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+Name of Paper: Stay On-Topic: Generating Context-specific Fake Restaurant Reviews
+
+Question: Which dataset do they use a starting point in generating fake reviews?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Background",
+    "System Model",
+    "Attack Model",
+    "Generative Model"
+  ],
+  "paragraphs": [
+    [
+      "Automatically generated fake reviews have only recently become natural enough to fool human readers. Yao et al. BIBREF0 use a deep neural network (a so-called 2-layer LSTM BIBREF1 ) to generate fake reviews, and concluded that these fake reviews look sufficiently genuine to fool native English speakers. They train their model using real restaurant reviews from yelp.com BIBREF2 . Once trained, the model is used to generate reviews character-by-character. Due to the generation methodology, it cannot be easily targeted for a specific context (meaningful side information). Consequently, the review generation process may stray off-topic. For instance, when generating a review for a Japanese restaurant in Las Vegas, the review generation process may include references to an Italian restaurant in Baltimore. The authors of BIBREF0 apply a post-processing step (customization), which replaces food-related words with more suitable ones (sampled from the targeted restaurant). The word replacement strategy has drawbacks: it can miss certain words and replace others independent of their surrounding words, which may alert savvy readers. As an example: when we applied the customization technique described in BIBREF0 to a review for a Japanese restaurant it changed the snippet garlic knots for breakfast with garlic knots for sushi).",
+      "We propose a methodology based on neural machine translation (NMT) that improves the generation process by defining a context for the each generated fake review. Our context is a clear-text sequence of: the review rating, restaurant name, city, state and food tags (e.g. Japanese, Italian). We show that our technique generates review that stay on topic. We can instantiate our basic technique into several variants. We vet them on Amazon Mechanical Turk and find that native English speakers are very poor at recognizing our fake generated reviews. For one variant, the participants' performance is close to random: the class-averaged F-score of detection is INLINEFORM0 (whereas random would be INLINEFORM1 given the 1:6 imbalance in the test). Via a user study with experienced, highly educated participants, we compare this variant (which we will henceforth refer to as NMT-Fake* reviews) with fake reviews generated using the char-LSTM-based technique from BIBREF0 .",
+      "We demonstrate that NMT-Fake* reviews constitute a new category of fake reviews that cannot be detected by classifiers trained only using previously known categories of fake reviews BIBREF0 , BIBREF3 , BIBREF4 . Therefore, NMT-Fake* reviews may go undetected in existing online review sites. To meet this challenge, we develop an effective classifier that detects NMT-Fake* reviews effectively (97% F-score). Our main contributions are:"
+    ],
+    [
+      "Fake reviews User-generated content BIBREF5 is an integral part of the contemporary user experience on the web. Sites like tripadvisor.com, yelp.com and Google Play use user-written reviews to provide rich information that helps other users choose where to spend money and time. User reviews are used for rating services or products, and for providing qualitative opinions. User reviews and ratings may be used to rank services in recommendations. Ratings have an affect on the outwards appearance. Already 8 years ago, researchers estimated that a one-star rating increase affects the business revenue by 5 \u2013 9% on yelp.com BIBREF6 .",
+      "Due to monetary impact of user-generated content, some businesses have relied on so-called crowd-turfing agents BIBREF7 that promise to deliver positive ratings written by workers to a customer in exchange for a monetary compensation. Crowd-turfing ethics are complicated. For example, Amazon community guidelines prohibit buying content relating to promotions, but the act of writing fabricated content is not considered illegal, nor is matching workers to customers BIBREF8 . Year 2015, approximately 20% of online reviews on yelp.com were suspected of being fake BIBREF9 .",
+      "Nowadays, user-generated review sites like yelp.com use filters and fraudulent review detection techniques. These factors have resulted in an increase in the requirements of crowd-turfed reviews provided to review sites, which in turn has led to an increase in the cost of high-quality review. Due to the cost increase, researchers hypothesize the existence of neural network-generated fake reviews. These neural-network-based fake reviews are statistically different from human-written fake reviews, and are not caught by classifiers trained on these BIBREF0 .",
+      "Detecting fake reviews can either be done on an individual level or as a system-wide detection tool (i.e. regulation). Detecting fake online content on a personal level requires knowledge and skills in critical reading. In 2017, the National Literacy Trust assessed that young people in the UK do not have the skillset to differentiate fake news from real news BIBREF10 . For example, 20% of children that use online news sites in age group 12-15 believe that all information on news sites are true.",
+      "Neural Networks Neural networks are function compositions that map input data through INLINEFORM0 subsequent layers: DISPLAYFORM0 ",
+      "where the functions INLINEFORM0 are typically non-linear and chosen by experts partly for known good performance on datasets and partly for simplicity of computational evaluation. Language models (LMs) BIBREF11 are generative probability distributions that assign probabilities to sequences of tokens ( INLINEFORM1 ): DISPLAYFORM0 ",
+      "such that the language model can be used to predict how likely a specific token at time step INLINEFORM0 is, based on the INLINEFORM1 previous tokens. Tokens are typically either words or characters.",
+      "For decades, deep neural networks were thought to be computationally too difficult to train. However, advances in optimization, hardware and the availability of frameworks have shown otherwise BIBREF1 , BIBREF12 . Neural language models (NLMs) have been one of the promising application areas. NLMs are typically various forms of recurrent neural networks (RNNs), which pass through the data sequentially and maintain a memory representation of the past tokens with a hidden context vector. There are many RNN architectures that focus on different ways of updating and maintaining context vectors: Long Short-Term Memory units (LSTM) and Gated Recurrent Units (GRUs) are perhaps most popular. Neural LMs have been used for free-form text generation. In certain application areas, the quality has been high enough to sometimes fool human readers BIBREF0 . Encoder-decoder (seq2seq) models BIBREF13 are architectures of stacked RNNs, which have the ability to generate output sequences based on input sequences. The encoder network reads in a sequence of tokens, and passes it to a decoder network (a LM). In contrast to simpler NLMs, encoder-decoder networks have the ability to use additional context for generating text, which enables more accurate generation of text. Encoder-decoder models are integral in Neural Machine Translation (NMT) BIBREF14 , where the task is to translate a source text from one language to another language. NMT models additionally use beam search strategies to heuristically search the set of possible translations. Training datasets are parallel corpora; large sets of paired sentences in the source and target languages. The application of NMT techniques for online machine translation has significantly improved the quality of translations, bringing it closer to human performance BIBREF15 .",
+      "Neural machine translation models are efficient at mapping one expression to another (one-to-one mapping). Researchers have evaluated these models for conversation generation BIBREF16 , with mixed results. Some researchers attribute poor performance to the use of the negative log likelihood cost function during training, which emphasizes generation of high-confidence phrases rather than diverse phrases BIBREF17 . The results are often generic text, which lacks variation. Li et al. have suggested various augmentations to this, among others suppressing typical responses in the decoder language model to promote response diversity BIBREF17 ."
+    ],
+    [
+      "We discuss the attack model, our generative machine learning method and controlling the generative process in this section."
+    ],
+    [
+      "Wang et al. BIBREF7 described a model of crowd-turfing attacks consisting of three entities: customers who desire to have fake reviews for a particular target (e.g. their restaurant) on a particular platform (e.g. Yelp), agents who offer fake review services to customers, and workers who are orchestrated by the agent to compose and post fake reviews.",
+      "Automated crowd-turfing attacks (ACA) replace workers by a generative model. This has several benefits including better economy and scalability (human workers are more expensive and slower) and reduced detectability (agent can better control the rate at which fake reviews are generated and posted).",
+      "We assume that the agent has access to public reviews on the review platform, by which it can train its generative model. We also assume that it is easy for the agent to create a large number of accounts on the review platform so that account-based detection or rate-limiting techniques are ineffective against fake reviews.",
+      "The quality of the generative model plays a crucial role in the attack. Yao et al. BIBREF0 propose the use of a character-based LSTM as base for generative model. LSTMs are not conditioned to generate reviews for a specific target BIBREF1 , and may mix-up concepts from different contexts during free-form generation. Mixing contextually separate words is one of the key criteria that humans use to identify fake reviews. These may result in violations of known indicators for fake content BIBREF18 . For example, the review content may not match prior expectations nor the information need that the reader has. We improve the attack model by considering a more capable generative model that produces more appropriate reviews: a neural machine translation (NMT) model."
+    ],
+    [
+      "We propose the use of NMT models for fake review generation. The method has several benefits: 1) the ability to learn how to associate context (keywords) to reviews, 2) fast training time, and 3) a high-degree of customization during production time, e.g. introduction of specific waiter or food items names into reviews.",
+      "NMT models are constructions of stacked recurrent neural networks (RNNs). They include an encoder network and a decoder network, which are jointly optimized to produce a translation of one sequence to another. The encoder rolls over the input data in sequence and produces one INLINEFORM0 -dimensional context vector representation for the sentence. The decoder then generates output sequences based on the embedding vector and an attention module, which is taught to associate output words with certain input words. The generation typically continues until a specific EOS (end of sentence) token is encountered. The review length can be controlled in many ways, e.g. by setting the probability of generating the EOS token to zero until the required length is reached.",
+      "NMT models often also include a beam search BIBREF14 , which generates several hypotheses and chooses the best ones amongst them. In our work, we use the greedy beam search technique. We forgo the use of additional beam searches as we found that the quality of the output was already adequate and the translation phase time consumption increases linearly for each beam used.",
+      "We use the Yelp Challenge dataset BIBREF2 for our fake review generation. The dataset (Aug 2017) contains 2.9 million 1 \u20135 star restaurant reviews. We treat all reviews as genuine human-written reviews for the purpose of this work, since wide-scale deployment of machine-generated review attacks are not yet reported (Sep 2017) BIBREF19 . As preprocessing, we remove non-printable (non-ASCII) characters and excessive white-space. We separate punctuation from words. We reserve 15,000 reviews for validation and 3,000 for testing, and the rest we use for training. NMT models require a parallel corpus of source and target sentences, i.e. a large set of (source, target)-pairs. We set up a parallel corpus by constructing (context, review)-pairs from the dataset. Next, we describe how we created our input context.",
+      "The Yelp Challenge dataset includes metadata about restaurants, including their names, food tags, cities and states these restaurants are located in. For each restaurant review, we fetch this metadata and use it as our input context in the NMT model. The corresponding restaurant review is similarly set as the target sentence. This method produced 2.9 million pairs of sentences in our parallel corpus. We show one example of the parallel training corpus in Example 1 below:",
+      "5 Public House Las Vegas NV Gastropubs Restaurants > Excellent",
+      "food and service . Pricey , but well worth it . I would recommend",
+      "the bone marrow and sampler platter for appetizers . \\end{verbatim}",
+      " ",
+      " ",
+      "\\noindent The order {\\textbf{[rating name city state tags]}} is kept constant.",
+      "Training the model conditions it to associate certain sequences of words in the input sentence with others in the output.",
+      " ",
+      "\\subsubsection{Training Settings}",
+      " ",
+      "We train our NMT model on a commodity PC with a i7-4790k CPU (4.00GHz), with 32GB RAM and one NVidia GeForce GTX 980 GPU. Our system can process approximately 1,300 \\textendash 1,500 source tokens/s and approximately 5,730 \\textendash 5,830 output tokens/s. Training one epoch takes in average 72 minutes. The model is trained for 8 epochs, i.e. over night. We call fake review generated by this model \\emph{NMT-Fake reviews}. We only need to train one model to produce reviews of different ratings.",
+      "We use the training settings: adam optimizer \\cite{kingma2014adam} with the suggested learning rate 0.001 \\cite{klein2017opennmt}. For most parts, parameters are at their default values. Notably, the maximum sentence length of input and output is 50 tokens by default.",
+      "We leverage the framework openNMT-py \\cite{klein2017opennmt} to teach the our NMT model.",
+      "We list used openNMT-py commands in Appendix Table~\\ref{table:openNMT-py_commands}.",
+      " ",
+      "\\begin{figure}[t]",
+      "\\begin{center}",
+      " \\begin{tabular}{ | l | }",
+      " \\hline",
+      "Example 2. Greedy NMT \\\\",
+      "Great food, \\underline{great} service, \\underline{great} \\textit{\\textit{beer selection}}. I had the \\textit{Gastropubs burger} and it",
+      "\\\\",
+      "was delicious. The \\underline{\\textit{beer selection}} was also \\underline{great}. \\\\",
+      "\\\\",
+      "Example 3. NMT-Fake* \\\\",
+      "I love this restaurant. Great food, great service. It's \\textit{a little pricy} but worth\\\\",
+      "it for the \\textit{quality} of the \\textit{beer} and atmosphere you can see in \\textit{Vegas}",
+      "\\\\",
+      " \\hline",
+      " \\end{tabular}",
+      " \\label{table:output_comparison}",
+      "\\end{center}",
+      "\\caption{Na\\\"{i}ve text generation with NMT vs. generation using our NTM model. Repetitive patterns are \\underline{underlined}. Contextual words are \\emph{italicized}. Both examples here are generated based on the context given in Example~1.}",
+      "\\label{fig:comparison}",
+      "\\end{figure}",
+      " ",
+      "\\subsection{Controlling generation of fake reviews}",
+      "\\label{sec:generating}",
+      " ",
+      "Greedy NMT beam searches are practical in many NMT cases. However, the results are simply repetitive, when naively applied to fake review generation (See Example~2 in Figure~\\ref{fig:comparison}).",
+      "The NMT model produces many \\emph{high-confidence} word predictions, which are repetitive and obviously fake. We calculated that in fact, 43\\% of the generated sentences started with the phrase ``Great food''. The lack of diversity in greedy use of NMTs for text generation is clear.",
+      " ",
+      " ",
+      "\\begin{algorithm}[!b]",
+      " \\KwData{Desired review context $C_\\mathrm{input}$ (given as cleartext), NMT model}",
+      " \\KwResult{Generated review $out$ for input context $C_\\mathrm{input}$}",
+      "set $b=0.3$, $\\lambda=-5$, $\\alpha=\\frac{2}{3}$, $p_\\mathrm{typo}$, $p_\\mathrm{spell}$ \\\\",
+      "$\\log p \\leftarrow \\text{NMT.decode(NMT.encode(}C_\\mathrm{input}\\text{))}$ \\\\",
+      "out $\\leftarrow$ [~] \\\\",
+      "$i \\leftarrow 0$ \\\\",
+      "$\\log p \\leftarrow \\text{Augment}(\\log p$, $b$, $\\lambda$, $1$, $[~]$, 0)~~~~~~~~~~~~~~~ |~random penalty~\\\\",
+      "\\While{$i=0$ or $o_i$ not EOS}{",
+      "$\\log \\Tilde{p} \\leftarrow \\text{Augment}(\\log p$, $b$, $\\lambda$, $\\alpha$, $o_i$, $i$)~~~~~~~~~~~ |~start \\& memory penalty~\\\\",
+      "$o_i \\leftarrow$ \\text{NMT.beam}($\\log \\Tilde{p}$, out) \\\\",
+      "out.append($o_i$) \\\\",
+      "$i \\leftarrow i+1$",
+      "}\\text{return}~$\\text{Obfuscate}$(out,~$p_\\mathrm{typo}$,~$p_\\mathrm{spell}$)",
+      "\\caption{Generation of NMT-Fake* reviews.}",
+      "\\label{alg:base}",
+      "\\end{algorithm}",
+      " ",
+      "In this work, we describe how we succeeded in creating more diverse and less repetitive generated reviews, such as Example 3 in Figure~\\ref{fig:comparison}.",
+      "We outline pseudocode for our methodology of generating fake reviews in Algorithm~\\ref{alg:base}. There are several parameters in our algorithm.",
+      "The details of the algorithm will be shown later.",
+      "We modify the openNMT-py translation phase by changing log-probabilities before passing them to the beam search.",
+      "We notice that reviews generated with openNMT-py contain almost no language errors. As an optional post-processing step, we obfuscate reviews by introducing natural typos/misspellings randomly. In the next sections, we describe how we succeeded in generating more natural sentences from our NMT model, i.e. generating reviews like Example~3 instead of reviews like Example~2.",
+      " ",
+      "\\subsubsection{Variation in word content}",
+      " ",
+      "Example 2 in Figure~\\ref{fig:comparison} repeats commonly occurring words given for a specific context (e.g. \\textit{great, food, service, beer, selection, burger} for Example~1). Generic review generation can be avoided by decreasing probabilities (log-likelihoods \\cite{murphy2012machine}) of the generators LM, the decoder.",
+      "We constrain the generation of sentences by randomly \\emph{imposing penalties to words}.",
+      "We tried several forms of added randomness, and found that adding constant penalties to a \\emph{random subset} of the target words resulted in the most natural sentence flow. We call these penalties \\emph{Bernoulli penalties}, since the random variables are chosen as either 1 or 0 (on or off).",
+      " ",
+      " ",
+      "\\paragraph{Bernoulli penalties to language model}",
+      "To avoid generic sentences components, we augment the default language model $p(\\cdot)$ of the decoder by",
+      " ",
+      "\\begin{equation}",
+      "\\log \\Tilde{p}(t_k) = \\log p(t_k | t_i, \\dots, t_1) + \\lambda q,",
+      "\\end{equation}",
+      " ",
+      "where $q \\in R^{V}$ is a vector of Bernoulli-distributed random values that obtain values $1$ with probability $b$ and value $0$ with probability $1-b_i$, and $\\lambda < 0$. Parameter $b$ controls how much of the vocabulary is forgotten and $\\lambda$ is a soft penalty of including ``forgotten'' words in a review.",
+      "$\\lambda q_k$ emphasizes sentence forming with non-penalized words. The randomness is reset at the start of generating a new review.",
+      "Using Bernoulli penalties in the language model, we can ``forget'' a certain proportion of words and essentially ``force'' the creation of less typical sentences. We will test the effect of these two parameters, the Bernoulli probability $b$ and log-likelihood penalty of including ``forgotten'' words $\\lambda$, with a user study in Section~\\ref{sec:varying}.",
+      " ",
+      "\\paragraph{Start penalty}",
+      "We introduce start penalties to avoid generic sentence starts (e.g. ``Great food, great service''). Inspired by \\cite{li2016diversity}, we add a random start penalty $\\lambda s^\\mathrm{i}$, to our language model, which decreases monotonically for each generated token. We set $\\alpha \\leftarrow 0.66$ as it's effect decreases by 90\\% every 5 words generated.",
+      " ",
+      "\\paragraph{Penalty for reusing words}",
+      "Bernoulli penalties do not prevent excessive use of certain words in a sentence (such as \\textit{great} in Example~2).",
+      "To avoid excessive reuse of words, we included a memory penalty for previously used words in each translation.",
+      "Concretely, we add the penalty $\\lambda$ to each word that has been generated by the greedy search.",
+      " ",
+      "\\subsubsection{Improving sentence coherence}",
+      "\\label{sec:grammar}",
+      "We visually analyzed reviews after applying these penalties to our NMT model. While the models were clearly diverse, they were \\emph{incoherent}: the introduction of random penalties had degraded the grammaticality of the sentences. Amongst others, the use of punctuation was erratic, and pronouns were used semantically wrongly (e.g. \\emph{he}, \\emph{she} might be replaced, as could ``and''/``but''). To improve the authenticity of our reviews, we added several \\emph{grammar-based rules}.",
+      " ",
+      "English language has several classes of words which are important for the natural flow of sentences.",
+      "We built a list of common pronouns (e.g. I, them, our), conjunctions (e.g. and, thus, if), punctuation (e.g. ,/.,..), and apply only half memory penalties for these words. We found that this change made the reviews more coherent. The pseudocode for this and the previous step is shown in Algorithm~\\ref{alg:aug}.",
+      "The combined effect of grammar-based rules and LM augmentation is visible in Example~3, Figure~\\ref{fig:comparison}.",
+      " ",
+      "\\begin{algorithm}[!t]",
+      " \\KwData{Initial log LM $\\log p$, Bernoulli probability $b$, soft-penalty $\\lambda$, monotonic factor $\\alpha$, last generated token $o_i$, grammar rules set $G$}",
+      " \\KwResult{Augmented log LM $\\log \\Tilde{p}$}",
+      "\\begin{algorithmic}[1]",
+      "\\Procedure {Augment}{$\\log p$, $b$, $\\lambda$, $\\alpha$, $o_i$, $i$}{ \\\\",
+      "generate $P_{\\mathrm{1:N}} \\leftarrow Bernoulli(b)$~~~~~~~~~~~~~~~|~$\\text{One value} \\in \\{0,1\\}~\\text{per token}$~ \\\\",
+      "$I \\leftarrow P>0$ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~|~Select positive indices~\\\\",
+      "$\\log \\Tilde{p} \\leftarrow$ $\\text{Discount}$($\\log p$, $I$, $\\lambda \\cdot \\alpha^i$,$G$) ~~~~~~ |~start penalty~\\\\",
+      "$\\log \\Tilde{p} \\leftarrow$ $\\text{Discount}$($\\log \\Tilde{p}$, $[o_i]$, $\\lambda$,$G$) ~~~~~~~~~ |~memory penalty~\\\\",
+      "\\textbf{return}~$\\log \\Tilde{p}$",
+      "}",
+      "\\EndProcedure",
+      "\\\\",
+      "\\Procedure {Discount}{$\\log p$, $I$, $\\lambda$, $G$}{",
+      "\\State{\\For{$i \\in I$}{",
+      "\\eIf{$o_i \\in G$}{",
+      "$\\log p_{i} \\leftarrow \\log p_{i} + \\lambda/2$",
+      "}{",
+      "$\\log p_{i} \\leftarrow \\log p_{i} + \\lambda$}",
+      "}\\textbf{return}~$\\log p$",
+      "\\EndProcedure",
+      "}}",
+      "\\end{algorithmic}",
+      "\\caption{Pseudocode for augmenting language model. }",
+      "\\label{alg:aug}",
+      "\\end{algorithm}",
+      " ",
+      "\\subsubsection{Human-like errors}",
+      "\\label{sec:obfuscation}",
+      "We notice that our NMT model produces reviews without grammar mistakes.",
+      "This is unlike real human writers, whose sentences contain two types of language mistakes 1) \\emph{typos} that are caused by mistakes in the human motoric input, and 2) \\emph{common spelling mistakes}.",
+      "We scraped a list of common English language spelling mistakes from Oxford dictionary\\footnote{\\url{https://en.oxforddictionaries.com/spelling/common-misspellings}} and created 80 rules for randomly \\emph{re-introducing spelling mistakes}.",
+      "Similarly, typos are randomly reintroduced based on the weighted edit distance\\footnote{\\url{https://pypi.python.org/pypi/weighted-levenshtein/0.1}}, such that typos resulting in real English words with small perturbations are emphasized.",
+      "We use autocorrection tools\\footnote{\\url{https://pypi.python.org/pypi/autocorrect/0.1.0}} for finding these words.",
+      "We call these augmentations \\emph{obfuscations}, since they aim to confound the reader to think a human has written them. We omit the pseudocode description for brevity.",
+      " ",
+      "\\subsection{Experiment: Varying generation parameters in our NMT model}",
+      "\\label{sec:varying}",
+      " ",
+      "Parameters $b$ and $\\lambda$ control different aspects in fake reviews.",
+      "We show six different examples of generated fake reviews in Table~\\ref{table:categories}.",
+      "Here, the largest differences occur with increasing values of $b$: visibly, the restaurant reviews become more extreme.",
+      "This occurs because a large portion of vocabulary is ``forgotten''. Reviews with $b \\geq 0.7$ contain more rare word combinations, e.g. ``!!!!!'' as punctuation, and they occasionally break grammaticality (''experience was awesome'').",
+      "Reviews with lower $b$ are more generic: they contain safe word combinations like ``Great place, good service'' that occur in many reviews. Parameter $\\lambda$'s is more subtle: it affects how random review starts are and to a degree, the discontinuation between statements within the review.",
+      "We conducted an Amazon Mechanical Turk (MTurk) survey in order to determine what kind of NMT-Fake reviews are convincing to native English speakers. We describe the survey and results in the next section.",
+      " ",
+      " ",
+      "\\begin{table}[!b]",
+      "\\caption{Six different parametrizations of our NMT reviews and one example for each. The context is ``5 P~.~F~.~Chang ' s Scottsdale AZ'' in all examples.}",
+      "\\begin{center}",
+      " \\begin{tabular}{ | l | l | }",
+      " \\hline",
+      " $(b, \\lambda)$ & Example review for context \\\\ \\hline",
+      " \\hline",
+      " $(0.3, -3)$ & I love this location! Great service, great food and the best drinks in Scottsdale. \\\\",
+      " & The staff is very friendly and always remembers u when we come in\\\\\\hline",
+      " $(0.3, -5)$ & Love love the food here! I always go for lunch. They have a great menu and \\\\",
+      " & they make it fresh to order. Great place, good service and nice staff\\\\\\hline",
+      " $(0.5, -4)$ & I love their chicken lettuce wraps and fried rice!! The service is good, they are\\\\",
+      " & always so polite. They have great happy hour specials and they have a lot\\\\",
+      " & of options.\\\\\\hline",
+      " $(0.7, -3)$ & Great place to go with friends! They always make sure your dining \\\\",
+      " & experience was awesome.\\\\ \\hline",
+      " $(0.7, -5)$ & Still haven't ordered an entree before but today we tried them once..\\\\",
+      " & both of us love this restaurant....\\\\\\hline",
+      " $(0.9, -4)$ & AMAZING!!!!! Food was awesome with excellent service. Loved the lettuce \\\\",
+      " & wraps. Great drinks and wine! Can't wait to go back so soon!!\\\\ \\hline",
+      " \\end{tabular}",
+      " \\label{table:categories}",
+      "\\end{center}",
+      "\\end{table}",
+      " ",
+      "\\subsubsection{MTurk study}",
+      "\\label{sec:amt}",
+      "We created 20 jobs, each with 100 questions, and requested master workers in MTurk to complete the jobs.",
+      "We randomly generated each survey for the participants. Each review had a 50\\% chance to be real or fake. The fake ones further were chosen among six (6) categories of fake reviews (Table~\\ref{table:categories}).",
+      "The restaurant and the city was given as contextual information to the participants. Our aim was to use this survey to understand how well English-speakers react to different parametrizations of NMT-Fake reviews.",
+      "Table~\\ref{table:amt_pop} in Appendix summarizes the statistics for respondents in the survey. All participants were native English speakers from America. The base rate (50\\%) was revealed to the participants prior to the study.",
+      " ",
+      "We first investigated overall detection of any NMT-Fake reviews (1,006 fake reviews and 994 real reviews). We found that the participants had big difficulties in detecting our fake reviews. In average, the reviews were detected with class-averaged \\emph{F-score of only 56\\%}, with 53\\% F-score for fake review detection and 59\\% F-score for real review detection. The results are very close to \\emph{random detection}, where precision, recall and F-score would each be 50\\%. Results are recorded in Table~\\ref{table:MTurk_super}. Overall, the fake review generation is very successful, since human detection rate across categories is close to random.",
+      " ",
+      "\\begin{table}[t]",
+      "\\caption{Effectiveness of Mechanical Turkers in distinguishing human-written reviews from fake reviews generated by our NMT model (all variants).}",
+      "\\begin{center}",
+      " \\begin{tabular}{ | c | c |c |c | c | }",
+      " \\hline",
+      " \\multicolumn{5}{|c|}{Classification report}",
+      " \\\\ \\hline",
+      " Review Type & Precision & Recall & F-score & Support \\\\ \\hline",
+      " \\hline",
+      " Human & 55\\% & 63\\% & 59\\% & 994\\\\",
+      " NMT-Fake & 57\\% & 50\\% & 53\\% & 1006 \\\\",
+      " \\hline",
+      " \\end{tabular}",
+      " \\label{table:MTurk_super}",
+      "\\end{center}",
+      "\\end{table}",
+      " ",
+      "We noticed some variation in the detection of different fake review categories. The respondents in our MTurk survey had most difficulties recognizing reviews of category $(b=0.3, \\lambda=-5)$, where true positive rate was $40.4\\%$, while the true negative rate of the real class was $62.7\\%$. The precision were $16\\%$ and $86\\%$, respectively. The class-averaged F-score is $47.6\\%$, which is close to random. Detailed classification reports are shown in Table~\\ref{table:MTurk_sub} in Appendix. Our MTurk-study shows that \\emph{our NMT-Fake reviews pose a significant threat to review systems}, since \\emph{ordinary native English-speakers have very big difficulties in separating real reviews from fake reviews}. We use the review category $(b=0.3, \\lambda=-5)$ for future user tests in this paper, since MTurk participants had most difficulties detecting these reviews. We refer to this category as NMT-Fake* in this paper.",
+      " ",
+      "\\section{Evaluation}",
+      "\\graphicspath{ {figures/}}",
+      " ",
+      "We evaluate our fake reviews by first comparing them statistically to previously proposed types of fake reviews, and proceed with a user study with experienced participants. We demonstrate the statistical difference to existing fake review types \\cite{yao2017automated,mukherjee2013yelp,rayana2015collective} by training classifiers to detect previous types and investigate classification performance.",
+      " ",
+      "\\subsection{Replication of state-of-the-art model: LSTM}",
+      "\\label{sec:repl}",
+      " ",
+      "Yao et al. \\cite{yao2017automated} presented the current state-of-the-art generative model for fake reviews. The model is trained over the Yelp Challenge dataset using a two-layer character-based LSTM model.",
+      "We requested the authors of \\cite{yao2017automated} for access to their LSTM model or a fake review dataset generated by their model. Unfortunately they were not able to share either of these with us. We therefore replicated their model as closely as we could, based on their paper and e-mail correspondence\\footnote{We are committed to sharing our code with bonafide researchers for the sake of reproducibility.}.",
+      " ",
+      "We used the same graphics card (GeForce GTX) and trained using the same framework (torch-RNN in lua). We downloaded the reviews from Yelp Challenge and preprocessed the data to only contain printable ASCII characters, and filtered out non-restaurant reviews. We trained the model for approximately 72 hours. We post-processed the reviews using the customization methodology described in \\cite{yao2017automated} and email correspondence. We call fake reviews generated by this model LSTM-Fake reviews.",
+      " ",
+      "\\subsection{Similarity to existing fake reviews}",
+      "\\label{sec:automated}",
+      " ",
+      "We now want to understand how NMT-Fake* reviews compare to a) LSTM fake reviews and b) human-generated fake reviews. We do this by comparing the statistical similarity between these classes.",
+      " ",
+      "For `a' (Figure~\\ref{fig:lstm}), we use the Yelp Challenge dataset. We trained a classifier using 5,000 random reviews from the Yelp Challenge dataset (``human'') and 5,000 fake reviews generated by LSTM-Fake. Yao et al. \\cite{yao2017automated} found that character features are essential in identifying LSTM-Fake reviews. Consequently, we use character features (n-grams up to 3).",
+      " ",
+      "For `b' (Figure~\\ref{fig:shill}),we the ``Yelp Shills'' dataset (combination of YelpZip \\cite{mukherjee2013yelp}, YelpNYC \\cite{mukherjee2013yelp}, YelpChi \\cite{rayana2015collective}). This dataset labels entries that are identified as fraudulent by Yelp's filtering mechanism (''shill reviews'')\\footnote{Note that shill reviews are probably generated by human shills \\cite{zhao2017news}.}. The rest are treated as genuine reviews from human users (''genuine''). We use 100,000 reviews from each category to train a classifier. We use features from the commercial psychometric tool LIWC2015 \\cite{pennebaker2015development} to generated features.",
+      " ",
+      "In both cases, we use AdaBoost (with 200 shallow decision trees) for training. For testing each classifier, we use a held out test set of 1,000 reviews from both classes in each case. In addition, we test 1,000 NMT-Fake* reviews. Figures~\\ref{fig:lstm} and~\\ref{fig:shill} show the results. The classification threshold of 50\\% is marked with a dashed line.",
+      " ",
+      "\\begin{figure}",
+      " \\begin{subfigure}[b]{0.5\\columnwidth}",
+      " \\includegraphics[width=\\columnwidth]{figures/lstm.png}",
+      " \\caption{Human--LSTM reviews.}",
+      " \\label{fig:lstm}",
+      " \\end{subfigure}",
+      " \\begin{subfigure}[b]{0.5\\columnwidth}",
+      " \\includegraphics[width=\\columnwidth]{figures/distribution_shill.png}",
+      " \\caption{Genuine--Shill reviews.}",
+      " \\label{fig:shill}",
+      " \\end{subfigure}",
+      " \\caption{",
+      " Histogram comparison of NMT-Fake* reviews with LSTM-Fake reviews and human-generated (\\emph{genuine} and \\emph{shill}) reviews. Figure~\\ref{fig:lstm} shows that a classifier trained to distinguish ``human'' vs. LSTM-Fake cannot distinguish ``human'' vs NMT-Fake* reviews. Figure~\\ref{fig:shill} shows NMT-Fake* reviews are more similar to \\emph{genuine} reviews than \\emph{shill} reviews.",
+      " }",
+      " \\label{fig:statistical_similarity}",
+      "\\end{figure}",
+      " ",
+      "We can see that our new generated reviews do not share strong attributes with previous known categories of fake reviews. If anything, our fake reviews are more similar to genuine reviews than previous fake reviews. We thus conjecture that our NMT-Fake* fake reviews present a category of fake reviews that may go undetected on online review sites.",
+      " ",
+      " ",
+      "\\subsection{Comparative user study}",
+      "\\label{sec:comparison}",
+      "We wanted to evaluate the effectiveness of fake reviews againsttech-savvy users who understand and know to expect machine-generated fake reviews. We conducted a user study with 20 participants, all with computer science education and at least one university degree. Participant demographics are shown in Table~\\ref{table:amt_pop} in the Appendix. Each participant first attended a training session where they were asked to label reviews (fake and genuine) and could later compare them to the correct answers -- we call these participants \\emph{experienced participants}.",
+      "No personal data was collected during the user study.",
+      " ",
+      "Each person was given two randomly selected sets of 30 of reviews (a total of 60 reviews per person) with reviews containing 10 \\textendash 50 words each.",
+      "Each set contained 26 (87\\%) real reviews from Yelp and 4 (13\\%) machine-generated reviews,",
+      "numbers chosen based on suspicious review prevalence on Yelp~\\cite{mukherjee2013yelp,rayana2015collective}.",
+      "One set contained machine-generated reviews from one of the two models (NMT ($b=0.3, \\lambda=-5$) or LSTM),",
+      "and the other set reviews from the other in randomized order. The number of fake reviews was revealed to each participant in the study description. Each participant was requested to mark four (4) reviews as fake.",
+      " ",
+      "Each review targeted a real restaurant. A screenshot of that restaurant's Yelp page was shown to each participant prior to the study. Each participant evaluated reviews for one specific, randomly selected, restaurant. An example of the first page of the user study is shown in Figure~\\ref{fig:screenshot} in Appendix.",
+      " ",
+      "\\begin{figure}[!ht]",
+      "\\centering",
+      "\\includegraphics[width=.7\\columnwidth]{detection2.png}",
+      "\\caption{Violin plots of detection rate in comparative study. Mean and standard deviations for number of detected fakes are $0.8\\pm0.7$ for NMT-Fake* and $2.5\\pm1.0$ for LSTM-Fake. $n=20$. A sample of random detection is shown as comparison.}",
+      "\\label{fig:aalto}",
+      "\\end{figure}",
+      " ",
+      " ",
+      "Figure~\\ref{fig:aalto} shows the distribution of detected reviews of both types. A hypothetical random detector is shown for comparison.",
+      "NMT-Fake* reviews are significantly more difficult to detect for our experienced participants. In average, detection rate (recall) is $20\\%$ for NMT-Fake* reviews, compared to $61\\%$ for LSTM-based reviews.",
+      "The precision (and F-score) is the same as the recall in our study, since participants labeled 4 fakes in each set of 30 reviews \\cite{murphy2012machine}.",
+      "The distribution of the detection across participants is shown in Figure~\\ref{fig:aalto}. \\emph{The difference is statistically significant with confidence level $99\\%$} (Welch's t-test).",
+      "We compared the detection rate of NMT-Fake* reviews to a random detector, and find that \\emph{our participants detection rate of NMT-Fake* reviews is not statistically different from random predictions with 95\\% confidence level} (Welch's t-test).",
+      " ",
+      " ",
+      "\\section{Defenses}",
+      " ",
+      "\\label{sec:detection}",
+      " ",
+      "We developed an AdaBoost-based classifier to detect our new fake reviews, consisting of 200 shallow decision trees (depth 2). The features we used are recorded in Table~\\ref{table:features_adaboost} (Appendix).",
+      "We used word-level features based on spaCy-tokenization \\cite{honnibal-johnson:2015:EMNLP} and constructed n-gram representation of POS-tags and dependency tree tags. We added readability features from NLTK~\\cite{bird2004nltk}.",
+      " ",
+      "\\begin{figure}[ht]",
+      "\\centering",
+      "\\includegraphics[width=.7\\columnwidth]{obf_score_fair_2.png}",
+      "\\caption{",
+      "Adaboost-based classification of NMT-Fake and human-written reviews.",
+      "Effect of varying $b$ and $\\lambda$ in fake review generation.",
+      "The variant native speakers had most difficulties detecting is well detectable by AdaBoost (97\\%).}",
+      "\\label{fig:adaboost_matrix_b_lambda}",
+      "\\end{figure}",
+      " ",
+      " ",
+      "Figure~\\ref{fig:adaboost_matrix_b_lambda} shows our AdaBoost classifier's class-averaged F-score at detecting different kind of fake reviews. The classifier is very effective in detecting reviews that humans have difficulties detecting. For example, the fake reviews MTurk users had most difficulty detecting ($b=0.3, \\lambda=-5$) are detected with an excellent 97\\% F-score.",
+      "The most important features for the classification were counts for frequently occurring words in fake reviews (such as punctuation, pronouns, articles) as well as the readability feature ``Automated Readability Index''. We thus conclude that while NMT-Fake reviews are difficult to detect for humans, they can be well detected with the right tools.",
+      " ",
+      "\\section{Related Work}",
+      " ",
+      "Kumar and Shah~\\cite{kumar2018false} survey and categorize false information research. Automatically generated fake reviews are a form of \\emph{opinion-based false information}, where the creator of the review may influence reader's opinions or decisions.",
+      "Yao et al. \\cite{yao2017automated} presented their study on machine-generated fake reviews. Contrary to us, they investigated character-level language models, without specifying a specific context before generation. We leverage existing NMT tools to encode a specific context to the restaurant before generating reviews.",
+      "Supporting our study, Everett et al~\\cite{Everett2016Automated} found that security researchers were less likely to be fooled by Markov chain-generated Reddit comments compared to ordinary Internet users.",
+      " ",
+      "Diversification of NMT model outputs has been studied in \\cite{li2016diversity}. The authors proposed the use of a penalty to commonly occurring sentences (\\emph{n-grams}) in order to emphasize maximum mutual information-based generation.",
+      "The authors investigated the use of NMT models in chatbot systems.",
+      "We found that unigram penalties to random tokens (Algorithm~\\ref{alg:aug}) was easy to implement and produced sufficiently diverse responses.",
+      " ",
+      "\\section {Discussion and Future Work}",
+      " ",
+      "\\paragraph{What makes NMT-Fake* reviews difficult to detect?} First, NMT models allow the encoding of a relevant context for each review, which narrows down the possible choices of words that the model has to choose from. Our NMT model had a perplexity of approximately $25$, while the model of \\cite{yao2017automated} had a perplexity of approximately $90$ \\footnote{Personal communication with the authors}. Second, the beam search in NMT models narrows down choices to natural-looking sentences. Third, we observed that the NMT model produced \\emph{better structure} in the generated sentences (i.e. a more coherent story).",
+      " ",
+      "\\paragraph{Cost of generating reviews} With our setup, generating one review took less than one second. The cost of generation stems mainly from the overnight training. Assuming an electricity cost of 16 cents / kWh (California) and 8 hours of training, training the NMT model requires approximately 1.30 USD. This is a 90\\% reduction in time compared to the state-of-the-art \\cite{yao2017automated}. Furthermore, it is possible to generate both positive and negative reviews with the same model.",
+      " ",
+      "\\paragraph{Ease of customization} We experimented with inserting specific words into the text by increasing their log likelihoods in the beam search. We noticed that the success depended on the prevalence of the word in the training set. For example, adding a +5 to \\emph{Mike} in the log-likelihood resulted in approximately 10\\% prevalence of this word in the reviews. An attacker can therefore easily insert specific keywords to reviews, which can increase evasion probability.",
+      " ",
+      "\\paragraph{Ease of testing} Our diversification scheme is applicable during \\emph{generation phase}, and does not affect the training setup of the network in any way. Once the NMT model is obtained, it is easy to obtain several different variants of NMT-Fake reviews by varying parameters $b$ and $\\lambda$.",
+      " ",
+      " ",
+      " ",
+      "\\paragraph{Languages} The generation methodology is not per-se language-dependent. The requirement for successful generation is that sufficiently much data exists in the targeted language. However, our language model modifications require some knowledge of that target language's grammar to produce high-quality reviews.",
+      " ",
+      "\\paragraph{Generalizability of detection techniques} Currently, fake reviews are not universally detectable. Our results highlight that it is difficult to claim detection performance on unseen types of fake reviews (Section~\\ref{sec:automated}). We see this an open problem that deserves more attention in fake reviews research.",
+      " ",
+      "\\paragraph{Generalizability to other types of datasets} Our technique can be applied to any dataset, as long as there is sufficient training data for the NMT model. We used approximately 2.9 million reviews for this work.",
+      " ",
+      "\\section{Conclusion}",
+      " ",
+      "In this paper, we showed that neural machine translation models can be used to generate fake reviews that are very effective in deceiving even experienced, tech-savvy users.",
+      "This supports anecdotal evidence \\cite{national2017commission}.",
+      "Our technique is more effective than state-of-the-art \\cite{yao2017automated}.",
+      "We conclude that machine-aided fake review detection is necessary since human users are ineffective in identifying fake reviews.",
+      "We also showed that detectors trained using one type of fake reviews are not effective in identifying other types of fake reviews.",
+      "Robust detection of fake reviews is thus still an open problem.",
+      " ",
+      " ",
+      "\\section*{Acknowledgments}",
+      "We thank Tommi Gr\\\"{o}ndahl for assistance in planning user studies and the",
+      "participants of the user study for their time and feedback. We also thank",
+      "Luiza Sayfullina for comments that improved the manuscript.",
+      "We thank the authors of \\cite{yao2017automated} for answering questions about",
+      "their work.",
+      " ",
+      " ",
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+      " ",
+      " ",
+      " ",
+      "\\section*{Appendix}",
+      " ",
+      "We present basic demographics of our MTurk study and the comparative study with experienced users in Table~\\ref{table:amt_pop}.",
+      " ",
+      "\\begin{table}",
+      "\\caption{User study statistics.}",
+      "\\begin{center}",
+      " \\begin{tabular}{ | l | c | c | }",
+      " \\hline",
+      " Quality & Mechanical Turk users & Experienced users\\\\",
+      " \\hline",
+      " Native English Speaker & Yes (20) & Yes (1) No (19) \\\\",
+      " Fluent in English & Yes (20) & Yes (20) \\\\",
+      " Age & 21-40 (17) 41-60 (3) & 21-25 (8) 26-30 (7) 31-35 (4) 41-45 (1)\\\\",
+      " Gender & Male (14) Female (6) & Male (17) Female (3)\\\\",
+      " Highest Education & High School (10) Bachelor (10) & Bachelor (9) Master (6) Ph.D. (5) \\\\",
+      " \\hline",
+      " \\end{tabular}",
+      " \\label{table:amt_pop}",
+      "\\end{center}",
+      "\\end{table}",
+      " ",
+      " ",
+      "Table~\\ref{table:openNMT-py_commands} shows a listing of the openNMT-py commands we used to create our NMT model and to generate fake reviews.",
+      " ",
+      "\\begin{table}[t]",
+      "\\caption{Listing of used openNMT-py commands.}",
+      "\\begin{center}",
+      " \\begin{tabular}{ | l | l | }",
+      " \\hline",
+      " Phase & Bash command \\\\",
+      " \\hline",
+      " Preprocessing & \\begin{lstlisting}[language=bash]",
+      "python preprocess.py -train_src context-train.txt",
+      "-train_tgt reviews-train.txt -valid_src context-val.txt",
+      "-valid_tgt reviews-val.txt -save_data model",
+      "-lower -tgt_words_min_frequency 10",
+      "\\end{lstlisting}",
+      " \\\\ & \\\\",
+      " Training & \\begin{lstlisting}[language=bash]",
+      "python train.py -data model -save_model model -epochs 8",
+      "-gpuid 0 -learning_rate_decay 0.5 -optim adam",
+      "-learning_rate 0.001 -start_decay_at 3\\end{lstlisting}",
+      " \\\\ & \\\\",
+      " Generation & \\begin{lstlisting}[language=bash]",
+      "python translate.py -model model_acc_35.54_ppl_25.68_e8.pt",
+      "-src context-tst.txt -output pred-e8.txt -replace_unk",
+      "-verbose -max_length 50 -gpu 0",
+      " \\end{lstlisting} \\\\",
+      " \\hline",
+      " \\end{tabular}",
+      " \\label{table:openNMT-py_commands}",
+      "\\end{center}",
+      "\\end{table}",
+      " ",
+      " ",
+      "Table~\\ref{table:MTurk_sub} shows the classification performance of Amazon Mechanical Turkers, separated across different categories of NMT-Fake reviews. The category with best performance ($b=0.3, \\lambda=-5$) is denoted as NMT-Fake*.",
+      " ",
+      "\\begin{table}[b]",
+      "\\caption{MTurk study subclass classification reports. Classes are imbalanced in ratio 1:6. Random predictions are $p_\\mathrm{human} = 86\\%$ and $p_\\mathrm{machine} = 14\\%$, with $r_\\mathrm{human} = r_\\mathrm{machine} = 50\\%$. Class-averaged F-scores for random predictions are $42\\%$.}",
+      "\\begin{center}",
+      " \\begin{tabular}{ | c || c |c |c | c | }",
+      " \\hline",
+      " $(b=0.3, \\lambda = -3)$ & Precision & Recall & F-score & Support \\\\ \\hline",
+      " Human & 89\\% & 63\\% & 73\\% & 994\\\\",
+      " NMT-Fake & 15\\% & 45\\% & 22\\% & 146 \\\\",
+      " \\hline",
+      " \\hline",
+      " $(b=0.3, \\lambda = -5)$ & Precision & Recall & F-score & Support \\\\ \\hline",
+      " Human & 86\\% & 63\\% & 73\\% & 994\\\\",
+      " NMT-Fake* & 16\\% & 40\\% & 23\\% & 171 \\\\",
+      " \\hline",
+      " \\hline",
+      " $(b=0.5, \\lambda = -4)$ & Precision & Recall & F-score & Support \\\\ \\hline",
+      " Human & 88\\% & 63\\% & 73\\% & 994\\\\",
+      " NMT-Fake & 21\\% & 55\\% & 30\\% & 181 \\\\",
+      " \\hline",
+      " \\hline",
+      " $(b=0.7, \\lambda = -3)$ & Precision & Recall & F-score & Support \\\\ \\hline",
+      " Human & 88\\% & 63\\% & 73\\% & 994\\\\",
+      " NMT-Fake & 19\\% & 50\\% & 27\\% & 170 \\\\",
+      " \\hline",
+      " \\hline",
+      " $(b=0.7, \\lambda = -5)$ & Precision & Recall & F-score & Support \\\\ \\hline",
+      " Human & 89\\% & 63\\% & 74\\% & 994\\\\",
+      " NMT-Fake & 21\\% & 57\\% & 31\\% & 174 \\\\",
+      " \\hline",
+      " \\hline",
+      " $(b=0.9, \\lambda = -4)$ & Precision & Recall & F-score & Support \\\\ \\hline",
+      " Human & 88\\% & 63\\% & 73\\% & 994\\\\",
+      " NMT-Fake & 18\\% & 50\\% & 27\\% & 164 \\\\",
+      " \\hline",
+      " \\end{tabular}",
+      " \\label{table:MTurk_sub}",
+      "\\end{center}",
+      "\\end{table}",
+      " ",
+      "Figure~\\ref{fig:screenshot} shows screenshots of the first two pages of our user study with experienced participants.",
+      " ",
+      "\\begin{figure}[ht]",
+      "\\centering",
+      "\\includegraphics[width=1.\\columnwidth]{figures/screenshot_7-3.png}",
+      "\\caption{",
+      "Screenshots of the first two pages in the user study. Example 1 is a NMT-Fake* review, the rest are human-written.",
+      "}",
+      "\\label{fig:screenshot}",
+      "\\end{figure}",
+      " ",
+      "Table~\\ref{table:features_adaboost} shows the features used to detect NMT-Fake reviews using the AdaBoost classifier.",
+      " ",
+      "\\begin{table}",
+      "\\caption{Features used in NMT-Fake review detector.}",
+      "\\begin{center}",
+      " \\begin{tabular}{ | l | c | }",
+      " \\hline",
+      " Feature type & Number of features \\\\ \\hline",
+      " \\hline",
+      " Readability features & 13 \\\\ \\hline",
+      " Unique POS tags & $~20$ \\\\ \\hline",
+      " Word unigrams & 22,831 \\\\ \\hline",
+      " 1/2/3/4-grams of simple part-of-speech tags & 54,240 \\\\ \\hline",
+      " 1/2/3-grams of detailed part-of-speech tags & 112,944 \\\\ \\hline",
+      " 1/2/3-grams of syntactic dependency tags & 93,195 \\\\ \\hline",
+      " \\end{tabular}",
+      " \\label{table:features_adaboost}",
+      "\\end{center}",
+      "\\end{table}",
+      " ",
+      "\\end{document}",
+      ""
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0034/instruction.md b/qasper-0034/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..4fe87a5d16d31a22bb0ba4a7972de41f07e914d3
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+++ b/qasper-0034/instruction.md
@@ -0,0 +1,673 @@
+Name of Paper: Stay On-Topic: Generating Context-specific Fake Restaurant Reviews
+
+Question: Do they use a pretrained NMT model to help generating reviews?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Background",
+    "System Model",
+    "Attack Model",
+    "Generative Model"
+  ],
+  "paragraphs": [
+    [
+      "Automatically generated fake reviews have only recently become natural enough to fool human readers. Yao et al. BIBREF0 use a deep neural network (a so-called 2-layer LSTM BIBREF1 ) to generate fake reviews, and concluded that these fake reviews look sufficiently genuine to fool native English speakers. They train their model using real restaurant reviews from yelp.com BIBREF2 . Once trained, the model is used to generate reviews character-by-character. Due to the generation methodology, it cannot be easily targeted for a specific context (meaningful side information). Consequently, the review generation process may stray off-topic. For instance, when generating a review for a Japanese restaurant in Las Vegas, the review generation process may include references to an Italian restaurant in Baltimore. The authors of BIBREF0 apply a post-processing step (customization), which replaces food-related words with more suitable ones (sampled from the targeted restaurant). The word replacement strategy has drawbacks: it can miss certain words and replace others independent of their surrounding words, which may alert savvy readers. As an example: when we applied the customization technique described in BIBREF0 to a review for a Japanese restaurant it changed the snippet garlic knots for breakfast with garlic knots for sushi).",
+      "We propose a methodology based on neural machine translation (NMT) that improves the generation process by defining a context for the each generated fake review. Our context is a clear-text sequence of: the review rating, restaurant name, city, state and food tags (e.g. Japanese, Italian). We show that our technique generates review that stay on topic. We can instantiate our basic technique into several variants. We vet them on Amazon Mechanical Turk and find that native English speakers are very poor at recognizing our fake generated reviews. For one variant, the participants' performance is close to random: the class-averaged F-score of detection is INLINEFORM0 (whereas random would be INLINEFORM1 given the 1:6 imbalance in the test). Via a user study with experienced, highly educated participants, we compare this variant (which we will henceforth refer to as NMT-Fake* reviews) with fake reviews generated using the char-LSTM-based technique from BIBREF0 .",
+      "We demonstrate that NMT-Fake* reviews constitute a new category of fake reviews that cannot be detected by classifiers trained only using previously known categories of fake reviews BIBREF0 , BIBREF3 , BIBREF4 . Therefore, NMT-Fake* reviews may go undetected in existing online review sites. To meet this challenge, we develop an effective classifier that detects NMT-Fake* reviews effectively (97% F-score). Our main contributions are:"
+    ],
+    [
+      "Fake reviews User-generated content BIBREF5 is an integral part of the contemporary user experience on the web. Sites like tripadvisor.com, yelp.com and Google Play use user-written reviews to provide rich information that helps other users choose where to spend money and time. User reviews are used for rating services or products, and for providing qualitative opinions. User reviews and ratings may be used to rank services in recommendations. Ratings have an affect on the outwards appearance. Already 8 years ago, researchers estimated that a one-star rating increase affects the business revenue by 5 \u2013 9% on yelp.com BIBREF6 .",
+      "Due to monetary impact of user-generated content, some businesses have relied on so-called crowd-turfing agents BIBREF7 that promise to deliver positive ratings written by workers to a customer in exchange for a monetary compensation. Crowd-turfing ethics are complicated. For example, Amazon community guidelines prohibit buying content relating to promotions, but the act of writing fabricated content is not considered illegal, nor is matching workers to customers BIBREF8 . Year 2015, approximately 20% of online reviews on yelp.com were suspected of being fake BIBREF9 .",
+      "Nowadays, user-generated review sites like yelp.com use filters and fraudulent review detection techniques. These factors have resulted in an increase in the requirements of crowd-turfed reviews provided to review sites, which in turn has led to an increase in the cost of high-quality review. Due to the cost increase, researchers hypothesize the existence of neural network-generated fake reviews. These neural-network-based fake reviews are statistically different from human-written fake reviews, and are not caught by classifiers trained on these BIBREF0 .",
+      "Detecting fake reviews can either be done on an individual level or as a system-wide detection tool (i.e. regulation). Detecting fake online content on a personal level requires knowledge and skills in critical reading. In 2017, the National Literacy Trust assessed that young people in the UK do not have the skillset to differentiate fake news from real news BIBREF10 . For example, 20% of children that use online news sites in age group 12-15 believe that all information on news sites are true.",
+      "Neural Networks Neural networks are function compositions that map input data through INLINEFORM0 subsequent layers: DISPLAYFORM0 ",
+      "where the functions INLINEFORM0 are typically non-linear and chosen by experts partly for known good performance on datasets and partly for simplicity of computational evaluation. Language models (LMs) BIBREF11 are generative probability distributions that assign probabilities to sequences of tokens ( INLINEFORM1 ): DISPLAYFORM0 ",
+      "such that the language model can be used to predict how likely a specific token at time step INLINEFORM0 is, based on the INLINEFORM1 previous tokens. Tokens are typically either words or characters.",
+      "For decades, deep neural networks were thought to be computationally too difficult to train. However, advances in optimization, hardware and the availability of frameworks have shown otherwise BIBREF1 , BIBREF12 . Neural language models (NLMs) have been one of the promising application areas. NLMs are typically various forms of recurrent neural networks (RNNs), which pass through the data sequentially and maintain a memory representation of the past tokens with a hidden context vector. There are many RNN architectures that focus on different ways of updating and maintaining context vectors: Long Short-Term Memory units (LSTM) and Gated Recurrent Units (GRUs) are perhaps most popular. Neural LMs have been used for free-form text generation. In certain application areas, the quality has been high enough to sometimes fool human readers BIBREF0 . Encoder-decoder (seq2seq) models BIBREF13 are architectures of stacked RNNs, which have the ability to generate output sequences based on input sequences. The encoder network reads in a sequence of tokens, and passes it to a decoder network (a LM). In contrast to simpler NLMs, encoder-decoder networks have the ability to use additional context for generating text, which enables more accurate generation of text. Encoder-decoder models are integral in Neural Machine Translation (NMT) BIBREF14 , where the task is to translate a source text from one language to another language. NMT models additionally use beam search strategies to heuristically search the set of possible translations. Training datasets are parallel corpora; large sets of paired sentences in the source and target languages. The application of NMT techniques for online machine translation has significantly improved the quality of translations, bringing it closer to human performance BIBREF15 .",
+      "Neural machine translation models are efficient at mapping one expression to another (one-to-one mapping). Researchers have evaluated these models for conversation generation BIBREF16 , with mixed results. Some researchers attribute poor performance to the use of the negative log likelihood cost function during training, which emphasizes generation of high-confidence phrases rather than diverse phrases BIBREF17 . The results are often generic text, which lacks variation. Li et al. have suggested various augmentations to this, among others suppressing typical responses in the decoder language model to promote response diversity BIBREF17 ."
+    ],
+    [
+      "We discuss the attack model, our generative machine learning method and controlling the generative process in this section."
+    ],
+    [
+      "Wang et al. BIBREF7 described a model of crowd-turfing attacks consisting of three entities: customers who desire to have fake reviews for a particular target (e.g. their restaurant) on a particular platform (e.g. Yelp), agents who offer fake review services to customers, and workers who are orchestrated by the agent to compose and post fake reviews.",
+      "Automated crowd-turfing attacks (ACA) replace workers by a generative model. This has several benefits including better economy and scalability (human workers are more expensive and slower) and reduced detectability (agent can better control the rate at which fake reviews are generated and posted).",
+      "We assume that the agent has access to public reviews on the review platform, by which it can train its generative model. We also assume that it is easy for the agent to create a large number of accounts on the review platform so that account-based detection or rate-limiting techniques are ineffective against fake reviews.",
+      "The quality of the generative model plays a crucial role in the attack. Yao et al. BIBREF0 propose the use of a character-based LSTM as base for generative model. LSTMs are not conditioned to generate reviews for a specific target BIBREF1 , and may mix-up concepts from different contexts during free-form generation. Mixing contextually separate words is one of the key criteria that humans use to identify fake reviews. These may result in violations of known indicators for fake content BIBREF18 . For example, the review content may not match prior expectations nor the information need that the reader has. We improve the attack model by considering a more capable generative model that produces more appropriate reviews: a neural machine translation (NMT) model."
+    ],
+    [
+      "We propose the use of NMT models for fake review generation. The method has several benefits: 1) the ability to learn how to associate context (keywords) to reviews, 2) fast training time, and 3) a high-degree of customization during production time, e.g. introduction of specific waiter or food items names into reviews.",
+      "NMT models are constructions of stacked recurrent neural networks (RNNs). They include an encoder network and a decoder network, which are jointly optimized to produce a translation of one sequence to another. The encoder rolls over the input data in sequence and produces one INLINEFORM0 -dimensional context vector representation for the sentence. The decoder then generates output sequences based on the embedding vector and an attention module, which is taught to associate output words with certain input words. The generation typically continues until a specific EOS (end of sentence) token is encountered. The review length can be controlled in many ways, e.g. by setting the probability of generating the EOS token to zero until the required length is reached.",
+      "NMT models often also include a beam search BIBREF14 , which generates several hypotheses and chooses the best ones amongst them. In our work, we use the greedy beam search technique. We forgo the use of additional beam searches as we found that the quality of the output was already adequate and the translation phase time consumption increases linearly for each beam used.",
+      "We use the Yelp Challenge dataset BIBREF2 for our fake review generation. The dataset (Aug 2017) contains 2.9 million 1 \u20135 star restaurant reviews. We treat all reviews as genuine human-written reviews for the purpose of this work, since wide-scale deployment of machine-generated review attacks are not yet reported (Sep 2017) BIBREF19 . As preprocessing, we remove non-printable (non-ASCII) characters and excessive white-space. We separate punctuation from words. We reserve 15,000 reviews for validation and 3,000 for testing, and the rest we use for training. NMT models require a parallel corpus of source and target sentences, i.e. a large set of (source, target)-pairs. We set up a parallel corpus by constructing (context, review)-pairs from the dataset. Next, we describe how we created our input context.",
+      "The Yelp Challenge dataset includes metadata about restaurants, including their names, food tags, cities and states these restaurants are located in. For each restaurant review, we fetch this metadata and use it as our input context in the NMT model. The corresponding restaurant review is similarly set as the target sentence. This method produced 2.9 million pairs of sentences in our parallel corpus. We show one example of the parallel training corpus in Example 1 below:",
+      "5 Public House Las Vegas NV Gastropubs Restaurants > Excellent",
+      "food and service . Pricey , but well worth it . I would recommend",
+      "the bone marrow and sampler platter for appetizers . \\end{verbatim}",
+      " ",
+      " ",
+      "\\noindent The order {\\textbf{[rating name city state tags]}} is kept constant.",
+      "Training the model conditions it to associate certain sequences of words in the input sentence with others in the output.",
+      " ",
+      "\\subsubsection{Training Settings}",
+      " ",
+      "We train our NMT model on a commodity PC with a i7-4790k CPU (4.00GHz), with 32GB RAM and one NVidia GeForce GTX 980 GPU. Our system can process approximately 1,300 \\textendash 1,500 source tokens/s and approximately 5,730 \\textendash 5,830 output tokens/s. Training one epoch takes in average 72 minutes. The model is trained for 8 epochs, i.e. over night. We call fake review generated by this model \\emph{NMT-Fake reviews}. We only need to train one model to produce reviews of different ratings.",
+      "We use the training settings: adam optimizer \\cite{kingma2014adam} with the suggested learning rate 0.001 \\cite{klein2017opennmt}. For most parts, parameters are at their default values. Notably, the maximum sentence length of input and output is 50 tokens by default.",
+      "We leverage the framework openNMT-py \\cite{klein2017opennmt} to teach the our NMT model.",
+      "We list used openNMT-py commands in Appendix Table~\\ref{table:openNMT-py_commands}.",
+      " ",
+      "\\begin{figure}[t]",
+      "\\begin{center}",
+      " \\begin{tabular}{ | l | }",
+      " \\hline",
+      "Example 2. Greedy NMT \\\\",
+      "Great food, \\underline{great} service, \\underline{great} \\textit{\\textit{beer selection}}. I had the \\textit{Gastropubs burger} and it",
+      "\\\\",
+      "was delicious. The \\underline{\\textit{beer selection}} was also \\underline{great}. \\\\",
+      "\\\\",
+      "Example 3. NMT-Fake* \\\\",
+      "I love this restaurant. Great food, great service. It's \\textit{a little pricy} but worth\\\\",
+      "it for the \\textit{quality} of the \\textit{beer} and atmosphere you can see in \\textit{Vegas}",
+      "\\\\",
+      " \\hline",
+      " \\end{tabular}",
+      " \\label{table:output_comparison}",
+      "\\end{center}",
+      "\\caption{Na\\\"{i}ve text generation with NMT vs. generation using our NTM model. Repetitive patterns are \\underline{underlined}. Contextual words are \\emph{italicized}. Both examples here are generated based on the context given in Example~1.}",
+      "\\label{fig:comparison}",
+      "\\end{figure}",
+      " ",
+      "\\subsection{Controlling generation of fake reviews}",
+      "\\label{sec:generating}",
+      " ",
+      "Greedy NMT beam searches are practical in many NMT cases. However, the results are simply repetitive, when naively applied to fake review generation (See Example~2 in Figure~\\ref{fig:comparison}).",
+      "The NMT model produces many \\emph{high-confidence} word predictions, which are repetitive and obviously fake. We calculated that in fact, 43\\% of the generated sentences started with the phrase ``Great food''. The lack of diversity in greedy use of NMTs for text generation is clear.",
+      " ",
+      " ",
+      "\\begin{algorithm}[!b]",
+      " \\KwData{Desired review context $C_\\mathrm{input}$ (given as cleartext), NMT model}",
+      " \\KwResult{Generated review $out$ for input context $C_\\mathrm{input}$}",
+      "set $b=0.3$, $\\lambda=-5$, $\\alpha=\\frac{2}{3}$, $p_\\mathrm{typo}$, $p_\\mathrm{spell}$ \\\\",
+      "$\\log p \\leftarrow \\text{NMT.decode(NMT.encode(}C_\\mathrm{input}\\text{))}$ \\\\",
+      "out $\\leftarrow$ [~] \\\\",
+      "$i \\leftarrow 0$ \\\\",
+      "$\\log p \\leftarrow \\text{Augment}(\\log p$, $b$, $\\lambda$, $1$, $[~]$, 0)~~~~~~~~~~~~~~~ |~random penalty~\\\\",
+      "\\While{$i=0$ or $o_i$ not EOS}{",
+      "$\\log \\Tilde{p} \\leftarrow \\text{Augment}(\\log p$, $b$, $\\lambda$, $\\alpha$, $o_i$, $i$)~~~~~~~~~~~ |~start \\& memory penalty~\\\\",
+      "$o_i \\leftarrow$ \\text{NMT.beam}($\\log \\Tilde{p}$, out) \\\\",
+      "out.append($o_i$) \\\\",
+      "$i \\leftarrow i+1$",
+      "}\\text{return}~$\\text{Obfuscate}$(out,~$p_\\mathrm{typo}$,~$p_\\mathrm{spell}$)",
+      "\\caption{Generation of NMT-Fake* reviews.}",
+      "\\label{alg:base}",
+      "\\end{algorithm}",
+      " ",
+      "In this work, we describe how we succeeded in creating more diverse and less repetitive generated reviews, such as Example 3 in Figure~\\ref{fig:comparison}.",
+      "We outline pseudocode for our methodology of generating fake reviews in Algorithm~\\ref{alg:base}. There are several parameters in our algorithm.",
+      "The details of the algorithm will be shown later.",
+      "We modify the openNMT-py translation phase by changing log-probabilities before passing them to the beam search.",
+      "We notice that reviews generated with openNMT-py contain almost no language errors. As an optional post-processing step, we obfuscate reviews by introducing natural typos/misspellings randomly. In the next sections, we describe how we succeeded in generating more natural sentences from our NMT model, i.e. generating reviews like Example~3 instead of reviews like Example~2.",
+      " ",
+      "\\subsubsection{Variation in word content}",
+      " ",
+      "Example 2 in Figure~\\ref{fig:comparison} repeats commonly occurring words given for a specific context (e.g. \\textit{great, food, service, beer, selection, burger} for Example~1). Generic review generation can be avoided by decreasing probabilities (log-likelihoods \\cite{murphy2012machine}) of the generators LM, the decoder.",
+      "We constrain the generation of sentences by randomly \\emph{imposing penalties to words}.",
+      "We tried several forms of added randomness, and found that adding constant penalties to a \\emph{random subset} of the target words resulted in the most natural sentence flow. We call these penalties \\emph{Bernoulli penalties}, since the random variables are chosen as either 1 or 0 (on or off).",
+      " ",
+      " ",
+      "\\paragraph{Bernoulli penalties to language model}",
+      "To avoid generic sentences components, we augment the default language model $p(\\cdot)$ of the decoder by",
+      " ",
+      "\\begin{equation}",
+      "\\log \\Tilde{p}(t_k) = \\log p(t_k | t_i, \\dots, t_1) + \\lambda q,",
+      "\\end{equation}",
+      " ",
+      "where $q \\in R^{V}$ is a vector of Bernoulli-distributed random values that obtain values $1$ with probability $b$ and value $0$ with probability $1-b_i$, and $\\lambda < 0$. Parameter $b$ controls how much of the vocabulary is forgotten and $\\lambda$ is a soft penalty of including ``forgotten'' words in a review.",
+      "$\\lambda q_k$ emphasizes sentence forming with non-penalized words. The randomness is reset at the start of generating a new review.",
+      "Using Bernoulli penalties in the language model, we can ``forget'' a certain proportion of words and essentially ``force'' the creation of less typical sentences. We will test the effect of these two parameters, the Bernoulli probability $b$ and log-likelihood penalty of including ``forgotten'' words $\\lambda$, with a user study in Section~\\ref{sec:varying}.",
+      " ",
+      "\\paragraph{Start penalty}",
+      "We introduce start penalties to avoid generic sentence starts (e.g. ``Great food, great service''). Inspired by \\cite{li2016diversity}, we add a random start penalty $\\lambda s^\\mathrm{i}$, to our language model, which decreases monotonically for each generated token. We set $\\alpha \\leftarrow 0.66$ as it's effect decreases by 90\\% every 5 words generated.",
+      " ",
+      "\\paragraph{Penalty for reusing words}",
+      "Bernoulli penalties do not prevent excessive use of certain words in a sentence (such as \\textit{great} in Example~2).",
+      "To avoid excessive reuse of words, we included a memory penalty for previously used words in each translation.",
+      "Concretely, we add the penalty $\\lambda$ to each word that has been generated by the greedy search.",
+      " ",
+      "\\subsubsection{Improving sentence coherence}",
+      "\\label{sec:grammar}",
+      "We visually analyzed reviews after applying these penalties to our NMT model. While the models were clearly diverse, they were \\emph{incoherent}: the introduction of random penalties had degraded the grammaticality of the sentences. Amongst others, the use of punctuation was erratic, and pronouns were used semantically wrongly (e.g. \\emph{he}, \\emph{she} might be replaced, as could ``and''/``but''). To improve the authenticity of our reviews, we added several \\emph{grammar-based rules}.",
+      " ",
+      "English language has several classes of words which are important for the natural flow of sentences.",
+      "We built a list of common pronouns (e.g. I, them, our), conjunctions (e.g. and, thus, if), punctuation (e.g. ,/.,..), and apply only half memory penalties for these words. We found that this change made the reviews more coherent. The pseudocode for this and the previous step is shown in Algorithm~\\ref{alg:aug}.",
+      "The combined effect of grammar-based rules and LM augmentation is visible in Example~3, Figure~\\ref{fig:comparison}.",
+      " ",
+      "\\begin{algorithm}[!t]",
+      " \\KwData{Initial log LM $\\log p$, Bernoulli probability $b$, soft-penalty $\\lambda$, monotonic factor $\\alpha$, last generated token $o_i$, grammar rules set $G$}",
+      " \\KwResult{Augmented log LM $\\log \\Tilde{p}$}",
+      "\\begin{algorithmic}[1]",
+      "\\Procedure {Augment}{$\\log p$, $b$, $\\lambda$, $\\alpha$, $o_i$, $i$}{ \\\\",
+      "generate $P_{\\mathrm{1:N}} \\leftarrow Bernoulli(b)$~~~~~~~~~~~~~~~|~$\\text{One value} \\in \\{0,1\\}~\\text{per token}$~ \\\\",
+      "$I \\leftarrow P>0$ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~|~Select positive indices~\\\\",
+      "$\\log \\Tilde{p} \\leftarrow$ $\\text{Discount}$($\\log p$, $I$, $\\lambda \\cdot \\alpha^i$,$G$) ~~~~~~ |~start penalty~\\\\",
+      "$\\log \\Tilde{p} \\leftarrow$ $\\text{Discount}$($\\log \\Tilde{p}$, $[o_i]$, $\\lambda$,$G$) ~~~~~~~~~ |~memory penalty~\\\\",
+      "\\textbf{return}~$\\log \\Tilde{p}$",
+      "}",
+      "\\EndProcedure",
+      "\\\\",
+      "\\Procedure {Discount}{$\\log p$, $I$, $\\lambda$, $G$}{",
+      "\\State{\\For{$i \\in I$}{",
+      "\\eIf{$o_i \\in G$}{",
+      "$\\log p_{i} \\leftarrow \\log p_{i} + \\lambda/2$",
+      "}{",
+      "$\\log p_{i} \\leftarrow \\log p_{i} + \\lambda$}",
+      "}\\textbf{return}~$\\log p$",
+      "\\EndProcedure",
+      "}}",
+      "\\end{algorithmic}",
+      "\\caption{Pseudocode for augmenting language model. }",
+      "\\label{alg:aug}",
+      "\\end{algorithm}",
+      " ",
+      "\\subsubsection{Human-like errors}",
+      "\\label{sec:obfuscation}",
+      "We notice that our NMT model produces reviews without grammar mistakes.",
+      "This is unlike real human writers, whose sentences contain two types of language mistakes 1) \\emph{typos} that are caused by mistakes in the human motoric input, and 2) \\emph{common spelling mistakes}.",
+      "We scraped a list of common English language spelling mistakes from Oxford dictionary\\footnote{\\url{https://en.oxforddictionaries.com/spelling/common-misspellings}} and created 80 rules for randomly \\emph{re-introducing spelling mistakes}.",
+      "Similarly, typos are randomly reintroduced based on the weighted edit distance\\footnote{\\url{https://pypi.python.org/pypi/weighted-levenshtein/0.1}}, such that typos resulting in real English words with small perturbations are emphasized.",
+      "We use autocorrection tools\\footnote{\\url{https://pypi.python.org/pypi/autocorrect/0.1.0}} for finding these words.",
+      "We call these augmentations \\emph{obfuscations}, since they aim to confound the reader to think a human has written them. We omit the pseudocode description for brevity.",
+      " ",
+      "\\subsection{Experiment: Varying generation parameters in our NMT model}",
+      "\\label{sec:varying}",
+      " ",
+      "Parameters $b$ and $\\lambda$ control different aspects in fake reviews.",
+      "We show six different examples of generated fake reviews in Table~\\ref{table:categories}.",
+      "Here, the largest differences occur with increasing values of $b$: visibly, the restaurant reviews become more extreme.",
+      "This occurs because a large portion of vocabulary is ``forgotten''. Reviews with $b \\geq 0.7$ contain more rare word combinations, e.g. ``!!!!!'' as punctuation, and they occasionally break grammaticality (''experience was awesome'').",
+      "Reviews with lower $b$ are more generic: they contain safe word combinations like ``Great place, good service'' that occur in many reviews. Parameter $\\lambda$'s is more subtle: it affects how random review starts are and to a degree, the discontinuation between statements within the review.",
+      "We conducted an Amazon Mechanical Turk (MTurk) survey in order to determine what kind of NMT-Fake reviews are convincing to native English speakers. We describe the survey and results in the next section.",
+      " ",
+      " ",
+      "\\begin{table}[!b]",
+      "\\caption{Six different parametrizations of our NMT reviews and one example for each. The context is ``5 P~.~F~.~Chang ' s Scottsdale AZ'' in all examples.}",
+      "\\begin{center}",
+      " \\begin{tabular}{ | l | l | }",
+      " \\hline",
+      " $(b, \\lambda)$ & Example review for context \\\\ \\hline",
+      " \\hline",
+      " $(0.3, -3)$ & I love this location! Great service, great food and the best drinks in Scottsdale. \\\\",
+      " & The staff is very friendly and always remembers u when we come in\\\\\\hline",
+      " $(0.3, -5)$ & Love love the food here! I always go for lunch. They have a great menu and \\\\",
+      " & they make it fresh to order. Great place, good service and nice staff\\\\\\hline",
+      " $(0.5, -4)$ & I love their chicken lettuce wraps and fried rice!! The service is good, they are\\\\",
+      " & always so polite. They have great happy hour specials and they have a lot\\\\",
+      " & of options.\\\\\\hline",
+      " $(0.7, -3)$ & Great place to go with friends! They always make sure your dining \\\\",
+      " & experience was awesome.\\\\ \\hline",
+      " $(0.7, -5)$ & Still haven't ordered an entree before but today we tried them once..\\\\",
+      " & both of us love this restaurant....\\\\\\hline",
+      " $(0.9, -4)$ & AMAZING!!!!! Food was awesome with excellent service. Loved the lettuce \\\\",
+      " & wraps. Great drinks and wine! Can't wait to go back so soon!!\\\\ \\hline",
+      " \\end{tabular}",
+      " \\label{table:categories}",
+      "\\end{center}",
+      "\\end{table}",
+      " ",
+      "\\subsubsection{MTurk study}",
+      "\\label{sec:amt}",
+      "We created 20 jobs, each with 100 questions, and requested master workers in MTurk to complete the jobs.",
+      "We randomly generated each survey for the participants. Each review had a 50\\% chance to be real or fake. The fake ones further were chosen among six (6) categories of fake reviews (Table~\\ref{table:categories}).",
+      "The restaurant and the city was given as contextual information to the participants. Our aim was to use this survey to understand how well English-speakers react to different parametrizations of NMT-Fake reviews.",
+      "Table~\\ref{table:amt_pop} in Appendix summarizes the statistics for respondents in the survey. All participants were native English speakers from America. The base rate (50\\%) was revealed to the participants prior to the study.",
+      " ",
+      "We first investigated overall detection of any NMT-Fake reviews (1,006 fake reviews and 994 real reviews). We found that the participants had big difficulties in detecting our fake reviews. In average, the reviews were detected with class-averaged \\emph{F-score of only 56\\%}, with 53\\% F-score for fake review detection and 59\\% F-score for real review detection. The results are very close to \\emph{random detection}, where precision, recall and F-score would each be 50\\%. Results are recorded in Table~\\ref{table:MTurk_super}. Overall, the fake review generation is very successful, since human detection rate across categories is close to random.",
+      " ",
+      "\\begin{table}[t]",
+      "\\caption{Effectiveness of Mechanical Turkers in distinguishing human-written reviews from fake reviews generated by our NMT model (all variants).}",
+      "\\begin{center}",
+      " \\begin{tabular}{ | c | c |c |c | c | }",
+      " \\hline",
+      " \\multicolumn{5}{|c|}{Classification report}",
+      " \\\\ \\hline",
+      " Review Type & Precision & Recall & F-score & Support \\\\ \\hline",
+      " \\hline",
+      " Human & 55\\% & 63\\% & 59\\% & 994\\\\",
+      " NMT-Fake & 57\\% & 50\\% & 53\\% & 1006 \\\\",
+      " \\hline",
+      " \\end{tabular}",
+      " \\label{table:MTurk_super}",
+      "\\end{center}",
+      "\\end{table}",
+      " ",
+      "We noticed some variation in the detection of different fake review categories. The respondents in our MTurk survey had most difficulties recognizing reviews of category $(b=0.3, \\lambda=-5)$, where true positive rate was $40.4\\%$, while the true negative rate of the real class was $62.7\\%$. The precision were $16\\%$ and $86\\%$, respectively. The class-averaged F-score is $47.6\\%$, which is close to random. Detailed classification reports are shown in Table~\\ref{table:MTurk_sub} in Appendix. Our MTurk-study shows that \\emph{our NMT-Fake reviews pose a significant threat to review systems}, since \\emph{ordinary native English-speakers have very big difficulties in separating real reviews from fake reviews}. We use the review category $(b=0.3, \\lambda=-5)$ for future user tests in this paper, since MTurk participants had most difficulties detecting these reviews. We refer to this category as NMT-Fake* in this paper.",
+      " ",
+      "\\section{Evaluation}",
+      "\\graphicspath{ {figures/}}",
+      " ",
+      "We evaluate our fake reviews by first comparing them statistically to previously proposed types of fake reviews, and proceed with a user study with experienced participants. We demonstrate the statistical difference to existing fake review types \\cite{yao2017automated,mukherjee2013yelp,rayana2015collective} by training classifiers to detect previous types and investigate classification performance.",
+      " ",
+      "\\subsection{Replication of state-of-the-art model: LSTM}",
+      "\\label{sec:repl}",
+      " ",
+      "Yao et al. \\cite{yao2017automated} presented the current state-of-the-art generative model for fake reviews. The model is trained over the Yelp Challenge dataset using a two-layer character-based LSTM model.",
+      "We requested the authors of \\cite{yao2017automated} for access to their LSTM model or a fake review dataset generated by their model. Unfortunately they were not able to share either of these with us. We therefore replicated their model as closely as we could, based on their paper and e-mail correspondence\\footnote{We are committed to sharing our code with bonafide researchers for the sake of reproducibility.}.",
+      " ",
+      "We used the same graphics card (GeForce GTX) and trained using the same framework (torch-RNN in lua). We downloaded the reviews from Yelp Challenge and preprocessed the data to only contain printable ASCII characters, and filtered out non-restaurant reviews. We trained the model for approximately 72 hours. We post-processed the reviews using the customization methodology described in \\cite{yao2017automated} and email correspondence. We call fake reviews generated by this model LSTM-Fake reviews.",
+      " ",
+      "\\subsection{Similarity to existing fake reviews}",
+      "\\label{sec:automated}",
+      " ",
+      "We now want to understand how NMT-Fake* reviews compare to a) LSTM fake reviews and b) human-generated fake reviews. We do this by comparing the statistical similarity between these classes.",
+      " ",
+      "For `a' (Figure~\\ref{fig:lstm}), we use the Yelp Challenge dataset. We trained a classifier using 5,000 random reviews from the Yelp Challenge dataset (``human'') and 5,000 fake reviews generated by LSTM-Fake. Yao et al. \\cite{yao2017automated} found that character features are essential in identifying LSTM-Fake reviews. Consequently, we use character features (n-grams up to 3).",
+      " ",
+      "For `b' (Figure~\\ref{fig:shill}),we the ``Yelp Shills'' dataset (combination of YelpZip \\cite{mukherjee2013yelp}, YelpNYC \\cite{mukherjee2013yelp}, YelpChi \\cite{rayana2015collective}). This dataset labels entries that are identified as fraudulent by Yelp's filtering mechanism (''shill reviews'')\\footnote{Note that shill reviews are probably generated by human shills \\cite{zhao2017news}.}. The rest are treated as genuine reviews from human users (''genuine''). We use 100,000 reviews from each category to train a classifier. We use features from the commercial psychometric tool LIWC2015 \\cite{pennebaker2015development} to generated features.",
+      " ",
+      "In both cases, we use AdaBoost (with 200 shallow decision trees) for training. For testing each classifier, we use a held out test set of 1,000 reviews from both classes in each case. In addition, we test 1,000 NMT-Fake* reviews. Figures~\\ref{fig:lstm} and~\\ref{fig:shill} show the results. The classification threshold of 50\\% is marked with a dashed line.",
+      " ",
+      "\\begin{figure}",
+      " \\begin{subfigure}[b]{0.5\\columnwidth}",
+      " \\includegraphics[width=\\columnwidth]{figures/lstm.png}",
+      " \\caption{Human--LSTM reviews.}",
+      " \\label{fig:lstm}",
+      " \\end{subfigure}",
+      " \\begin{subfigure}[b]{0.5\\columnwidth}",
+      " \\includegraphics[width=\\columnwidth]{figures/distribution_shill.png}",
+      " \\caption{Genuine--Shill reviews.}",
+      " \\label{fig:shill}",
+      " \\end{subfigure}",
+      " \\caption{",
+      " Histogram comparison of NMT-Fake* reviews with LSTM-Fake reviews and human-generated (\\emph{genuine} and \\emph{shill}) reviews. Figure~\\ref{fig:lstm} shows that a classifier trained to distinguish ``human'' vs. LSTM-Fake cannot distinguish ``human'' vs NMT-Fake* reviews. Figure~\\ref{fig:shill} shows NMT-Fake* reviews are more similar to \\emph{genuine} reviews than \\emph{shill} reviews.",
+      " }",
+      " \\label{fig:statistical_similarity}",
+      "\\end{figure}",
+      " ",
+      "We can see that our new generated reviews do not share strong attributes with previous known categories of fake reviews. If anything, our fake reviews are more similar to genuine reviews than previous fake reviews. We thus conjecture that our NMT-Fake* fake reviews present a category of fake reviews that may go undetected on online review sites.",
+      " ",
+      " ",
+      "\\subsection{Comparative user study}",
+      "\\label{sec:comparison}",
+      "We wanted to evaluate the effectiveness of fake reviews againsttech-savvy users who understand and know to expect machine-generated fake reviews. We conducted a user study with 20 participants, all with computer science education and at least one university degree. Participant demographics are shown in Table~\\ref{table:amt_pop} in the Appendix. Each participant first attended a training session where they were asked to label reviews (fake and genuine) and could later compare them to the correct answers -- we call these participants \\emph{experienced participants}.",
+      "No personal data was collected during the user study.",
+      " ",
+      "Each person was given two randomly selected sets of 30 of reviews (a total of 60 reviews per person) with reviews containing 10 \\textendash 50 words each.",
+      "Each set contained 26 (87\\%) real reviews from Yelp and 4 (13\\%) machine-generated reviews,",
+      "numbers chosen based on suspicious review prevalence on Yelp~\\cite{mukherjee2013yelp,rayana2015collective}.",
+      "One set contained machine-generated reviews from one of the two models (NMT ($b=0.3, \\lambda=-5$) or LSTM),",
+      "and the other set reviews from the other in randomized order. The number of fake reviews was revealed to each participant in the study description. Each participant was requested to mark four (4) reviews as fake.",
+      " ",
+      "Each review targeted a real restaurant. A screenshot of that restaurant's Yelp page was shown to each participant prior to the study. Each participant evaluated reviews for one specific, randomly selected, restaurant. An example of the first page of the user study is shown in Figure~\\ref{fig:screenshot} in Appendix.",
+      " ",
+      "\\begin{figure}[!ht]",
+      "\\centering",
+      "\\includegraphics[width=.7\\columnwidth]{detection2.png}",
+      "\\caption{Violin plots of detection rate in comparative study. Mean and standard deviations for number of detected fakes are $0.8\\pm0.7$ for NMT-Fake* and $2.5\\pm1.0$ for LSTM-Fake. $n=20$. A sample of random detection is shown as comparison.}",
+      "\\label{fig:aalto}",
+      "\\end{figure}",
+      " ",
+      " ",
+      "Figure~\\ref{fig:aalto} shows the distribution of detected reviews of both types. A hypothetical random detector is shown for comparison.",
+      "NMT-Fake* reviews are significantly more difficult to detect for our experienced participants. In average, detection rate (recall) is $20\\%$ for NMT-Fake* reviews, compared to $61\\%$ for LSTM-based reviews.",
+      "The precision (and F-score) is the same as the recall in our study, since participants labeled 4 fakes in each set of 30 reviews \\cite{murphy2012machine}.",
+      "The distribution of the detection across participants is shown in Figure~\\ref{fig:aalto}. \\emph{The difference is statistically significant with confidence level $99\\%$} (Welch's t-test).",
+      "We compared the detection rate of NMT-Fake* reviews to a random detector, and find that \\emph{our participants detection rate of NMT-Fake* reviews is not statistically different from random predictions with 95\\% confidence level} (Welch's t-test).",
+      " ",
+      " ",
+      "\\section{Defenses}",
+      " ",
+      "\\label{sec:detection}",
+      " ",
+      "We developed an AdaBoost-based classifier to detect our new fake reviews, consisting of 200 shallow decision trees (depth 2). The features we used are recorded in Table~\\ref{table:features_adaboost} (Appendix).",
+      "We used word-level features based on spaCy-tokenization \\cite{honnibal-johnson:2015:EMNLP} and constructed n-gram representation of POS-tags and dependency tree tags. We added readability features from NLTK~\\cite{bird2004nltk}.",
+      " ",
+      "\\begin{figure}[ht]",
+      "\\centering",
+      "\\includegraphics[width=.7\\columnwidth]{obf_score_fair_2.png}",
+      "\\caption{",
+      "Adaboost-based classification of NMT-Fake and human-written reviews.",
+      "Effect of varying $b$ and $\\lambda$ in fake review generation.",
+      "The variant native speakers had most difficulties detecting is well detectable by AdaBoost (97\\%).}",
+      "\\label{fig:adaboost_matrix_b_lambda}",
+      "\\end{figure}",
+      " ",
+      " ",
+      "Figure~\\ref{fig:adaboost_matrix_b_lambda} shows our AdaBoost classifier's class-averaged F-score at detecting different kind of fake reviews. The classifier is very effective in detecting reviews that humans have difficulties detecting. For example, the fake reviews MTurk users had most difficulty detecting ($b=0.3, \\lambda=-5$) are detected with an excellent 97\\% F-score.",
+      "The most important features for the classification were counts for frequently occurring words in fake reviews (such as punctuation, pronouns, articles) as well as the readability feature ``Automated Readability Index''. We thus conclude that while NMT-Fake reviews are difficult to detect for humans, they can be well detected with the right tools.",
+      " ",
+      "\\section{Related Work}",
+      " ",
+      "Kumar and Shah~\\cite{kumar2018false} survey and categorize false information research. Automatically generated fake reviews are a form of \\emph{opinion-based false information}, where the creator of the review may influence reader's opinions or decisions.",
+      "Yao et al. \\cite{yao2017automated} presented their study on machine-generated fake reviews. Contrary to us, they investigated character-level language models, without specifying a specific context before generation. We leverage existing NMT tools to encode a specific context to the restaurant before generating reviews.",
+      "Supporting our study, Everett et al~\\cite{Everett2016Automated} found that security researchers were less likely to be fooled by Markov chain-generated Reddit comments compared to ordinary Internet users.",
+      " ",
+      "Diversification of NMT model outputs has been studied in \\cite{li2016diversity}. The authors proposed the use of a penalty to commonly occurring sentences (\\emph{n-grams}) in order to emphasize maximum mutual information-based generation.",
+      "The authors investigated the use of NMT models in chatbot systems.",
+      "We found that unigram penalties to random tokens (Algorithm~\\ref{alg:aug}) was easy to implement and produced sufficiently diverse responses.",
+      " ",
+      "\\section {Discussion and Future Work}",
+      " ",
+      "\\paragraph{What makes NMT-Fake* reviews difficult to detect?} First, NMT models allow the encoding of a relevant context for each review, which narrows down the possible choices of words that the model has to choose from. Our NMT model had a perplexity of approximately $25$, while the model of \\cite{yao2017automated} had a perplexity of approximately $90$ \\footnote{Personal communication with the authors}. Second, the beam search in NMT models narrows down choices to natural-looking sentences. Third, we observed that the NMT model produced \\emph{better structure} in the generated sentences (i.e. a more coherent story).",
+      " ",
+      "\\paragraph{Cost of generating reviews} With our setup, generating one review took less than one second. The cost of generation stems mainly from the overnight training. Assuming an electricity cost of 16 cents / kWh (California) and 8 hours of training, training the NMT model requires approximately 1.30 USD. This is a 90\\% reduction in time compared to the state-of-the-art \\cite{yao2017automated}. Furthermore, it is possible to generate both positive and negative reviews with the same model.",
+      " ",
+      "\\paragraph{Ease of customization} We experimented with inserting specific words into the text by increasing their log likelihoods in the beam search. We noticed that the success depended on the prevalence of the word in the training set. For example, adding a +5 to \\emph{Mike} in the log-likelihood resulted in approximately 10\\% prevalence of this word in the reviews. An attacker can therefore easily insert specific keywords to reviews, which can increase evasion probability.",
+      " ",
+      "\\paragraph{Ease of testing} Our diversification scheme is applicable during \\emph{generation phase}, and does not affect the training setup of the network in any way. Once the NMT model is obtained, it is easy to obtain several different variants of NMT-Fake reviews by varying parameters $b$ and $\\lambda$.",
+      " ",
+      " ",
+      " ",
+      "\\paragraph{Languages} The generation methodology is not per-se language-dependent. The requirement for successful generation is that sufficiently much data exists in the targeted language. However, our language model modifications require some knowledge of that target language's grammar to produce high-quality reviews.",
+      " ",
+      "\\paragraph{Generalizability of detection techniques} Currently, fake reviews are not universally detectable. Our results highlight that it is difficult to claim detection performance on unseen types of fake reviews (Section~\\ref{sec:automated}). We see this an open problem that deserves more attention in fake reviews research.",
+      " ",
+      "\\paragraph{Generalizability to other types of datasets} Our technique can be applied to any dataset, as long as there is sufficient training data for the NMT model. We used approximately 2.9 million reviews for this work.",
+      " ",
+      "\\section{Conclusion}",
+      " ",
+      "In this paper, we showed that neural machine translation models can be used to generate fake reviews that are very effective in deceiving even experienced, tech-savvy users.",
+      "This supports anecdotal evidence \\cite{national2017commission}.",
+      "Our technique is more effective than state-of-the-art \\cite{yao2017automated}.",
+      "We conclude that machine-aided fake review detection is necessary since human users are ineffective in identifying fake reviews.",
+      "We also showed that detectors trained using one type of fake reviews are not effective in identifying other types of fake reviews.",
+      "Robust detection of fake reviews is thus still an open problem.",
+      " ",
+      " ",
+      "\\section*{Acknowledgments}",
+      "We thank Tommi Gr\\\"{o}ndahl for assistance in planning user studies and the",
+      "participants of the user study for their time and feedback. We also thank",
+      "Luiza Sayfullina for comments that improved the manuscript.",
+      "We thank the authors of \\cite{yao2017automated} for answering questions about",
+      "their work.",
+      " ",
+      " ",
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+      " ",
+      " ",
+      " ",
+      "\\section*{Appendix}",
+      " ",
+      "We present basic demographics of our MTurk study and the comparative study with experienced users in Table~\\ref{table:amt_pop}.",
+      " ",
+      "\\begin{table}",
+      "\\caption{User study statistics.}",
+      "\\begin{center}",
+      " \\begin{tabular}{ | l | c | c | }",
+      " \\hline",
+      " Quality & Mechanical Turk users & Experienced users\\\\",
+      " \\hline",
+      " Native English Speaker & Yes (20) & Yes (1) No (19) \\\\",
+      " Fluent in English & Yes (20) & Yes (20) \\\\",
+      " Age & 21-40 (17) 41-60 (3) & 21-25 (8) 26-30 (7) 31-35 (4) 41-45 (1)\\\\",
+      " Gender & Male (14) Female (6) & Male (17) Female (3)\\\\",
+      " Highest Education & High School (10) Bachelor (10) & Bachelor (9) Master (6) Ph.D. (5) \\\\",
+      " \\hline",
+      " \\end{tabular}",
+      " \\label{table:amt_pop}",
+      "\\end{center}",
+      "\\end{table}",
+      " ",
+      " ",
+      "Table~\\ref{table:openNMT-py_commands} shows a listing of the openNMT-py commands we used to create our NMT model and to generate fake reviews.",
+      " ",
+      "\\begin{table}[t]",
+      "\\caption{Listing of used openNMT-py commands.}",
+      "\\begin{center}",
+      " \\begin{tabular}{ | l | l | }",
+      " \\hline",
+      " Phase & Bash command \\\\",
+      " \\hline",
+      " Preprocessing & \\begin{lstlisting}[language=bash]",
+      "python preprocess.py -train_src context-train.txt",
+      "-train_tgt reviews-train.txt -valid_src context-val.txt",
+      "-valid_tgt reviews-val.txt -save_data model",
+      "-lower -tgt_words_min_frequency 10",
+      "\\end{lstlisting}",
+      " \\\\ & \\\\",
+      " Training & \\begin{lstlisting}[language=bash]",
+      "python train.py -data model -save_model model -epochs 8",
+      "-gpuid 0 -learning_rate_decay 0.5 -optim adam",
+      "-learning_rate 0.001 -start_decay_at 3\\end{lstlisting}",
+      " \\\\ & \\\\",
+      " Generation & \\begin{lstlisting}[language=bash]",
+      "python translate.py -model model_acc_35.54_ppl_25.68_e8.pt",
+      "-src context-tst.txt -output pred-e8.txt -replace_unk",
+      "-verbose -max_length 50 -gpu 0",
+      " \\end{lstlisting} \\\\",
+      " \\hline",
+      " \\end{tabular}",
+      " \\label{table:openNMT-py_commands}",
+      "\\end{center}",
+      "\\end{table}",
+      " ",
+      " ",
+      "Table~\\ref{table:MTurk_sub} shows the classification performance of Amazon Mechanical Turkers, separated across different categories of NMT-Fake reviews. The category with best performance ($b=0.3, \\lambda=-5$) is denoted as NMT-Fake*.",
+      " ",
+      "\\begin{table}[b]",
+      "\\caption{MTurk study subclass classification reports. Classes are imbalanced in ratio 1:6. Random predictions are $p_\\mathrm{human} = 86\\%$ and $p_\\mathrm{machine} = 14\\%$, with $r_\\mathrm{human} = r_\\mathrm{machine} = 50\\%$. Class-averaged F-scores for random predictions are $42\\%$.}",
+      "\\begin{center}",
+      " \\begin{tabular}{ | c || c |c |c | c | }",
+      " \\hline",
+      " $(b=0.3, \\lambda = -3)$ & Precision & Recall & F-score & Support \\\\ \\hline",
+      " Human & 89\\% & 63\\% & 73\\% & 994\\\\",
+      " NMT-Fake & 15\\% & 45\\% & 22\\% & 146 \\\\",
+      " \\hline",
+      " \\hline",
+      " $(b=0.3, \\lambda = -5)$ & Precision & Recall & F-score & Support \\\\ \\hline",
+      " Human & 86\\% & 63\\% & 73\\% & 994\\\\",
+      " NMT-Fake* & 16\\% & 40\\% & 23\\% & 171 \\\\",
+      " \\hline",
+      " \\hline",
+      " $(b=0.5, \\lambda = -4)$ & Precision & Recall & F-score & Support \\\\ \\hline",
+      " Human & 88\\% & 63\\% & 73\\% & 994\\\\",
+      " NMT-Fake & 21\\% & 55\\% & 30\\% & 181 \\\\",
+      " \\hline",
+      " \\hline",
+      " $(b=0.7, \\lambda = -3)$ & Precision & Recall & F-score & Support \\\\ \\hline",
+      " Human & 88\\% & 63\\% & 73\\% & 994\\\\",
+      " NMT-Fake & 19\\% & 50\\% & 27\\% & 170 \\\\",
+      " \\hline",
+      " \\hline",
+      " $(b=0.7, \\lambda = -5)$ & Precision & Recall & F-score & Support \\\\ \\hline",
+      " Human & 89\\% & 63\\% & 74\\% & 994\\\\",
+      " NMT-Fake & 21\\% & 57\\% & 31\\% & 174 \\\\",
+      " \\hline",
+      " \\hline",
+      " $(b=0.9, \\lambda = -4)$ & Precision & Recall & F-score & Support \\\\ \\hline",
+      " Human & 88\\% & 63\\% & 73\\% & 994\\\\",
+      " NMT-Fake & 18\\% & 50\\% & 27\\% & 164 \\\\",
+      " \\hline",
+      " \\end{tabular}",
+      " \\label{table:MTurk_sub}",
+      "\\end{center}",
+      "\\end{table}",
+      " ",
+      "Figure~\\ref{fig:screenshot} shows screenshots of the first two pages of our user study with experienced participants.",
+      " ",
+      "\\begin{figure}[ht]",
+      "\\centering",
+      "\\includegraphics[width=1.\\columnwidth]{figures/screenshot_7-3.png}",
+      "\\caption{",
+      "Screenshots of the first two pages in the user study. Example 1 is a NMT-Fake* review, the rest are human-written.",
+      "}",
+      "\\label{fig:screenshot}",
+      "\\end{figure}",
+      " ",
+      "Table~\\ref{table:features_adaboost} shows the features used to detect NMT-Fake reviews using the AdaBoost classifier.",
+      " ",
+      "\\begin{table}",
+      "\\caption{Features used in NMT-Fake review detector.}",
+      "\\begin{center}",
+      " \\begin{tabular}{ | l | c | }",
+      " \\hline",
+      " Feature type & Number of features \\\\ \\hline",
+      " \\hline",
+      " Readability features & 13 \\\\ \\hline",
+      " Unique POS tags & $~20$ \\\\ \\hline",
+      " Word unigrams & 22,831 \\\\ \\hline",
+      " 1/2/3/4-grams of simple part-of-speech tags & 54,240 \\\\ \\hline",
+      " 1/2/3-grams of detailed part-of-speech tags & 112,944 \\\\ \\hline",
+      " 1/2/3-grams of syntactic dependency tags & 93,195 \\\\ \\hline",
+      " \\end{tabular}",
+      " \\label{table:features_adaboost}",
+      "\\end{center}",
+      "\\end{table}",
+      " ",
+      "\\end{document}",
+      ""
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0050/instruction.md b/qasper-0050/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..573d57eaf9e1b519ddd21cdbd65d37dde370fa99
--- /dev/null
+++ b/qasper-0050/instruction.md
@@ -0,0 +1,56 @@
+Name of Paper: Is there Gender bias and stereotype in Portuguese Word Embeddings?
+
+Question: Which word embeddings are analysed?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Portuguese Embedding",
+    "Proposed Approach",
+    "Experiments",
+    "Final Remarks"
+  ],
+  "paragraphs": [
+    [
+      "Recently, the transformative potential of machine learning (ML) has propelled ML into the forefront of mainstream media. In Brazil, the use of such technique has been widely diffused gaining more space. Thus, it is used to search for patterns, regularities or even concepts expressed in data sets BIBREF0 , and can be applied as a form of aid in several areas of everyday life.",
+      "Among the different definitions, ML can be seen as the ability to improve performance in accomplishing a task through the experience BIBREF1 . Thus, BIBREF2 presents this as a method of inferences of functions or hypotheses capable of solving a problem algorithmically from data representing instances of the problem. This is an important way to solve different types of problems that permeate computer science and other areas.",
+      "One of the main uses of ML is in text processing, where the analysis of the content the entry point for various learning algorithms. However, the use of this content can represent the insertion of different types of bias in training and may vary with the context worked. This work aims to analyze and remove gender stereotypes from word embedding in Portuguese, analogous to what was done in BIBREF3 for the English language. Hence, we propose to employ a public word2vec model pre-trained to analyze gender bias in the Portuguese language, quantifying biases present in the model so that it is possible to reduce the spreading of sexism of such models. There is also a stage of bias reducing over the results obtained in the model, where it is sought to analyze the effects of the application of gender distinction reduction techniques.",
+      "This paper is organized as follows: Section SECREF2 discusses related works. Section SECREF3 presents the Portuguese word2vec embeddings model used in this paper and Section SECREF4 proposes our method. Section SECREF5 presents experimental results, whose purpose is to verify results of a de-bias algorithm application in Portuguese embeddings word2vec model and a short discussion about it. Section SECREF6 brings our concluding remarks."
+    ],
+    [
+      "There is a wide range of techniques that provide interesting results in the context of ML algorithms geared to the classification of data without discrimination; these techniques range from the pre-processing of data BIBREF4 to the use of bias removal techniques BIBREF5 in fact. Approaches linked to the data pre-processing step usually consist of methods based on improving the quality of the dataset after which the usual classification tools can be used to train a classifier. So, it starts from a baseline already stipulated by the execution of itself. On the other side of the spectrum, there are Unsupervised and semi-supervised learning techniques, that are attractive because they do not imply the cost of corpus annotation BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 .",
+      "The bias reduction is studied as a way to reduce discrimination through classification through different approaches BIBREF10 BIBREF11 . In BIBREF12 the authors propose to specify, implement, and evaluate the \u201cfairness-aware\" ML interface called themis-ml. In this interface, the main idea is to pick up a data set from a modified dataset. Themis-ml implements two methods for training fairness-aware models. The tool relies on two methods to make agnostic model type predictions: Reject Option Classification and Discrimination-Aware Ensemble Classification, these procedures being used to post-process predictions in a way that reduces potentially discriminatory predictions. According to the authors, it is possible to perceive the potential use of the method as a means of reducing bias in the use of ML algorithms.",
+      "In BIBREF3 , the authors propose a method to hardly reduce bias in English word embeddings collected from Google News. Using word2vec, they performed a geometric analysis of gender direction of the bias contained in the data. Using this property with the generation of gender-neutral analogies, a methodology was provided for modifying an embedding to remove gender stereotypes. Some metrics were defined to quantify both direct and indirect gender biases in embeddings and to develop algorithms to reduce bias in some embedding. Hence, the authors show that embeddings can be used in applications without amplifying gender bias."
+    ],
+    [
+      "In BIBREF13 , the quality of the representation of words through vectors in several models is discussed. According to the authors, the ability to train high-quality models using simplified architectures is useful in models composed of predictive methods that try to predict neighboring words with one or more context words, such as Word2Vec. Word embeddings have been used to provide meaningful representations for words in an efficient way.",
+      "In BIBREF14 , several word embedding models trained in a large Portuguese corpus are evaluated. Within the Word2Vec model, two training strategies were used. In the first, namely Skip-Gram, the model is given the word and attempts to predict its neighboring words. The second, Continuous Bag-of-Words (CBOW), the model is given the sequence of words without the middle one and attempts to predict this omitted word. The latter was chosen for application in the present proposal.",
+      "The authors of BIBREF14 claim to have collected a large corpus from several sources to obtain a multi-genre corpus representative of the Portuguese language. Hence, it comprehensively covers different expressions of the language, making it possible to analyze gender bias and stereotype in Portuguese word embeddings. The dataset used was tokenized and normalized by the authors to reduce the corpus vocabulary size, under the premise that vocabulary reduction provides more representative vectors."
+    ],
+    [
+      "Some linguists point out that the female gender is, in Portuguese, a particularization of the masculine. In this way the only gender mark is the feminine, the others being considered without gender (including names considered masculine). In BIBREF15 the gender representation in Portuguese is associated with a set of phenomena, not only from a linguistic perspective but also from a socio-cultural perspective. Since most of the termination of words (e.g., advogada and advogado) are used to indicate to whom the expression refers, stereotypes can be explained through communication. This implies the presence of biases when dealing with terms such as those referring to professions.",
+      "Figure FIGREF1 illustrates the approach proposed in this work. First, using a list of professions relating the identification of female and male who perform it as a parameter, we evaluate the accuracy of similarity generated by the embeddings. Then, getting the biased results, we apply the De-bias algorithm BIBREF3 aiming to reduce sexist analogies previous generated. Thus, all the results are analyzed by comparing the accuracies.",
+      "Using the word2vec model available in a public repository BIBREF14 , the proposal involves the analysis of the most similar analogies generated before and after the application of the BIBREF3 . The work is focused on the analysis of gender bias associated with professions in word embeddings. So therefore into the evaluation of the accuracy of the associations generated, aiming at achieving results as good as possible without prejudicing the evaluation metrics.",
+      "Algorithm SECREF4 describes the method performed during the evaluation of the gender bias presence. In this method we try to evaluate the accuracy of the analogies generated through the model, that is, to verify the cases of association matching generated between the words.",
+      "[!htb] Model Evaluation [1]",
+      "w2v_evaluate INLINEFORM0 open_model( INLINEFORM1 ) count = 0 INLINEFORM2 in INLINEFORM3 read list of tuples x = model.most_similar(positive=[`ela', male], negative=[`ele'])",
+      "x = female count += 1 accuracy = count/size(profession_pairs) return accuracy"
+    ],
+    [
+      "The purpose of this section is to perform different analysis concerning bias in word2vec models with Portuguese embeddings. The Continuous Bag-of-Words model used was provided by BIBREF14 (described in Section SECREF3 ). For these experiments, we use a model containing 934966 words of dimension 300 per vector representation. To realize the experiments, a list containing fifty professions labels for female and male was used as the parameter of similarity comparison.",
+      "Using the python library gensim, we evaluate the extreme analogies generated when comparing vectors like: INLINEFORM0 , where INLINEFORM1 represents the item from professions list and INLINEFORM2 the expected association. The most similarity function finds the top-N most similar entities, computing cosine similarity between a simple mean of the projection weight vectors of the given docs. Figure FIGREF4 presents the most extreme analogies results obtained from the model using these comparisons.",
+      "Applying the Algorithm SECREF4 , we check the accuracy obtained with the similarity function before and after the application of the de-bias method. Table TABREF3 presents the corresponding results. In cases like the analogy of `gar\u00e7onete' to `stripper' (Figure FIGREF4 , line 8), it is possible to observe that the relationship stipulated between terms with sexual connotation and females is closer than between females and professions. While in the male model, even in cases of non-compliance, the closest analogy remains in the professional environment.",
+      "Using a confidence factor of 99%, when comparing the correctness levels of the model with and without the reduction of bias, the prediction of the model with bias is significantly better. Different authors BIBREF16 BIBREF17 show that the removal of bias in models produces a negative impact on the quality of the model. On the other hand, it is observed that even with a better hit rate the correctness rate in the prediction of related terms is still low."
+    ],
+    [
+      "This paper presents an analysis of the presence of gender bias in Portuguese word embeddings. Even though it is a work in progress, the proposal showed promising results in analyzing predicting models.",
+      "A possible extension of the work involves deepening the analysis of the results obtained, seeking to achieve higher accuracy rates and fairer models to be used in machine learning techniques. Thus, these studies can involve tests with different methods of pre-processing the data to the use of different models, as well as other factors that may influence the results generated. This deepening is necessary since the model's accuracy is not high.",
+      "To conclude, we believe that the presence of gender bias and stereotypes in the Portuguese language is found in different spheres of language, and it is important to study ways of mitigating different types of discrimination. As such, it can be easily applied to analyze racists bias into the language, such as different types of preconceptions."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0056/instruction.md b/qasper-0056/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..0874629c1f2e1c2b3d808ed77ae9bd77660ac770
--- /dev/null
+++ b/qasper-0056/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: LAXARY: A Trustworthy Explainable Twitter Analysis Model for Post-Traumatic Stress Disorder Assessment
+
+Question: How is the intensity of the PTSD established?
\ No newline at end of file
diff --git a/qasper-0057/instruction.md b/qasper-0057/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..86e0cdf6ff03fdb9101878e4012090381bebd37c
--- /dev/null
+++ b/qasper-0057/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: LAXARY: A Trustworthy Explainable Twitter Analysis Model for Post-Traumatic Stress Disorder Assessment
+
+Question: How is LIWC incorporated into this system?
\ No newline at end of file
diff --git a/qasper-0058/instruction.md b/qasper-0058/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..0ad8f0330419fcaf4b0477a580d01111adb1ba90
--- /dev/null
+++ b/qasper-0058/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: LAXARY: A Trustworthy Explainable Twitter Analysis Model for Post-Traumatic Stress Disorder Assessment
+
+Question: How many twitter users are surveyed using the clinically validated survey?
\ No newline at end of file
diff --git a/qasper-0059/instruction.md b/qasper-0059/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..e324b2caceb724c0178b7a17d48cc85e85676b9c
--- /dev/null
+++ b/qasper-0059/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: LAXARY: A Trustworthy Explainable Twitter Analysis Model for Post-Traumatic Stress Disorder Assessment
+
+Question: Which clinically validated survey tools are used?
\ No newline at end of file
diff --git a/qasper-0060/instruction.md b/qasper-0060/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..4347a05b8d7acdb339df91f97cdd44c7398ca170
--- /dev/null
+++ b/qasper-0060/instruction.md
@@ -0,0 +1,70 @@
+Name of Paper: Comprehensive Named Entity Recognition on CORD-19 with Distant or Weak Supervision
+
+Question: Did they experiment with the dataset?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "CORD-19-NER Dataset ::: Corpus",
+    "CORD-19-NER Dataset ::: NER Methods",
+    "Results ::: NER Annotation Results",
+    "Results ::: Top-Frequent Entity Summarization",
+    "Conclusion",
+    "Acknowledgment"
+  ],
+  "paragraphs": [
+    [
+      "Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The disease was first identified in 2019 in Wuhan, Central China, and has since spread globally, resulting in the 2019\u20132020 coronavirus pandemic. On March 16th, 2020, researchers and leaders from the Allen Institute for AI, Chan Zuckerberg Initiative (CZI), Georgetown University\u2019s Center for Security and Emerging Technology (CSET), Microsoft, and the National Library of Medicine (NLM) at the National Institutes of Health released the COVID-19 Open Research Dataset (CORD-19) of scholarly literature about COVID-19, SARS-CoV-2, and the coronavirus group.",
+      "Named entity recognition (NER) is a fundamental step in text mining system development to facilitate the COVID-19 studies. There is critical need for NER methods that can quickly adapt to all the COVID-19 related new types without much human effort for training data annotation. We created this CORD-19-NER dataset with comprehensive named entity annotation on the CORD-19 corpus (2020-03-13). This dataset covers 75 fine-grained named entity types. CORD-19-NER is automatically generated by combining the annotation results from four sources. In the following sections, we introduce the details of CORD-19-NER dataset construction. We also show some NER annotation results in this dataset."
+    ],
+    [
+      "The corpus is generated from the 29,500 documents in the CORD-19 corpus (2020-03-13). We first merge all the meta-data (all_sources_metadata_2020-03-13.csv) with their corresponding full-text papers. Then we create a tokenized corpus (CORD-19-corpus.json) for further NER annotations.",
+      "Corpus Tokenization. The raw corpus is a combination of the \u201ctitle\", \u201cabstract\" and \u201cfull-text\" from the CORD-19 corpus. We first conduct automatic phrase mining on the raw corpus using AutoPhrase BIBREF0. Then we do the second round of tokenization with Spacy on the phrase-replaced corpus. We have observed that keeping the AutoPhrase results will significantly improve the distantly- and weakly-supervised NER performance.",
+      "Key Items. The tokenized corpus includes the following items:",
+      "doc_id: the line number (0-29499) in \u201call_sources_metadata_2020-03-13.csv\" in the CORD-19 corpus (2020-03-13).",
+      "sents: [sent_id, sent_tokens], tokenized sentences and words as described above.",
+      "source: CZI (1236 records), PMC (27337), bioRxiv (566) and medRxiv (361).",
+      "doi: populated for all BioRxiv/MedRxiv paper records and most of the other records (26357 non null).",
+      "pmcid: populated for all PMC paper records (27337 non null).",
+      "pubmed_id: populated for some of the records.",
+      "Other keys: publish_time, authors and journal.",
+      "The tokenized corpus (CORD-19-corpus.json) with the file schema and detailed descriptions can be found in our CORD-19-NER dataset."
+    ],
+    [
+      "CORD-19-NER annotation is a combination from four sources with different NER methods:",
+      "Pre-trained NER on 18 general entity types from Spacy using the model \u201cen_core_web_sm\".",
+      "Pre-trained NER on 18 biomedical entity types from SciSpacy using the model \u201cen_ner_bionlp13cg_md\".",
+      "Knowledge base (KB)-guided NER on 127 biomedical entity types with our distantly-supervised NER methods BIBREF1, BIBREF2. We do not require any human annotated training data for the NER model training. Instead, We rely on UMLS as the input KB for distant supervision.",
+      "Seed-guided NER on 9 new entity types (specifically related to the COVID-19 studies) with our weakly-supervised NER method. We only require several (10-20) human-input seed entities for each new type. Then we expand the seed entity sets with CatE BIBREF3 and apply our distant NER method for the new entity type recognition.",
+      "The 9 new entity types with examples of their input seed are as follows:",
+      "Coronavirus: COVID-19, SARS, MERS, etc.",
+      "Viral Protein: Hemagglutinin, GP120, etc.",
+      "Livestock: cattle, sheep, pig, etc.",
+      "Wildlife: bats, wild animals, wild birds, etc",
+      "Evolution: genetic drift, natural selection, mutation rate, etc",
+      "Physical Science: atomic charge, Amber force fields, Van der Waals interactions, etc.",
+      "Substrate: blood, sputum, urine, etc.",
+      "Material: copper, stainless steel, plastic, etc.",
+      "Immune Response: adaptive immune response, cell mediated immunity, innate immunity, etc.",
+      "We merged all the entity types from the four sources and reorganized them into one entity type hierarchy. Specifically, we align all the types from SciSpacy to UMLS. We also merge some fine-grained UMLS entity types to their more coarse-grained types based on the corpus count. Then we get a final entity type hierarchy with 75 fine-grained entity types used in our annotations. The entity type hierarchy (CORD-19-types.xlsx) can be found in our CORD-19-NER dataset.",
+      "Then we conduct named entity annotation with the four NER methods on the 75 fine-grained entity types. After we get the NER annotation results with the four different methods, we merge the results into one file. The conflicts are resolved by giving priority to different entity types annotated by different methods according to their annotation quality. The final entity annotation results (CORD-19-ner.json) with the file schema and detailed descriptions can be found in our CORD-19-NER dataset."
+    ],
+    [
+      "In Figure FIGREF28, we show some examples of the annotation results in CORD-19-NER. We can see that our distantly- or weakly supervised methods achieve high quality recognizing the new entity types, requiring only several seed examples as the input. For example, we recognized \u201cSARS-CoV-2\" as the \u201cCORONAVIRUS\" type, \u201cbat\" and \u201cpangolins\" as the \u201cWILDLIFE\" type and \u201cVan der Waals forces\" as the \u201cPHYSICAL_SCIENCE\" type. This NER annotation results help downstream text mining tasks in discovering the origin and the physical nature of the virus. Our NER methods are domain-independent that can be applied to corpus in different domains. In addition, we show another example of NER annotation on New York Times with our system in Figure FIGREF29.",
+      "In Figure FIGREF30, we show the comparison of our annotation results with existing NER/BioNER systems. In Figure FIGREF30, we can see that only our method can identify \u201cSARS-CoV-2\" as a coronavirus. In Figure FIGREF30, we can see that our method can identify many more entities such as \u201cpylogenetic\" as a evolution term and \u201cbat\" as a wildlife. In Figure FIGREF30, we can also see that our method can identify many more entities such as \u201cracism\" as a social behavior. In summary, our distantly- and weakly-supervised NER methods are reliable for high-quality entity recognition without requiring human effort for training data annotation."
+    ],
+    [
+      "In Table TABREF34, we show some examples of the most frequent entities in the annotated corpus. Specifically, we show the entity types including both our new types and some UMLS types that have not been manually annotated before. We find our annotated entities very informative for the COVID-19 studies. For example, the most frequent entities for the type \u201cSIGN_OR_SYMPTOM behavior\" includes \u201ccough\" and \u201crespiratory symptoms\" that are the most common symptoms for COVID-19 . The most frequent entities for the type \u201cINDIVIDUAL_BEHAVIOR\" includes \u201chand hygiene\", \u201cdisclosures\" and \u201cabsenteeism\", which indicates that people focus more on hand cleaning for the COVID-19 issue. Also, the most frequent entities for the type \u201cMACHINE_ACTIVITY\" includes \u201cmachine learning\", \u201cdata processing\" and \u201cautomation\", which indicates that people focus more on the automated methods that can process massive data for the COVID-19 studies. This type also includes \u201ctelecommunication\" as the top results, which is quite reasonable under the current COVID-19 situation. More examples can be found in our dataset."
+    ],
+    [
+      "In the future, we will further improve the CORD-19-NER dataset quality. We will also build text mining systems based on the CORD-19-NER dataset with richer functionalities. We hope this dataset can help the text mining community build downstream applications. We also hope this dataset can bring insights for the COVID-19 studies, both on the biomedical side and on the social side."
+    ],
+    [
+      "Research was sponsored in part by US DARPA KAIROS Program No. FA8750-19-2-1004 and SocialSim Program No. W911NF-17-C-0099, National Science Foundation IIS 16-18481, IIS 17-04532, and IIS-17-41317, and DTRA HDTRA11810026. Any opinions, findings, and conclusions or recommendations expressed herein are those of the authors and should not be interpreted as necessarily representing the views, either expressed or implied, of DARPA or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for government purposes notwithstanding any copyright annotation hereon. The views and conclusions contained in this paper are those of the authors and should not be interpreted as representing any funding agencies."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0061/instruction.md b/qasper-0061/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..3afd6195675821ad4ca647f1ed882debccf2b592
--- /dev/null
+++ b/qasper-0061/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Comprehensive Named Entity Recognition on CORD-19 with Distant or Weak Supervision
+
+Question: What is the size of this dataset?
\ No newline at end of file
diff --git a/qasper-0066/instruction.md b/qasper-0066/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..4634fe304abc378a433a50d5ccc61439ae708d53
--- /dev/null
+++ b/qasper-0066/instruction.md
@@ -0,0 +1,154 @@
+Name of Paper: Word Sense Disambiguation for 158 Languages using Word Embeddings Only
+
+Question: Is the method described in this work a clustering-based method?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "",
+    " ::: ",
+    " :::  ::: ",
+    "Introduction",
+    "Related Work",
+    "Algorithm for Word Sense Induction",
+    "Algorithm for Word Sense Induction ::: SenseGram: A Baseline Graph-based Word Sense Induction Algorithm",
+    "Algorithm for Word Sense Induction ::: egvi (Ego-Graph Vector Induction): A Novel Word Sense Induction Algorithm ::: Induction of Sense Inventories",
+    "Algorithm for Word Sense Induction ::: egvi (Ego-Graph Vector Induction): A Novel Word Sense Induction Algorithm ::: Labelling of Induced Senses",
+    "Algorithm for Word Sense Induction ::: egvi (Ego-Graph Vector Induction): A Novel Word Sense Induction Algorithm ::: Word Sense Disambiguation",
+    "System Design",
+    "System Design ::: Construction of Sense Inventories",
+    "System Design ::: Word Sense Disambiguation System",
+    "Evaluation",
+    "Evaluation ::: Lexical Similarity and Relatedness ::: Experimental Setup",
+    "Evaluation ::: Lexical Similarity and Relatedness ::: Discussion of Results",
+    "Evaluation ::: Word Sense Disambiguation",
+    "Evaluation ::: Word Sense Disambiguation ::: Experimental Setup",
+    "Evaluation ::: Word Sense Disambiguation ::: Discussion of Results",
+    "Evaluation ::: Analysis",
+    "Conclusions and Future Work",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "1.1em"
+    ],
+    [
+      "1.1.1em"
+    ],
+    [
+      "1.1.1.1em",
+      "ru=russian",
+      "",
+      "$^1$Skolkovo Institute of Science and Technology, Moscow, Russia",
+      "v.logacheva@skoltech.ru",
+      "$^2$Ural Federal University, Yekaterinburg, Russia",
+      "$^3$Universit\u00e4t Hamburg, Hamburg, Germany",
+      "$^4$Universit\u00e4t Mannheim, Mannheim, Germany",
+      "$^5$University of Oslo, Oslo, Norway",
+      "$^6$Higher School of Economics, Moscow, Russia",
+      "Disambiguation of word senses in context is easy for humans, but is a major challenge for automatic approaches. Sophisticated supervised and knowledge-based models were developed to solve this task. However, (i) the inherent Zipfian distribution of supervised training instances for a given word and/or (ii) the quality of linguistic knowledge representations motivate the development of completely unsupervised and knowledge-free approaches to word sense disambiguation (WSD). They are particularly useful for under-resourced languages which do not have any resources for building either supervised and/or knowledge-based models. In this paper, we present a method that takes as input a standard pre-trained word embedding model and induces a fully-fledged word sense inventory, which can be used for disambiguation in context. We use this method to induce a collection of sense inventories for 158 languages on the basis of the original pre-trained fastText word embeddings by Grave:18, enabling WSD in these languages. Models and system are available online.",
+      "word sense induction, word sense disambiguation, word embeddings, sense embeddings, graph clustering"
+    ],
+    [
+      "There are many polysemous words in virtually any language. If not treated as such, they can hamper the performance of all semantic NLP tasks BIBREF0. Therefore, the task of resolving the polysemy and choosing the most appropriate meaning of a word in context has been an important NLP task for a long time. It is usually referred to as Word Sense Disambiguation (WSD) and aims at assigning meaning to a word in context.",
+      "The majority of approaches to WSD are based on the use of knowledge bases, taxonomies, and other external manually built resources BIBREF1, BIBREF2. However, different senses of a polysemous word occur in very diverse contexts and can potentially be discriminated with their help. The fact that semantically related words occur in similar contexts, and diverse words do not share common contexts, is known as distributional hypothesis and underlies the technique of constructing word embeddings from unlabelled texts. The same intuition can be used to discriminate between different senses of individual words. There exist methods of training word embeddings that can detect polysemous words and assign them different vectors depending on their contexts BIBREF3, BIBREF4. Unfortunately, many wide-spread word embedding models, such as GloVe BIBREF5, word2vec BIBREF6, fastText BIBREF7, do not handle polysemous words. Words in these models are represented with single vectors, which were constructed from diverse sets of contexts corresponding to different senses. In such cases, their disambiguation needs knowledge-rich approaches.",
+      "We tackle this problem by suggesting a method of post-hoc unsupervised WSD. It does not require any external knowledge and can separate different senses of a polysemous word using only the information encoded in pre-trained word embeddings. We construct a semantic similarity graph for words and partition it into densely connected subgraphs. This partition allows for separating different senses of polysemous words. Thus, the only language resource we need is a large unlabelled text corpus used to train embeddings. This makes our method applicable to under-resourced languages. Moreover, while other methods of unsupervised WSD need to train embeddings from scratch, we perform retrofitting of sense vectors based on existing word embeddings.",
+      "We create a massively multilingual application for on-the-fly word sense disambiguation. When receiving a text, the system identifies its language and performs disambiguation of all the polysemous words in it based on pre-extracted word sense inventories. The system works for 158 languages, for which pre-trained fastText embeddings available BIBREF8. The created inventories are based on these embeddings. To the best of our knowledge, our system is the only WSD system for the majority of the presented languages. Although it does not match the state of the art for resource-rich languages, it is fully unsupervised and can be used for virtually any language.",
+      "The contributions of our work are the following:",
+      "[noitemsep]",
+      "We release word sense inventories associated with fastText embeddings for 158 languages.",
+      "We release a system that allows on-the-fly word sense disambiguation for 158 languages.",
+      "We present egvi (Ego-Graph Vector Induction), a new algorithm of unsupervised word sense induction, which creates sense inventories based on pre-trained word vectors."
+    ],
+    [
+      "There are two main scenarios for WSD: the supervised approach that leverages training corpora explicitly labelled for word sense, and the knowledge-based approach that derives sense representation from lexical resources, such as WordNet BIBREF9. In the supervised case WSD can be treated as a classification problem. Knowledge-based approaches construct sense embeddings, i.e. embeddings that separate various word senses.",
+      "SupWSD BIBREF10 is a state-of-the-art system for supervised WSD. It makes use of linear classifiers and a number of features such as POS tags, surrounding words, local collocations, word embeddings, and syntactic relations. GlossBERT model BIBREF11, which is another implementation of supervised WSD, achieves a significant improvement by leveraging gloss information. This model benefits from sentence-pair classification approach, introduced by Devlin:19 in their BERT contextualized embedding model. The input to the model consists of a context (a sentence which contains an ambiguous word) and a gloss (sense definition) from WordNet. The context-gloss pair is concatenated through a special token ([SEP]) and classified as positive or negative.",
+      "On the other hand, sense embeddings are an alternative to traditional word vector models such as word2vec, fastText or GloVe, which represent monosemous words well but fail for ambiguous words. Sense embeddings represent individual senses of polysemous words as separate vectors. They can be linked to an explicit inventory BIBREF12 or induce a sense inventory from unlabelled data BIBREF13. LSTMEmbed BIBREF13 aims at learning sense embeddings linked to BabelNet BIBREF14, at the same time handling word ordering, and using pre-trained embeddings as an objective. Although it was tested only on English, the approach can be easily adapted to other languages present in BabelNet. However, manually labelled datasets as well as knowledge bases exist only for a small number of well-resourced languages. Thus, to disambiguate polysemous words in other languages one has to resort to fully unsupervised techniques.",
+      "The task of Word Sense Induction (WSI) can be seen as an unsupervised version of WSD. WSI aims at clustering word senses and does not require to map each cluster to a predefined sense. Instead of that, word sense inventories are induced automatically from the clusters, treating each cluster as a single sense of a word. WSI approaches fall into three main groups: context clustering, word ego-network clustering and synonyms (or substitute) clustering.",
+      "Context clustering approaches consist in creating vectors which characterise words' contexts and clustering these vectors. Here, the definition of context may vary from window-based context to latent topic-alike context. Afterwards, the resulting clusters are either used as senses directly BIBREF15, or employed further to learn sense embeddings via Chinese Restaurant Process algorithm BIBREF16, AdaGram, a Bayesian extension of the Skip-Gram model BIBREF17, AutoSense, an extension of the LDA topic model BIBREF18, and other techniques.",
+      "Word ego-network clustering is applied to semantic graphs. The nodes of a semantic graph are words, and edges between them denote semantic relatedness which is usually evaluated with cosine similarity of the corresponding embeddings BIBREF19 or by PMI-like measures BIBREF20. Word senses are induced via graph clustering algorithms, such as Chinese Whispers BIBREF21 or MaxMax BIBREF22. The technique suggested in our work belongs to this class of methods and is an extension of the method presented by Pelevina:16.",
+      "Synonyms and substitute clustering approaches create vectors which represent synonyms or substitutes of polysemous words. Such vectors are created using synonymy dictionaries BIBREF23 or context-dependent substitutes obtained from a language model BIBREF24. Analogously to previously described techniques, word senses are induced by clustering these vectors."
+    ],
+    [
+      "The majority of word vector models do not discriminate between multiple senses of individual words. However, a polysemous word can be identified via manual analysis of its nearest neighbours\u2014they reflect different senses of the word. Table TABREF7 shows manually sense-labelled most similar terms to the word Ruby according to the pre-trained fastText model BIBREF8. As it was suggested early by Widdows:02, the distributional properties of a word can be used to construct a graph of words that are semantically related to it, and if a word is polysemous, such graph can easily be partitioned into a number of densely connected subgraphs corresponding to different senses of this word. Our algorithm is based on the same principle."
+    ],
+    [
+      "SenseGram is the method proposed by Pelevina:16 that separates nearest neighbours to induce word senses and constructs sense embeddings for each sense. It starts by constructing an ego-graph (semantic graph centred at a particular word) of the word and its nearest neighbours. The edges between the words denote their semantic relatedness, e.g. the two nodes are joined with an edge if cosine similarity of the corresponding embeddings is higher than a pre-defined threshold. The resulting graph can be clustered into subgraphs which correspond to senses of the word.",
+      "The sense vectors are then constructed by averaging embeddings of words in each resulting cluster. In order to use these sense vectors for word sense disambiguation in text, the authors compute the probabilities of sense vectors of a word given its context or the similarity of the sense vectors to the context."
+    ],
+    [
+      "One of the downsides of the described above algorithm is noise in the generated graph, namely, unrelated words and wrong connections. They hamper the separation of the graph. Another weak point is the imbalance in the nearest neighbour list, when a large part of it is attributed to the most frequent sense, not sufficiently representing the other senses. This can lead to construction of incorrect sense vectors.",
+      "We suggest a more advanced procedure of graph construction that uses the interpretability of vector addition and subtraction operations in word embedding space BIBREF6 while the previous algorithm only relies on the list of nearest neighbours in word embedding space. The key innovation of our algorithm is the use of vector subtraction to find pairs of most dissimilar graph nodes and construct the graph only from the nodes included in such \u201canti-edges\u201d. Thus, our algorithm is based on graph-based word sense induction, but it also relies on vector-based operations between word embeddings to perform filtering of graph nodes. Analogously to the work of Pelevina:16, we construct a semantic relatedness graph from a list of nearest neighbours, but we filter this list using the following procedure:",
+      "Extract a list $\\mathcal {N}$ = {$w_{1}$, $w_{2}$, ..., $w_{N}$} of $N$ nearest neighbours for the target (ego) word vector $w$.",
+      "Compute a list $\\Delta $ = {$\\delta _{1}$, $\\delta _{2}$, ..., $\\delta _{N}$} for each $w_{i}$ in $\\mathcal {N}$, where $\\delta _{i}~=~w-w_{i}$. The vectors in $\\delta $ contain the components of sense of $w$ which are not related to the corresponding nearest neighbours from $\\mathcal {N}$.",
+      "Compute a list $\\overline{\\mathcal {N}}$ = {$\\overline{w_{1}}$, $\\overline{w_{2}}$, ..., $\\overline{w_{N}}$}, such that $\\overline{w_{i}}$ is in the top nearest neighbours of $\\delta _{i}$ in the embedding space. In other words, $\\overline{w_{i}}$ is a word which is the most similar to the target (ego) word $w$ and least similar to its neighbour $w_{i}$. We refer to $\\overline{w_{i}}$ as an anti-pair of $w_{i}$. The set of $N$ nearest neighbours and their anti-pairs form a set of anti-edges i.e. pairs of most dissimilar nodes \u2013 those which should not be connected: $\\overline{E} = \\lbrace (w_{1},\\overline{w_{1}}), (w_{2},\\overline{w_{2}}), ..., (w_{N},\\overline{w_{N}})\\rbrace $.",
+      "To clarify this, consider the target (ego) word $w = \\textit {python}$, its top similar term $w_1 = \\textit {Java}$ and the resulting anti-pair $\\overline{w_i} = \\textit {snake}$ which is the top related term of $\\delta _1 = w - w_1$. Together they form an anti-edge $(w_i,\\overline{w_i})=(\\textit {Java}, \\textit {snake})$ composed of a pair of semantically dissimilar terms.",
+      "Construct $V$, the set of vertices of our semantic graph $G=(V,E)$ from the list of anti-edges $\\overline{E}$, with the following recurrent procedure: $V = V \\cup \\lbrace w_{i}, \\overline{w_{i}}: w_{i} \\in \\mathcal {N}, \\overline{w_{i}} \\in \\mathcal {N}\\rbrace $, i.e. we add a word from the list of nearest neighbours and its anti-pair only if both of them are nearest neighbours of the original word $w$. We do not add $w$'s nearest neighbours if their anti-pairs do not belong to $\\mathcal {N}$. Thus, we add only words which can help discriminating between different senses of $w$.",
+      "Construct the set of edges $E$ as follows. For each $w_{i}~\\in ~\\mathcal {N}$ we extract a set of its $K$ nearest neighbours $\\mathcal {N}^{\\prime }_{i} = \\lbrace u_{1}, u_{2}, ..., u_{K}\\rbrace $ and define $E = \\lbrace (w_{i}, u_{j}): w_{i}~\\in ~V, u_j~\\in ~V, u_{j}~\\in ~\\mathcal {N}^{\\prime }_{i}, u_{j}~\\ne ~\\overline{w_{i}}\\rbrace $. In other words, we remove edges between a word $w_{i}$ and its nearest neighbour $u_j$ if $u_j$ is also its anti-pair. According to our hypothesis, $w_{i}$ and $\\overline{w_{i}}$ belong to different senses of $w$, so they should not be connected (i.e. we never add anti-edges into $E$). Therefore, we consider any connection between them as noise and remove it.",
+      "Note that $N$ (the number of nearest neighbours for the target word $w$) and $K$ (the number of nearest neighbours of $w_{ci}$) do not have to match. The difference between these parameters is the following. $N$ defines how many words will be considered for the construction of ego-graph. On the other hand, $K$ defines the degree of relatedness between words in the ego-graph \u2014 if $K = 50$, then we will connect vertices $w$ and $u$ with an edge only if $u$ is in the list of 50 nearest neighbours of $w$. Increasing $K$ increases the graph connectivity and leads to lower granularity of senses.",
+      "According to our hypothesis, nearest neighbours of $w$ are grouped into clusters in the vector space, and each of the clusters corresponds to a sense of $w$. The described vertices selection procedure allows picking the most representative members of these clusters which are better at discriminating between the clusters. In addition to that, it helps dealing with the cases when one of the clusters is over-represented in the nearest neighbour list. In this case, many elements of such a cluster are not added to $V$ because their anti-pairs fall outside the nearest neighbour list. This also improves the quality of clustering.",
+      "After the graph construction, the clustering is performed using the Chinese Whispers algorithm BIBREF21. This is a bottom-up clustering procedure that does not require to pre-define the number of clusters, so it can correctly process polysemous words with varying numbers of senses as well as unambiguous words.",
+      "Figure FIGREF17 shows an example of the resulting pruned graph of for the word Ruby for $N = 50$ nearest neighbours in terms of the fastText cosine similarity. In contrast to the baseline method by BIBREF19 where all 50 terms are clustered, in the method presented in this section we sparsify the graph by removing 13 nodes which were not in the set of the \u201canti-edges\u201d i.e. pairs of most dissimilar terms out of these 50 neighbours. Examples of anti-edges i.e. pairs of most dissimilar terms for this graph include: (Haskell, Sapphire), (Garnet, Rails), (Opal, Rubyist), (Hazel, RubyOnRails), and (Coffeescript, Opal)."
+    ],
+    [
+      "We label each word cluster representing a sense to make them and the WSD results interpretable by humans. Prior systems used hypernyms to label the clusters BIBREF25, BIBREF26, e.g. \u201canimal\u201d in the \u201cpython (animal)\u201d. However, neither hypernyms nor rules for their automatic extraction are available for all 158 languages. Therefore, we use a simpler method to select a keyword which would help to interpret each cluster. For each graph node $v \\in V$ we count the number of anti-edges it belongs to: $count(v) = | \\lbrace (w_i,\\overline{w_i}) : (w_i,\\overline{w_i}) \\in \\overline{E} \\wedge (v = w_i \\vee v = \\overline{w_i}) \\rbrace |$. A graph clustering yields a partition of $V$ into $n$ clusters: $V~=~\\lbrace V_1, V_2, ..., V_n\\rbrace $. For each cluster $V_i$ we define a keyword $w^{key}_i$ as the word with the largest number of anti-edges $count(\\cdot )$ among words in this cluster."
+    ],
+    [
+      "We use keywords defined above to obtain vector representations of senses. In particular, we simply use word embedding of the keyword $w^{key}_i$ as a sense representation $\\mathbf {s}_i$ of the target word $w$ to avoid explicit computation of sense embeddings like in BIBREF19. Given a sentence $\\lbrace w_1, w_2, ..., w_{j}, w, w_{j+1}, ..., w_n\\rbrace $ represented as a matrix of word vectors, we define the context of the target word $w$ as $\\textbf {c}_w = \\dfrac{\\sum _{j=1}^{n} w_j}{n}$. Then, we define the most appropriate sense $\\hat{s}$ as the sense with the highest cosine similarity to the embedding of the word's context:"
+    ],
+    [
+      "We release a system for on-the-fly WSD for 158 languages. Given textual input, it identifies polysemous words and retrieves senses that are the most appropriate in the context."
+    ],
+    [
+      "To build word sense inventories (sense vectors) for 158 languages, we utilised GPU-accelerated routines for search of similar vectors implemented in Faiss library BIBREF27. The search of nearest neighbours takes substantial time, therefore, acceleration with GPUs helps to significantly reduce the word sense construction time. To further speed up the process, we keep all intermediate results in memory, which results in substantial RAM consumption of up to 200 Gb.",
+      "The construction of word senses for all of the 158 languages takes a lot of computational resources and imposes high requirements to the hardware. For calculations, we use in parallel 10\u201320 nodes of the Zhores cluster BIBREF28 empowered with Nvidia Tesla V100 graphic cards. For each of the languages, we construct inventories based on 50, 100, and 200 neighbours for 100,000 most frequent words. The vocabulary was limited in order to make the computation time feasible. The construction of inventories for one language takes up to 10 hours, with $6.5$ hours on average. Building the inventories for all languages took more than 1,000 hours of GPU-accelerated computations. We release the constructed sense inventories for all the available languages. They contain all the necessary information for using them in the proposed WSD system or in other downstream tasks."
+    ],
+    [
+      "The first text pre-processing step is language identification, for which we use the fastText language identification models by Bojanowski:17. Then the input is tokenised. For languages which use Latin, Cyrillic, Hebrew, or Greek scripts, we employ the Europarl tokeniser. For Chinese, we use the Stanford Word Segmenter BIBREF29. For Japanese, we use Mecab BIBREF30. We tokenise Vietnamese with UETsegmenter BIBREF31. All other languages are processed with the ICU tokeniser, as implemented in the PyICU project. After the tokenisation, the system analyses all the input words with pre-extracted sense inventories and defines the most appropriate sense for polysemous words.",
+      "Figure FIGREF19 shows the interface of the system. It has a textual input form. The automatically identified language of text is shown above. A click on any of the words displays a prompt (shown in black) with the most appropriate sense of a word in the specified context and the confidence score. In the given example, the word Jaguar is correctly identified as a car brand. This system is based on the system by Ustalov:18, extending it with a back-end for multiple languages, language detection, and sense browsing capabilities."
+    ],
+    [
+      "We first evaluate our converted embedding models on multi-language lexical similarity and relatedness tasks, as a sanity check, to make sure the word sense induction process did not hurt the general performance of the embeddings. Then, we test the sense embeddings on WSD task."
+    ],
+    [
+      "We use the SemR-11 datasets BIBREF32, which contain word pairs with manually assigned similarity scores from 0 (words are not related) to 10 (words are fully interchangeable) for 12 languages: English (en), Arabic (ar), German (de), Spanish (es), Farsi (fa), French (fr), Italian (it), Dutch (nl), Portuguese (pt), Russian (ru), Swedish (sv), Chinese (zh). The task is to assign relatedness scores to these pairs so that the ranking of the pairs by this score is close to the ranking defined by the oracle score. The performance is measured with Pearson correlation of the rankings. Since one word can have several different senses in our setup, we follow Remus:18 and define the relatedness score for a pair of words as the maximum cosine similarity between any of their sense vectors.",
+      "We extract the sense inventories from fastText embedding vectors. We set $N=K$ for all our experiments, i.e. the number of vertices in the graph and the maximum number of vertices' nearest neighbours match. We conduct experiments with $N=K$ set to 50, 100, and 200. For each cluster $V_i$ we create a sense vector $s_i$ by averaging vectors that belong to this cluster. We rely on the methodology of BIBREF33 shifting the generated sense vector to the direction of the original word vector: $s_i~=~\\lambda ~w + (1-\\lambda )~\\dfrac{1}{n}~\\sum _{u~\\in ~V_i} cos(w, u)\\cdot u, $ where, $\\lambda \\in [0, 1]$, $w$ is the embedding of the original word, $cos(w, u)$ is the cosine similarity between $w$ and $u$, and $n=|V_i|$. By introducing the linear combination of $w$ and $u~\\in ~V_i$ we enforce the similarity of sense vectors to the original word important for this task. In addition to that, we weight $u$ by their similarity to the original word, so that more similar neighbours contribute more to the sense vector. The shifting parameter $\\lambda $ is set to $0.5$, following Remus:18.",
+      "A fastText model is able to generate a vector for each word even if it is not represented in the vocabulary, due to the use of subword information. However, our system cannot assemble sense vectors for out-of-vocabulary words, for such words it returns their original fastText vector. Still, the coverage of the benchmark datasets by our vocabulary is at least 85% and approaches 100% for some languages, so we do not have to resort to this back-off strategy very often.",
+      "We use the original fastText vectors as a baseline. In this case, we compute the relatedness scores of the two words as a cosine similarity of their vectors."
+    ],
+    [
+      "We compute the relatedness scores for all benchmark datasets using our sense vectors and compare them to cosine similarity scores of original fastText vectors. The results vary for different languages. Figure FIGREF28 shows the change in Pearson correlation score when switching from the baseline fastText embeddings to our sense vectors. The new vectors significantly improve the relatedness detection for German, Farsi, Russian, and Chinese, whereas for Italian, Dutch, and Swedish the score slightly falls behind the baseline. For other languages, the performance of sense vectors is on par with regular fastText."
+    ],
+    [
+      "The purpose of our sense vectors is disambiguation of polysemous words. Therefore, we test the inventories constructed with egvi on the Task 13 of SemEval-2013 \u2014 Word Sense Induction BIBREF34. The task is to identify the different senses of a target word in context in a fully unsupervised manner."
+    ],
+    [
+      "The dataset consists of a set of polysemous words: 20 nouns, 20 verbs, and 10 adjectives and specifies 20 to 100 contexts per word, with the total of 4,664 contexts, drawn from the Open American National Corpus. Given a set of contexts of a polysemous word, the participants of the competition had to divide them into clusters by sense of the word. The contexts are manually labelled with WordNet senses of the target words, the gold standard clustering is generated from this labelling.",
+      "The task allows two setups: graded WSI where participants can submit multiple senses per word and provide the probability of each sense in a particular context, and non-graded WSI where a model determines a single sense for a word in context. In our experiments we performed non-graded WSI. We considered the most suitable sense as the one with the highest cosine similarity with embeddings of the context, as described in Section SECREF9.",
+      "The performance of WSI models is measured with three metrics that require mapping of sense inventories (Jaccard Index, Kendall's $\\tau $, and WNDCG) and two cluster comparison metrics (Fuzzy NMI and Fuzzy B-Cubed)."
+    ],
+    [
+      "We compare our model with the models that participated in the task, the baseline ego-graph clustering model by Pelevina:16, and AdaGram BIBREF17, a method that learns sense embeddings based on a Bayesian extension of the Skip-gram model. Besides that, we provide the scores of the simple baselines originally used in the task: assigning one sense to all words, assigning the most frequent sense to all words, and considering each context as expressing a different sense. The evaluation of our model was performed using the open source context-eval tool.",
+      "Table TABREF31 shows the performance of these models on the SemEval dataset. Due to space constraints, we only report the scores of the best-performing SemEval participants, please refer to jurgens-klapaftis-2013-semeval for the full results. The performance of AdaGram and SenseGram models is reported according to Pelevina:16.",
+      "The table shows that the performance of egvi is similar to state-of-the-art word sense disambiguation and word sense induction models. In particular, we can see that it outperforms SenseGram on the majority of metrics. We should note that this comparison is not fully rigorous, because SenseGram induces sense inventories from word2vec as opposed to fastText vectors used in our work."
+    ],
+    [
+      "In order to see how the separation of word contexts that we perform corresponds to actual senses of polysemous words, we visualise ego-graphs produced by our method. Figure FIGREF17 shows the nearest neighbours clustering for the word Ruby, which divides the graph into five senses: Ruby-related programming tools, e.g. RubyOnRails (orange cluster), female names, e.g. Josie (magenta cluster), gems, e.g. Sapphire (yellow cluster), programming languages in general, e.g. Haskell (red cluster). Besides, this is typical for fastText embeddings featuring sub-string similarity, one can observe a cluster of different spelling of the word Ruby in green.",
+      "Analogously, the word python (see Figure FIGREF35) is divided into the senses of animals, e.g. crocodile (yellow cluster), programming languages, e.g. perl5 (magenta cluster), and conference, e.g. pycon (red cluster).",
+      "In addition, we show a qualitative analysis of senses of mouse and apple. Table TABREF38 shows nearest neighbours of the original words separated into clusters (labels for clusters were assigned manually). These inventories demonstrate clear separation of different senses, although it can be too fine-grained. For example, the first and the second cluster for mouse both refer to computer mouse, but the first one addresses the different types of computer mice, and the second one is used in the context of mouse actions. Similarly, we see that iphone and macbook are separated into two clusters. Interestingly, fastText handles typos, code-switching, and emojis by correctly associating all non-standard variants to the word they refer, and our method is able to cluster them appropriately. Both inventories were produced with $K=200$, which ensures stronger connectivity of graph. However, we see that this setting still produces too many clusters. We computed the average numbers of clusters produced by our model with $K=200$ for words from the word relatedness datasets and compared these numbers with the number of senses in WordNet for English and RuWordNet BIBREF35 for Russian (see Table TABREF37). We can see that the number of senses extracted by our method is consistently higher than the real number of senses.",
+      "We also compute the average number of senses per word for all the languages and different values of $K$ (see Figure FIGREF36). The average across languages does not change much as we increase $K$. However, for larger $K$ the average exceed the median value, indicating that more languages have lower number of senses per word. At the same time, while at smaller $K$ the maximum average number of senses per word does not exceed 6, larger values of $K$ produce outliers, e.g. English with $12.5$ senses.",
+      "Notably, there are no languages with an average number of senses less than 2, while numbers on English and Russian WordNets are considerably lower. This confirms that our method systematically over-generates senses. The presence of outliers shows that this effect cannot be eliminated by further increasing $K$, because the $i$-th nearest neighbour of a word for $i>200$ can be only remotely related to this word, even if the word is rare. Thus, our sense clustering algorithm needs a method of merging spurious senses."
+    ],
+    [
+      "We present egvi, a new algorithm for word sense induction based on graph clustering that is fully unsupervised and relies on graph operations between word vectors. We apply this algorithm to a large collection of pre-trained fastText word embeddings, releasing sense inventories for 158 languages. These inventories contain all the necessary information for constructing sense vectors and using them in downstream tasks. The sense vectors for polysemous words can be directly retrofitted with the pre-trained word embeddings and do not need any external resources. As one application of these multilingual sense inventories, we present a multilingual word sense disambiguation system that performs unsupervised and knowledge-free WSD for 158 languages without the use of any dictionary or sense-labelled corpus.",
+      "The evaluation of quality of the produced sense inventories is performed on multilingual word similarity benchmarks, showing that our sense vectors improve the scores compared to non-disambiguated word embeddings. Therefore, our system in its present state can improve WSD and downstream tasks for languages where knowledge bases, taxonomies, and annotated corpora are not available and supervised WSD models cannot be trained.",
+      "A promising direction for future work is combining distributional information from the induced sense inventories with lexical knowledge bases to improve WSD performance. Besides, we encourage the use of the produced word sense inventories in other downstream tasks."
+    ],
+    [
+      "We acknowledge the support of the Deutsche Forschungsgemeinschaft (DFG) foundation under the \u201cJOIN-T 2\u201d and \u201cACQuA\u201d projects. Ekaterina Artemova was supported by the framework of the HSE University Basic Research Program and Russian Academic Excellence Project \u201c5-100\u201d."
+    ]
+  ]
+}
+```
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+Name of Paper: Word Sense Disambiguation for 158 Languages using Word Embeddings Only
+
+Question: How are the different senses annotated/labeled? 
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+Name of Paper: Word Sense Disambiguation for 158 Languages using Word Embeddings Only
+
+Question: Was any extrinsic evaluation carried out?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "",
+    " ::: ",
+    " :::  ::: ",
+    "Introduction",
+    "Related Work",
+    "Algorithm for Word Sense Induction",
+    "Algorithm for Word Sense Induction ::: SenseGram: A Baseline Graph-based Word Sense Induction Algorithm",
+    "Algorithm for Word Sense Induction ::: egvi (Ego-Graph Vector Induction): A Novel Word Sense Induction Algorithm ::: Induction of Sense Inventories",
+    "Algorithm for Word Sense Induction ::: egvi (Ego-Graph Vector Induction): A Novel Word Sense Induction Algorithm ::: Labelling of Induced Senses",
+    "Algorithm for Word Sense Induction ::: egvi (Ego-Graph Vector Induction): A Novel Word Sense Induction Algorithm ::: Word Sense Disambiguation",
+    "System Design",
+    "System Design ::: Construction of Sense Inventories",
+    "System Design ::: Word Sense Disambiguation System",
+    "Evaluation",
+    "Evaluation ::: Lexical Similarity and Relatedness ::: Experimental Setup",
+    "Evaluation ::: Lexical Similarity and Relatedness ::: Discussion of Results",
+    "Evaluation ::: Word Sense Disambiguation",
+    "Evaluation ::: Word Sense Disambiguation ::: Experimental Setup",
+    "Evaluation ::: Word Sense Disambiguation ::: Discussion of Results",
+    "Evaluation ::: Analysis",
+    "Conclusions and Future Work",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "1.1em"
+    ],
+    [
+      "1.1.1em"
+    ],
+    [
+      "1.1.1.1em",
+      "ru=russian",
+      "",
+      "$^1$Skolkovo Institute of Science and Technology, Moscow, Russia",
+      "v.logacheva@skoltech.ru",
+      "$^2$Ural Federal University, Yekaterinburg, Russia",
+      "$^3$Universit\u00e4t Hamburg, Hamburg, Germany",
+      "$^4$Universit\u00e4t Mannheim, Mannheim, Germany",
+      "$^5$University of Oslo, Oslo, Norway",
+      "$^6$Higher School of Economics, Moscow, Russia",
+      "Disambiguation of word senses in context is easy for humans, but is a major challenge for automatic approaches. Sophisticated supervised and knowledge-based models were developed to solve this task. However, (i) the inherent Zipfian distribution of supervised training instances for a given word and/or (ii) the quality of linguistic knowledge representations motivate the development of completely unsupervised and knowledge-free approaches to word sense disambiguation (WSD). They are particularly useful for under-resourced languages which do not have any resources for building either supervised and/or knowledge-based models. In this paper, we present a method that takes as input a standard pre-trained word embedding model and induces a fully-fledged word sense inventory, which can be used for disambiguation in context. We use this method to induce a collection of sense inventories for 158 languages on the basis of the original pre-trained fastText word embeddings by Grave:18, enabling WSD in these languages. Models and system are available online.",
+      "word sense induction, word sense disambiguation, word embeddings, sense embeddings, graph clustering"
+    ],
+    [
+      "There are many polysemous words in virtually any language. If not treated as such, they can hamper the performance of all semantic NLP tasks BIBREF0. Therefore, the task of resolving the polysemy and choosing the most appropriate meaning of a word in context has been an important NLP task for a long time. It is usually referred to as Word Sense Disambiguation (WSD) and aims at assigning meaning to a word in context.",
+      "The majority of approaches to WSD are based on the use of knowledge bases, taxonomies, and other external manually built resources BIBREF1, BIBREF2. However, different senses of a polysemous word occur in very diverse contexts and can potentially be discriminated with their help. The fact that semantically related words occur in similar contexts, and diverse words do not share common contexts, is known as distributional hypothesis and underlies the technique of constructing word embeddings from unlabelled texts. The same intuition can be used to discriminate between different senses of individual words. There exist methods of training word embeddings that can detect polysemous words and assign them different vectors depending on their contexts BIBREF3, BIBREF4. Unfortunately, many wide-spread word embedding models, such as GloVe BIBREF5, word2vec BIBREF6, fastText BIBREF7, do not handle polysemous words. Words in these models are represented with single vectors, which were constructed from diverse sets of contexts corresponding to different senses. In such cases, their disambiguation needs knowledge-rich approaches.",
+      "We tackle this problem by suggesting a method of post-hoc unsupervised WSD. It does not require any external knowledge and can separate different senses of a polysemous word using only the information encoded in pre-trained word embeddings. We construct a semantic similarity graph for words and partition it into densely connected subgraphs. This partition allows for separating different senses of polysemous words. Thus, the only language resource we need is a large unlabelled text corpus used to train embeddings. This makes our method applicable to under-resourced languages. Moreover, while other methods of unsupervised WSD need to train embeddings from scratch, we perform retrofitting of sense vectors based on existing word embeddings.",
+      "We create a massively multilingual application for on-the-fly word sense disambiguation. When receiving a text, the system identifies its language and performs disambiguation of all the polysemous words in it based on pre-extracted word sense inventories. The system works for 158 languages, for which pre-trained fastText embeddings available BIBREF8. The created inventories are based on these embeddings. To the best of our knowledge, our system is the only WSD system for the majority of the presented languages. Although it does not match the state of the art for resource-rich languages, it is fully unsupervised and can be used for virtually any language.",
+      "The contributions of our work are the following:",
+      "[noitemsep]",
+      "We release word sense inventories associated with fastText embeddings for 158 languages.",
+      "We release a system that allows on-the-fly word sense disambiguation for 158 languages.",
+      "We present egvi (Ego-Graph Vector Induction), a new algorithm of unsupervised word sense induction, which creates sense inventories based on pre-trained word vectors."
+    ],
+    [
+      "There are two main scenarios for WSD: the supervised approach that leverages training corpora explicitly labelled for word sense, and the knowledge-based approach that derives sense representation from lexical resources, such as WordNet BIBREF9. In the supervised case WSD can be treated as a classification problem. Knowledge-based approaches construct sense embeddings, i.e. embeddings that separate various word senses.",
+      "SupWSD BIBREF10 is a state-of-the-art system for supervised WSD. It makes use of linear classifiers and a number of features such as POS tags, surrounding words, local collocations, word embeddings, and syntactic relations. GlossBERT model BIBREF11, which is another implementation of supervised WSD, achieves a significant improvement by leveraging gloss information. This model benefits from sentence-pair classification approach, introduced by Devlin:19 in their BERT contextualized embedding model. The input to the model consists of a context (a sentence which contains an ambiguous word) and a gloss (sense definition) from WordNet. The context-gloss pair is concatenated through a special token ([SEP]) and classified as positive or negative.",
+      "On the other hand, sense embeddings are an alternative to traditional word vector models such as word2vec, fastText or GloVe, which represent monosemous words well but fail for ambiguous words. Sense embeddings represent individual senses of polysemous words as separate vectors. They can be linked to an explicit inventory BIBREF12 or induce a sense inventory from unlabelled data BIBREF13. LSTMEmbed BIBREF13 aims at learning sense embeddings linked to BabelNet BIBREF14, at the same time handling word ordering, and using pre-trained embeddings as an objective. Although it was tested only on English, the approach can be easily adapted to other languages present in BabelNet. However, manually labelled datasets as well as knowledge bases exist only for a small number of well-resourced languages. Thus, to disambiguate polysemous words in other languages one has to resort to fully unsupervised techniques.",
+      "The task of Word Sense Induction (WSI) can be seen as an unsupervised version of WSD. WSI aims at clustering word senses and does not require to map each cluster to a predefined sense. Instead of that, word sense inventories are induced automatically from the clusters, treating each cluster as a single sense of a word. WSI approaches fall into three main groups: context clustering, word ego-network clustering and synonyms (or substitute) clustering.",
+      "Context clustering approaches consist in creating vectors which characterise words' contexts and clustering these vectors. Here, the definition of context may vary from window-based context to latent topic-alike context. Afterwards, the resulting clusters are either used as senses directly BIBREF15, or employed further to learn sense embeddings via Chinese Restaurant Process algorithm BIBREF16, AdaGram, a Bayesian extension of the Skip-Gram model BIBREF17, AutoSense, an extension of the LDA topic model BIBREF18, and other techniques.",
+      "Word ego-network clustering is applied to semantic graphs. The nodes of a semantic graph are words, and edges between them denote semantic relatedness which is usually evaluated with cosine similarity of the corresponding embeddings BIBREF19 or by PMI-like measures BIBREF20. Word senses are induced via graph clustering algorithms, such as Chinese Whispers BIBREF21 or MaxMax BIBREF22. The technique suggested in our work belongs to this class of methods and is an extension of the method presented by Pelevina:16.",
+      "Synonyms and substitute clustering approaches create vectors which represent synonyms or substitutes of polysemous words. Such vectors are created using synonymy dictionaries BIBREF23 or context-dependent substitutes obtained from a language model BIBREF24. Analogously to previously described techniques, word senses are induced by clustering these vectors."
+    ],
+    [
+      "The majority of word vector models do not discriminate between multiple senses of individual words. However, a polysemous word can be identified via manual analysis of its nearest neighbours\u2014they reflect different senses of the word. Table TABREF7 shows manually sense-labelled most similar terms to the word Ruby according to the pre-trained fastText model BIBREF8. As it was suggested early by Widdows:02, the distributional properties of a word can be used to construct a graph of words that are semantically related to it, and if a word is polysemous, such graph can easily be partitioned into a number of densely connected subgraphs corresponding to different senses of this word. Our algorithm is based on the same principle."
+    ],
+    [
+      "SenseGram is the method proposed by Pelevina:16 that separates nearest neighbours to induce word senses and constructs sense embeddings for each sense. It starts by constructing an ego-graph (semantic graph centred at a particular word) of the word and its nearest neighbours. The edges between the words denote their semantic relatedness, e.g. the two nodes are joined with an edge if cosine similarity of the corresponding embeddings is higher than a pre-defined threshold. The resulting graph can be clustered into subgraphs which correspond to senses of the word.",
+      "The sense vectors are then constructed by averaging embeddings of words in each resulting cluster. In order to use these sense vectors for word sense disambiguation in text, the authors compute the probabilities of sense vectors of a word given its context or the similarity of the sense vectors to the context."
+    ],
+    [
+      "One of the downsides of the described above algorithm is noise in the generated graph, namely, unrelated words and wrong connections. They hamper the separation of the graph. Another weak point is the imbalance in the nearest neighbour list, when a large part of it is attributed to the most frequent sense, not sufficiently representing the other senses. This can lead to construction of incorrect sense vectors.",
+      "We suggest a more advanced procedure of graph construction that uses the interpretability of vector addition and subtraction operations in word embedding space BIBREF6 while the previous algorithm only relies on the list of nearest neighbours in word embedding space. The key innovation of our algorithm is the use of vector subtraction to find pairs of most dissimilar graph nodes and construct the graph only from the nodes included in such \u201canti-edges\u201d. Thus, our algorithm is based on graph-based word sense induction, but it also relies on vector-based operations between word embeddings to perform filtering of graph nodes. Analogously to the work of Pelevina:16, we construct a semantic relatedness graph from a list of nearest neighbours, but we filter this list using the following procedure:",
+      "Extract a list $\\mathcal {N}$ = {$w_{1}$, $w_{2}$, ..., $w_{N}$} of $N$ nearest neighbours for the target (ego) word vector $w$.",
+      "Compute a list $\\Delta $ = {$\\delta _{1}$, $\\delta _{2}$, ..., $\\delta _{N}$} for each $w_{i}$ in $\\mathcal {N}$, where $\\delta _{i}~=~w-w_{i}$. The vectors in $\\delta $ contain the components of sense of $w$ which are not related to the corresponding nearest neighbours from $\\mathcal {N}$.",
+      "Compute a list $\\overline{\\mathcal {N}}$ = {$\\overline{w_{1}}$, $\\overline{w_{2}}$, ..., $\\overline{w_{N}}$}, such that $\\overline{w_{i}}$ is in the top nearest neighbours of $\\delta _{i}$ in the embedding space. In other words, $\\overline{w_{i}}$ is a word which is the most similar to the target (ego) word $w$ and least similar to its neighbour $w_{i}$. We refer to $\\overline{w_{i}}$ as an anti-pair of $w_{i}$. The set of $N$ nearest neighbours and their anti-pairs form a set of anti-edges i.e. pairs of most dissimilar nodes \u2013 those which should not be connected: $\\overline{E} = \\lbrace (w_{1},\\overline{w_{1}}), (w_{2},\\overline{w_{2}}), ..., (w_{N},\\overline{w_{N}})\\rbrace $.",
+      "To clarify this, consider the target (ego) word $w = \\textit {python}$, its top similar term $w_1 = \\textit {Java}$ and the resulting anti-pair $\\overline{w_i} = \\textit {snake}$ which is the top related term of $\\delta _1 = w - w_1$. Together they form an anti-edge $(w_i,\\overline{w_i})=(\\textit {Java}, \\textit {snake})$ composed of a pair of semantically dissimilar terms.",
+      "Construct $V$, the set of vertices of our semantic graph $G=(V,E)$ from the list of anti-edges $\\overline{E}$, with the following recurrent procedure: $V = V \\cup \\lbrace w_{i}, \\overline{w_{i}}: w_{i} \\in \\mathcal {N}, \\overline{w_{i}} \\in \\mathcal {N}\\rbrace $, i.e. we add a word from the list of nearest neighbours and its anti-pair only if both of them are nearest neighbours of the original word $w$. We do not add $w$'s nearest neighbours if their anti-pairs do not belong to $\\mathcal {N}$. Thus, we add only words which can help discriminating between different senses of $w$.",
+      "Construct the set of edges $E$ as follows. For each $w_{i}~\\in ~\\mathcal {N}$ we extract a set of its $K$ nearest neighbours $\\mathcal {N}^{\\prime }_{i} = \\lbrace u_{1}, u_{2}, ..., u_{K}\\rbrace $ and define $E = \\lbrace (w_{i}, u_{j}): w_{i}~\\in ~V, u_j~\\in ~V, u_{j}~\\in ~\\mathcal {N}^{\\prime }_{i}, u_{j}~\\ne ~\\overline{w_{i}}\\rbrace $. In other words, we remove edges between a word $w_{i}$ and its nearest neighbour $u_j$ if $u_j$ is also its anti-pair. According to our hypothesis, $w_{i}$ and $\\overline{w_{i}}$ belong to different senses of $w$, so they should not be connected (i.e. we never add anti-edges into $E$). Therefore, we consider any connection between them as noise and remove it.",
+      "Note that $N$ (the number of nearest neighbours for the target word $w$) and $K$ (the number of nearest neighbours of $w_{ci}$) do not have to match. The difference between these parameters is the following. $N$ defines how many words will be considered for the construction of ego-graph. On the other hand, $K$ defines the degree of relatedness between words in the ego-graph \u2014 if $K = 50$, then we will connect vertices $w$ and $u$ with an edge only if $u$ is in the list of 50 nearest neighbours of $w$. Increasing $K$ increases the graph connectivity and leads to lower granularity of senses.",
+      "According to our hypothesis, nearest neighbours of $w$ are grouped into clusters in the vector space, and each of the clusters corresponds to a sense of $w$. The described vertices selection procedure allows picking the most representative members of these clusters which are better at discriminating between the clusters. In addition to that, it helps dealing with the cases when one of the clusters is over-represented in the nearest neighbour list. In this case, many elements of such a cluster are not added to $V$ because their anti-pairs fall outside the nearest neighbour list. This also improves the quality of clustering.",
+      "After the graph construction, the clustering is performed using the Chinese Whispers algorithm BIBREF21. This is a bottom-up clustering procedure that does not require to pre-define the number of clusters, so it can correctly process polysemous words with varying numbers of senses as well as unambiguous words.",
+      "Figure FIGREF17 shows an example of the resulting pruned graph of for the word Ruby for $N = 50$ nearest neighbours in terms of the fastText cosine similarity. In contrast to the baseline method by BIBREF19 where all 50 terms are clustered, in the method presented in this section we sparsify the graph by removing 13 nodes which were not in the set of the \u201canti-edges\u201d i.e. pairs of most dissimilar terms out of these 50 neighbours. Examples of anti-edges i.e. pairs of most dissimilar terms for this graph include: (Haskell, Sapphire), (Garnet, Rails), (Opal, Rubyist), (Hazel, RubyOnRails), and (Coffeescript, Opal)."
+    ],
+    [
+      "We label each word cluster representing a sense to make them and the WSD results interpretable by humans. Prior systems used hypernyms to label the clusters BIBREF25, BIBREF26, e.g. \u201canimal\u201d in the \u201cpython (animal)\u201d. However, neither hypernyms nor rules for their automatic extraction are available for all 158 languages. Therefore, we use a simpler method to select a keyword which would help to interpret each cluster. For each graph node $v \\in V$ we count the number of anti-edges it belongs to: $count(v) = | \\lbrace (w_i,\\overline{w_i}) : (w_i,\\overline{w_i}) \\in \\overline{E} \\wedge (v = w_i \\vee v = \\overline{w_i}) \\rbrace |$. A graph clustering yields a partition of $V$ into $n$ clusters: $V~=~\\lbrace V_1, V_2, ..., V_n\\rbrace $. For each cluster $V_i$ we define a keyword $w^{key}_i$ as the word with the largest number of anti-edges $count(\\cdot )$ among words in this cluster."
+    ],
+    [
+      "We use keywords defined above to obtain vector representations of senses. In particular, we simply use word embedding of the keyword $w^{key}_i$ as a sense representation $\\mathbf {s}_i$ of the target word $w$ to avoid explicit computation of sense embeddings like in BIBREF19. Given a sentence $\\lbrace w_1, w_2, ..., w_{j}, w, w_{j+1}, ..., w_n\\rbrace $ represented as a matrix of word vectors, we define the context of the target word $w$ as $\\textbf {c}_w = \\dfrac{\\sum _{j=1}^{n} w_j}{n}$. Then, we define the most appropriate sense $\\hat{s}$ as the sense with the highest cosine similarity to the embedding of the word's context:"
+    ],
+    [
+      "We release a system for on-the-fly WSD for 158 languages. Given textual input, it identifies polysemous words and retrieves senses that are the most appropriate in the context."
+    ],
+    [
+      "To build word sense inventories (sense vectors) for 158 languages, we utilised GPU-accelerated routines for search of similar vectors implemented in Faiss library BIBREF27. The search of nearest neighbours takes substantial time, therefore, acceleration with GPUs helps to significantly reduce the word sense construction time. To further speed up the process, we keep all intermediate results in memory, which results in substantial RAM consumption of up to 200 Gb.",
+      "The construction of word senses for all of the 158 languages takes a lot of computational resources and imposes high requirements to the hardware. For calculations, we use in parallel 10\u201320 nodes of the Zhores cluster BIBREF28 empowered with Nvidia Tesla V100 graphic cards. For each of the languages, we construct inventories based on 50, 100, and 200 neighbours for 100,000 most frequent words. The vocabulary was limited in order to make the computation time feasible. The construction of inventories for one language takes up to 10 hours, with $6.5$ hours on average. Building the inventories for all languages took more than 1,000 hours of GPU-accelerated computations. We release the constructed sense inventories for all the available languages. They contain all the necessary information for using them in the proposed WSD system or in other downstream tasks."
+    ],
+    [
+      "The first text pre-processing step is language identification, for which we use the fastText language identification models by Bojanowski:17. Then the input is tokenised. For languages which use Latin, Cyrillic, Hebrew, or Greek scripts, we employ the Europarl tokeniser. For Chinese, we use the Stanford Word Segmenter BIBREF29. For Japanese, we use Mecab BIBREF30. We tokenise Vietnamese with UETsegmenter BIBREF31. All other languages are processed with the ICU tokeniser, as implemented in the PyICU project. After the tokenisation, the system analyses all the input words with pre-extracted sense inventories and defines the most appropriate sense for polysemous words.",
+      "Figure FIGREF19 shows the interface of the system. It has a textual input form. The automatically identified language of text is shown above. A click on any of the words displays a prompt (shown in black) with the most appropriate sense of a word in the specified context and the confidence score. In the given example, the word Jaguar is correctly identified as a car brand. This system is based on the system by Ustalov:18, extending it with a back-end for multiple languages, language detection, and sense browsing capabilities."
+    ],
+    [
+      "We first evaluate our converted embedding models on multi-language lexical similarity and relatedness tasks, as a sanity check, to make sure the word sense induction process did not hurt the general performance of the embeddings. Then, we test the sense embeddings on WSD task."
+    ],
+    [
+      "We use the SemR-11 datasets BIBREF32, which contain word pairs with manually assigned similarity scores from 0 (words are not related) to 10 (words are fully interchangeable) for 12 languages: English (en), Arabic (ar), German (de), Spanish (es), Farsi (fa), French (fr), Italian (it), Dutch (nl), Portuguese (pt), Russian (ru), Swedish (sv), Chinese (zh). The task is to assign relatedness scores to these pairs so that the ranking of the pairs by this score is close to the ranking defined by the oracle score. The performance is measured with Pearson correlation of the rankings. Since one word can have several different senses in our setup, we follow Remus:18 and define the relatedness score for a pair of words as the maximum cosine similarity between any of their sense vectors.",
+      "We extract the sense inventories from fastText embedding vectors. We set $N=K$ for all our experiments, i.e. the number of vertices in the graph and the maximum number of vertices' nearest neighbours match. We conduct experiments with $N=K$ set to 50, 100, and 200. For each cluster $V_i$ we create a sense vector $s_i$ by averaging vectors that belong to this cluster. We rely on the methodology of BIBREF33 shifting the generated sense vector to the direction of the original word vector: $s_i~=~\\lambda ~w + (1-\\lambda )~\\dfrac{1}{n}~\\sum _{u~\\in ~V_i} cos(w, u)\\cdot u, $ where, $\\lambda \\in [0, 1]$, $w$ is the embedding of the original word, $cos(w, u)$ is the cosine similarity between $w$ and $u$, and $n=|V_i|$. By introducing the linear combination of $w$ and $u~\\in ~V_i$ we enforce the similarity of sense vectors to the original word important for this task. In addition to that, we weight $u$ by their similarity to the original word, so that more similar neighbours contribute more to the sense vector. The shifting parameter $\\lambda $ is set to $0.5$, following Remus:18.",
+      "A fastText model is able to generate a vector for each word even if it is not represented in the vocabulary, due to the use of subword information. However, our system cannot assemble sense vectors for out-of-vocabulary words, for such words it returns their original fastText vector. Still, the coverage of the benchmark datasets by our vocabulary is at least 85% and approaches 100% for some languages, so we do not have to resort to this back-off strategy very often.",
+      "We use the original fastText vectors as a baseline. In this case, we compute the relatedness scores of the two words as a cosine similarity of their vectors."
+    ],
+    [
+      "We compute the relatedness scores for all benchmark datasets using our sense vectors and compare them to cosine similarity scores of original fastText vectors. The results vary for different languages. Figure FIGREF28 shows the change in Pearson correlation score when switching from the baseline fastText embeddings to our sense vectors. The new vectors significantly improve the relatedness detection for German, Farsi, Russian, and Chinese, whereas for Italian, Dutch, and Swedish the score slightly falls behind the baseline. For other languages, the performance of sense vectors is on par with regular fastText."
+    ],
+    [
+      "The purpose of our sense vectors is disambiguation of polysemous words. Therefore, we test the inventories constructed with egvi on the Task 13 of SemEval-2013 \u2014 Word Sense Induction BIBREF34. The task is to identify the different senses of a target word in context in a fully unsupervised manner."
+    ],
+    [
+      "The dataset consists of a set of polysemous words: 20 nouns, 20 verbs, and 10 adjectives and specifies 20 to 100 contexts per word, with the total of 4,664 contexts, drawn from the Open American National Corpus. Given a set of contexts of a polysemous word, the participants of the competition had to divide them into clusters by sense of the word. The contexts are manually labelled with WordNet senses of the target words, the gold standard clustering is generated from this labelling.",
+      "The task allows two setups: graded WSI where participants can submit multiple senses per word and provide the probability of each sense in a particular context, and non-graded WSI where a model determines a single sense for a word in context. In our experiments we performed non-graded WSI. We considered the most suitable sense as the one with the highest cosine similarity with embeddings of the context, as described in Section SECREF9.",
+      "The performance of WSI models is measured with three metrics that require mapping of sense inventories (Jaccard Index, Kendall's $\\tau $, and WNDCG) and two cluster comparison metrics (Fuzzy NMI and Fuzzy B-Cubed)."
+    ],
+    [
+      "We compare our model with the models that participated in the task, the baseline ego-graph clustering model by Pelevina:16, and AdaGram BIBREF17, a method that learns sense embeddings based on a Bayesian extension of the Skip-gram model. Besides that, we provide the scores of the simple baselines originally used in the task: assigning one sense to all words, assigning the most frequent sense to all words, and considering each context as expressing a different sense. The evaluation of our model was performed using the open source context-eval tool.",
+      "Table TABREF31 shows the performance of these models on the SemEval dataset. Due to space constraints, we only report the scores of the best-performing SemEval participants, please refer to jurgens-klapaftis-2013-semeval for the full results. The performance of AdaGram and SenseGram models is reported according to Pelevina:16.",
+      "The table shows that the performance of egvi is similar to state-of-the-art word sense disambiguation and word sense induction models. In particular, we can see that it outperforms SenseGram on the majority of metrics. We should note that this comparison is not fully rigorous, because SenseGram induces sense inventories from word2vec as opposed to fastText vectors used in our work."
+    ],
+    [
+      "In order to see how the separation of word contexts that we perform corresponds to actual senses of polysemous words, we visualise ego-graphs produced by our method. Figure FIGREF17 shows the nearest neighbours clustering for the word Ruby, which divides the graph into five senses: Ruby-related programming tools, e.g. RubyOnRails (orange cluster), female names, e.g. Josie (magenta cluster), gems, e.g. Sapphire (yellow cluster), programming languages in general, e.g. Haskell (red cluster). Besides, this is typical for fastText embeddings featuring sub-string similarity, one can observe a cluster of different spelling of the word Ruby in green.",
+      "Analogously, the word python (see Figure FIGREF35) is divided into the senses of animals, e.g. crocodile (yellow cluster), programming languages, e.g. perl5 (magenta cluster), and conference, e.g. pycon (red cluster).",
+      "In addition, we show a qualitative analysis of senses of mouse and apple. Table TABREF38 shows nearest neighbours of the original words separated into clusters (labels for clusters were assigned manually). These inventories demonstrate clear separation of different senses, although it can be too fine-grained. For example, the first and the second cluster for mouse both refer to computer mouse, but the first one addresses the different types of computer mice, and the second one is used in the context of mouse actions. Similarly, we see that iphone and macbook are separated into two clusters. Interestingly, fastText handles typos, code-switching, and emojis by correctly associating all non-standard variants to the word they refer, and our method is able to cluster them appropriately. Both inventories were produced with $K=200$, which ensures stronger connectivity of graph. However, we see that this setting still produces too many clusters. We computed the average numbers of clusters produced by our model with $K=200$ for words from the word relatedness datasets and compared these numbers with the number of senses in WordNet for English and RuWordNet BIBREF35 for Russian (see Table TABREF37). We can see that the number of senses extracted by our method is consistently higher than the real number of senses.",
+      "We also compute the average number of senses per word for all the languages and different values of $K$ (see Figure FIGREF36). The average across languages does not change much as we increase $K$. However, for larger $K$ the average exceed the median value, indicating that more languages have lower number of senses per word. At the same time, while at smaller $K$ the maximum average number of senses per word does not exceed 6, larger values of $K$ produce outliers, e.g. English with $12.5$ senses.",
+      "Notably, there are no languages with an average number of senses less than 2, while numbers on English and Russian WordNets are considerably lower. This confirms that our method systematically over-generates senses. The presence of outliers shows that this effect cannot be eliminated by further increasing $K$, because the $i$-th nearest neighbour of a word for $i>200$ can be only remotely related to this word, even if the word is rare. Thus, our sense clustering algorithm needs a method of merging spurious senses."
+    ],
+    [
+      "We present egvi, a new algorithm for word sense induction based on graph clustering that is fully unsupervised and relies on graph operations between word vectors. We apply this algorithm to a large collection of pre-trained fastText word embeddings, releasing sense inventories for 158 languages. These inventories contain all the necessary information for constructing sense vectors and using them in downstream tasks. The sense vectors for polysemous words can be directly retrofitted with the pre-trained word embeddings and do not need any external resources. As one application of these multilingual sense inventories, we present a multilingual word sense disambiguation system that performs unsupervised and knowledge-free WSD for 158 languages without the use of any dictionary or sense-labelled corpus.",
+      "The evaluation of quality of the produced sense inventories is performed on multilingual word similarity benchmarks, showing that our sense vectors improve the scores compared to non-disambiguated word embeddings. Therefore, our system in its present state can improve WSD and downstream tasks for languages where knowledge bases, taxonomies, and annotated corpora are not available and supervised WSD models cannot be trained.",
+      "A promising direction for future work is combining distributional information from the induced sense inventories with lexical knowledge bases to improve WSD performance. Besides, we encourage the use of the produced word sense inventories in other downstream tasks."
+    ],
+    [
+      "We acknowledge the support of the Deutsche Forschungsgemeinschaft (DFG) foundation under the \u201cJOIN-T 2\u201d and \u201cACQuA\u201d projects. Ekaterina Artemova was supported by the framework of the HSE University Basic Research Program and Russian Academic Excellence Project \u201c5-100\u201d."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0069/instruction.md b/qasper-0069/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..b5261f724c663d5bf383d977c3896e5b5a42f9c8
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+++ b/qasper-0069/instruction.md
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+Name of Paper: Spoken Language Identification using ConvNets
+
+Question: Does the model use both spectrogram images and raw waveforms as features?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Proposed Method ::: Motivations",
+    "Proposed Method ::: Description of Features",
+    "Proposed Method ::: Model Description",
+    "Proposed Method ::: Model Details: 1D ConvNet",
+    "Proposed Method ::: Model Details: 1D ConvNet ::: Hyperparameter Optimization:",
+    "Proposed Method ::: Model Details: 2D ConvNet with Attention and bi-directional GRU",
+    "Proposed Method ::: Model Details: 2D ConvNet with Attention and bi-directional GRU ::: ",
+    "Proposed Method ::: Model Details: 2D ConvNet with Attention and bi-directional GRU ::: Hyperparameter Optimization:",
+    "Proposed Method ::: Model details: 2D-ConvNet",
+    "Proposed Method ::: Dataset",
+    "Results and Discussion",
+    "Results and Discussion ::: Misclassification",
+    "Results and Discussion ::: Future Scope",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Language Identification (LI) is a problem which involves classifying the language being spoken by a speaker. LI systems can be used in call centers to route international calls to an operator who is fluent in that identified language BIBREF0. In speech-based assistants, LI acts as the first step which chooses the corresponding grammar from a list of available languages for its further semantic analysis BIBREF1. It can also be used in multi-lingual voice-controlled information retrieval systems, for example, Apple Siri and Amazon Alexa.",
+      "Over the years, studies have utilized many prosodic and acoustic features to construct machine learning models for LI systems BIBREF2. Every language is composed of phonemes, which are distinct unit of sounds in that language, such as b of black and g of green. Several prosodic and acoustic features are based on phonemes, which become the underlying features on whom the performance of the statistical model depends BIBREF3, BIBREF4. If two languages have many overlapping phonemes, then identifying them becomes a challenging task for a classifier. For example, the word cat in English, kat in Dutch, katze in German have different consonants but when used in a speech they all would sound quite similar.",
+      "Due to such drawbacks several studies have switched over to using Deep Neural Networks (DNNs) to harness their novel auto-extraction techniques BIBREF1, BIBREF5. This work follows an implicit approach for identifying six languages with overlapping phonemes on the VoxForge BIBREF6 dataset and achieves 95.4% overall accuracy.",
+      "In previous studies BIBREF1, BIBREF7, BIBREF5, authors use log-Mel spectrum of a raw audio as inputs to their models. One of our contributions is to enhance the performance of this approach by utilising recent techniques like Mixup augmentation of inputs and exploring the effectiveness of Attention mechanism in enhancing performance of neural network. As log-Mel spectrum needs to be computed for each raw audio input and processing time for generating log-Mel spectrum increases linearly with length of audio, this acts as a bottleneck for these models. Hence, we propose the use of raw audio waveforms as inputs to deep neural network which boosts performance by avoiding additional overhead of computing log-Mel spectrum for each audio. Our 1D-ConvNet architecture auto-extracts and classifies features from this raw audio input.",
+      "The structure of the work is as follows. In Section 2 we discuss about the previous related studies in this field. The model architecture for both the raw waveforms and log-Mel spectrogram images is discussed in Section 3 along with the a discussion on hyperparameter space exploration. In Section 4 we present the experimental results. Finally, in Section 5 we discuss the conclusions drawn from the experiment and future work."
+    ],
+    [
+      "Extraction of language dependent features like prosody and phonemes was a popular approach to classify spoken languages BIBREF8, BIBREF9, BIBREF10. Following their success in speaker verification systems, i-vectors have also been used as features in various classification networks. These approaches required significant domain knowledge BIBREF11, BIBREF9. Nowadays most of the attempts on spoken language identification rely on neural networks for meaningful feature extraction and classification BIBREF12, BIBREF13.",
+      "Revay et al. BIBREF5 used the ResNet50 BIBREF14 architecture for classifying languages by generating the log-Mel spectra of each raw audio. The model uses a cyclic learning rate where learning rate increases and then decreases linearly. Maximum learning rate for a cycle is set by finding the optimal learning rate using fastai BIBREF15 library. The model classified six languages \u2013 English, French, Spanish, Russian, Italian and German \u2013 and achieving an accuracy of 89.0%.",
+      "Gazeau et al. BIBREF16 in his research showed how Neural Networks, Support Vector Machine and Hidden Markov Model (HMM) can be used to identify French, English, Spanish and German. Dataset was prepared using voice samples from Youtube News BIBREF17and VoxForge BIBREF6 datasets. Hidden Markov models convert speech into a sequence of vectors, was used to capture temporal features in speech. HMMs trained on VoxForge BIBREF6 dataset performed best in comparison to other models proposed by him on same VoxForge dataset. They reported an accuracy of 70.0%.",
+      "Bartz et al. BIBREF1 proposed two different hybrid Convolutional Recurrent Neural Networks for language identification. They proposed a new architecture for extracting spatial features from log-Mel spectra of raw audio using CNNs and then using RNNs for capturing temporal features to identify the language. This model achieved an accuracy of 91.0% on Youtube News Dataset BIBREF17. In their second architecture they used the Inception-v3 BIBREF18 architecture to extract spatial features which were then used as input for bi-directional LSTMs to predict the language accurately. This model achieved an accuracy of 96.0% on four languages which were English, German, French and Spanish. They also trained their CNN model (obtained after removing RNN from CRNN model) and the Inception-v3 on their dataset. However they were not able to achieve better results achieving and reported 90% and 95% accuracies, respectively.",
+      "Kumar et al. BIBREF0 used Mel-frequency cepstral coefficients (MFCC), Perceptual linear prediction coefficients (PLP), Bark Frequency Cepstral Coefficients (BFCC) and Revised Perceptual Linear Prediction Coefficients (RPLP) as features for language identification. BFCC and RPLP are hybrid features derived using MFCC and PLP. They used two different models based on Vector Quantization (VQ) with Dynamic Time Warping (DTW) and Gaussian Mixture Model (GMM) for classification. These classification models were trained with different features. The authors were able to show that these models worked better with hybrid features (BFCC and RPLP) as compared to conventional features (MFCC and PLP). GMM combined with RPLP features gave the most promising results and achieved an accuracy of 88.8% on ten languages. They designed their own dataset comprising of ten languages being Dutch, English, French, German, Italian, Russian, Spanish, Hindi, Telegu, and Bengali.",
+      "Montavon BIBREF7 generated Mel spectrogram as features for a time-delay neural network (TDNN). This network had two-dimensional convolutional layers for feature extraction. An elaborate analysis of how deep architectures outperform their shallow counterparts is presented in this reseacrch. The difficulties in classifying perceptually similar languages like German and English were also put forward in this work. It is mentioned that the proposed approach is less robust to new speakers present in the test dataset. This method was able to achieve an accuracy of 91.2% on dataset comprising of 3 languages \u2013 English, French and German.",
+      "In Table TABREF1, we summarize the quantitative results of the above previous studies. It includes the model basis, feature description, languages classified and the used dataset along with accuracy obtained. The table also lists the overall results of our proposed models (at the top). The languages used by various authors along with their acronyms are English (En), Spanish (Es), French (Fr), German (De), Russian (Ru), Italian (It), Bengali (Ben), Hindi (Hi) and Telegu (Tel)."
+    ],
+    [
+      "Several state-of-the-art results on various audio classification tasks have been obtained by using log-Mel spectrograms of raw audio, as features BIBREF19. Convolutional Neural Networks have demonstrated an excellent performance gain in classification of these features BIBREF20, BIBREF21 against other machine learning techniques. It has been shown that using attention layers with ConvNets further enhanced their performance BIBREF22. This motivated us to develop a CNN-based architecture with attention since this approach hasn\u2019t been applied to the task of language identification before.",
+      "Recently, using raw audio waveform as features to neural networks has become a popular approach in audio classification BIBREF23, BIBREF22. Raw waveforms have several artifacts which are not effectively captured by various conventional feature extraction techniques like Mel Frequency Cepstral Coefficients (MFCC), Constant Q Transform (CQT), Fast Fourier Transform (FFT), etc.",
+      "Audio files are a sequence of spoken words, hence they have temporal features too.A CNN is better at capturing spatial features only and RNNs are better at capturing temporal features as demonstrated by Bartz et al. BIBREF1 using audio files. Therefore, we combined both of these to make a CRNN model.",
+      "We propose three types of models to tackle the problem with different approaches, discussed as follows."
+    ],
+    [
+      "As an average human's voice is around 300 Hz and according to Nyquist-Shannon sampling theorem all the useful frequencies (0-300 Hz) are preserved with sampling at 8 kHz, therefore, we sampled raw audio files from all six languages at 8 kHz",
+      "The average length of audio files in this dataset was about 10.4 seconds and standard deviation was 2.3 seconds. For our experiments, the audio length was set to 10 seconds. If the audio files were shorter than 10 second, then the data was repeated and concatenated. If audio files were longer, then the data was truncated."
+    ],
+    [
+      "We applied the following design principles to all our models:",
+      "Every convolutional layer is always followed by an appropriate max pooling layer. This helps in containing the explosion of parameters and keeps the model small and nimble.",
+      "Convolutional blocks are defined as an individual block with multiple pairs of one convolutional layer and one max pooling layer. Each convolutional block is preceded or succeded by a convolutional layer.",
+      "Batch Normalization and Rectified linear unit activations were applied after each convolutional layer. Batch Normalization helps speed up convergence during training of a neural network.",
+      "Model ends with a dense layer which acts the final output layer."
+    ],
+    [
+      "As the sampling rate is 8 kHz and audio length is 10 s, hence the input is raw audio to the models with input size of (batch size, 1, 80000). In Table TABREF10, we present a detailed layer-by-layer illustration of the model along with its hyperparameter.",
+      "-10pt"
+    ],
+    [
+      "Tuning hyperparameters is a cumbersome process as the hyperparamter space expands exponentially with the number of parameters, therefore efficient exploration is needed for any feasible study. We used the random search algorithm supported by Hyperopt BIBREF24 library to randomly search for an optimal set of hyperparameters from a given parameter space. In Fig. FIGREF12, various hyperparameters we considered are plotted against the validation accuracy as violin plots. Our observations for each hyperparameter are summarized below:",
+      "Number of filters in first layer: We observe that having 128 filters gives better results as compared to other filter values of 32 and 64 in the first layer. A higher number of filters in the first layer of network is able to preserve most of the characteristics of input.",
+      "Kernel Size: We varied the receptive fields of convolutional layers by choosing the kernel size from among the set of {3, 5, 7, 9}. We observe that a kernel size of 9 gives better accuracy at the cost of increased computation time and larger number of parameters. A large kernel size is able to capture longer patterns in its input due to bigger receptive power which results in an improved accuracy.",
+      "Dropout: Dropout randomly turns-off (sets to 0) various individual nodes during training of the network. In a deep CNN it is important that nodes do not develop a co-dependency amongst each other during training in order to prevent overfitting on training data BIBREF25. Dropout rate of $0.1$ works well for our model. When using a higher dropout rate the network is not able to capture the patterns in training dataset.",
+      "Batch Size: We chose batch sizes from amongst the set {32, 64, 128}. There is more noise while calculating error in a smaller batch size as compared to a larger one. This tends to have a regularizing effect during training of the network and hence gives better results. Thus, batch size of 32 works best for the model.",
+      "Layers in Convolutional block 1 and 2: We varied the number of layers in both the convolutional blocks. If the number of layers is low, then the network does not have enough depth to capture patterns in the data whereas having large number of layers leads to overfitting on the data. In our network, two layers in the first block and one layer in the second block give optimal results."
+    ],
+    [
+      "Log-Mel spectrogram is the most commonly used method for converting audio into the image domain. The audio data was again sampled at 8 kHz. The input to this model was the log-Mel spectra. We generated log-Mel spectrogram using the LibROSA BIBREF26 library. In Table TABREF16, we present a detailed layer-by-layer illustration of the model along with its hyperparameter."
+    ],
+    [
+      "We took some specific design choices for this model, which are as follows:",
+      "We added residual connections with each convolutional layer. Residual connections in a way makes the model selective of the contributing layers, determines the optimal number of layers required for training and solves the problem of vanishing gradients. Residual connections or skip connections skip training of those layers that do not contribute much in the overall outcome of model.",
+      "We added spatial attention BIBREF27 networks to help the model in focusing on specific regions or areas in an image. Spatial attention aids learning irrespective of transformations, scaling and rotation done on the input images making the model more robust and helping it to achieve better results.",
+      "We added Channel Attention networks so as to help the model to find interdependencies among color channels of log-Mel spectra. It adaptively assigns importance to each color channel in a deep convolutional multi-channel network. In our model we apply channel and spatial attention just before feeding the input into bi-directional GRU. This helps the model to focus on selected regions and at the same time find patterns among channels to better determine the language."
+    ],
+    [
+      "We used the random search algorithm supported by Hyperopt BIBREF24 library to randomly search for an optimal set of hyperparameters from a given parameter space. In Fig. FIGREF19 ,various hyperparameters we tuned are plotted against the validation accuracy. Our observations for each hyperparameter are summarized below:",
+      "Filter Size: 64 filters in the first layer of network can preserve most of the characteristics of input, but increasing it to 128 is inefficient as overfitting occurs.",
+      "Kernel Size: There is a trade-off between kernel size and capturing complex non-linear features. Using a small kernel size will require more layers to capture features whereas using a large kernel size will require less layers. Large kernels capture simple non-linear features whereas using a smaller kernel will help us capture more complex non-linear features. However, with more layers, backpropagation necessitates the need for a large memory. We experimented with large kernel size and gradually increased the layers in order to capture more complex features. The results are not conclusive and thus we chose kernel size of 7 against 3.",
+      "Dropout: Dropout rate of 0.1 works well for our data. When using a higher dropout rate the network is not able to capture the patterns in training dataset.",
+      "Batch Size: There is always a trade-off between batch size and getting accurate gradients. Using a large batch size helps the model to get more accurate gradients since the model tries to optimize gradients over a large set of images. We found that using a batch size of 128 helped the model to train faster and get better results than using a batch size less than 128.",
+      "Number of hidden units in bi-directional GRU: Varying the number of hidden units and layers in GRU helps the model to capture temporal features which can play a significant role in identifying the language correctly. The optimal number of hidden units and layers depends on the complexity of the dataset. Using less number of hidden units may capture less features whereas using large number of hidden units may be computationally expensive. In our case we found that using 1536 hidden units in a single bi-directional GRU layer leads to the best result.",
+      "Image Size: We experimented with log-Mel spectra images of sizes $64 \\times 64$ and $128 \\times 128$ pixels and found that our model worked best with images of size of $128 \\times 128$ pixels.",
+      "We also evaluated our model on data with mixup augmentation BIBREF28. It is a data augmentation technique that also acts as a regularization technique and prevents overfitting. Instead of directly taking images from the training dataset as input, mixup takes a linear combination of any two random images and feeds it as input. The following equations were used to prepared a mixed-up dataset:",
+      "and",
+      "where $\\alpha \\in [0, 1]$ is a random variable from a $\\beta $-distribution, $I_1$."
+    ],
+    [
+      "This model is a similar model to 2D-ConvNet with Attention and bi-directional GRU described in section SECREF13 except that it lacks skip connections, attention layers, bi-directional GRU and the embedding layer incorporated in the previous model."
+    ],
+    [
+      "We classified six languages (English, French, German, Spanish, Russian and Italian) from the VoxForge BIBREF6 dataset. VoxForge is an open-source speech corpus which primarily consists of samples recorded and submitted by users using their own microphone. This results in significant variation of speech quality between samples making it more representative of real world scenarios.",
+      "Our dataset consists of 1,500 samples for each of six languages. Out of 1,500 samples for each language, 1,200 were randomly selected as training dataset for that language and rest 300 as validation dataset using k-fold cross-validation. To sum up, we trained our model on 7,200 samples and validated it on 1800 samples comprising six languages. The results are discussed in next section."
+    ],
+    [
+      "This paper discusses two end-to-end approaches which achieve state-of-the-art results in both the image as well as audio domain on the VoxForge dataset BIBREF6. In Table TABREF25, we present all the classification accuracies of the two models of the cases with and without mixup for six and four languages.",
+      "In the audio domain (using raw audio waveform as input), 1D-ConvNet achieved a mean accuracy of 93.7% with a standard deviation of 0.3% on running k-fold cross validation. In Fig FIGREF27 (a) we present the confusion matrix for the 1D-ConvNet model.",
+      "In the image domain (obtained by taking log-Mel spectra of raw audio), 2D-ConvNet with 2D attention (channel and spatial attention) and bi-directional GRU achieved a mean accuracy of 95.0% with a standard deviation of 1.2% for six languages. This model performed better when mixup regularization was applied. 2D-ConvNet achieved a mean accuracy of 95.4% with standard deviation of 0.6% on running k-fold cross validation for six languages when mixup was applied. In Fig FIGREF27 (b) we present the confusion matrix for the 2D-ConvNet model. 2D attention models focused on the important features extracted by convolutional layers and bi-directional GRU captured the temporal features."
+    ],
+    [
+      "Several of the spoken languages in Europe belong to the Indo-European family. Within this family, the languages are divided into three phyla which are Romance, Germanic and Slavic. Of the 6 languages that we selected Spanish (Es), French (Fr) and Italian (It) belong to the Romance phyla, English and German belong to Germanic phyla and Russian in Slavic phyla. Our model also confuses between languages belonging to the similar phyla which acts as an insanity check since languages in same phyla have many similar pronounced words such as cat in English becomes Katze in German and Ciao in Italian becomes Chao in Spanish.",
+      "Our model confuses between French (Fr) and Russian (Ru) while these languages belong to different phyla, many words from French were adopted into Russian such as automate (oot-oo-mate) in French becomes ABTOMaT (aff-taa-maat) in Russian which have similar pronunciation.",
+      ""
+    ],
+    [
+      "The performance of raw audio waveforms as input features to ConvNet can be further improved by applying silence removal in the audio. Also, there is scope for improvement by augmenting available data through various conventional techniques like pitch shifting, adding random noise and changing speed of audio. These help in making neural networks more robust to variations which might be present in real world scenarios. There can be further exploration of various feature extraction techniques like Constant-Q transform and Fast Fourier Transform and assessment of their impact on Language Identification.",
+      "There can be further improvements in neural network architectures like concatenating the high level features obtained from 1D-ConvNet and 2D-ConvNet, before performing classification. There can be experiments using deeper networks with skip connections and Inception modules. These are known to have positively impacted the performance of Convolutional Neural Networks."
+    ],
+    [
+      "There are two main contributions of this paper in the domain of spoken language identification. Firstly, we presented an extensive analysis of raw audio waveforms as input features to 1D-ConvNet. We experimented with various hyperparameters in our 1D-ConvNet and evaluated their effect on validation accuracy. This method is able to bypass the computational overhead of conventional approaches which depend on generation of spectrograms as a necessary pre-procesing step. We were able to achieve an accauracy of 93.7% using this technique.",
+      "Next, we discussed the enhancement in performance of 2D-ConvNet using mixup augmentation, which is a recently developed technique to prevent over\ufb01tting on test data.This approach achieved an accuracy of 95.4%. We also analysed how attention mechanism and recurrent layers impact the performance of networks. This approach achieved an accuracy of 95.0%."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0092/instruction.md b/qasper-0092/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..842863c01e724c938aa8e7f8a20bfcff33915daa
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+++ b/qasper-0092/instruction.md
@@ -0,0 +1,77 @@
+Name of Paper: Gunrock: A Social Bot for Complex and Engaging Long Conversations
+
+Question: How do they correlate user backstory queries to user satisfaction?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "System Architecture",
+    "System Architecture ::: Automatic Speech Recognition",
+    "System Architecture ::: Natural Language Understanding",
+    "System Architecture ::: Dialog Manager",
+    "System Architecture ::: Knowledge Databases",
+    "System Architecture ::: Natural Language Generation",
+    "System Architecture ::: Text To Speech",
+    "Analysis",
+    "Analysis ::: Response Depth: Mean Word Count",
+    "Analysis ::: Gunrock's Backstory and Persona",
+    "Analysis ::: Interleaving Personal and Factual Information: Animal Module",
+    "Conclusion",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "Amazon Alexa Prize BIBREF0 provides a platform to collect real human-machine conversation data and evaluate performance on speech-based social conversational systems. Our system, Gunrock BIBREF1 addresses several limitations of prior chatbots BIBREF2, BIBREF3, BIBREF4 including inconsistency and difficulty in complex sentence understanding (e.g., long utterances) and provides several contributions: First, Gunrock's multi-step language understanding modules enable the system to provide more useful information to the dialog manager, including a novel dialog act scheme. Additionally, the natural language understanding (NLU) module can handle more complex sentences, including those with coreference. Second, Gunrock interleaves actions to elicit users' opinions and provide responses to create an in-depth, engaging conversation; while a related strategy to interleave task- and non-task functions in chatbots has been proposed BIBREF5, no chatbots to our knowledge have employed a fact/opinion interleaving strategy. Finally, we use an extensive persona database to provide coherent profile information, a critical challenge in building social chatbots BIBREF3. Compared to previous systems BIBREF4, Gunrock generates more balanced conversations between human and machine by encouraging and understanding more human inputs (see Table TABREF2 for an example)."
+    ],
+    [
+      "Figure FIGREF3 provides an overview of Gunrock's architecture. We extend the Amazon Conversational Bot Toolkit (CoBot) BIBREF6 which is a flexible event-driven framework. CoBot provides ASR results and natural language processing pipelines through the Alexa Skills Kit (ASK) BIBREF7. Gunrock corrects ASR according to the context (asr) and creates a natural language understanding (NLU) (nlu) module where multiple components analyze the user utterances. A dialog manager (DM) (dm) uses features from NLU to select topic dialog modules and defines an individual dialog flow. Each dialog module leverages several knowledge bases (knowledge). Then a natural language generation (NLG) (nlg) module generates a corresponding response. Finally, we markup the synthesized responses and return to the users through text to speech (TTS) (tts). While we provide an overview of the system in the following sections, for detailed system implementation details, please see the technical report BIBREF1."
+    ],
+    [
+      "Gunrock receives ASR results with the raw text and timestep information for each word in the sequence (without case information and punctuation). Keywords, especially named entities such as movie names, are prone to generate ASR errors without contextual information, but are essential for NLU and NLG. Therefore, Gunrock uses domain knowledge to correct these errors by comparing noun phrases to a knowledge base (e.g. a list of the most popular movies names) based on their phonetic information. We extract the primary and secondary code using The Double Metaphone Search Algorithm BIBREF8 for noun phrases (extracted by noun trunks) and the selected knowledge base, and suggest a potential fix by code matching. An example can be seen in User_3 and Gunrock_3 in Table TABREF2."
+    ],
+    [
+      "Gunrock is designed to engage users in deeper conversation; accordingly, a user utterance can consist of multiple units with complete semantic meanings. We first split the corrected raw ASR text into sentences by inserting break tokens. An example is shown in User_3 in Table TABREF2. Meanwhile, we mask named entities before segmentation so that a named entity will not be segmented into multiple parts and an utterance with a complete meaning is maintained (e.g.,\u201ci like the movie a star is born\"). We also leverage timestep information to filter out false positive corrections. After segmentation, our coreference implementation leverages entity knowledge (such as person versus event) and replaces nouns with their actual reference by entity ranking. We implement coreference resolution on entities both within segments in a single turn as well as across multiple turns. For instance, \u201chim\" in the last segment in User_5 is replaced with \u201cbradley cooper\" in Table TABREF2. Next, we use a constituency parser to generate noun phrases from each modified segment. Within the sequence pipeline to generate complete segments, Gunrock detects (1) topic, (2) named entities, and (3) sentiment using ASK in parallel. The NLU module uses knowledge graphs including Google Knowledge Graph to call for a detailed description of each noun phrase for understanding.",
+      "In order to extract the intent for each segment, we designed MIDAS, a human-machine dialog act scheme with 23 tags and implemented a multi-label dialog act classification model using contextual information BIBREF9. Next, the NLU components analyzed on each segment in a user utterance are sent to the DM and NLG module for state tracking and generation, respectively."
+    ],
+    [
+      "We implemented a hierarchical dialog manager, consisting of a high level and low level DMs. The former leverages NLU outputs for each segment and selects the most important segment for the system as the central element using heuristics. For example, \u201ci just finished reading harry potter,\" triggers Sub-DM: Books. Utilizing the central element and features extracted from NLU, input utterances are mapped onto 11 possible topic dialog modules (e.g., movies, books, animals, etc.), including a backup module, retrieval.",
+      "Low level dialog management is handled by the separate topic dialog modules, which use modular finite state transducers to execute various dialog segments processed by the NLU. Using topic-specific modules enables deeper conversations that maintain the context. We design dialog flows in each of the finite state machines, as well. Dialog flow is determined by rule-based transitions between a specified fixed set of dialog states. To ensure that our states and transitions are effective, we leverage large scale user data to find high probability responses and high priority responses to handle in different contexts. Meanwhile, dialog flow is customized to each user by tracking user attributes as dialog context. In addition, each dialog flow is adaptive to user responses to show acknowledgement and understanding (e.g., talking about pet ownership in the animal module). Based on the user responses, many dialog flow variations exist to provide a fresh experience each time. This reduces the feeling of dialogs being scripted and repetitive. Our dialog flows additionally interleave facts, opinions, experiences, and questions to make the conversation flexible and interesting.",
+      "In the meantime, we consider feedback signals such as \u201ccontinue\" and \u201cstop\" from the current topic dialog module, indicating whether it is able to respond to the following request in the dialog flow, in order to select the best response module. Additionally, in all modules we allow mixed-initiative interactions; users can trigger a new dialog module when they want to switch topics while in any state. For example, users can start a new conversation about movies from any other topic module."
+    ],
+    [
+      "All topic dialog modules query knowledge bases to provide information to the user. To respond to general factual questions, Gunrock queries the EVI factual database , as well as other up-to-date scraped information appropriate for the submodule, such as news and current showing movies in a specific location from databases including IMDB. One contribution of Gunrock is the extensive Gunrock Persona Backstory database, consisting of over 1,000 responses to possible questions for Gunrock as well as reasoning for her responses for roughly 250 questions (see Table 2). We designed the system responses to elicit a consistent personality within and across modules, modeled as a female individual who is positive, outgoing, and is interested in science and technology."
+    ],
+    [
+      "In order to avoid repetitive and non-specific responses commonly seen in dialog systems BIBREF10, Gunrock uses a template manager to select from a handcrafted response templates based on the dialog state. One dialog state can map to multiple response templates with similar semantic or functional content but differing surface forms. Among these response templates for the same dialog state, one is randomly selected without repetition to provide variety unless all have been exhausted. When a response template is selected, any slots are substituted with actual contents, including queried information for news and specific data for weather. For example, to ground a movie name due to ASR errors or multiple versions, one template is \u201cAre you talking about {movie_title} released in {release_year} starring {actor_name} as {actor_role}?\". Module-specific templates were generated for each topic (e.g., animals), but some of the templates are generalizable across different modules (e.g., \u201cWhat\u2019s your favorite [movie $|$ book $|$ place to visit]?\")",
+      "In many cases, response templates corresponding to different dialog acts are dynamically composed to give the final response. For example, an appropriate acknowledgement for the user\u2019s response can be combined with a predetermined follow-up question."
+    ],
+    [
+      "After NLG, we adjust the TTS of the system to improve the expressiveness of the voice to convey that the system is an engaged and active participant in the conversation. We use a rule-based system to systematically add interjections, specifically Alexa Speechcons, and fillers to approximate human-like cognitive-emotional expression BIBREF11. For more on the framework and analysis of the TTS modifications, see BIBREF12."
+    ],
+    [
+      "From January 5, 2019 to March 5, 2019, we collected conversational data for Gunrock. During this time, no other code updates occurred. We analyzed conversations for Gunrock with at least 3 user turns to avoid conversations triggered by accident. Overall, this resulted in a total of 34,432 user conversations. Together, these users gave Gunrock an average rating of 3.65 (median: 4.0), which was elicited at the end of the conversation (\u201cOn a scale from 1 to 5 stars, how do you feel about talking to this socialbot again?\"). Users engaged with Gunrock for an average of 20.92 overall turns (median 13.0), with an average of 6.98 words per utterance, and had an average conversation time of 7.33 minutes (median: 2.87 min.). We conducted three principal analyses: users' response depth (wordcount), backstory queries (backstorypersona), and interleaving of personal and factual responses (pets)."
+    ],
+    [
+      "Two unique features of Gunrock are its ability to dissect longer, complex sentences, and its methods to encourage users to be active conversationalists, elaborating on their responses. In prior work, even if users are able to drive the conversation, often bots use simple yes/no questions to control the conversational flow to improve understanding; as a result, users are more passive interlocutors in the conversation. We aimed to improve user engagement by designing the conversation to have more open-ended opinion/personal questions, and show that the system can understand the users' complex utterances (See nlu for details on NLU). Accordingly, we ask if users' speech behavior will reflect Gunrock's technical capability and conversational strategy, producing longer sentences.",
+      "We assessed the degree of conversational depth by measuring users' mean word count. Prior work has found that an increase in word count has been linked to improved user engagement (e.g., in a social dialog system BIBREF13). For each user conversation, we extracted the overall rating, the number of turns of the interaction, and the user's per-utterance word count (averaged across all utterances). We modeled the relationship between word count and the two metrics of user engagement (overall rating, mean number of turns) in separate linear regressions.",
+      "Results showed that users who, on average, produced utterances with more words gave significantly higher ratings ($\\beta $=0.01, SE=0.002, t=4.79, p$<$0.001)(see Figure 2) and engaged with Gunrock for significantly greater number of turns ($\\beta $=1.85, SE=0.05, t=35.58, p$<$0.001) (see Figure 2). These results can be interpreted as evidence for Gunrock's ability to handle complex sentences, where users are not constrained to simple responses to be understood and feel engaged in the conversation \u2013 and evidence that individuals are more satisfied with the conversation when they take a more active role, rather than the system dominating the dialog. On the other hand, another interpretation is that users who are more talkative may enjoy talking to the bot in general, and thus give higher ratings in tandem with higher average word counts."
+    ],
+    [
+      "We assessed the user's interest in Gunrock by tagging instances where the user triggered Gunrock's backstory (e.g., \u201cWhat's your favorite color?\"). For users with at least one backstory question, we modeled overall (log) Rating with a linear regression by the (log) `Number of Backstory Questions Asked' (log transformed due to the variables' nonlinear relationship). We hypothesized that users who show greater curiosity about Gunrock will display higher overall ratings for the conversation based on her responses. Overall, the number of times users queried Gunrock's backstory was strongly related to the rating they gave at the end of the interaction (log:$\\beta $=0.10, SE=0.002, t=58.4, p$<$0.001)(see Figure 3). This suggests that maintaining a consistent personality \u2014 and having enough responses to questions the users are interested in \u2014 may improve user satisfaction."
+    ],
+    [
+      "Gunrock includes a specific topic module on animals, which includes a factual component where the system provides animal facts, as well as a more personalized component about pets. Our system is designed to engage users about animals in a more casual conversational style BIBREF14, eliciting follow-up questions if the user indicates they have a pet; if we are able to extract the pet's name, we refer to it in the conversation (e.g., \u201cOliver is a great name for a cat!\", \u201cHow long have you had Oliver?\"). In cases where the user does not indicate that they have a pet, the system solely provides animal facts. Therefore, the animal module can serve as a test of our interleaving strategy: we hypothesized that combining facts and personal questions \u2014 in this case about the user's pet \u2014 would lead to greater user satisfaction overall.",
+      "We extracted conversations where Gunrock asked the user if they had ever had a pet and categorized responses as \u201cYes\", \u201cNo\", or \u201cNA\" (if users did not respond with an affirmative or negative response). We modeled user rating with a linear regression model, with predictor of \u201cHas Pet' (2 levels: Yes, No). We found that users who talked to Gunrock about their pet showed significantly higher overall ratings of the conversation ($\\beta $=0.15, SE=0.06, t=2.53, p$=$0.016) (see Figure 4). One interpretation is that interleaving factual information with more in-depth questions about their pet result in improved user experience. Yet, another interpretation is that pet owners may be more friendly and amenable to a socialbot; for example, prior research has linked differences in personality to pet ownership BIBREF15."
+    ],
+    [
+      "Gunrock is a social chatbot that focuses on having long and engaging speech-based conversations with thousands of real users. Accordingly, our architecture employs specific modules to handle longer and complex utterances and encourages users to be more active in a conversation. Analysis shows that users' speech behavior reflects these capabilities. Longer sentences and more questions about Gunrocks's backstory positively correlate with user experience. Additionally, we find evidence for interleaved dialog flow, where combining factual information with personal opinions and stories improve user satisfaction. Overall, this work has practical applications, in applying these design principles to other social chatbots, as well as theoretical implications, in terms of the nature of human-computer interaction (cf. 'Computers are Social Actors' BIBREF16). Our results suggest that users are engaging with Gunrock in similar ways to other humans: in chitchat about general topics (e.g., animals, movies, etc.), taking interest in Gunrock's backstory and persona, and even producing more information about themselves in return."
+    ],
+    [
+      "We would like to acknowledge the help from Amazon in terms of financial and technical support."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0093/instruction.md b/qasper-0093/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..05b6ca83a339b2985e7a3eb525b1110646458b4a
--- /dev/null
+++ b/qasper-0093/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Towards Detection of Subjective Bias using Contextualized Word Embeddings
+
+Question: Do the authors report only on English?
\ No newline at end of file
diff --git a/qasper-0094/instruction.md b/qasper-0094/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..360b37acd432a4abf3fe52ec9eaecb57bfd9c927
--- /dev/null
+++ b/qasper-0094/instruction.md
@@ -0,0 +1,52 @@
+Name of Paper: Towards Detection of Subjective Bias using Contextualized Word Embeddings
+
+Question: What is the baseline for the experiments?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Baselines and Approach",
+    "Baselines and Approach ::: Baselines",
+    "Baselines and Approach ::: Proposed Approaches",
+    "Experiments ::: Dataset and Experimental Settings",
+    "Experiments ::: Experimental Results",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "In natural language, subjectivity refers to the aspects of communication used to express opinions, evaluations, and speculationsBIBREF0, often influenced by one's emotional state and viewpoints. Writers and editors of texts like news and textbooks try to avoid the use of biased language, yet subjective bias is pervasive in these texts. More than $56\\%$ of Americans believe that news sources do not report the news objectively , thus implying the prevalence of the bias. Therefore, when presenting factual information, it becomes necessary to differentiate subjective language from objective language.",
+      "There has been considerable work on capturing subjectivity using text-classification models ranging from linguistic-feature-based modelsBIBREF1 to finetuned pre-trained word embeddings like BERTBIBREF2. The detection of bias-inducing words in a Wikipedia statement was explored in BIBREF1. The authors propose the \"Neutral Point of View\" (NPOV) corpus made using Wikipedia revision history, containing Wikipedia edits that are specifically designed to remove subjective bias. They use logistic regression with linguistic features, including factive verbs, hedges, and subjective intensifiers to detect bias-inducing words. In BIBREF2, the authors extend this work by mitigating subjective bias after detecting bias-inducing words using a BERT-based model. However, they primarily focused on detecting and mitigating subjective bias for single-word edits. We extend their work by incorporating multi-word edits by detecting bias at the sentence level. We further use their version of the NPOV corpus called Wiki Neutrality Corpus(WNC) for this work.",
+      "The task of detecting sentences containing subjective bias rather than individual words inducing the bias has been explored in BIBREF3. However, they conduct majority of their experiments in controlled settings, limiting the type of articles from which the revisions were extracted. Their attempt to test their models in a general setting is dwarfed by the fact that they used revisions from a single Wikipedia article resulting in just 100 instances to evaluate their proposed models robustly. Consequently, we perform our experiments in the complete WNC corpus, which consists of $423,823$ revisions in Wikipedia marked by its editors over a period of 15 years, to simulate a more general setting for the bias.",
+      "In this work, we investigate the application of BERT-based models for the task of subjective language detection. We explore various BERT-based models, including BERT, RoBERTa, ALBERT, with their base and large specifications along with their native classifiers. We propose an ensemble model exploiting predictions from these models using multiple ensembling techniques. We show that our model outperforms the baselines by a margin of $5.6$ of F1 score and $5.95\\%$ of Accuracy."
+    ],
+    [
+      "In this section, we outline baseline models like $BERT_{large}$. We further propose three approaches: optimized BERT-based models, distilled pretrained models, and the use of ensemble methods for the task of subjectivity detection."
+    ],
+    [
+      "FastTextBIBREF4: It uses bag of words and bag of n-grams as features for text classification, capturing partial information about the local word order efficiently.",
+      "BiLSTM: Unlike feedforward neural networks, recurrent neural networks like BiLSTMs use memory based on history information to learn long-distance features and then predict the output. We use a two-layer BiLSTM architecture with GloVe word embeddings as a strong RNN baseline.",
+      "BERT BIBREF5: It is a contextualized word representation model that uses bidirectional transformers, pretrained on a large $3.3B$ word corpus. We use the $BERT_{large}$ model finetuned on the training dataset."
+    ],
+    [
+      "Optimized BERT-based models: We use BERT-based models optimized as in BIBREF6 and BIBREF7, pretrained on a dataset as large as twelve times as compared to $BERT_{large}$, with bigger batches, and longer sequences. ALBERT, introduced in BIBREF7, uses factorized embedding parameterization and cross-layer parameter sharing for parameter reduction. These optimizations have led both the models to outperform $BERT_{large}$ in various benchmarking tests, like GLUE for text classification and SQuAD for Question Answering.",
+      "Distilled BERT-based models: Secondly, we propose to use distilled BERT-based models, as introduced in BIBREF8. They are smaller general-purpose language representation model, pre-trained by leveraging distillation knowledge. This results in significantly smaller and faster models with performance comparable to their undistilled versions. We finetune these pretrained distilled models on the training corpus to efficiently detect subjectivity.",
+      "BERT-based ensemble models: Lastly, we use the weighted-average ensembling technique to exploit the predictions made by different variations of the above models. Ensembling methodology entails engendering a predictive model by utilizing predictions from multiple models in order to improve Accuracy and F1, decrease variance, and bias. We experiment with variations of $RoBERTa_{large}$, $ALBERT_{xxlarge.v2}$, $DistilRoBERTa$ and $BERT$ and outline selected combinations in tab:experimental-results."
+    ],
+    [
+      "We perform our experiments on the WNC dataset open-sourced by the authors of BIBREF2. It consists of aligned pre and post neutralized sentences made by Wikipedia editors under the neutral point of view. It contains $180k$ biased sentences, and their neutral counterparts crawled from $423,823$ Wikipedia revisions between 2004 and 2019. We randomly shuffled these sentences and split this dataset into two parts in a $90:10$ Train-Test split and perform the evaluation on the held-out test dataset.",
+      "For all BERT-based models, we use a learning rate of $2*10^{-5}$, a maximum sequence length of 50, and a weight decay of $0.01$ while finetuning the model. We use FastText's recently open-sourced automatic hyperparameter optimization functionality while training the model. For the BiLSTM baseline, we use a dropout of $0.05$ along with a recurrent dropout of $0.2$ in two 64 unit sized stacked BiLSTMs, using softmax activation layer as the final dense layer."
+    ],
+    [
+      "tab:experimental-results shows the performance of different models on the WNC corpus evaluated on the following four metrics: Precision, Recall, F1, and Accuracy. Our proposed methodology, the use of finetuned optimized BERT based models, and BERT-based ensemble models outperform the baselines for all the metrics.",
+      "Among the optimized BERT based models, $RoBERTa_{large}$ outperforms all other non-ensemble models and the baselines for all metrics. It further achieves a maximum recall of $0.681$ for all the proposed models. We note that DistillRoBERTa, a distilled model, performs competitively, achieving $69.69\\%$ accuracy, and $0.672$ F1 score. This observation shows that distilled pretrained models can replace their undistilled counterparts in a low-computing environment.",
+      "We further observe that ensemble models perform better than optimized BERT-based models and distilled pretrained models. Our proposed ensemble comprising of $RoBERTa_{large}$, $ALBERT_{xxlarge.v2}$, $DistilRoBERTa$ and $BERT$ outperforms all the proposed models obtaining $0.704$ F1 score, $0.733$ precision, and $71.61\\%$ Accuracy."
+    ],
+    [
+      "In this paper, we investigated BERT-based architectures for sentence level subjective bias detection. We perform our experiments on a general Wikipedia corpus consisting of more than $360k$ pre and post subjective bias neutralized sentences. We found our proposed architectures to outperform the existing baselines significantly. BERT-based ensemble consisting of RoBERTa, ALBERT, DistillRoBERTa, and BERT led to the highest F1 and Accuracy. In the future, we would like to explore document-level detection of subjective bias, multi-word mitigation of the bias, applications of detecting the bias in recommendation systems."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0095/instruction.md b/qasper-0095/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..0bc72914a246fe1e9999a132729f614c5f4e90ae
--- /dev/null
+++ b/qasper-0095/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Towards Detection of Subjective Bias using Contextualized Word Embeddings
+
+Question: Which experiments are perfomed?
\ No newline at end of file
diff --git a/qasper-0104/instruction.md b/qasper-0104/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..b9b4e6685213866dec06097f0fb33bd4272be756
--- /dev/null
+++ b/qasper-0104/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Unsupervised Machine Commenting with Neural Variational Topic Model
+
+Question: Which lexicon-based models did they compare with?
\ No newline at end of file
diff --git a/qasper-0112/instruction.md b/qasper-0112/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..58f60b87055f9555740fd7d03962a965b9ca0616
--- /dev/null
+++ b/qasper-0112/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Diachronic Topics in New High German Poetry
+
+Question: Is the outcome of the LDA analysis evaluated in any way?
\ No newline at end of file
diff --git a/qasper-0113/instruction.md b/qasper-0113/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..e91e1bb1cc5ebbc73447fe5da7be0dd710442ff9
--- /dev/null
+++ b/qasper-0113/instruction.md
@@ -0,0 +1,47 @@
+Name of Paper: Diachronic Topics in New High German Poetry
+
+Question: What is the corpus used in the study?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Corpus",
+    "Experiments",
+    "Experiments ::: Topic Trends",
+    "Experiments ::: Classification of Time Periods and Authorship",
+    "Experiments ::: Conclusion & Future Work"
+  ],
+  "paragraphs": [
+    [
+      "The Digital Library in the TextGrid Repository represents an extensive collection of German texts in digital form BIBREF3. It was mined from http://zeno.org and covers a time period from the mid 16th century up to the first decades of the 20th century. It contains many important texts that can be considered as part of the literary canon, even though it is far from complete (e.g. it contains only half of Rilke\u2019s work). We find that around 51k texts are annotated with the label \u2019verse\u2019 (TGRID-V), not distinguishing between \u2019lyric verse\u2019 and \u2019epic verse\u2019. However, the average length of these texts is around 150 token, dismissing most epic verse tales. Also, the poems are distributed over 229 authors, where the average author contributed 240 poems (median 131 poems). A drawback of TGRID-V is the circumstance that it contains a noticeable amount of French, Dutch and Latin (over 400 texts). To constrain our dataset to German, we filter foreign language material with a stopword list, as training a dedicated language identification classifier is far beyond the scope of this work."
+    ],
+    [
+      "We approach diachronic variation of poetry from two perspectives. First, as distant reading task to visualize the development of clearly interpretable topics over time. Second, as a downstream task, i.e. supervised machine learning task to determine the year (the time-slot) of publication for a given poem. We infer topic distributions over documents as features and pit them against a simple style baseline.",
+      "We use the implementation of LDA as it is provided in genism BIBREF4. LDA assumes that a particular document contains a mixture of few salient topics, where words are semantically related. We transform our documents (of wordforms) to a bag of words representation, filter stopwords (function words), and set the desired number of topics=100 and train for 50 epochs to attain a reasonable distinctness of topics. We choose 100 topics (rather than a lower number that might be more straightforward to interpret) as we want to later use these topics as features for downstream tasks. We find that wordforms (instead of lemma) are more useful for poetry topic models, as these capture style features (rhyme), orthographic variations ('hertz' instead of 'herz'), and generally offer more interpretable results."
+    ],
+    [
+      "We retrieve the most important (likely) words for all 100 topics and interpret these (sorted) word lists as aggregated topics, e.g. topic 27 (figure 2) contains: Tugend (virtue), Kunst (art), Ruhm (fame), Geist (spirit), Verstand (mind) and Lob (praise). This topic as a whole describes the concept of \u2019artistic virtue\u2019.",
+      "In certain clusters (topics) we find poetic residuals, such that rhyme words often cluster together (as they stand in proximity), e.g. topic 52 with: Mund (mouth), Grund (cause, ground), rund (round).",
+      "To discover trends of topics over time, we bin our documents into time slots of 25 years width each. See figure 1 for a plot of the number of documents per bin. The chosen binning slots offer enough documents per slot for our experiments. To visualize trends of singular topics over time, we aggregate all documents d in slot s and add the probabilities of topic t given d and divide by the number of all d in s. This gives us the average probability of a topic per timeslot. We then plot the trajectories for each single topic. See figures 2\u20136 for a selection of interpretable topic trends. Please note that the scaling on the y-axis differ for each topic, as some topics are more pronounced in the whole dataset overall.",
+      "Some topic plots are already very revealing. The topic \u2018artistic virtue\u2019 (figure 2, left) shows a sharp peak around 1700\u20141750, outlining the period of Enlightenment. Several topics indicate Romanticism, such as \u2018flowers\u2019 (figure 2, right), \u2018song\u2019 (figure 3, left) or \u2018dust, ghosts, depths\u2019 (not shown). The period of 'Vorm\u00e4rz' or 'Young Germany' is quite clear with the topic \u2018German Nation\u2019 (figure 3, right). It is however hardly distinguishable from romantic topics.",
+      "We find that the topics 'Beautiful Girls' (figure 4, left) and 'Life & Death' (figure 4, right) are always quite present over time, while 'Girls' is more prounounced in Romanticism, and 'Death' in Barock.",
+      "We find that the topic 'Fire' (figure 5, left) is a fairly modern concept, that steadily rises into modernity, possibly because of the trope 'love is fire'. Next to it, the topic 'Family' (figure 5, right) shows wild fluctuation over time.",
+      "Finally, figure 6 shows topics that are most informative for the downstream classification task: Topic 11 'World, Power, Time' (left) is very clearly a Barock topic, ending at 1750, while topic 19 'Heaven, Depth, Silence' is a topic that rises from Romanticism into Modernity."
+    ],
+    [
+      "To test whether topic models can be used for dating poetry or attributing authorship, we perform supervised classification experiments with Random Forest Ensemble classifiers. We find that we obtain better results by training and testing on stanzas instead of full poems, as we have more data available. Also, we use 50 year slots (instead of 25) to ease the task.",
+      "For each document we determine a class label for a time slot. The slot 1575\u20131624 receives the label 0, the slot 1625\u20131674 the label 1, etc.. In total, we have 7 classes (time slots).",
+      "As a baseline, we implement rather straightforward style features, such as line length, poem length (in token, syllables, lines), cadence (number of syllables of last word in line), soundscape (ratio of closed to open syllables, see BIBREF5), and a proxy for metre, the number of syllables of the first word in the line.",
+      "We split the data randomly 70:30 training:testing, where a 50:50 shows (5 points) worse performance. We then train Random Forest Ensemble classifiers and perform a grid search over their parameters to determine the best classifier. Please note that our class sizes are quite imbalanced.",
+      "The Style baseline achieves an Accuracy of 83%, LDA features 89% and a combination of the two gets 90%. However, training on full poems reduces this to 42\u201452%.",
+      "The most informative features (by information gain) are: Topic11 (.067), Topic 37 (.055), Syllables Per Line (.046), Length of poem in syllables (.031), Topic19 (.029), Topic98 (.025), Topic27 ('virtue') (.023), and Soundscape (.023).",
+      "For authorship attribution, we also use a 70:30 random train:test split and use the author name as class label. We only choose the most frequent 180 authors. We find that training on stanzas gives us 71% Accuracy, but when trained on full poems, we only get 13% Accuracy. It should be further investigated is this is only because of a surplus of data."
+    ],
+    [
+      "We have shown the viability of Latent Dirichlet Allocation for a visualization of topic trends (the evolution of what people talk about in poetry). While most topics are easily interpretable and show a clear trend, others are quite noisy. For an exploratory experiment, the classification into time slots and for authors attribution is very promising, however far from perfect. It should be investigated whether using stanzas instead of whole poems only improves results because of more available data. Also, it needs to be determined if better topic models can deliver a better baseline for diachronic change in poetry, and if better style features will outperform semantics. Finally, only selecting clear trending and peaking topics (through co-variance) might further improve the results."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0114/instruction.md b/qasper-0114/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..339d46721f9e601872c1f670f1dacd8bfa653927
--- /dev/null
+++ b/qasper-0114/instruction.md
@@ -0,0 +1,98 @@
+Name of Paper: Important Attribute Identification in Knowledge Graph
+
+Question: What are the traditional methods to identifying important attributes?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "The problem we solve in this paper",
+    "Related Research",
+    "What we propose and what we have done",
+    "Our proposed Method",
+    "Application Scenario",
+    "FastText Introduction",
+    "Matching",
+    "Data introduction",
+    "Data preprocessing",
+    "Proposed method vs previous methods",
+    "Result Analysis",
+    "Conclusions and Future work "
+  ],
+  "paragraphs": [
+    [
+      "Knowledge graph(KG) has been proposed for several years and its most prominent application is in web search, for example, Google search triggers a certain entity card when a user's query matches or mentions an entity based on some statistical model. The core potential of a knowledge graph is about its capability of reasoning and inferring, and we have not seen revolutionary breakthrough in such areas yet. One main obstacle is obviously the lack of sufficient knowledge graph data, including entities, entities' descriptions, entities' attributes, and relationship between entities. A full functional knowledge graph supporting general purposed reasoning and inference might still require long years of the community's innovation and hardworking. On the other hand, many less demanding applications have great potential benefiting from the availability of information from the knowledge graph, such as query understanding and document understanding in information retrieval/search engines, simple inference in question answering systems, and easy reasoning in domain-limited decision support tools. Not only academy, but also industry companies have been heavily investing in knowledge graphs, such as Google's knowledge graph, Amazon's product graph, Facebook's Graph API, IBM's Watson, and Microsoft's Satori etc.",
+      "In the existing knowledge graph, such as Wikidata and DBpedia, usually attributes do not have order or priorities, and we don't know which attributes are more important and of more interest to users. Such importance score of attributes is a vital piece of information in many applications of knowledge graph. The most important application is the triggered entity card in search engine when a customer's query gets hit for an entity. An entity usually has a large amount of attributes, but an entity card has limited space and can only show the most significant information; attribute importance's presence can make the displaying of an entity card easy to implement. Attribute importance also has great potential of playing a significant role in search engine, how to decide the matching score between the query and attribute values. If the query matches a very important attribute, and the relevance contribution from such a match should be higher than matching an ignorable attribute. Another application is in e-commerce communications, and one buyer initiates a communication cycle with a seller by sending a product enquiry. Writing the enquiry on a mobile phone is inconvenient and automatic composing assistance has great potential of improving customer experience by alleviating the writing burden. In the product enquiry, customers need to specify their requirements and ask questions about products, and their requirements and questions are usually about the most important attributes of the products. If we can identify out important attributes of products, we can help customers to draft the enquiry automatically to reduce their input time."
+    ],
+    [
+      "Many proposed approaches formulate the entity attribute ranking problem as a post processing step of automated attribute-value extraction. In BIBREF0 , BIBREF1 , BIBREF2 , Pasca et al. firstly extract potential class-attribute pairs using linguistically motivated patterns from unstructured text including query logs and query sessions, and then score the attributes using the Bayes model. In BIBREF3 , Rahul Rai proposed to identify product attributes from customer online reviews using part-of-speech(POS) tagging patterns, and to evaluate their importance with several different frequency metrics. In BIBREF4 , Lee et al. developed a system to extract concept-attribute pairs from multiple data sources, such as Probase, general web documents, query logs and external knowledge base, and aggregate the weights from different sources into one consistent typicality score using a Ranking SVM model. Those approaches typically suffer from the poor quality of the pattern rules, and the ranking process is used to identify relatively more precise attributes from all attribute candidates.",
+      "As for an already existing knowledge graph, there is plenty of work in literature dealing with ranking entities by relevance without or with a query. In BIBREF5 , Li et al. introduced the OntoRank algorithm for ranking the importance of semantic web objects at three levels of granularity: document, terms and RDF graphs. The algorithm is based on the rational surfer model, successfully used in the Swoogle semantic web search engine. In BIBREF6 , Hogan et al. presented an approach that adapted the well-known PageRank/HITS algorithms to semantic web data, which took advantage of property values to rank entities. In BIBREF7 , BIBREF8 , authors also focused on ranking entities, sorting the semantic web resources based on importance, relevance and query length, and aggregating the features together with an overall ranking model.",
+      "Just a few works were designated to specifically address the problem of computing attribute rankings in a given Knowledge Graph. Ibminer BIBREF9 introduced a tool for infobox(alias of an entity card) template suggestion, which collected attributes from different sources and then sorted them by popularity based on their co-occurrences in the dataset. In BIBREF10 , using the structured knowledge base, intermediate features were computed, including the importance or popularity of each entity type, IDF computation for each attribute on a global basis, IDF computation for entity types etc., and then the features were aggregated to train a classifier. Also, a similar approach in BIBREF11 was designed with more features extracted from GoogleSuggestChars data. In BIBREF12 , Ali et al. introduced a new set of features that utilizes semantic information about entities as well as information from top-ranked documents from a general search engine. In order to experiment their approach, they collected a dataset by exploiting Wikipedia infoboxes, whose ordering of attributes reflect the collaborative effort of a large community of users, which might not be accurate."
+    ],
+    [
+      "There have been broad researches on entity detection, relationship extraction, and also missing relationship prediction. For example: BIBREF13 , BIBREF14 and BIBREF15 explained how to construct a knowledge graph and how to perform representation learning on knowledge graphs. Some research has been performed on attribute extraction, such as BIBREF16 and BIBREF4 ; the latter one is quite special that it also simultaneously computes the attribute importance. As for modeling attribute importance for an existing knowledge graph which has completed attribute extractions, we found only a few existing research, all of which used simple co-occurrences to rank entity attributes. In reality, many knowledge graphs do not contain attribute importance information, for example, in the most famous Wikidata, a large amount of entities have many attributes, and it is difficult to know which attributes are significant and deserve more attention. In this research we focus on identifying important attributes in existing knowledge graphs. Specifically, we propose a new method of using extra user generated data source for evaluating the attribute importance, and we use the recently proposed state-of-the-art word/sub-word embedding techniques to match the external data with the attribute definition and values from entities in knowledge graphs. And then we use the statistics obtained from the matching to compare the attribute importance. Our method has general extensibility to any knowledge graph without attribute importance. When there is a possibility of finding external textual data source, our proposed method will work, even if the external data does not exactly match the attribute textual data, since the vector embedding performs semantic matching and does not require exact string matching.",
+      "The remaining of the paper is organized as follows: Section SECREF2 explains our proposed method in detail, including what kind of external data is required, and how to process the external data, and also how to perform the semantic matching and how to rank the attributes by statistics. Section SECREF3 introduces our experimentations, including our experimentation setup, data introduction and experimental result compared to other methods we do not employ. Section SECREF3 also briefly introduces our real world application scenario in e-commerce communication. Section SECREF4 draws the conclusion from our experimentations and analysis, and also we point out promising future research directions."
+    ],
+    [
+      "In this section, we will introduce our proposed method in detail. We use our application scenario to explain the logic behind the method, but the scope is not limited to our use case, and it is possible to extend to any existing knowledge graph without attribute importance information."
+    ],
+    [
+      "Alibaba.com is currently the world's largest cross-border business to business(B2B) E-commerce platform and it supports 17 languages for customers from all over the world. On the website, English is the dorminant language and accounts for around 50% of the traffic. The website has already accumulated a very large knowledge graph of products, and the entity here is the product or the product category; and every entity has lots of information such as the entity name, images and many attributes without ordering information. The entities are also connected by taxonomy structure and similar products usually belong to the same category/sub-category.",
+      "Since the B2B procurement usually involves a large amount of money, the business will be a long process beginning with a product enquiry. Generally speaking, when customers are interested in some product, they will start a communication cycle with a seller by sending a product enquiry to the seller. In the product enquiry, customers will specify their requirements and ask questions about the product. Their requirements and questions usually refer to the most important attributes of the product. Fig. FIGREF5 shows an enquery example. Alibaba.com has accumulated tens of millions of product enquires, and we would like to leverage these information, in combination of the product knowledge graph we have, to figure out the most important attributes for each category of products.",
+      "In our application scenario, the product knowledge graph is the existing knowledge graph and the enquiry data is the external textual data source. From now on, we will use our application scenario to explain the details of our proposed algorithm.",
+      "We propose an unsupervised learning framework for extracting important product attributes from product enquiries. By calculating the semantic similarity between each enquiry sentence and each attribute of the product to which the enquiry corresponds to, we identify the product attributes that the customer cares about most.",
+      "The attributes described in the enquiry may contain attribute names or attribute values or other expressions, for example, either the word \u201ccolor\u201d or a color instance word \u201cpurple\u201d is mentioned. Therefore, when calculating the semantic similarity between enquiry sentences and product attributes, we need both attribute names and attribute values. The same as any other knowledge graph, the product attributes in our knowledge graph we use contain noises and mistakes. We need to clean and normalize the attribute data before consuming it. We will introduce the detail of our data cleaning process in Section SECREF14 ."
+    ],
+    [
+      "FastText is a library created by the Facebook Research for efficient learning of word representations and sentence classification. Here, we just use the word representation functionality of it.",
+      "FastText models morphology by considering subword units, and representing words by a sum of its character n-grams BIBREF17 . In the original model the authors choose to use the binary logistic loss and the loss for a single instance is written as below: INLINEFORM0 ",
+      "By denoting the logistic loss function INLINEFORM0 , the loss over a sentence is: INLINEFORM1 ",
+      "The scoring function between a word INLINEFORM0 and a context word INLINEFORM1 is: INLINEFORM2 ",
+      "In the above functions, INLINEFORM0 is a set of negative examples sampled from the vocabulary, INLINEFORM1 is the set of indices of words surrounding word INLINEFORM2 , INLINEFORM3 is the set of n-grams appearing in word INLINEFORM4 , INLINEFORM5 is the size of the dictionary we have for n-grams, INLINEFORM6 is a vector representation to each n-gram INLINEFORM7 .",
+      "Compared with word2vec or glove, FastText has following advantages:",
+      "It is able to cover rare words and out-of-vocabulary(OOV) words. Since the basic modeling units in FastText are ngrams, and both rare words and OOV ones can obtain efficient word representations from their composing ngrams. Word2vec and glove both fail to provide accurate vector representations for these words. In our application, the training data is written by end customers, and there are many misspellings which easily become OOV words.",
+      "Character n-grams embeddings tend to perform superior to word2vec and glove on smaller datasets.",
+      "FastText is more efficient and its training is relatively fast."
+    ],
+    [
+      "In this section, how to compute the matching between an enquiry sentence and a product attribute is explained in detail. Our explanation here is for a certain product category, and other categories are the same.",
+      "As you can see in Fig. FIGREF12 , each sentence is compared with each attribute of a product category that the product belongs to. We now get a score between a sentence INLINEFORM0 and an attribute INLINEFORM1 , INLINEFORM2 INLINEFORM3 ",
+      "where INLINEFORM0 is all the possible values for this INLINEFORM1 , INLINEFORM2 is the word vector for INLINEFORM3 . According to this formula, we can get top two attributes whose scores are above the threshold INLINEFORM4 for each sentence. We choose two attributes instead of one because there may be more than one attribute for each sentence. In addition, some sentences are greetings or self-introduction and do not contain the attribute information of the product, so we require that the score to be higher than a certain threshold."
+    ],
+    [
+      "For our knowledge graph data, entity(product) attributes can be roughly divided into clusters of transaction order specific ones and product specific ones, in this paper, we choose the product specific ones for further study. We also need to point out that we only focus on the recommended communication language on the Alibaba.com platform, which is English.",
+      "To construct the evaluation dataset, top 14 categories are first chosen based on their business promotion features, and 3 millions typical products under each category were then chosen to form the attribute candidates. After preprocessing and basic filtering, top product specific attributes from the 14 different categories are chosen to be manually labeled by our annotators.",
+      "For each category, annotators each are asked to choose at most 10 important attributes from buyers perspective. After all annotators complete their annotations, attributes are then sorted according to the summed votes. In the end, 111 important attributes from the 14 categories are kept for final evaluation.",
+      "Outside of the evaluation explained in this paper, we actually have performed the matching on more than 4,000 catetories covering more than 100 million products and more than 20 million enquires. Due to limited annotation resources, we can only sample a small numbered categories(14 here) to evaluate the proposed algorithm here."
+    ],
+    [
+      "The product enquiries and attributes data preprocessing is shown in Algorithm 1. algorithmAlgorithm Data Preprocess Algorithm [1] INLINEFORM0 INLINEFORM1 : INLINEFORM2 INLINEFORM3 INLINEFORM4 : INLINEFORM5 Invalid INLINEFORM6 filter INLINEFORM7 Split INLINEFORM8 to sentences sentence INLINEFORM9 in INLINEFORM10 INLINEFORM11 INLINEFORM12 return INLINEFORM13 ",
+      "Firstly, for every product enquiry, we convert the original html textual data into the plain text. Secondly we filter out the useless enquires, such as non-English enquires and spams. The regular expressions and spam detection are used to detect non-English enquiries and spams respectively. Thirdly we get sentence list INLINEFORM0 with spliting every enquiry into sentences as described in section 2.2. Then for every sentence INLINEFORM1 in INLINEFORM2 , we need to do extra three processes: a)Spelling Correction. b)Regular Measures and Numbers. c)Stop Words Dropping.",
+      "Spelling Correction. Since quite a lot of the product enquires and self-filled attributes were misspelled, we have replaced the exact words by fuzzyfied search using Levenshtein distance. The method uses fuzzyfied search, only if the exact match is not found. Some attributes are actually the same, such as \"type\" and \"product type\", we merge these same attributes by judging whether the attributes are contained.",
+      "Regular Measures and Numbers. Attributes of number type have their values composed of numbers and units, such as INLINEFORM0 , INLINEFORM1 , INLINEFORM2 , INLINEFORM3 , etc. We replace all numbers (in any notation, e.g., floating point, scientific, arithmetical expression, etc.) with a unique token ( INLINEFORM4 ). For the same reason, each unit of measure is replaced with a corresponding token, eg., INLINEFORM5 is replaced with centimeter area.",
+      "Stop Words Dropping. Stop words appear to be of little value in the proposed matching algorithm. By removing the stop words we can focus on the important words instead. In our business scenario, we built a stop words list for foreign trade e-commerce.",
+      "Finally, we get the valid sentences INLINEFORM0 ."
+    ],
+    [
+      "The existing co-occurrence methods do not suit our application scenario at all, since exact string matching is too strong a requirement and initial trial has shown its incompetency. In stead we implemented an improved version of their method based on TextRank as our baseline. In addition, we also tested multiple semantic matching algorithms for comparison with our chosen method.",
+      "TextRank: TextRank is a graph-based ranking model for text processing. BIBREF18 It is an unsupervised algorithm for keyword extraction. Since product attributes are usually the keywords in enquiries, we can compare these keywords with the category attributes and find the most important attributes. This method consists of three steps. The first step is to merge all enquiries under one category as an article. The second step is to extract the top 50 keywords for each category. The third step is to find the most important attributes by comparing top keywords with category attributes.",
+      "Word2vec BIBREF19 : We use the word vector trained by BIBREF19 as the distributed representation of words. Then we get the enquiry sentence representation and category attribute representation. Finally we collect the statistics about the matched attributes of each category, and select the most frequent attributes under the same category.",
+      "GloVe BIBREF20 : GloVe is a global log-bilinear regression model for the unsupervised learning of word representations, which utilizes the ratios of word-word co-occurrence probabilities. We use the GloVe method to train the distributed representation of words. And attribute selection procedure is the same as word2vec.",
+      "Proposed method: the detail of our proposed algorithm has been carefully explained in Section SECREF2 . There are several thresholds we need to pick in the experimentation setup. Based on trial and error analysis, we choose 0.75 as the sentence and attribute similarity threshold, which balances the precision and recall relatively well. In our application, due to product enquiry length limitation, customers usually don't refer to more than five attributes in their initial approach to the seller, we choose to keep 5 most important attributes for each category.",
+      "Evaluation is conducted by comparing the output of the systems with the manual annotated answers, and we calculate the precision and recall rate. INLINEFORM0 INLINEFORM1 ",
+      "where INLINEFORM0 is the manually labeled attributes , INLINEFORM1 is the detected important attributes.",
+      "Table 1 depicts the algorithm performance of each category and the overall average metrics among all categories for our approach and other methods. It can be observed that our proposed method achieves the best performance. The average F1-measure of our approach is 0.47, while the average F1-measure values of \u201cGloVe\u201d, \u201cword2vect\u201d and \"TextRank\" are 0.46, 0.42 and 0.20 respectively."
+    ],
+    [
+      "In all our experiments, we find that FastText method outperforms other methods. By analyzing all results, we observe that semantic similarity based methods are more effective than the previous method which we implemented based on TextRank. This conclusion is understandable because lots of enquiries do not simply mention attribute words exactly, but some semantically related words are also used.",
+      "Evaluating FastText, GloVe and word2vec, we show that compared to other word representation learning algorithms, the FastText performs best. We sample and analyze the category attributes and find that many self-filled attributes contain misspellings. The FastText algorithm represents words by a sum of its character n-grams and it is much robust against problems like misspellings. In summary, FastText has greater advantages in dealing with natural language corpus usually with spelling mistakes.",
+      "We also applied the detected attributes in the automatic enquiry generation task and we obtained significantly better generated enquiries compared to previous rigid templates. Due to space limitation, we skip the explanation and leave it for future publications."
+    ],
+    [
+      "In this paper, we proposed a new general method of identifying important attributes for entities from a knowledge graph. This is a relatively new task and our proposed method of using external textual data and performing semantic matching via word/sub-word embeddings obtained better result compared to other work of using naive string matching and counting. In addition, we also successfully applied the detected important attributes in our real world application of smart composing. In summary, the method is extensible to any knowledge graph without attribute importance information and outperforms previous method.",
+      "In future work, there are two major areas with potential of improving the detection accuracy. The first one is about sentence splitting. What we are trying to get is semantic cohesive unit, which can be used to match an attribute, and there might be more comprehensive method than the simple splitting by sentence ending punctuations. The second one is about improving the word embedding quality. We have implemented an in-house improved version of Fasttext, which is adapted to our data source. It is highly possible to use the improved word embedding on purpose of obtaining higher semantic matching precision. As for the application, we will try to use more statistical models in the natural language generation part of the smart composing framework of consuming the detected important attributes."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0115/instruction.md b/qasper-0115/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..ebe9cc8c42971d00352e88e1774d059728b33b50
--- /dev/null
+++ b/qasper-0115/instruction.md
@@ -0,0 +1,98 @@
+Name of Paper: Important Attribute Identification in Knowledge Graph
+
+Question: What do you use to calculate word/sub-word embeddings
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "The problem we solve in this paper",
+    "Related Research",
+    "What we propose and what we have done",
+    "Our proposed Method",
+    "Application Scenario",
+    "FastText Introduction",
+    "Matching",
+    "Data introduction",
+    "Data preprocessing",
+    "Proposed method vs previous methods",
+    "Result Analysis",
+    "Conclusions and Future work "
+  ],
+  "paragraphs": [
+    [
+      "Knowledge graph(KG) has been proposed for several years and its most prominent application is in web search, for example, Google search triggers a certain entity card when a user's query matches or mentions an entity based on some statistical model. The core potential of a knowledge graph is about its capability of reasoning and inferring, and we have not seen revolutionary breakthrough in such areas yet. One main obstacle is obviously the lack of sufficient knowledge graph data, including entities, entities' descriptions, entities' attributes, and relationship between entities. A full functional knowledge graph supporting general purposed reasoning and inference might still require long years of the community's innovation and hardworking. On the other hand, many less demanding applications have great potential benefiting from the availability of information from the knowledge graph, such as query understanding and document understanding in information retrieval/search engines, simple inference in question answering systems, and easy reasoning in domain-limited decision support tools. Not only academy, but also industry companies have been heavily investing in knowledge graphs, such as Google's knowledge graph, Amazon's product graph, Facebook's Graph API, IBM's Watson, and Microsoft's Satori etc.",
+      "In the existing knowledge graph, such as Wikidata and DBpedia, usually attributes do not have order or priorities, and we don't know which attributes are more important and of more interest to users. Such importance score of attributes is a vital piece of information in many applications of knowledge graph. The most important application is the triggered entity card in search engine when a customer's query gets hit for an entity. An entity usually has a large amount of attributes, but an entity card has limited space and can only show the most significant information; attribute importance's presence can make the displaying of an entity card easy to implement. Attribute importance also has great potential of playing a significant role in search engine, how to decide the matching score between the query and attribute values. If the query matches a very important attribute, and the relevance contribution from such a match should be higher than matching an ignorable attribute. Another application is in e-commerce communications, and one buyer initiates a communication cycle with a seller by sending a product enquiry. Writing the enquiry on a mobile phone is inconvenient and automatic composing assistance has great potential of improving customer experience by alleviating the writing burden. In the product enquiry, customers need to specify their requirements and ask questions about products, and their requirements and questions are usually about the most important attributes of the products. If we can identify out important attributes of products, we can help customers to draft the enquiry automatically to reduce their input time."
+    ],
+    [
+      "Many proposed approaches formulate the entity attribute ranking problem as a post processing step of automated attribute-value extraction. In BIBREF0 , BIBREF1 , BIBREF2 , Pasca et al. firstly extract potential class-attribute pairs using linguistically motivated patterns from unstructured text including query logs and query sessions, and then score the attributes using the Bayes model. In BIBREF3 , Rahul Rai proposed to identify product attributes from customer online reviews using part-of-speech(POS) tagging patterns, and to evaluate their importance with several different frequency metrics. In BIBREF4 , Lee et al. developed a system to extract concept-attribute pairs from multiple data sources, such as Probase, general web documents, query logs and external knowledge base, and aggregate the weights from different sources into one consistent typicality score using a Ranking SVM model. Those approaches typically suffer from the poor quality of the pattern rules, and the ranking process is used to identify relatively more precise attributes from all attribute candidates.",
+      "As for an already existing knowledge graph, there is plenty of work in literature dealing with ranking entities by relevance without or with a query. In BIBREF5 , Li et al. introduced the OntoRank algorithm for ranking the importance of semantic web objects at three levels of granularity: document, terms and RDF graphs. The algorithm is based on the rational surfer model, successfully used in the Swoogle semantic web search engine. In BIBREF6 , Hogan et al. presented an approach that adapted the well-known PageRank/HITS algorithms to semantic web data, which took advantage of property values to rank entities. In BIBREF7 , BIBREF8 , authors also focused on ranking entities, sorting the semantic web resources based on importance, relevance and query length, and aggregating the features together with an overall ranking model.",
+      "Just a few works were designated to specifically address the problem of computing attribute rankings in a given Knowledge Graph. Ibminer BIBREF9 introduced a tool for infobox(alias of an entity card) template suggestion, which collected attributes from different sources and then sorted them by popularity based on their co-occurrences in the dataset. In BIBREF10 , using the structured knowledge base, intermediate features were computed, including the importance or popularity of each entity type, IDF computation for each attribute on a global basis, IDF computation for entity types etc., and then the features were aggregated to train a classifier. Also, a similar approach in BIBREF11 was designed with more features extracted from GoogleSuggestChars data. In BIBREF12 , Ali et al. introduced a new set of features that utilizes semantic information about entities as well as information from top-ranked documents from a general search engine. In order to experiment their approach, they collected a dataset by exploiting Wikipedia infoboxes, whose ordering of attributes reflect the collaborative effort of a large community of users, which might not be accurate."
+    ],
+    [
+      "There have been broad researches on entity detection, relationship extraction, and also missing relationship prediction. For example: BIBREF13 , BIBREF14 and BIBREF15 explained how to construct a knowledge graph and how to perform representation learning on knowledge graphs. Some research has been performed on attribute extraction, such as BIBREF16 and BIBREF4 ; the latter one is quite special that it also simultaneously computes the attribute importance. As for modeling attribute importance for an existing knowledge graph which has completed attribute extractions, we found only a few existing research, all of which used simple co-occurrences to rank entity attributes. In reality, many knowledge graphs do not contain attribute importance information, for example, in the most famous Wikidata, a large amount of entities have many attributes, and it is difficult to know which attributes are significant and deserve more attention. In this research we focus on identifying important attributes in existing knowledge graphs. Specifically, we propose a new method of using extra user generated data source for evaluating the attribute importance, and we use the recently proposed state-of-the-art word/sub-word embedding techniques to match the external data with the attribute definition and values from entities in knowledge graphs. And then we use the statistics obtained from the matching to compare the attribute importance. Our method has general extensibility to any knowledge graph without attribute importance. When there is a possibility of finding external textual data source, our proposed method will work, even if the external data does not exactly match the attribute textual data, since the vector embedding performs semantic matching and does not require exact string matching.",
+      "The remaining of the paper is organized as follows: Section SECREF2 explains our proposed method in detail, including what kind of external data is required, and how to process the external data, and also how to perform the semantic matching and how to rank the attributes by statistics. Section SECREF3 introduces our experimentations, including our experimentation setup, data introduction and experimental result compared to other methods we do not employ. Section SECREF3 also briefly introduces our real world application scenario in e-commerce communication. Section SECREF4 draws the conclusion from our experimentations and analysis, and also we point out promising future research directions."
+    ],
+    [
+      "In this section, we will introduce our proposed method in detail. We use our application scenario to explain the logic behind the method, but the scope is not limited to our use case, and it is possible to extend to any existing knowledge graph without attribute importance information."
+    ],
+    [
+      "Alibaba.com is currently the world's largest cross-border business to business(B2B) E-commerce platform and it supports 17 languages for customers from all over the world. On the website, English is the dorminant language and accounts for around 50% of the traffic. The website has already accumulated a very large knowledge graph of products, and the entity here is the product or the product category; and every entity has lots of information such as the entity name, images and many attributes without ordering information. The entities are also connected by taxonomy structure and similar products usually belong to the same category/sub-category.",
+      "Since the B2B procurement usually involves a large amount of money, the business will be a long process beginning with a product enquiry. Generally speaking, when customers are interested in some product, they will start a communication cycle with a seller by sending a product enquiry to the seller. In the product enquiry, customers will specify their requirements and ask questions about the product. Their requirements and questions usually refer to the most important attributes of the product. Fig. FIGREF5 shows an enquery example. Alibaba.com has accumulated tens of millions of product enquires, and we would like to leverage these information, in combination of the product knowledge graph we have, to figure out the most important attributes for each category of products.",
+      "In our application scenario, the product knowledge graph is the existing knowledge graph and the enquiry data is the external textual data source. From now on, we will use our application scenario to explain the details of our proposed algorithm.",
+      "We propose an unsupervised learning framework for extracting important product attributes from product enquiries. By calculating the semantic similarity between each enquiry sentence and each attribute of the product to which the enquiry corresponds to, we identify the product attributes that the customer cares about most.",
+      "The attributes described in the enquiry may contain attribute names or attribute values or other expressions, for example, either the word \u201ccolor\u201d or a color instance word \u201cpurple\u201d is mentioned. Therefore, when calculating the semantic similarity between enquiry sentences and product attributes, we need both attribute names and attribute values. The same as any other knowledge graph, the product attributes in our knowledge graph we use contain noises and mistakes. We need to clean and normalize the attribute data before consuming it. We will introduce the detail of our data cleaning process in Section SECREF14 ."
+    ],
+    [
+      "FastText is a library created by the Facebook Research for efficient learning of word representations and sentence classification. Here, we just use the word representation functionality of it.",
+      "FastText models morphology by considering subword units, and representing words by a sum of its character n-grams BIBREF17 . In the original model the authors choose to use the binary logistic loss and the loss for a single instance is written as below: INLINEFORM0 ",
+      "By denoting the logistic loss function INLINEFORM0 , the loss over a sentence is: INLINEFORM1 ",
+      "The scoring function between a word INLINEFORM0 and a context word INLINEFORM1 is: INLINEFORM2 ",
+      "In the above functions, INLINEFORM0 is a set of negative examples sampled from the vocabulary, INLINEFORM1 is the set of indices of words surrounding word INLINEFORM2 , INLINEFORM3 is the set of n-grams appearing in word INLINEFORM4 , INLINEFORM5 is the size of the dictionary we have for n-grams, INLINEFORM6 is a vector representation to each n-gram INLINEFORM7 .",
+      "Compared with word2vec or glove, FastText has following advantages:",
+      "It is able to cover rare words and out-of-vocabulary(OOV) words. Since the basic modeling units in FastText are ngrams, and both rare words and OOV ones can obtain efficient word representations from their composing ngrams. Word2vec and glove both fail to provide accurate vector representations for these words. In our application, the training data is written by end customers, and there are many misspellings which easily become OOV words.",
+      "Character n-grams embeddings tend to perform superior to word2vec and glove on smaller datasets.",
+      "FastText is more efficient and its training is relatively fast."
+    ],
+    [
+      "In this section, how to compute the matching between an enquiry sentence and a product attribute is explained in detail. Our explanation here is for a certain product category, and other categories are the same.",
+      "As you can see in Fig. FIGREF12 , each sentence is compared with each attribute of a product category that the product belongs to. We now get a score between a sentence INLINEFORM0 and an attribute INLINEFORM1 , INLINEFORM2 INLINEFORM3 ",
+      "where INLINEFORM0 is all the possible values for this INLINEFORM1 , INLINEFORM2 is the word vector for INLINEFORM3 . According to this formula, we can get top two attributes whose scores are above the threshold INLINEFORM4 for each sentence. We choose two attributes instead of one because there may be more than one attribute for each sentence. In addition, some sentences are greetings or self-introduction and do not contain the attribute information of the product, so we require that the score to be higher than a certain threshold."
+    ],
+    [
+      "For our knowledge graph data, entity(product) attributes can be roughly divided into clusters of transaction order specific ones and product specific ones, in this paper, we choose the product specific ones for further study. We also need to point out that we only focus on the recommended communication language on the Alibaba.com platform, which is English.",
+      "To construct the evaluation dataset, top 14 categories are first chosen based on their business promotion features, and 3 millions typical products under each category were then chosen to form the attribute candidates. After preprocessing and basic filtering, top product specific attributes from the 14 different categories are chosen to be manually labeled by our annotators.",
+      "For each category, annotators each are asked to choose at most 10 important attributes from buyers perspective. After all annotators complete their annotations, attributes are then sorted according to the summed votes. In the end, 111 important attributes from the 14 categories are kept for final evaluation.",
+      "Outside of the evaluation explained in this paper, we actually have performed the matching on more than 4,000 catetories covering more than 100 million products and more than 20 million enquires. Due to limited annotation resources, we can only sample a small numbered categories(14 here) to evaluate the proposed algorithm here."
+    ],
+    [
+      "The product enquiries and attributes data preprocessing is shown in Algorithm 1. algorithmAlgorithm Data Preprocess Algorithm [1] INLINEFORM0 INLINEFORM1 : INLINEFORM2 INLINEFORM3 INLINEFORM4 : INLINEFORM5 Invalid INLINEFORM6 filter INLINEFORM7 Split INLINEFORM8 to sentences sentence INLINEFORM9 in INLINEFORM10 INLINEFORM11 INLINEFORM12 return INLINEFORM13 ",
+      "Firstly, for every product enquiry, we convert the original html textual data into the plain text. Secondly we filter out the useless enquires, such as non-English enquires and spams. The regular expressions and spam detection are used to detect non-English enquiries and spams respectively. Thirdly we get sentence list INLINEFORM0 with spliting every enquiry into sentences as described in section 2.2. Then for every sentence INLINEFORM1 in INLINEFORM2 , we need to do extra three processes: a)Spelling Correction. b)Regular Measures and Numbers. c)Stop Words Dropping.",
+      "Spelling Correction. Since quite a lot of the product enquires and self-filled attributes were misspelled, we have replaced the exact words by fuzzyfied search using Levenshtein distance. The method uses fuzzyfied search, only if the exact match is not found. Some attributes are actually the same, such as \"type\" and \"product type\", we merge these same attributes by judging whether the attributes are contained.",
+      "Regular Measures and Numbers. Attributes of number type have their values composed of numbers and units, such as INLINEFORM0 , INLINEFORM1 , INLINEFORM2 , INLINEFORM3 , etc. We replace all numbers (in any notation, e.g., floating point, scientific, arithmetical expression, etc.) with a unique token ( INLINEFORM4 ). For the same reason, each unit of measure is replaced with a corresponding token, eg., INLINEFORM5 is replaced with centimeter area.",
+      "Stop Words Dropping. Stop words appear to be of little value in the proposed matching algorithm. By removing the stop words we can focus on the important words instead. In our business scenario, we built a stop words list for foreign trade e-commerce.",
+      "Finally, we get the valid sentences INLINEFORM0 ."
+    ],
+    [
+      "The existing co-occurrence methods do not suit our application scenario at all, since exact string matching is too strong a requirement and initial trial has shown its incompetency. In stead we implemented an improved version of their method based on TextRank as our baseline. In addition, we also tested multiple semantic matching algorithms for comparison with our chosen method.",
+      "TextRank: TextRank is a graph-based ranking model for text processing. BIBREF18 It is an unsupervised algorithm for keyword extraction. Since product attributes are usually the keywords in enquiries, we can compare these keywords with the category attributes and find the most important attributes. This method consists of three steps. The first step is to merge all enquiries under one category as an article. The second step is to extract the top 50 keywords for each category. The third step is to find the most important attributes by comparing top keywords with category attributes.",
+      "Word2vec BIBREF19 : We use the word vector trained by BIBREF19 as the distributed representation of words. Then we get the enquiry sentence representation and category attribute representation. Finally we collect the statistics about the matched attributes of each category, and select the most frequent attributes under the same category.",
+      "GloVe BIBREF20 : GloVe is a global log-bilinear regression model for the unsupervised learning of word representations, which utilizes the ratios of word-word co-occurrence probabilities. We use the GloVe method to train the distributed representation of words. And attribute selection procedure is the same as word2vec.",
+      "Proposed method: the detail of our proposed algorithm has been carefully explained in Section SECREF2 . There are several thresholds we need to pick in the experimentation setup. Based on trial and error analysis, we choose 0.75 as the sentence and attribute similarity threshold, which balances the precision and recall relatively well. In our application, due to product enquiry length limitation, customers usually don't refer to more than five attributes in their initial approach to the seller, we choose to keep 5 most important attributes for each category.",
+      "Evaluation is conducted by comparing the output of the systems with the manual annotated answers, and we calculate the precision and recall rate. INLINEFORM0 INLINEFORM1 ",
+      "where INLINEFORM0 is the manually labeled attributes , INLINEFORM1 is the detected important attributes.",
+      "Table 1 depicts the algorithm performance of each category and the overall average metrics among all categories for our approach and other methods. It can be observed that our proposed method achieves the best performance. The average F1-measure of our approach is 0.47, while the average F1-measure values of \u201cGloVe\u201d, \u201cword2vect\u201d and \"TextRank\" are 0.46, 0.42 and 0.20 respectively."
+    ],
+    [
+      "In all our experiments, we find that FastText method outperforms other methods. By analyzing all results, we observe that semantic similarity based methods are more effective than the previous method which we implemented based on TextRank. This conclusion is understandable because lots of enquiries do not simply mention attribute words exactly, but some semantically related words are also used.",
+      "Evaluating FastText, GloVe and word2vec, we show that compared to other word representation learning algorithms, the FastText performs best. We sample and analyze the category attributes and find that many self-filled attributes contain misspellings. The FastText algorithm represents words by a sum of its character n-grams and it is much robust against problems like misspellings. In summary, FastText has greater advantages in dealing with natural language corpus usually with spelling mistakes.",
+      "We also applied the detected attributes in the automatic enquiry generation task and we obtained significantly better generated enquiries compared to previous rigid templates. Due to space limitation, we skip the explanation and leave it for future publications."
+    ],
+    [
+      "In this paper, we proposed a new general method of identifying important attributes for entities from a knowledge graph. This is a relatively new task and our proposed method of using external textual data and performing semantic matching via word/sub-word embeddings obtained better result compared to other work of using naive string matching and counting. In addition, we also successfully applied the detected important attributes in our real world application of smart composing. In summary, the method is extensible to any knowledge graph without attribute importance information and outperforms previous method.",
+      "In future work, there are two major areas with potential of improving the detection accuracy. The first one is about sentence splitting. What we are trying to get is semantic cohesive unit, which can be used to match an attribute, and there might be more comprehensive method than the simple splitting by sentence ending punctuations. The second one is about improving the word embedding quality. We have implemented an in-house improved version of Fasttext, which is adapted to our data source. It is highly possible to use the improved word embedding on purpose of obtaining higher semantic matching precision. As for the application, we will try to use more statistical models in the natural language generation part of the smart composing framework of consuming the detected important attributes."
+    ]
+  ]
+}
+```
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+Name of Paper: What Drives the International Development Agenda? An NLP Analysis of the United Nations General Debate 1970-2016
+
+Question: How are the main international development topics that states raise identified?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "The UN General Debate and international development",
+    "Estimation of topic models",
+    "Topics in the UN General Debate",
+    "Explaining the rhetoric",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Decisions made in international organisations are fundamental to international development efforts and initiatives. It is in these global governance arenas that the rules of the global economic system, which have a huge impact on development outcomes are agreed on; decisions are made about large-scale funding for development issues, such as health and infrastructure; and key development goals and targets are agreed on, as can be seen with the Millennium Development Goals (MDGs). More generally, international organisations have a profound influence on the ideas that shape international development efforts BIBREF0 .",
+      "Yet surprisingly little is known about the agenda-setting process for international development in global governance institutions. This is perhaps best demonstrated by the lack of information on how the different goals and targets of the MDGs were decided, which led to much criticism and concern about the global governance of development BIBREF1 . More generally, we know little about the types of development issues that different countries prioritise, or whether country-specific factors such as wealth or democracy make countries more likely to push for specific development issues to be put on the global political agenda.",
+      "The lack of knowledge about the agenda setting process in the global governance of development is in large part due to the absence of obvious data sources on states' preferences about international development issues. To address this gap we employ a novel approach based on the application of natural language processing (NLP) to countries' speeches in the UN. Every September, the heads of state and other high-level country representatives gather in New York at the start of a new session of the United Nations General Assembly (UNGA) and address the Assembly in the General Debate. The General Debate (GD) provides the governments of the almost two hundred UN member states with an opportunity to present their views on key issues in international politics \u2013 including international development. As such, the statements made during GD are an invaluable and, largely untapped, source of information on governments' policy preferences on international development over time.",
+      "An important feature of these annual country statements is that they are not institutionally connected to decision-making in the UN. This means that governments face few external constraints when delivering these speeches, enabling them to raise the issues that they consider the most important. Therefore, the General Debate acts \u201cas a barometer of international opinion on important issues, even those not on the agenda for that particular session\u201d BIBREF2 . In fact, the GD is usually the first item for each new session of the UNGA, and as such it provides a forum for governments to identify like-minded members, and to put on the record the issues they feel the UNGA should address. Therefore, the GD can be viewed as a key forum for governments to put different policy issues on international agenda.",
+      "We use a new dataset of GD statements from 1970 to 2016, the UN General Debate Corpus (UNGDC), to examine the international development agenda in the UN BIBREF3 . Our application of NLP to these statements focuses in particular on structural topic models (STMs) BIBREF4 . The paper makes two contributions using this approach: (1) It sheds light on the main international development issues that governments prioritise in the UN; and (2) It identifies the key country-specific factors associated with governments discussing development issues in their GD statements."
+    ],
+    [
+      "In the analysis we consider the nature of international development issues raised in the UN General Debates, and the effect of structural covariates on the level of developmental rhetoric in the GD statements. To do this, we first implement a structural topic model BIBREF4 . This enables us to identify the key international development topics discussed in the GD. We model topic prevalence in the context of the structural covariates. In addition, we control for region fixed effects and time trend. The aim is to allow the observed metadata to affect the frequency with which a topic is discussed in General Debate speeches. This allows us to test the degree of association between covariates (and region/time effects) and the average proportion of a document discussing a topic."
+    ],
+    [
+      "We assess the optimal number of topics that need to be specified for the STM analysis. We follow the recommendations of the original STM paper and focus on exclusivity and semantic coherence measures. BIBREF5 propose semantic coherence measure, which is closely related to point-wise mutual information measure posited by BIBREF6 to evaluate topic quality. BIBREF5 show that semantic coherence corresponds to expert judgments and more general human judgments in Amazon's Mechanical Turk experiments.",
+      "Exclusivity scores for each topic follows BIBREF7 . Highly frequent words in a given topic that do not appear very often in other topics are viewed as making that topic exclusive. Cohesive and exclusive topics are more semantically useful. Following BIBREF8 we generate a set of candidate models ranging between 3 and 50 topics. We then plot the exclusivity and semantic coherence (numbers closer to 0 indicate higher coherence), with a linear regression overlaid (Figure FIGREF3 ). Models above the regression line have a \u201cbetter\u201d exclusivity-semantic coherence trade off. We select the 16-topic model, which has the largest positive residual in the regression fit, and provides higher exclusivity at the same level of semantic coherence. The topic quality is usually evaluated by highest probability words, which is presented in Figure FIGREF4 ."
+    ],
+    [
+      "Figure FIGREF4 provides a list of the main topics (and the highest probability words associated these topics) that emerge from the STM of UN General Debate statements. In addition to the highest probability words, we use several other measures of key words (not presented here) to interpret the dimensions. This includes the FREX metric (which combines exclusivity and word frequency), the lift (which gives weight to words that appear less frequently in other topics), and the score (which divides the log frequency of the word in the topic by the log frequency of the word in other topics). We provide a brief description of each of the 16 topics here.",
+      "Topic 1 - Security and cooperation in Europe.",
+      "The first topic is related to issues of security and cooperation, with a focus on Central and Eastern Europe.",
+      "Topic 2 - Economic development and the global system.",
+      "This topic is related to economic development, particularly around the global economic system. The focus on `trade', `growth', `econom-', `product', `growth', `financ-', and etc. suggests that Topic 2 represent a more traditional view of international development in that the emphasis is specifically on economic processes and relations.",
+      "Topic 3 - Nuclear disarmament.",
+      "This topic picks up the issue of nuclear weapons, which has been a major issue in the UN since its founding.",
+      "Topic 4 - Post-conflict development.",
+      "This topic relates to post-conflict development. The countries that feature in the key words (e.g. Rwanda, Liberia, Bosnia) have experienced devastating civil wars, and the emphasis on words such as `develop', `peace', `hope', and `democrac-' suggest that this topic relates to how these countries recover and move forward.",
+      "Topic 5 - African independence / decolonisation.",
+      "This topic picks up the issue of African decolonisation and independence. It includes the issue of apartheid in South Africa, as well as racism and imperialism more broadly.",
+      "Topic 6 - Africa.",
+      "While the previous topic focused explicitly on issues of African independence and decolonisation, this topic more generally picks up issues linked to Africa, including peace, governance, security, and development.",
+      "Topic 7 - Sustainable development.",
+      "This topic centres on sustainable development, picking up various issues linked to development and climate change. In contrast to Topic 2, this topic includes some of the newer issues that have emerged in the international development agenda, such as sustainability, gender, education, work and the MDGs.",
+      "Topic 8 - Functional topic.",
+      "This topic appears to be comprised of functional or process-oriented words e.g. `problem', `solution', `effort', `general', etc.",
+      "Topic 9 - War.",
+      "This topic directly relates to issues of war. The key words appear to be linked to discussions around ongoing wars.",
+      "Topic 10 - Conflict in the Middle East.",
+      "This topic clearly picks up issues related to the Middle East \u2013 particularly around peace and conflict in the Middle East.",
+      "Topic 11 - Latin America.",
+      "This is another topic with a regional focus, picking up on issues related to Latin America.",
+      "Topic 12 - Commonwealth.",
+      "This is another of the less obvious topics to emerge from the STM in that the key words cover a wide range of issues. However, the places listed (e.g. Australia, Sri Lanka, Papua New Guinea) suggest the topic is related to the Commonwealth (or former British colonies).",
+      "Topic 13 - International security.",
+      "This topic broadly captures international security issues (e.g. terrorism, conflict, peace) and in particularly the international response to security threats, such as the deployment of peacekeepers.",
+      "Topic 14 - International law.",
+      "This topic picks up issues related to international law, particularly connected to territorial disputes.",
+      "Topic 15 - Decolonisation.",
+      "This topic relates more broadly to decolonisation. As well as specific mention of decolonisation, the key words include a range of issues and places linked to the decolonisation process.",
+      "Topic 16 - Cold War.",
+      "This is another of the less tightly defined topics. The topics appears to pick up issues that are broadly related to the Cold War. There is specific mention of the Soviet Union, and detente, as well as issues such as nuclear weapons, and the Helsinki Accords.",
+      "Based on these topics, we examine Topic 2 and Topic 7 as the principal \u201cinternational development\u201d topics. While a number of other topics \u2013 for example post-conflict development, Africa, Latin America, etc. \u2013 are related to development issues, Topic 2 and Topic 7 most directly capture aspects of international development. We consider these two topics more closely by contrasting the main words linked to these two topics. In Figure FIGREF6 , the word clouds show the 50 words most likely to mentioned in relation to each of the topics.",
+      "The word clouds provide further support for Topic 2 representing a more traditional view of international development focusing on economic processes. In addition to a strong emphasis on 'econom-', other key words, such as `trade', `debt', `market', `growth', `industri-', `financi-', `technolog-', `product', and `argicultur-', demonstrate the narrower economic focus on international development captured by Topic 2. In contrast, Topic 7 provides a much broader focus on development, with key words including `climat-', `sustain', `environ-', `educ-', `health', `women', `work', `mdgs', `peac-', `govern-', and `right'. Therefore, Topic 7 captures many of the issues that feature in the recent Sustainable Development Goals (SDGs) agenda BIBREF9 .",
+      "Figure FIGREF7 calculates the difference in probability of a word for the two topics, normalized by the maximum difference in probability of any word between the two topics. The figure demonstrates that while there is a much high probability of words, such as `econom-', `trade', and even `develop-' being used to discuss Topic 2; words such as `climat-', `govern-', `sustain', `goal', and `support' being used in association with Topic 7. This provides further support for the Topic 2 representing a more economistic view of international development, while Topic 7 relating to a broader sustainable development agenda.",
+      "We also assess the relationship between topics in the STM framework, which allows correlations between topics to be examined. This is shown in the network of topics in Figure FIGREF8 . The figure shows that Topic 2 and Topic 7 are closely related, which we would expect as they both deal with international development (and share key words on development, such as `develop-', `povert-', etc.). It is also worth noting that while Topic 2 is more closely correlated with the Latin America topic (Topic 11), Topic 7 is more directly correlated with the Africa topic (Topic 6)."
+    ],
+    [
+      "We next look at the relationship between topic proportions and structural factors. The data for these structural covariates is taken from the World Bank's World Development Indicators (WDI) unless otherwise stated. Confidence intervals produced by the method of composition in STM allow us to pick up statistical uncertainty in the linear regression model.",
+      "Figure FIGREF9 demonstrates the effect of wealth (GDP per capita) on the the extent to which states discuss the two international development topics in their GD statements. The figure shows that the relationship between wealth and the topic proportions linked to international development differs across Topic 2 and Topic 7. Discussion of Topic 2 (economic development) remains far more constant across different levels of wealth than Topic 7. The poorest states tend to discuss both topics more than other developing nations. However, this effect is larger for Topic 7. There is a decline in the proportion of both topics as countries become wealthier until around $30,000 when there is an increase in discussion of Topic 7. There is a further pronounced increase in the extent countries discuss Topic 7 at around $60,000 per capita. However, there is a decline in expected topic proportions for both Topic 2 and Topic 7 for the very wealthiest countries.",
+      "Figure FIGREF10 shows the expected topic proportions for Topic 2 and Topic 7 associated with different population sizes. The figure shows a slight surge in the discussion of both development topics for countries with the very smallest populations. This reflects the significant amount of discussion of development issues, particularly sustainable development (Topic 7) by the small island developing states (SIDs). The discussion of Topic 2 remains relatively constant across different population sizes, with a slight increase in the expected topic proportion for the countries with the very largest populations. However, with Topic 7 there is an increase in expected topic proportion until countries have a population of around 300 million, after which there is a decline in discussion of Topic 7. For countries with populations larger than 500 million there is no effect of population on discussion of Topic 7. It is only with the very largest populations that we see a positive effect on discussion of Topic 7.",
+      "We would also expect the extent to which states discuss international development in their GD statements to be impacted by the amount of aid or official development assistance (ODA) they receive. Figure FIGREF11 plots the expected topic proportion according to the amount of ODA countries receive. Broadly-speaking the discussion of development topics remains largely constant across different levels of ODA received. There is, however, a slight increase in the expected topic proportions of Topic 7 according to the amount of ODA received. It is also worth noting the spikes in discussion of Topic 2 and Topic 7 for countries that receive negative levels of ODA. These are countries that are effectively repaying more in loans to lenders than they are receiving in ODA. These countries appear to raise development issues far more in their GD statements, which is perhaps not altogether surprising.",
+      "We also consider the effects of democracy on the expected topic proportions of both development topics using the Polity IV measure of democracy BIBREF10 . Figure FIGREF12 shows the extent to which states discuss the international development topics according to their level of democracy. Discussion of Topic 2 is fairly constant across different levels of democracy (although there are some slight fluctuations). However, the extent to which states discuss Topic 7 (sustainable development) varies considerably across different levels of democracy. Somewhat surprisingly the most autocratic states tend to discuss Topic 7 more than the slightly less autocratic states. This may be because highly autocratic governments choose to discuss development and environmental issues to avoid a focus on democracy and human rights. There is then an increase in the expected topic proportion for Topic 7 as levels of democracy increase reaching a peak at around 5 on the Polity scale, after this there is a gradual decline in discussion of Topic 7. This would suggest that democratizing or semi-democratic countries (which are more likely to be developing countries with democratic institutions) discuss sustainable development more than established democracies (that are more likely to be developed countries).",
+      "We also plot the results of the analysis as the difference in topic proportions for two different values of the effect of conflict. Our measure of whether a country is experiencing a civil conflict comes from the UCDP/PRIO Armed Conflict Dataset BIBREF11 . Point estimates and 95% confidence intervals are plotted in Figure FIGREF13 . The figure shows that conflict affects only Topic 7 and not Topic 2. Countries experiencing conflict are less likely to discuss Topic 7 (sustainable development) than countries not experiencing conflict. The most likely explanation is that these countries are more likely to devote a greater proportion of their annual statements to discussing issues around conflict and security than development. The fact that there is no effect of conflict on Topic 2 is interesting in this regard.",
+      "Finally, we consider regional effects in Figure FIGREF14 . We use the World Bank's classifications of regions: Latin America and the Caribbean (LCN), South Asia (SAS), Sub-Saharan Africa (SSA), Europe and Central Asia (ECS), Middle East and North Africa (MEA), East Asia and the Pacific (EAS), North America (NAC). The figure shows that states in South Asia, and Latin America and the Caribbean are likely to discuss Topic 2 the most. States in South Asia and East Asia and the Pacific discuss Topic 7 the most. The figure shows that countries in North America are likely to speak about Topic 7 least.",
+      "The analysis of discussion of international development in annual UN General Debate statements therefore uncovers two principle development topics: economic development and sustainable development. We find that discussion of Topic 2 is not significantly impacted by country-specific factors, such as wealth, population, democracy, levels of ODA, and conflict (although there are regional effects). However, we find that the extent to which countries discuss sustainable development (Topic 7) in their annual GD statements varies considerably according to these different structural factors. The results suggest that broadly-speaking we do not observe linear trends in the relationship between these country-specific factors and discussion of Topic 7. Instead, we find that there are significant fluctuations in the relationship between factors such as wealth, democracy, etc., and the extent to which these states discuss sustainable development in their GD statements. These relationships require further analysis and exploration."
+    ],
+    [
+      "Despite decisions taken in international organisations having a huge impact on development initiatives and outcomes, we know relatively little about the agenda-setting process around the global governance of development. Using a novel approach that applies NLP methods to a new dataset of speeches in the UN General Debate, this paper has uncovered the main development topics discussed by governments in the UN, and the structural factors that influence the degree to which governments discuss international development. In doing so, the paper has shed some light on state preferences regarding the international development agenda in the UN. The paper more broadly demonstrates how text analytic approaches can help us to better understand different aspects of global governance."
+    ]
+  ]
+}
+```
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+Name of Paper: QnAMaker: Data to Bot in 2 Minutes
+
+Question: What experiments do the authors present to validate their system?
\ No newline at end of file
diff --git a/qasper-0124/instruction.md b/qasper-0124/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..84b0d1ee63d89ec2819613f9a958a4d60fe0fdcf
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@@ -0,0 +1,93 @@
+Name of Paper: QnAMaker: Data to Bot in 2 Minutes
+
+Question: How does the conversation layer work?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "System description ::: Architecture",
+    "System description ::: Bot Development Process",
+    "System description ::: Extraction",
+    "System description ::: Retrieval And Ranking",
+    "System description ::: Retrieval And Ranking ::: Pre-Processing",
+    "System description ::: Retrieval And Ranking ::: Features",
+    "System description ::: Retrieval And Ranking ::: Contextual Features",
+    "System description ::: Retrieval And Ranking ::: Modeling and Training",
+    "System description ::: Persona Based Chit-Chat",
+    "System description ::: Active Learning",
+    "Evaluation and Insights",
+    "Demonstration",
+    "Future Work"
+  ],
+  "paragraphs": [
+    [
+      "QnAMaker aims to simplify the process of bot creation by extracting Question-Answer (QA) pairs from data given by users into a Knowledge Base (KB) and providing a conversational layer over it. KB here refers to one instance of azure search index, where the extracted QA are stored. Whenever a developer creates a KB using QnAMaker, they automatically get all NLP capabilities required to answer user's queries. There are other systems such as Google's Dialogflow, IBM's Watson Discovery which tries to solve this problem. QnAMaker provides unique features for the ease of development such as the ability to add a persona-based chit-chat layer on top of the bot. Additionally, bot developers get automatic feedback from the system based on end-user traffic and interaction which helps them in enriching the KB; we call this feature active-learning. Our system also allows user to add Multi-Turn structure to KB using hierarchical extraction and contextual ranking. QnAMaker today supports over 35 languages, and is the only system among its competitors to follow a Server-Client architecture; all the KB data rests only in the client's subscription, giving users total control over their data. QnAMaker is part of Microsoft Cognitive Service and currently runs using the Microsoft Azure Stack."
+    ],
+    [
+      "As shown in Figure FIGREF4, humans can have two different kinds of roles in the system: Bot-Developers who want to create a bot using the data they have, and End-Users who will chat with the bot(s) created by bot-developers. The components involved in the process are:",
+      "QnAMaker Portal: This is the Graphical User Interface (GUI) for using QnAMaker. This website is designed to ease the use of management APIs. It also provides a test pane.",
+      "QnaMaker Management APIs: This is used for the extraction of Question-Answer (QA) pairs from semi-structured content. It then passes these QA pairs to the web app to create the Knowledge Base Index.",
+      "Azure Search Index: Stores the KB with questions and answers as indexable columns, thus acting as a retrieval layer.",
+      "QnaMaker WebApp: Acts as a layer between the Bot, Management APIs, and Azure Search Index. WebApp does ranking on top of retrieved results. WebApp also handles feedback management for active learning.",
+      "Bot: Calls the WebApp with the User's query to get results."
+    ],
+    [
+      "Creating a bot is a 3-step process for a bot developer:",
+      "Create a QnaMaker Resource in Azure: This creates a WebApp with binaries required to run QnAMaker. It also creates an Azure Search Service for populating the index with any given knowledge base, extracted from user data",
+      "Use Management APIs to Create/Update/Delete your KB: The Create API automatically extracts the QA pairs and sends the Content to WebApp, which indexes it in Azure Search Index. Developers can also add persona-based chat content and synonyms while creating and updating their KBs.",
+      "Bot Creation: Create a bot using any framework and call the WebApp hosted in Azure to get your queries answered. There are Bot-Framework templates provided for the same."
+    ],
+    [
+      "The Extraction component is responsible for understanding a given document and extracting potential QA pairs. These QA pairs are in turn used to create a KB to be consumed later on by the QnAMaker WebApp to answer user queries. First, the basic blocks from given documents such as text, lines are extracted. Then the layout of the document such as columns, tables, lists, paragraphs, etc is extracted. This is done using Recursive X-Y cut BIBREF0. Following Layout Understanding, each element is tagged as headers, footers, table of content, index, watermark, table, image, table caption, image caption, heading, heading level, and answers. Agglomerative clustering BIBREF1 is used to identify heading and hierarchy to form an intent tree. Leaf nodes from the hierarchy are considered as QA pairs. In the end, the intent tree is further augmented with entities using CRF-based sequence labeling. Intents that are repeated in and across documents are further augmented with their parent intent, adding more context to resolve potential ambiguity."
+    ],
+    [
+      "QnAMaker uses Azure Search Index as it's retrieval layer, followed by re-ranking on top of retrieved results (Figure FIGREF21). Azure Search is based on inverted indexing and TF-IDF scores. Azure Search provides fuzzy matching based on edit-distance, thus making retrieval robust to spelling mistakes. It also incorporates lemmatization and normalization. These indexes can scale up to millions of documents, lowering the burden on QnAMaker WebApp which gets less than 100 results to re-rank.",
+      "Different customers may use QnAMaker for different scenarios such as banking task completion, answering FAQs on company policies, or fun and engagement. The number of QAs, length of questions and answers, number of alternate questions per QA can vary significantly across different types of content. Thus, the ranker model needs to use features that are generic enough to be relevant across all use cases."
+    ],
+    [
+      "The pre-processing layer uses components such as Language Detection, Lemmatization, Speller, and Word Breaker to normalize user queries. It also removes junk characters and stop-words from the user's query."
+    ],
+    [
+      "Going into granular features and the exact empirical formulas used is out of the scope of this paper. The broad level features used while ranking are:",
+      "WordNet: There are various features generated using WordNet BIBREF2 matching with questions and answers. This takes care of word-level semantics. For instance, if there is information about \u201cprice of furniture\" in a KB and the end-user asks about \u201cprice of table\", the user will likely get a relevant answer. The scores of these WordNet features are calculated as a function of:",
+      "Distance of 2 words in the WordNet graph",
+      "Distance of Lowest Common Hypernym from the root",
+      "Knowledge-Base word importance (Local IDFs)",
+      "Global word importance (Global IDFs)",
+      "This is the most important feature in our model as it has the highest relative feature gain.",
+      "CDSSM: Convolutional Deep Structured Semantic Models BIBREF3 are used for sentence-level semantic matching. This is a dual encoder model that converts text strings (sentences, queries, predicates, entity mentions, etc) into their vector representations. These models are trained using millions of Bing Query Title Click-Through data. Using the source-model for vectorizing user query and target-model for vectorizing answer, we compute the cosine similarity between these two vectors, giving the relevance of answer corresponding to the query.",
+      "TF-IDF: Though sentence-to-vector models are trained on huge datasets, they fail to effectively disambiguate KB specific data. This is where a standard TF-IDF BIBREF4 featurizer with local and global IDFs helps."
+    ],
+    [
+      "We extend the features for contextual ranking by modifying the candidate QAs and user query in these ways:",
+      "$Query_{modified}$ = Query + Previous Answer; For instance, if user query is \u201cyes\" and the previous answer is \u201cdo you want to know about XYZ\", the current query becomes \u201cdo you want to know about XYZ yes\".",
+      "Candidate QnA pairs are appended with its parent Questions and Answers; no contextual information is used from the user's query. For instance, if a candidate QnA has a question \u201cbenefits\" and its parent question was \u201cknow about XYZ\", the candidate QA's question is changed to \u201cknow about XYZ benefits\".",
+      "The features mentioned in Section SECREF20 are calculated for the above combinations also. These features carry contextual information."
+    ],
+    [
+      "We use gradient-boosted decision trees as our ranking model to combine all the features. Early stopping BIBREF5 based on Generality-to-Progress ratio is used to decide the number of step trees and Tolerant Pruning BIBREF6 helps prevent overfitting. We follow incremental training if there is small changes in features or training data so that the score distribution is not changed drastically."
+    ],
+    [
+      "We add support for bot-developers to directly enable handling chit-chat queries like \u201chi\", \u201cthank you\", \u201cwhat's up\" in their QnAMaker bots. In addition to chit-chat, we also give bot developers the flexibility to ground responses for such queries in a specific personality: professional, witty, friendly, caring, or enthusiastic. For example, the \u201cHumorous\" personality can be used for a casual bot, whereas a \u201cProfessional\" personality is more suited in case of banking FAQs or task-completion bots. There is a list of 100+ predefined intents BIBREF7. There is a curated list of queries for each of these intents, along with a separate query understanding layer for ranking these intents. The arbitration between chit-chat answers and user's knowledge base answers is handled by using a chat-domain classifier BIBREF8."
+    ],
+    [
+      "The majority of the KBs are created using existing FAQ pages or manuals but to improve the quality it requires effort from the developers. Active learning generates suggestions based on end-user feedback as well as ranker's implicit signals. For instance, if for a query, CDSSM feature was confident that one QnA should be ranked higher whereas wordnet feature thought other QnA should be ranked higher, active learning system will try to disambiguate it by showing this as a suggestion to the bot developer. To avoid showing similar suggestions to developers, DB-Scan clustering is done which optimizes the number of suggestions shown."
+    ],
+    [
+      "QnAMaker is not domain-specific and can be used for any type of data. To support this claim, we measure our system's performance for datasets across various domains. The evaluations are done by managed judges who understands the knowledge base and then judge user queries relevance to the QA pairs (binary labels). Each query-QA pair is judged by two judges. We filter out data for which judges do not agree on the label. Chit-chat in itself can be considered as a domain. Thus, we evaluate performance on given KB both with and without chit-chat data (last two rows in Table TABREF19), as well as performance on just chit-chat data (2nd row in Table TABREF19). Hybrid of deep learning(CDSSM) and machine learning features give our ranking model low computation cost, high explainability and significant F1/AUC score. Based on QnAMaker usage, we observed these trends:",
+      "Around 27% of the knowledge bases created use pre-built persona-based chitchat, out of which, $\\sim $4% of the knowledge bases are created for chit-chat alone. The highest used personality is Professional which is used in 9% knowledge bases.",
+      "Around $\\sim $25% developers have enabled active learning suggestions. The acceptance to reject ratio for active learning suggestions is 0.31.",
+      "25.5% of the knowledge bases use one URL as a source while creation. $\\sim $41% of the knowledge bases created use different sources like multiple URLs. 15.19% of the knowledge bases use both URL and editorial content as sources. Rest use just editorial content."
+    ],
+    [
+      "We demonstrate QnAMaker: a service to add a conversational layer over semi-structured user data. In addition to query-answering, we support novel features like personality-grounded chit-chat, active learning based on user-interaction feedback (Figure FIGREF40), and hierarchical extraction for multi-turn conversations (Figure FIGREF41). The goal of the demonstration will be to show how easy it is to create an intelligent bot using QnAMaker. All the demonstrations will be done on the production website Demo Video can be seen here."
+    ],
+    [
+      "The system currently doesn't highlight the answer span and does not generate answers taking the KB as grounding. We will be soon supporting Answer Span BIBREF9 and KB-grounded response generation BIBREF10 in QnAMaker. We are also working on user-defined personas for chit-chat (automatically learned from user-documents). We aim to enhance our extraction to be able to work for any unstructured document as well as images. We are also experimenting on improving our ranking system by using semantic vector-based search as our retrieval and transformer-based models for re-ranking."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0125/instruction.md b/qasper-0125/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..6b59551a663ead6eb45a194c263dc3dced8b2c45
--- /dev/null
+++ b/qasper-0125/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: QnAMaker: Data to Bot in 2 Minutes
+
+Question: What components is the QnAMaker composed of?
\ No newline at end of file
diff --git a/qasper-0140/instruction.md b/qasper-0140/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..6444fbede25ab2f4ace37801b4256c63ed5baa87
--- /dev/null
+++ b/qasper-0140/instruction.md
@@ -0,0 +1,110 @@
+Name of Paper: Procedural Reasoning Networks for Understanding Multimodal Procedures
+
+Question: How better is accuracy of new model compared to previously reported models?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Visual Reasoning in RecipeQA",
+    "Procedural Reasoning Networks",
+    "Procedural Reasoning Networks ::: Input Module",
+    "Procedural Reasoning Networks ::: Reasoning Module",
+    "Procedural Reasoning Networks ::: Attention Module",
+    "Procedural Reasoning Networks ::: Modeling Module",
+    "Procedural Reasoning Networks ::: Output Module",
+    "Experiments",
+    "Experiments ::: Entity Extraction",
+    "Experiments ::: Training Details",
+    "Experiments ::: Baselines",
+    "Experiments ::: Results",
+    "Related Work",
+    "Conclusion",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "A great deal of commonsense knowledge about the world we live is procedural in nature and involves steps that show ways to achieve specific goals. Understanding and reasoning about procedural texts (e.g. cooking recipes, how-to guides, scientific processes) are very hard for machines as it demands modeling the intrinsic dynamics of the procedures BIBREF0, BIBREF1, BIBREF2. That is, one must be aware of the entities present in the text, infer relations among them and even anticipate changes in the states of the entities after each action. For example, consider the cheeseburger recipe presented in Fig. FIGREF2. The instruction \u201csalt and pepper each patty and cook for 2 to 3 minutes on the first side\u201d in Step 5 entails mixing three basic ingredients, the ground beef, salt and pepper, together and then applying heat to the mix, which in turn causes chemical changes that alter both the appearance and the taste. From a natural language understanding perspective, the main difficulty arises when a model sees the word patty again at a later stage of the recipe. It still corresponds to the same entity, but its form is totally different.",
+      "Over the past few years, many new datasets and approaches have been proposed that address this inherently hard problem BIBREF0, BIBREF1, BIBREF3, BIBREF4. To mitigate the aforementioned challenges, the existing works rely mostly on heavy supervision and focus on predicting the individual state changes of entities at each step. Although these models can accurately learn to make local predictions, they may lack global consistency BIBREF3, BIBREF4, not to mention that building such annotated corpora is very labor-intensive. In this work, we take a different direction and explore the problem from a multimodal standpoint. Our basic motivation, as illustrated in Fig. FIGREF2, is that accompanying images provide complementary cues about causal effects and state changes. For instance, it is quite easy to distinguish raw meat from cooked one in visual domain.",
+      "In particular, we take advantage of recently proposed RecipeQA dataset BIBREF2, a dataset for multimodal comprehension of cooking recipes, and ask whether it is possible to have a model which employs dynamic representations of entities in answering questions that require multimodal understanding of procedures. To this end, inspired from BIBREF5, we propose Procedural Reasoning Networks (PRN) that incorporates entities into the comprehension process and allows to keep track of entities, understand their interactions and accordingly update their states across time. We report that our proposed approach significantly improves upon previously published results on visual reasoning tasks in RecipeQA, which test understanding causal and temporal relations from images and text. We further show that the dynamic entity representations can capture semantics of the state information in the corresponding steps."
+    ],
+    [
+      "In our study, we particularly focus on the visual reasoning tasks of RecipeQA, namely visual cloze, visual coherence, and visual ordering tasks, each of which examines a different reasoning skill. We briefly describe these tasks below.",
+      "Visual Cloze. In the visual cloze task, the question is formed by a sequence of four images from consecutive steps of a recipe where one of them is replaced by a placeholder. A model should select the correct one from a multiple-choice list of four answer candidates to fill in the missing piece. In that regard, the task inherently requires aligning visual and textual information and understanding temporal relationships between the cooking actions and the entities.",
+      "Visual Coherence. The visual coherence task tests the ability to identify the image within a sequence of four images that is inconsistent with the text instructions of a cooking recipe. To succeed in this task, a model should have a clear understanding of the procedure described in the recipe and at the same time connect language and vision.",
+      "Visual Ordering. The visual ordering task is about grasping the temporal flow of visual events with the help of the given recipe text. The questions show a set of four images from the recipe and the task is to sort jumbled images into the correct order. Here, a model needs to infer the temporal relations between the images and align them with the recipe steps."
+    ],
+    [
+      "In the following, we explain our Procedural Reasoning Networks model. Its architecture is based on a bi-directional attention flow (BiDAF) model BIBREF6, but also equipped with an explicit reasoning module that acts on entity-specific relational memory units. Fig. FIGREF4 shows an overview of the network architecture. It consists of five main modules: An input module, an attention module, a reasoning module, a modeling module, and an output module. Note that the question answering tasks we consider here are multimodal in that while the context is a procedural text, the question and the multiple choice answers are composed of images.",
+      "Input Module extracts vector representations of inputs at different levels of granularity by using several different encoders.",
+      "Reasoning Module scans the procedural text and tracks the states of the entities and their relations through a recurrent relational memory core unit BIBREF5.",
+      "Attention Module computes context-aware query vectors and query-aware context vectors as well as query-aware memory vectors.",
+      "Modeling Module employs two multi-layered RNNs to encode previous layers outputs.",
+      "Output Module scores a candidate answer from the given multiple-choice list.",
+      "At a high level, as the model is reading the cooking recipe, it continually updates the internal memory representations of the entities (ingredients) based on the content of each step \u2013 it keeps track of changes in the states of the entities, providing an entity-centric summary of the recipe. The response to a question and a possible answer depends on the representation of the recipe text as well as the last states of the entities. All this happens in a series of implicit relational reasoning steps and there is no need for explicitly encoding the state in terms of a predefined vocabulary."
+    ],
+    [
+      "Let the triple $(\\mathbf {R},\\mathbf {Q},\\mathbf {A})$ be a sample input. Here, $\\mathbf {R}$ denotes the input recipe which contains textual instructions composed of $N$ words in total. $\\mathbf {Q}$ represents the question that consists of a sequence of $M$ images. $\\mathbf {A}$ denotes an answer that is either a single image or a series of $L$ images depending on the reasoning task. In particular, for the visual cloze and the visual coherence type questions, the answer contains a single image ($L=1$) and for the visual ordering task, it includes a sequence.",
+      "We encode the input recipe $\\mathbf {R}$ at character, word, and step levels. Character-level embedding layer uses a convolutional neural network, namely CharCNN model by BIBREF7, which outputs character level embeddings for each word and alleviates the issue of out-of-vocabulary (OOV) words. In word embedding layer, we use a pretrained GloVe model BIBREF8 and extract word-level embeddings. The concatenation of the character and the word embeddings are then fed to a two-layer highway network BIBREF10 to obtain a contextual embedding for each word in the recipe. This results in the matrix $\\mathbf {R}^{\\prime } \\in \\mathbb {R}^{2d \\times N}$.",
+      "On top of these layers, we have another layer that encodes the steps of the recipe in an individual manner. Specifically, we obtain a step-level contextual embedding of the input recipe containing $T$ steps as $\\mathcal {S}=(\\mathbf {s}_1,\\mathbf {s}_2,\\dots ,\\mathbf {s}_T)$ where $\\mathbf {s}_i$ represents the final state of a BiLSTM encoding the $i$-th step of the recipe obtained from the character and word-level embeddings of the tokens exist in the corresponding step.",
+      "We represent both the question $\\mathbf {Q}$ and the answer $\\mathbf {A}$ in terms of visual embeddings. Here, we employ a pretrained ResNet-50 model BIBREF11 trained on ImageNet dataset BIBREF12 and represent each image as a real-valued 2048-d vector using features from the penultimate average-pool layer. Then these embeddings are passed first to a multilayer perceptron (MLP) and then its outputs are fed to a BiLSTM. We then form a matrix $\\mathbf {Q}^{\\prime } \\in \\mathbb {R}^{2d \\times M}$ for the question by concatenating the cell states of the BiLSTM. For the visual ordering task, to represent the sequence of images in the answer with a single vector, we additionally use a BiLSTM and define the answering embedding by the summation of the cell states of the BiLSTM. Finally, for all tasks, these computations produce answer embeddings denoted by $\\mathbf {a} \\in \\mathbb {R}^{2d \\times 1}$."
+    ],
+    [
+      "As mentioned before, comprehending a cooking recipe is mostly about entities (basic ingredients) and actions (cooking activities) described in the recipe instructions. Each action leads to changes in the states of the entities, which usually affects their visual characteristics. A change rarely occurs in isolation; in most cases, the action affects multiple entities at once. Hence, in our reasoning module, we have an explicit memory component implemented with relational memory units BIBREF5. This helps us to keep track of the entities, their state changes and their relations in relation to each other over the course of the recipe (see Fig. FIGREF14). As we will examine in more detail in Section SECREF4, it also greatly improves the interpretability of model outputs.",
+      "Specifically, we set up the memory with a memory matrix $\\mathbf {E} \\in \\mathbb {R}^{d_E \\times K}$ by extracting $K$ entities (ingredients) from the first step of the recipe. We initialize each memory cell $\\mathbf {e}_i$ representing a specific entity by its CharCNN and pre-trained GloVe embeddings. From now on, we will use the terms memory cells and entities interchangeably throughout the paper. Since the input recipe is given in the form of a procedural text decomposed into a number of steps, we update the memory cells after each step, reflecting the state changes happened on the entities. This update procedure is modelled via a relational recurrent neural network (R-RNN), recently proposed by BIBREF5. It is built on a 2-dimensional LSTM model whose matrix of cell states represent our memory matrix $\\mathbf {E}$. Here, each row $i$ of the matrix $\\mathbf {E}$ refers to a specific entity $\\mathbf {e}_i$ and is updated after each recipe step $t$ as follows:",
+      "where $\\mathbf {s}_{t}$ denotes the embedding of recipe step $t$ and $\\mathbf {\\phi }_{i,t}=(\\mathbf {h}_{i,t},\\mathbf {e}_{i,t})$ is the cell state of the R-RNN at step $t$ with $\\mathbf {h}_{i,t}$ and $\\mathbf {e}_{i,t}$ being the $i$-th row of the hidden state of the R-RNN and the dynamic representation of entity $\\mathbf {e}_{i}$ at the step $t$, respectively. The R-RNN model exploits a multi-headed self-attention mechanism BIBREF13 that allows memory cells to interact with each other and attend multiple locations simultaneously during the update phase.",
+      "In Fig. FIGREF14, we illustrate how this interaction takes place in our relational memory module by considering a sample cooking recipe and by presenting how the attention matrix changes throughout the recipe. In particular, the attention matrix at a specific time shows the attention flow from one entity (memory cell) to another along with the attention weights to the corresponding recipe step (offset column). The color intensity shows the magnitude of the attention weights. As can be seen from the figure, the internal representations of the entities are actively updated at each step. Moreover, as argued in BIBREF5, this can be interpreted as a form of relational reasoning as each update on a specific memory cell is operated in relation to others. Here, we should note that it is often difficult to make sense of these attention weights. However, we observe that the attention matrix changes very gradually near the completion of the recipe."
+    ],
+    [
+      "Attention module is in charge of linking the question with the recipe text and the entities present in the recipe. It takes the matrices $\\mathbf {Q^{\\prime }}$ and $\\mathbf {R}^{\\prime }$ from the input module, and $\\mathbf {E}$ from the reasoning module and constructs the question-aware recipe representation $\\mathbf {G}$ and the question-aware entity representation $\\mathbf {Y}$. Following the attention flow mechanism described in BIBREF14, we specifically calculate attentions in four different directions: (1) from question to recipe, (2) from recipe to question, (3) from question to entities, and (4) from entities to question.",
+      "The first two of these attentions require computing a shared affinity matrix $\\mathbf {S}^R \\in \\mathbb {R}^{N \\times M}$ with $\\mathbf {S}^R_{i,j}$ indicating the similarity between $i$-th recipe word and $j$-th image in the question estimated by",
+      "where $\\mathbf {w}^{\\top }_{R}$ is a trainable weight vector, $\\circ $ and $[;]$ denote elementwise multiplication and concatenation operations, respectively.",
+      "Recipe-to-question attention determines the images within the question that is most relevant to each word of the recipe. Let $\\mathbf {\\tilde{Q}} \\in \\mathbb {R}^{2d \\times N}$ represent the recipe-to-question attention matrix with its $i$-th column being given by $ \\mathbf {\\tilde{Q}}_i=\\sum _j \\mathbf {a}_{ij}\\mathbf {Q}^{\\prime }_j$ where the attention weight is computed by $\\mathbf {a}_i=\\operatorname{softmax}(\\mathbf {S}^R_{i}) \\in \\mathbb {R}^M$.",
+      "Question-to-recipe attention signifies the words within the recipe that have the closest similarity to each image in the question, and construct an attended recipe vector given by $ \\tilde{\\mathbf {r}}=\\sum _{i}\\mathbf {b}_i\\mathbf {R}^{\\prime }_i$ with the attention weight is calculated by $\\mathbf {b}=\\operatorname{softmax}(\\operatorname{max}_{\\mathit {col}}(\\mathbf {S}^R)) \\in \\mathbb {R}^{N}$ where $\\operatorname{max}_{\\mathit {col}}$ denotes the maximum function across the column. The question-to-recipe matrix is then obtained by replicating $\\tilde{\\mathbf {r}}$ $N$ times across the column, giving $\\tilde{\\mathbf {R}} \\in \\mathbb {R}^{2d \\times N}$.",
+      "Then, we construct the question aware representation of the input recipe, $\\mathbf {G}$, with its $i$-th column $\\mathbf {G}_i \\in \\mathbb {R}^{8d \\times N}$ denoting the final embedding of $i$-th word given by",
+      "Attentions from question to entities, and from entities to question are computed in a way similar to the ones described above. The only difference is that it uses a different shared affinity matrix to be computed between the memory encoding entities $\\mathbf {E}$ and the question $\\mathbf {Q}^{\\prime }$. These attentions are then used to construct the question aware representation of entities, denoted by $\\mathbf {Y}$, that links and integrates the images in the question and the entities in the input recipe."
+    ],
+    [
+      "Modeling module takes the question-aware representations of the recipe $\\mathbf {G}$ and the entities $\\mathbf {Y}$, and forms their combined vector representation. For this purpose, we first use a two-layer BiLSTM to read the question-aware recipe $\\mathbf {G}$ and to encode the interactions among the words conditioned on the question. For each direction of BiLSTM , we use its hidden state after reading the last token as its output. In the end, we obtain a vector embedding $\\mathbf {c} \\in \\mathbb {R}^{2d \\times 1}$. Similarly, we employ a second BiLSTM, this time, over the entities $\\mathbf {Y}$, which results in another vector embedding $\\mathbf {f} \\in \\mathbb {R}^{2d_E \\times 1}$. Finally, these vector representations are concatenated and then projected to a fixed size representation using $\\mathbf {o}=\\varphi _o(\\left[\\mathbf {c}; \\mathbf {f}\\right]) \\in \\mathbb {R}^{2d \\times 1}$ where $\\varphi _o$ is a multilayer perceptron with $\\operatorname{tanh}$ activation function."
+    ],
+    [
+      "The output module takes the output of the modeling module, encoding vector embeddings of the question-aware recipe and the entities $\\mathbf {Y}$, and the embedding of the answer $\\mathbf {A}$, and returns a similarity score which is used while determining the correct answer. Among all the candidate answer, the one having the highest similarity score is chosen as the correct answer. To train our proposed procedural reasoning network, we employ a hinge ranking loss BIBREF15, similar to the one used in BIBREF2, given below.",
+      "where $\\gamma $ is the margin parameter, $\\mathbf {a}_+$ and $\\mathbf {a}_{-}$ are the correct and the incorrect answers, respectively."
+    ],
+    [
+      "In this section, we describe our experimental setup and then analyze the results of the proposed Procedural Reasoning Networks (PRN) model."
+    ],
+    [
+      "Given a recipe, we automatically extract the entities from the initial step of a recipe by using a dictionary of ingredients. While determining the ingredients, we exploit Recipe1M BIBREF16 and Kaggle What\u2019s Cooking Recipes BIBREF17 datasets, and form our dictionary using the most commonly used ingredients in the training set of RecipeQA. For the cases when no entity can be extracted from the recipe automatically (20 recipes in total), we manually annotate those recipes with the related entities."
+    ],
+    [
+      "In our experiments, we separately trained models on each task, as well as we investigated multi-task learning where a single model is trained to solve all these tasks at once. In total, the PRN architecture consists of $\\sim $12M trainable parameters. We implemented our models in PyTorch BIBREF18 using AllenNLP library BIBREF6. We used Adam optimizer with a learning rate of 1e-4 with an early stopping criteria with the patience set to 10 indicating that the training procedure ends after 10 iterations if the performance would not improve. We considered a batch size of 32 due to our hardware constraints. In the multi-task setting, batches are sampled round-robin from all tasks, where each batch is solely composed of examples from one task. We performed our experiments on a system containing four NVIDIA GTX-1080Ti GPUs, and training a single model took around 2 hours. We employed the same hyperparameters for all the baseline systems. We plan to share our code and model implementation after the review process."
+    ],
+    [
+      "We compare our model with several baseline models as described below. We note that the results of the first two are previously reported in BIBREF2.",
+      "Hasty Student BIBREF2 is a heuristics-based simple model which ignores the recipe and gives an answer by examining only the question and the answer set using distances in the visual feature space.",
+      "Impatient Reader BIBREF19 is a simple neural model that takes its name from the fact that it repeatedly computes attention over the recipe after observing each image in the query.",
+      "BiDAF BIBREF14 is a strong reading comprehension model that employs a bi-directional attention flow mechanism to obtain a question-aware representation and bases its predictions on this representation. Originally, it is a span-selection model from the input context. Here, we adapt it to work in a multimodal setting and answer multiple choice questions instead.",
+      "BiDAF w/ static memory is an extended version of the BiDAF model which resembles our proposed PRN model in that it includes a memory unit for the entities. However, it does not make any updates on the memory cells. That is, it uses the static entity embeeddings initialized with GloVe word vectors. We propose this baseline to test the significance of the use of relational memory updates."
+    ],
+    [
+      "Table TABREF29 presents the quantitative results for the visual reasoning tasks in RecipeQA. In single-task training setting, PRN gives state-of-the-art results compared to other neural models. Moreover, it achieves the best performance on average. These results demonstrate the importance of having a dynamic memory and keeping track of entities extracted from the recipe. In multi-task training setting where a single model is trained to solve all the tasks at once, PRN and BIDAF w/ static memory perform comparably and give much better results than BIDAF. Note that the model performances in the multi-task training setting are worse than single-task performances. We believe that this is due to the nature of the tasks that some are more difficult than the others. We think that the performance could be improved by employing a carefully selected curriculum strategy BIBREF20.",
+      "In Fig. FIGREF28, we illustrate the entity embeddings space by projecting the learned embeddings from the step-by-step memory snapshots through time with t-SNE to 3-d space from 200-d vector space. Color codes denote the categories of the cooking recipes. As can be seen, these step-aware embeddings show clear clustering of these categories. Moreover, within each cluster, the entities are grouped together in terms of their state characteristics. For instance, in the zoomed parts of the figure, chopped and sliced, or stirred and whisked entities are placed close to each other.",
+      "Fig. FIGREF30 demonstrates the entity arithmetics using the learned embeddings from each entity step. Here, we show that the learned embedding from the memory snapshots can effectively capture the contextual information about the entities at each time point in the corresponding step while taking into account of the recipe data. This basic arithmetic operation suggests that the proposed model can successfully capture the semantics of each entity's state in the corresponding step."
+    ],
+    [
+      "In recent years, tracking entities and their state changes have been explored in the literature from a variety of perspectives. In an early work, BIBREF21 proposed a dynamic memory based network which updates entity states using a gating mechanism while reading the text. BIBREF22 presented a more structured memory augmented model which employs memory slots for representing both entities and their relations. BIBREF23 suggested a conceptually similar model in which the pairwise relations between attended memories are utilized to encode the world state. The main difference between our approach and these works is that by utilizing relational memory core units we also allow memories to interact with each other during each update.",
+      "BIBREF24 showed that similar ideas can be used to compile supporting memories in tracking dialogue state. BIBREF25 has shown the importance of coreference signals for reading comprehension task. More recently, BIBREF26 introduced a specialized recurrent layer which uses coreference annotations for improving reading comprehension tasks. On language modeling task, BIBREF27 proposed a language model which can explicitly incorporate entities while dynamically updating their representations for a variety of tasks such as language modeling, coreference resolution, and entity prediction.",
+      "Our work builds upon and contributes to the growing literature on tracking states changes in procedural text. BIBREF0 presented a neural model that can learn to explicitly predict state changes of ingredients at different points in a cooking recipe. BIBREF1 proposed another entity-aware model to track entity states in scientific processes. BIBREF3 demonstrated that the prediction quality can be boosted by including hard and soft constraints to eliminate unlikely or favor probable state changes. In a follow-up work, BIBREF4 exploited the notion of label consistency in training to enforce similar predictions in similar procedural contexts. BIBREF28 proposed a model that dynamically constructs a knowledge graph while reading the procedural text to track the ever-changing entities states. As discussed in the introduction, however, these previous methods use a strong inductive bias and assume that state labels are present during training. In our study, we deliberately focus on unlabeled procedural data and ask the question: Can multimodality help to identify and provide insights to understanding state changes."
+    ],
+    [
+      "We have presented a new neural architecture called Procedural Reasoning Networks (PRN) for multimodal understanding of step-by-step instructions. Our proposed model is based on the successful BiDAF framework but also equipped with an explicit memory unit that provides an implicit mechanism to keep track of the changes in the states of the entities over the course of the procedure. Our experimental analysis on visual reasoning tasks in the RecipeQA dataset shows that the model significantly improves the results of the previous models, indicating that it better understands the procedural text and the accompanying images. Additionally, we carefully analyze our results and find that our approach learns meaningful dynamic representations of entities without any entity-level supervision. Although we achieve state-of-the-art results on RecipeQA, clearly there is still room for improvement compared to human performance. We also believe that the PRN architecture will be of value to other visual and textual sequential reasoning tasks."
+    ],
+    [
+      "We thank the anonymous reviewers and area chairs for their invaluable feedback. This work was supported by TUBA GEBIP fellowship awarded to E. Erdem; and by the MMVC project via an Institutional Links grant (Project No. 217E054) under the Newton-Katip \u00c7elebi Fund partnership funded by the Scientific and Technological Research Council of Turkey (TUBITAK) and the British Council. We also thank NVIDIA Corporation for the donation of GPUs used in this research."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0141/instruction.md b/qasper-0141/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..658aa339b18d6c30e36dcfa6d9625a8fd822ab0b
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+++ b/qasper-0141/instruction.md
@@ -0,0 +1,116 @@
+Name of Paper: Active Learning for Chinese Word Segmentation in Medical Text
+
+Question: How does the scoring model work?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Chinese Word Segmentation",
+    "Active Learning",
+    "Active Learning for Chinese Word Segmentation",
+    "CRF-based Word Segmenter",
+    "Information Entropy Based Scoring Model",
+    "Datasets",
+    "Parameter Settings",
+    "Experimental Results",
+    "Conclusion and Future Work",
+    "Acknowledgment"
+  ],
+  "paragraphs": [
+    [
+      "Electronic health records (EHRs) systematically collect patients' clinical information, such as health profiles, histories of present illness, past medical histories, examination results and treatment plans BIBREF0 . By analyzing EHRs, many useful information, closely related to patients, can be discovered BIBREF1 . Since Chinese EHRs are recorded without explicit word delimiters (e.g., \u201cUTF8gkai\u7cd6\u5c3f\u75c5\u916e\u75c7\u9178\u4e2d\u6bd2\u201d (diabetic ketoacidosis)), Chinese word segmentation (CWS) is a prerequisite for processing EHRs. Currently, state-of-the-art CWS methods usually require large amounts of manually-labeled data to reach their full potential. However, there are many challenges inherent in labeling EHRs. First, EHRs have many medical terminologies, such as \u201cUTF8gkai\u9ad8\u8840\u538b\u6027\u5fc3\u810f\u75c5\u201d (hypertensive heart disease) and \u201cUTF8gkai\u7f57\u6c0f\u82ac\u201d (Rocephin), so only annotators with medical backgrounds can be qualified to label EHRs. Second, EHRs may involve personal privacies of patients. Therefore, they cannot be openly published on a large scale for labeling. The above two problems lead to the high annotation cost and insufficient training corpus in the research of CWS in medical text.",
+      "CWS was usually formulated as a sequence labeling task BIBREF2 , which can be solved by supervised learning approaches, such as hidden markov model (HMM) BIBREF3 and conditional random field (CRF) BIBREF4 . However, these methods rely heavily on handcrafted features. To relieve the efforts of feature engineering, neural network-based methods are beginning to thrive BIBREF5 , BIBREF6 , BIBREF7 . However, due to insufficient annotated training data, conventional models for CWS trained on open corpus often suffer from significant performance degradation when transferred to a domain-specific text. Moreover, the task in medical domain is rarely dabbled, and only one related work on transfer learning is found in recent literatures BIBREF8 . However, researches related to transfer learning mostly remain in general domains, causing a major problem that a considerable amount of manually annotated data is required, when introducing the models into specific domains.",
+      "One of the solutions for this obstacle is to use active learning, where only a small scale of samples are selected and labeled in an active manner. Active learning methods are favored by the researchers in many natural language processing (NLP) tasks, such as text classification BIBREF9 and named entity recognition (NER) BIBREF10 . However, only a handful of works are conducted on CWS BIBREF2 , and few focuses on medical domain tasks.",
+      "Given the aforementioned challenges and current researches, we propose a word segmentation method based on active learning. To model the segmentation history, we incorporate a sampling strategy consisting of word score, link score and sequence score, which effectively evaluates the segmentation decisions. Specifically, we combine information branch and gated neural network to determine if the segment is a legal word, i.e., word score. Meanwhile, we use the hidden layer output of the long short-term memory (LSTM) BIBREF11 to find out how the word is linked to its surroundings, i.e., link score. The final decision on the selection of labeling samples is made by calculating the average of word and link scores on the whole segmented sentence, i.e., sequence score. Besides, to capture coherence over characters, we additionally add K-means clustering features to the input of CRF-based word segmenter.",
+      "To sum up, the main contributions of our work are summarized as follows:",
+      "The rest of this paper is organized as follows. Section SECREF2 briefly reviews the related work on CWS and active learning. Section SECREF3 presents an active learning method for CWS. We experimentally evaluate our proposed method in Section SECREF4 . Finally, Section SECREF5 concludes the paper and envisions on future work."
+    ],
+    [
+      "In past decades, researches on CWS have a long history and various methods have been proposed BIBREF13 , BIBREF14 , BIBREF15 , which is an important task for Chinese NLP BIBREF7 . These methods are mainly focus on two categories: supervised learning and deep learning BIBREF2 .",
+      "Supervised Learning Methods. Initially, supervised learning methods were widely-used in CWS. Xue BIBREF13 employed a maximum entropy tagger to automatically assign Chinese characters. Zhao et al. BIBREF16 used a conditional random field for tag decoding and considered both feature template selection and tag set selection. However, these methods greatly rely on manual feature engineering BIBREF17 , while handcrafted features are difficult to design, and the size of these features is usually very large BIBREF6 .",
+      "Deep Learning Methods. Recently, neural networks have been applied in CWS tasks. To name a few, Zheng et al. BIBREF14 used deep layers of neural networks to learn feature representations of characters. Chen et al. BIBREF6 adopted LSTM to capture the previous important information. Chen et al. BIBREF18 proposed a gated recursive neural network (GRNN), which contains reset and update gates to incorporate the complicated combinations of characters. Jiang and Tang BIBREF19 proposed a sequence-to-sequence transformer model to avoid overfitting and capture character information at the distant site of a sentence. Yang et al. BIBREF20 investigated subword information for CWS and integrated subword embeddings into a Lattice LSTM (LaLSTM) network. However, general word segmentation models do not work well in specific field due to lack of annotated training data.",
+      "Currently, a handful of domain-specific CWS approaches have been studied, but they focused on decentralized domains. In the metallurgical field, Shao et al. BIBREF15 proposed a domain-specific CWS method based on Bi-LSTM model. In the medical field, Xing et al. BIBREF8 proposed an adaptive multi-task transfer learning framework to fully leverage domain-invariant knowledge from high resource domain to medical domain. Meanwhile, transfer learning still greatly focuses on the corpus in general domain. When it comes to the specific domain, large amounts of manually-annotated data is necessary. Active learning can solve this problem to a certain extent. However, due to the challenges faced by performing active learning on CWS, only a few studies have been conducted. On judgements, Yan et al. BIBREF21 adopted the local annotation strategy, which selects substrings around the informative characters in active learning. However, their method still stays at the statistical level. Unlike the above method, we propose an active learning approach for CWS in medical text, which combines information entropy with neural network to effectively reduce annotation cost."
+    ],
+    [
+      "Active learning BIBREF22 mainly aims to ease the data collection process by automatically deciding which instances should be labeled by annotators to train a model as quickly and effectively as possible BIBREF23 . The sampling strategy plays a key role in active learning. In the past decade, the rapid development of active learning has resulted in various sampling strategies, such as uncertainty sampling BIBREF24 , query-by-committee BIBREF25 and information gain BIBREF26 . Currently, the most mainstream sampling strategy is uncertainty sampling. It focuses its selection on samples closest to the decision boundary of the classifier and then chooses these samples for annotators to relabel BIBREF27 .",
+      "The formal definition of uncertainty sampling is to select a sample INLINEFORM0 that maximizes the entropy INLINEFORM1 over the probability of predicted classes: DISPLAYFORM0 ",
+      "where INLINEFORM0 is a multi-dimensional feature vector, INLINEFORM1 is its binary label, and INLINEFORM2 is the predicted probability, through which a classifier trained on training sets can map features to labels. However, in some complicated tasks, such as CWS and NER, only considering the uncertainty of classifier is obviously not enough."
+    ],
+    [
+      "Active learning methods can generally be described into two parts: a learning engine and a selection engine BIBREF28 . The learning engine is essentially a classifier, which is mainly used for training of classification problems. The selection engine is based on the sampling strategy, which chooses samples that need to be relabeled by annotators from unlabeled data. Then, relabeled samples are added to training set for classifier to re-train, thus continuously improving the accuracy of the classifier. In this paper, a CRF-based segmenter and a scoring model are employed as learning engine and selection engine, respectively.",
+      "Fig. FIGREF7 and Algorithm SECREF3 demonstrate the procedure of CWS based on active learning. First, we train a CRF-based segmenter by train set. Then, the segmenter is employed to annotate the unlabeled set roughly. Subsequently, information entropy based scoring model picks INLINEFORM0 -lowest ranking samples for annotators to relabel. Meanwhile, the train sets and unlabeled sets are updated. Finally, we re-train the segmenter. The above steps iterate until the desired accuracy is achieved or the number of iterations has reached a predefined threshold. [!ht] Active Learning for Chinese Word Segmentation labeled data INLINEFORM1 , unlabeled data INLINEFORM2 , the number of iterations INLINEFORM3 , the number of samples selected per iteration INLINEFORM4 , partitioning function INLINEFORM5 , size INLINEFORM6 a word segmentation model INLINEFORM7 with the smallest test set loss INLINEFORM8 Initialize: INLINEFORM9 ",
+      " train a word segmenter INLINEFORM0 ",
+      " estimate the test set loss INLINEFORM0 ",
+      " label INLINEFORM0 by INLINEFORM1 ",
+      " INLINEFORM0 to INLINEFORM1 INLINEFORM2 compute INLINEFORM3 by branch information entropy based scoring model",
+      " select INLINEFORM0 -lowest ranking samples INLINEFORM1 ",
+      "relabel INLINEFORM0 by annotators",
+      "form a new labeled dataset INLINEFORM0 ",
+      "form a new unlabeled dataset INLINEFORM0 ",
+      "train a word segmenter INLINEFORM0 ",
+      "estimate the new test loss INLINEFORM0 ",
+      "compute the loss reduction INLINEFORM0 ",
+      " INLINEFORM0 INLINEFORM1 ",
+      " INLINEFORM0 ",
+      " INLINEFORM0 INLINEFORM1 with the smallest test set loss INLINEFORM2 INLINEFORM3 "
+    ],
+    [
+      "CWS can be formalized as a sequence labeling problem with character position tags, which are (`B', `M', `E', `S'). So, we convert the labeled data into the `BMES' format, in which each character in the sequence is assigned into a label as follows one by one: B=beginning of a word, M=middle of a word, E=end of a word and S=single word.",
+      "In this paper, we use CRF as a training model for CWS task. Given the observed sequence, CRF has a single exponential model for the joint probability of the entire sequence of labels, while maximum entropy markov model (MEMM) BIBREF29 uses per-state exponential models for the conditional probabilities of next states BIBREF4 . Therefore, it can solve the label bias problem effectively. Compared with neural networks, it has less dependency on the corpus size.",
+      "First, we pre-process EHRs at the character-level, separating each character of raw EHRs. For instance, given a sentence INLINEFORM0 , where INLINEFORM1 represents the INLINEFORM2 -th character, the separated form is INLINEFORM3 . Then, we employ Word2Vec BIBREF30 to train pre-processed EHRs to get character embeddings. To capture interactions between adjacent characters, K-means clustering algorithm BIBREF31 is utilized to feature the coherence over characters. In general, K-means divides INLINEFORM4 EHR characters into INLINEFORM5 groups of clusters and the similarity of EHR characters in the same cluster is higher. With each iteration, K-means can classify EHR characters into the nearest cluster based on distance to the mean vector. Then, recalculating and adjusting the mean vectors of these clusters until the mean vector converges. K-means features explicitly show the difference between two adjacent characters and even multiple characters. Finally, we additionally add K-means clustering features to the input of CRF-based segmenter. The segmenter makes positional tagging decisions over individual characters. For example, a Chinese segmented sentence UTF8gkai\u201c\u75c5\u4eba/\u957f\u671f/\u4e8e/\u6211\u9662/\u80be\u75c5\u79d1/\u4f4f\u9662/\u6cbb\u7597/\u3002/\" (The patient was hospitalized for a long time in the nephrology department of our hospital.) is labeled as `BEBESBEBMEBEBES'."
+    ],
+    [
+      "To select the most appropriate sentences in a large number of unlabeled corpora, we propose a scoring model based on information entropy and neural network as the sampling strategy of active learning, which is inspired by Cai and Zhao BIBREF32 . The score of a segmented sentence is computed as follows. First, mapping the segmented sentence to a sequence of candidate word embeddings. Then, the scoring model takes the word embedding sequence as input, scoring over each individual candidate word from two perspectives: (1) the possibility that the candidate word itself can be regarded as a legal word; (2) the rationality of the link that the candidate word directly follows previous segmentation history. Fig. FIGREF10 illustrates the entire scoring model. A gated neural network is employed over character embeddings to generate distributed representations of candidate words, which are sent to a LSTM model.",
+      "We use gated neural network and information entropy to capture the likelihood of the segment being a legal word. The architecture of word score model is depicted in Fig. FIGREF12 .",
+      "Gated Combination Neural Network (GCNN)",
+      "To effectively learn word representations through character embeddings, we use GCNN BIBREF32 . The architecture of GCNN is demonstrated in Fig. FIGREF13 , which includes update gate and reset gate. The gated mechanism not only captures the characteristics of the characters themselves, but also utilizes the interaction between the characters. There are two types of gates in this network structure: reset gates and update gates. These two gated vectors determine the final output of the gated recurrent neural network, where the update gate helps the model determine what to be passed, and the reset gate primarily helps the model decide what to be cleared. In particular, the word embedding of a word with INLINEFORM0 characters can be computed as: DISPLAYFORM0 ",
+      "where INLINEFORM0 and INLINEFORM1 are update gates for new combination vector INLINEFORM2 and the i-th character INLINEFORM3 respectively, the combination vector INLINEFORM4 is formalized as: DISPLAYFORM0 ",
+      "where INLINEFORM0 and INLINEFORM1 are reset gates for characters.",
+      "Left and Right Branch Information Entropy In general, each string in a sentence may be a word. However, compared with a string which is not a word, the string of a word is significantly more independent. The branch information entropy is usually used to judge whether each character in a string is tightly linked through the statistical characteristics of the string, which reflects the likelihood of a string being a word. The left and right branch information entropy can be formalized as follows: DISPLAYFORM0 DISPLAYFORM1 ",
+      "where INLINEFORM0 denotes the INLINEFORM1 -th candidate word, INLINEFORM2 denotes the character set, INLINEFORM3 denotes the probability that character INLINEFORM4 is on the left of word INLINEFORM5 and INLINEFORM6 denotes the probability that character INLINEFORM7 is on the right of word INLINEFORM8 . INLINEFORM9 and INLINEFORM10 respectively represent the left and right branch information entropy of the candidate word INLINEFORM11 . If the left and right branch information entropy of a candidate word is relatively high, the probability that the candidate word can be combined with the surrounded characters to form a word is low, thus the candidate word is likely to be a legal word.",
+      "To judge whether the candidate words in a segmented sentence are legal words, we compute the left and right entropy of each candidate word, then take average as the measurement standard: DISPLAYFORM0 ",
+      "We represent a segmented sentence with INLINEFORM0 candidate words as [ INLINEFORM1 , INLINEFORM2 ,..., INLINEFORM3 ], so the INLINEFORM4 ( INLINEFORM5 ) of the INLINEFORM6 -th candidate word is computed by its average entropy: DISPLAYFORM0 ",
+      "In this paper, we use LSTM to capture the coherence between words in a segmented sentence. This neural network is mainly an optimization for traditional RNN. RNN is widely used to deal with time-series prediction problems. The result of its current hidden layer is determined by the input of the current layer and the output of the previous hidden layer BIBREF33 . Therefore, RNN can remember historical results. However, traditional RNN has problems of vanishing gradient and exploding gradient when training long sequences BIBREF34 . By adding a gated mechanism to RNN, LSTM effectively solves these problems, which motivates us to get the link score with LSTM. Formally, the LSTM unit performs the following operations at time step INLINEFORM0 : DISPLAYFORM0 DISPLAYFORM1 ",
+      "where INLINEFORM0 , INLINEFORM1 , INLINEFORM2 are the inputs of LSTM, all INLINEFORM3 and INLINEFORM4 are a set of parameter matrices to be trained, and INLINEFORM5 is a set of bias parameter matrices to be trained. INLINEFORM6 and INLINEFORM7 operation respectively represent matrix element-wise multiplication and sigmoid function. In the LSTM unit, there are two hidden layers ( INLINEFORM8 , INLINEFORM9 ), where INLINEFORM10 is the internal memory cell for dealing with vanishing gradient, while INLINEFORM11 is the main output of the LSTM unit for complex operations in subsequent layers.",
+      "We denotes INLINEFORM0 as the word embedding of time step INLINEFORM1 , a prediction INLINEFORM2 of next word embedding INLINEFORM3 can be computed by hidden layer INLINEFORM4 : DISPLAYFORM0 ",
+      "Therefore, link score of next word embedding INLINEFORM0 can be computed as: DISPLAYFORM0 ",
+      "Due to the structure of LSTM, vector INLINEFORM0 contains important information of entire segmentation decisions. In this way, the link score gets the result of the sequence-level word segmentation, not just word-level.",
+      "Intuitively, we can compute the score of a segmented sequence by summing up word scores and link scores. However, we find that a sequence with more candidate words tends to have higher sequence scores. Therefore, to alleviate the impact of the number of candidate words on sequence scores, we calculate final scores as follows: DISPLAYFORM0 ",
+      "where INLINEFORM0 denotes the INLINEFORM1 -th segmented sequence with INLINEFORM2 candidate words, and INLINEFORM3 represents the INLINEFORM4 -th candidate words in the segmented sequence.",
+      "When training the model, we seek to minimize the sequence score of the corrected segmented sentence and the predicted segmented sentence. DISPLAYFORM0 ",
+      "where INLINEFORM0 is the loss function."
+    ],
+    [
+      "We collect 204 EHRs with cardiovascular diseases from the Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine and each contains 27 types of records. We choose 4 different types with a total of 3868 records from them, which are first course reports, medical records, chief ward round records and discharge records. The detailed information of EHRs are listed in Table TABREF32 .",
+      "We split our datasets as follows. First, we randomly select 3200 records from 3868 records as unlabeled set. Then, we manually annotate remaining 668 records as labeled set, which contains 1170 sentences. Finally, we divide labeled set into train set and test set with the ratio of 7:3 randomly. Statistics of datasets are listed in Table TABREF33 ."
+    ],
+    [
+      "To determine suitable parameters, we divide training set into two sets, the first 80% sentences as training set and the rest 20% sentences as validation set.",
+      "Character embedding dimensions and K-means clusters are two main parameters in the CRF-based word segmenter.",
+      "In this paper, we choose character-based CRF without any features as baseline. First, we use Word2Vec to train character embeddings with dimensions of [`50', `100', `150', `200', `300', `400'] respectively, thus we obtain 6 different dimensional character embeddings. Second, these six types of character embeddings are used as the input to K-means algorithm with the number of clusters [`50', `100', `200', `300', `400', `500', `600'] respectively to capture the corresponding features of character embeddings. Then, we add K-means clustering features to baseline for training. As can be seen from Fig. FIGREF36 , when the character embedding dimension INLINEFORM0 = 150 and the number of clusters INLINEFORM1 = 400, CRF-based word segmenter performs best, so these two parameters are used in subsequent experiments.",
+      "Hyper-parameters of neural network have a great impact on the performance. The hyper-parameters we choose are listed in Table TABREF38 .",
+      "The dimension of character embeddings is set as same as the parameter used in CRF-based word segmenter and the number of hidden units is also set to be the same as it. Maximum word length is ralated to the number of parameters in GCNN unit. Since there are many long medical terminologies in EHRs, we set the maximum word length as 6. In addition, dropout is an effective way to prevent neural networks from overfitting BIBREF35 . To avoid overfitting, we drop the input layer of the scoring model with the rate of 20%."
+    ],
+    [
+      "Our work experimentally compares two mainstream CWS tools (LTP and Jieba) on training and testing sets. These two tools are widely used and recognized due to their high INLINEFORM0 -score of word segmentation in general fields. However, in specific fields, there are many terminologies and uncommon words, which lead to the unsatisfactory performance of segmentation results. To solve the problem of word segmentation in specific fields, these two tools provide a custom dictionary for users. In the experiments, we also conduct a comparative experiment on whether external domain dictionary has an effect on the experimental results. We manually construct the dictionary when labeling EHRs.",
+      "From the results in Table TABREF41 , we find that Jieba benefits a lot from the external dictionary. However, the Recall of LTP decreases when joining the domain dictionary. Generally speaking, since these two tools are trained by general domain corpus, the results are not ideal enough to cater to the needs of subsequent NLP of EHRs when applied to specific fields.",
+      "To investigate the effectiveness of K-means features in CRF-based segmenter, we also compare K-means with 3 different clustering features, including MeanShift BIBREF36 , SpectralClustering BIBREF37 and DBSCAN BIBREF38 on training and testing sets. From the results in Table TABREF43 , by adding additional clustering features in CRF-based segmenter, there is a significant improvement of INLINEFORM0 -score, which indicates that clustering features can effectively capture the semantic coherence between characters. Among these clustering features, K-means performs best, so we utlize K-means results as additional features for CRF-based segmenter.",
+      "In this experiment, since uncertainty sampling is the most popular strategy in real applications for its simpleness and effectiveness BIBREF27 , we compare our proposed strategy with uncertainty sampling in active learning. We conduct our experiments as follows. First, we employ CRF-based segmenter to annotate the unlabeled set. Then, sampling strategy in active learning selects a part of samples for annotators to relabel. Finally, the relabeled samples are added to train set for segmenter to re-train. Our proposed scoring strategy selects samples according to the sequence scores of the segmented sentences, while uncertainty sampling suggests relabeling samples that are closest to the segmenter\u2019s decision boundary.",
+      "Generally, two main parameters in active learning are the numbers of iterations and samples selected per iteration. To fairly investigate the influence of two parameters, we compare our proposed strategy with uncertainty sampling on the same parameter. We find that though the number of iterations is large enough, it has a limited impact on the performance of segmenter. Therefore, we choose 30 as the number of iterations, which is a good trade-off between speed and performance. As for the number of samples selected per iteration, there are 6078 sentences in unlabeled set, considering the high cost of relabeling, we set four sizes of samples selected per iteration, which are 2%, 5%, 8% and 11%.",
+      "The experimental results of two sampling strategies with 30 iterations on four different proportions of relabeled data are shown in Fig. FIGREF45 , where x-axis represents the number of iterations and y-axis denotes the INLINEFORM0 -score of the segmenter. Scoring strategy shows consistent improvements over uncertainty sampling in the early iterations, indicating that scoring strategy is more capable of selecting representative samples.",
+      "Furthermore, we also investigate the relations between the best INLINEFORM0 -score and corresponding number of iteration on two sampling strategies, which is depicted in Fig. FIGREF46 .",
+      "It is observed that in our proposed scoring model, with the proportion of relabeled data increasing, the iteration number of reaching the optimal word segmentation result is decreasing, but the INLINEFORM0 -score of CRF-based word segmenter is also gradually decreasing. When the proportion is 2%, the segmenter reaches the highest INLINEFORM1 -score: 90.62%. Obviously, our proposed strategy outperforms uncertainty sampling by a large margin. Our proposed method needs only 2% relabeled samples to obtain INLINEFORM2 -score of 90.62%, while uncertainty sampling requires 8% samples to reach its best INLINEFORM3 -score of 88.98%, which indicates that with our proposed method, we only need to manually relabel a small number of samples to achieve a desired segmentation result."
+    ],
+    [
+      "To relieve the efforts of EHRs annotation, we propose an effective word segmentation method based on active learning, in which the sampling strategy is a scoring model combining information entropy with neural network. Compared with the mainstream uncertainty sampling, our strategy selects samples from statistical perspective and deep learning level. In addition, to capture coherence between characters, we add K-means clustering features to CRF-based word segmenter. Based on EHRs collected from the Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, we evaluate our method on CWS task. Compared with uncertainty sampling, our method requires 6% less relabeled samples to achieve better performance, which proves that our method can save the cost of manual annotation to a certain extent.",
+      "In future, we plan to employ other widely-used deep neural networks, such as convolutional neural network and attention mechanism, in the research of EHRs segmentation. Then, we believe that our method can be applied to other tasks as well, so we will fully investigate the application of our method in other tasks, such as NER and relation extraction."
+    ],
+    [
+      "The authors would like to appreciate any suggestions or comments from the anonymous reviewers. This work was supported by the National Natural Science Foundation of China (No. 61772201) and the National Key R&D Program of China for \u201cPrecision medical research\" (No. 2018YFC0910550)."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0146/instruction.md b/qasper-0146/instruction.md
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+Name of Paper: InScript: Narrative texts annotated with script information
+
+Question: How many subjects have been used to create the annotations?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Motivation",
+    "Collection via Amazon M-Turk",
+    "Data Statistics",
+    "Annotation",
+    "Annotation Schema",
+    "Development of the Schema",
+    "First Annotation Phase",
+    "Modification of the Schema",
+    "Special Cases",
+    "Inter-Annotator Agreement",
+    "Annotated Corpus Statistics",
+    "Comparison to the DeScript Corpus",
+    "Conclusion",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "A script is \u201ca standardized sequence of events that describes some stereotypical human activity such as going to a restaurant or visiting a doctor\u201d BIBREF0 . Script events describe an action/activity along with the involved participants. For example, in the script describing a visit to a restaurant, typical events are entering the restaurant, ordering food or eating. Participants in this scenario can include animate objects like the waiter and the customer, as well as inanimate objects such as cutlery or food.",
+      "Script knowledge has been shown to play an important role in text understanding (cullingford1978script, miikkulainen1995script, mueller2004understanding, Chambers2008, Chambers2009, modi2014inducing, rudinger2015learning). It guides the expectation of the reader, supports coreference resolution as well as common-sense knowledge inference and enables the appropriate embedding of the current sentence into the larger context. Figure 1 shows the first few sentences of a story describing the scenario taking a bath. Once the taking a bath scenario is evoked by the noun phrase (NP) \u201ca bath\u201d, the reader can effortlessly interpret the definite NP \u201cthe faucet\u201d as an implicitly present standard participant of the taking a bath script. Although in this story, \u201centering the bath room\u201d, \u201cturning on the water\u201d and \u201cfilling the tub\u201d are explicitly mentioned, a reader could nevertheless have inferred the \u201cturning on the water\u201d event, even if it was not explicitly mentioned in the text. Table 1 gives an example of typical events and participants for the script describing the scenario taking a bath.",
+      "A systematic study of the influence of script knowledge in texts is far from trivial. Typically, text documents (e.g. narrative texts) describing various scenarios evoke many different scripts, making it difficult to study the effect of a single script. Efforts have been made to collect scenario-specific script knowledge via crowdsourcing, for example the OMICS and SMILE corpora (singh2002open, Regneri:2010, Regneri2013), but these corpora describe script events in a pointwise telegram style rather than in full texts.",
+      "This paper presents the InScript corpus (Narrative Texts Instantiating Script structure). It is a corpus of simple narrative texts in the form of stories, wherein each story is centered around a specific scenario. The stories have been collected via Amazon Mechanical Turk (M-Turk). In this experiment, turkers were asked to write down a concrete experience about a bus ride, a grocery shopping event etc. We concentrated on 10 scenarios and collected 100 stories per scenario, giving a total of 1,000 stories with about 200,000 words. Relevant verbs and noun phrases in all stories are annotated with event types and participant types respectively. Additionally, the texts have been annotated with coreference information in order to facilitate the study of the interdependence between script structure and coreference.",
+      "The InScript corpus is a unique resource that provides a basis for studying various aspects of the role of script knowledge in language processing by humans. The acquisition of this corpus is part of a larger research effort that aims at using script knowledge to model the surprisal and information density in written text. Besides InScript, this project also released a corpus of generic descriptions of script activities called DeScript (for Describing Script Structure, Wanzare2016). DeScript contains a range of short and textually simple phrases that describe script events in the style of OMICS or SMILE (singh2002open, Regneri:2010). These generic telegram-style descriptions are called Event Descriptions (EDs); a sequence of such descriptions that cover a complete script is called an Event Sequence Description (ESD). Figure 2 shows an excerpt of a script in the baking a cake scenario. The figure shows event descriptions for 3 different events in the DeScript corpus (left) and fragments of a story in the InScript corpus (right) that instantiate the same event type."
+    ],
+    [
+      "We selected 10 scenarios from different available scenario lists (e.g. Regneri:2010 , VanDerMeer2009, and the OMICS corpus BIBREF1 ), including scripts of different complexity (Taking a bath vs. Flying in an airplane) and specificity (Riding a public bus vs. Repairing a flat bicycle tire). For the full scenario list see Table 2 .",
+      "Texts were collected via the Amazon Mechanical Turk platform, which provides an opportunity to present an online task to humans (a.k.a. turkers). In order to gauge the effect of different M-Turk instructions on our task, we first conducted pilot experiments with different variants of instructions explaining the task. We finalized the instructions for the full data collection, asking the turkers to describe a scenario in form of a story as if explaining it to a child and to use a minimum of 150 words. The selected instruction variant resulted in comparably simple and explicit scenario-related stories. In the future we plan to collect more complex stories using different instructions. In total 190 turkers participated. All turkers were living in the USA and native speakers of English. We paid USD $0.50 per story to each turker. On average, the turkers took 9.37 minutes per story with a maximum duration of 17.38 minutes."
+    ],
+    [
+      "Statistics for the corpus are given in Table 2 . On average, each story has a length of 12 sentences and 217 words with 98 word types on average. Stories are coherent and concentrate mainly on the corresponding scenario. Neglecting auxiliaries, modals and copulas, on average each story has 32 verbs, out of which 58% denote events related to the respective scenario. As can be seen in Table 2 , there is some variation in stories across scenarios: The flying in an airplane scenario, for example, is most complex in terms of the number of sentences, tokens and word types that are used. This is probably due to the inherent complexity of the scenario: Taking a flight, for example, is more complicated and takes more steps than taking a bath. The average count of sentences, tokens and types is also very high for the baking a cake scenario. Stories from the scenario often resemble cake recipes, which usually contain very detailed steps, so people tend to give more detailed descriptions in the stories.",
+      "For both flying in an airplane and baking a cake, the standard deviation is higher in comparison to other scenarios. This indicates that different turkers described the scenario with a varying degree of detail and can also be seen as an indicator for the complexity of both scenarios. In general, different people tend to describe situations subjectively, with a varying degree of detail. In contrast, texts from the taking a bath and planting a tree scenarios contain a relatively smaller number of sentences and fewer word types and tokens. Both planting a tree and taking a bath are simpler activities, which results in generally less complex texts.",
+      "The average pairwise word type overlap can be seen as a measure of lexical variety among stories: If it is high, the stories resemble each other more. We can see that stories in the flying in an airplane and baking a cake scenarios have the highest values here, indicating that most turkers used a similar vocabulary in their stories.",
+      "In general, the response quality was good. We had to discard 9% of the stories as these lacked the quality we were expecting. In total, we selected 910 stories for annotation."
+    ],
+    [
+      "This section deals with the annotation of the data. We first describe the final annotation schema. Then, we describe the iterative process of corpus annotation and the refinement of the schema. This refinement was necessary due to the complexity of the annotation."
+    ],
+    [
+      "For each of the scenarios, we designed a specific annotation template. A script template consists of scenario-specific event and participant labels. An example of a template is shown in Table 1 . All NP heads in the corpus were annotated with a participant label; all verbs were annotated with an event label. For both participants and events, we also offered the label unclear if the annotator could not assign another label. We additionally annotated coreference chains between NPs. Thus, the process resulted in three layers of annotation: event types, participant types and coreference annotation. These are described in detail below.",
+      "As a first layer, we annotated event types. There are two kinds of event type labels, scenario-specific event type labels and general labels. The general labels are used across every scenario and mark general features, for example whether an event belongs to the scenario at all. For the scenario-specific labels, we designed an unique template for every scenario, with a list of script-relevant event types that were used as labels. Such labels include for example ScrEv_close_drain in taking a bath as in Example UID10 (see Figure 1 for a complete list for the taking a bath scenario)",
+      "I start by closing $_{\\textsc {\\scriptsize ScrEv\\_close\\_drain}}$ the drain at the bottom of the tub.",
+      "The general labels that were used in addition to the script-specific labels in every scenario are listed below:",
+      "ScrEv_other. An event that belongs to the scenario, but its event type occurs too infrequently (for details, see below, Section \"Modification of the Schema\" ). We used the label \u201cother\" because event classification would become too finegrained otherwise.",
+      "Example: After I am dried I put my new clothes on and clean up $_{\\textsc {\\scriptsize ScrEv\\_other}}$ the bathroom.",
+      "RelNScrEv. Related non-script event. An event that can plausibly happen during the execution of the script and is related to it, but that is not part of the script.",
+      "Example: After finding on what I wanted to wear, I went into the bathroom and shut $_{\\textsc {\\scriptsize RelNScrEv}}$ the door.",
+      "UnrelEv. An event that is unrelated to the script.",
+      "Example: I sank into the bubbles and took $_{\\textsc {\\scriptsize UnrelEv}}$ a deep breath.",
+      "Additionally, the annotators were asked to annotate verbs and phrases that evoke the script without explicitly referring to a script event with the label Evoking, as shown in Example UID10 . Today I took a bath $_{\\textsc {\\scriptsize Evoking}}$ in my new apartment.",
+      "As in the case of the event type labels, there are two kinds of participant labels: general labels and scenario-specific labels. The latter are part of the scenario-specific templates, e.g. ScrPart_drain in the taking a bath scenario, as can be seen in Example UID15 .",
+      "I start by closing the drain $_{\\textsc {\\scriptsize ScrPart\\_drain}}$ at the bottom of the tub.",
+      "The general labels that are used across all scenarios mark noun phrases with scenario-independent features. There are the following general labels:",
+      "ScrPart_other. A participant that belongs to the scenario, but its participant type occurs only infrequently.",
+      "Example: I find my bath mat $_{\\textsc {\\scriptsize ScrPart\\_other}}$ and lay it on the floor to keep the floor dry.",
+      "NPart. Non-participant. A referential NP that does not belong to the scenario.",
+      "Example: I washed myself carefully because I did not want to spill water onto the floor $_{\\textsc {\\scriptsize NPart}}$ .labeled",
+      "SuppVComp. A support verb complement. For further discussion of this label, see Section \"Special Cases\" ",
+      "Example: I sank into the bubbles and took a deep breath $_{\\textsc {\\scriptsize SuppVComp}}$ .",
+      "Head_of_Partitive. The head of a partitive or a partitive-like construction. For a further discussion of this label cf. Section \"Special Cases\" ",
+      "Example: I grabbed a bar $_{\\textsc {\\scriptsize Head\\_of\\_Partitive}}$ of soap and lathered my body.",
+      "No_label. A non-referential noun phrase that cannot be labeled with another label. Example: I sat for a moment $_{\\textsc {\\scriptsize No\\_label}}$ , relaxing, allowing the warm water to sooth my skin.",
+      "All NPs labeled with one of the labels SuppVComp, Head_of_Partitive or No_label are considered to be non-referential. No_label is used mainly in four cases in our data: non-referential time expressions (in a while, a million times better), idioms (no matter what), the non-referential \u201cit\u201d (it felt amazing, it is better) and other abstracta (a lot better, a little bit).",
+      "In the first annotation phase, annotators were asked to mark verbs and noun phrases that have an event or participant type, that is not listed in the template, as MissScrEv/ MissScrPart (missing script event or participant, resp.). These annotations were used as a basis for extending the templates (see Section \"Modification of the Schema\" ) and replaced later by newly introduced labels or ScrEv_other and ScrPart_other respectively.",
+      "All noun phrases were annotated with coreference information indicating which entities denote the same discourse referent. The annotation was done by linking heads of NPs (see Example UID21 , where the links are indicated by coindexing). As a rule, we assume that each element of a coreference chain is marked with the same participant type label.",
+      "I $ _{\\textsc {\\scriptsize Coref1}}$ washed my $ _{\\textsc {\\scriptsize Coref1}}$ entire body $ _{\\textsc {\\scriptsize Coref2}}$ , starting with my $ _{\\textsc {\\scriptsize Coref1}}$ face $ _{\\textsc {\\scriptsize Coref3}} $ and ending with the toes $ _{\\textsc {\\scriptsize Coref4}} $ . I $ _{\\textsc {\\scriptsize Coref1}}$ always wash my $ _{\\textsc {\\scriptsize Coref1}}$ toes $_{\\textsc {\\scriptsize Coref4}}$ very thoroughly ...",
+      "The assignment of an entity to a referent is not always trivial, as is shown in Example UID21 . There are some cases in which two discourse referents are grouped in a plural NP. In the example, those things refers to the group made up of shampoo, soap and sponge. In this case, we asked annotators to introduce a new coreference label, the name of which indicates which referents are grouped together (Coref_group_washing_tools). All NPs are then connected to the group phrase, resulting in an additional coreference chain.",
+      "I $ _{\\textsc {\\scriptsize Coref1}}$ made sure that I $ _{\\textsc {\\scriptsize Coref1}}$ have my $ _{\\textsc {\\scriptsize Coref1}}$ shampoo $ _{\\textsc {\\scriptsize Coref2 + Coref\\_group\\_washing\\_tools}}$ , soap $_{\\textsc {\\scriptsize Coref3 + Coref\\_group\\_washing\\_tools}}$ and sponge $ _{\\textsc {\\scriptsize Coref4 + Coref\\_group\\_washing\\_tools}}$ ready to get in. Once I $ _{\\textsc {\\scriptsize Coref1}}$ have those things $ _{\\textsc {\\scriptsize Coref\\_group\\_washing\\_tools}}$ I $ _{\\textsc {\\scriptsize Coref1}}$ sink into the bath. ... I $ _{\\textsc {\\scriptsize Coref1}}$ applied some soap $ _{\\textsc {\\scriptsize Coref1}}$0 on my $ _{\\textsc {\\scriptsize Coref1}}$1 body and used the sponge $ _{\\textsc {\\scriptsize Coref1}}$2 to scrub a bit. ... I $ _{\\textsc {\\scriptsize Coref1}}$3 rinsed the shampoo $ _{\\textsc {\\scriptsize Coref1}}$4 . Example UID21 thus contains the following coreference chains: Coref1: I $ _{\\textsc {\\scriptsize Coref1}}$5 I $ _{\\textsc {\\scriptsize Coref1}}$6 my $ _{\\textsc {\\scriptsize Coref1}}$7 I $ _{\\textsc {\\scriptsize Coref1}}$8 I $ _{\\textsc {\\scriptsize Coref1}}$9 I $ _{\\textsc {\\scriptsize Coref1}}$0 my $ _{\\textsc {\\scriptsize Coref1}}$1 I",
+      "Coref2: shampoo $\\rightarrow $ shampoo",
+      "Coref3: soap $\\rightarrow $ soap",
+      "Coref4: sponge $\\rightarrow $ sponge",
+      "Coref_group_washing_ tools: shampoo $\\rightarrow $ soap $\\rightarrow $ sponge $\\rightarrow $ things"
+    ],
+    [
+      "The templates were carefully designed in an iterated process. For each scenario, one of the authors of this paper provided a preliminary version of the template based on the inspection of some of the stories. For a subset of the scenarios, preliminary templates developed at our department for a psycholinguistic experiment on script knowledge were used as a starting point. Subsequently, the authors manually annotated 5 randomly selected texts for each of the scenarios based on the preliminary template. Necessary extensions and changes in the templates were discussed and agreed upon. Most of the cases of disagreement were related to the granularity of the event and participant types. We agreed on the script-specific functional equivalence as a guiding principle. For example, reading a book, listening to music and having a conversation are subsumed under the same event label in the flight scenario, because they have the common function of in-flight entertainment in the scenario. In contrast, we assumed different labels for the cake tin and other utensils (bowls etc.), since they have different functions in the baking a cake scenario and accordingly occur with different script events.",
+      "Note that scripts and templates as such are not meant to describe an activity as exhaustively as possible and to mention all steps that are logically necessary. Instead, scripts describe cognitively prominent events in an activity. An example can be found in the flight scenario. While more than a third of the turkers mentioned the event of fastening the seat belts in the plane (buckle_seat_belt), no person wrote about undoing their seat belts again, although in reality both events appear equally often. Consequently, we added an event type label for buckling up, but no label for undoing the seat belts."
+    ],
+    [
+      "We used the WebAnno annotation tool BIBREF2 for our project. The stories from each scenario were distributed among four different annotators. In a calibration phase, annotators were presented with some sample texts for test annotations; the results were discussed with the authors. Throughout the whole annotation phase, annotators could discuss any emerging issues with the authors. All annotations were done by undergraduate students of computational linguistics. The annotation was rather time-consuming due to the complexity of the task, and thus we decided for single annotation mode. To assess annotation quality, a small sample of texts was annotated by all four annotators and their inter-annotator agreement was measured (see Section \"Inter-Annotator Agreement\" ). It was found to be sufficiently high.",
+      "Annotation of the corpus together with some pre- and post-processing of the data required about 500 hours of work. All stories were annotated with event and participant types (a total of 12,188 and 43,946 instances, respectively). On average there were 7 coreference chains per story with an average length of 6 tokens."
+    ],
+    [
+      "After the first annotation round, we extended and changed the templates based on the results. As mentioned before, we used MissScrEv and MissScrPart labels to mark verbs and noun phrases instantiating events and participants for which no appropriate labels were available in the templates. Based on the instances with these labels (a total of 941 and 1717 instances, respectively), we extended the guidelines to cover the sufficiently frequent cases. In order to include new labels for event and participant types, we tried to estimate the number of instances that would fall under a certain label. We added new labels according to the following conditions:",
+      "For the participant annotations, we added new labels for types that we expected to appear at least 10 times in total in at least 5 different stories (i.e. in approximately 5% of the stories).",
+      "For the event annotations, we chose those new labels for event types that would appear in at least 5 different stories.",
+      "In order to avoid too fine a granularity of the templates, all other instances of MissScrEv and MissScrPart were re-labeled with ScrEv_other and ScrPart_other. We also relabeled participants and events from the first annotation phase with ScrEv_other and ScrPart_other, if they did not meet the frequency requirements. The event label air_bathroom (the event of letting fresh air into the room after the bath), for example, was only used once in the stories, so we relabeled that instance to ScrEv_other.",
+      "Additionally, we looked at the DeScript corpus BIBREF3 , which contains manually clustered event paraphrase sets for the 10 scenarios that are also covered by InScript (see Section \"Comparison to the DeScript Corpus\" ). Every such set contains event descriptions that describe a certain event type. We extended our templates with additional labels for these events, if they were not yet part of the template."
+    ],
+    [
+      "Noun-noun compounds were annotated twice with the same label (whole span plus the head noun), as indicated by Example UID31 . This redundant double annotation is motivated by potential processing requirements.",
+      "I get my (wash (cloth $ _{\\textsc {\\scriptsize ScrPart\\_washing\\_tools}} ))$ , $_{\\textsc {\\scriptsize ScrPart\\_washing\\_tools}} $ and put it under the water.",
+      "A special treatment was given to support verb constructions such as take time, get home or take a seat in Example UID32 . The semantics of the verb itself is highly underspecified in such constructions; the event type is largely dependent on the object NP. As shown in Example UID32 , we annotate the head verb with the event type described by the whole construction and label its object with SuppVComp (support verb complement), indicating that it does not have a proper reference.",
+      "I step into the tub and take $ _{\\textsc {\\scriptsize ScrEv\\_sink\\_water}} $ a seat $ _{\\textsc {\\scriptsize SuppVComp}} $ .",
+      "We used the Head_of_Partitive label for the heads in partitive constructions, assuming that the only referential part of the construction is the complement. This is not completely correct, since different partitive heads vary in their degree of concreteness (cf. Examples UID33 and UID33 ), but we did not see a way to make the distinction sufficiently transparent to the annotators. Our seats were at the back $ _{\\textsc {\\scriptsize Head\\_of\\_Partitive}} $ of the train $ _{\\textsc {\\scriptsize ScrPart\\_train}} $ . In the library you can always find a couple $ _{\\textsc {\\scriptsize Head\\_of\\_Partitive}} $ of interesting books $ _{\\textsc {\\scriptsize ScrPart\\_book}} $ .",
+      "Group denoting NPs sometimes refer to groups whose members are instances of different participant types. In Example UID34 , the first-person plural pronoun refers to the group consisting of the passenger (I) and a non-participant (my friend). To avoid a proliferation of event type labels, we labeled these cases with Unclear.",
+      "I $ _{\\textsc {\\scriptsize {ScrPart\\_passenger}}}$ wanted to visit my $_{\\textsc {\\scriptsize {ScrPart\\_passenger}}}$ friend $ _{\\textsc {\\scriptsize {NPart}}}$ in New York. ... We $_{\\textsc {\\scriptsize Unclear}}$ met at the train station.",
+      "We made an exception for the Getting a Haircut scenario, where the mixed participant group consisting of the hairdresser and the customer occurs very often, as in Example UID34 . Here, we introduced the additional ad-hoc participant label Scr_Part_hairdresser_customer.",
+      "While Susan $_{\\textsc {\\scriptsize {ScrPart\\_hairdresser}}}$ is cutting my $_{\\textsc {\\scriptsize {ScrPart\\_customer}}}$ hair we $_{\\textsc {\\scriptsize Scr\\_Part\\_hairdresser\\_customer}}$ usually talk a bit."
+    ],
+    [
+      "In order to calculate inter-annotator agreement, a total of 30 stories from 6 scenarios were randomly chosen for parallel annotation by all 4 annotators after the first annotation phase. We checked the agreement on these data using Fleiss' Kappa BIBREF4 . The results are shown in Figure 4 and indicate moderate to substantial agreement BIBREF5 . Interestingly, if we calculated the Kappa only on the subset of cases that were annotated with script-specific event and participant labels by all annotators, results were better than those of the evaluation on all labeled instances (including also unrelated and related non-script events). This indicates one of the challenges of the annotation task: In many cases it is difficult to decide whether a particular event should be considered a central script event, or an event loosely related or unrelated to the script.",
+      "For coreference chain annotation, we calculated the percentage of pairs which were annotated by at least 3 annotators (qualified majority vote) compared to the set of those pairs annotated by at least one person (see Figure 4 ). We take the result of 90.5% between annotators to be a good agreement."
+    ],
+    [
+      "Figure 5 gives an overview of the number of event and participant types provided in the templates. Taking a flight and getting a haircut stand out with a large number of both event and participant types, which is due to the inherent complexity of the scenarios. In contrast, planting a tree and going on a train contain the fewest labels. There are 19 event and participant types on average.",
+      "Figure 6 presents overview statistics about the usage of event labels, participant labels and coreference chain annotations. As can be seen, there are usually many more mentions of participants than events. For coreference chains, there are some chains that are really long (which also results in a large scenario-wise standard deviation). Usually, these chains describe the protagonist.",
+      "We also found again that the flying in an airplane scenario stands out in terms of participant mentions, event mentions and average number of coreference chains.",
+      "Figure 7 shows for every participant label in the baking a cake scenario the number of stories which they occurred in. This indicates how relevant a participant is for the script. As can be seen, a small number of participants are highly prominent: cook, ingredients and cake are mentioned in every story. The fact that the protagonist appears most often consistently holds for all other scenarios, where the acting person appears in every story, and is mentioned most frequently.",
+      "Figure 8 shows the distribution of participant/event type labels over all appearances over all scenarios on average. The groups stand for the most frequently appearing label, the top 2 to 5 labels in terms of frequency and the top 6 to 10. ScrEv_other and ScrPart_other are shown separately. As can be seen, the most frequently used participant label (the protagonist) makes up about 40% of overall participant instances. The four labels that follow the protagonist in terms of frequency together appear in 37% of the cases. More than 2 out of 3 participants in total belong to one of only 5 labels.",
+      "In contrast, the distribution for events is more balanced. 14% of all event instances have the most prominent event type. ScrEv_other and ScrPart_other both appear as labels in at most 5% of all event and participant instantiations: The specific event and participant type labels in our templates cover by far most of the instances.",
+      "In Figure 9 , we grouped participants similarly into the first, the top 2-5 and top 6-10 most frequently appearing participant types. The figure shows for each of these groups the average frequency per story, and in the rightmost column the overall average. The results correspond to the findings from the last paragraph."
+    ],
+    [
+      "As mentioned previously, the InScript corpus is part of a larger research project, in which also a corpus of a different kind, the DeScript corpus, was created. DeScript covers 40 scenarios, and also contains the 10 scenarios from InScript. This corpus contains texts that describe scripts on an abstract and generic level, while InScript contains instantiations of scripts in narrative texts. Script events in DeScript are described in a very simple, telegram-style language (see Figure 2 ). Since one of the long-term goals of the project is to align the InScript texts with the script structure given from DeScript, it is interesting to compare both resources.",
+      "The InScript corpus exhibits much more lexical variation than DeScript. Many approaches use the type-token ratio to measure this variance. However, this measure is known to be sensitive to text length (see e.g. Tweedie1998), which would result in very small values for InScript and relatively large ones for DeScript, given the large average difference of text lengths between the corpora. Instead, we decided to use the Measure of Textual Lexical Diversity (MTLD) (McCarthy2010, McCarthy2005), which is familiar in corpus linguistics. This metric measures the average number of tokens in a text that are needed to retain a type-token ratio above a certain threshold. If the MTLD for a text is high, many tokens are needed to lower the type-token ratio under the threshold, so the text is lexically diverse. In contrast, a low MTLD indicates that only a few words are needed to make the type-token ratio drop, so the lexical diversity is smaller. We use the threshold of 0.71, which is proposed by the authors as a well-proven value.",
+      "Figure 10 compares the lexical diversity of both resources. As can be seen, the InScript corpus with its narrative texts is generally much more diverse than the DeScript corpus with its short event descriptions, across all scenarios. For both resources, the flying in an airplane scenario is most diverse (as was also indicated above by the mean word type overlap). However, the difference in the variation of lexical variance of scenarios is larger for DeScript than for InScript. Thus, the properties of a scenario apparently influence the lexical variance of the event descriptions more than the variance of the narrative texts. We used entropy BIBREF6 over lemmas to measure the variance of lexical realizations for events. We excluded events for which there were less than 10 occurrences in DeScript or InScript. Since there is only an event annotation for 50 ESDs per scenario in DeScript, we randomly sampled 50 texts from InScript for computing the entropy to make the numbers more comparable.",
+      "Figure 11 shows as an example the entropy values for the event types in the going on a train scenario. As can be seen in the graph, the entropy for InScript is in general higher than for DeScript. In the stories, a wider variety of verbs is used to describe events. There are also large differences between events: While wait has a really low entropy, spend_time_train has an extremely high entropy value. This event type covers many different activities such as reading, sleeping etc."
+    ],
+    [
+      "In this paper we described the InScript corpus of 1,000 narrative texts annotated with script structure and coreference information. We described the annotation process, various difficulties encountered during annotation and different remedies that were taken to overcome these. One of the future research goals of our project is also concerned with finding automatic methods for text-to-script mapping, i.e. for the alignment of text segments with script states. We consider InScript and DeScript together as a resource for studying this alignment. The corpus shows rich lexical variation and will serve as a unique resource for the study of the role of script knowledge in natural language processing."
+    ],
+    [
+      "This research was funded by the German Research Foundation (DFG) as part of SFB 1102 'Information Density and Linguistic Encoding'."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: Investigating Robustness and Interpretability of Link Prediction via Adversarial Modifications
+
+Question: What datasets are used to evaluate this approach?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Background and Notation",
+    "Completion Robustness and Interpretability via Adversarial Graph Edits ()",
+    "Removing a fact ()",
+    "Adding a new fact ()",
+    "Challenges",
+    "Efficiently Identifying the Modification",
+    "First-order Approximation of Influence",
+    "Continuous Optimization for Search",
+    "Experiments",
+    "Influence Function vs ",
+    "Robustness of Link Prediction Models",
+    "Interpretability of Models",
+    "Finding Errors in Knowledge Graphs",
+    "Related Work",
+    "Conclusions",
+    "Acknowledgements",
+    "Appendix",
+    "Modifications of the Form \u2329s,r ' ,o ' \u232a\\langle s, r^{\\prime }, o^{\\prime } \\rangle ",
+    "Modifications of the Form \u2329s,r ' ,o\u232a\\langle s, r^{\\prime }, o \\rangle ",
+    "First-order Approximation of the Change For TransE",
+    "Sample Adversarial Attacks"
+  ],
+  "paragraphs": [
+    [
+      "Knowledge graphs (KG) play a critical role in many real-world applications such as search, structured data management, recommendations, and question answering. Since KGs often suffer from incompleteness and noise in their facts (links), a number of recent techniques have proposed models that embed each entity and relation into a vector space, and use these embeddings to predict facts. These dense representation models for link prediction include tensor factorization BIBREF0 , BIBREF1 , BIBREF2 , algebraic operations BIBREF3 , BIBREF4 , BIBREF5 , multiple embeddings BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , and complex neural models BIBREF10 , BIBREF11 . However, there are only a few studies BIBREF12 , BIBREF13 that investigate the quality of the different KG models. There is a need to go beyond just the accuracy on link prediction, and instead focus on whether these representations are robust and stable, and what facts they make use of for their predictions. In this paper, our goal is to design approaches that minimally change the graph structure such that the prediction of a target fact changes the most after the embeddings are relearned, which we collectively call Completion Robustness and Interpretability via Adversarial Graph Edits (). First, we consider perturbations that red!50!blackremove a neighboring link for the target fact, thus identifying the most influential related fact, providing an explanation for the model's prediction. As an example, consider the excerpt from a KG in Figure 1 with two observed facts, and a target predicted fact that Princes Henriette is the parent of Violante Bavaria. Our proposed graph perturbation, shown in Figure 1 , identifies the existing fact that Ferdinal Maria is the father of Violante Bavaria as the one when removed and model retrained, will change the prediction of Princes Henriette's child. We also study attacks that green!50!blackadd a new, fake fact into the KG to evaluate the robustness and sensitivity of link prediction models to small additions to the graph. An example attack for the original graph in Figure 1 , is depicted in Figure 1 . Such perturbations to the the training data are from a family of adversarial modifications that have been applied to other machine learning tasks, known as poisoning BIBREF14 , BIBREF15 , BIBREF16 , BIBREF17 .",
+      "Since the setting is quite different from traditional adversarial attacks, search for link prediction adversaries brings up unique challenges. To find these minimal changes for a target link, we need to identify the fact that, when added into or removed from the graph, will have the biggest impact on the predicted score of the target fact. Unfortunately, computing this change in the score is expensive since it involves retraining the model to recompute the embeddings. We propose an efficient estimate of this score change by approximating the change in the embeddings using Taylor expansion. The other challenge in identifying adversarial modifications for link prediction, especially when considering addition of fake facts, is the combinatorial search space over possible facts, which is intractable to enumerate. We introduce an inverter of the original embedding model, to decode the embeddings to their corresponding graph components, making the search of facts tractable by performing efficient gradient-based continuous optimization. We evaluate our proposed methods through following experiments. First, on relatively small KGs, we show that our approximations are accurate compared to the true change in the score. Second, we show that our additive attacks can effectively reduce the performance of state of the art models BIBREF2 , BIBREF10 up to $27.3\\%$ and $50.7\\%$ in Hits@1 for two large KGs: WN18 and YAGO3-10. We also explore the utility of adversarial modifications in explaining the model predictions by presenting rule-like descriptions of the most influential neighbors. Finally, we use adversaries to detect errors in the KG, obtaining up to $55\\%$ accuracy in detecting errors."
+    ],
+    [
+      "In this section, we briefly introduce some notations, and existing relational embedding approaches that model knowledge graph completion using dense vectors. In KGs, facts are represented using triples of subject, relation, and object, $\\langle s, r, o\\rangle $ , where $s,o\\in \\xi $ , the set of entities, and $r\\in $ , the set of relations. To model the KG, a scoring function $\\psi :\\xi \\times \\times \\xi \\rightarrow $ is learned to evaluate whether any given fact is true. In this work, we focus on multiplicative models of link prediction, specifically DistMult BIBREF2 because of its simplicity and popularity, and ConvE BIBREF10 because of its high accuracy. We can represent the scoring function of such methods as $\\psi (s,r,o) = , ) \\cdot $ , where $,,\\in ^d$ are embeddings of the subject, relation, and object respectively. In DistMult, $, ) = \\odot $ , where $\\odot $ is element-wise multiplication operator. Similarly, in ConvE, $, )$ is computed by a convolution on the concatenation of $$ and $s,o\\in \\xi $0 .",
+      "We use the same setup as BIBREF10 for training, i.e., incorporate binary cross-entropy loss over the triple scores. In particular, for subject-relation pairs $(s,r)$ in the training data $G$ , we use binary $y^{s,r}_o$ to represent negative and positive facts. Using the model's probability of truth as $\\sigma (\\psi (s,r,o))$ for $\\langle s,r,o\\rangle $ , the loss is defined as: (G) = (s,r)o ys,ro(((s,r,o)))",
+      "+ (1-ys,ro)(1 - ((s,r,o))). Gradient descent is used to learn the embeddings $,,$ , and the parameters of $, if any.\n$ "
+    ],
+    [
+      "For adversarial modifications on KGs, we first define the space of possible modifications. For a target triple $\\langle s, r, o\\rangle $ , we constrain the possible triples that we can remove (or inject) to be in the form of $\\langle s^{\\prime }, r^{\\prime }, o\\rangle $ i.e $s^{\\prime }$ and $r^{\\prime }$ may be different from the target, but the object is not. We analyze other forms of modifications such as $\\langle s, r^{\\prime }, o^{\\prime }\\rangle $ and $\\langle s, r^{\\prime }, o\\rangle $ in appendices \"Modifications of the Form \u2329s,r ' ,o ' \u232a\\langle s, r^{\\prime }, o^{\\prime } \\rangle \" and \"Modifications of the Form \u2329s,r ' ,o\u232a\\langle s, r^{\\prime }, o \\rangle \" , and leave empirical evaluation of these modifications for future work."
+    ],
+    [
+      "For explaining a target prediction, we are interested in identifying the observed fact that has the most influence (according to the model) on the prediction. We define influence of an observed fact on the prediction as the change in the prediction score if the observed fact was not present when the embeddings were learned. Previous work have used this concept of influence similarly for several different tasks BIBREF19 , BIBREF20 . Formally, for the target triple ${s,r,o}$ and observed graph $G$ , we want to identify a neighboring triple ${s^{\\prime },r^{\\prime },o}\\in G$ such that the score $\\psi (s,r,o)$ when trained on $G$ and the score $\\overline{\\psi }(s,r,o)$ when trained on $G-\\lbrace {s^{\\prime },r^{\\prime },o}\\rbrace $ are maximally different, i.e. *argmax(s', r')Nei(o) (s',r')(s,r,o) where $\\Delta _{(s^{\\prime },r^{\\prime })}(s,r,o)=\\psi (s, r, o)-\\overline{\\psi }(s,r,o)$ , and $\\text{Nei}(o)=\\lbrace (s^{\\prime },r^{\\prime })|\\langle s^{\\prime },r^{\\prime },o \\rangle \\in G \\rbrace $ ."
+    ],
+    [
+      "We are also interested in investigating the robustness of models, i.e., how sensitive are the predictions to small additions to the knowledge graph. Specifically, for a target prediction ${s,r,o}$ , we are interested in identifying a single fake fact ${s^{\\prime },r^{\\prime },o}$ that, when added to the knowledge graph $G$ , changes the prediction score $\\psi (s,r,o)$ the most. Using $\\overline{\\psi }(s,r,o)$ as the score after training on $G\\cup \\lbrace {s^{\\prime },r^{\\prime },o}\\rbrace $ , we define the adversary as: *argmax(s', r') (s',r')(s,r,o) where $\\Delta _{(s^{\\prime },r^{\\prime })}(s,r,o)=\\psi (s, r, o)-\\overline{\\psi }(s,r,o)$ . The search here is over any possible $s^{\\prime }\\in \\xi $ , which is often in the millions for most real-world KGs, and $r^{\\prime }\\in $ . We also identify adversaries that increase the prediction score for specific false triple, i.e., for a target fake fact ${s,r,o}$ , the adversary is ${s^{\\prime },r^{\\prime },o}$0 , where ${s^{\\prime },r^{\\prime },o}$1 is defined as before."
+    ],
+    [
+      "There are a number of crucial challenges when conducting such adversarial attack on KGs. First, evaluating the effect of changing the KG on the score of the target fact ( $\\overline{\\psi }(s,r,o)$ ) is expensive since we need to update the embeddings by retraining the model on the new graph; a very time-consuming process that is at least linear in the size of $G$ . Second, since there are many candidate facts that can be added to the knowledge graph, identifying the most promising adversary through search-based methods is also expensive. Specifically, the search size for unobserved facts is $|\\xi | \\times ||$ , which, for example in YAGO3-10 KG, can be as many as $4.5 M$ possible facts for a single target prediction."
+    ],
+    [
+      "In this section, we propose algorithms to address mentioned challenges by (1) approximating the effect of changing the graph on a target prediction, and (2) using continuous optimization for the discrete search over potential modifications."
+    ],
+    [
+      "We first study the addition of a fact to the graph, and then extend it to cover removal as well. To capture the effect of an adversarial modification on the score of a target triple, we need to study the effect of the change on the vector representations of the target triple. We use $$ , $$ , and $$ to denote the embeddings of $s,r,o$ at the solution of $\\operatornamewithlimits{argmin} (G)$ , and when considering the adversarial triple $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $ , we use $$ , $$ , and $$ for the new embeddings of $s,r,o$ , respectively. Thus $$0 is a solution to $$1 , which can also be written as $$2 . Similarly, $$3 s', r', o $$4 $$5 $$6 $$7 o $$8 $$9 $$0 $$1 $$2 $$3 O(n3) $$4 $$5 $$6 (s,r,o)-(s, r, o) $$7 - $$8 s, r = ,) $$9 - $s,r,o$0 (G)= (G)+(s', r', o ) $s,r,o$1 $s,r,o$2 s', r' = ',') $s,r,o$3 = ((s',r',o)) $s,r,o$4 eo (G)=0 $s,r,o$5 eo (G) $s,r,o$6 Ho $s,r,o$7 dd $s,r,o$8 o $s,r,o$9 $\\operatornamewithlimits{argmin} (G)$0 - $\\operatornamewithlimits{argmin} (G)$1 -= $\\operatornamewithlimits{argmin} (G)$2 Ho $\\operatornamewithlimits{argmin} (G)$3 Ho + (1-) s',r's',r' $\\operatornamewithlimits{argmin} (G)$4 Ho $\\operatornamewithlimits{argmin} (G)$5 dd $\\operatornamewithlimits{argmin} (G)$6 d $\\operatornamewithlimits{argmin} (G)$7 s,r,s',r'd $\\operatornamewithlimits{argmin} (G)$8 s, r, o $\\operatornamewithlimits{argmin} (G)$9 s', r', o $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $0 $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $1 $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $2 "
+    ],
+    [
+      "Using the approximations provided in the previous section, Eq. () and (), we can use brute force enumeration to find the adversary $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $ . This approach is feasible when removing an observed triple since the search space of such modifications is usually small; it is the number of observed facts that share the object with the target. On the other hand, finding the most influential unobserved fact to add requires search over a much larger space of all possible unobserved facts (that share the object). Instead, we identify the most influential unobserved fact $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $ by using a gradient-based algorithm on vector $_{s^{\\prime },r^{\\prime }}$ in the embedding space (reminder, $_{s^{\\prime },r^{\\prime }}=^{\\prime },^{\\prime })$ ), solving the following continuous optimization problem in $^d$ : *argmaxs', r' (s',r')(s,r,o). After identifying the optimal $_{s^{\\prime }, r^{\\prime }}$ , we still need to generate the pair $(s^{\\prime },r^{\\prime })$ . We design a network, shown in Figure 2 , that maps the vector $_{s^{\\prime },r^{\\prime }}$ to the entity-relation space, i.e., translating it into $(s^{\\prime },r^{\\prime })$ . In particular, we train an auto-encoder where the encoder is fixed to receive the $s$ and $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $0 as one-hot inputs, and calculates $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $1 in the same way as the DistMult and ConvE encoders respectively (using trained embeddings). The decoder is trained to take $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $2 as input and produce $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $3 and $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $4 , essentially inverting $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $5 s, r $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $6 s $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $7 r $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $8 s, r $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $9 We evaluate the performance of our inverter networks (one for each model/dataset) on correctly recovering the pairs of subject and relation from the test set of our benchmarks, given the $_{s^{\\prime },r^{\\prime }}$0 . The accuracy of recovered pairs (and of each argument) is given in Table 1 . As shown, our networks achieve a very high accuracy, demonstrating their ability to invert vectors $_{s^{\\prime },r^{\\prime }}$1 to $_{s^{\\prime },r^{\\prime }}$2 pairs."
+    ],
+    [
+      "We evaluate by ( \"Influence Function vs \" ) comparing estimate with the actual effect of the attacks, ( \"Robustness of Link Prediction Models\" ) studying the effect of adversarial attacks on evaluation metrics, ( \"Interpretability of Models\" ) exploring its application to the interpretability of KG representations, and ( \"Finding Errors in Knowledge Graphs\" ) detecting incorrect triples."
+    ],
+    [
+      "To evaluate the quality of our approximations and compare with influence function (IF), we conduct leave one out experiments. In this setup, we take all the neighbors of a random target triple as candidate modifications, remove them one at a time, retrain the model each time, and compute the exact change in the score of the target triple. We can use the magnitude of this change in score to rank the candidate triples, and compare this exact ranking with ranking as predicted by: , influence function with and without Hessian matrix, and the original model score (with the intuition that facts that the model is most confident of will have the largest impact when removed). Similarly, we evaluate by considering 200 random triples that share the object entity with the target sample as candidates, and rank them as above. The average results of Spearman's $\\rho $ and Kendall's $\\tau $ rank correlation coefficients over 10 random target samples is provided in Table 3 . performs comparably to the influence function, confirming that our approximation is accurate. Influence function is slightly more accurate because they use the complete Hessian matrix over all the parameters, while we only approximate the change by calculating the Hessian over $$ . The effect of this difference on scalability is dramatic, constraining IF to very small graphs and small embedding dimensionality ( $d\\le 10$ ) before we run out of memory. In Figure 3 , we show the time to compute a single adversary by IF compared to , as we steadily grow the number of entities (randomly chosen subgraphs), averaged over 10 random triples. As it shows, is mostly unaffected by the number of entities while IF increases quadratically. Considering that real-world KGs have tens of thousands of times more entities, making IF unfeasible for them."
+    ],
+    [
+      "Now we evaluate the effectiveness of to successfully attack link prediction by adding false facts. The goal here is to identify the attacks for triples in the test data, and measuring their effect on MRR and Hits@ metrics (ranking evaluations) after conducting the attack and retraining the model.",
+      "Since this is the first work on adversarial attacks for link prediction, we introduce several baselines to compare against our method. For finding the adversarial fact to add for the target triple $\\langle s, r, o \\rangle $ , we consider two baselines: 1) choosing a random fake fact $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $ (Random Attack); 2) finding $(s^{\\prime }, r^{\\prime })$ by first calculating $, )$ and then feeding $-, )$ to the decoder of the inverter function (Opposite Attack). In addition to , we introduce two other alternatives of our method: (1) , that uses to increase the score of fake fact over a test triple, i.e., we find the fake fact the model ranks second after the test triple, and identify the adversary for them, and (2) that selects between and attacks based on which has a higher estimated change in score.",
+      "All-Test The result of the attack on all test facts as targets is provided in the Table 4 . outperforms the baselines, demonstrating its ability to effectively attack the KG representations. It seems DistMult is more robust against random attacks, while ConvE is more robust against designed attacks. is more effective than since changing the score of a fake fact is easier than of actual facts; there is no existing evidence to support fake facts. We also see that YAGO3-10 models are more robust than those for WN18. Looking at sample attacks (provided in Appendix \"Sample Adversarial Attacks\" ), mostly tries to change the type of the target object by associating it with a subject and a relation for a different entity type.",
+      "Uncertain-Test To better understand the effect of attacks, we consider a subset of test triples that 1) the model predicts correctly, 2) difference between their scores and the negative sample with the highest score is minimum. This \u201cUncertain-Test\u201d subset contains 100 triples from each of the original test sets, and we provide results of attacks on this data in Table 4 . The attacks are much more effective in this scenario, causing a considerable drop in the metrics. Further, in addition to significantly outperforming other baselines, they indicate that ConvE's confidence is much more robust.",
+      "Relation Breakdown We perform additional analysis on the YAGO3-10 dataset to gain a deeper understanding of the performance of our model. As shown in Figure 4 , both DistMult and ConvE provide a more robust representation for isAffiliatedTo and isConnectedTo relations, demonstrating the confidence of models in identifying them. Moreover, the affects DistMult more in playsFor and isMarriedTo relations while affecting ConvE more in isConnectedTo relations.",
+      "Examples Sample adversarial attacks are provided in Table 5 . attacks mostly try to change the type of the target triple's object by associating it with a subject and a relation that require a different entity types."
+    ],
+    [
+      "To be able to understand and interpret why a link is predicted using the opaque, dense embeddings, we need to find out which part of the graph was most influential on the prediction. To provide such explanations for each predictions, we identify the most influential fact using . Instead of focusing on individual predictions, we aggregate the explanations over the whole dataset for each relation using a simple rule extraction technique: we find simple patterns on subgraphs that surround the target triple and the removed fact from , and appear more than $90\\%$ of the time. We only focus on extracting length-2 horn rules, i.e., $R_1(a,c)\\wedge R_2(c,b)\\Rightarrow R(a,b)$ , where $R(a,b)$ is the target and $R_2(c,b)$ is the removed fact. Table 6 shows extracted YAGO3-10 rules that are common to both models, and ones that are not. The rules show several interesting inferences, such that hasChild is often inferred via married parents, and isLocatedIn via transitivity. There are several differences in how the models reason as well; DistMult often uses the hasCapital as an intermediate step for isLocatedIn, while ConvE incorrectly uses isNeighbor. We also compare against rules extracted by BIBREF2 for YAGO3-10 that utilizes the structure of DistMult: they require domain knowledge on types and cannot be applied to ConvE. Interestingly, the extracted rules contain all the rules provided by , demonstrating that can be used to accurately interpret models, including ones that are not interpretable, such as ConvE. These are preliminary steps toward interpretability of link prediction models, and we leave more analysis of interpretability to future work."
+    ],
+    [
+      "Here, we demonstrate another potential use of adversarial modifications: finding erroneous triples in the knowledge graph. Intuitively, if there is an error in the graph, the triple is likely to be inconsistent with its neighborhood, and thus the model should put least trust on this triple. In other words, the error triple should have the least influence on the model's prediction of the training data. Formally, to find the incorrect triple $\\langle s^{\\prime }, r^{\\prime }, o\\rangle $ in the neighborhood of the train triple $\\langle s, r, o\\rangle $ , we need to find the triple $\\langle s^{\\prime },r^{\\prime },o\\rangle $ that results in the least change $\\Delta _{(s^{\\prime },r^{\\prime })}(s,r,o)$ when removed from the graph.",
+      "To evaluate this application, we inject random triples into the graph, and measure the ability of to detect the errors using our optimization. We consider two types of incorrect triples: 1) incorrect triples in the form of $\\langle s^{\\prime }, r, o\\rangle $ where $s^{\\prime }$ is chosen randomly from all of the entities, and 2) incorrect triples in the form of $\\langle s^{\\prime }, r^{\\prime }, o\\rangle $ where $s^{\\prime }$ and $r^{\\prime }$ are chosen randomly. We choose 100 random triples from the observed graph, and for each of them, add an incorrect triple (in each of the two scenarios) to its neighborhood. Then, after retraining DistMult on this noisy training data, we identify error triples through a search over the neighbors of the 100 facts. The result of choosing the neighbor with the least influence on the target is provided in the Table 7 . When compared with baselines that randomly choose one of the neighbors, or assume that the fact with the lowest score is incorrect, we see that outperforms both of these with a considerable gap, obtaining an accuracy of $42\\%$ and $55\\%$ in detecting errors."
+    ],
+    [
+      "Learning relational knowledge representations has been a focus of active research in the past few years, but to the best of our knowledge, this is the first work on conducting adversarial modifications on the link prediction task. Knowledge graph embedding There is a rich literature on representing knowledge graphs in vector spaces that differ in their scoring functions BIBREF21 , BIBREF22 , BIBREF23 . Although is primarily applicable to multiplicative scoring functions BIBREF0 , BIBREF1 , BIBREF2 , BIBREF24 , these ideas apply to additive scoring functions BIBREF18 , BIBREF6 , BIBREF7 , BIBREF25 as well, as we show in Appendix \"First-order Approximation of the Change For TransE\" .",
+      "Furthermore, there is a growing body of literature that incorporates an extra types of evidence for more informed embeddings such as numerical values BIBREF26 , images BIBREF27 , text BIBREF28 , BIBREF29 , BIBREF30 , and their combinations BIBREF31 . Using , we can gain a deeper understanding of these methods, especially those that build their embeddings wit hmultiplicative scoring functions.",
+      "Interpretability and Adversarial Modification There has been a significant recent interest in conducting an adversarial attacks on different machine learning models BIBREF16 , BIBREF32 , BIBREF33 , BIBREF34 , BIBREF35 , BIBREF36 to attain the interpretability, and further, evaluate the robustness of those models. BIBREF20 uses influence function to provide an approach to understanding black-box models by studying the changes in the loss occurring as a result of changes in the training data. In addition to incorporating their established method on KGs, we derive a novel approach that differs from their procedure in two ways: (1) instead of changes in the loss, we consider the changes in the scoring function, which is more appropriate for KG representations, and (2) in addition to searching for an attack, we introduce a gradient-based method that is much faster, especially for \u201cadding an attack triple\u201d (the size of search space make the influence function method infeasible). Previous work has also considered adversaries for KGs, but as part of training to improve their representation of the graph BIBREF37 , BIBREF38 . Adversarial Attack on KG Although this is the first work on adversarial attacks for link prediction, there are two approaches BIBREF39 , BIBREF17 that consider the task of adversarial attack on graphs. There are a few fundamental differences from our work: (1) they build their method on top of a path-based representations while we focus on embeddings, (2) they consider node classification as the target of their attacks while we attack link prediction, and (3) they conduct the attack on small graphs due to restricted scalability, while the complexity of our method does not depend on the size of the graph, but only the neighborhood, allowing us to attack real-world graphs."
+    ],
+    [
+      "Motivated by the need to analyze the robustness and interpretability of link prediction models, we present a novel approach for conducting adversarial modifications to knowledge graphs. We introduce , completion robustness and interpretability via adversarial graph edits: identifying the fact to add into or remove from the KG that changes the prediction for a target fact. uses (1) an estimate of the score change for any target triple after adding or removing another fact, and (2) a gradient-based algorithm for identifying the most influential modification. We show that can effectively reduce ranking metrics on link prediction models upon applying the attack triples. Further, we incorporate the to study the interpretability of KG representations by summarizing the most influential facts for each relation. Finally, using , we introduce a novel automated error detection method for knowledge graphs. We have release the open-source implementation of our models at: https://pouyapez.github.io/criage."
+    ],
+    [
+      "We would like to thank Matt Gardner, Marco Tulio Ribeiro, Zhengli Zhao, Robert L. Logan IV, Dheeru Dua and the anonymous reviewers for their detailed feedback and suggestions. This work is supported in part by Allen Institute for Artificial Intelligence (AI2) and in part by NSF awards #IIS-1817183 and #IIS-1756023. The views expressed are those of the authors and do not reflect the official policy or position of the funding agencies."
+    ],
+    [
+      "We approximate the change on the score of the target triple upon applying attacks other than the $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $ ones. Since each relation appears many times in the training triples, we can assume that applying a single attack will not considerably affect the relations embeddings. As a result, we just need to study the attacks in the form of $\\langle s, r^{\\prime }, o \\rangle $ and $\\langle s, r^{\\prime }, o^{\\prime } \\rangle $ . Defining the scoring function as $\\psi (s,r,o) = , ) \\cdot = _{s,r} \\cdot $ , we further assume that $\\psi (s,r,o) =\\cdot (, ) =\\cdot _{r,o}$ ."
+    ],
+    [
+      "Using similar argument as the attacks in the form of $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $ , we can calculate the effect of the attack, $\\overline{\\psi }{(s,r,o)}-\\psi (s, r, o)$ as: (s,r,o)-(s, r, o)=(-) s, r where $_{s, r} = (,)$ .",
+      "We now derive an efficient computation for $(-)$ . First, the derivative of the loss $(\\overline{G})= (G)+(\\langle s, r^{\\prime }, o^{\\prime } \\rangle )$ over $$ is: es (G) = es (G) - (1-) r', o' where $_{r^{\\prime }, o^{\\prime }} = (^{\\prime },^{\\prime })$ , and $\\varphi = \\sigma (\\psi (s,r^{\\prime },o^{\\prime }))$ . At convergence, after retraining, we expect $\\nabla _{e_s} (\\overline{G})=0$ . We perform first order Taylor approximation of $\\nabla _{e_s} (\\overline{G})$ to get: 0 - (1-)r',o'+",
+      "(Hs+(1-)r',o' r',o')(-) where $H_s$ is the $d\\times d$ Hessian matrix for $s$ , i.e. second order derivative of the loss w.r.t. $$ , computed sparsely. Solving for $-$ gives us: -=",
+      "(1-) (Hs + (1-) r',o'r',o')-1 r',o' In practice, $H_s$ is positive definite, making $H_s + \\varphi (1-\\varphi ) _{r^{\\prime },o^{\\prime }}^\\intercal _{r^{\\prime },o^{\\prime }}$ positive definite as well, and invertible. Then, we compute the score change as: (s,r,o)-(s, r, o)= r,o (-) =",
+      " ((1-) (Hs + (1-) r',o'r',o')-1 r',o')r,o."
+    ],
+    [
+      "In this section we approximate the effect of attack in the form of $\\langle s, r^{\\prime }, o \\rangle $ . In contrast to $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $ attacks, for this scenario we need to consider the change in the $$ , upon applying the attack, in approximation of the change in the score as well. Using previous results, we can approximate the $-$ as: -=",
+      "(1-) (Ho + (1-) s,r's,r')-1 s,r' and similarly, we can approximate $-$ as: -=",
+      " (1-) (Hs + (1-) r',or',o)-1 r',o where $H_s$ is the Hessian matrix over $$ . Then using these approximations: s,r(-) =",
+      " s,r ((1-) (Ho + (1-) s,r's,r')-1 s,r') and: (-) r,o=",
+      " ((1-) (Hs + (1-) r',or',o)-1 r',o) r,o and then calculate the change in the score as: (s,r,o)-(s, r, o)=",
+      " s,r.(-) +(-).r,o =",
+      " s,r ((1-) (Ho + (1-) s,r's,r')-1 s,r')+",
+      " ((1-) (Hs + (1-) r',or',o)-1 r',o) r, o"
+    ],
+    [
+      "In here we derive the approximation of the change in the score upon applying an adversarial modification for TransE BIBREF18 . Using similar assumptions and parameters as before, to calculate the effect of the attack, $\\overline{\\psi }{(s,r,o)}$ (where $\\psi {(s,r,o)}=|+-|$ ), we need to compute $$ . To do so, we need to derive an efficient computation for $$ . First, the derivative of the loss $(\\overline{G})= (G)+(\\langle s^{\\prime }, r^{\\prime }, o \\rangle )$ over $$ is: eo (G) = eo (G) + (1-) s', r'-(s',r',o) where $_{s^{\\prime }, r^{\\prime }} = ^{\\prime }+ ^{\\prime }$ , and $\\varphi = \\sigma (\\psi (s^{\\prime },r^{\\prime },o))$ . At convergence, after retraining, we expect $\\nabla _{e_o} (\\overline{G})=0$ . We perform first order Taylor approximation of $\\nabla _{e_o} (\\overline{G})$ to get: 0",
+      " (1-) (s', r'-)(s',r',o)+(Ho - Hs',r',o)(-)",
+      " Hs',r',o = (1-)(s', r'-)(s', r'-)(s',r',o)2+",
+      " 1-(s',r',o)-(1-) (s', r'-)(s', r'-)(s',r',o)3 where $H_o$ is the $d\\times d$ Hessian matrix for $o$ , i.e., second order derivative of the loss w.r.t. $$ , computed sparsely. Solving for $$ gives us: = -(1-) (Ho - Hs',r',o)-1 (s', r'-)(s',r',o)",
+      " + Then, we compute the score change as: (s,r,o)= |+-|",
+      "= |++(1-) (Ho - Hs',r',o)-1",
+      " (s', r'-)(s',r',o) - |",
+      "Calculating this expression is efficient since $H_o$ is a $d\\times d$ matrix."
+    ],
+    [
+      "In this section, we provide the output of the for some target triples. Sample adversarial attacks are provided in Table 5 . As it shows, attacks mostly try to change the type of the target triple's object by associating it with a subject and a relation that require a different entity types."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0148/instruction.md b/qasper-0148/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..a5577f3e8688b6049d7620efbc8122e78e64860b
--- /dev/null
+++ b/qasper-0148/instruction.md
@@ -0,0 +1,131 @@
+Name of Paper: Investigating Robustness and Interpretability of Link Prediction via Adversarial Modifications
+
+Question: How is this approach used to detect incorrect facts?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Background and Notation",
+    "Completion Robustness and Interpretability via Adversarial Graph Edits ()",
+    "Removing a fact ()",
+    "Adding a new fact ()",
+    "Challenges",
+    "Efficiently Identifying the Modification",
+    "First-order Approximation of Influence",
+    "Continuous Optimization for Search",
+    "Experiments",
+    "Influence Function vs ",
+    "Robustness of Link Prediction Models",
+    "Interpretability of Models",
+    "Finding Errors in Knowledge Graphs",
+    "Related Work",
+    "Conclusions",
+    "Acknowledgements",
+    "Appendix",
+    "Modifications of the Form \u2329s,r ' ,o ' \u232a\\langle s, r^{\\prime }, o^{\\prime } \\rangle ",
+    "Modifications of the Form \u2329s,r ' ,o\u232a\\langle s, r^{\\prime }, o \\rangle ",
+    "First-order Approximation of the Change For TransE",
+    "Sample Adversarial Attacks"
+  ],
+  "paragraphs": [
+    [
+      "Knowledge graphs (KG) play a critical role in many real-world applications such as search, structured data management, recommendations, and question answering. Since KGs often suffer from incompleteness and noise in their facts (links), a number of recent techniques have proposed models that embed each entity and relation into a vector space, and use these embeddings to predict facts. These dense representation models for link prediction include tensor factorization BIBREF0 , BIBREF1 , BIBREF2 , algebraic operations BIBREF3 , BIBREF4 , BIBREF5 , multiple embeddings BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , and complex neural models BIBREF10 , BIBREF11 . However, there are only a few studies BIBREF12 , BIBREF13 that investigate the quality of the different KG models. There is a need to go beyond just the accuracy on link prediction, and instead focus on whether these representations are robust and stable, and what facts they make use of for their predictions. In this paper, our goal is to design approaches that minimally change the graph structure such that the prediction of a target fact changes the most after the embeddings are relearned, which we collectively call Completion Robustness and Interpretability via Adversarial Graph Edits (). First, we consider perturbations that red!50!blackremove a neighboring link for the target fact, thus identifying the most influential related fact, providing an explanation for the model's prediction. As an example, consider the excerpt from a KG in Figure 1 with two observed facts, and a target predicted fact that Princes Henriette is the parent of Violante Bavaria. Our proposed graph perturbation, shown in Figure 1 , identifies the existing fact that Ferdinal Maria is the father of Violante Bavaria as the one when removed and model retrained, will change the prediction of Princes Henriette's child. We also study attacks that green!50!blackadd a new, fake fact into the KG to evaluate the robustness and sensitivity of link prediction models to small additions to the graph. An example attack for the original graph in Figure 1 , is depicted in Figure 1 . Such perturbations to the the training data are from a family of adversarial modifications that have been applied to other machine learning tasks, known as poisoning BIBREF14 , BIBREF15 , BIBREF16 , BIBREF17 .",
+      "Since the setting is quite different from traditional adversarial attacks, search for link prediction adversaries brings up unique challenges. To find these minimal changes for a target link, we need to identify the fact that, when added into or removed from the graph, will have the biggest impact on the predicted score of the target fact. Unfortunately, computing this change in the score is expensive since it involves retraining the model to recompute the embeddings. We propose an efficient estimate of this score change by approximating the change in the embeddings using Taylor expansion. The other challenge in identifying adversarial modifications for link prediction, especially when considering addition of fake facts, is the combinatorial search space over possible facts, which is intractable to enumerate. We introduce an inverter of the original embedding model, to decode the embeddings to their corresponding graph components, making the search of facts tractable by performing efficient gradient-based continuous optimization. We evaluate our proposed methods through following experiments. First, on relatively small KGs, we show that our approximations are accurate compared to the true change in the score. Second, we show that our additive attacks can effectively reduce the performance of state of the art models BIBREF2 , BIBREF10 up to $27.3\\%$ and $50.7\\%$ in Hits@1 for two large KGs: WN18 and YAGO3-10. We also explore the utility of adversarial modifications in explaining the model predictions by presenting rule-like descriptions of the most influential neighbors. Finally, we use adversaries to detect errors in the KG, obtaining up to $55\\%$ accuracy in detecting errors."
+    ],
+    [
+      "In this section, we briefly introduce some notations, and existing relational embedding approaches that model knowledge graph completion using dense vectors. In KGs, facts are represented using triples of subject, relation, and object, $\\langle s, r, o\\rangle $ , where $s,o\\in \\xi $ , the set of entities, and $r\\in $ , the set of relations. To model the KG, a scoring function $\\psi :\\xi \\times \\times \\xi \\rightarrow $ is learned to evaluate whether any given fact is true. In this work, we focus on multiplicative models of link prediction, specifically DistMult BIBREF2 because of its simplicity and popularity, and ConvE BIBREF10 because of its high accuracy. We can represent the scoring function of such methods as $\\psi (s,r,o) = , ) \\cdot $ , where $,,\\in ^d$ are embeddings of the subject, relation, and object respectively. In DistMult, $, ) = \\odot $ , where $\\odot $ is element-wise multiplication operator. Similarly, in ConvE, $, )$ is computed by a convolution on the concatenation of $$ and $s,o\\in \\xi $0 .",
+      "We use the same setup as BIBREF10 for training, i.e., incorporate binary cross-entropy loss over the triple scores. In particular, for subject-relation pairs $(s,r)$ in the training data $G$ , we use binary $y^{s,r}_o$ to represent negative and positive facts. Using the model's probability of truth as $\\sigma (\\psi (s,r,o))$ for $\\langle s,r,o\\rangle $ , the loss is defined as: (G) = (s,r)o ys,ro(((s,r,o)))",
+      "+ (1-ys,ro)(1 - ((s,r,o))). Gradient descent is used to learn the embeddings $,,$ , and the parameters of $, if any.\n$ "
+    ],
+    [
+      "For adversarial modifications on KGs, we first define the space of possible modifications. For a target triple $\\langle s, r, o\\rangle $ , we constrain the possible triples that we can remove (or inject) to be in the form of $\\langle s^{\\prime }, r^{\\prime }, o\\rangle $ i.e $s^{\\prime }$ and $r^{\\prime }$ may be different from the target, but the object is not. We analyze other forms of modifications such as $\\langle s, r^{\\prime }, o^{\\prime }\\rangle $ and $\\langle s, r^{\\prime }, o\\rangle $ in appendices \"Modifications of the Form \u2329s,r ' ,o ' \u232a\\langle s, r^{\\prime }, o^{\\prime } \\rangle \" and \"Modifications of the Form \u2329s,r ' ,o\u232a\\langle s, r^{\\prime }, o \\rangle \" , and leave empirical evaluation of these modifications for future work."
+    ],
+    [
+      "For explaining a target prediction, we are interested in identifying the observed fact that has the most influence (according to the model) on the prediction. We define influence of an observed fact on the prediction as the change in the prediction score if the observed fact was not present when the embeddings were learned. Previous work have used this concept of influence similarly for several different tasks BIBREF19 , BIBREF20 . Formally, for the target triple ${s,r,o}$ and observed graph $G$ , we want to identify a neighboring triple ${s^{\\prime },r^{\\prime },o}\\in G$ such that the score $\\psi (s,r,o)$ when trained on $G$ and the score $\\overline{\\psi }(s,r,o)$ when trained on $G-\\lbrace {s^{\\prime },r^{\\prime },o}\\rbrace $ are maximally different, i.e. *argmax(s', r')Nei(o) (s',r')(s,r,o) where $\\Delta _{(s^{\\prime },r^{\\prime })}(s,r,o)=\\psi (s, r, o)-\\overline{\\psi }(s,r,o)$ , and $\\text{Nei}(o)=\\lbrace (s^{\\prime },r^{\\prime })|\\langle s^{\\prime },r^{\\prime },o \\rangle \\in G \\rbrace $ ."
+    ],
+    [
+      "We are also interested in investigating the robustness of models, i.e., how sensitive are the predictions to small additions to the knowledge graph. Specifically, for a target prediction ${s,r,o}$ , we are interested in identifying a single fake fact ${s^{\\prime },r^{\\prime },o}$ that, when added to the knowledge graph $G$ , changes the prediction score $\\psi (s,r,o)$ the most. Using $\\overline{\\psi }(s,r,o)$ as the score after training on $G\\cup \\lbrace {s^{\\prime },r^{\\prime },o}\\rbrace $ , we define the adversary as: *argmax(s', r') (s',r')(s,r,o) where $\\Delta _{(s^{\\prime },r^{\\prime })}(s,r,o)=\\psi (s, r, o)-\\overline{\\psi }(s,r,o)$ . The search here is over any possible $s^{\\prime }\\in \\xi $ , which is often in the millions for most real-world KGs, and $r^{\\prime }\\in $ . We also identify adversaries that increase the prediction score for specific false triple, i.e., for a target fake fact ${s,r,o}$ , the adversary is ${s^{\\prime },r^{\\prime },o}$0 , where ${s^{\\prime },r^{\\prime },o}$1 is defined as before."
+    ],
+    [
+      "There are a number of crucial challenges when conducting such adversarial attack on KGs. First, evaluating the effect of changing the KG on the score of the target fact ( $\\overline{\\psi }(s,r,o)$ ) is expensive since we need to update the embeddings by retraining the model on the new graph; a very time-consuming process that is at least linear in the size of $G$ . Second, since there are many candidate facts that can be added to the knowledge graph, identifying the most promising adversary through search-based methods is also expensive. Specifically, the search size for unobserved facts is $|\\xi | \\times ||$ , which, for example in YAGO3-10 KG, can be as many as $4.5 M$ possible facts for a single target prediction."
+    ],
+    [
+      "In this section, we propose algorithms to address mentioned challenges by (1) approximating the effect of changing the graph on a target prediction, and (2) using continuous optimization for the discrete search over potential modifications."
+    ],
+    [
+      "We first study the addition of a fact to the graph, and then extend it to cover removal as well. To capture the effect of an adversarial modification on the score of a target triple, we need to study the effect of the change on the vector representations of the target triple. We use $$ , $$ , and $$ to denote the embeddings of $s,r,o$ at the solution of $\\operatornamewithlimits{argmin} (G)$ , and when considering the adversarial triple $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $ , we use $$ , $$ , and $$ for the new embeddings of $s,r,o$ , respectively. Thus $$0 is a solution to $$1 , which can also be written as $$2 . Similarly, $$3 s', r', o $$4 $$5 $$6 $$7 o $$8 $$9 $$0 $$1 $$2 $$3 O(n3) $$4 $$5 $$6 (s,r,o)-(s, r, o) $$7 - $$8 s, r = ,) $$9 - $s,r,o$0 (G)= (G)+(s', r', o ) $s,r,o$1 $s,r,o$2 s', r' = ',') $s,r,o$3 = ((s',r',o)) $s,r,o$4 eo (G)=0 $s,r,o$5 eo (G) $s,r,o$6 Ho $s,r,o$7 dd $s,r,o$8 o $s,r,o$9 $\\operatornamewithlimits{argmin} (G)$0 - $\\operatornamewithlimits{argmin} (G)$1 -= $\\operatornamewithlimits{argmin} (G)$2 Ho $\\operatornamewithlimits{argmin} (G)$3 Ho + (1-) s',r's',r' $\\operatornamewithlimits{argmin} (G)$4 Ho $\\operatornamewithlimits{argmin} (G)$5 dd $\\operatornamewithlimits{argmin} (G)$6 d $\\operatornamewithlimits{argmin} (G)$7 s,r,s',r'd $\\operatornamewithlimits{argmin} (G)$8 s, r, o $\\operatornamewithlimits{argmin} (G)$9 s', r', o $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $0 $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $1 $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $2 "
+    ],
+    [
+      "Using the approximations provided in the previous section, Eq. () and (), we can use brute force enumeration to find the adversary $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $ . This approach is feasible when removing an observed triple since the search space of such modifications is usually small; it is the number of observed facts that share the object with the target. On the other hand, finding the most influential unobserved fact to add requires search over a much larger space of all possible unobserved facts (that share the object). Instead, we identify the most influential unobserved fact $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $ by using a gradient-based algorithm on vector $_{s^{\\prime },r^{\\prime }}$ in the embedding space (reminder, $_{s^{\\prime },r^{\\prime }}=^{\\prime },^{\\prime })$ ), solving the following continuous optimization problem in $^d$ : *argmaxs', r' (s',r')(s,r,o). After identifying the optimal $_{s^{\\prime }, r^{\\prime }}$ , we still need to generate the pair $(s^{\\prime },r^{\\prime })$ . We design a network, shown in Figure 2 , that maps the vector $_{s^{\\prime },r^{\\prime }}$ to the entity-relation space, i.e., translating it into $(s^{\\prime },r^{\\prime })$ . In particular, we train an auto-encoder where the encoder is fixed to receive the $s$ and $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $0 as one-hot inputs, and calculates $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $1 in the same way as the DistMult and ConvE encoders respectively (using trained embeddings). The decoder is trained to take $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $2 as input and produce $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $3 and $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $4 , essentially inverting $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $5 s, r $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $6 s $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $7 r $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $8 s, r $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $9 We evaluate the performance of our inverter networks (one for each model/dataset) on correctly recovering the pairs of subject and relation from the test set of our benchmarks, given the $_{s^{\\prime },r^{\\prime }}$0 . The accuracy of recovered pairs (and of each argument) is given in Table 1 . As shown, our networks achieve a very high accuracy, demonstrating their ability to invert vectors $_{s^{\\prime },r^{\\prime }}$1 to $_{s^{\\prime },r^{\\prime }}$2 pairs."
+    ],
+    [
+      "We evaluate by ( \"Influence Function vs \" ) comparing estimate with the actual effect of the attacks, ( \"Robustness of Link Prediction Models\" ) studying the effect of adversarial attacks on evaluation metrics, ( \"Interpretability of Models\" ) exploring its application to the interpretability of KG representations, and ( \"Finding Errors in Knowledge Graphs\" ) detecting incorrect triples."
+    ],
+    [
+      "To evaluate the quality of our approximations and compare with influence function (IF), we conduct leave one out experiments. In this setup, we take all the neighbors of a random target triple as candidate modifications, remove them one at a time, retrain the model each time, and compute the exact change in the score of the target triple. We can use the magnitude of this change in score to rank the candidate triples, and compare this exact ranking with ranking as predicted by: , influence function with and without Hessian matrix, and the original model score (with the intuition that facts that the model is most confident of will have the largest impact when removed). Similarly, we evaluate by considering 200 random triples that share the object entity with the target sample as candidates, and rank them as above. The average results of Spearman's $\\rho $ and Kendall's $\\tau $ rank correlation coefficients over 10 random target samples is provided in Table 3 . performs comparably to the influence function, confirming that our approximation is accurate. Influence function is slightly more accurate because they use the complete Hessian matrix over all the parameters, while we only approximate the change by calculating the Hessian over $$ . The effect of this difference on scalability is dramatic, constraining IF to very small graphs and small embedding dimensionality ( $d\\le 10$ ) before we run out of memory. In Figure 3 , we show the time to compute a single adversary by IF compared to , as we steadily grow the number of entities (randomly chosen subgraphs), averaged over 10 random triples. As it shows, is mostly unaffected by the number of entities while IF increases quadratically. Considering that real-world KGs have tens of thousands of times more entities, making IF unfeasible for them."
+    ],
+    [
+      "Now we evaluate the effectiveness of to successfully attack link prediction by adding false facts. The goal here is to identify the attacks for triples in the test data, and measuring their effect on MRR and Hits@ metrics (ranking evaluations) after conducting the attack and retraining the model.",
+      "Since this is the first work on adversarial attacks for link prediction, we introduce several baselines to compare against our method. For finding the adversarial fact to add for the target triple $\\langle s, r, o \\rangle $ , we consider two baselines: 1) choosing a random fake fact $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $ (Random Attack); 2) finding $(s^{\\prime }, r^{\\prime })$ by first calculating $, )$ and then feeding $-, )$ to the decoder of the inverter function (Opposite Attack). In addition to , we introduce two other alternatives of our method: (1) , that uses to increase the score of fake fact over a test triple, i.e., we find the fake fact the model ranks second after the test triple, and identify the adversary for them, and (2) that selects between and attacks based on which has a higher estimated change in score.",
+      "All-Test The result of the attack on all test facts as targets is provided in the Table 4 . outperforms the baselines, demonstrating its ability to effectively attack the KG representations. It seems DistMult is more robust against random attacks, while ConvE is more robust against designed attacks. is more effective than since changing the score of a fake fact is easier than of actual facts; there is no existing evidence to support fake facts. We also see that YAGO3-10 models are more robust than those for WN18. Looking at sample attacks (provided in Appendix \"Sample Adversarial Attacks\" ), mostly tries to change the type of the target object by associating it with a subject and a relation for a different entity type.",
+      "Uncertain-Test To better understand the effect of attacks, we consider a subset of test triples that 1) the model predicts correctly, 2) difference between their scores and the negative sample with the highest score is minimum. This \u201cUncertain-Test\u201d subset contains 100 triples from each of the original test sets, and we provide results of attacks on this data in Table 4 . The attacks are much more effective in this scenario, causing a considerable drop in the metrics. Further, in addition to significantly outperforming other baselines, they indicate that ConvE's confidence is much more robust.",
+      "Relation Breakdown We perform additional analysis on the YAGO3-10 dataset to gain a deeper understanding of the performance of our model. As shown in Figure 4 , both DistMult and ConvE provide a more robust representation for isAffiliatedTo and isConnectedTo relations, demonstrating the confidence of models in identifying them. Moreover, the affects DistMult more in playsFor and isMarriedTo relations while affecting ConvE more in isConnectedTo relations.",
+      "Examples Sample adversarial attacks are provided in Table 5 . attacks mostly try to change the type of the target triple's object by associating it with a subject and a relation that require a different entity types."
+    ],
+    [
+      "To be able to understand and interpret why a link is predicted using the opaque, dense embeddings, we need to find out which part of the graph was most influential on the prediction. To provide such explanations for each predictions, we identify the most influential fact using . Instead of focusing on individual predictions, we aggregate the explanations over the whole dataset for each relation using a simple rule extraction technique: we find simple patterns on subgraphs that surround the target triple and the removed fact from , and appear more than $90\\%$ of the time. We only focus on extracting length-2 horn rules, i.e., $R_1(a,c)\\wedge R_2(c,b)\\Rightarrow R(a,b)$ , where $R(a,b)$ is the target and $R_2(c,b)$ is the removed fact. Table 6 shows extracted YAGO3-10 rules that are common to both models, and ones that are not. The rules show several interesting inferences, such that hasChild is often inferred via married parents, and isLocatedIn via transitivity. There are several differences in how the models reason as well; DistMult often uses the hasCapital as an intermediate step for isLocatedIn, while ConvE incorrectly uses isNeighbor. We also compare against rules extracted by BIBREF2 for YAGO3-10 that utilizes the structure of DistMult: they require domain knowledge on types and cannot be applied to ConvE. Interestingly, the extracted rules contain all the rules provided by , demonstrating that can be used to accurately interpret models, including ones that are not interpretable, such as ConvE. These are preliminary steps toward interpretability of link prediction models, and we leave more analysis of interpretability to future work."
+    ],
+    [
+      "Here, we demonstrate another potential use of adversarial modifications: finding erroneous triples in the knowledge graph. Intuitively, if there is an error in the graph, the triple is likely to be inconsistent with its neighborhood, and thus the model should put least trust on this triple. In other words, the error triple should have the least influence on the model's prediction of the training data. Formally, to find the incorrect triple $\\langle s^{\\prime }, r^{\\prime }, o\\rangle $ in the neighborhood of the train triple $\\langle s, r, o\\rangle $ , we need to find the triple $\\langle s^{\\prime },r^{\\prime },o\\rangle $ that results in the least change $\\Delta _{(s^{\\prime },r^{\\prime })}(s,r,o)$ when removed from the graph.",
+      "To evaluate this application, we inject random triples into the graph, and measure the ability of to detect the errors using our optimization. We consider two types of incorrect triples: 1) incorrect triples in the form of $\\langle s^{\\prime }, r, o\\rangle $ where $s^{\\prime }$ is chosen randomly from all of the entities, and 2) incorrect triples in the form of $\\langle s^{\\prime }, r^{\\prime }, o\\rangle $ where $s^{\\prime }$ and $r^{\\prime }$ are chosen randomly. We choose 100 random triples from the observed graph, and for each of them, add an incorrect triple (in each of the two scenarios) to its neighborhood. Then, after retraining DistMult on this noisy training data, we identify error triples through a search over the neighbors of the 100 facts. The result of choosing the neighbor with the least influence on the target is provided in the Table 7 . When compared with baselines that randomly choose one of the neighbors, or assume that the fact with the lowest score is incorrect, we see that outperforms both of these with a considerable gap, obtaining an accuracy of $42\\%$ and $55\\%$ in detecting errors."
+    ],
+    [
+      "Learning relational knowledge representations has been a focus of active research in the past few years, but to the best of our knowledge, this is the first work on conducting adversarial modifications on the link prediction task. Knowledge graph embedding There is a rich literature on representing knowledge graphs in vector spaces that differ in their scoring functions BIBREF21 , BIBREF22 , BIBREF23 . Although is primarily applicable to multiplicative scoring functions BIBREF0 , BIBREF1 , BIBREF2 , BIBREF24 , these ideas apply to additive scoring functions BIBREF18 , BIBREF6 , BIBREF7 , BIBREF25 as well, as we show in Appendix \"First-order Approximation of the Change For TransE\" .",
+      "Furthermore, there is a growing body of literature that incorporates an extra types of evidence for more informed embeddings such as numerical values BIBREF26 , images BIBREF27 , text BIBREF28 , BIBREF29 , BIBREF30 , and their combinations BIBREF31 . Using , we can gain a deeper understanding of these methods, especially those that build their embeddings wit hmultiplicative scoring functions.",
+      "Interpretability and Adversarial Modification There has been a significant recent interest in conducting an adversarial attacks on different machine learning models BIBREF16 , BIBREF32 , BIBREF33 , BIBREF34 , BIBREF35 , BIBREF36 to attain the interpretability, and further, evaluate the robustness of those models. BIBREF20 uses influence function to provide an approach to understanding black-box models by studying the changes in the loss occurring as a result of changes in the training data. In addition to incorporating their established method on KGs, we derive a novel approach that differs from their procedure in two ways: (1) instead of changes in the loss, we consider the changes in the scoring function, which is more appropriate for KG representations, and (2) in addition to searching for an attack, we introduce a gradient-based method that is much faster, especially for \u201cadding an attack triple\u201d (the size of search space make the influence function method infeasible). Previous work has also considered adversaries for KGs, but as part of training to improve their representation of the graph BIBREF37 , BIBREF38 . Adversarial Attack on KG Although this is the first work on adversarial attacks for link prediction, there are two approaches BIBREF39 , BIBREF17 that consider the task of adversarial attack on graphs. There are a few fundamental differences from our work: (1) they build their method on top of a path-based representations while we focus on embeddings, (2) they consider node classification as the target of their attacks while we attack link prediction, and (3) they conduct the attack on small graphs due to restricted scalability, while the complexity of our method does not depend on the size of the graph, but only the neighborhood, allowing us to attack real-world graphs."
+    ],
+    [
+      "Motivated by the need to analyze the robustness and interpretability of link prediction models, we present a novel approach for conducting adversarial modifications to knowledge graphs. We introduce , completion robustness and interpretability via adversarial graph edits: identifying the fact to add into or remove from the KG that changes the prediction for a target fact. uses (1) an estimate of the score change for any target triple after adding or removing another fact, and (2) a gradient-based algorithm for identifying the most influential modification. We show that can effectively reduce ranking metrics on link prediction models upon applying the attack triples. Further, we incorporate the to study the interpretability of KG representations by summarizing the most influential facts for each relation. Finally, using , we introduce a novel automated error detection method for knowledge graphs. We have release the open-source implementation of our models at: https://pouyapez.github.io/criage."
+    ],
+    [
+      "We would like to thank Matt Gardner, Marco Tulio Ribeiro, Zhengli Zhao, Robert L. Logan IV, Dheeru Dua and the anonymous reviewers for their detailed feedback and suggestions. This work is supported in part by Allen Institute for Artificial Intelligence (AI2) and in part by NSF awards #IIS-1817183 and #IIS-1756023. The views expressed are those of the authors and do not reflect the official policy or position of the funding agencies."
+    ],
+    [
+      "We approximate the change on the score of the target triple upon applying attacks other than the $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $ ones. Since each relation appears many times in the training triples, we can assume that applying a single attack will not considerably affect the relations embeddings. As a result, we just need to study the attacks in the form of $\\langle s, r^{\\prime }, o \\rangle $ and $\\langle s, r^{\\prime }, o^{\\prime } \\rangle $ . Defining the scoring function as $\\psi (s,r,o) = , ) \\cdot = _{s,r} \\cdot $ , we further assume that $\\psi (s,r,o) =\\cdot (, ) =\\cdot _{r,o}$ ."
+    ],
+    [
+      "Using similar argument as the attacks in the form of $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $ , we can calculate the effect of the attack, $\\overline{\\psi }{(s,r,o)}-\\psi (s, r, o)$ as: (s,r,o)-(s, r, o)=(-) s, r where $_{s, r} = (,)$ .",
+      "We now derive an efficient computation for $(-)$ . First, the derivative of the loss $(\\overline{G})= (G)+(\\langle s, r^{\\prime }, o^{\\prime } \\rangle )$ over $$ is: es (G) = es (G) - (1-) r', o' where $_{r^{\\prime }, o^{\\prime }} = (^{\\prime },^{\\prime })$ , and $\\varphi = \\sigma (\\psi (s,r^{\\prime },o^{\\prime }))$ . At convergence, after retraining, we expect $\\nabla _{e_s} (\\overline{G})=0$ . We perform first order Taylor approximation of $\\nabla _{e_s} (\\overline{G})$ to get: 0 - (1-)r',o'+",
+      "(Hs+(1-)r',o' r',o')(-) where $H_s$ is the $d\\times d$ Hessian matrix for $s$ , i.e. second order derivative of the loss w.r.t. $$ , computed sparsely. Solving for $-$ gives us: -=",
+      "(1-) (Hs + (1-) r',o'r',o')-1 r',o' In practice, $H_s$ is positive definite, making $H_s + \\varphi (1-\\varphi ) _{r^{\\prime },o^{\\prime }}^\\intercal _{r^{\\prime },o^{\\prime }}$ positive definite as well, and invertible. Then, we compute the score change as: (s,r,o)-(s, r, o)= r,o (-) =",
+      " ((1-) (Hs + (1-) r',o'r',o')-1 r',o')r,o."
+    ],
+    [
+      "In this section we approximate the effect of attack in the form of $\\langle s, r^{\\prime }, o \\rangle $ . In contrast to $\\langle s^{\\prime }, r^{\\prime }, o \\rangle $ attacks, for this scenario we need to consider the change in the $$ , upon applying the attack, in approximation of the change in the score as well. Using previous results, we can approximate the $-$ as: -=",
+      "(1-) (Ho + (1-) s,r's,r')-1 s,r' and similarly, we can approximate $-$ as: -=",
+      " (1-) (Hs + (1-) r',or',o)-1 r',o where $H_s$ is the Hessian matrix over $$ . Then using these approximations: s,r(-) =",
+      " s,r ((1-) (Ho + (1-) s,r's,r')-1 s,r') and: (-) r,o=",
+      " ((1-) (Hs + (1-) r',or',o)-1 r',o) r,o and then calculate the change in the score as: (s,r,o)-(s, r, o)=",
+      " s,r.(-) +(-).r,o =",
+      " s,r ((1-) (Ho + (1-) s,r's,r')-1 s,r')+",
+      " ((1-) (Hs + (1-) r',or',o)-1 r',o) r, o"
+    ],
+    [
+      "In here we derive the approximation of the change in the score upon applying an adversarial modification for TransE BIBREF18 . Using similar assumptions and parameters as before, to calculate the effect of the attack, $\\overline{\\psi }{(s,r,o)}$ (where $\\psi {(s,r,o)}=|+-|$ ), we need to compute $$ . To do so, we need to derive an efficient computation for $$ . First, the derivative of the loss $(\\overline{G})= (G)+(\\langle s^{\\prime }, r^{\\prime }, o \\rangle )$ over $$ is: eo (G) = eo (G) + (1-) s', r'-(s',r',o) where $_{s^{\\prime }, r^{\\prime }} = ^{\\prime }+ ^{\\prime }$ , and $\\varphi = \\sigma (\\psi (s^{\\prime },r^{\\prime },o))$ . At convergence, after retraining, we expect $\\nabla _{e_o} (\\overline{G})=0$ . We perform first order Taylor approximation of $\\nabla _{e_o} (\\overline{G})$ to get: 0",
+      " (1-) (s', r'-)(s',r',o)+(Ho - Hs',r',o)(-)",
+      " Hs',r',o = (1-)(s', r'-)(s', r'-)(s',r',o)2+",
+      " 1-(s',r',o)-(1-) (s', r'-)(s', r'-)(s',r',o)3 where $H_o$ is the $d\\times d$ Hessian matrix for $o$ , i.e., second order derivative of the loss w.r.t. $$ , computed sparsely. Solving for $$ gives us: = -(1-) (Ho - Hs',r',o)-1 (s', r'-)(s',r',o)",
+      " + Then, we compute the score change as: (s,r,o)= |+-|",
+      "= |++(1-) (Ho - Hs',r',o)-1",
+      " (s', r'-)(s',r',o) - |",
+      "Calculating this expression is efficient since $H_o$ is a $d\\times d$ matrix."
+    ],
+    [
+      "In this section, we provide the output of the for some target triples. Sample adversarial attacks are provided in Table 5 . As it shows, attacks mostly try to change the type of the target triple's object by associating it with a subject and a relation that require a different entity types."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0149/instruction.md b/qasper-0149/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..6966e8807239f45c04c9f1e97949a4c5256170c0
--- /dev/null
+++ b/qasper-0149/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Investigating Robustness and Interpretability of Link Prediction via Adversarial Modifications
+
+Question: Can this adversarial approach be used to directly improve model accuracy?
\ No newline at end of file
diff --git a/qasper-0156/instruction.md b/qasper-0156/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..0838e340a1ccae9bc029b75e2c39cad36204c738
--- /dev/null
+++ b/qasper-0156/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: BERTRAM: Improved Word Embeddings Have Big Impact on Contextualized Model Performance
+
+Question: What models other than standalone BERT is new model compared to?
\ No newline at end of file
diff --git a/qasper-0158/instruction.md b/qasper-0158/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..d1c6ad50b8507d729f19f1a815cdcd166791d696
--- /dev/null
+++ b/qasper-0158/instruction.md
@@ -0,0 +1,123 @@
+Name of Paper: BERTRAM: Improved Word Embeddings Have Big Impact on Contextualized Model Performance
+
+Question: What are three downstream task datasets?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Model ::: Form-Context Model",
+    "Model ::: Bertram",
+    "Model ::: Training",
+    "Generation of Rare Word Datasets",
+    "Generation of Rare Word Datasets ::: Dataset Splitting",
+    "Generation of Rare Word Datasets ::: Baseline Training",
+    "Generation of Rare Word Datasets ::: Test Set Generation",
+    "Evaluation ::: Setup",
+    "Evaluation ::: WNLaMPro",
+    "Evaluation ::: Downstream Task Datasets",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "As traditional word embedding algorithms BIBREF1 are known to struggle with rare words, several techniques for improving their representations have been proposed over the last few years. These approaches exploit either the contexts in which rare words occur BIBREF2, BIBREF3, BIBREF4, BIBREF5, their surface-form BIBREF6, BIBREF7, BIBREF8, or both BIBREF9, BIBREF10. However, all of these approaches are designed for and evaluated on uncontextualized word embeddings.",
+      "With the recent shift towards contextualized representations obtained from pretrained deep language models BIBREF11, BIBREF12, BIBREF13, BIBREF14, the question naturally arises whether these approaches are facing the same problem. As all of them already handle rare words implicitly \u2013 using methods such as byte-pair encoding BIBREF15 and WordPiece embeddings BIBREF16, or even character-level CNNs BIBREF17 \u2013, it is unclear whether these models even require special treatment of rare words. However, the listed methods only make use of surface-form information, whereas BIBREF9 found that for covering a wide range of rare words, it is crucial to consider both surface-form and contexts.",
+      "Consistently, BIBREF0 recently showed that for BERT BIBREF13, a popular pretrained language model based on a Transformer architecture BIBREF18, performance on a rare word probing task can significantly be improve by relearning representations of rare words using Attentive Mimicking BIBREF19. However, their proposed model is limited in two important respects:",
+      "For processing contexts, it uses a simple bag-of-words model, throwing away much of the available information.",
+      "It combines form and context only in a shallow fashion, thus preventing both input signals from sharing information in any sophisticated manner.",
+      "Importantly, this limitation applies not only to their model, but to all previous work on obtaining representations for rare words by leveraging form and context. While using bag-of-words models is a reasonable choice for uncontextualized embeddings, which are often themselves based on such models BIBREF1, BIBREF7, it stands to reason that they are suboptimal for contextualized embeddings based on position-aware deep neural architectures.",
+      "To overcome these limitations, we introduce Bertram (BERT for Attentive Mimicking), a novel architecture for understanding rare words that combines a pretrained BERT language model with Attentive Mimicking BIBREF19. Unlike previous approaches making use of language models BIBREF5, our approach integrates BERT in an end-to-end fashion and directly makes use of its hidden states. By giving Bertram access to both surface form and context information already at its very lowest layer, we allow for a deep connection and exchange of information between both input signals.",
+      "For various reasons, assessing the effectiveness of methods like Bertram in a contextualized setting poses a huge difficulty: While most previous work on rare words was evaluated on datasets explicitly focusing on such words BIBREF6, BIBREF3, BIBREF4, BIBREF5, BIBREF10, all of these datasets are tailored towards context-independent embeddings and thus not suitable for evaluating our proposed model. Furthermore, understanding rare words is of negligible importance for most commonly used downstream task datasets. To evaluate our proposed model, we therefore introduce a novel procedure that allows us to automatically turn arbitrary text classification datasets into ones where rare words are guaranteed to be important. This is achieved by replacing classification-relevant frequent words with rare synonyms obtained using semantic resources such as WordNet BIBREF20.",
+      "Using this procedure, we extract rare word datasets from three commonly used text (or text pair) classification datasets: MNLI BIBREF21, AG's News BIBREF22 and DBPedia BIBREF23. On both the WNLaMPro dataset of BIBREF0 and all three so-obtained datasets, our proposed Bertram model outperforms previous work by a large margin.",
+      "In summary, our contributions are as follows:",
+      "We show that a pretrained BERT instance can be integrated into Attentive Mimicking, resulting in much better context representations and a deeper connection of form and context.",
+      "We design a procedure that allows us to automatically transform text classification datasets into datasets for which rare words are guaranteed to be important.",
+      "We show that Bertram achieves a new state-of-the-art on the WNLaMPro probing task BIBREF0 and beats all baselines on rare word instances of AG's News, MNLI and DBPedia, resulting in an absolute improvement of up to 24% over a BERT baseline."
+    ],
+    [
+      "Incorporating surface-form information (e.g., morphemes, characters or character $n$-grams) is a commonly used technique for improving word representations. For context-independent word embeddings, this information can either be injected into a given embedding space BIBREF6, BIBREF8, or a model can directly be given access to it during training BIBREF7, BIBREF24, BIBREF25. In the area of contextualized representations, many architectures employ subword segmentation methods BIBREF12, BIBREF13, BIBREF26, BIBREF14, whereas others use convolutional neural networks to directly access character-level information BIBREF27, BIBREF11, BIBREF17.",
+      "Complementary to surface form, another useful source of information for understanding rare words are the contexts in which they occur BIBREF2, BIBREF3, BIBREF4. As recently shown by BIBREF19, BIBREF9, combining form and context leads to significantly better results than using just one of both input signals for a wide range of tasks. While all aforementioned methods are based on simple bag-of-words models, BIBREF5 recently proposed an architecture based on the context2vec language model BIBREF28. However, in contrast to our work, they (i) do not incorporate surface-form information and (ii) do not directly access the hidden states of the language model, but instead simply use its output distribution.",
+      "There are several datasets explicitly focusing on rare words, e.g. the Stanford Rare Word dataset of BIBREF6, the Definitional Nonce dataset of BIBREF3 and the Contextual Rare Word dataset BIBREF4. However, all of these datasets are only suitable for evaluating context-independent word representations.",
+      "Our proposed method of generating rare word datasets is loosely related to adversarial example generation methods such as HotFlip BIBREF29, which manipulate the input to change a model's prediction. We use a similar mechanism to determine which words in a given sentence are most important and replace these words with rare synonyms."
+    ],
+    [
+      "We review the architecture of the form-context model (FCM) BIBREF9, which forms the basis for our model. Given a set of $d$-dimensional high-quality embeddings for frequent words, FCM can be used to induce embeddings for infrequent words that are appropriate for the given embedding space. This is done as follows: Given a word $w$ and a context $C$ in which it occurs, a surface-form embedding $v_{(w,{C})}^\\text{form} \\in \\mathbb {R}^d$ is obtained similar to BIBREF7 by averaging over embeddings of all $n$-grams in $w$; these $n$-gram embeddings are learned during training. Similarly, a context embedding $v_{(w,{C})}^\\text{context} \\in \\mathbb {R}^d$ is obtained by averaging over the embeddings of all words in $C$. The so-obtained form and context embeddings are then combined using a gate",
+      "with parameters $w \\in \\mathbb {R}^{2d}, b \\in \\mathbb {R}$ and $\\sigma $ denoting the sigmoid function, allowing the model to decide for each pair $(x,y)$ of form and context embeddings how much attention should be paid to $x$ and $y$, respectively.",
+      "The final representation of $w$ is then simply a weighted sum of form and context embeddings:",
+      "where $\\alpha = g(v_{(w,C)}^\\text{form}, v_{(w,C)}^\\text{context})$ and $A$ is a $d\\times d$ matrix that is learned during training.",
+      "While the context-part of FCM is able to capture the broad topic of numerous rare words, in many cases it is not able to obtain a more concrete and detailed understanding thereof BIBREF9. This is hardly surprising given the model's simplicity; it does, for example, make no use at all of the relative positions of context words. Furthermore, the simple gating mechanism results in only a shallow combination of form and context. That is, the model is not able to combine form and context until the very last step: While it can choose how much to attend to form and context, respectively, the corresponding embeddings do not share any information and thus cannot influence each other in any way."
+    ],
+    [
+      "To overcome both limitations described above, we introduce Bertram, an approach that combines a pretrained BERT language model BIBREF13 with Attentive Mimicking BIBREF19. To this end, let $d_h$ be the hidden dimension size and $l_\\text{max}$ be the number of layers for the BERT model being used. We denote with $e_{t}$ the (uncontextualized) embedding assigned to a token $t$ by BERT and, given a sequence of such uncontextualized embeddings $\\mathbf {e} = e_1, \\ldots , e_n$, we denote by $\\textbf {h}_j^l(\\textbf {e})$ the contextualized representation of the $j$-th token at layer $l$ when the model is given $\\mathbf {e}$ as input.",
+      "Given a word $w$ and a context $C = w_1, \\ldots , w_n$ in which it occurs, let $\\mathbf {t} = t_1, \\ldots , t_{m}$ with $m \\ge n$ be the sequence obtained from $C$ by (i) replacing $w$ with a [MASK] token and (ii) tokenizing the so-obtained sequence to match the BERT vocabulary; furthermore, let $i$ denote the index for which $t_i = \\texttt {[MASK]}$. Perhaps the most simple approach for obtaining a context embedding from $C$ using BERT is to define",
+      "where $\\mathbf {e} = e_{t_1}, \\ldots , e_{t_m}$. The so-obtained context embedding can then be combined with its form counterpart as described in Eq. DISPLAY_FORM8. While this achieves our first goal of using a more sophisticated context model that can potentially gain a deeper understanding of a word than just its broad topic, the so-obtained architecture still only combines form and context in a shallow fashion. We thus refer to it as the shallow variant of our model and investigate two alternative approaches (replace and add) that work as follows:",
+      "Replace: Before computing the context embedding, we replace the uncontextualized embedding of the [MASK] token with the word's surface-form embedding:",
+      "As during BERT pretraining, words chosen for prediction are replaced with [MASK] tokens only 80% of the time and kept unchanged 10% of the time, we hypothesize that even without further training, BERT is able to make use of form embeddings ingested this way.",
+      "Add: Before computing the context embedding, we prepad the input with the surface-form embedding of $w$, followed by a colon:",
+      "We also experimented with various other prefixes, but ended up choosing this particular strategy because we empirically found that after masking a token $t$, adding the sequence \u201c$t :$\u201d at the beginning helps BERT the most in recovering this very token at the masked position.",
+      "tnode/.style=rectangle, inner sep=0.1cm, minimum height=4ex, text centered,text height=1.5ex, text depth=0.25ex, opnode/.style=draw, rectangle, rounded corners, minimum height=4ex, minimum width=4ex, text centered, arrow/.style=draw,->,>=stealth",
+      "As for both variants, surface-form information is directly and deeply integrated into the computation of the context embedding, we do not require any further gating mechanism and may directly set $v_{(w,C)} = A \\cdot v^\\text{context}_{(w,C)}$.",
+      "However, we note that for the add variant, the contextualized representation of the [MASK] token is not the only natural candidate to be used for computing the final embedding: We might just as well look at the contextualized representation of the surface-form based embedding added at the very first position. Therefore, we also try a shallow combination of both embeddings. Note, however, that unlike FCM, we combine the contextualized representations \u2013 that is, the form part was already influenced by the context part and vice versa before combining them using a gate. For this combination, we define",
+      "with $A^{\\prime } \\in \\mathbb {R}^{d \\times d_h}$ being an additional learnable parameter. We then combine the two contextualized embeddings similar to Eq. DISPLAY_FORM8 as",
+      "where $\\alpha = g(h^\\text{form}_{(w,C)}, h^\\text{context}_{(w,C)})$. We refer to this final alternative as the add-gated approach. The model architecture for this variant can be seen in Figure FIGREF14 (left).",
+      "As in many cases, not just one, but a handful of contexts is known for a rare word, we follow the approach of BIBREF19 to deal with multiple contexts: We add an Attentive Mimicking head on top of our model, as can be seen in Figure FIGREF14 (right). That is, given a set of contexts $\\mathcal {C} = \\lbrace C_1, \\ldots , C_m\\rbrace $ and the corresponding embeddings $v_{(w,C_1)}, \\ldots , v_{(w,C_m)}$, we apply a self-attention mechanism to all embeddings, allowing the model to distinguish informative contexts from uninformative ones. The final embedding $v_{(w, \\mathcal {C})}$ is then a linear combination of the embeddings obtained from each context, where the weight of each embedding is determined based on the self-attention layer. For further details on this mechanism, we refer to BIBREF19."
+    ],
+    [
+      "Like previous work, we use mimicking BIBREF8 as a training objective. That is, given a frequent word $w$ with known embedding $e_w$ and a set of corresponding contexts $\\mathcal {C}$, Bertram is trained to minimize $\\Vert e_w - v_{(w, \\mathcal {C})}\\Vert ^2$.",
+      "As training Bertram end-to-end requires much computation (processing a single training instance $(w,\\mathcal {C})$ is as costly as processing an entire batch of $|\\mathcal {C}|$ examples in the original BERT architecture), we resort to the following three-stage training process:",
+      "We train only the form part, i.e. our loss for a single example $(w, \\mathcal {C})$ is $\\Vert e_w - v^\\text{form}_{(w, \\mathcal {C})} \\Vert ^2$.",
+      "We train only the context part, minimizing $\\Vert e_w - A \\cdot v^\\text{context}_{(w, \\mathcal {C})} \\Vert ^2$ where the context embedding is obtained using the shallow variant of Bertram. Furthermore, we exclude all of BERT's parameters from our optimization.",
+      "We combine the pretrained form-only and context-only model and train all additional parameters.",
+      "Pretraining the form and context parts individually allows us to train the full model for much fewer steps with comparable results. Importantly, for the first two stages of our training procedure, we do not have to backpropagate through the entire BERT model to obtain all required gradients, drastically increasing the training speed."
+    ],
+    [
+      "To measure the quality of rare word representations in a contextualized setting, we would ideally need text classification datasets with the following two properties:",
+      "A model that has no understanding of rare words at all should perform close to 0%.",
+      "A model that perfectly understands rare words should be able to classify every instance correctly.",
+      "Unfortunately, this requirement is not even remotely fulfilled by most commonly used datasets, simply because rare words occur in only a few entries and when they do, they are often of negligible importance.",
+      "To solve this problem, we devise a procedure to automatically transform existing text classification datasets such that rare words become important. For this procedure, we require a pretrained language model $M$ as a baseline, an arbitrary text classification dataset $\\mathcal {D}$ containing labelled instances $(\\mathbf {x}, y)$ and a substitution dictionary $S$, mapping each word $w$ to a set of rare synonyms $S(w)$. Given these ingredients, our procedure consists of three steps: (i) splitting the dataset into a train set and a set of test candidates, (ii) training the baseline model on the train set and (iii) modifying a subset of the test candidates to generate the final test set."
+    ],
+    [
+      "We partition $\\mathcal {D}$ into a train set $\\mathcal {D}_\\text{train}$ and a set of test candidates, $\\mathcal {D}_\\text{cand}$, with the latter containing all instances $(\\mathbf {x},y) \\in \\mathcal {D}$ such that for at least one word $w$ in $\\mathbf {x}$, $S(w) \\ne \\emptyset $. Additionally, we require that the training set consists of at least one third of the entire data."
+    ],
+    [
+      "We finetune $M$ on $\\mathcal {D}_\\text{train}$. Let $(\\mathbf {x}, y) \\in \\mathcal {D}_\\text{train}$ where $\\mathbf {x} = w_1, \\ldots , w_n$ is a sequence of words. We deviate from the standard finetuning procedure of BIBREF13 in three respects:",
+      "We randomly replace 5% of all words in $\\mathbf {x}$ with a [MASK] token. This allows the model to cope with missing or unknown words, a prerequisite for our final test set generation.",
+      "As an alternative to overwriting the language model's uncontextualized embeddings for rare words, we also want to allow models to simply add an alternative representation during test time, in which case we simply separate both representations by a slash. To accustom the language model to this duplication of words, we replace each word $w_i$ with \u201c$w_i$ / $w_i$\u201d with a probability of 10%. To make sure that the model does not simply learn to always focus on the first instance during training, we randomly mask each of the two repetitions with probability 25%.",
+      "We do not finetune the model's embedding layer. In preliminary experiments, we found this not to hurt performance."
+    ],
+    [
+      "Let $p(y \\mid \\mathbf {x})$ be the probability that the finetuned model $M$ assigns to class $y$ given input $\\mathbf {x}$, and let",
+      "be the model's prediction for input $\\mathbf {x}$ where $\\mathcal {Y}$ denotes the set of all labels. For generating our test set, we only consider candidates that are classified correctly by the baseline model, i.e. candidates $(\\mathbf {x}, y) \\in \\mathcal {D}_\\text{cand}$ with $M(\\mathbf {x}) = y$. For each such entry, let $\\mathbf {x} = w_1, \\ldots , w_n$ and let $\\mathbf {x}_{w_i = t}$ be the sequence obtained from $\\mathbf {x}$ by replacing $w_i$ with $t$. We compute",
+      "i.e., we select the word $w_i$ whose masking pushes the model's prediction the furthest away from the correct label. If removing this word already changes the model's prediction \u2013 that is, $M(\\mathbf {x}_{w_i = \\texttt {[MASK]}}) \\ne y$ \u2013, we select a random rare synonym $\\hat{w}_i \\in S(w_i)$ and add $(\\mathbf {x}_{w_i = \\hat{w}_i}, y)$ to the test set. Otherwise, we repeat the above procedure; if the label still has not changed after masking up to 5 words, we discard the corresponding entry. All so-obtained test set entries $(\\mathbf {x}_{w_{i_1} = \\hat{w}_{i_1}, \\ldots , w_{i_k} = \\hat{w}_{i_k} }, y)$ have the following properties:",
+      "If each $w_{i_j}$ is replaced by a [MASK] token, the entry is classified incorrectly by $M$. In other words, understanding the words $w_{i_j}$ is essential for $M$ to determine the correct label.",
+      "If the model's internal representation of each $\\hat{w}_{i_j}$ is equal to its representation of $w_{i_j}$, the entry is classified correctly by $M$. That is, if the model is able to understand the rare words $\\hat{w}_{i_j}$ and to identify them as synonyms of ${w_{i_j}}$, it predicts the correct label for each instance.",
+      "It is important to notice that the so-obtained test set is very closely coupled to the baseline model $M$, because we selected the words to replace based on the model's predictions. Importantly, however, the model is never queried with any rare synonym during test set generation, so its representations of rare words are not taken into account for creating the test set. Thus, while the test set is not suitable for comparing $M$ with an entirely different model $M^{\\prime }$, it allows us to compare various strategies for representing rare words in the embedding space of $M$. A similar constraint can be found in the Definitional Nonce dataset BIBREF3, which is tied to a given embedding space based on Word2Vec BIBREF1."
+    ],
+    [
+      "For our evaluation of Bertram, we largely follow the experimental setup of BIBREF0. Our implementation of Bertram is based on PyTorch BIBREF30 and the Transformers library of BIBREF31. Throughout all of our experiments, we use BERT$_\\text{base}$ as the underlying language model for Bertram. To obtain embeddings for frequent multi-token words during training, we use one-token approximation BIBREF0. Somewhat surprisingly, we found in preliminary experiments that excluding BERT's parameters from the finetuning procedure outlined in Section SECREF17 improves performance while speeding up training; we thus exclude them in the third step of our training procedure.",
+      "While BERT was trained on BooksCorpus BIBREF32 and a large Wikipedia dump, we follow previous work and train Bertram on only the much smaller Westbury Wikipedia Corpus (WWC) BIBREF33; this of course gives BERT a clear advantage over our proposed method. In order to at least partially compensate for this, in our downstream task experiments we gather the set of contexts $\\mathcal {C}$ for a given rare word from both the WWC and BooksCorpus during inference."
+    ],
+    [
+      "We evalute Bertram on the WNLaMPro dataset of BIBREF0. This dataset consists of cloze-style phrases like",
+      "and the task is to correctly fill the slot (____) with one of several acceptable target words (e.g., \u201cfruit\u201d, \u201cbush\u201d and \u201cberry\u201d), which requires knowledge of the phrase's keyword (\u201clingonberry\u201d in the above example). As the goal of this dataset is to probe a language model's ability to understand rare words without any task-specific finetuning, BIBREF0 do not provide a training set. Furthermore, the dataset is partitioned into three subsets; this partition is based on the frequency of the keyword, with keywords occurring less than 10 times in the WWC forming the rare subset, those occurring between 10 and 100 times forming the medium subset, and all remaining words forming the frequent subset. As our focus is on improving representations for rare words, we evaluate our model only on the former two sets.",
+      "Results on WNLaMPro rare and medium are shown in Table TABREF34, where the mean reciprocal rank (MRR) is reported for BERT, Attentive Mimicking and Bertram. As can be seen, supplementing BERT with any of the proposed relearning methods results in noticeable improvements for the rare subset, with add clearly outperforming replace. Moreover, the add and add-gated variants of Bertram perform surprisingly well for more frequent words, improving the score for WNLaMPro-medium by 50% compared to BERT$_\\text{base}$ and 31% compared to Attentive Mimicking. This makes sense considering that compared to Attentive Mimicking, the key enhancement of Bertram lies in improving context representations and interconnection of form and context; naturally, the more contexts are given, the more this comes into play. Noticeably, despite being both based on and integrated into a BERT$_\\text{base}$ model, our architecture even outperforms a standalone BERT$_\\text{large}$ model by a large margin."
+    ],
+    [
+      "To measure the effect of adding Bertram to BERT on downstream tasks, we apply the procedure described in Section SECREF4 to a commonly used textual entailment dataset as well as two text classification datasets: MNLI BIBREF21, AG's News BIBREF22 and DBPedia BIBREF23. For all three datasets, we use BERT$_\\text{base}$ as a baseline model and create the substitution dictionary $S$ using the synonym relation of WordNet BIBREF20 and the pattern library BIBREF34 to make sure that all synonyms have consistent parts of speech. As an additional source of word substitutions, we make use of the misspellings dataset of BIBREF25, which is based on query logs of a search engine. To prevent misspellings from dominating the resulting dataset, we only assign misspelling-based substitutes to randomly selected 10% of the words contained in each sentence. Motivated by the results on WNLaMPro-medium, we consider every word that occurs less than 100 times in the WWC and our BooksCorpus replica combined as being rare. Some examples of entries in the resulting datasets can be seen in Table TABREF35.",
+      "Just like for WNLaMPro, our default way of injecting Bertram embeddings into the baseline model is to replace the sequence of uncontextualized WordPiece tokens for a given rare word with its Bertram-based embedding. That is, given a sequence of uncontextualized token embeddings $\\mathbf {e} = e_1, \\ldots , e_n$ where $e_{i}, \\ldots , e_{i+j}$ with $1 \\le i \\le i+j \\le n$ is the sequence of WordPiece embeddings for a single rare word $w$, we replace $\\mathbf {e}$ with",
+      "By default, the set of contexts $\\mathcal {C}$ required for this replacement is obtained by collecting all sentences from the WWC and BooksCorpus in which $w$ occurs. As our model architecture allows us to easily include new contexts without requiring any additional training, we also try a variant where we add in-domain contexts by giving the model access to the texts found in the test set.",
+      "In addition to the procedure described above, we also try a variant where instead of replacing the original WordPiece embeddings for a given rare word, we merely add the Bertram-based embedding, separating both representations using a single slash:",
+      "As it performs best on the rare and medium subsets of WNLaMPro combined, we use only the add-gated variant of Bertram for all datasets. Results can be seen in Table TABREF37, where for each task, we report the accuracy on the entire dataset as well as scores obtained considering only instances where at least one word was replaced by a misspelling or a WordNet synonym, respectively. Consistent with results on WNLaMPro, combining BERT with Bertram outperforms both a standalone BERT model and one combined with Attentive Mimicking across all tasks. While keeping the original BERT embeddings in addition to Bertram's representation brings no benefit, adding in-domain data clearly helps for two out of three datasets. This makes sense as for rare words, every single additional context can be crucial for gaining a deeper understanding.",
+      "To further understand for which words using Bertram is helpful, in Figure FIGREF39 we look at the accuracy of BERT both with and without Bertram on all three tasks as a function of word frequency. That is, we compute the accuracy scores for both models when considering only entries $(\\mathbf {x}_{w_{i_1} = \\hat{w}_{i_1}, \\ldots , w_{i_k} = \\hat{w}_{i_k} }, y)$ where each substituted word $\\hat{w}_{i_j}$ occurs less than $c_\\text{max}$ times in WWC and BooksCorpus, for various values of $c_\\text{max}$. As one would expect, $c_\\text{max}$ is positively correlated with the accuracies of both models, showing that the rarer a word is, the harder it is to understand. Perhaps more interestingly, for all three datasets the gap between Bertram and BERT remains more or less constant regardless of $c_\\text{max}$. This indicates that using Bertram might also be useful for even more frequent words than the ones considered."
+    ],
+    [
+      "We have introduced Bertram, a novel architecture for relearning high-quality representations of rare words. This is achieved by employing a powerful pretrained language model and deeply connecting surface-form and context information. By replacing important words with rare synonyms, we have created various downstream task datasets focusing on rare words; on all of these datasets, Bertram improves over a BERT model without special handling of rare words, demonstrating the usefulness of our proposed method.",
+      "As our analysis has shown that even for the most frequent words considered, using Bertram is still beneficial, future work might further investigate the limits of our proposed method. Furthermore, it would be interesting to explore more complex ways of incorporating surface-form information \u2013 e.g., by using a character-level CNN similar to the one of BIBREF27 \u2013 to balance out the potency of Bertram's form and context parts."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0160/instruction.md b/qasper-0160/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..b964da601cc1adef883e1be712481f0c9caa7bfb
--- /dev/null
+++ b/qasper-0160/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Joint Entity Linking with Deep Reinforcement Learning
+
+Question: How fast is the model compared to baselines?
\ No newline at end of file
diff --git a/qasper-0167/instruction.md b/qasper-0167/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..c3dff59c8d3392cdc696db30c8f6ca5081dc2a16
--- /dev/null
+++ b/qasper-0167/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Classification Betters Regression in Query-based Multi-document Summarisation Techniques for Question Answering: Macquarie University at BioASQ7b
+
+Question: Did classification models perform better than previous regression one?
\ No newline at end of file
diff --git a/qasper-0169/instruction.md b/qasper-0169/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..62adcdfb84468bfff896e258772e900956f94d32
--- /dev/null
+++ b/qasper-0169/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Marrying Universal Dependencies and Universal Morphology
+
+Question: Do they look for inconsistencies between different languages' annotations in UniMorph?
\ No newline at end of file
diff --git a/qasper-0170/instruction.md b/qasper-0170/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..8961ac8b9cd8ca6c94b6c37afb5836b6cd1c6af7
--- /dev/null
+++ b/qasper-0170/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Marrying Universal Dependencies and Universal Morphology
+
+Question: Do they look for inconsistencies between different UD treebanks?
\ No newline at end of file
diff --git a/qasper-0171/instruction.md b/qasper-0171/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..c2bceda6e33a0fcf18d171d533b9fafb9bcc5750
--- /dev/null
+++ b/qasper-0171/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Marrying Universal Dependencies and Universal Morphology
+
+Question: Which languages do they validate on?
\ No newline at end of file
diff --git a/qasper-0176/instruction.md b/qasper-0176/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..5c75ff8cd3e042094a84bfd6767fec5753ec8fb5
--- /dev/null
+++ b/qasper-0176/instruction.md
@@ -0,0 +1,89 @@
+Name of Paper: Revisiting Low-Resource Neural Machine Translation: A Case Study
+
+Question: what amounts of size were used on german-english?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Low-Resource Translation Quality Compared Across Systems",
+    "Improving Low-Resource Neural Machine Translation",
+    "Mainstream Improvements",
+    "Language Representation",
+    "Hyperparameter Tuning",
+    "Lexical Model",
+    "Data and Preprocessing",
+    "PBSMT Baseline",
+    "NMT Systems",
+    "Results",
+    "Conclusions",
+    "Acknowledgments",
+    "Hyperparameters",
+    "Sample Translations"
+  ],
+  "paragraphs": [
+    [
+      "While neural machine translation (NMT) has achieved impressive performance in high-resource data conditions, becoming dominant in the field BIBREF0 , BIBREF1 , BIBREF2 , recent research has argued that these models are highly data-inefficient, and underperform phrase-based statistical machine translation (PBSMT) or unsupervised methods in low-data conditions BIBREF3 , BIBREF4 . In this paper, we re-assess the validity of these results, arguing that they are the result of lack of system adaptation to low-resource settings. Our main contributions are as follows:"
+    ],
+    [
+      "Figure FIGREF4 reproduces a plot by BIBREF3 which shows that their NMT system only outperforms their PBSMT system when more than 100 million words (approx. 5 million sentences) of parallel training data are available. Results shown by BIBREF4 are similar, showing that unsupervised NMT outperforms supervised systems if few parallel resources are available. In both papers, NMT systems are trained with hyperparameters that are typical for high-resource settings, and the authors did not tune hyperparameters, or change network architectures, to optimize NMT for low-resource conditions."
+    ],
+    [
+      "The bulk of research on low-resource NMT has focused on exploiting monolingual data, or parallel data involving other language pairs. Methods to improve NMT with monolingual data range from the integration of a separately trained language model BIBREF5 to the training of parts of the NMT model with additional objectives, including a language modelling objective BIBREF5 , BIBREF6 , BIBREF7 , an autoencoding objective BIBREF8 , BIBREF9 , or a round-trip objective, where the model is trained to predict monolingual (target-side) training data that has been back-translated into the source language BIBREF6 , BIBREF10 , BIBREF11 . As an extreme case, models that rely exclusively on monolingual data have been shown to work BIBREF12 , BIBREF13 , BIBREF14 , BIBREF4 . Similarly, parallel data from other language pairs can be used to pre-train the network or jointly learn representations BIBREF15 , BIBREF16 , BIBREF17 , BIBREF18 , BIBREF19 , BIBREF20 , BIBREF21 .",
+      "While semi-supervised and unsupervised approaches have been shown to be very effective for some language pairs, their effectiveness depends on the availability of large amounts of suitable auxiliary data, and other conditions being met. For example, the effectiveness of unsupervised methods is impaired when languages are morphologically different, or when training domains do not match BIBREF22 ",
+      "More broadly, this line of research still accepts the premise that NMT models are data-inefficient and require large amounts of auxiliary data to train. In this work, we want to re-visit this point, and will focus on techniques to make more efficient use of small amounts of parallel training data. Low-resource NMT without auxiliary data has received less attention; work in this direction includes BIBREF23 , BIBREF24 ."
+    ],
+    [
+      "We consider the hyperparameters used by BIBREF3 to be our baseline. This baseline does not make use of various advances in NMT architectures and training tricks. In contrast to the baseline, we use a BiDeep RNN architecture BIBREF25 , label smoothing BIBREF26 , dropout BIBREF27 , word dropout BIBREF28 , layer normalization BIBREF29 and tied embeddings BIBREF30 ."
+    ],
+    [
+      "Subword representations such as BPE BIBREF31 have become a popular choice to achieve open-vocabulary translation. BPE has one hyperparameter, the number of merge operations, which determines the size of the final vocabulary. For high-resource settings, the effect of vocabulary size on translation quality is relatively small; BIBREF32 report mixed results when comparing vocabularies of 30k and 90k subwords.",
+      "In low-resource settings, large vocabularies result in low-frequency (sub)words being represented as atomic units at training time, and the ability to learn good high-dimensional representations of these is doubtful. BIBREF33 propose a minimum frequency threshold for subword units, and splitting any less frequent subword into smaller units or characters. We expect that such a threshold reduces the need to carefully tune the vocabulary size to the dataset, leading to more aggressive segmentation on smaller datasets."
+    ],
+    [
+      "Due to long training times, hyperparameters are hard to optimize by grid search, and are often re-used across experiments. However, best practices differ between high-resource and low-resource settings. While the trend in high-resource settings is towards using larger and deeper models, BIBREF24 use smaller and fewer layers for smaller datasets. Previous work has argued for larger batch sizes in NMT BIBREF35 , BIBREF36 , but we find that using smaller batches is beneficial in low-resource settings. More aggressive dropout, including dropping whole words at random BIBREF37 , is also likely to be more important. We report results on a narrow hyperparameter search guided by previous work and our own intuition."
+    ],
+    [
+      "Finally, we implement and test the lexical model by BIBREF24 , which has been shown to be beneficial in low-data conditions. The core idea is to train a simple feed-forward network, the lexical model, jointly with the original attentional NMT model. The input of the lexical model at time step INLINEFORM0 is the weighted average of source embeddings INLINEFORM1 (the attention weights INLINEFORM2 are shared with the main model). After a feedforward layer (with skip connection), the lexical model's output INLINEFORM3 is combined with the original model's hidden state INLINEFORM4 before softmax computation. INLINEFORM5 ",
+      " Our implementation adds dropout and layer normalization to the lexical model.",
+      "",
+      ""
+    ],
+    [
+      "We use the TED data from the IWSLT 2014 German INLINEFORM0 English shared translation task BIBREF38 . We use the same data cleanup and train/dev split as BIBREF39 , resulting in 159000 parallel sentences of training data, and 7584 for development.",
+      "As a second language pair, we evaluate our systems on a Korean\u2013English dataset with around 90000 parallel sentences of training data, 1000 for development, and 2000 for testing.",
+      "For both PBSMT and NMT, we apply the same tokenization and truecasing using Moses scripts. For NMT, we also learn BPE subword segmentation with 30000 merge operations, shared between German and English, and independently for Korean INLINEFORM0 English.",
+      "To simulate different amounts of training resources, we randomly subsample the IWSLT training corpus 5 times, discarding half of the data at each step. Truecaser and BPE segmentation are learned on the full training corpus; as one of our experiments, we set the frequency threshold for subword units to 10 in each subcorpus (see SECREF7 ). Table TABREF14 shows statistics for each subcorpus, including the subword vocabulary.",
+      "Translation outputs are detruecased, detokenized, and compared against the reference with cased BLEU using sacreBLEU BIBREF40 , BIBREF41 . Like BIBREF39 , we report BLEU on the concatenated dev sets for IWSLT 2014 (tst2010, tst2011, tst2012, dev2010, dev2012)."
+    ],
+    [
+      "We use Moses BIBREF42 to train a PBSMT system. We use MGIZA BIBREF43 to train word alignments, and lmplz BIBREF44 for a 5-gram LM. Feature weights are optimized on the dev set to maximize BLEU with batch MIRA BIBREF45 \u2013 we perform multiple runs where indicated. Unlike BIBREF3 , we do not use extra data for the LM. Both PBSMT and NMT can benefit from monolingual data, so the availability of monolingual data is no longer an exclusive advantage of PBSMT (see SECREF5 )."
+    ],
+    [
+      "We train neural systems with Nematus BIBREF46 . Our baseline mostly follows the settings in BIBREF3 ; we use adam BIBREF47 and perform early stopping based on dev set BLEU. We express our batch size in number of tokens, and set it to 4000 in the baseline (comparable to a batch size of 80 sentences used in previous work).",
+      "We subsequently add the methods described in section SECREF3 , namely the bideep RNN, label smoothing, dropout, tied embeddings, layer normalization, changes to the BPE vocabulary size, batch size, model depth, regularization parameters and learning rate. Detailed hyperparameters are reported in Appendix SECREF7 ."
+    ],
+    [
+      "Table TABREF18 shows the effect of adding different methods to the baseline NMT system, on the ultra-low data condition (100k words of training data) and the full IWSLT 14 training corpus (3.2M words). Our \"mainstream improvements\" add around 6\u20137 BLEU in both data conditions.",
+      "In the ultra-low data condition, reducing the BPE vocabulary size is very effective (+4.9 BLEU). Reducing the batch size to 1000 token results in a BLEU gain of 0.3, and the lexical model yields an additional +0.6 BLEU. However, aggressive (word) dropout (+3.4 BLEU) and tuning other hyperparameters (+0.7 BLEU) has a stronger effect than the lexical model, and adding the lexical model (9) on top of the optimized configuration (8) does not improve performance. Together, the adaptations to the ultra-low data setting yield 9.4 BLEU (7.2 INLINEFORM2 16.6). The model trained on full IWSLT data is less sensitive to our changes (31.9 INLINEFORM3 32.8 BLEU), and optimal hyperparameters differ depending on the data condition. Subsequently, we still apply the hyperparameters that were optimized to the ultra-low data condition (8) to other data conditions, and Korean INLINEFORM4 English, for simplicity.",
+      "For a comparison with PBSMT, and across different data settings, consider Figure FIGREF19 , which shows the result of PBSMT, our NMT baseline, and our optimized NMT system. Our NMT baseline still performs worse than the PBSMT system for 3.2M words of training data, which is consistent with the results by BIBREF3 . However, our optimized NMT system shows strong improvements, and outperforms the PBSMT system across all data settings. Some sample translations are shown in Appendix SECREF8 .",
+      "For comparison to previous work, we report lowercased and tokenized results on the full IWSLT 14 training set in Table TABREF20 . Our results far outperform the RNN-based results reported by BIBREF48 , and are on par with the best reported results on this dataset.",
+      "Table TABREF21 shows results for Korean INLINEFORM0 English, using the same configurations (1, 2 and 8) as for German\u2013English. Our results confirm that the techniques we apply are successful across datasets, and result in stronger systems than previously reported on this dataset, achieving 10.37 BLEU as compared to 5.97 BLEU reported by gu-EtAl:2018:EMNLP1."
+    ],
+    [
+      "Our results demonstrate that NMT is in fact a suitable choice in low-data settings, and can outperform PBSMT with far less parallel training data than previously claimed. Recently, the main trend in low-resource MT research has been the better exploitation of monolingual and multilingual resources. Our results show that low-resource NMT is very sensitive to hyperparameters such as BPE vocabulary size, word dropout, and others, and by following a set of best practices, we can train competitive NMT systems without relying on auxiliary resources. This has practical relevance for languages where large amounts of monolingual data, or multilingual data involving related languages, are not available. Even though we focused on only using parallel data, our results are also relevant for work on using auxiliary data to improve low-resource MT. Supervised systems serve as an important baseline to judge the effectiveness of semisupervised or unsupervised approaches, and the quality of supervised systems trained on little data can directly impact semi-supervised workflows, for instance for the back-translation of monolingual data."
+    ],
+    [
+      "Rico Sennrich has received funding from the Swiss National Science Foundation in the project CoNTra (grant number 105212_169888). Biao Zhang acknowledges the support of the Baidu Scholarship."
+    ],
+    [
+      "Table TABREF23 lists hyperparameters used for the different experiments in the ablation study (Table 2). Hyperparameters were kept constant across different data settings, except for the validation interval and subword vocabulary size (see Table 1)."
+    ],
+    [
+      "Table TABREF24 shows some sample translations that represent typical errors of our PBSMT and NMT systems, trained with ultra-low (100k words) and low (3.2M words) amounts of data. For unknown words such as blutbefleckten (`bloodstained') or Spaniern (`Spaniards', `Spanish'), PBSMT systems default to copying, while NMT systems produce translations on a subword-level, with varying success (blue-flect, bleed; spaniers, Spanians). NMT systems learn some syntactic disambiguation even with very little data, for example the translation of das and die as relative pronouns ('that', 'which', 'who'), while PBSMT produces less grammatical translation. On the flip side, the ultra low-resource NMT system ignores some unknown words in favour of a more-or-less fluent, but semantically inadequate translation: erobert ('conquered') is translated into doing, and richtig aufgezeichnet ('registered correctly', `recorded correctly') into really the first thing."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0177/instruction.md b/qasper-0177/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..5a32f3d776ad5d05cb6c64fe7c5bf4fa9a3099ef
--- /dev/null
+++ b/qasper-0177/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Revisiting Low-Resource Neural Machine Translation: A Case Study
+
+Question: what were their experimental results in the low-resource dataset?
\ No newline at end of file
diff --git a/qasper-0178/instruction.md b/qasper-0178/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..7dc6820f7a56aeb20fbe4e4fda7553973fb82ab5
--- /dev/null
+++ b/qasper-0178/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Revisiting Low-Resource Neural Machine Translation: A Case Study
+
+Question: what are the methods they compare with in the korean-english dataset?
\ No newline at end of file
diff --git a/qasper-0179/instruction.md b/qasper-0179/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..a4406026b7b4e3f5f119418799c93ea34e76bd95
--- /dev/null
+++ b/qasper-0179/instruction.md
@@ -0,0 +1,89 @@
+Name of Paper: Revisiting Low-Resource Neural Machine Translation: A Case Study
+
+Question: what pitfalls are mentioned in the paper?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Low-Resource Translation Quality Compared Across Systems",
+    "Improving Low-Resource Neural Machine Translation",
+    "Mainstream Improvements",
+    "Language Representation",
+    "Hyperparameter Tuning",
+    "Lexical Model",
+    "Data and Preprocessing",
+    "PBSMT Baseline",
+    "NMT Systems",
+    "Results",
+    "Conclusions",
+    "Acknowledgments",
+    "Hyperparameters",
+    "Sample Translations"
+  ],
+  "paragraphs": [
+    [
+      "While neural machine translation (NMT) has achieved impressive performance in high-resource data conditions, becoming dominant in the field BIBREF0 , BIBREF1 , BIBREF2 , recent research has argued that these models are highly data-inefficient, and underperform phrase-based statistical machine translation (PBSMT) or unsupervised methods in low-data conditions BIBREF3 , BIBREF4 . In this paper, we re-assess the validity of these results, arguing that they are the result of lack of system adaptation to low-resource settings. Our main contributions are as follows:"
+    ],
+    [
+      "Figure FIGREF4 reproduces a plot by BIBREF3 which shows that their NMT system only outperforms their PBSMT system when more than 100 million words (approx. 5 million sentences) of parallel training data are available. Results shown by BIBREF4 are similar, showing that unsupervised NMT outperforms supervised systems if few parallel resources are available. In both papers, NMT systems are trained with hyperparameters that are typical for high-resource settings, and the authors did not tune hyperparameters, or change network architectures, to optimize NMT for low-resource conditions."
+    ],
+    [
+      "The bulk of research on low-resource NMT has focused on exploiting monolingual data, or parallel data involving other language pairs. Methods to improve NMT with monolingual data range from the integration of a separately trained language model BIBREF5 to the training of parts of the NMT model with additional objectives, including a language modelling objective BIBREF5 , BIBREF6 , BIBREF7 , an autoencoding objective BIBREF8 , BIBREF9 , or a round-trip objective, where the model is trained to predict monolingual (target-side) training data that has been back-translated into the source language BIBREF6 , BIBREF10 , BIBREF11 . As an extreme case, models that rely exclusively on monolingual data have been shown to work BIBREF12 , BIBREF13 , BIBREF14 , BIBREF4 . Similarly, parallel data from other language pairs can be used to pre-train the network or jointly learn representations BIBREF15 , BIBREF16 , BIBREF17 , BIBREF18 , BIBREF19 , BIBREF20 , BIBREF21 .",
+      "While semi-supervised and unsupervised approaches have been shown to be very effective for some language pairs, their effectiveness depends on the availability of large amounts of suitable auxiliary data, and other conditions being met. For example, the effectiveness of unsupervised methods is impaired when languages are morphologically different, or when training domains do not match BIBREF22 ",
+      "More broadly, this line of research still accepts the premise that NMT models are data-inefficient and require large amounts of auxiliary data to train. In this work, we want to re-visit this point, and will focus on techniques to make more efficient use of small amounts of parallel training data. Low-resource NMT without auxiliary data has received less attention; work in this direction includes BIBREF23 , BIBREF24 ."
+    ],
+    [
+      "We consider the hyperparameters used by BIBREF3 to be our baseline. This baseline does not make use of various advances in NMT architectures and training tricks. In contrast to the baseline, we use a BiDeep RNN architecture BIBREF25 , label smoothing BIBREF26 , dropout BIBREF27 , word dropout BIBREF28 , layer normalization BIBREF29 and tied embeddings BIBREF30 ."
+    ],
+    [
+      "Subword representations such as BPE BIBREF31 have become a popular choice to achieve open-vocabulary translation. BPE has one hyperparameter, the number of merge operations, which determines the size of the final vocabulary. For high-resource settings, the effect of vocabulary size on translation quality is relatively small; BIBREF32 report mixed results when comparing vocabularies of 30k and 90k subwords.",
+      "In low-resource settings, large vocabularies result in low-frequency (sub)words being represented as atomic units at training time, and the ability to learn good high-dimensional representations of these is doubtful. BIBREF33 propose a minimum frequency threshold for subword units, and splitting any less frequent subword into smaller units or characters. We expect that such a threshold reduces the need to carefully tune the vocabulary size to the dataset, leading to more aggressive segmentation on smaller datasets."
+    ],
+    [
+      "Due to long training times, hyperparameters are hard to optimize by grid search, and are often re-used across experiments. However, best practices differ between high-resource and low-resource settings. While the trend in high-resource settings is towards using larger and deeper models, BIBREF24 use smaller and fewer layers for smaller datasets. Previous work has argued for larger batch sizes in NMT BIBREF35 , BIBREF36 , but we find that using smaller batches is beneficial in low-resource settings. More aggressive dropout, including dropping whole words at random BIBREF37 , is also likely to be more important. We report results on a narrow hyperparameter search guided by previous work and our own intuition."
+    ],
+    [
+      "Finally, we implement and test the lexical model by BIBREF24 , which has been shown to be beneficial in low-data conditions. The core idea is to train a simple feed-forward network, the lexical model, jointly with the original attentional NMT model. The input of the lexical model at time step INLINEFORM0 is the weighted average of source embeddings INLINEFORM1 (the attention weights INLINEFORM2 are shared with the main model). After a feedforward layer (with skip connection), the lexical model's output INLINEFORM3 is combined with the original model's hidden state INLINEFORM4 before softmax computation. INLINEFORM5 ",
+      " Our implementation adds dropout and layer normalization to the lexical model.",
+      "",
+      ""
+    ],
+    [
+      "We use the TED data from the IWSLT 2014 German INLINEFORM0 English shared translation task BIBREF38 . We use the same data cleanup and train/dev split as BIBREF39 , resulting in 159000 parallel sentences of training data, and 7584 for development.",
+      "As a second language pair, we evaluate our systems on a Korean\u2013English dataset with around 90000 parallel sentences of training data, 1000 for development, and 2000 for testing.",
+      "For both PBSMT and NMT, we apply the same tokenization and truecasing using Moses scripts. For NMT, we also learn BPE subword segmentation with 30000 merge operations, shared between German and English, and independently for Korean INLINEFORM0 English.",
+      "To simulate different amounts of training resources, we randomly subsample the IWSLT training corpus 5 times, discarding half of the data at each step. Truecaser and BPE segmentation are learned on the full training corpus; as one of our experiments, we set the frequency threshold for subword units to 10 in each subcorpus (see SECREF7 ). Table TABREF14 shows statistics for each subcorpus, including the subword vocabulary.",
+      "Translation outputs are detruecased, detokenized, and compared against the reference with cased BLEU using sacreBLEU BIBREF40 , BIBREF41 . Like BIBREF39 , we report BLEU on the concatenated dev sets for IWSLT 2014 (tst2010, tst2011, tst2012, dev2010, dev2012)."
+    ],
+    [
+      "We use Moses BIBREF42 to train a PBSMT system. We use MGIZA BIBREF43 to train word alignments, and lmplz BIBREF44 for a 5-gram LM. Feature weights are optimized on the dev set to maximize BLEU with batch MIRA BIBREF45 \u2013 we perform multiple runs where indicated. Unlike BIBREF3 , we do not use extra data for the LM. Both PBSMT and NMT can benefit from monolingual data, so the availability of monolingual data is no longer an exclusive advantage of PBSMT (see SECREF5 )."
+    ],
+    [
+      "We train neural systems with Nematus BIBREF46 . Our baseline mostly follows the settings in BIBREF3 ; we use adam BIBREF47 and perform early stopping based on dev set BLEU. We express our batch size in number of tokens, and set it to 4000 in the baseline (comparable to a batch size of 80 sentences used in previous work).",
+      "We subsequently add the methods described in section SECREF3 , namely the bideep RNN, label smoothing, dropout, tied embeddings, layer normalization, changes to the BPE vocabulary size, batch size, model depth, regularization parameters and learning rate. Detailed hyperparameters are reported in Appendix SECREF7 ."
+    ],
+    [
+      "Table TABREF18 shows the effect of adding different methods to the baseline NMT system, on the ultra-low data condition (100k words of training data) and the full IWSLT 14 training corpus (3.2M words). Our \"mainstream improvements\" add around 6\u20137 BLEU in both data conditions.",
+      "In the ultra-low data condition, reducing the BPE vocabulary size is very effective (+4.9 BLEU). Reducing the batch size to 1000 token results in a BLEU gain of 0.3, and the lexical model yields an additional +0.6 BLEU. However, aggressive (word) dropout (+3.4 BLEU) and tuning other hyperparameters (+0.7 BLEU) has a stronger effect than the lexical model, and adding the lexical model (9) on top of the optimized configuration (8) does not improve performance. Together, the adaptations to the ultra-low data setting yield 9.4 BLEU (7.2 INLINEFORM2 16.6). The model trained on full IWSLT data is less sensitive to our changes (31.9 INLINEFORM3 32.8 BLEU), and optimal hyperparameters differ depending on the data condition. Subsequently, we still apply the hyperparameters that were optimized to the ultra-low data condition (8) to other data conditions, and Korean INLINEFORM4 English, for simplicity.",
+      "For a comparison with PBSMT, and across different data settings, consider Figure FIGREF19 , which shows the result of PBSMT, our NMT baseline, and our optimized NMT system. Our NMT baseline still performs worse than the PBSMT system for 3.2M words of training data, which is consistent with the results by BIBREF3 . However, our optimized NMT system shows strong improvements, and outperforms the PBSMT system across all data settings. Some sample translations are shown in Appendix SECREF8 .",
+      "For comparison to previous work, we report lowercased and tokenized results on the full IWSLT 14 training set in Table TABREF20 . Our results far outperform the RNN-based results reported by BIBREF48 , and are on par with the best reported results on this dataset.",
+      "Table TABREF21 shows results for Korean INLINEFORM0 English, using the same configurations (1, 2 and 8) as for German\u2013English. Our results confirm that the techniques we apply are successful across datasets, and result in stronger systems than previously reported on this dataset, achieving 10.37 BLEU as compared to 5.97 BLEU reported by gu-EtAl:2018:EMNLP1."
+    ],
+    [
+      "Our results demonstrate that NMT is in fact a suitable choice in low-data settings, and can outperform PBSMT with far less parallel training data than previously claimed. Recently, the main trend in low-resource MT research has been the better exploitation of monolingual and multilingual resources. Our results show that low-resource NMT is very sensitive to hyperparameters such as BPE vocabulary size, word dropout, and others, and by following a set of best practices, we can train competitive NMT systems without relying on auxiliary resources. This has practical relevance for languages where large amounts of monolingual data, or multilingual data involving related languages, are not available. Even though we focused on only using parallel data, our results are also relevant for work on using auxiliary data to improve low-resource MT. Supervised systems serve as an important baseline to judge the effectiveness of semisupervised or unsupervised approaches, and the quality of supervised systems trained on little data can directly impact semi-supervised workflows, for instance for the back-translation of monolingual data."
+    ],
+    [
+      "Rico Sennrich has received funding from the Swiss National Science Foundation in the project CoNTra (grant number 105212_169888). Biao Zhang acknowledges the support of the Baidu Scholarship."
+    ],
+    [
+      "Table TABREF23 lists hyperparameters used for the different experiments in the ablation study (Table 2). Hyperparameters were kept constant across different data settings, except for the validation interval and subword vocabulary size (see Table 1)."
+    ],
+    [
+      "Table TABREF24 shows some sample translations that represent typical errors of our PBSMT and NMT systems, trained with ultra-low (100k words) and low (3.2M words) amounts of data. For unknown words such as blutbefleckten (`bloodstained') or Spaniern (`Spaniards', `Spanish'), PBSMT systems default to copying, while NMT systems produce translations on a subword-level, with varying success (blue-flect, bleed; spaniers, Spanians). NMT systems learn some syntactic disambiguation even with very little data, for example the translation of das and die as relative pronouns ('that', 'which', 'who'), while PBSMT produces less grammatical translation. On the flip side, the ultra low-resource NMT system ignores some unknown words in favour of a more-or-less fluent, but semantically inadequate translation: erobert ('conquered') is translated into doing, and richtig aufgezeichnet ('registered correctly', `recorded correctly') into really the first thing."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0182/instruction.md b/qasper-0182/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..a5e5683dca8c4fc75d4839f952505673d6ad04e0
--- /dev/null
+++ b/qasper-0182/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Facilitating on-line opinion dynamics by mining expressions of causation. The case of climate change debates on The Guardian
+
+Question: What is the technique used for text analysis and mining?
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+Name of Paper: Facilitating on-line opinion dynamics by mining expressions of causation. The case of climate change debates on The Guardian
+
+Question: What are the causal mapping methods employed?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction ::: Background",
+    "Introduction ::: Objective",
+    "Introduction ::: Data: the communicative setting of TheGuardian.com",
+    "Mining opinions and beliefs from texts",
+    "Mining opinions and beliefs from texts ::: Causal mapping methods and the climate change debate",
+    "Mining opinions and beliefs from texts ::: Automated causation tracking with the Penelope semantic frame extractor",
+    "Analyses and applications",
+    "Analyses and applications ::: Aggregation",
+    "Analyses and applications ::: Spatial renditions of TheGuardian.com's opinion landscape",
+    "Analyses and applications ::: Spatial renditions of TheGuardian.com's opinion landscape ::: A macro-level overview: causes addressed in the climate change debate",
+    "Analyses and applications ::: Spatial renditions of TheGuardian.com's opinion landscape ::: Micro-level investigations: opinions on nuclear power and global warming",
+    "From opinion observation to debate facilitation",
+    "From opinion observation to debate facilitation ::: Debate facilitation through models of alignment and polarization",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Over the past two decades, the rise of social media and the digitization of news and discussion platforms have radically transformed how individuals and groups create, process and share news and information. As Alan Rusbridger, former-editor-in-chief of the newspaper The Guardian has it, these technologically-driven shifts in the ways people communicate, organize themselves and express their beliefs and opinions, have",
+      "empower[ed] those that were never heard, creating a a new form of politics and turning traditional news corporations inside out. It is impossible to think of Donald Trump; of Brexit; of Bernie Sanders; of Podemos; of the growth of the far right in Europe; of the spasms of hope and violent despair in the Middle East and North Africa without thinking also of the total inversion of how news is created, shared and distributed. Much of it is liberating and and inspiring. Some of it is ugly and dark. And something - the centuries-old craft of journalism - is in danger of being lost BIBREF0.",
+      "Rusbridger's observation that the present media-ecology puts traditional notions of politics, journalism, trust and truth at stake is a widely shared one BIBREF1, BIBREF2, BIBREF3. As such, it has sparked interdisciplinary investigations, diagnoses and ideas for remedies across the economical, socio-political, and technological spectrum, challenging our existing assumptions and epistemologies BIBREF4, BIBREF5. Among these lines of inquiry, particular strands of research from the computational social sciences are addressing pressing questions of how emerging technologies and digital methods might be operationalized to regain a grip on the dynamics that govern the flow of on-line news and its associated multitudes of voices, opinions and conflicts. Could the information circulating on on-line (social) news platforms for instance be mined to better understand and analyze the problems facing our contemporary society? Might such data mining and analysis help us to monitor the growing number of social conflicts and crises due to cultural differences and diverging world-views? And finally, would such an approach potentially facilitate early detection of conflicts and even ways to resolve them before they turn violent?",
+      "Answering these questions requires further advances in the study of cultural conflict based on digital media data. This includes the development of fine-grained representations of cultural conflict based on theoretically-informed text analysis, the integration of game-theoretical approaches to models of polarization and alignment, as well as the construction of accessible tools and media-monitoring observatories: platforms that foster insight into the complexities of social behaviour and opinion dynamics through automated computational analyses of (social) media data. Through an interdisciplinary approach, the present article aims to make both a practical and theoretical contribution to these aspects of the study of opinion dynamics and conflict in new media environments."
+    ],
+    [
+      "The objective of the present article is to critically examine possibilities and limitations of machine-guided exploration and potential facilitation of on-line opinion dynamics on the basis of an experimental data analytics pipeline or observatory for mining and analyzing climate change-related user comments from the news website of The Guardian (TheGuardian.com). Combining insights from the social and political sciences with computational methods for the linguistic analysis of texts, this observatory provides a series of spatial (network) representations of the opinion landscapes on climate change on the basis of causation frames expressed in news website comments. This allows for the exploration of opinion spaces at different levels of detail and aggregation.",
+      "Technical and theoretical questions related to the proposed method and infrastructure for the exploration and facilitation of debates will be discussed in three sections. The first section concerns notions of how to define what constitutes a belief or opinion and how these can be mined from texts. To this end, an approach based on the automated extraction of semantic frames expressing causation is proposed. The observatory thus builds on the theoretical premise that expressions of causation such as `global warming causes rises in sea levels' can be revelatory for a person or group's underlying belief systems. Through a further technical description of the observatory's data-analytical components, section two of the paper deals with matters of spatially modelling the output of the semantic frame extractor and how this might be achieved without sacrificing nuances of meaning. The final section of the paper, then, discusses how insights gained from technologically observing opinion dynamics can inform conceptual modelling efforts and approaches to on-line opinion facilitation. As such, the paper brings into view and critically evaluates the fundamental conceptual leap from machine-guided observation to debate facilitation and intervention.",
+      "Through the case examples from The Guardian's website and the theoretical discussions explored in these sections, the paper intends to make a twofold contribution to the fields of media studies, opinion dynamics and computational social science. Firstly, the paper introduces and chains together a number of data analytics components for social media monitoring (and facilitation) that were developed in the context of the <project name anonymized for review> infrastructure project. The <project name anonymized for review> infrastructure makes the components discussed in this paper available as open web services in order to foster reproducibility and further experimentation and development <infrastructure reference URL anonymized for review>. Secondly, and supplementing these technological and methodological gains, the paper addresses a number of theoretical, epistemological and ethical questions that are raised by experimental approaches to opinion exploration and facilitation. This notably includes methodological questions on the preservation of meaning through text and data mining, as well as the role of human interpretation, responsibility and incentivisation in observing and potentially facilitating opinion dynamics."
+    ],
+    [
+      "In order to study on-line opinion dynamics and build the corresponding climate change opinion observatory discussed in this paper, a corpus of climate-change related news articles and news website comments was analyzed. Concretely, articles from the \u2018climate change\u2019 subsection from the news website of The Guardian dated from 2009 up to April 2019 were processed, along with up to 200 comments and associated metadata for articles where commenting was enabled at the time of publication. The choice for studying opinion dynamics using data from The Guardian is motivated by this news website's prominent position in the media landscape as well as its communicative setting, which is geared towards user engagement. Through this interaction with readers, the news platform embodies many of the recent shifts that characterize our present-day media ecology.",
+      "TheGuardian.com is generally acknowledged to be one of the UK's leading online newspapers, with 8,2 million unique visitors per month as of May 2013 BIBREF6. The website consists of a core news site, as well as a range of subsections that allow for further classification and navigation of articles. Articles related to climate change can for instance be accessed by navigating through the `News' section, over the subsection `environment', to the subsubsection `climate change' BIBREF7. All articles on the website can be read free of charge, as The Guardian relies on a business model that combines revenues from advertising, voluntary donations and paid subscriptions.",
+      "Apart from offering high-quality, independent journalism on a range of topics, a distinguishing characteristic of The Guardian is its penchant for reader involvement and engagement. Adopting to the changing media landscape and appropriating business models that fit the transition from print to on-line news media, the Guardian has transformed itself into a platform that enables forms of citizen journalism, blogging, and welcomes readers comments on news articles BIBREF0. In order for a reader to comment on articles, it is required that a user account is made, which provides a user with a unique user name and a user profile page with a stable URL. According to the website's help pages, providing users with an identity that is consistently recognized by the community fosters proper on-line community behaviour BIBREF8. Registered users can post comments on content that is open to commenting, and these comments are moderated by a dedicated moderation team according to The Guardian's community standards and participation guidelines BIBREF9. In support of digital methods and innovative approaches to journalism and data mining, The Guardian has launched an open API (application programming interface) through which developers can access different types of content BIBREF10. It should be noted that at the moment of writing this article, readers' comments are not accessible through this API. For the scientific and educational purposes of this paper, comments were thus consulted using a dedicated scraper.",
+      "Taking into account this community and technologically-driven orientation, the communicative setting of The Guardian from which opinions are to be mined and the underlying belief system revealed, is defined by articles, participating commenters and comment spheres (that is, the actual comments aggregated by user, individual article or collection of articles) (see Figure FIGREF4).",
+      "In this setting, articles (and previous comments on those articles) can be commented on by participating commenters, each of which bring to the debate his or her own opinions or belief system. What this belief system might consists of can be inferred on a number of levels, with varying degrees of precision. On the most general level, a generic description of the profile of the average reader of The Guardian can be informative. Such profiles have been compiled by market researchers with the purpose of informing advertisers about the demographic that might be reached through this news website (and other products carrying The Guardian's brand). As of the writing of this article, the audience The Guardian is presented to advertisers as a `progressive' audience:",
+      "Living in a world of unprecedented societal change, with the public narratives around politics, gender, body image, sexuality and diet all being challenged. The Guardian is committed to reflecting the progressive agenda, and reaching the crowd that uphold those values. It\u2019s helpful that we reach over half of progressives in the UK BIBREF11.",
+      "A second, equally high-level indicator of the beliefs that might be present on the platform, are the links through which articles on climate change can be accessed. An article on climate change might for instance be consulted through the environment section of the news website, but also through the business section. Assuming that business interests might potentially be at odds with environmental concerns, it could be hypothesized that the particular comment sphere for that article consists of at least two potentially clashing frames of mind or belief systems.",
+      "However, as will be expanded upon further in this article, truly capturing opinion dynamics requires a more systemic and fine-grained approach. The present article therefore proposes a method for harvesting opinions from the actual comment texts. The presupposition is thereby that comment spheres are marked by a diversity of potentially related opinions and beliefs. Opinions might for instance be connected through the reply structure that marks the comment section of an article, but this connection might also manifest itself on a semantic level (that is, the level of meaning or the actual contents of the comments). To capture this multidimensional, interconnected nature of the comment spheres, it is proposed to represent comment spheres as networks, where the nodes represent opinions and beliefs, and edges the relationships between these beliefs (see the spatial representation of beliefs infra). The use of precision language tools to extract such beliefs and their mutual relationships, as will be explored in the following sections, can open up new pathways of model validation and creation."
+    ],
+    [
+      "In traditional experimental settings, survey techniques and associated statistical models provide researchers with established methods to gauge and analyze the opinions of a population. When studying opinion landscapes through on-line social media, however, harvesting beliefs from big textual data such as news website comments and developing or appropriating models for their analysis is a non-trivial task BIBREF12, BIBREF13, BIBREF14.",
+      "In the present context, two challenges related to data-gathering and text mining need to be addressed: (1) defining what constitutes an expression of an opinion or belief, and (2) associating this definition with a pattern that might be extracted from texts. Recent scholarship in the fields of natural language processing (NLP) and argumentation mining has yielded a range of instruments and methods for the (automatic) identification of argumentative claims in texts BIBREF15, BIBREF16. Adding to these instruments and methods, the present article proposes an approach in which belief systems or opinions on climate change are accessed through expressions of causation."
+    ],
+    [
+      "The climate change debate is often characterized by expressions of causation, that is, expressions linking a certain cause with a certain effect. Cultural or societal clashes on climate change might for instance concern diverging assessments of whether global warming is man-made or not BIBREF17. Based on such examples, it can be stated that expressions of causation are closely associated with opinions or beliefs, and that as such, these expressions can be considered a valuable indicator for the range and diversity of the opinions and beliefs that constitute the climate change debate. The observatory under discussion therefore focuses on the extraction and analysis of linguistic patterns called causation frames. As will be further demonstrated in this section, the benefit of this causation-based approach is that it offers a systemic approach to opinion dynamics that comprises different layers of meaning, notably the cognitive or social meaningfulness of patterns on account of their being expressions of causation, as well as further lexical and semantic information that might be used for analysis and comparison.",
+      "The study of expressions of causation as a method for accessing and assessing belief systems and opinions has been formalized and streamlined since the 1970s. Pioneered by political scientist Robert Axelrod and others, this causal mapping method (also referred to as `cognitive mapping') was introduced as a means of reconstructing and evaluating administrative and political decision-making processes, based on the principle that",
+      "the notion of causation is vital to the process of evaluating alternatives. Regardless of philosophical difficulties involved in the meaning of causation, people do evaluate complex policy alternatives in terms of the consequences a particular choice would cause, and ultimately of what the sum of these effects would be. Indeed, such causal analysis is built into our language, and it would be very difficult for us to think completely in other terms, even if we tried BIBREF18.",
+      "Axelrod's causal mapping method comprises a set of conventions to graphically represent networks of causes and effects (the nodes in a network) as well as the qualitative aspects of this relation (the network\u2019s directed edges, notably assertions of whether the causal linkage is positive or negative). These causes and effects are to be extracted from relevant sources by means of a series of heuristics and an encoding scheme (it should be noted that for this task Axelrod had human readers in mind). The graphs resulting from these efforts provide a structural overview of the relations among causal assertions (and thus beliefs):",
+      "The basic elements of the proposed system are quite simple. The concepts a person uses are represented as points, and the causal links between these concepts are represented as arrows between these points. This gives a pictorial representation of the causal assertions of a person as a graph of points and arrows. This kind of representation of assertions as a graph will be called a cognitive map. The policy alternatives, all of the various causes and effects, the goals, and the ultimate utility of the decision maker can all be thought of as concept variables, and represented as points in the cognitive map. The real power of this approach appears when a cognitive map is pictured in graph form; it is then relatively easy to see how each of the concepts and causal relationships relate to each other, and to see the overall structure of the whole set of portrayed assertions BIBREF18.",
+      "In order to construct these cognitive maps based on textual information, Margaret Tucker Wrightson provides a set of reading and coding rules for extracting cause concepts, linkages (relations) and effect concepts from expressions in the English language. The assertion `Our present topic is the militarism of Germany, which is maintaining a state of tension in the Baltic Area' might for instance be encoded as follows: `the militarism of Germany' (cause concept), /+/ (a positive relationship), `maintaining a state of tension in the Baltic area' (effect concept) BIBREF19. Emphasizing the role of human interpretation, it is acknowledged that no strict set of rules can capture the entire spectrum of causal assertions:",
+      "The fact that the English language is as varied as those who use it makes the coder's task complex and difficult. No set of rules will completely solve the problems he or she might encounter. These rules, however, provide the coder with guidelines which, if conscientiously followed, will result in outcomes meeting social scientific standards of comparative validity and reliability BIBREF19.",
+      "To facilitate the task of encoders, the causal mapping method has gone through various iterations since its original inception, all the while preserving its original premises. Recent software packages have for instance been devised to support the data encoding and drawing process BIBREF20. As such, causal or cognitive mapping has become an established opinion and decision mining method within political science, business and management, and other domains. It has notably proven to be a valuable method for the study of recent societal and cultural conflicts. Thomas Homer-Dixon et al. for instance rely on cognitive-affective maps created from survey data to analyze interpretations of the housing crisis in Germany, Israeli attitudes toward the Western Wall, and moderate versus skeptical positions on climate change BIBREF21. Similarly, Duncan Shaw et al. venture to answer the question of `Why did Brexit happen?' by building causal maps of nine televised debates that were broadcast during the four weeks leading up to the Brexit referendum BIBREF22.",
+      "In order to appropriate the method of causal mapping to the study of on-line opinion dynamics, it needs to expanded from applications at the scale of human readers and relatively small corpora of archival documents and survey answers, to the realm of `big' textual data and larger quantities of information. This attuning of cognitive mapping methods to the large-scale processing of texts required for media monitoring necessarily involves a degree of automation, as will be explored in the next section."
+    ],
+    [
+      "As outlined in the previous section, causal mapping is based on the extraction of so-called cause concepts, (causal) relations, and effect concepts from texts. The complexity of each of these these concepts can range from the relatively simple (as illustrated by the easily-identifiable cause and effect relation in the example of `German militarism' cited earlier), to more complex assertions such as `The development of international cooperation in all fields across the ideological frontiers will gradually remove the hostility and fear that poison international relations', which contains two effect concepts (viz. `the hostility that poisons international relations' and `the fear that poisons international relations'). As such, this statement would have to be encoded as a double relationship BIBREF19.",
+      "The coding guidelines in BIBREF19 further reflect that extracting cause and effect concepts from texts is an operation that works on both the syntactical and semantic levels of assertions. This can be illustrated by means of the guidelines for analyzing the aforementioned causal assertion on German militarism:",
+      "1. The first step is the realization of the relationship. Does a subject affect an object? 2. Having recognized that it does, the isolation of the cause and effects concepts is the second step. As the sentence structure indicates, \"the militarism of Germany\" is the causal concept, because it is the initiator of the action, while the direct object clause, \"a state of tension in the Baltic area,\" constitutes that which is somehow influenced, the effect concept BIBREF19.",
+      "In the field of computational linguistics, from which the present paper borrows part of its methods, this procedure for extracting information related to causal assertions from texts can be considered an instance of an operation called semantic frame extraction BIBREF23. A semantic frame captures a coherent part of the meaning of a sentence in a structured way. As documented in the FrameNet project BIBREF24, the Causation frame is defined as follows:",
+      "A Cause causes an Effect. Alternatively, an Actor, a participant of a (implicit) Cause, may stand in for the Cause. The entity Affected by the Causation may stand in for the overall Effect situation or event BIBREF25.",
+      "In a linguistic utterance such as a statement in a news website comment, the Causation frame can be evoked by a series of lexical units, such as `cause', `bring on', etc. In the example `If such a small earthquake CAUSES problems, just imagine a big one!', the Causation frame is triggered by the verb `causes', which therefore is called the frame evoking element. The Cause slot is filled by `a small earthquake', the Effect slot by `problems' BIBREF25.",
+      "In order to automatically mine cause and effects concepts from the corpus of comments on The Guardian, the present paper uses the Penelope semantic frame extractor: a tool that exploits the fact that semantic frames can be expressed as form-meaning mappings called constructions. Notably, frames were extracted from Guardian comments by focusing on the following lexical units (verbs, prepositions and conjunctions), listed in FrameNet as frame evoking elements of the Causation frame: Cause.v, Due to.prep, Because.c, Because of.prep, Give rise to.v, Lead to.v or Result in.v.",
+      "As illustrated by the following examples, the strings output by the semantic frame extractor adhere closely to the original utterance, preserving all of the the comments' causation frames real-world noisiness:",
+      "The output of the semantic frame extractor as such is used as the input for the ensuing pipeline components in the climate change opinion observatory. The aim of a further analysis of these frames is to find patterns in the beliefs and opinions they express. As will be discussed in the following section, which focuses on applications and cases, maintaining semantic nuances in this further analytic process foregrounds the role of models and aggregation levels."
+    ],
+    [
+      "Based on the presupposition that relations between causation frames reveal beliefs, the output of the semantic frame extractor creates various opportunities for exploring opinion landscapes and empirically validating conceptual models for opinion dynamics.",
+      "In general, any alignment of conceptual models and real-world data is an exercise in compromising, as the idealized, abstract nature of models is likely to be at odds with the messiness of the actual data. Finding such a compromise might for instance involve a reduction of the simplicity or elegance of the model, or, on the other hand, an increased aggregation (and thus reduced granularity) of the data.",
+      "Addressing this challenge, the current section reflects on questions of data modelling, aggregation and meaning by exploring, through case examples, different spatial representations of opinion landscapes mined from the TheGuardian.com's comment sphere. These spatial renditions will be understood as network visualizations in which nodes represent argumentative statements (beliefs) and edges the degree of similarity between these statements. On the most general level, then, such a representation can consists of an overview of all the causes expressed in the corpus of climate change-related Guardian comments. This type of visualization provides a birds-eye view of the entire opinion landscape as mined from the comment texts. In turn, such a general overview might elicit more fine-grained, micro-level investigations, in which a particular cause is singled out and its more specific associated effects are mapped. These macro and micro level overviews come with their own proper potential for theory building and evaluation, as well as distinct requirements for the depth or detail of meaning that needs to be represented. To get the most general sense of an opinion landscape one might for instance be more tolerant of abstract renditions of beliefs (e.g. by reducing statements to their most frequently used terms), but for more fine-grained analysis one requires more context and nuance (e.g. adhering as closely as possible to the original comment)."
+    ],
+    [
+      "As follows from the above, one of the most fundamental questions when building automated tools to observe opinion dynamics that potentially aim at advising means of debate facilitation concerns the level of meaning aggregation. A clear argumentative or causal association between, for instance, climate change and catastrophic events such as floods or hurricanes may become detectable by automatic causal frame tracking at the scale of large collections of articles where this association might appear statistically more often, but detection comes with great challenges when the aim is to classify certain sets of only a few statements in more free expression environments such as comment spheres.",
+      "In other words, the problem of meaning aggregation is closely related to issues of scale and aggregation over utterances. The more fine-grained the semantic resolution is, that is, the more specific the cause or effect is that one is interested in, the less probable it is to observe the same statement twice. Moreover, with every independent variable (such as time, different commenters or user groups, etc.), less data on which fine-grained opinion statements are to be detected is available. In the present case of parsed comments from TheGuardian.com, providing insights into the belief system of individual commenters, even if all their statements are aggregated over time, relies on a relatively small set of argumentative statements. This relative sparseness is in part due to the fact that the scope of the semantic frame extractor is confined to the frame evoking elements listed earlier, thus omitting more implicit assertions of causation (i.e. expressions of causation that can only be derived from context and from reading between the lines).",
+      "Similarly, as will be explored in the ensuing paragraphs, matters of scale and aggregation determine the types of further linguistic analyses that can be performed on the output of the frame extractor. Within the field of computational linguistics, various techniques have been developed to represent the meaning of words as vectors that capture the contexts in which these words are typically used. Such analyses might reveal patterns of statistical significance, but it is also likely that in creating novel, numerical representations of the original utterances, the semantic structure of argumentatively linked beliefs is lost.",
+      "In sum, developing opinion observatories and (potential) debate facilitators entails finding a trade-off, or, in fact, a middle way between macro- and micro-level analyses. On the one hand, one needs to leverage automated analysis methods at the scale of larger collections to maximum advantage. But one also needs to integrate opportunities to interactively zoom into specific aspects of interest and provide more fine-grained information at these levels down to the actual statements. This interplay between macro- and micro-level analyses is explored in the case studies below."
+    ],
+    [
+      "The main purpose of the observatory under discussion is to provide insight into the belief structures that characterize the opinion landscape on climate change. For reasons outlined above, this raises questions of how to represent opinions and, correspondingly, determining which representation is most suited as the atomic unit of comparison between opinions. In general terms, the desired outcome of further processing of the output of the semantic frame extractor is a network representation in which similar cause or effect strings are displayed in close proximity to one another. A high-level description of the pipeline under discussion thus goes as follows. In a first step, it can be decided whether one wants to map cause statements or effect statements. Next, the selected statements are grouped per commenter (i.e. a list is made of all cause statements or effect statements per commenter). These statements are filtered in order to retain only nouns, adjectives and verbs (thereby also omitting frequently occurring verbs such as \u2018to be\u2019). The remaining words are then lemmatized, that is, reduced to their dictionary forms. This output is finally translated into a network representation, whereby nodes represent (aggregated) statements, and edges express the semantic relatedness between statements (based on a set overlap whereby the number of shared lemmata are counted).",
+      "As illustrated by two spatial renditions that were created using this approach and visualized using the network analysis tool Gephi BIBREF26, the labels assigned to these nodes (lemmata, full statements, or other) can be appropriated to the scope of the analysis."
+    ],
+    [
+      "Suppose one wants to get a first idea about the scope and diversity of an opinion landscape, without any preconceived notions of this landscape's structure or composition. One way of doing this would be to map all of the causes that are mentioned in comments related to articles on climate change, that is, creating an overview of all the causes that have been retrieved by the frame extractor in a single representation. Such a representation would not immediately provide the granularity to state what the beliefs or opinions in the debates actually are, but rather, it might inspire a sense of what those opinions might be about, thus pointing towards potentially interesting phenomena that might warrant closer examination.",
+      "Figure FIGREF10, a high-level overview of the opinion landscape, reveals a number of areas to which opinions and beliefs might pertain. The top-left clusters in the diagram for instance reveal opinions about the role of people and countries, whereas on the right-hand side, we find a complementary cluster that might point to beliefs concerning the influence of high or increased CO2-emissions. In between, there is a cluster on power and energy sources, reflecting the energy debate's association to both issues of human responsibility and CO2 emissions. As such, the overview can already inspire, potentially at best, some very general hypotheses about the types of opinions that figure in the climate change debate."
+    ],
+    [
+      "Based on the range of topics on which beliefs are expressed, a micro-level analysis can be conducted to reveal what those beliefs are and, for instance, whether they align or contradict each other. This can be achieved by singling out a cause of interest, and mapping out its associated effects.",
+      "As revealed by the global overview of the climate change opinion landscape, a portion of the debate concerns power and energy sources. One topic with a particularly interesting role in this debate is nuclear power. Figure FIGREF12 illustrates how a more detailed representation of opinions on this matter can be created by spatially representing all of the effects associated with causes containing the expression `nuclear power'. Again, similar beliefs (in terms of words used in the effects) are positioned closer to each other, thus facilitating the detection of clusters. Commenters on The Guardian for instance express concerns about the deaths or extinction that might be caused by this energy resource. They also voice opinions on its cleanliness, whether or not it might decrease pollution or be its own source of pollution, and how it reduces CO2-emissions in different countries.",
+      "Whereas the detailed opinion landscape on `nuclear power' is relatively limited in terms of the number of mined opinions, other topics might reveal more elaborate belief systems. This is for instance the case for the phenomenon of `global warming'. As shown in Figure FIGREF13, opinions on global warming are clustered around the idea of `increases', notably in terms of evaporation, drought, heat waves, intensity of cyclones and storms, etc. An adjacent cluster is related to `extremes', such as extreme summers and weather events, but also extreme colds."
+    ],
+    [
+      "The observatory introduced in the preceding paragraphs provides preliminary insights into the range and scope of the beliefs that figure in climate change debates on TheGuardian.com. The observatory as such takes a distinctly descriptive stance, and aims to satisfy, at least in part, the information needs of researchers, activists, journalists and other stakeholders whose main concern is to document, investigate and understand on-line opinion dynamics. However, in the current information sphere, which is marked by polarization, misinformation and a close entanglement with real-world conflicts, taking a mere descriptive or neutral stance might not serve every stakeholder's needs. Indeed, given the often skewed relations between power and information, questions arise as to how media observations might in turn be translated into (political, social or economic) action. Knowledge about opinion dynamics might for instance inform interventions that remedy polarization or disarm conflict. In other words, the construction of (social) media observatories unavoidably lifts questions about the possibilities, limitations and, especially, implications of the machine-guided and human-incentivized facilitation of on-line discussions and debates.",
+      "Addressing these questions, the present paragraph introduces and explores the concept of a debate facilitator, that is, a device that extends the capabilities of the previously discussed observatory to also promote more interesting and constructive discussions. Concretely, we will conceptualize a device that reveals how the personal opinion landscapes of commenters relate to each other (in terms of overlap or lack thereof), and we will discuss what steps might potentially be taken on the basis of such representation to balance the debate. Geared towards possible interventions in the debate, such a device may thus go well beyond the observatory's objectives of making opinion processes and conflicts more transparent, which concomitantly raises a number of serious concerns that need to be acknowledged.",
+      "On rather fundamental ground, tools that steer debates in one way or another may easily become manipulative and dangerous instruments in the hands of certain interest groups. Various aspects of our daily lives are for instance already implicitly guided by recommender systems, the purpose and impact of which can be rather opaque. For this reason, research efforts across disciplines are directed at scrutinizing and rendering such systems more transparent BIBREF28. Such scrutiny is particularly pressing in the context of interventions on on-line communication platforms, which have already been argued to enforce affective communication styles that feed rather than resolve conflict. The objectives behind any facilitation device should therefore be made maximally transparent and potential biases should be fully acknowledged at every level, from data ingest to the dissemination of results BIBREF29. More concretely, the endeavour of constructing opinion observatories and facilitators foregrounds matters of `openness' of data and tools, security, ensuring data quality and representative sampling, accounting for evolving data legislation and policy, building communities and trust, and envisioning beneficial implications. By documenting the development process for a potential facilitation device, the present paper aims to contribute to these on-going investigations and debates. Furthermore, every effort has been made to protect the identities of the commenters involved. In the words of media and technology visionary Jaron Lanier, developers and computational social scientists entering this space should remain fundamentally aware of the fact that `digital information is really just people in disguise' BIBREF30.",
+      "With these reservations in mind, the proposed approach can be situated among ongoing efforts that lead from debate observation to facilitation. One such pathway, for instance, involves the construction of filters to detect hate speech, misinformation and other forms of expression that might render debates toxic BIBREF31, BIBREF32. Combined with community outreach, language-based filtering and detection tools have proven to raise awareness among social media users about the nature and potential implications of their on-line contributions BIBREF33. Similarly, advances can be expected from approaches that aim to extend the scope of analysis beyond descriptions of a present debate situation in order to model how a debate might evolve over time and how intentions of the participants could be included in such an analysis.",
+      "Progress in any of these areas hinges on a further integration of real-world data in the modelling process, as well as a further socio-technical and media-theoretical investigation of how activity on social media platforms and technologies correlate to real-world conflicts. The remainder of this section therefore ventures to explore how conceptual argument communication models for polarization and alignment BIBREF34 might be reconciled with real-world data, and how such models might inform debate facilitation efforts."
+    ],
+    [
+      "As discussed in previous sections, news websites like TheGuardian.com establish a communicative settings in which agents (users, commenters) exchange arguments about different issues or topics. For those seeking to establish a healthy debate, it could thus be of interest to know how different users relate to each other in terms of their beliefs about a certain issue or topic (in this case climate change). Which beliefs are for instance shared by users and which ones are not? In other words, can we map patterns of alignment or polarization among users?",
+      "Figure FIGREF15 ventures to demonstrate how representations of opinion landscapes (generated using the methods outlined above) can be enriched with user information to answer such questions. Specifically, the graph represents the beliefs of two among the most active commenters in the corpus. The opinions of each user are marked using a colour coding scheme: red nodes represent the beliefs of the first user, blue nodes represent the beliefs of the second user. Nodes with a green colour represent beliefs that are shared by both users.",
+      "Taking into account again the factors of aggregation that were discussed in the previous section, Figure FIGREF15 supports some preliminary observations about the relationship between the two users in terms of their beliefs. Generally, given the fact that the graph concerns the two most active commenters on the website, it can be seen that the rendered opinion landscape is quite extensive. It is also clear that the belief systems of both users are not unrelated, as nodes of all colours can be found distributed throughout the graph. This is especially the case for the right-hand top cluster and right-hand bottom cluster of the graph, where green, red, and blue nodes are mixed. Since both users are discussing on articles on climate change, a degree of affinity between opinions or beliefs is to be expected.",
+      "Upon closer examination, a number of disparities between the belief systems of the two commenters can be detected. Considering the left-hand top cluster and center of the graph, it becomes clear that exclusively the red commenter is using a selection of terms related to the economical and socio-political realm (e.g. `people', `american', `nation', `government') and industry (e.g. `fuel', `industry', `car', etc.). The blue commenter, on the other hand, exclusively engages in using a range of terms that could be deemed more technical and scientific in nature (e.g. `feedback', `property', `output', `trend', `variability', etc.). From the graph, it also follows that the blue commenter does not enter into the red commenter's `social' segments of the graph as frequently as the red commenter enters the more scientifically-oriented clusters of the graph (although in the latter cases the red commenter does not use the specific technical terminology of the blue commenter). The cluster where both beliefs mingle the most (and where overlap can be observed), is the top right cluster. This overlap is constituted by very general terms (e.g. `climate', `change', and `science'). In sum, the graph reveals that the commenters' beliefs are positioned most closely to each other on the most general aspects of the debate, whereas there is less relatedness on the social and more technical aspects of the debate. In this regard, the depicted situation seemingly evokes currently on-going debates about the role or responsibilities of the people or individuals versus that of experts when it comes to climate change BIBREF35, BIBREF36, BIBREF37.",
+      "What forms of debate facilitation, then, could be based on these observations? And what kind of collective effects can be expected? As follows from the above, beliefs expressed by the two commenters shown here (which are selected based on their active participation rather than actual engagement or dialogue with one another) are to some extent complementary, as the blue commenter, who displays a scientifically-oriented system of beliefs, does not readily engage with the social topics discussed by the red commenter. As such, the overall opinion landscape of the climate change could potentially be enriched with novel perspectives if the blue commenter was invited to engage in a debate about such topics as industry and government. Similarly, one could explore the possibility of providing explanatory tools or additional references on occasions where the debate takes a more technical turn.",
+      "However, argument-based models of collective attitude formation BIBREF38, BIBREF34 also tell us to be cautious about such potential interventions. Following the theory underlying these models, different opinion groups prevailing during different periods of a debate will activate different argumentative associations. Facilitating exchange between users with complementary arguments supporting similar opinions may enforce biased argument pools BIBREF39 and lead to increasing polarization at the collective level. In the example considered here the two commenters agree on the general topic, but the analysis suggests that they might have different opinions about the adequate direction of specific climate change action. A more fine\u2013grained automatic detection of cognitive and evaluative associations between arguments and opinions is needed for a reliable use of models to predict what would come out of facilitating exchange between two specific users. In this regard, computational approaches to the linguistic analysis of texts such as semantic frame extraction offer productive opportunities for empirically modelling opinion dynamics. Extraction of causation frames allows one to disentangle cause-effect relations between semantic units, which provides a productive step towards mapping and measuring structures of cognitive associations. These opportunities are to be explored by future work."
+    ],
+    [
+      "Ongoing transitions from a print-based media ecology to on-line news and discussion platforms have put traditional forms of news production and consumption at stake. Many challenges related to how information is currently produced and consumed come to a head in news website comment sections, which harbour the potential of providing new insights into how cultural conflicts emerge and evolve. On the basis of an observatory for analyzing climate change-related comments from TheGuardian.com, this article has critically examined possibilities and limitations of the machine-assisted exploration and possible facilitation of on-line opinion dynamics and debates.",
+      "Beyond technical and modelling pathways, this examination brings into view broader methodological and epistemological aspects of the use of digital methods to capture and study the flow of on-line information and opinions. Notably, the proposed approaches lift questions of computational analysis and interpretation that can be tied to an overarching tension between `distant' and `close reading' BIBREF40. In other words, monitoring on-line opinion dynamics means embracing the challenges and associated trade-offs that come with investigating large quantities of information through computational, text-analytical means, but doing this in such a way that nuance and meaning are not lost in the process.",
+      "Establishing productive cross-overs between the level of opinions mined at scale (for instance through the lens of causation frames) and the detailed, closer looks at specific conversations, interactions and contexts depends on a series of preliminaries. One of these is the continued availability of high-quality, accessible data. As the current on-line media ecology is recovering from recent privacy-related scandals (e.g. Cambridge Analytica), such data for obvious reasons is not always easy to come by. In the same legal and ethical vein, reproducibility and transparency of models is crucial to the further development of analytical tools and methods. As the experiments discussed in this paper have revealed, a key factor in this undertaking are human faculties of interpretation. Just like the encoding schemes introduced by Axelrod and others before the wide-spread use of computational methods, present-day pipelines and tools foreground the role of human agents as the primary source of meaning attribution.",
+      "<This project has received funding from the European Union\u2019s Horizon 2020 research and innovation programme under grant agreement No 732942 (Opinion Dynamics and Cultural Conflict in European Spaces \u2013 www.Odycceus.eu).>"
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: "Hinglish"Language -- Modeling a Messy Code-Mixed Language
+
+Question: What is the previous work's model?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Introduction ::: Modeling challenges",
+    "Related Work ::: Transfer learning based approaches",
+    "Related Work ::: Hybrid models",
+    "Dataset and Features",
+    "Dataset and Features ::: Challenges",
+    "Model Architecture",
+    "Model Architecture ::: Loss function",
+    "Model Architecture ::: Models",
+    "Model Architecture ::: Hyper parameters",
+    "Results",
+    "Conclusion and Future work",
+    "References"
+  ],
+  "paragraphs": [
+    [
+      "Hinglish is a linguistic blend of Hindi (very widely spoken language in India) and English (an associate language of urban areas) and is spoken by upwards of 350 million people in India. While the name is based on the Hindi language, it does not refer exclusively to Hindi, but is used in India, with English words blending with Punjabi, Gujarati, Marathi and Hindi. Sometimes, though rarely, Hinglish is used to refer to Hindi written in English script and mixing with English words or phrases. This makes analyzing the language very interesting. Its rampant usage in social media like Twitter, Facebook, Online blogs and reviews has also led to its usage in delivering hate and abuses in similar platforms. We aim to find such content in the social media focusing on the tweets. Hypothetically, if we can classify such tweets, we might be able to detect them and isolate them for further analysis before it reaches public. This will a great application of AI to the social cause and thus is motivating. An example of a simple, non offensive message written in Hinglish could be:",
+      "\"Why do you waste your time with <redacted content>. Aapna ghar sambhalta nahi(<redacted content>). Chale dusro ko basane..!!\"",
+      "The second part of the above sentence is written in Hindi while the first part is in English. Second part calls for an action to a person to bring order to his/her home before trying to settle others."
+    ],
+    [
+      "From the modeling perspective there are couple of challenges introduced by the language and the labelled dataset. Generally, Hinglish follows largely fuzzy set of rules which evolves and is dependent upon the users preference. It doesn't have any formal definitions and thus the rules of usage are ambiguous. Thus, when used by different users the text produced may differ. Overall the challenges posed by this problem are:",
+      "Geographical variation: Depending upon the geography of origination, the content may be be highly influenced by the underlying region.",
+      "Language and phonetics variation: Based on a census in 2001, India has 122 major languages and 1599 other languages. The use of Hindi and English in a code switched setting is highly influenced by these language.",
+      "No grammar rules: Hinglish has no fixed set of grammar rules. The rules are inspired from both Hindi and English and when mixed with slur and slang produce large variation.",
+      "Spelling variation: There is no agreement on the spellings of the words which are mixed with English. For example to express love, a code mixed spelling, specially when used social platforms might be pyaar, pyar or pyr.",
+      "Dataset: Based on some earlier work, only available labelled dataset had 3189 rows of text messages of average length of 116 words and with a range of 1, 1295. Prior work addresses this concern by using Transfer Learning on an architecture learnt on about 14,500 messages with an accuracy of 83.90. We addressed this concern using data augmentation techniques applied on text data."
+    ],
+    [
+      "Mathur et al. in their paper for detecting offensive tweets proposed a Ternary Trans-CNN model where they train a model architecture comprising of 3 layers of Convolution 1D having filter sizes of 15, 12 and 10 and kernel size of 3 followed by 2 dense fully connected layer of size 64 and 3. The first dense FC layer has ReLU activation while the last Dense layer had Softmax activation. They were able to train this network on a parallel English dataset provided by Davidson et al. The authors were able to achieve Accuracy of 83.9%, Precision of 80.2%, Recall of 69.8%.",
+      "The approach looked promising given that the dataset was merely 3189 sentences divided into three categories and thus we replicated the experiment but failed to replicate the results. The results were poor than what the original authors achieved. But, most of the model hyper-parameter choices where inspired from this work."
+    ],
+    [
+      "In another localized setting of Vietnamese language, Nguyen et al. in 2017 proposed a Hybrid multi-channel CNN and LSTM model where they build feature maps for Vietnamese language using CNN to capture shorterm dependencies and LSTM to capture long term dependencies and concatenate both these feature sets to learn a unified set of features on the messages. These concatenated feature vectors are then sent to a few fully connected layers. They achieved an accuracy rate of 87.3% with this architecture."
+    ],
+    [
+      "We used dataset, HEOT obtained from one of the past studies done by Mathur et al. where they annotated a set of cleaned tweets obtained from twitter for the conversations happening in Indian subcontinent. A labelled dataset for a corresponding english tweets were also obtained from a study conducted by Davidson et al. This dataset was important to employ Transfer Learning to our task since the number of labeled dataset was very small. Basic summary and examples of the data from the dataset are below:"
+    ],
+    [
+      "The obtained data set had many challenges and thus a data preparation task was employed to clean the data and make it ready for the deep learning pipeline. The challenges and processes that were applied are stated below:",
+      "Messy text messages: The tweets had urls, punctuations, username mentions, hastags, emoticons, numbers and lots of special characters. These were all cleaned up in a preprocessing cycle to clean the data.",
+      "Stop words: Stop words corpus obtained from NLTK was used to eliminate most unproductive words which provide little information about individual tweets.",
+      "Transliteration: Followed by above two processes, we translated Hinglish tweets into English words using a two phase process",
+      "Transliteration: In phase I, we used translation API's provided by Google translation services and exposed via a SDK, to transliteration the Hinglish messages to English messages.",
+      "Translation: After transliteration, words that were specific to Hinglish were translated to English using an Hinglish-English dictionary. By doing this we converted the Hinglish message to and assortment of isolated words being presented in the message in a sequence that can also be represented using word to vector representation.",
+      "Data augmentation: Given the data set was very small with a high degree of imbalance in the labelled messages for three different classes, we employed a data augmentation technique to boost the learning of the deep network. Following techniques from the paper by Jason et al. was utilized in this setting that really helped during the training phase.Thsi techniques wasnt used in previous studies. The techniques were:",
+      "Synonym Replacement (SR):Randomly choose n words from the sentence that are not stop words. Replace each of these words with one of its synonyms chosen at random.",
+      "Random Insertion (RI):Find a random synonym of a random word in the sentence that is not a stop word. Insert that synonym into a random position in the sentence. Do this n times.",
+      "Random Swap (RS):Randomly choose two words in the sentence and swap their positions. Do this n times.",
+      "Random Deletion (RD):For each word in the sentence, randomly remove it with probability p.",
+      "Word Representation: We used word embedding representations by Glove for creating word embedding layers and to obtain the word sequence vector representations of the processed tweets. The pre-trained embedding dimension were one of the hyperparamaters for model. Further more, we introduced another bit flag hyperparameter that determined if to freeze these learnt embedding.",
+      "Train-test split: The labelled dataset that was available for this task was very limited in number of examples and thus as noted above few data augmentation techniques were applied to boost the learning of the network. Before applying augmentation, a train-test split of 78%-22% was done from the original, cleansed data set. Thus, 700 tweets/messages were held out for testing. All model evaluation were done in on the test set that got generated by this process. The results presented in this report are based on the performance of the model on the test set. The training set of 2489 messages were however sent to an offline pipeline for augmenting the data. The resulting training dataset was thus 7934 messages. the final distribution of messages for training and test was thus below:"
+    ],
+    [
+      "We tested the performance of various model architectures by running our experiment over 100 times on a CPU based compute which later as migrated to GPU based compute to overcome the slow learning progress. Our universal metric for minimizing was the validation loss and we employed various operational techniques for optimizing on the learning process. These processes and its implementation details will be discussed later but they were learning rate decay, early stopping, model checkpointing and reducing learning rate on plateau."
+    ],
+    [
+      "For the loss function we chose categorical cross entropy loss in finding the most optimal weights/parameters of the model. Formally this loss function for the model is defined as below:",
+      "The double sum is over the number of observations and the categories respectively. While the model probability is the probability that the observation i belongs to category c."
+    ],
+    [
+      "Among the model architectures we experimented with and without data augmentation were:",
+      "Fully Connected dense networks: Model hyperparameters were inspired from the previous work done by Vo et al and Mathur et al. This was also used as a baseline model but we did not get appreciable performance on such architecture due to FC networks not being able to capture local and long term dependencies.",
+      "Convolution based architectures: Architecture and hyperparameter choices were chosen from the past study Deon on the subject. We were able to boost the performance as compared to only FC based network but we noticed better performance from architectures that are suitable to sequences such as text messages or any timeseries data.",
+      "Sequence models: We used SimpleRNN, LSTM, GRU, Bidirectional LSTM model architecture to capture long term dependencies of the messages in determining the class the message or the tweet belonged to.",
+      "Based on all the experiments we conducted below model had best performance related to metrics - Recall rate, F1 score and Overall accuracy."
+    ],
+    [
+      "Choice of model parameters were in the above models were inspired from previous work done but then were tuned to the best performance of the Test dataset. Following parameters were considered for tuning.",
+      "Learning rate: Based on grid search the best performance was achieved when learning rate was set to 0.01. This value was arrived by a grid search on lr parameter.",
+      "Number of Bidirectional LSTM units: A set of 32, 64, 128 hidden activation units were considered for tuning the model. 128 was a choice made by Vo et al in modeling for Vietnamese language but with our experiments and with a small dataset to avoid overfitting to train dataset, a smaller unit sizes were considered.",
+      "Embedding dimension: 50, 100 and 200 dimension word representation from Glove word embedding were considered and the best results were obtained with 100d representation, consistent with choices made in the previous work.",
+      "Transfer learning on Embedding; Another bit flag for training the embedding on the train data or freezing the embedding from Glove was used. It was determined that set of pre-trained weights from Glove was best when it was fine tuned with Hinglish data. It provides evidence that a separate word or sentence level embedding when learnt for Hinglish text analysis will be very useful.",
+      "Number of dense FC layers.",
+      "Maximum length of the sequence to be considered: The max length of tweets/message in the dataset was 1265 while average was 116. We determined that choosing 200 resulted in the best performance."
+    ],
+    [
+      "During our experimentation, it was evident that this is a hard problem especially detecting the hate speech, text in a code- mixed language. The best recall rate of 77 % for hate speech was obtained by a Bidirectional LSTM with 32 units with a recurrent drop out rate of 0.2. Precision wise GRU type of RNN sequence model faired better than other kinds for hate speech detection. On the other hand for detecting offensive and non offensive tweets, fairly satisfactory results were obtained. For offensive tweets, 92 % precision was and recall rate of 88% was obtained with GRU versus BiLSTM based models. Comparatively, Recall of 85 % and precision of 76 % was obtained by again GRU and BiLSTM based models as shown and marked in the results."
+    ],
+    [
+      "The results of the experiments are encouraging on detective offensive vs non offensive tweets and messages written in Hinglish in social media. The utilization of data augmentation technique in this classification task was one of the vital contributions which led us to surpass results obtained by previous state of the art Hybrid CNN-LSTM based models. However, the results of the model for predicting hateful tweets on the contrary brings forth some shortcomings of the model. The biggest shortcoming on the model based on error analysis indicates less than generalized examples presented by the dataset. We also note that the embedding learnt from the Hinglish data set may be lacking and require extensive training to have competent word representations of Hinglish text. Given this learning's, we identify that creating word embeddings on much larger Hinglish corpora may have significant results. We also hypothesize that considering alternate methods than translation and transliteration may prove beneficial."
+    ],
+    [
+      "[1] Mathur, Puneet and Sawhney, Ramit and Ayyar, Meghna and Shah, Rajiv, Did you offend me? classification of offensive tweets in hinglish language, Proceedings of the 2nd Workshop on Abusive Language Online (ALW2)",
+      "[2] Mathur, Puneet and Shah, Rajiv and Sawhney, Ramit and Mahata, Debanjan Detecting offensive tweets in hindi-english code-switched language Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media",
+      "[3] Vo, Quan-Hoang and Nguyen, Huy-Tien and Le, Bac and Nguyen, Minh-Le Multi-channel LSTM-CNN model for Vietnamese sentiment analysis 2017 9th international conference on knowledge and systems engineering (KSE)",
+      "[4] Hochreiter, Sepp and Schmidhuber, J\u00fcrgen Long short-term memory Neural computation 1997",
+      "[5] Sinha, R Mahesh K and Thakur, Anil Multi-channel LSTM-CNN model for Vietnamese sentiment analysis 2017 9th international conference on knowledge and systems engineering (KSE)",
+      "[6] Pennington, Jeffrey and Socher, Richard and Manning, Christopher Glove: Global vectors for word representation Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP)",
+      "[7] Zhang, Lei and Wang, Shuai and Liu, Bing Deep learning for sentiment analysis: A survey Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery",
+      "[8] Caruana, Rich and Lawrence, Steve and Giles, C Lee Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping Advances in neural information processing systems",
+      "[9] Beale, Mark Hudson and Hagan, Martin T and Demuth, Howard B Neural network toolbox user\u2019s guide The MathWorks Incs",
+      "[10] Chollet, Fran\u00e7ois and others Keras: The python deep learning library Astrophysics Source Code Library",
+      "[11] Wei, Jason and Zou, Kai EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)"
+    ]
+  ]
+}
+```
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new file mode 100644
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+++ b/qasper-0185/instruction.md
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+Name of Paper: "Hinglish"Language -- Modeling a Messy Code-Mixed Language
+
+Question: What dataset is used?
\ No newline at end of file
diff --git a/qasper-0193/instruction.md b/qasper-0193/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..8197f0573963d5918a3634f2d2c4f2abf254af47
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+++ b/qasper-0193/instruction.md
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+Name of Paper: How Language-Neutral is Multilingual BERT?
+
+Question: Are language-specific and language-neutral components disjunctive?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Centering mBERT Representations",
+    "Probing Tasks",
+    "Probing Tasks ::: Language Identification.",
+    "Probing Tasks ::: Language Similarity.",
+    "Probing Tasks ::: Parallel Sentence Retrieval.",
+    "Probing Tasks ::: Word Alignment.",
+    "Probing Tasks ::: MT Quality Estimation.",
+    "Experimental Setup",
+    "Results ::: Language Identification.",
+    "Results ::: Language Similarity.",
+    "Results ::: Parallel Sentence Retrieval.",
+    "Results ::: Word Alignment.",
+    "Results ::: MT Quality Estimation.",
+    "Fine-tuning mBERT",
+    "Fine-tuning mBERT ::: UDify",
+    "Fine-tuning mBERT ::: lng-free",
+    "Conclusions"
+  ],
+  "paragraphs": [
+    [
+      "Multilingual BERT (mBERT; BIBREF0) is gaining popularity as a contextual representation for various multilingual tasks, such as dependency parsing BIBREF1, BIBREF2, cross-lingual natural language inference (XNLI) or named-entity recognition (NER) BIBREF3, BIBREF4, BIBREF5.",
+      "BIBREF3 present an exploratory paper showing that mBERT can be used cross-lingually for zero-shot transfer in morphological and syntactic tasks, at least for typologically similar languages. They also study an interesting semantic task, sentence-retrieval, with promising initial results. Their work leaves many open questions in terms of how good the cross-lingual mBERT representation is for semantics, motivating our work.",
+      "In this paper, we directly assess the semantic cross-lingual properties of mBERT. To avoid methodological issues with zero-shot transfer (possible language overfitting, hyper-parameter tuning), we selected tasks that only involve a direct comparison of the representations: cross-lingual sentence retrieval, word alignment, and machine translation quality estimation (MT QE). Additionally, we explore how the language is represented in the embeddings by training language identification classifiers and assessing how the representation similarity corresponds to phylogenetic language families.",
+      "Our results show that the mBERT representations, even after language-agnostic fine-tuning, are not very language-neutral. However, the identity of the language can be approximated as a constant shift in the representation space. An even higher language-neutrality can still be achieved by a linear projection fitted on a small amount of parallel data.",
+      "Finally, we present attempts to strengthen the language-neutral component via fine-tuning: first, for multi-lingual syntactic and morphological analysis; second, towards language identity removal via a adversarial classifier."
+    ],
+    [
+      "Since the publication of mBERT BIBREF0, many positive experimental results were published.",
+      "BIBREF2 reached impressive results in zero-shot dependency parsing. However, the representation used for the parser was a bilingual projection of the contextual embeddings based on word-alignment trained on parallel data.",
+      "BIBREF3 recently examined the cross-lingual properties of mBERT on zero-shot NER and part-of-speech (POS) tagging but the success of zero-shot transfer strongly depends on how typologically similar the languages are. Similarly, BIBREF4 trained good multilingual models for POS tagging, NER, and XNLI, but struggled to achieve good results in the zero-shot setup.",
+      "BIBREF3 assessed mBERT on cross-lingual sentence retrieval between three language pairs. They observed that if they subtract the average difference between the embeddings from the target language representation, the retrieval accuracy significantly increases. We systematically study this idea in the later sections.",
+      "Many experiments show BIBREF4, BIBREF5, BIBREF1 that downstream task models can extract relevant features from the multilingual representations. But these results do not directly show language-neutrality, i.e., to what extent are similar phenomena are represented similarly across languages. The models can obtain the task-specific information based on the knowledge of the language, which (as we show later) can be easily identified. Our choice of evaluation tasks eliminates this risk by directly comparing the representations. Limited success in zero-shot setups and the need for explicit bilingual projection in order to work well BIBREF3, BIBREF4, BIBREF6 also shows limited language neutrality of mBERT."
+    ],
+    [
+      "Following BIBREF3, we hypothesize that a sentence representation in mBERT is composed of a language-specific component, which identifies the language of the sentence, and a language-neutral component, which captures the meaning of the sentence in a language-independent way. We assume that the language-specific component is similar across all sentences in the language.",
+      "We thus try to remove the language-specific information from the representations by centering the representations of sentences in each language so that their average lies at the origin of the vector space. We do this by estimating the language centroid as the mean of the mBERT representations for a set of sentences in that language and subtracting the language centroid from the contextual embeddings.",
+      "We then analyze the semantic properties of both the original and the centered representations using a range of probing tasks. For all tasks, we test all layers of the model. For tasks utilizing a single-vector sentence representation, we test both the vector corresponding to the [cls] token and mean-pooled states."
+    ],
+    [
+      "We employ five probing tasks to evaluate the language neutrality of the representations."
+    ],
+    [
+      "With a representation that captures all phenomena in a language-neutral way, it should be difficult to determine what language the sentence is written in. Unlike other tasks, language identification does require fitting a classifier. We train a linear classifier on top of a sentence representation to try to classify the language of the sentence."
+    ],
+    [
+      "Experiments with POS tagging BIBREF3 suggest that similar languages tend to get similar representations on average. We quantify that observation by measuring how languages tend to cluster by the language families using V-measure over hierarchical clustering of the language centeroid BIBREF7."
+    ],
+    [
+      "For each sentence in a multi-parallel corpus, we compute the cosine distance of its representation with representations of all sentences on the parallel side of the corpus and select the sentence with the smallest distance.",
+      "Besides the plain and centered [cls] and mean-pooled representations, we evaluate explicit projection into the \u201cEnglish space\u201d. For each language, we fit a linear regression projecting the representations into English representation space using a small set of parallel sentences."
+    ],
+    [
+      "While sentence retrieval could be done with keyword spotting, computing bilingual alignment requires resolving detailed correspondence on the word level.",
+      "We find the word alignment as a minimum weighted edge cover of a bipartite graph. The graph connects the tokens of the sentences in the two languages and edges between them are weighted with the cosine distance of the token representation. Tokens that get split into multiple subwords are represented using the average of the embeddings of the subwords. Note that this algorithm is invariant to representation centering which would only change the edge weights by a constant offset.",
+      "We evaluate the alignment using the F$_1$ score over both sure and possible alignment links in a manually aligned gold standard."
+    ],
+    [
+      "MT QE assesses the quality of an MT system output without having access to a reference translation.",
+      "The standard evaluation metric is the correlation with the Human-targeted Translation Error Rate which is the number of edit operations a human translator would need to do to correct the system output. This is a more challenging task than the two previous ones because it requires capturing more fine-grained differences in meaning.",
+      "We evaluate how cosine distance of the representation of the source sentence and of the MT output reflects the translation quality. In addition to plain and centered representations, we also test trained bilingual projection, and a fully supervised regression trained on training data."
+    ],
+    [
+      "We use a pre-trained mBERT model that was made public with the BERT release. The model dimension is 768, hidden layer dimension 3072, self-attention uses 12 heads, the model has 12 layers. It uses a vocabulary of 120k wordpieces that is shared for all languages.",
+      "To train the language identification classifier, for each of the BERT languages we randomly selected 110k sentences of at least 20 characters from Wikipedia, and keep 5k for validation and 5k for testing for each language. The training data are also used for estimating the language centroids.",
+      "For parallel sentence retrieval, we use a multi-parallel corpus of test data from the WMT14 evaluation campaign BIBREF8 with 3,000 sentences in Czech, English, French, German, Hindi, and Russian. The linear projection experiment uses the WMT14 development data.",
+      "We use manually annotated word alignment datasets to evaluate word alignment between English on one side and Czech (2.5k sent.; BIBREF9), Swedish (192 sent.; BIBREF10), German (508 sent.), French (447 sent.; BIBREF11) and Romanian (248 sent.; BIBREF12) on the other side. We compare the results with FastAlign BIBREF13 that was provided with 1M additional parallel sentences from ParaCrawl BIBREF14 in addition to the test data.",
+      "For MT QE, we use English-German data provided for the WMT19 QE Shared Task BIBREF15 consisting training and test data with source senteces, their automatic translations, and manually corrections."
+    ],
+    [
+      "Table TABREF7 shows that centering the sentence representations considerably decreases the accuracy of language identification, especially in the case of mean-pooled embeddings. This indicates that the proposed centering procedure does indeed remove the language-specific information to a great extent."
+    ],
+    [
+      "Figure FIGREF9 is a tSNE plot BIBREF16 of the language centroids, showing that the similarity of the centroids tends to correspond to the similarity of the languages. Table TABREF10 confirms that the hierarchical clustering of the language centroids mostly corresponds to the language families."
+    ],
+    [
+      "Results in Table TABREF12 reveal that the representation centering dramatically improves the retrieval accuracy, showing that it makes the representations more language-neutral. However, an explicitly learned projection of the representations leads to a much greater improvement, reaching a close-to-perfect accuracy, even though the projection was fitted on relatively small parallel data. The accuracy is higher for mean-pooled states than for the [cls] embedding and varies according to the layer of mBERT used (see Figure FIGREF13)."
+    ],
+    [
+      "Table TABREF15 shows that word-alignment based on mBERT representations surpasses the outputs of the standard FastAlign tool even if it was provided large parallel corpus. This suggests that word-level semantics are well captured by mBERT contextual embeddings. For this task, learning an explicit projection had a negligible effect on the performance."
+    ],
+    [
+      "Qualitative results of MT QE are tabulated in Table TABREF18. Unlike sentence retrieval, QE is more sensitive to subtle differences between sentences. Measuring the distance of the non-centered sentence vectors does not correlate with translation quality at all. Centering or explicit projection only leads to a mild correlation, much lower than a supervisedly trained regression;and even better performance is possible BIBREF15. The results show that the linear projection between the representations only captures a rough semantic correspondence, which does not seem to be sufficient for QE, where the most indicative feature appears to be sentence complexity."
+    ],
+    [
+      "We also considered model fine-tuning towards stronger language neutrality. We evaluate two fine-tuned versions of mBERT: UDify, tuned for a multi-lingual dependency parser, and lng-free, tuned to jettison the language-specific information from the representations."
+    ],
+    [
+      "The UDify model BIBREF1 uses mBERT to train a single model for dependency parsing and morphological analysis of 75 languages. During the parser training, mBERT is fine-tuned, which improves the parser accuracy. Results on zero-shot parsing suggest that the fine-tuning leads to more cross-lingual representations with respect to morphology and syntax.",
+      "However, our analyses show that fine-tuning mBERT for multilingual dependency parsing does not remove the language identity information from the representations and actually makes the representations less semantically cross-lingual."
+    ],
+    [
+      "In this experiment, we try to make the representations more language-neutral by removing the language identity from the model using an adversarial approach. We continue training mBERT in a multi-task learning setup with the masked LM objective with the same sampling procedure BIBREF0 jointly with adversarial language ID classifiers BIBREF17. For each layer, we train one classifier for the [cls] token and one for the mean-pooled hidden states with the gradient reversal layer BIBREF18 between mBERT and the classifier.",
+      "The results reveal that the adversarial removal of language information succeeds in dramatically decreasing the accuracy of the language identification classifier; the effect is strongest in deeper layers for which the standard mBERT tend to perform better (see Figure FIGREF22). However, other tasksare not affected by the adversarial fine-tuning."
+    ],
+    [
+      "Using a set of semantically oriented tasks that require explicit semantic cross-lingual representations, we showed that mBERT contextual embeddings do not represent similar semantic phenomena similarly and therefore they are not directly usable for zero-shot cross-lingual tasks.",
+      "Contextual embeddings of mBERT capture similarities between languages and cluster the languages by their families. Neither cross-lingual fine-tuning nor adversarial language identity removal breaks this property. A part of language information is encoded by the position in the embedding space, thus a certain degree of cross-linguality can be achieved by centering the representations for each language. Exploiting this property allows a good cross-lingual sentence retrieval performance and bilingual word alignment (which is invariant to the shift). A good cross-lingual representation can be achieved by fitting a supervised projection on a small parallel corpus."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: How Language-Neutral is Multilingual BERT?
+
+Question: How they show that mBERT representations can be split into a language-specific component and a language-neutral component?
\ No newline at end of file
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+Name of Paper: Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?
+
+Question: What faithfulness criteria does they propose?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Faithfulness vs. Plausibility",
+    "Inherently Interpretable?",
+    "Evaluation via Utility",
+    "Guidelines for Evaluating Faithfulness",
+    "Guidelines for Evaluating Faithfulness ::: Be explicit in what you evaluate.",
+    "Guidelines for Evaluating Faithfulness ::: Faithfulness evaluation should not involve human-judgement on the quality of interpretation.",
+    "Guidelines for Evaluating Faithfulness ::: Faithfulness evaluation should not involve human-provided gold labels.",
+    "Guidelines for Evaluating Faithfulness ::: Do not trust \u201cinherent interpretability\u201d claims.",
+    "Guidelines for Evaluating Faithfulness ::: Faithfulness evaluation of IUI systems should not rely on user performance.",
+    "Defining Faithfulness",
+    "Defining Faithfulness ::: Assumption 1 (The Model Assumption).",
+    "Defining Faithfulness ::: Assumption 2 (The Prediction Assumption).",
+    "Defining Faithfulness ::: Assumption 3 (The Linearity Assumption).",
+    "Is Faithful Interpretation Impossible?",
+    "Towards Better Faithfulness Criteria",
+    "Conclusion",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "Fueled by recent advances in deep-learning and language processing, NLP systems are increasingly being used for prediction and decision-making in many fields BIBREF0, including sensitive ones such as health, commerce and law BIBREF1. Unfortunately, these highly flexible and highly effective neural models are also opaque. There is therefore a critical need for explaining learning-based models' decisions.",
+      "The emerging research topic of interpretability or explainability has grown rapidly in recent years. Unfortunately, not without growing pains.",
+      "One such pain is the challenge of defining\u2014and evaluating\u2014what constitutes a quality interpretation. Current approaches define interpretation in a rather ad-hoc manner, motivated by practical use-cases and applications. However, this view often fails to distinguish between distinct aspects of the interpretation's quality, such as readability, plausibility and faithfulness BIBREF2. We argue (\u00a7SECREF2, \u00a7SECREF5) such conflation is harmful, and that faithfulness should be defined and evaluated explicitly, and independently from plausibility.",
+      "Our main focus is the evaluation of the faithfulness of an explanation. Intuitively, a faithful interpretation is one that accurately represents the reasoning process behind the model's prediction. We find this to be a pressing issue in explainability: in cases where an explanation is required to be faithful, imperfect or misleading evaluation can have disastrous effects.",
+      "While literature in this area may implicitly or explicitly evaluate faithfulness for specific explanation techniques, there is no consistent and formal definition of faithfulness. We uncover three assumptions that underlie all these attempts. By making the assumptions explicit and organizing the literature around them, we \u201cconnect the dots\u201d between seemingly distinct evaluation methods, and also provide a basis for discussion regarding the desirable properties of faithfulness (\u00a7SECREF6).",
+      "Finally, we observe a trend by which faithfulness is treated as a binary property, followed by showing that an interpretation method is not faithful. We claim that this is unproductive (\u00a7SECREF7), as the assumptions are nearly impossible to satisfy fully, and it is all too easy to disprove the faithfulness of an interpretation method via a counter-example. What can be done? We argue for a more practical view of faithfulness, calling for a graded criteria that measures the extent and likelihood of an interpretation to be faithful, in practice (\u00a7SECREF8). While we started to work in this area, we pose the exact formalization of these criteria, and concrete evaluations methods for them, as a central challenge to the community for the coming future."
+    ],
+    [
+      "There is considerable research effort in attempting to define and categorize the desiderata of a learned system's interpretation, most of which revolves around specific use-cases BIBREF17, BIBREF15.",
+      "Two particularly notable criteria, each useful for a different purposes, are plausibility and faithfulness. \u201cPlausibility\u201d refers to how convincing the interpretation is to humans, while \u201cfaithfulness\u201d refers to how accurately it reflects the true reasoning process of the model BIBREF2, BIBREF18.",
+      "Naturally, it is possible to satisfy one of these properties without the other. For example, consider the case of interpretation via post-hoc text generation\u2014where an additional \u201cgenerator\u201d component outputs a textual explanation of the model's decision, and the generator is learned with supervision of textual explanations BIBREF19, BIBREF20, BIBREF21. In this case, plausibility is the dominating property, while there is no faithfulness guarantee.",
+      "Despite the difference between the two criteria, many authors do not clearly make the distinction, and sometimes conflate the two. Moreoever, the majority of works do not explicitly name the criteria under consideration, even when they clearly belong to one camp or the other.",
+      "We argue that this conflation is dangerous. For example, consider the case of recidivism prediction, where a judge is exposed to a model's prediction and its interpretation, and the judge believes the interpretation to reflect the model's reasoning process. Since the interpretation's faithfulness carries legal consequences, a plausible but unfaithful interpretation may be the worst-case scenario. The lack of explicit claims by research may cause misinformation to potential users of the technology, who are not versed in its inner workings. Therefore, clear distinction between these terms is critical."
+    ],
+    [
+      "A distinction is often made between two methods of achieving interpretability: (1) interpreting existing models via post-hoc techniques; and (2) designing inherently interpretable models. BIBREF29 argues in favor of inherently interpretable models, which by design claim to provide more faithful interpretations than post-hoc interpretation of black-box models.",
+      "We warn against taking this argumentation at face-value: a method being \u201cinherently interpretable\u201d is merely a claim that needs to be verified before it can be trusted. Indeed, while attention mechanisms have been considered as \u201cinherently interpretable\u201d BIBREF30, BIBREF31, recent work cast doubt regarding their faithfulness BIBREF32, BIBREF33, BIBREF18."
+    ],
+    [
+      "While explanations have many different use-cases, such as model debugging, lawful guarantees or health-critical guarantees, one other possible use-case with particularly prominent evaluation literature is Intelligent User Interfaces (IUI), via Human-Computer Interaction (HCI), of automatic models assisting human decision-makers. In this case, the goal of the explanation is to increase the degree of trust between the user and the system, giving the user more nuance towards whether the system's decision is likely correct, or not. In the general case, the final evaluation metric is the performance of the user at their task BIBREF34. For example, BIBREF35 evaluate various explanations of a model in a setting of trivia question answering.",
+      "However, in the context of faithfulness, we must warn against HCI-inspired evaluation, as well: increased performance in this setting is not indicative of faithfulness; rather, it is indicative of correlation between the plausibility of the explanations and the model's performance.",
+      "To illustrate, consider the following fictional case of a non-faithful explanation system, in an HCI evaluation setting: the explanation given is a heat-map of the textual input, attributing scores to various tokens. Assume the system explanations behave in the following way: when the output is correct, the explanation consists of random content words; and when the output is incorrect, it consists of random punctuation marks. In other words, the explanation is more likely to appear plausible when the model is correct, while at the same time not reflecting the true decision process of the model. The user, convinced by the nicer-looking explanations, performs better using this system. However, the explanation consistently claimed random tokens to be highly relevant to the model's reasoning process. While the system is concretely useful, the claims given by the explanation do not reflect the model's decisions whatsoever (by design).",
+      "While the above scenario is extreme, this misunderstanding is not entirely unlikely, since any degree of correlation between plausibility and model performance will result in increased user performance, regardless of any notion of faithfulness."
+    ],
+    [
+      "We propose the following guidelines for evaluating the faithfulness of explanations. These guidelines address common pitfalls and sub-optimal practices we observed in the literature."
+    ],
+    [
+      "Conflating plausability and faithfulness is harmful. You should be explicit on which one of them you evaluate, and use suitable methodologies for each one. Of course, the same applies when designing interpretation techniques\u2014be clear about which properties are being prioritized."
+    ],
+    [
+      "We note that: (1) humans cannot judge if an interpretation is faithful or not: if they understood the model, interpretation would be unnecessary; (2) for similar reasons, we cannot obtain supervision for this problem, either. Therefore, human judgement should not be involved in evaluation for faithfulness, as human judgement measures plausability."
+    ],
+    [
+      "We should be able to interpret incorrect model predictions, just the same as correct ones. Evaluation methods that rely on gold labels are influenced by human priors on what should the model do, and again push the evaluation in the direction of plausability."
+    ],
+    [
+      "Inherent interpretability is a claim until proven otherwise. Explanations provided by \u201cinherently interpretable\u201d models must be held to the same standards as post-hoc interpretation methods, and be evaluated for faithfulness using the same set of evaluation techniques."
+    ],
+    [
+      "End-task user performance in HCI settings is merely indicative of correlation between plausibility and model performance, however small this correlation is. While important to evaluate the utility of the interpretations for some use-cases, it is unrelated to faithfulness."
+    ],
+    [
+      "What does it mean for an interpretation method to be faithful? Intuitively, we would like the provided interpretation to reflect the true reasoning process of the model when making a decision. But what is a reasoning process of a model, and how can reasoning processes be compared to each other?",
+      "Lacking a standard definition, different works evaluate their methods by introducing tests to measure properties that they believe good interpretations should satisfy. Some of these tests measure aspects of faithfulness. These ad-hoc definitions are often unique to each paper and inconsistent with each other, making it hard to find commonalities.",
+      "We uncover three assumptions that underlie all these methods, enabling us to organize the literature along standardized axes, and relate seemingly distinct lines of work. Moreover, exposing the underlying assumptions enables an informed discussion regarding their validity and merit (we leave such a discussion for future work, by us or others).",
+      "These assumptions, to our knowledge, encapsulate the current working definitions of faithfulness used by the research community."
+    ],
+    [
+      "Two models will make the same predictions if and only if they use the same reasoning process.",
+      "Corollary 1.1. An interpretation system is unfaithful if it results in different interpretations of models that make the same decisions.",
+      "As demonstrated by a recent example concerning NLP models, it can be used for proof by counter-example. Theoretically, if all possible models which can perfectly mimic the model's decisions also provide the same interpretations, then they could be deemed faithful. Conversely, showing that two models provide the same results but different interpretations, disprove the faithfulness of the method. BIBREF18 show how these counter-examples can be derived with adversarial training of models which can mimic the original model, yet provide different explanations.",
+      "Corollary 1.2. An interpretation is unfaithful if it results in different decisions than the model it interprets.",
+      "A more direct application of the Model Assumption is via the notion of fidelity BIBREF15, BIBREF8. For cases in which the explanation is itself a model capable of making decisions (e.g., decision trees or rule lists BIBREF36), fidelity is defined as the degree to which the explanation model can mimic the original model's decisions (as an accuracy score). For cases where the explanation is not a computable model, BIBREF37 propose a simple way of mapping explanations to decisions via crowd-sourcing, by asking humans to simulate the model's decision without any access to the model, and only access to the input and explanation (termed forward simulation). This idea is further explored and used in practice by BIBREF38."
+    ],
+    [
+      "On similar inputs, the model makes similar decisions if and only if its reasoning is similar.",
+      "Corollary 2. An interpretation system is unfaithful if it provides different interpretations for similar inputs and outputs.",
+      "Since the interpretation serves as a proxy for the model's \u201creasoning\u201d, it should satisfy the same constraints. In other words, interpretations of similar decisions should be similar, and interpretations of dissimilar decisions should be dissimilar.",
+      "This assumption is more useful to disprove the faithfulness of an interpretation rather than prove it, since a disproof requires finding appropriate cases where the assumption doesn't hold, where a proof would require checking a (very large) satisfactory quantity of examples, or even the entire input space.",
+      "One recent discussion in the NLP community BIBREF33, BIBREF18 concerns the use of this underlying assumption for evaluating attention heat-maps as explanations. The former attempts to provide different explanations of similar decisions per instance. The latter critiques the former and is based more heavily on the model assumption, described above.",
+      "Additionally, BIBREF39 propose to introduce a constant shift to the input space, and evaluate whether the explanation changes significantly as the final decision stays the same. BIBREF16 formalize a generalization of this technique under the term interpretability robustness: interpretations should be invariant to small perturbations in the input (a direct consequence of the prediction assumption). BIBREF40 further expand on this notion as \u201cconsistency of the explanation with respect to the model\u201d. Unfortunately, robustness measures are difficult to apply in NLP settings due to the discrete input."
+    ],
+    [
+      "Certain parts of the input are more important to the model reasoning than others. Moreover, the contributions of different parts of the input are independent from each other.",
+      "Corollary 3. Under certain circumstances, heat-map interpretations can be faithful.",
+      "This assumption is employed by methods that consider heat-maps (e.g., attention maps) over the input as explanations, particularly popular in NLP. Heat-maps are claims about which parts of the input are more relevant than others to the model's decision. As such, we can design \u201cstress tests\u201d to verify whether they uphold their claims.",
+      "One method proposed to do so is erasure, where the \u201cmost relevant\u201d parts of the input\u2014according to the explanation\u2014are erased from the input, in expectation that the model's decision will change BIBREF25, BIBREF42, BIBREF32. Otherwise, the \u201cleast relevant\u201d parts of the input may be erased, in expectation that the model's decision will not change BIBREF43. BIBREF44, BIBREF45 propose two measures of comprehensiveness and sufficiency as a formal generalization of erasure: as the degree by which the model is influenced by the removal of the high-ranking features, or by inclusion of solely the high-ranking features."
+    ],
+    [
+      "The aforementioned assumptions are currently utilized to evaluate faithfulness in a binary manner, whether an interpretation is strictly faithful or not. Specifically, they are most often used to show that a method is not faithful, by constructing cases in which the assumptions do not hold for the suggested method. In other words, there is a clear trend of proof via counter-example, for various interpretation methods, that they are not globally faithful.",
+      "We claim that this is unproductive, as we expect these various methods to consistently result in negative (not faithful) results, continuing the current trend. This follows because an interpretation functions as an approximation of the model or decision's true reasoning process, so it by definition loses information. By the pigeonhole principle, there will be inputs with deviation between interpretation and reasoning.",
+      "This is observed in practice, in numerous work that show adversarial behavior, or pathological behaviours, that arise from the deeply non-linear and high-dimensional decision boundaries of current models. Furthermore, because we lack supervision regarding which models or decisions are indeed mappable to human-readable concepts, we cannot ignore the approximation errors.",
+      "This poses a high bar for explanation methods to fulfill, a bar which we estimate will not be overcome soon, if at all. What should we do, then, if we desire a system that provides faithful explanations?"
+    ],
+    [
+      "We argue that a way out of this standstill is in a more practical and nuanced methodology for defining and evaluating faithfulness. We propose the following challenge to the community: We must develop formal definition and evaluation for faithfulness that allows us the freedom to say when a method is sufficiently faithful to be useful in practice.",
+      "We note two possible approaches to this end:",
+      "Across models and tasks: The degree (as grayscale) of faithfulness at the level of specific models and tasks. Perhaps some models or tasks allow sufficiently faithful interpretation, even if the same is not true for others.",
+      "For example, the method may not be faithful for some question-answering task, but faithful for movie review sentiment, perhaps based on various syntactic and semantic attributes of those tasks.",
+      "Across input space: The degree of faithfulness at the level of subspaces of the input space, such as neighborhoods of similar inputs, or singular inputs themselves. If we are able to say with some degree of confidence whether a specific decision's explanation is faithful to the model, even if the interpretation method is not considered universally faithful, it can be used with respect to those specific areas or instances only."
+    ],
+    [
+      "The opinion proposed in this paper is two-fold:",
+      "First, interpretability evaluation often conflates evaluating faithfulness and plausibility together. We should tease apart the two definitions and focus solely on evaluating faithfulness without any supervision or influence of the convincing power of the interpretation.",
+      "Second, faithfulness is often evaluated in a binary \u201cfaithful or not faithful\u201d manner, and we believe strictly faithful interpretation is a \u201cunicorn\u201d which will likely never be found. We should instead evaluate faithfulness on a more nuanced \u201cgrayscale\u201d that allows interpretations to be useful even if they are not globally and definitively faithful."
+    ],
+    [
+      "We thank Yanai Elazar for welcome input on the presentation and organization of the paper. We also thank the reviewers for additional feedback and pointing to relevant literature in HCI and IUI.",
+      "This project has received funding from the Europoean Research Council (ERC) under the Europoean Union's Horizon 2020 research and innovation programme, grant agreement No. 802774 (iEXTRACT)."
+    ]
+  ]
+}
+```
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+Name of Paper: Interpreting Recurrent and Attention-Based Neural Models: a Case Study on Natural Language Inference
+
+Question: How many layers are there in their model?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Task and Model",
+    "Visualization of Attention and Gating",
+    "Attention",
+    "LSTM Gating Signals",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Deep learning has achieved tremendous success for many NLP tasks. However, unlike traditional methods that provide optimized weights for human understandable features, the behavior of deep learning models is much harder to interpret. Due to the high dimensionality of word embeddings, and the complex, typically recurrent architectures used for textual data, it is often unclear how and why a deep learning model reaches its decisions.",
+      "There are a few attempts toward explaining/interpreting deep learning-based models, mostly by visualizing the representation of words and/or hidden states, and their importances (via saliency or erasure) on shallow tasks like sentiment analysis and POS tagging BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 . In contrast, we focus on interpreting the gating and attention signals of the intermediate layers of deep models in the challenging task of Natural Language Inference. A key concept in explaining deep models is saliency, which determines what is critical for the final decision of a deep model. So far, saliency has only been used to illustrate the impact of word embeddings. In this paper, we extend this concept to the intermediate layer of deep models to examine the saliency of attention as well as the LSTM gating signals to understand the behavior of these components and their impact on the final decision.",
+      "We make two main contributions. First, we introduce new strategies for interpreting the behavior of deep models in their intermediate layers, specifically, by examining the saliency of the attention and the gating signals. Second, we provide an extensive analysis of the state-of-the-art model for the NLI task and show that our methods reveal interesting insights not available from traditional methods of inspecting attention and word saliency.",
+      "In this paper, our focus was on NLI, which is a fundamental NLP task that requires both understanding and reasoning. Furthermore, the state-of-the-art NLI models employ complex neural architectures involving key mechanisms, such as attention and repeated reading, widely seen in successful models for other NLP tasks. As such, we expect our methods to be potentially useful for other natural understanding tasks as well."
+    ],
+    [
+      "In NLI BIBREF4 , we are given two sentences, a premise and a hypothesis, the goal is to decide the logical relationship (Entailment, Neutral, or Contradiction) between them.",
+      "Many of the top performing NLI models BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , are variants of the ESIM model BIBREF11 , which we choose to analyze in this paper. ESIM reads the sentences independently using LSTM at first, and then applies attention to align/contrast the sentences. Another round of LSTM reading then produces the final representations, which are compared to make the prediction. Detailed description of ESIM can be found in the Appendix.",
+      "Using the SNLI BIBREF4 data, we train two variants of ESIM, with dimensionality 50 and 300 respectively, referred to as ESIM-50 and ESIM-300 in the remainder of the paper."
+    ],
+    [
+      "In this work, we are primarily interested in the internal workings of the NLI model. In particular, we focus on the attention and the gating signals of LSTM readers, and how they contribute to the decisions of the model."
+    ],
+    [
+      "Attention has been widely used in many NLP tasks BIBREF12 , BIBREF13 , BIBREF14 and is probably one of the most critical parts that affects the inference decisions. Several pieces of prior work in NLI have attempted to visualize the attention layer to provide some understanding of their models BIBREF5 , BIBREF15 . Such visualizations generate a heatmap representing the similarity between the hidden states of the premise and the hypothesis (Eq. 19 of Appendix). Unfortunately the similarities are often the same regardless of the decision.",
+      "Let us consider the following example, where the same premise \u201cA kid is playing in the garden\u201d, is paired with three different hypotheses:",
+      "A kid is taking a nap in the garden",
+      "A kid is having fun in the garden with her family",
+      "A kid is having fun in the garden",
+      " Note that the ground truth relationships are Contradiction, Neutral, and Entailment, respectively.",
+      "The first row of Fig. 1 shows the visualization of normalized attention for the three cases produced by ESIM-50, which makes correct predictions for all of them. As we can see from the figure, the three attention maps are fairly similar despite the completely different decisions. The key issue is that the attention visualization only allows us to see how the model aligns the premise with the hypothesis, but does not show how such alignment impacts the decision. This prompts us to consider the saliency of attention.",
+      "The concept of saliency was first introduced in vision for visualizing the spatial support on an image for a particular object class BIBREF16 . In NLP, saliency has been used to study the importance of words toward a final decision BIBREF0 .",
+      "We propose to examine the saliency of attention. Specifically, given a premise-hypothesis pair and the model's decision $y$ , we consider the similarity between a pair of premise and hypothesis hidden states $e_{ij}$ as a variable. The score of the decision $S(y)$ is thus a function of $e_{ij}$ for all $i$ and $j$ . The saliency of $e_{ij}$ is then defined to be $|\\frac{\\partial S(y)}{\\partial {e_{ij}}}|$ .",
+      "The second row of Fig. 1 presents the attention saliency map for the three examples acquired by the same ESIM-50 model. Interestingly, the saliencies are clearly different across the examples, each highlighting different parts of the alignment. Specifically, for h1, we see the alignment between \u201cis playing\u201d and \u201ctaking a nap\u201d and the alignment of \u201cin a garden\u201d to have the most prominent saliency toward the decision of Contradiction. For h2, the alignment of \u201ckid\u201d and \u201cher family\u201d seems to be the most salient for the decision of Neutral. Finally, for h3, the alignment between \u201cis having fun\u201d and \u201ckid is playing\u201d have the strongest impact toward the decision of Entailment.",
+      "From this example, we can see that by inspecting the attention saliency, we effectively pinpoint which part of the alignments contribute most critically to the final prediction whereas simply visualizing the attention itself reveals little information.",
+      "In the previous examples, we study the behavior of the same model on different inputs. Now we use the attention saliency to compare the two different ESIM models: ESIM-50 and ESIM-300.",
+      "Consider two examples with a shared hypothesis of \u201cA man ordered a book\u201d and premise:",
+      "John ordered a book from amazon",
+      "Mary ordered a book from amazon",
+      " Here ESIM-50 fails to capture the gender connections of the two different names and predicts Neutral for both inputs, whereas ESIM-300 correctly predicts Entailment for the first case and Contradiction for the second.",
+      "In the first two columns of Fig. 2 (column a and b) we visualize the attention of the two examples for ESIM-50 (left) and ESIM-300 (right) respectively. Although the two models make different predictions, their attention maps appear qualitatively similar.",
+      "In contrast, columns 3-4 of Fig. 2 (column c and d) present the attention saliency for the two examples by ESIM-50 and ESIM-300 respectively. We see that for both examples, ESIM-50 primarily focused on the alignment of \u201cordered\u201d, whereas ESIM-300 focused more on the alignment of \u201cJohn\u201d and \u201cMary\u201d with \u201cman\u201d. It is interesting to note that ESIM-300 does not appear to learn significantly different similarity values compared to ESIM-50 for the two critical pairs of words (\u201cJohn\u201d, \u201cman\u201d) and (\u201cMary\u201d, \u201cman\u201d) based on the attention map. The saliency map, however, reveals that the two models use these values quite differently, with only ESIM-300 correctly focusing on them."
+    ],
+    [
+      "LSTM gating signals determine the flow of information. In other words, they indicate how LSTM reads the word sequences and how the information from different parts is captured and combined. LSTM gating signals are rarely analyzed, possibly due to their high dimensionality and complexity. In this work, we consider both the gating signals and their saliency, which is computed as the partial derivative of the score of the final decision with respect to each gating signal.",
+      "Instead of considering individual dimensions of the gating signals, we aggregate them to consider their norm, both for the signal and for its saliency. Note that ESIM models have two LSTM layers, the first (input) LSTM performs the input encoding and the second (inference) LSTM generates the representation for inference.",
+      "In Fig. 3 we plot the normalized signal and saliency norms for different gates (input, forget, output) of the Forward input (bottom three rows) and inference (top three rows) LSTMs. These results are produced by the ESIM-50 model for the three examples of Section 3.1, one for each column.",
+      "From the figure, we first note that the saliency tends to be somewhat consistent across different gates within the same LSTM, suggesting that we can interpret them jointly to identify parts of the sentence important for the model's prediction.",
+      "Comparing across examples, we see that the saliency curves show pronounced differences across the examples. For instance, the saliency pattern of the Neutral example is significantly different from the other two examples, and heavily concentrated toward the end of the sentence (\u201cwith her family\u201d). Note that without this part of the sentence, the relationship would have been Entailment. The focus (evidenced by its strong saliency and strong gating signal) on this particular part, which presents information not available from the premise, explains the model's decision of Neutral.",
+      "Comparing the behavior of the input LSTM and the inference LSTM, we observe interesting shifts of focus. In particular, we see that the inference LSTM tends to see much more concentrated saliency over key parts of the sentence, whereas the input LSTM sees more spread of saliency. For example, for the Contradiction example, the input LSTM sees high saliency for both \u201ctaking\u201d and \u201cin\u201d, whereas the inference LSTM primarily focuses on \u201cnap\u201d, which is the key word suggesting a Contradiction. Note that ESIM uses attention between the input and inference LSTM layers to align/contrast the sentences, hence it makes sense that the inference LSTM is more focused on the critical differences between the sentences. This is also observed for the Neutral example as well.",
+      "It is worth noting that, while revealing similar general trends, the backward LSTM can sometimes focus on different parts of the sentence (e.g., see Fig. 11 of Appendix), suggesting the forward and backward readings provide complementary understanding of the sentence."
+    ],
+    [
+      "We propose new visualization and interpretation strategies for neural models to understand how and why they work. We demonstrate the effectiveness of the proposed strategies on a complex task (NLI). Our strategies are able to provide interesting insights not achievable by previous explanation techniques. Our future work will extend our study to consider other NLP tasks and models with the goal of producing useful insights for further improving these models. Model In this section we describe the ESIM model. We divide ESIM to three main parts: 1) input encoding, 2) attention, and 3) inference. Figure 4 demonstrates a high-level view of the ESIM framework. Let $u=[u_1, \\cdots , u_n]$ and $v=[v_1, \\cdots , v_m]$ be the given premise with length $n$ and hypothesis with length $m$ respectively, where $u_i, v_j \\in \\mathbb {R}^r$ are word embeddings of $r$ -dimensional vector. The goal is to predict a label $y$ that indicates the logical relationship between premise $u$ and hypothesis $v$ . Below we briefly explain the aforementioned parts. Input Encoding It utilizes a bidirectional LSTM (BiLSTM) for encoding the given premise and hypothesis using Equations 16 and 17 respectively. ",
+      "$$\\hat{u} \\in \\mathbb {R}^{n \\times 2d}$$   (Eq. ) ",
+      "$$\\hat{v} \\in \\mathbb {R}^{m \\times 2d}$$   (Eq. ) where $u$ and $v=[v_1, \\cdots , v_m]$0 are the reading sequences of $v=[v_1, \\cdots , v_m]$1 and $v=[v_1, \\cdots , v_m]$2 respectively. Attention It employs a soft alignment method to associate the relevant sub-components between the given premise and hypothesis. Equation 19 (energy function) computes the unnormalized attention weights as the similarity of hidden states of the premise and hypothesis. ",
+      "$$u$$   (Eq. ) where $v=[v_1, \\cdots , v_m]$3 and $v=[v_1, \\cdots , v_m]$4 are the hidden representations of $v=[v_1, \\cdots , v_m]$5 and $v=[v_1, \\cdots , v_m]$6 respectively which are computed earlier in Equations 16 and 17 . Next, for each word in either premise or hypothesis, the relevant semantics in the other sentence is extracted and composed according to $v=[v_1, \\cdots , v_m]$7 . Equations 20 and 21 provide formal and specific details of this procedure. ",
+      "$$\\tilde{v}_j$$   (Eq. ) ",
+      "$$\\hat{u}$$   (Eq. ) where $v=[v_1, \\cdots , v_m]$8 represents the extracted relevant information of $v=[v_1, \\cdots , v_m]$9 by attending to $n$0 while $n$1 represents the extracted relevant information of $n$2 by attending to $n$3 . Next, it passes the enriched information through a projector layer which produce the final output of attention stage. Equations 22 and 23 formally represent this process. ",
+      "$$p$$   (Eq. ) ",
+      "$$q$$   (Eq. ) Here $n$4 stands for element-wise product while $n$5 and $n$6 are the trainable weights and biases of the projector layer respectively. $n$7 and $n$8 indicate the output of attention devision for premise and hypothesis respectively. Inference During this phase, it uses another BiLSTM to aggregate the two sequences of computed matching vectors, $n$9 and $m$0 from the attention stage (Equations 27 and 28 ). ",
+      "$$\\emph {softmax}$$   (Eq. ) ",
+      "$$\\hat{u} = \\textit {BiLSTM}(u)$$   (Eq. 16) where $m$1 and $m$2 are the reading sequences of $m$3 and $m$4 respectively. Finally the concatenation max and average pooling of $m$5 and $m$6 are pass through a multilayer perceptron (MLP) classifier that includes a hidden layer with $m$7 activation and $m$8 output layer. The model is trained in an end-to-end manner. Attention Study Here we provide more examples on the NLI task which intend to examine specific behavior in this model. Such examples indicate interesting observation that we can analyze them in the future works. Table 1 shows the list of all example. LSTM Gating Signal Finally, Figure 11 depicts the backward LSTM gating signals study. "
+    ]
+  ]
+}
+```
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+Name of Paper: Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction
+
+Question: How higher are F1 scores compared to previous work?
\ No newline at end of file
diff --git a/qasper-0252/instruction.md b/qasper-0252/instruction.md
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+Name of Paper: Talk the Walk: Navigating New York City through Grounded Dialogue
+
+Question: What data did they use?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    null,
+    "Introduction",
+    "Talk The Walk",
+    "Task",
+    "Data Collection",
+    "Dataset Statistics",
+    "Experiments",
+    "Tourist Localization",
+    "Model",
+    "The Tourist",
+    "The Guide",
+    "Comparisons",
+    "Results and Discussion",
+    "Analysis of Localization Task",
+    "Emergent Language Localization",
+    "Natural Language Localization",
+    "Localization-based Baseline",
+    "Conclusion",
+    "Related Work",
+    "Implementation Details",
+    "Additional Natural Language Experiments",
+    "Tourist Generation Models",
+    "Localization from Human Utterances",
+    "Visualizing MASC predictions",
+    "Evaluation on Full Setup",
+    "Landmark Classification",
+    "Dataset Details"
+  ],
+  "paragraphs": [
+    [
+      "0pt0.03.03 *",
+      "0pt0.030.03 *",
+      "0pt0.030.03",
+      "We introduce \u201cTalk The Walk\u201d, the first large-scale dialogue dataset grounded in action and perception. The task involves two agents (a \u201cguide\u201d and a \u201ctourist\u201d) that communicate via natural language in order to achieve a common goal: having the tourist navigate to a given target location. The task and dataset, which are described in detail, are challenging and their full solution is an open problem that we pose to the community. We (i) focus on the task of tourist localization and develop the novel Masked Attention for Spatial Convolutions (MASC) mechanism that allows for grounding tourist utterances into the guide's map, (ii) show it yields significant improvements for both emergent and natural language communication, and (iii) using this method, we establish non-trivial baselines on the full task."
+    ],
+    [
+      "As artificial intelligence plays an ever more prominent role in everyday human lives, it becomes increasingly important to enable machines to communicate via natural language\u2014not only with humans, but also with each other. Learning algorithms for natural language understanding, such as in machine translation and reading comprehension, have progressed at an unprecedented rate in recent years, but still rely on static, large-scale, text-only datasets that lack crucial aspects of how humans understand and produce natural language. Namely, humans develop language capabilities by being embodied in an environment which they can perceive, manipulate and move around in; and by interacting with other humans. Hence, we argue that we should incorporate all three fundamental aspects of human language acquisition\u2014perception, action and interactive communication\u2014and develop a task and dataset to that effect.",
+      "We introduce the Talk the Walk dataset, where the aim is for two agents, a \u201cguide\u201d and a \u201ctourist\u201d, to interact with each other via natural language in order to achieve a common goal: having the tourist navigate towards the correct location. The guide has access to a map and knows the target location, but does not know where the tourist is; the tourist has a 360-degree view of the world, but knows neither the target location on the map nor the way to it. The agents need to work together through communication in order to successfully solve the task. An example of the task is given in Figure FIGREF3 .",
+      "Grounded language learning has (re-)gained traction in the AI community, and much attention is currently devoted to virtual embodiment\u2014the development of multi-agent communication tasks in virtual environments\u2014which has been argued to be a viable strategy for acquiring natural language semantics BIBREF0 . Various related tasks have recently been introduced, but in each case with some limitations. Although visually grounded dialogue tasks BIBREF1 , BIBREF2 comprise perceptual grounding and multi-agent interaction, their agents are passive observers and do not act in the environment. By contrast, instruction-following tasks, such as VNL BIBREF3 , involve action and perception but lack natural language interaction with other agents. Furthermore, some of these works use simulated environments BIBREF4 and/or templated language BIBREF5 , which arguably oversimplifies real perception or natural language, respectively. See Table TABREF15 for a comparison.",
+      "Talk The Walk is the first task to bring all three aspects together: perception for the tourist observing the world, action for the tourist to navigate through the environment, and interactive dialogue for the tourist and guide to work towards their common goal. To collect grounded dialogues, we constructed a virtual 2D grid environment by manually capturing 360-views of several neighborhoods in New York City (NYC). As the main focus of our task is on interactive dialogue, we limit the difficulty of the control problem by having the tourist navigating a 2D grid via discrete actions (turning left, turning right and moving forward). Our street view environment was integrated into ParlAI BIBREF6 and used to collect a large-scale dataset on Mechanical Turk involving human perception, action and communication.",
+      "We argue that for artificial agents to solve this challenging problem, some fundamental architecture designs are missing, and our hope is that this task motivates their innovation. To that end, we focus on the task of localization and develop the novel Masked Attention for Spatial Convolutions (MASC) mechanism. To model the interaction between language and action, this architecture repeatedly conditions the spatial dimensions of a convolution on the communicated message sequence.",
+      "This work makes the following contributions: 1) We present the first large scale dialogue dataset grounded in action and perception; 2) We introduce the MASC architecture for localization and show it yields improvements for both emergent and natural language; 4) Using localization models, we establish initial baselines on the full task; 5) We show that our best model exceeds human performance under the assumption of \u201cperfect perception\u201d and with a learned emergent communication protocol, and sets a non-trivial baseline with natural language."
+    ],
+    [
+      "We create a perceptual environment by manually capturing several neighborhoods of New York City (NYC) with a 360 camera. Most parts of the city are grid-like and uniform, which makes it well-suited for obtaining a 2D grid. For Talk The Walk, we capture parts of Hell's Kitchen, East Village, the Financial District, Williamsburg and the Upper East Side\u2014see Figure FIGREF66 in Appendix SECREF14 for their respective locations within NYC. For each neighborhood, we choose an approximately 5x5 grid and capture a 360 view on all four corners of each intersection, leading to a grid-size of roughly 10x10 per neighborhood.",
+      "The tourist's location is given as a tuple INLINEFORM0 , where INLINEFORM1 are the coordinates and INLINEFORM2 signifies the orientation (north, east, south or west). The tourist can take three actions: turn left, turn right and go forward. For moving forward, we add INLINEFORM3 , INLINEFORM4 , INLINEFORM5 , INLINEFORM6 to the INLINEFORM7 coordinates for the respective orientations. Upon a turning action, the orientation is updated by INLINEFORM8 where INLINEFORM9 for left and INLINEFORM10 for right. If the tourist moves outside the grid, we issue a warning that they cannot go in that direction and do not update the location. Moreover, tourists are shown different types of transitions: a short transition for actions that bring the tourist to a different corner of the same intersection; and a longer transition for actions that bring them to a new intersection.",
+      "The guide observes a map that corresponds to the tourist's environment. We exploit the fact that urban areas like NYC are full of local businesses, and overlay the map with these landmarks as localization points for our task. Specifically, we manually annotate each corner of the intersection with a set of landmarks INLINEFORM0 , each coming from one of the following categories:",
+      " Bar Playfield Bank Hotel Shop Subway Coffee Shop Restaurant Theater ",
+      "The right-side of Figure FIGREF3 illustrates how the map is presented. Note that within-intersection transitions have a smaller grid distance than transitions to new intersections. To ensure that the localization task is not too easy, we do not include street names in the overhead map and keep the landmark categories coarse. That is, the dialogue is driven by uncertainty in the tourist's current location and the properties of the target location: if the exact location and orientation of the tourist were known, it would suffice to communicate a sequence of actions."
+    ],
+    [
+      "For the Talk The Walk task, we randomly choose one of the five neighborhoods, and subsample a 4x4 grid (one block with four complete intersections) from the entire grid. We specify the boundaries of the grid by the top-left and bottom-right corners INLINEFORM0 . Next, we construct the overhead map of the environment, i.e. INLINEFORM1 with INLINEFORM2 and INLINEFORM3 . We subsequently sample a start location and orientation INLINEFORM4 and a target location INLINEFORM5 at random.",
+      "The shared goal of the two agents is to navigate the tourist to the target location INLINEFORM0 , which is only known to the guide. The tourist perceives a \u201cstreet view\u201d planar projection INLINEFORM1 of the 360 image at location INLINEFORM2 and can simultaneously chat with the guide and navigate through the environment. The guide's role consists of reading the tourist description of the environment, building a \u201cmental map\u201d of their current position and providing instructions for navigating towards the target location. Whenever the guide believes that the tourist has reached the target location, they instruct the system to evaluate the tourist's location. The task ends when the evaluation is successful\u2014i.e., when INLINEFORM3 \u2014or otherwise continues until a total of three failed attempts. The additional attempts are meant to ease the task for humans, as we found that they otherwise often fail at the task but still end up close to the target location, e.g., at the wrong corner of the correct intersection."
+    ],
+    [
+      "We crowd-sourced the collection of the dataset on Amazon Mechanical Turk (MTurk). We use the MTurk interface of ParlAI BIBREF6 to render 360 images via WebGL and dynamically display neighborhood maps with an HTML5 canvas. Detailed task instructions, which were also given to our workers before they started their task, are shown in Appendix SECREF15 . We paired Turkers at random and let them alternate between the tourist and guide role across different HITs."
+    ],
+    [
+      "The Talk The Walk dataset consists of over 10k successful dialogues\u2014see Table FIGREF66 in the appendix for the dataset statistics split by neighborhood. Turkers successfully completed INLINEFORM0 of all finished tasks (we use this statistic as the human success rate). More than six hundred participants successfully completed at least one Talk The Walk HIT. Although the Visual Dialog BIBREF2 and GuessWhat BIBREF1 datasets are larger, the collected Talk The Walk dialogs are significantly longer. On average, Turkers needed more than 62 acts (i.e utterances and actions) before they successfully completed the task, whereas Visual Dialog requires 20 acts. The majority of acts comprise the tourist's actions, with on average more than 44 actions per dialogue. The guide produces roughly 9 utterances per dialogue, slightly more than the tourist's 8 utterances. Turkers use diverse discourse, with a vocabulary size of more than 10K (calculated over all successful dialogues). An example from the dataset is shown in Appendix SECREF14 . The dataset is available at https://github.com/facebookresearch/talkthewalk."
+    ],
+    [
+      "We investigate the difficulty of the proposed task by establishing initial baselines. The final Talk The Walk task is challenging and encompasses several important sub-tasks, ranging from landmark recognition to tourist localization and natural language instruction-giving. Arguably the most important sub-task is localization: without such capabilities the guide can not tell whether the tourist reached the target location. In this work, we establish a minimal baseline for Talk The Walk by utilizing agents trained for localization. Specifically, we let trained tourist models undertake random walks, using the following protocol: at each step, the tourist communicates its observations and actions to the guide, who predicts the tourist's location. If the guide predicts that the tourist is at target, we evaluate its location. If successful, the task ends, otherwise we continue until there have been three wrong evaluations. The protocol is given as pseudo-code in Appendix SECREF12 ."
+    ],
+    [
+      "The designed navigation protocol relies on a trained localization model that predicts the tourist's location from a communicated message. Before we formalize this localization sub-task in Section UID21 , we further introduce two simplifying assumptions\u2014perfect perception and orientation-agnosticism\u2014so as to overcome some of the difficulties we encountered in preliminary experiments.",
+      "paragraph4 0.1ex plus0.1ex minus.1ex-1em Perfect Perception Early experiments revealed that perceptual grounding of landmarks is difficult: we set up a landmark classification problem, on which models with extracted CNN BIBREF7 or text recognition features BIBREF8 barely outperform a random baseline\u2014see Appendix SECREF13 for full details. This finding implies that localization models from image input are limited by their ability to recognize landmarks, and, as a result, would not generalize to unseen environments. To ensure that perception is not the limiting factor when investigating the landmark-grounding and action-grounding capabilities of localization models, we assume \u201cperfect perception\u201d: in lieu of the 360 image view, the tourist is given the landmarks at its current location. More formally, each state observation INLINEFORM0 now equals the set of landmarks at the INLINEFORM1 -location, i.e. INLINEFORM2 . If the INLINEFORM3 -location does not have any visible landmarks, we return a single \u201cempty corner\u201d symbol. We stress that our findings\u2014including a novel architecture for grounding actions into an overhead map, see Section UID28 \u2014should carry over to settings without the perfect perception assumption.",
+      "paragraph4 0.1ex plus0.1ex minus.1ex-1em Orientation-agnosticism We opt to ignore the tourist's orientation, which simplifies the set of actions to [Left, Right, Up, Down], corresponding to adding [(-1, 0), (1, 0), (0, 1), (0, -1)] to the current INLINEFORM0 coordinates, respectively. Note that actions are now coupled to an orientation on the map\u2014e.g. up is equal to going north\u2014and this implicitly assumes that the tourist has access to a compass. This also affects perception, since the tourist now has access to views from all orientations: in conjunction with \u201cperfect perception\u201d, implying that only landmarks at the current corner are given, whereas landmarks from different corners (e.g. across the street) are not visible.",
+      "Even with these simplifications, the localization-based baseline comes with its own set of challenges. As we show in Section SECREF34 , the task requires communication about a short (random) path\u2014i.e., not only a sequence of observations but also actions\u2014in order to achieve high localization accuracy. This means that the guide needs to decode observations from multiple time steps, as well as understand their 2D spatial arrangement as communicated via the sequence of actions. Thus, in order to get to a good understanding of the task, we thoroughly examine whether the agents can learn a communication protocol that simultaneously grounds observations and actions into the guide's map. In doing so, we thoroughly study the role of the communication channel in the localization task, by investigating increasingly constrained forms of communication: from differentiable continuous vectors to emergent discrete symbols to the full complexity of natural language.",
+      "The full navigation baseline hinges on a localization model from random trajectories. While we can sample random actions in the emergent communication setup, this is not possible for the natural language setup because the messages are coupled to the trajectories of the human annotators. This leads to slightly different problem setups, as described below.",
+      "paragraph4 0.1ex plus0.1ex minus.1ex-1em Emergent language A tourist, starting from a random location, takes INLINEFORM0 random actions INLINEFORM1 to reach target location INLINEFORM2 . Every location in the environment has a corresponding set of landmarks INLINEFORM3 for each of the INLINEFORM4 coordinates. As the tourist navigates, the agent perceives INLINEFORM5 state-observations INLINEFORM6 where each observation INLINEFORM7 consists of a set of INLINEFORM8 landmark symbols INLINEFORM9 . Given the observations INLINEFORM10 and actions INLINEFORM11 , the tourist generates a message INLINEFORM12 which is communicated to the other agent. The objective of the guide is to predict the location INLINEFORM13 from the tourist's message INLINEFORM14 .",
+      "paragraph4 0.1ex plus0.1ex minus.1ex-1em Natural language In contrast to our emergent communication experiments, we do not take random actions but instead extract actions, observations, and messages from the dataset. Specifically, we consider each tourist utterance (i.e. at any point in the dialogue), obtain the current tourist location as target location INLINEFORM0 , the utterance itself as message INLINEFORM1 , and the sequence of observations and actions that took place between the current and previous tourist utterance as INLINEFORM2 and INLINEFORM3 , respectively. Similar to the emergent language setting, the guide's objective is to predict the target location INLINEFORM4 models from the tourist message INLINEFORM5 . We conduct experiments with INLINEFORM6 taken from the dataset and with INLINEFORM7 generated from the extracted observations INLINEFORM8 and actions INLINEFORM9 ."
+    ],
+    [
+      "This section outlines the tourist and guide architectures. We first describe how the tourist produces messages for the various communication channels across which the messages are sent. We subsequently describe how these messages are processed by the guide, and introduce the novel Masked Attention for Spatial Convolutions (MASC) mechanism that allows for grounding into the 2D overhead map in order to predict the tourist's location."
+    ],
+    [
+      "For each of the communication channels, we outline the procedure for generating a message INLINEFORM0 . Given a set of state observations INLINEFORM1 , we represent each observation by summing the INLINEFORM2 -dimensional embeddings of the observed landmarks, i.e. for INLINEFORM3 , INLINEFORM4 , where INLINEFORM5 is the landmark embedding lookup table. In addition, we embed action INLINEFORM6 into a INLINEFORM7 -dimensional embedding INLINEFORM8 via a look-up table INLINEFORM9 . We experiment with three types of communication channel.",
+      "paragraph4 0.1ex plus0.1ex minus.1ex-1em Continuous vectors The tourist has access to observations of several time steps, whose order is important for accurate localization. Because summing embeddings is order-invariant, we introduce a sum over positionally-gated embeddings, which, conditioned on time step INLINEFORM0 , pushes embedding information into the appropriate dimensions. More specifically, we generate an observation message INLINEFORM1 , where INLINEFORM2 is a learned gating vector for time step INLINEFORM3 . In a similar fashion, we produce action message INLINEFORM4 and send the concatenated vectors INLINEFORM5 as message to the guide. We can interpret continuous vector communication as a single, monolithic model because its architecture is end-to-end differentiable, enabling gradient-based optimization for training.",
+      "paragraph4 0.1ex plus0.1ex minus.1ex-1em Discrete symbols Like the continuous vector communication model, with discrete communication the tourist also uses separate channels for observations and actions, as well as a sum over positionally gated embeddings to generate observation embedding INLINEFORM0 . We pass this embedding through a sigmoid and generate a message INLINEFORM1 by sampling from the resulting Bernoulli distributions:",
+      " INLINEFORM0 ",
+      "The action message INLINEFORM0 is produced in the same way, and we obtain the final tourist message INLINEFORM1 through concatenating the messages.",
+      "The communication channel's sampling operation yields the model non-differentiable, so we use policy gradients BIBREF9 , BIBREF10 to train the parameters INLINEFORM0 of the tourist model. That is, we estimate the gradient by INLINEFORM1 ",
+      " where the reward function INLINEFORM0 is the negative guide's loss (see Section SECREF25 ) and INLINEFORM1 a state-value baseline to reduce variance. We use a linear transformation over the concatenated embeddings as baseline prediction, i.e. INLINEFORM2 , and train it with a mean squared error loss.",
+      "paragraph4 0.1ex plus0.1ex minus.1ex-1em Natural Language Because observations and actions are of variable-length, we use an LSTM encoder over the sequence of observations embeddings INLINEFORM0 , and extract its last hidden state INLINEFORM1 . We use a separate LSTM encoder for action embeddings INLINEFORM2 , and concatenate both INLINEFORM3 and INLINEFORM4 to the input of the LSTM decoder at each time step: DISPLAYFORM0 ",
+      " where INLINEFORM0 a look-up table, taking input tokens INLINEFORM1 . We train with teacher-forcing, i.e. we optimize the cross-entropy loss: INLINEFORM2 . At test time, we explore the following decoding strategies: greedy, sampling and a beam-search. We also fine-tune a trained tourist model (starting from a pre-trained model) with policy gradients in order to minimize the guide's prediction loss."
+    ],
+    [
+      "Given a tourist message INLINEFORM0 describing their observations and actions, the objective of the guide is to predict the tourist's location on the map. First, we outline the procedure for extracting observation embedding INLINEFORM1 and action embeddings INLINEFORM2 from the message INLINEFORM3 for each of the types of communication. Next, we discuss the MASC mechanism that takes the observations and actions in order to ground them on the guide's map in order to predict the tourist's location.",
+      "paragraph4 0.1ex plus0.1ex minus.1ex-1em Continuous For the continuous communication model, we assign the observation message to the observation embedding, i.e. INLINEFORM0 . To extract the action embedding for time step INLINEFORM1 , we apply a linear layer to the action message, i.e. INLINEFORM2 .",
+      "paragraph4 0.1ex plus0.1ex minus.1ex-1em Discrete For discrete communication, we obtain observation INLINEFORM0 by applying a linear layer to the observation message, i.e. INLINEFORM1 . Similar to the continuous communication model, we use a linear layer over action message INLINEFORM2 to obtain action embedding INLINEFORM3 for time step INLINEFORM4 .",
+      "paragraph4 0.1ex plus0.1ex minus.1ex-1em Natural Language The message INLINEFORM0 contains information about observations and actions, so we use a recurrent neural network with attention mechanism to extract the relevant observation and action embeddings. Specifically, we encode the message INLINEFORM1 , consisting of INLINEFORM2 tokens INLINEFORM3 taken from vocabulary INLINEFORM4 , with a bidirectional LSTM: DISPLAYFORM0 ",
+      " where INLINEFORM0 is the word embedding look-up table. We obtain observation embedding INLINEFORM1 through an attention mechanism over the hidden states INLINEFORM2 : DISPLAYFORM0 ",
+      "where INLINEFORM0 is a learned control embedding who is updated through a linear transformation of the previous control and observation embedding: INLINEFORM1 . We use the same mechanism to extract the action embedding INLINEFORM2 from the hidden states. For the observation embedding, we obtain the final representation by summing positionally gated embeddings, i.e., INLINEFORM3 .",
+      "We represent the guide's map as INLINEFORM0 , where in this case INLINEFORM1 , where each INLINEFORM2 -dimensional INLINEFORM3 location embedding INLINEFORM4 is computed as the sum of the guide's landmark embeddings for that location.",
+      "paragraph4 0.1ex plus0.1ex minus.1ex-1em Motivation While the guide's map representation contains only local landmark information, the tourist communicates a trajectory of the map (i.e. actions and observations from multiple locations), implying that directly comparing the tourist's message with the individual landmark embeddings is probably suboptimal. Instead, we want to aggregate landmark information from surrounding locations by imputing trajectories over the map to predict locations. We propose a mechanism for translating landmark embeddings according to state transitions (left, right, up, down), which can be expressed as a 2D convolution over the map embeddings. For simplicity, let us assume that the map embedding INLINEFORM0 is 1-dimensional, then a left action can be realized through application of the following INLINEFORM1 kernel: INLINEFORM2 which effectively shifts all values of INLINEFORM3 one position to the left. We propose to learn such state-transitions from the tourist message through a differentiable attention-mask over the spatial dimensions of a 3x3 convolution.",
+      "paragraph4 0.1ex plus0.1ex minus.1ex-1em MASC We linearly project each predicted action embedding INLINEFORM0 to a 9-dimensional vector INLINEFORM1 , normalize it by a softmax and subsequently reshape the vector into a 3x3 mask INLINEFORM2 : DISPLAYFORM0 ",
+      " We learn a 3x3 convolutional kernel INLINEFORM0 , with INLINEFORM1 features, and apply the mask INLINEFORM2 to the spatial dimensions of the convolution by first broadcasting its values along the feature dimensions, i.e. INLINEFORM3 , and subsequently taking the Hadamard product: INLINEFORM4 . For each action step INLINEFORM5 , we then apply a 2D convolution with masked weight INLINEFORM6 to obtain a new map embedding INLINEFORM7 , where we zero-pad the input to maintain identical spatial dimensions.",
+      "paragraph4 0.1ex plus0.1ex minus.1ex-1em Prediction model We repeat the MASC operation INLINEFORM0 times (i.e. once for each action), and then aggregate the map embeddings by a sum over positionally-gated embeddings: INLINEFORM1 . We score locations by taking the dot-product of the observation embedding INLINEFORM2 , which contains information about the sequence of observed landmarks by the tourist, and the map. We compute a distribution over the locations of the map INLINEFORM3 by taking a softmax over the computed scores: DISPLAYFORM0 ",
+      "paragraph4 0.1ex plus0.1ex minus.1ex-1em Predicting T While emergent communication models use a fixed length trasjectory INLINEFORM0 , natural language messages may differ in the number of communicated observations and actions. Hence, we predict INLINEFORM1 from the communicated message. Specifically, we use a softmax regression layer over the last hidden state INLINEFORM2 of the RNN, and subsequently sample INLINEFORM3 from the resulting multinomial distribution: DISPLAYFORM0 ",
+      "We jointly train the INLINEFORM0 -prediction model via REINFORCE, with the guide's loss as reward function and a mean-reward baseline."
+    ],
+    [
+      "To better analyze the performance of the models incorporating MASC, we compare against a no-MASC baseline in our experiments, as well as a prediction upper bound.",
+      "paragraph4 0.1ex plus0.1ex minus.1ex-1em No MASC We compare the proposed MASC model with a model that does not include this mechanism. Whereas MASC predicts a convolution mask from the tourist message, the \u201cNo MASC\u201d model uses INLINEFORM0 , the ordinary convolutional kernel to convolve the map embedding INLINEFORM1 to obtain INLINEFORM2 . We also share the weights of this convolution at each time step.",
+      "paragraph4 0.1ex plus0.1ex minus.1ex-1em Prediction upper-bound Because we have access to the class-conditional likelihood INLINEFORM0 , we are able to compute the Bayes error rate (or irreducible error). No model (no matter how expressive) with any amount of data can ever obtain better localization accuracy as there are multiple locations consistent with the observations and actions."
+    ],
+    [
+      "In this section, we describe the findings of various experiments. First, we analyze how much information needs to be communicated for accurate localization in the Talk The Walk environment, and find that a short random path (including actions) is necessary. Next, for emergent language, we show that the MASC architecture can achieve very high localization accuracy, significantly outperforming the baseline that does not include this mechanism. We then turn our attention to the natural language experiments, and find that localization from human utterances is much harder, reaching an accuracy level that is below communicating a single landmark observation. We show that generated utterances from a conditional language model leads to significantly better localization performance, by successfully grounding the utterance on a single landmark observation (but not yet on multiple observations and actions). Finally, we show performance of the localization baseline on the full task, which can be used for future comparisons to this work."
+    ],
+    [
+      "paragraph4 0.1ex plus0.1ex minus.1ex-1em Task is not too easy The upper-bound on localization performance in Table TABREF32 suggest that communicating a single landmark observation is not sufficient for accurate localization of the tourist ( INLINEFORM0 35% accuracy). This is an important result for the full navigation task because the need for two-way communication disappears if localization is too easy; if the guide knows the exact location of the tourist it suffices to communicate a list of instructions, which is then executed by the tourist. The uncertainty in the tourist's location is what drives the dialogue between the two agents.",
+      "paragraph4 0.1ex plus0.1ex minus.1ex-1em Importance of actions We observe that the upperbound for only communicating observations plateaus around 57% (even for INLINEFORM0 actions), whereas it exceeds 90% when we also take actions into account. This implies that, at least for random walks, it is essential to communicate a trajectory, including observations and actions, in order to achieve high localization accuracy."
+    ],
+    [
+      "We first report the results for tourist localization with emergent language in Table TABREF32 .",
+      "paragraph4 0.1ex plus0.1ex minus.1ex-1em MASC improves performance The MASC architecture significantly improves performance compared to models that do not include this mechanism. For instance, for INLINEFORM0 action, MASC already achieves 56.09 % on the test set and this further increases to 69.85% for INLINEFORM1 . On the other hand, no-MASC models hit a plateau at 43%. In Appendix SECREF11 , we analyze learned MASC values, and show that communicated actions are often mapped to corresponding state-transitions.",
+      "paragraph4 0.1ex plus0.1ex minus.1ex-1em Continuous vs discrete We observe similar performance for continuous and discrete emergent communication models, implying that a discrete communication channel is not a limiting factor for localization performance."
+    ],
+    [
+      "We report the results of tourist localization with natural language in Table TABREF36 . We compare accuracy of the guide model (with MASC) trained on utterances from (i) humans, (ii) a supervised model with various decoding strategies, and (iii) a policy gradient model optimized with respect to the loss of a frozen, pre-trained guide model on human utterances.",
+      "paragraph4 0.1ex plus0.1ex minus.1ex-1em Human utterances Compared to emergent language, localization from human utterances is much harder, achieving only INLINEFORM0 on the test set. Here, we report localization from a single utterance, but in Appendix SECREF45 we show that including up to five dialogue utterances only improves performance to INLINEFORM1 . We also show that MASC outperform no-MASC models for natural language communication.",
+      "paragraph4 0.1ex plus0.1ex minus.1ex-1em Generated utterances We also investigate generated tourist utterances from conditional language models. Interestingly, we observe that the supervised model (with greedy and beam-search decoding) as well as the policy gradient model leads to an improvement of more than 10 accuracy points over the human utterances. However, their level of accuracy is slightly below the baseline of communicating a single observation, indicating that these models only learn to ground utterances in a single landmark observation.",
+      "paragraph4 0.1ex plus0.1ex minus.1ex-1em Better grounding of generated utterances We analyze natural language samples in Table TABREF38 , and confirm that, unlike human utterances, the generated utterances are talking about the observed landmarks. This observation explains why the generated utterances obtain higher localization accuracy. The current language models are most successful when conditioned on a single landmark observation; We show in Appendix UID43 that performance quickly deteriorates when the model is conditioned on more observations, suggesting that it can not produce natural language utterances about multiple time steps."
+    ],
+    [
+      "Table TABREF36 shows results for the best localization models on the full task, evaluated via the random walk protocol defined in Algorithm SECREF12 .",
+      "paragraph4 0.1ex plus0.1ex minus.1ex-1em Comparison with human annotators Interestingly, our best localization model (continuous communication, with MASC, and INLINEFORM0 ) achieves 88.33% on the test set and thus exceed human performance of 76.74% on the full task. While emergent models appear to be stronger localizers, humans might cope with their localization uncertainty through other mechanisms (e.g. better guidance, bias towards taking particular paths, etc). The simplifying assumption of perfect perception also helps.",
+      "paragraph4 0.1ex plus0.1ex minus.1ex-1em Number of actions Unsurprisingly, humans take fewer steps (roughly 15) than our best random walk model (roughly 34). Our human annotators likely used some form of guidance to navigate faster to the target."
+    ],
+    [
+      "We introduced the Talk The Walk task and dataset, which consists of crowd-sourced dialogues in which two human annotators collaborate to navigate to target locations in the virtual streets of NYC. For the important localization sub-task, we proposed MASC\u2014a novel grounding mechanism to learn state-transition from the tourist's message\u2014and showed that it improves localization performance for emergent and natural language. We use the localization model to provide baseline numbers on the Talk The Walk task, in order to facilitate future research."
+    ],
+    [
+      "The Talk the Walk task and dataset facilitate future research on various important subfields of artificial intelligence, including grounded language learning, goal-oriented dialogue research and situated navigation. Here, we describe related previous work in these areas.",
+      "paragraph4 0.1ex plus0.1ex minus.1ex-1em Related tasks There has been a long line of work involving related tasks. Early work on task-oriented dialogue dates back to the early 90s with the introduction of the Map Task BIBREF11 and Maze Game BIBREF25 corpora. Recent efforts have led to larger-scale goal-oriented dialogue datasets, for instance to aid research on visually-grounded dialogue BIBREF2 , BIBREF1 , knowledge-base-grounded discourse BIBREF29 or negotiation tasks BIBREF36 . At the same time, there has been a big push to develop environments for embodied AI, many of which involve agents following natural language instructions with respect to an environment BIBREF13 , BIBREF50 , BIBREF5 , BIBREF39 , BIBREF19 , BIBREF18 , following-up on early work in this area BIBREF38 , BIBREF20 . An early example of navigation using neural networks is BIBREF28 , who propose an online learning approach for robot navigation. Recently, there has been increased interest in using end-to-end trainable neural networks for learning to navigate indoor scenes BIBREF27 , BIBREF26 or large cities BIBREF17 , BIBREF40 , but, unlike our work, without multi-agent communication. Also the task of localization (without multi-agent communication) has recently been studied BIBREF18 , BIBREF48 .",
+      "paragraph4 0.1ex plus0.1ex minus.1ex-1em Grounded language learning Grounded language learning is motivated by the observation that humans learn language embodied (grounded) in sensorimotor experience of the physical world BIBREF15 , BIBREF45 . On the one hand, work in multi-modal semantics has shown that grounding can lead to practical improvements on various natural language understanding tasks BIBREF14 , BIBREF31 . In robotics, researchers dissatisfied with purely symbolic accounts of meaning attempted to build robotic systems with the aim of grounding meaning in physical experience of the world BIBREF44 , BIBREF46 . Recently, grounding has also been applied to the learning of sentence representations BIBREF32 , image captioning BIBREF37 , BIBREF49 , visual question answering BIBREF12 , BIBREF22 , visual reasoning BIBREF30 , BIBREF42 , and grounded machine translation BIBREF43 , BIBREF23 . Grounding also plays a crucial role in the emergent research of multi-agent communication, where, agents communicate (in natural language or otherwise) in order to solve a task, with respect to their shared environment BIBREF35 , BIBREF21 , BIBREF41 , BIBREF24 , BIBREF36 , BIBREF47 , BIBREF34 ."
+    ],
+    [
+      "For the emergent communication models, we use an embedding size INLINEFORM0 . The natural language experiments use 128-dimensional word embeddings and a bidirectional RNN with 256 units. In all experiments, we train the guide with a cross entropy loss using the ADAM optimizer with default hyper-parameters BIBREF33 . We perform early stopping on the validation accuracy, and report the corresponding train, valid and test accuracy. We optimize the localization models with continuous, discrete and natural language communication channels for 200, 200, and 25 epochs, respectively. To facilitate further research on Talk The Walk, we make our code base for reproducing experiments publicly available at https://github.com/facebookresearch/talkthewalk."
+    ],
+    [
+      "First, we investigate the sensitivity of tourist generation models to the trajectory length, finding that the model conditioned on a single observation (i.e. INLINEFORM0 ) achieves best performance. In the next subsection, we further analyze localization models from human utterances by investigating MASC and no-MASC models with increasing dialogue context."
+    ],
+    [
+      "After training the supervised tourist model (conditioned on observations and action from human expert trajectories), there are two ways to train an accompanying guide model. We can optimize a location prediction model on either (i) extracted human trajectories (as in the localization setup from human utterances) or (ii) on all random paths of length INLINEFORM0 (as in the full task evaluation). Here, we investigate the impact of (1) using either human or random trajectories for training the guide model, and (2) the effect of varying the path length INLINEFORM1 during the full-task evaluation. For random trajectories, guide training uses the same path length INLINEFORM2 as is used during evaluation. We use a pre-trained tourist model with greedy decoding for generating the tourist utterances. Table TABREF40 summarizes the results.",
+      "paragraph4 0.1ex plus0.1ex minus.1ex-1em Human vs random trajectories We only observe small improvements for training on random trajectories. Human trajectories are thus diverse enough to generalize to random trajectories.",
+      "paragraph4 0.1ex plus0.1ex minus.1ex-1em Effect of path length There is a strong negative correlation between task success and the conditioned trajectory length. We observe that the full task performance quickly deteriorates for both human and random trajectories. This suggests that the tourist generation model can not produce natural language utterances that describe multiple observations and actions. Although it is possible that the guide model can not process such utterances, this is not very likely because the MASC architectures handles such messages successfully for emergent communication.",
+      "We report localization performance of tourist utterances generated by beam search decoding of varying beam size in Table TABREF40 . We find that performance decreases from 29.05% to 20.87% accuracy on the test set when we increase the beam-size from one to eight."
+    ],
+    [
+      "We conduct an ablation study for MASC on natural language with varying dialogue context. Specifically, we compare localization accuracy of MASC and no-MASC models trained on the last [1, 3, 5] utterances of the dialogue (including guide utterances). We report these results in Table TABREF41 . In all cases, MASC outperforms the no-MASC models by several accuracy points. We also observe that mean predicted INLINEFORM0 (over the test set) increases from 1 to 2 when more dialogue context is included."
+    ],
+    [
+      "Figure FIGREF46 shows the MASC values for a learned model with emergent discrete communications and INLINEFORM0 actions. Specifically, we look at the predicted MASC values for different action sequences taken by the tourist. We observe that the first action is always mapped to the correct state-transition, but that the second and third MASC values do not always correspond to right state-transitions."
+    ],
+    [
+      "We provide pseudo-code for evaluation of localization models on the full task in Algorithm SECREF12 , as well as results for all emergent communication models in Table TABREF55 .",
+      " INLINEFORM0 INLINEFORM1 ",
+      " INLINEFORM0 take new action INLINEFORM1 INLINEFORM2 ",
+      "Performance evaluation of location prediction model on full Talk The Walk setup"
+    ],
+    [
+      "While the guide has access to the landmark labels, the tourist needs to recognize these landmarks from raw perceptual information. In this section, we study landmark classification as a supervised learning problem to investigate the difficulty of perceptual grounding in Talk The Walk.",
+      "The Talk The Walk dataset contains a total of 307 different landmarks divided among nine classes, see Figure FIGREF62 for how they are distributed. The class distribution is fairly imbalanced, with shops and restaurants as the most frequent landmarks and relatively few play fields and theaters. We treat landmark recognition as a multi-label classification problem as there can be multiple landmarks on a corner.",
+      "For the task of landmark classification, we extract the relevant views of the 360 image from which a landmark is visible. Because landmarks are labeled to be on a specific corner of an intersection, we assume that they are visible from one of the orientations facing away from the intersection. For example, for a landmark on the northwest corner of an intersection, we extract views from both the north and west direction. The orientation-specific views are obtained by a planar projection of the full 360-image with a small field of view (60 degrees) to limit distortions. To cover the full field of view, we extract two images per orientation, with their horizontal focus point 30 degrees apart. Hence, we obtain eight images per 360 image with corresponding orientation INLINEFORM0 .",
+      "We run the following pre-trained feature extractors over the extracted images:",
+      "For the text recognition model, we use a learned look-up table INLINEFORM0 to embed the extracted text features INLINEFORM1 , and fuse all embeddings of four images through a bag of embeddings, i.e., INLINEFORM2 . We use a linear layer followed by a sigmoid to predict the probability for each class, i.e. INLINEFORM3 . We also experiment with replacing the look-up embeddings with pre-trained FastText embeddings BIBREF16 . For the ResNet model, we use a bag of embeddings over the four ResNet features, i.e. INLINEFORM4 , before we pass it through a linear layer to predict the class probabilities: INLINEFORM5 . We also conduct experiments where we first apply PCA to the extracted ResNet and FastText features before we feed them to the model.",
+      "To account for class imbalance, we train all described models with a binary cross entropy loss weighted by the inverted class frequency. We create a 80-20 class-conditional split of the dataset into a training and validation set. We train for 100 epochs and perform early stopping on the validation loss.",
+      "The F1 scores for the described methods in Table TABREF65 . We compare to an \u201call positive\u201d baseline that always predicts that the landmark class is visible and observe that all presented models struggle to outperform this baseline. Although 256-dimensional ResNet features achieve slightly better precision on the validation set, it results in much worse recall and a lower F1 score. Our results indicate that perceptual grounding is a difficult task, which easily merits a paper of its own right, and so we leave further improvements (e.g. better text recognizers) for future work."
+    ],
+    [
+      "paragraph4 0.1ex plus0.1ex minus.1ex-1em Dataset split We split the full dataset by assigning entire 4x4 grids (independent of the target location) to the train, valid or test set. Specifically, we design the split such that the valid set contains at least one intersection (out of four) is not part of the train set. For the test set, all four intersections are novel. See our source code, available at URL ANONYMIZED, for more details on how this split is realized.",
+      "paragraph4 0.1ex plus0.1ex minus.1ex-1em Example",
+      "Tourist: ACTION:TURNRIGHT ACTION:TURNRIGHT",
+      "Guide: Hello, what are you near?",
+      "Tourist: ACTION:TURNLEFT ACTION:TURNLEFT ACTION:TURNLEFT",
+      "Tourist: Hello, in front of me is a Brooks Brothers",
+      "Tourist: ACTION:TURNLEFT ACTION:FORWARD ACTION:TURNLEFT ACTION:TURNLEFT",
+      "Guide: Is that a shop or restaurant?",
+      "Tourist: ACTION:TURNLEFT",
+      "Tourist: It is a clothing shop.",
+      "Tourist: ACTION:TURNLEFT",
+      "Guide: You need to go to the intersection in the northwest corner of the map",
+      "Tourist: ACTION:TURNLEFT",
+      "Tourist: There appears to be a bank behind me.",
+      "Tourist: ACTION:TURNLEFT ACTION:TURNLEFT ACTION:TURNRIGHT ACTION:TURNRIGHT",
+      "Guide: Ok, turn left then go straight up that road",
+      "Tourist: ACTION:TURNLEFT ACTION:TURNLEFT ACTION:TURNLEFT ACTION:FORWARD ACTION:TURNRIGHT",
+      " ACTION:FORWARD ACTION:FORWARD ACTION:TURNLEFT ACTION:TURNLEFT ACTION:TURNLEFT",
+      "Guide: There should be shops on two of the corners but you",
+      " need to go to the corner without a shop.",
+      "Tourist: ACTION:FORWARD ACTION:FORWARD ACTION:FORWARD ACTION:TURNLEFT ACTION:TURNLEFT",
+      "Guide: let me know when you get there.",
+      "Tourist: on my left is Radio city Music hall",
+      "Tourist: ACTION:TURNLEFT ACTION:FORWARD ACTION:TURNLEFT ACTION:TURNRIGHT ACTION:TURNRIGHT",
+      "Tourist: I can't go straight any further.",
+      "Guide: ok. turn so that the theater is on your right.",
+      "Guide: then go straight",
+      "Tourist: That would be going back the way I came",
+      "Guide: yeah. I was looking at the wrong bank",
+      "Tourist: I'll notify when I am back at the brooks brothers, and the bank.",
+      "Tourist: ACTION:TURNRIGHT",
+      "Guide: make a right when the bank is on your left",
+      "Tourist: ACTION:FORWARD ACTION:FORWARD ACTION:TURNRIGHT",
+      "Tourist: Making the right at the bank.",
+      "Tourist: ACTION:FORWARD ACTION:FORWARD",
+      "Tourist: I can't go that way.",
+      "Tourist: ACTION:TURNLEFT",
+      "Tourist: Bank is ahead of me on the right",
+      "Tourist: ACTION:FORWARD ACTION:FORWARD ACTION:TURNLEFT",
+      "Guide: turn around on that intersection",
+      "Tourist: I can only go to the left or back the way I just came.",
+      "Tourist: ACTION:TURNLEFT",
+      "Guide: you're in the right place. do you see shops on the corners?",
+      "Guide: If you're on the corner with the bank, cross the street",
+      "Tourist: I'm back where I started by the shop and the bank.",
+      "Tourist: ACTION:TURNRIGHT",
+      "Guide: on the same side of the street?",
+      "Tourist: crossing the street now",
+      "Tourist: ACTION:FORWARD ACTION:FORWARD ACTION:TURNLEFT",
+      "Tourist: there is an I love new york shop across the street on the left from me now",
+      "Tourist: ACTION:TURNRIGHT ACTION:FORWARD",
+      "Guide: ok. I'll see if it's right.",
+      "Guide: EVALUATE_LOCATION",
+      "Guide: It's not right.",
+      "Tourist: What should I be on the look for?",
+      "Tourist: ACTION:TURNRIGHT ACTION:TURNRIGHT ACTION:TURNRIGHT",
+      "Guide: There should be shops on two corners but you need to be on one of the corners",
+      " without the shop.",
+      "Guide: Try the other corner.",
+      "Tourist: this intersection has 2 shop corners and a bank corner",
+      "Guide: yes. that's what I see on the map.",
+      "Tourist: should I go to the bank corner? or one of the shop corners?",
+      " or the blank corner (perhaps a hotel)",
+      "Tourist: ACTION:TURNLEFT ACTION:TURNLEFT ACTION:TURNRIGHT ACTION:TURNRIGHT",
+      "Guide: Go to the one near the hotel. The map says the hotel is a little",
+      " further down but it might be a little off.",
+      "Tourist: It's a big hotel it's possible.",
+      "Tourist: ACTION:FORWARD ACTION:TURNLEFT ACTION:FORWARD ACTION:TURNRIGHT",
+      "Tourist: I'm on the hotel corner",
+      "Guide: EVALUATE_LOCATION"
+    ]
+  ]
+}
+```
\ No newline at end of file
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+++ b/qasper-0253/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Real-time Claim Detection from News Articles and Retrieval of Semantically-Similar Factchecks
+
+Question: Do the authors report results only on English data?
\ No newline at end of file
diff --git a/qasper-0254/instruction.md b/qasper-0254/instruction.md
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+Name of Paper: Real-time Claim Detection from News Articles and Retrieval of Semantically-Similar Factchecks
+
+Question: How is the accuracy of the system measured?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Method",
+    "Choosing an embedding",
+    "Clustering Method",
+    "Next Steps"
+  ],
+  "paragraphs": [
+    [
+      "In recent years, the spread of misinformation has become a growing concern for researchers and the public at large BIBREF1 . Researchers at MIT found that social media users are more likely to share false information than true information BIBREF2 . Due to renewed focus on finding ways to foster healthy political conversation, the profile of factcheckers has been raised.",
+      "Factcheckers positively influence public debate by publishing good quality information and asking politicians and journalists to retract misleading or false statements. By calling out lies and the blurring of the truth, they make those in positions of power accountable. This is a result of labour intensive work that involves monitoring the news for spurious claims and carrying out rigorous research to judge credibility. So far, it has only been possible to scale their output upwards by hiring more personnel. This is problematic because newsrooms need significant resources to employ factcheckers. Publication budgets have been decreasing, resulting in a steady decline in the size of their workforce BIBREF0 . Factchecking is not a directly profitable activity, which negatively affects the allocation of resources towards it in for-profit organisations. It is often taken on by charities and philanthropists instead.",
+      "To compensate for this shortfall, our strategy is to harness the latest developments in NLP to make factchecking more efficient and therefore less costly. To this end, the new field of automated factchecking has captured the imagination of both non-profits and start-ups BIBREF3 , BIBREF4 , BIBREF5 . It aims to speed up certain aspects of the factchecking process rather than create AI that can replace factchecking personnel. This includes monitoring claims that are made in the news, aiding decisions about which statements are the most important to check and automatically retrieving existing factchecks that are relevant to a new claim.",
+      "The claim detection and claim clustering methods that we set out in this paper can be applied to each of these. We sought to devise a system that would automatically detect claims in articles and compare them to previously submitted claims. Storing the results to allow a factchecker's work on one of these claims to be easily transferred to others in the same cluster."
+    ],
+    [
+      "It is important to decide what sentences are claims before attempting to cluster them. The first such claim detection system to have been created is ClaimBuster BIBREF6 , which scores sentences with an SVM to determine how likely they are to be politically pertinent statements. Similarly, ClaimRank BIBREF7 uses real claims checked by factchecking institutions as training data in order to surface sentences that are worthy of factchecking.",
+      "These methods deal with the question of what is a politically interesting claim. In order to classify the objective qualities of what set apart different types of claims, the ClaimBuster team created PolitiTax BIBREF8 , a taxonomy of claims, and factchecking organisation Full Fact BIBREF9 developed their preferred annotation schema for statements in consultation with their own factcheckers. This research provides a more solid framework within which to construct claim detection classifiers.",
+      "The above considers whether or not a sentence is a claim, but often claims are subsections of sentences and multiple claims might be found in one sentence. In order to accommodate this, BIBREF10 proposes extracting phrases called Context Dependent Claims (CDC) that are relevant to a certain `Topic'. Along these lines, BIBREF11 proposes new definitions for frames to be incorporated into FrameNet BIBREF12 that are specific to facts, in particular those found in a political context.",
+      "Traditional text clustering methods, using TFIDF and some clustering algorithm, are poorly suited to the problem of clustering and comparing short texts, as they can be semantically very similar but use different words. This is a manifestation of the the data sparsity problem with Bag-of-Words (BoW) models. BIBREF16 . Dimensionality reduction methods such as Latent Dirichlet Allocation (LDA) can help solve this problem by giving a dense approximation of this sparse representation BIBREF17 . More recently, efforts in this area have used text embedding-based systems in order to capture dense representation of the texts BIBREF18 . Much of this recent work has relied on the increase of focus in word and text embeddings. Text embeddings have been an increasingly popular tool in NLP since the introduction of Word2Vec BIBREF19 , and since then the number of different embeddings has exploded. While many focus on giving a vector representation of a word, an increasing number now exist that will give a vector representation of a entire sentence or text. Following on from this work, we seek to devise a system that can run online, performing text clustering on the embeddings of texts one at a time",
+      "Some considerations to bear in mind when deciding on an embedding scheme to use are: the size of the final vector, the complexity of the model itself and, if using a pretrained implementation, the data the model has been trained on and whether it is trained in a supervised or unsupervised manner.",
+      "The size of the embedding can have numerous results downstream. In our example we will be doing distance calculations on the resultant vectors and therefore any increase in length will increase the complexity of those distance calculations. We would therefore like as short a vector as possible, but we still wish to capture all salient information about the claim; longer vectors have more capacity to store information, both salient and non-salient.",
+      "A similar effect is seen for the complexity of the model. A more complicated model, with more trainable parameters, may be able to capture finer details about the text, but it will require a larger corpus to achieve this, and will require more computational time to calculate the embeddings. We should therefore attempt to find the simplest embedding system that can accurately solve our problem.",
+      "When attempting to use pretrained models to help in other areas, it is always important to ensure that the models you are using are trained on similar material, to increase the chance that their findings will generalise to the new problem. Many unsupervised text embeddings are trained on the CommonCrawl dataset of approx. 840 billion tokens. This gives a huge amount of data across many domains, but requires a similarly huge amount of computing power to train on the entire dataset. Supervised datasets are unlikely ever to approach such scale as they require human annotations which can be expensive to assemble. The SNLI entailment dataset is an example of a large open source dataset BIBREF20 . It features pairs of sentences along with labels specifying whether or not one entails the other. Google's Universal Sentence Encoder (USE) BIBREF14 is a sentence embedding created with a hybrid supervised/unsupervised method, leveraging both the vast amounts of unsupervised training data and the extra detail that can be derived from a supervised method. The SNLI dataset and the related MultiNLI dataset are often used for this because textual entailment is seen as a good basis for general Natural Language Understanding (NLU) BIBREF21 ."
+    ],
+    [
+      "It is much easier to build a dataset and reliably evaluate a model if the starting definitions are clear and objective. Questions around what is an interesting or pertinent claim are inherently subjective. For example, it is obvious that a politician will judge their opponents' claims to be more important to factcheck than their own.",
+      "Therefore, we built on the methodologies that dealt with the objective qualities of claims, which were the PolitiTax and Full Fact taxonomies. We annotated sentences from our own database of news articles based on a combination of these. We also used the Full Fact definition of a claim as a statement about the world that can be checked. Some examples of claims according to this definition are shown in Table TABREF3 . We decided the first statement was a claim since it declares the occurrence of an event, while the second was considered not to be a claim as it is an expression of feeling.",
+      "Full Fact's approach centred around using sentence embeddings as a feature engineering step, followed by a simple classifier such as logistic regression, which is what we used. They used Facebook's sentence embeddings, InferSent BIBREF13 , which was a recent breakthrough at the time. Such is the speed of new development in the field that since then, several papers describing textual embeddings have been published. Due to the fact that we had already evaluated embeddings for clustering, and therefore knew our system would rely on Google USE Large BIBREF14 , we decided to use this instead. We compared this to TFIDF and Full Fact's results as baselines. The results are displayed in Table TABREF4 .",
+      "However, ClaimBuster and Full Fact focused on live factchecking of TV debates. Logically is a news aggregator and we analyse the bodies of published news stories. We found that in our corpus, the majority of sentences are claims and therefore our model needed to be as selective as possible. In practice, we choose to filter out sentences that are predictions since generally the substance of the claim cannot be fully checked until after the event has occurred. Likewise, we try to remove claims based on personal experience or anecdotal evidence as they are difficult to verify."
+    ],
+    [
+      "In order to choose an embedding, we sought a dataset to represent our problem. Although no perfect matches exist, we decided upon the Quora duplicate question dataset BIBREF22 as the best match. To study the embeddings, we computed the euclidean distance between the two questions using various embeddings, to study the distance between semantically similar and dissimilar questions.",
+      "The graphs in figure 1 show the distances between duplicate and non-duplicate questions using different embedding systems. The X axis shows the euclidean distance between vectors and the Y axis frequency. A perfect result would be a blue peak to the left and an entirely disconnected orange spike to the right, showing that all non-duplicate questions have a greater euclidean distance than the least similar duplicate pair of questions. As can be clearly seen in the figure above, Elmo BIBREF23 and Infersent BIBREF13 show almost no separation and therefore cannot be considered good models for this problem. A much greater disparity is shown by the Google USE models BIBREF14 , and even more for the Google USE Large model. In fact the Google USE Large achieved a F1 score of 0.71 for this task without any specific training, simply by choosing a threshold below which all sentence pairs are considered duplicates.",
+      "In order to test whether these results generalised to our domain, we devised a test that would make use of what little data we had to evaluate. We had no original data on whether sentences were semantically similar, but we did have a corpus of articles clustered into stories. Working on the assumption that similar claims would be more likely to be in the same story, we developed an equation to judge how well our corpus of sentences was clustered, rewarding clustering which matches the article clustering and the total number of claims clustered. The precise formula is given below, where INLINEFORM0 is the proportion of claims in clusters from one story cluster, INLINEFORM1 is the proportion of claims in the correct claim cluster, where they are from the most common story cluster, and INLINEFORM2 is the number of claims placed in clusters. A,B and C are parameters to tune. INLINEFORM3 ",
+      " figureFormula to assess the correctness of claim clusters based on article clusters",
+      "This method is limited in how well it can represent the problem, but it can give indications as to a good or bad clustering method or embedding, and can act as a check that the findings we obtained from the Quora dataset will generalise to our domain. We ran code which vectorized 2,000 sentences and then used the DBScan clustering method BIBREF24 to cluster using a grid search to find the best INLINEFORM0 value, maximizing this formula. We used DBScan as it mirrored the clustering method used to derive the original article clusters. The results for this experiment can be found in Table TABREF10 . We included TFIDF in the experiment as a baseline to judge other results. It is not suitable for our eventual purposes, but it the basis of the original keyword-based model used to build the clusters . That being said, TFIDF performs very well, with only Google USE Large and Infersent coming close in terms of `accuracy'. In the case of Infersent, this comes with the penalty of a much smaller number of claims included in the clusters. Google USE Large, however, clusters a greater number and for this reason we chose to use Google's USE Large. ",
+      "Since Google USE Large was the best-performing embedding in both the tests we devised, this was our chosen embedding to use for clustering. However as can be seen from the results shown above, this is not a perfect solution and the inaccuracy here will introduce inaccuracy further down the clustering pipeline."
+    ],
+    [
+      "We decided to follow a methodology upon the DBScan method of clustering BIBREF24 . DBScan considers all distances between pairs of points. If they are under INLINEFORM0 then those two are linked. Once the number of connected points exceeds a minimum size threshold, they are considered a cluster and all other points are considered to be unclustered. This method is advantageous for our purposes because unlike other methods, such as K-Means, it does not require the number of clusters to be specified. To create a system that can build clusters dynamically, adding one point at a time, we set the minimum cluster size to one, meaning that every point is a member of a cluster.",
+      "A potential disadvantage of this method is that because points require only one connection to a cluster to join it, they may only be related to one point in the cluster, but be considered in the same cluster as all of them. In small examples this is not a problem as all points in the cluster should be very similar. However as the number of points being considered grows, this behaviour raises the prospect of one or several borderline clustering decisions leading to massive clusters made from tenuous connections between genuine clusters. To mitigate this problem we used a method described in the Newslens paper BIBREF25 to solve a similar problem when clustering entire articles. We stored all of our claims in a graph with the connections between them added when the distance between them was determined to be less than INLINEFORM0 . To determine the final clusters we run a Louvain Community Detection BIBREF26 over this graph to split it into defined communities. This improved the compactness of a cluster. When clustering claims one by one, this algorithm can be performed on the connected subgraph featuring the new claim, to reduce the computation required.",
+      "As this method involves distance calculations between the claim being added and every existing claim, the time taken to add one claim will increase roughly linearly with respect to the number of previous claims. Through much optimization we have brought the computational time down to approximately 300ms per claim, which stays fairly static with respect to the number of previous claims."
+    ],
+    [
+      "The clustering described above is heavily dependent on the embedding used. The rate of advances in this field has been rapid in recent years, but an embedding will always be an imperfect representation of an claim and therefore always an area of improvement. A domain specific-embedding will likely offer a more accurate representation but creates problems with clustering claims from different domains. They also require a huge amount of data to give a good model and that is not possible in all domains."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0255/instruction.md b/qasper-0255/instruction.md
new file mode 100644
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--- /dev/null
+++ b/qasper-0255/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Real-time Claim Detection from News Articles and Retrieval of Semantically-Similar Factchecks
+
+Question: How is an incoming claim used to retrieve similar factchecked claims?
\ No newline at end of file
diff --git a/qasper-0262/instruction.md b/qasper-0262/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..9b1dc6b8bb16bffafa58d25b7cb5d2d8955fa0a6
--- /dev/null
+++ b/qasper-0262/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: RC-QED: Evaluating Natural Language Derivations in Multi-Hop Reading Comprehension
+
+Question: What is the source of the proposed dataset?
\ No newline at end of file
diff --git a/qasper-0264/instruction.md b/qasper-0264/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..08e3fe8ffeffbfecf13f3f3ec3443f6e2755c55a
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+++ b/qasper-0264/instruction.md
@@ -0,0 +1,139 @@
+Name of Paper: Event Outcome Prediction using Sentiment Analysis and Crowd Wisdom in Microblog Feeds
+
+Question: What is the interannotator agreement of the crowd sourced users?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Data Set and Preprocessing ::: Data Collection",
+    "Data Set and Preprocessing ::: Preprocessing",
+    "Methodology ::: Procedure",
+    "Methodology ::: Machine Learning Models",
+    "Methodology ::: Machine Learning Models ::: Single-label Classification",
+    "Methodology ::: Machine Learning Models ::: Multi-label Classification",
+    "Methodology ::: Feature Space",
+    "Data Analysis",
+    "Evaluation Metrics",
+    "Evaluation Metrics ::: Sentiment Analysis",
+    "Evaluation Metrics ::: Outcome Prediction",
+    "Results ::: Sentiment Analysis",
+    "Results ::: Results for Outcome Prediction",
+    "Results ::: Results for Outcome Prediction ::: Presidential Debates",
+    "Results ::: Results for Outcome Prediction ::: Grammy Awards",
+    "Results ::: Results for Outcome Prediction ::: Super Bowl",
+    "Conclusions"
+  ],
+  "paragraphs": [
+    [
+      "Over the past few years, microblogs have become one of the most popular online social networks. Microblogging websites have evolved to become a source of varied kinds of information. This is due to the nature of microblogs: people post real-time messages about their opinions and express sentiment on a variety of topics, discuss current issues, complain, etc. Twitter is one such popular microblogging service where users create status messages (called \u201ctweets\"). With over 400 million tweets per day on Twitter, microblog users generate large amount of data, which cover very rich topics ranging from politics, sports to celebrity gossip. Because the user generated content on microblogs covers rich topics and expresses sentiment/opinions of the mass, mining and analyzing this information can prove to be very beneficial both to the industrial and the academic community. Tweet classification has attracted considerable attention because it has become very important to analyze peoples' sentiments and opinions over social networks.",
+      "Most of the current work on analysis of tweets is focused on sentiment analysis BIBREF0, BIBREF1, BIBREF2. Not much has been done on predicting outcomes of events based on the tweet sentiments, for example, predicting winners of presidential debates based on the tweets by analyzing the users' sentiments. This is possible intuitively because the sentiment of the users in their tweets towards the candidates is proportional to the performance of the candidates in the debate.",
+      "In this paper, we analyze three such events: 1) US Presidential Debates 2015-16, 2) Grammy Awards 2013, and 3) Super Bowl 2013. The main focus is on the analysis of the presidential debates. For the Grammys and the Super Bowl, we just perform sentiment analysis and try to predict the outcomes in the process. For the debates, in addition to the analysis done for the Grammys and Super Bowl, we also perform a trend analysis. Our analysis of the tweets for the debates is 3-fold as shown below.",
+      "Sentiment: Perform a sentiment analysis on the debates. This involves: building a machine learning model which learns the sentiment-candidate pair (candidate is the one to whom the tweet is being directed) from the training data and then using this model to predict the sentiment-candidate pairs of new tweets.",
+      "Predicting Outcome: Here, after predicting the sentiment-candidate pairs on the new data, we predict the winner of the debates based on the sentiments of the users.",
+      "Trends: Here, we analyze certain trends of the debates like the change in sentiments of the users towards the candidates over time (hours, days, months) and how the opinion of experts such as Washington Post affect the sentiments of the users.",
+      "For the sentiment analysis, we look at our problem in a multi-label setting, our two labels being sentiment polarity and the candidate/category in consideration. We test both single-label classifiers and multi-label ones on the problem and as intuition suggests, the multi-label classifier RaKel performs better. A combination of document-embedding features BIBREF3 and topic features (essentially the document-topic probabilities) BIBREF4 is shown to give the best results. These features make sense intuitively because the document-embedding features take context of the text into account, which is important for sentiment polarity classification, and topic features take into account the topic of the tweet (who/what is it about).",
+      "The prediction of outcomes of debates is very interesting in our case. Most of the results seem to match with the views of some experts such as the political pundits of the Washington Post. This implies that certain rules that were used to score the candidates in the debates by said-experts were in fact reflected by reading peoples' sentiments expressed over social media. This opens up a wide variety of learning possibilities from users' sentiments on social media, which is sometimes referred to as the wisdom of crowd.",
+      "We do find out that the public sentiments are not always coincident with the views of the experts. In this case, it is interesting to check whether the views of the experts can affect the public, for example, by spreading through the social media microblogs such as Twitter. Hence, we also conduct experiments to compare the public sentiment before and after the experts' views become public and thus notice the impact of the experts' views on the public sentiment. In our analysis of the debates, we observe that in certain debates, such as the 5th Republican Debate, held on December 15, 2015, the opinions of the users vary from the experts. But we see the effect of the experts on the sentiment of the users by looking at their opinions of the same candidates the next day.",
+      "Our contributions are mainly: we want to see how predictive the sentiment/opinion of the users are in social media microblogs and how it compares to that of the experts. In essence, we find that the crowd wisdom in the microblog domain matches that of the experts in most cases. There are cases, however, where they don't match but we observe that the crowd's sentiments are actually affected by the experts. This can be seen in our analysis of the presidential debates.",
+      "The rest of the paper is organized as follows: in section SECREF2, we review some of the literature. In section SECREF3, we discuss the collection and preprocessing of the data. Section SECREF4 details the approach taken, along with the features and the machine learning methods used. Section SECREF7 discusses the results of the experiments conducted and lastly section SECREF8 ends with a conclusion on the results including certain limitations and scopes for improvement to work on in the future."
+    ],
+    [
+      "Sentiment analysis as a Natural Language Processing task has been handled at many levels of granularity. Specifically on the microblog front, some of the early results on sentiment analysis are by BIBREF0, BIBREF1, BIBREF2, BIBREF5, BIBREF6. Go et al. BIBREF0 applied distant supervision to classify tweet sentiment by using emoticons as noisy labels. Kouloumpis et al. BIBREF7 exploited hashtags in tweets to build training data. Chenhao Tan et al. BIBREF8 determined user-level sentiments on particular topics with the help of the social network graph.",
+      "There has been some work in event detection and extraction in microblogs as well. In BIBREF9, the authors describe a way to extract major life events of a user based on tweets that either congratulate/offer condolences. BIBREF10 build a key-word graph from the data and then detect communities in this graph (cluster) to find events. This works because words that describe similar events will form clusters. In BIBREF11, the authors use distant supervision to extract events. There has also been some work on event retrieval in microblogs BIBREF12. In BIBREF13, the authors detect time points in the twitter stream when an important event happens and then classify such events based on the sentiments they evoke using only non-textual features to do so. In BIBREF14, the authors study how much of the opinion extracted from Online Social Networks (OSN) user data is reflective of the opinion of the larger population. Researchers have also mined Twitter dataset to analyze public reaction to various events: from election debate performance BIBREF15, where the authors demonstrate visuals and metrics that can be used to detect sentiment pulse, anomalies in that pulse, and indications of controversial topics that can be used to inform the design of visual analytic systems for social media events, to movie box-office predictions on the release day BIBREF16. Mishne and Glance BIBREF17 correlate sentiments in blog posts with movie box-office scores. The correlations they observed for positive sentiments are fairly low and not sufficient to use for predictive purposes. Recently, several approaches involving machine learning and deep learning have also been used in the visual and language domains BIBREF18, BIBREF19, BIBREF20, BIBREF21, BIBREF22, BIBREF23, BIBREF24."
+    ],
+    [
+      "Twitter is a social networking and microblogging service that allows users to post real-time messages, called tweets. Tweets are very short messages, a maximum of 140 characters in length. Due to such a restriction in length, people tend to use a lot of acronyms, shorten words etc. In essence, the tweets are usually very noisy. There are several aspects to tweets such as: 1) Target: Users use the symbol \u201c@\" in their tweets to refer to other users on the microblog. 2) Hashtag: Hashtags are used by users to mark topics. This is done to increase the visibility of the tweets.",
+      "We conduct experiments on 3 different datasets, as mentioned earlier: 1) US Presidential Debates, 2) Grammy Awards 2013, 3) Superbowl 2013. To construct our presidential debates dataset, we have used the Twitter Search API to collect the tweets. Since there was no publicly available dataset for this, we had to collect the data manually. The data was collected on 10 different presidential debates: 7 republican and 3 democratic, from October 2015 to March 2016. Different hashtags like \u201c#GOP, #GOPDebate\u201d were used to filter out tweets specific to the debate. This is given in Table TABREF2. We extracted only english tweets for our dataset. We collected a total of 104961 tweets were collected across all the debates. But there were some limitations with the API. Firstly, the server imposes a rate limit and discards tweets when the limit is reached. The second problem is that the API returns many duplicates. Thus, after removing the duplicates and irrelevant tweets, we were left with a total of 17304 tweets. This includes the tweets only on the day of the debate. We also collected tweets on the days following the debate.",
+      "As for the other two datasets, we collected them from available-online repositories. There were a total of 2580062 tweets for the Grammy Awards 2013, and a total of 2428391 tweets for the Superbowl 2013. The statistics are given in Tables TABREF3 and TABREF3. The tweets for the grammy were before the ceremony and during. However, we only use the tweets before the ceremony (after the nominations were announced), to predict the winners. As for the superbowl, the tweets collected were during the game. But we can predict interesting things like Most Valuable Player etc. from the tweets. The tweets for both of these datasets were annotated and thus did not require any human intervention. However, the tweets for the debates had to be annotated.",
+      "Since we are using a supervised approach in this paper, we have all the tweets (for debates) in the training set human-annotated. The tweets were already annotated for the Grammys and Super Bowl. Some statistics about our datasets are presented in Tables TABREF3, TABREF3 and TABREF3. The annotations for the debate dataset comprised of 2 labels for each tweet: 1) Candidate: This is the candidate of the debate to whom the tweet refers to, 2) Sentiment: This represents the sentiment of the tweet towards that candidate. This is either positive or negative.",
+      "The task then becomes a case of multi-label classification. The candidate labels are not so trivial to obtain, because there are tweets that do not directly contain any candidates' name. For example, the tweets, \u201ca business man for president??\u201d and \u201ca doctor might sure bring about a change in America!\u201d are about Donal Trump and Ben Carson respectively. Thus, it is meaningful to have a multi-label task.",
+      "The annotations for the other two datasets are similar, in that one of the labels was the sentiment and the other was category-dependent in the outcome-prediction task, as discussed in the sections below. For example, if we are trying to predict the \"Album of the Year\" winners for the Grammy dataset, the second label would be the nominees for that category (album of the year)."
+    ],
+    [
+      "As noted earlier, tweets are generally noisy and thus require some preprocessing done before using them. Several filters were applied to the tweets such as: (1) Usernames: Since users often include usernames in their tweets to direct their message, we simplify it by replacing the usernames with the token \u201cUSER\u201d. For example, @michael will be replaced by USER. (2) URLs: In most of the tweets, users include links that add on to their text message. We convert/replace the link address to the token \u201cURL\u201d. (3) Repeated Letters: Oftentimes, users use repeated letters in a word to emphasize their notion. For example, the word \u201clol\u201d (which stands for \u201claugh out loud\u201d) is sometimes written as \u201clooooool\u201d to emphasize the degree of funnyness. We replace such repeated occurrences of letters (more than 2), with just 3 occurrences. We replace with 3 occurrences and not 2, so that we can distinguish the exaggerated usage from the regular ones. (4) Multiple Sentiments: Tweets which contain multiple sentiments are removed, such as \"I hate Donald Trump, but I will vote for him\". This is done so that there is no ambiguity. (5) Retweets: In Twitter, many times tweets of a person are copied and posted by another user. This is known as retweeting and they are commonly abbreviated with \u201cRT\u201d. These are removed and only the original tweets are processed. (6) Repeated Tweets: The Twitter API sometimes returns a tweet multiple times. We remove such duplicates to avoid putting extra weight on any particular tweet."
+    ],
+    [
+      "Our analysis of the debates is 3-fold including sentiment analysis, outcome prediction, and trend analysis.",
+      "Sentiment Analysis: To perform a sentiment analysis on the debates, we first extract all the features described below from all the tweets in the training data. We then build the different machine learning models described below on these set of features. After that, we evaluate the output produced by the models on unseen test data. The models essentially predict candidate-sentiment pairs for each tweet.",
+      "Outcome Prediction: Predict the outcome of the debates. After obtaining the sentiments on the test data for each tweet, we can compute the net normalized sentiment for each presidential candidate in the debate. This is done by looking at the number of positive and negative sentiments for each candidate. We then normalize the sentiment scores of each candidate to be in the same scale (0-1). After that, we rank the candidates based on the sentiment scores and predict the top $k$ as the winners.",
+      "Trend Analysis: We also analyze some certain trends of the debates. Firstly, we look at the change in sentiments of the users towards the candidates over time (hours, days, months). This is done by computing the sentiment scores for each candidate in each of the debates and seeing how it varies over time, across debates. Secondly, we examine the effect of Washington Post on the views of the users. This is done by looking at the sentiments of the candidates (to predict winners) of a debate before and after the winners are announced by the experts in Washington Post. This way, we can see if Washington Post has had any effect on the sentiments of the users. Besides that, to study the behavior of the users, we also look at the correlation of the tweet volume with the number of viewers as well as the variation of tweet volume over time (hours, days, months) for debates.",
+      "As for the Grammys and the Super Bowl, we only perform the sentiment analysis and predict the outcomes."
+    ],
+    [
+      "We compare 4 different models for performing our task of sentiment classification. We then pick the best performing model for the task of outcome prediction. Here, we have two categories of algorithms: single-label and multi-label (We already discussed above why it is meaningful to have a multi-label task earlier), because one can represent $<$candidate, sentiment$>$ as a single class label or have candidate and sentiment as two separate labels. They are listed below:"
+    ],
+    [
+      "Naive Bayes: We use a multinomial Naive Bayes model. A tweet $t$ is assigned a class $c^{*}$ such that",
+      "where there are $m$ features and $f_i$ represents the $i^{th}$ feature.",
+      "Support Vector Machines: Support Vector Machines (SVM) constructs a hyperplane or a set of hyperplanes in a high-dimensional space, which can then be used for classification. In our case, we use SVM with Sequential Minimal Optimization (SMO) BIBREF25, which is an algorithm for solving the quadratic programming (QP) problem that arises during the training of SVMs.",
+      "Elman Recurrent Neural Network: Recurrent Neural Networks (RNNs) are gaining popularity and are being applied to a wide variety of problems. They are a class of artificial neural networks, where connections between units form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior. The Elman RNN was proposed by Jeff Elman in the year 1990 BIBREF26. We use this in our task."
+    ],
+    [
+      "RAkEL (RAndom k labELsets): RAkEL BIBREF27 is a multi-label classification algorithm that uses labeled powerset (LP) transformation: it basically creates a single binary classifier for every label combination and then uses multiple LP classifiers, each trained on a random subset of the actual labels, for classification."
+    ],
+    [
+      "In order to classify the tweets, a set of features is extracted from each of the tweets, such as n-gram, part-of-speech etc. The details of these features are given below:",
+      "n-gram: This represents the frequency counts of n-grams, specifically that of unigrams and bigrams.",
+      "punctuation: The number of occurrences of punctuation symbols such as commas, exclamation marks etc.",
+      "POS (part-of-speech): The frequency of each POS tagger is used as the feature.",
+      "prior polarity scoring: Here, we obtain the prior polarity of the words BIBREF6 using the Dictionary of Affect in Language (DAL) BIBREF28. This dictionary (DAL) of about 8000 English words assigns a pleasantness score to each word on a scale of 1-3. After normalizing, we can assign the words with polarity higher than $0.8$ as positive and less than $0.5$ as negative. If a word is not present in the dictionary, we lookup its synonyms in WordNet: if this word is there in the dictionary, we assign the original word its synonym's score.",
+      "Twitter Specific features:",
+      "Number of hashtags ($\\#$ symbol)",
+      "Number of mentioning users ( symbol)",
+      "Number of hyperlinks",
+      "Document embedding features: Here, we use the approach proposed by Mikolov et al. BIBREF3 to embed an entire tweet into a vector of features",
+      "Topic features: Here, LDA (Latent Dirichlet Allocation) BIBREF4 is used to extract topic-specific features for a tweet (document). This is basically the topic-document probability that is outputted by the model.",
+      "In the following experiments, we use 1-$gram$, 2-$gram$ and $(1+2)$-$gram$ to denote unigram, bigram and a combination of unigram and bigram features respectively. We also combine punctuation and the other features as miscellaneous features and use $MISC$ to denote this. We represent the document-embedding features by $DOC$ and topic-specific features by $TOPIC$."
+    ],
+    [
+      "In this section, we analyze the presidential debates data and show some trends.",
+      "First, we look at the trend of the tweet frequency. Figure FIGREF21 shows the trends of the tweet frequency and the number of TV viewers as the debates progress over time. We observe from Figures FIGREF21 and FIGREF21 that for the first 5 debates considered, the trend of the number of TV viewers matches the trend of the number of tweets. However, we can see that towards the final debates, the frequency of the tweets decreases consistently. This shows an interesting fact that although the people still watch the debates, the number of people who tweet about it are greatly reduced. But the tweeting community are mainly youngsters and this shows that most of the tweeting community, who actively tweet, didn't watch the later debates. Because if they did, then the trends should ideally match.",
+      "Next we look at how the tweeting activity is on days of the debate: specifically on the day of the debate, the next day and two days later. Figure FIGREF22 shows the trend of the tweet frequency around the day of the 5th republican debate, i.e December 15, 2015. As can be seen, the average number of people tweet more on the day of the debate than a day or two after it. This makes sense intuitively because the debate would be fresh in their heads.",
+      "Then, we look at how people tweet in the hours of the debate: specifically during the debate, one hour after and then two hours after. Figure FIGREF23 shows the trend of the tweet frequency around the hour of the 5th republican debate, i.e December 15, 2015. We notice that people don't tweet much during the debate but the activity drastically increases after two hours. This might be because people were busy watching the debate and then taking some time to process things, so that they can give their opinion.",
+      "We have seen the frequency of tweets over time in the previous trends. Now, we will look at how the sentiments of the candidates change over time.",
+      "First, Figure FIGREF24 shows how the sentiments of the candidates changed across the debates. We find that many of the candidates have had ups and downs towards in the debates. But these trends are interesting in that, it gives some useful information about what went down in the debate that caused the sentiments to change (sometimes drastically). For example, if we look at the graph for Donald Trump, we see that his sentiment was at its lowest point during the debate held on December 15. Looking into the debate, we can easily see why this was the case. At a certain point in the debate, Trump was asked about his ideas for the nuclear triad. It is very important that a presidential candidate knows about this, but Trump had no idea what the nuclear triad was and, in a transparent attempt to cover his tracks, resorted to a \u201cwe need to be strong\" speech. It can be due to this embarrassment that his sentiment went down during this debate.",
+      "Next, we investigate how the sentiments of the users towards the candidates change before and after the debate. In essence, we examine how the debate and the results of the debates given by the experts affects the sentiment of the candidates. Figure FIGREF25 shows the sentiments of the users towards the candidate during the 5th Republican Debate, 15th December 2015. It can be seen that the sentiments of the users towards the candidates does indeed change over the course of two days. One particular example is that of Jeb Bush. It seems that the populace are generally prejudiced towards the candidates, which is reflected in their sentiments of the candidates on the day of the debate. The results of the Washington Post are released in the morning after the debate. One can see the winners suggested by the Washington Post in Table TABREF35. One of the winners in that debate according to them is Jeb Bush. Coincidentally, Figure FIGREF25 suggests that the sentiment of Bush has gone up one day after the debate (essentially, one day after the results given by the experts are out).",
+      "There is some influence, for better or worse, of these experts on the minds of the users in the early debates, but towards the final debates the sentiments of the users are mostly unwavering, as can be seen in Figure FIGREF25. Figure FIGREF25 is on the last Republican debate, and suggests that the opinions of the users do not change much with time. Essentially the users have seen enough debates to make up their own minds and their sentiments are not easily wavered."
+    ],
+    [
+      "In this section, we define the different evaluation metrics that we use for different tasks. We have two tasks at hand: 1) Sentiment Analysis, 2) Outcome Prediction. We use different metrics for these two tasks."
+    ],
+    [
+      "In the study of sentiment analysis, we use \u201cHamming Loss\u201d to evaluate the performance of the different methods. Hamming Loss, based on Hamming distance, takes into account the prediction error and the missing error, normalized over the total number of classes and total number of examples BIBREF29. The Hamming Loss is given below:",
+      "where $|D|$ is the number of examples in the dataset and $|L|$ is the number of labels. $S_i$ and $Y_i$ denote the sets of true and predicted labels for instance $i$ respectively. $\\oplus $ stands for the XOR operation BIBREF30. Intuitively, the performance is better, when the Hamming Loss is smaller. 0 would be the ideal case."
+    ],
+    [
+      "For the case of outcome prediction, we will have a predicted set and an actual set of results. Thus, we can use common information retrieval metrics to evaluate the prediction performance. Those metrics are listed below:",
+      "Mean F-measure: F-measure takes into account both the precision and recall of the results. In essence, it takes into account how many of the relevant results are returned and also how many of the returned results are relevant.",
+      "where $|D|$ is the number of queries (debates/categories for grammy winners etc.), $P_i$ and $R_i$ are the precision and recall for the $i^{th}$ query.",
+      "Mean Average Precision: As a standard metric used in information retrieval, Mean Average Precision for a set of queries is mean of the average precision scores for each query:",
+      "where $|D|$ is the number of queries (e.g., debates), $P_i(k)$ is the precision at $k$ ($P@k$) for the $i^{th}$ query, $rel_i(k)$ is an indicator function, equaling 1 if the document at position $k$ for the $i^th$ query is relevant, else 0, and $|RD_i|$ is the number of relevant documents for the $i^{th}$ query."
+    ],
+    [
+      "We use 3 different datasets for the problem of sentiment analysis, as already mentioned. We test the four different algorithms mentioned in Section SECREF6, with a different combination of features that are described in Section SECREF10. To evaluate our models, we use the \u201cHamming Loss\u201d metric as discussed in Section SECREF6. We use this metric because our problem is in the multi-class classification domain. However, the single-label classifiers like SVM, Naive Bayes, Elman RNN cannot be evaluated against this metric directly. To do this, we split the predicted labels into a format that is consistent with that of multi-label classifiers like RaKel. The results of the experiments for each of the datasets are given in Tables TABREF34, TABREF34 and TABREF34. In the table, $f_1$, $f_2$, $f_3$, $f_4$, $f_5$ and $f_6$ denote the features 1-$gram$, 2-$gram$, $(1+2)$-$gram$, $(1+2)$-$gram + MISC$, $DOC$ and $DOC + TOPIC$ respectively. Note that lower values of hamming losses are more desirable. We find that RaKel performs the best out of all the algorithms. RaKel is more suited for the task because our task is a multi-class classification. Among all the single-label classifiers, SVM performs the best. We also observe that as we use more complex feature spaces, the performance increases. This is true for almost all of the algorithms listed.",
+      "Our best performing features is a combination of paragraph embedding features and topic features from LDA. This makes sense intuitively because paragraph-embedding takes into account the context in the text. Context is very important in determining the sentiment of a short text: having negative words in the text does not always mean that the text contains a negative sentiment. For example, the sentence \u201cnever say never is not a bad thing\u201d has many negative words; but the sentence as a whole does not have a negative sentiment. This is why we need some kind of context information to accurately determine the sentiment. Thus, with these embedded features, one would be able to better determine the polarity of the sentence. The other label is the entity (candidate/song etc.) in consideration. Topic features here make sense because this can be considered as the topic of the tweet in some sense. This can be done because that label captures what or whom the tweet is about."
+    ],
+    [
+      "In this section, we show the results for the outcome-prediction of the events. RaKel, as the best performing method, is trained to predict the sentiment-labels for the unlabeled data. The sentiment labels are then used to determine the outcome of the events. In the Tables (TABREF35, TABREF36, TABREF37) of outputs given, we only show as many predictions as there are winners."
+    ],
+    [
+      "The results obtained for the outcome prediction task for the US presidential debates is shown in Table TABREF35. Table TABREF35 shows the winners as given in the Washington Post (3rd column) and the winners that are predicted by our system (2nd column). By comparing the set of results obtained from both the sources, we find that the set of candidates predicted match to a large extent with the winners given out by the Washington Post. The result suggests that the opinions of the social media community match with that of the journalists in most cases."
+    ],
+    [
+      "A Grammy Award is given to outstanding achievement in the music industry. There are two types of awards: \u201cGeneral Field\u201d awards, which are not restricted by genre, and genre-specific awards. Since, there can be upto 80 categories of awards, we only focus on the main 4: 1) Album of the Year, 2) Record of the Year, 3) Song of the Year, and 4) Best New Artist. These categories are the main in the awards ceremony and most looked forward to. That is also why we choose to predict the outcomes of these categories based on the tweets. We use the tweets before the ceremony (but after the nominations) to predict the outcomes.",
+      "Basically, we have a list of nominations for each category. We filter the tweets based on these nominations and then predict the winner as with the case of the debates. The outcomes are listed in Table TABREF36. We see that largely, the opinion of the users on the social network, agree with the deciding committee of the awards. The winners agree for all the categories except \u201cSong of the Year\u201d."
+    ],
+    [
+      "The Super Bowl is the annual championship game of the National Football League. We have collected the data for the year 2013. Here, the match was between the Baltimore Ravens and the San Francisco 49ers. The tweets that we have collected are during the game. From these tweets, one could trivially determine the winner. But an interesting outcome would be to predict the Most Valuable Player (MVP) during the game. To determine this, all the tweets were looked at and we determined the candidate with the highest positive sentiment by the end of the game. The result in Table TABREF37 suggests that we are able to determine the outcomes accurately.",
+      "Table TABREF43 displays some evaluation metrics for this task. These were computed based on the predicted outcomes and the actual outcomes for each of the different datasets. Since the number of participants varies from debate-to-debate or category-to-category for Grammy etc., we cannot return a fixed number of winners for everything. So, the size of our returned ranked-list is set to half of the number of participants (except for the MVP for Super Bowl; there are so many players and returning half of them when only one of them is relevant is meaningless. So, we just return the top 10 players). As we can see from the metrics, the predicted outcomes match quite well with the actual ones (or the ones given by the experts)."
+    ],
+    [
+      "This paper presents a study that compares the opinions of users on microblogs, which is essentially the crowd wisdom, to that of the experts in the field. Specifically, we explore three datasets: US Presidential Debates 2015-16, Grammy Awards 2013, Super Bowl 2013. We determined if the opinions of the crowd and the experts match by using the sentiments of the tweets to predict the outcomes of the debates/Grammys/Super Bowl. We observed that in most of the cases, the predictions were right indicating that crowd wisdom is indeed worth looking at and mining sentiments in microblogs is useful. In some cases where there were disagreements, however, we observed that the opinions of the experts did have some influence on the opinions of the users. We also find that the features that were most useful in our case of multi-label classification was a combination of the document-embedding and topic features."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0265/instruction.md b/qasper-0265/instruction.md
new file mode 100644
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+Name of Paper: Event Outcome Prediction using Sentiment Analysis and Crowd Wisdom in Microblog Feeds
+
+Question: Who are the experts?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Data Set and Preprocessing ::: Data Collection",
+    "Data Set and Preprocessing ::: Preprocessing",
+    "Methodology ::: Procedure",
+    "Methodology ::: Machine Learning Models",
+    "Methodology ::: Machine Learning Models ::: Single-label Classification",
+    "Methodology ::: Machine Learning Models ::: Multi-label Classification",
+    "Methodology ::: Feature Space",
+    "Data Analysis",
+    "Evaluation Metrics",
+    "Evaluation Metrics ::: Sentiment Analysis",
+    "Evaluation Metrics ::: Outcome Prediction",
+    "Results ::: Sentiment Analysis",
+    "Results ::: Results for Outcome Prediction",
+    "Results ::: Results for Outcome Prediction ::: Presidential Debates",
+    "Results ::: Results for Outcome Prediction ::: Grammy Awards",
+    "Results ::: Results for Outcome Prediction ::: Super Bowl",
+    "Conclusions"
+  ],
+  "paragraphs": [
+    [
+      "Over the past few years, microblogs have become one of the most popular online social networks. Microblogging websites have evolved to become a source of varied kinds of information. This is due to the nature of microblogs: people post real-time messages about their opinions and express sentiment on a variety of topics, discuss current issues, complain, etc. Twitter is one such popular microblogging service where users create status messages (called \u201ctweets\"). With over 400 million tweets per day on Twitter, microblog users generate large amount of data, which cover very rich topics ranging from politics, sports to celebrity gossip. Because the user generated content on microblogs covers rich topics and expresses sentiment/opinions of the mass, mining and analyzing this information can prove to be very beneficial both to the industrial and the academic community. Tweet classification has attracted considerable attention because it has become very important to analyze peoples' sentiments and opinions over social networks.",
+      "Most of the current work on analysis of tweets is focused on sentiment analysis BIBREF0, BIBREF1, BIBREF2. Not much has been done on predicting outcomes of events based on the tweet sentiments, for example, predicting winners of presidential debates based on the tweets by analyzing the users' sentiments. This is possible intuitively because the sentiment of the users in their tweets towards the candidates is proportional to the performance of the candidates in the debate.",
+      "In this paper, we analyze three such events: 1) US Presidential Debates 2015-16, 2) Grammy Awards 2013, and 3) Super Bowl 2013. The main focus is on the analysis of the presidential debates. For the Grammys and the Super Bowl, we just perform sentiment analysis and try to predict the outcomes in the process. For the debates, in addition to the analysis done for the Grammys and Super Bowl, we also perform a trend analysis. Our analysis of the tweets for the debates is 3-fold as shown below.",
+      "Sentiment: Perform a sentiment analysis on the debates. This involves: building a machine learning model which learns the sentiment-candidate pair (candidate is the one to whom the tweet is being directed) from the training data and then using this model to predict the sentiment-candidate pairs of new tweets.",
+      "Predicting Outcome: Here, after predicting the sentiment-candidate pairs on the new data, we predict the winner of the debates based on the sentiments of the users.",
+      "Trends: Here, we analyze certain trends of the debates like the change in sentiments of the users towards the candidates over time (hours, days, months) and how the opinion of experts such as Washington Post affect the sentiments of the users.",
+      "For the sentiment analysis, we look at our problem in a multi-label setting, our two labels being sentiment polarity and the candidate/category in consideration. We test both single-label classifiers and multi-label ones on the problem and as intuition suggests, the multi-label classifier RaKel performs better. A combination of document-embedding features BIBREF3 and topic features (essentially the document-topic probabilities) BIBREF4 is shown to give the best results. These features make sense intuitively because the document-embedding features take context of the text into account, which is important for sentiment polarity classification, and topic features take into account the topic of the tweet (who/what is it about).",
+      "The prediction of outcomes of debates is very interesting in our case. Most of the results seem to match with the views of some experts such as the political pundits of the Washington Post. This implies that certain rules that were used to score the candidates in the debates by said-experts were in fact reflected by reading peoples' sentiments expressed over social media. This opens up a wide variety of learning possibilities from users' sentiments on social media, which is sometimes referred to as the wisdom of crowd.",
+      "We do find out that the public sentiments are not always coincident with the views of the experts. In this case, it is interesting to check whether the views of the experts can affect the public, for example, by spreading through the social media microblogs such as Twitter. Hence, we also conduct experiments to compare the public sentiment before and after the experts' views become public and thus notice the impact of the experts' views on the public sentiment. In our analysis of the debates, we observe that in certain debates, such as the 5th Republican Debate, held on December 15, 2015, the opinions of the users vary from the experts. But we see the effect of the experts on the sentiment of the users by looking at their opinions of the same candidates the next day.",
+      "Our contributions are mainly: we want to see how predictive the sentiment/opinion of the users are in social media microblogs and how it compares to that of the experts. In essence, we find that the crowd wisdom in the microblog domain matches that of the experts in most cases. There are cases, however, where they don't match but we observe that the crowd's sentiments are actually affected by the experts. This can be seen in our analysis of the presidential debates.",
+      "The rest of the paper is organized as follows: in section SECREF2, we review some of the literature. In section SECREF3, we discuss the collection and preprocessing of the data. Section SECREF4 details the approach taken, along with the features and the machine learning methods used. Section SECREF7 discusses the results of the experiments conducted and lastly section SECREF8 ends with a conclusion on the results including certain limitations and scopes for improvement to work on in the future."
+    ],
+    [
+      "Sentiment analysis as a Natural Language Processing task has been handled at many levels of granularity. Specifically on the microblog front, some of the early results on sentiment analysis are by BIBREF0, BIBREF1, BIBREF2, BIBREF5, BIBREF6. Go et al. BIBREF0 applied distant supervision to classify tweet sentiment by using emoticons as noisy labels. Kouloumpis et al. BIBREF7 exploited hashtags in tweets to build training data. Chenhao Tan et al. BIBREF8 determined user-level sentiments on particular topics with the help of the social network graph.",
+      "There has been some work in event detection and extraction in microblogs as well. In BIBREF9, the authors describe a way to extract major life events of a user based on tweets that either congratulate/offer condolences. BIBREF10 build a key-word graph from the data and then detect communities in this graph (cluster) to find events. This works because words that describe similar events will form clusters. In BIBREF11, the authors use distant supervision to extract events. There has also been some work on event retrieval in microblogs BIBREF12. In BIBREF13, the authors detect time points in the twitter stream when an important event happens and then classify such events based on the sentiments they evoke using only non-textual features to do so. In BIBREF14, the authors study how much of the opinion extracted from Online Social Networks (OSN) user data is reflective of the opinion of the larger population. Researchers have also mined Twitter dataset to analyze public reaction to various events: from election debate performance BIBREF15, where the authors demonstrate visuals and metrics that can be used to detect sentiment pulse, anomalies in that pulse, and indications of controversial topics that can be used to inform the design of visual analytic systems for social media events, to movie box-office predictions on the release day BIBREF16. Mishne and Glance BIBREF17 correlate sentiments in blog posts with movie box-office scores. The correlations they observed for positive sentiments are fairly low and not sufficient to use for predictive purposes. Recently, several approaches involving machine learning and deep learning have also been used in the visual and language domains BIBREF18, BIBREF19, BIBREF20, BIBREF21, BIBREF22, BIBREF23, BIBREF24."
+    ],
+    [
+      "Twitter is a social networking and microblogging service that allows users to post real-time messages, called tweets. Tweets are very short messages, a maximum of 140 characters in length. Due to such a restriction in length, people tend to use a lot of acronyms, shorten words etc. In essence, the tweets are usually very noisy. There are several aspects to tweets such as: 1) Target: Users use the symbol \u201c@\" in their tweets to refer to other users on the microblog. 2) Hashtag: Hashtags are used by users to mark topics. This is done to increase the visibility of the tweets.",
+      "We conduct experiments on 3 different datasets, as mentioned earlier: 1) US Presidential Debates, 2) Grammy Awards 2013, 3) Superbowl 2013. To construct our presidential debates dataset, we have used the Twitter Search API to collect the tweets. Since there was no publicly available dataset for this, we had to collect the data manually. The data was collected on 10 different presidential debates: 7 republican and 3 democratic, from October 2015 to March 2016. Different hashtags like \u201c#GOP, #GOPDebate\u201d were used to filter out tweets specific to the debate. This is given in Table TABREF2. We extracted only english tweets for our dataset. We collected a total of 104961 tweets were collected across all the debates. But there were some limitations with the API. Firstly, the server imposes a rate limit and discards tweets when the limit is reached. The second problem is that the API returns many duplicates. Thus, after removing the duplicates and irrelevant tweets, we were left with a total of 17304 tweets. This includes the tweets only on the day of the debate. We also collected tweets on the days following the debate.",
+      "As for the other two datasets, we collected them from available-online repositories. There were a total of 2580062 tweets for the Grammy Awards 2013, and a total of 2428391 tweets for the Superbowl 2013. The statistics are given in Tables TABREF3 and TABREF3. The tweets for the grammy were before the ceremony and during. However, we only use the tweets before the ceremony (after the nominations were announced), to predict the winners. As for the superbowl, the tweets collected were during the game. But we can predict interesting things like Most Valuable Player etc. from the tweets. The tweets for both of these datasets were annotated and thus did not require any human intervention. However, the tweets for the debates had to be annotated.",
+      "Since we are using a supervised approach in this paper, we have all the tweets (for debates) in the training set human-annotated. The tweets were already annotated for the Grammys and Super Bowl. Some statistics about our datasets are presented in Tables TABREF3, TABREF3 and TABREF3. The annotations for the debate dataset comprised of 2 labels for each tweet: 1) Candidate: This is the candidate of the debate to whom the tweet refers to, 2) Sentiment: This represents the sentiment of the tweet towards that candidate. This is either positive or negative.",
+      "The task then becomes a case of multi-label classification. The candidate labels are not so trivial to obtain, because there are tweets that do not directly contain any candidates' name. For example, the tweets, \u201ca business man for president??\u201d and \u201ca doctor might sure bring about a change in America!\u201d are about Donal Trump and Ben Carson respectively. Thus, it is meaningful to have a multi-label task.",
+      "The annotations for the other two datasets are similar, in that one of the labels was the sentiment and the other was category-dependent in the outcome-prediction task, as discussed in the sections below. For example, if we are trying to predict the \"Album of the Year\" winners for the Grammy dataset, the second label would be the nominees for that category (album of the year)."
+    ],
+    [
+      "As noted earlier, tweets are generally noisy and thus require some preprocessing done before using them. Several filters were applied to the tweets such as: (1) Usernames: Since users often include usernames in their tweets to direct their message, we simplify it by replacing the usernames with the token \u201cUSER\u201d. For example, @michael will be replaced by USER. (2) URLs: In most of the tweets, users include links that add on to their text message. We convert/replace the link address to the token \u201cURL\u201d. (3) Repeated Letters: Oftentimes, users use repeated letters in a word to emphasize their notion. For example, the word \u201clol\u201d (which stands for \u201claugh out loud\u201d) is sometimes written as \u201clooooool\u201d to emphasize the degree of funnyness. We replace such repeated occurrences of letters (more than 2), with just 3 occurrences. We replace with 3 occurrences and not 2, so that we can distinguish the exaggerated usage from the regular ones. (4) Multiple Sentiments: Tweets which contain multiple sentiments are removed, such as \"I hate Donald Trump, but I will vote for him\". This is done so that there is no ambiguity. (5) Retweets: In Twitter, many times tweets of a person are copied and posted by another user. This is known as retweeting and they are commonly abbreviated with \u201cRT\u201d. These are removed and only the original tweets are processed. (6) Repeated Tweets: The Twitter API sometimes returns a tweet multiple times. We remove such duplicates to avoid putting extra weight on any particular tweet."
+    ],
+    [
+      "Our analysis of the debates is 3-fold including sentiment analysis, outcome prediction, and trend analysis.",
+      "Sentiment Analysis: To perform a sentiment analysis on the debates, we first extract all the features described below from all the tweets in the training data. We then build the different machine learning models described below on these set of features. After that, we evaluate the output produced by the models on unseen test data. The models essentially predict candidate-sentiment pairs for each tweet.",
+      "Outcome Prediction: Predict the outcome of the debates. After obtaining the sentiments on the test data for each tweet, we can compute the net normalized sentiment for each presidential candidate in the debate. This is done by looking at the number of positive and negative sentiments for each candidate. We then normalize the sentiment scores of each candidate to be in the same scale (0-1). After that, we rank the candidates based on the sentiment scores and predict the top $k$ as the winners.",
+      "Trend Analysis: We also analyze some certain trends of the debates. Firstly, we look at the change in sentiments of the users towards the candidates over time (hours, days, months). This is done by computing the sentiment scores for each candidate in each of the debates and seeing how it varies over time, across debates. Secondly, we examine the effect of Washington Post on the views of the users. This is done by looking at the sentiments of the candidates (to predict winners) of a debate before and after the winners are announced by the experts in Washington Post. This way, we can see if Washington Post has had any effect on the sentiments of the users. Besides that, to study the behavior of the users, we also look at the correlation of the tweet volume with the number of viewers as well as the variation of tweet volume over time (hours, days, months) for debates.",
+      "As for the Grammys and the Super Bowl, we only perform the sentiment analysis and predict the outcomes."
+    ],
+    [
+      "We compare 4 different models for performing our task of sentiment classification. We then pick the best performing model for the task of outcome prediction. Here, we have two categories of algorithms: single-label and multi-label (We already discussed above why it is meaningful to have a multi-label task earlier), because one can represent $<$candidate, sentiment$>$ as a single class label or have candidate and sentiment as two separate labels. They are listed below:"
+    ],
+    [
+      "Naive Bayes: We use a multinomial Naive Bayes model. A tweet $t$ is assigned a class $c^{*}$ such that",
+      "where there are $m$ features and $f_i$ represents the $i^{th}$ feature.",
+      "Support Vector Machines: Support Vector Machines (SVM) constructs a hyperplane or a set of hyperplanes in a high-dimensional space, which can then be used for classification. In our case, we use SVM with Sequential Minimal Optimization (SMO) BIBREF25, which is an algorithm for solving the quadratic programming (QP) problem that arises during the training of SVMs.",
+      "Elman Recurrent Neural Network: Recurrent Neural Networks (RNNs) are gaining popularity and are being applied to a wide variety of problems. They are a class of artificial neural networks, where connections between units form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior. The Elman RNN was proposed by Jeff Elman in the year 1990 BIBREF26. We use this in our task."
+    ],
+    [
+      "RAkEL (RAndom k labELsets): RAkEL BIBREF27 is a multi-label classification algorithm that uses labeled powerset (LP) transformation: it basically creates a single binary classifier for every label combination and then uses multiple LP classifiers, each trained on a random subset of the actual labels, for classification."
+    ],
+    [
+      "In order to classify the tweets, a set of features is extracted from each of the tweets, such as n-gram, part-of-speech etc. The details of these features are given below:",
+      "n-gram: This represents the frequency counts of n-grams, specifically that of unigrams and bigrams.",
+      "punctuation: The number of occurrences of punctuation symbols such as commas, exclamation marks etc.",
+      "POS (part-of-speech): The frequency of each POS tagger is used as the feature.",
+      "prior polarity scoring: Here, we obtain the prior polarity of the words BIBREF6 using the Dictionary of Affect in Language (DAL) BIBREF28. This dictionary (DAL) of about 8000 English words assigns a pleasantness score to each word on a scale of 1-3. After normalizing, we can assign the words with polarity higher than $0.8$ as positive and less than $0.5$ as negative. If a word is not present in the dictionary, we lookup its synonyms in WordNet: if this word is there in the dictionary, we assign the original word its synonym's score.",
+      "Twitter Specific features:",
+      "Number of hashtags ($\\#$ symbol)",
+      "Number of mentioning users ( symbol)",
+      "Number of hyperlinks",
+      "Document embedding features: Here, we use the approach proposed by Mikolov et al. BIBREF3 to embed an entire tweet into a vector of features",
+      "Topic features: Here, LDA (Latent Dirichlet Allocation) BIBREF4 is used to extract topic-specific features for a tweet (document). This is basically the topic-document probability that is outputted by the model.",
+      "In the following experiments, we use 1-$gram$, 2-$gram$ and $(1+2)$-$gram$ to denote unigram, bigram and a combination of unigram and bigram features respectively. We also combine punctuation and the other features as miscellaneous features and use $MISC$ to denote this. We represent the document-embedding features by $DOC$ and topic-specific features by $TOPIC$."
+    ],
+    [
+      "In this section, we analyze the presidential debates data and show some trends.",
+      "First, we look at the trend of the tweet frequency. Figure FIGREF21 shows the trends of the tweet frequency and the number of TV viewers as the debates progress over time. We observe from Figures FIGREF21 and FIGREF21 that for the first 5 debates considered, the trend of the number of TV viewers matches the trend of the number of tweets. However, we can see that towards the final debates, the frequency of the tweets decreases consistently. This shows an interesting fact that although the people still watch the debates, the number of people who tweet about it are greatly reduced. But the tweeting community are mainly youngsters and this shows that most of the tweeting community, who actively tweet, didn't watch the later debates. Because if they did, then the trends should ideally match.",
+      "Next we look at how the tweeting activity is on days of the debate: specifically on the day of the debate, the next day and two days later. Figure FIGREF22 shows the trend of the tweet frequency around the day of the 5th republican debate, i.e December 15, 2015. As can be seen, the average number of people tweet more on the day of the debate than a day or two after it. This makes sense intuitively because the debate would be fresh in their heads.",
+      "Then, we look at how people tweet in the hours of the debate: specifically during the debate, one hour after and then two hours after. Figure FIGREF23 shows the trend of the tweet frequency around the hour of the 5th republican debate, i.e December 15, 2015. We notice that people don't tweet much during the debate but the activity drastically increases after two hours. This might be because people were busy watching the debate and then taking some time to process things, so that they can give their opinion.",
+      "We have seen the frequency of tweets over time in the previous trends. Now, we will look at how the sentiments of the candidates change over time.",
+      "First, Figure FIGREF24 shows how the sentiments of the candidates changed across the debates. We find that many of the candidates have had ups and downs towards in the debates. But these trends are interesting in that, it gives some useful information about what went down in the debate that caused the sentiments to change (sometimes drastically). For example, if we look at the graph for Donald Trump, we see that his sentiment was at its lowest point during the debate held on December 15. Looking into the debate, we can easily see why this was the case. At a certain point in the debate, Trump was asked about his ideas for the nuclear triad. It is very important that a presidential candidate knows about this, but Trump had no idea what the nuclear triad was and, in a transparent attempt to cover his tracks, resorted to a \u201cwe need to be strong\" speech. It can be due to this embarrassment that his sentiment went down during this debate.",
+      "Next, we investigate how the sentiments of the users towards the candidates change before and after the debate. In essence, we examine how the debate and the results of the debates given by the experts affects the sentiment of the candidates. Figure FIGREF25 shows the sentiments of the users towards the candidate during the 5th Republican Debate, 15th December 2015. It can be seen that the sentiments of the users towards the candidates does indeed change over the course of two days. One particular example is that of Jeb Bush. It seems that the populace are generally prejudiced towards the candidates, which is reflected in their sentiments of the candidates on the day of the debate. The results of the Washington Post are released in the morning after the debate. One can see the winners suggested by the Washington Post in Table TABREF35. One of the winners in that debate according to them is Jeb Bush. Coincidentally, Figure FIGREF25 suggests that the sentiment of Bush has gone up one day after the debate (essentially, one day after the results given by the experts are out).",
+      "There is some influence, for better or worse, of these experts on the minds of the users in the early debates, but towards the final debates the sentiments of the users are mostly unwavering, as can be seen in Figure FIGREF25. Figure FIGREF25 is on the last Republican debate, and suggests that the opinions of the users do not change much with time. Essentially the users have seen enough debates to make up their own minds and their sentiments are not easily wavered."
+    ],
+    [
+      "In this section, we define the different evaluation metrics that we use for different tasks. We have two tasks at hand: 1) Sentiment Analysis, 2) Outcome Prediction. We use different metrics for these two tasks."
+    ],
+    [
+      "In the study of sentiment analysis, we use \u201cHamming Loss\u201d to evaluate the performance of the different methods. Hamming Loss, based on Hamming distance, takes into account the prediction error and the missing error, normalized over the total number of classes and total number of examples BIBREF29. The Hamming Loss is given below:",
+      "where $|D|$ is the number of examples in the dataset and $|L|$ is the number of labels. $S_i$ and $Y_i$ denote the sets of true and predicted labels for instance $i$ respectively. $\\oplus $ stands for the XOR operation BIBREF30. Intuitively, the performance is better, when the Hamming Loss is smaller. 0 would be the ideal case."
+    ],
+    [
+      "For the case of outcome prediction, we will have a predicted set and an actual set of results. Thus, we can use common information retrieval metrics to evaluate the prediction performance. Those metrics are listed below:",
+      "Mean F-measure: F-measure takes into account both the precision and recall of the results. In essence, it takes into account how many of the relevant results are returned and also how many of the returned results are relevant.",
+      "where $|D|$ is the number of queries (debates/categories for grammy winners etc.), $P_i$ and $R_i$ are the precision and recall for the $i^{th}$ query.",
+      "Mean Average Precision: As a standard metric used in information retrieval, Mean Average Precision for a set of queries is mean of the average precision scores for each query:",
+      "where $|D|$ is the number of queries (e.g., debates), $P_i(k)$ is the precision at $k$ ($P@k$) for the $i^{th}$ query, $rel_i(k)$ is an indicator function, equaling 1 if the document at position $k$ for the $i^th$ query is relevant, else 0, and $|RD_i|$ is the number of relevant documents for the $i^{th}$ query."
+    ],
+    [
+      "We use 3 different datasets for the problem of sentiment analysis, as already mentioned. We test the four different algorithms mentioned in Section SECREF6, with a different combination of features that are described in Section SECREF10. To evaluate our models, we use the \u201cHamming Loss\u201d metric as discussed in Section SECREF6. We use this metric because our problem is in the multi-class classification domain. However, the single-label classifiers like SVM, Naive Bayes, Elman RNN cannot be evaluated against this metric directly. To do this, we split the predicted labels into a format that is consistent with that of multi-label classifiers like RaKel. The results of the experiments for each of the datasets are given in Tables TABREF34, TABREF34 and TABREF34. In the table, $f_1$, $f_2$, $f_3$, $f_4$, $f_5$ and $f_6$ denote the features 1-$gram$, 2-$gram$, $(1+2)$-$gram$, $(1+2)$-$gram + MISC$, $DOC$ and $DOC + TOPIC$ respectively. Note that lower values of hamming losses are more desirable. We find that RaKel performs the best out of all the algorithms. RaKel is more suited for the task because our task is a multi-class classification. Among all the single-label classifiers, SVM performs the best. We also observe that as we use more complex feature spaces, the performance increases. This is true for almost all of the algorithms listed.",
+      "Our best performing features is a combination of paragraph embedding features and topic features from LDA. This makes sense intuitively because paragraph-embedding takes into account the context in the text. Context is very important in determining the sentiment of a short text: having negative words in the text does not always mean that the text contains a negative sentiment. For example, the sentence \u201cnever say never is not a bad thing\u201d has many negative words; but the sentence as a whole does not have a negative sentiment. This is why we need some kind of context information to accurately determine the sentiment. Thus, with these embedded features, one would be able to better determine the polarity of the sentence. The other label is the entity (candidate/song etc.) in consideration. Topic features here make sense because this can be considered as the topic of the tweet in some sense. This can be done because that label captures what or whom the tweet is about."
+    ],
+    [
+      "In this section, we show the results for the outcome-prediction of the events. RaKel, as the best performing method, is trained to predict the sentiment-labels for the unlabeled data. The sentiment labels are then used to determine the outcome of the events. In the Tables (TABREF35, TABREF36, TABREF37) of outputs given, we only show as many predictions as there are winners."
+    ],
+    [
+      "The results obtained for the outcome prediction task for the US presidential debates is shown in Table TABREF35. Table TABREF35 shows the winners as given in the Washington Post (3rd column) and the winners that are predicted by our system (2nd column). By comparing the set of results obtained from both the sources, we find that the set of candidates predicted match to a large extent with the winners given out by the Washington Post. The result suggests that the opinions of the social media community match with that of the journalists in most cases."
+    ],
+    [
+      "A Grammy Award is given to outstanding achievement in the music industry. There are two types of awards: \u201cGeneral Field\u201d awards, which are not restricted by genre, and genre-specific awards. Since, there can be upto 80 categories of awards, we only focus on the main 4: 1) Album of the Year, 2) Record of the Year, 3) Song of the Year, and 4) Best New Artist. These categories are the main in the awards ceremony and most looked forward to. That is also why we choose to predict the outcomes of these categories based on the tweets. We use the tweets before the ceremony (but after the nominations) to predict the outcomes.",
+      "Basically, we have a list of nominations for each category. We filter the tweets based on these nominations and then predict the winner as with the case of the debates. The outcomes are listed in Table TABREF36. We see that largely, the opinion of the users on the social network, agree with the deciding committee of the awards. The winners agree for all the categories except \u201cSong of the Year\u201d."
+    ],
+    [
+      "The Super Bowl is the annual championship game of the National Football League. We have collected the data for the year 2013. Here, the match was between the Baltimore Ravens and the San Francisco 49ers. The tweets that we have collected are during the game. From these tweets, one could trivially determine the winner. But an interesting outcome would be to predict the Most Valuable Player (MVP) during the game. To determine this, all the tweets were looked at and we determined the candidate with the highest positive sentiment by the end of the game. The result in Table TABREF37 suggests that we are able to determine the outcomes accurately.",
+      "Table TABREF43 displays some evaluation metrics for this task. These were computed based on the predicted outcomes and the actual outcomes for each of the different datasets. Since the number of participants varies from debate-to-debate or category-to-category for Grammy etc., we cannot return a fixed number of winners for everything. So, the size of our returned ranked-list is set to half of the number of participants (except for the MVP for Super Bowl; there are so many players and returning half of them when only one of them is relevant is meaningless. So, we just return the top 10 players). As we can see from the metrics, the predicted outcomes match quite well with the actual ones (or the ones given by the experts)."
+    ],
+    [
+      "This paper presents a study that compares the opinions of users on microblogs, which is essentially the crowd wisdom, to that of the experts in the field. Specifically, we explore three datasets: US Presidential Debates 2015-16, Grammy Awards 2013, Super Bowl 2013. We determined if the opinions of the crowd and the experts match by using the sentiments of the tweets to predict the outcomes of the debates/Grammys/Super Bowl. We observed that in most of the cases, the predictions were right indicating that crowd wisdom is indeed worth looking at and mining sentiments in microblogs is useful. In some cases where there were disagreements, however, we observed that the opinions of the experts did have some influence on the opinions of the users. We also find that the features that were most useful in our case of multi-label classification was a combination of the document-embedding and topic features."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0290/instruction.md b/qasper-0290/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..fe25d9e7433d85124c3e7c659929bb51d72f19ba
--- /dev/null
+++ b/qasper-0290/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: An Unsupervised Word Sense Disambiguation System for Under-Resourced Languages
+
+Question: Do the authors offer any hypothesis about why the dense mode outperformed the sparse one?
\ No newline at end of file
diff --git a/qasper-0291/instruction.md b/qasper-0291/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..afee1ce18f77ae8cf6cccaa51e73b5249ad8fc81
--- /dev/null
+++ b/qasper-0291/instruction.md
@@ -0,0 +1,85 @@
+Name of Paper: An Unsupervised Word Sense Disambiguation System for Under-Resourced Languages
+
+Question: What evaluation is conducted?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Watasense, an Unsupervised System for Word Sense Disambiguation",
+    "System Architecture",
+    "User Interface",
+    "Word Sense Disambiguation",
+    "Evaluation",
+    "Quality Measure",
+    "Dataset",
+    "Results",
+    "Conclusion",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "Word sense disambiguation (WSD) is a natural language processing task of identifying the particular word senses of polysemous words used in a sentence. Recently, a lot of attention was paid to the problem of WSD for the Russian language BIBREF0 , BIBREF1 , BIBREF2 . This problem is especially difficult because of both linguistic issues \u2013 namely, the rich morphology of Russian and other Slavic languages in general \u2013 and technical challenges like the lack of software and language resources required for addressing the problem.",
+      "To address these issues, we present Watasense, an unsupervised system for word sense disambiguation. We describe its architecture and conduct an evaluation on three datasets for Russian. The choice of an unsupervised system is motivated by the absence of resources that would enable a supervised system for under-resourced languages. Watasense is not strictly tied to the Russian language and can be applied to any language for which a tokenizer, part-of-speech tagger, lemmatizer, and a sense inventory are available.",
+      "The rest of the paper is organized as follows. Section 2 reviews related work. Section 3 presents the Watasense word sense disambiguation system, presents its architecture, and describes the unsupervised word sense disambiguation methods bundled with it. Section 4 evaluates the system on a gold standard for Russian. Section 5 concludes with final remarks."
+    ],
+    [
+      "Although the problem of WSD has been addressed in many SemEval campaigns BIBREF3 , BIBREF4 , BIBREF5 , we focus here on word sense disambiguation systems rather than on the research methodologies.",
+      "Among the freely available systems, IMS (\u201cIt Makes Sense\u201d) is a supervised WSD system designed initially for the English language BIBREF6 . The system uses a support vector machine classifier to infer the particular sense of a word in the sentence given its contextual sentence-level features. Pywsd is an implementation of several popular WSD algorithms implemented in a library for the Python programming language. It offers both the classical Lesk algorithm for WSD and path-based algorithms that heavily use the WordNet and similar lexical ontologies. DKPro WSD BIBREF7 is a general-purpose framework for WSD that uses a lexical ontology as the sense inventory and offers the variety of WordNet-based algorithms. Babelfy BIBREF8 is a WSD system that uses BabelNet, a large-scale multilingual lexical ontology available for most natural languages. Due to the broad coverage of BabelNet, Babelfy offers entity linking as part of the WSD functionality.",
+      "Panchenko:17:emnlp present an unsupervised WSD system that is also knowledge-free: its sense inventory is induced based on the JoBimText framework, and disambiguation is performed by computing the semantic similarity between the context and the candidate senses BIBREF9 . Pelevina:16 proposed a similar approach to WSD, but based on dense vector representations (word embeddings), called SenseGram. Similarly to SenseGram, our WSD system is based on averaging of word embeddings on the basis of an automatically induced sense inventory. A crucial difference, however, is that we induce our sense inventory from synonymy dictionaries and not distributional word vectors. While this requires more manually created resources, a potential advantage of our approach is that the resulting inventory contains less noise."
+    ],
+    [
+      "Watasense is implemented in the Python programming language using the scikit-learn BIBREF10 and Gensim BIBREF11 libraries. Watasense offers a Web interface (Figure FIGREF2 ), a command-line tool, and an application programming interface (API) for deployment within other applications."
+    ],
+    [
+      "A sentence is represented as a list of spans. A span is a quadruple: INLINEFORM0 , where INLINEFORM1 is the word or the token, INLINEFORM2 is the part of speech tag, INLINEFORM3 is the lemma, INLINEFORM4 is the position of the word in the sentence. These data are provided by tokenizer, part-of-speech tagger, and lemmatizer that are specific for the given language. The WSD results are represented as a map of spans to the corresponding word sense identifiers.",
+      "The sense inventory is a list of synsets. A synset is represented by three bag of words: the synonyms, the hypernyms, and the union of two former \u2013 the bag. Due to the performance reasons, on initialization, an inverted index is constructed to map a word to the set of synsets it is included into.",
+      "Each word sense disambiguation method extends the BaseWSD class. This class provides the end user with a generic interface for WSD and also encapsulates common routines for data pre-processing. The inherited classes like SparseWSD and DenseWSD should implement the disambiguate_word(...) method that disambiguates the given word in the given sentence. Both classes use the bag representation of synsets on the initialization. As the result, for WSD, not just the synonyms are used, but also the hypernyms corresponding to the synsets. The UML class diagram is presented in Figure FIGREF4 .",
+      "Watasense supports two sources of word vectors: it can either read the word vector dataset in the binary Word2Vec format or use Word2Vec-Pyro4, a general-purpose word vector server. The use of a remote word vector server is recommended due to the reduction of memory footprint per each Watasense process."
+    ],
+    [
+      " FIGREF2 shows the Web interface of Watasense. It is composed of two primary activities. The first is the text input and the method selection ( FIGREF2 ). The second is the display of the disambiguation results with part of speech highlighting ( FIGREF7 ). Those words with resolved polysemy are underlined; the tooltips with the details are raised on hover."
+    ],
+    [
+      "We use two different unsupervised approaches for word sense disambiguation. The first, called `sparse model', uses a straightforward sparse vector space model, as widely used in Information Retrieval, to represent contexts and synsets. The second, called `dense model', represents synsets and contexts in a dense, low-dimensional space by averaging word embeddings.",
+      "In the vector space model approach, we follow the sparse context-based disambiguated method BIBREF12 , BIBREF13 . For estimating the sense of the word INLINEFORM0 in a sentence, we search for such a synset INLINEFORM1 that maximizes the cosine similarity to the sentence vector: DISPLAYFORM0 ",
+      "where INLINEFORM0 is the set of words forming the synset, INLINEFORM1 is the set of words forming the sentence. On initialization, the synsets represented in the sense inventory are transformed into the INLINEFORM2 -weighted word-synset sparse matrix efficiently represented in the memory using the compressed sparse row format. Given a sentence, a similar transformation is done to obtain the sparse vector representation of the sentence in the same space as the word-synset matrix. Then, for each word to disambiguate, we retrieve the synset containing this word that maximizes the cosine similarity between the sparse sentence vector and the sparse synset vector. Let INLINEFORM3 be the maximal number of synsets containing a word and INLINEFORM4 be the maximal size of a synset. Therefore, disambiguation of the whole sentence INLINEFORM5 requires INLINEFORM6 operations using the efficient sparse matrix representation.",
+      "In the synset embeddings model approach, we follow SenseGram BIBREF14 and apply it to the synsets induced from a graph of synonyms. We transform every synset into its dense vector representation by averaging the word embeddings corresponding to each constituent word: DISPLAYFORM0 ",
+      "where INLINEFORM0 denotes the word embedding of INLINEFORM1 . We do the same transformation for the sentence vectors. Then, given a word INLINEFORM2 , a sentence INLINEFORM3 , we find the synset INLINEFORM4 that maximizes the cosine similarity to the sentence: DISPLAYFORM0 ",
+      "On initialization, we pre-compute the dense synset vectors by averaging the corresponding word embeddings. Given a sentence, we similarly compute the dense sentence vector by averaging the vectors of the words belonging to non-auxiliary parts of speech, i.e., nouns, adjectives, adverbs, verbs, etc. Then, given a word to disambiguate, we retrieve the synset that maximizes the cosine similarity between the dense sentence vector and the dense synset vector. Thus, given the number of dimensions INLINEFORM0 , disambiguation of the whole sentence INLINEFORM1 requires INLINEFORM2 operations."
+    ],
+    [
+      "We conduct our experiments using the evaluation methodology of SemEval 2010 Task 14: Word Sense Induction & Disambiguation BIBREF5 . In the gold standard, each word is provided with a set of instances, i.e., the sentences containing the word. Each instance is manually annotated with the single sense identifier according to a pre-defined sense inventory. Each participating system estimates the sense labels for these ambiguous words, which can be viewed as a clustering of instances, according to sense labels. The system's clustering is compared to the gold-standard clustering for evaluation."
+    ],
+    [
+      "The original SemEval 2010 Task 14 used the V-Measure external clustering measure BIBREF5 . However, this measure is maximized by clustering each sentence into his own distinct cluster, i.e., a `dummy' singleton baseline. This is achieved by the system deciding that every ambiguous word in every sentence corresponds to a different word sense. To cope with this issue, we follow a similar study BIBREF1 and use instead of the adjusted Rand index (ARI) proposed by Hubert:85 as an evaluation measure.",
+      "In order to provide the overall value of ARI, we follow the addition approach used in BIBREF1 . Since the quality measure is computed for each lemma individually, the total value is a weighted sum, namely DISPLAYFORM0 ",
+      "where INLINEFORM0 is the lemma, INLINEFORM1 is the set of the instances for the lemma INLINEFORM2 , INLINEFORM3 is the adjusted Rand index computed for the lemma INLINEFORM4 . Thus, the contribution of each lemma to the total score is proportional to the number of instances of this lemma."
+    ],
+    [
+      "We evaluate the word sense disambiguation methods in Watasense against three baselines: an unsupervised approach for learning multi-prototype word embeddings called AdaGram BIBREF15 , same sense for all the instances per lemma (One), and one sense per instance (Singletons). The AdaGram model is trained on the combination of RuWac, Lib.Ru, and the Russian Wikipedia with the overall vocabulary size of 2 billion tokens BIBREF1 .",
+      "As the gold-standard dataset, we use the WSD training dataset for Russian created during RUSSE'2018: A Shared Task on Word Sense Induction and Disambiguation for the Russian Language BIBREF16 . The dataset has 31 words covered by INLINEFORM0 instances in the bts-rnc subset and 5 words covered by 439 instances in the wiki-wiki subset.",
+      "The following different sense inventories have been used during the evaluation:",
+      "[leftmargin=4mm]",
+      "Watlink, a word sense network constructed automatically. It uses the synsets induced in an unsupervised way by the Watset[CWnolog, MCL] method BIBREF2 and the semantic relations from such dictionaries as Wiktionary referred as Joint INLINEFORM0 Exp INLINEFORM1 SWN in Ustalov:17:dialogue. This is the only automatically built inventory we use in the evaluation.",
+      "RuThes, a large-scale lexical ontology for Russian created by a group of expert lexicographers BIBREF17 .",
+      "RuWordNet, a semi-automatic conversion of the RuThes lexical ontology into a WordNet-like structure BIBREF18 .",
+      "Since the Dense model requires word embeddings, we used the 500-dimensional word vectors from the Russian Distributional Thesaurus BIBREF19 . These vectors are obtained using the Skip-gram approach trained on the lib.rus.ec text corpus."
+    ],
+    [
+      "We compare the evaluation results obtained for the Sparse and Dense approaches with three baselines: the AdaGram model (AdaGram), the same sense for all the instances per lemma (One) and one sense per instance (Singletons). The evaluation results are presented in Table TABREF25 . The columns bts-rnc and wiki-wiki represent the overall value of ARI according to Equation ( EQREF15 ). The column Avg. consists of the weighted average of the datasets w.r.t. the number of instances.",
+      "We observe that the SenseGram-based approach for word sense disambiguation yields substantially better results in every case (Table TABREF25 ). The primary reason for that is the implicit handling of similar words due to the averaging of dense word vectors for semantically related words. Thus, we recommend using the dense approach in further studies. Although the AdaGram approach trained on a large text corpus showed better results according to the weighted average, this result does not transfer to languages with less available corpus size."
+    ],
+    [
+      "In this paper, we presented Watasense, an open source unsupervised word sense disambiguation system that is parameterized only by a word sense inventory. It supports both sparse and dense sense representations. We were able to show that the dense approach substantially boosts the performance of the sparse approach on three different sense inventories for Russian. We recommend using the dense approach in further studies due to its smoothing capabilities that reduce sparseness. In further studies, we will look at the problem of phrase neighbors that influence the sentence vector representations.",
+      "Finally, we would like to emphasize the fact that Watasense has a simple API for integrating different algorithms for WSD. At the same time, it requires only a basic set of language processing tools to be available: tokenizer, a part-of-speech tagger, lemmatizer, and a sense inventory, which means that low-resourced language can benefit of its usage."
+    ],
+    [
+      "We acknowledge the support of the Deutsche Forschungsgemeinschaft (DFG) under the project \u201cJoining Ontologies and Semantics Induced from Text\u201d (JOIN-T), the RFBR under the projects no. 16-37-00203 mol_a and no. 16-37-00354 mol_a, and the RFH under the project no. 16-04-12019. The research was supported by the Ministry of Education and Science of the Russian Federation Agreement no. 02.A03.21.0006. The calculations were carried out using the supercomputer \u201cUran\u201d at the Krasovskii Institute of Mathematics and Mechanics."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0296/instruction.md b/qasper-0296/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..2406dc6dda538a8a31f591acfd14bdb57d43c344
--- /dev/null
+++ b/qasper-0296/instruction.md
@@ -0,0 +1,138 @@
+Name of Paper: Error Analysis for Vietnamese Named Entity Recognition on Deep Neural Network Models
+
+Question: What type of errors were produced by the BLSTM-CNN-CRF system?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related work",
+    "Error-analysis method",
+    "Data and model ::: Data sets",
+    "Data and model ::: Pre-trained word Embeddings",
+    "Data and model ::: Model",
+    "Experiment and Results",
+    "Experiment and Results ::: Error analysis on gold data",
+    "Experiment and Results ::: Analysis on predicted data",
+    "Experiment and Results ::: Errors of annotators",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Named Entity Recognition (NER) is one of information extraction subtasks that is responsible for detecting entity elements from raw text and can determine the category in which the element belongs, these categories include the names of persons, organizations, locations, expressions of times, quantities, monetary values and percentages.",
+      "The problem of NER is described as follow:",
+      "Input: A sentence S consists a sequence of $n$ words: $S= w_1,w_2,w_3,\u2026,w_n$ ($w_i$: the $i^{th}$ word)",
+      "Output: The sequence of $n$ labels $y_1,y_2,y_3,\u2026,y_n$. Each $y_i$ label represents the category which $w_i$ belongs to.",
+      "For example, given a sentence:",
+      "Input: vietnamGi\u00e1m \u0111\u1ed1c \u0111i\u1ec1u h\u00e0nh Tim Cook c\u1ee7a Apple v\u1eeba gi\u1edbi thi\u1ec7u 2 \u0111i\u1ec7n tho\u1ea1i iPhone, \u0111\u1ed3ng h\u1ed3 th\u00f4ng minh m\u1edbi, l\u1edbn h\u01a1n \u1edf s\u1ef1 ki\u1ec7n Flint Center, Cupertino.",
+      "(Apple CEO Tim Cook introduces 2 new, larger iPhones, Smart Watch at Cupertino Flint Center event)",
+      "The algorithm will output:",
+      "Output: vietnam\u27e8O\u27e9Gi\u00e1m \u0111\u1ed1c \u0111i\u1ec1u h\u00e0nh\u27e8O\u27e9 \u27e8PER\u27e9Tim Cook\u27e8PER\u27e9 \u27e8O\u27e9c\u1ee7a\u27e8O\u27e9 \u27e8ORG\u27e9Apple\u27e8ORG\u27e9 \u27e8O\u27e9v\u1eeba gi\u1edbi thi\u1ec7u 2 \u0111i\u1ec7n tho\u1ea1i iPhone, \u0111\u1ed3ng h\u1ed3 th\u00f4ng minh m\u1edbi, l\u1edbn h\u01a1n \u1edf s\u1ef1 ki\u1ec7n\u27e8O\u27e9 \u27e8ORG\u27e9Flint Center\u27e8ORG\u27e9, \u27e8LOC\u27e9Cupertino\u27e8LOC\u27e9.",
+      "With LOC, PER, ORG is Name of location, person, organization respectively. Note that O means Other (Not a Name entity). We will not denote the O label in the following examples in this article because we only care about name of entities.",
+      "In this paper, we analyze common errors of the previous state-of-the-art techniques using Deep Neural Network (DNN) on VLSP Corpus. This may contribute to the later researchers the common errors from the results of these state-of-the-art models, then they can rely on to improve the model.",
+      "Section 2 discusses the related works to this paper. We will present a method for evaluating and analyzing the types of errors in Section 3. The data used for testing and analysis of errors will be introduced in Section 4, we also talk about deep neural network methods and pre-trained word embeddings for experimentation in this section. Section 5 will detail the errors and evaluations. In the end is our contribution to improve the above errors."
+    ],
+    [
+      "Previously publicly available NER systems do not use DNN, for example, the MITRE Identification Scrubber Toolkit (MIST) BIBREF0, Stanford NER BIBREF1, BANNER BIBREF2 and NERsuite BIBREF3. NER systems for Vietnamese language processing used traditional machine learning methods such as Maximum Entropy Markov Model (MEMM), Support Vector Machine (SVM) and Conditional Random Field (CRF). In particular, most of the toolkits for NER task attempted to use MEMM BIBREF4, and CRF BIBREF5 to solve this problem.",
+      "Nowadays, because of the increase in data, DNN methods are used a lot. They have archived great results when it comes to NER tasks, for example, Guillaume Lample et al with BLSTM-CRF in BIBREF6 report 90.94 F1 score, Chiu et al with BLSTM-CNN in BIBREF7 got 91.62 F1 score, Xeuzhe Ma and Eduard Hovy with BLSTM-CNN-CRF in BIBREF8 achieved F1 score of 91.21, Thai-Hoang Pham and Phuong Le-Hong with BLSTM-CNN-CRF in BIBREF9 got 88.59% F1 score. These DNN models are also the state-of-the-art models."
+    ],
+    [
+      "The results of our analysis experiments are reported in precision and recall over all labels (name of person, location, organization and miscellaneous). The process of analyzing errors has 2 steps:",
+      "Step 1: We use two state-of-the-art models including BLSTM-CNN-CRF and BLSTM-CRF to train and test on VLSP\u2019s NER corpus. In our experiments, we implement word embeddings as features to the two systems.",
+      "Step 2: Based on the best results (BLSTM-CNN-CRF), error analysis is performed based on five types of errors (No extraction, No annotation, Wrong range, Wrong tag, Wrong range and tag), in a way similar to BIBREF10, but we analyze on both gold labels and predicted labels (more detail in figure 1 and 2).",
+      "A token (an entity name maybe contain more than one word) will be extracted as a correct entity by the model if both of the followings are correct:",
+      "The length of it (range) is correct: The word beginning and the end is the same as gold data (annotator).",
+      "The label (tag) of it is correct: The label is the same as in gold data.",
+      "If it is not meet two above requirements, it will be the wrong entity (an error). Therefore, we divide the errors into five different types which are described in detail as follows:",
+      "No extraction: The error where the model did not extract tokens as a name entity (NE) though the tokens were annotated as a NE.",
+      "LSTM-CNN-CRF: vietnam Vi\u1ec7t_Nam",
+      "Annotator: vietnam\u27e8LOC\u27e9 Vi\u1ec7t_Nam \u27e8LOC\u27e9",
+      "No annotation: The error where the model extracted tokens as an NE though the tokens were not annotated as a NE.",
+      "LSTM-CNN-CRF: vietnam\u27e8PER\u27e9 Ch\u00e2u \u00c2u \u27e8PER\u27e9",
+      "Annotator: vietnamCh\u00e2u \u00c2u",
+      "Wrong range: The error where the model extracted tokens as an NE and only the range was wrong. (The extracted tokens were partially annotated or they were the part of the annotated tokens).",
+      "LSTM-CNN-CRF: vietnam\u27e8PER\u27e9 Ca_s\u0129 Nguy\u1ec5n V\u0103n A \u27e8PER\u27e9",
+      "Annotator:",
+      "vietnamCa_s\u0129 \u27e8PER\u27e9 Nguy\u1ec5n V\u0103n A \u27e8PER\u27e9",
+      "Wrong tag: The error where the model extracted tokens as an NE and only the tag type was wrong.",
+      "LSTM-CNN-CRF: vietnamKh\u00e1m ph\u00e1 \u27e8PER\u27e9 Yangsuri \u27e8PER\u27e9",
+      "Annotator:",
+      "vietnamKh\u00e1m ph\u00e1 \u27e8LOC\u27e9 Yangsuri \u27e8LOC\u27e9",
+      "Wrong range and tag: The error where the model extracted tokens as an NE and both the range and the tag type were wrong.",
+      "LSTM-CNN-CRF: vietnam\u27e8LOC\u27e9 gian_h\u00e0ng Apple \u27e8LOC\u27e9",
+      "Annotator:",
+      "vietnamgian_h\u00e0ng \u27e8ORG\u27e9 Apple \u27e8ORG\u27e9",
+      "We compare the predicted NEs to the gold NEs ($Fig. 1$), if they have the same range, the predicted NE is a correct or Wrong tag. If it has different range with the gold NE, we will see what type of wrong it is. If it does not have any overlap, it is a No extraction. If it has an overlap and the tag is the same at gold NE, it is a Wrong range. Finally, it is a Wrong range and tag if it has an overlap but the tag is different. The steps in Fig. 2 is the same at Fig. 1 and the different only is we compare the gold NE to the predicted NE, and No extraction type will be No annotation."
+    ],
+    [
+      "To conduct error analysis of the model, we used the corpus which are provided by VLSP 2016 - Named Entity Recognition. The dataset contains four different types of label: Location (LOC), Person (PER), Organization (ORG) and Miscellaneous - Name of an entity that do not belong to 3 types above (Table TABREF15). Although the corpus has more information about the POS and chunks, but we do not use them as features in our model.",
+      "There are two folders with 267 text files of training data and 45 text files of test data. They all have their own format. We take 21 first text files and 22 last text files and 22 sentences of the 22th text file and 55 sentences of the 245th text file to be a development data. The remaining files are going to be the training data. The test file is the same at the file VSLP gave. Finally, we have 3 text files only based on the CoNLL 2003 format: train, dev and test."
+    ],
+    [
+      "We use the word embeddings for Vietnamese that created by Kyubyong Park and Edouard Grave at al:",
+      "Kyubyong Park: In his project, he uses two methods including fastText and word2vec to generate word embeddings from wikipedia database backup dumps. His word embedding is the vector of 100 dimension and it has about 10k words.",
+      "Edouard Grave et al BIBREF11: They use fastText tool to generate word embeddings from Wikipedia. The format is the same at Kyubyong's, but their embedding is the vector of 300 dimension, and they have about 200k words"
+    ],
+    [
+      "Based on state-of-the-art methods for NER, BLSTM-CNN-CRF is the end-to-end deep neural network model that achieves the best result on F-score BIBREF9. Therefore, we decide to conduct the experiment on this model and analyze the errors.",
+      "We run experiment with the Ma and Hovy (2016) model BIBREF8, source code provided by (Motoki Sato) and analysis the errors from this result. Before we decide to analysis on this result, we have run some other methods, but this one with Vietnamese pre-trained word embeddings provided by Kyubyong Park obtains the best result. Other results are shown in the Table 2."
+    ],
+    [
+      "Table 2 shows our experiments on two models with and without different pre-trained word embedding \u2013 KP means the Kyubyong Park\u2019s pre-trained word embeddings and EG means Edouard Grave\u2019s pre-trained word embeddings.",
+      "We compare the outputs of BLSTM-CNN-CRF model (predicted) to the annotated data (gold) and analyzed the errors. Table 3 shows perfomance of the BLSTM-CNN-CRF model. In our experiments, we use three evaluation parameters (precision, recall, and F1 score) to access our experimental result. They will be described as follow in Table 3. The \"correctNE\", the number of correct label for entity that the model can found. The \"goldNE\", number of the real label annotated by annotator in the gold data. The \"foundNE\", number of the label the model find out (no matter if they are correct or not).",
+      "In Table 3 above, we can see that recall score on ORG label is lowest. The reason is almost all the ORG label on test file is name of some brands that do not appear on training data and pre-trained word embedding. On the other side, the characters inside these brand names also inside the other names of person in the training data. The context from both side of the sentence (future- and past-feature) also make the model \"think\" the name entity not as it should be.",
+      "Table 4 shows that the biggest number of errors is No extraction. The errors were counted by using logical sum (OR) of the gold labels and predicted labels (predicted by the model). The second most frequent error was Wrong tag means the model extract it's a NE but wrong tag."
+    ],
+    [
+      "First of all, we will compare the predicted NEs to the gold NEs (Fig. 1). Table 4 shows the summary of errors by types based on the gold labels, the \"correct\" is the number of gold tag that the model predicted correctly, \"error\" is the number of gold tag that the model predicted incorrectly, and \"total\" is sum of them. Four columns next show the number of type errors on each label.",
+      "Table 5 shows that Person, Location and Organization is the main reason why No extraction and Wrong tag are high.",
+      "After analyzing based on the gold NEs, we figure out the reason is:",
+      "Almost all the NEs is wrong, they do not appear on training data and pre-trained embedding. These NEs vector will be initial randomly, therefore, these vectors are poor which means have no semantic aspect.",
+      "The \"weird\" ORG NE in the sentence appear together with other words have context of PER, so this \"weird\" ORG NE is going to be label at PER.",
+      "For example:",
+      "gold data: vietnamV\u0110V \u0111\u01b0\u1ee3c xem l\u00e0 \u0111\u1ea7u_ti\u00ean k\u00fd h\u1ee3p_\u0111\u1ed3ng qu\u1ea3ng_c\u00e1o l\u00e0 v\u00f5_s\u0129 \u27e8PER\u27e9 Tr\u1ea7n Quang H\u1ea1 \u27e8PER\u27e9 sau khi \u0111o\u1ea1t HCV taekwondo Asiad \u27e8LOC\u27e9 Hiroshima \u27e8LOC\u27e9.",
+      "(The athlete is considered the first to sign a contract of boxing Tran Quang Ha after winning the gold medal Asiad Hiroshima)",
+      "predicted data: vietnam\u2026l\u00e0 v\u00f5_s\u0129 \u27e8PER\u27e9Tr\u1ea7n Quang H\u1ea1\u27e8PER\u27e9 sau khi \u0111o\u1ea1t HCV taekwondo Asiad \u27e8PER\u27e9Hiroshima\u27e8PER\u27e9.",
+      "Some mistakes of the model are from training set, for example, anonymous person named \"P.\" appears many times in the training set, so when model meets \"P.\" in context of \"P. 3 vietnamQu\u1eadn 9\" (Ward 3, District 9) \u2013 \"P.\" stands for vietnam\"Ph\u01b0\u1eddng\" (Ward) model will predict \"P.\" as a PER.",
+      "Training data: vietnamn\u1ebfu \u27e8PER\u27e9P.\u27e8PER\u27e9 c\u00f3 \u1edf \u0111\u00e2y \u2013 (If P. were here) Predicted data: vietnam\u27e8PER\u27e9P. 3\u27e8PER\u27e9, G\u00f2_v\u1ea5p \u2013 (Ward 3, Go_vap District)"
+    ],
+    [
+      "Table 6 shows the summary of errors by types based on the predicted data. After analyzing the errors on predicted and gold data, we noticed that the difference of these errors are mainly in the No anotation and No extraction. Therefore, we only mention the main reasons for the No anotation:",
+      "Most of the wrong labels that model assigns are brand names (Ex: Charriol, Dream, Jupiter, ...), words are abbreviated vietnam(XKLD \u2013 xu\u1ea5t kh\u1ea9u lao \u0111\u1ed9ng (labour export)), movie names, \u2026 All of these words do not appear in training data and word embedding. Perhaps these reasons are the followings:",
+      "The vectors of these words are random so the semantic aspect is poor.",
+      "The hidden states of these words also rely on past feature (forward pass) and future feature (backward pass) of the sentence. Therefore, they are assigned wrongly because of their context.",
+      "These words are primarily capitalized or all capital letters, so they are assigned as a name entity. This error is caused by the CNN layer extract characters information of the word.",
+      "Table 7 shows the detail of errors on predicted data where we will see number kind of errors on each label."
+    ],
+    [
+      "After considering the training and test data, we realized that this data has many problems need to be fixed in the next run experiments. The annotators are not consistent between the training data and the test data, more details are shown as follow:",
+      "The organizations are labeled in the train data but not labeled in the test data:",
+      "Training data: vietnam\u27e8ORG\u27e9 S\u1edf Y_t\u1ebf \u27e8ORG\u27e9 (Department of Health)",
+      "Test data: vietnamS\u1edf Y_t\u1ebf (Department of Health)",
+      "Explanation: vietnam\"S\u1edf Y_t\u1ebf\" in train and test are the same name of organization entity. However the one in test data is not labeled.",
+      "The entity has the same meaning but is assigned differently between the train data and the test:",
+      "Training data: vietnam\u27e8MISC\u27e9 ng\u01b0\u1eddi Vi\u1ec7t \u27e8MISC\u27e9 (Vietnamese people)",
+      "Test data: vietnamd\u00e2n \u27e8LOC\u27e9 Vi\u1ec7t \u27e8LOC\u27e9 (Vietnamese people)",
+      "Explanation: vietnamBoth \"ng\u01b0\u1eddi Vi\u1ec7t\" in train data and \"d\u00e2n Vi\u1ec7t\" in test data are the same meaning, but they are assigned differently.",
+      "The range of entities are differently between the train data and the test data:",
+      "Training data: vietnam\u27e8LOC\u27e9 l\u00e0ng At\u00e2u \u27e8LOC\u27e9 (At\u00e2u village)",
+      "Test data: vietnaml\u00e0ng \u27e8LOC\u27e9 H\u00e0n_Qu\u1ed1c \u27e8LOC\u27e9 (Korea village)",
+      "Explanation: The two villages differ only in name, but they are labeled differently in range",
+      "Capitalization rules are not unified with a token is considered an entity:",
+      "Training data: vietnam\u27e8ORG\u27e9 C\u00f4ng_ty Inmasco \u27e8ORG\u27e9 (Inmasco Company)",
+      "Training data: vietnamc\u00f4ng_ty con (Subsidiaries)",
+      "Test data: vietnamc\u00f4ng_ty \u27e8ORG\u27e9 Yeon Young Entertainment \u27e8ORG\u27e9 (Yeon Young Entertainment company)",
+      "Explanation: If it comes to a company with a specific name, it should be labeled vietnam\u27e8ORG\u27e9 C\u00f4ng_ty Yeon Young Entertainment \u27e8ORG\u27e9 with \"C\" in capital letters."
+    ],
+    [
+      "In this paper, we have presented a thorough study of distinctive error distributions produced by Bi-LSTM-CNN-CRF for the Vietnamese language. This would be helpful for researchers to create better NER models.",
+      "Based on the analysis results, we suggest some possible directions for improvement of model and for the improvement of data-driven NER for the Vietnamese language in future:",
+      "The word at the begin of the sentence is capitalized, so, if the name of person is at this position, model will ignore them (no extraction). To improve this issue, we can use the POS feature together with BIO format (Inside, Outside, Beginning) BIBREF6 at the top layer (CRF).",
+      "If we can unify the labeling of the annotators between the train, dev and test sets. We will improve data quality and classifier.",
+      "It is better if there is a pre-trained word embeddings that overlays the data, and segmentation algorithm need to be more accurately."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: Recurrent Neural Network Encoder with Attention for Community Question Answering
+
+Question: What supplemental tasks are used for multitask learning?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Method",
+    "LSTM Models",
+    "Neural Attention",
+    "Predicting Relationships of Object Pairs with an Attention Model",
+    "Modeling Question-External Comments",
+    "Experiments",
+    "Preliminary Results",
+    "Robust Parameter Initialization",
+    "Multitask Learning",
+    "Augmented data",
+    "Augmented features",
+    "Comparison with Other Systems",
+    "Analysis of Attention Mechanism",
+    "Short Sentences",
+    "Long Sentences",
+    "Noisy Sentence",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Community question answering (cQA) is a paradigm that provides forums for users to ask or answer questions on any topic with barely any restrictions. In the past decade, these websites have attracted a great number of users, and have accumulated a large collection of question-comment threads generated by these users. However, the low restriction results in a high variation in answer quality, which makes it time-consuming to search for useful information from the existing content. It would therefore be valuable to automate the procedure of ranking related questions and comments for users with a new question, or when looking for solutions from comments of an existing question.",
+      "Automation of cQA forums can be divided into three tasks: question-comment relevance (Task A), question-question relevance (Task B), and question-external comment relevance (Task C). One might think that classic retrieval models like language models for information retrieval BIBREF0 could solve these tasks. However, a big challenge for cQA tasks is that users are used to expressing similar meanings with different words, which creates gaps when matching questions based on common words. Other challenges include informal usage of language, highly diverse content of comments, and variation in the length of both questions and comments.",
+      "To overcome these issues, most previous work (e.g. SemEval 2015 BIBREF1 ) relied heavily on additional features and reasoning capabilities. In BIBREF2 , a neural attention-based model was proposed for automatically recognizing entailment relations between pairs of natural language sentences. In this study, we first modify this model for all three cQA tasks. We also extend this framework into a jointly trained model when the external resources are available, i.e. selecting an external comment when we know the question that the external comment answers (Task C).",
+      "Our ultimate objective is to classify relevant questions and comments without complicated handcrafted features. By applying RNN-based encoders, we avoid heavily engineered features and learn the representation automatically. In addition, an attention mechanism augments encoders with the ability to attend to past outputs directly. This becomes helpful when encoding longer sequences, since we no longer need to compress all information into a fixed-length vector representation.",
+      "In our view, existing annotated cQA corpora are generally too small to properly train an end-to-end neural network. To address this, we investigate transfer learning by pretraining the recurrent systems on other corpora, and also generating additional instances from existing cQA corpus."
+    ],
+    [
+      "Earlier work of community question answering relied heavily on feature engineering, linguistic tools, and external resource. BIBREF3 and BIBREF4 utilized rich non-textual features such as answer's profile. BIBREF5 syntactically analyzed the question and extracted name entity features. BIBREF6 demonstrated a textual entailment system can enhance cQA task by casting question answering to logical entailment.",
+      "More recent work incorporated word vector into their feature extraction system and based on it designed different distance metric for question and answer BIBREF7 BIBREF8 . While these approaches showed effectiveness, it is difficult to generalize them to common cQA tasks since linguistic tools and external resource may be restrictive in other languages and features are highly customized for each cQA task.",
+      "Very recent work on answer selection also involved the use of neural networks. BIBREF9 used LSTM to construct a joint vector based on both the question and the answer and then converted it into a learning to rank problem. BIBREF10 proposed several convolutional neural network (CNN) architectures for cQA. Our method differs in that RNN encoder is applied here and by adding attention mechanism we jointly learn which words in question to focus and hence available to conduct qualitative analysis. During classification, we feed the extracted vector into a feed-forward neural network directly instead of using mean/max pooling on top of each time steps."
+    ],
+    [
+      "In this section, we first discuss long short-term memory (LSTM) units and an associated attention mechanism. Next, we explain how we can encode a pair of sentences into a dense vector for predicting relationships using an LSTM with an attention mechanism. Finally, we apply these models to predict question-question similarity, question-comment similarity, and question-external comment similarity."
+    ],
+    [
+      "LSTMs have shown great success in many different fields. An LSTM unit contains a memory cell with self-connections, as well as three multiplicative gates to control information flow. Given input vector $x_t$ , previous hidden outputs $h_{t-1}$ , and previous cell state $c_{t-1}$ , LSTM units operate as follows: ",
+      "$$X &= \\begin{bmatrix}\nx_t\\\\[0.3em]\nh_{t-1}\\\\[0.3em]\n\\end{bmatrix}\\\\\ni_t &= \\sigma (\\mathbf {W_{iX}}X + \\mathbf {W_{ic}}c_{t-1} + \\mathbf {b_i})\\\\\nf_t &= \\sigma (\\mathbf {W_{fX}}X + \\mathbf {W_{fc}}c_{t-1} + \\mathbf {b_f})\\\\\no_t &= \\sigma (\\mathbf {W_{oX}}X + \\mathbf {W_{oc}}c_{t-1} + \\mathbf {b_o})\\\\\nc_t &= f_t \\odot c_{t-1} + i_t \\odot tanh(\\mathbf {W_{cX}}X + \\mathbf {b_c})\\\\\nh_t &= o_t \\odot tanh(c_t)$$   (Eq. 3) ",
+      "where $i_t$ , $f_t$ , $o_t$ are input, forget, and output gates, respectively. The sigmoid function $\\sigma ()$ is a soft gate function controlling the amount of information flow. $W$ s and $b$ s are model parameters to learn."
+    ],
+    [
+      "A traditional RNN encoder-decoder approach BIBREF11 first encodes an arbitrary length input sequence into a fixed-length dense vector that can be used as input to subsequent classification models, or to initialize the hidden state of a secondary decoder. However, the requirement to compress all necessary information into a single fixed length vector can be problematic. A neural attention model BIBREF12 BIBREF13 has been recently proposed to alleviate this issue by enabling the network to attend to past outputs when decoding. Thus, the encoder no longer needs to represent an entire sequence with one vector; instead, it encodes information into a sequence of vectors, and adaptively chooses a subset of the vectors when decoding."
+    ],
+    [
+      "In our cQA tasks, the pair of objects are (question, question) or (question, comment), and the relationship is relevant/irrelevant. The left side of Figure 1 shows one intuitive way to predict relationships using RNNs. Parallel LSTMs encode two objects independently, and then concatenate their outputs as an input to a feed-forward neural network (FNN) with a softmax output layer for classification.",
+      "The representations of the two objects are generated independently in this manner. However, we are more interested in the relationship instead of the object representations themselves. Therefore, we consider a serialized LSTM-encoder model in the right side of Figure 1 that is similar to that in BIBREF2 , but also allows an augmented feature input to the FNN classifier.",
+      "Figure 2 illustrates our attention framework in more detail. The first LSTM reads one object, and passes information through hidden units to the second LSTM. The second LSTM then reads the other object and generates the representation of this pair after the entire sequence is processed. We build another FNN that takes this representation as input to classify the relationship of this pair.",
+      "By adding an attention mechanism to the encoder, we allow the second LSTM to attend to the sequence of output vectors from the first LSTM, and hence generate a weighted representation of first object according to both objects. Let $h_N$ be the last output of second LSTM and $M = [h_1, h_2, \\cdots , h_L]$ be the sequence of output vectors of the first object. The weighted representation of the first object is ",
+      "$$h^{\\prime } = \\sum _{i=1}^{L} \\alpha _i h_i$$   (Eq. 7) ",
+      "The weight is computed by ",
+      "$$\\alpha _i = \\dfrac{exp(a(h_i,h_N))}{\\sum _{j=1}^{L}exp(a(h_j,h_N))}$$   (Eq. 8) ",
+      "where $a()$ is the importance model that produces a higher score for $(h_i, h_N)$ if $h_i$ is useful to determine the object pair's relationship. We parametrize this model using another FNN. Note that in our framework, we also allow other augmented features (e.g., the ranking score from the IR system) to enhance the classifier. So the final input to the classifier will be $h_N$ , $h^{\\prime }$ , as well as augmented features."
+    ],
+    [
+      "For task C, in addition to an original question (oriQ) and an external comment (relC), the question which relC commented on is also given (relQ). To incorporate this extra information, we consider a multitask learning framework which jointly learns to predict the relationships of the three pairs (oriQ/relQ, oriQ/relC, relQ/relC).",
+      "Figure 3 shows our framework: the three lower models are separate serialized LSTM-encoders for the three respective object pairs, whereas the upper model is an FNN that takes as input the concatenation of the outputs of three encoders, and predicts the relationships for all three pairs. More specifically, the output layer consists of three softmax layers where each one is intended to predict the relationship of one particular pair.",
+      "For the overall loss function, we combine three separate loss functions using a heuristic weight vector $\\beta $ that allocates a higher weight to the main task (oriQ-relC relationship prediction) as follows: ",
+      "$$\\mathcal {L} = \\beta _1 \\mathcal {L}_1 + \\beta _2 \\mathcal {L}_2 + \\beta _3 \\mathcal {L}_3$$   (Eq. 11) ",
+      "By doing so, we hypothesize that the related tasks can improve the main task by leveraging commonality among all tasks."
+    ],
+    [
+      "We evaluate our approach on all three cQA tasks. We use the cQA datasets provided by the Semeval 2016 task . The cQA data is organized as follows: there are 267 original questions, each question has 10 related question, and each related question has 10 comments. Therefore, for task A, there are a total number of 26,700 question-comment pairs. For task B, there are 2,670 question-question pairs. For task C, there are 26,700 question-comment pairs. The test dataset includes 50 questions, 500 related questions and 5,000 comments which do not overlap with the training set. To evaluate the performance, we use mean average precision (MAP) and F1 score."
+    ],
+    [
+      "Table 2 shows the initial results using the RNN encoder for different tasks. We observe that the attention model always gets better results than the RNN without attention, especially for task C. However, the RNN model achieves a very low F1 score. For task B, it is even worse than the random baseline. We believe the reason is because for task B, there are only 2,670 pairs for training which is very limited training for a reasonable neural network. For task C, we believe the problem is highly imbalanced data. Since the related comments did not directly comment on the original question, more than $90\\%$ of the comments are labeled as irrelevant to the original question. The low F1 (with high precision and low recall) means our system tends to label most comments as irrelevant. In the following section, we investigate methods to address these issues."
+    ],
+    [
+      "One way to improve models trained on limited data is to use external data to pretrain the neural network. We therefore considered two different datasets for this task.",
+      "Cross-domain: The Stanford natural language inference (SNLI) corpus BIBREF17 has a huge amount of cleaned premise and hypothesis pairs. Unfortunately the pairs are for a different task. The relationship between the premise and hypothesis may be similar to the relation between questions and comments, but may also be different.",
+      "In-domain: since task A seems has reasonable performance, and the network is also well-trained, we could use it directly to initialize task B.",
+      "To utilize the data, we first trained the model on each auxiliary data (SNLI or Task A) and then removed the softmax layer. After that, we retrain the network using the target data with a softmax layer that was randomly initialized.",
+      "For task A, the SNLI cannot improve MAP or F1 scores. Actually it slightly hurts the performance. We surmise that it is probably because the domain is different. Further investigation is needed: for example, we could only use the parameter for embedding layers etc. For task B, the SNLI yields a slight improvement on MAP ( $0.2\\%$ ), and Task A could give ( $1.2\\%$ ) on top of that. No improvement was observed on F1. For task C, pretraining by task A is also better than using SNLI (task A is $1\\%$ better than the baseline, while SNLI is almost the same).",
+      "In summary, the in-domain pretraining seems better, but overall, the improvement is less than we expected, especially for task B, which only has very limited target data. We will not make a conclusion here since more investigation is needed."
+    ],
+    [
+      "As mentioned in Section \"Modeling Question-External Comments\" , we also explored a multitask learning framework that jointly learns to predict the relationships of all three tasks. We set $0.8$ for the main task (task C) and $0.1$ for the other auxiliary tasks. The MAP score did not improve, but F1 increases to $0.1617$ . We believe this is because other tasks have more balanced labels, which improves the shared parameters for task C."
+    ],
+    [
+      "There are many sources of external question-answer pairs that could be used in our tasks. For example: WebQuestion (was introduced by the authors of SEMPRE system BIBREF18 ) and The SimpleQuestions dataset . All of them are positive examples for our task and we can easily create negative examples from it. Initial experiments indicate that it is very easy to overfit these obvious negative examples. We believe this is because our negative examples are non-informative for our task and just introduce noise.",
+      "Since the external data seems to hurt the performance, we try to use the in-domain pairs to enhance task B and task C. For task B, if relative question 1 (rel1) and relative question 2 (rel2) are both relevant to the original question, then we add a positive sample (rel1, rel2, 1). If either rel1 and rel2 is irrelevant and the other is relevant, we add a negative sample (rel1, rel2, 0). After doing this, the samples of task B increase from $2,670$ to $11,810$ . By applying this method, the MAP score increased slightly from $0.5723$ to $0.5789$ but the F1 score improved from $0.4334$ to $0.5860$ .",
+      "For task C, we used task A's data directly. The results are very similar with a slight improvement on MAP, but large improvement on F1 score from $0.1449$ to $0.2064$ ."
+    ],
+    [
+      "To further enhance the system, we incorporate a one hot vector of the original IR ranking as an additional feature into the FNN classifier. Table 3 shows the results. In comparing the models with and without augmented features, we can see large improvement for task B and C. The F1 score for task A degrades slightly but MAP improves. This might be because task A already had a substantial amount of training data."
+    ],
+    [
+      "Table 4 gives the final comparison between different models (we only list the MAP score because it is the official score for the challenge). Since the two baseline models did not use any additional data, in this table our system was also restricted to the provided training data. For task A, we can see that if there is enough training data our single system already performs better than a very strong feature-rich based system. For task B, since only limited training data is given, both feature-rich based system and our system are worse than the IR system. For task C, our system also got comparable results with the feature-rich based system. If we do a simple system combination (average the rank score) between our system and the IR system, the combined system will give large gains on tasks B and C. This implies that our system is complimentary with the IR system."
+    ],
+    [
+      "In addition to quantitative analysis, it is natural to qualitatively evaluate the performance of the attention mechanism by visualizing the weight distribution of each instance. We randomly picked several instances from the test set in task A, for which the sentence lengths are more moderate for demonstration. These examples are shown in Figure 5 , and categorized into short, long, and noisy sentences for discussion. A darker blue patch refers to a larger weight relative to other words in the same sentence."
+    ],
+    [
+      "Figure 5 illustrates two cQA examples whose questions are relatively short. The comments corresponding to these questions are \u201c...snorkeling two days ago off the coast of dukhan...\u201d and \u201cthe doha international airport...\u201d. We can observe that our model successfully learns to focus on the most representative part of the question pertaining to classifying the relationship, which is \"place for snorkeling\" for the first example and \u201cplace can ... visited in qatar\u201d for the second example."
+    ],
+    [
+      "In Figure 5 , we investigate two examples with longer questions, which both contain 63 words. Interestingly, the distribution of weights does not become more uniform; the model still focuses attention on a small number of hot words, for example, \u201cpuppy dog for ... mall\u201d and \u201chectic driving in doha ... car insurance ... quite costly\u201d. Additionally, some words that appear frequently but carry little information for classification are assigned very small weights, such as I/we/my, is/am, like, and to."
+    ],
+    [
+      "Due to the open nature of cQA forums, some content is noisy. Figure 5 is an example with excessive usage of question marks. Again, our model exhibits its robustness by allocating very low weights to the noise symbols and therefore excludes the noninformative content."
+    ],
+    [
+      "In this paper, we demonstrate that a general RNN encoder framework can be applied to community question answering tasks. By adding a neural attention mechanism, we showed quantitatively and qualitatively that attention can improve the RNN encoder framework. To deal with a more realistic scenario, we expanded the framework to incorporate metadata as augmented inputs to a FNN classifier, and pretrained models on larger datasets, increasing both stability and performance. Our model is consistently better than or comparable to a strong feature-rich baseline system, and is superior to an IR-based system when there is a reasonable amount of training data.",
+      "Our model is complimentary with an IR-based system that uses vast amounts of external resources but trained for general purposes. By combining the two systems, it exceeds the feature-rich and IR-based system in all three tasks.",
+      "Moreover, our approach is also language independent. We have also performed preliminary experiments on the Arabic portion of the SemEval-2016 cQA task. The results are competitive with a hand-tuned strong baseline from SemEval-2015.",
+      "Future work could proceed in two directions: first, we can enrich the existing system by incorporating available metadata and preprocessing data with morphological normalization and out-of-vocabulary mappings; second, we can reinforce our model by carrying out word-by-word and history-aware attention mechanisms instead of attending only when reading the last word."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0299/instruction.md b/qasper-0299/instruction.md
new file mode 100644
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+Name of Paper: Recurrent Neural Network Encoder with Attention for Community Question Answering
+
+Question: Is the improvement actually coming from using an RNN?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Method",
+    "LSTM Models",
+    "Neural Attention",
+    "Predicting Relationships of Object Pairs with an Attention Model",
+    "Modeling Question-External Comments",
+    "Experiments",
+    "Preliminary Results",
+    "Robust Parameter Initialization",
+    "Multitask Learning",
+    "Augmented data",
+    "Augmented features",
+    "Comparison with Other Systems",
+    "Analysis of Attention Mechanism",
+    "Short Sentences",
+    "Long Sentences",
+    "Noisy Sentence",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Community question answering (cQA) is a paradigm that provides forums for users to ask or answer questions on any topic with barely any restrictions. In the past decade, these websites have attracted a great number of users, and have accumulated a large collection of question-comment threads generated by these users. However, the low restriction results in a high variation in answer quality, which makes it time-consuming to search for useful information from the existing content. It would therefore be valuable to automate the procedure of ranking related questions and comments for users with a new question, or when looking for solutions from comments of an existing question.",
+      "Automation of cQA forums can be divided into three tasks: question-comment relevance (Task A), question-question relevance (Task B), and question-external comment relevance (Task C). One might think that classic retrieval models like language models for information retrieval BIBREF0 could solve these tasks. However, a big challenge for cQA tasks is that users are used to expressing similar meanings with different words, which creates gaps when matching questions based on common words. Other challenges include informal usage of language, highly diverse content of comments, and variation in the length of both questions and comments.",
+      "To overcome these issues, most previous work (e.g. SemEval 2015 BIBREF1 ) relied heavily on additional features and reasoning capabilities. In BIBREF2 , a neural attention-based model was proposed for automatically recognizing entailment relations between pairs of natural language sentences. In this study, we first modify this model for all three cQA tasks. We also extend this framework into a jointly trained model when the external resources are available, i.e. selecting an external comment when we know the question that the external comment answers (Task C).",
+      "Our ultimate objective is to classify relevant questions and comments without complicated handcrafted features. By applying RNN-based encoders, we avoid heavily engineered features and learn the representation automatically. In addition, an attention mechanism augments encoders with the ability to attend to past outputs directly. This becomes helpful when encoding longer sequences, since we no longer need to compress all information into a fixed-length vector representation.",
+      "In our view, existing annotated cQA corpora are generally too small to properly train an end-to-end neural network. To address this, we investigate transfer learning by pretraining the recurrent systems on other corpora, and also generating additional instances from existing cQA corpus."
+    ],
+    [
+      "Earlier work of community question answering relied heavily on feature engineering, linguistic tools, and external resource. BIBREF3 and BIBREF4 utilized rich non-textual features such as answer's profile. BIBREF5 syntactically analyzed the question and extracted name entity features. BIBREF6 demonstrated a textual entailment system can enhance cQA task by casting question answering to logical entailment.",
+      "More recent work incorporated word vector into their feature extraction system and based on it designed different distance metric for question and answer BIBREF7 BIBREF8 . While these approaches showed effectiveness, it is difficult to generalize them to common cQA tasks since linguistic tools and external resource may be restrictive in other languages and features are highly customized for each cQA task.",
+      "Very recent work on answer selection also involved the use of neural networks. BIBREF9 used LSTM to construct a joint vector based on both the question and the answer and then converted it into a learning to rank problem. BIBREF10 proposed several convolutional neural network (CNN) architectures for cQA. Our method differs in that RNN encoder is applied here and by adding attention mechanism we jointly learn which words in question to focus and hence available to conduct qualitative analysis. During classification, we feed the extracted vector into a feed-forward neural network directly instead of using mean/max pooling on top of each time steps."
+    ],
+    [
+      "In this section, we first discuss long short-term memory (LSTM) units and an associated attention mechanism. Next, we explain how we can encode a pair of sentences into a dense vector for predicting relationships using an LSTM with an attention mechanism. Finally, we apply these models to predict question-question similarity, question-comment similarity, and question-external comment similarity."
+    ],
+    [
+      "LSTMs have shown great success in many different fields. An LSTM unit contains a memory cell with self-connections, as well as three multiplicative gates to control information flow. Given input vector $x_t$ , previous hidden outputs $h_{t-1}$ , and previous cell state $c_{t-1}$ , LSTM units operate as follows: ",
+      "$$X &= \\begin{bmatrix}\nx_t\\\\[0.3em]\nh_{t-1}\\\\[0.3em]\n\\end{bmatrix}\\\\\ni_t &= \\sigma (\\mathbf {W_{iX}}X + \\mathbf {W_{ic}}c_{t-1} + \\mathbf {b_i})\\\\\nf_t &= \\sigma (\\mathbf {W_{fX}}X + \\mathbf {W_{fc}}c_{t-1} + \\mathbf {b_f})\\\\\no_t &= \\sigma (\\mathbf {W_{oX}}X + \\mathbf {W_{oc}}c_{t-1} + \\mathbf {b_o})\\\\\nc_t &= f_t \\odot c_{t-1} + i_t \\odot tanh(\\mathbf {W_{cX}}X + \\mathbf {b_c})\\\\\nh_t &= o_t \\odot tanh(c_t)$$   (Eq. 3) ",
+      "where $i_t$ , $f_t$ , $o_t$ are input, forget, and output gates, respectively. The sigmoid function $\\sigma ()$ is a soft gate function controlling the amount of information flow. $W$ s and $b$ s are model parameters to learn."
+    ],
+    [
+      "A traditional RNN encoder-decoder approach BIBREF11 first encodes an arbitrary length input sequence into a fixed-length dense vector that can be used as input to subsequent classification models, or to initialize the hidden state of a secondary decoder. However, the requirement to compress all necessary information into a single fixed length vector can be problematic. A neural attention model BIBREF12 BIBREF13 has been recently proposed to alleviate this issue by enabling the network to attend to past outputs when decoding. Thus, the encoder no longer needs to represent an entire sequence with one vector; instead, it encodes information into a sequence of vectors, and adaptively chooses a subset of the vectors when decoding."
+    ],
+    [
+      "In our cQA tasks, the pair of objects are (question, question) or (question, comment), and the relationship is relevant/irrelevant. The left side of Figure 1 shows one intuitive way to predict relationships using RNNs. Parallel LSTMs encode two objects independently, and then concatenate their outputs as an input to a feed-forward neural network (FNN) with a softmax output layer for classification.",
+      "The representations of the two objects are generated independently in this manner. However, we are more interested in the relationship instead of the object representations themselves. Therefore, we consider a serialized LSTM-encoder model in the right side of Figure 1 that is similar to that in BIBREF2 , but also allows an augmented feature input to the FNN classifier.",
+      "Figure 2 illustrates our attention framework in more detail. The first LSTM reads one object, and passes information through hidden units to the second LSTM. The second LSTM then reads the other object and generates the representation of this pair after the entire sequence is processed. We build another FNN that takes this representation as input to classify the relationship of this pair.",
+      "By adding an attention mechanism to the encoder, we allow the second LSTM to attend to the sequence of output vectors from the first LSTM, and hence generate a weighted representation of first object according to both objects. Let $h_N$ be the last output of second LSTM and $M = [h_1, h_2, \\cdots , h_L]$ be the sequence of output vectors of the first object. The weighted representation of the first object is ",
+      "$$h^{\\prime } = \\sum _{i=1}^{L} \\alpha _i h_i$$   (Eq. 7) ",
+      "The weight is computed by ",
+      "$$\\alpha _i = \\dfrac{exp(a(h_i,h_N))}{\\sum _{j=1}^{L}exp(a(h_j,h_N))}$$   (Eq. 8) ",
+      "where $a()$ is the importance model that produces a higher score for $(h_i, h_N)$ if $h_i$ is useful to determine the object pair's relationship. We parametrize this model using another FNN. Note that in our framework, we also allow other augmented features (e.g., the ranking score from the IR system) to enhance the classifier. So the final input to the classifier will be $h_N$ , $h^{\\prime }$ , as well as augmented features."
+    ],
+    [
+      "For task C, in addition to an original question (oriQ) and an external comment (relC), the question which relC commented on is also given (relQ). To incorporate this extra information, we consider a multitask learning framework which jointly learns to predict the relationships of the three pairs (oriQ/relQ, oriQ/relC, relQ/relC).",
+      "Figure 3 shows our framework: the three lower models are separate serialized LSTM-encoders for the three respective object pairs, whereas the upper model is an FNN that takes as input the concatenation of the outputs of three encoders, and predicts the relationships for all three pairs. More specifically, the output layer consists of three softmax layers where each one is intended to predict the relationship of one particular pair.",
+      "For the overall loss function, we combine three separate loss functions using a heuristic weight vector $\\beta $ that allocates a higher weight to the main task (oriQ-relC relationship prediction) as follows: ",
+      "$$\\mathcal {L} = \\beta _1 \\mathcal {L}_1 + \\beta _2 \\mathcal {L}_2 + \\beta _3 \\mathcal {L}_3$$   (Eq. 11) ",
+      "By doing so, we hypothesize that the related tasks can improve the main task by leveraging commonality among all tasks."
+    ],
+    [
+      "We evaluate our approach on all three cQA tasks. We use the cQA datasets provided by the Semeval 2016 task . The cQA data is organized as follows: there are 267 original questions, each question has 10 related question, and each related question has 10 comments. Therefore, for task A, there are a total number of 26,700 question-comment pairs. For task B, there are 2,670 question-question pairs. For task C, there are 26,700 question-comment pairs. The test dataset includes 50 questions, 500 related questions and 5,000 comments which do not overlap with the training set. To evaluate the performance, we use mean average precision (MAP) and F1 score."
+    ],
+    [
+      "Table 2 shows the initial results using the RNN encoder for different tasks. We observe that the attention model always gets better results than the RNN without attention, especially for task C. However, the RNN model achieves a very low F1 score. For task B, it is even worse than the random baseline. We believe the reason is because for task B, there are only 2,670 pairs for training which is very limited training for a reasonable neural network. For task C, we believe the problem is highly imbalanced data. Since the related comments did not directly comment on the original question, more than $90\\%$ of the comments are labeled as irrelevant to the original question. The low F1 (with high precision and low recall) means our system tends to label most comments as irrelevant. In the following section, we investigate methods to address these issues."
+    ],
+    [
+      "One way to improve models trained on limited data is to use external data to pretrain the neural network. We therefore considered two different datasets for this task.",
+      "Cross-domain: The Stanford natural language inference (SNLI) corpus BIBREF17 has a huge amount of cleaned premise and hypothesis pairs. Unfortunately the pairs are for a different task. The relationship between the premise and hypothesis may be similar to the relation between questions and comments, but may also be different.",
+      "In-domain: since task A seems has reasonable performance, and the network is also well-trained, we could use it directly to initialize task B.",
+      "To utilize the data, we first trained the model on each auxiliary data (SNLI or Task A) and then removed the softmax layer. After that, we retrain the network using the target data with a softmax layer that was randomly initialized.",
+      "For task A, the SNLI cannot improve MAP or F1 scores. Actually it slightly hurts the performance. We surmise that it is probably because the domain is different. Further investigation is needed: for example, we could only use the parameter for embedding layers etc. For task B, the SNLI yields a slight improvement on MAP ( $0.2\\%$ ), and Task A could give ( $1.2\\%$ ) on top of that. No improvement was observed on F1. For task C, pretraining by task A is also better than using SNLI (task A is $1\\%$ better than the baseline, while SNLI is almost the same).",
+      "In summary, the in-domain pretraining seems better, but overall, the improvement is less than we expected, especially for task B, which only has very limited target data. We will not make a conclusion here since more investigation is needed."
+    ],
+    [
+      "As mentioned in Section \"Modeling Question-External Comments\" , we also explored a multitask learning framework that jointly learns to predict the relationships of all three tasks. We set $0.8$ for the main task (task C) and $0.1$ for the other auxiliary tasks. The MAP score did not improve, but F1 increases to $0.1617$ . We believe this is because other tasks have more balanced labels, which improves the shared parameters for task C."
+    ],
+    [
+      "There are many sources of external question-answer pairs that could be used in our tasks. For example: WebQuestion (was introduced by the authors of SEMPRE system BIBREF18 ) and The SimpleQuestions dataset . All of them are positive examples for our task and we can easily create negative examples from it. Initial experiments indicate that it is very easy to overfit these obvious negative examples. We believe this is because our negative examples are non-informative for our task and just introduce noise.",
+      "Since the external data seems to hurt the performance, we try to use the in-domain pairs to enhance task B and task C. For task B, if relative question 1 (rel1) and relative question 2 (rel2) are both relevant to the original question, then we add a positive sample (rel1, rel2, 1). If either rel1 and rel2 is irrelevant and the other is relevant, we add a negative sample (rel1, rel2, 0). After doing this, the samples of task B increase from $2,670$ to $11,810$ . By applying this method, the MAP score increased slightly from $0.5723$ to $0.5789$ but the F1 score improved from $0.4334$ to $0.5860$ .",
+      "For task C, we used task A's data directly. The results are very similar with a slight improvement on MAP, but large improvement on F1 score from $0.1449$ to $0.2064$ ."
+    ],
+    [
+      "To further enhance the system, we incorporate a one hot vector of the original IR ranking as an additional feature into the FNN classifier. Table 3 shows the results. In comparing the models with and without augmented features, we can see large improvement for task B and C. The F1 score for task A degrades slightly but MAP improves. This might be because task A already had a substantial amount of training data."
+    ],
+    [
+      "Table 4 gives the final comparison between different models (we only list the MAP score because it is the official score for the challenge). Since the two baseline models did not use any additional data, in this table our system was also restricted to the provided training data. For task A, we can see that if there is enough training data our single system already performs better than a very strong feature-rich based system. For task B, since only limited training data is given, both feature-rich based system and our system are worse than the IR system. For task C, our system also got comparable results with the feature-rich based system. If we do a simple system combination (average the rank score) between our system and the IR system, the combined system will give large gains on tasks B and C. This implies that our system is complimentary with the IR system."
+    ],
+    [
+      "In addition to quantitative analysis, it is natural to qualitatively evaluate the performance of the attention mechanism by visualizing the weight distribution of each instance. We randomly picked several instances from the test set in task A, for which the sentence lengths are more moderate for demonstration. These examples are shown in Figure 5 , and categorized into short, long, and noisy sentences for discussion. A darker blue patch refers to a larger weight relative to other words in the same sentence."
+    ],
+    [
+      "Figure 5 illustrates two cQA examples whose questions are relatively short. The comments corresponding to these questions are \u201c...snorkeling two days ago off the coast of dukhan...\u201d and \u201cthe doha international airport...\u201d. We can observe that our model successfully learns to focus on the most representative part of the question pertaining to classifying the relationship, which is \"place for snorkeling\" for the first example and \u201cplace can ... visited in qatar\u201d for the second example."
+    ],
+    [
+      "In Figure 5 , we investigate two examples with longer questions, which both contain 63 words. Interestingly, the distribution of weights does not become more uniform; the model still focuses attention on a small number of hot words, for example, \u201cpuppy dog for ... mall\u201d and \u201chectic driving in doha ... car insurance ... quite costly\u201d. Additionally, some words that appear frequently but carry little information for classification are assigned very small weights, such as I/we/my, is/am, like, and to."
+    ],
+    [
+      "Due to the open nature of cQA forums, some content is noisy. Figure 5 is an example with excessive usage of question marks. Again, our model exhibits its robustness by allocating very low weights to the noise symbols and therefore excludes the noninformative content."
+    ],
+    [
+      "In this paper, we demonstrate that a general RNN encoder framework can be applied to community question answering tasks. By adding a neural attention mechanism, we showed quantitatively and qualitatively that attention can improve the RNN encoder framework. To deal with a more realistic scenario, we expanded the framework to incorporate metadata as augmented inputs to a FNN classifier, and pretrained models on larger datasets, increasing both stability and performance. Our model is consistently better than or comparable to a strong feature-rich baseline system, and is superior to an IR-based system when there is a reasonable amount of training data.",
+      "Our model is complimentary with an IR-based system that uses vast amounts of external resources but trained for general purposes. By combining the two systems, it exceeds the feature-rich and IR-based system in all three tasks.",
+      "Moreover, our approach is also language independent. We have also performed preliminary experiments on the Arabic portion of the SemEval-2016 cQA task. The results are competitive with a hand-tuned strong baseline from SemEval-2015.",
+      "Future work could proceed in two directions: first, we can enrich the existing system by incorporating available metadata and preprocessing data with morphological normalization and out-of-vocabulary mappings; second, we can reinforce our model by carrying out word-by-word and history-aware attention mechanisms instead of attending only when reading the last word."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0309/instruction.md b/qasper-0309/instruction.md
new file mode 100644
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+Name of Paper: DENS: A Dataset for Multi-class Emotion Analysis
+
+Question: How many emotions do they look at?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Background",
+    "Dataset",
+    "Dataset ::: Plutchik\u2019s Wheel of Emotions",
+    "Dataset ::: Passage Selection",
+    "Dataset ::: Mechanical Turk (MTurk)",
+    "Dataset ::: Dataset Statistics",
+    "Benchmarks",
+    "Benchmarks ::: Bag-of-Words-based Benchmarks",
+    "Benchmarks ::: Doc2Vec + SVM",
+    "Benchmarks ::: Hierarchical RNN",
+    "Benchmarks ::: Bi-directional RNN and Self-Attention (BiRNN + Self-Attention)",
+    "Benchmarks ::: ELMo embedding and Bi-directional RNN (ELMo + BiRNN)",
+    "Benchmarks ::: Fine-tuned BERT",
+    "Conclusion",
+    "Appendices ::: Sample Data"
+  ],
+  "paragraphs": [
+    [
+      "Humans experience a variety of complex emotions in daily life. These emotions are heavily reflected in our language, in both spoken and written forms.",
+      "Many recent advances in natural language processing on emotions have focused on product reviews BIBREF0 and tweets BIBREF1, BIBREF2. These datasets are often limited in length (e.g. by the number of words in tweets), purpose (e.g. product reviews), or emotional spectrum (e.g. binary classification).",
+      "Character dialogues and narratives in storytelling usually carry strong emotions. A memorable story is often one in which the emotional journey of the characters resonates with the reader. Indeed, emotion is one of the most important aspects of narratives. In order to characterize narrative emotions properly, we must move beyond binary constraints (e.g. good or bad, happy or sad).",
+      "In this paper, we introduce the Dataset for Emotions of Narrative Sequences (DENS) for emotion analysis, consisting of passages from long-form fictional narratives from both classic literature and modern stories in English. The data samples consist of self-contained passages that span several sentences and a variety of subjects. Each sample is annotated by using one of 9 classes and an indicator for annotator agreement."
+    ],
+    [
+      "Using the categorical basic emotion model BIBREF3, BIBREF4, BIBREF5 studied creating lexicons from tweets for use in emotion analysis. Recently, BIBREF1, BIBREF6 and BIBREF2 proposed shared-tasks for multi-class emotion analysis based on tweets.",
+      "Fewer works have been reported on understanding emotions in narratives. Emotional Arc BIBREF7 is one recent advance in this direction. The work used lexicons and unsupervised learning methods based on unlabelled passages from titles in Project Gutenberg.",
+      "For labelled datasets on narratives, BIBREF8 provided a sentence-level annotated corpus of childrens' stories and BIBREF9 provided phrase-level annotations on selected Project Gutenberg titles.",
+      "To the best of our knowledge, the dataset in this work is the first to provide multi-class emotion labels on passages, selected from both Project Gutenberg and modern narratives. The dataset is available upon request for non-commercial, research only purposes."
+    ],
+    [
+      "In this section, we describe the process used to collect and annotate the dataset."
+    ],
+    [
+      "The dataset is annotated based on a modified Plutchik\u2019s wheel of emotions.",
+      "The original Plutchik\u2019s wheel consists of 8 primary emotions: Joy, Sadness, Anger, Fear, Anticipation, Surprise, Trust, Disgust. In addition, more complex emotions can be formed by combing two basic emotions. For example, Love is defined as a combination of Joy and Trust (Fig. 1).",
+      "The intensity of an emotion is also captured in Plutchik's wheel. For example, the primary emotion of Anger can vary between Annoyance (mild) and Rage (intense).",
+      "We conducted an initial survey based on 100 stories with a significant fraction sampled from the romance genre. We asked readers to identify the major emotion exhibited in each story from a choice of the original 8 primary emotions.",
+      "We found that readers have significant difficulty in identifying Trust as an emotion associated with romantic stories. Hence, we modified our annotation scheme by removing Trust and adding Love. We also added the Neutral category to denote passages that do not exhibit any emotional content.",
+      "The final annotation categories for the dataset are: Joy, Sadness, Anger, Fear, Anticipation, Surprise, Love, Disgust, Neutral."
+    ],
+    [
+      "We selected both classic and modern narratives in English for this dataset. The modern narratives were sampled based on popularity from Wattpad. We parsed selected narratives into passages, where a passage is considered to be eligible for annotation if it contained between 40 and 200 tokens.",
+      "In long-form narratives, many non-conversational passages are intended for transition or scene introduction, and may not carry any emotion. We divided the eligible passages into two parts, and one part was pruned using selected emotion-rich but ambiguous lexicons such as cry, punch, kiss, etc.. Then we mixed this pruned part with the unpruned part for annotation in order to reduce the number of neutral passages. See Appendix SECREF25 for the lexicons used."
+    ],
+    [
+      "MTurk was set up using the standard sentiment template and instructed the crowd annotators to `pick the best/major emotion embodied in the passage'.",
+      "We further provided instructions to clarify the intensity of an emotion, such as: \u201cRage/Annoyance is a form of Anger\u201d, \u201cSerenity/Ecstasy is a form of Joy\u201d, and \u201cLove includes Romantic/Family/Friendship\u201d, along with sample passages.",
+      "We required all annotators have a `master' MTurk qualification. Each passage was labelled by 3 unique annotators. Only passages with a majority agreement between annotators were accepted as valid. This is equivalent to a Fleiss's $\\kappa $ score of greater than $0.4$.",
+      "For passages without majority agreement between annotators, we consolidated their labels using in-house data annotators who are experts in narrative content. A passage is accepted as valid if the in-house annotator's label matched any one of the MTurk annotators' labels. The remaining passages are discarded. We provide the fraction of annotator agreement for each label in the dataset.",
+      "Though passages may lose some emotional context when read independently of the complete narrative, we believe annotator agreement on our dataset supports the assertion that small excerpts can still convey coherent emotions.",
+      "During the annotation process, several annotators had suggested for us to include additional emotions such as confused, pain, and jealousy, which are common to narratives. As they were not part of the original Plutchik\u2019s wheel, we decided to not include them. An interesting future direction is to study the relationship between emotions such as \u2018pain versus sadness\u2019 or \u2018confused versus surprise\u2019 and improve the emotion model for narratives."
+    ],
+    [
+      "The dataset contains a total of 9710 passages, with an average of 6.24 sentences per passage, 16.16 words per sentence, and an average length of 86 words.",
+      "The vocabulary size is 28K (when lowercased). It contains over 1600 unique titles across multiple categories, including 88 titles (1520 passages) from Project Gutenberg. All of the modern narratives were written after the year 2000, with notable amount of themes in coming-of-age, strong-female-lead, and LGBTQ+. The genre distribution is listed in Table TABREF8.",
+      "In the final dataset, 21.0% of the data has consensus between all annotators, 73.5% has majority agreement, and 5.48% has labels assigned after consultation with in-house annotators.",
+      "The distribution of data points over labels with top lexicons (lower-cased, normalized) is shown in Table TABREF9. Note that the Disgust category is very small and should be discarded. Furthermore, we suspect that the data labelled as Surprise may be noisier than other categories and should be discarded as well.",
+      "Table TABREF10 shows a few examples labelled data from classic titles. More examples can be found in Table TABREF26 in the Appendix SECREF27."
+    ],
+    [
+      "We performed benchmark experiments on the dataset using several different algorithms. In all experiments, we have discarded the data labelled with Surprise and Disgust.",
+      "We pre-processed the data by using the SpaCy pipeline. We masked out named entities with entity-type specific placeholders to reduce the chance of benchmark models utilizing named entities as a basis for classification.",
+      "Benchmark results are shown in Table TABREF17. The dataset is approximately balanced after discarding the Surprise and Disgust classes. We report the average micro-F1 scores, with 5-fold cross validation for each technique.",
+      "We provide a brief overview of each benchmark experiment below. Among all of the benchmarks, Bidirectional Encoder Representations from Transformers (BERT) BIBREF11 achieved the best performance with a 0.604 micro-F1 score.",
+      "Overall, we observed that deep-learning based techniques performed better than lexical based methods. This suggests that a method which attends to context and themes could do well on the dataset."
+    ],
+    [
+      "We computed bag-of-words-based benchmarks using the following methods:",
+      "Classification with TF-IDF + Linear SVM (TF-IDF + SVM)",
+      "Classification with Depeche++ Emotion lexicons BIBREF12 + Linear SVM (Depeche + SVM)",
+      "Classification with NRC Emotion lexicons BIBREF13, BIBREF14 + Linear SVM (NRC + SVM)",
+      "Combination of TF-IDF and NRC Emotion lexicons (TF-NRC + SVM)"
+    ],
+    [
+      "We also used simple classification models with learned embeddings. We trained a Doc2Vec model BIBREF15 using the dataset and used the embedding document vectors as features for a linear SVM classifier."
+    ],
+    [
+      "For this benchmark, we considered a Hierarchical RNN, following BIBREF16. We used two BiLSTMs BIBREF17 with 256 units each to model sentences and documents. The tokens of a sentence were processed independently of other sentence tokens. For each direction in the token-level BiLSTM, the last outputs were concatenated and fed into the sentence-level BiLSTM as inputs.",
+      "The outputs of the BiLSTM were connected to 2 dense layers with 256 ReLU units and a Softmax layer. We initialized tokens with publicly available embeddings trained with GloVe BIBREF18. Sentence boundaries were provided by SpaCy. Dropout was applied to the dense hidden layers during training."
+    ],
+    [
+      "One challenge with RNN-based solutions for text classification is finding the best way to combine word-level representations into higher-level representations.",
+      "Self-attention BIBREF19, BIBREF20, BIBREF21 has been adapted to text classification, providing improved interpretability and performance. We used BIBREF20 as the basis of this benchmark.",
+      "The benchmark used a layered Bi-directional RNN (60 units) with GRU cells and a dense layer. Both self-attention layers were 60 units in size and cross-entropy was used as the cost function.",
+      "Note that we have omitted the orthogonal regularizer term, since this dataset is relatively small compared to the traditional datasets used for training such a model. We did not observe any significant performance gain while using the regularizer term in our experiments."
+    ],
+    [
+      "Deep Contextualized Word Representations (ELMo) BIBREF22 have shown recent success in a number of NLP tasks. The unsupervised nature of the language model allows it to utilize a large amount of available unlabelled data in order to learn better representations of words.",
+      "We used the pre-trained ELMo model (v2) available on Tensorhub for this benchmark. We fed the word embeddings of ELMo as input into a one layer Bi-directional RNN (16 units) with GRU cells (with dropout) and a dense layer. Cross-entropy was used as the cost function."
+    ],
+    [
+      "Bidirectional Encoder Representations from Transformers (BERT) BIBREF11 has achieved state-of-the-art results on several NLP tasks, including sentence classification.",
+      "We used the fine-tuning procedure outlined in the original work to adapt the pre-trained uncased BERT$_\\textrm {{\\scriptsize LARGE}}$ to a multi-class passage classification task. This technique achieved the best result among our benchmarks, with an average micro-F1 score of 60.4%."
+    ],
+    [
+      "We introduce DENS, a dataset for multi-class emotion analysis from long-form narratives in English. We provide a number of benchmark results based on models ranging from bag-of-word models to methods based on pre-trained language models (ELMo and BERT).",
+      "Our benchmark results demonstrate that this dataset provides a novel challenge in emotion analysis. The results also demonstrate that attention-based models could significantly improve performance on classification tasks such as emotion analysis.",
+      "Interesting future directions for this work include: 1. incorporating common-sense knowledge into emotion analysis to capture semantic context and 2. using few-shot learning to bootstrap and improve performance of underrepresented emotions.",
+      "Finally, as narrative passages often involve interactions between multiple emotions, one avenue for future datasets could be to focus on the multi-emotion complexities of human language and their contextual interactions."
+    ],
+    [
+      "Table TABREF26 shows sample passages from classic titles with corresponding labels."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0310/instruction.md b/qasper-0310/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..5201753615351c6f2e0cf07683f9007db2ab259c
--- /dev/null
+++ b/qasper-0310/instruction.md
@@ -0,0 +1,116 @@
+Name of Paper: DENS: A Dataset for Multi-class Emotion Analysis
+
+Question: What are the baseline benchmarks?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Background",
+    "Dataset",
+    "Dataset ::: Plutchik\u2019s Wheel of Emotions",
+    "Dataset ::: Passage Selection",
+    "Dataset ::: Mechanical Turk (MTurk)",
+    "Dataset ::: Dataset Statistics",
+    "Benchmarks",
+    "Benchmarks ::: Bag-of-Words-based Benchmarks",
+    "Benchmarks ::: Doc2Vec + SVM",
+    "Benchmarks ::: Hierarchical RNN",
+    "Benchmarks ::: Bi-directional RNN and Self-Attention (BiRNN + Self-Attention)",
+    "Benchmarks ::: ELMo embedding and Bi-directional RNN (ELMo + BiRNN)",
+    "Benchmarks ::: Fine-tuned BERT",
+    "Conclusion",
+    "Appendices ::: Sample Data"
+  ],
+  "paragraphs": [
+    [
+      "Humans experience a variety of complex emotions in daily life. These emotions are heavily reflected in our language, in both spoken and written forms.",
+      "Many recent advances in natural language processing on emotions have focused on product reviews BIBREF0 and tweets BIBREF1, BIBREF2. These datasets are often limited in length (e.g. by the number of words in tweets), purpose (e.g. product reviews), or emotional spectrum (e.g. binary classification).",
+      "Character dialogues and narratives in storytelling usually carry strong emotions. A memorable story is often one in which the emotional journey of the characters resonates with the reader. Indeed, emotion is one of the most important aspects of narratives. In order to characterize narrative emotions properly, we must move beyond binary constraints (e.g. good or bad, happy or sad).",
+      "In this paper, we introduce the Dataset for Emotions of Narrative Sequences (DENS) for emotion analysis, consisting of passages from long-form fictional narratives from both classic literature and modern stories in English. The data samples consist of self-contained passages that span several sentences and a variety of subjects. Each sample is annotated by using one of 9 classes and an indicator for annotator agreement."
+    ],
+    [
+      "Using the categorical basic emotion model BIBREF3, BIBREF4, BIBREF5 studied creating lexicons from tweets for use in emotion analysis. Recently, BIBREF1, BIBREF6 and BIBREF2 proposed shared-tasks for multi-class emotion analysis based on tweets.",
+      "Fewer works have been reported on understanding emotions in narratives. Emotional Arc BIBREF7 is one recent advance in this direction. The work used lexicons and unsupervised learning methods based on unlabelled passages from titles in Project Gutenberg.",
+      "For labelled datasets on narratives, BIBREF8 provided a sentence-level annotated corpus of childrens' stories and BIBREF9 provided phrase-level annotations on selected Project Gutenberg titles.",
+      "To the best of our knowledge, the dataset in this work is the first to provide multi-class emotion labels on passages, selected from both Project Gutenberg and modern narratives. The dataset is available upon request for non-commercial, research only purposes."
+    ],
+    [
+      "In this section, we describe the process used to collect and annotate the dataset."
+    ],
+    [
+      "The dataset is annotated based on a modified Plutchik\u2019s wheel of emotions.",
+      "The original Plutchik\u2019s wheel consists of 8 primary emotions: Joy, Sadness, Anger, Fear, Anticipation, Surprise, Trust, Disgust. In addition, more complex emotions can be formed by combing two basic emotions. For example, Love is defined as a combination of Joy and Trust (Fig. 1).",
+      "The intensity of an emotion is also captured in Plutchik's wheel. For example, the primary emotion of Anger can vary between Annoyance (mild) and Rage (intense).",
+      "We conducted an initial survey based on 100 stories with a significant fraction sampled from the romance genre. We asked readers to identify the major emotion exhibited in each story from a choice of the original 8 primary emotions.",
+      "We found that readers have significant difficulty in identifying Trust as an emotion associated with romantic stories. Hence, we modified our annotation scheme by removing Trust and adding Love. We also added the Neutral category to denote passages that do not exhibit any emotional content.",
+      "The final annotation categories for the dataset are: Joy, Sadness, Anger, Fear, Anticipation, Surprise, Love, Disgust, Neutral."
+    ],
+    [
+      "We selected both classic and modern narratives in English for this dataset. The modern narratives were sampled based on popularity from Wattpad. We parsed selected narratives into passages, where a passage is considered to be eligible for annotation if it contained between 40 and 200 tokens.",
+      "In long-form narratives, many non-conversational passages are intended for transition or scene introduction, and may not carry any emotion. We divided the eligible passages into two parts, and one part was pruned using selected emotion-rich but ambiguous lexicons such as cry, punch, kiss, etc.. Then we mixed this pruned part with the unpruned part for annotation in order to reduce the number of neutral passages. See Appendix SECREF25 for the lexicons used."
+    ],
+    [
+      "MTurk was set up using the standard sentiment template and instructed the crowd annotators to `pick the best/major emotion embodied in the passage'.",
+      "We further provided instructions to clarify the intensity of an emotion, such as: \u201cRage/Annoyance is a form of Anger\u201d, \u201cSerenity/Ecstasy is a form of Joy\u201d, and \u201cLove includes Romantic/Family/Friendship\u201d, along with sample passages.",
+      "We required all annotators have a `master' MTurk qualification. Each passage was labelled by 3 unique annotators. Only passages with a majority agreement between annotators were accepted as valid. This is equivalent to a Fleiss's $\\kappa $ score of greater than $0.4$.",
+      "For passages without majority agreement between annotators, we consolidated their labels using in-house data annotators who are experts in narrative content. A passage is accepted as valid if the in-house annotator's label matched any one of the MTurk annotators' labels. The remaining passages are discarded. We provide the fraction of annotator agreement for each label in the dataset.",
+      "Though passages may lose some emotional context when read independently of the complete narrative, we believe annotator agreement on our dataset supports the assertion that small excerpts can still convey coherent emotions.",
+      "During the annotation process, several annotators had suggested for us to include additional emotions such as confused, pain, and jealousy, which are common to narratives. As they were not part of the original Plutchik\u2019s wheel, we decided to not include them. An interesting future direction is to study the relationship between emotions such as \u2018pain versus sadness\u2019 or \u2018confused versus surprise\u2019 and improve the emotion model for narratives."
+    ],
+    [
+      "The dataset contains a total of 9710 passages, with an average of 6.24 sentences per passage, 16.16 words per sentence, and an average length of 86 words.",
+      "The vocabulary size is 28K (when lowercased). It contains over 1600 unique titles across multiple categories, including 88 titles (1520 passages) from Project Gutenberg. All of the modern narratives were written after the year 2000, with notable amount of themes in coming-of-age, strong-female-lead, and LGBTQ+. The genre distribution is listed in Table TABREF8.",
+      "In the final dataset, 21.0% of the data has consensus between all annotators, 73.5% has majority agreement, and 5.48% has labels assigned after consultation with in-house annotators.",
+      "The distribution of data points over labels with top lexicons (lower-cased, normalized) is shown in Table TABREF9. Note that the Disgust category is very small and should be discarded. Furthermore, we suspect that the data labelled as Surprise may be noisier than other categories and should be discarded as well.",
+      "Table TABREF10 shows a few examples labelled data from classic titles. More examples can be found in Table TABREF26 in the Appendix SECREF27."
+    ],
+    [
+      "We performed benchmark experiments on the dataset using several different algorithms. In all experiments, we have discarded the data labelled with Surprise and Disgust.",
+      "We pre-processed the data by using the SpaCy pipeline. We masked out named entities with entity-type specific placeholders to reduce the chance of benchmark models utilizing named entities as a basis for classification.",
+      "Benchmark results are shown in Table TABREF17. The dataset is approximately balanced after discarding the Surprise and Disgust classes. We report the average micro-F1 scores, with 5-fold cross validation for each technique.",
+      "We provide a brief overview of each benchmark experiment below. Among all of the benchmarks, Bidirectional Encoder Representations from Transformers (BERT) BIBREF11 achieved the best performance with a 0.604 micro-F1 score.",
+      "Overall, we observed that deep-learning based techniques performed better than lexical based methods. This suggests that a method which attends to context and themes could do well on the dataset."
+    ],
+    [
+      "We computed bag-of-words-based benchmarks using the following methods:",
+      "Classification with TF-IDF + Linear SVM (TF-IDF + SVM)",
+      "Classification with Depeche++ Emotion lexicons BIBREF12 + Linear SVM (Depeche + SVM)",
+      "Classification with NRC Emotion lexicons BIBREF13, BIBREF14 + Linear SVM (NRC + SVM)",
+      "Combination of TF-IDF and NRC Emotion lexicons (TF-NRC + SVM)"
+    ],
+    [
+      "We also used simple classification models with learned embeddings. We trained a Doc2Vec model BIBREF15 using the dataset and used the embedding document vectors as features for a linear SVM classifier."
+    ],
+    [
+      "For this benchmark, we considered a Hierarchical RNN, following BIBREF16. We used two BiLSTMs BIBREF17 with 256 units each to model sentences and documents. The tokens of a sentence were processed independently of other sentence tokens. For each direction in the token-level BiLSTM, the last outputs were concatenated and fed into the sentence-level BiLSTM as inputs.",
+      "The outputs of the BiLSTM were connected to 2 dense layers with 256 ReLU units and a Softmax layer. We initialized tokens with publicly available embeddings trained with GloVe BIBREF18. Sentence boundaries were provided by SpaCy. Dropout was applied to the dense hidden layers during training."
+    ],
+    [
+      "One challenge with RNN-based solutions for text classification is finding the best way to combine word-level representations into higher-level representations.",
+      "Self-attention BIBREF19, BIBREF20, BIBREF21 has been adapted to text classification, providing improved interpretability and performance. We used BIBREF20 as the basis of this benchmark.",
+      "The benchmark used a layered Bi-directional RNN (60 units) with GRU cells and a dense layer. Both self-attention layers were 60 units in size and cross-entropy was used as the cost function.",
+      "Note that we have omitted the orthogonal regularizer term, since this dataset is relatively small compared to the traditional datasets used for training such a model. We did not observe any significant performance gain while using the regularizer term in our experiments."
+    ],
+    [
+      "Deep Contextualized Word Representations (ELMo) BIBREF22 have shown recent success in a number of NLP tasks. The unsupervised nature of the language model allows it to utilize a large amount of available unlabelled data in order to learn better representations of words.",
+      "We used the pre-trained ELMo model (v2) available on Tensorhub for this benchmark. We fed the word embeddings of ELMo as input into a one layer Bi-directional RNN (16 units) with GRU cells (with dropout) and a dense layer. Cross-entropy was used as the cost function."
+    ],
+    [
+      "Bidirectional Encoder Representations from Transformers (BERT) BIBREF11 has achieved state-of-the-art results on several NLP tasks, including sentence classification.",
+      "We used the fine-tuning procedure outlined in the original work to adapt the pre-trained uncased BERT$_\\textrm {{\\scriptsize LARGE}}$ to a multi-class passage classification task. This technique achieved the best result among our benchmarks, with an average micro-F1 score of 60.4%."
+    ],
+    [
+      "We introduce DENS, a dataset for multi-class emotion analysis from long-form narratives in English. We provide a number of benchmark results based on models ranging from bag-of-word models to methods based on pre-trained language models (ELMo and BERT).",
+      "Our benchmark results demonstrate that this dataset provides a novel challenge in emotion analysis. The results also demonstrate that attention-based models could significantly improve performance on classification tasks such as emotion analysis.",
+      "Interesting future directions for this work include: 1. incorporating common-sense knowledge into emotion analysis to capture semantic context and 2. using few-shot learning to bootstrap and improve performance of underrepresented emotions.",
+      "Finally, as narrative passages often involve interactions between multiple emotions, one avenue for future datasets could be to focus on the multi-emotion complexities of human language and their contextual interactions."
+    ],
+    [
+      "Table TABREF26 shows sample passages from classic titles with corresponding labels."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0311/instruction.md b/qasper-0311/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..e33bba3f64ede888a77972e224c7a01b67a48e8e
--- /dev/null
+++ b/qasper-0311/instruction.md
@@ -0,0 +1,116 @@
+Name of Paper: DENS: A Dataset for Multi-class Emotion Analysis
+
+Question: What is the size of this dataset?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Background",
+    "Dataset",
+    "Dataset ::: Plutchik\u2019s Wheel of Emotions",
+    "Dataset ::: Passage Selection",
+    "Dataset ::: Mechanical Turk (MTurk)",
+    "Dataset ::: Dataset Statistics",
+    "Benchmarks",
+    "Benchmarks ::: Bag-of-Words-based Benchmarks",
+    "Benchmarks ::: Doc2Vec + SVM",
+    "Benchmarks ::: Hierarchical RNN",
+    "Benchmarks ::: Bi-directional RNN and Self-Attention (BiRNN + Self-Attention)",
+    "Benchmarks ::: ELMo embedding and Bi-directional RNN (ELMo + BiRNN)",
+    "Benchmarks ::: Fine-tuned BERT",
+    "Conclusion",
+    "Appendices ::: Sample Data"
+  ],
+  "paragraphs": [
+    [
+      "Humans experience a variety of complex emotions in daily life. These emotions are heavily reflected in our language, in both spoken and written forms.",
+      "Many recent advances in natural language processing on emotions have focused on product reviews BIBREF0 and tweets BIBREF1, BIBREF2. These datasets are often limited in length (e.g. by the number of words in tweets), purpose (e.g. product reviews), or emotional spectrum (e.g. binary classification).",
+      "Character dialogues and narratives in storytelling usually carry strong emotions. A memorable story is often one in which the emotional journey of the characters resonates with the reader. Indeed, emotion is one of the most important aspects of narratives. In order to characterize narrative emotions properly, we must move beyond binary constraints (e.g. good or bad, happy or sad).",
+      "In this paper, we introduce the Dataset for Emotions of Narrative Sequences (DENS) for emotion analysis, consisting of passages from long-form fictional narratives from both classic literature and modern stories in English. The data samples consist of self-contained passages that span several sentences and a variety of subjects. Each sample is annotated by using one of 9 classes and an indicator for annotator agreement."
+    ],
+    [
+      "Using the categorical basic emotion model BIBREF3, BIBREF4, BIBREF5 studied creating lexicons from tweets for use in emotion analysis. Recently, BIBREF1, BIBREF6 and BIBREF2 proposed shared-tasks for multi-class emotion analysis based on tweets.",
+      "Fewer works have been reported on understanding emotions in narratives. Emotional Arc BIBREF7 is one recent advance in this direction. The work used lexicons and unsupervised learning methods based on unlabelled passages from titles in Project Gutenberg.",
+      "For labelled datasets on narratives, BIBREF8 provided a sentence-level annotated corpus of childrens' stories and BIBREF9 provided phrase-level annotations on selected Project Gutenberg titles.",
+      "To the best of our knowledge, the dataset in this work is the first to provide multi-class emotion labels on passages, selected from both Project Gutenberg and modern narratives. The dataset is available upon request for non-commercial, research only purposes."
+    ],
+    [
+      "In this section, we describe the process used to collect and annotate the dataset."
+    ],
+    [
+      "The dataset is annotated based on a modified Plutchik\u2019s wheel of emotions.",
+      "The original Plutchik\u2019s wheel consists of 8 primary emotions: Joy, Sadness, Anger, Fear, Anticipation, Surprise, Trust, Disgust. In addition, more complex emotions can be formed by combing two basic emotions. For example, Love is defined as a combination of Joy and Trust (Fig. 1).",
+      "The intensity of an emotion is also captured in Plutchik's wheel. For example, the primary emotion of Anger can vary between Annoyance (mild) and Rage (intense).",
+      "We conducted an initial survey based on 100 stories with a significant fraction sampled from the romance genre. We asked readers to identify the major emotion exhibited in each story from a choice of the original 8 primary emotions.",
+      "We found that readers have significant difficulty in identifying Trust as an emotion associated with romantic stories. Hence, we modified our annotation scheme by removing Trust and adding Love. We also added the Neutral category to denote passages that do not exhibit any emotional content.",
+      "The final annotation categories for the dataset are: Joy, Sadness, Anger, Fear, Anticipation, Surprise, Love, Disgust, Neutral."
+    ],
+    [
+      "We selected both classic and modern narratives in English for this dataset. The modern narratives were sampled based on popularity from Wattpad. We parsed selected narratives into passages, where a passage is considered to be eligible for annotation if it contained between 40 and 200 tokens.",
+      "In long-form narratives, many non-conversational passages are intended for transition or scene introduction, and may not carry any emotion. We divided the eligible passages into two parts, and one part was pruned using selected emotion-rich but ambiguous lexicons such as cry, punch, kiss, etc.. Then we mixed this pruned part with the unpruned part for annotation in order to reduce the number of neutral passages. See Appendix SECREF25 for the lexicons used."
+    ],
+    [
+      "MTurk was set up using the standard sentiment template and instructed the crowd annotators to `pick the best/major emotion embodied in the passage'.",
+      "We further provided instructions to clarify the intensity of an emotion, such as: \u201cRage/Annoyance is a form of Anger\u201d, \u201cSerenity/Ecstasy is a form of Joy\u201d, and \u201cLove includes Romantic/Family/Friendship\u201d, along with sample passages.",
+      "We required all annotators have a `master' MTurk qualification. Each passage was labelled by 3 unique annotators. Only passages with a majority agreement between annotators were accepted as valid. This is equivalent to a Fleiss's $\\kappa $ score of greater than $0.4$.",
+      "For passages without majority agreement between annotators, we consolidated their labels using in-house data annotators who are experts in narrative content. A passage is accepted as valid if the in-house annotator's label matched any one of the MTurk annotators' labels. The remaining passages are discarded. We provide the fraction of annotator agreement for each label in the dataset.",
+      "Though passages may lose some emotional context when read independently of the complete narrative, we believe annotator agreement on our dataset supports the assertion that small excerpts can still convey coherent emotions.",
+      "During the annotation process, several annotators had suggested for us to include additional emotions such as confused, pain, and jealousy, which are common to narratives. As they were not part of the original Plutchik\u2019s wheel, we decided to not include them. An interesting future direction is to study the relationship between emotions such as \u2018pain versus sadness\u2019 or \u2018confused versus surprise\u2019 and improve the emotion model for narratives."
+    ],
+    [
+      "The dataset contains a total of 9710 passages, with an average of 6.24 sentences per passage, 16.16 words per sentence, and an average length of 86 words.",
+      "The vocabulary size is 28K (when lowercased). It contains over 1600 unique titles across multiple categories, including 88 titles (1520 passages) from Project Gutenberg. All of the modern narratives were written after the year 2000, with notable amount of themes in coming-of-age, strong-female-lead, and LGBTQ+. The genre distribution is listed in Table TABREF8.",
+      "In the final dataset, 21.0% of the data has consensus between all annotators, 73.5% has majority agreement, and 5.48% has labels assigned after consultation with in-house annotators.",
+      "The distribution of data points over labels with top lexicons (lower-cased, normalized) is shown in Table TABREF9. Note that the Disgust category is very small and should be discarded. Furthermore, we suspect that the data labelled as Surprise may be noisier than other categories and should be discarded as well.",
+      "Table TABREF10 shows a few examples labelled data from classic titles. More examples can be found in Table TABREF26 in the Appendix SECREF27."
+    ],
+    [
+      "We performed benchmark experiments on the dataset using several different algorithms. In all experiments, we have discarded the data labelled with Surprise and Disgust.",
+      "We pre-processed the data by using the SpaCy pipeline. We masked out named entities with entity-type specific placeholders to reduce the chance of benchmark models utilizing named entities as a basis for classification.",
+      "Benchmark results are shown in Table TABREF17. The dataset is approximately balanced after discarding the Surprise and Disgust classes. We report the average micro-F1 scores, with 5-fold cross validation for each technique.",
+      "We provide a brief overview of each benchmark experiment below. Among all of the benchmarks, Bidirectional Encoder Representations from Transformers (BERT) BIBREF11 achieved the best performance with a 0.604 micro-F1 score.",
+      "Overall, we observed that deep-learning based techniques performed better than lexical based methods. This suggests that a method which attends to context and themes could do well on the dataset."
+    ],
+    [
+      "We computed bag-of-words-based benchmarks using the following methods:",
+      "Classification with TF-IDF + Linear SVM (TF-IDF + SVM)",
+      "Classification with Depeche++ Emotion lexicons BIBREF12 + Linear SVM (Depeche + SVM)",
+      "Classification with NRC Emotion lexicons BIBREF13, BIBREF14 + Linear SVM (NRC + SVM)",
+      "Combination of TF-IDF and NRC Emotion lexicons (TF-NRC + SVM)"
+    ],
+    [
+      "We also used simple classification models with learned embeddings. We trained a Doc2Vec model BIBREF15 using the dataset and used the embedding document vectors as features for a linear SVM classifier."
+    ],
+    [
+      "For this benchmark, we considered a Hierarchical RNN, following BIBREF16. We used two BiLSTMs BIBREF17 with 256 units each to model sentences and documents. The tokens of a sentence were processed independently of other sentence tokens. For each direction in the token-level BiLSTM, the last outputs were concatenated and fed into the sentence-level BiLSTM as inputs.",
+      "The outputs of the BiLSTM were connected to 2 dense layers with 256 ReLU units and a Softmax layer. We initialized tokens with publicly available embeddings trained with GloVe BIBREF18. Sentence boundaries were provided by SpaCy. Dropout was applied to the dense hidden layers during training."
+    ],
+    [
+      "One challenge with RNN-based solutions for text classification is finding the best way to combine word-level representations into higher-level representations.",
+      "Self-attention BIBREF19, BIBREF20, BIBREF21 has been adapted to text classification, providing improved interpretability and performance. We used BIBREF20 as the basis of this benchmark.",
+      "The benchmark used a layered Bi-directional RNN (60 units) with GRU cells and a dense layer. Both self-attention layers were 60 units in size and cross-entropy was used as the cost function.",
+      "Note that we have omitted the orthogonal regularizer term, since this dataset is relatively small compared to the traditional datasets used for training such a model. We did not observe any significant performance gain while using the regularizer term in our experiments."
+    ],
+    [
+      "Deep Contextualized Word Representations (ELMo) BIBREF22 have shown recent success in a number of NLP tasks. The unsupervised nature of the language model allows it to utilize a large amount of available unlabelled data in order to learn better representations of words.",
+      "We used the pre-trained ELMo model (v2) available on Tensorhub for this benchmark. We fed the word embeddings of ELMo as input into a one layer Bi-directional RNN (16 units) with GRU cells (with dropout) and a dense layer. Cross-entropy was used as the cost function."
+    ],
+    [
+      "Bidirectional Encoder Representations from Transformers (BERT) BIBREF11 has achieved state-of-the-art results on several NLP tasks, including sentence classification.",
+      "We used the fine-tuning procedure outlined in the original work to adapt the pre-trained uncased BERT$_\\textrm {{\\scriptsize LARGE}}$ to a multi-class passage classification task. This technique achieved the best result among our benchmarks, with an average micro-F1 score of 60.4%."
+    ],
+    [
+      "We introduce DENS, a dataset for multi-class emotion analysis from long-form narratives in English. We provide a number of benchmark results based on models ranging from bag-of-word models to methods based on pre-trained language models (ELMo and BERT).",
+      "Our benchmark results demonstrate that this dataset provides a novel challenge in emotion analysis. The results also demonstrate that attention-based models could significantly improve performance on classification tasks such as emotion analysis.",
+      "Interesting future directions for this work include: 1. incorporating common-sense knowledge into emotion analysis to capture semantic context and 2. using few-shot learning to bootstrap and improve performance of underrepresented emotions.",
+      "Finally, as narrative passages often involve interactions between multiple emotions, one avenue for future datasets could be to focus on the multi-emotion complexities of human language and their contextual interactions."
+    ],
+    [
+      "Table TABREF26 shows sample passages from classic titles with corresponding labels."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0316/instruction.md b/qasper-0316/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..eae641ec84231bb06b350c8688943ef430b6311b
--- /dev/null
+++ b/qasper-0316/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Filling Gender&Number Gaps in Neural Machine Translation with Black-box Context Injection
+
+Question: How is it demonstrated that the correct gender and number information is injected using this system?
\ No newline at end of file
diff --git a/qasper-0317/instruction.md b/qasper-0317/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..7ece18be33e747cfaab12a1cf052d29e0f4d784c
--- /dev/null
+++ b/qasper-0317/instruction.md
@@ -0,0 +1,64 @@
+Name of Paper: Filling Gender&Number Gaps in Neural Machine Translation with Black-box Context Injection
+
+Question: Which neural machine translation system is used?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Morphological Ambiguity in Translation",
+    "Black-Box Knowledge Injection",
+    "Experiments & Results",
+    "Quantitative Results",
+    "Qualitative Results",
+    "Comparison to vanmassenhove-hardmeier-way:2018:EMNLP",
+    "Other Languages",
+    "Related Work",
+    "Conclusions"
+  ],
+  "paragraphs": [
+    [
+      "A common way for marking information about gender, number, and case in language is morphology, or the structure of a given word in the language. However, different languages mark such information in different ways \u2013 for example, in some languages gender may be marked on the head word of a syntactic dependency relation, while in other languages it is marked on the dependent, on both, or on none of them BIBREF0 . This morphological diversity creates a challenge for machine translation, as there are ambiguous cases where more than one correct translation exists for the same source sentence. For example, while the English sentence \u201cI love language\u201d is ambiguous with respect to the gender of the speaker, Hebrew marks verbs for the gender of their subject and does not allow gender-neutral translation. This allows two possible Hebrew translations \u2013 one in a masculine and the other in a feminine form. As a consequence, a sentence-level translator (either human or machine) must commit to the gender of the speaker, adding information that is not present in the source. Without additional context, this choice must be done arbitrarily by relying on language conventions, world knowledge or statistical (stereotypical) knowledge.",
+      "Indeed, the English sentence \u201cI work as a doctor\u201d is translated into Hebrew by Google Translate using the masculine verb form oved, indicating a male speaker, while \u201cI work as a nurse\u201d is translated with the feminine form ovedet, indicating a female speaker (verified on March 2019). While this is still an issue, there have been recent efforts to reduce it for specific language pairs.",
+      "We present a simple black-box method to influence the interpretation chosen by an NMT system in these ambiguous cases. More concretely, we construct pre-defined textual hints about the gender and number of the speaker and the audience (the interlocutors), which we concatenate to a given input sentence that we would like to translate accordingly. We then show that a black-box NMT system makes the desired morphological decisions according to the given hint, even when no other evidence is available on the source side. While adding those hints results in additional text on the target side, we show that it is simple to remove, leaving only the desired translation.",
+      "Our method is appealing as it only requires simple pre-and-post processing of the inputs and outputs, without considering the system internals, or requiring specific annotated data and training procedure as in previous work BIBREF1 . We show that in spite of its simplicity, it is effective in resolving many of the ambiguities and improves the translation quality in up to 2.3 BLEU when given the correct hints, which may be inferred from text metadata or other sources. Finally, we perform a fine-grained syntactic analysis of the translations generated using our method which shows its effectiveness."
+    ],
+    [
+      "Different languages use different morphological features marking different properties on different elements. For example, English marks for number, case, aspect, tense, person, and degree of comparison. However, English does not mark gender on nouns and verbs. Even when a certain property is marked, languages differ in the form and location of the marking BIBREF0 . For example, marking can occur on the head of a syntactic dependency construction, on its argument, on both (requiring agreement), or on none of them. Translation systems must generate correct target-language morphology as part of the translation process. This requires knowledge of both the source-side and target-side morphology. Current state-of-the-art translation systems do capture many aspects of natural language, including morphology, when a relevant context is available BIBREF2 , BIBREF3 , but resort to \u201cguessing\u201d based on the training-data statistics when it is not. Complications arise when different languages convey different kinds of information in their morphological systems. In such cases, a translation system may be required to remove information available in the source sentence, or to add information not available in it, where the latter can be especially tricky."
+    ],
+    [
+      "Our goal is to supply an NMT system with knowledge regarding the speaker and interlocutor of first-person sentences, in order to produce the desired target-side morphology when the information is not available in the source sentence. The approach we take in the current work is that of black-box injection, in which we attempt to inject knowledge to the input in order to influence the output of a trained NMT system, without having access to its internals or its training procedure as proposed by vanmassenhove-hardmeier-way:2018:EMNLP.",
+      "We are motivated by recent work by BIBREF4 who showed that NMT systems learn to track coreference chains when presented with sufficient discourse context. We conjecture that there are enough sentence-internal pronominal coreference chains appearing in the training data of large-scale NMT systems, such that state-of-the-art NMT systems can and do track sentence-internal coreference. We devise a wrapper method to make use of this coreference tracking ability by introducing artificial antecedents that unambiguously convey the desired gender and number properties of the speaker and audience.",
+      "More concretely, a sentence such as \u201cI love you\u201d is ambiguous with respect to the gender of the speaker and the gender and number of the audience. However, sentences such as \u201cI love you, she told him\u201d are unambiguous given the coreference groups {I, she} and {you, him} which determine I to be feminine singular and you to be masculine singular. We can thus inject the desired information by prefixing a sentence with short generic sentence fragment such as \u201cShe told him:\u201d or \u201cShe told them that\u201d, relying on the NMT system's coreference tracking abilities to trigger the correctly marked translation, and then remove the redundant translated prefix from the generated target sentence. We observed that using a parataxis construction (i.e. \u201cshe said to him:\u201d) almost exclusively results in target-side parataxis as well (in 99.8% of our examples), making it easy to identify and strip the translated version from the target side. Moreover, because the parataxis construction is grammatically isolated from the rest of the sentence, it can be stripped without requiring additional changes or modification to the rest of the sentence, ensuring grammaticality."
+    ],
+    [
+      "To demonstrate our method in a black-box setting, we focus our experiments on Google's machine translation system (GMT), accessed through its Cloud API. To test the method on real-world sentences, we consider a monologue from the stand-up comedy show \u201cSarah Silverman: A Speck of Dust\u201d. The monologue consists of 1,244 English sentences, all by a female speaker conveyed to a plural, gender-neutral audience. Our parallel corpora consists of the 1,244 English sentences from the transcript, and their corresponding Hebrew translations based on the Hebrew subtitles. We translate the monologue one sentence at a time through the Google Cloud API. Eyeballing the results suggest that most of the translations use the incorrect, but default, masculine and singular forms for the speaker and the audience, respectively. We expect that by adding the relevant condition of \u201cfemale speaking to an audience\u201d we will get better translations, affecting both the gender of the speaker and the number of the audience.",
+      "To verify this, we experiment with translating the sentences with the following variations: No Prefix\u2014The baseline translation as returned by the GMT system. \u201cHe said:\u201d\u2014Signaling a male speaker. We expect to further skew the system towards masculine forms. \u201cShe said:\u201d\u2014Signaling a female speaker and unknown audience. As this matches the actual speaker's gender, we expect an improvement in translation of first-person pronouns and verbs with first-person pronouns as subjects. \u201cI said to them:\u201d\u2014Signaling an unknown speaker and plural audience. \u201cHe said to them:\u201d\u2014Masculine speaker and plural audience. \u201cShe said to them:\u201d\u2014Female speaker and plural audience\u2014the complete, correct condition. We expect the best translation accuracy on this setup. \u201cHe/she said to him/her\u201d\u2014Here we set an (incorrect) singular gender-marked audience, to investigate our ability to control the audience morphology."
+    ],
+    [
+      "We compare the different conditions by comparing BLEU BIBREF5 with respect to the reference Hebrew translations. We use the multi-bleu.perl script from the Moses toolkit BIBREF6 . Table shows BLEU scores for the different prefixes. The numbers match our expectations: Generally, providing an incorrect speaker and/or audience information decreases the BLEU scores, while providing the correct information substantially improves it - we see an increase of up to 2.3 BLEU over the baseline. We note the BLEU score improves in all cases, even when given the wrong gender of either the speaker or the audience. We hypothesise this improvement stems from the addition of the word \u201csaid\u201d which hints the model to generate a more \u201cspoken\u201d language which matches the tested scenario. Providing correct information for both speaker and audience usually helps more than providing correct information to either one of them individually. The one outlier is providing \u201cShe\u201d for the speaker and \u201cher\u201d for the audience. While this is not the correct scenario, we hypothesise it gives an improvement in BLEU as it further reinforces the female gender in the sentence."
+    ],
+    [
+      "The BLEU score is an indication of how close the automated translation is to the reference translation, but does not tell us what exactly changed concerning the gender and number properties we attempt to control. We perform a finer-grained analysis focusing on the relation between the injected speaker and audience information, and the morphological realizations of the corresponding elements. We parse the translations and the references using a Hebrew dependency parser. In addition to the parse structure, the parser also performs morphological analysis and tagging of the individual tokens. We then perform the following analysis.",
+      "Speaker's Gender Effects: We search for first-person singular pronouns with subject case (ani, unmarked for gender, corresponding to the English I), and consider the gender of its governing verb (or adjectives in copular constructions such as `I am nice'). The possible genders are `masculine', `feminine' and `both', where the latter indicates a case where the none-diacriticized written form admits both a masculine and a feminine reading. We expect the gender to match the ones requested in the prefix.",
+      "Interlocutors' Gender and Number Effects: We search for second-person pronouns and consider their gender and number. For pronouns in subject position, we also consider the gender and number of their governing verbs (or adjectives in copular constructions). For a singular audience, we expect the gender and number to match the requested ones. For a plural audience, we expect the masculine-plural forms.",
+      "Results: Speaker. Figure FIGREF3 shows the result for controlling the morphological properties of the speaker ({he, she, I} said). It shows the proportion of gender-inflected verbs for the various conditions and the reference. We see that the baseline system severely under-predicts the feminine form of verbs as compared to the reference. The \u201cHe said\u201d conditions further decreases the number of feminine verbs, while the \u201cI said\u201d conditions bring it back to the baseline level. Finally, the \u201cShe said\u201d prefixes substantially increase the number of feminine-marked verbs, bringing the proportion much closer to that of the reference (though still under-predicting some of the feminine cases).",
+      "Results: Audience. The chart in Figure FIGREF3 shows the results for controlling the number of the audience (...to them vs nothing). It shows the proportion of singular vs. plural second-person pronouns on the various conditions. It shows a similar trend: the baseline system severely under-predicts the plural forms with respect to the reference translation, while adding the \u201cto them\u201d condition brings the proportion much closer to that of the reference."
+    ],
+    [
+      "Closely related to our work, vanmassenhove-hardmeier-way:2018:EMNLP proposed a method and an English-French test set to evaluate gender-aware translation, based on the Europarl corpus BIBREF7 . We evaluate our method (using Google Translate and the given prefixes) on their test set to see whether it is applicable to another language pair and domain. Table shows the results of our approach vs. their published results and the Google Translate baseline. As may be expected, Google Translate outperforms their system as it is trained on a different corpus and may use more complex machine translation models. Using our method improves the BLEU score even further."
+    ],
+    [
+      "To test our method\u2019s outputs on multiple languages, we run our pre-and post-processing steps with Google Translate using examples we sourced from native speakers of different languages. For every example we have an English sentence and two translations in the corresponding language, one in masculine and one in feminine form. Not all examples are using the same source English sentence as different languages mark different information. Table shows that for these specific examples our method worked on INLINEFORM0 of the languages we had examples for, while for INLINEFORM1 languages both translations are masculine, and for 1 language both are feminine."
+    ],
+    [
+      "E17-1101 showed that given input with author traits like gender, it is possible to retain those traits in Statistical Machine Translation (SMT) models. W17-4727 showed that incorporating morphological analysis in the decoder improves NMT performance for morphologically rich languages. burlot:hal-01618387 presented a new protocol for evaluating the morphological competence of MT systems, indicating that current translation systems only manage to capture some morphological phenomena correctly. Regarding the application of constraints in NMT, N16-1005 presented a method for controlling the politeness level in the generated output. DBLP:journals/corr/FiclerG17aa showed how to guide a neural text generation system towards style and content parameters like the level of professionalism, subjective/objective, sentiment and others. W17-4811 showed that incorporating more context when translating subtitles can improve the coherence of the generated translations. Most closely to our work, vanmassenhove-hardmeier-way:2018:EMNLP also addressed the missing gender information by training proprietary models with a gender-indicating-prefix. We differ from this work by treating the problem in a black-box manner, and by addressing additional information like the number of the speaker and the gender and number of the audience."
+    ],
+    [
+      "We highlight the problem of translating between languages with different morphological systems, in which the target translation must contain gender and number information that is not available in the source. We propose a method for injecting such information into a pre-trained NMT model in a black-box setting. We demonstrate the effectiveness of this method by showing an improvement of 2.3 BLEU in an English-to-Hebrew translation setting where the speaker and audience gender can be inferred. We also perform a fine-grained syntactic analysis that shows how our method enables to control the morphological realization of first and second-person pronouns, together with verbs and adjectives related to them. In future work we would like to explore automatic generation of the injected context, or the use of cross-sentence context to infer the injected information."
+    ]
+  ]
+}
+```
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diff --git a/qasper-0318/instruction.md b/qasper-0318/instruction.md
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index 0000000000000000000000000000000000000000..19fda9bd52652ebfd3c387570db8ba03a671b0d6
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+++ b/qasper-0318/instruction.md
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+Name of Paper: Filling Gender&Number Gaps in Neural Machine Translation with Black-box Context Injection
+
+Question: What are the components of the black-box context injection system?
\ No newline at end of file
diff --git a/qasper-0319/instruction.md b/qasper-0319/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..208cba5d979a298ee7f9704cc09841715e46e139
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+Name of Paper: Exploring End-to-End Techniques for Low-Resource Speech Recognition
+
+Question: What normalization techniques are mentioned?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related work",
+    "Basic setup",
+    "Experiments with architecture",
+    "Loss modification: segmenting during training",
+    "Using different features",
+    "Varying model size and number of layers",
+    "Training the best model",
+    "Conclusions and future work",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "Although development of the first speech recognition systems began half a century ago, there has been a significant increase of the accuracy of ASR systems and number of their applications for the recent ten years, even for low-resource languages BIBREF0 , BIBREF1 .",
+      "This is mainly due to widespread applying of deep learning and very effective performance of neural networks in hybrid recognition systems (DNN-HMM). However, for last few years there has been a trend to change traditional ASR training paradigm. End-to-end training systems gradually displace complex multistage learning process (including training of GMMs BIBREF2 , clustering of allophones\u2019 states, aligning of speech to clustered senones, training neural networks with cross-entropy loss, followed by retraining with sequence-discriminative criterion). The new approach implies training the system in one global step, working only with acoustic data and reference texts, and significantly simplifies or even completely excludes in some cases the decoding process. It also avoids the problem of out-of-vocabulary words (OOV), because end-to-end system, trained with parts of the words as targets, can construct new words itself using graphemes or subword units, while traditional DNN-HMM systems are limited with language model vocabulary.",
+      "The whole variety of end-to-end systems can be divided into 3 main categories: Connectionist Temporal Classification (CTC) BIBREF3 ; Sequence-to-sequence models with attention mechanism BIBREF4 ; RNN-Transducers BIBREF5 .",
+      "Connectionist Temporal Classification (CTC) approach uses loss functions that utilize all possible alignments between reference text and audio data. Targets for CTC-based system can be phonemes, graphemes, syllables and other subword units and even whole words. However, a lot more data is usually required to train such systems well, compared to traditional hybrid systems.",
+      "Sequence-to-sequence models are used to map entire input sequences to output sequences without any assumptions about their alignment. The most popular architecture for sequence-to-sequence models is encoder-decoder model with attention. Encoder and decoder are usually constructed using recurrent neural networks, basic attention mechanism calculates energy weights that emphasize importance of encoder vectors for decoding on this step, and then sums all these vectors with energy weights. Encoder-decoder models with attention mechanism show results close to traditional DNN-HMM systems and in some cases surpass them, but for a number of reasons their usage is still rather limited. First of all, this is related to the fact, that such systems show best results when the duration of real utterances is close to the duration of utterances from training data. However, when the duration difference increases, the performance degrades significantly BIBREF4 .",
+      "Moreover, the entire utterance must be preprocessed by encoder before start of decoder's work. This is the reason, why it is hard to apply the approach to recognize long recordings or streaming audio. Segmenting long recordings into shorter utterances solves the duration issue, but leads to a context break, and eventually negatively affects recognition accuracy. Secondly, the computational complexity of encoder-decoder models is high because of recurrent networks usage, so these models are rather slow and hard to parallelize.",
+      "The idea of RNN-Transducer is an extension of CTC and provides the ability to model inner dependencies separately and jointly between elements of both input (audio frames) and output (phonemes and other subword units) sequences. Despite of mathematical elegance, such systems are very complicated and hard to implement, so they are still rarely used, although several impressive results were obtained using this technique.",
+      "CTC-based approach is easier to implement, better scaled and has many \u201cdegrees of freedom\u201d, which allows to significantly improve baseline systems and achieve results close to state-of-the-art. Moreover, CTC-based systems are well compatible with traditional WFST-decoders and can be easily integrated with conventional ASR systems.",
+      "Besides, as already mentioned, CTC-systems are rather sensitive to the amount of training data, so it is very relevant to study how to build effective CTC-based recognition system using a small amount of training samples. It is especially actual for low-resource languages, where we have only a few dozen hours of speech. Building ASR system for low-resource languages is one of the aims of international Babel program, funded by the Intelligence Advanced Research Projects Activity (IARPA). Within the program extensive research was carried out, resulting in creation of a number of modern ASR systems for low-resource languages. Recently, end-to-end approaches were applied to this task, showing expectedly worse results than traditional systems, although the difference is rather small.",
+      "In this paper we explore a number of ways to improve end-to-end CTC-based systems in low-resource scenarios using the Turkish language dataset from the IARPA Babel collection. In the next section we describe in more details different versions of CTC-systems and their application for low-resource speech recognition. Section 3 describes the experiments and their results. Section 4 summarizes the results and discusses possible ways for further work."
+    ],
+    [
+      "Development of CTC-based systems originates from the paper BIBREF3 where CTC loss was introduced. This loss is a total probability of labels sequence given observation sequence, which takes into account all possible alignments induced by a given words sequence.",
+      "Although a number of possible alignments increases exponentially with sequences\u2019 lengths, there is an efficient algorithm to compute CTC loss based on dynamic programming principle (known as Forward-Backward algorithm). This algorithm operates with posterior probabilities of any output sequence element observation given the time frame and CTC loss is differentiable with respect to these probabilities.",
+      "Therefore, if an acoustic model is based on the neural network which estimates these posteriors, its training may be performed with a conventional error back-propagation gradient descent BIBREF6 . Training of ASR system based on such a model does not require an explicit alignment of input utterance to the elements of output sequence and thus may be performed in end-to-end fashion. It is also important that CTC loss accumulates the information about the whole output sequence, and hence its optimization is in some sense an alternative to the traditional fine-tuning of neural network acoustic models by means of sequence-discriminative criteria such as sMBR BIBREF7 etc. The implementation of CTC is conventionally based on RNN/LSTM networks, including bidirectional ones as acoustic models, since they are known to model long context effectively.",
+      "The important component of CTC is a special \u201cblank\u201d symbol which fills in gaps between meaningful elements of output sequence to equalize its length to the number of frames in the input sequence. It corresponds to a separate output neuron, and blank symbols are deleted from the recognized sequence to obtain the final result. In BIBREF8 a modification of CTC loss was proposed, referred as Auto SeGmentation criterion (ASG loss), which does not use blank symbols. Instead of using \u201cblank\u201d, a simple transition probability model for an output symbols is introduced. This leads to a significant simplification and speedup of computations. Moreover, the improved recognition results compared to basic CTC loss were obtained.",
+      "DeepSpeech BIBREF9 developed by Baidu Inc. was one of the first systems that demonstrated an effectiveness of CTC-based speech recognition in LVCSR tasks. Being trained on 2300 hours of English Conversational Telephone Speech data, it demonstrated state-of-the-art results on Hub5'00 evaluation set. Research in this direction continued and resulted in DeepSpeech2 architecture BIBREF10 , composed of both convolutional and recurrent layers. This system demonstrates improved accuracy of recognition of both English and Mandarin speech. Another successful example of applying CTC to LVCSR tasks is EESEN system BIBREF11 . It integrates an RNN-based model trained with CTC criterion to the conventional WFST-based decoder from the Kaldi toolkit BIBREF12 . The paper BIBREF13 shows that end-to-end systems may be successfully built from convolutional layers only instead of recurrent ones. It was demonstrated that using Gated Convolutional Units (GLU-CNNs) and training with ASG-loss leads to the state-of-the-art results on the LibriSpeech database (960 hours of training data).",
+      "Recently, a new modification of DeepSpeech2 architecture was proposed in BIBREF14 . Several lower convolutional layers were replaced with a deep residual network with depth-wise separable convolutions. This modification along with using strong regularization and data augmentation techniques leads to the results close to DeepSpeech2 in spite of significantly lower amount of data used for training. Indeed, one of the models was trained with only 80 hours of speech data (which were augmented with noisy and speed-perturbed versions of original data).",
+      "These results suggest that CTC can be successfully applied for the training of ASR systems for low-resource languages, in particular, for those included in Babel research program (the amount of training data for them is normally 40 to 80 hours of speech).",
+      "Currently, Babel corpus contains data for more than 20 languages, and for most of them quite good traditional ASR system were built BIBREF15 , BIBREF16 , BIBREF17 . In order to improve speech recognition accuracy for a given language, data from other languages is widely used as well. It can be used to train multilingual system via multitask learning or to obtain high-level multilingual representations, usually bottleneck features, extracted from a pre-trained multilingual network.",
+      "One of the first attempts to build ASR system for low-resource BABEL languages using CTC-based end-to-end training was made recently BIBREF18 . Despite the obtained results are somewhat worse compared to the state-of-the-art traditional systems, they still demonstrate that CTC-based approach is viable for building low-resource ASR systems. The aim of our work is to investigate some ways to improve the obtained results."
+    ],
+    [
+      "For all experiments we used conversational speech from IARPA Babel Turkish Language Pack (LDC2016S10). This corpus contains about 80 hours of transcribed speech for training and 10 hours for development. The dataset is rather small compared to widely used benchmarks for conversational speech: English Switchboard corpus (300 hours, LDC97S62) and Fisher dataset (2000 hours, LDC2004S13 and LDC2005S13).",
+      "As targets we use 32 symbols: 29 lowercase characters of Turkish alphabet BIBREF19 , apostrophe, space and special \u3008blank\u3009 character that means \u201cno output\u201d. Thus we do not use any prior linguistic knowledge and also avoid OOV problem as the system can construct new words directly.",
+      "All models are trained with CTC-loss. Input features are 40 mel-scaled log filterbank enegries (FBanks) computed every 10 ms with 25 ms window, concatenated with deltas and delta-deltas (120 features in vector). We also tried to use spectrogram and experimented with different normalization techniques.",
+      "For decoding we used character-based beam search BIBREF20 with 3-gram language model build with SRILM package BIBREF21 finding sequence of characters INLINEFORM0 that maximizes the following objective BIBREF9 : INLINEFORM1 ",
+      "where INLINEFORM0 is language model weight and INLINEFORM1 is word insertion penalty.",
+      "For all experiments we used INLINEFORM0 , INLINEFORM1 , and performed decoding with beam width equal to 100 and 2000, which is not very large compared to 7000 and more active hypotheses used in traditional WFST decoders (e.g. many Kaldi recipes do decoding with INLINEFORM2 ).",
+      "To compare with other published results BIBREF18 , BIBREF22 we used Sclite BIBREF23 scoring package to measure results of decoding with beam width 2000, that takes into account incomplete words and spoken noise in reference texts and doesn't penalize model if it incorrectly recognize these pieces.",
+      "Also we report WER (word error rate) for simple argmax decoder (taking labels with maximum output on each time step and than applying CTC decoding rule \u2013 collapse repeated labels and remove \u201cblanks\u201d)."
+    ],
+    [
+      "We tried to explore the behavior of different neural network architectures in case when rather small data is available. We used multi-layer bidirectional LSTM networks, tried fully-convolutional architecture similar to Wav2Letter BIBREF8 and explored DeepSpeech-like architecture developed by Salesforce (DS-SF) BIBREF14 .",
+      "The convolutional model consists of 11 convolutional layers with batch normalization after each layer. The DeepSpeech-like architecture consists of 5-layers residual network with depth-wise separable convolutions followed by 4-layer bidirectional Gated Recurrent Unit (GRU) as described in BIBREF14 .",
+      "Our baseline bidirectional LSTM is 6-layers network with 320 hidden units per direction as in BIBREF18 . Also we tried to use bLSTM to label every second frame (20 ms) concatenating every first output from first layer with second and taking this as input for second model layer.",
+      "The performance of our baseline models is shown in Table TABREF6 ."
+    ],
+    [
+      "It is known that CTC-loss is very unstable for long utterances BIBREF3 , and smaller utterances are more useful for this task. Some techniques were developed to help model converge faster, e.g. sortagrad BIBREF10 (using shorter segments at the beginning of training).",
+      "To compute CTC-loss we use all possible alignments between audio features and reference text, but only some of the alignments make sense. Traditional DNN-HMM systems also use iterative training with finding best alignment and then training neural network to approximate this alignment. Therefore, we propose the following algorithm to use segmentation during training:",
+      "compute CTC-alignment (find the sequence of targets with minimal loss that can be mapped to real targets by collapsing repeated characters and removing blanks)",
+      "perform greedy decoding (argmax on each step)",
+      "find \u201cwell-recognized\u201d words with INLINEFORM0 ( INLINEFORM1 is a hyperparameter): segment should start and end with space; word is \u201cwell-recognized\u201d when argmax decoding is equal to computed alignment",
+      "if the word is \u201cwell-recognized\u201d, divide the utterance into 5 segments: left segment before space, left space, the word, right space and right segment",
+      "compute CTC-loss for all this segments separately and do back-propagation as usual",
+      "The results of training with this criterion are shown in Table TABREF13 . The proposed criterion doesn't lead to consistent improvement while decoding with large beam width (2000), but shows significant improvement when decoding with smaller beam (100). We plan to further explore utilizing alignment information during training."
+    ],
+    [
+      "We explored different normalization techniques. FBanks with cepstral mean normalization (CMN) perform better than raw FBanks. We found using variance with mean normalization (CMVN) unnecessary for the task. Using deltas and delta-deltas improves model, so we used them in other experiments. Models trained with spectrogram features converge slower and to worse minimum, but the difference when using CMN is not very big compared to FBanks."
+    ],
+    [
+      "Experiments with varying number of hidden units of 6-layer bLSTM models are presented in Table TABREF17 . Models with 512 and 768 hidden units are worse than with 320, but model with 1024 hidden units is significantly better than others. We also observed that model with 6 layers performs better than others."
+    ],
+    [
+      "To train our best model we chose the best network from our experiments (6-layer bLSTM with 1024 hidden units), trained it with Adam optimizer and fine-tuned with SGD with momentum using exponential learning rate decay. The best model trained with speed and volume perturbation BIBREF24 achieved 45.8% WER, which is the best published end-to-end result on Babel Turkish dataset using in-domain data. For comparison, WER of model trained using in-domain data in BIBREF18 is 53.1%, using 4 additional languages (including English Switchboard dataset) \u2013 48.7%. It is also not far from Kaldi DNN-HMM system BIBREF22 with 43.8% WER."
+    ],
+    [
+      "In this paper we explored different end-to-end architectures in low-resource ASR task using Babel Turkish dataset. We considered different ways to improve performance and proposed promising CTC-loss modification that uses segmentation during training. Our final system achieved 45.8% WER using in-domain data only, which is the best published result for Turkish end-to-end systems. Our work also shows than well-tuned end-to-end system can achieve results very close to traditional DNN-HMM systems even for low-resource languages. In future work we plan to further investigate different loss modifications (Gram-CTC, ASG) and try to use RNN-Transducers and multi-task learning."
+    ],
+    [
+      "This work was financially supported by the Ministry of Education and Science of the Russian Federation, Contract 14.575.21.0132 (IDRFMEFI57517X0132)."
+    ]
+  ]
+}
+```
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diff --git a/qasper-0320/instruction.md b/qasper-0320/instruction.md
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+++ b/qasper-0320/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Exploring End-to-End Techniques for Low-Resource Speech Recognition
+
+Question: What features do they experiment with?
\ No newline at end of file
diff --git a/qasper-0321/instruction.md b/qasper-0321/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..b3c3ff4b0f90a1792fc2a20a0f0200231de7964a
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+Name of Paper: Exploring End-to-End Techniques for Low-Resource Speech Recognition
+
+Question: Which architecture is their best model?
\ No newline at end of file
diff --git a/qasper-0326/instruction.md b/qasper-0326/instruction.md
new file mode 100644
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+Name of Paper: Tag-based Multi-Span Extraction in Reading Comprehension
+
+Question: What is the performance of proposed model on entire DROP dataset?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Model",
+    "Model ::: NABERT+",
+    "Model ::: NABERT+ ::: Heads Shared with NABERT+",
+    "Model ::: Multi-Span Head",
+    "Model ::: Objective and Training",
+    "Model ::: Objective and Training ::: Multi-Span Head Training Objective",
+    "Model ::: Objective and Training ::: Multi-Span Head Correct Tag Sequences",
+    "Model ::: Objective and Training ::: Dealing with too Many Correct Tag Sequences",
+    "Model ::: Tag Sequence Prediction with the Multi-Span Head",
+    "Model ::: Tag Sequence Prediction with the Multi-Span Head ::: Viterbi Decoding",
+    "Model ::: Tag Sequence Prediction with the Multi-Span Head ::: Beam Search",
+    "Model ::: Tag Sequence Prediction with the Multi-Span Head ::: Greedy Tagging",
+    "Preprocessing",
+    "Preprocessing ::: Simple Preprocessing ::: Improved Textual Parsing",
+    "Preprocessing ::: Simple Preprocessing ::: Improved Handling of Numbers",
+    "Preprocessing ::: Using NER for Cleaning Up Multi-Span Questions",
+    "Training",
+    "Results and Discussion ::: Performance on DROP's Development Set",
+    "Results and Discussion ::: Performance on DROP's Development Set ::: Comparison to the NABERT+ Baseline",
+    "Results and Discussion ::: Performance on DROP's Development Set ::: Comparison to MTMSN",
+    "Results and Discussion ::: Performance on DROP's Test Set",
+    "Results and Discussion ::: Ablation Studies",
+    "Conclusion",
+    "Future Work ::: A Different Loss for Multi-span Questions",
+    "Future Work ::: Explore Utilization of Non-First Wordpiece Sub-Tokens"
+  ],
+  "paragraphs": [
+    [
+      "The task of reading comprehension, where systems must understand a single passage of text well enough to answer arbitrary questions about it, has seen significant progress in the last few years. With models reaching human performance on the popular SQuAD dataset BIBREF0, and with much of the most popular reading comprehension datasets having been solved BIBREF1, BIBREF2, a new dataset, DROP BIBREF3, was recently published.",
+      "DROP aimed to present questions that require more complex reasoning in order to answer than that of previous datasets, in a hope to push the field towards a more comprehensive analysis of paragraphs of text. In addition to questions whose answers are a single continuous span from the paragraph text (questions of a type already included in SQuAD), DROP introduced additional types of questions. Among these new types were questions that require simple numerical reasoning, i.e questions whose answer is the result of a simple arithmetic expression containing numbers from the passage, and questions whose answers consist of several spans taken from the paragraph or the question itself, what we will denote as \"multi-span questions\".",
+      "Of all the existing models that tried to tackle DROP, only one model BIBREF4 directly targeted multi-span questions in a manner that wasn't just a by-product of the model's overall performance. In this paper, we propose a new method for tackling multi-span questions. Our method takes a different path from that of the aforementioned model. It does not try to generalize the existing approach for tackling single-span questions, but instead attempts to attack this issue with a new, tag-based, approach."
+    ],
+    [
+      "Numerically-aware QANet (NAQANet) BIBREF3 was the model released with DROP. It uses QANET BIBREF5, at the time the best-performing published model on SQuAD 1.1 BIBREF0 (without data augmentation or pretraining), as the encoder. On top of QANET, NAQANet adds four different output layers, which we refer to as \"heads\". Each of these heads is designed to tackle a specific question type from DROP, where these types where identified by DROP's authors post-creation of the dataset. These four heads are (1) Passage span head, designed for producing answers that consist of a single span from the passage. This head deals with the type of questions already introduced in SQuAD. (2) Question span head, for answers that consist of a single span from the question. (3) Arithmetic head, for answers that require adding or subtracting numbers from the passage. (4) Count head, for answers that require counting and sorting entities from the text. In addition, to determine which head should be used to predict an answer, a 4-way categorical variable, as per the number of heads, is trained. We denote this categorical variable as the \"head predictor\".",
+      "Numerically-aware BERT (NABERT+) BIBREF6 introduced two main improvements over NAQANET. The first was to replace the QANET encoder with BERT. This change alone resulted in an absolute improvement of more than eight points in both EM and F1 metrics. The second improvement was to the arithmetic head, consisting of the addition of \"standard numbers\" and \"templates\". Standard numbers were predefined numbers which were added as additional inputs to the arithmetic head, regardless of their occurrence in the passage. Templates were an attempt to enrich the head's arithmetic capabilities, by adding the ability of doing simple multiplications and divisions between up to three numbers.",
+      "MTMSN BIBREF4 is the first, and only model so far, that specifically tried to tackle the multi-span questions of DROP. Their approach consisted of two parts. The first was to train a dedicated categorical variable to predict the number of spans to extract. The second was to generalize the single-span head method of extracting a span, by utilizing the non-maximum suppression (NMS) algorithm BIBREF7 to find the most probable set of non-overlapping spans. The number of spans to extract was determined by the aforementioned categorical variable.",
+      "Additionally, MTMSN introduced two new other, non span-related, components. The first was a new \"negation\" head, meant to deal with questions deemed as requiring logical negation (e.g. \"How many percent were not German?\"). The second was improving the arithmetic head by using beam search to re-rank candidate arithmetic expressions."
+    ],
+    [
+      "Problem statement. Given a pair $(x^P,x^Q)$ of a passage and a question respectively, both comprised of tokens from a vocabulary $V$, we wish to predict an answer $y$. The answer could be either a collection of spans from the input, or a number, supposedly arrived to by performing arithmetic reasoning on the input. We want to estimate $p(y;x^P,x^Q)$.",
+      "The basic structure of our model is shared with NABERT+, which in turn is shared with that of NAQANET (the model initially released with DROP). Consequently, meticulously presenting every part of our model would very likely prove redundant. As a reasonable compromise, we will introduce the shared parts with more brevity, and will go into greater detail when presenting our contributions."
+    ],
+    [
+      "Assume there are $K$ answer heads in the model and their weights denoted by $\\theta $. For each pair $(x^P,x^Q)$ we assume a latent categorical random variable $z\\in \\left\\lbrace 1,\\ldots \\,K\\right\\rbrace $ such that the probability of an answer $y$ is",
+      "where each component of the mixture corresponds to an output head such that",
+      "Note that a head is not always capable of producing the correct answer $y_\\text{gold}$ for each type of question, in which case $p\\left(y_\\text{gold} \\vert z ; x^{P},x^{Q},\\theta \\right)=0$. For example, the arithmetic head, whose output is always a single number, cannot possibly produce a correct answer for a multi-span question.",
+      "For a multi-span question with an answer composed of $l$ spans, denote $y_{{\\text{gold}}_{\\textit {MS}}}=\\left\\lbrace y_{{\\text{gold}}_1}, \\ldots , y_{{\\text{gold}}_l} \\right\\rbrace $. NAQANET and NABERT+ had no head capable of outputting correct answers for multi-span questions. Instead of ignoring them in training, both models settled on using \"semi-correct answers\": each $y_\\text{gold} \\in y_{{\\text{gold}}_{\\textit {MS}}}$ was considered to be a correct answer (only in training). By deliberately encouraging the model to provide partial answers for multi-span questions, they were able to improve the corresponding F1 score. As our model does have a head with the ability to answer multi-span questions correctly, we didn't provide the aforementioned semi-correct answers to any of the other heads. Otherwise, we would have skewed the predictions of the head predictor and effectively mislead the other heads to believe they could predict correct answers for multi-span questions."
+    ],
+    [
+      "Before going over the answer heads, two additional components should be introduced - the summary vectors, and the head predictor.",
+      "Summary vectors. The summary vectors are two fixed-size learned representations of the question and the passage, which serve as an input for some of the heads. To create the summary vectors, first define $\\mathbf {T}$ as BERT's output on a $(x^{P},x^{Q})$ input. Then, let $\\mathbf {T}^{P}$ and $\\mathbf {T}^{Q}$ be subsequences of T that correspond to $x^P$ and $x^Q$ respectively. Finally, let us also define Bdim as the dimension of the tokens in $\\mathbf {T}$ (e.g 768 for BERTbase), and have $\\mathbf {W}^P \\in \\mathbb {R}^\\texttt {Bdim}$ and $\\mathbf {W}^Q \\in \\mathbb {R}^\\texttt {Bdim}$ as learned linear layers. Then, the summary vectors are computed as:",
+      "Head predictor. A learned categorical variable with its number of outcomes equal to the number of answer heads in the model. Used to assign probabilities for using each of the heads in prediction.",
+      "where FFN is a two-layer feed-forward network with RELU activation.",
+      "Passage span. Define $\\textbf {W}^S \\in \\mathbb {R}^\\texttt {Bdim}$ and $\\textbf {W}^E \\in \\mathbb {R}^\\texttt {Bdim}$ as learned vectors. Then the probabilities of the start and end positions of a passage span are computed as",
+      "Question span. The probabilities of the start and end positions of a question span are computed as",
+      "where $\\textbf {e}^{|\\textbf {T}^Q|}\\otimes \\textbf {h}^P$ repeats $\\textbf {h}^P$ for each component of $\\textbf {T}^Q$.",
+      "Count. Counting is treated as a multi-class prediction problem with the numbers 0-9 as possible labels. The label probabilities are computed as",
+      "Arithmetic. As in NAQNET, this head obtains all of the numbers from the passage, and assigns a plus, minus or zero (\"ignore\") for each number. As BERT uses wordpiece tokenization, some numbers are broken up into multiple tokens. Following NABERT+, we chose to represent each number by its first wordpiece. That is, if $\\textbf {N}^i$ is the set of tokens corresponding to the $i^\\text{th}$ number, we define a number representation as $\\textbf {h}_i^N = \\textbf {N}^i_0$.",
+      "The selection of the sign for each number is a multi-class prediction problem with options $\\lbrace 0, +, -\\rbrace $, and the probabilities for the signs are given by",
+      "As for NABERT+'s two additional arithmetic features, we decided on using only the standard numbers, as the benefits from using templates were deemed inconclusive. Note that unlike the single-span heads, which are related to our introduction of a multi-span head, the arithmetic and count heads were not intended to play a significant role in our work. We didn't aim to improve results on these types of questions, perhaps only as a by-product of improving the general reading comprehension ability of our model."
+    ],
+    [
+      "A subset of questions that wasn't directly dealt with by the base models (NAQANET, NABERT+) is questions that have an answer which is composed of multiple non-continuous spans. We suggest a head that will be able to deal with both single-span and multi-span questions.",
+      "To model an answer which is a collection of spans, the multi-span head uses the $\\mathtt {BIO}$ tagging format BIBREF8: $\\mathtt {B}$ is used to mark the beginning of a span, $\\mathtt {I}$ is used to mark the inside of a span and $\\mathtt {O}$ is used to mark tokens not included in a span. In this way, we get a sequence of chunks that can be decoded to a final answer - a collection of spans.",
+      "As words are broken up by the wordpiece tokenization for BERT, we decided on only considering the representation of the first sub-token of the word to tag, following the NER task from BIBREF2.",
+      "For the $i$-th token of an input, the probability to be assigned a $\\text{tag} \\in \\left\\lbrace {\\mathtt {B},\\mathtt {I},\\mathtt {O}} \\right\\rbrace $ is computed as"
+    ],
+    [
+      "To train our model, we try to maximize the log-likelihood of the correct answer $p(y_\\text{gold};x^{P},x^{Q},\\theta )$ as defined in Section SECREF2. If no head is capable of predicting the gold answer, the sample is skipped.",
+      "We enumerate over every answer head $z\\in \\left\\lbrace \\textit {PS}, \\textit {QS}, \\textit {C}, \\textit {A}, \\textit {MS}\\right\\rbrace $ (Passage Span, Question Span, Count, Arithmetic, Multi-Span) to compute each of the objective's addends:",
+      "Note that we are in a weakly supervised setup: the answer type is not given, and neither is the correct arithmetic expression required for deriving some answers. Therefore, it is possible that $y_\\text{gold}$ could be derived by more than one way, even from the same head, with no indication of which is the \"correct\" one.",
+      "We use the weakly supervised training method used in NABERT+ and NAQANET. Based on BIBREF9, for each head we find all the executions that evaluate to the correct answer and maximize their marginal likelihood .",
+      "For a datapoint $\\left(y, x^{P}, x^{Q} \\right)$ let $\\chi ^z$ be the set of all possible ways to get $y$ for answer head $z\\in \\left\\lbrace \\textit {PS}, \\textit {QS}, \\textit {C}, \\textit {A}, \\textit {MS}\\right\\rbrace $. Then, as in NABERT+, we have",
+      "Finally, for the arithmetic head, let $\\mu $ be the set of all the standard numbers and the numbers from the passage, and let $\\mathbf {\\chi }^{\\textit {A}}$ be the set of correct sign assignments to these numbers. Then, we have"
+    ],
+    [
+      "Denote by ${\\chi }^{\\textit {MS}}$ the set of correct tag sequences. If the concatenation of a question and a passage is $m$ tokens long, then denote a correct tag sequence as $\\left(\\text{tag}_1,\\ldots ,\\text{tag}_m\\right)$.",
+      "We approximate the likelihood of a tag sequence by assuming independence between the sequence's positions, and multiplying the likelihoods of all the correct tags in the sequence. Then, we have"
+    ],
+    [
+      "Since a given multi-span answer is a collection of spans, it is required to obtain its matching tag sequences in order to compute the training objective.",
+      "In what we consider to be a correct tag sequence, each answer span will be marked at least once. Due to the weakly supervised setup, we consider all the question/passage spans that match the answer spans as being correct. To illustrate, consider the following simple example. Given the text \"X Y Z Z\" and the correct multi-span answer [\"Y\", \"Z\"], there are three correct tag sequences: $\\mathtt {O\\,B\\,B\\,B}$,$\\quad $ $\\mathtt {O\\,B\\,B\\,O}$,$\\quad $ $\\mathtt {O\\,B\\,O\\,B}$."
+    ],
+    [
+      "The number of correct tag sequences can be expressed by",
+      "where $s$ is the number of spans in the answer and $\\#_i$ is the number of times the $i^\\text{th}$ span appears in the text.",
+      "For questions with a reasonable amount of correct tag sequences, we generate all of them before the training starts. However, there is a small group of questions for which the amount of such sequences is between 10,000 and 100,000,000 - too many to generate and train on. In such cases, inspired by BIBREF9, instead of just using an arbitrary subset of the correct sequences, we use beam search to generate the top-k predictions of the training model, and then filter out the incorrect sequences. Compared to using an arbitrary subset, using these sequences causes the optimization to be done with respect to answers more compatible with the model. If no correct tag sequences were predicted within the top-k, we use the tag sequence that has all of the answer spans marked."
+    ],
+    [
+      "Based on the outputs $\\textbf {p}_{i}^{{\\text{tag}}_{i}}$ we would like to predict the most likely sequence given the $\\mathtt {BIO}$ constraints. Denote $\\textit {validSeqs}$ as the set of all $\\mathtt {BIO}$ sequences of length $m$ that are valid according to the rules specified in Section SECREF5. The $\\mathtt {BIO}$ tag sequence to predict is then",
+      "We considered the following approaches:"
+    ],
+    [
+      "A natural candidate for getting the most likely sequence is Viterbi decoding, BIBREF10 with transition probabilities learned by a $\\mathtt {BIO}$ constrained Conditional Random Field (CRF) BIBREF11. However, further inspection of our sequence's properties reveals that such a computational effort is probably not necessary, as explained in following paragraphs."
+    ],
+    [
+      "Due to our use of $\\mathtt {BIO}$ tags and their constraints, observe that past tag predictions only affect future tag predictions from the last $\\mathtt {B}$ prediction and as long as the best tag to predict is $\\mathtt {I}$. Considering the frequency and length of the correct spans in the question and the passage, effectively there's no effect of past sequence's positions on future ones, other than a very few positions ahead. Together with the fact that at each prediction step there are no more than 3 tags to consider, it means using beam search to get the most likely sequence is very reasonable and even allows near-optimal results with small beam width values."
+    ],
+    [
+      "Notice that greedy tagging does not enforce the $\\mathtt {BIO}$ constraints. However, since the multi-span head's training objective adheres to the $\\mathtt {BIO}$ constraints via being given the correct tag sequences, we can expect that even with greedy tagging the predictions will mostly adhere to these constraints as well. In case there are violations, their amendment is required post-prediction. Albeit faster, greedy tagging resulted in a small performance hit, as seen in Table TABREF26."
+    ],
+    [
+      "We tokenize the passage, question, and all answer texts using the BERT uncased wordpiece tokenizer from huggingface. The tokenization resulting from each $(x^P,x^Q)$ input pair is truncated at 512 tokens so it can be fed to BERT as an input. However, before tokenizing the dataset texts, we perform additional preprocessing as listed below."
+    ],
+    [
+      "The raw dataset included almost a thousand of HTML entities that did not get parsed properly, e.g \"&#160;\" instead of a simple space. In addition, we fixed some quirks that were introduced by the original Wikipedia parsing method. For example, when encountering a reference to an external source that included a specific page from that reference, the original parser ended up introducing a redundant \":<PAGE NUMBER>\" into the parsed text."
+    ],
+    [
+      "Although we previously stated that we aren't focusing on improving arithmetic performance, while analyzing the training process we encountered two arithmetic-related issues that could be resolved rather quickly: a precision issue and a number extraction issue. Regarding precision, we noticed that while either generating expressions for the arithmetic head, or using the arithmetic head to predict a numeric answer, the value resulting from an arithmetic operation would not always yield the exact result due to floating point precision limitations. For example, $5.8 + 6.6 = 12.3999...$ instead of $12.4$. This issue has caused a significant performance hit of about 1.5 points for both F1 and EM and was fixed by simply rounding numbers to 5 decimal places, assuming that no answer requires a greater precision. Regarding number extraction, we noticed that some numeric entities, required in order to produce a correct answer, weren't being extracted from the passage. Examples include ordinals (121st, 189th) and some \"per-\" units (1,580.7/km2, 1050.95/month)."
+    ],
+    [
+      "The training dataset contains multi-span questions with answers that are clearly incorrect, with examples shown in Table TABREF22. In order to mitigate this, we applied an answer-cleaning technique using a pretrained Named Entity Recognition (NER) model BIBREF12 in the following manner: (1) Pre-define question prefixes whose answer spans are expected to contain only a specific entity type and filter the matching questions. (2) For a given answer of a filtered question, remove any span that does not contain at least one token of the expected type, where the types are determined by applying the NER model on the passage. For example, if a question starts with \"who scored\", we expect that any valid span will include a person entity ($\\mathtt {PER}$). By applying such rules, we discovered that at least 3% of the multi-span questions in the training dataset included incorrect spans. As our analysis of prefixes wasn't exhaustive, we believe that this method could yield further gains. Table TABREF22 shows a few of our cleaning method results, where we perfectly clean the first two questions, and partially clean a third question."
+    ],
+    [
+      "The starting point for our implementation was the NABERT+ model, which in turn was based on allenai's NAQANET. Our implementation can be found on GitHub. All three models utilize the allennlp framework. The pretrained BERT models were supplied by huggingface. For our base model we used bert-base-uncased. For our large models we used the standard bert-large-uncased-whole-word-masking and the squad fine-tuned bert-large-uncased- whole-word-masking-finetuned-squad.",
+      "Due to limited computational resources, we did not perform any hyperparameter searching. We preferred to focus our efforts on the ablation studies, in hope to gain further insights on the effect of the components that we ourselves introduced. For ease of performance comparison, we followed NABERT+'s training settings: we used the BERT Adam optimizer from huggingface with default settings and a learning rate of $1e^{-5}$. The only difference was that we used a batch size of 12. We trained our base model for 20 epochs. For the large models we used a batch size of 3 with a learning rate of $5e^{-6}$ and trained for 5 epochs, except for the model without the single-span heads that was trained with a batch size of 2 for 7 epochs. F1 was used as our validation metric. All models were trained on a single GPU with 12-16GB of memory."
+    ],
+    [
+      "Table TABREF24 shows the results on DROP's development set. Compared to our base models, our large models exhibit a substantial improvement across all metrics."
+    ],
+    [
+      "We can see that our base model surpasses the NABERT+ baseline in every metric. The major improvement in multi-span performance was expected, as our multi-span head was introduced specifically to tackle this type of questions. For the other types, most of the improvement came from better preprocessing. A more detailed discussion could be found in Section (SECREF36)."
+    ],
+    [
+      "Notice that different BERTlarge models were used, so the comparison is less direct. Overall, our large models exhibits similar results to those of MTMSNlarge.",
+      "For multi-span questions we achieve a significantly better performance. While a breakdown of metrics was only available for MTMSNlarge, notice that even when comparing these metrics to our base model, we still achieve a 12.2 absolute improvement in EM, and a 2.3 improvement in F1. All that, while keeping in mind we compare a base model to a large model (for reference, note the 8 point improvement between MTMSNbase and MTMSNlarge in both EM and F1). Our best model, large-squad, exhibits a huge improvement of 29.7 in EM and 15.1 in F1 compared to MTMSNlarge.",
+      "When comparing single-span performance, our best model exhibits slightly better results, but it should be noted that it retains the single-span heads from NABERT+, while in MTMSN they have one head to predict both single-span and multi-span answers. For a fairer comparison, we trained our model with the single-span heads removed, where our multi-span head remained the only head aimed for handling span questions. With this no-single-span-heads setting, while our multi-span performance even improved a bit, our single-span performance suffered a slight drop, ending up trailing by 0.8 in EM and 0.6 in F1 compared to MTMSN. Therefore, it could prove beneficial to try and analyze the reasons behind each model's (ours and MTMSN) relative advantages, and perhaps try to combine them into a more holistic approach of tackling span questions."
+    ],
+    [
+      "Table TABREF25 shows the results on DROP's test set, with our model being the best overall as of the time of writing, and not just on multi-span questions."
+    ],
+    [
+      "In order to analyze the effect of each of our changes, we conduct ablation studies on the development set, depicted in Table TABREF26.",
+      "Not using the simple preprocessing from Section SECREF17 resulted in a 2.5 point decrease in both EM and F1. The numeric questions were the most affected, with their performance dropping by 3.5 points. Given that number questions make up about 61% of the dataset, we can deduce that our improved number handling is responsible for about a 2.1 point gain, while the rest could be be attributed to the improved Wikipedia parsing.",
+      "Although NER span cleaning (Section SECREF23) affected only 3% of the multi-span questions, it provided a solid improvement of 5.4 EM in multi-span questions and 1.5 EM in single-span questions. The single-span improvement is probably due to the combination of better multi-span head learning as a result of fixing multi-span questions and the fact that the multi-span head can answer single-span questions as well.",
+      "Not using the single-span heads results in a slight drop in multi-span performance, and a noticeable drop in single-span performance. However when performing the same comparison between our large models (see Table TABREF24), this performance gap becomes significantly smaller.",
+      "As expected, not using the multi-span head causes the multi-span performance to plummet. Note that for this ablation test the single-span heads were permitted to train on multi-span questions.",
+      "Compared to using greedy decoding in the prediction of multi-span questions, using beam search results in a small improvement. We used a beam with of 5, and didn't perform extensive tuning of the beam width."
+    ],
+    [
+      "In this work, we introduced a new approach for tackling multi-span questions in reading comprehension datasets. This approach is based on individually tagging each token with a categorical tag, relying on the tokens' contextual representation to bridge the information gap resulting from the tokens being tagged individually.",
+      "First, we show that integrating this new approach into an existing model, NABERT+, does not hinder performance on other questions types, while substantially improving the results on multi-span questions. Later, we compare our results to the current state-of-the-art on multi-span questions. We show that our model has a clear advantage in handling multi-span questions, with a 29.7 absolute improvement in EM, and a 15.1 absolute improvement in F1. Furthermore, we show that our model slightly eclipses the current state-of-the-art results on the entire DROP dataeset. Finally, we present some ablation studies, analyzing the benefit gained from individual components of our model.",
+      "We believe that combining our tag-based approach for handling multi-span questions with current successful techniques for handling single-span questions could prove beneficial in finding better, more holistic ways, of tackling span questions in general."
+    ],
+    [
+      "Currently, For each individual span, we optimize the average likelihood over all its possible tag sequences (see Section SECREF9). A different approach could be not taking each possible tag sequence into account but only the most likely one. This could provide the model more flexibility during training and the ability to focus on the more \"correct\" tag sequences."
+    ],
+    [
+      "As mentioned in Section SECREF5, we only considered the representation of the first wordpiece sub-token in our model. It would be interesting to see how different approaches to utilize the other sub-tokens' representations in the tagging task affect the results."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0327/instruction.md b/qasper-0327/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..199acfacb74ead279a59578c04873a7a19d4db19
--- /dev/null
+++ b/qasper-0327/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Tag-based Multi-Span Extraction in Reading Comprehension
+
+Question: What is the previous model that attempted to tackle multi-span questions as a part of its design?
\ No newline at end of file
diff --git a/qasper-0328/instruction.md b/qasper-0328/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..e9d246dddbdb43ad4993404f71ae73dde2f18520
--- /dev/null
+++ b/qasper-0328/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Transfer Learning Between Related Tasks Using Expected Label Proportions
+
+Question: How much more data does the model trained using XR loss have access to, compared to the fully supervised model?
\ No newline at end of file
diff --git a/qasper-0329/instruction.md b/qasper-0329/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..46941f72417b1e897f3a8eedd463573451c8ab33
--- /dev/null
+++ b/qasper-0329/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Transfer Learning Between Related Tasks Using Expected Label Proportions
+
+Question: Does the system trained only using XR loss outperform the fully supervised neural system?
\ No newline at end of file
diff --git a/qasper-0336/instruction.md b/qasper-0336/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..ebee60a29d8e0ce79ba4748ce00c4aa4e433269b
--- /dev/null
+++ b/qasper-0336/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Interactive Machine Comprehension with Information Seeking Agents
+
+Question: What are the models evaluated on?
\ No newline at end of file
diff --git a/qasper-0342/instruction.md b/qasper-0342/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..34ffceb447daf7d73bf7536e44bef5054256eebc
--- /dev/null
+++ b/qasper-0342/instruction.md
@@ -0,0 +1,106 @@
+Name of Paper: Exploring Hate Speech Detection in Multimodal Publications
+
+Question: What is the results of multimodal compared to unimodal models?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work ::: Hate Speech Detection",
+    "Related Work ::: Visual and Textual Data Fusion",
+    "The MMHS150K dataset",
+    "The MMHS150K dataset ::: Tweets Gathering",
+    "The MMHS150K dataset ::: Textual Image Filtering",
+    "The MMHS150K dataset ::: Annotation",
+    "Methodology ::: Unimodal Treatment ::: Images.",
+    "Methodology ::: Unimodal Treatment ::: Tweet Text.",
+    "Methodology ::: Unimodal Treatment ::: Image Text.",
+    "Methodology ::: Multimodal Architectures",
+    "Methodology ::: Multimodal Architectures ::: Feature Concatenation Model (FCM)",
+    "Methodology ::: Multimodal Architectures ::: Spatial Concatenation Model (SCM)",
+    "Methodology ::: Multimodal Architectures ::: Textual Kernels Model (TKM)",
+    "Methodology ::: Multimodal Architectures ::: Training",
+    "Results",
+    "Conclusions"
+  ],
+  "paragraphs": [
+    [
+      "Social Media platforms such as Facebook, Twitter or Reddit have empowered individuals' voices and facilitated freedom of expression. However they have also been a breeding ground for hate speech and other types of online harassment. Hate speech is defined in legal literature as speech (or any form of expression) that expresses (or seeks to promote, or has the capacity to increase) hatred against a person or a group of people because of a characteristic they share, or a group to which they belong BIBREF0. Twitter develops this definition in its hateful conduct policy as violence against or directly attack or threaten other people on the basis of race, ethnicity, national origin, sexual orientation, gender, gender identity, religious affiliation, age, disability, or serious disease.",
+      "In this work we focus on hate speech detection. Due to the inherent complexity of this task, it is important to distinguish hate speech from other types of online harassment. In particular, although it might be offensive to many people, the sole presence of insulting terms does not itself signify or convey hate speech. And, the other way around, hate speech may denigrate or threaten an individual or a group of people without the use of any profanities. People from the african-american community, for example, often use the term nigga online, in everyday language, without malicious intentions to refer to folks within their community, and the word cunt is often used in non hate speech publications and without any sexist purpose. The goal of this work is not to discuss if racial slur, such as nigga, should be pursued. The goal is to distinguish between publications using offensive terms and publications attacking communities, which we call hate speech.",
+      "Modern social media content usually include images and text. Some of these multimodal publications are only hate speech because of the combination of the text with a certain image. That is because, as we have stated, the presence of offensive terms does not itself signify hate speech, and the presence of hate speech is often determined by the context of a publication. Moreover, users authoring hate speech tend to intentionally construct publications where the text is not enough to determine they are hate speech. This happens especially in Twitter, where multimodal tweets are formed by an image and a short text, which in many cases is not enough to judge them. In those cases, the image might give extra context to make a proper judgement. Fig. FIGREF5 shows some of such examples in MMHS150K.",
+      "The contributions of this work are as follows:",
+      "[noitemsep,leftmargin=*]",
+      "We propose the novel task of hate speech detection in multimodal publications, collect, annotate and publish a large scale dataset.",
+      "We evaluate state of the art multimodal models on this specific task and compare their performance with unimodal detection. Even though images are proved to be useful for hate speech detection, the proposed multimodal models do not outperform unimodal textual models.",
+      "We study the challenges of the proposed task, and open the field for future research."
+    ],
+    [
+      "The literature on detecting hate speech on online textual publications is extensive. Schmidt and Wiegand BIBREF1 recently provided a good survey of it, where they review the terminology used over time, the features used, the existing datasets and the different approaches. However, the field lacks a consistent dataset and evaluation protocol to compare proposed methods. Saleem et al. BIBREF2 compare different classification methods detecting hate speech in Reddit and other forums. Wassem and Hovy BIBREF3 worked on hate speech detection on twitter, published a manually annotated dataset and studied its hate distribution. Later Wassem BIBREF4 extended the previous published dataset and compared amateur and expert annotations, concluding that amateur annotators are more likely than expert annotators to label items as hate speech. Park and Fung BIBREF5 worked on Wassem datasets and proposed a classification method using a CNN over Word2Vec BIBREF6 word embeddings, showing also classification results on racism and sexism hate sub-classes. Davidson et al. BIBREF7 also worked on hate speech detection on twitter, publishing another manually annotated dataset. They test different classifiers such as SVMs and decision trees and provide a performance comparison. Malmasi and Zampieri BIBREF8 worked on Davidson's dataset improving his results using more elaborated features. ElSherief et al. BIBREF9 studied hate speech on twitter and selected the most frequent terms in hate tweets based on Hatebase, a hate expression repository. They propose a big hate dataset but it lacks manual annotations, and all the tweets containing certain hate expressions are considered hate speech. Zhang et al. BIBREF10 recently proposed a more sophisticated approach for hate speech detection, using a CNN and a GRU BIBREF11 over Word2Vec BIBREF6 word embeddings. They show experiments in different datasets outperforming previous methods. Next, we summarize existing hate speech datasets:",
+      "[noitemsep,leftmargin=*]",
+      "RM BIBREF10: Formed by $2,435$ tweets discussing Refugees and Muslims, annotated as hate or non-hate.",
+      "DT BIBREF7: Formed by $24,783$ tweets annotated as hate, offensive language or neither. In our work, offensive language tweets are considered as non-hate.",
+      "WZ-LS BIBREF5: A combination of Wassem datasets BIBREF4, BIBREF3 labeled as racism, sexism, neither or both that make a total of $18,624$ tweets.",
+      "Semi-Supervised BIBREF9: Contains $27,330$ general hate speech Twitter tweets crawled in a semi-supervised manner.",
+      "Although often modern social media publications include images, not too many contributions exist that exploit visual information. Zhong et al. BIBREF12 worked on classifying Instagram images as potential cyberbullying targets, exploiting both the image content, the image caption and the comments. However, their visual information processing is limited to the use of features extracted by a pre-trained CNN, the use of which does not achieve any improvement. Hosseinmardi et al. BIBREF13 also address the problem of detecting cyberbullying incidents on Instagram exploiting both textual and image content. But, again, their visual information processing is limited to use the features of a pre-trained CNN, and the improvement when using visual features on cyberbullying classification is only of 0.01%."
+    ],
+    [
+      "A typical task in multimodal visual and textual analysis is to learn an alignment between feature spaces. To do that, usually a CNN and a RNN are trained jointly to learn a joint embedding space from aligned multimodal data. This approach is applied in tasks such as image captioning BIBREF14, BIBREF15 and multimodal image retrieval BIBREF16, BIBREF17. On the other hand, instead of explicitly learning an alignment between two spaces, the goal of Visual Question Answering (VQA) is to merge both data modalities in order to decide which answer is correct. This problem requires modeling very precise correlations between the image and the question representations. The VQA task requirements are similar to our hate speech detection problem in multimodal publications, where we have a visual and a textual input and we need to combine both sources of information to understand the global context and make a decision. We thus take inspiration from the VQA literature for the tested models. Early VQA methods BIBREF18 fuse textual and visual information by feature concatenation. Later methods, such as Multimodal Compact Bilinear pooling BIBREF19, utilize bilinear pooling to learn multimodal features. An important limitation of these methods is that the multimodal features are fused in the latter model stage, so the textual and visual relationships are modeled only in the last layers. Another limitation is that the visual features are obtained by representing the output of the CNN as a one dimensional vector, which losses the spatial information of the input images. In a recent work, Gao et al. BIBREF20 propose a feature fusion scheme to overcome these limitations. They learn convolution kernels from the textual information \u2013which they call question-guided kernels\u2013 and convolve them with the visual information in an earlier stage to get the multimodal features. Margffoy-Tuay et al. BIBREF21 use a similar approach to combine visual and textual information, but they address a different task: instance segmentation guided by natural language queries. We inspire in these latest feature fusion works to build the models for hate speech detection."
+    ],
+    [
+      "Existing hate speech datasets contain only textual data. Moreover, a reference benchmark does not exists. Most of the published datasets are crawled from Twitter and distributed as tweet IDs but, since Twitter removes reported user accounts, an important amount of their hate tweets is no longer accessible. We create a new manually annotated multimodal hate speech dataset formed by $150,000$ tweets, each one of them containing text and an image. We call the dataset MMHS150K, and made it available online . In this section, we explain the dataset creation steps."
+    ],
+    [
+      "We used the Twitter API to gather real-time tweets from September 2018 until February 2019, selecting the ones containing any of the 51 Hatebase terms that are more common in hate speech tweets, as studied in BIBREF9. We filtered out retweets, tweets containing less than three words and tweets containing porn related terms. From that selection, we kept the ones that included images and downloaded them. Twitter applies hate speech filters and other kinds of content control based on its policy, although the supervision is based on users' reports. Therefore, as we are gathering tweets from real-time posting, the content we get has not yet passed any filter."
+    ],
+    [
+      "We aim to create a multimodal hate speech database where all the instances contain visual and textual information that we can later process to determine if a tweet is hate speech or not. But a considerable amount of the images of the selected tweets contain only textual information, such as screenshots of other tweets. To ensure that all the dataset instances contain both visual and textual information, we remove those tweets. To do that, we use TextFCN BIBREF22, BIBREF23 , a Fully Convolutional Network that produces a pixel wise text probability map of an image. We set empirical thresholds to discard images that have a substantial total text probability, filtering out $23\\%$ of the collected tweets."
+    ],
+    [
+      "We annotate the gathered tweets using the crowdsourcing platform Amazon Mechanical Turk. There, we give the workers the definition of hate speech and show some examples to make the task clearer. We then show the tweet text and image and we ask them to classify it in one of 6 categories: No attacks to any community, racist, sexist, homophobic, religion based attacks or attacks to other communities. Each one of the $150,000$ tweets is labeled by 3 different workers to palliate discrepancies among workers.",
+      "We received a lot of valuable feedback from the annotators. Most of them had understood the task correctly, but they were worried because of its subjectivity. This is indeed a subjective task, highly dependent on the annotator convictions and sensitivity. However, we expect to get cleaner annotations the more strong the attack is, which are the publications we are more interested on detecting. We also detected that several users annotate tweets for hate speech just by spotting slur. As already said previously, just the use of particular words can be offensive to many people, but this is not the task we aim to solve. We have not included in our experiments those hits that were made in less than 3 seconds, understanding that it takes more time to grasp the multimodal context and make a decision.",
+      "We do a majority voting between the three annotations to get the tweets category. At the end, we obtain $112,845$ not hate tweets and $36,978$ hate tweets. The latest are divided in $11,925$ racist, $3,495$ sexist, $3,870$ homophobic, 163 religion-based hate and $5,811$ other hate tweets (Fig. FIGREF17). In this work, we do not use hate sub-categories, and stick to the hate / not hate split. We separate balanced validation ($5,000$) and test ($10,000$) sets. The remaining tweets are used for training.",
+      "We also experimented using hate scores for each tweet computed given the different votes by the three annotators instead of binary labels. The results did not present significant differences to those shown in the experimental part of this work, but the raw annotations will be published nonetheless for further research.",
+      "As far as we know, this dataset is the biggest hate speech dataset to date, and the first multimodal hate speech dataset. One of its challenges is to distinguish between tweets using the same key offensive words that constitute or not an attack to a community (hate speech). Fig. FIGREF18 shows the percentage of hate and not hate tweets of the top keywords."
+    ],
+    [
+      "All images are resized such that their shortest size has 500 pixels. During training, online data augmentation is applied as random cropping of $299\\times 299$ patches and mirroring. We use a CNN as the image features extractor which is an Imagenet BIBREF24 pre-trained Google Inception v3 architecture BIBREF25. The fine-tuning process of the Inception v3 layers aims to modify its weights to extract the features that, combined with the textual information, are optimal for hate speech detection."
+    ],
+    [
+      "We train a single layer LSTM with a 150-dimensional hidden state for hate / not hate classification. The input dimensionality is set to 100 and GloVe BIBREF26 embeddings are used as word input representations. Since our dataset is not big enough to train a GloVe word embedding model, we used a pre-trained model that has been trained in two billion tweets. This ensures that the model will be able to produce word embeddings for slang and other words typically used in Twitter. To process the tweets text before generating the word embeddings, we use the same pipeline as the model authors, which includes generating symbols to encode Twitter special interactions such as user mentions (@user) or hashtags (#hashtag). To encode the tweet text and input it later to multimodal models, we use the LSTM hidden state after processing the last tweet word. Since the LSTM has been trained for hate speech classification, it extracts the most useful information for this task from the text, which is encoded in the hidden state after inputting the last tweet word."
+    ],
+    [
+      "The text in the image can also contain important information to decide if a publication is hate speech or not, so we extract it and also input it to our model. To do so, we use Google Vision API Text Detection module BIBREF27. We input the tweet text and the text from the image separately to the multimodal models, so it might learn different relations between them and between them and the image. For instance, the model could learn to relate the image text with the area in the image where the text appears, so it could learn to interpret the text in a different way depending on the location where it is written in the image. The image text is also encoded by the LSTM as the hidden state after processing its last word."
+    ],
+    [
+      "The objective of this work is to build a hate speech detector that leverages both textual and visual data and detects hate speech publications based on the context given by both data modalities. To study how the multimodal context can boost the performance compared to an unimodal context we evaluate different models: a Feature Concatenation Model (FCM), a Spatial Concatenation Model (SCM) and a Textual Kernels Model (TKM). All of them are CNN+RNN models with three inputs: the tweet image, the tweet text and the text appearing in the image (if any)."
+    ],
+    [
+      "The image is fed to the Inception v3 architecture and the 2048 dimensional feature vector after the last average pooling layer is used as the visual representation. This vector is then concatenated with the 150 dimension vectors of the LSTM last word hidden states of the image text and the tweet text, resulting in a 2348 feature vector. This vector is then processed by three fully connected layers of decreasing dimensionality $(2348, 1024, 512)$ with following corresponding batch normalization and ReLu layers until the dimensions are reduced to two, the number of classes, in the last classification layer. The FCM architecture is illustrated in Fig. FIGREF26."
+    ],
+    [
+      "Instead of using the latest feature vector before classification of the Inception v3 as the visual representation, in the SCM we use the $8\\times 8\\times 2048$ feature map after the last Inception module. Then we concatenate the 150 dimension vectors encoding the tweet text and the tweet image text at each spatial location of that feature map. The resulting multimodal feature map is processed by two Inception-E blocks BIBREF28. After that, dropout and average pooling are applied and, as in the FCM model, three fully connected layers are used to reduce the dimensionality until the classification layer."
+    ],
+    [
+      "The TKM design, inspired by BIBREF20 and BIBREF21, aims to capture interactions between the two modalities more expressively than concatenation models. As in SCM we use the $8\\times 8\\times 2048$ feature map after the last Inception module as the visual representation. From the 150 dimension vector encoding the tweet text, we learn $K_t$ text dependent kernels using independent fully connected layers that are trained together with the rest of the model. The resulting $K_t$ text dependent kernels will have dimensionality of $1\\times 1\\times 2048$. We do the same with the feature vector encoding the image text, learning $K_{it}$ kernels. The textual kernels are convolved with the visual feature map in the channel dimension at each spatial location, resulting in a $8\\times 8\\times (K_i+K_{it})$ multimodal feature map, and batch normalization is applied. Then, as in the SCM, the 150 dimension vectors encoding the tweet text and the tweet image text are concatenated at each spatial dimension. The rest of the architecture is the same as in SCM: two Inception-E blocks, dropout, average pooling and three fully connected layers until the classification layer. The number of tweet textual kernels $K_t$ and tweet image textual kernels $K_it$ is set to $K_t = 10$ and $K_it = 5$. The TKM architecture is illustrated in Fig. FIGREF29."
+    ],
+    [
+      "We train the multimodal models with a Cross-Entropy loss with Softmax activations and an ADAM optimizer with an initial learning rate of $1e-4$. Our dataset suffers from a high class imbalance, so we weight the contribution to the loss of the samples to totally compensate for it. One of the goals of this work is to explore how every one of the inputs contributes to the classification and to prove that the proposed model can learn concurrences between visual and textual data useful to improve the hate speech classification results on multimodal data. To do that we train different models where all or only some inputs are available. When an input is not available, we set it to zeros, and we do the same when an image has no text."
+    ],
+    [
+      "Table TABREF31 shows the F-score, the Area Under the ROC Curve (AUC) and the mean accuracy (ACC) of the proposed models when different inputs are available. $TT$ refers to the tweet text, $IT$ to the image text and $I$ to the image. It also shows results for the LSTM, for the Davison method proposed in BIBREF7 trained with MMHS150K, and for random scores. Fig. FIGREF32 shows the Precision vs Recall plot and the ROC curve (which plots the True Positive Rate vs the False Positive Rate) of the different models.",
+      "First, notice that given the subjectivity of the task and the discrepancies between annotators, getting optimal scores in the evaluation metrics is virtually impossible. However, a system with relatively low metric scores can still be very useful for hate speech detection in a real application: it will fire on publications for which most annotators agree they are hate, which are often the stronger attacks. The proposed LSTM to detect hate speech when only text is available, gets similar results as the method presented in BIBREF7, which we trained with MMHS150K and the same splits. However, more than substantially advancing the state of the art on hate speech detection in textual publications, our key purpose in this work is to introduce and work on its detection on multimodal publications. We use LSTM because it provides a strong representation of the tweet texts.",
+      "The FCM trained only with images gets decent results, considering that in many publications the images might not give any useful information for the task. Fig. FIGREF33 shows some representative examples of the top hate and not hate scored images of this model. Many hate tweets are accompanied by demeaning nudity images, being sexist or homophobic. Other racist tweets are accompanied by images caricaturing black people. Finally, MEMES are also typically used in hate speech publications. The top scored images for not hate are portraits of people belonging to minorities. This is due to the use of slur inside these communities without an offensive intention, such as the word nigga inside the afro-american community or the word dyke inside the lesbian community. These results show that images can be effectively used to discriminate between offensive and non-offensive uses of those words.",
+      "Despite the model trained only with images proves that they are useful for hate speech detection, the proposed multimodal models are not able to improve the detection compared to the textual models. Besides the different architectures, we have tried different training strategies, such as initializing the CNN weights with a model already trained solely with MMHS150K images or using dropout to force the multimodal models to use the visual information. Eventually, though, these models end up using almost only the text input for the prediction and producing very similar results to those of the textual models. The proposed multimodal models, such as TKM, have shown good performance in other tasks, such as VQA. Next, we analyze why they do not perform well in this task and with this data:",
+      "[noitemsep,leftmargin=*]",
+      "Noisy data. A major challenge of this task is the discrepancy between annotations due to subjective judgement. Although this affects also detection using only text, its repercussion is bigger in more complex tasks, such as detection using images or multimodal detection.",
+      "Complexity and diversity of multimodal relations. Hate speech multimodal publications employ a lot of background knowledge which makes the relations between visual and textual elements they use very complex and diverse, and therefore difficult to learn by a neural network.",
+      "Small set of multimodal examples. Fig. FIGREF5 shows some of the challenging multimodal hate examples that we aimed to detect. But although we have collected a big dataset of $150K$ tweets, the subset of multimodal hate there is still too small to learn the complex multimodal relations needed to identify multimodal hate."
+    ],
+    [
+      "In this work we have explored the task of hate speech detection on multimodal publications. We have created MMHS150K, to our knowledge the biggest available hate speech dataset, and the first one composed of multimodal data, namely tweets formed by image and text. We have trained different textual, visual and multimodal models with that data, and found out that, despite the fact that images are useful for hate speech detection, the multimodal models do not outperform the textual models. Finally, we have analyzed the challenges of the proposed task and dataset. Given that most of the content in Social Media nowadays is multimodal, we truly believe on the importance of pushing forward this research. The code used in this work is available in ."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0343/instruction.md b/qasper-0343/instruction.md
new file mode 100644
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@@ -0,0 +1,106 @@
+Name of Paper: Exploring Hate Speech Detection in Multimodal Publications
+
+Question: What is author's opinion on why current multimodal models cannot outperform models analyzing only text?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work ::: Hate Speech Detection",
+    "Related Work ::: Visual and Textual Data Fusion",
+    "The MMHS150K dataset",
+    "The MMHS150K dataset ::: Tweets Gathering",
+    "The MMHS150K dataset ::: Textual Image Filtering",
+    "The MMHS150K dataset ::: Annotation",
+    "Methodology ::: Unimodal Treatment ::: Images.",
+    "Methodology ::: Unimodal Treatment ::: Tweet Text.",
+    "Methodology ::: Unimodal Treatment ::: Image Text.",
+    "Methodology ::: Multimodal Architectures",
+    "Methodology ::: Multimodal Architectures ::: Feature Concatenation Model (FCM)",
+    "Methodology ::: Multimodal Architectures ::: Spatial Concatenation Model (SCM)",
+    "Methodology ::: Multimodal Architectures ::: Textual Kernels Model (TKM)",
+    "Methodology ::: Multimodal Architectures ::: Training",
+    "Results",
+    "Conclusions"
+  ],
+  "paragraphs": [
+    [
+      "Social Media platforms such as Facebook, Twitter or Reddit have empowered individuals' voices and facilitated freedom of expression. However they have also been a breeding ground for hate speech and other types of online harassment. Hate speech is defined in legal literature as speech (or any form of expression) that expresses (or seeks to promote, or has the capacity to increase) hatred against a person or a group of people because of a characteristic they share, or a group to which they belong BIBREF0. Twitter develops this definition in its hateful conduct policy as violence against or directly attack or threaten other people on the basis of race, ethnicity, national origin, sexual orientation, gender, gender identity, religious affiliation, age, disability, or serious disease.",
+      "In this work we focus on hate speech detection. Due to the inherent complexity of this task, it is important to distinguish hate speech from other types of online harassment. In particular, although it might be offensive to many people, the sole presence of insulting terms does not itself signify or convey hate speech. And, the other way around, hate speech may denigrate or threaten an individual or a group of people without the use of any profanities. People from the african-american community, for example, often use the term nigga online, in everyday language, without malicious intentions to refer to folks within their community, and the word cunt is often used in non hate speech publications and without any sexist purpose. The goal of this work is not to discuss if racial slur, such as nigga, should be pursued. The goal is to distinguish between publications using offensive terms and publications attacking communities, which we call hate speech.",
+      "Modern social media content usually include images and text. Some of these multimodal publications are only hate speech because of the combination of the text with a certain image. That is because, as we have stated, the presence of offensive terms does not itself signify hate speech, and the presence of hate speech is often determined by the context of a publication. Moreover, users authoring hate speech tend to intentionally construct publications where the text is not enough to determine they are hate speech. This happens especially in Twitter, where multimodal tweets are formed by an image and a short text, which in many cases is not enough to judge them. In those cases, the image might give extra context to make a proper judgement. Fig. FIGREF5 shows some of such examples in MMHS150K.",
+      "The contributions of this work are as follows:",
+      "[noitemsep,leftmargin=*]",
+      "We propose the novel task of hate speech detection in multimodal publications, collect, annotate and publish a large scale dataset.",
+      "We evaluate state of the art multimodal models on this specific task and compare their performance with unimodal detection. Even though images are proved to be useful for hate speech detection, the proposed multimodal models do not outperform unimodal textual models.",
+      "We study the challenges of the proposed task, and open the field for future research."
+    ],
+    [
+      "The literature on detecting hate speech on online textual publications is extensive. Schmidt and Wiegand BIBREF1 recently provided a good survey of it, where they review the terminology used over time, the features used, the existing datasets and the different approaches. However, the field lacks a consistent dataset and evaluation protocol to compare proposed methods. Saleem et al. BIBREF2 compare different classification methods detecting hate speech in Reddit and other forums. Wassem and Hovy BIBREF3 worked on hate speech detection on twitter, published a manually annotated dataset and studied its hate distribution. Later Wassem BIBREF4 extended the previous published dataset and compared amateur and expert annotations, concluding that amateur annotators are more likely than expert annotators to label items as hate speech. Park and Fung BIBREF5 worked on Wassem datasets and proposed a classification method using a CNN over Word2Vec BIBREF6 word embeddings, showing also classification results on racism and sexism hate sub-classes. Davidson et al. BIBREF7 also worked on hate speech detection on twitter, publishing another manually annotated dataset. They test different classifiers such as SVMs and decision trees and provide a performance comparison. Malmasi and Zampieri BIBREF8 worked on Davidson's dataset improving his results using more elaborated features. ElSherief et al. BIBREF9 studied hate speech on twitter and selected the most frequent terms in hate tweets based on Hatebase, a hate expression repository. They propose a big hate dataset but it lacks manual annotations, and all the tweets containing certain hate expressions are considered hate speech. Zhang et al. BIBREF10 recently proposed a more sophisticated approach for hate speech detection, using a CNN and a GRU BIBREF11 over Word2Vec BIBREF6 word embeddings. They show experiments in different datasets outperforming previous methods. Next, we summarize existing hate speech datasets:",
+      "[noitemsep,leftmargin=*]",
+      "RM BIBREF10: Formed by $2,435$ tweets discussing Refugees and Muslims, annotated as hate or non-hate.",
+      "DT BIBREF7: Formed by $24,783$ tweets annotated as hate, offensive language or neither. In our work, offensive language tweets are considered as non-hate.",
+      "WZ-LS BIBREF5: A combination of Wassem datasets BIBREF4, BIBREF3 labeled as racism, sexism, neither or both that make a total of $18,624$ tweets.",
+      "Semi-Supervised BIBREF9: Contains $27,330$ general hate speech Twitter tweets crawled in a semi-supervised manner.",
+      "Although often modern social media publications include images, not too many contributions exist that exploit visual information. Zhong et al. BIBREF12 worked on classifying Instagram images as potential cyberbullying targets, exploiting both the image content, the image caption and the comments. However, their visual information processing is limited to the use of features extracted by a pre-trained CNN, the use of which does not achieve any improvement. Hosseinmardi et al. BIBREF13 also address the problem of detecting cyberbullying incidents on Instagram exploiting both textual and image content. But, again, their visual information processing is limited to use the features of a pre-trained CNN, and the improvement when using visual features on cyberbullying classification is only of 0.01%."
+    ],
+    [
+      "A typical task in multimodal visual and textual analysis is to learn an alignment between feature spaces. To do that, usually a CNN and a RNN are trained jointly to learn a joint embedding space from aligned multimodal data. This approach is applied in tasks such as image captioning BIBREF14, BIBREF15 and multimodal image retrieval BIBREF16, BIBREF17. On the other hand, instead of explicitly learning an alignment between two spaces, the goal of Visual Question Answering (VQA) is to merge both data modalities in order to decide which answer is correct. This problem requires modeling very precise correlations between the image and the question representations. The VQA task requirements are similar to our hate speech detection problem in multimodal publications, where we have a visual and a textual input and we need to combine both sources of information to understand the global context and make a decision. We thus take inspiration from the VQA literature for the tested models. Early VQA methods BIBREF18 fuse textual and visual information by feature concatenation. Later methods, such as Multimodal Compact Bilinear pooling BIBREF19, utilize bilinear pooling to learn multimodal features. An important limitation of these methods is that the multimodal features are fused in the latter model stage, so the textual and visual relationships are modeled only in the last layers. Another limitation is that the visual features are obtained by representing the output of the CNN as a one dimensional vector, which losses the spatial information of the input images. In a recent work, Gao et al. BIBREF20 propose a feature fusion scheme to overcome these limitations. They learn convolution kernels from the textual information \u2013which they call question-guided kernels\u2013 and convolve them with the visual information in an earlier stage to get the multimodal features. Margffoy-Tuay et al. BIBREF21 use a similar approach to combine visual and textual information, but they address a different task: instance segmentation guided by natural language queries. We inspire in these latest feature fusion works to build the models for hate speech detection."
+    ],
+    [
+      "Existing hate speech datasets contain only textual data. Moreover, a reference benchmark does not exists. Most of the published datasets are crawled from Twitter and distributed as tweet IDs but, since Twitter removes reported user accounts, an important amount of their hate tweets is no longer accessible. We create a new manually annotated multimodal hate speech dataset formed by $150,000$ tweets, each one of them containing text and an image. We call the dataset MMHS150K, and made it available online . In this section, we explain the dataset creation steps."
+    ],
+    [
+      "We used the Twitter API to gather real-time tweets from September 2018 until February 2019, selecting the ones containing any of the 51 Hatebase terms that are more common in hate speech tweets, as studied in BIBREF9. We filtered out retweets, tweets containing less than three words and tweets containing porn related terms. From that selection, we kept the ones that included images and downloaded them. Twitter applies hate speech filters and other kinds of content control based on its policy, although the supervision is based on users' reports. Therefore, as we are gathering tweets from real-time posting, the content we get has not yet passed any filter."
+    ],
+    [
+      "We aim to create a multimodal hate speech database where all the instances contain visual and textual information that we can later process to determine if a tweet is hate speech or not. But a considerable amount of the images of the selected tweets contain only textual information, such as screenshots of other tweets. To ensure that all the dataset instances contain both visual and textual information, we remove those tweets. To do that, we use TextFCN BIBREF22, BIBREF23 , a Fully Convolutional Network that produces a pixel wise text probability map of an image. We set empirical thresholds to discard images that have a substantial total text probability, filtering out $23\\%$ of the collected tweets."
+    ],
+    [
+      "We annotate the gathered tweets using the crowdsourcing platform Amazon Mechanical Turk. There, we give the workers the definition of hate speech and show some examples to make the task clearer. We then show the tweet text and image and we ask them to classify it in one of 6 categories: No attacks to any community, racist, sexist, homophobic, religion based attacks or attacks to other communities. Each one of the $150,000$ tweets is labeled by 3 different workers to palliate discrepancies among workers.",
+      "We received a lot of valuable feedback from the annotators. Most of them had understood the task correctly, but they were worried because of its subjectivity. This is indeed a subjective task, highly dependent on the annotator convictions and sensitivity. However, we expect to get cleaner annotations the more strong the attack is, which are the publications we are more interested on detecting. We also detected that several users annotate tweets for hate speech just by spotting slur. As already said previously, just the use of particular words can be offensive to many people, but this is not the task we aim to solve. We have not included in our experiments those hits that were made in less than 3 seconds, understanding that it takes more time to grasp the multimodal context and make a decision.",
+      "We do a majority voting between the three annotations to get the tweets category. At the end, we obtain $112,845$ not hate tweets and $36,978$ hate tweets. The latest are divided in $11,925$ racist, $3,495$ sexist, $3,870$ homophobic, 163 religion-based hate and $5,811$ other hate tweets (Fig. FIGREF17). In this work, we do not use hate sub-categories, and stick to the hate / not hate split. We separate balanced validation ($5,000$) and test ($10,000$) sets. The remaining tweets are used for training.",
+      "We also experimented using hate scores for each tweet computed given the different votes by the three annotators instead of binary labels. The results did not present significant differences to those shown in the experimental part of this work, but the raw annotations will be published nonetheless for further research.",
+      "As far as we know, this dataset is the biggest hate speech dataset to date, and the first multimodal hate speech dataset. One of its challenges is to distinguish between tweets using the same key offensive words that constitute or not an attack to a community (hate speech). Fig. FIGREF18 shows the percentage of hate and not hate tweets of the top keywords."
+    ],
+    [
+      "All images are resized such that their shortest size has 500 pixels. During training, online data augmentation is applied as random cropping of $299\\times 299$ patches and mirroring. We use a CNN as the image features extractor which is an Imagenet BIBREF24 pre-trained Google Inception v3 architecture BIBREF25. The fine-tuning process of the Inception v3 layers aims to modify its weights to extract the features that, combined with the textual information, are optimal for hate speech detection."
+    ],
+    [
+      "We train a single layer LSTM with a 150-dimensional hidden state for hate / not hate classification. The input dimensionality is set to 100 and GloVe BIBREF26 embeddings are used as word input representations. Since our dataset is not big enough to train a GloVe word embedding model, we used a pre-trained model that has been trained in two billion tweets. This ensures that the model will be able to produce word embeddings for slang and other words typically used in Twitter. To process the tweets text before generating the word embeddings, we use the same pipeline as the model authors, which includes generating symbols to encode Twitter special interactions such as user mentions (@user) or hashtags (#hashtag). To encode the tweet text and input it later to multimodal models, we use the LSTM hidden state after processing the last tweet word. Since the LSTM has been trained for hate speech classification, it extracts the most useful information for this task from the text, which is encoded in the hidden state after inputting the last tweet word."
+    ],
+    [
+      "The text in the image can also contain important information to decide if a publication is hate speech or not, so we extract it and also input it to our model. To do so, we use Google Vision API Text Detection module BIBREF27. We input the tweet text and the text from the image separately to the multimodal models, so it might learn different relations between them and between them and the image. For instance, the model could learn to relate the image text with the area in the image where the text appears, so it could learn to interpret the text in a different way depending on the location where it is written in the image. The image text is also encoded by the LSTM as the hidden state after processing its last word."
+    ],
+    [
+      "The objective of this work is to build a hate speech detector that leverages both textual and visual data and detects hate speech publications based on the context given by both data modalities. To study how the multimodal context can boost the performance compared to an unimodal context we evaluate different models: a Feature Concatenation Model (FCM), a Spatial Concatenation Model (SCM) and a Textual Kernels Model (TKM). All of them are CNN+RNN models with three inputs: the tweet image, the tweet text and the text appearing in the image (if any)."
+    ],
+    [
+      "The image is fed to the Inception v3 architecture and the 2048 dimensional feature vector after the last average pooling layer is used as the visual representation. This vector is then concatenated with the 150 dimension vectors of the LSTM last word hidden states of the image text and the tweet text, resulting in a 2348 feature vector. This vector is then processed by three fully connected layers of decreasing dimensionality $(2348, 1024, 512)$ with following corresponding batch normalization and ReLu layers until the dimensions are reduced to two, the number of classes, in the last classification layer. The FCM architecture is illustrated in Fig. FIGREF26."
+    ],
+    [
+      "Instead of using the latest feature vector before classification of the Inception v3 as the visual representation, in the SCM we use the $8\\times 8\\times 2048$ feature map after the last Inception module. Then we concatenate the 150 dimension vectors encoding the tweet text and the tweet image text at each spatial location of that feature map. The resulting multimodal feature map is processed by two Inception-E blocks BIBREF28. After that, dropout and average pooling are applied and, as in the FCM model, three fully connected layers are used to reduce the dimensionality until the classification layer."
+    ],
+    [
+      "The TKM design, inspired by BIBREF20 and BIBREF21, aims to capture interactions between the two modalities more expressively than concatenation models. As in SCM we use the $8\\times 8\\times 2048$ feature map after the last Inception module as the visual representation. From the 150 dimension vector encoding the tweet text, we learn $K_t$ text dependent kernels using independent fully connected layers that are trained together with the rest of the model. The resulting $K_t$ text dependent kernels will have dimensionality of $1\\times 1\\times 2048$. We do the same with the feature vector encoding the image text, learning $K_{it}$ kernels. The textual kernels are convolved with the visual feature map in the channel dimension at each spatial location, resulting in a $8\\times 8\\times (K_i+K_{it})$ multimodal feature map, and batch normalization is applied. Then, as in the SCM, the 150 dimension vectors encoding the tweet text and the tweet image text are concatenated at each spatial dimension. The rest of the architecture is the same as in SCM: two Inception-E blocks, dropout, average pooling and three fully connected layers until the classification layer. The number of tweet textual kernels $K_t$ and tweet image textual kernels $K_it$ is set to $K_t = 10$ and $K_it = 5$. The TKM architecture is illustrated in Fig. FIGREF29."
+    ],
+    [
+      "We train the multimodal models with a Cross-Entropy loss with Softmax activations and an ADAM optimizer with an initial learning rate of $1e-4$. Our dataset suffers from a high class imbalance, so we weight the contribution to the loss of the samples to totally compensate for it. One of the goals of this work is to explore how every one of the inputs contributes to the classification and to prove that the proposed model can learn concurrences between visual and textual data useful to improve the hate speech classification results on multimodal data. To do that we train different models where all or only some inputs are available. When an input is not available, we set it to zeros, and we do the same when an image has no text."
+    ],
+    [
+      "Table TABREF31 shows the F-score, the Area Under the ROC Curve (AUC) and the mean accuracy (ACC) of the proposed models when different inputs are available. $TT$ refers to the tweet text, $IT$ to the image text and $I$ to the image. It also shows results for the LSTM, for the Davison method proposed in BIBREF7 trained with MMHS150K, and for random scores. Fig. FIGREF32 shows the Precision vs Recall plot and the ROC curve (which plots the True Positive Rate vs the False Positive Rate) of the different models.",
+      "First, notice that given the subjectivity of the task and the discrepancies between annotators, getting optimal scores in the evaluation metrics is virtually impossible. However, a system with relatively low metric scores can still be very useful for hate speech detection in a real application: it will fire on publications for which most annotators agree they are hate, which are often the stronger attacks. The proposed LSTM to detect hate speech when only text is available, gets similar results as the method presented in BIBREF7, which we trained with MMHS150K and the same splits. However, more than substantially advancing the state of the art on hate speech detection in textual publications, our key purpose in this work is to introduce and work on its detection on multimodal publications. We use LSTM because it provides a strong representation of the tweet texts.",
+      "The FCM trained only with images gets decent results, considering that in many publications the images might not give any useful information for the task. Fig. FIGREF33 shows some representative examples of the top hate and not hate scored images of this model. Many hate tweets are accompanied by demeaning nudity images, being sexist or homophobic. Other racist tweets are accompanied by images caricaturing black people. Finally, MEMES are also typically used in hate speech publications. The top scored images for not hate are portraits of people belonging to minorities. This is due to the use of slur inside these communities without an offensive intention, such as the word nigga inside the afro-american community or the word dyke inside the lesbian community. These results show that images can be effectively used to discriminate between offensive and non-offensive uses of those words.",
+      "Despite the model trained only with images proves that they are useful for hate speech detection, the proposed multimodal models are not able to improve the detection compared to the textual models. Besides the different architectures, we have tried different training strategies, such as initializing the CNN weights with a model already trained solely with MMHS150K images or using dropout to force the multimodal models to use the visual information. Eventually, though, these models end up using almost only the text input for the prediction and producing very similar results to those of the textual models. The proposed multimodal models, such as TKM, have shown good performance in other tasks, such as VQA. Next, we analyze why they do not perform well in this task and with this data:",
+      "[noitemsep,leftmargin=*]",
+      "Noisy data. A major challenge of this task is the discrepancy between annotations due to subjective judgement. Although this affects also detection using only text, its repercussion is bigger in more complex tasks, such as detection using images or multimodal detection.",
+      "Complexity and diversity of multimodal relations. Hate speech multimodal publications employ a lot of background knowledge which makes the relations between visual and textual elements they use very complex and diverse, and therefore difficult to learn by a neural network.",
+      "Small set of multimodal examples. Fig. FIGREF5 shows some of the challenging multimodal hate examples that we aimed to detect. But although we have collected a big dataset of $150K$ tweets, the subset of multimodal hate there is still too small to learn the complex multimodal relations needed to identify multimodal hate."
+    ],
+    [
+      "In this work we have explored the task of hate speech detection on multimodal publications. We have created MMHS150K, to our knowledge the biggest available hate speech dataset, and the first one composed of multimodal data, namely tweets formed by image and text. We have trained different textual, visual and multimodal models with that data, and found out that, despite the fact that images are useful for hate speech detection, the multimodal models do not outperform the textual models. Finally, we have analyzed the challenges of the proposed task and dataset. Given that most of the content in Social Media nowadays is multimodal, we truly believe on the importance of pushing forward this research. The code used in this work is available in ."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: Exploring Hate Speech Detection in Multimodal Publications
+
+Question: What metrics are used to benchmark the results?
\ No newline at end of file
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+Name of Paper: Exploring Hate Speech Detection in Multimodal Publications
+
+Question: How is data collected, manual collection or Twitter api?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work ::: Hate Speech Detection",
+    "Related Work ::: Visual and Textual Data Fusion",
+    "The MMHS150K dataset",
+    "The MMHS150K dataset ::: Tweets Gathering",
+    "The MMHS150K dataset ::: Textual Image Filtering",
+    "The MMHS150K dataset ::: Annotation",
+    "Methodology ::: Unimodal Treatment ::: Images.",
+    "Methodology ::: Unimodal Treatment ::: Tweet Text.",
+    "Methodology ::: Unimodal Treatment ::: Image Text.",
+    "Methodology ::: Multimodal Architectures",
+    "Methodology ::: Multimodal Architectures ::: Feature Concatenation Model (FCM)",
+    "Methodology ::: Multimodal Architectures ::: Spatial Concatenation Model (SCM)",
+    "Methodology ::: Multimodal Architectures ::: Textual Kernels Model (TKM)",
+    "Methodology ::: Multimodal Architectures ::: Training",
+    "Results",
+    "Conclusions"
+  ],
+  "paragraphs": [
+    [
+      "Social Media platforms such as Facebook, Twitter or Reddit have empowered individuals' voices and facilitated freedom of expression. However they have also been a breeding ground for hate speech and other types of online harassment. Hate speech is defined in legal literature as speech (or any form of expression) that expresses (or seeks to promote, or has the capacity to increase) hatred against a person or a group of people because of a characteristic they share, or a group to which they belong BIBREF0. Twitter develops this definition in its hateful conduct policy as violence against or directly attack or threaten other people on the basis of race, ethnicity, national origin, sexual orientation, gender, gender identity, religious affiliation, age, disability, or serious disease.",
+      "In this work we focus on hate speech detection. Due to the inherent complexity of this task, it is important to distinguish hate speech from other types of online harassment. In particular, although it might be offensive to many people, the sole presence of insulting terms does not itself signify or convey hate speech. And, the other way around, hate speech may denigrate or threaten an individual or a group of people without the use of any profanities. People from the african-american community, for example, often use the term nigga online, in everyday language, without malicious intentions to refer to folks within their community, and the word cunt is often used in non hate speech publications and without any sexist purpose. The goal of this work is not to discuss if racial slur, such as nigga, should be pursued. The goal is to distinguish between publications using offensive terms and publications attacking communities, which we call hate speech.",
+      "Modern social media content usually include images and text. Some of these multimodal publications are only hate speech because of the combination of the text with a certain image. That is because, as we have stated, the presence of offensive terms does not itself signify hate speech, and the presence of hate speech is often determined by the context of a publication. Moreover, users authoring hate speech tend to intentionally construct publications where the text is not enough to determine they are hate speech. This happens especially in Twitter, where multimodal tweets are formed by an image and a short text, which in many cases is not enough to judge them. In those cases, the image might give extra context to make a proper judgement. Fig. FIGREF5 shows some of such examples in MMHS150K.",
+      "The contributions of this work are as follows:",
+      "[noitemsep,leftmargin=*]",
+      "We propose the novel task of hate speech detection in multimodal publications, collect, annotate and publish a large scale dataset.",
+      "We evaluate state of the art multimodal models on this specific task and compare their performance with unimodal detection. Even though images are proved to be useful for hate speech detection, the proposed multimodal models do not outperform unimodal textual models.",
+      "We study the challenges of the proposed task, and open the field for future research."
+    ],
+    [
+      "The literature on detecting hate speech on online textual publications is extensive. Schmidt and Wiegand BIBREF1 recently provided a good survey of it, where they review the terminology used over time, the features used, the existing datasets and the different approaches. However, the field lacks a consistent dataset and evaluation protocol to compare proposed methods. Saleem et al. BIBREF2 compare different classification methods detecting hate speech in Reddit and other forums. Wassem and Hovy BIBREF3 worked on hate speech detection on twitter, published a manually annotated dataset and studied its hate distribution. Later Wassem BIBREF4 extended the previous published dataset and compared amateur and expert annotations, concluding that amateur annotators are more likely than expert annotators to label items as hate speech. Park and Fung BIBREF5 worked on Wassem datasets and proposed a classification method using a CNN over Word2Vec BIBREF6 word embeddings, showing also classification results on racism and sexism hate sub-classes. Davidson et al. BIBREF7 also worked on hate speech detection on twitter, publishing another manually annotated dataset. They test different classifiers such as SVMs and decision trees and provide a performance comparison. Malmasi and Zampieri BIBREF8 worked on Davidson's dataset improving his results using more elaborated features. ElSherief et al. BIBREF9 studied hate speech on twitter and selected the most frequent terms in hate tweets based on Hatebase, a hate expression repository. They propose a big hate dataset but it lacks manual annotations, and all the tweets containing certain hate expressions are considered hate speech. Zhang et al. BIBREF10 recently proposed a more sophisticated approach for hate speech detection, using a CNN and a GRU BIBREF11 over Word2Vec BIBREF6 word embeddings. They show experiments in different datasets outperforming previous methods. Next, we summarize existing hate speech datasets:",
+      "[noitemsep,leftmargin=*]",
+      "RM BIBREF10: Formed by $2,435$ tweets discussing Refugees and Muslims, annotated as hate or non-hate.",
+      "DT BIBREF7: Formed by $24,783$ tweets annotated as hate, offensive language or neither. In our work, offensive language tweets are considered as non-hate.",
+      "WZ-LS BIBREF5: A combination of Wassem datasets BIBREF4, BIBREF3 labeled as racism, sexism, neither or both that make a total of $18,624$ tweets.",
+      "Semi-Supervised BIBREF9: Contains $27,330$ general hate speech Twitter tweets crawled in a semi-supervised manner.",
+      "Although often modern social media publications include images, not too many contributions exist that exploit visual information. Zhong et al. BIBREF12 worked on classifying Instagram images as potential cyberbullying targets, exploiting both the image content, the image caption and the comments. However, their visual information processing is limited to the use of features extracted by a pre-trained CNN, the use of which does not achieve any improvement. Hosseinmardi et al. BIBREF13 also address the problem of detecting cyberbullying incidents on Instagram exploiting both textual and image content. But, again, their visual information processing is limited to use the features of a pre-trained CNN, and the improvement when using visual features on cyberbullying classification is only of 0.01%."
+    ],
+    [
+      "A typical task in multimodal visual and textual analysis is to learn an alignment between feature spaces. To do that, usually a CNN and a RNN are trained jointly to learn a joint embedding space from aligned multimodal data. This approach is applied in tasks such as image captioning BIBREF14, BIBREF15 and multimodal image retrieval BIBREF16, BIBREF17. On the other hand, instead of explicitly learning an alignment between two spaces, the goal of Visual Question Answering (VQA) is to merge both data modalities in order to decide which answer is correct. This problem requires modeling very precise correlations between the image and the question representations. The VQA task requirements are similar to our hate speech detection problem in multimodal publications, where we have a visual and a textual input and we need to combine both sources of information to understand the global context and make a decision. We thus take inspiration from the VQA literature for the tested models. Early VQA methods BIBREF18 fuse textual and visual information by feature concatenation. Later methods, such as Multimodal Compact Bilinear pooling BIBREF19, utilize bilinear pooling to learn multimodal features. An important limitation of these methods is that the multimodal features are fused in the latter model stage, so the textual and visual relationships are modeled only in the last layers. Another limitation is that the visual features are obtained by representing the output of the CNN as a one dimensional vector, which losses the spatial information of the input images. In a recent work, Gao et al. BIBREF20 propose a feature fusion scheme to overcome these limitations. They learn convolution kernels from the textual information \u2013which they call question-guided kernels\u2013 and convolve them with the visual information in an earlier stage to get the multimodal features. Margffoy-Tuay et al. BIBREF21 use a similar approach to combine visual and textual information, but they address a different task: instance segmentation guided by natural language queries. We inspire in these latest feature fusion works to build the models for hate speech detection."
+    ],
+    [
+      "Existing hate speech datasets contain only textual data. Moreover, a reference benchmark does not exists. Most of the published datasets are crawled from Twitter and distributed as tweet IDs but, since Twitter removes reported user accounts, an important amount of their hate tweets is no longer accessible. We create a new manually annotated multimodal hate speech dataset formed by $150,000$ tweets, each one of them containing text and an image. We call the dataset MMHS150K, and made it available online . In this section, we explain the dataset creation steps."
+    ],
+    [
+      "We used the Twitter API to gather real-time tweets from September 2018 until February 2019, selecting the ones containing any of the 51 Hatebase terms that are more common in hate speech tweets, as studied in BIBREF9. We filtered out retweets, tweets containing less than three words and tweets containing porn related terms. From that selection, we kept the ones that included images and downloaded them. Twitter applies hate speech filters and other kinds of content control based on its policy, although the supervision is based on users' reports. Therefore, as we are gathering tweets from real-time posting, the content we get has not yet passed any filter."
+    ],
+    [
+      "We aim to create a multimodal hate speech database where all the instances contain visual and textual information that we can later process to determine if a tweet is hate speech or not. But a considerable amount of the images of the selected tweets contain only textual information, such as screenshots of other tweets. To ensure that all the dataset instances contain both visual and textual information, we remove those tweets. To do that, we use TextFCN BIBREF22, BIBREF23 , a Fully Convolutional Network that produces a pixel wise text probability map of an image. We set empirical thresholds to discard images that have a substantial total text probability, filtering out $23\\%$ of the collected tweets."
+    ],
+    [
+      "We annotate the gathered tweets using the crowdsourcing platform Amazon Mechanical Turk. There, we give the workers the definition of hate speech and show some examples to make the task clearer. We then show the tweet text and image and we ask them to classify it in one of 6 categories: No attacks to any community, racist, sexist, homophobic, religion based attacks or attacks to other communities. Each one of the $150,000$ tweets is labeled by 3 different workers to palliate discrepancies among workers.",
+      "We received a lot of valuable feedback from the annotators. Most of them had understood the task correctly, but they were worried because of its subjectivity. This is indeed a subjective task, highly dependent on the annotator convictions and sensitivity. However, we expect to get cleaner annotations the more strong the attack is, which are the publications we are more interested on detecting. We also detected that several users annotate tweets for hate speech just by spotting slur. As already said previously, just the use of particular words can be offensive to many people, but this is not the task we aim to solve. We have not included in our experiments those hits that were made in less than 3 seconds, understanding that it takes more time to grasp the multimodal context and make a decision.",
+      "We do a majority voting between the three annotations to get the tweets category. At the end, we obtain $112,845$ not hate tweets and $36,978$ hate tweets. The latest are divided in $11,925$ racist, $3,495$ sexist, $3,870$ homophobic, 163 religion-based hate and $5,811$ other hate tweets (Fig. FIGREF17). In this work, we do not use hate sub-categories, and stick to the hate / not hate split. We separate balanced validation ($5,000$) and test ($10,000$) sets. The remaining tweets are used for training.",
+      "We also experimented using hate scores for each tweet computed given the different votes by the three annotators instead of binary labels. The results did not present significant differences to those shown in the experimental part of this work, but the raw annotations will be published nonetheless for further research.",
+      "As far as we know, this dataset is the biggest hate speech dataset to date, and the first multimodal hate speech dataset. One of its challenges is to distinguish between tweets using the same key offensive words that constitute or not an attack to a community (hate speech). Fig. FIGREF18 shows the percentage of hate and not hate tweets of the top keywords."
+    ],
+    [
+      "All images are resized such that their shortest size has 500 pixels. During training, online data augmentation is applied as random cropping of $299\\times 299$ patches and mirroring. We use a CNN as the image features extractor which is an Imagenet BIBREF24 pre-trained Google Inception v3 architecture BIBREF25. The fine-tuning process of the Inception v3 layers aims to modify its weights to extract the features that, combined with the textual information, are optimal for hate speech detection."
+    ],
+    [
+      "We train a single layer LSTM with a 150-dimensional hidden state for hate / not hate classification. The input dimensionality is set to 100 and GloVe BIBREF26 embeddings are used as word input representations. Since our dataset is not big enough to train a GloVe word embedding model, we used a pre-trained model that has been trained in two billion tweets. This ensures that the model will be able to produce word embeddings for slang and other words typically used in Twitter. To process the tweets text before generating the word embeddings, we use the same pipeline as the model authors, which includes generating symbols to encode Twitter special interactions such as user mentions (@user) or hashtags (#hashtag). To encode the tweet text and input it later to multimodal models, we use the LSTM hidden state after processing the last tweet word. Since the LSTM has been trained for hate speech classification, it extracts the most useful information for this task from the text, which is encoded in the hidden state after inputting the last tweet word."
+    ],
+    [
+      "The text in the image can also contain important information to decide if a publication is hate speech or not, so we extract it and also input it to our model. To do so, we use Google Vision API Text Detection module BIBREF27. We input the tweet text and the text from the image separately to the multimodal models, so it might learn different relations between them and between them and the image. For instance, the model could learn to relate the image text with the area in the image where the text appears, so it could learn to interpret the text in a different way depending on the location where it is written in the image. The image text is also encoded by the LSTM as the hidden state after processing its last word."
+    ],
+    [
+      "The objective of this work is to build a hate speech detector that leverages both textual and visual data and detects hate speech publications based on the context given by both data modalities. To study how the multimodal context can boost the performance compared to an unimodal context we evaluate different models: a Feature Concatenation Model (FCM), a Spatial Concatenation Model (SCM) and a Textual Kernels Model (TKM). All of them are CNN+RNN models with three inputs: the tweet image, the tweet text and the text appearing in the image (if any)."
+    ],
+    [
+      "The image is fed to the Inception v3 architecture and the 2048 dimensional feature vector after the last average pooling layer is used as the visual representation. This vector is then concatenated with the 150 dimension vectors of the LSTM last word hidden states of the image text and the tweet text, resulting in a 2348 feature vector. This vector is then processed by three fully connected layers of decreasing dimensionality $(2348, 1024, 512)$ with following corresponding batch normalization and ReLu layers until the dimensions are reduced to two, the number of classes, in the last classification layer. The FCM architecture is illustrated in Fig. FIGREF26."
+    ],
+    [
+      "Instead of using the latest feature vector before classification of the Inception v3 as the visual representation, in the SCM we use the $8\\times 8\\times 2048$ feature map after the last Inception module. Then we concatenate the 150 dimension vectors encoding the tweet text and the tweet image text at each spatial location of that feature map. The resulting multimodal feature map is processed by two Inception-E blocks BIBREF28. After that, dropout and average pooling are applied and, as in the FCM model, three fully connected layers are used to reduce the dimensionality until the classification layer."
+    ],
+    [
+      "The TKM design, inspired by BIBREF20 and BIBREF21, aims to capture interactions between the two modalities more expressively than concatenation models. As in SCM we use the $8\\times 8\\times 2048$ feature map after the last Inception module as the visual representation. From the 150 dimension vector encoding the tweet text, we learn $K_t$ text dependent kernels using independent fully connected layers that are trained together with the rest of the model. The resulting $K_t$ text dependent kernels will have dimensionality of $1\\times 1\\times 2048$. We do the same with the feature vector encoding the image text, learning $K_{it}$ kernels. The textual kernels are convolved with the visual feature map in the channel dimension at each spatial location, resulting in a $8\\times 8\\times (K_i+K_{it})$ multimodal feature map, and batch normalization is applied. Then, as in the SCM, the 150 dimension vectors encoding the tweet text and the tweet image text are concatenated at each spatial dimension. The rest of the architecture is the same as in SCM: two Inception-E blocks, dropout, average pooling and three fully connected layers until the classification layer. The number of tweet textual kernels $K_t$ and tweet image textual kernels $K_it$ is set to $K_t = 10$ and $K_it = 5$. The TKM architecture is illustrated in Fig. FIGREF29."
+    ],
+    [
+      "We train the multimodal models with a Cross-Entropy loss with Softmax activations and an ADAM optimizer with an initial learning rate of $1e-4$. Our dataset suffers from a high class imbalance, so we weight the contribution to the loss of the samples to totally compensate for it. One of the goals of this work is to explore how every one of the inputs contributes to the classification and to prove that the proposed model can learn concurrences between visual and textual data useful to improve the hate speech classification results on multimodal data. To do that we train different models where all or only some inputs are available. When an input is not available, we set it to zeros, and we do the same when an image has no text."
+    ],
+    [
+      "Table TABREF31 shows the F-score, the Area Under the ROC Curve (AUC) and the mean accuracy (ACC) of the proposed models when different inputs are available. $TT$ refers to the tweet text, $IT$ to the image text and $I$ to the image. It also shows results for the LSTM, for the Davison method proposed in BIBREF7 trained with MMHS150K, and for random scores. Fig. FIGREF32 shows the Precision vs Recall plot and the ROC curve (which plots the True Positive Rate vs the False Positive Rate) of the different models.",
+      "First, notice that given the subjectivity of the task and the discrepancies between annotators, getting optimal scores in the evaluation metrics is virtually impossible. However, a system with relatively low metric scores can still be very useful for hate speech detection in a real application: it will fire on publications for which most annotators agree they are hate, which are often the stronger attacks. The proposed LSTM to detect hate speech when only text is available, gets similar results as the method presented in BIBREF7, which we trained with MMHS150K and the same splits. However, more than substantially advancing the state of the art on hate speech detection in textual publications, our key purpose in this work is to introduce and work on its detection on multimodal publications. We use LSTM because it provides a strong representation of the tweet texts.",
+      "The FCM trained only with images gets decent results, considering that in many publications the images might not give any useful information for the task. Fig. FIGREF33 shows some representative examples of the top hate and not hate scored images of this model. Many hate tweets are accompanied by demeaning nudity images, being sexist or homophobic. Other racist tweets are accompanied by images caricaturing black people. Finally, MEMES are also typically used in hate speech publications. The top scored images for not hate are portraits of people belonging to minorities. This is due to the use of slur inside these communities without an offensive intention, such as the word nigga inside the afro-american community or the word dyke inside the lesbian community. These results show that images can be effectively used to discriminate between offensive and non-offensive uses of those words.",
+      "Despite the model trained only with images proves that they are useful for hate speech detection, the proposed multimodal models are not able to improve the detection compared to the textual models. Besides the different architectures, we have tried different training strategies, such as initializing the CNN weights with a model already trained solely with MMHS150K images or using dropout to force the multimodal models to use the visual information. Eventually, though, these models end up using almost only the text input for the prediction and producing very similar results to those of the textual models. The proposed multimodal models, such as TKM, have shown good performance in other tasks, such as VQA. Next, we analyze why they do not perform well in this task and with this data:",
+      "[noitemsep,leftmargin=*]",
+      "Noisy data. A major challenge of this task is the discrepancy between annotations due to subjective judgement. Although this affects also detection using only text, its repercussion is bigger in more complex tasks, such as detection using images or multimodal detection.",
+      "Complexity and diversity of multimodal relations. Hate speech multimodal publications employ a lot of background knowledge which makes the relations between visual and textual elements they use very complex and diverse, and therefore difficult to learn by a neural network.",
+      "Small set of multimodal examples. Fig. FIGREF5 shows some of the challenging multimodal hate examples that we aimed to detect. But although we have collected a big dataset of $150K$ tweets, the subset of multimodal hate there is still too small to learn the complex multimodal relations needed to identify multimodal hate."
+    ],
+    [
+      "In this work we have explored the task of hate speech detection on multimodal publications. We have created MMHS150K, to our knowledge the biggest available hate speech dataset, and the first one composed of multimodal data, namely tweets formed by image and text. We have trained different textual, visual and multimodal models with that data, and found out that, despite the fact that images are useful for hate speech detection, the multimodal models do not outperform the textual models. Finally, we have analyzed the challenges of the proposed task and dataset. Given that most of the content in Social Media nowadays is multimodal, we truly believe on the importance of pushing forward this research. The code used in this work is available in ."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: Self-Taught Convolutional Neural Networks for Short Text Clustering
+
+Question: By how much did they outperform the other methods?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Short Text Clustering",
+    "Deep Neural Networks",
+    "Methodology",
+    "Deep Convolutional Neural Networks",
+    "Unsupervised Dimensionality Reduction",
+    "Learning",
+    "K-means for Clustering",
+    "Datasets",
+    "Pre-trained Word Vectors",
+    "Comparisons",
+    "Evaluation Metrics",
+    "Hyperparameter Settings",
+    "Results and Analysis",
+    "Conclusions",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "Short text clustering is of great importance due to its various applications, such as user profiling BIBREF0 and recommendation BIBREF1 , for nowaday's social media dataset emerged day by day. However, short text clustering has the data sparsity problem and most words only occur once in each short text BIBREF2 . As a result, the Term Frequency-Inverse Document Frequency (TF-IDF) measure cannot work well in short text setting. In order to address this problem, some researchers work on expanding and enriching the context of data from Wikipedia BIBREF3 or an ontology BIBREF4 . However, these methods involve solid Natural Language Processing (NLP) knowledge and still use high-dimensional representation which may result in a waste of both memory and computation time. Another way to overcome these issues is to explore some sophisticated models to cluster short texts. For example, Yin and Wang BIBREF5 proposed a Dirichlet multinomial mixture model-based approach for short text clustering. Yet how to design an effective model is an open question, and most of these methods directly trained based on Bag-of-Words (BoW) are shallow structures which cannot preserve the accurate semantic similarities.",
+      "Recently, with the help of word embedding, neural networks demonstrate their great performance in terms of constructing text representation, such as Recursive Neural Network (RecNN) BIBREF6 , BIBREF7 and Recurrent Neural Network (RNN) BIBREF8 . However, RecNN exhibits high time complexity to construct the textual tree, and RNN, using the hidden layer computed at the last word to represent the text, is a biased model where later words are more dominant than earlier words BIBREF9 . Whereas for the non-biased models, the learned representation of one text can be extracted from all the words in the text with non-dominant learned weights. More recently, Convolution Neural Network (CNN), as the most popular non-biased model and applying convolutional filters to capture local features, has achieved a better performance in many NLP applications, such as sentence modeling BIBREF10 , relation classification BIBREF11 , and other traditional NLP tasks BIBREF12 . Most of the previous works focus CNN on solving supervised NLP tasks, while in this paper we aim to explore the power of CNN on one unsupervised NLP task, short text clustering.",
+      "We systematically introduce a simple yet surprisingly powerful Self-Taught Convolutional neural network framework for Short Text Clustering, called STC INLINEFORM0 . An overall architecture of our proposed approach is illustrated in Figure FIGREF5 . We, inspired by BIBREF13 , BIBREF14 , utilize a self-taught learning framework into our task. In particular, the original raw text features are first embedded into compact binary codes INLINEFORM1 with the help of one traditional unsupervised dimensionality reduction function. Then text matrix INLINEFORM2 projected from word embeddings are fed into CNN model to learn the deep feature representation INLINEFORM3 and the output units are used to fit the pre-trained binary codes INLINEFORM4 . After obtaining the learned features, K-means algorithm is employed on them to cluster texts into clusters INLINEFORM5 . Obviously, we call our approach \u201cself-taught\u201d because the CNN model is learnt from the pseudo labels generated from the previous stage, which is quite different from the term \u201cself-taught\u201d in BIBREF15 . Our main contributions can be summarized as follows:",
+      "This work is an extension of our conference paper BIBREF16 , and they differ in the following aspects. First, we put forward a general a self-taught CNN framework in this paper which can flexibly couple various semantic features, whereas the conference version can be seen as a specific example of this work. Second, in this paper we use a new short text dataset, Biomedical, in the experiment to verify the effectiveness of our approach. Third, we put much effort on studying the influence of various different semantic features integrated in our self-taught CNN framework, which is not involved in the conference paper.",
+      "For the purpose of reproducibility, we make the datasets and software used in our experiments publicly available at the website.",
+      "The remainder of this paper is organized as follows: In Section SECREF2 , we first briefly survey several related works. In Section SECREF3 , we describe the proposed approach STC INLINEFORM0 and implementation details. Experimental results and analyses are presented in Section SECREF4 . Finally, conclusions are given in the last Section."
+    ],
+    [
+      "In this section, we review the related work from the following two perspectives: short text clustering and deep neural networks."
+    ],
+    [
+      "There have been several studies that attempted to overcome the sparseness of short text representation. One way is to expand and enrich the context of data. For example, Banerjee et al. BIBREF3 proposed a method of improving the accuracy of short text clustering by enriching their representation with additional features from Wikipedia, and Fodeh et al. BIBREF4 incorporate semantic knowledge from an ontology into text clustering. However, these works need solid NLP knowledge and still use high-dimensional representation which may result in a waste of both memory and computation time. Another direction is to map the original features into reduced space, such as Latent Semantic Analysis (LSA) BIBREF17 , Laplacian Eigenmaps (LE) BIBREF18 , and Locality Preserving Indexing (LPI) BIBREF19 . Even some researchers explored some sophisticated models to cluster short texts. For example, Yin and Wang BIBREF5 proposed a Dirichlet multinomial mixture model-based approach for short text clustering. Moreover, some studies even focus the above both two streams. For example, Tang et al. BIBREF20 proposed a novel framework which enrich the text features by employing machine translation and reduce the original features simultaneously through matrix factorization techniques.",
+      "Despite the above clustering methods can alleviate sparseness of short text representation to some extent, most of them ignore word order in the text and belong to shallow structures which can not fully capture accurate semantic similarities."
+    ],
+    [
+      "Recently, there is a revival of interest in DNN and many researchers have concentrated on using Deep Learning to learn features. Hinton and Salakhutdinov BIBREF21 use DAE to learn text representation. During the fine-tuning procedure, they use backpropagation to find codes that are good at reconstructing the word-count vector.",
+      "More recently, researchers propose to use external corpus to learn a distributed representation for each word, called word embedding BIBREF22 , to improve DNN performance on NLP tasks. The Skip-gram and continuous bag-of-words models of Word2vec BIBREF23 propose a simple single-layer architecture based on the inner product between two word vectors, and Pennington et al. BIBREF24 introduce a new model for word representation, called GloVe, which captures the global corpus statistics.",
+      "In order to learn the compact representation vectors of sentences, Le and Mikolov BIBREF25 directly extend the previous Word2vec BIBREF23 by predicting words in the sentence, which is named Paragraph Vector (Para2vec). Para2vec is still a shallow window-based method and need a larger corpus to yield better performance. More neural networks utilize word embedding to capture true meaningful syntactic and semantic regularities, such as RecNN BIBREF6 , BIBREF7 and RNN BIBREF8 . However, RecNN exhibits high time complexity to construct the textual tree, and RNN, using the layer computed at the last word to represent the text, is a biased model. Recently, Long Short-Term Memory (LSTM) BIBREF26 and Gated Recurrent Unit (GRU) BIBREF27 , as sophisticated recurrent hidden units of RNN, has presented its advantages in many sequence generation problem, such as machine translation BIBREF28 , speech recognition BIBREF29 , and text conversation BIBREF30 . While, CNN is better to learn non-biased implicit features which has been successfully exploited for many supervised NLP learning tasks as described in Section SECREF1 , and various CNN based variants are proposed in the recent works, such as Dynamic Convolutional Neural Network (DCNN) BIBREF10 , Gated Recursive Convolutional Neural Network (grConv) BIBREF31 and Self-Adaptive Hierarchical Sentence model (AdaSent) BIBREF32 .",
+      "In the past few days, Visin et al. BIBREF33 have attempted to replace convolutional layer in CNN to learn non-biased features for object recognition with four RNNs, called ReNet, that sweep over lower-layer features in different directions: (1) bottom to top, (2) top to bottom, (3) left to right and (4) right to left. However, ReNet does not outperform state-of-the-art convolutional neural networks on any of the three benchmark datasets, and it is also a supervised learning model for classification. Inspired by Skip-gram of word2vec BIBREF34 , BIBREF23 , Skip-thought model BIBREF35 describe an approach for unsupervised learning of a generic, distributed sentence encoder. Similar as Skip-gram model, Skip-thought model trains an encoder-decoder model that tries to reconstruct the surrounding sentences of an encoded sentence and released an off-the-shelf encoder to extract sentence representation. Even some researchers introduce continuous Skip-gram and negative sampling to CNN for learning visual representation in an unsupervised manner BIBREF36 . This paper, from a new perspective, puts forward a general self-taught CNN framework which can flexibly couple various semantic features and achieve a good performance on one unsupervised learning task, short text clustering."
+    ],
+    [
+      "Assume that we are given a dataset of INLINEFORM0 training texts denoted as: INLINEFORM1 , where INLINEFORM2 is the dimensionality of the original BoW representation. Denote its tag set as INLINEFORM3 and the pre-trained word embedding set as INLINEFORM4 , where INLINEFORM5 is the dimensionality of word vectors and INLINEFORM6 is the vocabulary size. In order to learn the INLINEFORM7 -dimensional deep feature representation INLINEFORM8 from CNN in an unsupervised manner, some unsupervised dimensionality reduction methods INLINEFORM9 are employed to guide the learning of CNN model. Our goal is to cluster these texts INLINEFORM10 into clusters INLINEFORM11 based on the learned deep feature representation while preserving the semantic consistency.",
+      "As depicted in Figure FIGREF5 , the proposed framework consist of three components, deep convolutional neural network (CNN), unsupervised dimensionality reduction function and K-means module. In the rest sections, we first present the first two components respectively, and then give the trainable parameters and the objective function to learn the deep feature representation. Finally, the last section describe how to perform clustering on the learned features."
+    ],
+    [
+      "In this section, we briefly review one popular deep convolutional neural network, Dynamic Convolutional Neural Network (DCNN) BIBREF10 as an instance of CNN in the following sections, which as the foundation of our proposed method has been successfully proposed for the completely supervised learning task, text classification.",
+      "Taking a neural network with two convolutional layers in Figure FIGREF9 as an example, the network transforms raw input text to a powerful representation. Particularly, each raw text vector INLINEFORM0 is projected into a matrix representation INLINEFORM1 by looking up a word embedding INLINEFORM2 , where INLINEFORM3 is the length of one text. We also let INLINEFORM4 and INLINEFORM5 denote the weights of the neural networks. The network defines a transformation INLINEFORM6 INLINEFORM7 which transforms an input raw text INLINEFORM8 to a INLINEFORM9 -dimensional deep representation INLINEFORM10 . There are three basic operations described as follows:",
+      "Wide one-dimensional convolution This operation INLINEFORM0 is applied to an individual row of the sentence matrix INLINEFORM1 , and yields a resulting matrix INLINEFORM2 , where INLINEFORM3 is the width of convolutional filter.",
+      "Folding In this operation, every two rows in a feature map are simply summed component-wisely. For a map of INLINEFORM0 rows, folding returns a map of INLINEFORM1 rows, thus halving the size of the representation and yielding a matrix feature INLINEFORM2 . Note that folding operation does not introduce any additional parameters.",
+      "Dynamic INLINEFORM0 -max pooling Assuming the pooling parameter as INLINEFORM1 , INLINEFORM2 -max pooling selects the sub-matrix INLINEFORM3 of the INLINEFORM4 highest values in each row of the matrix INLINEFORM5 . For dynamic INLINEFORM6 -max pooling, the pooling parameter INLINEFORM7 is dynamically selected in order to allow for a smooth extraction of higher-order and longer-range features BIBREF10 . Given a fixed pooling parameter INLINEFORM8 for the topmost convolutional layer, the parameter INLINEFORM9 of INLINEFORM10 -max pooling in the INLINEFORM11 -th convolutional layer can be computed as follows: DISPLAYFORM0 ",
+      "where INLINEFORM0 is the total number of convolutional layers in the network."
+    ],
+    [
+      "As described in Figure FIGREF5 , the dimensionality reduction function is defined as follows: DISPLAYFORM0 ",
+      "where, INLINEFORM0 are the INLINEFORM1 -dimensional reduced latent space representations. Here, we take four popular dimensionality reduction methods as examples in our framework.",
+      "Average Embedding (AE): This method directly averages the word embeddings which are respectively weighted with TF and TF-IDF. Huang et al. BIBREF37 used this strategy as the global context in their task, and Socher et al. BIBREF7 and Lai et al. BIBREF9 used this method for text classification. The weighted average of all word vectors in one text can be computed as follows: DISPLAYFORM0 ",
+      "where INLINEFORM0 can be any weighting function that captures the importance of word INLINEFORM1 in the text INLINEFORM2 .",
+      "Latent Semantic Analysis (LSA): LSA BIBREF17 is the most popular global matrix factorization method, which applies a dimension reducing linear projection, Singular Value Decomposition (SVD), of the corresponding term/document matrix. Suppose the rank of INLINEFORM0 is INLINEFORM1 , LSA decompose INLINEFORM2 into the product of three other matrices: DISPLAYFORM0 ",
+      "where INLINEFORM0 and INLINEFORM1 are the singular values of INLINEFORM2 , INLINEFORM3 is a set of left singular vectors and INLINEFORM4 is a set of right singular vectors. LSA uses the top INLINEFORM5 vectors in INLINEFORM6 as the transformation matrix to embed the original text features into a INLINEFORM7 -dimensional subspace INLINEFORM8 BIBREF17 .",
+      "Laplacian Eigenmaps (LE): The top eigenvectors of graph Laplacian, defined on the similarity matrix of texts, are used in the method, which can discover the manifold structure of the text space BIBREF18 . In order to avoid storing the dense similarity matrix, many approximation techniques are proposed to reduce the memory usage and computational complexity for LE. There are two representative approximation methods, sparse similarity matrix and Nystr INLINEFORM0 m approximation. Following previous studies BIBREF38 , BIBREF13 , we select the former technique to construct the INLINEFORM1 local similarity matrix INLINEFORM2 by using heat kernel as follows: DISPLAYFORM0 ",
+      "where, INLINEFORM0 is a tuning parameter (default is 1) and INLINEFORM1 represents the set of INLINEFORM2 -nearest-neighbors of INLINEFORM3 . By introducing a diagonal INLINEFORM4 matrix INLINEFORM5 whose entries are given by INLINEFORM6 , the graph Laplacian INLINEFORM7 can be computed by ( INLINEFORM8 ). The optimal INLINEFORM9 real-valued matrix INLINEFORM10 can be obtained by solving the following objective function: DISPLAYFORM0 ",
+      "where INLINEFORM0 is the trace function, INLINEFORM1 requires the different dimensions to be uncorrelated, and INLINEFORM2 requires each dimension to achieve equal probability as positive or negative).",
+      "Locality Preserving Indexing (LPI): This method extends LE to deal with unseen texts by approximating the linear function INLINEFORM0 BIBREF13 , and the subspace vectors are obtained by finding the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the Riemannian manifold BIBREF19 . Similar as LE, we first construct the local similarity matrix INLINEFORM1 , then the graph Laplacian INLINEFORM2 can be computed by ( INLINEFORM3 ), where INLINEFORM4 measures the local density around INLINEFORM5 and is equal to INLINEFORM6 . Compute the eigenvectors INLINEFORM7 and eigenvalues INLINEFORM8 of the following generalized eigen-problem: DISPLAYFORM0 ",
+      "The mapping function INLINEFORM0 can be obtained and applied to the unseen data BIBREF38 .",
+      "All of the above methods claim a better performance in capturing semantic similarity between texts in the reduced latent space representation INLINEFORM0 than in the original representation INLINEFORM1 , while the performance of short text clustering can be further enhanced with the help of our framework, self-taught CNN."
+    ],
+    [
+      "The last layer of CNN is an output layer as follows: DISPLAYFORM0 ",
+      "where, INLINEFORM0 is the deep feature representation, INLINEFORM1 is the output vector and INLINEFORM2 is weight matrix.",
+      "In order to incorporate the latent semantic features INLINEFORM0 , we first binary the real-valued vectors INLINEFORM1 to the binary codes INLINEFORM2 by setting the threshold to be the media vector INLINEFORM3 . Then, the output vector INLINEFORM4 is used to fit the binary codes INLINEFORM5 via INLINEFORM6 logistic operations as follows: DISPLAYFORM0 ",
+      "All parameters to be trained are defined as INLINEFORM0 . DISPLAYFORM0 ",
+      "Given the training text collection INLINEFORM0 , and the pre-trained binary codes INLINEFORM1 , the log likelihood of the parameters can be written down as follows: DISPLAYFORM0 ",
+      "Following the previous work BIBREF10 , we train the network with mini-batches by back-propagation and perform the gradient-based optimization using the Adagrad update rule BIBREF39 . For regularization, we employ dropout with 50% rate to the penultimate layer BIBREF10 , BIBREF40 ."
+    ],
+    [
+      "With the given short texts, we first utilize the trained deep neural network to obtain the semantic representations INLINEFORM0 , and then employ traditional K-means algorithm to perform clustering."
+    ],
+    [
+      "We test our proposed approach on three public short text datasets. The summary statistics and semantic topics of these datasets are described in Table TABREF24 and Table TABREF25 .",
+      "SearchSnippets. This dataset was selected from the results of web search transaction using predefined phrases of 8 different domains by Phan et al. BIBREF41 .",
+      "StackOverflow. We use the challenge data published in Kaggle.com. The raw dataset consists 3,370,528 samples through July 31st, 2012 to August 14, 2012. In our experiments, we randomly select 20,000 question titles from 20 different tags as in Table TABREF25 .",
+      "Biomedical. We use the challenge data published in BioASQ's official website. In our experiments, we randomly select 20, 000 paper titles from 20 different MeSH major topics as in Table TABREF25 . As described in Table TABREF24 , the max length of selected paper titles is 53.",
+      "For these datasets, we randomly select 10% of data as the development set. Since SearchSnippets has been pre-processed by Phan et al. BIBREF41 , we do not further process this dataset. In StackOverflow, texts contain lots of computer terminology, and symbols and capital letters are meaningful, thus we do not do any pre-processed procedures. For Biomedical, we remove the symbols and convert letters into lower case."
+    ],
+    [
+      "We use the publicly available word2vec tool to train word embeddings, and the most parameters are set as same as Mikolov et al. BIBREF23 to train word vectors on Google News setting, except of vector dimensionality using 48 and minimize count using 5. For SearchSnippets, we train word vectors on Wikipedia dumps. For StackOverflow, we train word vectors on the whole corpus of the StackOverflow dataset described above which includes the question titles and post contents. For Biomedical, we train word vectors on all titles and abstracts of 2014 training articles. The coverage of these learned vectors on three datasets are listed in Table TABREF32 , and the words not present in the set of pre-trained words are initialized randomly."
+    ],
+    [
+      "In our experiment, some widely used text clustering methods are compared with our approach. Besides K-means, Skip-thought Vectors, Recursive Neural Network and Paragraph Vector based clustering methods, four baseline clustering methods are directly based on the popular unsupervised dimensionality reduction methods as described in Section SECREF11 . We further compare our approach with some other non-biased neural networks, such as bidirectional RNN. More details are listed as follows:",
+      "K-means K-means BIBREF42 on original keyword features which are respectively weighted with term frequency (TF) and term frequency-inverse document frequency (TF-IDF).",
+      "Skip-thought Vectors (SkipVec) This baseline BIBREF35 gives an off-the-shelf encoder to produce highly generic sentence representations. The encoder is trained using a large collection of novels and provides three encoder modes, that are unidirectional encoder (SkipVec (Uni)) with 2,400 dimensions, bidirectional encoder (SkipVec (Bi)) with 2,400 dimensions and combined encoder (SkipVec (Combine)) with SkipVec (Uni) and SkipVec (Bi) of 2,400 dimensions each. K-means is employed on the these vector representations respectively.",
+      "Recursive Neural Network (RecNN) In BIBREF6 , the tree structure is firstly greedy approximated via unsupervised recursive autoencoder. Then, semi-supervised recursive autoencoders are used to capture the semantics of texts based on the predicted structure. In order to make this recursive-based method completely unsupervised, we remove the cross-entropy error in the second phrase to learn vector representation and subsequently employ K-means on the learned vectors of the top tree node and the average of all vectors in the tree.",
+      "Paragraph Vector (Para2vec) K-means on the fixed size feature vectors generated by Paragraph Vector (Para2vec) BIBREF25 which is an unsupervised method to learn distributed representation of words and paragraphs. In our experiments, we use the open source software released by Mesnil et al. BIBREF43 .",
+      "Average Embedding (AE) K-means on the weighted average vectors of the word embeddings which are respectively weighted with TF and TF-IDF. The dimension of average vectors is equal to and decided by the dimension of word vectors used in our experiments.",
+      "Latent Semantic Analysis (LSA) K-means on the reduced subspace vectors generated by Singular Value Decomposition (SVD) method. The dimension of subspace is default set to the number of clusters, we also iterate the dimensions ranging from 10:10:200 to get the best performance, that is 10 on SearchSnippets, 20 on StackOverflow and 20 on Biomedical in our experiments.",
+      "Laplacian Eigenmaps (LE) This baseline, using Laplacian Eigenmaps and subsequently employing K-means algorithm, is well known as spectral clustering BIBREF44 . The dimension of subspace is default set to the number of clusters BIBREF18 , BIBREF38 , we also iterate the dimensions ranging from 10:10:200 to get the best performance, that is 20 on SearchSnippets, 70 on StackOverflow and 30 on Biomedical in our experiments.",
+      "Locality Preserving Indexing (LPI) This baseline, projecting the texts into a lower dimensional semantic space, can discover both the geometric and discriminating structures of the original feature space BIBREF38 . The dimension of subspace is default set to the number of clusters BIBREF38 , we also iterate the dimensions ranging from 10:10:200 to get the best performance, that is 20 on SearchSnippets, 80 on StackOverflow and 30 on Biomedical in our experiments.",
+      "bidirectional RNN (bi-RNN) We replace the CNN model in our framework as in Figure FIGREF5 with some bi-RNN models. Particularly, LSTM and GRU units are used in the experiments. In order to generate the fixed-length document representation from the variable-length vector sequences, for both bi-LSTM and bi-GRU based clustering methods, we further utilize three pooling methods: last pooling (using the last hidden state), mean pooling and element-wise max pooling. These pooling methods are respectively used in the previous works BIBREF45 , BIBREF27 , BIBREF46 and BIBREF9 . For regularization, the training gradients of all parameters with an INLINEFORM0 2 norm larger than 40 are clipped to 40, as the previous work BIBREF47 ."
+    ],
+    [
+      "The clustering performance is evaluated by comparing the clustering results of texts with the tags/labels provided by the text corpus. Two metrics, the accuracy (ACC) and the normalized mutual information metric (NMI), are used to measure the clustering performance BIBREF38 , BIBREF48 . Given a text INLINEFORM0 , let INLINEFORM1 and INLINEFORM2 be the obtained cluster label and the label provided by the corpus, respectively. Accuracy is defined as: DISPLAYFORM0 ",
+      "where, INLINEFORM0 is the total number of texts, INLINEFORM1 is the indicator function that equals one if INLINEFORM2 and equals zero otherwise, and INLINEFORM3 is the permutation mapping function that maps each cluster label INLINEFORM4 to the equivalent label from the text data by Hungarian algorithm BIBREF49 .",
+      "Normalized mutual information BIBREF50 between tag/label set INLINEFORM0 and cluster set INLINEFORM1 is a popular metric used for evaluating clustering tasks. It is defined as follows: DISPLAYFORM0 ",
+      "where, INLINEFORM0 is the mutual information between INLINEFORM1 and INLINEFORM2 , INLINEFORM3 is entropy and the denominator INLINEFORM4 is used for normalizing the mutual information to be in the range of [0, 1]."
+    ],
+    [
+      "The most of parameters are set uniformly for these datasets. Following previous study BIBREF38 , the number of nearest neighbors in Eqn. ( EQREF15 ) is fixed to 15 when constructing the graph structures for LE and LPI. For CNN model, the networks has two convolutional layers. The widths of the convolutional filters are both 3. The value of INLINEFORM0 for the top INLINEFORM1 -max pooling in Eqn. ( EQREF10 ) is 5. The number of feature maps at the first convolutional layer is 12, and 8 feature maps at the second convolutional layer. Both those two convolutional layers are followed by a folding layer. We further set the dimension of word embeddings INLINEFORM2 as 48. Finally, the dimension of the deep feature representation INLINEFORM3 is fixed to 480. Moreover, we set the learning rate INLINEFORM4 as 0.01 and the mini-batch training size as 200. The output size INLINEFORM5 in Eqn. ( EQREF19 ) is set same as the best dimensions of subspace in the baseline method, as described in Section SECREF37 .",
+      "For initial centroids have significant impact on clustering results when utilizing the K-means algorithms, we repeat K-means for multiple times with random initial centroids (specifically, 100 times for statistical significance) as Huang BIBREF48 . The all subspace vectors are normalized to 1 before applying K-means and the final results reported are the average of 5 trials with all clustering methods on three text datasets."
+    ],
+    [
+      "In Table TABREF43 and Table TABREF44 , we report the ACC and NMI performance of our proposed approaches and four baseline methods, K-means, SkipVec, RecNN and Para2vec based clustering methods. Intuitively, we get a general observation that (1) BoW based approaches, including K-means (TF) and K-means (TF-IDF), and SkipVec based approaches perform not well; (2) RecNN based approaches, both RecNN (Ave.) and RecNN (Top+Ave.), do better; (3) Para2vec makes a comparable performance with the most baselines; and (4) the evaluation clearly demonstrate the superiority of our proposed methods STC INLINEFORM0 . It is an expected results. For SkipVec based approaches, the off-the-shelf encoders are trained on the BookCorpus datasets BIBREF51 , and then applied to our datasets to extract the sentence representations. The SkipVec encoders can produce generic sentence representations but may not perform well for specific datasets, in our experiments, StackOverflow and Biomedical datasets consist of many computer terms and medical terms, such as \u201cASP.NET\u201d, \u201cXML\u201d, \u201cC#\u201d, \u201cserum\u201d and \u201cglycolytic\u201d. When we take a more careful look, we find that RecNN (Top) does poorly, even worse than K-means (TF-IDF). The reason maybe that although recursive neural models introduce tree structure to capture compositional semantics, the vector of the top node mainly captures a biased semantic while the average of all vectors in the tree nodes, such as RecNN (Ave.), can be better to represent sentence level semantic. And we also get another observation that, although our proposed STC INLINEFORM1 -LE and STC INLINEFORM2 -LPI outperform both BoW based and RecNN based approaches across all three datasets, STC INLINEFORM3 -AE and STC INLINEFORM4 -LSA do just exhibit some similar performances as RecNN (Ave.) and RecNN (Top+Ave.) do in the datasets of StackOverflow and Biomedical.",
+      "We further replace the CNN model in our framework as in Figure FIGREF5 with some other non-biased models, such as bi-LSTM and bi-GRU, and report the results in Table TABREF46 and Table TABREF47 . As an instance, the binary codes generated from LPI are used to guide the learning of bi-LSTM/bi-GRU models. From the results, we can see that bi-GRU and bi-LSTM based clustering methods do equally well, no clear winner, and both achieve great enhancements compared with LPI (best). Compared with these bi-LSTM/bi-GRU based models, the evaluation results still demonstrate the superiority of our approach methods, CNN based clustering model, in the most cases. As the results reported by Visin et al. BIBREF33 , despite bi-directional or multi-directional RNN models perform a good non-biased feature extraction, they yet do not outperform state-of-the-art CNN on some tasks.",
+      "In order to make clear what factors make our proposed method work, we report the bar chart results of ACC and MNI of our proposed methods and the corresponding baseline methods in Figure FIGREF49 and Figure FIGREF53 . It is clear that, although AE and LSA does well or even better than LE and LPI, especially in dataset of both StackOverflow and Biomedical, STC INLINEFORM0 -LE and STC INLINEFORM1 -LPI achieve a much larger performance enhancements than STC INLINEFORM2 -AE and STC INLINEFORM3 -LSA do. The possible reason is that the information the pseudo supervision used to guide the learning of CNN model that make difference. Especially, for AE case, the input features fed into CNN model and the pseudo supervision employed to guide the learning of CNN model are all come from word embeddings. There are no different semantic features to be used into our proposed method, thus the performance enhancements are limited in STC INLINEFORM4 -AE. For LSA case, as we known, LSA is to make matrix factorization to find the best subspace approximation of the original feature space to minimize the global reconstruction error. And as BIBREF24 , BIBREF52 recently point out that word embeddings trained with word2vec or some variances, is essentially to do an operation of matrix factorization. Therefore, the information between input and the pseudo supervision in CNN is not departed very largely from each other, and the performance enhancements of STC INLINEFORM5 -AE is also not quite satisfactory. For LE and LPI case, as we known that LE extracts the manifold structure of the original feature space, and LPI extracts both geometric and discriminating structure of the original feature space BIBREF38 . We guess that our approach STC INLINEFORM6 -LE and STC INLINEFORM7 -LPI achieve enhancements compared with both LE and LPI by a large margin, because both of LE and LPI get useful semantic features, and these features are also different from word embeddings used as input of CNN. From this view, we say that our proposed STC has potential to behave more effective when the pseudo supervision is able to get semantic meaningful features, which is different enough from the input of CNN.",
+      "Furthermore, from the results of K-means and AE in Table TABREF43 - TABREF44 and Figure FIGREF49 - FIGREF53 , we note that TF-IDF weighting gives a more remarkable improvement for K-means, while TF weighting works better than TF-IDF weighting for Average Embedding. Maybe the reason is that pre-trained word embeddings encode some useful information from external corpus and are able to get even better results without TF-IDF weighting. Meanwhile, we find that LE get quite unusual good performance than LPI, LSA and AE in SearchSnippets dataset, which is not found in the other two datasets. To get clear about this, and also to make a much better demonstration about our proposed approaches and other baselines, we further report 2-dimensional text embeddings on SearchSnippets in Figure FIGREF58 , using t-SNE BIBREF53 to get distributed stochastic neighbor embedding of the feature representations used in the clustering methods. We can see that the results of from AE and LSA seem to be fairly good or even better than the ones from LE and LPI, which is not the same as the results from ACC and NMI in Figure FIGREF49 - FIGREF53 . Meanwhile, RecNN (Ave.) performs better than BoW (both TF and TF-IDF) while RecNN (Top) does not, which is the same as the results from ACC and NMI in Table TABREF43 and Table TABREF44 . Then we guess that both \u201dthe same as\u201d and \u201dnot the same as\u201d above, is just a good example to illustrate that visualization tool, such as t-SNE, get some useful information for measuring results, which is different from the ones of ACC and NMI. Moreover, from this complementary view of t-SNE, we can see that our STC INLINEFORM0 -AE, STC INLINEFORM1 -LSA, STC INLINEFORM2 -LE, and STC INLINEFORM3 -LPI show more clear-cut margins among different semantic topics (that is, tags/labels), compared with AE, LSA, LE and LPI, respectively, as well as compared with both baselines, BoW and RecNN based ones.",
+      "From all these results, with three measures of ACC, NMI and t-SNE under three datasets, we can get a solid conclusion that our proposed approaches is an effective approaches to get useful semantic features for short text clustering."
+    ],
+    [
+      "With the emergence of social media, short text clustering has become an increasing important task. This paper explores a new perspective to cluster short texts based on deep feature representation learned from the proposed self-taught convolutional neural networks. Our framework can be successfully accomplished without using any external tags/labels and complicated NLP pre-processing, and and our approach is a flexible framework, in which the traditional dimension reduction approaches could be used to get performance enhancement. Our extensive experimental study on three short text datasets shows that our approach can achieve a significantly better performance. In the future, how to select and incorporate more effective semantic features into the proposed framework would call for more research."
+    ],
+    [
+      "We would like to thank reviewers for their comments, and acknowledge Kaggle and BioASQ for making the datasets available. This work is supported by the National Natural Science Foundation of China (No. 61602479, No. 61303172, No. 61403385) and the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB02070005)."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0355/instruction.md b/qasper-0355/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..d74e352bcfe5b6142621c6140032cebe22477267
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+++ b/qasper-0355/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Solving Arithmetic Word Problems Automatically Using Transformer and Unambiguous Representations
+
+Question: Does pre-training on general text corpus improve performance?
\ No newline at end of file
diff --git a/qasper-0364/instruction.md b/qasper-0364/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..12264ca7649e374083e222004e8d6c3bb6113315
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+Name of Paper: A Measure of Similarity in Textual Data Using Spearman's Rank Correlation Coefficient
+
+Question: Which dataset(s) do they use?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Background",
+    "Background ::: Document Representation",
+    "Background ::: Measures of Similarity",
+    "Related Work",
+    "The Spearman's Rank Correlation Coefficient Similarity Measure",
+    "The Spearman's Rank Correlation Coefficient Similarity Measure ::: Spearman's Rank Correlation Coefficient",
+    "The Spearman's Rank Correlation Coefficient Similarity Measure ::: Spearman's Rank Correlation Coefficient ::: An Illustration of the Ranking TF-IDF Vectors",
+    "Experiments",
+    "Experiments ::: Comparison Between Similarity Measures",
+    "Experiments ::: Non-linearity of Documents",
+    "Conclusion and Future Work"
+  ],
+  "paragraphs": [
+    [
+      "Over the past few years, the term big data has become an important key point for research into data mining and information retrieval. Through the years, the quantity of data managed across enterprises has evolved from a simple and imperceptible task to an extent to which it has become the central performance improvement problem. In other words, it evolved to be the next frontier for innovation, competition and productivity BIBREF0. Extracting knowledge from data is now a very competitive environment. Many companies process vast amounts of customer/user data in order to improve the quality of experience (QoE) of their customers. For instance, a typical use-case scenario would be a book seller that performs an automatic extraction of the content of the books a customer has bought, and subsequently extracts knowledge of what customers prefer to read. The knowledge extracted could then be used to recommend other books. Book recommending systems are typical examples where data mining techniques should be considered as the primary tool for making future decisions BIBREF1.",
+      "KE from TDs is an essential field of research in data mining and it certainly requires techniques that are reliable and accurate in order to neutralize (or even eliminate) uncertainty in future decisions. Grouping TDs based on their content and mutual key information is referred to as clustering. Clustering is mostly performed with respect to a measure of similarity between TDs, which must be represented as vectors in a vector space beforehand BIBREF2. News aggregation engines can be considered as a typical representative where such techniques are extensively applied as a sub-field of natural language processing (NLP).",
+      "In this paper we present a new technique for measuring similarity between TDs, represented in a vector space, based on SRCC - \"a statistical measure of association between two things\" BIBREF3, which in this case things refer to TDs. The mathematical properties of SRCC (such as the ability to detect nonlinear correlation) make it compelling to be researched into. Our motivation is to provide a new technique of improving the quality of KE based on the well-known association measure SRCC, as opposed to other well-known TD similarity measures.",
+      "The paper is organized as follows: Section SECREF2 gives a brief overview of the vector space representation of a TD and the corresponding similarity measures, in Section SECREF3 we address conducted research of the role of SRCC in data mining and trend prediction. Section SECREF4 is a detailed description of the proposed technique, and later, in Section SECREF5 we present clustering and classification experiments conducted on several sets of TDs, while Section SECREF6 summarizes our research and contribution to the broad area of statistical text analysis."
+    ],
+    [
+      "In this section we provide a brief background of vector space representation of TDs and existing similarity measures that have been widely used in statistical text analysis. To begin with, we consider the representation of documents."
+    ],
+    [
+      "A document $d$ can be defined as a finite sequence of terms (independent textual entities within a document, for example, words), namely $d=(t_1,t_2,\\dots ,t_n)$. A general idea is to associate weight to each term $t_i$ within $d$, such that",
+      "which has proven superior in prior extensive research BIBREF4. The most common weight measure is Term Frequency - Inverse Document Frequency (TF-IDF). TF is the frequency of a term within a single document, and IDF represents the importance, or uniqueness of a term within a set of documents $D=\\lbrace d_1, d_2, \\dots ,d_m\\rbrace $. TF-IDF is defined as follows:",
+      "where",
+      "such that $f$ is the number of occurrences of $t$ in $d$ and $\\log $ is used to avoid very small values close to zero.",
+      "Having these measures defined, it becomes obvious that each $w_i$, for $i=1,\\dots ,n$ is assigned the TF-IDF value of the corresponding term. It turns out that each document is represented as a vector of TF-IDF weights within a vector space model (VSM) with its properties BIBREF5."
+    ],
+    [
+      "Different ways of computing the similarity of two vector exist. There are two main approaches in similarity computation:",
+      "Deterministic - similarity measures exploiting algebraic properties of vectors and their geometrical interpretation. These include, for instance, cosine similarity (CS), Jaccard coefficients (for binary representations), etc.",
+      "Stochastic - similarity measures in which uncertainty is taken into account. These include, for instance, statistics such as Pearson's Correlation Coefficient (PCC) BIBREF6.",
+      "Let $\\mathbf {u}$ and $\\mathbf {v}$ be the vector representations of two documents $d_1$ and $d_2$. Cosine similarity simply measures $cos\\theta $, where $\\theta $ is the angle between $\\mathbf {u}$ and $\\mathbf {v}$",
+      "(cosine similarity)",
+      "(PCC)",
+      "where",
+      "All of the above measures are widely used and have proven efficient, but an important aspect is the lack of importance of the order of terms in textual data. It is easy for one to conclude that, two documents containing a single sentence each, but in a reverse order of terms, most deterministic methods fail to express that these are actually very similar. On the other hand, PCC detects only linear correlation, which constraints the diversity present in textual data. In the following section, we study relevant research in solving this problem, and then in Sections SECREF4 and SECREF5 we present our solution and results."
+    ],
+    [
+      "A significant number of similarity measures have been proposed and this topic has been thoroughly elaborated. Its main application is considered to be clustering and classification of textual data organized in TDs. In this section, we provide an overview of relevant research on this topic, to which we can later compare our proposed technique for computing vector similarity.",
+      "KE (also referred to as knowledge discovery) techniques are used to extract information from unstructured data, which can be subsequently used for applying supervised or unsupervised learning techniques, such as clustering and classification of the content BIBREF7. Text clustering should address several challenges such as vast amounts of data, very high dimensionality of more than 10,000 terms (dimensions), and most importantly - an understandable description of the clusters BIBREF8, which essentially implies the demand for high quality of extracted information.",
+      "Regarding high quality KE and information accuracy, much effort has been put into improving similarity measurements. An improvement based on linear algebra, known as Singular Value Decomposition (SVD), is oriented towards word similarity, but instead, its main application is document similarity BIBREF9. Alluring is the fact that this measure takes the advantage of synonym recognition and has been used to achieve human-level scores on multiple-choice synonym questions from the Test of English as a Foreign Language (TOEFL) in a technique known as Latent Semantic Analysis (LSA) BIBREF10 BIBREF5.",
+      "Other semantic term similarity measures have been also proposed, based on information exclusively derived from large corpora of words, such as Pointwise Mutual Information (PMI), which has been reported to have achieved a large degree of correctness in the synonym questions in the TOEFL and SAT tests BIBREF11.",
+      "Moreover, normalized knowledge-based measures, such as Leacock & Chodrow BIBREF12, Lesk (\"how to tell a pine cone from an ice-cream cone\" BIBREF13, or measures for the depth of two concepts (preferably vebs) in the Word-Net taxonomy BIBREF14 have experimentally proven to be efficient. Their accuracy converges to approximately 69%, Leacock & Chodrow and Lesk have showed the highest precision, and having them combined turns out to be the approximately optimal solution BIBREF11."
+    ],
+    [
+      "The main idea behind our proposed technique is to introduce uncertainty in the calculations of the similarity between TDs represented in a vector space model, based on the nonlinear properties of SRCC. Unlike PCC, which is only able to detect linear correlation, SRCC's nonlinear ability provides a convenient way of taking different ordering of terms into account."
+    ],
+    [
+      "The Spreaman's Rank Correlation Coefficient BIBREF3, denoted $\\rho $, has a from which is very similar to PCC. Namely, for $n$ raw scores $U_i, V_i$ for $i=1,\\dots ,n$ denoting TF-IDF values for two document vectors $\\mathbf {U}, \\mathbf {V}$,",
+      "where $u_i$ and $v_i$ are the corresponding ranks of $U_i$ and $V_i$, for $i=0,\\dots ,n-1$. A metric to assign the ranks of each of the TF-IDF values has to be determined beforehand. Each $U_i$ is assigned a rank value $u_i$, such that $u_i=0,1,\\dots ,n-1$. It is important to note that the metric by which the TF-IDF values are ranked is essentially their sorting criteria. A convenient way of determining this criteria when dealing with TF-IDF values, which emphasize the importance of a term within a TD set, is to sort these values in an ascending order. Thus, the largest (or most important) TF-IDF value within a TD vector is assigned the rank value of $n-1$, and the least important is assigned a value of 0."
+    ],
+    [
+      "Consider two TDs $d_1$ and $d_2$, each containing a single sentence.",
+      "Document 1: John had asked Mary to marry him before she left.",
+      "Document 2: Before she left, Mary was asked by John to be his wife.",
+      "Now consider these sentences lemmatized:",
+      "Document 1: John have ask Mary marry before leave.",
+      "Document 2: Before leave Mary ask John his wife.",
+      "Let us now represent $d_1$ and $d_2$ as TF-IDF vectors for the vocabulary in our small corpus.",
+      "The results in Table TABREF7 show that SRCC performs much better in knowledge extraction. The two documents' contents contain the same idea expressed by terms in a different order that John had asked Mary to marry him before she left. It is obvious that cosine similarity cannot recognize this association, but SRCC has successfully recognized it and produced a similarity value of -0.285714.",
+      "SRCC is essentially conducive to semantic similarity. Rising the importance of a term in a TD will eventually rise its importance in another TD. But if the two TDs are of different size, the terms' importance values will also differ, by which a nonlinear association will emerge. This association will not be recognized by PCC at all (as it only detects linear association), but SRCC will definitely catch this detail and produce the desirable similarity value. The idea is to use SRCC to catch such terms which drive the semantic context of a TD, which will follow a nonlinear and lie on a polynomial curve, and not on the line $x=y$.",
+      "In our approach, we use a non-standard measure of similarity in textual data with simple and common frequency values, such as TF-IDF, in contrast to the statement that simple frequencies are not enough for high-quality knowledge extraction BIBREF5. In the next section, we will present our experiments and discuss the results we have obtained."
+    ],
+    [
+      "In order to test our proposed approach, we have conducted a series of experiments. In this section, we briefly discuss the outcome and provide a clear view of whether our approach is suitable for knowledge extraction from textual data in a semantic context.",
+      "We have used a dataset of 14 TDs to conduct our experiments. There are several subjects on which their content is based: (aliens, stories, law, news) BIBREF15."
+    ],
+    [
+      "In this part, we have compared the similarity values produced by each of the similarity measures CS, SRCC and PCC. We have picked a few notable results and they are summarized in Table TABREF9 below.",
+      "In Table TABREF9 that SRCC mostly differs from CS and PCC, which also differ in some cases.For instance, $d_1$ refers to leadership in the nineties, while $d_5$ refers to the family and medical lead act of 1993. We have empirically observed that the general topics discussed in these two textual documents are very different. Namely, discusses different frameworks for leadership empowerment, while $d_5$ discusses medical treatment and self-care of employees. We have observed that the term employee is the only connection between $d_1$ and $d_5$. The similarity value of CS of 0.36 is very unreal in this case, while PCC (0.05), and especially SRCC (0.0018) provide a much more realistic view of the semantic knowledge aggregated in these documents. Another example are $d_8$ and $d_9$. The contents of these documents are very straightforward and very similar, because they discuss aliens seen by Boeing-747 pilots and $d_9$ discusses angels that were considered to be aliens. It is obvious that SRCC is able to detect this association as good as CS and PCC which are very good in such straightforward cases.",
+      "We have observed that SRCC does not perform worse than any other of these similarity measures. It does not always produce the most suitable similarity value, but it indeed does perform at least equally good as other measures. The values in Table TABREF9 are very small, and suggest that SRCC performs well in extracting tiny associations in such cases. It is mostly a few times larger than CS and PCC when there actually exist associations between the documents.",
+      "These results are visually summarized in Figure FIGREF10. The two above-described examples can be clearly seen as standing out."
+    ],
+    [
+      "In this part we will briefly present the nonlinear association between some of the TDs we have used in our experiments. Our purpose is to point out that $(d_6,d_{10})$ and $(d_7,d_{12})$ are the pairs where SRCC is the most appropriate measure for the observed content, and as such, it is able to detect the nonlinear association between them. This can be seen in Figure FIGREF12 below. The straightforward case of $d_8$ and $d_9$ also stands out here (SRCC can also detect it very well).",
+      "The obtained results showed that our technique shows good performance on similarity computing, although it is not a perfect measure. But, it sure comes close to convenient and widely used similarity measures such as CS and PCC. The next section provides a conclusion of our research and suggestions for further work."
+    ],
+    [
+      "In this paper we have presented a non-standard technique for computing the similarity between TF-IDF vectors. We have propagated our idea and contributed a portion of new knowledge in this field of text analysis. We have proposed a technique that is widely used in similar fields, and our goal is to provide starting information to other researches in this area. We consider our observations promising and they should be extensively researched.",
+      "Our experiments have proved that our technique should be a subject for further research. Our future work will concentrate on the implementation of machine learning techniques, such as clustering and subsequent classification of textual data. We expect an information of good quality to be extracted. To summarize, the rapidly emerging area of big data and information retrieval is where our technique should reside and where it should be applied."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0372/instruction.md b/qasper-0372/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..d63b3610d1c6463100d714a63d4b4fd4d0ae2ccb
--- /dev/null
+++ b/qasper-0372/instruction.md
@@ -0,0 +1,150 @@
+Name of Paper: CamemBERT: a Tasty French Language Model
+
+Question: What data is used for training CamemBERT?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work ::: From non-contextual to contextual word embeddings",
+    "Related Work ::: Non-contextual word embeddings for languages other than English",
+    "Related Work ::: Contextualised models for languages other than English",
+    "CamemBERT",
+    "CamemBERT ::: Architecture",
+    "CamemBERT ::: Pretraining objective",
+    "CamemBERT ::: Optimisation",
+    "CamemBERT ::: Segmentation into subword units",
+    "CamemBERT ::: Pretraining data",
+    "Evaluation ::: Part-of-speech tagging and dependency parsing",
+    "Evaluation ::: Part-of-speech tagging and dependency parsing ::: Baselines",
+    "Evaluation ::: Named Entity Recognition",
+    "Evaluation ::: Named Entity Recognition ::: Baselines",
+    "Evaluation ::: Natural Language Inference",
+    "Evaluation ::: Natural Language Inference ::: Baselines",
+    "Experiments",
+    "Experiments ::: Experimental Setup ::: Pretraining",
+    "Experiments ::: Experimental Setup ::: Fine-tuning",
+    "Experiments ::: Results ::: Part-of-Speech tagging and dependency parsing",
+    "Experiments ::: Results ::: Natural Language Inference: XNLI",
+    "Experiments ::: Results ::: Named-Entity Recognition",
+    "Experiments ::: Discussion",
+    "Conclusion",
+    "Acknowledgments",
+    "Appendix ::: Impact of Whole-Word Masking"
+  ],
+  "paragraphs": [
+    [
+      "Pretrained word representations have a long history in Natural Language Processing (NLP), from non-neural methods BIBREF0, BIBREF1, BIBREF2 to neural word embeddings BIBREF3, BIBREF4 and to contextualised representations BIBREF5, BIBREF6. Approaches shifted more recently from using these representations as an input to task-specific architectures to replacing these architectures with large pretrained language models. These models are then fine-tuned to the task at hand with large improvements in performance over a wide range of tasks BIBREF7, BIBREF8, BIBREF9, BIBREF10.",
+      "These transfer learning methods exhibit clear advantages over more traditional task-specific approaches, probably the most important being that they can be trained in an unsupervised manner. They nevertheless come with implementation challenges, namely the amount of data and computational resources needed for pretraining that can reach hundreds of gigabytes of uncompressed text and require hundreds of GPUs BIBREF11, BIBREF9. The latest transformer architecture has gone uses as much as 750GB of plain text and 1024 TPU v3 for pretraining BIBREF10. This has limited the availability of these state-of-the-art models to the English language, at least in the monolingual setting. Even though multilingual models give remarkable results, they are often larger and their results still lag behind their monolingual counterparts BIBREF12. This is particularly inconvenient as it hinders their practical use in NLP systems as well as the investigation of their language modeling capacity, something that remains to be investigated in the case of, for instance, morphologically rich languages.",
+      "We take advantage of the newly available multilingual corpus OSCAR BIBREF13 and train a monolingual language model for French using the RoBERTa architecture. We pretrain the model - which we dub CamemBERT- and evaluate it in four different downstream tasks for French: part-of-speech (POS) tagging, dependency parsing, named entity recognition (NER) and natural language inference (NLI). CamemBERT improves the state of the art for most tasks over previous monolingual and multilingual approaches, which confirms the effectiveness of large pretrained language models for French.",
+      "We summarise our contributions as follows:",
+      "We train a monolingual BERT model on the French language using recent large-scale corpora.",
+      "We evaluate our model on four downstream tasks (POS tagging, dependency parsing, NER and natural language inference (NLI)), achieving state-of-the-art results in most tasks, confirming the effectiveness of large BERT-based models for French.",
+      "We release our model in a user-friendly format for popular open-source libraries so that it can serve as a strong baseline for future research and be useful for French NLP practitioners."
+    ],
+    [
+      "The first neural word vector representations were non-contextualised word embeddings, most notably word2vec BIBREF3, GloVe BIBREF4 and fastText BIBREF14, which were designed to be used as input to task-specific neural architectures. Contextualised word representations such as ELMo BIBREF5 and flair BIBREF6, improved the expressivity of word embeddings by taking context into account. They improved the performance of downstream tasks when they replaced traditional word representations. This paved the way towards larger contextualised models that replaced downstream architectures in most tasks. These approaches, trained with language modeling objectives, range from LSTM-based architectures such as ULMFiT BIBREF15 to the successful transformer-based architectures such as GPT2 BIBREF8, BERT BIBREF7, RoBERTa BIBREF9 and more recently ALBERT BIBREF16 and T5 BIBREF10."
+    ],
+    [
+      "Since the introduction of word2vec BIBREF3, many attempts have been made to create monolingual models for a wide range of languages. For non-contextual word embeddings, the first two attempts were by BIBREF17 and BIBREF18 who created word embeddings for a large number of languages using Wikipedia. Later BIBREF19 trained fastText word embeddings for 157 languages using Common Crawl and showed that using crawled data significantly increased the performance of the embeddings relatively to those trained only on Wikipedia."
+    ],
+    [
+      "Following the success of large pretrained language models, they were extended to the multilingual setting with multilingual BERT , a single multilingual model for 104 different languages trained on Wikipedia data, and later XLM BIBREF12, which greatly improved unsupervised machine translation. A few monolingual models have been released: ELMo models for Japanese, Portuguese, German and Basque and BERT for Simplified and Traditional Chinese and German.",
+      "However, to the best of our knowledge, no particular effort has been made toward training models for languages other than English, at a scale similar to the latest English models (e.g. RoBERTa trained on more than 100GB of data)."
+    ],
+    [
+      "Our approach is based on RoBERTa BIBREF9, which replicates and improves the initial BERT by identifying key hyper-parameters for more robust performance.",
+      "In this section, we describe the architecture, training objective, optimisation setup and pretraining data that was used for CamemBERT.",
+      "CamemBERT differs from RoBERTa mainly with the addition of whole-word masking and the usage of SentencePiece tokenisation BIBREF20."
+    ],
+    [
+      "Similar to RoBERTa and BERT, CamemBERT is a multi-layer bidirectional Transformer BIBREF21. Given the widespread usage of Transformers, we do not describe them in detail here and refer the reader to BIBREF21. CamemBERT uses the original BERT $_{\\small \\textsc {BASE}}$ configuration: 12 layers, 768 hidden dimensions, 12 attention heads, which amounts to 110M parameters."
+    ],
+    [
+      "We train our model on the Masked Language Modeling (MLM) task. Given an input text sequence composed of $N$ tokens $x_1, ..., x_N$, we select $15\\%$ of tokens for possible replacement. Among those selected tokens, 80% are replaced with the special $<$mask$>$ token, 10% are left unchanged and 10% are replaced by a random token. The model is then trained to predict the initial masked tokens using cross-entropy loss.",
+      "Following RoBERTa we dynamically mask tokens instead of fixing them statically for the whole dataset during preprocessing. This improves variability and makes the model more robust when training for multiple epochs.",
+      "Since we segment the input sentence into subwords using SentencePiece, the input tokens to the models can be subwords. An upgraded version of BERT and BIBREF22 have shown that masking whole words instead of individual subwords leads to improved performance. Whole-word masking (WWM) makes the training task more difficult because the model has to predict a whole word instead of predicting only part of the word given the rest. As a result, we used WWM for CamemBERT by first randomly sampling 15% of the words in the sequence and then considering all subword tokens in each of these 15% words for candidate replacement. This amounts to a proportion of selected tokens that is close to the original 15%. These tokens are then either replaced by $<$mask$>$ tokens (80%), left unchanged (10%) or replaced by a random token.",
+      "Subsequent work has shown that the next sentence prediction task (NSP) originally used in BERT does not improve downstream task performance BIBREF12, BIBREF9, we do not use NSP as a consequence."
+    ],
+    [
+      "Following BIBREF9, we optimise the model using Adam BIBREF23 ($\\beta _1 = 0.9$, $\\beta _2 = 0.98$) for 100k steps. We use large batch sizes of 8192 sequences. Each sequence contains at most 512 tokens. We enforce each sequence to only contain complete sentences. Additionally, we used the DOC-SENTENCES scenario from BIBREF9, consisting of not mixing multiple documents in the same sequence, which showed slightly better results."
+    ],
+    [
+      "We segment the input text into subword units using SentencePiece BIBREF20. SentencePiece is an extension of Byte-Pair encoding (BPE) BIBREF24 and WordPiece BIBREF25 that does not require pre-tokenisation (at the word or token level), thus removing the need for language-specific tokenisers. We use a vocabulary size of 32k subword tokens. These are learned on $10^7$ sentences sampled from the pretraining dataset. We do not use subword regularisation (i.e. sampling from multiple possible segmentations) in our implementation for simplicity."
+    ],
+    [
+      "Pretrained language models can be significantly improved by using more data BIBREF9, BIBREF10. Therefore we used French text extracted from Common Crawl, in particular, we use OSCAR BIBREF13 a pre-classified and pre-filtered version of the November 2018 Common Craw snapshot.",
+      "OSCAR is a set of monolingual corpora extracted from Common Crawl, specifically from the plain text WET format distributed by Common Crawl, which removes all HTML tags and converts all text encodings to UTF-8. OSCAR follows the same approach as BIBREF19 by using a language classification model based on the fastText linear classifier BIBREF26, BIBREF27 pretrained on Wikipedia, Tatoeba and SETimes, which supports 176 different languages.",
+      "OSCAR performs a deduplication step after language classification and without introducing a specialised filtering scheme, other than only keeping paragraphs containing 100 or more UTF-8 encoded characters, making OSCAR quite close to the original Crawled data.",
+      "We use the unshuffled version of the French OSCAR corpus, which amounts to 138GB of uncompressed text and 32.7B SentencePiece tokens."
+    ],
+    [
+      "We fist evaluate CamemBERT on the two downstream tasks of part-of-speech (POS) tagging and dependency parsing. POS tagging is a low-level syntactic task, which consists in assigning to each word its corresponding grammatical category. Dependency parsing consists in predicting the labeled syntactic tree capturing the syntactic relations between words.",
+      "We run our experiments using the Universal Dependencies (UD) paradigm and its corresponding UD POS tag set BIBREF28 and UD treebank collection version 2.2 BIBREF29, which was used for the CoNLL 2018 shared task. We perform our work on the four freely available French UD treebanks in UD v2.2: GSD, Sequoia, Spoken, and ParTUT.",
+      "GSD BIBREF30 is the second-largest treebank available for French after the FTB (described in subsection SECREF25), it contains data from blogs, news articles, reviews, and Wikipedia. The Sequoia treebank BIBREF31, BIBREF32 comprises more than 3000 sentences, from the French Europarl, the regional newspaper L\u2019Est R\u00e9publicain, the French Wikipedia and documents from the European Medicines Agency. Spoken is a corpus converted automatically from the Rhapsodie treebank BIBREF33, BIBREF34 with manual corrections. It consists of 57 sound samples of spoken French with orthographic transcription and phonetic transcription aligned with sound (word boundaries, syllables, and phonemes), syntactic and prosodic annotations. Finally, ParTUT is a conversion of a multilingual parallel treebank developed at the University of Turin, and consisting of a variety of text genres, including talks, legal texts, and Wikipedia articles, among others; ParTUT data is derived from the already-existing parallel treebank Par(allel)TUT BIBREF35 . Table TABREF23 contains a summary comparing the sizes of the treebanks.",
+      "We evaluate the performance of our models using the standard UPOS accuracy for POS tagging, and Unlabeled Attachment Score (UAS) and Labeled Attachment Score (LAS) for dependency parsing. We assume gold tokenisation and gold word segmentation as provided in the UD treebanks."
+    ],
+    [
+      "To demonstrate the value of building a dedicated version of BERT for French, we first compare CamemBERT to the multilingual cased version of BERT (designated as mBERT). We then compare our models to UDify BIBREF36. UDify is a multitask and multilingual model based on mBERT that is near state-of-the-art on all UD languages including French for both POS tagging and dependency parsing.",
+      "It is relevant to compare CamemBERT to UDify on those tasks because UDify is the work that pushed the furthest the performance in fine-tuning end-to-end a BERT-based model on downstream POS tagging and dependency parsing. Finally, we compare our model to UDPipe Future BIBREF37, a model ranked 3rd in dependency parsing and 6th in POS tagging during the CoNLL 2018 shared task BIBREF38. UDPipe Future provides us a strong baseline that does not make use of any pretrained contextual embedding.",
+      "We will compare to the more recent cross-lingual language model XLM BIBREF12, as well as the state-of-the-art CoNLL 2018 shared task results with predicted tokenisation and segmentation in an updated version of the paper."
+    ],
+    [
+      "Named Entity Recognition (NER) is a sequence labeling task that consists in predicting which words refer to real-world objects, such as people, locations, artifacts and organisations. We use the French Treebank (FTB) BIBREF39 in its 2008 version introduced by cc-clustering:09short and with NER annotations by sagot2012annotation. The NER-annotated FTB contains more than 12k sentences and more than 350k tokens extracted from articles of the newspaper Le Monde published between 1989 and 1995. In total, it contains 11,636 entity mentions distributed among 7 different types of entities, namely: 2025 mentions of \u201cPerson\u201d, 3761 of \u201cLocation\u201d, 2382 of \u201cOrganisation\u201d, 3357 of \u201cCompany\u201d, 67 of \u201cProduct\u201d, 15 of \u201cPOI\u201d (Point of Interest) and 29 of \u201cFictional Character\u201d.",
+      "A large proportion of the entity mentions in the treebank are multi-word entities. For NER we therefore report the 3 metrics that are commonly used to evaluate models: precision, recall, and F1 score. Here precision measures the percentage of entities found by the system that are correctly tagged, recall measures the percentage of named entities present in the corpus that are found and the F1 score combines both precision and recall measures giving a general idea of a model's performance."
+    ],
+    [
+      "Most of the advances in NER haven been achieved on English, particularly focusing on the CoNLL 2003 BIBREF40 and the Ontonotes v5 BIBREF41, BIBREF42 English corpora. NER is a task that was traditionally tackled using Conditional Random Fields (CRF) BIBREF43 which are quite suited for NER; CRFs were later used as decoding layers for Bi-LSTM architectures BIBREF44, BIBREF45 showing considerable improvements over CRFs alone. These Bi-LSTM-CRF architectures were later enhanced with contextualised word embeddings which yet again brought major improvements to the task BIBREF5, BIBREF6. Finally, large pretrained architectures settled the current state of the art showing a small yet important improvement over previous NER-specific architectures BIBREF7, BIBREF46.",
+      "In non-English NER the CoNLL 2002 shared task included NER corpora for Spanish and Dutch corpora BIBREF47 while the CoNLL 2003 included a German corpus BIBREF40. Here the recent efforts of BIBREF48 settled the state of the art for Spanish and Dutch, while BIBREF6 did it for German.",
+      "In French, no extensive work has been done due to the limited availability of NER corpora. We compare our model with the strong baselines settled by BIBREF49, who trained both CRF and BiLSTM-CRF architectures on the FTB and enhanced them using heuristics and pretrained word embeddings."
+    ],
+    [
+      "We also evaluate our model on the Natural Language Inference (NLI) task, using the French part of the XNLI dataset BIBREF50. NLI consists in predicting whether a hypothesis sentence is entailed, neutral or contradicts a premise sentence.",
+      "The XNLI dataset is the extension of the Multi-Genre NLI (MultiNLI) corpus BIBREF51 to 15 languages by translating the validation and test sets manually into each of those languages. The English training set is also machine translated for all languages. The dataset is composed of 122k train, 2490 valid and 5010 test examples. As usual, NLI performance is evaluated using accuracy.",
+      "To evaluate a model on a language other than English (such as French), we consider the two following settings:",
+      "TRANSLATE-TEST: The French test set is machine translated into English, and then used with an English classification model. This setting provides a reasonable, although imperfect, way to circumvent the fact that no such data set exists for French, and results in very strong baseline scores.",
+      "TRANSLATE-TRAIN: The French model is fine-tuned on the machine-translated English training set and then evaluated on the French test set. This is the setting that we used for CamemBERT."
+    ],
+    [
+      "For the TRANSLATE-TEST setting, we report results of the English RoBERTa to act as a reference.",
+      "In the TRANSLATE-TRAIN setting, we report the best scores from previous literature along with ours. BiLSTM-max is the best model in the original XNLI paper, mBERT which has been reported in French in BIBREF52 and XLM (MLM+TLM) is the best-presented model from BIBREF50."
+    ],
+    [
+      "In this section, we measure the performance of CamemBERT by evaluating it on the four aforementioned tasks: POS tagging, dependency parsing, NER and NLI."
+    ],
+    [
+      "We use the RoBERTa implementation in the fairseq library BIBREF53. Our learning rate is warmed up for 10k steps up to a peak value of $0.0007$ instead of the original $0.0001$ given our large batch size (8192). The learning rate fades to zero with polynomial decay. We pretrain our model on 256 Nvidia V100 GPUs (32GB each) for 100k steps during 17h."
+    ],
+    [
+      "For each task, we append the relevant predictive layer on top of CamemBERT's Transformer architecture. Following the work done on BERT BIBREF7, for sequence tagging and sequence labeling we append a linear layer respectively to the $<$s$>$ special token and to the first subword token of each word. For dependency parsing, we plug a bi-affine graph predictor head as inspired by BIBREF54 following the work done on multilingual parsing with BERT by BIBREF36. We refer the reader to these two articles for more details on this module.",
+      "We fine-tune independently CamemBERT for each task and each dataset. We optimise the model using the Adam optimiser BIBREF23 with a fixed learning rate. We run a grid search on a combination of learning rates and batch sizes. We select the best model on the validation set out of the 30 first epochs.",
+      "Although this might push the performances even further, for all tasks except NLI, we don't apply any regularisation techniques such as weight decay, learning rate warm-up or discriminative fine-tuning. We show that fine-tuning CamemBERT in a straight-forward manner leads to state-of-the-art results on most tasks and outperforms the existing BERT-based models in most cases.",
+      "The POS tagging, dependency parsing, and NER experiments are run using hugging face's Transformer library extended to support CamemBERT and dependency parsing BIBREF55. The NLI experiments use the fairseq library following the RoBERTa implementation."
+    ],
+    [
+      "For POS tagging and dependency parsing, we compare CamemBERT to three other near state-of-the-art models in Table TABREF32. CamemBERT outperforms UDPipe Future by a large margin for all treebanks and all metrics. Despite a much simpler optimisation process, CamemBERT beats UDify performances on all the available French treebanks.",
+      "CamemBERT also demonstrates higher performances than mBERT on those tasks. We observe a larger error reduction for parsing than for tagging. For POS tagging, we observe error reductions of respectively 0.71% for GSD, 0.81% for Sequoia, 0.7% for Spoken and 0.28% for ParTUT. For parsing, we observe error reductions in LAS of 2.96% for GSD, 3.33% for Sequoia, 1.70% for Spoken and 1.65% for ParTUT."
+    ],
+    [
+      "On the XNLI benchmark, CamemBERT obtains improved performance over multilingual language models on the TRANSLATE-TRAIN setting (81.2 vs. 80.2 for XLM) while using less than half the parameters (110M vs. 250M). However, its performance still lags behind models trained on the original English training set in the TRANSLATE-TEST setting, 81.2 vs. 82.91 for RoBERTa. It should be noted that CamemBERT uses far fewer parameters than RoBERTa (110M vs. 355M parameters)."
+    ],
+    [
+      "For named entity recognition, our experiments show that CamemBERT achieves a slightly better precision than the traditional CRF-based SEM architectures described above in Section SECREF25 (CRF and Bi-LSTM+CRF), but shows a dramatic improvement in finding entity mentions, raising the recall score by 3.5 points. Both improvements result in a 2.36 point increase in the F1 score with respect to the best SEM architecture (BiLSTM-CRF), giving CamemBERT the state of the art for NER on the FTB. One other important finding is the results obtained by mBERT. Previous work with this model showed increased performance in NER for German, Dutch and Spanish when mBERT is used as contextualised word embedding for an NER-specific model BIBREF48, but our results suggest that the multilingual setting in which mBERT was trained is simply not enough to use it alone and fine-tune it for French NER, as it shows worse performance than even simple CRF models, suggesting that monolingual models could be better at NER."
+    ],
+    [
+      "CamemBERT displays improved performance compared to prior work for the 4 downstream tasks considered. This confirms the hypothesis that pretrained language models can be effectively fine-tuned for various downstream tasks, as observed for English in previous work. Moreover, our results also show that dedicated monolingual models still outperform multilingual ones. We explain this point in two ways. First, the scale of data is possibly essential to the performance of CamemBERT. Indeed, we use 138GB of uncompressed text vs. 57GB for mBERT. Second, with more data comes more diversity in the pretraining distribution. Reaching state-of-the-art performances on 4 different tasks and 6 different datasets requires robust pretrained models. Our results suggest that the variability in the downstream tasks and datasets considered is handled more efficiently by a general language model than by Wikipedia-pretrained models such as mBERT."
+    ],
+    [
+      "CamemBERT improves the state of the art for multiple downstream tasks in French. It is also lighter than other BERT-based approaches such as mBERT or XLM. By releasing our model, we hope that it can serve as a strong baseline for future research in French NLP, and expect our experiments to be reproduced in many other languages. We will publish an updated version in the near future where we will explore and release models trained for longer, with additional downstream tasks, baselines (e.g. XLM) and analysis, we will also train additional models with potentially cleaner corpora such as CCNet BIBREF56 for more accurate performance evaluation and more complete ablation."
+    ],
+    [
+      "This work was partly funded by three French National grants from the Agence Nationale de la Recherche, namely projects PARSITI (ANR-16-CE33-0021), SoSweet (ANR-15-CE38-0011) and BASNUM (ANR-18-CE38-0003), as well as by the last author's chair in the PRAIRIE institute."
+    ],
+    [
+      "We analyze the addition of whole-word masking on the downstream performance of CamemBERT. As reported for English on other downstream tasks, whole word masking improves downstream performances for all tasks but NER as seen in Table TABREF46. NER is highly sensitive to capitalisation, prefixes, suffixes and other subword features that could be used by a model to correctly identify entity mentions. Thus the added information by learning the masking at a subword level rather than at whole-word level seems to have a detrimental effect on downstream NER results."
+    ]
+  ]
+}
+```
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+Name of Paper: Vocabulary-based Method for Quantifying Controversy in Social Media
+
+Question: What are the state of the art measures?
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+Name of Paper: Vocabulary-based Method for Quantifying Controversy in Social Media
+
+Question: What controversial topics are experimented with?
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+Name of Paper: Vocabulary-based Method for Quantifying Controversy in Social Media
+
+Question: What datasets did they use?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related work",
+    "Method",
+    "Experiments",
+    "Experiments ::: Topic definition",
+    "Experiments ::: Datasets",
+    "Experiments ::: Results",
+    "Discussions",
+    "Discussions ::: Limitations",
+    "Discussions ::: Conclusions",
+    "Details on the discussions"
+  ],
+  "paragraphs": [
+    [
+      "Controversy is a phenomenom with a high impact at various levels. It has been broadly studied from the perspective of different disciplines, ranging from the seminal analysis of the conflicts within the members of a karate club BIBREF0 to political issues in modern times BIBREF1, BIBREF2. The irruption of digital social networks BIBREF3 gave raise to new ways of intentionally intervening on them for taking some advantage BIBREF4, BIBREF5. Moreover highly contrasting points of view in some groups tend to provoke conflicts that lead to attacks from one community to the other by harassing, \u201cbrigading\u201d, or \u201ctrolling\u201d it BIBREF6. The existing literature shows different issues that controversy brings up such as splitting of communities, biased information, hateful discussions and attacks between groups, generally proposing ways to solve them. For example, Kumar, Srijan, et al. BIBREF6 analyze many techniques to defend us from attacks in Reddit while Stewart, et al. BIBREF4 insinuate that there was external interference in Twitter during the 2016 US presidential elections to benefit one candidate. Also, as shown in BIBREF7, detecting controversy could provide the basis to improve the \u201cnews diet\" of readers, offering the possibility to connect users with different points of views by recommending them new content to read BIBREF8.",
+      "Moreover, other studies on \u201cbridging echo chambers\u201d BIBREF9 and the positive effects of intergroup dialogue BIBREF10, BIBREF11 suggest that direct engagement could be effective for mitigating such conflicts. Therefore, easily and automatically identifying controversial topics could allow us to quickly implement different strategies for preventing miss-information, fights and bias. Quantifying the controversy is even more powerful, as it allows us to establish controversy levels, and in particular to classify controversial and non-controversial topics by establishing a threshold score that separates the two types of topics. With this aim, we propose in this work a systematic, language-agnostic method to quantify controversy on social networks taking tweet's content as root input. Our main contribution is a new vocabulary-based method that works in any language and equates the performance of state-of-the-art structure-based methods. Finally, controversy quantification through vocabulary analysis opens several research avenues to analyze whether polarization is being created, maintained or augmented by the ways of talking of each community.",
+      "Having this in mind and if we draw from the premise that when a discussion has a high controversy it is in general due to the presence of two principal communities fighting each other (or, conversely, that when there is no controversy there is just one principal community the members of which share a common point of view), we can measure the controversy by detecting if the discussion has one or two principal jargons in use. Our method is tested on Twitter datasets. This microblogging platform has been widely used to analyze discussions and polarization BIBREF12, BIBREF13, BIBREF14, BIBREF15, BIBREF2. It is a natural choice for these kind of problems, as it represents one of the main fora for public debate in online social media BIBREF15, it is a common destination for affiliative expressions BIBREF16 and is often used to report and read news about current events BIBREF17. An extra advantage of Twitter for this kind of studies is the availability of real-time data generated by millions of users. Other social media platforms offer similar data-sharing services, but few can match the amount of data and the accompanied documentation provided by Twitter. One last asset of Twitter for our work is given by retweets, whom typically indicate endorsement BIBREF18 and hence become a useful concept to model discussions as we can set \u201cwho is with who\". However, our method has a general approach and it could be used a priori in any social network. In this work we report excellent result tested on Twitter but in future work we are going to test it in other social networks.",
+      "Our paper is organized as follows: in Section SECREF2, we review related work. Section SECREF3 contains the detailed explanation of the pipeline we use for quantifying controversy of a topic, and each of its stages. In Section SECREF4 we report the results of an extensive empirical evaluation of the proposed measure of controversy. Finally, Section SECREF5 is devoted to discuss possible improvements and directions for future work, as well as lessons learned."
+    ],
+    [
+      "Many previous works are dedicated to quantifying the polarization observed in online social networks and social media BIBREF1, BIBREF19, BIBREF20, BIBREF21, BIBREF22, BIBREF23. The main characteristic of those works is that the measures proposed are based on the structural characteristics of the underlying graph. Among them, we highlight the work of Garimella et al.BIBREF23 that presents an extensive comparison of controversy measures, different graph-building approaches, and data sources, achieving the best performance of all. In their research they propose different metrics to measure polarization on Twitter. Their techniques based on the structure of the endorsement graph can successfully detect whether a discussion (represented by a set of tweets), is controversial or not regardless of the context and most importantly, without the need of any domain expertise. They also consider two different methods to measure controversy based on the analysis of the posts contents, but both fail when used to create a measure of controversy.",
+      "Matakos et al. BIBREF24 develop a polarization index. Their measure captures the tendency of opinions to concentrate in network communities, creating echo-chambers. They obtain a good performance at identifying controversy by taking into account both the network structure and the existing opinions of users. However, they model opinions as positive or negative with a real number between -1 and 1. Their performance is good, but although it is an opinion-based method it is not a text-related one.Other recent works BIBREF25, BIBREF26, BIBREF27 have shown that communities may express themselves with different terms or ways of speaking, use different jargon, which in turn can be detected with the use of text-related techniques.",
+      "In his thesis BIBREF28, Jang explains controversy via generating a summary of two conflicting stances that make up the controversy. This work shows that a specific sub-set of tweets could represent the two opposite positions in a polarized debate.",
+      "A good tool to see how communities interact is ForceAtlas2 BIBREF29, a force-directed layout widely used for visualization. This layout has been recently found to be very useful at visualizing community interactions BIBREF30, as this algorithm will draw groups with little communication between them in different areas, whereas, if they have many interactions they will be drawn closer to each other. Therefore, whenever there is controversy the layout will show two well separated groups and will tend to show only one big community otherwise.",
+      "The method we propose to measure the controversy equates in accuracy the one developed by Garimella et al.BIBREF23 and improves considerably computing time and robustness wrt the amount of data needed to effectively apply it. Our method is also based on a graph approach but it has its main focus on the vocabulary. We first train an NLP classifier that estimates opinion polarity of main users, then we run label-propagation BIBREF31 on the endorsement graph to get polarity of the whole network. Finally we compute the controversy score through a computation inspired in Dipole Moment, a measure used in physics to estimate electric polarity on a system. In our experiments we use the same data-sets from other works BIBREF32, BIBREF23, BIBREF33 as well as other datasets that we collected by us using a similar criterion (described in Section SECREF4)."
+    ],
+    [
+      "Our approach to measuring controversy is based on a systematic way of characterizing social media activity through its content. We employ a pipeline with five stages, namely graph building, community identification, model training, predicting and controversy measure. The final output of the pipeline is a value that measures how controversial a topic is, with higher values corresponding to higher degrees of controversy. The method is based on analysing posts content through Fasttext BIBREF34, a library for efficient learning of word representations and sentence classification developed by Facebook Research team. In short, our method works as follows: through Fasttext we train a language-agnostic model which can predict the community of many users by their jargon. Then we take there predictions and compute a score based on the physic notion Dipole Moment using a language approach to identify core or characteristic users and set the polarity trough them. We provide a detailed description of each stage in the following.",
+      "Graph Building",
+      "This paragraph provides details about the approach used to build graphs from raw data. As we said in Section SECREF1, we extract our discussions from Twitter. Our purpose is to build a conversation graph that represents activity related to a single topic of discussion -a debate about a specific event.",
+      "For each topic, we build a graph $G$ where we assign a vertex to each user who contributes to it and we add a directed edge from node $u$ to node $v$ whenever user $u$ retweets a tweet posted by $v$. Retweets typically indicate endorsement BIBREF18: users who retweet signal endorsement of the opinion expressed in the original tweet by propagating it further. Retweets are not constrained to occur only between users who are connected in Twitter's social network, but users are allowed to retweet posts generated by any other user. As many other works in literature BIBREF5, BIBREF35, BIBREF36, BIBREF37, BIBREF4, BIBREF2 we establish that one retweet among a pair of users are needed to define an edge between them.",
+      "Community Identification",
+      "To identify a community's jargon we need to be very accurate at defining its members. If we, in our will of finding two principal communities, force the partition of the graph in that precise number of communities, we may be adding noise in the jargon of the principal communities that are fighting each other. Because of that, we decide to cluster the graph trying two popular algorithms: Walktrap BIBREF38 and Louvain BIBREF39. Both are structure-based algorithms that have very good performance with respect to the Modularity Q measure. These techniques does not detect a fixed number of clusters; their output will depend on the Modularity Q optimization, resulting in less \u201cnoisy\" communities. The main differences between the two methods, in what regards our work, are that Louvain is a much faster heuristic algorithm but produces clusters with worse Modularity Q. Therefore, in order to analyze the trade-off between computing time and quality we decide to test both methods. At this step we want to capture the tweets of the principal communities to create the model that could differentiate them. Therefore, we take the two communities identified by the cluster algorithm that have the maximum number of users, and use them for the following step of our method.",
+      "Model Training",
+      "After detecting the principal communities we create our training dataset to feed the model. To do that, we extract the tweets of each cluster, we sanitize and we subject them to some transformations. First, we remove duplicate tweets -e.g. retweets without additional text. Second, we remove from the text of the tweets user names, links, punctuation, tabs, leading and lagging blanks, general spaces and \u201cRT\" - the text that points that a tweet is in fact a retweet.",
+      "As shown in previous works, emojis are correlated with sentiment BIBREF40. Moreover, as we think that communities will express different sentiment during discussion, it is forseeable that emojis will play an important role as separators of tweets that differentiate between the two sides. Accordingly, we decide to add them to the train-set by translating each emoji into a different word. For example, the emoji :) will be translated into happy and :( into sad. Relations between emojis and words are defined in the R library textclean.",
+      "Finally, we group tweets by user concatenating them in one string and labeling them with the user's community, namely with tags C1 and C2, corresponding respectively to the biggest and second biggest groups. It is important to note that we take the same number of users of each community to prevent bias in the model. Thus, we use the number of users of the smallest principal community.",
+      "The train-set built that way is used to feed the model. As we said, we use Fasttext BIBREF34 to do this training. To define the values of the hyper-parameters we use the findings of BIBREF41. In their work they investigate the best hyper-parameters to train word embedding models using Fasttext BIBREF34 and Twitter data. We also change the default value of the hyper-parameter epoch to 20 instead of 5 because we want more convergence preventing as much as possible the variance between different training. These values could change in other context or social networks where we have more text per user or different discussion dynamics.",
+      "Predicting",
+      "The next stage consists of identifying the characteristic users of each side the discussion. These are the users that better represent the jargon of each side. To do that, tweets of the users belonging to the largest connected component of the graph are sanitized and transformed exactly as in the Training step.",
+      "We decide to restrict to the largest connected component because in all cases it contains more than 90% of the nodes. The remaining 10% of the users don't participate in the discussion from a collective point of view but rather in an isolated way and this kind of intervention does not add interesting information to our approach. Then, we remove from this component users with degree smaller or equal to 2 (i.e. users that were retweeted by another user or retweeted other person less than three times in total). Their participation in the discussion is marginal, consequently they are not relevant wrt controversy as they add more noise than information at measuring time. This step could be adjusted differently in a different social network. We name this result component root-graph.",
+      "Finally, let's see how we do classification. Considering that Fasttext returns for each classification both the predicted tag and the probability of the prediction, we classify each user of the resulting component by his sanitized tweets with our trained model, and take users that were tagged with a probability greater or equal than 0.9. These are the characteristic users that will be used in next step to compute the controversy measure.",
+      "Controversy Measure",
+      "This section describes the controversy measures used in this work. This computation is inspired in the measure presented by Morales et al. BIBREF2, and is based on the notion of dipole moment that has its origin in physics.",
+      "First, we assign to the characteristic users the probability returned by the model, negativizing them if the predicted tag was C2. Therefore, these users are assigned values in the set [-1,-0.9] $\\cup $ [0.9,1]. Then, we set values for the rest of the users of the root-graph by label-propagation BIBREF31 - an iterative algorithm to propagate values through a graph by node's neighborhood.",
+      "Let $n^{+}$ and $n^{-}$ be the number of vertices $V$ with positive and negative values, respectively, and $\\Delta A = \\dfrac{\\mid n^{+} - n^{-}\\mid }{\\mid V \\mid }$ the absolute difference of their normalized size. Moreover, let $gc^{+}$ ($gc^{-}$) be the average value among vertices $n^{+}$ ($n^{-}$) and set $\\tau $ as half their absolute difference, $\\tau = \\dfrac{\\mid gc^{+} - gc^{- }\\mid }{2}$. The dipole moment content controversy measure is defined as: $\\textit {DMC} = (1 -\\Delta A)\\tau $.",
+      "The rationale for this measure is that if the two sides are well separated, then label propagation will assign different extreme values to the two partitions, where users from one community will have values near to 1 and users from the other to -1, leading to higher values of the DMC measure. Note also that larger differences in the size of the two partitions (reflected in the value of $\\Delta A$) lead to smaller values for the measure, which takes values between zero and one."
+    ],
+    [
+      "In this section we report the results obtained by running the above proposed method over different discussions."
+    ],
+    [
+      "In the literature, a topic is often defined by a single hashtag. However, this might be too restrictive in many cases. In our approach, a topic is operationalized as an specific hashtags or key words. Sometimes a discussion in a particular moment could not have a defined hashtag but it could be around a certain keyword, i.e. a word or expression that is not specifically a hashtag but it is widely used in the topic. For example during the Brazilian presidential elections in 2018 we captured the discussion by the mentions to the word Bolsonaro, that is the principal candidate's surname.",
+      "Thus, for each topic we retrieve all the tweets that contain one of its hashtags or the keyword and that are generated during the observation window. We also ensure that the selected topic is associated with a large enough volume of activity."
+    ],
+    [
+      "In this section we detail the discussions we use to test our metric and how we determine the ground truth (i.e. if the discussion is controversial or not). We use thirty different discussions that took place between March 2015 and June 2019, half of them with controversy and half without it. We considered discussions in four different languages: English, Portuguese, Spanish and French, occurring in five regions over the world: South and North America, Western Europe, Central and Southern Asia. We also studied these discussions taking first 140 characters and then 280 from each tweet to analyze the difference in performance and computing time wrt the length of the posts.",
+      "To define the amount of data needed to run our method we established that the Fasttext model has to predict at least one user of each community with a probability greater or equal than 0.9 during ten different trainings. If that is not the case, we are not able to use DPC method. This decision made us consider only a subset of the datasets used in BIBREF23, because due to the time elapsed since their work, many tweets had been deleted and consequently the volume of the data was not enough for our framework. To enlarge our experiment base we added new debates, more detailed information about each one is shown in Table TABREF24 in UNKREF6. To select new discussions and to determine if they are controversial or not we looked for topics widely covered by mainstream media, and that have generated ample discussion, both online and offline. For non-controversy discussions we focused on \u201csoft news\" and entertainment, but also to events that, while being impactful and/or dramatic, did not generate large controversies. To validate that intuition, we manually checked a sample of tweets, being unable to identify any clear instance of controversy On the other side, for controversial debates we focused on political events such as elections, corruption cases or justice decisions.",
+      "To furtherly establish the presence of absence of controversy of our datasets, we visualized the corresponding networks through ForceAtlas2 BIBREF29. Figures FIGREF9 and FIGREF9 show an example of how non-controversial and controversial discussions look like respectively with ForceAtlas2 layout. As we can see in these figures, in a controversial discussion this layout tends to show two well separated groups while in a non-controversial one it tends to be only one big group. More information on the discussions is given in Table TABREF24.",
+      "To avoid potential overfitting, we use only twelve graphs as testbed during the development of the measures, half of them controversial (netanyahu, ukraine, @mauriciomacri 1-11 Jan, Kavanaugh 3 Oct, @mauriciomacri 11-18 Mar, Bolsonaro 27 Oct) and half non-controversial (sxsw, germanwings, onedirection, ultralive, nepal, mothersday). This procedure resembles a 40/60% train/test split in traditional machine learning applications.",
+      "Some of the discussions we consider refer to the same topics but in different periods of time. We needed to split them because our computing infrastructure does not allow us to compute such an enormous amount of data. However, being able to estimate controversy with only a subset of the discussion is an advantage, because discussions could take many days or months and we want to identify controversy as soon as possible, without the need of downloading the whole discussion. Moreover, for very long lasting discussions in social networks gathering the whole data would be impractical for any method.",
+      ""
+    ],
+    [
+      "Training a Fasttext model is not a deterministic process, as different runs could yield different results even using the same training set in each one. To analyze if these differences are significant, we decide to compute 20 scores for each discussion. The standard deviations among these 20 scores were low in all cases, with mean 0.01 and maximum 0.05. Consequently, we decided to report in this paper the average between the 20 scores, in practice taking the average between 5 runs would be enough. Figure FIGREF18 reports the scores computed by our measure in each topic for the two cluster methods. The beanplot shows the estimated probability density function for a measure computed on the topics, the individual observations are shown as small white lines in a one-dimensional scatter plot, and the median as a longer black line. The beanplot is divided into two groups, one for controversial topics (left/dark) and one for non-controversial ones (right/light). Hence, the black group shows the score distribution over controversial discussions and the white group over non-controversial ones. A larger separation of the two distributions indicates that the measure is better at capturing the characteristics of controversial topics, because a good separation allows to establish a threshold in the score that separates controversial and non-controversial discussions.",
+      "As we may see in the figure, the medians are well separated in both cases, with little overlapping. To better quantify this overlap we measure the sensitivity BIBREF42 of these predictions by measuring the area under the ROC curve (AUC ROC), obtaining a value of 0.98 for Walktrap clustering and 0.967 for Louvain (where 1 represents a perfect separation and 0.5 means that they are indistinguishable).",
+      "As Garimella et al. BIBREF23 have made their code public , we reproduced their best method Randomwalk on our datasets and measured the AUC ROC, obtaining a score of 0.935. An interesting finding was that their method had a poor performance over their own datasets. This was due to the fact (already explained in Section SECREF4) that it was not possible to retrieve the complete discussions, moreover, in no case could we restore more than 50% of the tweets. So we decided to remove these discussions and measure again the AUC ROC of this method, obtaining a 0.99 value. Our hypothesis is that the performance of that method was seriously hurt by the incompleteness of the data. We also tested our method on these datasets, obtaining a 0.99 AUC ROC with Walktrap and 0.989 with Louvain clustering.",
+      "We conclude that our method works better, as in practice both approaches show same performances -specially with Walktrap, but in presence of incomplete information our measure is more robust. The performance of Louvain is slightly worse but, as we mentioned in Section SECREF3, this method is much faster. Therefore, we decided to compare the running time of our method with both clustering techniques and also with the Randomwalk algorithm. In figure FIGREF18 we can see the distribution of running times of all techniques through box plots. Both versions of our method are faster than Randomwalk, while Louvain is faster than Walktrap.",
+      "We now analyze the impact of the length of the considered text in our method. Figure FIGREF18 depicts the results of similar experiment as Figure FIGREF18, but considering only 140 characters per tweet. As we may see, here the overlapping is bigger, having an AUC of 0.88. As for the impact on computing time, we observe that despite of the results of BIBREF34 that reported a complexity of O(h $log_{2}$(k)) at training and test tasks, in practice we observed a linear growth. We measured the running times of the training and predicting phases (the two text-related phases of our method), the resulting times are reported in figure FIGREF18, which shows running time as a function of the text-size. We include also the best estimated function that approximate computing time as a function of text-set size. As it may be seen, time grows almost linearly, ranging from 30 seconds for a set of 111 KB to 84 seconds for a set of 11941 KB. Finally, we measured running times for the whole method over each dataset with 280 characters. Times were between 170 and 2467 seconds with a mean of 842, making it in practice a reasonable amount of time."
+    ],
+    [
+      "The task we address in this work is certainly not an easy one, and our study has some limitations, which we discuss in this section. Our work lead us to some conclusions regarding the overall possibility of measuring controversy through text, and what aspects need to be considered to deepen our work."
+    ],
+    [
+      "As our approach to controversy is similar to that of Garimella et al. BIBREF23, we share some of their limitations with respect to several aspects: Evaluation -difficulties to establish ground-truth, Multisided controversies -controversy with more than two sides, Choice of data - manually pick topics, and Overfitting - small set of experiments. Although we have more discussions, it is still small set from statistical point of view. Apart from that, our language-based approach has other limitations which we mention in the following, together with their solutions or mitigation.",
+      "Data-size. Training an NLP model that can predict tags with a probability greater or equal than 0.9 requires significant amount of text, therefore our method works only for \u201cbig\" discussions. Most interesting controversies are those that have consequence at a society level, in general big enough for our method.",
+      "Multi-language discussions. When multiple languages are participating in a discussion it is common that users tend to retweet more tweets in their own language, creating sub-communities. In this cases our model will tend to predict higher controversy scores. This is the case for example of #germanwings, where users tweet in English, German and Spanish and it has the highest score in no-controversial topics. However, the polarization that we tackle in this work is normally part of a society cell (a nation, a city, etc.), and thus developed in just one language. We think that limiting the effectiveness of our analysis to single-language discussions is not a serious limitation.",
+      "Twitter only. Our findings are based on datasets coming from Twitter. While this is certainly a limitation, Twitter is one of the main venues for online public discussion, and one of the few for which data is available. Hence, Twitter is a natural choice. However, Twitter's characteristic limit of 280 characters per message (140 till short time ago) is an intrinsic limitation of that network. We think that in other social networks as Facebook or Reddit our method will work even better, as having more text per user could redound on a better NLP model as we verified comparing the results with 140 and 280 characters per post."
+    ],
+    [
+      "In this article, we introduced the first large-scale systematic method for quantifying controversy in social media through content. We have shown that this method works on Spanish, English, French and Portuguese, it is context-agnostic and does not require the intervention of a domain expert.",
+      "We have compared its performance with state-of-the-art structure-based controversy measures showing that they have same performance and it is more robust. We also have shown that more text implies better performance and without significantly increasing computing time, therefore, it could be used in other contexts such as other social networks like Reddit or Facebook and we are going to test it in future works.",
+      "Training the model is not an expensive task since Fasttext has a good performance at this. However, the best performance for detecting principal communities is obtained by Walktrap. The complexity of that algorithm is O(m$n^2$)BIBREF38, where $m$ and $n$ are the number of edges and vertices respectively. This makes this method rather expensive to compute on big networks. Nevertheless, we have shown that with Louvain the method still obtains a very similar AUC ROC (0.99 with Walktrap and 0.989 with Louvain). With incomplete information its performance gets worse but it is still good (0.96) and better than previous state of the art.",
+      "This work opens several avenues for future research. One is identifying what words, semantics/concepts or language expressions make differ one community from the other. There are various ways to do this, for instance through the word-embbedings that Fasttext returns after training BIBREF34. Also we could use interpretability techniques on machine learning models BIBREF43. Finally, we could try other techniques for measuring controversy through text, using another NLP model as pre-trained neural network BERT BIBREF44 or, in a completely different approach measuring the dispersion index of the discussions word-embbedings BIBREF25. We are currently starting to follow this direction."
+    ],
+    [
+      "F"
+    ]
+  ]
+}
+```
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diff --git a/qasper-0380/instruction.md b/qasper-0380/instruction.md
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+Name of Paper: Semantic Sentiment Analysis of Twitter Data
+
+Question: What are the metrics to evaluate sentiment analysis on Twitter?
\ No newline at end of file
diff --git a/qasper-0381/instruction.md b/qasper-0381/instruction.md
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+Name of Paper: COSTRA 1.0: A Dataset of Complex Sentence Transformations
+
+Question: How many sentence transformations on average are available per unique sentence in dataset?
\ No newline at end of file
diff --git a/qasper-0386/instruction.md b/qasper-0386/instruction.md
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+++ b/qasper-0386/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: COSTRA 1.0: A Dataset of Complex Sentence Transformations
+
+Question: Are some baseline models trained on this dataset?
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@@ -0,0 +1,3 @@
+Name of Paper: COSTRA 1.0: A Dataset of Complex Sentence Transformations
+
+Question: Do they do any analysis of of how the modifications changed the starting set of sentences?
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diff --git a/qasper-0388/instruction.md b/qasper-0388/instruction.md
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+Name of Paper: COSTRA 1.0: A Dataset of Complex Sentence Transformations
+
+Question: How do they introduce language variation?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Background",
+    "Annotation",
+    "Annotation ::: First Round: Collecting Ideas",
+    "Annotation ::: Second Round: Collecting Data ::: Sentence Transformations",
+    "Annotation ::: Second Round: Collecting Data ::: Seed Data",
+    "Annotation ::: Second Round: Collecting Data ::: Spell-Checking",
+    "Dataset Description",
+    "Dataset Description ::: First Observations",
+    "Conclusion and Future Work"
+  ],
+  "paragraphs": [
+    [
+      "Vector representations are becoming truly essential in majority of natural language processing tasks. Word embeddings became widely popular with the introduction of word2vec BIBREF0 and GloVe BIBREF1 and their properties have been analyzed in length from various aspects.",
+      "Studies of word embeddings range from word similarity BIBREF2, BIBREF3, over the ability to capture derivational relations BIBREF4, linear superposition of multiple senses BIBREF5, the ability to predict semantic hierarchies BIBREF6 or POS tags BIBREF7 up to data efficiency BIBREF8.",
+      "Several studies BIBREF9, BIBREF10, BIBREF11, BIBREF12 show that word vector representations are capable of capturing meaningful syntactic and semantic regularities. These include, for example, male/female relation demonstrated by the pairs \u201cman:woman\u201d, \u201cking:queen\u201d and the country/capital relation (\u201cRussia:Moscow\u201d, \u201cJapan:Tokyo\u201d). These regularities correspond to simple arithmetic operations in the vector space.",
+      "Sentence embeddings are becoming equally ubiquitous in NLP, with novel representations appearing almost every other week. With an overwhelming number of methods to compute sentence vector representations, the study of their general properties becomes difficult. Furthermore, it is not so clear in which way the embeddings should be evaluated.",
+      "In an attempt to bring together more traditional representations of sentence meanings and the emerging vector representations, bojar:etal:jnle:representations:2019 introduce a number of aspects or desirable properties of sentence embeddings. One of them is denoted as \u201crelatability\u201d, which highlights the correspondence between meaningful differences between sentences and geometrical relations between their respective embeddings in the highly dimensional continuous vector space. If such a correspondence could be found, we could use geometrical operations in the space to induce meaningful changes in sentences.",
+      "In this work, we present COSTRA, a new dataset of COmplex Sentence TRAnsformations. In its first version, the dataset is limited to sample sentences in Czech. The goal is to support studies of semantic and syntactic relations between sentences in the continuous space. Our dataset is the prerequisite for one of possible ways of exploring sentence meaning relatability: we envision that the continuous space of sentences induced by an ideal embedding method would exhibit topological similarity to the graph of sentence variations. For instance, one could argue that a subset of sentences could be organized along a linear scale reflecting the formalness of the language used. Another set of sentences could form a partially ordered set of gradually less and less concrete statements. And yet another set, intersecting both of the previous ones in multiple sentences could be partially or linearly ordered according to the strength of the speakers confidence in the claim.",
+      "Our long term goal is to search for an embedding method which exhibits this behaviour, i.e. that the topological map of the embedding space corresponds to meaningful operations or changes in the set of sentences of a language (or more languages at once). We prefer this behaviour to emerge, as it happened for word vector operations, but regardless if the behaviour is emergent or trained, we need a dataset of sentences illustrating these patterns. If large enough, such a dataset could serve for training. If it will be smaller, it will provide a test set. In either case, these sentences could provide a \u201cskeleton\u201d to the continuous space of sentence embeddings.",
+      "The paper is structured as follows: related summarizes existing methods of sentence embeddings evaluation and related work. annotation describes our methodology for constructing our dataset. data details the obtained dataset and some first observations. We conclude and provide the link to the dataset in conclusion"
+    ],
+    [
+      "As hinted above, there are many methods of converting a sequence of words into a vector in a highly dimensional space. To name a few: BiLSTM with the max-pooling trained for natural language inference BIBREF13, masked language modeling and next sentence prediction using bidirectional Transformer BIBREF14, max-pooling last states of neural machine translation among many languages BIBREF15 or the encoder final state in attentionless neural machine translation BIBREF16.",
+      "The most common way of evaluating methods of sentence embeddings is extrinsic, using so called `transfer tasks', i.e. comparing embeddings via the performance in downstream tasks such as paraphrasing, entailment, sentence sentiment analysis, natural language inference and other assignments. However, even simple bag-of-words (BOW) approaches achieve often competitive results on such tasks BIBREF17.",
+      "Adi16 introduce intrinsic evaluation by measuring the ability of models to encode basic linguistic properties of a sentence such as its length, word order, and word occurrences. These so called `probing tasks' are further extended by a depth of the syntactic tree, top constituent or verb tense by DBLP:journals/corr/abs-1805-01070.",
+      "Both transfer and probing tasks are integrated in SentEval BIBREF18 framework for sentence vector representations. Later, Perone2018 applied SentEval to eleven different encoding methods revealing that there is no consistently well performing method across all tasks. SentEval was further criticized for pitfalls such as comparing different embedding sizes or correlation between tasks BIBREF19, BIBREF20.",
+      "shi-etal-2016-string show that NMT encoder is able to capture syntactic information about the source sentence. DBLP:journals/corr/BelinkovDDSG17 examine the ability of NMT to learn morphology through POS and morphological tagging.",
+      "Still, very little is known about semantic properties of sentence embeddings. Interestingly, cifka:bojar:meanings:2018 observe that the better self-attention embeddings serve in NMT, the worse they perform in most of SentEval tasks.",
+      "zhu-etal-2018-exploring generate automatically sentence variations such as:",
+      "Original sentence: A rooster pecked grain.",
+      "Synonym Substitution: A cock pecked grain.",
+      "Not-Negation: A rooster didn't peck grain.",
+      "Quantifier-Negation: There was no rooster pecking grain.",
+      "and compare their triplets by examining distances between their embeddings, i.e. distance between (1) and (2) should be smaller than distances between (1) and (3), (2) and (3), similarly, (3) and (4) should be closer together than (1)\u2013(3) or (1)\u2013(4).",
+      "In our previous study BIBREF21, we examined the effect of small sentence alternations in sentence vector spaces. We used sentence pairs automatically extracted from datasets for natural language inference BIBREF22, BIBREF23 and observed, that the simple vector difference, familiar from word embeddings, serves reasonably well also in sentence embedding spaces. The examined relations were however very simple: a change of gender, number, addition of an adjective, etc. The structure of the sentence and its wording remained almost identical.",
+      "We would like to move to more interesting non-trivial sentence comparison, beyond those in zhu-etal-2018-exploring or BaBo2019, such as change of style of a sentence, the introduction of a small modification that drastically changes the meaning of a sentence or reshuffling of words in a sentence that alters its meaning.",
+      "Unfortunately, such a dataset cannot be generated automatically and it is not available to our best knowledge. We try to start filling this gap with COSTRA 1.0."
+    ],
+    [
+      "We acquired the data in two rounds of annotation. In the first one, we were looking for original and uncommon sentence change suggestions. In the second one, we collected sentence alternations using ideas from the first round. The first and second rounds of annotation could be broadly called as collecting ideas and collecting data, respectively."
+    ],
+    [
+      "We manually selected 15 newspaper headlines. Eleven annotators were asked to modify each headline up to 20 times and describe the modification with a short name. They were given an example sentence and several of its possible alternations, see tab:firstroundexamples.",
+      "Unfortunately, these examples turned out to be highly influential on the annotators' decisions and they correspond to almost two thirds of all of modifications gathered in the first round. Other very common transformations include change of a word order or transformation into a interrogative/imperative sentence.",
+      "Other interesting modification were also proposed such as change into a fairy-tale style, excessive use of diminutives/vulgarisms or dadaism\u2014a swap of roles in the sentence so that the resulting sentence is grammatically correct but nonsensical in our world. Of these suggestions, we selected only the dadaistic swap of roles for the current exploration (see nonsense in Table TABREF7).",
+      "In total, we collected 984 sentences with 269 described unique changes. We use them as an inspiration for second round of annotation."
+    ],
+    [
+      "We selected 15 modifications types to collect COSTRA 1.0. They are presented in annotationinstructions.",
+      "We asked for two distinct paraphrases of each sentence because we believe that a good sentence embedding should put paraphrases close together in vector space.",
+      "Several modification types were specifically selected to constitute a thorough test of embeddings. In different meaning, the annotators should create a sentence with some other meaning using the same words as the original sentence. Other transformations which should be difficult for embeddings include minimal change, in which the sentence meaning should be significantly changed by using only very small modification, or nonsense, in which words of the source sentence should be shuffled so that it is grammatically correct, but without any sense."
+    ],
+    [
+      "The source sentences for annotations were selected from Czech data of Global Voices BIBREF24 and OpenSubtitles BIBREF25. We used two sources in order to have different styles of seed sentences, both journalistic and common spoken language. We considered only sentences with more than 5 and less than 15 words and we manually selected 150 of them for further annotation. This step was necessary to remove sentences that are:",
+      "too unreal, out of this world, such as:",
+      "Jedno fotonov\u00fd torp\u00e9do a je z tebe vesm\u00edrn\u00e1 topinka.",
+      "\u201cOne photon torpedo and you're a space toast.\u201d",
+      "photo captions (i.e. incomplete sentences), e.g.:",
+      "Zvl\u00e1\u0161tn\u00ed ekv\u00e1dorsk\u00fd p\u0159\u00edpad Correa vs. Crudo",
+      "\u201cSpecific Ecuadorian case Correa vs. Crudo\u201d",
+      "too vague, overly dependent on the context:",
+      "B\u011b\u017e tam a mluv na ni.",
+      "\u201cGo there and speak to her.\u201d",
+      "Many of the intended sentence transformations would be impossible to apply to such sentences and annotators' time would be wasted. Even after such filtering, it was still quite possible that a desired sentence modification could not be achieved for a sentence. For such a case, we gave the annotators the option to enter the keyword IMPOSSIBLE instead of the particular (impossible) modification.",
+      "This option allowed to explicitly state that no such transformation is possible. At the same time most of the transformations are likely to lead to a large number possible outcomes. As documented in scratching2013, Czech sentence might have hundreds of thousand of paraphrases. To support some minimal exploration of this possible diversity, most of sentences were assigned to several annotators."
+    ],
+    [
+      "The annotation is a challenging task and the annotators naturally make mistakes. Unfortunately, a single typo can significantly influence the resulting embedding BIBREF26. After collecting all the sentence variations, we applied the statistical spellchecker and grammar checker Korektor BIBREF27 in order to minimize influence of typos to performance of embedding methods. We manually inspected 519 errors identified by Korektor and fixed 129, which were identified correctly."
+    ],
+    [
+      "In the second round, we collected 293 annotations from 12 annotators. After Korektor, there are 4262 unique sentences (including 150 seed sentences) that form the COSTRA 1.0 dataset. Statistics of individual annotators are available in tab:statistics.",
+      "The time needed to carry out one piece of annotation (i.e. to provide one seed sentence with all 15 transformations) was on average almost 20 minutes but some annotators easily needed even half an hour. Out of the 4262 distinct sentences, only 188 was recorded more than once. In other words, the chance of two annotators producing the same output string is quite low. The most repeated transformations are by far past, future and ban. The least repeated is paraphrase with only single one repeated.",
+      "multiple-annots documents this in another way. The 293 annotations are split into groups depending on how many annotators saw the same input sentence: 30 annotations were annotated by one person only, 30 annotations by two different persons etc. The last column shows the number of unique outputs obtained in that group. Across all cases, 96.8% of produced strings were unique.",
+      "In line with instructions, the annotators were using the IMPOSSIBLE option scarcely (95 times, i.e. only 2%). It was also a case of 7 annotators only; the remaining 5 annotators were capable of producing all requested transformations. The top three transformations considered unfeasible were different meaning (using the same set of words), past (esp. for sentences already in the past tense) and simple sentence."
+    ],
+    [
+      "We embedded COSTRA sentences with LASER BIBREF15, the method that performed very well in revealing linear relations in BaBo2019. Having browsed a number of 2D visualizations (PCA and t-SNE) of the space, we have to conclude that visually, LASER space does not seem to exhibit any of the desired topological properties discussed above, see fig:pca for one example.",
+      "The lack of semantic relations in the LASER space is also reflected in vector similarities, summarized in similarities. The minimal change operation substantially changed the meaning of the sentence, and yet the embedding of the transformation lies very closely to the original sentence (average similarity of 0.930). Tense changes and some form of negation or banning also keep the vectors very similar.",
+      "The lowest average similarity was observed for generalization (0.739) and simplification (0.781), which is not any bad sign. However the fact that paraphrases have much smaller similarity (0.826) than opposite meaning (0.902) documents that the vector space lacks in terms of \u201crelatability\u201d."
+    ],
+    [
+      "We presented COSTRA 1.0, a small corpus of complex transformations of Czech sentences.",
+      "We plan to use this corpus to analyze a wide spectrum sentence embeddings methods to see to what extent the continuous space they induce reflects semantic relations between sentences in our corpus. The very first analysis using LASER embeddings indicates lack of \u201cmeaning relatability\u201d, i.e. the ability to move along a trajectory in the space in order to reach desired sentence transformations. Actually, not even paraphrases are found in close neighbourhoods of embedded sentences. More \u201csemantic\u201d sentence embeddings methods are thus to be sought for.",
+      "The corpus is freely available at the following link:",
+      "http://hdl.handle.net/11234/1-3123",
+      "Aside from extending the corpus in Czech and adding other language variants, we are also considering to wrap COSTRA 1.0 into an API such as SentEval, so that it is very easy for researchers to evaluate their sentence embeddings in terms of \u201crelatability\u201d."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0389/instruction.md b/qasper-0389/instruction.md
new file mode 100644
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--- /dev/null
+++ b/qasper-0389/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: COSTRA 1.0: A Dataset of Complex Sentence Transformations
+
+Question: Do they use external resources to make modifications to sentences?
\ No newline at end of file
diff --git a/qasper-0390/instruction.md b/qasper-0390/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..89d8a29941b474a4cb25759f94f312c9d2d92af9
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+++ b/qasper-0390/instruction.md
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+Name of Paper: Learning to Create Sentence Semantic Relation Graphs for Multi-Document Summarization
+
+Question: How big is dataset domain-specific embedding are trained on?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Method",
+    "Method ::: Sentence Semantic Relation Graph",
+    "Method ::: Sentence Encoder",
+    "Method ::: Graph Convolutional Network",
+    "Method ::: Saliency Estimation",
+    "Method ::: Training",
+    "Method ::: Summary Generation Process",
+    "Experiments ::: Datasets",
+    "Experiments ::: Evaluation Metric",
+    "Experiments ::: Model Settings",
+    "Experiments ::: Summarization Performance",
+    "Experiments ::: Sentence Semantic Relation Graph Construction",
+    "Experiments ::: Ablation Study",
+    "Experiments ::: Results and Discussion",
+    "Experiments ::: Results and Discussion ::: Summarization Performance",
+    "Experiments ::: Results and Discussion ::: Sentence Semantic Relation Graph",
+    "Experiments ::: Results and Discussion ::: Ablation Study",
+    "Related Work",
+    "Conclusion",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "Today's increasing flood of information on the web creates a need for automated multi-document summarization systems that produce high quality summaries. However, producing summaries in a multi-document setting is difficult, as the language used to display the same information in a sentence can vary significantly, making it difficult for summarization models to capture. Given the complexity of the task and the lack of datasets, most researchers use extractive summarization, where the final summary is composed of existing sentences in the input documents. More specifically, extractive summarization systems output summaries in two steps : via sentence ranking, where an importance score is assigned to each sentence, and via the subsequent sentence selection, where the most appropriate sentence is chosen, by considering 1) their importance and 2) their frequency among all documents. Due to data sparcity, models heavily rely on well-designed features at the word level BIBREF0, BIBREF1, BIBREF2, BIBREF3 or take advantage of other large, manually annotated datasets and then apply transfer learning BIBREF4. Additionally, most of the time, all sentences in the same collection of documents are processed independently and therefore, their relationships are lost.",
+      "In realistic scenarios, features are hard to craft, gathering additional annotated data is costly, and the large variety in expressing the same fact cannot be handled by the use of word-based features only, as is often the case. In this paper, we address these obstacles by proposing to simultaneously leverage two types of sentence embeddings, namely embeddings pre-trained on a large corpus that capture a variety of meanings and domain-specific embeddings learned during training. The former is typically trained on an unrelated corpus composed of high quality texts, allowing to cover additional contexts for each encountered word and sentence. Hereby, we build on the assumption that sentence embeddings capture both the syntactic and semantic content of sentences. We hypothesize that using two types of sentence embeddings, general and domain-specific, is beneficial for the task of multi-document summarization, as the former captures the most common semantic structures from a large, general corpus, while the latter captures the aspects related to the domain.",
+      "We present SemSentSum (Figure FIGREF3), a fully data-driven summarization system, which does not depend on hand-crafted features, nor additional data, and is thus domain-independent. It first makes use of general sentence embedding knowledge to build a sentenc semantic relation graph that captures sentence similarities (Section SECREF4). In a second step, it trains genre-specific sentence embeddings related to the domains of the collection of documents, by utilizing a sentence encoder (Section SECREF5). Both representations are afterwards merged, by using a graph convolutional network BIBREF5 (Section SECREF6). Then, it employs a linear layer to project high-level hidden features for individual sentences to salience scores (Section SECREF8). Finally, it greedily produces relevant and non-redundant summaries by using sentence embeddings to detect similarities between candidate sentences and the current summary (Section SECREF11).",
+      "The main contributions of this work are as follows :",
+      "We aggregate two types of sentences embeddings using a graph representation. They share different properties and are consequently complementary. The first one is trained on a large unrelated corpus to model general semantics among sentences, whereas the second is domain-specific to the dataset and learned during training. Together, they enable a model to be domain-independent as it can be applied easily on other domains. Moreover, it could be used for other tasks including detecting information cascades, query-focused summarization, keyphrase extraction and information retrieval.",
+      "We devise a competitive multi-document summarization system, which does not need hand-crafted features nor additional annotated data. Moreover, the results are competitive for 665-byte and 100-word summaries. Usually, models are compared in one of the two settings but not both and thus lack comparability."
+    ],
+    [
+      "Let $C$ denote a collection of related documents composed of a set of documents $\\lbrace D_i|i \\in [1,N]\\rbrace $ where $N$ is the number of documents. Moreover, each document $D_i$ consists of a set of sentences $\\lbrace S_{i,j}|j \\in [1,M]\\rbrace $, $M$ being the number of sentences in $D_i$. Given a collection of related documents $C$, our goal is to produce a summary $Sum$ using a subset of these in the input documents ordered in some way, such that $Sum = (S_{i_1,j_1},S_{i_2,j_2},...,S_{i_n,j_m})$.",
+      "In this section, we describe how SemSentSum estimates the salience score of each sentence and how it selects a subset of these to create the final summary. The architecture of SemSentSum is depicted in Figure FIGREF3.",
+      "In order to perform sentence selection, we first build our sentence semantic relation graph, where each vertex is a sentence and edges capture the semantic similarity among them. At the same time, each sentence is fed into a recurrent neural network, as a sentence encoder, to generate sentence embeddings using the last hidden states. A single-layer graph convolutional neural network is then applied on top, where the sentence semantic relation graph is the adjacency matrix and the sentence embeddings are the node features. Afterward, a linear layer is used to project high-level hidden features for individual sentences to salience scores, representing how salient a sentence is with respect to the final summary. Finally, based on this, we devise an innovative greedy method that leverages sentence embeddings to detect redundant sentences and select sentences until reaching the summary length limit."
+    ],
+    [
+      "We model the semantic relationship among sentences using a graph representation. In this graph, each vertex is a sentence $S_{i,j}$ ($j$'th sentence of document $D_i$) from the collection documents $C$ and an undirected edge between $S_{i_u,j_u}$ and $S_{i_v,j_v}$ indicates their degree of similarity. In order to compute the semantic similarity, we use the model of BIBREF6 trained on the English Wikipedia corpus. In this manner, we incorporate general knowledge (i.e. not domain-specific) that will complete the specialized sentence embeddings obtained during training (see Section SECREF5). We process sentences by their model and compute the cosine similarity between every sentence pair, resulting in a complete graph. However, having a complete graph alone does not allow the model to leverage the semantic structure across sentences significantly, as every sentence pair is connected, and likewise, a sparse graph does not contain enough information to exploit semantic similarities. Furthermore, all edges have a weight above zero, since it is very unlikely that two sentence embeddings are completely orthogonal. To overcome this problem, we introduce an edge-removal-method, where every edge below a certain threshold $t_{sim}^g$ is removed in order to emphasize high sentence similarity. Nonetheless, $t_{sim}^g$ should not be too large, as we otherwise found the model to be prone to overfitting. After removing edges below $t_{sim}^g$, our sentence semantic relation graph is used as the adjacency matrix $A$. The impact of $t_{sim}^g$ with different values is shown in Section SECREF26.",
+      "Based on our aforementioned hypothesis that a combination of general and genre-specific sentence embeddings is beneficial for the task of multi-document summarization, we further incorporate general sentence embeddings, pre-trained on Wikipedia entries, into edges between sentences. Additionally, we compute specialised sentence embeddings, which are related to the domains of the documents (see Section SECREF35).",
+      "Note that 1) the pre-trained sentence embeddings are only used to compute the weights of the edges and are not used by the summarization model (as others are produced by the sentence encoder) and 2) the edge weights are static and do not change during training."
+    ],
+    [
+      "Given a list of documents $C$, we encode each document's sentence $S_{i,j}$, where each has at most $L$ words $(w_{i,j,1}, w_{i,j,2}, ..., w_{i,j,L})$. In our experiments, all words are kept and converted into word embeddings, which are then fed to the sentence encoder in order to compute specialized sentence embeddings $S^{\\prime }_{i,j}$. We employ a single-layer forward recurrent neural network, using Long Short-Term Memory (LSTM) of BIBREF7 as sentence encoder, where the sentence embeddings are extracted from the last hidden states. We then concatenate all sentence embeddings into a matrix $X$ which constitutes the input node features that will be used by the graph convolutional network."
+    ],
+    [
+      "After having computed all sentence embeddings and the sentence semantic relation graph, we apply a single-layer Graph Convolutional Network (GCN) from BIBREF5, in order to capture high-level hidden features for each sentence, encapsulating sentence information as well as the graph structure.",
+      "We believe that our sentence semantic relation graph contains information not present in the data (via universal embeddings) and thus, we leverage this information by running a graph convolution on the first order neighborhood.",
+      "The GCN model takes as input the node features matrix $X$ and a squared adjacency matrix $A$. The former contains all sentence embeddings of the collection of documents, while the latter is our underlying sentence semantic relation graph. It outputs hidden representations for each node that encode both local graph structure and nodes's features. In order to take into account the sentences themselves during the information propagation, we add self-connections (i.e. the identity matrix) to $A$ such that $\\tilde{A} = A + I$.",
+      "Subsequently, we obtain our sentence hidden features by using Equation DISPLAY_FORM7.",
+      "where $W_i$ is the weight matrix of the $i$'th graph convolution layer and $b_i$ the bias vector. We choose the Exponential Linear Unit (ELU) activation function from BIBREF8 due to its ability to handle the vanishing gradient problem, by pushing the mean unit activations close to zero and consequently facilitating the backpropagation. By using only one hidden layer, as we only have one input-to-hidden layer and one hidden-to-output layer, we limit the information propagation to the first order neighborhood."
+    ],
+    [
+      "We use a simple linear layer to estimate a salience score for each sentence and then normalize the scores via softmax and obtain our estimated salience score $S^s_{i,j}$."
+    ],
+    [
+      "Our model SemSentSum is trained in an end-to-end manner and minimizes the cross-entropy loss of Equation DISPLAY_FORM10 between the salience score prediction and the ROUGE-1 $F_1$ score for each sentence.",
+      "$F_1(S)$ is computed as the ROUGE-1 $F_1$ score, unlike the common practice in the area of single and multi-document summarization as recall favors longer sentences whereas $F_1$ prevents this tendency. The scores are normalized via softmax."
+    ],
+    [
+      "While our model SemSentSum provides estimated saliency scores, we use a greedy strategy to construct an informative and non-redundant summary $Sum$. We first discard sentences having less than 9 words, as in BIBREF9, and then sort them in descending order of their estimated salience scores. We iteratively dequeue the sentence having the highest score and append it to the current summary $Sum$ if it is non-redundant with respect to the current content of $Sum$. We iterate until reaching the summary length limit.",
+      "To determine the similarity of a candidate sentence with the current summary, a sentence is considered as dissimilar if and only if the cosine similarity between its sentence embeddings and the embeddings of the current summary is below a certain threshold $t_{sim}^s$. We use the pre-trained model of BIBREF6 to compute sentence as well as summary embeddings, similarly to the sentence semantic relation graph construction. Our approach is novel, since it focuses on the semantic sentence structures and captures similarity between sentence meanings, instead of focusing on word similarities only, like previous TF-IDF approaches ( BIBREF0, BIBREF1, BIBREF3, BIBREF4)."
+    ],
+    [
+      "We conduct experiments on the most commonly used datasets for multi-document summarization from the Document Understanding Conferences (DUC). We use DUC 2001, 2002, 2003 and 2004 as the tasks of generic multi-document summarization, because they have been carried out during these years. We use DUC 2001, 2002, 2003 and 2004 for generic multi-document summarization, where DUC 2001/2002 are used for training, DUC 2003 for validation and finally, DUC 2004 for testing, following the common practice."
+    ],
+    [
+      "For the evaluation, we use ROUGE BIBREF10 with the official parameters of the DUC tasks and also truncate the summaries to 100 words for DUC 2001/2002/2003 and to 665 bytes for DUC 2004. Notably, we take ROUGE-1 and ROUGE-2 recall scores as the main metrics for comparison between produced summaries and golden ones as proposed by BIBREF11. The goal of the ROUGE-N metric is to compute the ratio of the number of N-grams from the generated summary matching these of the human reference summaries."
+    ],
+    [
+      "To define the edge weights of our sentence semantic relation graph, we employ the 600-dimensional pre-trained unigram model of BIBREF6, using English Wikipedia as source corpus. We keep only edges having a weight larger than $t_{sim}^g = 0.5$ (tuned on the validation set). For word embeddings, the 300-dimensional pre-trained GloVe embeddings BIBREF12 are used and fixed during training. The output dimension of the sentence embeddings produced by the sentence encoder is the same as that of the word embeddings, i.e. 300. For the graph convolutional network, the number of hidden units is 128 and the size of the generated hidden feature vectors is also 300. We use a batch size of 1, a learning rate of $0.0075$ using Adam optimizer BIBREF13 with $\\beta _1=0.9, \\beta _2=0.999$ and $\\epsilon =10^{-8}$. In order to make SemSentSum generalize better, we use dropout BIBREF14 of $0.2$, batch normalization BIBREF15, clip the gradient norm at $1.0$ if higher, add L2-norm regularizer with a regularization factor of $10^{-12}$ and train using early stopping with a patience of 10 iterations. Finally, the similarity threshold $t_{sim}^s$ in the summary generation process is $0.8$ (tuned on the validation set)."
+    ],
+    [
+      "We train our model SemSentSum on DUC 2001/2002, tune it on DUC 2003 and assess the performance on DUC 2004. In order to fairly compare SemSentSum with other models available in the literature, experiments are conducted with summaries truncated to 665 bytes (official summary length in the DUC competition), but also with summaries with a length constraint of 100 words. To the best of our knowledge, we are the first to conduct experiments on both summary lengths and compare our model with other systems producing either 100 words or 665 bytes summaries."
+    ],
+    [
+      "We investigate different methods to build our sentence semantic relation graph and vary the value of $t_{sim}^g$ from $0.0$ to $0.75$ to study the impact of the threshold cut-off. Among these are :",
+      "Cosine : Using cosine similarity ;",
+      "Tf-idf : Considering a node as the query and another as document. The weight corresponds to the cosine similarity between the query and the document ;",
+      "TextRank BIBREF16 : A weighted graph is created where nodes are sentences and edges defined by a similarity measure based on word overlap. Afterward, an algorithm similar to PageRank BIBREF17 is used to compute sentence importance and refined edge weights ;",
+      "LexRank BIBREF9 : An unsupervised multi-document summarizer based on the concept of eigenvector centrality in a graph of sentences to set up the edge weights ;",
+      "Approximate Discourse Graph (ADG) BIBREF2 : Approximation of a discourse graph where nodes are sentences and edges $(S_u,S_v)$ indicates sentence $S_v$ can be placed after $S_u$ in a coherent summary ;",
+      "Personalized ADG (PADG) BIBREF3 : Normalized version of ADG where sentence nodes are normalized over all edges."
+    ],
+    [
+      "In order to quantify the contribution of the different components of SemSentSum, we try variations of our model by removing different modules one at a time. Our two main elements are the sentence encoder (Sent) and the graph convolutional neural network (GCN). When we omit Sent, we substitute it with the pre-trained sentence embeddings used to build our sentence semantic relation graph."
+    ],
+    [
+      "Three dimensions are used to evaluate our model SemSentSum : 1) the summarization performance, to assess its capability 2) the impact of the sentence semantic relation graph generation using various methods and different thresholds $t_{sim}^g$ 3) an ablation study to analyze the importance of each component of SemSentSum."
+    ],
+    [
+      "We compare the results of SemSentSum for both settings : 665 bytes and 100 words summaries. We only include models using the same parameters to compute the ROUGE-1/ROUGE-2 score and recall as metrics.",
+      "The results for 665 bytes summaries are reported in Table TABREF28. We compare SemSentSum with three types of model relying on either 1) sentence or document embeddings 2) various hand-crafted features or 3) additional data.",
+      "For the first category, we significantly outperform MMR BIBREF18, PV-DBOW+BS BIBREF19 and PG-MMR BIBREF20. Although their methods are based on embeddings to represent the meaning, it shows that using only various distance metrics or encoder-decoder architecture on these is not efficient for the task of multi-document summarization (as also shown in the Ablation Study). We hypothesize that SemSentSum performs better by leveraging pre-trained sentence embeddings and hence lowering the effects of data scarcity.",
+      "Systems based on hand-crafted features include a widely-used learning-based summarization method, built on support vector regression SVR BIBREF21 ; a graph-based method based on approximating discourse graph G-Flow BIBREF2 ; Peer 65 which is the best peer systems participating in DUC evaluations ; and the recursive neural network R2N2 of BIBREF1 that learns automatically combinations of hand-crafted features. As can be seen, among these models completely dependent on hand-crafted features, SemSentSum achieves highest performance on both ROUGE scores. This denotes that using different linguistic and word-based features might not be enough to capture the semantic structures, in addition to being cumbersome to craft.",
+      "The last type of model is shown in TCSum BIBREF4 and uses transfer learning from a text classifier model, based on a domain-related dataset of $30\\,000$ documents from New York Times (sharing the same topics of the DUC datasets). In terms of ROUGE-1, SemSentSum significantly outperforms TCSum and performs similarly on ROUGE-2 score. This demonstrates that collecting more manually annotated data and training two models is unnecessary, in addition to being difficult to use in other domains, whereas SemSentSum is fully data driven, domain-independent and usable in realistic scenarios.",
+      "Table TABREF32 depicts models producing 100 words summaries, all depending on hand-crafted features. We use as baselines FreqSum BIBREF22 ; TsSum BIBREF23 ; traditional graph-based approaches such as Cont. LexRank BIBREF9 ; Centroid BIBREF24 ; CLASSY04 BIBREF25 ; its improved version CLASSY11 BIBREF26 and the greedy model GreedyKL BIBREF27. All of these models are significantly underperforming compared to SemSentSum. In addition, we include state-of-the-art models : RegSum BIBREF0 and GCN+PADG BIBREF3. We outperform both in terms of ROUGE-1. For ROUGE-2 scores we achieve better results than GCN+PADG but without any use of domain-specific hand-crafted features and a much smaller and simpler model. Finally, RegSum achieves a similar ROUGE-2 score but computes sentence saliences based on word scores, incorporating a rich set of word-level and domain-specific features. Nonetheless, our model is competitive and does not depend on hand-crafted features due to its full data-driven nature and thus, it is not limited to a single domain.",
+      "Consequently, the experiments show that achieving good performance for multi-document summarization without hand-crafted features or additional data is clearly feasible and SemSentSum produces competitive results without depending on these, is domain independent, fast to train and thus usable in real scenarios."
+    ],
+    [
+      "Table TABREF34 shows the results of different methods to create the sentence semantic relation graph with various thresholds $t_{sim}^g$ for 665 bytes summaries (we obtain similar results for 100 words). A first observation is that using cosine similarity with sentence embeddings significantly outperforms all other methods for ROUGE-1 and ROUGE-2 scores, mainly because it relies on the semantic of sentences instead of their individual words. A second is that different methods evolve similarly : PADG, Textrank, Tf-idf behave similarly to an U-shaped curve for both ROUGE scores while Cosine is the only one having an inverted U-shaped curve. The reason for this behavior is a consequence of its distribution being similar to a normal distribution because it relies on the semantic instead of words, while the others are more skewed towards zero. This confirms our hypothesis that 1) having a complete graph does not allow the model to leverage much the semantic 2) a sparse graph might not contain enough information to exploit similarities. Finally, Lexrank and ADG have different trends between both ROUGE scores."
+    ],
+    [
+      "We quantify the contribution of each module of SemSentSum in Table TABREF36 for 665 bytes summaries (we obtain similar results for 100 words). Removing the sentence encoder produces slightly lower results. This shows that the sentence semantic relation graph captures semantic attributes well, while the fine-tuned sentence embeddings obtained via the encoder help boost the performance, making these methods complementary. By disabling only the graph convolutional layer, a drastic drop in terms of performance is observed, which emphasizes that the relationship among sentences is indeed important and not present in the data itself. Therefore, our sentence semantic relation graph is able to capture sentence similarities by analyzing the semantic structures. Interestingly, if we remove the sentence encoder in addition to the graph convolutional layer, similar results are achieved. This confirms that alone, the sentence encoder is not able to compute an efficient representation of sentences for the task of multi-document summarization, probably due to the poor size of the DUC datasets. Finally, we can observe that the use of sentence embeddings only results in similar performance to the baselines, which rely on sentence or document embeddings BIBREF18, BIBREF19."
+    ],
+    [
+      "The idea of using multiple embeddings has been employed at the word level. BIBREF28 use an attention mechanism to combine the embeddings for each word for the task of natural language inference. BIBREF29, BIBREF30 concatenate the embeddings of each word into a vector before feeding a neural network for the tasks of aspect extraction and sentiment analysis. To our knowledge, we are the first to combine multiple types of sentence embeddings.",
+      "Extractive multi-document summarization has been addressed by a large range of approaches. Several of them employ graph-based methods. BIBREF31 introduced a cross-document structure theory, as a basis for multi-document summarization. BIBREF9 proposed LexRank, an unsupervised multi-document summarizer based on the concept of eigenvector centrality in a graph of sentences. Other works exploit shallow or deep features from the graph's topology BIBREF32, BIBREF33. BIBREF34 pairs graph-based methods (e.g. random walk) with clustering. BIBREF35 improved results by using a reinforced random walk model to rank sentences and keep non-redundant ones. The system by BIBREF2 does sentence selection, while balancing coherence and salience and by building a graph that approximates discourse relations across sentences BIBREF36.",
+      "Besides graph-based methods, other viable approaches include Maximum Marginal Relevance BIBREF37, which uses a greedy approach to select sentences and considers the tradeoff between relevance and redundancy ; support vector regression BIBREF21 ; conditional random field BIBREF38 ; or hidden markov model BIBREF25. Yet other approaches rely on n-grams regression as in BIBREF39. More recently, BIBREF1 built a recursive neural network, which tries to automatically detect combination of hand-crafted features. BIBREF4 employ a neural model for text classification on a large manually annotated dataset and apply transfer learning for multi-document summarization afterward.",
+      "The work most closely related to ours is BIBREF3. They create a normalized version of the approximate discourse graph BIBREF2, based on hand-crafted features, where sentence nodes are normalized over all the incoming edges. They then employ a deep neural network, composed of a sentence encoder, three graph convolutional layers, one document encoder and an attention mechanism. Afterward, they greedily select sentences using TF-IDF similarity to detect redundant sentences. Our model differs in four ways : 1) we build our sentence semantic relation graph by using pre-trained sentence embeddings with cosine similarity, where neither heavy preprocessing, nor hand-crafted features are necessary. Thus, our model is fully data-driven and domain-independent unlike other systems. In addition, the sentence semantic relation graph could be used for other tasks than multi-document summarization, such as detecting information cascades, query-focused summarization, keyphrase extraction or information retrieval, as it is not composed of hand-crafted features. 2) SemSentSum is much smaller and consequently has fewer parameters as it only uses a sentence encoder and a single convolutional layer. 3) The loss function is based on ROUGE-1 $F_1$ score instead of recall to prevent the tendency of choosing longer sentences. 4) Our method for summary generation is also different and novel as we leverage sentence embeddings to compute the similarity between a candidate sentence and the current summary instead of TF-IDF based approaches."
+    ],
+    [
+      "In this work, we propose a method to combine two types of sentence embeddings : 1) universal embeddings, pre-trained on a large corpus such as Wikipedia and incorporating general semantic structures across sentences and 2) domain-specific embeddings, learned during training. We merge them together by using a graph convolutional network that eliminates the need of hand-crafted features or additional annotated data.",
+      "We introduce a fully data-driven model SemSentSum that achieves competitive results for multi-document summarization on both kind of summary length (665 bytes and 100 words summaries), without requiring hand-crafted features or additional annotated data.",
+      "As SemSentSum is domain-independent, we believe that our sentence semantic relation graph and model can be used for other tasks including detecting information cascades, query-focused summarization, keyphrase extraction and information retrieval. In addition, we plan to leave the weights of the sentence semantic relation graph dynamic during training, and to integrate an attention mechanism directly into the graph."
+    ],
+    [
+      "We thank Michaela Benk for proofreading and helpful advice."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0399/instruction.md b/qasper-0399/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..ea7d960c2941d64ddbdcecbe573073d2d6b757b1
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+++ b/qasper-0399/instruction.md
@@ -0,0 +1,138 @@
+Name of Paper: Logic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph Embedding
+
+Question: Which knowledge graph completion tasks do they experiment with?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Transductive Embedding Models",
+    "Inductive Embedding Models",
+    "Notations",
+    "Framework",
+    "Logic Attention Network",
+    "Incorporating Neighborhood Attention",
+    "Training Objective",
+    "Experimental Configurations",
+    "Data Construction",
+    "Experiments on Triplet Classification",
+    "Experimental Setup",
+    "Evaluation Results",
+    "Experiments on Link Prediction",
+    "Experimental Results",
+    "Case Studies on Neighbors' Weights",
+    "Conclusion",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "Knowledge graphs (KGs) such as Freebase BIBREF0 , DBpedia BIBREF1 , and YAGO BIBREF2 play a critical role in various NLP tasks, including question answering BIBREF3 , information retrieval BIBREF4 , and personalized recommendation BIBREF5 . A typical KG consists of numerous facts about a predefined set of entities. Each fact is in the form of a triplet INLINEFORM0 (or INLINEFORM1 for short), where INLINEFORM2 and INLINEFORM3 are two entities and INLINEFORM4 is a relation the fact describes. Due to the discrete and incomplete natures of KGs, various KG embedding models are proposed to facilitate KG completion tasks, e.g., link prediction and triplet classification. After vectorizing entities and relations in a low-dimensional space, those models predict missing facts by manipulating the involved entity and relation embeddings.",
+      "Although proving successful in previous studies, traditional KG embedding models simply ignore the evolving nature of KGs. They require all entities to be present when training the embeddings. However, BIBREF6 shi2018open suggest that, on DBpedia, 200 new entities emerge on a daily basis between late 2015 and early 2016. Given the infeasibility of retraining embeddings from scratch whenever new entities come, missing facts about emerging entities are, unfortunately, not guaranteed to be inferred in time.",
+      "By transforming realistic networks, e.g., citation graphs, social networks, and protein interaction graphs, to simple graphs with single-typed and undirected edges, recent explorations BIBREF7 shed light on the evolution issue for homogeneous graphs. While learning embeddings for existing nodes, they inductively learn a neighborhood aggregator that represents a node by aggregating its neighbors' embeddings. The embeddings of unseen nodes can then be obtained by applying the aggregator on their existing neighbors.",
+      "It is well received that KGs differ from homogeneous graphs by their multi-relational structure BIBREF8 . Despite the difference, it seems promising to generalize the neighborhood aggregating scheme to embed emerging KG entities in an inductive manner. For example, in Figure FIGREF1 , a news article may describe an emerging entity (marked gray) as well as some facts involving existing entities. By generalizing structural information in the underlying KG, e.g., other entities residing in a similar neighborhood or involving similar relations, to the current entity's neighborhood, we can infer that it may probably live in Chicago.",
+      "Inspired by the above example, the inductive KG embedding problem boils down to designing a KG-specific neighborhood aggregator to capture essential neighborhood information. Intuitively, an ideal aggregator should have the following desired properties:",
+      "This paper concentrates on KG-specific neighborhood aggregators, which is of practical importance but only received limited focus BIBREF9 . To the best of our knowledge, neither conventional aggregators for homogeneous graphs nor those for KGs satisfy all the above three properties. In this regard, we employ the attention mechanism BIBREF10 and propose an aggregator called Logic Attention Network (LAN). Aggregating neighbors by a weighted combination of their transformed embeddings, LAN is inherently permutation invariant. To estimate the attention weights in LAN, we adopt two mechanisms to model relation- and neighbor-level information in a coarse-to-fine manner, At both levels, LAN is made aware of both neighborhood redundancy and query relation.",
+      "To summarize, our contributions are: (1) We propose three desired properties that decent neighborhood aggregators for KGs should possess. (2) We propose a novel aggregator, i.e., Logic Attention Network, to facilitate inductive KG embedding. (3) We conduct extensive comparisons with conventional aggregators on two KG completions tasks. The results validate the superiority of LAN w.r.t. the three properties."
+    ],
+    [
+      "In recent years, representation learning problems on KGs have received much attention due to the wide applications of the resultant entity and relation embeddings. Typical KG embedding models include TransE BIBREF11 , Distmult BIBREF12 , Complex BIBREF13 , Analogy BIBREF14 , to name a few. For more explorations, we refer readers to an extensive survey BIBREF15 . However, conventional approaches on KG embedding work in a transductive manner. They require that all entities should be seen during training. Such limitation hinders them from efficiently generalizing to emerging entities."
+    ],
+    [
+      "To relieve the issue of emerging entities, several inductive KG embedding models are proposed, including BIBREF16 xie2016representation, BIBREF6 shi2018open and BIBREF17 xie2016image which use description text or images as inputs. Although the resultant embeddings may be utilized for KG completion, it is not clear whether the embeddings are powerful enough to infer implicit or new facts beyond those expressed in the text/image. Moreover, when domain experts are recruited to introduce new entities via partial facts rather than text or images, those approaches may not help much.",
+      "In light of the above scenario, existing neighbors of an emerging entity are considered as another type of input for inductive models. In BIBREF9 ijcai2017-250, the authors propose applying Graph Neural Network (GNN) on the KG, which generates the embedding of a new entity by aggregating all its known neighbors. However, their model aggregates the neighbors via simple pooling functions, which neglects the difference among the neighbors. Other works like BIBREF18 fu2017hin2vec and BIBREF19 tang2015pte aim at embedding nodes for node classification given the entire graph and thus are inapplicable for inductive KG-specific tasks. BIBREF20 schlichtkrull2017modeling and BIBREF21 xiong2018one also rely on neighborhood structures to embed entities, but they either work transductively or focus on emerging relations.",
+      "Finally, we note another related line of studies on node representation learning for homogeneous graphs. Similar to text- or image-based inductive models for KGs, BIBREF22 duran2017learning, BIBREF23 yang2016revisiting, BIBREF24 velivckovic2017graph and BIBREF25 rossi2018deep exploit additional node attributes to embed unseen nodes. Another work more related to ours is BIBREF26 hamilton2017inductive. They tackle inductive node embedding by the neighborhood aggregation scheme. Their aggregators either trivially treat neighbors equally or unnecessarily require them to be ordered. Moreover, like all embedding models for homogeneous graphs, their model cannot be directly applied to KGs with multi-relational edges."
+    ],
+    [
+      "Let INLINEFORM0 and INLINEFORM1 be two sets of entities and relations of size INLINEFORM2 and INLINEFORM3 , respectively. A knowledge graph is composed of a set of triplet facts, namely DISPLAYFORM0 ",
+      "For each INLINEFORM0 , we denote the reverse of INLINEFORM1 by INLINEFORM2 , and add an additional triplet INLINEFORM3 to INLINEFORM4 .",
+      "For an entity INLINEFORM0 , we denote by INLINEFORM1 its neighborhood in INLINEFORM2 , i.e., all related entities with the involved relations. Formally, DISPLAYFORM0 ",
+      "We denote the projection of INLINEFORM0 on INLINEFORM1 and INLINEFORM2 by INLINEFORM3 and INLINEFORM4 , respectively. Here INLINEFORM5 are neighbors and INLINEFORM6 are neighboring relations. When the context is clear, we simplify the INLINEFORM7 -th entity INLINEFORM8 by its subscript INLINEFORM9 . We denote vectors by bold lower letters, and matrices or sets of vectors by bold upper letters.",
+      "Given a knowledge graph INLINEFORM0 , we would like to learn a neighborhood aggregator INLINEFORM1 that acts as follows:",
+      "For an entity INLINEFORM0 on INLINEFORM1 , INLINEFORM2 depends on INLINEFORM3 's neighborhood INLINEFORM4 to embed INLINEFORM5 as a low-dimensional vector INLINEFORM6 ;",
+      "For an unknown triplet INLINEFORM0 , the embeddings of INLINEFORM1 and INLINEFORM2 output by INLINEFORM3 suggest the plausibility of the triplet.",
+      "When a new entity emerges with some triplets involving INLINEFORM0 and INLINEFORM1 , we could apply such an aggregator INLINEFORM2 on its newly established neighborhood, and use the output embedding to infer new facts about it."
+    ],
+    [
+      "To obtain such a neighborhood aggregator INLINEFORM0 , we adopt an encoder-decoder framework as illustrated by Figure FIGREF12 . Given a training triplet, the encoder INLINEFORM1 encodes INLINEFORM2 and INLINEFORM3 into two embeddings with INLINEFORM4 . The decoder measures the plausibility of the triplet, and provides feedbacks to the encoder to adjust the parameters of INLINEFORM5 . In the remainder of this section, we describe general configurations of the two components.",
+      "As specified in Figure FIGREF12 , for an entity INLINEFORM0 on focus, the encoder works on a collection of input neighbor embeddings, and output INLINEFORM1 's embedding. To differentiate between input and output embeddings, we use superscripts INLINEFORM2 and INLINEFORM3 on the respective vectors. Let INLINEFORM4 , which is obtained from an embedding matrix INLINEFORM5 , be the embedding of a neighbor INLINEFORM6 , where INLINEFORM7 . To reflect the impact of relation INLINEFORM8 on INLINEFORM9 , we apply a relation-specific transforming function INLINEFORM10 on INLINEFORM11 as follows, DISPLAYFORM0 ",
+      "where INLINEFORM0 is the transforming vector for relation INLINEFORM1 and is restricted as a unit vector. We adopt this transformation from BIBREF27 wang2014knowledge since it does not involve matrix product operations and is of low computation complexity.",
+      "After neighbor embeddings are transformed, these transformed embeddings are fed to the aggregator INLINEFORM0 to output an embedding INLINEFORM1 for the target entity INLINEFORM2 , i.e., DISPLAYFORM0 ",
+      "By definition, an aggregator INLINEFORM0 essentially takes as input a collection of vectors INLINEFORM1 ( INLINEFORM2 ) and maps them to a single vector. With this observation, the following two types of functions seem to be natural choices for neighborhood aggregators, and have been adopted previously:",
+      "Pooling Functions. A typical pooling function is mean-pooling, which is defined by INLINEFORM0 . Besides mean-pooling, other previously adopted choices include sum- and max-pooling BIBREF9 . Due to their simple forms, pooling functions are permutation-invariant, but consider the neighbors equally. It is aware of neither potential redundancy in the neighborhood nor the query relations.",
+      "Recurrent Neural Networks (RNNs). In various natural language processing tasks, RNNs prove effective in modeling sequential dependencies. In BIBREF26 , the authors adopt an RNN variant LSTM BIBREF28 as neighborhood aggregator, i.e., INLINEFORM0 . To train and apply the LSTM-based aggregator, they have to randomly permute the neighbors, which violates the permutation variance property.",
+      "Given the subject and object embeddings INLINEFORM0 and INLINEFORM1 output by the encoder, the decoder is required to measure the plausibility of the training triplet. To avoid potential mixture with relations INLINEFORM2 in the neighborhood, we refer to the relation in the training triplet by query relation, and denote it by INLINEFORM3 instead. After looking up INLINEFORM4 's representation INLINEFORM5 from an embedding matrix INLINEFORM6 , the decoder scores the training triplet INLINEFORM7 with a scoring function INLINEFORM8 . Following BIBREF9 ijcai2017-250, we mainly investigate a scoring function based on TransE BIBREF11 defined by DISPLAYFORM0 ",
+      "where INLINEFORM0 denotes the L1 norm. To test whether the studied aggregators generalize among different scoring function, we will also consider several alternatives in experiments."
+    ],
+    [
+      "As discussed above, traditional neighborhood aggregators do not preserve all desired properties. In this section, we describe a novel aggregator, namely Logic Attention Network (LAN), which addresses all three properties. We also provide details in training the LAN aggregator."
+    ],
+    [
+      "Traditional neighborhood aggregators only depend on collections of transformed embeddings. They neglect other useful information in the neighborhood INLINEFORM0 and the query relation INLINEFORM1 , which may facilitate more effective aggregation of the transformed embeddings. To this end, we propose generalizing the aggregators from INLINEFORM2 to INLINEFORM3 .",
+      "Specifically, for an entity INLINEFORM0 , its neighbors INLINEFORM1 should contribute differently to INLINEFORM2 according to its importance in representing INLINEFORM3 . To consider the different contribution while preserving the permutation invariance property, we employ a weighted or attention-based aggregating approach on the transformed embeddings. The additional information in INLINEFORM4 and INLINEFORM5 is then exploited when estimating the attention weights. Formally, we obtain INLINEFORM6 by DISPLAYFORM0 ",
+      "Here INLINEFORM0 is the attention weight specified for each neighbor INLINEFORM1 given INLINEFORM2 and the query relation INLINEFORM3 .",
+      "To assign larger weights INLINEFORM0 to more important neighbors, from the perspective of INLINEFORM1 , we ask ourselves two questions at progressive levels: 1) What types of neighboring relations may lead us to potentially important neighbors? 2) Following those relations, which specific neighbor (in transformed embedding) may contain important information? Inspired by the two questions, we adopt the following two mechanisms to estimate INLINEFORM2 .",
+      "Relations in a KG are simply not independent of each other. For an entity INLINEFORM0 , one neighboring relation INLINEFORM1 may imply the existence of another neighboring relation INLINEFORM2 , though they may not necessarily connect INLINEFORM3 to the same neighbor. For example, a neighboring relation play_for may suggest the home city, i.e., live_in, of the current athlete entity. Following notations in logics, we denote potential dependency between INLINEFORM4 and INLINEFORM5 by a \u201clogic rule\u201d INLINEFORM6 . To measure the extent of such dependency, we define the confidence of a logic rule INLINEFORM7 as follows: DISPLAYFORM0 ",
+      "Here the function INLINEFORM0 equals 1 when INLINEFORM1 is true and 0 otherwise. As an empirical statistic over the entire KG, INLINEFORM2 is larger if more entities with neighboring relation INLINEFORM3 also have INLINEFORM4 as a neighboring relation.",
+      "With the confidence scores INLINEFORM0 between all relation pairs at hand, we are ready to characterize neighboring relations INLINEFORM1 that lead to important neighbors. On one hand, such a relation INLINEFORM2 should have a large INLINEFORM3 , i.e., it is statistically relevant to INLINEFORM4 . Following the above example, play_for should be consulted to if the query relation is live_in. On the other hand, INLINEFORM5 should not be implied by other relations in the neighborhood. For example, no matter whether the query relation is live_in or not, the neighboring relation work_as should not be assigned too much weight, because sufficient information is already provided by play_for.",
+      "Following the above intuitions, we implement the logic rule mechanism of measuring neighboring relations' usefulness as follow: DISPLAYFORM0 ",
+      "We note that INLINEFORM0 promotes relations INLINEFORM1 strongly implying INLINEFORM2 (the numerator) and demotes those implied by some other relation in the same neighborhood (the denominator). In this manner, our logic rule mechanism addresses both query relation awareness and neighborhood redundancy awareness.",
+      "With global statistics about relations, the logic rule mechanism guides the attention weight to be distributed at a coarse granularity of relations. However, it may be insufficient not to consult finer-grained information hidden in the transformed neighbor embeddings to determine which neighbor is important indeed. To take the transformed embeddings into consideration, we adopt an attention network BIBREF10 .",
+      "Specifically, given a query relation INLINEFORM0 , the importance of an entity INLINEFORM1 's neighbor INLINEFORM2 is measured by DISPLAYFORM0 ",
+      "Here the unnormalized attention weight INLINEFORM0 is given by an attention neural network as DISPLAYFORM0 ",
+      "In this equation, INLINEFORM0 and INLINEFORM1 are global attention parameters, while INLINEFORM2 is a relation-specific attention parameter for the query relation INLINEFORM3 . All those attention parameters are regarded as parameters of the encoder, and learned directly from the data.",
+      "Note that, unlike the logic rule mechanism at relation level, the computation of INLINEFORM0 concentrates more on the neighbor INLINEFORM1 itself. This is useful when the neighbor entity INLINEFORM2 is also helpful to explain the current training triplet. For example, in Figure FIGREF12 , the neighbor Chicago_Bulls could help to imply the object of live_in since there are other athletes playing for Chicago_Bulls while living in Chicago. Although working at the neighbor level, the dependency on transformed neighbor embeddings INLINEFORM3 and the relation-specific parameter INLINEFORM4 make INLINEFORM5 aware of both neighborhood redundancy and the query relation.",
+      "Finally, to incorporate these two weighting mechanisms together in measuring the importance of neighbors, we employ a double-view attention and reformulate Eq. ( EQREF22 ) as DISPLAYFORM0 "
+    ],
+    [
+      "To train the entire model in Figure FIGREF12 , we need both positive triplets and negative ones. All triplets INLINEFORM0 from the knowledge graph naturally serve as positive triplets, which we denote by INLINEFORM1 . To make up for the absence of negative triplets, for each INLINEFORM2 , we randomly corrupt the object or subject (but not both) by another entity in INLINEFORM3 , and denote the corresponding negative triplets by INLINEFORM4 . Formally, DISPLAYFORM0 ",
+      "To encourage the decoder to give high scores for positive triplets and low scores for negative ones, we apply a margin-based ranking loss on each triplet INLINEFORM0 , i.e., DISPLAYFORM0 ",
+      "Here INLINEFORM0 denotes the positive part of x, and INLINEFORM1 is a hyper-parameter for the margin. Finally, the training objective is defined by DISPLAYFORM0 ",
+      "The above training objective only optimizes the output of the aggregator, i.e., the output entity embeddings INLINEFORM0 . The input entity embeddings INLINEFORM1 , however, are not directly aware of the structure of the entire KG. To make the input embeddings and thus the aggregation more meaningful, we set up a subtask for LAN.",
+      "First, we define a second scoring function, which is similar to Eq. ( EQREF20 ) except that input embeddings INLINEFORM0 from INLINEFORM1 are used to represent the subject and object, i.e., DISPLAYFORM0 ",
+      "The embedding of query relation INLINEFORM0 is obtained from the same embedding matrix INLINEFORM1 as in the first scoring function. Then a similar margin-based ranking loss INLINEFORM2 as Eq. ( EQREF32 ) is defined for the subtask. Finally, we combine the subtask with the main task, and reformulate the overall training objective of LAN as DISPLAYFORM0 "
+    ],
+    [
+      "We evaluate the effectiveness of our LAN model on two typical knowledge graph completion tasks, i.e., link prediction and triplet classification. We compare our LAN with two baseline aggregators, MEAN and LSTM, as described in the Encoder section. MEAN is used on behalf of pooling functions since it leads to the best performance in BIBREF9 ijcai2017-250. LSTM is used due to its large expressive capability BIBREF26 ."
+    ],
+    [
+      "In both tasks, we need datasets whose test sets contain new entities unseen during training. For the task of triplet classification, we directly use the datasets released by BIBREF9 ijcai2017-250 which are based on WordNet11 BIBREF29 . Since they do not conduct experiments on the link prediction task, we construct the required datasets based on FB15K BIBREF11 following a similar protocol used in BIBREF9 ijcai2017-250 as follows.",
+      "Sampling unseen entities. Firstly, we randomly sample INLINEFORM0 of the original testing triplets to form a new test set INLINEFORM1 for our inductive scenario ( BIBREF9 ijcai2017-250 samples INLINEFORM2 testing triplets). Then two different strategies are used to construct the candidate unseen entities INLINEFORM6 . One is called Subject, where only entities appearing as the subjects in INLINEFORM7 are added to INLINEFORM8 . Another is called Object, where only objects in INLINEFORM9 are added to INLINEFORM10 . For an entity INLINEFORM11 , if it does not have any neighbor in the original training set, such an entity is filtered out, yielding the final unseen entity set INLINEFORM12 . For a triplet INLINEFORM13 , if INLINEFORM14 or INLINEFORM15 , it is removed from INLINEFORM16 .",
+      "Filtering and splitting data sets. The second step is to ensure that unseen entities would not appear in final training set or validation set. We split the original training set into two data sets, the new training set and auxiliary set. For a triplet INLINEFORM0 in original training set, if INLINEFORM1 , it is added to the new training set. If INLINEFORM2 or INLINEFORM3 , it is added to the auxiliary set, which serves as existing neighbors for unseen entities in INLINEFORM4 .",
+      "Finally, for a triplet INLINEFORM0 in the original validation set, if INLINEFORM1 or INLINEFORM2 , it is removed from the validation set.",
+      "The statistics for the resulting INLINEFORM0 datasets using Subject and Object strategies are in Table TABREF34 ."
+    ],
+    [
+      "Triplet classification aims at classifying a fact triplet INLINEFORM0 as true or false. In the dataset of BIBREF9 ijcai2017-250, triplets in the validation and testing sets are labeled as true or false, while triplets in the training set are all true ones.",
+      "To tackle this task, we preset a threshold INLINEFORM0 for each relation r. If INLINEFORM1 , the triplet is classified as positive, otherwise it is negative. We determine the optimal INLINEFORM2 by maximizing classification accuracy on the validation set."
+    ],
+    [
+      "Since this task is also conducted in BIBREF9 ijcai2017-250, we use the same configurations with learning rate INLINEFORM0 , embedding dimension INLINEFORM1 , and margin INLINEFORM2 for all datasets. We randomly sample 64 neighbors for each entity. Zero padding is used when the number of neighbors is less than 64. L2-regularization is applied on the parameters of LAN. The regularization rate is INLINEFORM3 .",
+      "We search the best hyper-parameters of all models according to the performance on validation set. In detail, we search learning rate INLINEFORM0 in INLINEFORM1 , embedding dimension for neighbors INLINEFORM2 in INLINEFORM3 , and margin INLINEFORM4 in INLINEFORM5 . The optimal configurations are INLINEFORM6 for all the datasets."
+    ],
+    [
+      "The results are reported in Table TABREF42 . Since we did not achieve the same results for MEAN as reported in BIBREF9 ijcai2017-250 with either our implementation or their released source code, the best results from their original paper are reported. From the table, we observe that, on one hand, LSTM results in poorer performance compared with MEAN, which involves fewer parameters though. This demonstrates the necessity of the permutation invariance for designing neighborhood aggregators for KGs. On the other hand, our LAN model consistently achieves the best results on all datasets, demonstrating the effectiveness of LAN on this KBC task."
+    ],
+    [
+      "Link prediction in the inductive setting aims at reasoning the missing part \u201c?\u201d in a triplet when given INLINEFORM0 or INLINEFORM1 with emerging entities INLINEFORM2 or INLINEFORM3 respectively. To tackle the task, we firstly hide the object (subject) of each testing triplet in Subject-R (Object-R) to produce a missing part. Then we replace the missing part with all entities in the entity set INLINEFORM4 to construct candidate triplets. We compute the scoring function INLINEFORM5 defined in Eq. ( EQREF20 ) for all candidate triplets, and rank them in descending order. Finally, we evaluate whether the ground-truth entities are ranked ahead of other entities. We use traditional evaluation metrics as in the KG completion literature, i.e., Mean Rank (MR), Mean Reciprocal Rank (MRR), and the proportion of ground truth entities ranked top-k (Hits@k, INLINEFORM6 ). Since certain candidate triplets might also be true, we follow previous works and filter out these fake negatives before ranking."
+    ],
+    [
+      "The results on Subject-10 and Object-10 are reported in Table TABREF43 . The results on other datasets are similar and we summarize them later in Figure FIGREF50 . From Table TABREF43 , we still observe consistent results for all the models as in the triplet classification task. Firstly, LSTM results in the poorest performance on all datasets. Secondly, our LAN model outperforms all the other baselines significantly, especially on the Hit@k metrics. The improvement on the MR metric of LAN might not be considerable. This is due to the flaw of the MR metric since it is more sensitive to lower positions of the ranking, which is actually of less importance. The MRR metric is proposed for this reason, where we could observe consistent improvements brought by LAN. The effectiveness of LAN on link prediction validates LAN's superiority to other aggregators and the necessities to treat the neighbors differently in a permutation invariant way. To analyze whether LAN outperforms the others for expected reasons and generalizes to other configurations, we conduct the following studies.",
+      "In this experiment, we would like to confirm that it's necessary for the aggregator to be aware of the query relation. Specifically, we investigate the attention neural network and design two degenerated baselines. One is referred to as Query-Attention and is simply an attention network as in LAN except that the logic rule mechanism is removed. The other is referred to as Global-Attention, which is also an attention network except that the query relation embedding INLINEFORM0 in Eq. ( EQREF28 ) is masked by a zero vector. The results are reported in Table TABREF46 . We observe that although superior to MEAN, Global-Attention is outperformed by Query-Attention, demonstrating the necessity of query relation awareness. The superiority of Global-Attention over MEAN could be attributed to the fact that the attention mechanism is effective to identify the neighbors which are globally important regardless of the query.",
+      "We find that the logic rules greatly help to improve the attention network in LAN. We confirm this point by conducting further experiments where the logic rule mechanism is isolated as a single model (referred to as Logic Rules Only). The results are also demonstrated in Table TABREF46 , from which we find that Query-Attention outperforms MEAN by a limited margin. Meanwhile, Logic Rules Only outperforms both MEAN and Query-Attention by significant margins. These results demonstrate the effectiveness of logic rules in assigning meaningful weights to the neighbors. Specifically, in order to generate representations for unseen entities, it is crucial to incorporate the logic rules to train the aggregator, instead of depending solely on neural networks to learn from the data. By combining the logic rules and neural networks, LAN takes a step further in outperforming all the other models.",
+      "To find out whether the superiority of LAN to the baselines can generalize to other scoring functions, we replace the scoring function in Eq. ( EQREF20 ) and Eq. ( EQREF36 ) by three typical scoring functions mentioned in Related Works. We omit the results of LSTM, for it is still inferior to MEAN. The results are listed in Table TABREF48 , from which we observe that with different scoring functions, LAN outperforms MEAN consistently by a large margin on all the evaluation metrics. Note that TransE leads to the best results on MEAN and LAN.",
+      "It's reasonable to suppose that when the ratio of the unseen entities over the training entities increases (namely the observed knowledge graph becomes sparser), all models' performance would deteriorate. To figure out whether our LAN could suffer less on sparse knowledge graphs, we conduct link prediction on datasets with different sample rates INLINEFORM0 as described in Step 1 of the Data Construction section. The results are displayed in Figure FIGREF50 . We observe that the increasing proportion of unseen entities certainly has a negative impact on all models. However, the performance of LAN does not decrease as drastically as that of MEAN and LSTM, indicating that LAN is more robust on sparse KGs."
+    ],
+    [
+      "In order to visualize how LAN specifies weights to neighbors, we sample some cases from the Subject-10 testing set. From Table FIGREF50 , we have the following observations. First, with the query relation, LAN could attribute higher weights to neighbors with more relevant relations. In the first case, when the query is origin, the top two neighbors are involved by place_lived and breed_origin, which are helpful to imply origin. In addition, in all three cases, neighbors with relation gender gain the lowest weights since they imply nothing about the query relation. Second, LAN could attribute higher weights to neighbor entities that are more informative. When the query relation is profession, the neighbors Aristotle, Metaphysics and Aesthetics are all relevant to the answer Philosopher. In the third case, we also observe similar situations. Here, the neighbor with the highest weight is (institution, University_of_Calgary) since the query relation place_lived helps the aggregator to focus on the neighboring relation institution, then the neighbor entity University_of_Calgary assists in locating the answer Calgary."
+    ],
+    [
+      "In this paper, we address inductive KG embedding, which helps embed emerging entities efficiently. We formulate three characteristics required for effective neighborhood aggregators. To meet the three characteristics, we propose LAN, which attributes different weights to an entity's neighbors in a permutation invariant manner, considering both the redundancy of neighbors and the query relation. The weights are estimated from data with logic rules at a coarse relation level, and neural attention network at a fine neighbor level. Experiments show that LAN outperforms baseline models significantly on two typical KG completion tasks."
+    ],
+    [
+      "We thank the three anonymous authors for their constructive comments. This work is supported by the National Natural Science Foundation of China (61472453, U1401256, U1501252, U1611264, U1711261, U1711262)."
+    ]
+  ]
+}
+```
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diff --git a/qasper-0402/instruction.md b/qasper-0402/instruction.md
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+++ b/qasper-0402/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Learning with Noisy Labels for Sentence-level Sentiment Classification
+
+Question: How does the model differ from Generative Adversarial Networks?
\ No newline at end of file
diff --git a/qasper-0403/instruction.md b/qasper-0403/instruction.md
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--- /dev/null
+++ b/qasper-0403/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Learning with Noisy Labels for Sentence-level Sentiment Classification
+
+Question: What is the dataset used to train the model?
\ No newline at end of file
diff --git a/qasper-0404/instruction.md b/qasper-0404/instruction.md
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index 0000000000000000000000000000000000000000..87f2b4d987b474d5edcecff31e48fbfe1ae13c33
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+++ b/qasper-0404/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Learning with Noisy Labels for Sentence-level Sentiment Classification
+
+Question: What is the performance of the model?
\ No newline at end of file
diff --git a/qasper-0405/instruction.md b/qasper-0405/instruction.md
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+++ b/qasper-0405/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Learning with Noisy Labels for Sentence-level Sentiment Classification
+
+Question: Is the model evaluated against a CNN baseline?
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diff --git a/qasper-0432/instruction.md b/qasper-0432/instruction.md
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+Name of Paper: Dynamic Memory Networks for Visual and Textual Question Answering
+
+Question: Why is supporting fact supervision necessary for DMN?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Dynamic Memory Networks",
+    "Improved Dynamic Memory Networks: DMN+",
+    "Input Module for Text QA",
+    "Input Module for VQA",
+    "The Episodic Memory Module",
+    "Related Work",
+    "Datasets",
+    "bAbI-10k",
+    "DAQUAR-ALL visual dataset",
+    "Visual Question Answering",
+    "Model Analysis",
+    "Comparison to state of the art using bAbI-10k",
+    "Comparison to state of the art using VQA",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Neural network based methods have made tremendous progress in image and text classification BIBREF0 , BIBREF1 . However, only recently has progress been made on more complex tasks that require logical reasoning. This success is based in part on the addition of memory and attention components to complex neural networks. For instance, memory networks BIBREF2 are able to reason over several facts written in natural language or (subject, relation, object) triplets. Attention mechanisms have been successful components in both machine translation BIBREF3 , BIBREF4 and image captioning models BIBREF5 .",
+      "The dynamic memory network BIBREF6 (DMN) is one example of a neural network model that has both a memory component and an attention mechanism. The DMN yields state of the art results on question answering with supporting facts marked during training, sentiment analysis, and part-of-speech tagging.",
+      "We analyze the DMN components, specifically the input module and memory module, to improve question answering. We propose a new input module which uses a two level encoder with a sentence reader and input fusion layer to allow for information flow between sentences. For the memory, we propose a modification to gated recurrent units (GRU) BIBREF7 . The new GRU formulation incorporates attention gates that are computed using global knowledge over the facts. Unlike before, the new DMN+ model does not require that supporting facts (i.e. the facts that are relevant for answering a particular question) are labeled during training. The model learns to select the important facts from a larger set.",
+      "In addition, we introduce a new input module to represent images. This module is compatible with the rest of the DMN architecture and its output is fed into the memory module. We show that the changes in the memory module that improved textual question answering also improve visual question answering. Both tasks are illustrated in Fig. 1 ."
+    ],
+    [
+      "We begin by outlining the DMN for question answering and the modules as presented in BIBREF6 .",
+      "The DMN is a general architecture for question answering (QA). It is composed of modules that allow different aspects such as input representations or memory components to be analyzed and improved independently. The modules, depicted in Fig. 1 , are as follows:",
+      "Input Module: This module processes the input data about which a question is being asked into a set of vectors termed facts, represented as $F=[f_1,\\hdots ,f_N]$ , where $N$ is the total number of facts. These vectors are ordered, resulting in additional information that can be used by later components. For text QA in BIBREF6 , the module consists of a GRU over the input words.",
+      "As the GRU is used in many components of the DMN, it is useful to provide the full definition. For each time step $i$ with input $x_i$ and previous hidden state $h_{i-1}$ , we compute the updated hidden state $h_i = GRU(x_i,h_{i-1})$ by ",
+      "$$u_i &=& \\sigma \\left(W^{(u)}x_{i} + U^{(u)} h_{i-1} + b^{(u)} \\right)\\\\\nr_i &=& \\sigma \\left(W^{(r)}x_{i} + U^{(r)} h_{i-1} + b^{(r)} \\right)\\\\\n\\tilde{h}_i &=& \\tanh \\left(Wx_{i} + r_i \\circ U h_{i-1} + b^{(h)}\\right)\\\\\nh_i &=& u_i\\circ \\tilde{h}_i + (1-u_i) \\circ h_{i-1}$$   (Eq. 2) ",
+      "where $\\sigma $ is the sigmoid activation function, $\\circ $ is an element-wise product, $W^{(z)}, W^{(r)}, W \\in \\mathbb {R}^{n_H \\times n_I}$ , $U^{(z)}, U^{(r)}, U \\in \\mathbb {R}^{n_H \\times n_H}$ , $n_H$ is the hidden size, and $n_I$ is the input size.",
+      "Question Module: This module computes a vector representation $q$ of the question, where $q \\in \\mathbb {R}^{n_H}$ is the final hidden state of a GRU over the words in the question.",
+      "Episodic Memory Module: Episode memory aims to retrieve the information required to answer the question $q$ from the input facts. To improve our understanding of both the question and input, especially if questions require transitive reasoning, the episode memory module may pass over the input multiple times, updating episode memory after each pass. We refer to the episode memory on the $t^{th}$ pass over the inputs as $m^t$ , where $m^t \\in \\mathbb {R}^{n_H}$ , the initial memory vector is set to the question vector: $m^0 = q$ .",
+      "The episodic memory module consists of two separate components: the attention mechanism and the memory update mechanism. The attention mechanism is responsible for producing a contextual vector $c^t$ , where $c^t \\in \\mathbb {R}^{n_H}$ is a summary of relevant input for pass $t$ , with relevance inferred by the question $q$ and previous episode memory $m^{t-1}$ . The memory update mechanism is responsible for generating the episode memory $m^t$ based upon the contextual vector $c^t$ and previous episode memory $m^{t-1}$ . By the final pass $T$ , the episodic memory $m^T$ should contain all the information required to answer the question $c^t \\in \\mathbb {R}^{n_H}$0 .",
+      "Answer Module: The answer module receives both $q$ and $m^T$ to generate the model's predicted answer. For simple answers, such as a single word, a linear layer with softmax activation may be used. For tasks requiring a sequence output, an RNN may be used to decode $a = [q ; m^T]$ , the concatenation of vectors $q$ and $m^T$ , to an ordered set of tokens. The cross entropy error on the answers is used for training and backpropagated through the entire network."
+    ],
+    [
+      "We propose and compare several modeling choices for two crucial components: input representation, attention mechanism and memory update. The final DMN+ model obtains the highest accuracy on the bAbI-10k dataset without supporting facts and the VQA dataset BIBREF8 . Several design choices are motivated by intuition and accuracy improvements on that dataset."
+    ],
+    [
+      "In the DMN specified in BIBREF6 , a single GRU is used to process all the words in the story, extracting sentence representations by storing the hidden states produced at the end of sentence markers. The GRU also provides a temporal component by allowing a sentence to know the content of the sentences that came before them. Whilst this input module worked well for bAbI-1k with supporting facts, as reported in BIBREF6 , it did not perform well on bAbI-10k without supporting facts (Sec. \"Model Analysis\" ).",
+      "We speculate that there are two main reasons for this performance disparity, all exacerbated by the removal of supporting facts. First, the GRU only allows sentences to have context from sentences before them, but not after them. This prevents information propagation from future sentences. Second, the supporting sentences may be too far away from each other on a word level to allow for these distant sentences to interact through the word level GRU.",
+      "Input Fusion Layer",
+      "For the DMN+, we propose replacing this single GRU with two different components. The first component is a sentence reader, responsible only for encoding the words into a sentence embedding. The second component is the input fusion layer, allowing for interactions between sentences. This resembles the hierarchical neural auto-encoder architecture of BIBREF9 and allows content interaction between sentences. We adopt the bi-directional GRU for this input fusion layer because it allows information from both past and future sentences to be used. As gradients do not need to propagate through the words between sentences, the fusion layer also allows for distant supporting sentences to have a more direct interaction.",
+      "Fig. 2 shows an illustration of an input module, where a positional encoder is used for the sentence reader and a bi-directional GRU is adopted for the input fusion layer. Each sentence encoding $f_i$ is the output of an encoding scheme taking the word tokens $[w^i_1, \\hdots , w^i_{M_i}]$ , where $M_i$ is the length of the sentence.",
+      "The sentence reader could be based on any variety of encoding schemes. We selected positional encoding described in BIBREF10 to allow for a comparison to their work. GRUs and LSTMs were also considered but required more computational resources and were prone to overfitting if auxiliary tasks, such as reconstructing the original sentence, were not used.",
+      "For the positional encoding scheme, the sentence representation is produced by $f_i = \\sum ^{j=1}_M l_j \\circ w^i_j$ , where $\\circ $ is element-wise multiplication and $l_j$ is a column vector with structure $l_{jd} = (1 - j / M) - (d / D) (1 - 2j / M)$ , where $d$ is the embedding index and $D$ is the dimension of the embedding.",
+      "The input fusion layer takes these input facts and enables an information exchange between them by applying a bi-directional GRU. ",
+      "$$\\overrightarrow{f_i} = GRU_{fwd}(f_i, \\overrightarrow{f_{i-1}}) \\\\\n\\overleftarrow{f_{i}} = GRU_{bwd}(f_{i}, \\overleftarrow{f_{i+1}}) \\\\\n\\overleftrightarrow{f_i} = \\overleftarrow{f_i} + \\overrightarrow{f_i}$$   (Eq. 5) ",
+      "where $f_i$ is the input fact at timestep $i$ , $ \\overrightarrow{f_i}$ is the hidden state of the forward GRU at timestep $i$ , and $\\overleftarrow{f_i}$ is the hidden state of the backward GRU at timestep $i$ . This allows contextual information from both future and past facts to impact $\\overleftrightarrow{f_i}$ .",
+      "We explored a variety of encoding schemes for the sentence reader, including GRUs, LSTMs, and the positional encoding scheme described in BIBREF10 . For simplicity and speed, we selected the positional encoding scheme. GRUs and LSTMs were also considered but required more computational resources and were prone to overfitting if auxiliary tasks, such as reconstructing the original sentence, were not used."
+    ],
+    [
+      "To apply the DMN to visual question answering, we introduce a new input module for images. The module splits an image into small local regions and considers each region equivalent to a sentence in the input module for text. The input module for VQA is composed of three parts, illustrated in Fig. 3 : local region feature extraction, visual feature embedding, and the input fusion layer introduced in Sec. \"Input Module for Text QA\" .",
+      "Local region feature extraction: To extract features from the image, we use a convolutional neural network BIBREF0 based upon the VGG-19 model BIBREF11 . We first rescale the input image to $448 \\times 448$ and take the output from the last pooling layer which has dimensionality $d = 512 \\times 14 \\times 14$ . The pooling layer divides the image into a grid of $14 \\times 14$ , resulting in 196 local regional vectors of $d = 512$ .",
+      "Visual feature embedding: As the VQA task involves both image features and text features, we add a linear layer with tanh activation to project the local regional vectors to the textual feature space used by the question vector $q$ .",
+      "Input fusion layer: The local regional vectors extracted from above do not yet have global information available to them. Without global information, their representational power is quite limited, with simple issues like object scaling or locational variance causing accuracy problems.",
+      "To solve this, we add an input fusion layer similar to that of the textual input module described in Sec. \"Input Module for Text QA\" . First, to produce the input facts $F$ , we traverse the image in a snake like fashion, as seen in Figure 3 . We then apply a bi-directional GRU over these input facts $F$ to produce the globally aware input facts $\\overleftrightarrow{F}$ . The bi-directional GRU allows for information propagation from neighboring image patches, capturing spatial information."
+    ],
+    [
+      "The episodic memory module, as depicted in Fig. 4 , retrieves information from the input facts $\\overleftrightarrow{F} = [\\overleftrightarrow{f_1}, \\hdots , \\overleftrightarrow{f_N}]$ provided to it by focusing attention on a subset of these facts. We implement this attention by associating a single scalar value, the attention gate $g^t_i$ , with each fact $\\overleftrightarrow{f}_i$ during pass $t$ . This is computed by allowing interactions between the fact and both the question representation and the episode memory state. ",
+      "$$z^t_i &=& [\\overleftrightarrow{f_i} \\circ q; \\overleftrightarrow{f_i} \\circ m^{t-1}; \\vert \\overleftrightarrow{f_i} - q \\vert ; \\vert \\overleftrightarrow{f_i} - m^{t-1} \\vert ] \\\\\nZ^t_i &=& W^{(2)} \\tanh \\left(W^{(1)}z^t_i + b^{(1)} \\right)+ b^{(2)} \\\\\ng^t_i &=& \\frac{\\exp (Z^t_i)}{\\sum _{k=1}^{M_i} \\exp (Z^t_k)} $$   (Eq. 10) ",
+      "where $\\overleftrightarrow{f_i}$ is the $i^{th}$ fact, $m^{t-1}$ is the previous episode memory, $q$ is the original question, $\\circ $ is the element-wise product, $|\\cdot |$ is the element-wise absolute value, and $;$ represents concatenation of the vectors.",
+      "The DMN implemented in BIBREF6 involved a more complex set of interactions within $z$ , containing the additional terms $[f; m^{t-1}; q; f^T W^{(b)} q; f^T W^{(b)} m^{t-1}]$ . After an initial analysis, we found these additional terms were not required.",
+      "Attention Mechanism",
+      "Once we have the attention gate $g^t_i$ we use an attention mechanism to extract a contextual vector $c^t$ based upon the current focus. We focus on two types of attention: soft attention and a new attention based GRU. The latter improves performance and is hence the final modeling choice for the DMN+.",
+      "Soft attention: Soft attention produces a contextual vector $c^t$ through a weighted summation of the sorted list of vectors $\\overleftrightarrow{F}$ and corresponding attention gates $g_i^t$ : $c^t = \\sum _{i=1}^N g^t_i \\overleftrightarrow{f}_i$ This method has two advantages. First, it is easy to compute. Second, if the softmax activation is spiky it can approximate a hard attention function by selecting only a single fact for the contextual vector whilst still being differentiable. However the main disadvantage to soft attention is that the summation process loses both positional and ordering information. Whilst multiple attention passes can retrieve some of this information, this is inefficient.",
+      "Attention based GRU: For more complex queries, we would like for the attention mechanism to be sensitive to both the position and ordering of the input facts $\\overleftrightarrow{F}$ . An RNN would be advantageous in this situation except they cannot make use of the attention gate from Equation .",
+      "We propose a modification to the GRU architecture by embedding information from the attention mechanism. The update gate $u_i$ in Equation 2 decides how much of each dimension of the hidden state to retain and how much should be updated with the transformed input $x_i$ from the current timestep. As $u_i$ is computed using only the current input and the hidden state from previous timesteps, it lacks any knowledge from the question or previous episode memory.",
+      "By replacing the update gate $u_i$ in the GRU (Equation 2 ) with the output of the attention gate $g^t_i$ (Equation ) in Equation , the GRU can now use the attention gate for updating its internal state. This change is depicted in Fig 5 . ",
+      "$$h_i &=& g^t_i \\circ \\tilde{h}_i + (1-g^t_i) \\circ h_{i-1}$$   (Eq. 12) ",
+      "An important consideration is that $g^t_i$ is a scalar, generated using a softmax activation, as opposed to the vector $u_i \\in \\mathbb {R}^{n_H}$ , generated using a sigmoid activation. This allows us to easily visualize how the attention gates activate over the input, later shown for visual QA in Fig. 6 . Though not explored, replacing the softmax activation in Equation with a sigmoid activation would result in $g^t_i \\in \\mathbb {R}^{n_H}$ . To produce the contextual vector $c^t$ used for updating the episodic memory state $m^t$ , we use the final hidden state of the attention based GRU.",
+      "Episode Memory Updates",
+      "After each pass through the attention mechanism, we wish to update the episode memory $m^{t-1}$ with the newly constructed contextual vector $c^t$ , producing $m^t$ . In the DMN, a GRU with the initial hidden state set to the question vector $q$ is used for this purpose. The episodic memory for pass $t$ is computed by ",
+      "$$m^t = GRU(c^t, m^{t-1})$$   (Eq. 13) ",
+      "The work of BIBREF10 suggests that using different weights for each pass through the episodic memory may be advantageous. When the model contains only one set of weights for all episodic passes over the input, it is referred to as a tied model, as in the \u201cMem Weights\u201d row in Table 1 .",
+      "Following the memory update component used in BIBREF10 and BIBREF12 we experiment with using a ReLU layer for the memory update, calculating the new episode memory state by ",
+      "$$m^t = ReLU\\left(W^t [m^{t-1} ; c^t ; q] + b\\right)$$   (Eq. 14) ",
+      "where $;$ is the concatenation operator, $W^t \\in \\mathbb {R}^{n_H \\times n_H}$ , $b \\in \\mathbb {R}^{n_H}$ , and $n_H$ is the hidden size. The untying of weights and using this ReLU formulation for the memory update improves accuracy by another 0.5% as shown in Table 1 in the last column. The final output of the memory network is passed to the answer module as in the original DMN."
+    ],
+    [
+      "The DMN is related to two major lines of recent work: memory and attention mechanisms. We work on both visual and textual question answering which have, until now, been developed in separate communities.",
+      "Neural Memory Models The earliest recent work with a memory component that is applied to language processing is that of memory networks BIBREF2 which adds a memory component for question answering over simple facts. They are similar to DMNs in that they also have input, scoring, attention and response mechanisms. However, unlike the DMN their input module computes sentence representations independently and hence cannot easily be used for other tasks such as sequence labeling. Like the original DMN, this memory network requires that supporting facts are labeled during QA training. End-to-end memory networks BIBREF10 do not have this limitation. In contrast to previous memory models with a variety of different functions for memory attention retrieval and representations, DMNs BIBREF6 have shown that neural sequence models can be used for input representation, attention and response mechanisms. Sequence models naturally capture position and temporality of both the inputs and transitive reasoning steps.",
+      "Neural Attention Mechanisms Attention mechanisms allow neural network models to use a question to selectively pay attention to specific inputs. They can benefit image classification BIBREF13 , generating captions for images BIBREF5 , among others mentioned below, and machine translation BIBREF14 , BIBREF3 , BIBREF4 . Other recent neural architectures with memory or attention which have proposed include neural Turing machines BIBREF15 , neural GPUs BIBREF16 and stack-augmented RNNs BIBREF17 .",
+      "Question Answering in NLP Question answering involving natural language can be solved in a variety of ways to which we cannot all do justice. If the potential input is a large text corpus, QA becomes a combination of information retrieval and extraction BIBREF18 . Neural approaches can include reasoning over knowledge bases, BIBREF19 , BIBREF20 or directly via sentences for trivia competitions BIBREF21 .",
+      "Visual Question Answering (VQA) In comparison to QA in NLP, VQA is still a relatively young task that is feasible only now that objects can be identified with high accuracy. The first large scale database with unconstrained questions about images was introduced by BIBREF8 . While VQA datasets existed before they did not include open-ended, free-form questions about general images BIBREF22 . Others are were too small to be viable for a deep learning approach BIBREF23 . The only VQA model which also has an attention component is the stacked attention network BIBREF24 . Their work also uses CNN based features. However, unlike our input fusion layer, they use a single layer neural network to map the features of each patch to the dimensionality of the question vector. Hence, the model cannot easily incorporate adjacency of local information in its hidden state. A model that also uses neural modules, albeit logically inspired ones, is that by BIBREF25 who evaluate on knowledgebase reasoning and visual question answering. We compare directly to their method on the latter task and dataset.",
+      "Related to visual question answering is the task of describing images with sentences BIBREF26 . BIBREF27 used deep learning methods to map images and sentences into the same space in order to describe images with sentences and to find images that best visualize a sentence. This was the first work to map both modalities into a joint space with deep learning methods, but it could only select an existing sentence to describe an image. Shortly thereafter, recurrent neural networks were used to generate often novel sentences based on images BIBREF28 , BIBREF29 , BIBREF30 , BIBREF5 ."
+    ],
+    [
+      "To analyze our proposed model changes and compare our performance with other architectures, we use three datasets."
+    ],
+    [
+      "For evaluating the DMN on textual question answering, we use bAbI-10k English BIBREF31 , a synthetic dataset which features 20 different tasks. Each example is composed of a set of facts, a question, the answer, and the supporting facts that lead to the answer. The dataset comes in two sizes, referring to the number of training examples each task has: bAbI-1k and bAbI-10k. The experiments in BIBREF10 found that their lowest error rates on the smaller bAbI-1k dataset were on average three times higher than on bAbI-10k."
+    ],
+    [
+      "The DAtaset for QUestion Answering on Real-world images (DAQUAR) BIBREF23 consists of 795 training images and 654 test images. Based upon these images, 6,795 training questions and 5,673 test questions were generated. Following the previously defined experimental method, we exclude multiple word answers BIBREF32 , BIBREF33 . The resulting dataset covers 90% of the original data. The evaluation method uses classification accuracy over the single words. We use this as a development dataset for model analysis (Sec. \"Model Analysis\" )."
+    ],
+    [
+      "The Visual Question Answering (VQA) dataset was constructed using the Microsoft COCO dataset BIBREF34 which contained 123,287 training/validation images and 81,434 test images. Each image has several related questions with each question answered by multiple people. This dataset contains 248,349 training questions, 121,512 validation questions, and 244,302 for testing. The testing data was split into test-development, test-standard and test-challenge in BIBREF8 .",
+      "Evaluation on both test-standard and test-challenge are implemented via a submission system. test-standard may only be evaluated 5 times and test-challenge is only evaluated at the end of the competition. To the best of our knowledge, VQA is the largest and most complex image dataset for the visual question answering task."
+    ],
+    [
+      "To understand the impact of the proposed module changes, we analyze the performance of a variety of DMN models on textual and visual question answering datasets.",
+      "The original DMN (ODMN) is the architecture presented in BIBREF6 without any modifications. DMN2 only replaces the input module with the input fusion layer (Sec. \"Input Module for Text QA\" ). DMN3, based upon DMN2, replaces the soft attention mechanism with the attention based GRU proposed in Sec. \"The Episodic Memory Module\" . Finally, DMN+, based upon DMN3, is an untied model, using a unique set of weights for each pass and a linear layer with a ReLU activation to compute the memory update. We report the performance of the model variations in Table 1 .",
+      "A large improvement to accuracy on both the bAbI-10k textual and DAQUAR visual datasets results from updating the input module, seen when comparing ODMN to DMN2. On both datasets, the input fusion layer improves interaction between distant facts. In the visual dataset, this improvement is purely from providing contextual information from neighboring image patches, allowing it to handle objects of varying scale or questions with a locality aspect. For the textual dataset, the improved interaction between sentences likely helps the path finding required for logical reasoning when multiple transitive steps are required.",
+      "The addition of the attention GRU in DMN3 helps answer questions where complex positional or ordering information may be required. This change impacts the textual dataset the most as few questions in the visual dataset are likely to require this form of logical reasoning. Finally, the untied model in the DMN+ overfits on some tasks compared to DMN3, but on average the error rate decreases.",
+      "From these experimental results, we find that the combination of all the proposed model changes results, culminating in DMN+, achieves the highest performance across both the visual and textual datasets."
+    ],
+    [
+      "We trained our models using the Adam optimizer BIBREF35 with a learning rate of 0.001 and batch size of 128. Training runs for up to 256 epochs with early stopping if the validation loss had not improved within the last 20 epochs. The model from the epoch with the lowest validation loss was then selected. Xavier initialization was used for all weights except for the word embeddings, which used random uniform initialization with range $[-\\sqrt{3}, \\sqrt{3}]$ . Both the embedding and hidden dimensions were of size $d = 80$ . We used $\\ell _2$ regularization on all weights except bias and used dropout on the initial sentence encodings and the answer module, keeping the input with probability $p=0.9$ . The last 10% of the training data on each task was chosen as the validation set. For all tasks, three passes were used for the episodic memory module, allowing direct comparison to other state of the art methods. Finally, we limited the input to the last 70 sentences for all tasks except QA3 for which we limited input to the last 130 sentences, similar to BIBREF10 .",
+      "On some tasks, the accuracy was not stable across multiple runs. This was particularly problematic on QA3, QA17, and QA18. To solve this, we repeated training 10 times using random initializations and evaluated the model that achieved the lowest validation set loss.",
+      "Text QA Results",
+      "We compare our best performing approach, DMN+, to two state of the art question answering architectures: the end to end memory network (E2E) BIBREF10 and the neural reasoner framework (NR) BIBREF12 . Neither approach use supporting facts for training.",
+      "The end-to-end memory network is a form of memory network BIBREF2 tested on both textual question answering and language modeling. The model features both explicit memory and a recurrent attention mechanism. We select the model from the paper that achieves the lowest mean error over the bAbI-10k dataset. This model utilizes positional encoding for input, RNN-style tied weights for the episode module, and a ReLU non-linearity for the memory update component.",
+      "The neural reasoner framework is an end-to-end trainable model which features a deep architecture for logical reasoning and an interaction-pooling mechanism for allowing interaction over multiple facts. While the neural reasoner framework was only tested on QA17 and QA19, these were two of the most challenging question types at the time.",
+      "In Table 2 we compare the accuracy of these question answering architectures, both as mean error and error on individual tasks. The DMN+ model reduces mean error by 1.4% compared to the the end-to-end memory network, achieving a new state of the art for the bAbI-10k dataset.",
+      "One notable deficiency in our model is that of QA16: Basic Induction. In BIBREF10 , an untied model using only summation for memory updates was able to achieve a near perfect error rate of $0.4$ . When the memory update was replaced with a linear layer with ReLU activation, the end-to-end memory network's overall mean error decreased but the error for QA16 rose sharply. Our model experiences the same difficulties, suggesting that the more complex memory update component may prevent convergence on certain simpler tasks.",
+      "The neural reasoner model outperforms both the DMN and end-to-end memory network on QA17: Positional Reasoning. This is likely as the positional reasoning task only involves minimal supervision - two sentences for input, yes/no answers for supervision, and only 5,812 unique examples after removing duplicates from the initial 10,000 training examples. BIBREF12 add an auxiliary task of reconstructing both the original sentences and question from their representations. This auxiliary task likely improves performance by preventing overfitting."
+    ],
+    [
+      "For the VQA dataset, each question is answered by multiple people and the answers may not be the same, the generated answers are evaluated using human consensus. For each predicted answer $a_i$ for the $i_{th}$ question with target answer set $T^{i}$ , the accuracy of VQA: $Acc_{VQA} = \\frac{1}{N}\\sum _{i=1}^Nmin(\\frac{\\sum _{t\\in T^i}{1}_{(a_i==t)}}{3},1)$ where ${1}_{(\\cdot )}$ is the indicator function. Simply put, the answer $a_i$ is only 100 $\\%$ accurate if at least 3 people provide that exact answer.",
+      "Training Details We use the Adam optimizer BIBREF35 with a learning rate of 0.003 and batch size of 100. Training runs for up to 256 epochs with early stopping if the validation loss has not improved in the last 10 epochs. For weight initialization, we sampled from a random uniform distribution with range $[-0.08, 0.08]$ . Both the word embedding and hidden layers were vectors of size $d=512$ . We apply dropout on the initial image output from the VGG convolutional neural network BIBREF11 as well as the input to the answer module, keeping input with probability $p=0.5$ .",
+      "Results and Analysis",
+      "The VQA dataset is composed of three question domains: Yes/No, Number, and Other. This enables us to analyze the performance of the models on various tasks that require different reasoning abilities.",
+      "The comparison models are separated into two broad classes: those that utilize a full connected image feature for classification and those that perform reasoning over multiple small image patches. Only the SAN and DMN approach use small image patches, while the rest use the fully-connected whole image feature approach.",
+      "Here, we show the quantitative and qualitative results in Table 3 and Fig. 6 , respectively. The images in Fig. 6 illustrate how the attention gate $g^t_i$ selectively activates over relevant portions of the image according to the query. In Table 3 , our method outperforms baseline and other state-of-the-art methods across all question domains (All) in both test-dev and test-std, and especially for Other questions, achieves a wide margin compared to the other architectures, which is likely as the small image patches allow for finely detailed reasoning over the image.",
+      "However, the granularity offered by small image patches does not always offer an advantage. The Number questions may be not solvable for both the SAN and DMN architectures, potentially as counting objects is not a simple task when an object crosses image patch boundaries."
+    ],
+    [
+      "We have proposed new modules for the DMN framework to achieve strong results without supervision of supporting facts. These improvements include the input fusion layer to allow interactions between input facts and a novel attention based GRU that allows for logical reasoning over ordered inputs. Our resulting model obtains state of the art results on both the VQA dataset and the bAbI-10k text question-answering dataset, proving the framework can be generalized across input domains."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0433/instruction.md b/qasper-0433/instruction.md
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+Name of Paper: Dynamic Memory Networks for Visual and Textual Question Answering
+
+Question: What does supporting fact supervision mean?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Dynamic Memory Networks",
+    "Improved Dynamic Memory Networks: DMN+",
+    "Input Module for Text QA",
+    "Input Module for VQA",
+    "The Episodic Memory Module",
+    "Related Work",
+    "Datasets",
+    "bAbI-10k",
+    "DAQUAR-ALL visual dataset",
+    "Visual Question Answering",
+    "Model Analysis",
+    "Comparison to state of the art using bAbI-10k",
+    "Comparison to state of the art using VQA",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Neural network based methods have made tremendous progress in image and text classification BIBREF0 , BIBREF1 . However, only recently has progress been made on more complex tasks that require logical reasoning. This success is based in part on the addition of memory and attention components to complex neural networks. For instance, memory networks BIBREF2 are able to reason over several facts written in natural language or (subject, relation, object) triplets. Attention mechanisms have been successful components in both machine translation BIBREF3 , BIBREF4 and image captioning models BIBREF5 .",
+      "The dynamic memory network BIBREF6 (DMN) is one example of a neural network model that has both a memory component and an attention mechanism. The DMN yields state of the art results on question answering with supporting facts marked during training, sentiment analysis, and part-of-speech tagging.",
+      "We analyze the DMN components, specifically the input module and memory module, to improve question answering. We propose a new input module which uses a two level encoder with a sentence reader and input fusion layer to allow for information flow between sentences. For the memory, we propose a modification to gated recurrent units (GRU) BIBREF7 . The new GRU formulation incorporates attention gates that are computed using global knowledge over the facts. Unlike before, the new DMN+ model does not require that supporting facts (i.e. the facts that are relevant for answering a particular question) are labeled during training. The model learns to select the important facts from a larger set.",
+      "In addition, we introduce a new input module to represent images. This module is compatible with the rest of the DMN architecture and its output is fed into the memory module. We show that the changes in the memory module that improved textual question answering also improve visual question answering. Both tasks are illustrated in Fig. 1 ."
+    ],
+    [
+      "We begin by outlining the DMN for question answering and the modules as presented in BIBREF6 .",
+      "The DMN is a general architecture for question answering (QA). It is composed of modules that allow different aspects such as input representations or memory components to be analyzed and improved independently. The modules, depicted in Fig. 1 , are as follows:",
+      "Input Module: This module processes the input data about which a question is being asked into a set of vectors termed facts, represented as $F=[f_1,\\hdots ,f_N]$ , where $N$ is the total number of facts. These vectors are ordered, resulting in additional information that can be used by later components. For text QA in BIBREF6 , the module consists of a GRU over the input words.",
+      "As the GRU is used in many components of the DMN, it is useful to provide the full definition. For each time step $i$ with input $x_i$ and previous hidden state $h_{i-1}$ , we compute the updated hidden state $h_i = GRU(x_i,h_{i-1})$ by ",
+      "$$u_i &=& \\sigma \\left(W^{(u)}x_{i} + U^{(u)} h_{i-1} + b^{(u)} \\right)\\\\\nr_i &=& \\sigma \\left(W^{(r)}x_{i} + U^{(r)} h_{i-1} + b^{(r)} \\right)\\\\\n\\tilde{h}_i &=& \\tanh \\left(Wx_{i} + r_i \\circ U h_{i-1} + b^{(h)}\\right)\\\\\nh_i &=& u_i\\circ \\tilde{h}_i + (1-u_i) \\circ h_{i-1}$$   (Eq. 2) ",
+      "where $\\sigma $ is the sigmoid activation function, $\\circ $ is an element-wise product, $W^{(z)}, W^{(r)}, W \\in \\mathbb {R}^{n_H \\times n_I}$ , $U^{(z)}, U^{(r)}, U \\in \\mathbb {R}^{n_H \\times n_H}$ , $n_H$ is the hidden size, and $n_I$ is the input size.",
+      "Question Module: This module computes a vector representation $q$ of the question, where $q \\in \\mathbb {R}^{n_H}$ is the final hidden state of a GRU over the words in the question.",
+      "Episodic Memory Module: Episode memory aims to retrieve the information required to answer the question $q$ from the input facts. To improve our understanding of both the question and input, especially if questions require transitive reasoning, the episode memory module may pass over the input multiple times, updating episode memory after each pass. We refer to the episode memory on the $t^{th}$ pass over the inputs as $m^t$ , where $m^t \\in \\mathbb {R}^{n_H}$ , the initial memory vector is set to the question vector: $m^0 = q$ .",
+      "The episodic memory module consists of two separate components: the attention mechanism and the memory update mechanism. The attention mechanism is responsible for producing a contextual vector $c^t$ , where $c^t \\in \\mathbb {R}^{n_H}$ is a summary of relevant input for pass $t$ , with relevance inferred by the question $q$ and previous episode memory $m^{t-1}$ . The memory update mechanism is responsible for generating the episode memory $m^t$ based upon the contextual vector $c^t$ and previous episode memory $m^{t-1}$ . By the final pass $T$ , the episodic memory $m^T$ should contain all the information required to answer the question $c^t \\in \\mathbb {R}^{n_H}$0 .",
+      "Answer Module: The answer module receives both $q$ and $m^T$ to generate the model's predicted answer. For simple answers, such as a single word, a linear layer with softmax activation may be used. For tasks requiring a sequence output, an RNN may be used to decode $a = [q ; m^T]$ , the concatenation of vectors $q$ and $m^T$ , to an ordered set of tokens. The cross entropy error on the answers is used for training and backpropagated through the entire network."
+    ],
+    [
+      "We propose and compare several modeling choices for two crucial components: input representation, attention mechanism and memory update. The final DMN+ model obtains the highest accuracy on the bAbI-10k dataset without supporting facts and the VQA dataset BIBREF8 . Several design choices are motivated by intuition and accuracy improvements on that dataset."
+    ],
+    [
+      "In the DMN specified in BIBREF6 , a single GRU is used to process all the words in the story, extracting sentence representations by storing the hidden states produced at the end of sentence markers. The GRU also provides a temporal component by allowing a sentence to know the content of the sentences that came before them. Whilst this input module worked well for bAbI-1k with supporting facts, as reported in BIBREF6 , it did not perform well on bAbI-10k without supporting facts (Sec. \"Model Analysis\" ).",
+      "We speculate that there are two main reasons for this performance disparity, all exacerbated by the removal of supporting facts. First, the GRU only allows sentences to have context from sentences before them, but not after them. This prevents information propagation from future sentences. Second, the supporting sentences may be too far away from each other on a word level to allow for these distant sentences to interact through the word level GRU.",
+      "Input Fusion Layer",
+      "For the DMN+, we propose replacing this single GRU with two different components. The first component is a sentence reader, responsible only for encoding the words into a sentence embedding. The second component is the input fusion layer, allowing for interactions between sentences. This resembles the hierarchical neural auto-encoder architecture of BIBREF9 and allows content interaction between sentences. We adopt the bi-directional GRU for this input fusion layer because it allows information from both past and future sentences to be used. As gradients do not need to propagate through the words between sentences, the fusion layer also allows for distant supporting sentences to have a more direct interaction.",
+      "Fig. 2 shows an illustration of an input module, where a positional encoder is used for the sentence reader and a bi-directional GRU is adopted for the input fusion layer. Each sentence encoding $f_i$ is the output of an encoding scheme taking the word tokens $[w^i_1, \\hdots , w^i_{M_i}]$ , where $M_i$ is the length of the sentence.",
+      "The sentence reader could be based on any variety of encoding schemes. We selected positional encoding described in BIBREF10 to allow for a comparison to their work. GRUs and LSTMs were also considered but required more computational resources and were prone to overfitting if auxiliary tasks, such as reconstructing the original sentence, were not used.",
+      "For the positional encoding scheme, the sentence representation is produced by $f_i = \\sum ^{j=1}_M l_j \\circ w^i_j$ , where $\\circ $ is element-wise multiplication and $l_j$ is a column vector with structure $l_{jd} = (1 - j / M) - (d / D) (1 - 2j / M)$ , where $d$ is the embedding index and $D$ is the dimension of the embedding.",
+      "The input fusion layer takes these input facts and enables an information exchange between them by applying a bi-directional GRU. ",
+      "$$\\overrightarrow{f_i} = GRU_{fwd}(f_i, \\overrightarrow{f_{i-1}}) \\\\\n\\overleftarrow{f_{i}} = GRU_{bwd}(f_{i}, \\overleftarrow{f_{i+1}}) \\\\\n\\overleftrightarrow{f_i} = \\overleftarrow{f_i} + \\overrightarrow{f_i}$$   (Eq. 5) ",
+      "where $f_i$ is the input fact at timestep $i$ , $ \\overrightarrow{f_i}$ is the hidden state of the forward GRU at timestep $i$ , and $\\overleftarrow{f_i}$ is the hidden state of the backward GRU at timestep $i$ . This allows contextual information from both future and past facts to impact $\\overleftrightarrow{f_i}$ .",
+      "We explored a variety of encoding schemes for the sentence reader, including GRUs, LSTMs, and the positional encoding scheme described in BIBREF10 . For simplicity and speed, we selected the positional encoding scheme. GRUs and LSTMs were also considered but required more computational resources and were prone to overfitting if auxiliary tasks, such as reconstructing the original sentence, were not used."
+    ],
+    [
+      "To apply the DMN to visual question answering, we introduce a new input module for images. The module splits an image into small local regions and considers each region equivalent to a sentence in the input module for text. The input module for VQA is composed of three parts, illustrated in Fig. 3 : local region feature extraction, visual feature embedding, and the input fusion layer introduced in Sec. \"Input Module for Text QA\" .",
+      "Local region feature extraction: To extract features from the image, we use a convolutional neural network BIBREF0 based upon the VGG-19 model BIBREF11 . We first rescale the input image to $448 \\times 448$ and take the output from the last pooling layer which has dimensionality $d = 512 \\times 14 \\times 14$ . The pooling layer divides the image into a grid of $14 \\times 14$ , resulting in 196 local regional vectors of $d = 512$ .",
+      "Visual feature embedding: As the VQA task involves both image features and text features, we add a linear layer with tanh activation to project the local regional vectors to the textual feature space used by the question vector $q$ .",
+      "Input fusion layer: The local regional vectors extracted from above do not yet have global information available to them. Without global information, their representational power is quite limited, with simple issues like object scaling or locational variance causing accuracy problems.",
+      "To solve this, we add an input fusion layer similar to that of the textual input module described in Sec. \"Input Module for Text QA\" . First, to produce the input facts $F$ , we traverse the image in a snake like fashion, as seen in Figure 3 . We then apply a bi-directional GRU over these input facts $F$ to produce the globally aware input facts $\\overleftrightarrow{F}$ . The bi-directional GRU allows for information propagation from neighboring image patches, capturing spatial information."
+    ],
+    [
+      "The episodic memory module, as depicted in Fig. 4 , retrieves information from the input facts $\\overleftrightarrow{F} = [\\overleftrightarrow{f_1}, \\hdots , \\overleftrightarrow{f_N}]$ provided to it by focusing attention on a subset of these facts. We implement this attention by associating a single scalar value, the attention gate $g^t_i$ , with each fact $\\overleftrightarrow{f}_i$ during pass $t$ . This is computed by allowing interactions between the fact and both the question representation and the episode memory state. ",
+      "$$z^t_i &=& [\\overleftrightarrow{f_i} \\circ q; \\overleftrightarrow{f_i} \\circ m^{t-1}; \\vert \\overleftrightarrow{f_i} - q \\vert ; \\vert \\overleftrightarrow{f_i} - m^{t-1} \\vert ] \\\\\nZ^t_i &=& W^{(2)} \\tanh \\left(W^{(1)}z^t_i + b^{(1)} \\right)+ b^{(2)} \\\\\ng^t_i &=& \\frac{\\exp (Z^t_i)}{\\sum _{k=1}^{M_i} \\exp (Z^t_k)} $$   (Eq. 10) ",
+      "where $\\overleftrightarrow{f_i}$ is the $i^{th}$ fact, $m^{t-1}$ is the previous episode memory, $q$ is the original question, $\\circ $ is the element-wise product, $|\\cdot |$ is the element-wise absolute value, and $;$ represents concatenation of the vectors.",
+      "The DMN implemented in BIBREF6 involved a more complex set of interactions within $z$ , containing the additional terms $[f; m^{t-1}; q; f^T W^{(b)} q; f^T W^{(b)} m^{t-1}]$ . After an initial analysis, we found these additional terms were not required.",
+      "Attention Mechanism",
+      "Once we have the attention gate $g^t_i$ we use an attention mechanism to extract a contextual vector $c^t$ based upon the current focus. We focus on two types of attention: soft attention and a new attention based GRU. The latter improves performance and is hence the final modeling choice for the DMN+.",
+      "Soft attention: Soft attention produces a contextual vector $c^t$ through a weighted summation of the sorted list of vectors $\\overleftrightarrow{F}$ and corresponding attention gates $g_i^t$ : $c^t = \\sum _{i=1}^N g^t_i \\overleftrightarrow{f}_i$ This method has two advantages. First, it is easy to compute. Second, if the softmax activation is spiky it can approximate a hard attention function by selecting only a single fact for the contextual vector whilst still being differentiable. However the main disadvantage to soft attention is that the summation process loses both positional and ordering information. Whilst multiple attention passes can retrieve some of this information, this is inefficient.",
+      "Attention based GRU: For more complex queries, we would like for the attention mechanism to be sensitive to both the position and ordering of the input facts $\\overleftrightarrow{F}$ . An RNN would be advantageous in this situation except they cannot make use of the attention gate from Equation .",
+      "We propose a modification to the GRU architecture by embedding information from the attention mechanism. The update gate $u_i$ in Equation 2 decides how much of each dimension of the hidden state to retain and how much should be updated with the transformed input $x_i$ from the current timestep. As $u_i$ is computed using only the current input and the hidden state from previous timesteps, it lacks any knowledge from the question or previous episode memory.",
+      "By replacing the update gate $u_i$ in the GRU (Equation 2 ) with the output of the attention gate $g^t_i$ (Equation ) in Equation , the GRU can now use the attention gate for updating its internal state. This change is depicted in Fig 5 . ",
+      "$$h_i &=& g^t_i \\circ \\tilde{h}_i + (1-g^t_i) \\circ h_{i-1}$$   (Eq. 12) ",
+      "An important consideration is that $g^t_i$ is a scalar, generated using a softmax activation, as opposed to the vector $u_i \\in \\mathbb {R}^{n_H}$ , generated using a sigmoid activation. This allows us to easily visualize how the attention gates activate over the input, later shown for visual QA in Fig. 6 . Though not explored, replacing the softmax activation in Equation with a sigmoid activation would result in $g^t_i \\in \\mathbb {R}^{n_H}$ . To produce the contextual vector $c^t$ used for updating the episodic memory state $m^t$ , we use the final hidden state of the attention based GRU.",
+      "Episode Memory Updates",
+      "After each pass through the attention mechanism, we wish to update the episode memory $m^{t-1}$ with the newly constructed contextual vector $c^t$ , producing $m^t$ . In the DMN, a GRU with the initial hidden state set to the question vector $q$ is used for this purpose. The episodic memory for pass $t$ is computed by ",
+      "$$m^t = GRU(c^t, m^{t-1})$$   (Eq. 13) ",
+      "The work of BIBREF10 suggests that using different weights for each pass through the episodic memory may be advantageous. When the model contains only one set of weights for all episodic passes over the input, it is referred to as a tied model, as in the \u201cMem Weights\u201d row in Table 1 .",
+      "Following the memory update component used in BIBREF10 and BIBREF12 we experiment with using a ReLU layer for the memory update, calculating the new episode memory state by ",
+      "$$m^t = ReLU\\left(W^t [m^{t-1} ; c^t ; q] + b\\right)$$   (Eq. 14) ",
+      "where $;$ is the concatenation operator, $W^t \\in \\mathbb {R}^{n_H \\times n_H}$ , $b \\in \\mathbb {R}^{n_H}$ , and $n_H$ is the hidden size. The untying of weights and using this ReLU formulation for the memory update improves accuracy by another 0.5% as shown in Table 1 in the last column. The final output of the memory network is passed to the answer module as in the original DMN."
+    ],
+    [
+      "The DMN is related to two major lines of recent work: memory and attention mechanisms. We work on both visual and textual question answering which have, until now, been developed in separate communities.",
+      "Neural Memory Models The earliest recent work with a memory component that is applied to language processing is that of memory networks BIBREF2 which adds a memory component for question answering over simple facts. They are similar to DMNs in that they also have input, scoring, attention and response mechanisms. However, unlike the DMN their input module computes sentence representations independently and hence cannot easily be used for other tasks such as sequence labeling. Like the original DMN, this memory network requires that supporting facts are labeled during QA training. End-to-end memory networks BIBREF10 do not have this limitation. In contrast to previous memory models with a variety of different functions for memory attention retrieval and representations, DMNs BIBREF6 have shown that neural sequence models can be used for input representation, attention and response mechanisms. Sequence models naturally capture position and temporality of both the inputs and transitive reasoning steps.",
+      "Neural Attention Mechanisms Attention mechanisms allow neural network models to use a question to selectively pay attention to specific inputs. They can benefit image classification BIBREF13 , generating captions for images BIBREF5 , among others mentioned below, and machine translation BIBREF14 , BIBREF3 , BIBREF4 . Other recent neural architectures with memory or attention which have proposed include neural Turing machines BIBREF15 , neural GPUs BIBREF16 and stack-augmented RNNs BIBREF17 .",
+      "Question Answering in NLP Question answering involving natural language can be solved in a variety of ways to which we cannot all do justice. If the potential input is a large text corpus, QA becomes a combination of information retrieval and extraction BIBREF18 . Neural approaches can include reasoning over knowledge bases, BIBREF19 , BIBREF20 or directly via sentences for trivia competitions BIBREF21 .",
+      "Visual Question Answering (VQA) In comparison to QA in NLP, VQA is still a relatively young task that is feasible only now that objects can be identified with high accuracy. The first large scale database with unconstrained questions about images was introduced by BIBREF8 . While VQA datasets existed before they did not include open-ended, free-form questions about general images BIBREF22 . Others are were too small to be viable for a deep learning approach BIBREF23 . The only VQA model which also has an attention component is the stacked attention network BIBREF24 . Their work also uses CNN based features. However, unlike our input fusion layer, they use a single layer neural network to map the features of each patch to the dimensionality of the question vector. Hence, the model cannot easily incorporate adjacency of local information in its hidden state. A model that also uses neural modules, albeit logically inspired ones, is that by BIBREF25 who evaluate on knowledgebase reasoning and visual question answering. We compare directly to their method on the latter task and dataset.",
+      "Related to visual question answering is the task of describing images with sentences BIBREF26 . BIBREF27 used deep learning methods to map images and sentences into the same space in order to describe images with sentences and to find images that best visualize a sentence. This was the first work to map both modalities into a joint space with deep learning methods, but it could only select an existing sentence to describe an image. Shortly thereafter, recurrent neural networks were used to generate often novel sentences based on images BIBREF28 , BIBREF29 , BIBREF30 , BIBREF5 ."
+    ],
+    [
+      "To analyze our proposed model changes and compare our performance with other architectures, we use three datasets."
+    ],
+    [
+      "For evaluating the DMN on textual question answering, we use bAbI-10k English BIBREF31 , a synthetic dataset which features 20 different tasks. Each example is composed of a set of facts, a question, the answer, and the supporting facts that lead to the answer. The dataset comes in two sizes, referring to the number of training examples each task has: bAbI-1k and bAbI-10k. The experiments in BIBREF10 found that their lowest error rates on the smaller bAbI-1k dataset were on average three times higher than on bAbI-10k."
+    ],
+    [
+      "The DAtaset for QUestion Answering on Real-world images (DAQUAR) BIBREF23 consists of 795 training images and 654 test images. Based upon these images, 6,795 training questions and 5,673 test questions were generated. Following the previously defined experimental method, we exclude multiple word answers BIBREF32 , BIBREF33 . The resulting dataset covers 90% of the original data. The evaluation method uses classification accuracy over the single words. We use this as a development dataset for model analysis (Sec. \"Model Analysis\" )."
+    ],
+    [
+      "The Visual Question Answering (VQA) dataset was constructed using the Microsoft COCO dataset BIBREF34 which contained 123,287 training/validation images and 81,434 test images. Each image has several related questions with each question answered by multiple people. This dataset contains 248,349 training questions, 121,512 validation questions, and 244,302 for testing. The testing data was split into test-development, test-standard and test-challenge in BIBREF8 .",
+      "Evaluation on both test-standard and test-challenge are implemented via a submission system. test-standard may only be evaluated 5 times and test-challenge is only evaluated at the end of the competition. To the best of our knowledge, VQA is the largest and most complex image dataset for the visual question answering task."
+    ],
+    [
+      "To understand the impact of the proposed module changes, we analyze the performance of a variety of DMN models on textual and visual question answering datasets.",
+      "The original DMN (ODMN) is the architecture presented in BIBREF6 without any modifications. DMN2 only replaces the input module with the input fusion layer (Sec. \"Input Module for Text QA\" ). DMN3, based upon DMN2, replaces the soft attention mechanism with the attention based GRU proposed in Sec. \"The Episodic Memory Module\" . Finally, DMN+, based upon DMN3, is an untied model, using a unique set of weights for each pass and a linear layer with a ReLU activation to compute the memory update. We report the performance of the model variations in Table 1 .",
+      "A large improvement to accuracy on both the bAbI-10k textual and DAQUAR visual datasets results from updating the input module, seen when comparing ODMN to DMN2. On both datasets, the input fusion layer improves interaction between distant facts. In the visual dataset, this improvement is purely from providing contextual information from neighboring image patches, allowing it to handle objects of varying scale or questions with a locality aspect. For the textual dataset, the improved interaction between sentences likely helps the path finding required for logical reasoning when multiple transitive steps are required.",
+      "The addition of the attention GRU in DMN3 helps answer questions where complex positional or ordering information may be required. This change impacts the textual dataset the most as few questions in the visual dataset are likely to require this form of logical reasoning. Finally, the untied model in the DMN+ overfits on some tasks compared to DMN3, but on average the error rate decreases.",
+      "From these experimental results, we find that the combination of all the proposed model changes results, culminating in DMN+, achieves the highest performance across both the visual and textual datasets."
+    ],
+    [
+      "We trained our models using the Adam optimizer BIBREF35 with a learning rate of 0.001 and batch size of 128. Training runs for up to 256 epochs with early stopping if the validation loss had not improved within the last 20 epochs. The model from the epoch with the lowest validation loss was then selected. Xavier initialization was used for all weights except for the word embeddings, which used random uniform initialization with range $[-\\sqrt{3}, \\sqrt{3}]$ . Both the embedding and hidden dimensions were of size $d = 80$ . We used $\\ell _2$ regularization on all weights except bias and used dropout on the initial sentence encodings and the answer module, keeping the input with probability $p=0.9$ . The last 10% of the training data on each task was chosen as the validation set. For all tasks, three passes were used for the episodic memory module, allowing direct comparison to other state of the art methods. Finally, we limited the input to the last 70 sentences for all tasks except QA3 for which we limited input to the last 130 sentences, similar to BIBREF10 .",
+      "On some tasks, the accuracy was not stable across multiple runs. This was particularly problematic on QA3, QA17, and QA18. To solve this, we repeated training 10 times using random initializations and evaluated the model that achieved the lowest validation set loss.",
+      "Text QA Results",
+      "We compare our best performing approach, DMN+, to two state of the art question answering architectures: the end to end memory network (E2E) BIBREF10 and the neural reasoner framework (NR) BIBREF12 . Neither approach use supporting facts for training.",
+      "The end-to-end memory network is a form of memory network BIBREF2 tested on both textual question answering and language modeling. The model features both explicit memory and a recurrent attention mechanism. We select the model from the paper that achieves the lowest mean error over the bAbI-10k dataset. This model utilizes positional encoding for input, RNN-style tied weights for the episode module, and a ReLU non-linearity for the memory update component.",
+      "The neural reasoner framework is an end-to-end trainable model which features a deep architecture for logical reasoning and an interaction-pooling mechanism for allowing interaction over multiple facts. While the neural reasoner framework was only tested on QA17 and QA19, these were two of the most challenging question types at the time.",
+      "In Table 2 we compare the accuracy of these question answering architectures, both as mean error and error on individual tasks. The DMN+ model reduces mean error by 1.4% compared to the the end-to-end memory network, achieving a new state of the art for the bAbI-10k dataset.",
+      "One notable deficiency in our model is that of QA16: Basic Induction. In BIBREF10 , an untied model using only summation for memory updates was able to achieve a near perfect error rate of $0.4$ . When the memory update was replaced with a linear layer with ReLU activation, the end-to-end memory network's overall mean error decreased but the error for QA16 rose sharply. Our model experiences the same difficulties, suggesting that the more complex memory update component may prevent convergence on certain simpler tasks.",
+      "The neural reasoner model outperforms both the DMN and end-to-end memory network on QA17: Positional Reasoning. This is likely as the positional reasoning task only involves minimal supervision - two sentences for input, yes/no answers for supervision, and only 5,812 unique examples after removing duplicates from the initial 10,000 training examples. BIBREF12 add an auxiliary task of reconstructing both the original sentences and question from their representations. This auxiliary task likely improves performance by preventing overfitting."
+    ],
+    [
+      "For the VQA dataset, each question is answered by multiple people and the answers may not be the same, the generated answers are evaluated using human consensus. For each predicted answer $a_i$ for the $i_{th}$ question with target answer set $T^{i}$ , the accuracy of VQA: $Acc_{VQA} = \\frac{1}{N}\\sum _{i=1}^Nmin(\\frac{\\sum _{t\\in T^i}{1}_{(a_i==t)}}{3},1)$ where ${1}_{(\\cdot )}$ is the indicator function. Simply put, the answer $a_i$ is only 100 $\\%$ accurate if at least 3 people provide that exact answer.",
+      "Training Details We use the Adam optimizer BIBREF35 with a learning rate of 0.003 and batch size of 100. Training runs for up to 256 epochs with early stopping if the validation loss has not improved in the last 10 epochs. For weight initialization, we sampled from a random uniform distribution with range $[-0.08, 0.08]$ . Both the word embedding and hidden layers were vectors of size $d=512$ . We apply dropout on the initial image output from the VGG convolutional neural network BIBREF11 as well as the input to the answer module, keeping input with probability $p=0.5$ .",
+      "Results and Analysis",
+      "The VQA dataset is composed of three question domains: Yes/No, Number, and Other. This enables us to analyze the performance of the models on various tasks that require different reasoning abilities.",
+      "The comparison models are separated into two broad classes: those that utilize a full connected image feature for classification and those that perform reasoning over multiple small image patches. Only the SAN and DMN approach use small image patches, while the rest use the fully-connected whole image feature approach.",
+      "Here, we show the quantitative and qualitative results in Table 3 and Fig. 6 , respectively. The images in Fig. 6 illustrate how the attention gate $g^t_i$ selectively activates over relevant portions of the image according to the query. In Table 3 , our method outperforms baseline and other state-of-the-art methods across all question domains (All) in both test-dev and test-std, and especially for Other questions, achieves a wide margin compared to the other architectures, which is likely as the small image patches allow for finely detailed reasoning over the image.",
+      "However, the granularity offered by small image patches does not always offer an advantage. The Number questions may be not solvable for both the SAN and DMN architectures, potentially as counting objects is not a simple task when an object crosses image patch boundaries."
+    ],
+    [
+      "We have proposed new modules for the DMN framework to achieve strong results without supervision of supporting facts. These improvements include the input fusion layer to allow interactions between input facts and a novel attention based GRU that allows for logical reasoning over ordered inputs. Our resulting model obtains state of the art results on both the VQA dataset and the bAbI-10k text question-answering dataset, proving the framework can be generalized across input domains."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: Dynamic Memory Networks for Visual and Textual Question Answering
+
+Question: What changes they did on input module?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Dynamic Memory Networks",
+    "Improved Dynamic Memory Networks: DMN+",
+    "Input Module for Text QA",
+    "Input Module for VQA",
+    "The Episodic Memory Module",
+    "Related Work",
+    "Datasets",
+    "bAbI-10k",
+    "DAQUAR-ALL visual dataset",
+    "Visual Question Answering",
+    "Model Analysis",
+    "Comparison to state of the art using bAbI-10k",
+    "Comparison to state of the art using VQA",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Neural network based methods have made tremendous progress in image and text classification BIBREF0 , BIBREF1 . However, only recently has progress been made on more complex tasks that require logical reasoning. This success is based in part on the addition of memory and attention components to complex neural networks. For instance, memory networks BIBREF2 are able to reason over several facts written in natural language or (subject, relation, object) triplets. Attention mechanisms have been successful components in both machine translation BIBREF3 , BIBREF4 and image captioning models BIBREF5 .",
+      "The dynamic memory network BIBREF6 (DMN) is one example of a neural network model that has both a memory component and an attention mechanism. The DMN yields state of the art results on question answering with supporting facts marked during training, sentiment analysis, and part-of-speech tagging.",
+      "We analyze the DMN components, specifically the input module and memory module, to improve question answering. We propose a new input module which uses a two level encoder with a sentence reader and input fusion layer to allow for information flow between sentences. For the memory, we propose a modification to gated recurrent units (GRU) BIBREF7 . The new GRU formulation incorporates attention gates that are computed using global knowledge over the facts. Unlike before, the new DMN+ model does not require that supporting facts (i.e. the facts that are relevant for answering a particular question) are labeled during training. The model learns to select the important facts from a larger set.",
+      "In addition, we introduce a new input module to represent images. This module is compatible with the rest of the DMN architecture and its output is fed into the memory module. We show that the changes in the memory module that improved textual question answering also improve visual question answering. Both tasks are illustrated in Fig. 1 ."
+    ],
+    [
+      "We begin by outlining the DMN for question answering and the modules as presented in BIBREF6 .",
+      "The DMN is a general architecture for question answering (QA). It is composed of modules that allow different aspects such as input representations or memory components to be analyzed and improved independently. The modules, depicted in Fig. 1 , are as follows:",
+      "Input Module: This module processes the input data about which a question is being asked into a set of vectors termed facts, represented as $F=[f_1,\\hdots ,f_N]$ , where $N$ is the total number of facts. These vectors are ordered, resulting in additional information that can be used by later components. For text QA in BIBREF6 , the module consists of a GRU over the input words.",
+      "As the GRU is used in many components of the DMN, it is useful to provide the full definition. For each time step $i$ with input $x_i$ and previous hidden state $h_{i-1}$ , we compute the updated hidden state $h_i = GRU(x_i,h_{i-1})$ by ",
+      "$$u_i &=& \\sigma \\left(W^{(u)}x_{i} + U^{(u)} h_{i-1} + b^{(u)} \\right)\\\\\nr_i &=& \\sigma \\left(W^{(r)}x_{i} + U^{(r)} h_{i-1} + b^{(r)} \\right)\\\\\n\\tilde{h}_i &=& \\tanh \\left(Wx_{i} + r_i \\circ U h_{i-1} + b^{(h)}\\right)\\\\\nh_i &=& u_i\\circ \\tilde{h}_i + (1-u_i) \\circ h_{i-1}$$   (Eq. 2) ",
+      "where $\\sigma $ is the sigmoid activation function, $\\circ $ is an element-wise product, $W^{(z)}, W^{(r)}, W \\in \\mathbb {R}^{n_H \\times n_I}$ , $U^{(z)}, U^{(r)}, U \\in \\mathbb {R}^{n_H \\times n_H}$ , $n_H$ is the hidden size, and $n_I$ is the input size.",
+      "Question Module: This module computes a vector representation $q$ of the question, where $q \\in \\mathbb {R}^{n_H}$ is the final hidden state of a GRU over the words in the question.",
+      "Episodic Memory Module: Episode memory aims to retrieve the information required to answer the question $q$ from the input facts. To improve our understanding of both the question and input, especially if questions require transitive reasoning, the episode memory module may pass over the input multiple times, updating episode memory after each pass. We refer to the episode memory on the $t^{th}$ pass over the inputs as $m^t$ , where $m^t \\in \\mathbb {R}^{n_H}$ , the initial memory vector is set to the question vector: $m^0 = q$ .",
+      "The episodic memory module consists of two separate components: the attention mechanism and the memory update mechanism. The attention mechanism is responsible for producing a contextual vector $c^t$ , where $c^t \\in \\mathbb {R}^{n_H}$ is a summary of relevant input for pass $t$ , with relevance inferred by the question $q$ and previous episode memory $m^{t-1}$ . The memory update mechanism is responsible for generating the episode memory $m^t$ based upon the contextual vector $c^t$ and previous episode memory $m^{t-1}$ . By the final pass $T$ , the episodic memory $m^T$ should contain all the information required to answer the question $c^t \\in \\mathbb {R}^{n_H}$0 .",
+      "Answer Module: The answer module receives both $q$ and $m^T$ to generate the model's predicted answer. For simple answers, such as a single word, a linear layer with softmax activation may be used. For tasks requiring a sequence output, an RNN may be used to decode $a = [q ; m^T]$ , the concatenation of vectors $q$ and $m^T$ , to an ordered set of tokens. The cross entropy error on the answers is used for training and backpropagated through the entire network."
+    ],
+    [
+      "We propose and compare several modeling choices for two crucial components: input representation, attention mechanism and memory update. The final DMN+ model obtains the highest accuracy on the bAbI-10k dataset without supporting facts and the VQA dataset BIBREF8 . Several design choices are motivated by intuition and accuracy improvements on that dataset."
+    ],
+    [
+      "In the DMN specified in BIBREF6 , a single GRU is used to process all the words in the story, extracting sentence representations by storing the hidden states produced at the end of sentence markers. The GRU also provides a temporal component by allowing a sentence to know the content of the sentences that came before them. Whilst this input module worked well for bAbI-1k with supporting facts, as reported in BIBREF6 , it did not perform well on bAbI-10k without supporting facts (Sec. \"Model Analysis\" ).",
+      "We speculate that there are two main reasons for this performance disparity, all exacerbated by the removal of supporting facts. First, the GRU only allows sentences to have context from sentences before them, but not after them. This prevents information propagation from future sentences. Second, the supporting sentences may be too far away from each other on a word level to allow for these distant sentences to interact through the word level GRU.",
+      "Input Fusion Layer",
+      "For the DMN+, we propose replacing this single GRU with two different components. The first component is a sentence reader, responsible only for encoding the words into a sentence embedding. The second component is the input fusion layer, allowing for interactions between sentences. This resembles the hierarchical neural auto-encoder architecture of BIBREF9 and allows content interaction between sentences. We adopt the bi-directional GRU for this input fusion layer because it allows information from both past and future sentences to be used. As gradients do not need to propagate through the words between sentences, the fusion layer also allows for distant supporting sentences to have a more direct interaction.",
+      "Fig. 2 shows an illustration of an input module, where a positional encoder is used for the sentence reader and a bi-directional GRU is adopted for the input fusion layer. Each sentence encoding $f_i$ is the output of an encoding scheme taking the word tokens $[w^i_1, \\hdots , w^i_{M_i}]$ , where $M_i$ is the length of the sentence.",
+      "The sentence reader could be based on any variety of encoding schemes. We selected positional encoding described in BIBREF10 to allow for a comparison to their work. GRUs and LSTMs were also considered but required more computational resources and were prone to overfitting if auxiliary tasks, such as reconstructing the original sentence, were not used.",
+      "For the positional encoding scheme, the sentence representation is produced by $f_i = \\sum ^{j=1}_M l_j \\circ w^i_j$ , where $\\circ $ is element-wise multiplication and $l_j$ is a column vector with structure $l_{jd} = (1 - j / M) - (d / D) (1 - 2j / M)$ , where $d$ is the embedding index and $D$ is the dimension of the embedding.",
+      "The input fusion layer takes these input facts and enables an information exchange between them by applying a bi-directional GRU. ",
+      "$$\\overrightarrow{f_i} = GRU_{fwd}(f_i, \\overrightarrow{f_{i-1}}) \\\\\n\\overleftarrow{f_{i}} = GRU_{bwd}(f_{i}, \\overleftarrow{f_{i+1}}) \\\\\n\\overleftrightarrow{f_i} = \\overleftarrow{f_i} + \\overrightarrow{f_i}$$   (Eq. 5) ",
+      "where $f_i$ is the input fact at timestep $i$ , $ \\overrightarrow{f_i}$ is the hidden state of the forward GRU at timestep $i$ , and $\\overleftarrow{f_i}$ is the hidden state of the backward GRU at timestep $i$ . This allows contextual information from both future and past facts to impact $\\overleftrightarrow{f_i}$ .",
+      "We explored a variety of encoding schemes for the sentence reader, including GRUs, LSTMs, and the positional encoding scheme described in BIBREF10 . For simplicity and speed, we selected the positional encoding scheme. GRUs and LSTMs were also considered but required more computational resources and were prone to overfitting if auxiliary tasks, such as reconstructing the original sentence, were not used."
+    ],
+    [
+      "To apply the DMN to visual question answering, we introduce a new input module for images. The module splits an image into small local regions and considers each region equivalent to a sentence in the input module for text. The input module for VQA is composed of three parts, illustrated in Fig. 3 : local region feature extraction, visual feature embedding, and the input fusion layer introduced in Sec. \"Input Module for Text QA\" .",
+      "Local region feature extraction: To extract features from the image, we use a convolutional neural network BIBREF0 based upon the VGG-19 model BIBREF11 . We first rescale the input image to $448 \\times 448$ and take the output from the last pooling layer which has dimensionality $d = 512 \\times 14 \\times 14$ . The pooling layer divides the image into a grid of $14 \\times 14$ , resulting in 196 local regional vectors of $d = 512$ .",
+      "Visual feature embedding: As the VQA task involves both image features and text features, we add a linear layer with tanh activation to project the local regional vectors to the textual feature space used by the question vector $q$ .",
+      "Input fusion layer: The local regional vectors extracted from above do not yet have global information available to them. Without global information, their representational power is quite limited, with simple issues like object scaling or locational variance causing accuracy problems.",
+      "To solve this, we add an input fusion layer similar to that of the textual input module described in Sec. \"Input Module for Text QA\" . First, to produce the input facts $F$ , we traverse the image in a snake like fashion, as seen in Figure 3 . We then apply a bi-directional GRU over these input facts $F$ to produce the globally aware input facts $\\overleftrightarrow{F}$ . The bi-directional GRU allows for information propagation from neighboring image patches, capturing spatial information."
+    ],
+    [
+      "The episodic memory module, as depicted in Fig. 4 , retrieves information from the input facts $\\overleftrightarrow{F} = [\\overleftrightarrow{f_1}, \\hdots , \\overleftrightarrow{f_N}]$ provided to it by focusing attention on a subset of these facts. We implement this attention by associating a single scalar value, the attention gate $g^t_i$ , with each fact $\\overleftrightarrow{f}_i$ during pass $t$ . This is computed by allowing interactions between the fact and both the question representation and the episode memory state. ",
+      "$$z^t_i &=& [\\overleftrightarrow{f_i} \\circ q; \\overleftrightarrow{f_i} \\circ m^{t-1}; \\vert \\overleftrightarrow{f_i} - q \\vert ; \\vert \\overleftrightarrow{f_i} - m^{t-1} \\vert ] \\\\\nZ^t_i &=& W^{(2)} \\tanh \\left(W^{(1)}z^t_i + b^{(1)} \\right)+ b^{(2)} \\\\\ng^t_i &=& \\frac{\\exp (Z^t_i)}{\\sum _{k=1}^{M_i} \\exp (Z^t_k)} $$   (Eq. 10) ",
+      "where $\\overleftrightarrow{f_i}$ is the $i^{th}$ fact, $m^{t-1}$ is the previous episode memory, $q$ is the original question, $\\circ $ is the element-wise product, $|\\cdot |$ is the element-wise absolute value, and $;$ represents concatenation of the vectors.",
+      "The DMN implemented in BIBREF6 involved a more complex set of interactions within $z$ , containing the additional terms $[f; m^{t-1}; q; f^T W^{(b)} q; f^T W^{(b)} m^{t-1}]$ . After an initial analysis, we found these additional terms were not required.",
+      "Attention Mechanism",
+      "Once we have the attention gate $g^t_i$ we use an attention mechanism to extract a contextual vector $c^t$ based upon the current focus. We focus on two types of attention: soft attention and a new attention based GRU. The latter improves performance and is hence the final modeling choice for the DMN+.",
+      "Soft attention: Soft attention produces a contextual vector $c^t$ through a weighted summation of the sorted list of vectors $\\overleftrightarrow{F}$ and corresponding attention gates $g_i^t$ : $c^t = \\sum _{i=1}^N g^t_i \\overleftrightarrow{f}_i$ This method has two advantages. First, it is easy to compute. Second, if the softmax activation is spiky it can approximate a hard attention function by selecting only a single fact for the contextual vector whilst still being differentiable. However the main disadvantage to soft attention is that the summation process loses both positional and ordering information. Whilst multiple attention passes can retrieve some of this information, this is inefficient.",
+      "Attention based GRU: For more complex queries, we would like for the attention mechanism to be sensitive to both the position and ordering of the input facts $\\overleftrightarrow{F}$ . An RNN would be advantageous in this situation except they cannot make use of the attention gate from Equation .",
+      "We propose a modification to the GRU architecture by embedding information from the attention mechanism. The update gate $u_i$ in Equation 2 decides how much of each dimension of the hidden state to retain and how much should be updated with the transformed input $x_i$ from the current timestep. As $u_i$ is computed using only the current input and the hidden state from previous timesteps, it lacks any knowledge from the question or previous episode memory.",
+      "By replacing the update gate $u_i$ in the GRU (Equation 2 ) with the output of the attention gate $g^t_i$ (Equation ) in Equation , the GRU can now use the attention gate for updating its internal state. This change is depicted in Fig 5 . ",
+      "$$h_i &=& g^t_i \\circ \\tilde{h}_i + (1-g^t_i) \\circ h_{i-1}$$   (Eq. 12) ",
+      "An important consideration is that $g^t_i$ is a scalar, generated using a softmax activation, as opposed to the vector $u_i \\in \\mathbb {R}^{n_H}$ , generated using a sigmoid activation. This allows us to easily visualize how the attention gates activate over the input, later shown for visual QA in Fig. 6 . Though not explored, replacing the softmax activation in Equation with a sigmoid activation would result in $g^t_i \\in \\mathbb {R}^{n_H}$ . To produce the contextual vector $c^t$ used for updating the episodic memory state $m^t$ , we use the final hidden state of the attention based GRU.",
+      "Episode Memory Updates",
+      "After each pass through the attention mechanism, we wish to update the episode memory $m^{t-1}$ with the newly constructed contextual vector $c^t$ , producing $m^t$ . In the DMN, a GRU with the initial hidden state set to the question vector $q$ is used for this purpose. The episodic memory for pass $t$ is computed by ",
+      "$$m^t = GRU(c^t, m^{t-1})$$   (Eq. 13) ",
+      "The work of BIBREF10 suggests that using different weights for each pass through the episodic memory may be advantageous. When the model contains only one set of weights for all episodic passes over the input, it is referred to as a tied model, as in the \u201cMem Weights\u201d row in Table 1 .",
+      "Following the memory update component used in BIBREF10 and BIBREF12 we experiment with using a ReLU layer for the memory update, calculating the new episode memory state by ",
+      "$$m^t = ReLU\\left(W^t [m^{t-1} ; c^t ; q] + b\\right)$$   (Eq. 14) ",
+      "where $;$ is the concatenation operator, $W^t \\in \\mathbb {R}^{n_H \\times n_H}$ , $b \\in \\mathbb {R}^{n_H}$ , and $n_H$ is the hidden size. The untying of weights and using this ReLU formulation for the memory update improves accuracy by another 0.5% as shown in Table 1 in the last column. The final output of the memory network is passed to the answer module as in the original DMN."
+    ],
+    [
+      "The DMN is related to two major lines of recent work: memory and attention mechanisms. We work on both visual and textual question answering which have, until now, been developed in separate communities.",
+      "Neural Memory Models The earliest recent work with a memory component that is applied to language processing is that of memory networks BIBREF2 which adds a memory component for question answering over simple facts. They are similar to DMNs in that they also have input, scoring, attention and response mechanisms. However, unlike the DMN their input module computes sentence representations independently and hence cannot easily be used for other tasks such as sequence labeling. Like the original DMN, this memory network requires that supporting facts are labeled during QA training. End-to-end memory networks BIBREF10 do not have this limitation. In contrast to previous memory models with a variety of different functions for memory attention retrieval and representations, DMNs BIBREF6 have shown that neural sequence models can be used for input representation, attention and response mechanisms. Sequence models naturally capture position and temporality of both the inputs and transitive reasoning steps.",
+      "Neural Attention Mechanisms Attention mechanisms allow neural network models to use a question to selectively pay attention to specific inputs. They can benefit image classification BIBREF13 , generating captions for images BIBREF5 , among others mentioned below, and machine translation BIBREF14 , BIBREF3 , BIBREF4 . Other recent neural architectures with memory or attention which have proposed include neural Turing machines BIBREF15 , neural GPUs BIBREF16 and stack-augmented RNNs BIBREF17 .",
+      "Question Answering in NLP Question answering involving natural language can be solved in a variety of ways to which we cannot all do justice. If the potential input is a large text corpus, QA becomes a combination of information retrieval and extraction BIBREF18 . Neural approaches can include reasoning over knowledge bases, BIBREF19 , BIBREF20 or directly via sentences for trivia competitions BIBREF21 .",
+      "Visual Question Answering (VQA) In comparison to QA in NLP, VQA is still a relatively young task that is feasible only now that objects can be identified with high accuracy. The first large scale database with unconstrained questions about images was introduced by BIBREF8 . While VQA datasets existed before they did not include open-ended, free-form questions about general images BIBREF22 . Others are were too small to be viable for a deep learning approach BIBREF23 . The only VQA model which also has an attention component is the stacked attention network BIBREF24 . Their work also uses CNN based features. However, unlike our input fusion layer, they use a single layer neural network to map the features of each patch to the dimensionality of the question vector. Hence, the model cannot easily incorporate adjacency of local information in its hidden state. A model that also uses neural modules, albeit logically inspired ones, is that by BIBREF25 who evaluate on knowledgebase reasoning and visual question answering. We compare directly to their method on the latter task and dataset.",
+      "Related to visual question answering is the task of describing images with sentences BIBREF26 . BIBREF27 used deep learning methods to map images and sentences into the same space in order to describe images with sentences and to find images that best visualize a sentence. This was the first work to map both modalities into a joint space with deep learning methods, but it could only select an existing sentence to describe an image. Shortly thereafter, recurrent neural networks were used to generate often novel sentences based on images BIBREF28 , BIBREF29 , BIBREF30 , BIBREF5 ."
+    ],
+    [
+      "To analyze our proposed model changes and compare our performance with other architectures, we use three datasets."
+    ],
+    [
+      "For evaluating the DMN on textual question answering, we use bAbI-10k English BIBREF31 , a synthetic dataset which features 20 different tasks. Each example is composed of a set of facts, a question, the answer, and the supporting facts that lead to the answer. The dataset comes in two sizes, referring to the number of training examples each task has: bAbI-1k and bAbI-10k. The experiments in BIBREF10 found that their lowest error rates on the smaller bAbI-1k dataset were on average three times higher than on bAbI-10k."
+    ],
+    [
+      "The DAtaset for QUestion Answering on Real-world images (DAQUAR) BIBREF23 consists of 795 training images and 654 test images. Based upon these images, 6,795 training questions and 5,673 test questions were generated. Following the previously defined experimental method, we exclude multiple word answers BIBREF32 , BIBREF33 . The resulting dataset covers 90% of the original data. The evaluation method uses classification accuracy over the single words. We use this as a development dataset for model analysis (Sec. \"Model Analysis\" )."
+    ],
+    [
+      "The Visual Question Answering (VQA) dataset was constructed using the Microsoft COCO dataset BIBREF34 which contained 123,287 training/validation images and 81,434 test images. Each image has several related questions with each question answered by multiple people. This dataset contains 248,349 training questions, 121,512 validation questions, and 244,302 for testing. The testing data was split into test-development, test-standard and test-challenge in BIBREF8 .",
+      "Evaluation on both test-standard and test-challenge are implemented via a submission system. test-standard may only be evaluated 5 times and test-challenge is only evaluated at the end of the competition. To the best of our knowledge, VQA is the largest and most complex image dataset for the visual question answering task."
+    ],
+    [
+      "To understand the impact of the proposed module changes, we analyze the performance of a variety of DMN models on textual and visual question answering datasets.",
+      "The original DMN (ODMN) is the architecture presented in BIBREF6 without any modifications. DMN2 only replaces the input module with the input fusion layer (Sec. \"Input Module for Text QA\" ). DMN3, based upon DMN2, replaces the soft attention mechanism with the attention based GRU proposed in Sec. \"The Episodic Memory Module\" . Finally, DMN+, based upon DMN3, is an untied model, using a unique set of weights for each pass and a linear layer with a ReLU activation to compute the memory update. We report the performance of the model variations in Table 1 .",
+      "A large improvement to accuracy on both the bAbI-10k textual and DAQUAR visual datasets results from updating the input module, seen when comparing ODMN to DMN2. On both datasets, the input fusion layer improves interaction between distant facts. In the visual dataset, this improvement is purely from providing contextual information from neighboring image patches, allowing it to handle objects of varying scale or questions with a locality aspect. For the textual dataset, the improved interaction between sentences likely helps the path finding required for logical reasoning when multiple transitive steps are required.",
+      "The addition of the attention GRU in DMN3 helps answer questions where complex positional or ordering information may be required. This change impacts the textual dataset the most as few questions in the visual dataset are likely to require this form of logical reasoning. Finally, the untied model in the DMN+ overfits on some tasks compared to DMN3, but on average the error rate decreases.",
+      "From these experimental results, we find that the combination of all the proposed model changes results, culminating in DMN+, achieves the highest performance across both the visual and textual datasets."
+    ],
+    [
+      "We trained our models using the Adam optimizer BIBREF35 with a learning rate of 0.001 and batch size of 128. Training runs for up to 256 epochs with early stopping if the validation loss had not improved within the last 20 epochs. The model from the epoch with the lowest validation loss was then selected. Xavier initialization was used for all weights except for the word embeddings, which used random uniform initialization with range $[-\\sqrt{3}, \\sqrt{3}]$ . Both the embedding and hidden dimensions were of size $d = 80$ . We used $\\ell _2$ regularization on all weights except bias and used dropout on the initial sentence encodings and the answer module, keeping the input with probability $p=0.9$ . The last 10% of the training data on each task was chosen as the validation set. For all tasks, three passes were used for the episodic memory module, allowing direct comparison to other state of the art methods. Finally, we limited the input to the last 70 sentences for all tasks except QA3 for which we limited input to the last 130 sentences, similar to BIBREF10 .",
+      "On some tasks, the accuracy was not stable across multiple runs. This was particularly problematic on QA3, QA17, and QA18. To solve this, we repeated training 10 times using random initializations and evaluated the model that achieved the lowest validation set loss.",
+      "Text QA Results",
+      "We compare our best performing approach, DMN+, to two state of the art question answering architectures: the end to end memory network (E2E) BIBREF10 and the neural reasoner framework (NR) BIBREF12 . Neither approach use supporting facts for training.",
+      "The end-to-end memory network is a form of memory network BIBREF2 tested on both textual question answering and language modeling. The model features both explicit memory and a recurrent attention mechanism. We select the model from the paper that achieves the lowest mean error over the bAbI-10k dataset. This model utilizes positional encoding for input, RNN-style tied weights for the episode module, and a ReLU non-linearity for the memory update component.",
+      "The neural reasoner framework is an end-to-end trainable model which features a deep architecture for logical reasoning and an interaction-pooling mechanism for allowing interaction over multiple facts. While the neural reasoner framework was only tested on QA17 and QA19, these were two of the most challenging question types at the time.",
+      "In Table 2 we compare the accuracy of these question answering architectures, both as mean error and error on individual tasks. The DMN+ model reduces mean error by 1.4% compared to the the end-to-end memory network, achieving a new state of the art for the bAbI-10k dataset.",
+      "One notable deficiency in our model is that of QA16: Basic Induction. In BIBREF10 , an untied model using only summation for memory updates was able to achieve a near perfect error rate of $0.4$ . When the memory update was replaced with a linear layer with ReLU activation, the end-to-end memory network's overall mean error decreased but the error for QA16 rose sharply. Our model experiences the same difficulties, suggesting that the more complex memory update component may prevent convergence on certain simpler tasks.",
+      "The neural reasoner model outperforms both the DMN and end-to-end memory network on QA17: Positional Reasoning. This is likely as the positional reasoning task only involves minimal supervision - two sentences for input, yes/no answers for supervision, and only 5,812 unique examples after removing duplicates from the initial 10,000 training examples. BIBREF12 add an auxiliary task of reconstructing both the original sentences and question from their representations. This auxiliary task likely improves performance by preventing overfitting."
+    ],
+    [
+      "For the VQA dataset, each question is answered by multiple people and the answers may not be the same, the generated answers are evaluated using human consensus. For each predicted answer $a_i$ for the $i_{th}$ question with target answer set $T^{i}$ , the accuracy of VQA: $Acc_{VQA} = \\frac{1}{N}\\sum _{i=1}^Nmin(\\frac{\\sum _{t\\in T^i}{1}_{(a_i==t)}}{3},1)$ where ${1}_{(\\cdot )}$ is the indicator function. Simply put, the answer $a_i$ is only 100 $\\%$ accurate if at least 3 people provide that exact answer.",
+      "Training Details We use the Adam optimizer BIBREF35 with a learning rate of 0.003 and batch size of 100. Training runs for up to 256 epochs with early stopping if the validation loss has not improved in the last 10 epochs. For weight initialization, we sampled from a random uniform distribution with range $[-0.08, 0.08]$ . Both the word embedding and hidden layers were vectors of size $d=512$ . We apply dropout on the initial image output from the VGG convolutional neural network BIBREF11 as well as the input to the answer module, keeping input with probability $p=0.5$ .",
+      "Results and Analysis",
+      "The VQA dataset is composed of three question domains: Yes/No, Number, and Other. This enables us to analyze the performance of the models on various tasks that require different reasoning abilities.",
+      "The comparison models are separated into two broad classes: those that utilize a full connected image feature for classification and those that perform reasoning over multiple small image patches. Only the SAN and DMN approach use small image patches, while the rest use the fully-connected whole image feature approach.",
+      "Here, we show the quantitative and qualitative results in Table 3 and Fig. 6 , respectively. The images in Fig. 6 illustrate how the attention gate $g^t_i$ selectively activates over relevant portions of the image according to the query. In Table 3 , our method outperforms baseline and other state-of-the-art methods across all question domains (All) in both test-dev and test-std, and especially for Other questions, achieves a wide margin compared to the other architectures, which is likely as the small image patches allow for finely detailed reasoning over the image.",
+      "However, the granularity offered by small image patches does not always offer an advantage. The Number questions may be not solvable for both the SAN and DMN architectures, potentially as counting objects is not a simple task when an object crosses image patch boundaries."
+    ],
+    [
+      "We have proposed new modules for the DMN framework to achieve strong results without supervision of supporting facts. These improvements include the input fusion layer to allow interactions between input facts and a novel attention based GRU that allows for logical reasoning over ordered inputs. Our resulting model obtains state of the art results on both the VQA dataset and the bAbI-10k text question-answering dataset, proving the framework can be generalized across input domains."
+    ]
+  ]
+}
+```
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diff --git a/qasper-0457/instruction.md b/qasper-0457/instruction.md
new file mode 100644
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+++ b/qasper-0457/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: RobBERT: a Dutch RoBERTa-based Language Model
+
+Question: What data did they use?
\ No newline at end of file
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--- /dev/null
+++ b/qasper-0468/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Text-based inference of moral sentiment change
+
+Question: How does the parameter-free model work?
\ No newline at end of file
diff --git a/qasper-0503/instruction.md b/qasper-0503/instruction.md
new file mode 100644
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@@ -0,0 +1,82 @@
+Name of Paper: Synchronising audio and ultrasound by learning cross-modal embeddings
+
+Question: What kind of neural network architecture do they use?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Background",
+    "Audiovisual synchronisation for lip videos",
+    "Lip videos vs. ultrasound tongue imaging (UTI)",
+    "Model",
+    "Data",
+    "Preparing the data",
+    "Creating samples using a self-supervision strategy",
+    "Dividing samples for training, validation and testing",
+    "Experiments",
+    "Conclusion",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "Ultrasound tongue imaging (UTI) is a non-invasive way of observing the vocal tract during speech production BIBREF0 . Instrumental speech therapy relies on capturing ultrasound videos of the patient's tongue simultaneously with their speech audio in order to provide a diagnosis, design treatments, and measure therapy progress BIBREF1 . The two modalities must be correctly synchronised, with a minimum shift of INLINEFORM0 45ms if the audio leads and INLINEFORM1 125ms if the audio lags, based on synchronisation standards for broadcast audiovisual signals BIBREF2 . Errors beyond this range can render the data unusable \u2013 indeed, synchronisation errors do occur, resulting in significant wasted effort if not corrected. No mechanism currently exists to automatically correct these errors, and although manual synchronisation is possible in the presence of certain audiovisual cues such as stop consonants BIBREF3 , it is time consuming and tedious.",
+      "In this work, we exploit the correlation between the two modalities to synchronise them. We utilise a two-stream neural network architecture for the task BIBREF4 , using as our only source of supervision pairs of ultrasound and audio segments which have been automatically generated and labelled as positive (correctly synchronised) or negative (randomly desynchronised); a process known as self-supervision BIBREF5 . We demonstrate how this approach enables us to correctly synchronise the majority of utterances in our test set, and in particular, those exhibiting natural variation in speech.",
+      "Section SECREF2 reviews existing approaches for audiovisual synchronisation, and describes the challenges specifically associated with UTI data, compared with lip videos for which automatic synchronisation has been previously attempted. Section SECREF3 describes our approach. Section SECREF4 describes the data we use, including data preprocessing and positive and negative sample creation using a self-supervision strategy. Section SECREF5 describes our experiments, followed by an analysis of the results. We conclude with a summary and future directions in Section SECREF6 ."
+    ],
+    [
+      "Ultrasound and audio are recorded using separate components, and hardware synchronisation is achieved by translating information from the visual signal into audio at recording time. Specifically, for every ultrasound frame recorded, the ultrasound beam-forming unit releases a pulse signal, which is translated by an external hardware synchroniser into an audio pulse signal and captured by the sound card BIBREF6 , BIBREF7 . Synchronisation is achieved by aligning the ultrasound frames with the audio pulse signal, which is already time-aligned with the speech audio BIBREF8 .",
+      "Hardware synchronisation can fail for a number of reasons. The synchroniser is an external device which needs to be correctly connected and operated by therapists. Incorrect use can lead to missing the pulse signal, which would cause synchronisation to fail for entire therapy sessions BIBREF9 . Furthermore, low-quality sound cards report an approximate, rather than the exact, sample rate which leads to errors in the offset calculation BIBREF8 . There is currently no recovery mechanism for when synchronisation fails, and to the best of our knowledge, there has been no prior work on automatically correcting the synchronisation error between ultrasound tongue videos and audio. There is, however, some prior work on synchronising lip movement with audio which we describe next."
+    ],
+    [
+      "Speech audio is generated by articulatory movement and is therefore fundamentally correlated with other manifestations of this movement, such as lip or tongue videos BIBREF10 . An alternative to the hardware approach is to exploit this correlation to find the offset. Previous approaches have investigated the effects of using different representations and feature extraction techniques on finding dimensions of high correlation BIBREF11 , BIBREF12 , BIBREF13 . More recently, neural networks, which learn features directly from input, have been employed for the task. SyncNet BIBREF4 uses a two-stream neural network and self-supervision to learn cross-modal embeddings, which are then used to synchronise audio with lip videos. It achieves near perfect accuracy ( INLINEFORM0 99 INLINEFORM1 ) using manual evaluation where lip-sync error is not detectable to a human. It has since been extended to use different sample creation methods for self-supervision BIBREF5 , BIBREF14 and different training objectives BIBREF14 . We adopt the original approach BIBREF4 , as it is both simpler and significantly less expensive to train than the more recent variants."
+    ],
+    [
+      "Videos of lip movement can be obtained from various sources including TV, films, and YouTube, and are often cropped to include only the lips BIBREF4 . UTI data, on the other hand, is recorded in clinics by trained therapists BIBREF15 . An ultrasound probe placed under the chin of the patient captures the midsaggital view of their oral cavity as they speak. UTI data consists of sequences of 2D matrices of raw ultrasound reflection data, which can be interpreted as greyscale images BIBREF15 . There are several challenges specifically associated with UTI data compared with lip videos, which can potentially lower the performance of models relative to results reported on lip video data. These include:",
+      "Poor image quality: Ultrasound data is noisy, containing arbitrary high-contrast edges, speckle noise, artefacts, and interruptions to the tongue's surface BIBREF0 , BIBREF16 , BIBREF17 . The oral cavity is not entirely visible, missing the lips, the palate, and the pharyngeal wall, and visually interpreting the data requires specialised training. In contrast, videos of lip movement are of much higher quality and suffer from none of these issues.",
+      "Probe placement variation: Surfaces that are orthogonal to the ultrasound beam image better than those at an angle. Small shifts in probe placement during recording lead to high variation between otherwise similar tongue shapes BIBREF0 , BIBREF18 , BIBREF17 . In contrast, while the scaling and rotations of lip videos lead to variation, they do not lead to a degradation in image quality.",
+      "Inter-speaker variation: Age and physiology affect the quality of ultrasound data, and subjects with smaller vocal tracts and less tissue fat image better BIBREF0 , BIBREF17 . Dryness in the mouth, as a result of nervousness during speech therapy, leads to poor imaging. While inter-speaker variation is expected in lip videos, again, the variation does not lead to quality degradation.",
+      "Limited amount of data: Existing UTI datasets are considerably smaller than lip movement datasets. Consider for example VoxCeleb and VoxCeleb2 used to train SyncNet BIBREF4 , BIBREF14 , which together contain 1 million utterances from 7,363 identities BIBREF19 , BIBREF20 . In contrast, the UltraSuite repository (used in this work) contains 13,815 spoken utterances from 86 identities.",
+      "Uncorrelated segments: Speech therapy data contains interactions between the therapist and patient. The audio therefore contains speech from both speakers, while the ultrasound captures only the patient's tongue BIBREF15 . As a result, parts of the recordings will consist of completely uncorrelated audio and ultrasound. This issue is similar to that of dubbed voices in lip videos BIBREF4 , but is more prevalent in speech therapy data."
+    ],
+    [
+      "We adopt the approach in BIBREF4 , modifying it to synchronise audio with UTI data. Our model, UltraSync, consists of two streams: the first takes as input a short segment of ultrasound and the second takes as input the corresponding audio. Both inputs are high-dimensional and are of different sizes. The objective is to learn a mapping from the inputs to a pair of low-dimensional vectors of the same length, such that the Euclidean distance between the two vectors is small when they correlate and large otherwise BIBREF21 , BIBREF22 . This model can be viewed as an extension of a siamese neural network BIBREF23 but with two asymmetrical streams and no shared parameters. Figure FIGREF1 illustrates the main architecture. The visual data INLINEFORM0 (ultrasound) and audio data INLINEFORM1 (MFCC), which have different shapes, are mapped to low dimensional embeddings INLINEFORM2 (visual) and INLINEFORM3 (audio) of the same size: DISPLAYFORM0 ",
+      "The model is trained using a contrastive loss function BIBREF21 , BIBREF22 , INLINEFORM0 , which minimises the Euclidean distance INLINEFORM1 between INLINEFORM2 and INLINEFORM3 for positive pairs ( INLINEFORM4 ), and maximises it for negative pairs ( INLINEFORM5 ), for a number of training samples INLINEFORM6 : DISPLAYFORM0 ",
+      "Given a pair of ultrasound and audio segments we can calculate the distance between them using our model. To predict the synchronisation offset for an utterance, we consider a discretised set of candidate offsets, calculate the average distance for each across utterance segments, and select the one with the minimum average distance. The candidate set is independent of the model, and is chosen based on task knowledge (Section SECREF5 )."
+    ],
+    [
+      "For our experiments, we select a dataset whose utterances have been correctly synchronised at recording time. This allows us to control how the model is trained and verify its performance using ground truth synchronisation offsets. We use UltraSuite: a repository of ultrasound and acoustic data gathered from child speech therapy sessions BIBREF15 . We used all three datasets from the repository: UXTD (recorded with typically developing children), and UXSSD and UPX (recorded with children with speech sound disorders). In total, the dataset contains 13,815 spoken utterances from 86 speakers, corresponding to 35.9 hours of recordings. The utterances have been categorised by the type of task the child was given, and are labelled as: Words (A), Non-words (B), Sentence (C), Articulatory (D), Non-speech (E), or Conversations (F). See BIBREF15 for details.",
+      "Each utterance consists of 3 files: audio, ultrasound, and parameter. The audio file is a RIFF wave file, sampled at 22.05 KHz, containing the speech of the child and therapist. The ultrasound file consists of a sequence of ultrasound frames capturing the midsagittal view of the child's tongue. A single ultrasound frame is recorded as a 2D matrix where each column represents the ultrasound reflection intensities along a single scan line. Each ultrasound frame consists of 63 scan lines of 412 data points each, and is sampled at a rate of INLINEFORM0 121.5 fps. Raw ultrasound frames can be visualised as greyscale images and can thus be interpreted as videos. The parameter file contains the synchronisation offset value (in milliseconds), determined using hardware synchronisation at recording time and confirmed by the therapists to be correct for this dataset."
+    ],
+    [
+      "First, we exclude utterances of type \u201cNon-speech\" (E) from our training data (and statistics). These are coughs recorded to obtain additional tongue shapes, or swallowing motions recorded to capture a trace of the hard palate. Both of these rarely contain audible content and are therefore not relevant to our task. Next, we apply the offset, which should be positive if the audio leads and negative if the audio lags. In this dataset, the offset is always positive. We apply it by cropping the leading audio and trimming the end of the longer signal to match the duration.",
+      "To process the ultrasound more efficiently, we first reduce the frame rate from INLINEFORM0 121.5 fps to INLINEFORM1 24.3 fps by retaining 1 out of every 5 frames. We then downsample by a factor of (1, 3), shrinking the frame size from 63x412 to 63x138 using max pixel value. This retains the number of ultrasound vectors (63), but reduces the number of pixels per vector (from 412 to 138).",
+      "The final pre-preprocessing step is to remove empty regions. UltraSuite was previously anonymised by zero-ing segments of audio which contained personally identifiable information. As a preprocessing step, we remove the zero regions from audio and corresponding ultrasound. We additionally experimented with removing regions of silence using voice activity detection, but obtained a higher performance by retaining them."
+    ],
+    [
+      "To train our model we need positive and negative training pairs. The model ingests short clips from each modality of INLINEFORM0 200ms long, calculated as INLINEFORM1 , where INLINEFORM2 is the time window, INLINEFORM3 is the number of ultrasound frames per window (5 in our case), and INLINEFORM4 is the ultrasound frame rate of the utterance ( INLINEFORM5 24.3 fps). For each recording, we split the ultrasound into non-overlapping windows of 5 frames each. We extract MFCC features (13 cepstral coefficients) from the audio using a window length of INLINEFORM6 20ms, calculated as INLINEFORM7 , and a step size of INLINEFORM8 10ms, calculated as INLINEFORM9 . This give us the input sizes shown in Figure FIGREF1 .",
+      "Positive samples are pairs of ultrasound windows and the corresponding MFCC frames. To create negative samples, we randomise pairings of ultrasound windows to MFCC frames within the same utterance, generating as many negative as positive samples to achieve a balanced dataset. We obtain 243,764 samples for UXTD (13.5hrs), 333,526 for UXSSD (18.5hrs), and 572,078 for UPX (31.8 hrs), or a total 1,149,368 samples (63.9hrs) which we divide into training, validation and test sets."
+    ],
+    [
+      "We aim to test whether our model generalises to data from new speakers, and to data from new sessions recorded with known speakers. To simulate this, we select a group of speakers from each dataset, and hold out all of their data either for validation or for testing. Additionally, we hold out one entire session from each of the remaining speakers, and use the rest of their data for training. We aim to reserve approximately 80% of the created samples for training, 10% for validation, and 10% for testing, and select speakers and sessions on this basis.",
+      "Each speaker in UXTD recorded 1 session, but sessions are of different durations. We reserve 45 speakers for training, 5 for validation, and 8 for testing. UXSSD and UPX contain fewer speakers, but each recorded multiple sessions. We hold out 1 speaker for validation and 1 for testing from each of the two datasets. We also hold out a session from the first half of the remaining speakers for validation, and a session from the second half of the remaining speakers for testing. This selection process results in 909,858 (pooled) samples for training (50.5hrs), 128,414 for validation (7.1hrs) and 111,096 for testing (6.2hrs). From the training set, we create shuffled batches which are balanced in the number of positive and negative samples."
+    ],
+    [
+      "We select the hyper-parameters of our model empirically by tuning on the validation set (Table ). Hyper-parameter exploration is guided by BIBREF24 . We train our model using the Adam optimiser BIBREF25 with a learning rate of 0.001, a batch size of 64 samples, and for 20 epochs. We implement learning rate scheduling which reduces the learning rate by a factor of 0.1 when the validation loss plateaus for 2 epochs.",
+      "Upon convergence, the model achieves 0.193 training loss, 0.215 validation loss, and 0.213 test loss. By placing a threshold of 0.5 on predicted distances, the model achieves 69.9% binary classification accuracy on training samples, 64.7% on validation samples, and 65.3% on test samples.",
+      "Synchronisation offset prediction: Section SECREF3 described briefly how to use our model to predict the synchronisation offset for test utterances. To obtain a discretised set of offset candidates, we retrieve the true offsets of the training utterances, and find that they fall in the range [0, 179] ms. We discretise this range taking 45ms steps and rendering 40 candidate values (45ms is the smaller of the absolute values of the detectability boundaries, INLINEFORM0 125 and INLINEFORM1 45 ms). We bin the true offsets in the candidate set and discard empty bins, reducing the set from 40 to 24 values. We consider all 24 candidates for each test utterance. We do this by aligning the two signals according to the given candidate, then producing the non-overlapping windows of ultrasound and MFCC pairs, as we did when preparing the data. We then use our model to predict the Euclidean distance for each pair, and average the distances. Finally, we select the offset with the smallest average distance as our prediction.",
+      "Evaluation: Because the true offsets are known, we evaluate the performance of our model by computing the discrepancy between the predicted and the true offset for each utterance. If the discrepancy falls within the minimum detectability range ( INLINEFORM0 125 INLINEFORM1 INLINEFORM2 INLINEFORM3 INLINEFORM4 45) then the prediction is correct. Random prediction (averaged over 1000 runs) yields 14.6% accuracy with a mean and standard deviation discrepancy of 328 INLINEFORM5 518ms. We achieve 82.9% accuracy with a mean and standard deviation discrepancy of 32 INLINEFORM6 223ms. SyncNet reports INLINEFORM7 99% accuracy on lip video synchronisation using a manual evaluation where the lip error is not detectable to a human observer BIBREF4 . However, we argue that our data is more challenging (Section SECREF4 ).",
+      "Analysis: We analyse the performance of our model across different conditions. Table shows the model accuracy broken down by utterance type. The model achieves 91.2% accuracy on utterances containing words, sentences, and conversations, all of which exhibit natural variation in speech. The model is less successful with Articulatory utterances, which contain isolated phones occurring once or repeated (e.g., \u201csh sh sh\"). Such utterances contain subtle tongue movement, making it more challenging to correlate the visual signal with the audio. And indeed, the model finds the correct offset for only 55.9% of Articulatory utterances. A further analysis shows that 84.4% (N INLINEFORM0 90) of stop consonants (e.g., \u201ct\u201d), which are relied upon by therapists as the most salient audiovisual synchronisation cues BIBREF3 , are correctly synchronised by our model, compared to 48.6% (N INLINEFORM1 140) of vowels, which contain less distinct movement and are also more challenging for therapists to synchronise.",
+      "Table shows accuracy broken down by test set. The model performs better on test sets containing entirely new speakers compared with test sets containing new sessions from previously seen speakers. This is contrary to expectation but could be due to the UTI challenges (described in Section SECREF4 ) affecting different subsets to different degrees. Table shows that the model performs considerably worse on UXTD compared to other test sets (64.8% accuracy). However, a further breakdown of the results in Table by test set and utterance type explains this poor performance; the majority of UXTD utterances (71%) are Articulatory utterances which the model struggles to correctly synchronise. In fact, for other utterance types (where there is a large enough sample, such as Words) performance on UXTD is on par with other test sets."
+    ],
+    [
+      "We have shown how a two-stream neural network originally designed to synchronise lip videos with audio can be used to synchronise UTI data with audio. Our model exploits the correlation between the modalities to learn cross-model embeddings which are used to find the synchronisation offset. It generalises well to held-out data, allowing us to correctly synchronise the majority of test utterances. The model is best-suited to utterances which contain natural variation in speech and least suited to those containing isolated phones, with the exception of stop consonants. Future directions include integrating the model and synchronisation offset prediction process into speech therapy software BIBREF6 , BIBREF7 , and using the learned embeddings for other tasks such as active speaker detection BIBREF4 ."
+    ],
+    [
+      "Supported by EPSRC Healthcare Partnerships Programme grant number EP/P02338X/1 (Ultrax2020)."
+    ]
+  ]
+}
+```
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+Name of Paper: Basic tasks of sentiment analysis
+
+Question: How are aspects identified in aspect extraction?
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+Name of Paper: Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping
+
+Question: How big are the datasets?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Overview of the Approach",
+    "Cross-Lingual Word Embeddings",
+    "Cross-Lingual Word Embeddings ::: Monolingual Word Embeddings",
+    "Cross-Lingual Word Embeddings ::: Bilingual Word Embedding Mapping",
+    "Cross-Lingual Word Embeddings ::: Bilingual Word Embedding Mapping ::: Length Normalization and Orthogonal Transformation",
+    "Cross-Lingual Word Embeddings ::: Bilingual Word Embedding Mapping ::: Semi-Supervised and Unsupervised Mappings",
+    "Neural Network RE Models",
+    "Neural Network RE Models ::: Embedding Layer",
+    "Neural Network RE Models ::: Context Layer",
+    "Neural Network RE Models ::: Context Layer ::: Bi-LSTM Context Layer",
+    "Neural Network RE Models ::: Context Layer ::: CNN Context Layer",
+    "Neural Network RE Models ::: Summarization Layer",
+    "Neural Network RE Models ::: Output Layer",
+    "Neural Network RE Models ::: Cross-Lingual RE Model Transfer",
+    "Experiments",
+    "Experiments ::: Datasets",
+    "Experiments ::: Source (English) RE Model Performance",
+    "Experiments ::: Cross-Lingual RE Performance",
+    "Experiments ::: Cross-Lingual RE Performance ::: Dictionary Size",
+    "Experiments ::: Cross-Lingual RE Performance ::: Comparison of Different Mappings",
+    "Experiments ::: Cross-Lingual RE Performance ::: Performance on Test Data",
+    "Experiments ::: Cross-Lingual RE Performance ::: Discussion",
+    "Related Work",
+    "Conclusion",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "Relation extraction (RE) is an important information extraction task that seeks to detect and classify semantic relationships between entities like persons, organizations, geo-political entities, locations, and events. It provides useful information for many NLP applications such as knowledge base construction, text mining and question answering. For example, the entity Washington, D.C. and the entity United States have a CapitalOf relationship, and extraction of such relationships can help answer questions like \u201cWhat is the capital city of the United States?\"",
+      "Traditional RE models (e.g., BIBREF0, BIBREF1, BIBREF2) require careful feature engineering to derive and combine various lexical, syntactic and semantic features. Recently, neural network RE models (e.g., BIBREF3, BIBREF4, BIBREF5, BIBREF6) have become very successful. These models employ a certain level of automatic feature learning by using word embeddings, which significantly simplifies the feature engineering task while considerably improving the accuracy, achieving the state-of-the-art performance for relation extraction.",
+      "All the above RE models are supervised machine learning models that need to be trained with large amounts of manually annotated RE data to achieve high accuracy. However, annotating RE data by human is expensive and time-consuming, and can be quite difficult for a new language. Moreover, most RE models require language-specific resources such as dependency parsers and part-of-speech (POS) taggers, which also makes it very challenging to transfer an RE model of a resource-rich language to a resource-poor language.",
+      "There are a few existing weakly supervised cross-lingual RE approaches that require no human annotation in the target languages, e.g., BIBREF7, BIBREF8, BIBREF9, BIBREF10. However, the existing approaches require aligned parallel corpora or machine translation systems, which may not be readily available in practice.",
+      "In this paper, we make the following contributions to cross-lingual RE:",
+      "We propose a new approach for direct cross-lingual RE model transfer based on bilingual word embedding mapping. It projects word embeddings from a target language to a source language (e.g., English), so that a well-trained source-language RE model can be directly applied to the target language, with no manually annotated RE data needed for the target language.",
+      "We design a deep neural network architecture for the source-language (English) RE model that uses word embeddings and generic language-independent features as the input. The English RE model achieves the-state-of-the-art performance without using language-specific resources.",
+      "We conduct extensive experiments which show that the proposed approach achieves very good performance (up to $79\\%$ of the accuracy of the supervised target-language RE model) for a number of target languages on both in-house and the ACE05 datasets BIBREF11, using a small bilingual dictionary with only 1K word pairs. To the best of our knowledge, this is the first work that includes empirical studies for cross-lingual RE on several languages across a variety of language families, without using aligned parallel corpora or machine translation systems.",
+      "We organize the paper as follows. In Section 2 we provide an overview of our approach. In Section 3 we describe how to build monolingual word embeddings and learn a linear mapping between two languages. In Section 4 we present a neural network architecture for the source-language (English). In Section 5 we evaluate the performance of the proposed approach for a number of target languages. We discuss related work in Section 6 and conclude the paper in Section 7."
+    ],
+    [
+      "We summarize the main steps of our neural cross-lingual RE model transfer approach as follows.",
+      "Build word embeddings for the source language and the target language separately using monolingual data.",
+      "Learn a linear mapping that projects the target-language word embeddings into the source-language embedding space using a small bilingual dictionary.",
+      "Build a neural network source-language RE model that uses word embeddings and generic language-independent features as the input.",
+      "For a target-language sentence and any two entities in it, project the word embeddings of the words in the sentence to the source-language word embeddings using the linear mapping, and then apply the source-language RE model on the projected word embeddings to classify the relationship between the two entities. An example is shown in Figure FIGREF4, where the target language is Portuguese and the source language is English.",
+      "We will describe each component of our approach in the subsequent sections."
+    ],
+    [
+      "In recent years, vector representations of words, known as word embeddings, become ubiquitous for many NLP applications BIBREF12, BIBREF13, BIBREF14.",
+      "A monolingual word embedding model maps words in the vocabulary $\\mathcal {V}$ of a language to real-valued vectors in $\\mathbb {R}^{d\\times 1}$. The dimension of the vector space $d$ is normally much smaller than the size of the vocabulary $V=|\\mathcal {V}|$ for efficient representation. It also aims to capture semantic similarities between the words based on their distributional properties in large samples of monolingual data.",
+      "Cross-lingual word embedding models try to build word embeddings across multiple languages BIBREF15, BIBREF16. One approach builds monolingual word embeddings separately and then maps them to the same vector space using a bilingual dictionary BIBREF17, BIBREF18. Another approach builds multilingual word embeddings in a shared vector space simultaneously, by generating mixed language corpora using aligned sentences BIBREF19, BIBREF20.",
+      "In this paper, we adopt the technique in BIBREF17 because it only requires a small bilingual dictionary of aligned word pairs, and does not require parallel corpora of aligned sentences which could be more difficult to obtain."
+    ],
+    [
+      "To build monolingual word embeddings for the source and target languages, we use a variant of the Continuous Bag-of-Words (CBOW) word2vec model BIBREF13.",
+      "The standard CBOW model has two matrices, the input word matrix $\\tilde{\\mathbf {X}} \\in \\mathbb {R}^{d\\times V}$ and the output word matrix $\\mathbf {X} \\in \\mathbb {R}^{d\\times V}$. For the $i$th word $w_i$ in $\\mathcal {V}$, let $\\mathbf {e}(w_i) \\in \\mathbb {R}^{V \\times 1}$ be a one-hot vector with 1 at index $i$ and 0s at other indexes, so that $\\tilde{\\mathbf {x}}_i = \\tilde{\\mathbf {X}}\\mathbf {e}(w_i)$ (the $i$th column of $\\tilde{\\mathbf {X}}$) is the input vector representation of word $w_i$, and $\\mathbf {x}_i = \\mathbf {X}\\mathbf {e}(w_i)$ (the $i$th column of $\\mathbf {X}$) is the output vector representation (i.e., word embedding) of word $w_i$.",
+      "Given a sequence of training words $w_1, w_2, ..., w_N$, the CBOW model seeks to predict a target word $w_t$ using a window of $2c$ context words surrounding $w_t$, by maximizing the following objective function:",
+      "The conditional probability is calculated using a softmax function:",
+      "where $\\mathbf {x}_t=\\mathbf {X}\\mathbf {e}(w_t)$ is the output vector representation of word $w_t$, and",
+      "is the sum of the input vector representations of the context words.",
+      "In our variant of the CBOW model, we use a separate input word matrix $\\tilde{\\mathbf {X}}_j$ for a context word at position $j, -c \\le j \\le c, j\\ne 0$. In addition, we employ weights that decay with the distances of the context words to the target word. Under these modifications, we have",
+      "We use the variant to build monolingual word embeddings because experiments on named entity recognition and word similarity tasks showed this variant leads to small improvements over the standard CBOW model BIBREF21."
+    ],
+    [
+      "BIBREF17 observed that word embeddings of different languages often have similar geometric arrangements, and suggested to learn a linear mapping between the vector spaces.",
+      "Let $\\mathcal {D}$ be a bilingual dictionary with aligned word pairs ($w_i, v_i)_{i=1,...,D}$ between a source language $s$ and a target language $t$, where $w_i$ is a source-language word and $v_i$ is the translation of $w_i$ in the target language. Let $\\mathbf {x}_i \\in \\mathbb {R}^{d \\times 1}$ be the word embedding of the source-language word $w_i$, $\\mathbf {y}_i \\in \\mathbb {R}^{d \\times 1}$ be the word embedding of the target-language word $v_i$.",
+      "We find a linear mapping (matrix) $\\mathbf {M}_{t\\rightarrow s}$ such that $\\mathbf {M}_{t\\rightarrow s}\\mathbf {y}_i$ approximates $\\mathbf {x}_i$, by solving the following least squares problem using the dictionary as the training set:",
+      "Using $\\mathbf {M}_{t\\rightarrow s}$, for any target-language word $v$ with word embedding $\\mathbf {y}$, we can project it into the source-language embedding space as $\\mathbf {M}_{t\\rightarrow s}\\mathbf {y}$."
+    ],
+    [
+      "To ensure that all the training instances in the dictionary $\\mathcal {D}$ contribute equally to the optimization objective in (DISPLAY_FORM14) and to preserve vector norms after projection, we have tried length normalization and orthogonal transformation for learning the bilingual mapping as in BIBREF22, BIBREF23, BIBREF24.",
+      "First, we normalize the source-language and target-language word embeddings to be unit vectors: $\\mathbf {x}^{\\prime }=\\frac{\\mathbf {x}}{||\\mathbf {x}||}$ for each source-language word embedding $\\mathbf {x}$, and $\\mathbf {y}^{\\prime }= \\frac{\\mathbf {y}}{||\\mathbf {y}||}$ for each target-language word embedding $\\mathbf {y}$.",
+      "Next, we add an orthogonality constraint to (DISPLAY_FORM14) such that $\\mathbf {M}$ is an orthogonal matrix, i.e., $\\mathbf {M}^\\mathrm {T}\\mathbf {M} = \\mathbf {I}$ where $\\mathbf {I}$ denotes the identity matrix:",
+      "$\\mathbf {M}^{O} _{t\\rightarrow s}$ can be computed using singular-value decomposition (SVD)."
+    ],
+    [
+      "The mapping learned in (DISPLAY_FORM14) or (DISPLAY_FORM16) requires a seed dictionary. To relax this requirement, BIBREF25 proposed a self-learning procedure that can be combined with a dictionary-based mapping technique. Starting with a small seed dictionary, the procedure iteratively 1) learns a mapping using the current dictionary; and 2) computes a new dictionary using the learned mapping.",
+      "BIBREF26 proposed an unsupervised method to learn the bilingual mapping without using a seed dictionary. The method first uses a heuristic to build an initial dictionary that aligns the vocabularies of two languages, and then applies a robust self-learning procedure to iteratively improve the mapping. Another unsupervised method based on adversarial training was proposed in BIBREF27.",
+      "We compare the performance of different mappings for cross-lingual RE model transfer in Section SECREF45."
+    ],
+    [
+      "For any two entities in a sentence, an RE model determines whether these two entities have a relationship, and if yes, classifies the relationship into one of the pre-defined relation types. We focus on neural network RE models since these models achieve the state-of-the-art performance for relation extraction. Most importantly, neural network RE models use word embeddings as the input, which are amenable to cross-lingual model transfer via cross-lingual word embeddings. In this paper, we use English as the source language.",
+      "Our neural network architecture has four layers. The first layer is the embedding layer which maps input words in a sentence to word embeddings. The second layer is a context layer which transforms the word embeddings to context-aware vector representations using a recurrent or convolutional neural network layer. The third layer is a summarization layer which summarizes the vectors in a sentence by grouping and pooling. The final layer is the output layer which returns the classification label for the relation type."
+    ],
+    [
+      "For an English sentence with $n$ words $\\mathbf {s}=(w_1,w_2,...,w_n)$, the embedding layer maps each word $w_t$ to a real-valued vector (word embedding) $\\mathbf {x}_t\\in \\mathbb {R}^{d \\times 1}$ using the English word embedding model (Section SECREF9). In addition, for each entity $m$ in the sentence, the embedding layer maps its entity type to a real-valued vector (entity label embedding) $\\mathbf {l}_m \\in \\mathbb {R}^{d_m \\times 1}$ (initialized randomly). In our experiments we use $d=300$ and $d_m = 50$."
+    ],
+    [
+      "Given the word embeddings $\\mathbf {x}_t$'s of the words in the sentence, the context layer tries to build a sentence-context-aware vector representation for each word. We consider two types of neural network layers that aim to achieve this."
+    ],
+    [
+      "The first type of context layer is based on Long Short-Term Memory (LSTM) type recurrent neural networks BIBREF28, BIBREF29. Recurrent neural networks (RNNs) are a class of neural networks that operate on sequential data such as sequences of words. LSTM networks are a type of RNNs that have been invented to better capture long-range dependencies in sequential data.",
+      "We pass the word embeddings $\\mathbf {x}_t$'s to a forward and a backward LSTM layer. A forward or backward LSTM layer consists of a set of recurrently connected blocks known as memory blocks. The memory block at the $t$-th word in the forward LSTM layer contains a memory cell $\\overrightarrow{\\mathbf {c}}_t$ and three gates: an input gate $\\overrightarrow{\\mathbf {i}}_t$, a forget gate $\\overrightarrow{\\mathbf {f}}_t$ and an output gate $\\overrightarrow{\\mathbf {o}}_t$ ($\\overrightarrow{\\cdot }$ indicates the forward direction), which are updated as follows:",
+      "where $\\sigma $ is the element-wise sigmoid function and $\\odot $ is the element-wise multiplication.",
+      "The hidden state vector $\\overrightarrow{\\mathbf {h}}_t$ in the forward LSTM layer incorporates information from the left (past) tokens of $w_t$ in the sentence. Similarly, we can compute the hidden state vector $\\overleftarrow{\\mathbf {h}}_t$ in the backward LSTM layer, which incorporates information from the right (future) tokens of $w_t$ in the sentence. The concatenation of the two vectors $\\mathbf {h}_t = [\\overrightarrow{\\mathbf {h}}_t, \\overleftarrow{\\mathbf {h}}_t]$ is a good representation of the word $w_t$ with both left and right contextual information in the sentence."
+    ],
+    [
+      "The second type of context layer is based on Convolutional Neural Networks (CNNs) BIBREF3, BIBREF4, which applies convolution-like operation on successive windows of size $k$ around each word in the sentence. Let $\\mathbf {z}_t = [\\mathbf {x}_{t-(k-1)/2},...,\\mathbf {x}_{t+(k-1)/2}]$ be the concatenation of $k$ word embeddings around $w_t$. The convolutional layer computes a hidden state vector",
+      "for each word $w_t$, where $\\mathbf {W}$ is a weight matrix and $\\mathbf {b}$ is a bias vector, and $\\tanh (\\cdot )$ is the element-wise hyperbolic tangent function."
+    ],
+    [
+      "After the context layer, the sentence $(w_1,w_2,...,w_n)$ is represented by $(\\mathbf {h}_1,....,\\mathbf {h}_n)$. Suppose $m_1=(w_{b_1},..,w_{e_1})$ and $m_2=(w_{b_2},..,w_{e_2})$ are two entities in the sentence where $m_1$ is on the left of $m_2$ (i.e., $e_1 < b_2$). As different sentences and entities may have various lengths, the summarization layer tries to build a fixed-length vector that best summarizes the representations of the sentence and the two entities for relation type classification.",
+      "We divide the hidden state vectors $\\mathbf {h}_t$'s into 5 groups:",
+      "$G_1=\\lbrace \\mathbf {h}_{1},..,\\mathbf {h}_{b_1-1}\\rbrace $ includes vectors that are left to the first entity $m_1$.",
+      "$G_2=\\lbrace \\mathbf {h}_{b_1},..,\\mathbf {h}_{e_1}\\rbrace $ includes vectors that are in the first entity $m_1$.",
+      "$G_3=\\lbrace \\mathbf {h}_{e_1+1},..,\\mathbf {h}_{b_2-1}\\rbrace $ includes vectors that are between the two entities.",
+      "$G_4=\\lbrace \\mathbf {h}_{b_2},..,\\mathbf {h}_{e_2}\\rbrace $ includes vectors that are in the second entity $m_2$.",
+      "$G_5=\\lbrace \\mathbf {h}_{e_2+1},..,\\mathbf {h}_{n}\\rbrace $ includes vectors that are right to the second entity $m_2$.",
+      "We perform element-wise max pooling among the vectors in each group:",
+      "where $d_h$ is the dimension of the hidden state vectors. Concatenating the $\\mathbf {h}_{G_i}$'s we get a fixed-length vector $\\mathbf {h}_s=[\\mathbf {h}_{G_1},...,\\mathbf {h}_{G_5}]$."
+    ],
+    [
+      "The output layer receives inputs from the previous layers (the summarization vector $\\mathbf {h}_s$, the entity label embeddings $\\mathbf {l}_{m_1}$ and $\\mathbf {l}_{m_2}$ for the two entities under consideration) and returns a probability distribution over the relation type labels:"
+    ],
+    [
+      "Given the word embeddings of a sequence of words in a target language $t$, $(\\mathbf {y}_1,...,\\mathbf {y}_n)$, we project them into the English embedding space by applying the linear mapping $\\mathbf {M}_{t\\rightarrow s}$ learned in Section SECREF13: $(\\mathbf {M}_{t\\rightarrow s}\\mathbf {y}_1, \\mathbf {M}_{t\\rightarrow s}\\mathbf {y}_2,...,\\mathbf {M}_{t\\rightarrow s}\\mathbf {y}_n)$. The neural network English RE model is then applied on the projected word embeddings and the entity label embeddings (which are language independent) to perform relationship classification.",
+      "Note that our models do not use language-specific resources such as dependency parsers or POS taggers because these resources might not be readily available for a target language. Also our models do not use precise word position features since word positions in sentences can vary a lot across languages."
+    ],
+    [
+      "In this section, we evaluate the performance of the proposed cross-lingual RE approach on both in-house dataset and the ACE (Automatic Content Extraction) 2005 multilingual dataset BIBREF11."
+    ],
+    [
+      "Our in-house dataset includes manually annotated RE data for 6 languages: English, German, Spanish, Italian, Japanese and Portuguese. It defines 56 entity types (e.g., Person, Organization, Geo-Political Entity, Location, Facility, Time, Event_Violence, etc.) and 53 relation types between the entities (e.g., AgentOf, LocatedAt, PartOf, TimeOf, AffectedBy, etc.).",
+      "The ACE05 dataset includes manually annotated RE data for 3 languages: English, Arabic and Chinese. It defines 7 entity types (Person, Organization, Geo-Political Entity, Location, Facility, Weapon, Vehicle) and 6 relation types between the entities (Agent-Artifact, General-Affiliation, ORG-Affiliation, Part-Whole, Personal-Social, Physical).",
+      "For both datasets, we create a class label \u201cO\" to denote that the two entities under consideration do not have a relationship belonging to one of the relation types of interest."
+    ],
+    [
+      "We build 3 neural network English RE models under the architecture described in Section SECREF4:",
+      "The first neural network RE model does not have a context layer and the word embeddings are directly passed to the summarization layer. We call it Pass-Through for short.",
+      "The second neural network RE model has a Bi-LSTM context layer. We call it Bi-LSTM for short.",
+      "The third neural network model has a CNN context layer with a window size 3. We call it CNN for short.",
+      "First we compare our neural network English RE models with the state-of-the-art RE models on the ACE05 English data. The ACE05 English data can be divided to 6 different domains: broadcast conversation (bc), broadcast news (bn), telephone conversation (cts), newswire (nw), usenet (un) and webblogs (wl). We apply the same data split in BIBREF31, BIBREF30, BIBREF6, which uses news (the union of bn and nw) as the training set, a half of bc as the development set and the remaining data as the test set.",
+      "We learn the model parameters using Adam BIBREF32. We apply dropout BIBREF33 to the hidden layers to reduce overfitting. The development set is used for tuning the model hyperparameters and for early stopping.",
+      "In Table TABREF40 we compare our models with the best models in BIBREF30 and BIBREF6. Our Bi-LSTM model outperforms the best model (single or ensemble) in BIBREF30 and the best single model in BIBREF6, without using any language-specific resources such as dependency parsers.",
+      "While the data split in the previous works was motivated by domain adaptation, the focus of this paper is on cross-lingual model transfer, and hence we apply a random data split as follows. For the source language English and each target language, we randomly select $80\\%$ of the data as the training set, $10\\%$ as the development set, and keep the remaining $10\\%$ as the test set. The sizes of the sets are summarized in Table TABREF41.",
+      "We report the Precision, Recall and $F_1$ score of the 3 neural network English RE models in Table TABREF42. Note that adding an additional context layer with either Bi-LSTM or CNN significantly improves the performance of our English RE model, compared with the simple Pass-Through model. Therefore, we will focus on the Bi-LSTM model and the CNN model in the subsequent experiments."
+    ],
+    [
+      "We apply the English RE models to the 7 target languages across a variety of language families."
+    ],
+    [
+      "The bilingual dictionary includes the most frequent target-language words and their translations in English. To determine how many word pairs are needed to learn an effective bilingual word embedding mapping for cross-lingual RE, we first evaluate the performance ($F_1$ score) of our cross-lingual RE approach on the target-language development sets with an increasing dictionary size, as plotted in Figure FIGREF35.",
+      "We found that for most target languages, once the dictionary size reaches 1K, further increasing the dictionary size may not improve the transfer performance. Therefore, we select the dictionary size to be 1K."
+    ],
+    [
+      "We compare the performance of cross-lingual RE model transfer under the following bilingual word embedding mappings:",
+      "Regular-1K: the regular mapping learned in (DISPLAY_FORM14) using 1K word pairs;",
+      "Orthogonal-1K: the orthogonal mapping with length normalization learned in (DISPLAY_FORM16) using 1K word pairs (in this case we train the English RE models with the normalized English word embeddings);",
+      "Semi-Supervised-1K: the mapping learned with 1K word pairs and improved by the self-learning method in BIBREF25;",
+      "Unsupervised: the mapping learned by the unsupervised method in BIBREF26.",
+      "The results are summarized in Table TABREF46. The regular mapping outperforms the orthogonal mapping consistently across the target languages. While the orthogonal mapping was shown to work better than the regular mapping for the word translation task BIBREF22, BIBREF23, BIBREF24, our cross-lingual RE approach directly maps target-language word embeddings to the English embedding space without conducting word translations. Moreover, the orthogonal mapping requires length normalization, but we observed that length normalization adversely affects the performance of the English RE models (about 2.0 $F_1$ points drop).",
+      "We apply the vecmap toolkit to obtain the semi-supervised and unsupervised mappings. The unsupervised mapping has the lowest average accuracy over the target languages, but it does not require a seed dictionary. Among all the mappings, the regular mapping achieves the best average accuracy over the target languages using a dictionary with only 1K word pairs, and hence we adopt it for the cross-lingual RE task."
+    ],
+    [
+      "The cross-lingual RE model transfer results for the in-house test data are summarized in Table TABREF52 and the results for the ACE05 test data are summarized in Table TABREF53, using the regular mapping learned with a bilingual dictionary of size 1K. In the tables, we also provide the performance of the supervised RE model (Bi-LSTM) for each target language, which is trained with a few hundred thousand tokens of manually annotated RE data in the target-language, and may serve as an upper bound for the cross-lingual model transfer performance.",
+      "Among the 2 neural network models, the Bi-LSTM model achieves a better cross-lingual RE performance than the CNN model for 6 out of the 7 target languages. In terms of absolute performance, the Bi-LSTM model achieves over $40.0$ $F_1$ scores for German, Spanish, Portuguese and Chinese. In terms of relative performance, it reaches over $75\\%$ of the accuracy of the supervised target-language RE model for German, Spanish, Italian and Portuguese. While Japanese and Arabic appear to be more difficult to transfer, it still achieves $55\\%$ and $52\\%$ of the accuracy of the supervised Japanese and Arabic RE model, respectively, without using any manually annotated RE data in Japanese/Arabic.",
+      "We apply model ensemble to further improve the accuracy of the Bi-LSTM model. We train 5 Bi-LSTM English RE models initiated with different random seeds, apply the 5 models on the target languages, and combine the outputs by selecting the relation type labels with the highest probabilities among the 5 models. This Ensemble approach improves the single model by 0.6-1.9 $F_1$ points, except for Arabic."
+    ],
+    [
+      "Since our approach projects the target-language word embeddings to the source-language embedding space preserving the word order, it is expected to work better for a target language that has more similar word order as the source language. This has been verified by our experiments. The source language, English, belongs to the SVO (Subject, Verb, Object) language family where in a sentence the subject comes first, the verb second, and the object third. Spanish, Italian, Portuguese, German (in conventional typology) and Chinese also belong to the SVO language family, and our approach achieves over $70\\%$ relative accuracy for these languages. On the other hand, Japanese belongs to the SOV (Subject, Object, Verb) language family and Arabic belongs to the VSO (Verb, Subject, Object) language family, and our approach achieves lower relative accuracy for these two languages."
+    ],
+    [
+      "There are a few weakly supervised cross-lingual RE approaches. BIBREF7 and BIBREF8 project annotated English RE data to Korean to create weakly labeled training data via aligned parallel corpora. BIBREF9 translates a target-language sentence into English, performs RE in English, and then projects the relation phrases back to the target-language sentence. BIBREF10 proposes an adversarial feature adaptation approach for cross-lingual relation classification, which uses a machine translation system to translate source-language sentences into target-language sentences. Unlike the existing approaches, our approach does not require aligned parallel corpora or machine translation systems. There are also several multilingual RE approaches, e.g., BIBREF34, BIBREF35, BIBREF36, where the focus is to improve monolingual RE by jointly modeling texts in multiple languages.",
+      "Many cross-lingual word embedding models have been developed recently BIBREF15, BIBREF16. An important application of cross-lingual word embeddings is to enable cross-lingual model transfer. In this paper, we apply the bilingual word embedding mapping technique in BIBREF17 to cross-lingual RE model transfer. Similar approaches have been applied to other NLP tasks such as dependency parsing BIBREF37, POS tagging BIBREF38 and named entity recognition BIBREF21, BIBREF39."
+    ],
+    [
+      "In this paper, we developed a simple yet effective neural cross-lingual RE model transfer approach, which has very low resource requirements (a small bilingual dictionary with 1K word pairs) and can be easily extended to a new language. Extensive experiments for 7 target languages across a variety of language families on both in-house and open datasets show that the proposed approach achieves very good performance (up to $79\\%$ of the accuracy of the supervised target-language RE model), which provides a strong baseline for building cross-lingual RE models with minimal resources."
+    ],
+    [
+      "We thank Mo Yu for sharing their ACE05 English data split and the anonymous reviewers for their valuable comments."
+    ]
+  ]
+}
+```
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diff --git a/qasper-0514/instruction.md b/qasper-0514/instruction.md
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--- /dev/null
+++ b/qasper-0514/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping
+
+Question: What datasets are used?
\ No newline at end of file
diff --git a/qasper-0515/instruction.md b/qasper-0515/instruction.md
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index 0000000000000000000000000000000000000000..ad40dbd0f310f92c444d35ff939f9290328cd3e1
--- /dev/null
+++ b/qasper-0515/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Visual Natural Language Query Auto-Completion for Estimating Instance Probabilities
+
+Question: How better does auto-completion perform when using both language and vision than only language?
\ No newline at end of file
diff --git a/qasper-0522/instruction.md b/qasper-0522/instruction.md
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index 0000000000000000000000000000000000000000..e286d52bbe624b8ea3b5fef501f79045d3c1b7cf
--- /dev/null
+++ b/qasper-0522/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Translating Navigation Instructions in Natural Language to a High-Level Plan for Behavioral Robot Navigation
+
+Question: What evaluation metrics are used?
\ No newline at end of file
diff --git a/qasper-0523/instruction.md b/qasper-0523/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..749e0e849ff9c2f09df6d35cc32b5b8c54fdd0b4
--- /dev/null
+++ b/qasper-0523/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Translating Navigation Instructions in Natural Language to a High-Level Plan for Behavioral Robot Navigation
+
+Question: Did the authors use a crowdsourcing platform?
\ No newline at end of file
diff --git a/qasper-0524/instruction.md b/qasper-0524/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..68eb11c4b7de4b69235ddc952f161ec879249825
--- /dev/null
+++ b/qasper-0524/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Translating Navigation Instructions in Natural Language to a High-Level Plan for Behavioral Robot Navigation
+
+Question: How were the navigation instructions collected?
\ No newline at end of file
diff --git a/qasper-0525/instruction.md b/qasper-0525/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..327d47d0977706a029740e61db340a058bcded13
--- /dev/null
+++ b/qasper-0525/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Translating Navigation Instructions in Natural Language to a High-Level Plan for Behavioral Robot Navigation
+
+Question: What language is the experiment done in?
\ No newline at end of file
diff --git a/qasper-0532/instruction.md b/qasper-0532/instruction.md
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+++ b/qasper-0532/instruction.md
@@ -0,0 +1,104 @@
+Name of Paper: Morphological Word Segmentation on Agglutinative Languages for Neural Machine Translation
+
+Question: Is the word segmentation method independently evaluated?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Approach",
+    "Approach ::: Morpheme Segmentation",
+    "Approach ::: Morpheme Segmentation ::: Stem with Combined Suffix",
+    "Approach ::: Morpheme Segmentation ::: Stem with Singular Suffix",
+    "Approach ::: Byte Pair Encoding (BPE)",
+    "Approach ::: Morphologically Motivated Segmentation",
+    "Approach ::: Morphologically Motivated Segmentation ::: BPE on Stem with Combined Suffix",
+    "Approach ::: Morphologically Motivated Segmentation ::: BPE on Stem with Singular Suffix",
+    "Experiments ::: Experimental Setup ::: Turkish-English Data :",
+    "Experiments ::: Experimental Setup ::: Uyghur-Chinese Data :",
+    "Experiments ::: Experimental Setup ::: Data Preprocessing :",
+    "Experiments ::: Experimental Setup ::: Number of Merge Operations :",
+    "Experiments ::: NMT Configuration",
+    "Results",
+    "Discussion",
+    "Related Work",
+    "Conclusion",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "Neural machine translation (NMT) has achieved impressive performance on machine translation task in recent years for many language pairs BIBREF0, BIBREF1, BIBREF2. However, in consideration of time cost and space capacity, the NMT model generally employs a limited-size vocabulary that only contains the top-N highest frequency words (commonly in the range of 30K to 80K) BIBREF3, which leads to the Out-of-Vocabulary (OOV) problem following with inaccurate and terrible translation results. Research indicated that sentences with too many unknown words tend to be translated much more poorly than sentences with mainly frequent words. For the low-resource and source-side morphologically-rich machine translation tasks, such as Turkish-English and Uyghur-Chinese, all the above issues are more serious due to the fact that the NMT model cannot effectively identify the complex morpheme structure or capture the linguistic and semantic information with too many rare and unknown words in the training corpus.",
+      "Both the Turkish and Uyghur are agglutinative and highly-inflected languages in which the word is formed by suffixes attaching to a stem BIBREF4. The word consists of smaller morpheme units without any splitter between them and its structure can be denoted as \u201cstem + suffix1 + suffix2 + ... + suffixN\u201d. A stem is attached in the rear by zero to many suffixes that have many inflected and morphological variants depending on case, number, gender, and so on. The complex morpheme structure and relatively free constituent order can produce very large vocabulary because of the derivational morphology, so when translating from the agglutinative languages, many words are unseen at training time. Moreover, due to the semantic context, the same word generally has different segmentation forms in the training corpus.",
+      "For the purpose of incorporating morphology knowledge of agglutinative languages into word segmentation for NMT, we propose a morphological word segmentation method on the source-side of Turkish-English and Uyghur-Chinese machine translation tasks, which segments the complex words into simple and effective morpheme units while reducing the vocabulary size for model training. In this paper, we investigate and compare the following segmentation strategies:",
+      "Stem with combined suffix",
+      "Stem with singular suffix",
+      "Byte Pair Encoding (BPE)",
+      "BPE on stem with combined suffix",
+      "BPE on stem with singular suffix",
+      "The latter two segmentation strategies are our newly proposed methods. Experimental results show that our morphologically motivated word segmentation method can achieve significant improvement of up to 1.2 and 2.5 BLEU points on Turkish-English and Uyghur-Chinese machine translation tasks over the strong baseline of pure BPE method respectively, indicating that it can provide better translation performance for the NMT model."
+    ],
+    [
+      "We will elaborate two popular word segmentation methods and our newly proposed segmentation strategies in this section. The two popular segmentation methods are morpheme segmentation BIBREF4 and Byte Pair Encoding (BPE) BIBREF5. After word segmentation, we additionally add an specific symbol behind each separated subword unit, which aims to assist the NMT model to identify the morpheme boundaries and capture the semantic information effectively. The sentence examples with different segmentation strategies for Turkish-English machine translation task are shown in Table 1."
+    ],
+    [
+      "The words of Turkish and Uyghur are formed by a stem followed with unlimited number of suffixes. Both of the stem and suffix are called morphemes, and they are the smallest functional unit in agglutinative languages. Study indicated that modeling language based on the morpheme units can provide better performance BIBREF6. Morpheme segmentation can segment the complex word into morpheme units of stem and suffix. This representation maintains a full description of the morphological properties of subwords while minimizing the data sparseness caused by inflection and allomorphy phenomenon in highly-inflected languages."
+    ],
+    [
+      "In this segmentation strategy, each word is segmented into a stem unit and a combined suffix unit. We add \u201c##\u201d behind the stem unit and add \u201c$$\u201d behind the combined suffix unit. We denote this method as SCS. The segmented word can be denoted as two parts of \u201cstem##\u201d and \u201csuffix1suffix2...suffixN$$\u201d. If the original word has no suffix unit, the word is treated as its stem unit. All the following segmentation strategies will follow this rule."
+    ],
+    [
+      "In this segmentation strategy, each word is segmented into a stem unit and a sequence of suffix units. We add \u201c##\u201d behind the stem unit and add \u201c$$\u201d behind each singular suffix unit. We denote this method as SSS. The segmented word can be denoted as a sequence of \u201cstem##\u201d, \u201csuffix1$$\u201d, \u201csuffix2$$\u201d until \u201csuffixN$$\u201d."
+    ],
+    [
+      "BPE BIBREF7 is originally a data compression technique and it is adapted by BIBREF5 for word segmentation and vocabulary reduction by encoding the rare and unknown words as a sequence of subword units, in which the most frequent character sequences are merged iteratively. Frequent character n-grams are eventually merged into a single symbol. This is based on the intuition that various word classes are translatable via smaller units than words. This method making the NMT model capable of open-vocabulary translation, which can generalize to translate and produce new words on the basis of these subword units. The BPE algorithm can be run on the dictionary extracted from a training text, with each word being weighted by its frequency. In this segmentation strategy, we add \u201c@@\u201d behind each no-final subword unit of the segmented word."
+    ],
+    [
+      "The problem with morpheme segmentation is that the vocabulary of stem units is still very large, which leads to many rare and unknown words at the training time. The problem with BPE is that it do not consider the morpheme boundaries inside words, which might cause a loss of morphological properties and semantic information. Hence, on the analyses of the above popular word segmentation methods, we propose the morphologically motivated segmentation strategy that combines the morpheme segmentation and BPE for further improving the translation performance of NMT.",
+      "Compared with the sentence of word surface forms, the corresponding sentence of stem units only contains the structure information without considering morphological information, which can make better generalization over inflectional variants of the same word and reduce data sparseness BIBREF8. Therefore, we learn a BPE model on the stem units in the training corpus rather than the words, and then apply it on the stem unit of each word after morpheme segmentation."
+    ],
+    [
+      "In this segmentation strategy, firstly we segment each word into a stem unit and a combined suffix unit as SCS. Secondly, we apply BPE on the stem unit. Thirdly, we add \u201c$$\u201d behind the combined suffix unit. If the stem unit is not segmented, we add \u201c##\u201d behind itself. Otherwise, we add \u201c@@\u201d behind each no-final subword of the segmented stem unit. We denote this method as BPE-SCS."
+    ],
+    [
+      "In this segmentation strategy, firstly we segment each word into a stem unit and a sequence of suffix units as SSS. Secondly, we apply BPE on the stem unit. Thirdly, we add \u201c$$\u201d behind each singular suffix unit. If the stem unit is not segmented, we add \u201c##\u201d behind itself. Otherwise, we add \u201c@@\u201d behind each no-final subword of the segmented stem unit. We denote this method as BPE-SSS."
+    ],
+    [
+      "Following BIBREF9, we use the WIT corpus BIBREF10 and SETimes corpus BIBREF11 for model training, and use the newsdev2016 from Workshop on Machine Translation in 2016 (WMT2016) for validation. The test data are newstest2016 and newstest2017."
+    ],
+    [
+      "We use the news data from China Workshop on Machine Translation in 2017 (CWMT2017) for model training, validation and test."
+    ],
+    [
+      "We utilize the Zemberek with a morphological disambiguation tool to segment the Turkish words into morpheme units, and utilize the morphology analysis tool BIBREF12 to segment the Uyghur words into morpheme units. We employ the python toolkits of jieba for Chinese word segmentation. We apply BPE on the target-side words and we set the number of merge operations to 35K for Chinese and 30K for English and we set the maximum sentence length to 150 tokens. The training corpus statistics of Turkish-English and Uyghur-Chinese machine translation tasks are shown in Table 2 and Table 3 respectively."
+    ],
+    [
+      "We set the number of merge operations on the stem units in the consideration of keeping the vocabulary size of BPE, BPE-SCS and BPE-SSS segmentation strategies on the same scale. We will elaborate the number settings for our proposed word segmentation strategies in this section.",
+      "In the Turkish-English machine translation task, for the pure BPE strategy, we set the number of merge operations on the words to 35K, set the number of merge operations on the stem units for BPE-SCS strategy to 15K, and set the number of merge operations on the stem units for BPE-SSS strategy to 25K. In the Uyghur-Chinese machine translation task, for the pure BPE strategy, we set the number of merge operations on the words to 38K, set the number of merge operations on the stem units for BPE-SCS strategy to 10K, and set the number of merge operations on the stem units for BPE-SSS strategy to 35K. The detailed training corpus statistics with different segmentation strategies of Turkish and Uyghur are shown in Table 4 and Table 5 respectively.",
+      "According to Table 4 and Table 5, we can find that both the Turkish and Uyghur have a very large vocabulary even in the low-resource training corpus. So we propose the morphological word segmentation strategies of BPE-SCS and BPE-SSS that additionally applying BPE on the stem units after morpheme segmentation, which not only consider the morphological properties but also eliminate the rare and unknown words."
+    ],
+    [
+      "We employ the Transformer model BIBREF13 with self-attention mechanism architecture implemented in Sockeye toolkit BIBREF14. Both the encoder and decoder have 6 layers. We set the number of hidden units to 512, the number of heads for self-attention to 8, the source and target word embedding size to 512, and the number of hidden units in feed-forward layers to 2048. We train the NMT model by using the Adam optimizer BIBREF15 with a batch size of 128 sentences, and we shuffle all the training data at each epoch. The label smoothing is set to 0.1. We report the result of averaging the parameters of the 4 best checkpoints on the validation perplexity. Decoding is performed by beam search with beam size of 5. To effectively evaluate the machine translation quality, we report case-sensitive BLEU score with standard tokenization and character n-gram ChrF3 score ."
+    ],
+    [
+      "In this paper, we investigate and compare morpheme segmentation, BPE and our proposed morphological segmentation strategies on the low resource and morphologically-rich agglutinative languages. Experimental results of Turkish-English and Uyghur-Chinese machine translation tasks are shown in Table 6 and Table 7 respectively."
+    ],
+    [
+      "According to Table 6 and Table 7, we can find that both the BPE-SCS and BPE-SSS strategies outperform morpheme segmentation and the strong baseline of pure BPE method. Especially, the BPE-SSS strategy is better and it achieves significant improvement of up to 1.2 BLEU points on Turkish-English machine translation task and 2.5 BLEU points on Uyghur-Chinese machine translation task. Furthermore, we also find that the translation performance of our proposed segmentation strategy on Turkish-English machine translation task is not obvious than Uyghur-Chinese machine translation task, the probable reasons are: the training corpus of Turkish-English consists of talk and news data while most of the talk data are short informal sentences compared with the news data, which cannot provide more language information for the NMT model. Moreover, the test corpus consists of news data, so due to the data domain is different, the improvement of machine translation quality is limited.",
+      "In addition, we estimate how the number of merge operations on the stem units for BPE-SSS strategy effects the machine translation quality. Experimental results are shown in Table 8 and Table 9. We find that the number of 25K for Turkish, 30K and 35K for Uyghur maximizes the translation performance. The probable reason is that these numbers of merge operations are able to generate a more appropriate vocabulary that containing effective morpheme units and moderate subword units, which makes better generalization over the morphologically-rich words."
+    ],
+    [
+      "The NMT system is typically trained with a limited vocabulary, which creates bottleneck on translation accuracy and generalization capability. Many word segmentation methods have been proposed to cope with the above problems, which consider the morphological properties of different languages.",
+      "Bradbury and Socher BIBREF16 employed the modified Morfessor to provide morphology knowledge into word segmentation, but they neglected the morphological varieties between subword units, which might result in ambiguous translation results. Sanchez-Cartagena and Toral BIBREF17 proposed a rule-based morphological word segmentation for Finnish, which applies BPE on all the morpheme units uniformly without distinguishing their inner morphological roles. Huck BIBREF18 explored target-side segmentation method for German, which shows that the cascading of suffix splitting and compound splitting with BPE can achieve better translation results. Ataman et al. BIBREF19 presented a linguistically motivated vocabulary reduction approach for Turkish, which optimizes the segmentation complexity with constraint on the vocabulary based on a category-based hidden markov model (HMM). Our work is closely related to their idea while ours are more simple and realizable. Tawfik et al. BIBREF20 confirmed that there is some advantage from using a high accuracy dialectal segmenter jointly with a language independent word segmentation method like BPE. The main difference is that their approach needs sufficient monolingual data additionally to train a segmentation model while ours do not need any external resources, which is very convenient for word segmentation on the low-resource and morphologically-rich agglutinative languages."
+    ],
+    [
+      "In this paper, we investigate morphological segmentation strategies on the low-resource and morphologically-rich languages of Turkish and Uyghur. Experimental results show that our proposed morphologically motivated word segmentation method is better suitable for NMT. And the BPE-SSS strategy achieves the best machine translation performance, as it can better preserve the syntactic and semantic information of the words with complex morphology as well as reduce the vocabulary size for model training. Moreover, we also estimate how the number of merge operations on the stem units for BPE-SSS strategy effects the translation quality, and we find that an appropriate vocabulary size is more useful for the NMT model.",
+      "In future work, we are planning to incorporate more linguistic and morphology knowledge into the training process of NMT to enhance its capacity of capturing syntactic structure and semantic information on the low-resource and morphologically-rich languages."
+    ],
+    [
+      "This work is supported by the National Natural Science Foundation of China, the Open Project of Key Laboratory of Xinjiang Uygur Autonomous Region, the Youth Innovation Promotion Association of the Chinese Academy of Sciences, and the High-level Talents Introduction Project of Xinjiang Uyghur Autonomous Region."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: Acquisition of Inflectional Morphology in Artificial Neural Networks With Prior Knowledge
+
+Question: What are the tree target languages studied in the paper?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Task",
+    "Task ::: Formal definition.",
+    "Model ::: Pointer\u2013Generator Network",
+    "Model ::: Pointer\u2013Generator Network ::: Encoders.",
+    "Model ::: Pointer\u2013Generator Network ::: Attention.",
+    "Model ::: Pointer\u2013Generator Network ::: Decoder.",
+    "Model ::: Pretraining and Finetuning",
+    "Experimental Design ::: Target Languages",
+    "Experimental Design ::: Source Languages",
+    "Experimental Design ::: Hyperparameters and Data",
+    "Quantitative Results",
+    "Qualitative Results",
+    "Qualitative Results ::: Stem Errors",
+    "Qualitative Results ::: Affix Errors",
+    "Qualitative Results ::: Miscellaneous Errors",
+    "Qualitative Results ::: Error Analysis: English",
+    "Qualitative Results ::: Error Analysis: Spanish",
+    "Qualitative Results ::: Error Analysis: Zulu",
+    "Qualitative Results ::: Limitations",
+    "Related Work ::: Neural network models for inflection.",
+    "Related Work ::: Cross-lingual transfer in NLP.",
+    "Related Work ::: Acquisition of morphological inflection.",
+    "Conclusion and Future Work",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "A widely agreed-on fact in language acquisition research is that learning of a second language (L2) is influenced by a learner's native language (L1) BIBREF0, BIBREF1. A language's morphosyntax seems to be no exception to this rule BIBREF2, but the exact nature of this influence remains unknown. For instance, it is unclear whether it is constraints imposed by the phonological or by the morphosyntactic attributes of the L1 that are more important during the process of learning an L2's morphosyntax.",
+      "Within the area of natural language processing (NLP) research, experimenting on neural network models just as if they were human subjects has recently been gaining popularity BIBREF3, BIBREF4, BIBREF5. Often, so-called probing tasks are used, which require a specific subset of linguistic knowledge and can, thus, be leveraged for qualitative evaluation. The goal is to answer the question: What do neural networks learn that helps them to succeed in a given task?",
+      "Neural network models, and specifically sequence-to-sequence models, have pushed the state of the art for morphological inflection \u2013 the task of learning a mapping from lemmata to their inflected forms \u2013 in the last years BIBREF6. Thus, in this work, we experiment on such models, asking not what they learn, but, motivated by the respective research on human subjects, the related question of how what they learn depends on their prior knowledge. We manually investigate the errors made by artificial neural networks for morphological inflection in a target language after pretraining on different source languages. We aim at finding answers to two main questions: (i) Do errors systematically differ between source languages? (ii) Do these differences seem explainable, given the properties of the source and target languages? In other words, we are interested in exploring if and how L2 acquisition of morphological inflection depends on the L1, i.e., the \"native language\", in neural network models.",
+      "To this goal, we select a diverse set of eight source languages from different language families \u2013 Basque, French, German, Hungarian, Italian, Navajo, Turkish, and Quechua \u2013 and three target languages \u2013 English, Spanish and Zulu. We pretrain a neural sequence-to-sequence architecture on each of the source languages and then fine-tune the resulting models on small datasets in each of the target languages. Analyzing the errors made by the systems, we find that (i) source and target language being closely related simplifies the successful learning of inflection in the target language, (ii) the task is harder to learn in a prefixing language if the source language is suffixing \u2013 as well as the other way around, and (iii) a source language which exhibits an agglutinative morphology simplifies learning of a second language's inflectional morphology."
+    ],
+    [
+      "Many of the world's languages exhibit rich inflectional morphology: the surface form of an individual lexical entry changes in order to express properties such as person, grammatical gender, or case. The citation form of a lexical entry is referred to as the lemma. The set of all possible surface forms or inflections of a lemma is called its paradigm. Each inflection within a paradigm can be associated with a tag, i.e., 3rdSgPres is the morphological tag associated with the inflection dances of the English lemma dance. We display the paradigms of dance and eat in Table TABREF1.",
+      "The presence of rich inflectional morphology is problematic for NLP systems as it increases word form sparsity. For instance, while English verbs can have up to 5 inflected forms, Archi verbs have thousands BIBREF7, even by a conservative count. Thus, an important task in the area of morphology is morphological inflection BIBREF8, BIBREF9, which consists of mapping a lemma to an indicated inflected form. An (irregular) English example would be",
+      "with PAST being the target tag, denoting the past tense form. Additionally, a rich inflectional morphology is also challenging for L2 language learners, since both rules and their exceptions need to be memorized.",
+      "In NLP, morphological inflection has recently frequently been cast as a sequence-to-sequence problem, where the sequence of target (sub-)tags together with the sequence of input characters constitute the input sequence, and the characters of the inflected word form the output. Neural models define the state of the art for the task and obtain high accuracy if an abundance of training data is available. Here, we focus on learning of inflection from limited data if information about another language's morphology is already known. We, thus, loosely simulate an L2 learning setting."
+    ],
+    [
+      "Let ${\\cal M}$ be the paradigm slots which are being expressed in a language, and $w$ a lemma in that language. We then define the paradigm $\\pi $ of $w$ as:",
+      "$f_k[w]$ denotes an inflected form corresponding to tag $t_{k}$, and $w$ and $f_k[w]$ are strings consisting of letters from an alphabet $\\Sigma $.",
+      "The task of morphological inflection consists of predicting a missing form $f_i[w]$ from a paradigm, given the lemma $w$ together with the tag $t_i$."
+    ],
+    [
+      "The models we experiment with are based on a pointer\u2013generator network architecture BIBREF10, BIBREF11, i.e., a recurrent neural network (RNN)-based sequence-to-sequence network with attention and a copy mechanism. A standard sequence-to-sequence model BIBREF12 has been shown to perform well for morphological inflection BIBREF13 and has, thus, been subject to cognitively motivated experiments BIBREF14 before. Here, however, we choose the pointer\u2013generator variant of sharma-katrapati-sharma:2018:K18-30, since it performs better in low-resource settings, which we will assume for our target languages. We will explain the model shortly in the following and refer the reader to the original paper for more details."
+    ],
+    [
+      "Our architecture employs two separate encoders, which are both bi-directional long short-term memory (LSTM) networks BIBREF15: The first processes the morphological tags which describe the desired target form one by one. The second encodes the sequence of characters of the input word."
+    ],
+    [
+      "Two separate attention mechanisms are used: one per encoder LSTM. Taking all respective encoder hidden states as well as the current decoder hidden state as input, each of them outputs a so-called context vector, which is a weighted sum of all encoder hidden states. The concatenation of the two individual context vectors results in the final context vector $c_t$, which is the input to the decoder at time step $t$."
+    ],
+    [
+      "Our decoder consists of a uni-directional LSTM. Unlike a standard sequence-to-sequence model, a pointer\u2013generator network is not limited to generating characters from the vocabulary to produce the output. Instead, the model gives certain probability to copying elements from the input over to the output. The probability of a character $y_t$ at time step $t$ is computed as a sum of the probability of $y_t$ given by the decoder and the probability of copying $y_t$, weighted by the probabilities of generating and copying:",
+      "$p_{\\textrm {dec}}(y_t)$ is calculated as an LSTM update and a projection of the decoder state to the vocabulary, followed by a softmax function. $p_{\\textrm {copy}}(y_t)$ corresponds to the attention weights for each input character. The model computes the probability $\\alpha $ with which it generates a new output character as",
+      "for context vector $c_t$, decoder state $s_t$, embedding of the last output $y_{t-1}$, weights $w_c$, $w_s$, $w_y$, and bias vector $b$. It has been shown empirically that the copy mechanism of the pointer\u2013generator network architecture is beneficial for morphological generation in the low-resource setting BIBREF16."
+    ],
+    [
+      "Pretraining and successive fine-tuning of neural network models is a common approach for handling of low-resource settings in NLP. The idea is that certain properties of language can be learned either from raw text, related tasks, or related languages. Technically, pretraining consists of estimating some or all model parameters on examples which do not necessarily belong to the final target task. Fine-tuning refers to continuing training of such a model on a target task, whose data is often limited. While the sizes of the pretrained model parameters usually remain the same between the two phases, the learning rate or other details of the training regime, e.g., dropout, might differ. Pretraining can be seen as finding a suitable initialization of model parameters, before training on limited amounts of task- or language-specific examples.",
+      "In the context of morphological generation, pretraining in combination with fine-tuning has been used by kann-schutze-2018-neural, which proposes to pretrain a model on general inflection data and fine-tune on examples from a specific paradigm whose remaining forms should be automatically generated. Famous examples for pretraining in the wider area of NLP include BERT BIBREF17 or GPT-2 BIBREF18: there, general properties of language are learned using large unlabeled corpora.",
+      "Here, we are interested in pretraining as a simulation of familiarity with a native language. By investigating a fine-tuned model we ask the question: How does extensive knowledge of one language influence the acquisition of another?"
+    ],
+    [
+      "We choose three target languages.",
+      "English (ENG) is a morphologically impoverished language, as far as inflectional morphology is concerned. Its verbal paradigm only consists of up to 5 different forms and its nominal paradigm of only up to 2. However, it is one of the most frequently spoken and taught languages in the world, making its acquisition a crucial research topic.",
+      "Spanish (SPA), in contrast, is morphologically rich, and disposes of much larger verbal paradigms than English. Like English, it is a suffixing language, and it additionally makes use of internal stem changes (e.g., o $\\rightarrow $ ue).",
+      "Since English and Spanish are both Indo-European languages, and, thus, relatively similar, we further add a third, unrelated target language. We choose Zulu (ZUL), a Bantoid language. In contrast to the first two, it is strongly prefixing."
+    ],
+    [
+      "For pretraining, we choose languages with different degrees of relatedness and varying morphological similarity to English, Spanish, and Zulu. We limit our experiments to languages which are written in Latin script.",
+      "As an estimate for morphological similarity we look at the features from the Morphology category mentioned in The World Atlas of Language Structures (WALS). An overview of the available features as well as the respective values for our set of languages is shown in Table TABREF13.",
+      "We decide on Basque (EUS), French (FRA), German (DEU), Hungarian (HUN), Italian (ITA), Navajo (NAV), Turkish (TUR), and Quechua (QVH) as source languages.",
+      "Basque is a language isolate. Its inflectional morphology makes similarly frequent use of prefixes and suffixes, with suffixes mostly being attached to nouns, while prefixes and suffixes can both be employed for verbal inflection.",
+      "French and Italian are Romance languages, and thus belong to the same family as the target language Spanish. Both are suffixing and fusional languages.",
+      "German, like English, belongs to the Germanic language family. It is a fusional, predominantly suffixing language and, similarly to Spanish, makes use of stem changes.",
+      "Hungarian, a Finno-Ugric language, and Turkish, a Turkic language, both exhibit an agglutinative morphology, and are predominantly suffixing. They further have vowel harmony systems.",
+      "Navajo is an Athabaskan language and the only source language which is strongly prefixing. It further exhibits consonant harmony among its sibilants BIBREF19, BIBREF20.",
+      "Finally, Quechua, a Quechuan language spoken in South America, is again predominantly suffixing and unrelated to all of our target languages."
+    ],
+    [
+      "We mostly use the default hyperparameters by sharma-katrapati-sharma:2018:K18-30. In particular, all RNNs have one hidden layer of size 100, and all input and output embeddings are 300-dimensional.",
+      "For optimization, we use ADAM BIBREF21. Pretraining on the source language is done for exactly 50 epochs. To obtain our final models, we then fine-tune different copies of each pretrained model for 300 additional epochs for each target language. We employ dropout BIBREF22 with a coefficient of 0.3 for pretraining and, since that dataset is smaller, with a coefficient of 0.5 for fine-tuning.",
+      "We make use of the datasets from the CoNLL\u2013SIGMORPHON 2018 shared task BIBREF9. The organizers provided a low, medium, and high setting for each language, with 100, 1000, and 10000 examples, respectively. For all L1 languages, we train our models on the high-resource datasets with 10000 examples. For fine-tuning, we use the low-resource datasets."
+    ],
+    [
+      "In Table TABREF18, we show the final test accuracy for all models and languages. Pretraining on EUS and NAV results in the weakest target language inflection models for ENG, which might be explained by those two languages being unrelated to ENG and making at least partial use of prefixing, while ENG is a suffixing language (cf. Table TABREF13). In contrast, HUN and ITA yield the best final models for ENG. This is surprising, since DEU is the language in our experiments which is closest related to ENG.",
+      "For SPA, again HUN performs best, followed closely by ITA. While the good performance of HUN as a source language is still unexpected, ITA is closely related to SPA, which could explain the high accuracy of the final model. As for ENG, pretraining on EUS and NAV yields the worst final models \u2013 importantly, accuracy is over $15\\%$ lower than for QVH, which is also an unrelated language. This again suggests that the prefixing morphology of EUS and NAV might play a role.",
+      "Lastly, for ZUL, all models perform rather poorly, with a minimum accuracy of 10.7 and 10.8 for the source languages QVH and EUS, respectively, and a maximum accuracy of 24.9 for a model pretrained on Turkish. The latter result hints at the fact that a regular and agglutinative morphology might be beneficial in a source language \u2013 something which could also account for the performance of models pretrained on HUN."
+    ],
+    [
+      "For our qualitative analysis, we make use of the validation set. Therefore, we show validation set accuracies in Table TABREF19 for comparison. As we can see, the results are similar to the test set results for all language combinations. We manually annotate the outputs for the first 75 development examples for each source\u2013target language combination. All found errors are categorized as belonging to one of the following categories."
+    ],
+    [
+      "SUB(X): This error consists of a wrong substitution of one character with another. SUB(V) and SUB(C) denote this happening with a vowel or a consonant, respectively. Letters that differ from each other by an accent count as different vowels.",
+      "Example: decultared instead of decultured",
+      "DEL(X): This happens when the system ommits a letter from the output. DEL(V) and DEL(C) refer to a missing vowel or consonant, respectively.",
+      "Example: firte instead of firtle",
+      "NO_CHG(X): This error occurs when inflecting the lemma to the gold form requires a change of either a vowel (NO_CHG(V)) or a consonant (NO_CHG(C)), but this is missing in the predicted form.",
+      "Example: verto instead of vierto",
+      "MULT: This describes cases where two or more errors occur in the stem. Errors concerning the affix are counted for separately.",
+      "Example: aconcoonaste instead of acondicionaste",
+      "ADD(X): This error occurs when a letter is mistakenly added to the inflected form. ADD(V) refers to an unnecessary vowel, ADD(C) refers to an unnecessary consonant.",
+      "Example: compillan instead of compilan",
+      "CHG2E(X): This error occurs when inflecting the lemma to the gold form requires a change of either a vowel (CHG2E(V)) or a consonant (CHG2E(C)), and this is done, but the resulting vowel or consonant is incorrect.",
+      "Example: propace instead of propague"
+    ],
+    [
+      "AFF: This error refers to a wrong affix. This can be either a prefix or a suffix, depending on the correct target form.",
+      "Example: ezoJulayi instead of esikaJulayi",
+      "CUT: This consists of cutting too much of the lemma's prefix or suffix before attaching the inflected form's prefix or suffix, respectively.",
+      "Example: irradiseis instead of irradiaseis"
+    ],
+    [
+      "REFL: This happens when a reflective pronoun is missing in the generated form.",
+      "Example: doli\u00e9ramos instead of nos doli\u00e9ramos",
+      "REFL_LOC: This error occurs if the reflective pronouns appears at an unexpected position within the generated form.",
+      "Example: taparsebais instead of os tapabais",
+      "OVERREG: Overregularization errors occur when the model predicts a form which would be correct if the lemma's inflections were regular but they are not.",
+      "Example: underteach instead of undertaught"
+    ],
+    [
+      "Table TABREF35 displays the errors found in the 75 first ENG development examples, for each source language. From Table TABREF19, we know that HUN $>$ ITA $>$ TUR $>$ DEU $>$ FRA $>$ QVH $>$ NAV $>$ EUS, and we get a similar picture when analyzing the first examples. Thus, especially keeping HUN and TUR in mind, we cautiously propose a first conclusion: familiarity with languages which exhibit an agglutinative morphology simplifies learning of a new language's morphology.",
+      "Looking at the types of errors, we find that EUS and NAV make the most stem errors. For QVH we find less, but still over 10 more than for the remaining languages. This makes it seem that models pretrained on prefixing or partly prefixing languages indeed have a harder time to learn ENG inflectional morphology, and, in particular, to copy the stem correctly. Thus, our second hypotheses is that familiarity with a prefixing language might lead to suspicion of needed changes to the part of the stem which should remain unaltered in a suffixing language. DEL(X) and ADD(X) errors are particularly frequent for EUS and NAV, which further suggests this conclusion.",
+      "Next, the relatively large amount of stem errors for QVH leads to our second hypothesis: language relatedness does play a role when trying to produce a correct stem of an inflected form. This is also implied by the number of MULT errors for EUS, NAV and QVH, as compared to the other languages.",
+      "Considering errors related to the affixes which have to be generated, we find that DEU, HUN and ITA make the fewest. This further suggests the conclusion that, especially since DEU is the language which is closest related to ENG, language relatedness plays a role for producing suffixes of inflected forms as well.",
+      "Our last observation is that many errors are not found at all in our data sample, e.g., CHG2E(X) or NO_CHG(C). This can be explained by ENG having a relatively poor inflectional morphology, which does not leave much room for mistakes."
+    ],
+    [
+      "The errors committed for SPA are shown in Table TABREF37, again listed by source language. Together with Table TABREF19 it gets clear that SPA inflectional morphology is more complex than that of ENG: systems for all source languages perform worse.",
+      "Similarly to ENG, however, we find that most stem errors happen for the source languages EUS and NAV, which is further evidence for our previous hypothesis that familiarity with prefixing languages impedes acquisition of a suffixing one. Especially MULT errors are much more frequent for EUS and NAV than for all other languages. ADD(X) happens a lot for EUS, while ADD(C) is also frequent for NAV. Models pretrained on either language have difficulties with vowel changes, which reflects in NO_CHG(V). Thus, we conclude that this phenomenon is generally hard to learn.",
+      "Analyzing next the errors concerning affixes, we find that models pretrained on HUN, ITA, DEU, and FRA (in that order) commit the fewest errors. This supports two of our previous hypotheses: First, given that ITA and FRA are both from the same language family as SPA, relatedness seems to be benficial for learning of the second language. Second, the system pretrained on HUN performing well suggests again that a source language with an agglutinative, as opposed to a fusional, morphology seems to be beneficial as well."
+    ],
+    [
+      "In Table TABREF39, the errors for Zulu are shown, and Table TABREF19 reveals the relative performance for different source languages: TUR $>$ HUN $>$ DEU $>$ ITA $>$ FRA $>$ NAV $>$ EUS $>$ QVH. Again, TUR and HUN obtain high accuracy, which is an additional indicator for our hypothesis that a source language with an agglutinative morphology facilitates learning of inflection in another language.",
+      "Besides that, results differ from those for ENG and SPA. First of all, more mistakes are made for all source languages. However, there are also several finer differences. For ZUL, the model pretrained on QVH makes the most stem errors, in particular 4 more than the EUS model, which comes second. Given that ZUL is a prefixing language and QVH is suffixing, this relative order seems important. QVH also committs the highest number of MULT errors.",
+      "The next big difference between the results for ZUL and those for ENG and SPA is that DEL(X) and ADD(X) errors, which previously have mostly been found for the prefixing or partially prefixing languages EUS and NAV, are now most present in the outputs of suffixing languages. Namely, DEL(C) occurs most for FRA and ITA, DEL(V) for FRA and QVH, and ADD(C) and ADD(V) for HUN. While some deletion and insertion errors are subsumed in MULT, this does not fully explain this difference. For instance, QVH has both the second most DEL(V) and the most MULT errors.",
+      "The overall number of errors related to the affix seems comparable between models with different source languages. This weakly supports the hypothesis that relatedness reduces affix-related errors, since none of the pretraining languages in our experiments is particularly close to ZUL. However, we do find more CUT errors for HUN and TUR: again, these are suffixing, while CUT for the target language SPA mostly happened for the prefixing languages EUS and NAV."
+    ],
+    [
+      "A limitation of our work is that we only include languages that are written in Latin script. An interesting question for future work might, thus, regard the effect of disjoint L1 and L2 alphabets.",
+      "Furthermore, none of the languages included in our study exhibits a templatic morphology. We make this choice because data for templatic languages is currently mostly available in non-Latin alphabets. Future work could investigate languages with templatic morphology as source or target languages, if needed by mapping the language's alphabet to Latin characters.",
+      "Finally, while we intend to choose a diverse set of languages for this study, our overall number of languages is still rather small. This affects the generalizability of the results, and future work might want to look at larger samples of languages."
+    ],
+    [
+      "Most research on inflectional morphology in NLP within the last years has been related to the SIGMORPHON and CoNLL\u2013SIGMORPHON shared tasks on morphological inflection, which have been organized yearly since 2016 BIBREF6. Traditionally being focused on individual languages, the 2019 edition BIBREF23 contained a task which asked for transfer learning from a high-resource to a low-resource language. However, source\u2013target pairs were predefined, and the question of how the source language influences learning besides the final accuracy score was not considered. Similarly to us, kyle performed a manual error analysis of morphological inflection systems for multiple languages. However, they did not investigate transfer learning, but focused on monolingual models.",
+      "Outside the scope of the shared tasks, kann-etal-2017-one investigated cross-lingual transfer for morphological inflection, but was limited to a quantitative analysis. Furthermore, that work experimented with a standard sequence-to-sequence model BIBREF12 in a multi-task training fashion BIBREF24, while we pretrain and fine-tune pointer\u2013generator networks. jin-kann-2017-exploring also investigated cross-lingual transfer in neural sequence-to-sequence models for morphological inflection. However, their experimental setup mimicked kann-etal-2017-one, and the main research questions were different: While jin-kann-2017-exploring asked how cross-lingual knowledge transfer works during multi-task training of neural sequence-to-sequence models on two languages, we investigate if neural inflection models demonstrate interesting differences in production errors depending on the pretraining language. Besides that, we differ in the artificial neural network architecture and language pairs we investigate."
+    ],
+    [
+      "Cross-lingual transfer learning has been used for a large variety NLP of tasks, e.g., automatic speech recognition BIBREF25, entity recognition BIBREF26, language modeling BIBREF27, or parsing BIBREF28, BIBREF29, BIBREF30. Machine translation has been no exception BIBREF31, BIBREF32, BIBREF33. Recent research asked how to automatically select a suitable source language for a given target language BIBREF34. This is similar to our work in that our findings could potentially be leveraged to find good source languages."
+    ],
+    [
+      "Finally, a lot of research has focused on human L1 and L2 acquisition of inflectional morphology BIBREF35, BIBREF36, BIBREF37, BIBREF38, BIBREF39, BIBREF40.",
+      "To name some specific examples, marques2011study investigated the effect of a stay abroad on Spanish L2 acquisition, including learning of its verbal morphology in English speakers. jia2003acquisition studied how Mandarin Chinese-speaking children learned the English plural morpheme. nicoladis2012young studied the English past tense acquisition in Chinese\u2013English and French\u2013English bilingual children. They found that, while both groups showed similar production accuracy, they differed slightly in the type of errors they made. Also considering the effect of the native language explicitly, yang2004impact investigated the acquisition of the tense-aspect system in an L2 for speakers of a native language which does not mark tense explicitly.",
+      "Finally, our work has been weakly motivated by bliss2006l2. There, the author asked a question for human subjects which is similar to the one we ask for neural models: How does the native language influence L2 acquisition of inflectional morphology?"
+    ],
+    [
+      "Motivated by the fact that, in humans, learning of a second language is influenced by a learner's native language, we investigated a similar question in artificial neural network models for morphological inflection: How does pretraining on different languages influence a model's learning of inflection in a target language?",
+      "We performed experiments on eight different source languages and three different target languages. An extensive error analysis of all final models showed that (i) for closely related source and target languages, acquisition of target language inflection gets easier; (ii) knowledge of a prefixing language makes learning of inflection in a suffixing language more challenging, as well as the other way around; and (iii) languages which exhibit an agglutinative morphology facilitate learning of inflection in a second language.",
+      "Future work might leverage those findings to improve neural network models for morphological inflection in low-resource languages, by choosing suitable source languages for pretraining.",
+      "Another interesting next step would be to investigate how the errors made by our models compare to those by human L2 learners with different native languages. If the exhibited patterns resemble each other, computational models could be used to predict errors a person will make, which, in turn, could be leveraged for further research or the development of educational material."
+    ],
+    [
+      "I would like to thank Samuel R. Bowman and Kyle Gorman for helpful discussions and suggestions. This work has benefited from the support of Samsung Research under the project Improving Deep Learning using Latent Structure and from the donation of a Titan V GPU by NVIDIA Corporation."
+    ]
+  ]
+}
+```
\ No newline at end of file
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new file mode 100644
index 0000000000000000000000000000000000000000..c9584262b9237089ecce8085eae00324bb2a0d5b
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+Name of Paper: How Does Language Influence Documentation Workflow? Unsupervised Word Discovery Using Translations in Multiple Languages
+
+Question: Is the model evaluated against any baseline?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Methodology ::: The Multilingual Mboshi Parallel Corpus:",
+    "Methodology ::: Bilingual Unsupervised Word Segmentation/Discovery Approach:",
+    "Methodology ::: Multilingual Leveraging:",
+    "Experiments",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "The Cambridge Handbook of Endangered Languages BIBREF3 estimates that at least half of the 7,000 languages currently spoken worldwide will no longer exist by the end of this century. For these endangered languages, data collection campaigns have to accommodate the challenge that many of them are from oral tradition, and producing transcriptions is costly. This transcription bottleneck problem can be handled by translating into a widely spoken language to ensure subsequent interpretability of the collected recordings, and such parallel corpora have been recently created by aligning the collected audio with translations in a well-resourced language BIBREF1, BIBREF2, BIBREF4. Moreover, some linguists suggested that more than one translation should be collected to capture deeper layers of meaning BIBREF5.",
+      "This work is a contribution to the Computational Language Documentation (CLD) research field, that aims to replace part of the manual steps performed by linguists during language documentation initiatives by automatic approaches. Here we investigate the unsupervised word discovery and segmentation task, using the bilingual-rooted approach from BIBREF6. There, words in the well-resourced language are aligned to unsegmented phonemes in the endangered language in order to identify group of phonemes, and to cluster them into word-like units. We experiment with the Mboshi-French parallel corpus, translating the French text into four other well-resourced languages in order to investigate language impact in this CLD approach. Our results hint that this language impact exists, and that models based on different languages will output different word-like units."
+    ],
+    [
+      "In this work we extend the bilingual Mboshi-French parallel corpus BIBREF2, fruit of the documentation process of Mboshi (Bantu C25), an endangered language spoken in Congo-Brazzaville. The corpus contains 5,130 utterances, for which it provides audio, transcriptions and translations in French. We translate the French into four other well-resourced languages through the use of the $DeepL$ translator. The languages added to the dataset are: English, German, Portuguese and Spanish. Table shows some statistics for the produced Multilingual Mboshi parallel corpus."
+    ],
+    [
+      "We use the bilingual neural-based Unsupervised Word Segmentation (UWS) approach from BIBREF6 to discover words in Mboshi. In this approach, Neural Machine Translation (NMT) models are trained between language pairs, using as source language the translation (word-level) and as target, the language to document (unsegmented phonemic sequence). Due to the attention mechanism present in these networks BIBREF7, posterior to training, it is possible to retrieve soft-alignment probability matrices between source and target sequences. These matrices give us sentence-level source-to-target alignment information, and by using it for clustering neighbor phonemes aligned to the same translation word, we are able to create segmentation in the target side. The product of this approach is a set of (discovered-units, translation words) pairs."
+    ],
+    [
+      "In this work we apply two simple methods for including multilingual information into the bilingual models from BIBREF6. The first one, Multilingual Voting, consists of merging the information learned by models trained with different language pairs by performing a voting over the final discovered boundaries. The voting is performed by applying an agreement threshold $T$ over the output boundaries. This threshold balances between accepting all boundaries from all the bilingual models (zero agreement) and accepting only input boundaries discovered by all these models (total agreement). The second method is ANE Selection. For every language pair and aligned sentence in the dataset, a soft-alignment probability matrix is generated. We use Average Normalized Entropy (ANE) BIBREF8 computed over these matrices for selecting the most confident one for segmenting each phoneme sequence. This exploits the idea that models trained on different language pairs will have language-related behavior, thus differing on the resulting alignment and segmentation over the same phoneme sequence."
+    ],
+    [
+      "The experiment settings from this paper and evaluation protocol for the Mboshi corpus (Boundary F-scores using the ZRC speech reference) are the same from BIBREF8. Table presents the results for bilingual UWS and multilingual leveraging. For the former, we reach our best result by using as aligned information the French, the original aligned language for this dataset. Languages closely related to French (Spanish and Portuguese) ranked better, while our worst result used German. English also performs notably well in our experiments. We believe this is due to the statistics features of the resulting text. We observe in Table that the English portion of the dataset contains the smallest vocabulary among all languages. Since we train our systems in very low-resource settings, vocabulary-related features can impact greatly the system's capacity to language-model, and consequently the final quality of the produced alignments. Even in high-resource settings, it was already attested that some languages are more difficult to model than others BIBREF9.",
+      "For the multilingual selection experiments, we experimented combining the languages from top to bottom as they appear Table (ranked by performance; e.g. 1-3 means the combination of FR(1), EN(2) and PT(3)). We observe that the performance improvement is smaller than the one observed in previous work BIBREF10, which we attribute to the fact that our dataset was artificially augmented. This could result in the available multilingual form of supervision not being as rich as in a manually generated dataset. Finally, the best boundary segmentation result is obtained by performing multilingual voting with all the languages and an agreement of 50%, which indicates that the information learned by different languages will provide additional complementary evidence.",
+      "Lastly, following the methodology from BIBREF8, we extract the most confident alignments (in terms of ANE) discovered by the bilingual models. Table presents the top 10 most confident (discovered type, translation) pairs. Looking at the pairs the bilingual models are most confident about, we observe there are some types discovered by all the bilingual models (e.g. Mboshi word itua, and the concatenation obo\u00e1+ng\u00e1). However, the models still differ for most of their alignments in the table. This hints that while a portion of the lexicon might be captured independently of the language used, other structures might be more dependent of the chosen language. On this note, BIBREF11 suggests the notion of word cannot always be meaningfully defined cross-linguistically."
+    ],
+    [
+      "In this work we train bilingual UWS models using the endangered language Mboshi as target and different well-resourced languages as aligned information. Results show that similar languages rank better in terms of segmentation performance, and that by combining the information learned by different models, segmentation is further improved. This might be due to the different language-dependent structures that are captured by using more than one language. Lastly, we extend the bilingual Mboshi-French parallel corpus, creating a multilingual corpus for the endangered language Mboshi that we make available to the community."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0546/instruction.md b/qasper-0546/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..ca97942fb99b9911a95f26c0c099b9dd0e63bec5
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+Name of Paper: Dense Information Flow for Neural Machine Translation
+
+Question: what are the baselines?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "DenseNMT",
+    "Dense encoder and decoder",
+    "Dense attention",
+    "Summary layers",
+    "Analysis of information flow",
+    "Datasets",
+    "Model and architect design",
+    "Training setting",
+    "Training curve",
+    "DenseNMT improves accuracy with similar architectures and model sizes",
+    "DenseNMT with smaller embedding size",
+    "DenseNMT compares with state-of-the-art results",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Neural machine translation (NMT) is a challenging task that attracts lots of attention in recent years. Starting from the encoder-decoder framework BIBREF0 , NMT starts to show promising results in many language pairs. The evolving structures of NMT models in recent years have made them achieve higher scores and become more favorable. The attention mechanism BIBREF1 added on top of encoder-decoder framework is shown to be very useful to automatically find alignment structure, and single-layer RNN-based structure has evolved into deeper models with more efficient transformation functions BIBREF2 , BIBREF3 , BIBREF4 .",
+      "One major challenge of NMT is that its models are hard to train in general due to the complexity of both the deep models and languages. From the optimization perspective, deeper models are hard to efficiently back-propagate the gradients, and this phenomenon as well as its solution is better explored in the computer vision society. Residual networks (ResNet) BIBREF5 achieve great performance in a wide range of tasks, including image classification and image segmentation. Residual connections allow features from previous layers to be accumulated to the next layer easily, and make the optimization of the model efficiently focus on refining upper layer features.",
+      "NMT is considered as a challenging problem due to its sequence-to-sequence generation framework, and the goal of comprehension and reorganizing from one language to the other. Apart from the encoder block that works as a feature generator, the decoder network combining with the attention mechanism bring new challenges to the optimization of the models. While nowadays best-performing NMT systems use residual connections, we question whether this is the most efficient way to propagate information through deep models. In this paper, inspired by the idea of using dense connections for training computer vision tasks BIBREF6 , we propose a densely connected NMT framework (DenseNMT) that efficiently propagates information from the encoder to the decoder through the attention component. Taking the CNN-based deep architecture as an example, we verify the efficiency of DenseNMT. Our contributions in this work include: (i) by comparing the loss curve, we show that DenseNMT allows the model to pass information more efficiently, and speeds up training; (ii) we show through ablation study that dense connections in all three blocks altogether help improve the performance, while not increasing the number of parameters; (iii) DenseNMT allows the models to achieve similar performance with much smaller embedding size; (iv) DenseNMT on IWSLT14 German-English and Turkish-English translation tasks achieves new benchmark BLEU scores, and the result on WMT14 English-German task is more competitive than the residual connections based baseline model."
+    ],
+    [
+      "In this section, we introduce our DenseNMT architecture. In general, compared with residual connected NMT models, DenseNMT allows each layer to provide its information to all subsequent layers directly. Figure FIGREF9 - FIGREF15 show the design of our model structure by parts.",
+      "We start with the formulation of a regular NMT model. Given a set of sentence pairs INLINEFORM0 , an NMT model learns parameter INLINEFORM1 by maximizing the log-likelihood function: DISPLAYFORM0 ",
+      "For every sentence pair INLINEFORM0 , INLINEFORM1 is calculated based on the decomposition: DISPLAYFORM0 ",
+      "where INLINEFORM0 is the length of sentence INLINEFORM1 . Typically, NMT models use the encoder-attention-decoder framework BIBREF1 , and potentially use multi-layer structure for both encoder and decoder. Given a source sentence INLINEFORM2 with length INLINEFORM3 , the encoder calculates hidden representations by layer. We denote the representation in the INLINEFORM4 -th layer as INLINEFORM5 , with dimension INLINEFORM6 , where INLINEFORM7 is the dimension of features in layer INLINEFORM8 . The hidden representation at each position INLINEFORM9 is either calculated by: DISPLAYFORM0 ",
+      "for recurrent transformation INLINEFORM0 such as LSTM and GRU, or by: DISPLAYFORM0 ",
+      "for parallel transformation INLINEFORM0 . On the other hand, the decoder layers INLINEFORM1 follow similar structure, while getting extra representations from the encoder side. These extra representations are also called attention, and are especially useful for capturing alignment information.",
+      "In our experiments, we use convolution based transformation for INLINEFORM0 due to both its efficiency and high performance, more formally, DISPLAYFORM0 ",
+      " INLINEFORM0 is the gated linear unit proposed in BIBREF11 and the kernel size is INLINEFORM1 . DenseNMT is agnostic to the transformation function, and we expect it to also work well combining with other transformations, such as LSTM, self-attention and depthwise separable convolution."
+    ],
+    [
+      "Different from residual connections, later layers in the dense encoder are able to use features from all previous layers by concatenating them: DISPLAYFORM0 ",
+      "Here, INLINEFORM0 is defined in Eq. ( EQREF10 ), INLINEFORM1 represents concatenation operation. Although this brings extra connections to the network, with smaller number of features per layer, the architecture encourages feature reuse, and can be more compact and expressive. As shown in Figure FIGREF9 , when designing the model, the hidden size in each layer is much smaller than the hidden size of the corresponding layer in the residual-connected model.",
+      "While each encoder layer perceives information from its previous layers, each decoder layer INLINEFORM0 has two information sources: previous layers INLINEFORM1 , and attention values INLINEFORM2 . Therefore, in order to allow dense information flow, we redefine the generation of INLINEFORM3 -th layer as a nonlinear function over all its previous decoder layers and previous attentions. This can be written as: DISPLAYFORM0 ",
+      "where INLINEFORM0 is the attention value using INLINEFORM1 -th decoder layer and information from encoder side, which will be specified later. Figure FIGREF13 shows the comparison of a dense decoder with a regular residual decoder. The dimensions of both attention values and hidden layers are chosen with smaller values, yet the perceived information for each layer consists of a higher dimension vector with more representation power. The output of the decoder is a linear transformation of the concatenation of all layers by default. To compromise to the increment of dimensions, we use summary layers, which will be introduced in Section 3.3. With summary layers, the output of the decoder is only a linear transformation of the concatenation of the upper few layers."
+    ],
+    [
+      "Prior works show a trend of designing more expressive attention mechanisms (as discussed in Section 2). However, most of them only use the last encoder layer. In order to pass more abundant information from the encoder side to the decoder side, the attention block needs to be more expressive. Following the recent development of designing attention architectures, we propose DenseAtt as the dense attention block, which serves for the dense connection between the encoder and the decoder side. More specifically, two options are proposed accordingly. For each decoding step in the corresponding decoder layer, the two options both calculate attention using multiple encoder layers. The first option is more compressed, while the second option is more expressive and flexible. We name them as DenseAtt-1 and DenseAtt-2 respectively. Figure FIGREF15 shows the architecture of (a) multi-step attention BIBREF2 , (b) DenseAtt-1, and (c) DenseAtt-2 in order. In general, a popular multiplicative attention module can be written as: DISPLAYFORM0 ",
+      "where INLINEFORM0 represent query, key, value respectively. We will use this function INLINEFORM1 in the following descriptions.",
+      "In the decoding phase, we use a layer-wise attention mechanism, such that each decoder layer absorbs different attention information to adjust its output. Instead of treating the last hidden layer as the encoder's output, we treat the concatenation of all hidden layers from encoder side as the output. The decoder layer multiplies with the encoder output to obtain the attention weights, which is then multiplied by a linear combination of the encoder output and the sentence embedding. The attention output of each layer INLINEFORM0 can be formally written as: DISPLAYFORM0 ",
+      "where INLINEFORM0 is the multiplicative attention function, INLINEFORM1 is a concatenation operation that combines all features, and INLINEFORM2 is a linear transformation function that maps each variable to a fixed dimension in order to calculate the attention value. Notice that we explicitly write the INLINEFORM3 term in ( EQREF19 ) to keep consistent with the multi-step attention mechanism, as pictorially shown in Figure FIGREF15 (a).",
+      "Notice that the transformation INLINEFORM0 in DenseAtt-1 forces the encoder layers to be mixed before doing attention. Since we use multiple hidden layers from the encoder side to get an attention value, we can alternatively calculate multiple attention values before concatenating them. In another word, the decoder layer can get different attention values from different encoder layers. This can be formally expressed as: DISPLAYFORM0 ",
+      "where the only difference from Eq. ( EQREF19 ) is that the concatenation operation is substituted by a summation operation, and is put after the attention function INLINEFORM0 . This method further increases the representation power in the attention block, while maintaining the same number of parameters in the model."
+    ],
+    [
+      "Since the number of features fed into nonlinear operation is accumulated along the path, the parameter size increases accordingly. For example, for the INLINEFORM0 -th encoder layer, the input dimension of features is INLINEFORM1 , where INLINEFORM2 is the feature dimension in previous layers, INLINEFORM3 is the embedding size. In order to avoid the calculation bottleneck for later layers due to large INLINEFORM4 , we introduce the summary layer for deeper models. It summarizes the features for all previous layers and projects back to the embedding size, so that later layers of both the encoder and the decoder side do not need to look back further. The summary layers can be considered as contextualized word vectors in a given sentence BIBREF12 . We add one summary layer after every INLINEFORM5 layers, where INLINEFORM6 is the hyperparameter we introduce. Accordingly, the input dimension of features is at most INLINEFORM7 for the last layer of the encoder. Moreover, combined with the summary layer setting, our DenseAtt mechanism allows each decoder layer to calculate the attention value focusing on the last few encoder layers, which consists of the last contextual embedding layer and several dense connected layers with low dimension. In practice, we set INLINEFORM8 as 5 or 6."
+    ],
+    [
+      "Figure FIGREF9 and Figure FIGREF13 show the difference of information flow compared with a residual-based encoder/decoder. For residual-based models, each layer can absorb a single high-dimensional vector from its previous layer as the only information, while for DenseNMT, each layer can utilize several low-dimensional vectors from its previous layers and a high-dimensional vector from the first layer (embedding layer) as its information. In DenseNMT, each layer directly provides information to its later layers. Therefore, the structure allows feature reuse, and encourages upper layers to focus on creating new features. Furthermore, the attention block allows the embedding vectors (as well as other hidden layers) to guide the decoder's generation more directly; therefore, during back-propagation, the gradient information can be passed directly to all encoder layers simultaneously."
+    ],
+    [
+      "We use three datasets for our experiments: IWSLT14 German-English, Turkish-English, and WMT14 English-German.",
+      "We preprocess the IWSLT14 German-English dataset following byte-pair-encoding (BPE) method BIBREF13 . We learn 25k BPE codes using the joint corpus of source and target languages. We randomly select 7k from IWSLT14 German-English as the development set , and the test set is a concatenation of dev2010, tst2010, tst2011 and tst2012, which is widely used in prior works BIBREF14 , BIBREF15 , BIBREF16 .",
+      "For the Turkish-English translation task, we use the data provided by IWSLT14 BIBREF17 and the SETimes corpus BIBREF17 following BIBREF18 . After removing sentence pairs with length ratio over 9, we obtain 360k sentence pairs. Since there is little commonality between the two languages, we learn 30k size BPE codes separately for Turkish and English. In addition to this, we give another preprocessing for Turkish sentences and use word-level English corpus. For Turkish sentences, following BIBREF19 , BIBREF18 , we use the morphology tool Zemberek with disambiguation by the morphological analysis BIBREF20 and removal of non-surface tokens. Following BIBREF18 , we concatenate tst2011, tst2012, tst2013, tst2014 as our test set. We concatenate dev2010 and tst2010 as the development set.",
+      "We preprocess the WMT14 English-German dataset using a BPE code size of 40k. We use the concatenation of newstest2013 and newstest2012 as the development set."
+    ],
+    [
+      "As the baseline model (BASE-4L) for IWSLT14 German-English and Turkish-English, we use a 4-layer encoder, 4-layer decoder, residual-connected model, with embedding and hidden size set as 256 by default. As a comparison, we design a densely connected model with same number of layers, but the hidden size is set as 128 in order to keep the model size consistent. The models adopting DenseAtt-1, DenseAtt-2 are named as DenseNMT-4L-1 and DenseNMT-4L-2 respectively. In order to check the effect of dense connections on deeper models, we also construct a series of 8-layer models. We set the hidden number to be 192, such that both 4-layer models and 8-layer models have similar number of parameters. For dense structured models, we set the dimension of hidden states to be 96.",
+      "Since NMT model usually allocates a large proportion of its parameters to the source/target sentence embedding and softmax matrix, we explore in our experiments to what extent decreasing the dimensions of the three parts would harm the BLEU score. We change the dimensions of the source embedding, the target embedding as well as the softmax matrix simultaneously to smaller values, and then project each word back to the original embedding dimension through a linear transformation. This significantly reduces the number of total parameters, while not influencing the upper layer structure of the model.",
+      "We also introduce three additional models we use for ablation study, all using 4-layer structure. Based on the residual connected BASE-4L model, (1) DenseENC-4L only makes encoder side dense, (2) DenseDEC-4L only makes decoder side dense, and (3) DenseAtt-4L only makes the attention dense using DenseAtt-2. There is no summary layer in the models, and both DenseENC-4L and DenseDEC-4L use hidden size 128. Again, by reducing the hidden size, we ensure that different 4-layer models have similar model sizes.",
+      "Our design for the WMT14 English-German model follows the best performance model provided in BIBREF2 . The construction of our model is straightforward: our 15-layer model DenseNMT-En-De-15 uses dense connection with DenseAtt-2, INLINEFORM0 . The hidden number in each layer is INLINEFORM1 that of the original model, while the kernel size maintains the same."
+    ],
+    [
+      "We use Nesterov Accelerated Gradient (NAG) BIBREF21 as our optimizer, and the initial learning rate is set to INLINEFORM0 . For German-English and Turkish-English experiments, the learning rate will shrink by 10 every time the validation loss increases. For the English-German dataset, in consistent with BIBREF2 , the learning rate will shrink by 10 every epoch since the first increment of validation loss. The system stops training until the learning rate is less than INLINEFORM1 . All models are trained end-to-end without any warmstart techniques. We set our batch size for the WMT14 English-German dataset to be 48, and additionally tune the length penalty parameter, in consistent with BIBREF2 . For other datasets, we set batch size to be 32. During inference, we use a beam size of 5."
+    ],
+    [
+      "We first show that DenseNMT helps information flow more efficiently by presenting the training loss curve. All hyperparameters are fixed in each plot, only the models are different. In Figure FIGREF30 , the loss curves for both training and dev sets (before entering the finetuning period) are provided for De-En, Tr-En and Tr-En-morph. For clarity, we compare DenseNMT-4L-2 with BASE-4L. We observe that DenseNMT models are consistently better than residual-connected models, since their loss curves are always below those of the baseline models. The effect is more obvious on the WMT14 English-German dataset. We rerun the best model provided by BIBREF2 and compare with our model. In Figure FIGREF33 , where train/test loss curve are provided, DenseNMT-En-De-15 reaches the same level of loss and starts finetuning (validation loss starts to increase) at epoch 13, which is 35% faster than the baseline.",
+      "Adding dense connections changes the architecture, and would slightly influence training speed. For the WMT14 En-De experiments, the computing time for both DenseNMT and the baseline (with similar number of parameters and same batch size) tested on single M40 GPU card are 1571 and 1710 word/s, respectively. While adding dense connections influences the per-iteration training slightly (8.1% reduction of speed), it uses many fewer epochs, and achieves a better BLEU score. In terms of training time, DenseNMT uses 29.3%(before finetuning)/22.9%(total) less time than the baseline."
+    ],
+    [
+      "Table TABREF32 shows the results for De-En, Tr-En, Tr-En-morph datasets, where the best accuracy for models with the same depth and of similar sizes are marked in boldface. In almost all genres, DenseNMT models are significantly better than the baselines. With embedding size 256, where all models achieve their best scores, DenseNMT outperforms baselines by 0.7-1.0 BLEU on De-En, 0.5-1.3 BLEU on Tr-En, 0.8-1.5 BLEU on Tr-En-morph. We observe significant gain using other embedding sizes as well.",
+      "Furthermore, in Table TABREF36 , we investigate DenseNMT models through ablation study. In order to make the comparison fair, six models listed have roughly the same number of parameters. On De-En, Tr-En and Tr-En-morph, we see improvement by making the encoder dense, making the decoder dense, and making the attention dense. Fully dense-connected model DenseNMT-4L-1 further improves the translation accuracy. By allowing more flexibility in dense attention, DenseNMT-4L-2 provides the highest BLEU scores for all three experiments.",
+      "From the experiments, we have seen that enlarging the information flow in the attention block benefits the models. The dense attention block provides multi-layer information transmission from the encoder to the decoder, and to the output as well. Meanwhile, as shown by the ablation study, the dense-connected encoder and decoder both give more powerful representations than the residual-connected counterparts. As a result, the integration of the three parts improve the accuracy significantly."
+    ],
+    [
+      "From Table TABREF32 , we also observe that DenseNMT performs better with small embedding sizes compared to residual-connected models with regular embedding size. For example, on Tr-En model, the 8-layer DenseNMT-8L-2 model with embedding size 64 matches the BLEU score of the 8-layer BASE model with embedding size 256, while the number of parameter of the former one is only INLINEFORM0 of the later one. In all genres, DenseNMT model with embedding size 128 is comparable or even better than the baseline model with embedding size 256.",
+      "While overlarge embedding sizes hurt accuracy because of overfitting issues, smaller sizes are not preferable because of insufficient representation power. However, our dense models show that with better model design, the embedding information can be well concentrated on fewer dimensions, e.g., 64. This is extremely helpful when building models on mobile and small devices where the model size is critical. While there are other works that stress the efficiency issue by using techniques such as separable convolution BIBREF3 , and shared embedding BIBREF4 , our DenseNMT framework is orthogonal to those approaches. We believe that other techniques would produce more efficient models through combining with our DenseNMT framework."
+    ],
+    [
+      "For the IWSLT14 German-English dataset, we compare with the best results reported from literatures. To be consistent with prior works, we also provide results using our model directly on the dataset without BPE preprocessing. As shown in Table TABREF39 , DenseNMT outperforms the phrase-structure based network NPMT BIBREF16 (with beam size 10) by 1.2 BLEU, using a smaller beam size, and outperforms the actor-critic method based algorithm BIBREF15 by 2.8 BLEU. For reference, our model trained on the BPE preprocessed dataset achieves 32.26 BLEU, which is 1.93 BLEU higher than our word-based model. For Turkish-English task, we compare with BIBREF19 which uses the same morphology preprocessing as our Tr-En-morph. As shown in Table TABREF37 , our baseline is higher than the previous result, and we further achieve new benchmark result with 24.36 BLEU average score. For WMT14 English-German, from Table TABREF41 , we can see that DenseNMT outperforms ConvS2S model by 0.36 BLEU score using 35% fewer training iterations and 20% fewer parameters. We also compare with another convolution based NMT model: SliceNet BIBREF3 , which explores depthwise separable convolution architectures. SliceNet-Full matches our result, and SliceNet-Super outperforms by 0.58 BLEU score. However, both models have 2.2x more parameters than our model. We expect DenseNMT structure could help improve their performance as well."
+    ],
+    [
+      "In this work, we have proposed DenseNMT as a dense-connection framework for translation tasks, which uses the information from embeddings more efficiently, and passes abundant information from the encoder side to the decoder side. Our experiments have shown that DenseNMT is able to speed up the information flow and improve translation accuracy. For the future work, we will combine dense connections with other deep architectures, such as RNNs BIBREF7 and self-attention networks BIBREF4 ."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0547/instruction.md b/qasper-0547/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..301219f970b430213fcd7b6bcad0335e979026b3
--- /dev/null
+++ b/qasper-0547/instruction.md
@@ -0,0 +1,97 @@
+Name of Paper: Dense Information Flow for Neural Machine Translation
+
+Question: did they outperform previous methods?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "DenseNMT",
+    "Dense encoder and decoder",
+    "Dense attention",
+    "Summary layers",
+    "Analysis of information flow",
+    "Datasets",
+    "Model and architect design",
+    "Training setting",
+    "Training curve",
+    "DenseNMT improves accuracy with similar architectures and model sizes",
+    "DenseNMT with smaller embedding size",
+    "DenseNMT compares with state-of-the-art results",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Neural machine translation (NMT) is a challenging task that attracts lots of attention in recent years. Starting from the encoder-decoder framework BIBREF0 , NMT starts to show promising results in many language pairs. The evolving structures of NMT models in recent years have made them achieve higher scores and become more favorable. The attention mechanism BIBREF1 added on top of encoder-decoder framework is shown to be very useful to automatically find alignment structure, and single-layer RNN-based structure has evolved into deeper models with more efficient transformation functions BIBREF2 , BIBREF3 , BIBREF4 .",
+      "One major challenge of NMT is that its models are hard to train in general due to the complexity of both the deep models and languages. From the optimization perspective, deeper models are hard to efficiently back-propagate the gradients, and this phenomenon as well as its solution is better explored in the computer vision society. Residual networks (ResNet) BIBREF5 achieve great performance in a wide range of tasks, including image classification and image segmentation. Residual connections allow features from previous layers to be accumulated to the next layer easily, and make the optimization of the model efficiently focus on refining upper layer features.",
+      "NMT is considered as a challenging problem due to its sequence-to-sequence generation framework, and the goal of comprehension and reorganizing from one language to the other. Apart from the encoder block that works as a feature generator, the decoder network combining with the attention mechanism bring new challenges to the optimization of the models. While nowadays best-performing NMT systems use residual connections, we question whether this is the most efficient way to propagate information through deep models. In this paper, inspired by the idea of using dense connections for training computer vision tasks BIBREF6 , we propose a densely connected NMT framework (DenseNMT) that efficiently propagates information from the encoder to the decoder through the attention component. Taking the CNN-based deep architecture as an example, we verify the efficiency of DenseNMT. Our contributions in this work include: (i) by comparing the loss curve, we show that DenseNMT allows the model to pass information more efficiently, and speeds up training; (ii) we show through ablation study that dense connections in all three blocks altogether help improve the performance, while not increasing the number of parameters; (iii) DenseNMT allows the models to achieve similar performance with much smaller embedding size; (iv) DenseNMT on IWSLT14 German-English and Turkish-English translation tasks achieves new benchmark BLEU scores, and the result on WMT14 English-German task is more competitive than the residual connections based baseline model."
+    ],
+    [
+      "In this section, we introduce our DenseNMT architecture. In general, compared with residual connected NMT models, DenseNMT allows each layer to provide its information to all subsequent layers directly. Figure FIGREF9 - FIGREF15 show the design of our model structure by parts.",
+      "We start with the formulation of a regular NMT model. Given a set of sentence pairs INLINEFORM0 , an NMT model learns parameter INLINEFORM1 by maximizing the log-likelihood function: DISPLAYFORM0 ",
+      "For every sentence pair INLINEFORM0 , INLINEFORM1 is calculated based on the decomposition: DISPLAYFORM0 ",
+      "where INLINEFORM0 is the length of sentence INLINEFORM1 . Typically, NMT models use the encoder-attention-decoder framework BIBREF1 , and potentially use multi-layer structure for both encoder and decoder. Given a source sentence INLINEFORM2 with length INLINEFORM3 , the encoder calculates hidden representations by layer. We denote the representation in the INLINEFORM4 -th layer as INLINEFORM5 , with dimension INLINEFORM6 , where INLINEFORM7 is the dimension of features in layer INLINEFORM8 . The hidden representation at each position INLINEFORM9 is either calculated by: DISPLAYFORM0 ",
+      "for recurrent transformation INLINEFORM0 such as LSTM and GRU, or by: DISPLAYFORM0 ",
+      "for parallel transformation INLINEFORM0 . On the other hand, the decoder layers INLINEFORM1 follow similar structure, while getting extra representations from the encoder side. These extra representations are also called attention, and are especially useful for capturing alignment information.",
+      "In our experiments, we use convolution based transformation for INLINEFORM0 due to both its efficiency and high performance, more formally, DISPLAYFORM0 ",
+      " INLINEFORM0 is the gated linear unit proposed in BIBREF11 and the kernel size is INLINEFORM1 . DenseNMT is agnostic to the transformation function, and we expect it to also work well combining with other transformations, such as LSTM, self-attention and depthwise separable convolution."
+    ],
+    [
+      "Different from residual connections, later layers in the dense encoder are able to use features from all previous layers by concatenating them: DISPLAYFORM0 ",
+      "Here, INLINEFORM0 is defined in Eq. ( EQREF10 ), INLINEFORM1 represents concatenation operation. Although this brings extra connections to the network, with smaller number of features per layer, the architecture encourages feature reuse, and can be more compact and expressive. As shown in Figure FIGREF9 , when designing the model, the hidden size in each layer is much smaller than the hidden size of the corresponding layer in the residual-connected model.",
+      "While each encoder layer perceives information from its previous layers, each decoder layer INLINEFORM0 has two information sources: previous layers INLINEFORM1 , and attention values INLINEFORM2 . Therefore, in order to allow dense information flow, we redefine the generation of INLINEFORM3 -th layer as a nonlinear function over all its previous decoder layers and previous attentions. This can be written as: DISPLAYFORM0 ",
+      "where INLINEFORM0 is the attention value using INLINEFORM1 -th decoder layer and information from encoder side, which will be specified later. Figure FIGREF13 shows the comparison of a dense decoder with a regular residual decoder. The dimensions of both attention values and hidden layers are chosen with smaller values, yet the perceived information for each layer consists of a higher dimension vector with more representation power. The output of the decoder is a linear transformation of the concatenation of all layers by default. To compromise to the increment of dimensions, we use summary layers, which will be introduced in Section 3.3. With summary layers, the output of the decoder is only a linear transformation of the concatenation of the upper few layers."
+    ],
+    [
+      "Prior works show a trend of designing more expressive attention mechanisms (as discussed in Section 2). However, most of them only use the last encoder layer. In order to pass more abundant information from the encoder side to the decoder side, the attention block needs to be more expressive. Following the recent development of designing attention architectures, we propose DenseAtt as the dense attention block, which serves for the dense connection between the encoder and the decoder side. More specifically, two options are proposed accordingly. For each decoding step in the corresponding decoder layer, the two options both calculate attention using multiple encoder layers. The first option is more compressed, while the second option is more expressive and flexible. We name them as DenseAtt-1 and DenseAtt-2 respectively. Figure FIGREF15 shows the architecture of (a) multi-step attention BIBREF2 , (b) DenseAtt-1, and (c) DenseAtt-2 in order. In general, a popular multiplicative attention module can be written as: DISPLAYFORM0 ",
+      "where INLINEFORM0 represent query, key, value respectively. We will use this function INLINEFORM1 in the following descriptions.",
+      "In the decoding phase, we use a layer-wise attention mechanism, such that each decoder layer absorbs different attention information to adjust its output. Instead of treating the last hidden layer as the encoder's output, we treat the concatenation of all hidden layers from encoder side as the output. The decoder layer multiplies with the encoder output to obtain the attention weights, which is then multiplied by a linear combination of the encoder output and the sentence embedding. The attention output of each layer INLINEFORM0 can be formally written as: DISPLAYFORM0 ",
+      "where INLINEFORM0 is the multiplicative attention function, INLINEFORM1 is a concatenation operation that combines all features, and INLINEFORM2 is a linear transformation function that maps each variable to a fixed dimension in order to calculate the attention value. Notice that we explicitly write the INLINEFORM3 term in ( EQREF19 ) to keep consistent with the multi-step attention mechanism, as pictorially shown in Figure FIGREF15 (a).",
+      "Notice that the transformation INLINEFORM0 in DenseAtt-1 forces the encoder layers to be mixed before doing attention. Since we use multiple hidden layers from the encoder side to get an attention value, we can alternatively calculate multiple attention values before concatenating them. In another word, the decoder layer can get different attention values from different encoder layers. This can be formally expressed as: DISPLAYFORM0 ",
+      "where the only difference from Eq. ( EQREF19 ) is that the concatenation operation is substituted by a summation operation, and is put after the attention function INLINEFORM0 . This method further increases the representation power in the attention block, while maintaining the same number of parameters in the model."
+    ],
+    [
+      "Since the number of features fed into nonlinear operation is accumulated along the path, the parameter size increases accordingly. For example, for the INLINEFORM0 -th encoder layer, the input dimension of features is INLINEFORM1 , where INLINEFORM2 is the feature dimension in previous layers, INLINEFORM3 is the embedding size. In order to avoid the calculation bottleneck for later layers due to large INLINEFORM4 , we introduce the summary layer for deeper models. It summarizes the features for all previous layers and projects back to the embedding size, so that later layers of both the encoder and the decoder side do not need to look back further. The summary layers can be considered as contextualized word vectors in a given sentence BIBREF12 . We add one summary layer after every INLINEFORM5 layers, where INLINEFORM6 is the hyperparameter we introduce. Accordingly, the input dimension of features is at most INLINEFORM7 for the last layer of the encoder. Moreover, combined with the summary layer setting, our DenseAtt mechanism allows each decoder layer to calculate the attention value focusing on the last few encoder layers, which consists of the last contextual embedding layer and several dense connected layers with low dimension. In practice, we set INLINEFORM8 as 5 or 6."
+    ],
+    [
+      "Figure FIGREF9 and Figure FIGREF13 show the difference of information flow compared with a residual-based encoder/decoder. For residual-based models, each layer can absorb a single high-dimensional vector from its previous layer as the only information, while for DenseNMT, each layer can utilize several low-dimensional vectors from its previous layers and a high-dimensional vector from the first layer (embedding layer) as its information. In DenseNMT, each layer directly provides information to its later layers. Therefore, the structure allows feature reuse, and encourages upper layers to focus on creating new features. Furthermore, the attention block allows the embedding vectors (as well as other hidden layers) to guide the decoder's generation more directly; therefore, during back-propagation, the gradient information can be passed directly to all encoder layers simultaneously."
+    ],
+    [
+      "We use three datasets for our experiments: IWSLT14 German-English, Turkish-English, and WMT14 English-German.",
+      "We preprocess the IWSLT14 German-English dataset following byte-pair-encoding (BPE) method BIBREF13 . We learn 25k BPE codes using the joint corpus of source and target languages. We randomly select 7k from IWSLT14 German-English as the development set , and the test set is a concatenation of dev2010, tst2010, tst2011 and tst2012, which is widely used in prior works BIBREF14 , BIBREF15 , BIBREF16 .",
+      "For the Turkish-English translation task, we use the data provided by IWSLT14 BIBREF17 and the SETimes corpus BIBREF17 following BIBREF18 . After removing sentence pairs with length ratio over 9, we obtain 360k sentence pairs. Since there is little commonality between the two languages, we learn 30k size BPE codes separately for Turkish and English. In addition to this, we give another preprocessing for Turkish sentences and use word-level English corpus. For Turkish sentences, following BIBREF19 , BIBREF18 , we use the morphology tool Zemberek with disambiguation by the morphological analysis BIBREF20 and removal of non-surface tokens. Following BIBREF18 , we concatenate tst2011, tst2012, tst2013, tst2014 as our test set. We concatenate dev2010 and tst2010 as the development set.",
+      "We preprocess the WMT14 English-German dataset using a BPE code size of 40k. We use the concatenation of newstest2013 and newstest2012 as the development set."
+    ],
+    [
+      "As the baseline model (BASE-4L) for IWSLT14 German-English and Turkish-English, we use a 4-layer encoder, 4-layer decoder, residual-connected model, with embedding and hidden size set as 256 by default. As a comparison, we design a densely connected model with same number of layers, but the hidden size is set as 128 in order to keep the model size consistent. The models adopting DenseAtt-1, DenseAtt-2 are named as DenseNMT-4L-1 and DenseNMT-4L-2 respectively. In order to check the effect of dense connections on deeper models, we also construct a series of 8-layer models. We set the hidden number to be 192, such that both 4-layer models and 8-layer models have similar number of parameters. For dense structured models, we set the dimension of hidden states to be 96.",
+      "Since NMT model usually allocates a large proportion of its parameters to the source/target sentence embedding and softmax matrix, we explore in our experiments to what extent decreasing the dimensions of the three parts would harm the BLEU score. We change the dimensions of the source embedding, the target embedding as well as the softmax matrix simultaneously to smaller values, and then project each word back to the original embedding dimension through a linear transformation. This significantly reduces the number of total parameters, while not influencing the upper layer structure of the model.",
+      "We also introduce three additional models we use for ablation study, all using 4-layer structure. Based on the residual connected BASE-4L model, (1) DenseENC-4L only makes encoder side dense, (2) DenseDEC-4L only makes decoder side dense, and (3) DenseAtt-4L only makes the attention dense using DenseAtt-2. There is no summary layer in the models, and both DenseENC-4L and DenseDEC-4L use hidden size 128. Again, by reducing the hidden size, we ensure that different 4-layer models have similar model sizes.",
+      "Our design for the WMT14 English-German model follows the best performance model provided in BIBREF2 . The construction of our model is straightforward: our 15-layer model DenseNMT-En-De-15 uses dense connection with DenseAtt-2, INLINEFORM0 . The hidden number in each layer is INLINEFORM1 that of the original model, while the kernel size maintains the same."
+    ],
+    [
+      "We use Nesterov Accelerated Gradient (NAG) BIBREF21 as our optimizer, and the initial learning rate is set to INLINEFORM0 . For German-English and Turkish-English experiments, the learning rate will shrink by 10 every time the validation loss increases. For the English-German dataset, in consistent with BIBREF2 , the learning rate will shrink by 10 every epoch since the first increment of validation loss. The system stops training until the learning rate is less than INLINEFORM1 . All models are trained end-to-end without any warmstart techniques. We set our batch size for the WMT14 English-German dataset to be 48, and additionally tune the length penalty parameter, in consistent with BIBREF2 . For other datasets, we set batch size to be 32. During inference, we use a beam size of 5."
+    ],
+    [
+      "We first show that DenseNMT helps information flow more efficiently by presenting the training loss curve. All hyperparameters are fixed in each plot, only the models are different. In Figure FIGREF30 , the loss curves for both training and dev sets (before entering the finetuning period) are provided for De-En, Tr-En and Tr-En-morph. For clarity, we compare DenseNMT-4L-2 with BASE-4L. We observe that DenseNMT models are consistently better than residual-connected models, since their loss curves are always below those of the baseline models. The effect is more obvious on the WMT14 English-German dataset. We rerun the best model provided by BIBREF2 and compare with our model. In Figure FIGREF33 , where train/test loss curve are provided, DenseNMT-En-De-15 reaches the same level of loss and starts finetuning (validation loss starts to increase) at epoch 13, which is 35% faster than the baseline.",
+      "Adding dense connections changes the architecture, and would slightly influence training speed. For the WMT14 En-De experiments, the computing time for both DenseNMT and the baseline (with similar number of parameters and same batch size) tested on single M40 GPU card are 1571 and 1710 word/s, respectively. While adding dense connections influences the per-iteration training slightly (8.1% reduction of speed), it uses many fewer epochs, and achieves a better BLEU score. In terms of training time, DenseNMT uses 29.3%(before finetuning)/22.9%(total) less time than the baseline."
+    ],
+    [
+      "Table TABREF32 shows the results for De-En, Tr-En, Tr-En-morph datasets, where the best accuracy for models with the same depth and of similar sizes are marked in boldface. In almost all genres, DenseNMT models are significantly better than the baselines. With embedding size 256, where all models achieve their best scores, DenseNMT outperforms baselines by 0.7-1.0 BLEU on De-En, 0.5-1.3 BLEU on Tr-En, 0.8-1.5 BLEU on Tr-En-morph. We observe significant gain using other embedding sizes as well.",
+      "Furthermore, in Table TABREF36 , we investigate DenseNMT models through ablation study. In order to make the comparison fair, six models listed have roughly the same number of parameters. On De-En, Tr-En and Tr-En-morph, we see improvement by making the encoder dense, making the decoder dense, and making the attention dense. Fully dense-connected model DenseNMT-4L-1 further improves the translation accuracy. By allowing more flexibility in dense attention, DenseNMT-4L-2 provides the highest BLEU scores for all three experiments.",
+      "From the experiments, we have seen that enlarging the information flow in the attention block benefits the models. The dense attention block provides multi-layer information transmission from the encoder to the decoder, and to the output as well. Meanwhile, as shown by the ablation study, the dense-connected encoder and decoder both give more powerful representations than the residual-connected counterparts. As a result, the integration of the three parts improve the accuracy significantly."
+    ],
+    [
+      "From Table TABREF32 , we also observe that DenseNMT performs better with small embedding sizes compared to residual-connected models with regular embedding size. For example, on Tr-En model, the 8-layer DenseNMT-8L-2 model with embedding size 64 matches the BLEU score of the 8-layer BASE model with embedding size 256, while the number of parameter of the former one is only INLINEFORM0 of the later one. In all genres, DenseNMT model with embedding size 128 is comparable or even better than the baseline model with embedding size 256.",
+      "While overlarge embedding sizes hurt accuracy because of overfitting issues, smaller sizes are not preferable because of insufficient representation power. However, our dense models show that with better model design, the embedding information can be well concentrated on fewer dimensions, e.g., 64. This is extremely helpful when building models on mobile and small devices where the model size is critical. While there are other works that stress the efficiency issue by using techniques such as separable convolution BIBREF3 , and shared embedding BIBREF4 , our DenseNMT framework is orthogonal to those approaches. We believe that other techniques would produce more efficient models through combining with our DenseNMT framework."
+    ],
+    [
+      "For the IWSLT14 German-English dataset, we compare with the best results reported from literatures. To be consistent with prior works, we also provide results using our model directly on the dataset without BPE preprocessing. As shown in Table TABREF39 , DenseNMT outperforms the phrase-structure based network NPMT BIBREF16 (with beam size 10) by 1.2 BLEU, using a smaller beam size, and outperforms the actor-critic method based algorithm BIBREF15 by 2.8 BLEU. For reference, our model trained on the BPE preprocessed dataset achieves 32.26 BLEU, which is 1.93 BLEU higher than our word-based model. For Turkish-English task, we compare with BIBREF19 which uses the same morphology preprocessing as our Tr-En-morph. As shown in Table TABREF37 , our baseline is higher than the previous result, and we further achieve new benchmark result with 24.36 BLEU average score. For WMT14 English-German, from Table TABREF41 , we can see that DenseNMT outperforms ConvS2S model by 0.36 BLEU score using 35% fewer training iterations and 20% fewer parameters. We also compare with another convolution based NMT model: SliceNet BIBREF3 , which explores depthwise separable convolution architectures. SliceNet-Full matches our result, and SliceNet-Super outperforms by 0.58 BLEU score. However, both models have 2.2x more parameters than our model. We expect DenseNMT structure could help improve their performance as well."
+    ],
+    [
+      "In this work, we have proposed DenseNMT as a dense-connection framework for translation tasks, which uses the information from embeddings more efficiently, and passes abundant information from the encoder side to the decoder side. Our experiments have shown that DenseNMT is able to speed up the information flow and improve translation accuracy. For the future work, we will combine dense connections with other deep architectures, such as RNNs BIBREF7 and self-attention networks BIBREF4 ."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0548/instruction.md b/qasper-0548/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..267a26c9c65dfba33d395105f07ea2c23aa02231
--- /dev/null
+++ b/qasper-0548/instruction.md
@@ -0,0 +1,97 @@
+Name of Paper: Dense Information Flow for Neural Machine Translation
+
+Question: what language pairs are explored?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "DenseNMT",
+    "Dense encoder and decoder",
+    "Dense attention",
+    "Summary layers",
+    "Analysis of information flow",
+    "Datasets",
+    "Model and architect design",
+    "Training setting",
+    "Training curve",
+    "DenseNMT improves accuracy with similar architectures and model sizes",
+    "DenseNMT with smaller embedding size",
+    "DenseNMT compares with state-of-the-art results",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Neural machine translation (NMT) is a challenging task that attracts lots of attention in recent years. Starting from the encoder-decoder framework BIBREF0 , NMT starts to show promising results in many language pairs. The evolving structures of NMT models in recent years have made them achieve higher scores and become more favorable. The attention mechanism BIBREF1 added on top of encoder-decoder framework is shown to be very useful to automatically find alignment structure, and single-layer RNN-based structure has evolved into deeper models with more efficient transformation functions BIBREF2 , BIBREF3 , BIBREF4 .",
+      "One major challenge of NMT is that its models are hard to train in general due to the complexity of both the deep models and languages. From the optimization perspective, deeper models are hard to efficiently back-propagate the gradients, and this phenomenon as well as its solution is better explored in the computer vision society. Residual networks (ResNet) BIBREF5 achieve great performance in a wide range of tasks, including image classification and image segmentation. Residual connections allow features from previous layers to be accumulated to the next layer easily, and make the optimization of the model efficiently focus on refining upper layer features.",
+      "NMT is considered as a challenging problem due to its sequence-to-sequence generation framework, and the goal of comprehension and reorganizing from one language to the other. Apart from the encoder block that works as a feature generator, the decoder network combining with the attention mechanism bring new challenges to the optimization of the models. While nowadays best-performing NMT systems use residual connections, we question whether this is the most efficient way to propagate information through deep models. In this paper, inspired by the idea of using dense connections for training computer vision tasks BIBREF6 , we propose a densely connected NMT framework (DenseNMT) that efficiently propagates information from the encoder to the decoder through the attention component. Taking the CNN-based deep architecture as an example, we verify the efficiency of DenseNMT. Our contributions in this work include: (i) by comparing the loss curve, we show that DenseNMT allows the model to pass information more efficiently, and speeds up training; (ii) we show through ablation study that dense connections in all three blocks altogether help improve the performance, while not increasing the number of parameters; (iii) DenseNMT allows the models to achieve similar performance with much smaller embedding size; (iv) DenseNMT on IWSLT14 German-English and Turkish-English translation tasks achieves new benchmark BLEU scores, and the result on WMT14 English-German task is more competitive than the residual connections based baseline model."
+    ],
+    [
+      "In this section, we introduce our DenseNMT architecture. In general, compared with residual connected NMT models, DenseNMT allows each layer to provide its information to all subsequent layers directly. Figure FIGREF9 - FIGREF15 show the design of our model structure by parts.",
+      "We start with the formulation of a regular NMT model. Given a set of sentence pairs INLINEFORM0 , an NMT model learns parameter INLINEFORM1 by maximizing the log-likelihood function: DISPLAYFORM0 ",
+      "For every sentence pair INLINEFORM0 , INLINEFORM1 is calculated based on the decomposition: DISPLAYFORM0 ",
+      "where INLINEFORM0 is the length of sentence INLINEFORM1 . Typically, NMT models use the encoder-attention-decoder framework BIBREF1 , and potentially use multi-layer structure for both encoder and decoder. Given a source sentence INLINEFORM2 with length INLINEFORM3 , the encoder calculates hidden representations by layer. We denote the representation in the INLINEFORM4 -th layer as INLINEFORM5 , with dimension INLINEFORM6 , where INLINEFORM7 is the dimension of features in layer INLINEFORM8 . The hidden representation at each position INLINEFORM9 is either calculated by: DISPLAYFORM0 ",
+      "for recurrent transformation INLINEFORM0 such as LSTM and GRU, or by: DISPLAYFORM0 ",
+      "for parallel transformation INLINEFORM0 . On the other hand, the decoder layers INLINEFORM1 follow similar structure, while getting extra representations from the encoder side. These extra representations are also called attention, and are especially useful for capturing alignment information.",
+      "In our experiments, we use convolution based transformation for INLINEFORM0 due to both its efficiency and high performance, more formally, DISPLAYFORM0 ",
+      " INLINEFORM0 is the gated linear unit proposed in BIBREF11 and the kernel size is INLINEFORM1 . DenseNMT is agnostic to the transformation function, and we expect it to also work well combining with other transformations, such as LSTM, self-attention and depthwise separable convolution."
+    ],
+    [
+      "Different from residual connections, later layers in the dense encoder are able to use features from all previous layers by concatenating them: DISPLAYFORM0 ",
+      "Here, INLINEFORM0 is defined in Eq. ( EQREF10 ), INLINEFORM1 represents concatenation operation. Although this brings extra connections to the network, with smaller number of features per layer, the architecture encourages feature reuse, and can be more compact and expressive. As shown in Figure FIGREF9 , when designing the model, the hidden size in each layer is much smaller than the hidden size of the corresponding layer in the residual-connected model.",
+      "While each encoder layer perceives information from its previous layers, each decoder layer INLINEFORM0 has two information sources: previous layers INLINEFORM1 , and attention values INLINEFORM2 . Therefore, in order to allow dense information flow, we redefine the generation of INLINEFORM3 -th layer as a nonlinear function over all its previous decoder layers and previous attentions. This can be written as: DISPLAYFORM0 ",
+      "where INLINEFORM0 is the attention value using INLINEFORM1 -th decoder layer and information from encoder side, which will be specified later. Figure FIGREF13 shows the comparison of a dense decoder with a regular residual decoder. The dimensions of both attention values and hidden layers are chosen with smaller values, yet the perceived information for each layer consists of a higher dimension vector with more representation power. The output of the decoder is a linear transformation of the concatenation of all layers by default. To compromise to the increment of dimensions, we use summary layers, which will be introduced in Section 3.3. With summary layers, the output of the decoder is only a linear transformation of the concatenation of the upper few layers."
+    ],
+    [
+      "Prior works show a trend of designing more expressive attention mechanisms (as discussed in Section 2). However, most of them only use the last encoder layer. In order to pass more abundant information from the encoder side to the decoder side, the attention block needs to be more expressive. Following the recent development of designing attention architectures, we propose DenseAtt as the dense attention block, which serves for the dense connection between the encoder and the decoder side. More specifically, two options are proposed accordingly. For each decoding step in the corresponding decoder layer, the two options both calculate attention using multiple encoder layers. The first option is more compressed, while the second option is more expressive and flexible. We name them as DenseAtt-1 and DenseAtt-2 respectively. Figure FIGREF15 shows the architecture of (a) multi-step attention BIBREF2 , (b) DenseAtt-1, and (c) DenseAtt-2 in order. In general, a popular multiplicative attention module can be written as: DISPLAYFORM0 ",
+      "where INLINEFORM0 represent query, key, value respectively. We will use this function INLINEFORM1 in the following descriptions.",
+      "In the decoding phase, we use a layer-wise attention mechanism, such that each decoder layer absorbs different attention information to adjust its output. Instead of treating the last hidden layer as the encoder's output, we treat the concatenation of all hidden layers from encoder side as the output. The decoder layer multiplies with the encoder output to obtain the attention weights, which is then multiplied by a linear combination of the encoder output and the sentence embedding. The attention output of each layer INLINEFORM0 can be formally written as: DISPLAYFORM0 ",
+      "where INLINEFORM0 is the multiplicative attention function, INLINEFORM1 is a concatenation operation that combines all features, and INLINEFORM2 is a linear transformation function that maps each variable to a fixed dimension in order to calculate the attention value. Notice that we explicitly write the INLINEFORM3 term in ( EQREF19 ) to keep consistent with the multi-step attention mechanism, as pictorially shown in Figure FIGREF15 (a).",
+      "Notice that the transformation INLINEFORM0 in DenseAtt-1 forces the encoder layers to be mixed before doing attention. Since we use multiple hidden layers from the encoder side to get an attention value, we can alternatively calculate multiple attention values before concatenating them. In another word, the decoder layer can get different attention values from different encoder layers. This can be formally expressed as: DISPLAYFORM0 ",
+      "where the only difference from Eq. ( EQREF19 ) is that the concatenation operation is substituted by a summation operation, and is put after the attention function INLINEFORM0 . This method further increases the representation power in the attention block, while maintaining the same number of parameters in the model."
+    ],
+    [
+      "Since the number of features fed into nonlinear operation is accumulated along the path, the parameter size increases accordingly. For example, for the INLINEFORM0 -th encoder layer, the input dimension of features is INLINEFORM1 , where INLINEFORM2 is the feature dimension in previous layers, INLINEFORM3 is the embedding size. In order to avoid the calculation bottleneck for later layers due to large INLINEFORM4 , we introduce the summary layer for deeper models. It summarizes the features for all previous layers and projects back to the embedding size, so that later layers of both the encoder and the decoder side do not need to look back further. The summary layers can be considered as contextualized word vectors in a given sentence BIBREF12 . We add one summary layer after every INLINEFORM5 layers, where INLINEFORM6 is the hyperparameter we introduce. Accordingly, the input dimension of features is at most INLINEFORM7 for the last layer of the encoder. Moreover, combined with the summary layer setting, our DenseAtt mechanism allows each decoder layer to calculate the attention value focusing on the last few encoder layers, which consists of the last contextual embedding layer and several dense connected layers with low dimension. In practice, we set INLINEFORM8 as 5 or 6."
+    ],
+    [
+      "Figure FIGREF9 and Figure FIGREF13 show the difference of information flow compared with a residual-based encoder/decoder. For residual-based models, each layer can absorb a single high-dimensional vector from its previous layer as the only information, while for DenseNMT, each layer can utilize several low-dimensional vectors from its previous layers and a high-dimensional vector from the first layer (embedding layer) as its information. In DenseNMT, each layer directly provides information to its later layers. Therefore, the structure allows feature reuse, and encourages upper layers to focus on creating new features. Furthermore, the attention block allows the embedding vectors (as well as other hidden layers) to guide the decoder's generation more directly; therefore, during back-propagation, the gradient information can be passed directly to all encoder layers simultaneously."
+    ],
+    [
+      "We use three datasets for our experiments: IWSLT14 German-English, Turkish-English, and WMT14 English-German.",
+      "We preprocess the IWSLT14 German-English dataset following byte-pair-encoding (BPE) method BIBREF13 . We learn 25k BPE codes using the joint corpus of source and target languages. We randomly select 7k from IWSLT14 German-English as the development set , and the test set is a concatenation of dev2010, tst2010, tst2011 and tst2012, which is widely used in prior works BIBREF14 , BIBREF15 , BIBREF16 .",
+      "For the Turkish-English translation task, we use the data provided by IWSLT14 BIBREF17 and the SETimes corpus BIBREF17 following BIBREF18 . After removing sentence pairs with length ratio over 9, we obtain 360k sentence pairs. Since there is little commonality between the two languages, we learn 30k size BPE codes separately for Turkish and English. In addition to this, we give another preprocessing for Turkish sentences and use word-level English corpus. For Turkish sentences, following BIBREF19 , BIBREF18 , we use the morphology tool Zemberek with disambiguation by the morphological analysis BIBREF20 and removal of non-surface tokens. Following BIBREF18 , we concatenate tst2011, tst2012, tst2013, tst2014 as our test set. We concatenate dev2010 and tst2010 as the development set.",
+      "We preprocess the WMT14 English-German dataset using a BPE code size of 40k. We use the concatenation of newstest2013 and newstest2012 as the development set."
+    ],
+    [
+      "As the baseline model (BASE-4L) for IWSLT14 German-English and Turkish-English, we use a 4-layer encoder, 4-layer decoder, residual-connected model, with embedding and hidden size set as 256 by default. As a comparison, we design a densely connected model with same number of layers, but the hidden size is set as 128 in order to keep the model size consistent. The models adopting DenseAtt-1, DenseAtt-2 are named as DenseNMT-4L-1 and DenseNMT-4L-2 respectively. In order to check the effect of dense connections on deeper models, we also construct a series of 8-layer models. We set the hidden number to be 192, such that both 4-layer models and 8-layer models have similar number of parameters. For dense structured models, we set the dimension of hidden states to be 96.",
+      "Since NMT model usually allocates a large proportion of its parameters to the source/target sentence embedding and softmax matrix, we explore in our experiments to what extent decreasing the dimensions of the three parts would harm the BLEU score. We change the dimensions of the source embedding, the target embedding as well as the softmax matrix simultaneously to smaller values, and then project each word back to the original embedding dimension through a linear transformation. This significantly reduces the number of total parameters, while not influencing the upper layer structure of the model.",
+      "We also introduce three additional models we use for ablation study, all using 4-layer structure. Based on the residual connected BASE-4L model, (1) DenseENC-4L only makes encoder side dense, (2) DenseDEC-4L only makes decoder side dense, and (3) DenseAtt-4L only makes the attention dense using DenseAtt-2. There is no summary layer in the models, and both DenseENC-4L and DenseDEC-4L use hidden size 128. Again, by reducing the hidden size, we ensure that different 4-layer models have similar model sizes.",
+      "Our design for the WMT14 English-German model follows the best performance model provided in BIBREF2 . The construction of our model is straightforward: our 15-layer model DenseNMT-En-De-15 uses dense connection with DenseAtt-2, INLINEFORM0 . The hidden number in each layer is INLINEFORM1 that of the original model, while the kernel size maintains the same."
+    ],
+    [
+      "We use Nesterov Accelerated Gradient (NAG) BIBREF21 as our optimizer, and the initial learning rate is set to INLINEFORM0 . For German-English and Turkish-English experiments, the learning rate will shrink by 10 every time the validation loss increases. For the English-German dataset, in consistent with BIBREF2 , the learning rate will shrink by 10 every epoch since the first increment of validation loss. The system stops training until the learning rate is less than INLINEFORM1 . All models are trained end-to-end without any warmstart techniques. We set our batch size for the WMT14 English-German dataset to be 48, and additionally tune the length penalty parameter, in consistent with BIBREF2 . For other datasets, we set batch size to be 32. During inference, we use a beam size of 5."
+    ],
+    [
+      "We first show that DenseNMT helps information flow more efficiently by presenting the training loss curve. All hyperparameters are fixed in each plot, only the models are different. In Figure FIGREF30 , the loss curves for both training and dev sets (before entering the finetuning period) are provided for De-En, Tr-En and Tr-En-morph. For clarity, we compare DenseNMT-4L-2 with BASE-4L. We observe that DenseNMT models are consistently better than residual-connected models, since their loss curves are always below those of the baseline models. The effect is more obvious on the WMT14 English-German dataset. We rerun the best model provided by BIBREF2 and compare with our model. In Figure FIGREF33 , where train/test loss curve are provided, DenseNMT-En-De-15 reaches the same level of loss and starts finetuning (validation loss starts to increase) at epoch 13, which is 35% faster than the baseline.",
+      "Adding dense connections changes the architecture, and would slightly influence training speed. For the WMT14 En-De experiments, the computing time for both DenseNMT and the baseline (with similar number of parameters and same batch size) tested on single M40 GPU card are 1571 and 1710 word/s, respectively. While adding dense connections influences the per-iteration training slightly (8.1% reduction of speed), it uses many fewer epochs, and achieves a better BLEU score. In terms of training time, DenseNMT uses 29.3%(before finetuning)/22.9%(total) less time than the baseline."
+    ],
+    [
+      "Table TABREF32 shows the results for De-En, Tr-En, Tr-En-morph datasets, where the best accuracy for models with the same depth and of similar sizes are marked in boldface. In almost all genres, DenseNMT models are significantly better than the baselines. With embedding size 256, where all models achieve their best scores, DenseNMT outperforms baselines by 0.7-1.0 BLEU on De-En, 0.5-1.3 BLEU on Tr-En, 0.8-1.5 BLEU on Tr-En-morph. We observe significant gain using other embedding sizes as well.",
+      "Furthermore, in Table TABREF36 , we investigate DenseNMT models through ablation study. In order to make the comparison fair, six models listed have roughly the same number of parameters. On De-En, Tr-En and Tr-En-morph, we see improvement by making the encoder dense, making the decoder dense, and making the attention dense. Fully dense-connected model DenseNMT-4L-1 further improves the translation accuracy. By allowing more flexibility in dense attention, DenseNMT-4L-2 provides the highest BLEU scores for all three experiments.",
+      "From the experiments, we have seen that enlarging the information flow in the attention block benefits the models. The dense attention block provides multi-layer information transmission from the encoder to the decoder, and to the output as well. Meanwhile, as shown by the ablation study, the dense-connected encoder and decoder both give more powerful representations than the residual-connected counterparts. As a result, the integration of the three parts improve the accuracy significantly."
+    ],
+    [
+      "From Table TABREF32 , we also observe that DenseNMT performs better with small embedding sizes compared to residual-connected models with regular embedding size. For example, on Tr-En model, the 8-layer DenseNMT-8L-2 model with embedding size 64 matches the BLEU score of the 8-layer BASE model with embedding size 256, while the number of parameter of the former one is only INLINEFORM0 of the later one. In all genres, DenseNMT model with embedding size 128 is comparable or even better than the baseline model with embedding size 256.",
+      "While overlarge embedding sizes hurt accuracy because of overfitting issues, smaller sizes are not preferable because of insufficient representation power. However, our dense models show that with better model design, the embedding information can be well concentrated on fewer dimensions, e.g., 64. This is extremely helpful when building models on mobile and small devices where the model size is critical. While there are other works that stress the efficiency issue by using techniques such as separable convolution BIBREF3 , and shared embedding BIBREF4 , our DenseNMT framework is orthogonal to those approaches. We believe that other techniques would produce more efficient models through combining with our DenseNMT framework."
+    ],
+    [
+      "For the IWSLT14 German-English dataset, we compare with the best results reported from literatures. To be consistent with prior works, we also provide results using our model directly on the dataset without BPE preprocessing. As shown in Table TABREF39 , DenseNMT outperforms the phrase-structure based network NPMT BIBREF16 (with beam size 10) by 1.2 BLEU, using a smaller beam size, and outperforms the actor-critic method based algorithm BIBREF15 by 2.8 BLEU. For reference, our model trained on the BPE preprocessed dataset achieves 32.26 BLEU, which is 1.93 BLEU higher than our word-based model. For Turkish-English task, we compare with BIBREF19 which uses the same morphology preprocessing as our Tr-En-morph. As shown in Table TABREF37 , our baseline is higher than the previous result, and we further achieve new benchmark result with 24.36 BLEU average score. For WMT14 English-German, from Table TABREF41 , we can see that DenseNMT outperforms ConvS2S model by 0.36 BLEU score using 35% fewer training iterations and 20% fewer parameters. We also compare with another convolution based NMT model: SliceNet BIBREF3 , which explores depthwise separable convolution architectures. SliceNet-Full matches our result, and SliceNet-Super outperforms by 0.58 BLEU score. However, both models have 2.2x more parameters than our model. We expect DenseNMT structure could help improve their performance as well."
+    ],
+    [
+      "In this work, we have proposed DenseNMT as a dense-connection framework for translation tasks, which uses the information from embeddings more efficiently, and passes abundant information from the encoder side to the decoder side. Our experiments have shown that DenseNMT is able to speed up the information flow and improve translation accuracy. For the future work, we will combine dense connections with other deep architectures, such as RNNs BIBREF7 and self-attention networks BIBREF4 ."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: Dense Information Flow for Neural Machine Translation
+
+Question: what datasets were used?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "DenseNMT",
+    "Dense encoder and decoder",
+    "Dense attention",
+    "Summary layers",
+    "Analysis of information flow",
+    "Datasets",
+    "Model and architect design",
+    "Training setting",
+    "Training curve",
+    "DenseNMT improves accuracy with similar architectures and model sizes",
+    "DenseNMT with smaller embedding size",
+    "DenseNMT compares with state-of-the-art results",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Neural machine translation (NMT) is a challenging task that attracts lots of attention in recent years. Starting from the encoder-decoder framework BIBREF0 , NMT starts to show promising results in many language pairs. The evolving structures of NMT models in recent years have made them achieve higher scores and become more favorable. The attention mechanism BIBREF1 added on top of encoder-decoder framework is shown to be very useful to automatically find alignment structure, and single-layer RNN-based structure has evolved into deeper models with more efficient transformation functions BIBREF2 , BIBREF3 , BIBREF4 .",
+      "One major challenge of NMT is that its models are hard to train in general due to the complexity of both the deep models and languages. From the optimization perspective, deeper models are hard to efficiently back-propagate the gradients, and this phenomenon as well as its solution is better explored in the computer vision society. Residual networks (ResNet) BIBREF5 achieve great performance in a wide range of tasks, including image classification and image segmentation. Residual connections allow features from previous layers to be accumulated to the next layer easily, and make the optimization of the model efficiently focus on refining upper layer features.",
+      "NMT is considered as a challenging problem due to its sequence-to-sequence generation framework, and the goal of comprehension and reorganizing from one language to the other. Apart from the encoder block that works as a feature generator, the decoder network combining with the attention mechanism bring new challenges to the optimization of the models. While nowadays best-performing NMT systems use residual connections, we question whether this is the most efficient way to propagate information through deep models. In this paper, inspired by the idea of using dense connections for training computer vision tasks BIBREF6 , we propose a densely connected NMT framework (DenseNMT) that efficiently propagates information from the encoder to the decoder through the attention component. Taking the CNN-based deep architecture as an example, we verify the efficiency of DenseNMT. Our contributions in this work include: (i) by comparing the loss curve, we show that DenseNMT allows the model to pass information more efficiently, and speeds up training; (ii) we show through ablation study that dense connections in all three blocks altogether help improve the performance, while not increasing the number of parameters; (iii) DenseNMT allows the models to achieve similar performance with much smaller embedding size; (iv) DenseNMT on IWSLT14 German-English and Turkish-English translation tasks achieves new benchmark BLEU scores, and the result on WMT14 English-German task is more competitive than the residual connections based baseline model."
+    ],
+    [
+      "In this section, we introduce our DenseNMT architecture. In general, compared with residual connected NMT models, DenseNMT allows each layer to provide its information to all subsequent layers directly. Figure FIGREF9 - FIGREF15 show the design of our model structure by parts.",
+      "We start with the formulation of a regular NMT model. Given a set of sentence pairs INLINEFORM0 , an NMT model learns parameter INLINEFORM1 by maximizing the log-likelihood function: DISPLAYFORM0 ",
+      "For every sentence pair INLINEFORM0 , INLINEFORM1 is calculated based on the decomposition: DISPLAYFORM0 ",
+      "where INLINEFORM0 is the length of sentence INLINEFORM1 . Typically, NMT models use the encoder-attention-decoder framework BIBREF1 , and potentially use multi-layer structure for both encoder and decoder. Given a source sentence INLINEFORM2 with length INLINEFORM3 , the encoder calculates hidden representations by layer. We denote the representation in the INLINEFORM4 -th layer as INLINEFORM5 , with dimension INLINEFORM6 , where INLINEFORM7 is the dimension of features in layer INLINEFORM8 . The hidden representation at each position INLINEFORM9 is either calculated by: DISPLAYFORM0 ",
+      "for recurrent transformation INLINEFORM0 such as LSTM and GRU, or by: DISPLAYFORM0 ",
+      "for parallel transformation INLINEFORM0 . On the other hand, the decoder layers INLINEFORM1 follow similar structure, while getting extra representations from the encoder side. These extra representations are also called attention, and are especially useful for capturing alignment information.",
+      "In our experiments, we use convolution based transformation for INLINEFORM0 due to both its efficiency and high performance, more formally, DISPLAYFORM0 ",
+      " INLINEFORM0 is the gated linear unit proposed in BIBREF11 and the kernel size is INLINEFORM1 . DenseNMT is agnostic to the transformation function, and we expect it to also work well combining with other transformations, such as LSTM, self-attention and depthwise separable convolution."
+    ],
+    [
+      "Different from residual connections, later layers in the dense encoder are able to use features from all previous layers by concatenating them: DISPLAYFORM0 ",
+      "Here, INLINEFORM0 is defined in Eq. ( EQREF10 ), INLINEFORM1 represents concatenation operation. Although this brings extra connections to the network, with smaller number of features per layer, the architecture encourages feature reuse, and can be more compact and expressive. As shown in Figure FIGREF9 , when designing the model, the hidden size in each layer is much smaller than the hidden size of the corresponding layer in the residual-connected model.",
+      "While each encoder layer perceives information from its previous layers, each decoder layer INLINEFORM0 has two information sources: previous layers INLINEFORM1 , and attention values INLINEFORM2 . Therefore, in order to allow dense information flow, we redefine the generation of INLINEFORM3 -th layer as a nonlinear function over all its previous decoder layers and previous attentions. This can be written as: DISPLAYFORM0 ",
+      "where INLINEFORM0 is the attention value using INLINEFORM1 -th decoder layer and information from encoder side, which will be specified later. Figure FIGREF13 shows the comparison of a dense decoder with a regular residual decoder. The dimensions of both attention values and hidden layers are chosen with smaller values, yet the perceived information for each layer consists of a higher dimension vector with more representation power. The output of the decoder is a linear transformation of the concatenation of all layers by default. To compromise to the increment of dimensions, we use summary layers, which will be introduced in Section 3.3. With summary layers, the output of the decoder is only a linear transformation of the concatenation of the upper few layers."
+    ],
+    [
+      "Prior works show a trend of designing more expressive attention mechanisms (as discussed in Section 2). However, most of them only use the last encoder layer. In order to pass more abundant information from the encoder side to the decoder side, the attention block needs to be more expressive. Following the recent development of designing attention architectures, we propose DenseAtt as the dense attention block, which serves for the dense connection between the encoder and the decoder side. More specifically, two options are proposed accordingly. For each decoding step in the corresponding decoder layer, the two options both calculate attention using multiple encoder layers. The first option is more compressed, while the second option is more expressive and flexible. We name them as DenseAtt-1 and DenseAtt-2 respectively. Figure FIGREF15 shows the architecture of (a) multi-step attention BIBREF2 , (b) DenseAtt-1, and (c) DenseAtt-2 in order. In general, a popular multiplicative attention module can be written as: DISPLAYFORM0 ",
+      "where INLINEFORM0 represent query, key, value respectively. We will use this function INLINEFORM1 in the following descriptions.",
+      "In the decoding phase, we use a layer-wise attention mechanism, such that each decoder layer absorbs different attention information to adjust its output. Instead of treating the last hidden layer as the encoder's output, we treat the concatenation of all hidden layers from encoder side as the output. The decoder layer multiplies with the encoder output to obtain the attention weights, which is then multiplied by a linear combination of the encoder output and the sentence embedding. The attention output of each layer INLINEFORM0 can be formally written as: DISPLAYFORM0 ",
+      "where INLINEFORM0 is the multiplicative attention function, INLINEFORM1 is a concatenation operation that combines all features, and INLINEFORM2 is a linear transformation function that maps each variable to a fixed dimension in order to calculate the attention value. Notice that we explicitly write the INLINEFORM3 term in ( EQREF19 ) to keep consistent with the multi-step attention mechanism, as pictorially shown in Figure FIGREF15 (a).",
+      "Notice that the transformation INLINEFORM0 in DenseAtt-1 forces the encoder layers to be mixed before doing attention. Since we use multiple hidden layers from the encoder side to get an attention value, we can alternatively calculate multiple attention values before concatenating them. In another word, the decoder layer can get different attention values from different encoder layers. This can be formally expressed as: DISPLAYFORM0 ",
+      "where the only difference from Eq. ( EQREF19 ) is that the concatenation operation is substituted by a summation operation, and is put after the attention function INLINEFORM0 . This method further increases the representation power in the attention block, while maintaining the same number of parameters in the model."
+    ],
+    [
+      "Since the number of features fed into nonlinear operation is accumulated along the path, the parameter size increases accordingly. For example, for the INLINEFORM0 -th encoder layer, the input dimension of features is INLINEFORM1 , where INLINEFORM2 is the feature dimension in previous layers, INLINEFORM3 is the embedding size. In order to avoid the calculation bottleneck for later layers due to large INLINEFORM4 , we introduce the summary layer for deeper models. It summarizes the features for all previous layers and projects back to the embedding size, so that later layers of both the encoder and the decoder side do not need to look back further. The summary layers can be considered as contextualized word vectors in a given sentence BIBREF12 . We add one summary layer after every INLINEFORM5 layers, where INLINEFORM6 is the hyperparameter we introduce. Accordingly, the input dimension of features is at most INLINEFORM7 for the last layer of the encoder. Moreover, combined with the summary layer setting, our DenseAtt mechanism allows each decoder layer to calculate the attention value focusing on the last few encoder layers, which consists of the last contextual embedding layer and several dense connected layers with low dimension. In practice, we set INLINEFORM8 as 5 or 6."
+    ],
+    [
+      "Figure FIGREF9 and Figure FIGREF13 show the difference of information flow compared with a residual-based encoder/decoder. For residual-based models, each layer can absorb a single high-dimensional vector from its previous layer as the only information, while for DenseNMT, each layer can utilize several low-dimensional vectors from its previous layers and a high-dimensional vector from the first layer (embedding layer) as its information. In DenseNMT, each layer directly provides information to its later layers. Therefore, the structure allows feature reuse, and encourages upper layers to focus on creating new features. Furthermore, the attention block allows the embedding vectors (as well as other hidden layers) to guide the decoder's generation more directly; therefore, during back-propagation, the gradient information can be passed directly to all encoder layers simultaneously."
+    ],
+    [
+      "We use three datasets for our experiments: IWSLT14 German-English, Turkish-English, and WMT14 English-German.",
+      "We preprocess the IWSLT14 German-English dataset following byte-pair-encoding (BPE) method BIBREF13 . We learn 25k BPE codes using the joint corpus of source and target languages. We randomly select 7k from IWSLT14 German-English as the development set , and the test set is a concatenation of dev2010, tst2010, tst2011 and tst2012, which is widely used in prior works BIBREF14 , BIBREF15 , BIBREF16 .",
+      "For the Turkish-English translation task, we use the data provided by IWSLT14 BIBREF17 and the SETimes corpus BIBREF17 following BIBREF18 . After removing sentence pairs with length ratio over 9, we obtain 360k sentence pairs. Since there is little commonality between the two languages, we learn 30k size BPE codes separately for Turkish and English. In addition to this, we give another preprocessing for Turkish sentences and use word-level English corpus. For Turkish sentences, following BIBREF19 , BIBREF18 , we use the morphology tool Zemberek with disambiguation by the morphological analysis BIBREF20 and removal of non-surface tokens. Following BIBREF18 , we concatenate tst2011, tst2012, tst2013, tst2014 as our test set. We concatenate dev2010 and tst2010 as the development set.",
+      "We preprocess the WMT14 English-German dataset using a BPE code size of 40k. We use the concatenation of newstest2013 and newstest2012 as the development set."
+    ],
+    [
+      "As the baseline model (BASE-4L) for IWSLT14 German-English and Turkish-English, we use a 4-layer encoder, 4-layer decoder, residual-connected model, with embedding and hidden size set as 256 by default. As a comparison, we design a densely connected model with same number of layers, but the hidden size is set as 128 in order to keep the model size consistent. The models adopting DenseAtt-1, DenseAtt-2 are named as DenseNMT-4L-1 and DenseNMT-4L-2 respectively. In order to check the effect of dense connections on deeper models, we also construct a series of 8-layer models. We set the hidden number to be 192, such that both 4-layer models and 8-layer models have similar number of parameters. For dense structured models, we set the dimension of hidden states to be 96.",
+      "Since NMT model usually allocates a large proportion of its parameters to the source/target sentence embedding and softmax matrix, we explore in our experiments to what extent decreasing the dimensions of the three parts would harm the BLEU score. We change the dimensions of the source embedding, the target embedding as well as the softmax matrix simultaneously to smaller values, and then project each word back to the original embedding dimension through a linear transformation. This significantly reduces the number of total parameters, while not influencing the upper layer structure of the model.",
+      "We also introduce three additional models we use for ablation study, all using 4-layer structure. Based on the residual connected BASE-4L model, (1) DenseENC-4L only makes encoder side dense, (2) DenseDEC-4L only makes decoder side dense, and (3) DenseAtt-4L only makes the attention dense using DenseAtt-2. There is no summary layer in the models, and both DenseENC-4L and DenseDEC-4L use hidden size 128. Again, by reducing the hidden size, we ensure that different 4-layer models have similar model sizes.",
+      "Our design for the WMT14 English-German model follows the best performance model provided in BIBREF2 . The construction of our model is straightforward: our 15-layer model DenseNMT-En-De-15 uses dense connection with DenseAtt-2, INLINEFORM0 . The hidden number in each layer is INLINEFORM1 that of the original model, while the kernel size maintains the same."
+    ],
+    [
+      "We use Nesterov Accelerated Gradient (NAG) BIBREF21 as our optimizer, and the initial learning rate is set to INLINEFORM0 . For German-English and Turkish-English experiments, the learning rate will shrink by 10 every time the validation loss increases. For the English-German dataset, in consistent with BIBREF2 , the learning rate will shrink by 10 every epoch since the first increment of validation loss. The system stops training until the learning rate is less than INLINEFORM1 . All models are trained end-to-end without any warmstart techniques. We set our batch size for the WMT14 English-German dataset to be 48, and additionally tune the length penalty parameter, in consistent with BIBREF2 . For other datasets, we set batch size to be 32. During inference, we use a beam size of 5."
+    ],
+    [
+      "We first show that DenseNMT helps information flow more efficiently by presenting the training loss curve. All hyperparameters are fixed in each plot, only the models are different. In Figure FIGREF30 , the loss curves for both training and dev sets (before entering the finetuning period) are provided for De-En, Tr-En and Tr-En-morph. For clarity, we compare DenseNMT-4L-2 with BASE-4L. We observe that DenseNMT models are consistently better than residual-connected models, since their loss curves are always below those of the baseline models. The effect is more obvious on the WMT14 English-German dataset. We rerun the best model provided by BIBREF2 and compare with our model. In Figure FIGREF33 , where train/test loss curve are provided, DenseNMT-En-De-15 reaches the same level of loss and starts finetuning (validation loss starts to increase) at epoch 13, which is 35% faster than the baseline.",
+      "Adding dense connections changes the architecture, and would slightly influence training speed. For the WMT14 En-De experiments, the computing time for both DenseNMT and the baseline (with similar number of parameters and same batch size) tested on single M40 GPU card are 1571 and 1710 word/s, respectively. While adding dense connections influences the per-iteration training slightly (8.1% reduction of speed), it uses many fewer epochs, and achieves a better BLEU score. In terms of training time, DenseNMT uses 29.3%(before finetuning)/22.9%(total) less time than the baseline."
+    ],
+    [
+      "Table TABREF32 shows the results for De-En, Tr-En, Tr-En-morph datasets, where the best accuracy for models with the same depth and of similar sizes are marked in boldface. In almost all genres, DenseNMT models are significantly better than the baselines. With embedding size 256, where all models achieve their best scores, DenseNMT outperforms baselines by 0.7-1.0 BLEU on De-En, 0.5-1.3 BLEU on Tr-En, 0.8-1.5 BLEU on Tr-En-morph. We observe significant gain using other embedding sizes as well.",
+      "Furthermore, in Table TABREF36 , we investigate DenseNMT models through ablation study. In order to make the comparison fair, six models listed have roughly the same number of parameters. On De-En, Tr-En and Tr-En-morph, we see improvement by making the encoder dense, making the decoder dense, and making the attention dense. Fully dense-connected model DenseNMT-4L-1 further improves the translation accuracy. By allowing more flexibility in dense attention, DenseNMT-4L-2 provides the highest BLEU scores for all three experiments.",
+      "From the experiments, we have seen that enlarging the information flow in the attention block benefits the models. The dense attention block provides multi-layer information transmission from the encoder to the decoder, and to the output as well. Meanwhile, as shown by the ablation study, the dense-connected encoder and decoder both give more powerful representations than the residual-connected counterparts. As a result, the integration of the three parts improve the accuracy significantly."
+    ],
+    [
+      "From Table TABREF32 , we also observe that DenseNMT performs better with small embedding sizes compared to residual-connected models with regular embedding size. For example, on Tr-En model, the 8-layer DenseNMT-8L-2 model with embedding size 64 matches the BLEU score of the 8-layer BASE model with embedding size 256, while the number of parameter of the former one is only INLINEFORM0 of the later one. In all genres, DenseNMT model with embedding size 128 is comparable or even better than the baseline model with embedding size 256.",
+      "While overlarge embedding sizes hurt accuracy because of overfitting issues, smaller sizes are not preferable because of insufficient representation power. However, our dense models show that with better model design, the embedding information can be well concentrated on fewer dimensions, e.g., 64. This is extremely helpful when building models on mobile and small devices where the model size is critical. While there are other works that stress the efficiency issue by using techniques such as separable convolution BIBREF3 , and shared embedding BIBREF4 , our DenseNMT framework is orthogonal to those approaches. We believe that other techniques would produce more efficient models through combining with our DenseNMT framework."
+    ],
+    [
+      "For the IWSLT14 German-English dataset, we compare with the best results reported from literatures. To be consistent with prior works, we also provide results using our model directly on the dataset without BPE preprocessing. As shown in Table TABREF39 , DenseNMT outperforms the phrase-structure based network NPMT BIBREF16 (with beam size 10) by 1.2 BLEU, using a smaller beam size, and outperforms the actor-critic method based algorithm BIBREF15 by 2.8 BLEU. For reference, our model trained on the BPE preprocessed dataset achieves 32.26 BLEU, which is 1.93 BLEU higher than our word-based model. For Turkish-English task, we compare with BIBREF19 which uses the same morphology preprocessing as our Tr-En-morph. As shown in Table TABREF37 , our baseline is higher than the previous result, and we further achieve new benchmark result with 24.36 BLEU average score. For WMT14 English-German, from Table TABREF41 , we can see that DenseNMT outperforms ConvS2S model by 0.36 BLEU score using 35% fewer training iterations and 20% fewer parameters. We also compare with another convolution based NMT model: SliceNet BIBREF3 , which explores depthwise separable convolution architectures. SliceNet-Full matches our result, and SliceNet-Super outperforms by 0.58 BLEU score. However, both models have 2.2x more parameters than our model. We expect DenseNMT structure could help improve their performance as well."
+    ],
+    [
+      "In this work, we have proposed DenseNMT as a dense-connection framework for translation tasks, which uses the information from embeddings more efficiently, and passes abundant information from the encoder side to the decoder side. Our experiments have shown that DenseNMT is able to speed up the information flow and improve translation accuracy. For the future work, we will combine dense connections with other deep architectures, such as RNNs BIBREF7 and self-attention networks BIBREF4 ."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0558/instruction.md b/qasper-0558/instruction.md
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+Name of Paper: Casting Light on Invisible Cities: Computationally Engaging with Literary Criticism
+
+Question: How do they obtain human judgements?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Literary analyses of Invisible Cities",
+    "A Computational Analysis",
+    "Embedding city descriptions",
+    "Clustering city representations",
+    "Evaluating clustering assignments",
+    "Quantitative comparison",
+    "Examining the learned clusters",
+    "Related work",
+    "Conclusion",
+    "Acknowledgement"
+  ],
+  "paragraphs": [
+    [
+      "Literary critics form interpretations of meaning in works of literature. Building computational models that can help form and test these interpretations is a fundamental goal of digital humanities research BIBREF0 . Within natural language processing, most previous work that engages with literature relies on \u201cdistant reading\u201d BIBREF1 , which involves discovering high-level patterns from large collections of stories BIBREF2 , BIBREF3 . We depart from this trend by showing that computational techniques can also engage with literary criticism at a closer distance: concretely, we use recent advances in text representation learning to test a single literary theory about the novel Invisible Cities by Italo Calvino.",
+      "Framed as a dialogue between the traveler Marco Polo and the emperor Kublai Khan, Invisible Cities consists of 55 prose poems, each of which describes an imaginary city. Calvino categorizes these cities into eleven thematic groups that deal with human emotions (e.g., desires, memories), general objects (eyes, sky, signs), and unusual properties (continuous, hidden, thin). Many critics argue that Calvino's labels are not meaningful, while others believe that there is a distinct thematic separation between the groups, including the author himself BIBREF4 . The unique structure of this novel \u2014 each city's description is short and self-contained (Figure FIGREF1 ) \u2014 allows us to computationally examine this debate.",
+      "As the book is too small to train any models, we leverage recent advances in large-scale language model-based representations BIBREF5 , BIBREF6 to compute a representation of each city. We feed these representations into a clustering algorithm that produces exactly eleven clusters of five cities each and evaluate them against both Calvino's original labels and crowdsourced human judgments. While the overall correlation with Calvino's labels is low, both computers and humans can reliably identify some thematic groups associated with concrete objects.",
+      "While prior work has computationally analyzed a single book BIBREF7 , our work goes beyond simple word frequency or n-gram counts by leveraging the power of pretrained language models to engage with literary criticism. Admittedly, our approach and evaluations are specific to Invisible Cities, but we believe that similar analyses of more conventionally-structured novels could become possible as text representation methods improve. We also highlight two challenges of applying computational methods to literary criticisms: (1) text representation methods are imperfect, especially when given writing as complex as Calvino's; and (2) evaluation is difficult because there is no consensus among literary critics on a single \u201ccorrect\u201d interpretation."
+    ],
+    [
+      "Before describing our method and results, we first review critical opinions on both sides of whether Calvino's thematic groups meaningfully characterize his city descriptions."
+    ],
+    [
+      "We focus on measuring to what extent computers can recover Calvino's thematic groupings when given just raw text of the city descriptions. At a high level, our approach (Figure FIGREF4 ) involves (1) computing a vector representation for every city and (2) performing unsupervised clustering of these representations. The rest of this section describes both of these steps in more detail."
+    ],
+    [
+      "While each of the city descriptions is relatively short, Calvino's writing is filled with rare words, complex syntactic structures, and figurative language. Capturing the essential components of each city in a single vector is thus not as simple as it is with more standard forms of text. Nevertheless, we hope that representations from language models trained over billions of words of text can extract some meaningful semantics from these descriptions. We experiment with three different pretrained representations: ELMo BIBREF5 , BERT BIBREF6 , and GloVe BIBREF18 . To produce a single city embedding, we compute the TF-IDF weighted element-wise mean of the token-level representations. For all pretrained methods, we additionally reduce the dimensionality of the city embeddings to 40 using PCA for increased compatibility with our clustering algorithm."
+    ],
+    [
+      "Given 55 city representations, how do we group them into eleven clusters of five cities each? Initially, we experimented with a graph-based community detection algorithm that maximizes cluster modularity BIBREF20 , but we found no simple way to constrain this method to produce a specific number of equally-sized clusters. The brute force approach of enumerating all possible cluster assignments is intractable given the large search space ( INLINEFORM0 possible assignments). We devise a simple clustering algorithm to approximate this process. First, we initialize with random cluster assignments and define \u201ccluster strength\u201d to be the relative difference between \u201cintra-group\u201d Euclidean distance and \u201cinter-group\u201d Euclidean distance. Then, we iteratively propose random exchanges of memberships, only accepting these proposals when the cluster strength increases, until convergence. To evaluate the quality of the computationally-derived clusters against those of Calvino, we measure cluster purity BIBREF21 : given a set of predicted clusters INLINEFORM1 and ground-truth clusters INLINEFORM2 that both partition a set of INLINEFORM3 data points, INLINEFORM4 "
+    ],
+    [
+      "While the results from the above section allow us to compare our three computational methods against each other, we additionally collect human judgments to further ground our results. In this section, we first describe our human experiment before quantitatively analyzing our results."
+    ],
+    [
+      "We compare clusters computed on different representations using community purity; additionally, we compare these computational methods to humans by their accuracy on the odd-one-out task.",
+      "City representations computed using language model-based representation (ELMo and BERT) achieve significantly higher purity than a clustering induced from random representations, indicating that there is at least some meaningful coherence to Calvino's thematic groups (first row of Table TABREF11 ). ELMo representations yield the highest purity among the three methods, which is surprising as BERT is a bigger model trained on data from books (among other domains). Both ELMo and BERT outperform GloVe, which intuitively makes sense because the latter do not model the order or structure of the words in each description.",
+      "While the purity of our methods is higher than that of a random clustering, it is still far below 1. To provide additional context to these results, we now switch to our \u201codd-one-out\u201d task and compare directly to human performance. For each triplet of cities, we identify the intruder as the city with the maximum Euclidean distance from the other two. Interestingly, crowd workers achieve only slightly higher accuracy than ELMo city representations; their interannotator agreement is also low, which indicates that close reading to analyze literary coherence between multiple texts is a difficult task, even for human annotators. Overall, results from both computational and human approaches suggests that the author-assigned labels are not entirely arbitrary, as we can reliably recover some of the thematic groups."
+    ],
+    [
+      "Our quantitative results suggest that while vector-based city representations capture some thematic similarities, there is much room for improvement. In this section, we first investigate whether the learned clusters provide evidence for any arguments put forth by literary critics on the novel. Then, we explore possible reasons that the learned clusters deviate from Calvino's."
+    ],
+    [
+      "Most previous work within the NLP community applies distant reading BIBREF1 to large collections of books, focusing on modeling different aspects of narratives such as plots and event sequences BIBREF22 , BIBREF23 , BIBREF24 , BIBREF25 , characters BIBREF2 , BIBREF26 , BIBREF27 , BIBREF28 , and narrative similarity BIBREF3 . In the same vein, researchers in computational literary analysis have combined statistical techniques and linguistics theories to perform quantitative analysis on large narrative texts BIBREF29 , BIBREF30 , BIBREF31 , BIBREF32 , BIBREF33 , but these attempts largely rely on techniques such as word counting, topic modeling, and naive Bayes classifiers and are therefore not able to capture the meaning of sentences or paragraphs BIBREF34 . While these works discover general patterns from multiple literary works, we are the first to use cutting-edge NLP techniques to engage with specific literary criticism about a single narrative.",
+      "There has been other computational work that focuses on just a single book or a small number of books, much of it focused on network analysis: BIBREF35 extract character social networks from Alice in Wonderland, while BIBREF36 recover social networks from 19th century British novels. BIBREF37 disentangles multiple narrative threads within the novel Infinite Jest, while BIBREF7 provides several automated statistical methods for close reading and test them on the award-winning novel Cloud Atlas (2004). Compared to this work, we push further on modeling the content of the narrative by leveraging pretrained language models."
+    ],
+    [
+      "Our work takes a first step towards computationally engaging with literary criticism on a single book using state-of-the-art text representation methods. While we demonstrate that NLP techniques can be used to support literary analyses and obtain new insights, they also have clear limitations (e.g., in understanding abstract themes). As text representation methods become more powerful, we hope that (1) computational tools will become useful for analyzing novels with more conventional structures, and (2) literary criticism will be used as a testbed for evaluating representations."
+    ],
+    [
+      "We thank the anonymous reviewers for their insightful comments. Additionally, we thank Nader Akoury, Garrett Bernstein, Chenghao Lv, Ari Kobren, Kalpesh Krishna, Saumya Lal, Tu Vu, Zhichao Yang, Mengxue Zhang and the UMass NLP group for suggestions that improved the paper's clarity, coverage of related work, and analysis experiments."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0560/instruction.md b/qasper-0560/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..50d28ef4f01d3986c3494330809b4153da0c6f2b
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@@ -0,0 +1,100 @@
+Name of Paper: Scalable and Accurate Dialogue State Tracking via Hierarchical Sequence Generation
+
+Question: Does this approach perform better in the multi-domain or single-domain setting?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Motivation",
+    "Hierarchical Sequence Generation for DST",
+    "Encoding Module",
+    "Conditional Memory Relation Decoder",
+    "Experimental Setting",
+    "Implementation Details",
+    "Results",
+    "Ablation Study",
+    "Qualitative Analysis",
+    "Related Work",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "A Dialogue State Tracker (DST) is a core component of a modular task-oriented dialogue system BIBREF7 . For each dialogue turn, a DST module takes a user utterance and the dialogue history as input, and outputs a belief estimate of the dialogue state. Then a machine action is decided based on the dialogue state according to a dialogue policy module, after which a machine response is generated.",
+      "Traditionally, a dialogue state consists of a set of requests and joint goals, both of which are represented by a set of slot-value pairs (e.g. (request, phone), (area, north), (food, Japanese)) BIBREF8 . In a recently proposed multi-domain dialogue state tracking dataset, MultiWoZ BIBREF9 , a representation of dialogue state consists of a hierarchical structure of domain, slot, and value is proposed. This is a more practical scenario since dialogues often include multiple domains simultaneously.",
+      "Many recently proposed DSTs BIBREF2 , BIBREF10 are based on pre-defined ontology lists that specify all possible slot values in advance. To generate a distribution over the candidate set, previous works often take each of the slot-value pairs as input for scoring. However, in real-world scenarios, it is often not practical to enumerate all possible slot value pairs and perform scoring from a large dynamically changing knowledge base BIBREF11 . To tackle this problem, a popular direction is to build a fixed-length candidate set that is dynamically updated throughout the dialogue development. cpt briefly summaries the inference time complexity of multiple state-of-the-art DST models following this direction. Since the inference complexity of all of previous model is at least proportional to the number of the slots, these models will struggle to scale to multi-domain datasets with much larger numbers of pre-defined slots.",
+      "In this work, we formulate the dialogue state tracking task as a sequence generation problem, instead of formulating the task as a pair-wise prediction problem as in existing work. We propose the COnditional MEmory Relation Network (COMER), a scalable and accurate dialogue state tracker that has a constant inference time complexity. ",
+      "Specifically, our model consists of an encoder-decoder network with a hierarchically stacked decoder to first generate the slot sequences in the belief state and then for each slot generate the corresponding value sequences. The parameters are shared among all of our decoders for the scalability of the depth of the hierarchical structure of the belief states. COMER applies BERT contextualized word embeddings BIBREF12 and BPE BIBREF13 for sequence encoding to ensure the uniqueness of the representations of the unseen words. The word embeddings for sequence generation are initialized and fixed with the static word embeddings generated from BERT to have the potential of generating unseen words."
+    ],
+    [
+      "f1 shows a multi-domain dialogue in which the user wants the system to first help book a train and then reserve a hotel. For each turn, the DST will need to track the slot-value pairs (e.g. (arrive by, 20:45)) representing the user goals as well as the domain that the slot-value pairs belongs to (e.g. train, hotel). Instead of representing the belief state via a hierarchical structure, one can also combine the domain and slot together to form a combined slot-value pair (e.g. (train; arrive by, 20:45) where the combined slot is \u201ctrain; arrive by\"), which ignores the subordination relationship between the domain and the slots.",
+      "A typical fallacy in dialogue state tracking datasets is that they make an assumption that the slot in a belief state can only be mapped to a single value in a dialogue turn. We call this the single value assumption. Figure 2 shows an example of this fallacy from the WoZ2.0 dataset: Based on the belief state label (food, seafood), it will be impossible for the downstream module in the dialogue system to generate sample responses that return information about Chinese restaurants. A correct representation of the belief state could be (food, seafood $>$ chinese). This would tell the system to first search the database for information about seafood and then Chinese restaurants. The logical operator \u201c $>$ \" indicates which retrieved information should have a higher priority to be returned to the user. Thus we are interested in building DST modules capable of generating structured sequences, since this kind of sequence representation of the value is critical for accurately capturing the belief states of a dialogue."
+    ],
+    [
+      "Given a dialogue $D$ which consists of $T$ turns of user utterances and system actions, our target is to predict the state at each turn. Different from previous methods which formulate multi-label state prediction as a collection of binary prediction problems, COMER adapts the task into a sequence generation problem via a Seq2Seq framework.",
+      "As shown in f3, COMER consists of three encoders and three hierarchically stacked decoders. We propose a novel Conditional Memory Relation Decoder (CMRD) for sequence decoding. Each encoder includes an embedding layer and a BiLSTM. The encoders take in the user utterance, the previous system actions, and the previous belief states at the current turn, and encodes them into the embedding space. The user encoder and the system encoder use the fixed BERT model as the embedding layer.",
+      "Since the slot value pairs are un-ordered set elements of a domain in the belief states, we first order the sequence of domain according to their frequencies as they appear in the training set BIBREF14 , and then order the slot value pairs in the domain according to the slot's frequencies of as they appear in a domain. After the sorting of the state elements, We represent the belief states following the paradigm: (Domain1- Slot1, Value1; Slot2, Value2; ... Domain2- Slot1, Value1; ...) for a more concise representation compared with the nested tuple representation.",
+      "All the CMRDs take the same representations from the system encoder, user encoder and the belief encoder as part of the input. In the procedure of hierarchical sequence generation, the first CMRD takes a zero vector for its condition input $\\mathbf {c}$ , and generates a sequence of the domains, $D$ , as well as the hidden representation of domains $H_D$ . For each $d$ in $D$ , the second CMRD then takes the corresponding $h_d$ as the condition input and generates the slot sequence $S_d$ , and representations, $H_{S,d}$ . Then for each $s$ in $S$ , the third CMRD generates the value sequence $D$0 based on the corresponding $D$1 . We update the belief state with the new $D$2 pairs and perform the procedure iteratively until a dialogue is completed. All the CMR decoders share all of their parameters.",
+      "Since our model generates domains and slots instead of taking pre-defined slots as inputs, and the number of domains and slots generated each turn is only related to the complexity of the contents covered in a specific dialogue, the inference time complexity of COMER is $O(1)$ with respect to the number of pre-defined slots and values."
+    ],
+    [
+      "Let $X$ represent a user utterance or system transcript consisting of a sequence of words $\\lbrace w_1,\\ldots ,w_T\\rbrace $ . The encoder first passes the sequence $\\lbrace \\mathit {[CLS]},w_1,\\ldots ,w_T,\\mathit {[SEP]}\\rbrace $ into a pre-trained BERT model and obtains its contextual embeddings $E_{X}$ . Specifically, we leverage the output of all layers of BERT and take the average to obtain the contextual embeddings.",
+      "For each domain/slot appeared in the training set, if it has more than one word, such as `price range', `leave at', etc., we feed it into BERT and take the average of the word vectors to form the extra slot embedding $E_{s}$ . In this way, we map each domain/slot to a fixed embedding, which allows us to generate a domain/slot as a whole instead of a token at each time step of domain/slot sequence decoding. We also construct a static vocabulary embedding $E_{v}$ by feeding each token in the BERT vocabulary into BERT. The final static word embedding $E$ is the concatenation of the $E_{v}$ and $E_{s}$ .",
+      "After we obtain the contextual embeddings for the user utterance, system action, and the static embeddings for the previous belief state, we feed each of them into a Bidirectional LSTM BIBREF15 . ",
+      "$$\\begin{aligned}\n\\mathbf {h}_{a_t} & = \\textrm {BiLSTM}(\\mathbf {e}_{X_{a_t}}, \\mathbf {h}_{a_{t-1}}) \\\\\n\\mathbf {h}_{u_t} & = \\textrm {BiLSTM}(\\mathbf {e}_{X_{u_t}}, \\mathbf {h}_{u_{t-1}}) \\\\\n\\mathbf {h}_{b_t} & = \\textrm {BiLSTM}(\\mathbf {e}_{X_{b_t}}, \\mathbf {h}_{b_{t-1}}) \\\\\n\\mathbf {h}_{a_0} & = \\mathbf {h}_{u_0} = \\mathbf {h}_{b_0} = c_{0}, \\\\\n\\end{aligned}$$   (Eq. 7) ",
+      "where $c_{0}$ is the zero-initialized hidden state for the BiLSTM. The hidden size of the BiLSTM is $d_m/2$ . We concatenate the forward and the backward hidden representations of each token from the BiLSTM to obtain the token representation $\\mathbf {h}_{k_t}\\in R^{d_m}$ , $k\\in \\lbrace a,u,b\\rbrace $ at each time step $t$ . The hidden states of all time steps are concatenated to obtain the final representation of $H_{k}\\in R^{T \\times d_m}, k \\in \\lbrace a,u,B\\rbrace $ . The parameters are shared between all of the BiLSTMs."
+    ],
+    [
+      "Inspired by Residual Dense Networks BIBREF16 , End-to-End Memory Networks BIBREF17 and Relation Networks BIBREF18 , we here propose the Conditional Memory Relation Decoder (CMRD). Given a token embedding, $\\mathbf {e}_x$ , CMRD outputs the next token, $s$ , and the hidden representation, $h_s$ , with the hierarchical memory access of different encoded information sources, $H_B$ , $H_a$ , $H_u$ , and the relation reasoning under a certain given condition $\\mathbf {c}$ , $\n\\mathbf {s}, \\mathbf {h}_s= \\textrm {CMRD}(\\mathbf {e}_x, \\mathbf {c}, H_B, H_a, H_u),\n$ ",
+      "the final output matrices $S,H_s \\in R^{l_s\\times d_m}$ are concatenations of all generated $\\mathbf {s}$ and $\\mathbf {h}_s$ (respectively) along the sequence length dimension, where $d_m$ is the model size, and $l_s$ is the generated sequence length. The general structure of the CMR decoder is shown in Figure 4 . Note that the CMR decoder can support additional memory sources by adding the residual connection and the attention block, but here we only show the structure with three sources: belief state representation ( $H_B$ ), system transcript representation ( $H_a$ ), and user utterance representation ( $H_u$ ), corresponding to a dialogue state tracking scenario. Since we share the parameters between all of the decoders, thus CMRD is actually a 2-dimensional auto-regressive model with respect to both the condition generation and the sequence generation task.",
+      "At each time step $t$ , the CMR decoder first embeds the token $x_t$ with a fixed token embedding $E\\in R^{d_e\\times d_v}$ , where $d_e$ is the embedding size and $d_v$ is the vocabulary size. The initial token $x_0$ is \u201c[CLS]\". The embedded vector $\\textbf {e}_{x_t}$ is then encoded with an LSTM, which emits a hidden representation $\\textbf {h}_0 \\in R^{d_m}$ , $\n\\textbf {h}_0= \\textrm {LSTM}(\\textbf {e}_{x_t},\\textbf {q}_{t-1}).\n$ ",
+      "where $\\textbf {q}_t$ is the hidden state of the LSTM. $\\textbf {q}_0$ is initialized with an average of the hidden states of the belief encoder, the system encoder and the user encoder which produces $H_B$ , $H_a$ , $H_u$ respectively.",
+      " $\\mathbf {h}_0$ is then summed (element-wise) with the condition representation $\\mathbf {c}\\in R^{d_m}$ to produce $\\mathbf {h}_1$ , which is (1) fed into the attention module; (2) used for residual connection; and (3) concatenated with other $\\mathbf {h}_i$ , ( $i>1$ ) to produce the concatenated working memory, $\\mathbf {r_0}$ , for relation reasoning, $\n\\mathbf {h}_1 & =\\mathbf {h}_0+\\mathbf {c},\\\\\n\\mathbf {h}_2 & =\\mathbf {h}_1+\\text{Attn}_{\\text{belief}}(\\mathbf {h}_1,H_e),\\\\\n\\mathbf {h}_3 & = \\mathbf {h}_2+\\text{Attn}_{\\text{sys}}(\\mathbf {h}_2,H_a),\\\\\n\\mathbf {h}_4 & = \\mathbf {h}_3+\\text{Attn}_{\\text{usr}}(\\mathbf {h}_3,H_u),\\\\\n\\mathbf {r} & = \\mathbf {h}_1\\oplus \\mathbf {h}_2\\oplus \\mathbf {h}_3\\oplus \\mathbf {h}_4 \\in R^{4d_m},\n$ ",
+      " where $\\text{Attn}_k$ ( $k\\in \\lbrace  \\text{belief}, \\text{sys},\\text{usr}\\rbrace $ ) are the attention modules applied respectively to $H_B$ , $H_a$ , $H_u$ , and $\\oplus $ means the concatenation operator. The gradients are blocked for $ \\mathbf {h}_1,\\mathbf {h}_2,\\mathbf {h}_3$ during the back-propagation stage, since we only need them to work as the supplementary memories for the relation reasoning followed.",
+      "The attention module takes a vector, $\\mathbf {h}\\in R^{d_m}$ , and a matrix, $H\\in R^{d_m\\times l}$ as input, where $l$ is the sequence length of the representation, and outputs $\\mathbf {h}_a$ , a weighted sum of the column vectors in $H$ . $\n\\mathbf {a} & =W_1^T\\mathbf {h}+\\mathbf {b}_1& &\\in R^{d_m},\\\\\n\\mathbf {c} &=\\text{softmax}(H^Ta)& &\\in R^l,\\\\\n\\mathbf {h} &=H\\mathbf {c}& &\\in R^{d_m},\\\\\n\\mathbf {h}_a &=W_2^T\\mathbf {h}+\\mathbf {b}_2& &\\in R^{d_m},\n$ ",
+      " where the weights $W_1\\in R^{d_m \\times d_m}$ , $W_2\\in R^{d_m \\times d_m}$ and the bias $b_1\\in R^{d_m}$ , $b_2\\in R^{d_m}$ are the learnable parameters.",
+      "The order of the attention modules, i.e., first attend to the system and the user and then the belief, is decided empirically. We can interpret this hierarchical structure as the internal order for the memory processing, since from the daily life experience, people tend to attend to the most contemporary memories (system/user utterance) first and then attend to the older history (belief states). All of the parameters are shared between the attention modules.",
+      "The concatenated working memory, $\\mathbf {r}_0$ , is then fed into a Multi-Layer Perceptron (MLP) with four layers, $\n\\mathbf {r}_1 & =\\sigma (W_1^T\\mathbf {r}_0+\\mathbf {b}_1),\\\\\n\\mathbf {r}_2 & =\\sigma (W_2^T\\mathbf {r}_1+\\mathbf {b}_2),\\\\\n\\mathbf {r}_3 & = \\sigma (W_3^T\\mathbf {r}_2+\\mathbf {b}_3),\\\\\n\\mathbf {h}_s & = \\sigma (W_4^T\\mathbf {r}_3+\\mathbf {b}_4),\n$ ",
+      " where $\\sigma $ is a non-linear activation, and the weights $W_1 \\in R^{4d_m \\times d_m}$ , $W_i \\in R^{d_m \\times d_m}$ and the bias $b_1 \\in R^{d_m}$ , $b_i \\in R^{d_m}$ are learnable parameters, and $2\\le i\\le 4$ . The number of layers for the MLP is decided by the grid search.",
+      "The hidden representation of the next token, $\\mathbf {h}_s$ , is then (1) emitted out of the decoder as a representation; and (2) fed into a dropout layer with drop rate $p$ , and a linear layer to generate the next token, $\n\\mathbf {h}_k & =\\text{dropout}(\\mathbf {h}_s)& &\\in R^{d_m},\\\\\n\\mathbf {h}_o & =W_k^T\\mathbf {h}_k+\\mathbf {b}_k& &\\in R^{d_e},\\\\\n\\mathbf {p}_s & =\\text{softmax}(E^T\\mathbf {h}_o)& &\\in R^{d_v},\\\\\ns & =\\text{argmax}(\\mathbf {p}_s)& &\\in R,\n$ ",
+      " where the weight $W_k\\in R^{d_m \\times d_e}$ and the bias $b_k\\in R^{d_e}$ are learnable parameters. Since $d_e$ is the embedding size and the model parameters are independent of the vocabulary size, the CMR decoder can make predictions on a dynamic vocabulary and implicitly supports the generation of unseen words. When training the model, we minimize the cross-entropy loss between the output probabilities, $\\mathbf {p}_s$ , and the given labels."
+    ],
+    [
+      "We first test our model on the single domain dataset, WoZ2.0 BIBREF19 . It consists of 1,200 dialogues from the restaurant reservation domain with three pre-defined slots: food, price range, and area. Since the name slot rarely occurs in the dataset, it is not included in our experiments, following previous literature BIBREF3 , BIBREF20 . Our model is also tested on the multi-domain dataset, MultiWoZ BIBREF9 . It has a more complex ontology with 7 domains and 25 predefined slots. Since the combined slot-value pairs representation of the belief states has to be applied for the model with $O(n)$ ITC, the total number of slots is 35. The statistics of these two datsets are shown in Table 2 .",
+      "Based on the statistics from these two datasets, we can calculate the theoretical Inference Time Multiplier (ITM), $K$ , as a metric of scalability. Given the inference time complexity, ITM measures how many times a model will be slower when being transferred from the WoZ2.0 dataset, $d_1$ , to the MultiWoZ dataset, $d_2$ , $\nK= h(t)h(s)h(n)h(m)\\\\\n$ $\nh(x)=\\left\\lbrace \n\\begin{array}{lcl}\n1 & &O(x)=O(1),\\\\\n\\frac{x_{d_2}}{x_{d_1}}& & \\text{otherwise},\\\\\n\\end{array}\\right.\n\n$ ",
+      "where $O(x)$ means the Inference Time Complexity (ITC) of the variable $x$ . For a model having an ITC of $O(1)$ with respect to the number of slots $n$ , and values $m$ , the ITM will be a multiplier of 2.15x, while for an ITC of $O(n)$ , it will be a multiplier of 25.1, and 1,143 for $O(mn)$ .",
+      "As a convention, the metric of joint goal accuracy is used to compare our model to previous work. The joint goal accuracy only regards the model making a successful belief state prediction if all of the slots and values predicted are exactly matched with the labels provided. This metric gives a strict measurement that tells how often the DST module will not propagate errors to the downstream modules in a dialogue system. In this work, the model with the highest joint accuracy on the validation set is evaluated on the test set for the test joint accuracy measurement."
+    ],
+    [
+      "We use the $\\text{BERT}_\\text{large}$ model for both contextual and static embedding generation. All LSTMs in the model are stacked with 2 layers, and only the output of the last layer is taken as a hidden representation. ReLU non-linearity is used for the activation function, $\\sigma $ .",
+      "The hyper-parameters of our model are identical for both the WoZ2.0 and the MultiwoZ datasets: dropout rate $p=0.5$ , model size $d_m=512$ , embedding size $d_e=1024$ . For training on WoZ2.0, the model is trained with a batch size of 32 and the ADAM optimizer BIBREF21 for 150 epochs, while for MultiWoZ, the AMSGrad optimizer BIBREF22 and a batch size of 16 is adopted for 15 epochs of training. For both optimizers, we use a learning rate of 0.0005 with a gradient clip of 2.0. We initialize all weights in our model with Kaiming initialization BIBREF23 and adopt zero initialization for the bias. All experiments are conducted on a single NVIDIA GTX 1080Ti GPU."
+    ],
+    [
+      "To measure the actual inference time multiplier of our model, we evaluate the runtime of the best-performing models on the validation sets of both the WoZ2.0 and MultiWoZ datasets. During evaluation, we set the batch size to 1 to avoid the influence of data parallelism and sequence padding. On the validation set of WoZ2.0, we obtain a runtime of 65.6 seconds, while on MultiWoZ, the runtime is 835.2 seconds. Results are averaged across 5 runs. Considering that the validation set of MultiWoZ is 5 times larger than that of WoZ2.0, the actual inference time multiplier is 2.54 for our model. Since the actual inference time multiplier roughly of the same magnitude as the theoretical value of 2.15, we can confirm empirically that we have the $O(1)$ inference time complexity and thus obtain full scalability to the number of slots and values pre-defined in an ontology.",
+      "c compares our model with the previous state-of-the-art on both the WoZ2.0 test set and the MultiWoZ test set. For the WoZ2.0 dataset, we maintain performance at the level of the state-of-the-art, with a marginal drop of 0.3% compared with previous work. Considering the fact that WoZ2.0 is a relatively small dataset, this small difference does not represent a significant big performance drop. On the muli-domain dataset, MultiWoZ, our model achieves a joint goal accuracy of 45.72%, which is significant better than most of the previous models other than TRADE which applies the copy mechanism and gains better generalization ability on named entity coping."
+    ],
+    [
+      "To prove the effectiveness of our structure of the Conditional Memory Relation Decoder (CMRD), we conduct ablation experiments on the WoZ2.0 dataset. We observe an accuracy drop of 1.95% after removing residual connections and the hierarchical stack of our attention modules. This proves the effectiveness of our hierarchical attention design. After the MLP is replaced with a linear layer of hidden size 512 and the ReLU activation function, the accuracy further drops by 3.45%. This drop is partly due to the reduction of the number of the model parameters, but it also proves that stacking more layers in an MLP can improve the relational reasoning performance given a concatenation of multiple representations from different sources.",
+      "We also conduct the ablation study on the MultiWoZ dataset for a more precise analysis on the hierarchical generation process. For joint domain accuracy, we calculate the probability that all domains generated in each turn are exactly matched with the labels provided. The joint domain-slot accuracy further calculate the probability that all domains and slots generated are correct, while the joint goal accuracy requires all the domains, slots and values generated are exactly matched with the labels. From abm, We can further calculate that given the correct slot prediction COMER has 83.52% chance to make the correct value prediction. While COMER has done great job on domain prediction (95.53%) and value prediction (83.52%), the accuracy of the slot prediction given the correct domain is only 57.30%. We suspect that this is because we only use the previous belief state to represent the dialogue history, and the inter-turn reasoning ability on the slot prediction suffers from the limited context and the accuracy is harmed due to the multi-turn mapping problem BIBREF4 . We can also see that the JDS Acc. has an absolute boost of 5.48% when we switch from the combined slot representation to the nested tuple representation. This is because the subordinate relationship between the domains and the slots can be captured by the hierarchical sequence generation, while this relationship is missed when generating the domain and slot together via the combined slot representation."
+    ],
+    [
+      "f5 shows an example of the belief state prediction result in one turn of a dialogue on the MultiWoZ test set. The visualization includes the CMRD attention scores over the belief states, system transcript and user utterance during the decoding stage of the slot sequence.",
+      "From the system attention (top right), since it is the first attention module and no previous context information is given, it can only find the information indicating the slot \u201cdeparture\u201d from the system utterance under the domain condition, and attend to the evidence \u201cleaving\u201d correctly during the generation step of \u201cdeparture\u201d. From the user attention, we can see that it captures the most helpful keywords that are necessary for correct prediction, such as \u201cafter\" for \u201cday\" and \u201cleave at\u201d, \u201cto\" for \u201cdestination\". Moreover, during the generation step of \u201cdeparture\u201d, the user attention successfully discerns that, based on the context, the word \u201cleave\u201d is not the evidence that need to be accumulated and choose to attend nothing in this step. For the belief attention, we can see that the belief attention module correctly attends to a previous slot for each generation step of a slot that has been presented in the previous state. For the generation step of the new slot \u201cdestination\", since the previous state does not have the \u201cdestination\" slot, the belief attention module only attends to the `-' mark after the `train' domain to indicate that the generated word should belong to this domain."
+    ],
+    [
+      "Semi-scalable Belief Tracker BIBREF1 proposed an approach that can generate fixed-length candidate sets for each of the slots from the dialogue history. Although they only need to perform inference for a fixed number of values, they still need to iterate over all slots defined in the ontology to make a prediction for a given dialogue turn. In addition, their method needs an external language understanding module to extract the exact entities from a dialogue to form candidates, which will not work if the label value is an abstraction and does not have the exact match with the words in the dialogue.",
+      "StateNet BIBREF3 achieves state-of-the-art performance with the property that its parameters are independent of the number of slot values in the candidate set, and it also supports online training or inference with dynamically changing slots and values. Given a slot that needs tracking, it only needs to perform inference once to make the prediction for a turn, but this also means that its inference time complexity is proportional to the number of slots.",
+      "TRADE BIBREF4 achieves state-of-the-art performance on the MultiWoZ dataset by applying the copy mechanism for the value sequence generation. Since TRADE takes $n$ combinations of the domains and slots as the input, the inference time complexity of TRADE is $O(n)$ . The performance improvement achieved by TRADE is mainly due to the fact that it incorporates the copy mechanism that can boost the accuracy on the \u2018name\u2019 slot, which mainly needs the ability in copying names from the dialogue history. However, TRADE does not report its performance on the WoZ2.0 dataset which does not have the \u2018name\u2019 slot.",
+      "DSTRead BIBREF6 formulate the dialogue state tracking task as a reading comprehension problem by asking slot specified questions to the BERT model and find the answer span in the dialogue history for each of the pre-defined combined slot. Thus its inference time complexity is still $O(n)$ . This method suffers from the fact that its generation vocabulary is limited to the words occurred in the dialogue history, and it has to do a manual combination strategy with another joint state tracking model on the development set to achieve better performance.",
+      "Contextualized Word Embedding (CWE) was first proposed by BIBREF25 . Based on the intuition that the meaning of a word is highly correlated with its context, CWE takes the complete context (sentences, passages, etc.) as the input, and outputs the corresponding word vectors that are unique under the given context. Recently, with the success of language models (e.g. BIBREF12 ) that are trained on large scale data, contextualizeds word embedding have been further improved and can achieve the same performance compared to (less flexible) finely-tuned pipelines.",
+      "Sequence Generation Models. Recently, sequence generation models have been successfully applied in the realm of multi-label classification (MLC) BIBREF14 . Different from traditional binary relevance methods, they proposed a sequence generation model for MLC tasks which takes into consideration the correlations between labels. Specifically, the model follows the encoder-decoder structure with an attention mechanism BIBREF26 , where the decoder generates a sequence of labels. Similar to language modeling tasks, the decoder output at each time step will be conditioned on the previous predictions during generation. Therefore the correlation between generated labels is captured by the decoder."
+    ],
+    [
+      "In this work, we proposed the Conditional Memory Relation Network (COMER), the first dialogue state tracking model that has a constant inference time complexity with respect to the number of domains, slots and values pre-defined in an ontology. Besides its scalability, the joint goal accuracy of our model also achieve the similar performance compared with the state-of-the-arts on both the MultiWoZ dataset and the WoZ dataset. Due to the flexibility of our hierarchical encoder-decoder framework and the CMR decoder, abundant future research direction remains as applying the transformer structure, incorporating open vocabulary and copy mechanism for explicit unseen words generation, and inventing better dialogue history access mechanism to accommodate efficient inter-turn reasoning.",
+      "Acknowledgements. This work is partly supported by NSF #1750063. We thank all the reviewers for their constructive suggestions. We also want to thank Zhuowen Tu and Shengnan Zhang for the early discussions of the project."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: A Simple Method for Commonsense Reasoning
+
+Question: Do they fine-tune their model on the end task?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Methods",
+    "Experimental settings",
+    "Main results",
+    "The first challenge in 2016: PDP-60",
+    "Winograd Schema Challenge",
+    "Customized training data for Winograd Schema Challenge",
+    "Discovery of special words in Winograd Schema",
+    "Partial scoring is better than full scoring.",
+    "Importance of training corpus",
+    "Conclusion",
+    "Recurrent language models",
+    "Data contamination in CommonCrawl"
+  ],
+  "paragraphs": [
+    [
+      "Although deep neural networks have achieved remarkable successes (e.g., BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 ), their dependence on supervised learning has been challenged as a significant weakness. This dependence prevents deep neural networks from being applied to problems where labeled data is scarce. An example of such problems is common sense reasoning, such as the Winograd Schema Challenge BIBREF0 , where the labeled set is typically very small, on the order of hundreds of examples. Below is an example question from this dataset:",
+      "Although it is straightforward for us to choose the answer to be \"the trophy\" according to our common sense, answering this type of question is a great challenge for machines because there is no training data, or very little of it.",
+      "In this paper, we present a surprisingly simple method for common sense reasoning with Winograd schema multiple choice questions. Key to our method is th e use of language models (LMs), trained on a large amount of unlabeled data, to score multiple choice questions posed by the challenge and similar datasets. More concretely, in the above example, we will first substitute the pronoun (\"it\") with the candidates (\"the trophy\" and \"the suitcase\"), and then use LMs to compute the probability of the two resulting sentences (\"The trophy doesn\u2019t fit in the suitcase because the trophy is too big.\" and \"The trophy doesn\u2019t fit in the suitcase because the suitcase is too big.\"). The substitution that results in a more probable sentence will be the correct answer.",
+      "A unique feature of Winograd Schema questions is the presence of a special word that decides the correct reference choice. In the above example, \"big\" is this special word. When \"big\" is replaced by \"small\", the correct answer switches to \"the suitcase\". Although detecting this feature is not part of the challenge, further analysis shows that our system successfully discovers this special word to make its decisions in many cases, indicating a good grasp of commonsense knowledge."
+    ],
+    [
+      "Unsupervised learning has been used to discover simple commonsense relationships. For example, Mikolov et al. BIBREF15 , BIBREF16 show that by learning to predict adjacent words in a sentence, word vectors can be used to answer analogy questions such as: Man:King::Woman:?. Our work uses a similar intuition that language modeling can naturally capture common sense knowledge. The difference is that Winograd Schema questions require more contextual information, hence our use of LMs instead of just word vectors.",
+      "Neural LMs have also been applied successfully to improve downstream applications BIBREF17 , BIBREF18 , BIBREF19 , BIBREF20 . In BIBREF17 , BIBREF18 , BIBREF19 , BIBREF20 , researchers have shown that pre-trained LMs can be used as feature representations for a sentence, or a paragraph to improve NLP applications such as document classification, machine translation, question answering, etc. The combined evidence suggests that LMs trained on a massive amount of unlabeled data can capture many aspects of natural language and the world's knowledge, especially commonsense information.",
+      "Previous attempts on solving the Winograd Schema Challenge usually involve heavy utilization of annotated knowledge bases, rule-based reasoning, or hand-crafted features BIBREF21 , BIBREF22 , BIBREF23 . In particular, Rahman and Ng BIBREF24 employ human annotators to build more supervised training data. Their model utilizes nearly 70K hand-crafted features, including querying data from Google Search API. Sharma et al. BIBREF25 rely on a semantic parser to understand the question, query texts through Google Search, and reason on the graph produced by the parser. Similarly, Sch\u00fcller BIBREF23 formalizes the knowledge-graph data structure and a reasoning process based on cognitive linguistics theories. Bailey et al. BIBREF22 introduces a framework for reasoning, using expensive annotated knowledge bases as axioms.",
+      "The current best approach makes use of the skip-gram model to learn word representations BIBREF26 . The model incorporates several knowledge bases to regularize its training process, resulting in Knowledge Enhanced Embeddings (KEE). A semantic similarity scorer and a deep neural network classifier are then combined on top of KEE to predict the answers. The final system, therefore, includes both supervised and unsupervised models, besides three different knowledge bases. In contrast, our unsupervised method is simpler while having significantly higher accuracy. Unsupervised training is done on text corpora which can be cheaply curated.",
+      "Using language models in reading comprehension tests also produced many great successes. Namely Chu et al. BIBREF27 used bi-directional RNNs to predict the last word of a passage in the LAMBADA challenge. Similarly, LMs are also used to produce features for a classifier in the Store Close Test 2017, giving best accuracy against other methods BIBREF28 . In a broader context, LMs are used to produce good word embeddings, significantly improved a wide variety of downstream tasks, including the general problem of question answering BIBREF19 , BIBREF29 ."
+    ],
+    [
+      "We first substitute the pronoun in the original sentence with each of the candidate choices. The problem of coreference resolution then reduces to identifying which substitution results in a more probable sentence. By reframing the problem this way, language modeling becomes a natural solution by its definition. Namely, LMs are trained on text corpora, which encodes human knowledge in the form of natural language. During inference, LMs are able to assign probability to any given text based on what they have learned from training data. An overview of our method is shown in Figure 1 .",
+      "Suppose the sentence $S$ of $n$ consecutive words has its pronoun to be resolved specified at the $k^{th}$ position: $S = \\lbrace w_1, .., w_{k-1}, w_{k} \\equiv p, w_{k+1}, .., w_{n}\\rbrace $ . We make use of a trained language model $P_\\theta (w_t | w_{1}, w_2, .., w_{t-1})$ , which defines the probability of word $w_t$ conditioned on the previous words $w_1, ..., w_{t-1}$ . The substitution of a candidate reference $c$ in to the pronoun position $k$ results in a new sentence $S_{w_k\\leftarrow c}$ (we use notation $n$0 to mean that word $n$1 is substituted by candidate $n$2 ). We consider two different ways of scoring the substitution:",
+      "which scores how probable the resulting full sentence is, and",
+      "which scores how probable the part of the resulting sentence following $c$ is, given its antecedent. In other words, it only scores a part $S_{w_k\\leftarrow c}$ conditioned on the rest of the substituted sentence. An example of these two scores is shown in Table 1 . In our experiments, we find that partial scoring strategy is generally better than the naive full scoring strategy."
+    ],
+    [
+      "In this section we describe tests for commonsense reasoning and the LMs used to solve these tasks. We also detail training text corpora used in our experiments."
+    ],
+    [
+      "Our experiments start with testing LMs trained on all text corpora with PDP-60 and WSC-273. Next, we show that it is possible to customize training data to obtain even better results."
+    ],
+    [
+      "We first examine unsupervised single-model resolvers on PDP-60 by training one character-level and one word-level LM on the Gutenberg corpus. In Table 2 , these two resolvers outperform previous results by a large margin. For this task, we found full scoring gives better results than partial scoring. In Section \"Partial scoring is better than full scoring.\" , we provide evidences that this is an atypical case due to the very small size of PDP-60.",
+      "Next, we allow systems to take in necessary components to maximize their test performance. This includes making use of supervised training data that maps commonsense reasoning questions to their correct answer. Here we simply train another three variants of LMs on LM-1-Billion, CommonCrawl, and SQuAD and ensemble all of them. As reported in Table 3 , this ensemble of five unsupervised models outperform the best system in the 2016 competition (58.3%) by a large margin. Specifically, we achieve 70.0% accuracy, better than the more recent reported results from Quan Liu et al (66.7%) BIBREF26 , who makes use of three knowledge bases and a supervised deep neural network."
+    ],
+    [
+      "On the harder task WSC-273, our single-model resolvers also outperform the current state-of-the-art by a large margin, as shown in Table 4 . Namely, our word-level resolver achieves an accuracy of 56.4%. By training another 4 LMs, each on one of the 4 text corpora LM-1-Billion, CommonCrawl, SQuAD, Gutenberg Books, and add to the previous ensemble, we are able to reach 61.5%, nearly 10% of accuracy above the previous best result. This is a drastic improvement considering this previous best system outperforms random guess by only 3% in accuracy.",
+      "This task is more difficult than PDP-60. First, the overall performance of all competing systems are much lower than that of PDP-60. Second, incorporating supervised learning and expensive annotated knowledge bases to USSM provides insignificant gain this time (+3%), comparing to the large gain on PDP-60 (+19%)."
+    ],
+    [
+      "As previous systems collect relevant data from knowledge bases after observing questions during evaluation BIBREF24 , BIBREF25 , we also explore using this option. Namely, we build a customized text corpus based on questions in commonsense reasoning tasks. It is important to note that this does not include the answers and therefore does not provide supervision to our resolvers. In particular, we aggregate documents from the CommonCrawl dataset that has the most overlapping n-grams with the questions. The score for each document is a weighted sum of $F_1(n)$ scores when counting overlapping n-grams: $Similarity\\_Score_{document} = \\frac{\\sum _{n=1}^4nF_1(n)}{\\sum _{n=1}^4n}$ ",
+      "The top 0.1% of highest ranked documents is chosen as our new training corpus. Details of the ranking is shown in Figure 2 . This procedure resulted in nearly 1,000,000 documents, with the highest ranking document having a score of $8\\times 10^{-2}$ , still relatively small to a perfect score of $1.0$ . We name this dataset STORIES since most of the constituent documents take the form of a story with long chain of coherent events.",
+      "We train four different LMs on STORIES and add them to the previous ensemble of 10 LMs, resulting in a gain of 2% accuracy in the final system as shown in Table 5 . Remarkably, single models trained on this corpus are already extremely strong, with a word-level LM achieving 62.6% accuracy, even better than the ensemble of 10 models previously trained on 4 other text corpora (61.5%)."
+    ],
+    [
+      "We introduce a method to potentially detect keywords at which our proposed resolvers make decision between the two candidates $c_{correct}$ and $c_{incorrect}$ . Namely, we look at the following ratio: $q_t = \\frac{P_\\theta (w_t | w_1, w_2, ..., w_{t-1}; w_k \\leftarrow c_{correct})}{P_\\theta (w_t | w_1, w_2, ..., w_{t-1}; w_k \\leftarrow c_{incorrect})}$ ",
+      "Where $1 \\le t \\le n$ for full scoring, and $k +1 \\le t \\le n$ for partial scoring. It follows that the choice between $c_{correct}$ or $c_{incorrect}$ is made by the value of $Q = \\prod _tq_t$ being bigger than $1.0$ or not. By looking at the value of each individual $q_t$ , it is possible to retrieve words with the largest values of $q_t$ and hence most responsible for the final value of $Q$ .",
+      "We visualize the probability ratios $q_t$ to have more insights into the decisions of our resolvers. Figure 3 displays a sample of incorrect decisions made by full scoring and is corrected by partial scoring. Interestingly, we found $q_t$ with large values coincides with the special keyword of each Winograd Schema in several cases. Intuitively, this means the LMs assigned very low probability for the keyword after observing the wrong substitution. It follows that we can predict the keyword in each the Winograd Schema question by selecting top word positions with the highest value of $q_t$ .",
+      "For questions with keyword appearing before the reference, we detect them by backward-scoring models. Namely, we ensemble 6 LMs, each trained on one text corpora with word order reversed. This ensemble also outperforms the previous best system on WSC-273 with a remarkable accuracy of 58.2%. Overall, we are able to discover a significant amount of special keywords (115 out of 178 correctly answered questions) as shown in Table 6 . This strongly indicates a correct understanding of the context and a good grasp of commonsense knowledge in the resolver's decision process."
+    ],
+    [
+      "In this set of experiments, we look at wrong predictions from a word-level LM. With full scoring strategy, we observe that $q_t$ at the pronoun position is most responsible for a very large percentage of incorrect decisions as shown in Figfure 3 and Table 7 . For example, with the test \"The trophy cannot fit in the suitcase because it is too big.\", the system might return $c_{incorrect} = $ \"suitcase\" simply because $c_{correct} = $ \"trophy\" is a very rare word in its training corpus and therefore, is assigned a very low probability, overpowering subsequent $q_t$ values.",
+      "Following this reasoning, we apply a simple fix to full scoring by normalizing its score with the unigram count of $c$ : $Score_{full~normalized} = Score_{full} / Count(c)$ . Partial scoring, on the other hand, disregards $c$ altogether. As shown in Figure 4 , this normalization fixes full scoring in 9 out of 10 tested LMs on PDP-122. On WSC-273, the result is very decisive as partial scoring strongly outperforms the other two scoring in all cases. Since PDP-122 is a larger superset of PDP-60, we attribute the different behaviour observed on PDP-60 as an atypical case due to its very small size."
+    ],
+    [
+      "In this set of experiments, we examine the effect of training data on commonsense reasoning test performance. Namely, we train both word-level and character-level LMs on each of the five corpora: LM-1-Billion, CommonCrawl, SQuAD, Gutenberg Books, and STORIES. A held-out dataset from each text corpus is used for early stopping on the corresponding training data.",
+      "To speed up training on these large corpora, we first train the models on the LM-1-Billion text corpus. Each trained model is then divided into three groups of parameters: Embedding, Recurrent Cell, and Softmax. Each of the three is optionally transferred to train the same architectures on CommonCrawl, SQuAD and Gutenberg Books. The best transferring combination is chosen by cross-validation.",
+      "Figure 5 -left and middle show that STORIES always yield the highest accuracy for both types of input processing. We next rank the text corpora based on ensemble performance for more reliable results. Namely, we compare the previous ensemble of 10 models against the same set of models trained on each single text corpus. This time, the original ensemble trained on a diverse set of text corpora outperforms all other single-corpus ensembles including STORIES. This highlights the important role of diversity in training data for commonsense reasoning accuracy of the final system."
+    ],
+    [
+      "We introduce a simple unsupervised method for Commonsense Reasoning tasks. Key to our proposal are large language models, trained on a number of massive and diverse text corpora. The resulting systems outperform previous best systems on both Pronoun Disambiguation Problems and Winograd Schema Challenge. Remarkably on the later benchmark, we are able to achieve 63.7% accuracy, comparing to 52.8% accuracy of the previous state-of-the-art, who utilizes supervised learning and expensively annotated knowledge bases. We analyze our system's answers and observe that it discovers key features of the question that decides the correct answer, indicating good understanding of the context and commonsense knowledge. We also demonstrated that ensembles of models benefit the most when trained on a diverse set of text corpora.",
+      "We anticipate that this simple technique will be a strong building block for future systems that utilize reasoning ability on commonsense knowledge."
+    ],
+    [
+      "The base model consists of two layers of Long-Short Term Memory (LSTM) BIBREF31 with 8192 hidden units. The output gate of each LSTM uses peepholes and a projection layer to reduce its output dimensionality to 1024. We perform drop-out on LSTM's outputs with probability 0.25.",
+      "For word inputs, we use an embedding lookup of 800000 words, each with dimension 1024. For character inputs, we use an embedding lookup of 256 characters, each with dimension 16. We concatenate all characters in each word into a tensor of shape (word length, 16) and add to its two ends the <begin of word> and <end of word> tokens. The resulting concatenation is zero-padded to produce a fixed size tensor of shape (50, 16). This tensor is then processed by eight different 1-D convolution (Conv) kernels of different sizes and number of output channels, listed in Table 8 , each followed by a ReLU acitvation. The output of all CNNs are then concatenated and processed by two other fully-connected layers with highway connection that persist the input dimensionality. The resulting tensor is projected down to a 1024-feature vector. For both word input and character input, we perform dropout on the tensors that go into LSTM layers with probability 0.25.",
+      "We use a single fully-connected layer followed by a $Softmax$ operator to process the LSTM's output and produce a distribution over word vocabulary of size 800K. During training, LM loss is evaluated using importance sampling with negative sample size of 8192. This loss is minimized using the AdaGrad BIBREF37 algorithm with a learning rate of 0.2. All gradients on LSTM parameters and Character Embedding parameters are clipped by their global norm at 1.0. To avoid storing large matrices in memory, we shard them into 32 equal-sized smaller pieces. In our experiments, we used 8 different variants of this base model as listed in Table 9 .",
+      "In Table 10 , we listed all LMs and their training text corpora used in each of the experiments in Section \"Main results\" ."
+    ],
+    [
+      "Using the similarity scoring technique in section \"Customized training data for Winograd Schema Challenge\" , we observe a large amount of low quality training text on the lower end of the ranking. Namely, these are documents whose content are mostly unintelligible or unrecognized by our vocabulary. Training LMs for commonsense reasoning tasks on full CommonCrawl, therefore, might not be ideal. On the other hand, we detected and removed a portion of PDP-122 questions presented as an extremely high ranked document."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology
+
+Question: Which model do they use to convert between masculine-inflected and feminine-inflected sentences?
\ No newline at end of file
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+Name of Paper: Representation of Constituents in Neural Language Models: Coordination Phrase as a Case Study
+
+Question: What is the performance achieved by the model described in the paper?
\ No newline at end of file
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+Name of Paper: Investigating Linguistic Pattern Ordering in Hierarchical Natural Language Generation
+
+Question: What datasets did they use?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Hierarchical Natural Language Generation (HNLG)",
+    "Attentional Hierarchical Decoder",
+    "Scheduled Sampling",
+    "Curriculum Learning",
+    "Repeat-Input Mechanism",
+    "Attention Mechanism",
+    "Training",
+    "Setup",
+    "Results and Analysis",
+    "Conclusion",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "Spoken dialogue systems that can help users to solve complex tasks have become an emerging research topic in artificial intelligence and natural language processing areas BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 . With a well-designed dialogue system as an intelligent personal assistant, people can accomplish certain tasks more easily via natural language interactions. Today, there are several virtual intelligent assistants, such as Apple's Siri, Google's Home, Microsoft's Cortana, and Amazon's Alexa, in the market. A typical dialogue system pipeline can be divided into several parts: a recognized result of a user's speech input is fed into a natural language understanding module (NLU) to classify the domain along with domain-specific intents and fill in a set of slots to form a semantic frame BIBREF4 , BIBREF5 , BIBREF6 . A dialogue state tracking (DST) module predicts the current state of the dialogue by means of the semantic frames extracted from multi-turn conversations. Then the dialogue policy determines the system action for the next step given the current dialogue state. Finally the semantic frame of the system action is then fed into a natural language generation (NLG) module to construct a response utterance to the user BIBREF7 , BIBREF8 .",
+      "As a key component to a dialogue system, the goal of NLG is to generate natural language sentences given the semantics provided by the dialogue manager to feedback to users. As the endpoint of interacting with users, the quality of generated sentences is crucial for better user experience. The common and mostly adopted method is the rule-based (or template-based) method BIBREF9 , which can ensure the natural language quality and fluency. In spite of robustness and adequacy of the rule-based methods, frequent repetition of identical, tedious output makes talking to a template-based machine unsatisfactory. Furthermore, scalability is an issue, because designing sophisticated rules for a specific domain is time-consuming BIBREF10 .",
+      "Recurrent neural network-based language model (RNNLM) have demonstrated the capability of modeling long-term dependency in sequence prediction by leveraging recurrent structures BIBREF11 , BIBREF12 . Previous work proposed an RNNLM-based NLG that can be trained on any corpus of dialogue act-utterance pairs without hand-crafted features and any semantic alignment BIBREF13 . The following work based on sequence-to-sequence (seq2seq) further obtained better performance by employing encoder-decoder structure with linguistic knowledge such as syntax trees BIBREF14 , BIBREF15 , BIBREF16 , BIBREF17 . However, due to grammar complexity and lack of diction knowledge, it is still challenging to generate long and complex sentences by a simple encoder-decoder structure.",
+      "To address the issue, previous work attempted separating decoding jobs in a decoding hierarchy, which is constructed in terms of part-of-speech (POS) tags BIBREF8 . The original single decoding process is separated into a multi-level decoding hierarchy, where each decoding layer generates words associated with a specific POS set. This paper extends the idea to a more flexible design by incorporating attention mechanisms into the decoding hierarchy. Because prior work designs the decoding hierarchy in a hand-crafted manner based on a subjective intuition BIBREF8 , in this work, we experiment on various generating hierarchies to investigate the importance of linguistic pattern ordering in hierarchical language generation. The experiments show that our proposed method outperforms the classic seq2seq model with a smaller model size; in addition, the concept of the hierarchical decoder is proven general enough for various generating hierarchies. Furthermore, this paper also provides the design guidelines and insights of designing the decoding hierarchy."
+    ],
+    [
+      "The framework of the proposed hierarchical NLG model is illustrated in Figure FIGREF2 , where the model architecture is based on an encoder-decoder (seq2seq) structure with attentional hierarchical decoders BIBREF14 , BIBREF15 . In the encoder-decoder architecture, a typical generation process includes encoding and decoding phases: First, a given semantic representation sequence INLINEFORM0 is fed into a RNN-based encoder to capture the temporal dependency and project the input to a latent feature space; the semantic representation sequence is also encoded into an one-hot representation as the initial state of the encoder in order to maintain the temporal-independent condition as shown in the left part of Figure FIGREF2 . The recurrent unit of the encoder is bidirectional gated recurrent unit (GRU) BIBREF14 , DISPLAYFORM0 ",
+      "Then the encoded semantic vector, INLINEFORM0 , is fed into an RNN-based decoder as the initial state to decode word sequences, as shown in the right part of Figure FIGREF2 ."
+    ],
+    [
+      "In spite of the intuitive and elegant design of the seq2seq model, it is still difficult to generate complex and decent sequences by a simple encoder-decoder structure, because a single decoder is not capable of learning all diction, grammar, and other related linguistic knowledge at the same time. Some prior work applied additional techniques such as reranker and beam-search to select a better result among multiple generated sequences BIBREF13 , BIBREF16 . However, it is still an unsolved issue to the NLG community.",
+      "Therefore, we propose a hierarchical decoder to address the above issue, where the core idea is to allow the decoding layers to focus on learning different types of patterns instead of learning all relevant knowledge together. The hierarchical decoder is composed of several decoding layers, each of which is only responsible for learning a portion of the required knowledge. Namely, the linguistic knowledge can be incorporated into the decoding process and divided into several subsets.",
+      "We use part-of-speech (POS) tags as the additional linguistic features to construct the decoding hierarchy in this paper, where POS tags of the words in the target sentence are separated into several subsets, and each layer is responsible for decoding the words associated with a specific set of POS patterns. An example is shown in the right part of Figure FIGREF2 , where the first layer at the bottom is in charge of decoding nouns, pronouns, and proper nouns, and the second layer is for verbs, and so on. The prior work manually designed the decoding hierarchy by considering the subjective intuition about how children learn to speak BIBREF8 : infants first learn to say keywords, which are often nouns. For example, when an infant says \u201cDaddy, toilet.\u201d, it actually means \u201cDaddy, I want to go to the toilet.\u201d. Along with the growth of the age, children learn more grammars and vocabulary and then start adding verbs to the sentences, further adding adverbs, and so on. However, the hand-crafted linguistic order may not be optimal, so we experiment and analyze the model on various generating linguistic hierarchies to deeply investigate the effect of linguistic pattern ordering.",
+      "In the hierarchical decoder, the initial state of each GRU-based decoding layer INLINEFORM0 is the extracted feature INLINEFORM1 from the encoder, and the input at every step is the last predicted token INLINEFORM2 concatenated with the output from the previous layer INLINEFORM3 , DISPLAYFORM0 ",
+      "where INLINEFORM0 is the INLINEFORM1 -th hidden state of the INLINEFORM2 -th GRU decoding layer and INLINEFORM3 is the INLINEFORM4 -th outputted word in the INLINEFORM5 -th layer. We use the cross entropy loss as our training objective for optimization, where the difference between the predicted distribution and target distribution is minimized. To facilitate training and improve the performance, several strategies including scheduled sampling, a repeat input mechanism, curriculum learning, and an attention mechanism are utilized."
+    ],
+    [
+      "Teacher forcing BIBREF18 is a strategy for training RNN that uses model output from a prior time step as an input, and it works by using the expected output at the current time step INLINEFORM0 as the input at the next time step, rather than the output generated by the network. The teacher forcing techniques can also be triggered only with a certain probability, which is known as the scheduled sampling approach BIBREF19 . We adopt scheduled sampling methods in our experiments. In the proposed framework, an input of a decoder contains not only the output from the last step but one from the last decoding layer. Therefore, we design two types of scheduled sampling approaches \u2013 inner-layer and inter-layer.",
+      "Inner-layer schedule sampling is the classic teacher forcing strategy: DISPLAYFORM0 ",
+      "Inter-layer schedule sampling uses the labels instead of the actual output tokens of the last layer: DISPLAYFORM0 "
+    ],
+    [
+      "The proposed hierarchical decoder consists of several decoding layers, the expected output sequences of upper layers are longer than the ones in the lower layers. The framework is suitable for applying the curriculum learning BIBREF20 , of which core concept is that a curriculum of progressively harder tasks could significantly accelerate a network\u2019s training. The training procedure is to train each decoding layer for some epochs from the bottommost layer to the topmost one."
+    ],
+    [
+      "The concept of the hierarchical decoding is to hierarchically generate the sequence, gradually adding words associated with different linguistic patterns. Therefore, the generated sequences from the decoders become longer as the generating process proceeds to the higher decoding layers, and the sequence generated by a upper layer should contain the words predicted by the lower layers. To facilitate the behavior, previous work designs a strategy that repeats the outputs from the last layer as inputs until the current decoding layer outputs the same token, so-called the repeat-input mechanism BIBREF8 . This approach offers at least two merits: (1) Repeating inputs tells the decoder that the repeated tokens are important to encourage the decoder to generate them. (2) If the expected output sequence of a layer is much shorter than the one of the next layer, the large difference in length becomes a critical issue of the hierarchical decoder, because the output sequence of a layer will be fed into the next layer. With the repeat-input mechanism, the impact of length difference can be mitigated."
+    ],
+    [
+      "In order to model the relationship between layers in a generating hierarchy, we further design attention mechanisms for the hierarchical decoder. The proposed attention mechanisms are content-based, which means the weights are determined based on hidden states of neural models: DISPLAYFORM0 ",
+      "where INLINEFORM0 is the hidden state at the current step, INLINEFORM1 are the hidden states from the previous decoder layer, and INLINEFORM2 is a learned weight matrix. At each decoding step, attention values INLINEFORM3 are calculated by these methods and then used to compute the weighted sum as a context vector, which is then concatenated to decoder inputs as additional information."
+    ],
+    [
+      "The objective of the proposed model is to optimize the conditional probability INLINEFORM0 , so that the difference between the predicted distribution and the target distribution, INLINEFORM1 , can be minimized: DISPLAYFORM0 ",
+      "where INLINEFORM0 is the number of samples and the labels INLINEFORM1 are the word labels. Each decoder in the hierarchical NLG is trained based on curriculum learning with the objective."
+    ],
+    [
+      "The E2E NLG challenge dataset BIBREF21 is utilized in our experiments, which is a crowd-sourced dataset of 50k instances in the restaurant domain. Our models are trained on the official training set and verified on the official testing set. As shown in Figure FIGREF2 , the inputs are semantic frames containing specific slots and corresponding values, and the outputs are the associated natural language utterances with the given semantics. For example, a semantic frame with the slot-value pairs \u201cname[Bibimbap House], food[English], priceRange[moderate], area [riverside], near [Clare Hall]\u201d corresponds to the target sentence \u201cBibimbap House is a moderately priced restaurant who's main cuisine is English food. You will find this local gem near Clare Hall in the Riverside area.\u201d.",
+      "The data preprocessing includes trimming punctuation marks, lemmatization, and turning all words into lowercase. To prepare the labels of each layer within the hierarchical structure of the proposed method, we utilize spaCy toolkit to perform POS tagging for the target word sequences. Some properties such as names of restaurants are delexicalized (for example, replaced with a symbol \u201cRESTAURANT_NAME\u201d) to avoid data sparsity. In our experiments, we perform six different generating linguistic orders, in which each hierarchy is constructed based on different permutations of the POS tag sets: (1) nouns, proper nouns, and pronouns (2) verbs (3) adjectives and adverbs (4) others.",
+      "The probability of activating inter-layer and inner-layer teacher forcing is set to 0.5, the probability of teacher forcing is attenuated every epoch, and the decaying ratio is 0.9. The models are trained for 20 training epochs without early stop; when curriculum learning is applied, only the first layer is trained during first five epochs, the second decoder layer starts to be trained at the sixth epoch, and so on. To evaluate the quality of the generated sequences regarding both precision and recall, the evaluation metrics include BLEU and ROUGE (1, 2, L) scores with multiple references BIBREF22 ."
+    ],
+    [
+      "In the experiments, we borrow the idea of hierarchical decoding proposed by the previous work BIBREF8 and investigate various extensions of generating hierarchies. To examine the effectiveness of hierarchical decoders, we control our model size to be smaller than the baseline's. Specifically, the decoder in the baseline seq2seq model has hidden layers of size 400, while our models with hierarchical decoders have four decoding layers of size 100 for fair comparison.",
+      "Table TABREF13 compares the performance between a baseline and proposed models with different generating linguistic orders. For all generating hierarchies with different orders, simply replacing the decoder by a hierarchical decoder achieves significant improvement in every evaluation metrics; for example, the topmost generating hierarchy in Table TABREF13 has 49.25% improvement in BLEU, 30.03% in ROUGE-1, 96.48% in ROUGE-2, and 25.99% in ROUGE-L respectively. In other words, separating the generation process into several phases is proven to be a promising method. Performing curriculum learning strategy offers a considerable improvement, take the topmost generating hierarchy in Table TABREF13 for example, this method yields a 102.07% improvement in BLEU, 48.26% in ROUGE-1, 144.8% in ROUGE-2, and 39.18% in ROUGE-L. Despite that applying repeat-input mechanism alone does not offer benefit, combining these two strategies together further achieves the best performance. Note that these methods do not require any additional parameters.",
+      "Unfortunately, even some of the attentional hierarchical decoders achieve the best results in the generating hierarchies (Table TABREF18 ). Mostly, the additional attention mechanisms are not capable of bringing benefit for model performance. The reason may be that the decoding process is designed for gradually importing words in the specific set of linguistic patterns to the output sequence, each decoder layer is responsible of copying the output tokens from the previous layer and insert new words into the sequence precisely. Because of this nature, a decoder needs explicit information of the structure of a sentence rather than implicit high-level latent information. For instance, when a decoder is trying to insert some Verb words into the output sequence, knowing the position of subject and object would be very helpful.",
+      "The above results show that among these six different generating hierarchy, the generating order: (1) verbs INLINEFORM0 (2) nouns, proper nouns, and pronouns INLINEFORM1 (3) adjectives and adverbs INLINEFORM2 (4) the other POS tags yields the worst performance. Table TABREF23 shows that the gap of average length of target sequences between the first and the second decoder layer is the largest among all the hierarchies; in average, the second decoder needs to insert up to 8 words into the sequence based on 3.62 words from the first decoder layer in this generation process, which is absolutely difficult. The essence of the hierarchical design is to separate the job of the decoder into several phases; if the job of each phase is balanced, it is intuitive that it is more suitable for applying curriculum learning and improve the model performance.",
+      "The model performance is also related to linguistic structures of sentences: the fifth and the sixth generating hierarchies in Table TABREF13 have very similar trends, where the length of target sentences of each decoder layer is almost identical as shown in Table TABREF23 . However, the model performance differs a lot. An adverb word could be used to modify anything but nouns and pronouns, which means that the number of adverbs used for modifying verbs would be a factor to determine the generating order as well. In our cases, almost all adverbs in the dataset are used to describe adjectives, indicating that generating verbs before inserting adverbs to sequences may not provide enough useful information; instead, it would possibly obstruct the model learning. We can also find that in all experiments, inserting adverbs before verbs would be better.",
+      "In summary, the concept of the hierarchical decoder is simple and useful, separating a difficult job to many phases is demonstrated to be a promising direction and not limited to a specific generating hierarchy. Furthermore, the generating linguistic orders should be determined based on the dataset, and the important factors include the distribution over length of subsequences and the linguistic nature of the dataset for designing a proper generating hierarchy in NLG."
+    ],
+    [
+      "This paper investigates the seq2seq-based model with a hierarchical decoder that leverages various linguistic patterns. The experiments on different generating linguistic orders demonstrates the generalization about the proposed hierarchical decoder, which is not limited to a specific generating hierarchy. However, there is no universal decoding hierarchy, while the main factor for designing a suitable generating order is the nature of the dataset."
+    ],
+    [
+      "We would like to thank reviewers for their insightful comments on the paper. This work was financially supported by Ministry of Science and Technology (MOST) in Taiwan."
+    ]
+  ]
+}
+```
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+Name of Paper: Deep Enhanced Representation for Implicit Discourse Relation Recognition
+
+Question: Why does their model do better than prior models?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Overview",
+    "Word-Level Module",
+    "Sentence-Level Module",
+    "Pair-Level Module",
+    "Classifier",
+    "ExperimentsThe code for this paper is available at https://github.com/diccooo/Deep_Enhanced_Repr_for_IDRR",
+    "11-way Classification",
+    "Binary and 4-way Classification",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      " This work is licenced under a Creative Commons Attribution 4.0 International Licence. Licence details: http://creativecommons.org/licenses/by/4.0/",
+      "Discourse parsing is a fundamental task in natural language processing (NLP) which determines the structure of the whole discourse and identifies the relations between discourse spans such as clauses and sentences. Improving this task can be helpful to many downstream tasks such as machine translation BIBREF0 , question answering BIBREF1 , and so on. As one of the important parts of discourse parsing, implicit discourse relation recognition task is to find the relation between two spans without explicit connectives (e.g., but, so, etc.), and needs recovering the relation from semantic understanding of texts.",
+      "The Penn Discourse Treebank 2.0 (PDTB 2.0) BIBREF2 is a benchmark corpus for discourse relations. In PDTB style, the connectives can be explicit or implicit, and one entry of the data is separated into Arg1 and Arg2, accompanied with a relation sense. Since the release of PDTB 2.0 dataset, many methods have been proposed, ranging from traditional feature-based methods BIBREF3 , BIBREF4 to latest neural-based methods BIBREF5 , BIBREF6 . Especially through many neural network methods used for this task such as convolutional neural network (CNN) BIBREF7 , recursive neural network BIBREF8 , embedding improvement BIBREF9 , attention mechanism BIBREF10 , gate mechanism BIBREF11 , multi-task method BIBREF6 , the performance of this task has improved a lot since it was first introduced. However, this task is still very challenging with the highest reported accuracy still lower than 50% due to the hardness for the machines to understand the text meaning and the relatively small task corpus.",
+      "In this work, we focus on improving the learned representations of sentence pairs to address the implicit discourse relation recognition. It is well known that text representation is the core part of state-of-the-art deep learning methods for NLP tasks, and improving the representation from all perspective will benefit the concerned task.",
+      "The representation is improved by two ways in our model through three-level hierarchy. The first way is embedding augmentation. Only with informative embeddings, can the final representations be better. This is implemented in our word-level module. We augment word embeddings with subword-level embeddings and pre-trained ELMo embeddings. Subwords coming from unsupervised segmentation demonstrate a better consequent performance than characters for being a better minimal representation unit. The pre-trained contextualized word embeddings (ELMo) can make the embeddings contain more contextual information which is also involved with character-level inputs. The second way is a deep residual bi-attention encoder. Since this task is about classifying sentence pairs, the encoder is implemented in sentence and sentence-pair levels. A deeper model can support richer representations but is hard to train, especially with a small dataset. So we apply residual connections BIBREF12 to each module for facilitating signal propagation and alleviating gradient degradation. The stacked encoder blocks make the single sentence representation richer and bi-attention module mixes two sentence representations focusingly. With introducing richer and deeper representation enhancement, we report the deepest model so far for the task.",
+      "Our representation enhanced model will be evaluated on the benchmark PDTB 2.0 and demonstrate state-of-the-art performance to verify its effectiveness.",
+      "This paper is organized as follows. Section 2 reviews related work. Section 3 introduces our model. Section 4 shows our experiments and analyses the results. Section 5 concludes this work."
+    ],
+    [
+      "After the release of Penn Discourse Treebank 2.0, many works have been made to solve this concerned task. lin-kan-ng:2009:EMNLP is the first work who considered the second-level classification of the task by empirically evaluating the impact of surface features. Feature based methods BIBREF4 , BIBREF13 , BIBREF14 , BIBREF15 mainly focused on using linguistic, or semantic features from the discourse units, or the relations between unit pairs and word pairs. zhang-EtAl:2015:EMNLP4 is the first one who modeled this task using end-to-end neural network and gained great performance improvement. Neural network methods also used by lots of works BIBREF16 , BIBREF17 for better performance. Since then, a lot of methods have been proposed. braud2015comparing found that word embeddings trained by neural networks is very useful to this task. qin-zhang-zhao:2016:COLING augmented their system with character-level and contextualized embeddings. Recurrent networks and convolutional networks have been used as basic blocks in many works BIBREF18 , BIBREF19 , BIBREF7 . TACL536 used recursive neural networks. Attention mechanism was used by liu-li:2016:EMNLP2016, cai2017discourse and others. wu-EtAl:2016:EMNLP2016 and lan-EtAl:2017:EMNLP20172 applied multi-task component. qin-EtAl:2017:Long utilized adversarial nets to migrate the connective-based features to implicit ones.",
+      "Sentence representation is a key component in many NLP tasks. Usually, better representation means better performance. Plenty of work on language modeling has been done, as language modeling can supply better sentence representations. Since the pioneering work of Bengio2006, neural language models have been well developed BIBREF20 , BIBREF21 , BIBREF22 . Sentence representation is directly handled in a series of work. lin2017structured used self attention mechanism and used matrix to represent sentence, and conneau-EtAl:2017:EMNLP2017 used encoders pre-trained on SNLI BIBREF23 and MultiNLI BIBREF24 .",
+      "Different from all the existing work, for the first time to our best knowledge, this work is devoted to an empirical study on different levels of representation enhancement for implicit discourse relation classification task."
+    ],
+    [
+      "Figure 1 illustrates an overview of our model, which is mainly consisted of three parts: word-level module, sentence-level module, and pair-level module. Token sequences of sentence pairs (Arg1 and Arg2) are encoded by word-level module first and every token becomes a word embedding augmented by subword and ELMo. Then these embeddings are fed to sentence-level module and processed by stacked encoder blocks (CNN or RNN encoder block). Every block layer outputs representation for each token. Furthermore, the output of each layer is processed by bi-attention module in the pair-level module, and concatenated to pair representation, which is finally sent to classifiers which are multiple layer perceptrons (MLP) with softmax. The model details are given in the rest of this section."
+    ],
+    [
+      "An inputed token sequence of length $N$ is encoded by the word-level module into an embedding sequence $(\\mathbf {e}_1, \\mathbf {e}_2, \\mathbf {e}_3, \\cdots , \\mathbf {e}_N)$ . For each embedded token $\\mathbf {e}_i$ , it is concatenated from three parts, ",
+      "$$\\mathbf {e}_i = [\\mathbf {e}_i^w;~ \\mathbf {e}_i^s;~ \\mathbf {e}_i^c] \\in \\mathbb {R}^{d_e}$$   (Eq. 4) ",
+      " $\\mathbf {e}_i^w \\in \\mathbb {R}^{d_w}$ is pre-trained word embedding for this token, and is fixed during the training procedure. Our experiments show that fine-tuning the embeddings slowed down the training without better performance. $\\mathbf {e}_i^s \\in \\mathbb {R}^{d_s}$ is subword-level embedding encoded by subword encoder. $\\mathbf {e}_i^c \\in \\mathbb {R}^{d_c}$ is contextualized word embedding encoded by pre-trained ELMo encoders, whose parameters are also fixed during training. Subword is merged from single-character segmentation and the input of ELMo encoder is also character.",
+      "Character-level embeddings have been used widely in lots of works and its effectiveness is verified for out-of-vocabulary (OOV) or rare word representation. However, character is not a natural minimal unit for there exists word internal structure, we thus introduce a subword-level embedding instead.",
+      "Subword units can be computationally discovered by unsupervised segmentation over words that are regarded as character sequences. We adopt byte pair encoding (BPE) algorithm introduced by sennrich-haddow-birch:2016:P16-12 for this segmentation. BPE segmentation actually relies on a series of iterative merging operation over bigrams with the highest frequency. The number of merging operation times is roughly equal to the result subword vocabulary size.",
+      "For each word, the subword-level embedding is encoded by a subword encoder as in Figure 2 . Firstly, the subword sequence (of length $n$ ) of the word is mapped to subword embedding sequence $(\\mathbf {se}_1, \\mathbf {se}_2, \\mathbf {se}_3, \\cdots , \\mathbf {se}_n)$ (after padding), which is randomly initialized. Then $K$ (we empirically set $K$ =2) convolutional operations $Conv_1, Conv_2, \\cdots , Conv_K$ followed by max pooling operation are applied to the embedding sequence, and the sequence is padded before the convolutional operation. For the $i$ -th convolution kernel $Conv_i$ , suppose the kernel size is $k_i$ , then the output of $Conv_i$ on embeddings $\\mathbf {se}_{j}$ to $(\\mathbf {se}_1, \\mathbf {se}_2, \\mathbf {se}_3, \\cdots , \\mathbf {se}_n)$0 is $(\\mathbf {se}_1, \\mathbf {se}_2, \\mathbf {se}_3, \\cdots , \\mathbf {se}_n)$1 ",
+      "The final output of $Conv_i$ after max pooling is $\\begin{split}\n\\mathbf {u}_i &= \\mathop {maxpool}{(\\mathbf {C}_1,~ \\cdots ,~ \\mathbf {C}_j,~ \\cdots ,~ \\mathbf {C}_n)}\n\\end{split}$ ",
+      "Finally, the $K$ outputs are concatenated, $\n\\mathbf {u} = [\\mathbf {u}_1;~ \\mathbf {u}_2;~ \\cdots ;~ \\mathbf {u}_K] \\in \\mathbb {R}^{d_s}\n$ ",
+      "to feed a highway network BIBREF25 , ",
+      "$$\\mathbf {g} &=& \\sigma (\\mathbf {W}_g \\mathbf {u}^T + \\mathbf {b}_g) \\in \\mathbb {R}^{d_s} \\nonumber \\\\\n\\mathbf {e}_i^s &=& \\mathbf {g} \\odot \\mathop {ReLU}(\\mathbf {W}_h \\mathbf {u}^T + \\mathbf {b}_h)\n+ (\\mathbf {1} - \\mathbf {g}) \\odot \\mathbf {u} \\nonumber \\\\\n&\\in & \\mathbb {R}^{d_s}$$   (Eq. 6) ",
+      "where $\\mathbf {g}$ denotes the gate, and $\\mathbf {W}_g \\in \\mathbb {R}^{d_s \\times d_s}, \\mathbf {b}_g \\in \\mathbb {R}^{d_s},\n\\mathbf {W}_h \\in \\mathbb {R}^{d_s \\times d_s}, \\mathbf {b}_h \\in \\mathbb {R}^{d_s}$ are parameters. $\\odot $ is element-wise multiplication. The above Eq. 6 gives the subword-level embedding for the $i$ -th word.",
+      "ELMo (Embeddings from Language Models) BIBREF26 is a pre-trained contextualized word embeddings involving character-level representation. It is shown useful in some works BIBREF27 , BIBREF28 . This embedding is trained by bidirectional language models on large corpus using character sequence for each word token as input. The ELMo encoder employs CNN and highway networks over characters, whose output is given to a multiple-layer biLSTM with residual connections. Then the output is contextualized embeddings for each word. It is also can be seen as a hybrid encoder for character, word, and sentence. This encoder can add lots of contextual information to each word, and can ease the semantics learning of the model.",
+      "For the pre-trained ELMo encoder, the output is the result of the last two biLSTM layers. Suppose $\\mathbf {c}_i$ is the character sequence of $i$ -th word in a sentence, then the encoder output is $\n[\\cdots , \\mathbf {h}_i^0, \\cdots ;~ \\cdots , \\mathbf {h}_i^1, \\cdots ]\n= \\mathop {ELMo}(\\cdots , \\mathbf {c}_i, \\cdots )\n$ ",
+      "where $\\mathbf {h}_i^0$ and $\\mathbf {h}_i^1$ denote the outputs of first and second layers of ELMo encoder for $i$ -th word.",
+      "Following Peters2018ELMo, we use a self-adjusted weighted average of $\\mathbf {h}_i^0, \\mathbf {h}_i^1$ , $\\begin{split}\n\\mathbf {s} &= \\mathop {softmax}(\\mathbf {w}) \\in \\mathbb {R}^2\\\\\n\\mathbf {h} &= \\gamma \\sum _{j=0}^1 s_j \\mathbf {h}_i^j \\in \\mathbb {R}^{d_c^{\\prime }}\n\\end{split}$ ",
+      "where $\\gamma \\in \\mathbb {R}$ and $\\mathbf {w} \\in \\mathbb {R}^2$ are parameters tuned during training and $d_c^{\\prime }$ is the dimension of the ELMo encoder's outputs. Then the result is fed to a feed forward network to reduce its dimension, ",
+      "$$\\mathbf {e}_i^c = \\mathbf {W}_c \\mathbf {h}^T + \\mathbf {b}_c \\in \\mathbb {R}^{d_c}$$   (Eq. 7) ",
+      " $\\mathbf {W}_c \\in \\mathbb {R}^{d_c^{\\prime } \\times d_c}$ and $\\mathbf {b}_c \\in \\mathbb {R}^{d_c}$ are parameters. The above Eq. 7 gives ELMo embedding for the $i$ -th word."
+    ],
+    [
+      "The resulting word embeddings $\\mathbf {e}_i$ (Eq. 4 ) are sent to sentence-level module. The sentence-level module is composed of stacked encoder blocks. The block in each layer receives output of the previous layer as input and sends output to next layer. It also sends its output to the pair-level module. Parameters in different layers are not the same.",
+      "We consider two encoder types, convolutional type and recurrent type. We only use one encoder type in one experiment.",
+      "For the sentence-level module for different arguments (Arg1 and Arg2), many previous works used same parameters to encode different arguments, that is, one encoder for two type arguments. But as indicated by prasad2008penn, Arg1 and Arg2 may have different semantic perspective, we thus introduce argument-aware parameter settings for different arguments.",
+      "Figure 3 is the convolutional encoder block. Suppose the input for the encoder block is $\\mathbf {x}_i ~ (i=1, \\cdots , N)$ , then $\\mathbf {x}_i \\in \\mathbb {R}^{d_e}$ . The input is sent to a convolutional layer and mapped to output $\\mathbf {y}_i = [\\mathbf {A}_i \\; \\mathbf {B}_i] \\in \\mathbb {R}^{2d_e}$ . After the convolutional operation, gated linear units (GLU) BIBREF29 is applied, i.e., $\n\\mathbf {z}_i = \\mathbf {A}_i \\odot \\sigma (\\mathbf {B}_i) \\in \\mathbb {R}^{d_e}\n$ ",
+      "There is also a residual connection (Res 1) in the block, which means adding the output of $\\mathop {GLU}$ and the input of the block as final output, so $\\mathbf {z}_i + \\mathbf {x}_i$ is the output of the block corresponding to the input $\\mathbf {x}_i$ . The output $\\mathbf {z}_i + \\mathbf {x}_i$ for all $i = 1, \\cdots , N$ is sent to both the next layer and the pair-level module as input.",
+      "Similar to the convolutional one, recurrent encoder block is shown in Figure 3 . The input $\\mathbf {x}_i$ is encoded by a biGRU BIBREF30 layer first, $\n\\mathbf {y}_i = \\mathop {biGRU}(\\mathbf {x}_i) \\in \\mathbb {R}^{2d_e}\n$ ",
+      "then this is sent to a feed forword network, ",
+      "$$\\mathbf {z}_i = \\mathbf {W}_r \\mathbf {y}_i^T + \\mathbf {b}_r \\in \\mathbb {R}^{d_e}$$   (Eq. 10) ",
+      " $\\mathbf {W}_r \\in \\mathbb {R}^{2d_e \\times d_e}$ and $\\mathbf {b}_r \\in \\mathbb {R}^{d_e}$ are parameters. There is also a similar residual connection (Res 1) in the block, so $\\mathbf {z}_i + \\mathbf {x}_i$ for all $i = 1, \\cdots , N$ is the final output of the recurrent encoder block."
+    ],
+    [
+      "Through the sentence-level module, the word representations are contextualized, and these contextualized representations of each layer are sent to pair-level module.",
+      "Suppose the encoder block layer number is $l$ , and the outputs of $j$ -th block layer for Arg1 and Arg2 are $\\mathbf {v}_1^j, \\mathbf {v}_2^j \\in \\mathbb {R}^{N \\times d_e}$ , each row of which is the embedding for the corresponding word. $N$ is the length of word sequence (sentence). Each sentence is padded or truncated to let all sentences have the same length. They are sent to a bi-attention module, the attention matrix is $\n\\mathbf {M}_j = (\\mathop {FFN}(\\mathbf {v}_1^j)) {\\mathbf {v}_2^j}^T\n\\in \\mathbb {R}^{N \\times N}\n$ ",
+      " $\\mathop {FFN}$ is a feed froward network (similar to Eq. 10 ) applied to the last dimension corresponding to the word. Then the projected representations are $\\begin{split}\n\\mathbf {w}_2^j &= \\mathop {softmax}(\\mathbf {M}_j) {\\mathbf {v}_2^j} \\in \\mathbb {R}^{N \\times d_e}\\\\\n\\mathbf {w}_1^j &= \\mathop {softmax}(\\mathbf {M}_j^T) {\\mathbf {v}_1^j} \\in \\mathbb {R}^{N \\times d_e}\n\\end{split}$ ",
+      "where the $\\mathop {softmax}$ is applied to each row of the matrix. We apply 2-max pooling on each projected representation and concatenate them as output of the $j$ -th bi-attention module $\n\\mathbf {o}_j = [\\mathop {top2}(\\mathbf {w}_1^j);~ \\mathop {top2}(\\mathbf {w}_2^j)]\n\\in \\mathbb {R}^{4 d_e}\n$ ",
+      "The number of max pooling operation (top-2) is selected from experiments and it is a balance of more salient features and less noise. The final pair representation is ",
+      "$$\\mathbf {o} = [\\mathbf {o}_1, \\mathbf {o}_2, \\cdots , \\mathbf {o}_l] \\in \\mathbb {R}^{4 l d_e}$$   (Eq. 12) ",
+      "Since the output is concatenated from different layers and the outputs of lower layers are sent directly to the final representation, this also can be seen as residual connections (Res 2). Then the output as Eq. 12 is fed to an MLP classifier with softmax. The parameters for bi-attention modules in different levels are shared."
+    ],
+    [
+      "We use two classifiers in our model. One is for relation classification, and another one is for connective classification. The classifier is only a multiple layer perceptron (MLP) with softmax layer. qin-EtAl:2017:Long used adversarial method to utilize the connectives, but this method is not suitable for our adopted attention module since the attended part of a sentence will be distinctly different when the argument is with and without connectives. They also proposed a multi-task method that augments the model with an additional classifier for connective prediction, and the input of it is also the pair representation. It is straightforward and simple enough, and can help the model learn better representations, so we include this module in our model. The implicit connectives are provided by PDTB 2.0 dataset, and the connective classifier is only used during training. The loss function for both classifiers is cross entropy loss, and the total loss is the sum of the two losses, i.e., $Loss = Loss_{relation} + Loss_{connective}$ ."
+    ],
+    [
+      "Our model is evaluated on the benchmark PDTB 2.0 for two types of classification tasks.",
+      "PDTB 2.0 has three levels of senses: Level-1 Class, Level-2 Type, and Level-3 Subtypes. The first level consists of four major relation Classes: COMPARISON, CONTINGENCY, EXPANSION, and TEMPORAL. The second level contains 16 Types.",
+      "All our experiments are implemented by PyTorch. The pre-trained ELMo encoder is from AllenNLP toolkit BIBREF31 ."
+    ],
+    [
+      "Following the settings of qin-EtAl:2017:Long, we use two splitting methods of PDTB dataset for comprehensive comparison. The first is PDTB-Lin BIBREF3 , which uses section 2-21, 22 and 23 as training, dev and test sets respectively. The second is PDTB-Ji BIBREF8 , which uses section 2-20, 0-1, and 21-22 as training, dev and test sets respectively. According to TACL536, five relation types have few training instances and no dev and test instance. Removing the five types, there remain 11 second level types. During training, instances with more than one annotated relation types are considered as multiple instances, each of which has one of the annotations. At test time, a prediction that matches one of the gold types is considered as correct. All sentences in the dataset are padded or truncated to keep the same 100-word length.",
+      "For the results of both splitting methods, we share some hyperparameters. Table 1 is some of the shared hyperparameter settings. The pre-trained word embeddings are 300-dim word2vec BIBREF32 pre-trained from Google News. So $d_w = 300, d_s = 100, d_c = 300$ , then for the final embedding ( $\\mathbf {e}_i$ ), $d_e = 700$ . For the encoder block in sentence-level module, kernel size is same for every layer. We use AdaGrad optimization BIBREF33 .",
+      "The encoder block layer number is different for the two splitting methods. The layer number for PDTB-Ji splitting method is 4, and the layer number for PDTB-Lin splitting method is 5.",
+      "Compared to other recent state-of-the-art systems in Table 2 , our model achieves new state-of-the-art performance in two splitting methods with great improvements. As to our best knowledge, our model is the first one that exceeds the 48% accuracy in 11-way classification.",
+      "Ablation Study",
+      "To illustrate the effectiveness of our model and the contribution of each module, we use the PTDB-Ji splitting method to do a group of experiments. For the baseline model, we use 4 layer stacked convolutional encoder blocks without the residual connection in the block with only pre-trained word embeddings. We only use the output of the last layer and the output is processed by 2-max pooling without attention and sent to the relation classifier and connective classifier. Without the two residual connections, using 4 layers may be not the best for baseline model but is more convenient to comparison.",
+      "Firstly, we add modules from high level to low level accumulatively to observe the performance improvement. Table 3 is the results, which demonstrate that every module has considerable effect on the performance.",
+      "Then we test the effects of the two residual connections on the performance. The results are in Table 3 . The baseline $^+$ means baseline + bi-attention, i.e., the second row of Table 3 . We find that Res 1 (residual connection in the block) is much more useful than Res 2 (residual connection for pair representation), and they work together can bring even better performance.",
+      "Without ELMo (the same setting as 4-th row in Table 3 ), our data settings is the same as qin-EtAl:2017:Long whose performance was state-of-the-art and will be compared directly. We see that even without the pre-trained ELMo encoder, our performance is better, which is mostly attributed to our better sentence pair representations.",
+      "Subword-Level Embedding For the usefulness of subword-level embedding, we compare its performance to a model with character-level embedding, which was ever used in qin-zhang-zhao:2016:COLING. We use the same model setting as the 4-th row of Table 3 , and then replace subword with character sequence. The subword embedding augmented result is 47.03%, while the character embedding result is 46.37%, which verifies that the former is a better input representation for the task.",
+      "Parameters for Sentence-Level Module As previously discussed, argument specific parameter settings may result in better sentence-level encoders. We use the model which is the same as the third row in Table 3 . If shared parameters are used, the result is 45.97%, which is lower than argument specific parameter settings (46.29%). The comparison shows argument specific parameter settings indeed capture the difference of argument representations and facilitate the sentence pair representation.",
+      "Encoder Block Type and Layer Number In section 3.3, we consider two encoder types, here we compare their effects on the model performance. Like the previous part, The model setting is also the same as the third row in Table 3 except for the block type and layer number. The results are shown in Figure 4 .",
+      "The results in the figure show that both types may reach similar level of top accuracies, as the order of word is not important to the task. We also try to add position information to the convolutional type encoder, and receive a dropped accuracy. This further verifies the order information does not matter too much for the task. For most of the other numbers of layers, the recurrent type shows better, as the number of layers has an impact on the window size of convolutional encoders. When convolutional type is used, the training procedure is much faster, but choosing the suitable kernel size needs extra efforts.",
+      "Bi-Attention",
+      "We visualize the attention weight of one instance in Figure 5 . For lower layers, the attended part is more concentrated. For higher layers, the weights are more average and the attended part moves to the sentence border. This is because the window size is bigger for higher layers, and the convolutional kernel may have higher weights on words at the window edge."
+    ],
+    [
+      "Settings For the first level classification, we perform both 4-way classification and one-vs-others binary classification. Following the settings of previous works, the dataset splitting method is the same as PDTB-Ji without removing instances. The model uses 5 block layers with kernel size 3, other details are the same as that for 11-way classification on PDTB-Ji.",
+      "Results Table 4 is the result comparison on first level classification. For binary classification, the result is computed by $F_1$ score (%), and for 4-way classification, the result is computed by macro average $F_1$ score (%). Our model gives the state-of-the-art performance for 4-way classification by providing an $F_1$ score greater than 50% for the first time according to our best knowledge."
+    ],
+    [
+      "In this paper, we propose a deeper neural model augmented by different grained text representations for implicit discourse relation recognition. These different module levels work together and produce task-related representations of the sentence pair. Our experiments show that the model is effective and achieve the state-of-the-art performance. As to our best knowledge, this is the first time that an implicit discourse relation classifier gives an accuracy higher than 48% for 11-way and an $F_1$ score higher than 50% for 4-way classification tasks."
+    ]
+  ]
+}
+```
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+Name of Paper: Detecting Potential Topics In News Using BERT, CRF and Wikipedia
+
+Question: What is the difference in recall score between the systems?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction & Related Work",
+    "Data Preparation",
+    "Experiments ::: Model Architecture",
+    "Experiments ::: Training",
+    "Experiments ::: Results",
+    "Experiments ::: Discussions",
+    "Conclusion and Future Work"
+  ],
+  "paragraphs": [
+    [
+      "Named-Entity-Recognition(NER) approaches can be categorised broadly in three types. Detecting NER with predefined dictionaries and rulesBIBREF2, with some statistical approachesBIBREF3 and with deep learning approachesBIBREF4.",
+      "Stanford CoreNLP NER is a widely used baseline for many applications BIBREF5. Authors have used approaches of Gibbs sampling and conditional random field (CRF) for non-local information gathering and then Viterbi algorithm to infer the most likely state in the CRF sequence outputBIBREF6.",
+      "Deep learning approaches in NLP use document, word or token representations instead of one-hot encoded vectors. With the rise of transfer learning, pretrained Word2VecBIBREF7, GloVeBIBREF8, fasttextBIBREF9 which provides word embeddings were being used with recurrent neural networks (RNN) to detect NERs. Using LSTM layers followed by CRF layes with pretrained word-embeddings as input has been explored hereBIBREF10. Also, CNNs with character embeddings as inputs followed by bi-directional LSTM and CRF layers, were explored hereBIBREF11.",
+      "With the introduction of attentions and transformersBIBREF12 many deep architectures emerged in last few years. Approach of using these pretrained models like ElmoBIBREF13, FlairBIBREF14 and BERTBIBREF0 for word representations followed by variety of LSMT and CRF combinations were tested by authors in BIBREF15 and these approaches show state-of-the-art performance.",
+      "There are very few approaches where caseless NER task is explored. In this recent paperBIBREF16 authors have explored effects of \"Cased\" entities and how variety of networks perform and they show that the most effective strategy is a concatenation of cased and lowercased training data, producing a single model with high performance on both cased and uncased text.",
+      "In another paperBIBREF17, authors have proposed True-Case pre-training before using BiLSTM+CRF approach to detect NERs effectively. Though it shows good results over previous approaches, it is not useful in Indian Languages context as there is no concept of cases.",
+      "In our approach, we are focusing more on data preparation for our definition of topics using some of the state-of-art architectures based on BERT, LSTM/GRU and CRF layers as they have been explored in previous approaches mentioned above. Detecting caseless topics with higher recall and reasonable precision has been given a priority over f1 score. And comparisons have been made with available and ready-to-use open-source libraries from the productionization perspective."
+    ],
+    [
+      "We need good amount of data to try deep learning state-of-the-art algorithms. There are lot of open datasets available for names, locations, organisations, but not for topics as defined in Abstract above. Also defining and inferring topics is an individual preference and there are no fix set of rules for its definition. But according to our definition, we can use wikipedia titles as our target topics. English wikipedia dataset has more than 18 million titles if we consider all versions of them till now. We had to clean up the titles to remove junk titles as wikipedia title almost contains all the words we use daily. To remove such titles, we deployed simple rules as follows -",
+      "Remove titles with common words : \"are\", \"the\", \"which\"",
+      "Remove titles with numeric values : 29, 101",
+      "Remove titles with technical components, driver names, transistor names : X00, lga-775",
+      "Remove 1-gram titles except locations (almost 80% of these also appear in remaining n-gram titles)",
+      "After doing some more cleaning we were left with 10 million titles. We have a dump of 15 million English news articles published in past 4 years. Further, we reduced number of articles by removing duplicate and near similar articles. We used our pre-trained doc2vec models and cosine similarity to detect almost similar news articles. Then selected minimum articles required to cover all possible 2-grams to 5-grams. This step is done to save some training time without loosing accuracy. Do note that, in future we are planning to use whole dataset and hope to see gains in F1 and Recall further. But as per manual inspection, our dataset contains enough variations of sentences with rich vocabulary which contains names of celebrities, politicians, local authorities, national/local organisations and almost all locations, India and International, mentioned in the news text, in last 4 years.",
+      "We then created a parallel corpus format as shown in Table 1. Using pre-trained Bert-Tokenizer from hugging-face, converted words in sentences to tokenes. Caseless-BERT pre-trained tokenizer is used. Notice that some of the topic words are broken into tokens and NER tag has been repeated accordingly. For example, in Table 1 second row, word \"harassment\" is broken into \"har ##ass ##ment\". Similarly, one \"NER\" tag is repeated three times to keep the length of sequence-pair same. Finally, for around 3 million news articles, parallel corpus is created, which is of around 150 million sentences, with around 3 billion words (all lower cased) and with around 5 billion tokens approximately."
+    ],
+    [
+      "We tried multiple variations of LSTM and GRU layes, with/without CRF layer. There is a marginal gain in using GRU layers over LSTM. Also, we saw gain in using just one layers of GRU instead of more. Finally, we settled on the architecture, shown in Figure 1 for the final training, based on validation set scores with sample training set.",
+      "Text had to be tokenized using pytorch-pretrained-bert as explained above before passing to the network. Architecture is built using tensorflow/keras. Coding inspiration taken from BERT-keras and for CRF layer keras-contrib. If one is more comfortable in pytorch there are many examples available on github, but pytorch-bert-crf-ner is better for an easy start.",
+      "We used BERT-Multilingual model so that we can train and fine-tune the same model for other Indian languages. You can take BERT-base or BERT-large for better performance with only English dataset. Or you can use DistilBERT for English and DistilmBERT for 104 languages for faster pre-training and inferences. Also, we did not choose AutoML approach for hyper-parameter tuning which could have resulted in much more accurate results but at the same time could have taken very long time as well. So instead, chose and tweaked the parameters based on initial results.",
+      "We trained two models, one with sequence length 512 to capture document level important n-grams and second with sequence length 64 to capture sentence/paragraph level important n-grams. Through experiments it was evident that, sequence length plays a vital role in deciding context and locally/globally important n-grams. Final output is a concatenation of both the model outputs."
+    ],
+    [
+      "Trained the topic model on single 32gb NVidia-V100 and it took around 50 hours to train the model with sequence length 512. We had to take 256gb ram machine to accommodate all data in memory for faster read/write. Also, trained model with 64 sequence length in around 17 hours.",
+      "It is very important to note that sequence length decides how many bert-tokens you can pass for inference and also decides training time and accuracy. Ideally more is better because inference would be faster as well. For 64 sequence length, we are moving 64-token window over whole token-text and recognising topics in each window. So, one should choose sequence length according to their use case. Also, we have explained before our motivation of choosing 2 separate sequence lengths models.",
+      "We stopped the training for both the models when it crossed 70% precision, 90% recall on training and testing sets, as we were just looking to get maximum recall and not bothered about precision in our case. Both the models reach this point at around 16 epochs."
+    ],
+    [
+      "Comparison with existing open-source NER libraries is not exactly fair as they are NOT trained for detecting topics and important n-grams, also NOT trained for case-less text. But they are useful in testing and benchmarking if our model is detecting traditional NERs or not, which it should capture, as Wikipedia titles contains almost all Names, Places and Organisation names. You can check the sample output here",
+      "Comparisons have been made among Flair-NER, Stanford-caseless-NER (used english.conll.4class.caseless as it performed better than 3class and 7class), Spacy-NER and our models. Of which only Stanford-NER provides case-less models. In Table 2, scores are calculated by taking traditional NER list as reference. In Table 4, same is done with Wikipedia Titles reference set.",
+      "As you can see in Table 2 & 3, recall is great for our model but precision is not good as Model is also trying to detect new potential topics which are not there even in reference Wikipedia-Titles and NER sets. In capturing Wikipedia topics our model clearly surpasses other models in all scores.",
+      "Spacy results are good despite not being trained for case-less data. In terms of F1 and overall stability Spacy did better than Stanford NER, on our News Validation set. Similarly, Stanford did well in Precision but could not catch up with Spacy and our model in terms of Recall. Flair overall performed poorly, but as said before these open-source models are not trained for our particular use-case."
+    ],
+    [
+      "Lets check some examples for detailed analysis of the models and their results. Following is the economy related news.",
+      "Example 1 : around $1\u20131.5 trillion or around two percent of global gdp, are lost to corruption every year, president of the natural resource governance institute nrgi has said. speaking at a panel on integrity in public governance during the world bank group and international monetary fund annual meeting on sunday, daniel kaufmann, president of nrgi, presented the statistic, result of a study by the nrgi, an independent, non-profit organisation based in new york. however, according to kaufmann, the figure is only the direct costs of corruption as it does not factor in the opportunities lost on innovation and productivity, xinhua news agency reported. a country that addresses corruption and significantly improves rule of law can expect a huge increase in per capita income in the long run, the study showed. it will also see similar gains in reducing infant mortality and improving education, said kaufmann.",
+      "Detected NERs can be seen per model in Table 4. Our model do not capture numbers as we have removed all numbers from my wiki-titles as topics. Reason behind the same is that we can easily write regex to detect currency, prices, time, date and deep learning is not required for the same. Following are few important n-grams only our models was able to capture -",
+      "capita income",
+      "infant mortality",
+      "international monetary fund annual meeting",
+      "natural resource governance institute",
+      "public governance",
+      "At the same time, we can see that Spacy did much better than Stanford-caseless NER and Flair could not capture any of the NERs. Another example of a news in political domain and detected NERs can be seen per model in Table 5.",
+      "Example 2 : wearing the aam aadmi party's trademark cap and with copies of the party's five-year report card in hand, sunita kejriwal appears completely at ease. it's a cold winter afternoon in delhi, as the former indian revenue service (irs) officer hits the campaign trail to support her husband and batchmate, chief minister arvind kejriwal. emerging from the background for the first time, she is lending her shoulder to the aap bandwagon in the new delhi assembly constituency from where the cm, then a political novice, had emerged as the giant killer by defeating congress incumbent sheila dikshit in 2013.",
+      "Correct n-grams captured only by our model are -",
+      "aam aadmi party",
+      "aap bandwagon",
+      "delhi assembly constituency",
+      "giant killer",
+      "indian revenue service",
+      "political novice",
+      "In this example, Stanford model did better and captured names properly, for example \"sheila dikshit\" which Spacy could not detect but Spacy captureed almost all numeric values along with numbers expressed in words.",
+      "It is important to note that, our model captures NERs with some additional words around them. For example, \"president of nrgi\" is detected by the model but not \"ngri\". But model output does convey more information than the later. To capture the same for all models (and to make comparison fair), partial match has been enabled and if correct NER is part of predictied NER then later one is marked as matched. This could be the reason for good score for Spacy. Note that, partial match is disabled for Wikipedia Titles match task as shown in Table 3. Here, our model outperformed all the models."
+    ],
+    [
+      "Through this exercise, we were able to test out the best suitable model architecture and data preparation steps so that similar models could be trained for Indian languages. Building cased or caseless NERs for English was not the final goal and this has already been benchmarked and explored before in previous approaches explained in \"Related Work\" section. We didn't use traditional datasets for model performance comparisons & benchmarks. As mentioned before, all the comparisons are being done with open-source models and libraries from the productionization point of view. We used a english-news validation dataset which is important and relevant to our specific task and all validation datasets and raw output results can be found at our github link .",
+      "Wikipedia titles for Indian languages are very very less and resulting tagged data is even less to run deep architectures. We are trying out translations/transliterations of the English-Wiki-Titles to improve Indic-languages entity/topics data.",
+      "This approach is also useful in building news-summarizing models as it detects almost all important n-grams present in the news. Output of this model can be introduced in a summarization network to add more bias towards important words and bias for their inclusion."
+    ]
+  ]
+}
+```
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diff --git a/qasper-0579/instruction.md b/qasper-0579/instruction.md
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+Name of Paper: Detecting Potential Topics In News Using BERT, CRF and Wikipedia
+
+Question: What is their f1 score and recall?
\ No newline at end of file
diff --git a/qasper-0582/instruction.md b/qasper-0582/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..4dcd6a172b2701a733710d7d5593a4e64206648f
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+++ b/qasper-0582/instruction.md
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+Name of Paper: Detecting Potential Topics In News Using BERT, CRF and Wikipedia
+
+Question: How large is the dataset they used?
\ No newline at end of file
diff --git a/qasper-0583/instruction.md b/qasper-0583/instruction.md
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index 0000000000000000000000000000000000000000..7c5561b58a8a89a0ab0cc8ebc0b1fa051bcae93f
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+++ b/qasper-0583/instruction.md
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+Name of Paper: Gender Bias in Coreference Resolution
+
+Question: Which coreference resolution systems are tested?
\ No newline at end of file
diff --git a/qasper-0584/instruction.md b/qasper-0584/instruction.md
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index 0000000000000000000000000000000000000000..24db9f3ff77d8bf9d85db01d81427a6f68392a8f
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+++ b/qasper-0584/instruction.md
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+Name of Paper: How Far are We from Effective Context Modeling ? An Exploratory Study on Semantic Parsing in Context
+
+Question: How big is improvement in performances of proposed model over state of the art?
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diff --git a/qasper-0585/instruction.md b/qasper-0585/instruction.md
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+Name of Paper: How Far are We from Effective Context Modeling ? An Exploratory Study on Semantic Parsing in Context
+
+Question: What two large datasets are used for evaluation?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Methodology",
+    "Methodology ::: Base Model",
+    "Methodology ::: Base Model ::: Question Encoder",
+    "Methodology ::: Base Model ::: Grammar-based Decoder",
+    "Methodology ::: Recent Questions as Context",
+    "Methodology ::: Recent Questions as Context ::: Concat",
+    "Methodology ::: Recent Questions as Context ::: Turn",
+    "Methodology ::: Recent Questions as Context ::: Gate",
+    "Methodology ::: Precedent SQL as Context",
+    "Methodology ::: Precedent SQL as Context ::: SQL Attn",
+    "Methodology ::: Precedent SQL as Context ::: Action Copy",
+    "Methodology ::: Precedent SQL as Context ::: Tree Copy",
+    "Methodology ::: BERT Enhanced Embedding",
+    "Experiment & Analysis",
+    "Experiment & Analysis ::: Experimental Setup ::: Dataset",
+    "Experiment & Analysis ::: Experimental Setup ::: Evaluation Metrics",
+    "Experiment & Analysis ::: Experimental Setup ::: Implementation Detail",
+    "Experiment & Analysis ::: Experimental Setup ::: Baselines",
+    "Experiment & Analysis ::: Model Comparison",
+    "Experiment & Analysis ::: Fine-grained Analysis",
+    "Experiment & Analysis ::: Fine-grained Analysis ::: Coreference",
+    "Experiment & Analysis ::: Fine-grained Analysis ::: Ellipsis",
+    "Related Work",
+    "Conclusion & Future Work"
+  ],
+  "paragraphs": [
+    [
+      "Semantic parsing, which translates a natural language sentence into its corresponding executable logic form (e.g. Structured Query Language, SQL), relieves users from the burden of learning techniques behind the logic form. The majority of previous studies on semantic parsing assume that queries are context-independent and analyze them in isolation. However, in reality, users prefer to interact with systems in a dialogue, where users are allowed to ask context-dependent incomplete questions BIBREF0. That arises the task of Semantic Parsing in Context (SPC), which is quite challenging as there are complex contextual phenomena. In general, there are two sorts of contextual phenomena in dialogues: Coreference and Ellipsis BIBREF1. Figure FIGREF1 shows a dialogue from the dataset SParC BIBREF2. After the question \u201cWhat is id of the car with the max horsepower?\u201d, the user poses an elliptical question \u201cHow about with the max mpg?\u201d, and a question containing pronouns \u201cShow its Make!\u201d. Only when completely understanding the context, could a parser successfully parse the incomplete questions into their corresponding SQL queries.",
+      "A number of context modeling methods have been suggested in the literature to address SPC BIBREF3, BIBREF4, BIBREF2, BIBREF5, BIBREF6. These methods proposed to leverage two categories of context: recent questions and precedent logic form. It is natural to leverage recent questions as context. Taking the example from Figure FIGREF1, when parsing $Q_3$, we also need to take $Q_1$ and $Q_2$ as input. We can either simply concatenate the input questions, or use a model to encode them hierarchically BIBREF4. As for the second category, instead of taking a bag of recent questions as input, it only considers the precedent logic form. For instance, when parsing $Q_3$, we only need to take $S_2$ as context. With such a context, the decoder can attend over it, or reuse it via a copy mechanism BIBREF4, BIBREF5. Intuitively, methods that fall into this category enjoy better generalizability, as they only rely on the last logic form as context, no matter at which turn. Notably, these two categories of context can be used simultaneously.",
+      "However, it remains unclear how far we are from effective context modeling. First, there is a lack of thorough comparisons of typical context modeling methods on complex SPC (e.g. cross-domain). Second, none of previous works verified their proposed context modeling methods with the grammar-based decoding technique, which has been developed for years and proven to be highly effective in semantic parsing BIBREF7, BIBREF8, BIBREF9. To obtain better performance, it is worthwhile to study how context modeling methods collaborate with the grammar-based decoding. Last but not the least, there is limited understanding of how context modeling methods perform on various contextual phenomena. An in-depth analysis can shed light on potential research directions.",
+      "In this paper, we try to fulfill the above insufficiency via an exploratory study on real-world semantic parsing in context. Concretely, we present a grammar-based decoding semantic parser and adapt typical context modeling methods on top of it. Through experiments on two large complex cross-domain datasets, SParC BIBREF2 and CoSQL BIBREF6, we carefully compare and analyze the performance of different context modeling methods. Our best model achieves state-of-the-art (SOTA) performances on both datasets with significant improvements. Furthermore, we summarize and generalize the most frequent contextual phenomena, with a fine-grained analysis on representative models. Through the analysis, we obtain some interesting findings, which may benefit the community on the potential research directions. We will open-source our code and materials to facilitate future work upon acceptance."
+    ],
+    [
+      "In the task of semantic parsing in context, we are given a dataset composed of dialogues. Denoting $\\langle \\mathbf {x}_1,...,\\mathbf {x}_n\\rangle $ a sequence of natural language questions in a dialogue, $\\langle \\mathbf {y}_1,...,\\mathbf {y}_n\\rangle $ are their corresponding SQL queries. Each SQL query is conditioned on a multi-table database schema, and the databases used in test do not appear in training. In this section, we first present a base model without considering context. Then we introduce 6 typical context modeling methods and describe how we equip the base model with these methods. Finally, we present how to augment the model with BERT BIBREF10."
+    ],
+    [
+      "We employ the popularly used attention-based sequence-to-sequence architecture BIBREF11, BIBREF12 to build our base model. As shown in Figure FIGREF6, the base model consists of a question encoder and a grammar-based decoder. For each question, the encoder provides contextual representations, while the decoder generates its corresponding SQL query according to a predefined grammar."
+    ],
+    [
+      "To capture contextual information within a question, we apply Bidirectional Long Short-Term Memory Neural Network (BiLSTM) as our question encoder BIBREF13, BIBREF14. Specifically, at turn $i$, firstly every token $x_{i,k}$ in $\\mathbf {x}_{i}$ is fed into a word embedding layer $\\mathbf {\\phi }^x$ to get its embedding representation $\\mathbf {\\phi }^x{(x_{i,k})}$. On top of the embedding representation, the question encoder obtains a contextual representation $\\mathbf {h}^{E}_{i,k}=[\\mathop {{\\mathbf {h}}^{\\overrightarrow{E}}_{i,k}}\\,;{\\mathbf {h}}^{\\overleftarrow{E}}_{i,k}]$, where the forward hidden state is computed as following:"
+    ],
+    [
+      "The decoder is grammar-based with attention on the input question BIBREF7. Different from producing a SQL query word by word, our decoder outputs a sequence of grammar rule (i.e. action). Such a sequence has one-to-one correspondence with the abstract syntax tree of the SQL query. Taking the SQL query in Figure FIGREF6 as an example, it is transformed to the action sequence $\\langle $ $\\rm \\scriptstyle {Start}\\rightarrow \\rm {Root}$, $\\rm \\scriptstyle {Root}\\rightarrow \\rm {Select\\ Order}$, $\\rm \\scriptstyle {Select}\\rightarrow \\rm {Agg}$, $\\rm \\scriptstyle {Agg}\\rightarrow \\rm {max\\ Col\\ Tab}$, $\\rm \\scriptstyle {Col}\\rightarrow \\rm {Id}$, $\\rm \\scriptstyle {Tab}\\rightarrow \\rm {CARS\\_DATA}$, $\\rm \\scriptstyle {Order}\\rightarrow \\rm {desc\\ limit\\ Agg}$, $\\rm \\scriptstyle {Agg}\\rightarrow \\rm {none\\ Col\\ Tab}$, $\\rm \\scriptstyle {Col}\\rightarrow \\rm {Horsepower}$, $\\rm \\scriptstyle {Tab}\\rightarrow \\rm {CARS\\_DATA}$ $\\rangle $ by left-to-right depth-first traversing on the tree. At each decoding step, a nonterminal is expanded using one of its corresponding grammar rules. The rules are either schema-specific (e.g. $\\rm \\scriptstyle {Col}\\rightarrow \\rm {Horsepower}$), or schema-agnostic (e.g. $\\rm \\scriptstyle {Start}\\rightarrow \\rm {Root}$). More specifically, as shown at the top of Figure FIGREF6, we make a little modification on $\\rm {Order}$-related rules upon the grammar proposed by BIBREF9, which has been proven to have better performance than vanilla SQL grammar. Denoting $\\mathbf {LSTM}^{\\overrightarrow{D}}$ the unidirectional LSTM used in the decoder, at each decoding step $j$ of turn $i$, it takes the embedding of the previous generated grammar rule $\\mathbf {\\phi }^y(y_{i,j-1})$ (indicated as the dash lines in Figure FIGREF6), and updates its hidden state as:",
+      "where $\\mathbf {c}_{i,j-1}$ is the context vector produced by attending on each encoder hidden state $\\mathbf {h}^E_{i,k}$ in the previous step:",
+      "where $\\mathbf {W}^e$ is a learned matrix. $\\mathbf {h}^{\\overrightarrow{D}}_{i,0}$ is initialized by the final encoder hidden state $\\mathbf {h}^E_{i,|\\mathbf {x}_{i}|}$, while $\\mathbf {c}_{i,0}$ is a zero-vector. For each schema-agnostic grammar rule, $\\mathbf {\\phi }^y$ returns a learned embedding. For schema-specific one, the embedding is obtained by passing its schema (i.e. table or column) through another unidirectional LSTM, namely schema encoder $\\mathbf {LSTM}^{\\overrightarrow{S}}$. For example, the embedding of $\\rm \\scriptstyle {Col}\\rightarrow \\rm {Id}$ is:",
+      "As for the output $y_{i,j}$, if the expanded nonterminal corresponds to schema-agnostic grammar rules, we can obtain the output probability of action ${\\gamma }$ as:",
+      "where $\\mathbf {W}^o$ is a learned matrix. When it comes to schema-specific grammar rules, the main challenge is that the model may encounter schemas never appeared in training due to the cross-domain setting. To deal with it, we do not directly compute the similarity between the decoder hidden state and the schema-specific grammar rule embedding. Instead, we first obtain the unnormalized linking score $l(x_{i,k},\\gamma )$ between the $k$-th token in $\\mathbf {x}_i$ and the schema in action $\\gamma $. It is computed by both handcraft features (e.g. word exact match) BIBREF15 and learned similarity (i.e. dot product between word embedding and grammar rule embedding). With the input question as bridge, we reuse the attention score $a_{i,k}$ in Equation DISPLAY_FORM8 to measure the probability of outputting a schema-specific action $\\gamma $ as:"
+    ],
+    [
+      "To take advantage of the question context, we provide the base model with recent $h$ questions as additional input. As shown in Figure FIGREF13, we summarize and generalize three ways to incorporate recent questions as context."
+    ],
+    [
+      "The method concatenates recent questions with the current question in order, making the input of the question encoder be $[\\mathbf {x}_{i-h},\\dots ,\\mathbf {x}_{i}]$, while the architecture of the base model remains the same. We do not insert special delimiters between questions, as there are punctuation marks."
+    ],
+    [
+      "A dialogue can be seen as a sequence of questions which, in turn, are sequences of words. Considering such hierarchy, BIBREF4 employed a turn-level encoder (i.e. an unidirectional LSTM) to encode recent questions hierarchically. At turn $i$, the turn-level encoder takes the previous question vector $[\\mathbf {h}^{\\overleftarrow{E}}_{i-1,1},\\mathbf {h}^{\\overrightarrow{E}}_{i-1,|\\mathbf {x}_{i-1}|}]$ as input, and updates its hidden state to $\\mathbf {h}^{\\overrightarrow{T}}_{i}$. Then $\\mathbf {h}^{\\overrightarrow{T}}_{i}$ is fed into $\\mathbf {LSTM}^E$ as an implicit context. Accordingly Equation DISPLAY_FORM4 is rewritten as:",
+      "Similar to Concat, BIBREF4 allowed the decoder to attend over all encoder hidden states. To make the decoder distinguish hidden states from different turns, they further proposed a relative distance embedding ${\\phi }^{d}$ in attention computing. Taking the above into account, Equation DISPLAY_FORM8 is as:",
+      "",
+      "where $t{\\in }[0,\\dots ,h]$ represents the relative distance."
+    ],
+    [
+      "To jointly model the decoder attention in token-level and question-level, inspired by the advances of open-domain dialogue area BIBREF16, we propose a gate mechanism to automatically compute the importance of each question. The importance is computed by:",
+      "where $\\lbrace \\mathbf {V}^{g},\\mathbf {W}^g,\\mathbf {U}^g\\rbrace $ are learned parameters and $0\\,{\\le }\\,t\\,{\\le }\\,h$. As done in Equation DISPLAY_FORM17 except for the relative distance embedding, the decoder of Gate also attends over all the encoder hidden states. And the question-level importance $\\bar{g}_{i-t}$ is employed as the coefficient of the attention scores at turn $i\\!-\\!t$."
+    ],
+    [
+      "Besides recent questions, as mentioned in Section SECREF1, the precedent SQL can also be context. As shown in Figure FIGREF27, the usage of $\\mathbf {y}_{i-1}$ requires a SQL encoder, where we employ another BiLSTM to achieve it. The $m$-th contextual action representation at turn $i\\!-\\!1$, $\\mathbf {h}^A_{i-1,m}$, can be obtained by passing the action sequence through the SQL encoder."
+    ],
+    [
+      "Attention over $\\mathbf {y}_{i-1}$ is a straightforward method to incorporate the SQL context. Given $\\mathbf {h}^A_{i-1,m}$, we employ a similar manner as Equation DISPLAY_FORM8 to compute attention score and thus obtain the SQL context vector. This vector is employed as an additional input for decoder in Equation DISPLAY_FORM7."
+    ],
+    [
+      "To reuse the precedent generated SQL, BIBREF5 presented a token-level copy mechanism on their non-grammar based parser. Inspired by them, we propose an action-level copy mechanism suited for grammar-based decoding. It enables the decoder to copy actions appearing in $\\mathbf {y}_{i-1}$, when the actions are compatible to the current expanded nonterminal. As the copied actions lie in the same semantic space with the generated ones, the output probability for action $\\gamma $ is a mix of generating ($\\mathbf {g}$) and copying ($\\mathbf {c}$). The generating probability $P(y_{i,j}\\!=\\!{\\gamma }\\,|\\,\\mathbf {g})$ follows Equation DISPLAY_FORM10 and DISPLAY_FORM11, while the copying probability is:",
+      "where $\\mathbf {W}^l$ is a learned matrix. Denoting $P^{copy}_{i,j}$ the probability of copying at decoding step $j$ of turn $i$, it can be obtained by $\\sigma (\\mathbf {W}^{c}\\mathbf {h}^{\\overrightarrow{D}}_{i,j}+\\mathbf {b}^{c})$, where $\\lbrace \\mathbf {W}^{c},\\mathbf {b}^{c}\\rbrace $ are learned parameters and $\\sigma $ is the sigmoid function. The final probability $P(y_{i,j}={\\gamma })$ is computed by:"
+    ],
+    [
+      "Besides the action-level copy, we also introduce a tree-level copy mechanism. As illustrated in Figure FIGREF27, tree-level copy mechanism enables the decoder to copy action subtrees extracted from $\\mathbf {y}_{i-1}$, which shrinks the number of decoding steps by a large margin. Similar idea has been proposed in a non-grammar based decoder BIBREF4. In fact, a subtree is an action sequence starting from specific nonterminals, such as ${\\rm Select}$. To give an example, $\\langle $ $\\rm \\scriptstyle {Select}\\rightarrow \\rm {Agg}$, $\\rm \\scriptstyle {Agg}\\rightarrow \\rm {max\\ Col\\ Tab}$, $\\rm \\scriptstyle {Col}\\rightarrow \\rm {Id}$, $\\rm \\scriptstyle {Tab}\\rightarrow \\rm {CARS\\_DATA}$ $\\rangle $ makes up a subtree for the tree in Figure FIGREF6. For a subtree $\\upsilon $, its representation $\\phi ^{t}(\\upsilon )$ is the final hidden state of SQL encoder, which encodes its corresponding action sequence. Then we can obtain the output probability of subtree $\\upsilon $ as:",
+      "where $\\mathbf {W}^t$ is a learned matrix. The output probabilities of subtrees are normalized together with Equation DISPLAY_FORM10 and DISPLAY_FORM11."
+    ],
+    [
+      "We employ BERT BIBREF10 to augment our model via enhancing the embedding of questions and schemas. We first concatenate the input question and all the schemas in a deterministic order with [SEP] as delimiter BIBREF17. For instance, the input for $Q_1$ in Figure FIGREF1 is \u201cWhat is id ... max horsepower? [SEP] CARS_NAMES [SEP] MakeId ... [SEP] Horsepower\u201d. Feeding it into BERT, we obtain the schema-aware question representations and question-aware schema representations. These contextual representations are used to substitute $\\phi ^x$ subsequently, while other parts of the model remain the same."
+    ],
+    [
+      "We conduct experiments to study whether the introduced methods are able to effectively model context in the task of SPC (Section SECREF36), and further perform a fine-grained analysis on various contextual phenomena (Section SECREF40)."
+    ],
+    [
+      "Two large complex cross-domain datasets are used: SParC BIBREF2 consists of 3034 / 422 dialogues for train / development, and CoSQL BIBREF6 consists of 2164 / 292 ones. The average turn numbers of SParC and CoSQL are $3.0$ and $5.2$, respectively."
+    ],
+    [
+      "We evaluate each predicted SQL query using exact set match accuracy BIBREF2. Based on it, we consider three metrics: Question Match (Ques.Match), the match accuracy over all questions, Interaction Match (Int.Match), the match accuracy over all dialogues, and Turn $i$ Match, the match accuracy over questions at turn $i$."
+    ],
+    [
+      "Our implementation is based on PyTorch BIBREF18, AllenNLP BIBREF19 and the library transformers BIBREF20. We adopt the Adam optimizer and set the learning rate as 1e-3 on all modules except for BERT, for which a learning rate of 1e-5 is used BIBREF21. The dimensions of word embedding, action embedding and distance embedding are 100, while the hidden state dimensions of question encoder, grammar-based decoder, turn-level encoder and SQL encoder are 200. We initialize word embedding using Glove BIBREF22 for non-BERT models. For methods which use recent $h$ questions, $h$ is set as 5 on both datasets."
+    ],
+    [
+      "We consider three models as our baselines. SyntaxSQL-con and CD-Seq2Seq are two strong baselines introduced in the SParC dataset paper BIBREF2. SyntaxSQL-con employs a BiLSTM model to encode dialogue history upon the SyntaxSQLNet model (analogous to our Turn) BIBREF23, while CD-Seq2Seq is adapted from BIBREF4 for cross-domain settings (analogous to our Turn+Tree Copy). EditSQL BIBREF5 is a STOA baseline which mainly makes use of SQL attention and token-level copy (analogous to our Turn+SQL Attn+Action Copy)."
+    ],
+    [
+      "Taking Concat as a representative, we compare the performance of our model with other models, as shown in Table TABREF34. As illustrated, our model outperforms baselines by a large margin with or without BERT, achieving new SOTA performances on both datasets. Compared with the previous SOTA without BERT on SParC, our model improves Ques.Match and Int.Match by $10.6$ and $5.4$ points, respectively.",
+      "To conduct a thorough comparison, we evaluate 13 different context modeling methods upon the same parser, including 6 methods introduced in Section SECREF2 and 7 selective combinations of them (e.g., Concat+Action Copy). The experimental results are presented in Figure FIGREF37. Taken as a whole, it is very surprising to observe that none of these methods can be consistently superior to the others. The experimental results on BERT-based models show the same trend. Diving deep into the methods only using recent questions as context, we observe that Concat and Turn perform competitively, outperforming Gate by a large margin. With respect to the methods only using precedent SQL as context, Action Copy significantly surpasses Tree Copy and SQL Attn in all metrics. In addition, we observe that there is little difference in the performance of Action Copy and Concat, which implies that using precedent SQL as context gives almost the same effect with using recent questions. In terms of the combinations of different context modeling methods, they do not significantly improve the performance as we expected.",
+      "As mentioned in Section SECREF1, intuitively, methods which only use the precedent SQL enjoys better generalizability. To validate it, we further conduct an out-of-distribution experiment to assess the generalizability of different context modeling methods. Concretely, we select three representative methods and train them on questions at turn 1 and 2, whereas test them at turn 3, 4 and beyond. As shown in Figure FIGREF38, Action Copy has a consistently comparable or better performance, validating the intuition. Meanwhile, Concat appears to be strikingly competitive, demonstrating it also has a good generalizability. Compared with them, Turn is more vulnerable to out-of-distribution questions.",
+      "In conclusion, existing context modeling methods in the task of SPC are not as effective as expected, since they do not show a significant advantage over the simple concatenation method."
+    ],
+    [
+      "By a careful investigation on contextual phenomena, we summarize them in multiple hierarchies. Roughly, there are three kinds of contextual phenomena in questions: semantically complete, coreference and ellipsis. Semantically complete means a question can reflect all the meaning of its corresponding SQL. Coreference means a question contains pronouns, while ellipsis means the question cannot reflect all of its SQL, even if resolving its pronouns. In the fine-grained level, coreference can be divided into 5 types according to its pronoun BIBREF1. Ellipsis can be characterized by its intention: continuation and substitution. Continuation is to augment extra semantics (e.g. ${\\rm Filter}$), and substitution refers to the situation where current question is intended to substitute particular semantics in the precedent question. Substitution can be further branched into 4 types: explicit vs. implicit and schema vs. operator. Explicit means the current question provides contextual clues (i.e. partial context overlaps with the precedent question) to help locate the substitution target, while implicit does not. On most cases, the target is schema or operator. In order to study the effect of context modeling methods on various phenomena, as shown in Table TABREF39, we take the development set of SParC as an example to perform our analysis. The analysis begins by presenting Ques.Match of three representative models on above fine-grained types in Figure FIGREF42. As shown, though different methods have different strengths, they all perform poorly on certain types, which will be elaborated below."
+    ],
+    [
+      "Diving deep into the coreference (left of Figure FIGREF42), we observe that all methods struggle with two fine-grained types: definite noun phrases and one anaphora. Through our study, we find the scope of antecedent is a key factor. An antecedent is one or more entities referred by a pronoun. Its scope is either whole, where the antecedent is the precedent answer, or partial, where the antecedent is part of the precedent question. The above-mentioned fine-grained types are more challenging as their partial proportion are nearly $40\\%$, while for demonstrative pronoun it is only $22\\%$. It is reasonable as partial requires complex inference on context. Considering the 4th example in Table TABREF39, \u201cone\u201d refers to \u201cpets\u201d instead of \u201cage\u201d because the accompanying verb is \u201cweigh\u201d. From this observation, we draw the conclusion that current context modeling methods do not succeed on pronouns which require complex inference on context."
+    ],
+    [
+      "As for ellipsis (right of Figure FIGREF42), we obtain three interesting findings by comparisons in three aspects. The first finding is that all models have a better performance on continuation than substitution. This is expected since there are redundant semantics in substitution, while not in continuation. Considering the 8th example in Table TABREF39, \u201chorsepower\u201d is a redundant semantic which may raise noise in SQL prediction. The second finding comes from the unexpected drop from implicit(substitution) to explicit(substitution). Intuitively, explicit should surpass implicit on substitution as it provides more contextual clues. The finding demonstrates that contextual clues are obviously not well utilized by the context modeling methods. Third, compared with schema(substitution), operator(substitution) achieves a comparable or better performance consistently. We believe it is caused by the cross-domain setting, which makes schema related substitution more difficult."
+    ],
+    [
+      "The most related work is the line of semantic parsing in context. In the topic of SQL, BIBREF24 proposed a context-independent CCG parser and then applied it to do context-dependent substitution, BIBREF3 applied a search-based method for sequential questions, and BIBREF4 provided the first sequence-to-sequence solution in the area. More recently, BIBREF5 presented a edit-based method to reuse the precedent generated SQL. With respect to other logic forms, BIBREF25 focuses on understanding execution commands in context, BIBREF26 on question answering over knowledge base in a conversation, and BIBREF27 on code generation in environment context. Our work is different from theirs as we perform an exploratory study, not fulfilled by previous works.",
+      "There are also several related works that provided studies on context. BIBREF17 explored the contextual representations in context-independent semantic parsing, and BIBREF28 studied how conversational agents use conversation history to generate response. Different from them, our task focuses on context modeling for semantic parsing. Under the same task, BIBREF1 summarized contextual phenomena in a coarse-grained level, while BIBREF0 performed a wizard-of-oz experiment to study the most frequent phenomena. What makes our work different from them is that we not only summarize contextual phenomena by fine-grained types, but also perform an analysis on context modeling methods."
+    ],
+    [
+      "This work conducts an exploratory study on semantic parsing in context, to realize how far we are from effective context modeling. Through a thorough comparison, we find that existing context modeling methods are not as effective as expected. A simple concatenation method can be much competitive. Furthermore, by performing a fine-grained analysis, we summarize two potential directions as our future work: incorporating common sense for better pronouns inference, and modeling contextual clues in a more explicit manner. By open-sourcing our code and materials, we believe our work can facilitate the community to debug models in a fine-grained level and make more progress."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0594/instruction.md b/qasper-0594/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..1123a5421733f8ff7fb60567ed89a9de60701e6b
--- /dev/null
+++ b/qasper-0594/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Shallow Discourse Annotation for Chinese TED Talks
+
+Question: How are resources adapted to properties of Chinese text?
\ No newline at end of file
diff --git a/qasper-0600/instruction.md b/qasper-0600/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..baf6f9147e59be27cc5e2c3ecc32035fc1b88183
--- /dev/null
+++ b/qasper-0600/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Textual Data for Time Series Forecasting
+
+Question: Is there any example where geometric property is visible for context similarity between words?
\ No newline at end of file
diff --git a/qasper-0601/instruction.md b/qasper-0601/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..a93deeb442988cb3a8b8762f44a75c98e924b819
--- /dev/null
+++ b/qasper-0601/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Textual Data for Time Series Forecasting
+
+Question: What geometric properties do embeddings display?
\ No newline at end of file
diff --git a/qasper-0606/instruction.md b/qasper-0606/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..debda9be1cc1c2bc89ec42514864de5d7a81f335
--- /dev/null
+++ b/qasper-0606/instruction.md
@@ -0,0 +1,116 @@
+Name of Paper: Emotion helps Sentiment: A Multi-task Model for Sentiment and Emotion Analysis
+
+Question: How is multi-tasking performed?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Proposed Methodology",
+    "Proposed Methodology ::: Two-Layered Multi-Task Attention Model ::: BiLSTM based word encoder",
+    "Proposed Methodology ::: Two-Layered Multi-Task Attention Model ::: Word Attention",
+    "Proposed Methodology ::: Two-Layered Multi-Task Attention Model ::: Sentence Attention",
+    "Proposed Methodology ::: Two-Layered Multi-Task Attention Model ::: Final Output",
+    "Proposed Methodology ::: Distributional Thesaurus",
+    "Proposed Methodology ::: Word Embeddings",
+    "Datasets, Experiments and Analysis",
+    "Datasets, Experiments and Analysis ::: Datasets",
+    "Datasets, Experiments and Analysis ::: Preprocessing",
+    "Datasets, Experiments and Analysis ::: Implementation Details",
+    "Datasets, Experiments and Analysis ::: Results and Analysis",
+    "Datasets, Experiments and Analysis ::: Error Analysis",
+    "Conclusion",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "The emergence of social media sites with limited character constraint has ushered in a new style of communication. Twitter users within 280 characters per tweet share meaningful and informative messages. These short messages have a powerful impact on how we perceive and interact with other human beings. Their compact nature allows them to be transmitted efficiently and assimilated easily. These short messages can shape people's thought and opinion. This makes them an interesting and important area of study. Tweets are not only important for an individual but also for the companies, political parties or any organization. Companies can use tweets to gauge the performance of their products and predict market trends BIBREF0. The public opinion is particularly interesting for political parties as it gives them an idea of voter's inclination and their support. Sentiment and emotion analysis can help to gauge product perception, predict stock prices and model public opinions BIBREF1.",
+      "Sentiment analysis BIBREF2 is an important area of research in natural language processing (NLP) where we automatically determine the sentiments (positive, negative, neutral). Emotion analysis focuses on the extraction of predefined emotion from documents. Discrete emotions BIBREF3, BIBREF4 are often classified into anger, anticipation, disgust, fear, joy, sadness, surprise and trust. Sentiments and emotions are subjective and hence they are understood similarly and often used interchangeably. This is also mostly because both emotions and sentiments refer to experiences that result from the combined influences of the biological, the cognitive, and the social BIBREF5. However, emotions are brief episodes and are shorter in length BIBREF6, whereas sentiments are formed and retained for a longer period. Moreover, emotions are not always target-centric whereas sentiments are directed. Another difference between emotion and sentiment is that a sentence or a document may contain multiple emotions but a single overall sentiment.",
+      "Prior studies show that sentiment and emotion are generally tackled as two separate problems. Although sentiment and emotion are not exactly the same, they are closely related. Emotions, like joy and trust, intrinsically have an association with a positive sentiment. Similarly, anger, disgust, fear and sadness have a negative tone. Moreover, sentiment analysis alone is insufficient at times in imparting complete information. A negative sentiment can arise due to anger, disgust, fear, sadness or a combination of these. Information about emotion along with sentiment helps to better understand the state of the person or object. The close association of emotion with sentiment motivates us to build a system for sentiment analysis using the information obtained from emotion analysis.",
+      "In this paper, we put forward a robust two-layered multi-task attention based neural network which performs sentiment analysis and emotion analysis simultaneously. The model uses two levels of attention - the first primary attention builds the best representation for each word using Distributional Thesaurus and the secondary attention mechanism creates the final sentence level representation. The system builds the representation hierarchically which gives it a good intuitive working insight. We perform several experiments to evaluate the usefulness of primary attention mechanism. Experimental results show that the two-layered multi-task system for sentiment analysis which uses emotion analysis as an auxiliary task improves over the existing state-of-the-art system of SemEval 2016 Task 6 BIBREF7.",
+      "The main contributions of the current work are two-fold: a) We propose a novel two-layered multi-task attention based system for joint sentiment and emotion analysis. This system has two levels of attention which builds a hierarchical representation. This provides an intuitive explanation of its working; b) We empirically show that emotion analysis is relevant and useful in sentiment analysis. The multi-task system utilizing fine-grained information of emotion analysis performs better than the single task system of sentiment analysis."
+    ],
+    [
+      "A survey of related literature reveals the use of both classical and deep-learning approaches for sentiment and emotion analysis. The system proposed in BIBREF8 relied on supervised statistical text classification which leveraged a variety of surface form, semantic, and sentiment features for short informal texts. A Support Vector Machine (SVM) based system for sentiment analysis was used in BIBREF9, whereas an ensemble of four different sub-systems for sentiment analysis was proposed in BIBREF10. It comprised of Long Short-Term Memory (LSTM) BIBREF11, Gated Recurrent Unit (GRU) BIBREF12, Convolutional Neural Network (CNN) BIBREF13 and Support Vector Regression (SVR) BIBREF14. BIBREF15 reported the results for emotion analysis using SVR, LSTM, CNN and Bi-directional LSTM (Bi-LSTM) BIBREF16. BIBREF17 proposed a lexicon based feature extraction for emotion text classification. A rule-based approach was adopted by BIBREF18 to extract emotion-specific semantics. BIBREF19 used a high-order Hidden Markov Model (HMM) for emotion detection. BIBREF20 explored deep learning techniques for end-to-end trainable emotion recognition. BIBREF21 proposed a multi-task learning model for fine-grained sentiment analysis. They used ternary sentiment classification (negative, neutral, positive) as an auxiliary task for fine-grained sentiment analysis (very-negative, negative, neutral, positive, very-positive). A CNN based system was proposed by BIBREF22 for three phase joint multi-task training. BIBREF23 presented a multi-task learning based model for joint sentiment analysis and semantic embedding learning tasks. BIBREF24 proposed a multi-task setting for emotion analysis based on a vector-valued Gaussian Process (GP) approach known as coregionalisation BIBREF25. A hierarchical document classification system based on sentence and document representation was proposed by BIBREF26. An attention framework for sentiment regression is described in BIBREF27. BIBREF28 proposed a DeepEmoji system based on transfer learning for sentiment, emotion and sarcasm detection through emoji prediction. However, the DeepEmoji system treats these independently, one at a time.",
+      "Our proposed system differs from the above works in the sense that none of these works addresses the problem of sentiment and emotion analysis concurrently. Our empirical analysis shows that performance of sentiment analysis is boosted significantly when this is jointly performed with emotion analysis. This may be because of the fine-grained characteristics of emotion analysis that provides useful evidences for sentiment analysis."
+    ],
+    [
+      "We propose a novel two-layered multi-task attention based neural network for sentiment analysis where emotion analysis is utilized to improve its efficiency. Figure FIGREF1 illustrates the overall architecture of the proposed multi-task system. The proposed system consists of a Bi-directional Long Short-Term Memory (BiLSTM) BIBREF16, a two-level attention mechanism BIBREF29, BIBREF30 and a shared representation for emotion and sentiment analysis tasks. The BiLSTM encodes the word representation of each word. This representation is shared between the subsystems of sentiment and emotion analysis. Each of the shared representations is then fed to the primary attention mechanism of both the subsystems. The primary attention mechanism finds the best representation for each word for each task. The secondary attention mechanism acts on top of the primary attention to extract the best sentence representation by focusing on the suitable context for each task. Finally, the representations of both the tasks are fed to two different feed-forward neural networks to produce two outputs - one for sentiment analysis and one for emotion analysis. Each component is explained in the subsequent subsections."
+    ],
+    [
+      "Recurrent Neural Networks (RNN) are a class of networks which take sequential input and computes a hidden state vector for each time step. The current hidden state vector depends on the current input and the previous hidden state vector. This makes them good for handling sequential data. However, they suffer from a vanishing or exploding gradient problem when presented with long sequences. The gradient for back-propagating error either reduces to a very small number or increases to a very high value which hinders the learning process. Long Short Term Memory (LSTM) BIBREF11, a variant of RNN solves this problem by the gating mechanisms. The input, forget and output gates control the information flow.",
+      "BiLSTM is a special type of LSTM which takes into account the output of two LSTMs - one working in the forward direction and one working in the backward direction. The presence of contextual information for both past and future helps the BiLSTM to make an informed decision. The concatenation of a hidden state vectors $\\overrightarrow{h_t}$ of the forward LSTM and $\\overleftarrow{h_t}$ of the backward LSTM at any time step t provides the complete information. Therefore, the output of the BiLSTM at any time step t is $h_t$ = [$\\overrightarrow{h_t}$, $\\overleftarrow{h_t}$]. The output of the BiLSTM is shared between the main task (Sentiment Analysis) and the auxiliary task (Emotion Analysis)."
+    ],
+    [
+      "The word level attention (primary attention) mechanism gives the model a flexibility to represent each word for each task differently. This improves the word representation as the model chooses the best representation for each word for each task. A Distributional Thesaurus (DT) identifies words that are semantically similar, based on whether they tend to occur in a similar context. It provides a word expansion list for words based on their contextual similarity. We use the top-4 words for each word as their candidate terms. We only use the top-4 words for each word as we observed that the expansion list with more words started to contain the antonyms of the current word which empirically reduced the system performance. Word embeddings of these four candidate terms and the hidden state vector $h_t$ of the input word are fed to the primary attention mechanism. The primary attention mechanism finds the best attention coefficient for each candidate term. At each time step $t$ we get V($x_t$) candidate terms for each input $x_t$ with $v_i$ being the embedding for each term (Distributional Thesaurus and word embeddings are described in the next section). The primary attention mechanism assigns an attention coefficient to each of the candidate terms having the index $i$ $\\in $ V($x_t$):",
+      "where $W_w$ and $b_{w}$ are jointly learned parameters.",
+      "Each embedding of the candidate term is weighted with the attention score $\\alpha _{ti}$ and then summed up. This produces $m_{t}$, the representation for the current input $x_{t}$ obtained from the Distributional Thesaurus using the candidate terms.",
+      "Finally, $m_{t}$ and $h_{t}$ are concatenated to get $\\widehat{h_{t}}$, the final output of the primary attention mechanism."
+    ],
+    [
+      "The sentence attention (secondary attention) part focuses on each word of the sentence and assigns the attention coefficients. The attention coefficients are assigned on the basis of words' importance and their contextual relevance. This helps the model to build the overall sentence representation by capturing the context while weighing different word representations individually. The final sentence representation is obtained by multiplying each word vector representation with their attention coefficient and summing them over. The attention coefficient $\\alpha _t$ for each word vector representation and the sentence representation $\\widehat{H}$ are calculated as:",
+      "where $W_s$ and $b_{s}$ are parameters to be learned.",
+      "$\\widehat{H}$ denotes the sentence representation for sentiment analysis. Similarly, we calculate $\\bar{H}$ which represents the sentence for emotion classification. The system has the flexibility to compute different representations for sentiment and emotion analysis both."
+    ],
+    [
+      "The final outputs for both sentiment and emotion analysis are computed by feeding $\\widehat{H}$ and $\\bar{H}$ to two different one-layer feed forward neural networks. For our task, the feed forward network for sentiment analysis has two output units, whereas the feed forward network for emotion analysis has eight output nodes performing multi-label classification."
+    ],
+    [
+      "Distributional Thesaurus (DT) BIBREF31 ranks words according to their semantic similarity. It is a resource which produces a list of words in decreasing order of their similarity for each word. We use the DT to expand each word of the sentence. The top-4 words serve as the candidate terms for each word. For example, the candidate terms for the word good are: great, nice awesome and superb. DT offers the primary attention mechanism external knowledge in the form of candidate terms. It assists the system to perform better when presented with unseen words during testing as the unseen words could have been a part of the DT expansion list. For example, the system may not come across the word superb during training but it can appear in the test set. Since the system has already seen the word superb in the DT expansion list of the word good, it can handle this case efficiently. This fact is established by our evaluation results as the model performs better when the DT expansion and primary attentions are a part of the final multi-task system."
+    ],
+    [
+      "Word embeddings represent words in a low-dimensional numerical form. They are useful for solving many NLP problems. We use the pre-trained 300 dimensional Google Word2Vec BIBREF32 embeddings. The word embedding for each word in the sentence is fed to the BiLSTM network to get the current hidden state. Moreover, the primary attention mechanism is also applied to the word embeddings of the candidate terms for the current word."
+    ],
+    [
+      "In this section we present the details of the datasets used for the experiments, results that we obtain and the necessary analysis."
+    ],
+    [
+      "We evaluate our proposed approach for joint sentiment and emotion analysis on the benchmark dataset of SemEval 2016 Task 6 BIBREF7 and Stance Sentiment Emotion Corpus (SSEC) BIBREF15. The SSEC corpus is an annotation of the SemEval 2016 Task 6 corpus with emotion labels. The re-annotation of the SemEval 2016 Task 6 corpus helps to bridge the gap between the unavailability of a corpus with sentiment and emotion labels. The SemEval 2016 corpus contains tweets which are classified into positive, negative or other. It contains 2,914 training and 1,956 test instances. The SSEC corpus is annotated with anger, anticipation, disgust, fear, joy, sadness, surprise and trust labels. Each tweet could belong to one or more emotion classes and one sentiment class. Table TABREF15 shows the data statistics of SemEval 2016 task 6 and SSEC which are used for sentiment and emotion analysis, respectively."
+    ],
+    [
+      "The SemEval 2016 task 6 corpus contains tweets from Twitter. Since the tweets are derived from an environment with the constraint on the number of characters, there is an inherent problem of word concatenation, contractions and use of hashtags. Example: #BeautifulDay, we've, etc. Usernames and URLs do not impart any sentiment and emotion information (e.g. @John). We use the Python package ekphrasis BIBREF33 for handling these situations. Ekphrasis helps to split the concatenated words into individual words and expand the contractions. For example, #BeautifulDay to # Beautiful Day and we've to we have. We replace usernames with $<$user$>$, number with $<number>$ and URLs with $<$url$>$ token."
+    ],
+    [
+      "We implement our model in Python using Tensorflow on a single GPU. We experiment with six different BiLSTM based architectures. The three architectures correspond to BiLSTM based systems without primary attention i.e. only with secondary attention for sentiment analysis (S1), emotion analysis (E1) and the multi-task system (M1) for joint sentiment and emotion analysis. The remaining three architectures correspond to the systems for sentiment analysis (S2), emotion analysis (E2) and multi-task system (M2), with both primary and secondary attention. The weight matrices were initialized randomly using numbers form a truncated normal distribution. The batch size was 64 and the dropout BIBREF34 was 0.6 with the Adam optimizer BIBREF35. The hidden state vectors of both the forward and backward LSTM were 300-dimensional, whereas the context vector was 150-dimensional. Relu BIBREF36 was used as the activation for the hidden layers, whereas in the output layer we used sigmoid as the activation function. Sigmoid cross-entropy was used as the loss function. F1-score was reported for the sentiment analysis BIBREF7 and precision, recall and F1-score were used as the evaluation metric for emotion analysis BIBREF15. Therefore, we report the F1-score for sentiment and precision, recall and F1-score for emotion analysis."
+    ],
+    [
+      "We compare the performance of our proposed system with the state-of-the-art systems of SemEval 2016 Task 6 and the systems of BIBREF15. Experimental results show that the proposed system improves the existing state-of-the-art systems for sentiment and emotion analysis. We summarize the results of evaluation in Table TABREF18.",
+      "The primary attention mechanism plays a key role in the overall system as it improves the score of both sentiment and emotion analysis in both single task as well as multi-task systems. The use of primary attention improves the performance of single task systems for sentiment and emotion analysis by 2.21 and 1.72 points, respectively.Similarly, when sentiment and emotion analysis are jointly performed the primary attention mechanism improves the score by 0.93 and 2.42 points for sentiment and emotion task, respectively. To further measure the usefulness of the primary attention mechanism and the Distributional Thesaurus, we remove it from the systems S2, E2, and M2 to get the systems S1, E1, and M1. In all the cases, with the removal of primary attention mechanism, the performance drops. This is clearly illustrated in Figure FIGREF21. These observations indicate that the primary attention mechanism is an important component of the two-layered multi-task attention based network for sentiment analysis. We also perform t-test BIBREF40 for computing statistical significance of the obtained results from the final two-layered multi-task system M2 for sentiment analysis by calculating the p-values and observe that the performance gain over M1 is significant with p-value = 0.001495. Similarly, we perform the statistical significance test for each emotion class. The p-values for anger, anticipation, fear, disgust, joy, sadness, surprise and trust are 0.000002, 0.000143, 0.00403, 0.000015, 0.004607, 0.069, 0.000001 and 0.000001, respectively. These results provide a good indication of statistical significance.",
+      "Table TABREF19 shows the comparison of our proposed system with the existing state-of-the-art system of SemEval 2016 Task 6 for the sentiment dataset. BIBREF7 used feature-based SVM, BIBREF39 used keyword rules, LitisMind relied on hashtag rules on external data, BIBREF38 utilized a combination of sentiment classifiers and rules, whereas BIBREF37 used a maximum entropy classifier with domain-specific features. Our system comfortably surpasses the existing best system at SemEval. Our system manages to improve the existing best system of SemEval 2016 task 6 by 3.2 F-score points for sentiment analysis.",
+      "We also compare our system with the state-of-the-art systems proposed by BIBREF15 on the emotion dataset. The comparison is demonstrated in Table TABREF22. Maximum entropy, SVM, LSTM, Bi-LSTM, and CNN were the five individual systems used by BIBREF15. Overall, our proposed system achieves an improvement of 5 F-Score points over the existing state-of-the-art system for emotion analysis. Individually, the proposed system improves the existing F-scores for all the emotions except surprise. The findings of BIBREF15 also support this behavior (i.e. worst result for the surprise class). This could be attributed to the data scarcity and a very low agreement between the annotators for the emotion surprise.",
+      "Experimental results indicate that the multi-task system which uses fine-grained information of emotion analysis helps to boost the performance of sentiment analysis. The system M1 comprises of the system S1 performing the main task (sentiment analysis) with E1 undertaking the auxiliary task (emotion analysis). Similarly, the system M2 is made up of S2 and E2 where S2 performs the main task (sentiment analysis) and E2 commits to the auxiliary task (emotion analysis). We observe that in both the situations, the auxiliary task, i.e. emotional information increases the performance of the main task, i.e. sentiment analysis when these two are jointly performed. Experimental results help us to establish the fact that emotion analysis benefits sentiment analysis. The implicit sentiment attached to the emotion words assists the multi-task system. Emotion such as joy and trust are inherently associated with a positive sentiment whereas, anger, disgust, fear and sadness bear a negative sentiment. Figure FIGREF21 illustrates the performance of various models for sentiment analysis.",
+      "As a concrete example which justifies the utility of emotion analysis in sentiment analysis is shown below.",
+      "@realMessi he is a real sportsman and deserves to be the skipper.",
+      "The gold labels for the example are anticipation, joy and trust emotion with a positive sentiment. Our system S2 (single task system for sentiment analysis with primary and secondary attention) had incorrectly labeled this example with a negative sentiment and the E2 system (single task system with both primary and secondary attention for emotion analysis) had tagged it with anticipation and joy only. However, M2 i.e. the multi-task system for joint sentiment and emotion analysis had correctly classified the sentiment as positive and assigned all the correct emotion tags. It predicted the trust emotion tag, in addition to anticipation and joy (which were predicted earlier by E2). This helped M2 to correctly identify the positive sentiment of the example. The presence of emotional information helped the system to alter its sentiment decision (negative by S2) as it had better understanding of the text.",
+      "A sentiment directly does not invoke a particular emotion always and a sentiment can be associated with more than one emotion. However, emotions like joy and trust are associated with positive sentiment mostly whereas, anger, disgust and sadness are associated with negative sentiment particularly. This might be the reason of the extra sentiment information not helping the multi-task system for emotion analysis and hence, a decreased performance for emotion analysis in the multi-task setting."
+    ],
+    [
+      "We perform quantitative error analysis for both sentiment and emotion for the M2 model. Table TABREF23 shows the confusion matrix for sentiment analysis. anger,anticipation,fear,disgust,joy,sadness,surprise,trust consist of the confusion matrices for anger, anticipation, fear, disgust, joy, sadness, surprise and trust. We observe from Table TABREF23 that the system fails to label many instances with the emotion surprise. This may be due to the reason that this particular class is the most underrepresented in the training set. A similar trend can also be observed for the emotion fear and trust in Table TABREF23 and Table TABREF23, respectively. These three emotions have the least share of training instances, making the system less confident towards these emotions.",
+      "Moreover, we closely analyze the outputs to understand the kind of errors that our proposed model faces. We observe that the system faces difficulties at times and wrongly predicts the sentiment class in the following scenarios:",
+      "$\\bullet $ Often real-world phrases/sentences have emotions of conflicting nature. These conflicting nature of emotions are directly not evident from the surface form and are left unsaid as these are implicitly understood by humans. The system gets confused when presented with such instances.",
+      "Text: When you become a father you realize that you are not the most important person in the room anymore... Your child is!",
+      "Actual Sentiment: positive",
+      "Actual Emotion: anticipation, joy, surprise, trust",
+      "Predicted Sentiment: negative",
+      "Predicted Emotion: anger, anticipation, sadness",
+      "The realization of not being the most important person in a room invokes anger, anticipation and sadness emotions, and a negative sentiment. However, it is a natural feeling of overwhelmingly positive sentiment when you understand that your own child is the most significant part of your life.",
+      "$\\bullet $ Occasionally, the system focuses on the less significant part of the sentences. Due to this the system might miss crucial information which can influence and even change the final sentiment or emotion. This sometimes lead to the incorrect prediction of the overall sentiment and emotion.",
+      "Text: I've been called many things, quitter is not one of them...",
+      "Actual Sentiment: positive",
+      "Actual Emotion: anticipation, joy, trust",
+      "Predicted Sentiment: negative",
+      "Predicted Emotion: anticipation, sadness",
+      "Here, the system focuses on the first part of the sentence where the speaker was called many things which denotes a negative sentiment. Hence, the system predicts a negative sentiment and, anticipation and sadness emotions. However, the speaker in the second part uplifts the overall tone by justifying that s/he has never been called a quitter. This changes the negative sentiment to a positive sentiment and the overall emotion."
+    ],
+    [
+      "In this paper, we have presented a novel two-layered multi-task attention based neural network which performs sentiment analysis through emotion analysis. The primary attention mechanism of the two-layered multi-task system relies on Distributional Thesaurus which acts as a source of external knowledge. The system hierarchically builds the final representation from the word level to the sentence level. This provides a working insight to the system and its ability to handle the unseen words. Evaluation on the benchmark dataset suggests an improvement of 3.2 F-score point for sentiment analysis and an overall performance boost of 5 F-score points for emotion analysis over the existing state-of-the-art systems. The system empirically establishes the fact that emotion analysis is both useful and relevant to sentiment analysis. The proposed system does not rely on any language dependent features or lexicons. This makes it extensible to other languages as well. In future, we would like to extend the two-layered multi-task attention based neural network to other languages."
+    ],
+    [
+      "Asif Ekbal acknowledges the Young Faculty Research Fellowship (YFRF), supported by Visvesvaraya PhD scheme for Electronics and IT, Ministry of Electronics and Information Technology (MeitY), Government of India, being implemented by Digital India Corporation (formerly Media Lab Asia)."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0608/instruction.md b/qasper-0608/instruction.md
new file mode 100644
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@@ -0,0 +1,116 @@
+Name of Paper: Emotion helps Sentiment: A Multi-task Model for Sentiment and Emotion Analysis
+
+Question: How many parameters does the model have?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Proposed Methodology",
+    "Proposed Methodology ::: Two-Layered Multi-Task Attention Model ::: BiLSTM based word encoder",
+    "Proposed Methodology ::: Two-Layered Multi-Task Attention Model ::: Word Attention",
+    "Proposed Methodology ::: Two-Layered Multi-Task Attention Model ::: Sentence Attention",
+    "Proposed Methodology ::: Two-Layered Multi-Task Attention Model ::: Final Output",
+    "Proposed Methodology ::: Distributional Thesaurus",
+    "Proposed Methodology ::: Word Embeddings",
+    "Datasets, Experiments and Analysis",
+    "Datasets, Experiments and Analysis ::: Datasets",
+    "Datasets, Experiments and Analysis ::: Preprocessing",
+    "Datasets, Experiments and Analysis ::: Implementation Details",
+    "Datasets, Experiments and Analysis ::: Results and Analysis",
+    "Datasets, Experiments and Analysis ::: Error Analysis",
+    "Conclusion",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "The emergence of social media sites with limited character constraint has ushered in a new style of communication. Twitter users within 280 characters per tweet share meaningful and informative messages. These short messages have a powerful impact on how we perceive and interact with other human beings. Their compact nature allows them to be transmitted efficiently and assimilated easily. These short messages can shape people's thought and opinion. This makes them an interesting and important area of study. Tweets are not only important for an individual but also for the companies, political parties or any organization. Companies can use tweets to gauge the performance of their products and predict market trends BIBREF0. The public opinion is particularly interesting for political parties as it gives them an idea of voter's inclination and their support. Sentiment and emotion analysis can help to gauge product perception, predict stock prices and model public opinions BIBREF1.",
+      "Sentiment analysis BIBREF2 is an important area of research in natural language processing (NLP) where we automatically determine the sentiments (positive, negative, neutral). Emotion analysis focuses on the extraction of predefined emotion from documents. Discrete emotions BIBREF3, BIBREF4 are often classified into anger, anticipation, disgust, fear, joy, sadness, surprise and trust. Sentiments and emotions are subjective and hence they are understood similarly and often used interchangeably. This is also mostly because both emotions and sentiments refer to experiences that result from the combined influences of the biological, the cognitive, and the social BIBREF5. However, emotions are brief episodes and are shorter in length BIBREF6, whereas sentiments are formed and retained for a longer period. Moreover, emotions are not always target-centric whereas sentiments are directed. Another difference between emotion and sentiment is that a sentence or a document may contain multiple emotions but a single overall sentiment.",
+      "Prior studies show that sentiment and emotion are generally tackled as two separate problems. Although sentiment and emotion are not exactly the same, they are closely related. Emotions, like joy and trust, intrinsically have an association with a positive sentiment. Similarly, anger, disgust, fear and sadness have a negative tone. Moreover, sentiment analysis alone is insufficient at times in imparting complete information. A negative sentiment can arise due to anger, disgust, fear, sadness or a combination of these. Information about emotion along with sentiment helps to better understand the state of the person or object. The close association of emotion with sentiment motivates us to build a system for sentiment analysis using the information obtained from emotion analysis.",
+      "In this paper, we put forward a robust two-layered multi-task attention based neural network which performs sentiment analysis and emotion analysis simultaneously. The model uses two levels of attention - the first primary attention builds the best representation for each word using Distributional Thesaurus and the secondary attention mechanism creates the final sentence level representation. The system builds the representation hierarchically which gives it a good intuitive working insight. We perform several experiments to evaluate the usefulness of primary attention mechanism. Experimental results show that the two-layered multi-task system for sentiment analysis which uses emotion analysis as an auxiliary task improves over the existing state-of-the-art system of SemEval 2016 Task 6 BIBREF7.",
+      "The main contributions of the current work are two-fold: a) We propose a novel two-layered multi-task attention based system for joint sentiment and emotion analysis. This system has two levels of attention which builds a hierarchical representation. This provides an intuitive explanation of its working; b) We empirically show that emotion analysis is relevant and useful in sentiment analysis. The multi-task system utilizing fine-grained information of emotion analysis performs better than the single task system of sentiment analysis."
+    ],
+    [
+      "A survey of related literature reveals the use of both classical and deep-learning approaches for sentiment and emotion analysis. The system proposed in BIBREF8 relied on supervised statistical text classification which leveraged a variety of surface form, semantic, and sentiment features for short informal texts. A Support Vector Machine (SVM) based system for sentiment analysis was used in BIBREF9, whereas an ensemble of four different sub-systems for sentiment analysis was proposed in BIBREF10. It comprised of Long Short-Term Memory (LSTM) BIBREF11, Gated Recurrent Unit (GRU) BIBREF12, Convolutional Neural Network (CNN) BIBREF13 and Support Vector Regression (SVR) BIBREF14. BIBREF15 reported the results for emotion analysis using SVR, LSTM, CNN and Bi-directional LSTM (Bi-LSTM) BIBREF16. BIBREF17 proposed a lexicon based feature extraction for emotion text classification. A rule-based approach was adopted by BIBREF18 to extract emotion-specific semantics. BIBREF19 used a high-order Hidden Markov Model (HMM) for emotion detection. BIBREF20 explored deep learning techniques for end-to-end trainable emotion recognition. BIBREF21 proposed a multi-task learning model for fine-grained sentiment analysis. They used ternary sentiment classification (negative, neutral, positive) as an auxiliary task for fine-grained sentiment analysis (very-negative, negative, neutral, positive, very-positive). A CNN based system was proposed by BIBREF22 for three phase joint multi-task training. BIBREF23 presented a multi-task learning based model for joint sentiment analysis and semantic embedding learning tasks. BIBREF24 proposed a multi-task setting for emotion analysis based on a vector-valued Gaussian Process (GP) approach known as coregionalisation BIBREF25. A hierarchical document classification system based on sentence and document representation was proposed by BIBREF26. An attention framework for sentiment regression is described in BIBREF27. BIBREF28 proposed a DeepEmoji system based on transfer learning for sentiment, emotion and sarcasm detection through emoji prediction. However, the DeepEmoji system treats these independently, one at a time.",
+      "Our proposed system differs from the above works in the sense that none of these works addresses the problem of sentiment and emotion analysis concurrently. Our empirical analysis shows that performance of sentiment analysis is boosted significantly when this is jointly performed with emotion analysis. This may be because of the fine-grained characteristics of emotion analysis that provides useful evidences for sentiment analysis."
+    ],
+    [
+      "We propose a novel two-layered multi-task attention based neural network for sentiment analysis where emotion analysis is utilized to improve its efficiency. Figure FIGREF1 illustrates the overall architecture of the proposed multi-task system. The proposed system consists of a Bi-directional Long Short-Term Memory (BiLSTM) BIBREF16, a two-level attention mechanism BIBREF29, BIBREF30 and a shared representation for emotion and sentiment analysis tasks. The BiLSTM encodes the word representation of each word. This representation is shared between the subsystems of sentiment and emotion analysis. Each of the shared representations is then fed to the primary attention mechanism of both the subsystems. The primary attention mechanism finds the best representation for each word for each task. The secondary attention mechanism acts on top of the primary attention to extract the best sentence representation by focusing on the suitable context for each task. Finally, the representations of both the tasks are fed to two different feed-forward neural networks to produce two outputs - one for sentiment analysis and one for emotion analysis. Each component is explained in the subsequent subsections."
+    ],
+    [
+      "Recurrent Neural Networks (RNN) are a class of networks which take sequential input and computes a hidden state vector for each time step. The current hidden state vector depends on the current input and the previous hidden state vector. This makes them good for handling sequential data. However, they suffer from a vanishing or exploding gradient problem when presented with long sequences. The gradient for back-propagating error either reduces to a very small number or increases to a very high value which hinders the learning process. Long Short Term Memory (LSTM) BIBREF11, a variant of RNN solves this problem by the gating mechanisms. The input, forget and output gates control the information flow.",
+      "BiLSTM is a special type of LSTM which takes into account the output of two LSTMs - one working in the forward direction and one working in the backward direction. The presence of contextual information for both past and future helps the BiLSTM to make an informed decision. The concatenation of a hidden state vectors $\\overrightarrow{h_t}$ of the forward LSTM and $\\overleftarrow{h_t}$ of the backward LSTM at any time step t provides the complete information. Therefore, the output of the BiLSTM at any time step t is $h_t$ = [$\\overrightarrow{h_t}$, $\\overleftarrow{h_t}$]. The output of the BiLSTM is shared between the main task (Sentiment Analysis) and the auxiliary task (Emotion Analysis)."
+    ],
+    [
+      "The word level attention (primary attention) mechanism gives the model a flexibility to represent each word for each task differently. This improves the word representation as the model chooses the best representation for each word for each task. A Distributional Thesaurus (DT) identifies words that are semantically similar, based on whether they tend to occur in a similar context. It provides a word expansion list for words based on their contextual similarity. We use the top-4 words for each word as their candidate terms. We only use the top-4 words for each word as we observed that the expansion list with more words started to contain the antonyms of the current word which empirically reduced the system performance. Word embeddings of these four candidate terms and the hidden state vector $h_t$ of the input word are fed to the primary attention mechanism. The primary attention mechanism finds the best attention coefficient for each candidate term. At each time step $t$ we get V($x_t$) candidate terms for each input $x_t$ with $v_i$ being the embedding for each term (Distributional Thesaurus and word embeddings are described in the next section). The primary attention mechanism assigns an attention coefficient to each of the candidate terms having the index $i$ $\\in $ V($x_t$):",
+      "where $W_w$ and $b_{w}$ are jointly learned parameters.",
+      "Each embedding of the candidate term is weighted with the attention score $\\alpha _{ti}$ and then summed up. This produces $m_{t}$, the representation for the current input $x_{t}$ obtained from the Distributional Thesaurus using the candidate terms.",
+      "Finally, $m_{t}$ and $h_{t}$ are concatenated to get $\\widehat{h_{t}}$, the final output of the primary attention mechanism."
+    ],
+    [
+      "The sentence attention (secondary attention) part focuses on each word of the sentence and assigns the attention coefficients. The attention coefficients are assigned on the basis of words' importance and their contextual relevance. This helps the model to build the overall sentence representation by capturing the context while weighing different word representations individually. The final sentence representation is obtained by multiplying each word vector representation with their attention coefficient and summing them over. The attention coefficient $\\alpha _t$ for each word vector representation and the sentence representation $\\widehat{H}$ are calculated as:",
+      "where $W_s$ and $b_{s}$ are parameters to be learned.",
+      "$\\widehat{H}$ denotes the sentence representation for sentiment analysis. Similarly, we calculate $\\bar{H}$ which represents the sentence for emotion classification. The system has the flexibility to compute different representations for sentiment and emotion analysis both."
+    ],
+    [
+      "The final outputs for both sentiment and emotion analysis are computed by feeding $\\widehat{H}$ and $\\bar{H}$ to two different one-layer feed forward neural networks. For our task, the feed forward network for sentiment analysis has two output units, whereas the feed forward network for emotion analysis has eight output nodes performing multi-label classification."
+    ],
+    [
+      "Distributional Thesaurus (DT) BIBREF31 ranks words according to their semantic similarity. It is a resource which produces a list of words in decreasing order of their similarity for each word. We use the DT to expand each word of the sentence. The top-4 words serve as the candidate terms for each word. For example, the candidate terms for the word good are: great, nice awesome and superb. DT offers the primary attention mechanism external knowledge in the form of candidate terms. It assists the system to perform better when presented with unseen words during testing as the unseen words could have been a part of the DT expansion list. For example, the system may not come across the word superb during training but it can appear in the test set. Since the system has already seen the word superb in the DT expansion list of the word good, it can handle this case efficiently. This fact is established by our evaluation results as the model performs better when the DT expansion and primary attentions are a part of the final multi-task system."
+    ],
+    [
+      "Word embeddings represent words in a low-dimensional numerical form. They are useful for solving many NLP problems. We use the pre-trained 300 dimensional Google Word2Vec BIBREF32 embeddings. The word embedding for each word in the sentence is fed to the BiLSTM network to get the current hidden state. Moreover, the primary attention mechanism is also applied to the word embeddings of the candidate terms for the current word."
+    ],
+    [
+      "In this section we present the details of the datasets used for the experiments, results that we obtain and the necessary analysis."
+    ],
+    [
+      "We evaluate our proposed approach for joint sentiment and emotion analysis on the benchmark dataset of SemEval 2016 Task 6 BIBREF7 and Stance Sentiment Emotion Corpus (SSEC) BIBREF15. The SSEC corpus is an annotation of the SemEval 2016 Task 6 corpus with emotion labels. The re-annotation of the SemEval 2016 Task 6 corpus helps to bridge the gap between the unavailability of a corpus with sentiment and emotion labels. The SemEval 2016 corpus contains tweets which are classified into positive, negative or other. It contains 2,914 training and 1,956 test instances. The SSEC corpus is annotated with anger, anticipation, disgust, fear, joy, sadness, surprise and trust labels. Each tweet could belong to one or more emotion classes and one sentiment class. Table TABREF15 shows the data statistics of SemEval 2016 task 6 and SSEC which are used for sentiment and emotion analysis, respectively."
+    ],
+    [
+      "The SemEval 2016 task 6 corpus contains tweets from Twitter. Since the tweets are derived from an environment with the constraint on the number of characters, there is an inherent problem of word concatenation, contractions and use of hashtags. Example: #BeautifulDay, we've, etc. Usernames and URLs do not impart any sentiment and emotion information (e.g. @John). We use the Python package ekphrasis BIBREF33 for handling these situations. Ekphrasis helps to split the concatenated words into individual words and expand the contractions. For example, #BeautifulDay to # Beautiful Day and we've to we have. We replace usernames with $<$user$>$, number with $<number>$ and URLs with $<$url$>$ token."
+    ],
+    [
+      "We implement our model in Python using Tensorflow on a single GPU. We experiment with six different BiLSTM based architectures. The three architectures correspond to BiLSTM based systems without primary attention i.e. only with secondary attention for sentiment analysis (S1), emotion analysis (E1) and the multi-task system (M1) for joint sentiment and emotion analysis. The remaining three architectures correspond to the systems for sentiment analysis (S2), emotion analysis (E2) and multi-task system (M2), with both primary and secondary attention. The weight matrices were initialized randomly using numbers form a truncated normal distribution. The batch size was 64 and the dropout BIBREF34 was 0.6 with the Adam optimizer BIBREF35. The hidden state vectors of both the forward and backward LSTM were 300-dimensional, whereas the context vector was 150-dimensional. Relu BIBREF36 was used as the activation for the hidden layers, whereas in the output layer we used sigmoid as the activation function. Sigmoid cross-entropy was used as the loss function. F1-score was reported for the sentiment analysis BIBREF7 and precision, recall and F1-score were used as the evaluation metric for emotion analysis BIBREF15. Therefore, we report the F1-score for sentiment and precision, recall and F1-score for emotion analysis."
+    ],
+    [
+      "We compare the performance of our proposed system with the state-of-the-art systems of SemEval 2016 Task 6 and the systems of BIBREF15. Experimental results show that the proposed system improves the existing state-of-the-art systems for sentiment and emotion analysis. We summarize the results of evaluation in Table TABREF18.",
+      "The primary attention mechanism plays a key role in the overall system as it improves the score of both sentiment and emotion analysis in both single task as well as multi-task systems. The use of primary attention improves the performance of single task systems for sentiment and emotion analysis by 2.21 and 1.72 points, respectively.Similarly, when sentiment and emotion analysis are jointly performed the primary attention mechanism improves the score by 0.93 and 2.42 points for sentiment and emotion task, respectively. To further measure the usefulness of the primary attention mechanism and the Distributional Thesaurus, we remove it from the systems S2, E2, and M2 to get the systems S1, E1, and M1. In all the cases, with the removal of primary attention mechanism, the performance drops. This is clearly illustrated in Figure FIGREF21. These observations indicate that the primary attention mechanism is an important component of the two-layered multi-task attention based network for sentiment analysis. We also perform t-test BIBREF40 for computing statistical significance of the obtained results from the final two-layered multi-task system M2 for sentiment analysis by calculating the p-values and observe that the performance gain over M1 is significant with p-value = 0.001495. Similarly, we perform the statistical significance test for each emotion class. The p-values for anger, anticipation, fear, disgust, joy, sadness, surprise and trust are 0.000002, 0.000143, 0.00403, 0.000015, 0.004607, 0.069, 0.000001 and 0.000001, respectively. These results provide a good indication of statistical significance.",
+      "Table TABREF19 shows the comparison of our proposed system with the existing state-of-the-art system of SemEval 2016 Task 6 for the sentiment dataset. BIBREF7 used feature-based SVM, BIBREF39 used keyword rules, LitisMind relied on hashtag rules on external data, BIBREF38 utilized a combination of sentiment classifiers and rules, whereas BIBREF37 used a maximum entropy classifier with domain-specific features. Our system comfortably surpasses the existing best system at SemEval. Our system manages to improve the existing best system of SemEval 2016 task 6 by 3.2 F-score points for sentiment analysis.",
+      "We also compare our system with the state-of-the-art systems proposed by BIBREF15 on the emotion dataset. The comparison is demonstrated in Table TABREF22. Maximum entropy, SVM, LSTM, Bi-LSTM, and CNN were the five individual systems used by BIBREF15. Overall, our proposed system achieves an improvement of 5 F-Score points over the existing state-of-the-art system for emotion analysis. Individually, the proposed system improves the existing F-scores for all the emotions except surprise. The findings of BIBREF15 also support this behavior (i.e. worst result for the surprise class). This could be attributed to the data scarcity and a very low agreement between the annotators for the emotion surprise.",
+      "Experimental results indicate that the multi-task system which uses fine-grained information of emotion analysis helps to boost the performance of sentiment analysis. The system M1 comprises of the system S1 performing the main task (sentiment analysis) with E1 undertaking the auxiliary task (emotion analysis). Similarly, the system M2 is made up of S2 and E2 where S2 performs the main task (sentiment analysis) and E2 commits to the auxiliary task (emotion analysis). We observe that in both the situations, the auxiliary task, i.e. emotional information increases the performance of the main task, i.e. sentiment analysis when these two are jointly performed. Experimental results help us to establish the fact that emotion analysis benefits sentiment analysis. The implicit sentiment attached to the emotion words assists the multi-task system. Emotion such as joy and trust are inherently associated with a positive sentiment whereas, anger, disgust, fear and sadness bear a negative sentiment. Figure FIGREF21 illustrates the performance of various models for sentiment analysis.",
+      "As a concrete example which justifies the utility of emotion analysis in sentiment analysis is shown below.",
+      "@realMessi he is a real sportsman and deserves to be the skipper.",
+      "The gold labels for the example are anticipation, joy and trust emotion with a positive sentiment. Our system S2 (single task system for sentiment analysis with primary and secondary attention) had incorrectly labeled this example with a negative sentiment and the E2 system (single task system with both primary and secondary attention for emotion analysis) had tagged it with anticipation and joy only. However, M2 i.e. the multi-task system for joint sentiment and emotion analysis had correctly classified the sentiment as positive and assigned all the correct emotion tags. It predicted the trust emotion tag, in addition to anticipation and joy (which were predicted earlier by E2). This helped M2 to correctly identify the positive sentiment of the example. The presence of emotional information helped the system to alter its sentiment decision (negative by S2) as it had better understanding of the text.",
+      "A sentiment directly does not invoke a particular emotion always and a sentiment can be associated with more than one emotion. However, emotions like joy and trust are associated with positive sentiment mostly whereas, anger, disgust and sadness are associated with negative sentiment particularly. This might be the reason of the extra sentiment information not helping the multi-task system for emotion analysis and hence, a decreased performance for emotion analysis in the multi-task setting."
+    ],
+    [
+      "We perform quantitative error analysis for both sentiment and emotion for the M2 model. Table TABREF23 shows the confusion matrix for sentiment analysis. anger,anticipation,fear,disgust,joy,sadness,surprise,trust consist of the confusion matrices for anger, anticipation, fear, disgust, joy, sadness, surprise and trust. We observe from Table TABREF23 that the system fails to label many instances with the emotion surprise. This may be due to the reason that this particular class is the most underrepresented in the training set. A similar trend can also be observed for the emotion fear and trust in Table TABREF23 and Table TABREF23, respectively. These three emotions have the least share of training instances, making the system less confident towards these emotions.",
+      "Moreover, we closely analyze the outputs to understand the kind of errors that our proposed model faces. We observe that the system faces difficulties at times and wrongly predicts the sentiment class in the following scenarios:",
+      "$\\bullet $ Often real-world phrases/sentences have emotions of conflicting nature. These conflicting nature of emotions are directly not evident from the surface form and are left unsaid as these are implicitly understood by humans. The system gets confused when presented with such instances.",
+      "Text: When you become a father you realize that you are not the most important person in the room anymore... Your child is!",
+      "Actual Sentiment: positive",
+      "Actual Emotion: anticipation, joy, surprise, trust",
+      "Predicted Sentiment: negative",
+      "Predicted Emotion: anger, anticipation, sadness",
+      "The realization of not being the most important person in a room invokes anger, anticipation and sadness emotions, and a negative sentiment. However, it is a natural feeling of overwhelmingly positive sentiment when you understand that your own child is the most significant part of your life.",
+      "$\\bullet $ Occasionally, the system focuses on the less significant part of the sentences. Due to this the system might miss crucial information which can influence and even change the final sentiment or emotion. This sometimes lead to the incorrect prediction of the overall sentiment and emotion.",
+      "Text: I've been called many things, quitter is not one of them...",
+      "Actual Sentiment: positive",
+      "Actual Emotion: anticipation, joy, trust",
+      "Predicted Sentiment: negative",
+      "Predicted Emotion: anticipation, sadness",
+      "Here, the system focuses on the first part of the sentence where the speaker was called many things which denotes a negative sentiment. Hence, the system predicts a negative sentiment and, anticipation and sadness emotions. However, the speaker in the second part uplifts the overall tone by justifying that s/he has never been called a quitter. This changes the negative sentiment to a positive sentiment and the overall emotion."
+    ],
+    [
+      "In this paper, we have presented a novel two-layered multi-task attention based neural network which performs sentiment analysis through emotion analysis. The primary attention mechanism of the two-layered multi-task system relies on Distributional Thesaurus which acts as a source of external knowledge. The system hierarchically builds the final representation from the word level to the sentence level. This provides a working insight to the system and its ability to handle the unseen words. Evaluation on the benchmark dataset suggests an improvement of 3.2 F-score point for sentiment analysis and an overall performance boost of 5 F-score points for emotion analysis over the existing state-of-the-art systems. The system empirically establishes the fact that emotion analysis is both useful and relevant to sentiment analysis. The proposed system does not rely on any language dependent features or lexicons. This makes it extensible to other languages as well. In future, we would like to extend the two-layered multi-task attention based neural network to other languages."
+    ],
+    [
+      "Asif Ekbal acknowledges the Young Faculty Research Fellowship (YFRF), supported by Visvesvaraya PhD scheme for Electronics and IT, Ministry of Electronics and Information Technology (MeitY), Government of India, being implemented by Digital India Corporation (formerly Media Lab Asia)."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0609/instruction.md b/qasper-0609/instruction.md
new file mode 100644
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@@ -0,0 +1,116 @@
+Name of Paper: Emotion helps Sentiment: A Multi-task Model for Sentiment and Emotion Analysis
+
+Question: What is the previous state-of-the-art model?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Proposed Methodology",
+    "Proposed Methodology ::: Two-Layered Multi-Task Attention Model ::: BiLSTM based word encoder",
+    "Proposed Methodology ::: Two-Layered Multi-Task Attention Model ::: Word Attention",
+    "Proposed Methodology ::: Two-Layered Multi-Task Attention Model ::: Sentence Attention",
+    "Proposed Methodology ::: Two-Layered Multi-Task Attention Model ::: Final Output",
+    "Proposed Methodology ::: Distributional Thesaurus",
+    "Proposed Methodology ::: Word Embeddings",
+    "Datasets, Experiments and Analysis",
+    "Datasets, Experiments and Analysis ::: Datasets",
+    "Datasets, Experiments and Analysis ::: Preprocessing",
+    "Datasets, Experiments and Analysis ::: Implementation Details",
+    "Datasets, Experiments and Analysis ::: Results and Analysis",
+    "Datasets, Experiments and Analysis ::: Error Analysis",
+    "Conclusion",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "The emergence of social media sites with limited character constraint has ushered in a new style of communication. Twitter users within 280 characters per tweet share meaningful and informative messages. These short messages have a powerful impact on how we perceive and interact with other human beings. Their compact nature allows them to be transmitted efficiently and assimilated easily. These short messages can shape people's thought and opinion. This makes them an interesting and important area of study. Tweets are not only important for an individual but also for the companies, political parties or any organization. Companies can use tweets to gauge the performance of their products and predict market trends BIBREF0. The public opinion is particularly interesting for political parties as it gives them an idea of voter's inclination and their support. Sentiment and emotion analysis can help to gauge product perception, predict stock prices and model public opinions BIBREF1.",
+      "Sentiment analysis BIBREF2 is an important area of research in natural language processing (NLP) where we automatically determine the sentiments (positive, negative, neutral). Emotion analysis focuses on the extraction of predefined emotion from documents. Discrete emotions BIBREF3, BIBREF4 are often classified into anger, anticipation, disgust, fear, joy, sadness, surprise and trust. Sentiments and emotions are subjective and hence they are understood similarly and often used interchangeably. This is also mostly because both emotions and sentiments refer to experiences that result from the combined influences of the biological, the cognitive, and the social BIBREF5. However, emotions are brief episodes and are shorter in length BIBREF6, whereas sentiments are formed and retained for a longer period. Moreover, emotions are not always target-centric whereas sentiments are directed. Another difference between emotion and sentiment is that a sentence or a document may contain multiple emotions but a single overall sentiment.",
+      "Prior studies show that sentiment and emotion are generally tackled as two separate problems. Although sentiment and emotion are not exactly the same, they are closely related. Emotions, like joy and trust, intrinsically have an association with a positive sentiment. Similarly, anger, disgust, fear and sadness have a negative tone. Moreover, sentiment analysis alone is insufficient at times in imparting complete information. A negative sentiment can arise due to anger, disgust, fear, sadness or a combination of these. Information about emotion along with sentiment helps to better understand the state of the person or object. The close association of emotion with sentiment motivates us to build a system for sentiment analysis using the information obtained from emotion analysis.",
+      "In this paper, we put forward a robust two-layered multi-task attention based neural network which performs sentiment analysis and emotion analysis simultaneously. The model uses two levels of attention - the first primary attention builds the best representation for each word using Distributional Thesaurus and the secondary attention mechanism creates the final sentence level representation. The system builds the representation hierarchically which gives it a good intuitive working insight. We perform several experiments to evaluate the usefulness of primary attention mechanism. Experimental results show that the two-layered multi-task system for sentiment analysis which uses emotion analysis as an auxiliary task improves over the existing state-of-the-art system of SemEval 2016 Task 6 BIBREF7.",
+      "The main contributions of the current work are two-fold: a) We propose a novel two-layered multi-task attention based system for joint sentiment and emotion analysis. This system has two levels of attention which builds a hierarchical representation. This provides an intuitive explanation of its working; b) We empirically show that emotion analysis is relevant and useful in sentiment analysis. The multi-task system utilizing fine-grained information of emotion analysis performs better than the single task system of sentiment analysis."
+    ],
+    [
+      "A survey of related literature reveals the use of both classical and deep-learning approaches for sentiment and emotion analysis. The system proposed in BIBREF8 relied on supervised statistical text classification which leveraged a variety of surface form, semantic, and sentiment features for short informal texts. A Support Vector Machine (SVM) based system for sentiment analysis was used in BIBREF9, whereas an ensemble of four different sub-systems for sentiment analysis was proposed in BIBREF10. It comprised of Long Short-Term Memory (LSTM) BIBREF11, Gated Recurrent Unit (GRU) BIBREF12, Convolutional Neural Network (CNN) BIBREF13 and Support Vector Regression (SVR) BIBREF14. BIBREF15 reported the results for emotion analysis using SVR, LSTM, CNN and Bi-directional LSTM (Bi-LSTM) BIBREF16. BIBREF17 proposed a lexicon based feature extraction for emotion text classification. A rule-based approach was adopted by BIBREF18 to extract emotion-specific semantics. BIBREF19 used a high-order Hidden Markov Model (HMM) for emotion detection. BIBREF20 explored deep learning techniques for end-to-end trainable emotion recognition. BIBREF21 proposed a multi-task learning model for fine-grained sentiment analysis. They used ternary sentiment classification (negative, neutral, positive) as an auxiliary task for fine-grained sentiment analysis (very-negative, negative, neutral, positive, very-positive). A CNN based system was proposed by BIBREF22 for three phase joint multi-task training. BIBREF23 presented a multi-task learning based model for joint sentiment analysis and semantic embedding learning tasks. BIBREF24 proposed a multi-task setting for emotion analysis based on a vector-valued Gaussian Process (GP) approach known as coregionalisation BIBREF25. A hierarchical document classification system based on sentence and document representation was proposed by BIBREF26. An attention framework for sentiment regression is described in BIBREF27. BIBREF28 proposed a DeepEmoji system based on transfer learning for sentiment, emotion and sarcasm detection through emoji prediction. However, the DeepEmoji system treats these independently, one at a time.",
+      "Our proposed system differs from the above works in the sense that none of these works addresses the problem of sentiment and emotion analysis concurrently. Our empirical analysis shows that performance of sentiment analysis is boosted significantly when this is jointly performed with emotion analysis. This may be because of the fine-grained characteristics of emotion analysis that provides useful evidences for sentiment analysis."
+    ],
+    [
+      "We propose a novel two-layered multi-task attention based neural network for sentiment analysis where emotion analysis is utilized to improve its efficiency. Figure FIGREF1 illustrates the overall architecture of the proposed multi-task system. The proposed system consists of a Bi-directional Long Short-Term Memory (BiLSTM) BIBREF16, a two-level attention mechanism BIBREF29, BIBREF30 and a shared representation for emotion and sentiment analysis tasks. The BiLSTM encodes the word representation of each word. This representation is shared between the subsystems of sentiment and emotion analysis. Each of the shared representations is then fed to the primary attention mechanism of both the subsystems. The primary attention mechanism finds the best representation for each word for each task. The secondary attention mechanism acts on top of the primary attention to extract the best sentence representation by focusing on the suitable context for each task. Finally, the representations of both the tasks are fed to two different feed-forward neural networks to produce two outputs - one for sentiment analysis and one for emotion analysis. Each component is explained in the subsequent subsections."
+    ],
+    [
+      "Recurrent Neural Networks (RNN) are a class of networks which take sequential input and computes a hidden state vector for each time step. The current hidden state vector depends on the current input and the previous hidden state vector. This makes them good for handling sequential data. However, they suffer from a vanishing or exploding gradient problem when presented with long sequences. The gradient for back-propagating error either reduces to a very small number or increases to a very high value which hinders the learning process. Long Short Term Memory (LSTM) BIBREF11, a variant of RNN solves this problem by the gating mechanisms. The input, forget and output gates control the information flow.",
+      "BiLSTM is a special type of LSTM which takes into account the output of two LSTMs - one working in the forward direction and one working in the backward direction. The presence of contextual information for both past and future helps the BiLSTM to make an informed decision. The concatenation of a hidden state vectors $\\overrightarrow{h_t}$ of the forward LSTM and $\\overleftarrow{h_t}$ of the backward LSTM at any time step t provides the complete information. Therefore, the output of the BiLSTM at any time step t is $h_t$ = [$\\overrightarrow{h_t}$, $\\overleftarrow{h_t}$]. The output of the BiLSTM is shared between the main task (Sentiment Analysis) and the auxiliary task (Emotion Analysis)."
+    ],
+    [
+      "The word level attention (primary attention) mechanism gives the model a flexibility to represent each word for each task differently. This improves the word representation as the model chooses the best representation for each word for each task. A Distributional Thesaurus (DT) identifies words that are semantically similar, based on whether they tend to occur in a similar context. It provides a word expansion list for words based on their contextual similarity. We use the top-4 words for each word as their candidate terms. We only use the top-4 words for each word as we observed that the expansion list with more words started to contain the antonyms of the current word which empirically reduced the system performance. Word embeddings of these four candidate terms and the hidden state vector $h_t$ of the input word are fed to the primary attention mechanism. The primary attention mechanism finds the best attention coefficient for each candidate term. At each time step $t$ we get V($x_t$) candidate terms for each input $x_t$ with $v_i$ being the embedding for each term (Distributional Thesaurus and word embeddings are described in the next section). The primary attention mechanism assigns an attention coefficient to each of the candidate terms having the index $i$ $\\in $ V($x_t$):",
+      "where $W_w$ and $b_{w}$ are jointly learned parameters.",
+      "Each embedding of the candidate term is weighted with the attention score $\\alpha _{ti}$ and then summed up. This produces $m_{t}$, the representation for the current input $x_{t}$ obtained from the Distributional Thesaurus using the candidate terms.",
+      "Finally, $m_{t}$ and $h_{t}$ are concatenated to get $\\widehat{h_{t}}$, the final output of the primary attention mechanism."
+    ],
+    [
+      "The sentence attention (secondary attention) part focuses on each word of the sentence and assigns the attention coefficients. The attention coefficients are assigned on the basis of words' importance and their contextual relevance. This helps the model to build the overall sentence representation by capturing the context while weighing different word representations individually. The final sentence representation is obtained by multiplying each word vector representation with their attention coefficient and summing them over. The attention coefficient $\\alpha _t$ for each word vector representation and the sentence representation $\\widehat{H}$ are calculated as:",
+      "where $W_s$ and $b_{s}$ are parameters to be learned.",
+      "$\\widehat{H}$ denotes the sentence representation for sentiment analysis. Similarly, we calculate $\\bar{H}$ which represents the sentence for emotion classification. The system has the flexibility to compute different representations for sentiment and emotion analysis both."
+    ],
+    [
+      "The final outputs for both sentiment and emotion analysis are computed by feeding $\\widehat{H}$ and $\\bar{H}$ to two different one-layer feed forward neural networks. For our task, the feed forward network for sentiment analysis has two output units, whereas the feed forward network for emotion analysis has eight output nodes performing multi-label classification."
+    ],
+    [
+      "Distributional Thesaurus (DT) BIBREF31 ranks words according to their semantic similarity. It is a resource which produces a list of words in decreasing order of their similarity for each word. We use the DT to expand each word of the sentence. The top-4 words serve as the candidate terms for each word. For example, the candidate terms for the word good are: great, nice awesome and superb. DT offers the primary attention mechanism external knowledge in the form of candidate terms. It assists the system to perform better when presented with unseen words during testing as the unseen words could have been a part of the DT expansion list. For example, the system may not come across the word superb during training but it can appear in the test set. Since the system has already seen the word superb in the DT expansion list of the word good, it can handle this case efficiently. This fact is established by our evaluation results as the model performs better when the DT expansion and primary attentions are a part of the final multi-task system."
+    ],
+    [
+      "Word embeddings represent words in a low-dimensional numerical form. They are useful for solving many NLP problems. We use the pre-trained 300 dimensional Google Word2Vec BIBREF32 embeddings. The word embedding for each word in the sentence is fed to the BiLSTM network to get the current hidden state. Moreover, the primary attention mechanism is also applied to the word embeddings of the candidate terms for the current word."
+    ],
+    [
+      "In this section we present the details of the datasets used for the experiments, results that we obtain and the necessary analysis."
+    ],
+    [
+      "We evaluate our proposed approach for joint sentiment and emotion analysis on the benchmark dataset of SemEval 2016 Task 6 BIBREF7 and Stance Sentiment Emotion Corpus (SSEC) BIBREF15. The SSEC corpus is an annotation of the SemEval 2016 Task 6 corpus with emotion labels. The re-annotation of the SemEval 2016 Task 6 corpus helps to bridge the gap between the unavailability of a corpus with sentiment and emotion labels. The SemEval 2016 corpus contains tweets which are classified into positive, negative or other. It contains 2,914 training and 1,956 test instances. The SSEC corpus is annotated with anger, anticipation, disgust, fear, joy, sadness, surprise and trust labels. Each tweet could belong to one or more emotion classes and one sentiment class. Table TABREF15 shows the data statistics of SemEval 2016 task 6 and SSEC which are used for sentiment and emotion analysis, respectively."
+    ],
+    [
+      "The SemEval 2016 task 6 corpus contains tweets from Twitter. Since the tweets are derived from an environment with the constraint on the number of characters, there is an inherent problem of word concatenation, contractions and use of hashtags. Example: #BeautifulDay, we've, etc. Usernames and URLs do not impart any sentiment and emotion information (e.g. @John). We use the Python package ekphrasis BIBREF33 for handling these situations. Ekphrasis helps to split the concatenated words into individual words and expand the contractions. For example, #BeautifulDay to # Beautiful Day and we've to we have. We replace usernames with $<$user$>$, number with $<number>$ and URLs with $<$url$>$ token."
+    ],
+    [
+      "We implement our model in Python using Tensorflow on a single GPU. We experiment with six different BiLSTM based architectures. The three architectures correspond to BiLSTM based systems without primary attention i.e. only with secondary attention for sentiment analysis (S1), emotion analysis (E1) and the multi-task system (M1) for joint sentiment and emotion analysis. The remaining three architectures correspond to the systems for sentiment analysis (S2), emotion analysis (E2) and multi-task system (M2), with both primary and secondary attention. The weight matrices were initialized randomly using numbers form a truncated normal distribution. The batch size was 64 and the dropout BIBREF34 was 0.6 with the Adam optimizer BIBREF35. The hidden state vectors of both the forward and backward LSTM were 300-dimensional, whereas the context vector was 150-dimensional. Relu BIBREF36 was used as the activation for the hidden layers, whereas in the output layer we used sigmoid as the activation function. Sigmoid cross-entropy was used as the loss function. F1-score was reported for the sentiment analysis BIBREF7 and precision, recall and F1-score were used as the evaluation metric for emotion analysis BIBREF15. Therefore, we report the F1-score for sentiment and precision, recall and F1-score for emotion analysis."
+    ],
+    [
+      "We compare the performance of our proposed system with the state-of-the-art systems of SemEval 2016 Task 6 and the systems of BIBREF15. Experimental results show that the proposed system improves the existing state-of-the-art systems for sentiment and emotion analysis. We summarize the results of evaluation in Table TABREF18.",
+      "The primary attention mechanism plays a key role in the overall system as it improves the score of both sentiment and emotion analysis in both single task as well as multi-task systems. The use of primary attention improves the performance of single task systems for sentiment and emotion analysis by 2.21 and 1.72 points, respectively.Similarly, when sentiment and emotion analysis are jointly performed the primary attention mechanism improves the score by 0.93 and 2.42 points for sentiment and emotion task, respectively. To further measure the usefulness of the primary attention mechanism and the Distributional Thesaurus, we remove it from the systems S2, E2, and M2 to get the systems S1, E1, and M1. In all the cases, with the removal of primary attention mechanism, the performance drops. This is clearly illustrated in Figure FIGREF21. These observations indicate that the primary attention mechanism is an important component of the two-layered multi-task attention based network for sentiment analysis. We also perform t-test BIBREF40 for computing statistical significance of the obtained results from the final two-layered multi-task system M2 for sentiment analysis by calculating the p-values and observe that the performance gain over M1 is significant with p-value = 0.001495. Similarly, we perform the statistical significance test for each emotion class. The p-values for anger, anticipation, fear, disgust, joy, sadness, surprise and trust are 0.000002, 0.000143, 0.00403, 0.000015, 0.004607, 0.069, 0.000001 and 0.000001, respectively. These results provide a good indication of statistical significance.",
+      "Table TABREF19 shows the comparison of our proposed system with the existing state-of-the-art system of SemEval 2016 Task 6 for the sentiment dataset. BIBREF7 used feature-based SVM, BIBREF39 used keyword rules, LitisMind relied on hashtag rules on external data, BIBREF38 utilized a combination of sentiment classifiers and rules, whereas BIBREF37 used a maximum entropy classifier with domain-specific features. Our system comfortably surpasses the existing best system at SemEval. Our system manages to improve the existing best system of SemEval 2016 task 6 by 3.2 F-score points for sentiment analysis.",
+      "We also compare our system with the state-of-the-art systems proposed by BIBREF15 on the emotion dataset. The comparison is demonstrated in Table TABREF22. Maximum entropy, SVM, LSTM, Bi-LSTM, and CNN were the five individual systems used by BIBREF15. Overall, our proposed system achieves an improvement of 5 F-Score points over the existing state-of-the-art system for emotion analysis. Individually, the proposed system improves the existing F-scores for all the emotions except surprise. The findings of BIBREF15 also support this behavior (i.e. worst result for the surprise class). This could be attributed to the data scarcity and a very low agreement between the annotators for the emotion surprise.",
+      "Experimental results indicate that the multi-task system which uses fine-grained information of emotion analysis helps to boost the performance of sentiment analysis. The system M1 comprises of the system S1 performing the main task (sentiment analysis) with E1 undertaking the auxiliary task (emotion analysis). Similarly, the system M2 is made up of S2 and E2 where S2 performs the main task (sentiment analysis) and E2 commits to the auxiliary task (emotion analysis). We observe that in both the situations, the auxiliary task, i.e. emotional information increases the performance of the main task, i.e. sentiment analysis when these two are jointly performed. Experimental results help us to establish the fact that emotion analysis benefits sentiment analysis. The implicit sentiment attached to the emotion words assists the multi-task system. Emotion such as joy and trust are inherently associated with a positive sentiment whereas, anger, disgust, fear and sadness bear a negative sentiment. Figure FIGREF21 illustrates the performance of various models for sentiment analysis.",
+      "As a concrete example which justifies the utility of emotion analysis in sentiment analysis is shown below.",
+      "@realMessi he is a real sportsman and deserves to be the skipper.",
+      "The gold labels for the example are anticipation, joy and trust emotion with a positive sentiment. Our system S2 (single task system for sentiment analysis with primary and secondary attention) had incorrectly labeled this example with a negative sentiment and the E2 system (single task system with both primary and secondary attention for emotion analysis) had tagged it with anticipation and joy only. However, M2 i.e. the multi-task system for joint sentiment and emotion analysis had correctly classified the sentiment as positive and assigned all the correct emotion tags. It predicted the trust emotion tag, in addition to anticipation and joy (which were predicted earlier by E2). This helped M2 to correctly identify the positive sentiment of the example. The presence of emotional information helped the system to alter its sentiment decision (negative by S2) as it had better understanding of the text.",
+      "A sentiment directly does not invoke a particular emotion always and a sentiment can be associated with more than one emotion. However, emotions like joy and trust are associated with positive sentiment mostly whereas, anger, disgust and sadness are associated with negative sentiment particularly. This might be the reason of the extra sentiment information not helping the multi-task system for emotion analysis and hence, a decreased performance for emotion analysis in the multi-task setting."
+    ],
+    [
+      "We perform quantitative error analysis for both sentiment and emotion for the M2 model. Table TABREF23 shows the confusion matrix for sentiment analysis. anger,anticipation,fear,disgust,joy,sadness,surprise,trust consist of the confusion matrices for anger, anticipation, fear, disgust, joy, sadness, surprise and trust. We observe from Table TABREF23 that the system fails to label many instances with the emotion surprise. This may be due to the reason that this particular class is the most underrepresented in the training set. A similar trend can also be observed for the emotion fear and trust in Table TABREF23 and Table TABREF23, respectively. These three emotions have the least share of training instances, making the system less confident towards these emotions.",
+      "Moreover, we closely analyze the outputs to understand the kind of errors that our proposed model faces. We observe that the system faces difficulties at times and wrongly predicts the sentiment class in the following scenarios:",
+      "$\\bullet $ Often real-world phrases/sentences have emotions of conflicting nature. These conflicting nature of emotions are directly not evident from the surface form and are left unsaid as these are implicitly understood by humans. The system gets confused when presented with such instances.",
+      "Text: When you become a father you realize that you are not the most important person in the room anymore... Your child is!",
+      "Actual Sentiment: positive",
+      "Actual Emotion: anticipation, joy, surprise, trust",
+      "Predicted Sentiment: negative",
+      "Predicted Emotion: anger, anticipation, sadness",
+      "The realization of not being the most important person in a room invokes anger, anticipation and sadness emotions, and a negative sentiment. However, it is a natural feeling of overwhelmingly positive sentiment when you understand that your own child is the most significant part of your life.",
+      "$\\bullet $ Occasionally, the system focuses on the less significant part of the sentences. Due to this the system might miss crucial information which can influence and even change the final sentiment or emotion. This sometimes lead to the incorrect prediction of the overall sentiment and emotion.",
+      "Text: I've been called many things, quitter is not one of them...",
+      "Actual Sentiment: positive",
+      "Actual Emotion: anticipation, joy, trust",
+      "Predicted Sentiment: negative",
+      "Predicted Emotion: anticipation, sadness",
+      "Here, the system focuses on the first part of the sentence where the speaker was called many things which denotes a negative sentiment. Hence, the system predicts a negative sentiment and, anticipation and sadness emotions. However, the speaker in the second part uplifts the overall tone by justifying that s/he has never been called a quitter. This changes the negative sentiment to a positive sentiment and the overall emotion."
+    ],
+    [
+      "In this paper, we have presented a novel two-layered multi-task attention based neural network which performs sentiment analysis through emotion analysis. The primary attention mechanism of the two-layered multi-task system relies on Distributional Thesaurus which acts as a source of external knowledge. The system hierarchically builds the final representation from the word level to the sentence level. This provides a working insight to the system and its ability to handle the unseen words. Evaluation on the benchmark dataset suggests an improvement of 3.2 F-score point for sentiment analysis and an overall performance boost of 5 F-score points for emotion analysis over the existing state-of-the-art systems. The system empirically establishes the fact that emotion analysis is both useful and relevant to sentiment analysis. The proposed system does not rely on any language dependent features or lexicons. This makes it extensible to other languages as well. In future, we would like to extend the two-layered multi-task attention based neural network to other languages."
+    ],
+    [
+      "Asif Ekbal acknowledges the Young Faculty Research Fellowship (YFRF), supported by Visvesvaraya PhD scheme for Electronics and IT, Ministry of Electronics and Information Technology (MeitY), Government of India, being implemented by Digital India Corporation (formerly Media Lab Asia)."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0630/instruction.md b/qasper-0630/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..6da47fb18e455e3eb9ea33d9057a467fd5f3071b
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+++ b/qasper-0630/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Predictive Embeddings for Hate Speech Detection on Twitter
+
+Question: What data are the embeddings trained on?
\ No newline at end of file
diff --git a/qasper-0631/instruction.md b/qasper-0631/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..993a83c4710c17fc29f6e26d206ba683a17e6a1f
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+++ b/qasper-0631/instruction.md
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+Name of Paper: Predictive Embeddings for Hate Speech Detection on Twitter
+
+Question: how much was the parameter difference between their model and previous methods?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Data",
+    "Transformed Word Embedding Model (TWEM)",
+    "Word Embeddings",
+    "Pooling",
+    "Output",
+    "Experimental Setup",
+    "Results and Discussion",
+    "Error Analysis",
+    "Conclusion",
+    "Supplemental Material",
+    "Preprocessing",
+    "Embedding Analysis"
+  ],
+  "paragraphs": [
+    [
+      "The increasing popularity of social media platforms like Twitter for both personal and political communication BIBREF0 has seen a well-acknowledged rise in the presence of toxic and abusive speech on these platforms BIBREF1 , BIBREF2 . Although the terms of services on these platforms typically forbid hateful and harassing speech, enforcing these rules has proved challenging, as identifying hate speech speech at scale is still a largely unsolved problem in the NLP community. BIBREF3 , for example, identify many ambiguities in classifying abusive communications, and highlight the difficulty of clearly defining the parameters of such speech. This problem is compounded by the fact that identifying abusive or harassing speech is a challenge for humans as well as automated systems.",
+      "Despite the lack of consensus around what constitutes abusive speech, some definition of hate speech must be used to build automated systems to address it. We rely on BIBREF4 's definition of hate speech, specifically: \u201clanguage that is used to express hatred towards a targeted group or is intended to be derogatory, to humiliate, or to insult the members of the group.\u201d",
+      "In this paper, we present a neural classification system that uses minimal preprocessing to take advantage of a modified Simple Word Embeddings-based Model BIBREF5 to predict the occurrence of hate speech. Our classifier features:",
+      "In the following sections, we discuss related work on hate speech classification, followed by a description of the datasets, methods and results of our study."
+    ],
+    [
+      "Many efforts have been made to classify hate speech using data scraped from online message forums and popular social media sites such as Twitter and Facebook. BIBREF3 applied a logistic regression model that used one- to four-character n-grams for classification of tweets labeled as racist, sexist or neither. BIBREF4 experimented in classification of hateful as well as offensive but not hateful tweets. They applied a logistic regression classifier with L2 regularization using word level n-grams and various part-of-speech, sentiment, and tweet-level metadata features.",
+      "Additional projects have built upon the data sets created by Waseem and/or Davidson. For example, BIBREF6 used a neural network approach with two binary classifiers: one to predict the presence abusive speech more generally, and another to discern the form of abusive speech.",
+      " BIBREF7 , meanwhile, used pre-trained word2vec embeddings, which were then fed into a convolutional neural network (CNN) with max pooling to produce input vectors for a Gated Recurrent Unit (GRU) neural network. Other researchers have experimented with using metadata features from tweets. BIBREF8 built a classifier composed of two separate neural networks, one for the text and the other for metadata of the Twitter user, that were trained jointly in interleaved fashion. Both networks used in combination - and especially when trained using transfer learning - achieved higher F1 scores than either neural network classifier alone.",
+      "In contrast to the methods described above, our approach relies on a simple word embedding (SWEM)-based architecture BIBREF5 , reducing the number of required parameters and length of training required, while still yielding improved performance and resilience across related classification tasks. Moreover, our network is able to learn flexible vector representations that demonstrate associations among words typically used in hateful communication. Finally, while metadata-based augmentation is intriguing, here we sought to develop an approach that would function well even in cases where such additional data was missing due to the deletion, suspension, or deactivation of accounts."
+    ],
+    [
+      "In this paper, we use three data sets from the literature to train and evaluate our own classifier. Although all address the category of hateful speech, they used different strategies of labeling the collected data. Table TABREF5 shows the characteristics of the datasets.",
+      "Data collected by BIBREF3 , which we term the Sexist/Racist (SR) data set, was collected using an initial Twitter search followed by analysis and filtering by the authors and their team who identified 17 common phrases, hashtags, and users that were indicative of abusive speech. BIBREF4 collected the HATE dataset by searching for tweets using a lexicon provided by Hatebase.org. The final data set we used, which we call HAR, was collected by BIBREF9 ; we removed all retweets reducing the dataset to 20,000 tweets. Tweets were labeled as \u201cHarrassing\u201d or \u201cNon-Harrassing\u201d; hate speech was not explicitly labeled, but treated as an unlabeled subset of the broader \u201cHarrassing\u201d category BIBREF9 ."
+    ],
+    [
+      "Our training set consists of INLINEFORM0 examples INLINEFORM1 where the input INLINEFORM2 is a sequence of tokens INLINEFORM3 , and the output INLINEFORM4 is the numerical class for the hate speech class. Each input instance represents a Twitter post and thus, is not limited to a single sentence.",
+      "We modify the SWEM-concat BIBREF5 architecture to allow better handling of infrequent and unknown words and to capture non-linear word combinations."
+    ],
+    [
+      "Each token in the input is mapped to an embedding. We used the 300 dimensional embeddings for all our experiments, so each word INLINEFORM0 is mapped to INLINEFORM1 . We denote the full embedded sequence as INLINEFORM2 . We then transform each word embedding by applying 300 dimensional 1-layer Multi Layer Perceptron (MLP) INLINEFORM3 with a Rectified Liner Unit (ReLU) activation to form an updated embedding space INLINEFORM4 . We find this better handles unseen or rare tokens in our training data by projecting the pretrained embedding into a space that the encoder can understand."
+    ],
+    [
+      "We make use of two pooling methods on the updated embedding space INLINEFORM0 . We employ a max pooling operation on INLINEFORM1 to capture salient word features from our input; this representation is denoted as INLINEFORM2 . This forces words that are highly indicative of hate speech to higher positive values within the updated embedding space. We also average the embeddings INLINEFORM3 to capture the overall meaning of the sentence, denoted as INLINEFORM4 , which provides a strong conditional factor in conjunction with the max pooling output. This also helps regularize gradient updates from the max pooling operation."
+    ],
+    [
+      "We concatenate INLINEFORM0 and INLINEFORM1 to form a document representation INLINEFORM2 and feed the representation into a 50 node 2 layer MLP followed by ReLU Activation to allow for increased nonlinear representation learning. This representation forms the preterminal layer and is passed to a fully connected softmax layer whose output is the probability distribution over labels."
+    ],
+    [
+      "We tokenize the data using Spacy BIBREF10 . We use 300 Dimensional Glove Common Crawl Embeddings (840B Token) BIBREF11 and fine tune them for the task. We experimented extensively with pre-processing variants and our results showed better performance without lemmatization and lower-casing (see supplement for details). We pad each input to 50 words. We train using RMSprop with a learning rate of .001 and a batch size of 512. We add dropout with a drop rate of 0.1 in the final layer to reduce overfitting BIBREF12 , batch size, and input length empirically through random hyperparameter search.",
+      "All of our results are produced from 10-fold cross validation to allow comparison with previous results. We trained a logistic regression baseline model (line 1 in Table TABREF10 ) using character ngrams and word unigrams using TF*IDF weighting BIBREF13 , to provide a baseline since HAR has no reported results. For the SR and HATE datasets, the authors reported their trained best logistic regression model's results on their respective datasets.",
+      "SR: Sexist/Racist BIBREF3 , HATE: Hate BIBREF4 HAR: Harassment BIBREF9 "
+    ],
+    [
+      "The approach we have developed establishes a new state of the art for classifying hate speech, outperforming previous results by as much as 12 F1 points. Table TABREF10 illustrates the robustness of our method, which often outperform previous results, measured by weighted F1. ",
+      "Using the Approximate Randomization (AR) Test BIBREF14 , we perform significance testing using a 75/25 train and test split",
+      "to compare against BIBREF3 and BIBREF4 , whose models we re-implemented. We found 0.001 significance compared to both methods. We also include in-depth precision and recall results for all three datasets in the supplement.",
+      "Our results indicate better performance than several more complex approaches, including BIBREF4 's best model (which used word and part-of-speech ngrams, sentiment, readability, text, and Twitter specific features), BIBREF6 (which used two fold classification and a hybrid of word and character CNNs, using approximately twice the parameters we use excluding the word embeddings) and even recent work by BIBREF8 , (whose best model relies on GRUs, metadata including popularity, network reciprocity, and subscribed lists).",
+      "On the SR dataset, we outperform BIBREF8 's text based model by 3 F1 points, while just falling short of the Text + Metadata Interleaved Training model. While we appreciate the potential added value of metadata, we believe a tweet-only classifier has merits because retrieving features from the social graph is not always tractable in production settings. Excluding the embedding weights, our model requires 100k parameters , while BIBREF8 requires 250k parameters."
+    ],
+    [
+      "False negatives",
+      "Many of the false negatives we see are specific references to characters in the TV show \u201cMy Kitchen Rules\u201d, rather than something about women in general. Such examples may be innocuous in isolation but could potentially be sexist or racist in context. While this may be a limitation of considering only the content of the tweet, it could also be a mislabel.",
+      "Debra are now my most hated team on #mkr after least night's ep. Snakes in the grass those two.",
+      "Along these lines, we also see correct predictions of innocuous speech, but find data mislabeled as hate speech:",
+      "@LoveAndLonging ...how is that example \"sexism\"?",
+      "@amberhasalamb ...in what way?",
+      "Another case our classifier misses is problematic speech within a hashtag:",
+      ":D @nkrause11 Dudes who go to culinary school: #why #findawife #notsexist :)",
+      "This limitation could be potentially improved through the use of character convolutions or subword tokenization.",
+      "False Positives",
+      "In certain cases, our model seems to be learning user names instead of semantic content:",
+      "RT @GrantLeeStone: @MT8_9 I don't even know what that is, or where it's from. Was that supposed to be funny? It wasn't.",
+      "Since the bulk of our model's weights are in the embedding and embedding-transformation matrices, we cluster the SR vocabulary using these transformed embeddings to clarify our intuitions about the model ( TABREF14 ). We elaborate on our clustering approach in the supplement. We see that the model learned general semantic groupings of words associated with hate speech as well as specific idiosyncrasies related to the dataset itself (e.g. katieandnikki)"
+    ],
+    [
+      "Despite minimal tuning of hyper-parameters, fewer weight parameters, minimal text preprocessing, and no additional metadata, the model performs remarkably well on standard hate speech datasets. Our clustering analysis adds interpretability enabling inspection of results.",
+      "Our results indicate that the majority of recent deep learning models in hate speech may rely on word embeddings for the bulk of predictive power and the addition of sequence-based parameters provide minimal utility. Sequence based approaches are typically important when phenomena such as negation, co-reference, and context-dependent phrases are salient in the text and thus, we suspect these cases are in the minority for publicly available datasets. We think it would be valuable to study the occurrence of such linguistic phenomena in existing datasets and construct new datasets that have a better representation of subtle forms of hate speech. In the future, we plan to investigate character based representations, using character CNNs and highway layers BIBREF15 along with word embeddings to allow robust representations for sparse words such as hashtags."
+    ],
+    [
+      "We experimented with several different preprocessing variants and were surprised to find that reducing preprocessing improved the performance on the task for all of our tasks. We go through each preprocessing variant with an example and then describe our analysis to compare and evaluate each of them."
+    ],
+    [
+      "Original text",
+      "RT @AGuyNamed_Nick Now, I'm not sexist in any way shape or form but I think women are better at gift wrapping. It's the XX chromosome thing",
+      "Tokenize (Basic Tokenize: Keeps case and words intact with limited sanitizing)",
+      "RT @AGuyNamed_Nick Now , I 'm not sexist in any way shape or form but I think women are better at gift wrapping . It 's the XX chromosome thing",
+      "Tokenize Lowercase: Lowercase the basic tokenize scheme",
+      "rt @aguynamed_nick now , i 'm not sexist in any way shape or form but i think women are better at gift wrapping . it 's the xx chromosome thing",
+      "Token Replace: Replaces entities and user names with placeholder)",
+      "ENT USER now , I 'm not sexist in any way shape or form but I think women are better at gift wrapping . It 's the xx chromosome thing",
+      "Token Replace Lowercase: Lowercase the Token Replace Scheme",
+      "ENT USER now , i 'm not sexist in any way shape or form but i think women are better at gift wrapping . it 's the xx chromosome thing",
+      "We did analysis on a validation set across multiple datasets to find that the \"Tokenize\" scheme was by far the best. We believe that keeping the case in tact provides useful information about the user. For example, saying something in all CAPS is a useful signal that the model can take advantage of."
+    ],
+    [
+      "Since our method was a simple word embedding based model, we explored the learned embedding space to analyze results. For this analysis, we only use the max pooling part of our architecture to help analyze the learned embedding space because it encourages salient words to increase their values to be selected. We projected the original pre-trained embeddings to the learned space using the time distributed MLP. We summed the embedding dimensions for each word and sorted by the sum in descending order to find the 1000 most salient word embeddings from our vocabulary. We then ran PCA BIBREF16 to reduce the dimensionality of the projected embeddings from 300 dimensions to 75 dimensions. This captured about 60% of the variance. Finally, we ran K means clustering for INLINEFORM0 clusters to organize the most salient embeddings in the projected space.",
+      "The learned clusters from the SR vocabulary were very illuminating (see Table TABREF14 ); they gave insights to how hate speech surfaced in the datasets. One clear grouping we found is the misogynistic and pornographic group, which contained words like breasts, blonds, and skank. Two other clusters had references to geopolitical and religious issues in the Middle East and disparaging and resentful epithets that could be seen as having an intellectual tone. This hints towards the subtle pedagogic forms of hate speech that surface. We ran silhouette analysis BIBREF17 on the learned clusters to find that the clusters from the learned representations had a 35% higher silhouette coefficient using the projected embeddings compared to the clusters created from the original pre-trained embeddings. This reinforces the claim that our training process pushed hate-speech related words together, and words from other clusters further away, thus, structuring the embedding space effectively for detecting hate speech."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0636/instruction.md b/qasper-0636/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..f34307123f94dff96da887c3efa43ab6be3ff108
--- /dev/null
+++ b/qasper-0636/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Incorporating Discrete Translation Lexicons into Neural Machine Translation
+
+Question: What datasets were used?
\ No newline at end of file
diff --git a/qasper-0637/instruction.md b/qasper-0637/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..919cc65ff762387e95e844361f14bf2d48aa832b
--- /dev/null
+++ b/qasper-0637/instruction.md
@@ -0,0 +1,128 @@
+Name of Paper: Incorporating Discrete Translation Lexicons into Neural Machine Translation
+
+Question: What language pairs did they experiment with?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Neural Machine Translation",
+    "Integrating Lexicons into NMT",
+    "Converting Lexicon Probabilities into Conditioned Predictive Proabilities",
+    "Combining Predictive Probabilities",
+    "Constructing Lexicon Probabilities",
+    "Automatically Learned Lexicons",
+    "Manual Lexicons",
+    "Hybrid Lexicons",
+    "Experiment & Result",
+    "Settings",
+    "Effect of Integrating Lexicons",
+    "Comparison of Integration Methods",
+    "Additional Experiments",
+    "Related Work",
+    "Conclusion & Future Work",
+    "Acknowledgment"
+  ],
+  "paragraphs": [
+    [
+      "Neural machine translation (NMT, \u00a7 SECREF2 ; kalchbrenner13emnlp, sutskever14nips) is a variant of statistical machine translation (SMT; brown93cl), using neural networks. NMT has recently gained popularity due to its ability to model the translation process end-to-end using a single probabilistic model, and for its state-of-the-art performance on several language pairs BIBREF0 , BIBREF1 .",
+      "One feature of NMT systems is that they treat each word in the vocabulary as a vector of continuous-valued numbers. This is in contrast to more traditional SMT methods such as phrase-based machine translation (PBMT; koehn03phrasebased), which represent translations as discrete pairs of word strings in the source and target languages. The use of continuous representations is a major advantage, allowing NMT to share statistical power between similar words (e.g. \u201cdog\u201d and \u201ccat\u201d) or contexts (e.g. \u201cthis is\u201d and \u201cthat is\u201d). However, this property also has a drawback in that NMT systems often mistranslate into words that seem natural in the context, but do not reflect the content of the source sentence. For example, Figure FIGREF2 is a sentence from our data where the NMT system mistakenly translated \u201cTunisia\u201d into the word for \u201cNorway.\u201d This variety of error is particularly serious because the content words that are often mistranslated by NMT are also the words that play a key role in determining the whole meaning of the sentence.",
+      "In contrast, PBMT and other traditional SMT methods tend to rarely make this kind of mistake. This is because they base their translations on discrete phrase mappings, which ensure that source words will be translated into a target word that has been observed as a translation at least once in the training data. In addition, because the discrete mappings are memorized explicitly, they can be learned efficiently from as little as a single instance (barring errors in word alignments). Thus we hypothesize that if we can incorporate a similar variety of information into NMT, this has the potential to alleviate problems with the previously mentioned fatal errors on low-frequency words.",
+      "In this paper, we propose a simple, yet effective method to incorporate discrete, probabilistic lexicons as an additional information source in NMT (\u00a7 SECREF3 ). First we demonstrate how to transform lexical translation probabilities (\u00a7 SECREF7 ) into a predictive probability for the next word by utilizing attention vectors from attentional NMT models BIBREF2 . We then describe methods to incorporate this probability into NMT, either through linear interpolation with the NMT probabilities (\u00a7 UID10 ) or as the bias to the NMT predictive distribution (\u00a7 UID9 ). We construct these lexicon probabilities by using traditional word alignment methods on the training data (\u00a7 SECREF11 ), other external parallel data resources such as a handmade dictionary (\u00a7 SECREF13 ), or using a hybrid between the two (\u00a7 SECREF14 ).",
+      "We perform experiments (\u00a7 SECREF5 ) on two English-Japanese translation corpora to evaluate the method's utility in improving translation accuracy and reducing the time required for training."
+    ],
+    [
+      "The goal of machine translation is to translate a sequence of source words INLINEFORM0 into a sequence of target words INLINEFORM1 . These words belong to the source vocabulary INLINEFORM2 , and the target vocabulary INLINEFORM3 respectively. NMT performs this translation by calculating the conditional probability INLINEFORM4 of the INLINEFORM5 th target word INLINEFORM6 based on the source INLINEFORM7 and the preceding target words INLINEFORM8 . This is done by encoding the context INLINEFORM9 a fixed-width vector INLINEFORM10 , and calculating the probability as follows: DISPLAYFORM0 ",
+      "where INLINEFORM0 and INLINEFORM1 are respectively weight matrix and bias vector parameters.",
+      "The exact variety of the NMT model depends on how we calculate INLINEFORM0 used as input. While there are many methods to perform this modeling, we opt to use attentional models BIBREF2 , which focus on particular words in the source sentence when calculating the probability of INLINEFORM1 . These models represent the current state of the art in NMT, and are also convenient for use in our proposed method. Specifically, we use the method of luong15emnlp, which we describe briefly here and refer readers to the original paper for details.",
+      "First, an encoder converts the source sentence INLINEFORM0 into a matrix INLINEFORM1 where each column represents a single word in the input sentence as a continuous vector. This representation is generated using a bidirectional encoder INLINEFORM2 ",
+      "Here the INLINEFORM0 function maps the words into a representation BIBREF3 , and INLINEFORM1 is a stacking long short term memory (LSTM) neural network BIBREF4 , BIBREF5 , BIBREF6 . Finally we concatenate the two vectors INLINEFORM2 and INLINEFORM3 into a bidirectional representation INLINEFORM4 . These vectors are further concatenated into the matrix INLINEFORM5 where the INLINEFORM6 th column corresponds to INLINEFORM7 .",
+      "Next, we generate the output one word at a time while referencing this encoded input sentence and tracking progress with a decoder LSTM. The decoder's hidden state INLINEFORM0 is a fixed-length continuous vector representing the previous target words INLINEFORM1 , initialized as INLINEFORM2 . Based on this INLINEFORM3 , we calculate a similarity vector INLINEFORM4 , with each element equal to DISPLAYFORM0 ",
+      " INLINEFORM0 can be an arbitrary similarity function, which we set to the dot product, following luong15emnlp. We then normalize this into an attention vector, which weights the amount of focus that we put on each word in the source sentence DISPLAYFORM0 ",
+      "This attention vector is then used to weight the encoded representation INLINEFORM0 to create a context vector INLINEFORM1 for the current time step INLINEFORM2 ",
+      "Finally, we create INLINEFORM0 by concatenating the previous hidden state INLINEFORM1 with the context vector, and performing an affine transform INLINEFORM2 ",
+      "Once we have this representation of the current state, we can calculate INLINEFORM0 according to Equation ( EQREF3 ). The next word INLINEFORM1 is chosen according to this probability, and we update the hidden state by inputting the chosen word into the decoder LSTM DISPLAYFORM0 ",
+      "If we define all the parameters in this model as INLINEFORM0 , we can then train the model by minimizing the negative log-likelihood of the training data INLINEFORM1 "
+    ],
+    [
+      "In \u00a7 SECREF2 we described how traditional NMT models calculate the probability of the next target word INLINEFORM0 . Our goal in this paper is to improve the accuracy of this probability estimate by incorporating information from discrete probabilistic lexicons. We assume that we have a lexicon that, given a source word INLINEFORM1 , assigns a probability INLINEFORM2 to target word INLINEFORM3 . For a source word INLINEFORM4 , this probability will generally be non-zero for a small number of translation candidates, and zero for the majority of words in INLINEFORM5 . In this section, we first describe how we incorporate these probabilities into NMT, and explain how we actually obtain the INLINEFORM6 probabilities in \u00a7 SECREF4 ."
+    ],
+    [
+      "First, we need to convert lexical probabilities INLINEFORM0 for the individual words in the source sentence INLINEFORM1 to a form that can be used together with INLINEFORM2 . Given input sentence INLINEFORM3 , we can construct a matrix in which each column corresponds to a word in the input sentence, each row corresponds to a word in the INLINEFORM4 , and the entry corresponds to the appropriate lexical probability: INLINEFORM5 ",
+      "This matrix can be precomputed during the encoding stage because it only requires information about the source sentence INLINEFORM0 .",
+      "Next we convert this matrix into a predictive probability over the next word: INLINEFORM0 . To do so we use the alignment probability INLINEFORM1 from Equation ( EQREF5 ) to weight each column of the INLINEFORM2 matrix: INLINEFORM3 ",
+      "This calculation is similar to the way how attentional models calculate the context vector INLINEFORM0 , but over a vector representing the probabilities of the target vocabulary, instead of the distributed representations of the source words. The process of involving INLINEFORM1 is important because at every time step INLINEFORM2 , the lexical probability INLINEFORM3 will be influenced by different source words."
+    ],
+    [
+      "After calculating the lexicon predictive probability INLINEFORM0 , next we need to integrate this probability with the NMT model probability INLINEFORM1 . To do so, we examine two methods: (1) adding it as a bias, and (2) linear interpolation.",
+      "In our first bias method, we use INLINEFORM0 to bias the probability distribution calculated by the vanilla NMT model. Specifically, we add a small constant INLINEFORM1 to INLINEFORM2 , take the logarithm, and add this adjusted log probability to the input of the softmax as follows: INLINEFORM3 ",
+      "We take the logarithm of INLINEFORM0 so that the values will still be in the probability domain after the softmax is calculated, and add the hyper-parameter INLINEFORM1 to prevent zero probabilities from becoming INLINEFORM2 after taking the log. When INLINEFORM3 is small, the model will be more heavily biased towards using the lexicon, and when INLINEFORM4 is larger the lexicon probabilities will be given less weight. We use INLINEFORM5 for this paper.",
+      "We also attempt to incorporate the two probabilities through linear interpolation between the standard NMT probability model probability INLINEFORM0 and the lexicon probability INLINEFORM1 . We will call this the linear method, and define it as follows: INLINEFORM2 ",
+      "where INLINEFORM0 is an interpolation coefficient that is the result of the sigmoid function INLINEFORM1 . INLINEFORM2 is a learnable parameter, and the sigmoid function ensures that the final interpolation level falls between 0 and 1. We choose INLINEFORM3 ( INLINEFORM4 ) at the beginning of training.",
+      "This notation is partly inspired by allamanis16icml and gu16acl who use linear interpolation to merge a standard attentional model with a \u201ccopy\u201d operator that copies a source word as-is into the target sentence. The main difference is that they use this to copy words into the output while our method uses it to influence the probabilities of all target words."
+    ],
+    [
+      "In the previous section, we have defined some ways to use predictive probabilities INLINEFORM0 based on word-to-word lexical probabilities INLINEFORM1 . Next, we define three ways to construct these lexical probabilities using automatically learned lexicons, handmade lexicons, or a combination of both."
+    ],
+    [
+      "In traditional SMT systems, lexical translation probabilities are generally learned directly from parallel data in an unsupervised fashion using a model such as the IBM models BIBREF7 , BIBREF8 . These models can be used to estimate the alignments and lexical translation probabilities INLINEFORM0 between the tokens of the two languages using the expectation maximization (EM) algorithm.",
+      "First in the expectation step, the algorithm estimates the expected count INLINEFORM0 . In the maximization step, lexical probabilities are calculated by dividing the expected count by all possible counts: INLINEFORM1 ",
+      "The IBM models vary in level of refinement, with Model 1 relying solely on these lexical probabilities, and latter IBM models (Models 2, 3, 4, 5) introducing more sophisticated models of fertility and relative alignment. Even though IBM models also occasionally have problems when dealing with the rare words (e.g. \u201cgarbage collecting\u201d effects BIBREF9 ), traditional SMT systems generally achieve better translation accuracies of low-frequency words than NMT systems BIBREF6 , indicating that these problems are less prominent than they are in NMT.",
+      "Note that in many cases, NMT limits the target vocabulary BIBREF10 for training speed or memory constraints, resulting in rare words not being covered by the NMT vocabulary INLINEFORM0 . Accordingly, we allocate the remaining probability assigned by the lexicon to the unknown word symbol INLINEFORM1 : DISPLAYFORM0 "
+    ],
+    [
+      "In addition, for many language pairs, broad-coverage handmade dictionaries exist, and it is desirable that we be able to use the information included in them as well. Unlike automatically learned lexicons, however, handmade dictionaries generally do not contain translation probabilities. To construct the probability INLINEFORM0 , we define the set of translations INLINEFORM1 existing in the dictionary for particular source word INLINEFORM2 , and assume a uniform distribution over these words: INLINEFORM3 ",
+      "Following Equation ( EQREF12 ), unknown source words will assign their probability mass to the INLINEFORM0 tag."
+    ],
+    [
+      "Handmade lexicons have broad coverage of words but their probabilities might not be as accurate as the learned ones, particularly if the automatic lexicon is constructed on in-domain data. Thus, we also test a hybrid method where we use the handmade lexicons to complement the automatically learned lexicon. Specifically, inspired by phrase table fill-up used in PBMT systems BIBREF11 , we use the probability of the automatically learned lexicons INLINEFORM1 by default, and fall back to the handmade lexicons INLINEFORM2 only for uncovered words: DISPLAYFORM0 "
+    ],
+    [
+      "In this section, we describe experiments we use to evaluate our proposed methods."
+    ],
+    [
+      "Dataset: We perform experiments on two widely-used tasks for the English-to-Japanese language pair: KFTT BIBREF12 and BTEC BIBREF13 . KFTT is a collection of Wikipedia article about city of Kyoto and BTEC is a travel conversation corpus. BTEC is an easier translation task than KFTT, because KFTT covers a broader domain, has a larger vocabulary of rare words, and has relatively long sentences. The details of each corpus are depicted in Table TABREF19 .",
+      "We tokenize English according to the Penn Treebank standard BIBREF14 and lowercase, and tokenize Japanese using KyTea BIBREF15 . We limit training sentence length up to 50 in both experiments and keep the test data at the original length. We replace words of frequency less than a threshold INLINEFORM0 in both languages with the INLINEFORM1 symbol and exclude them from our vocabulary. We choose INLINEFORM2 for BTEC and INLINEFORM3 for KFTT, resulting in INLINEFORM4 k, INLINEFORM5 k for BTEC and INLINEFORM6 k, INLINEFORM7 k for KFTT.",
+      "NMT Systems: We build the described models using the Chainer toolkit. The depth of the stacking LSTM is INLINEFORM0 and hidden node size INLINEFORM1 . We concatenate the forward and backward encodings (resulting in a 1600 dimension vector) and then perform a linear transformation to 800 dimensions.",
+      "We train the system using the Adam BIBREF16 optimization method with the default settings: INLINEFORM0 . Additionally, we add dropout BIBREF17 with drop rate INLINEFORM1 at the last layer of each stacking LSTM unit to prevent overfitting. We use a batch size of INLINEFORM2 and we run a total of INLINEFORM3 iterations for all data sets. All of the experiments are conducted on a single GeForce GTX TITAN X GPU with a 12 GB memory cache.",
+      "At test time, we use beam search with beam size INLINEFORM0 . We follow luong15acl in replacing every unknown token at position INLINEFORM1 with the target token that maximizes the probability INLINEFORM2 . We choose source word INLINEFORM3 according to the highest alignment score in Equation ( EQREF5 ). This unknown word replacement is applied to both baseline and proposed systems. Finally, because NMT models tend to give higher probabilities to shorter sentences BIBREF18 , we discount the probability of INLINEFORM4 token by INLINEFORM5 to correct for this bias.",
+      "Traditional SMT Systems: We also prepare two traditional SMT systems for comparison: a PBMT system BIBREF19 using Moses BIBREF20 , and a hierarchical phrase-based MT system BIBREF21 using Travatar BIBREF22 , Systems are built using the default settings, with models trained on the training data, and weights tuned on the development data.",
+      "Lexicons: We use a total of 3 lexicons for the proposed method, and apply bias and linear method for all of them, totaling 6 experiments. The first lexicon (auto) is built on the training data using the automatically learned lexicon method of \u00a7 SECREF11 separately for both the BTEC and KFTT experiments. Automatic alignment is performed using GIZA++ BIBREF8 . The second lexicon (man) is built using the popular English-Japanese dictionary Eijiro with the manual lexicon method of \u00a7 SECREF13 . Eijiro contains 104K distinct word-to-word translation entries. The third lexicon (hyb) is built by combining the first and second lexicon with the hybrid method of \u00a7 SECREF14 .",
+      "Evaluation: We use standard single reference BLEU-4 BIBREF23 to evaluate the translation performance. Additionally, we also use NIST BIBREF24 , which is a measure that puts a particular focus on low-frequency word strings, and thus is sensitive to the low-frequency words we are focusing on in this paper. We measure the statistical significant differences between systems using paired bootstrap resampling BIBREF25 with 10,000 iterations and measure statistical significance at the INLINEFORM0 and INLINEFORM1 levels.",
+      "Additionally, we also calculate the recall of rare words from the references. We define \u201crare words\u201d as words that appear less than eight times in the target training corpus or references, and measure the percentage of time they are recovered by each translation system."
+    ],
+    [
+      "In this section, we first a detailed examination of the utility of the proposed bias method when used with the auto or hyb lexicons, which empirically gave the best results, and perform a comparison among the other lexicon integration methods in the following section. Table TABREF20 shows the results of these methods, along with the corresponding baselines.",
+      "First, compared to the baseline attn, our bias method achieved consistently higher scores on both test sets. In particular, the gains on the more difficult KFTT set are large, up to 2.3 BLEU, 0.44 NIST, and 30% Recall, demonstrating the utility of the proposed method in the face of more diverse content and fewer high-frequency words.",
+      "Compared to the traditional pbmt systems hiero, particularly on KFTT we can see that the proposed method allows the NMT system to exceed the traditional SMT methods in BLEU. This is despite the fact that we are not performing ensembling, which has proven to be essential to exceed traditional systems in several previous works BIBREF6 , BIBREF0 , BIBREF1 . Interestingly, despite gains in BLEU, the NMT methods still fall behind in NIST score on the KFTT data set, demonstrating that traditional SMT systems still tend to have a small advantage in translating lower-frequency words, despite the gains made by the proposed method.",
+      "In Table TABREF27 , we show some illustrative examples where the proposed method (auto-bias) was able to obtain a correct translation while the normal attentional model was not. The first example is a mistake in translating \u201cextramarital affairs\u201d into the Japanese equivalent of \u201csoccer,\u201d entirely changing the main topic of the sentence. This is typical of the errors that we have observed NMT systems make (the mistake from Figure FIGREF2 is also from attn, and was fixed by our proposed method). The second example demonstrates how these mistakes can then affect the process of choosing the remaining words, propagating the error through the whole sentence.",
+      "Next, we examine the effect of the proposed method on the training time for each neural MT method, drawing training curves for the KFTT data in Figure FIGREF26 . Here we can see that the proposed bias training methods achieve reasonable BLEU scores in the upper 10s even after the first iteration. In contrast, the baseline attn method has a BLEU score of around 5 after the first iteration, and takes significantly longer to approach values close to its maximal accuracy. This shows that by incorporating lexical probabilities, we can effectively bootstrap the learning of the NMT system, allowing it to approach an appropriate answer in a more timely fashion.",
+      "It is also interesting to examine the alignment vectors produced by the baseline and proposed methods, a visualization of which we show in Figure FIGREF29 . For this sentence, the outputs of both methods were both identical and correct, but we can see that the proposed method (right) placed sharper attention on the actual source word corresponding to content words in the target sentence. This trend of peakier attention distributions in the proposed method held throughout the corpus, with the per-word entropy of the attention vectors being 3.23 bits for auto-bias, compared with 3.81 bits for attn, indicating that the auto-bias method places more certainty in its attention decisions."
+    ],
+    [
+      "Finally, we perform a full comparison between the various methods for integrating lexicons into the translation process, with results shown in Table TABREF31 . In general the bias method improves accuracy for the auto and hyb lexicon, but is less effective for the man lexicon. This is likely due to the fact that the manual lexicon, despite having broad coverage, did not sufficiently cover target-domain words (coverage of unique words in the source vocabulary was 35.3% and 9.7% for BTEC and KFTT respectively).",
+      "Interestingly, the trend is reversed for the linear method, with it improving man systems, but causing decreases when using the auto and hyb lexicons. This indicates that the linear method is more suited for cases where the lexicon does not closely match the target domain, and plays a more complementary role. Compared to the log-linear modeling of bias, which strictly enforces constraints imposed by the lexicon distribution BIBREF27 , linear interpolation is intuitively more appropriate for integrating this type of complimentary information.",
+      "On the other hand, the performance of linear interpolation was generally lower than that of the bias method. One potential reason for this is the fact that we use a constant interpolation coefficient that was set fixed in every context. gu16acl have recently developed methods to use the context information from the decoder to calculate the different interpolation coefficients for every decoding step, and it is possible that introducing these methods would improve our results."
+    ],
+    [
+      "To test whether the proposed method is useful on larger data sets, we also performed follow-up experiments on the larger Japanese-English ASPEC dataset BIBREF28 that consist of 2 million training examples, 63 million tokens, and 81,000 vocabulary size. We gained an improvement in BLEU score from 20.82 using the attn baseline to 22.66 using the auto-bias proposed method. This experiment shows that our method scales to larger datasets."
+    ],
+    [
+      "From the beginning of work on NMT, unknown words that do not exist in the system vocabulary have been focused on as a weakness of these systems. Early methods to handle these unknown words replaced them with appropriate words in the target vocabulary BIBREF10 , BIBREF29 according to a lexicon similar to the one used in this work. In contrast to our work, these only handle unknown words and do not incorporate information from the lexicon in the learning procedure.",
+      "There have also been other approaches that incorporate models that learn when to copy words as-is into the target language BIBREF30 , BIBREF31 , BIBREF32 . These models are similar to the linear approach of \u00a7 UID10 , but are only applicable to words that can be copied as-is into the target language. In fact, these models can be thought of as a subclass of the proposed approach that use a lexicon that assigns a all its probability to target words that are the same as the source. On the other hand, while we are simply using a static interpolation coefficient INLINEFORM0 , these works generally have a more sophisticated method for choosing the interpolation between the standard and \u201ccopy\u201d models. Incorporating these into our linear method is a promising avenue for future work.",
+      "In addition mi16acl have also recently proposed a similar approach by limiting the number of vocabulary being predicted by each batch or sentence. This vocabulary is made by considering the original HMM alignments gathered from the training corpus. Basically, this method is a specific version of our bias method that gives some of the vocabulary a bias of negative infinity and all other vocabulary a uniform distribution. Our method improves over this by considering actual translation probabilities, and also considering the attention vector when deciding how to combine these probabilities.",
+      "Finally, there have been a number of recent works that improve accuracy of low-frequency words using character-based translation models BIBREF33 , BIBREF34 , BIBREF35 . However, luong16acl have found that even when using character-based models, incorporating information about words allows for gains in translation accuracy, and it is likely that our lexicon-based method could result in improvements in these hybrid systems as well."
+    ],
+    [
+      "In this paper, we have proposed a method to incorporate discrete probabilistic lexicons into NMT systems to solve the difficulties that NMT systems have demonstrated with low-frequency words. As a result, we achieved substantial increases in BLEU (2.0-2.3) and NIST (0.13-0.44) scores, and observed qualitative improvements in the translations of content words.",
+      "For future work, we are interested in conducting the experiments on larger-scale translation tasks. We also plan to do subjective evaluation, as we expect that improvements in content word translation are critical to subjective impressions of translation results. Finally, we are also interested in improvements to the linear method where INLINEFORM0 is calculated based on the context, instead of using a fixed value."
+    ],
+    [
+      "We thank Makoto Morishita and Yusuke Oda for their help in this project. We also thank the faculty members of AHC lab for their supports and suggestions.",
+      "This work was supported by grants from the Ministry of Education, Culture, Sport, Science, and Technology of Japan and in part by JSPS KAKENHI Grant Number 16H05873."
+    ]
+  ]
+}
+```
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+++ b/qasper-0638/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Crowdsourcing a High-Quality Gold Standard for QA-SRL
+
+Question: How much more coverage is in the new dataset?
\ No newline at end of file
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+Name of Paper: Crowdsourcing a High-Quality Gold Standard for QA-SRL
+
+Question: How was coverage measured?
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+Name of Paper: What Gets Echoed? Understanding the"Pointers"in Explanations of Persuasive Arguments
+
+Question: What are overall baseline results on new this new task?
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+Name of Paper: What Gets Echoed? Understanding the"Pointers"in Explanations of Persuasive Arguments
+
+Question: What features are proposed?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Dataset",
+    "Understanding the Pointers in Explanations",
+    "Predicting Pointers",
+    "Predicting Pointers ::: Experiment setup",
+    "Predicting Pointers ::: Prediction Performance",
+    "Predicting Pointers ::: The Effect on Generating Explanations",
+    "Concluding Discussions",
+    "Acknowledgments",
+    "Supplemental Material ::: Preprocessing.",
+    "Supplemental Material ::: PC Echoing OP",
+    "Supplemental Material ::: Feature Calculation",
+    "Supplemental Material ::: Word\u2013level Prediction Task",
+    "Supplemental Material ::: Generating Explanations"
+  ],
+  "paragraphs": [
+    [
+      "Explanations are essential for understanding and learning BIBREF0. They can take many forms, ranging from everyday explanations for questions such as why one likes Star Wars, to sophisticated formalization in the philosophy of science BIBREF1, to simply highlighting features in recent work on interpretable machine learning BIBREF2.",
+      "Although everyday explanations are mostly encoded in natural language, natural language explanations remain understudied in NLP, partly due to a lack of appropriate datasets and problem formulations. To address these challenges, we leverage /r/ChangeMyView, a community dedicated to sharing counterarguments to controversial views on Reddit, to build a sizable dataset of naturally-occurring explanations. Specifically, in /r/ChangeMyView, an original poster (OP) first delineates the rationales for a (controversial) opinion (e.g., in Table TABREF1, \u201cmost hit music artists today are bad musicians\u201d). Members of /r/ChangeMyView are invited to provide counterarguments. If a counterargument changes the OP's view, the OP awards a $\\Delta $ to indicate the change and is required to explain why the counterargument is persuasive. In this work, we refer to what is being explained, including both the original post and the persuasive comment, as the explanandum.",
+      "An important advantage of explanations in /r/ChangeMyView is that the explanandum contains most of the required information to provide its explanation. These explanations often select key counterarguments in the persuasive comment and connect them with the original post. As shown in Table TABREF1, the explanation naturally points to, or echoes, part of the explanandum (including both the persuasive comment and the original post) and in this case highlights the argument of \u201cmusic serving different purposes.\u201d",
+      "These naturally-occurring explanations thus enable us to computationally investigate the selective nature of explanations: \u201cpeople rarely, if ever, expect an explanation that consists of an actual and complete cause of an event. Humans are adept at selecting one or two causes from a sometimes infinite number of causes to be the explanation\u201d BIBREF3. To understand the selective process of providing explanations, we formulate a word-level task to predict whether a word in an explanandum will be echoed in its explanation.",
+      "Inspired by the observation that words that are likely to be echoed are either frequent or rare, we propose a variety of features to capture how a word is used in the explanandum as well as its non-contextual properties in Section SECREF4. We find that a word's usage in the original post and in the persuasive argument are similarly related to being echoed, except in part-of-speech tags and grammatical relations. For instance, verbs in the original post are less likely to be echoed, while the relationship is reversed in the persuasive argument.",
+      "We further demonstrate that these features can significantly outperform a random baseline and even a neural model with significantly more knowledge of a word's context. The difficulty of predicting whether content words (i.e., non-stopwords) are echoed is much greater than that of stopwords, among which adjectives are the most difficult and nouns are relatively the easiest. This observation highlights the important role of nouns in explanations. We also find that the relationship between a word's usage in the original post and in the persuasive comment is crucial for predicting the echoing of content words. Our proposed features can also improve the performance of pointer generator networks with coverage in generating explanations BIBREF4.",
+      "To summarize, our main contributions are:",
+      "[itemsep=0pt,leftmargin=*,topsep=0pt]",
+      "We highlight the importance of computationally characterizing human explanations and formulate a concrete problem of predicting how information is selected from explananda to form explanations, including building a novel dataset of naturally-occurring explanations.",
+      "We provide a computational characterization of natural language explanations and demonstrate the U-shape in which words get echoed.",
+      "We identify interesting patterns in what gets echoed through a novel word-level classification task, including the importance of nouns in shaping explanations and the importance of contextual properties of both the original post and persuasive comment in predicting the echoing of content words.",
+      "We show that vanilla LSTMs fail to learn some of the features we develop and that the proposed features can even improve performance in generating explanations with pointer networks.",
+      "Our code and dataset is available at https://chenhaot.com/papers/explanation-pointers.html."
+    ],
+    [
+      "To provide background for our study, we first present a brief overview of explanations for the NLP community, and then discuss the connection of our study with pointer networks, linguistic accommodation, and argumentation mining.",
+      "The most developed discussion of explanations is in the philosophy of science. Extensive studies aim to develop formal models of explanations (e.g., the deductive-nomological model in BIBREF5, see BIBREF1 and BIBREF6 for a review). In this view, explanations are like proofs in logic. On the other hand, psychology and cognitive sciences examine \u201ceveryday explanations\u201d BIBREF0, BIBREF7. These explanations tend to be selective, are typically encoded in natural language, and shape our understanding and learning in life despite the absence of \u201caxioms.\u201d Please refer to BIBREF8 for a detailed comparison of these two modes of explanation.",
+      "Although explanations have attracted significant interest from the AI community thanks to the growing interest on interpretable machine learning BIBREF9, BIBREF10, BIBREF11, such studies seldom refer to prior work in social sciences BIBREF3. Recent studies also show that explanations such as highlighting important features induce limited improvement on human performance in detecting deceptive reviews and media biases BIBREF12, BIBREF13. Therefore, we believe that developing a computational understanding of everyday explanations is crucial for explainable AI. Here we provide a data-driven study of everyday explanations in the context of persuasion.",
+      "In particular, we investigate the \u201cpointers\u201d in explanations, inspired by recent work on pointer networks BIBREF14. Copying mechanisms allow a decoder to generate a token by copying from the source, and have been shown to be effective in generation tasks ranging from summarization to program synthesis BIBREF4, BIBREF15, BIBREF16. To the best of our knowledge, our work is the first to investigate the phenomenon of pointers in explanations.",
+      "Linguistic accommodation and studies on quotations also examine the phenomenon of reusing words BIBREF17, BIBREF18, BIBREF19, BIBREF20. For instance, BIBREF21 show that power differences are reflected in the echoing of function words; BIBREF22 find that news media prefer to quote locally distinct sentences in political debates. In comparison, our word-level formulation presents a fine-grained view of echoing words, and puts a stronger emphasis on content words than work on linguistic accommodation.",
+      "Finally, our work is concerned with an especially challenging problem in social interaction: persuasion. A battery of studies have done work to enhance our understanding of persuasive arguments BIBREF23, BIBREF24, BIBREF25, BIBREF26, BIBREF27, and the area of argumentation mining specifically investigates the structure of arguments BIBREF28, BIBREF29, BIBREF30. We build on previous work by BIBREF31 and leverage the dynamics of /r/ChangeMyView. Although our findings are certainly related to the persuasion process, we focus on understanding the self-described reasons for persuasion, instead of the structure of arguments or the factors that drive effective persuasion."
+    ],
+    [
+      "Our dataset is derived from the /r/ChangeMyView subreddit, which has more than 720K subscribers BIBREF31. /r/ChangeMyView hosts conversations where someone expresses a view and others then try to change that person's mind. Despite being fundamentally based on argument, /r/ChangeMyView has a reputation for being remarkably civil and productive BIBREF32, e.g., a journalist wrote \u201cIn a culture of brittle talking points that we guard with our lives, Change My View is a source of motion and surprise\u201d BIBREF33.",
+      "The delta mechanism in /r/ChangeMyView allows members to acknowledge opinion changes and enables us to identify explanations for opinion changes BIBREF34. Specifically, it requires \u201cAny user, whether they're the OP or not, should reply to a comment that changed their view with a delta symbol and an explanation of the change.\u201d As a result, we have access to tens of thousands of naturally-occurring explanations and associated explananda. In this work, we focus on the opinion changes of the original posters.",
+      "Throughout this paper, we use the following terminology:",
+      "[itemsep=-5pt,leftmargin=*,topsep=0pt]",
+      "An original post (OP) is an initial post where the original poster justifies his or her opinion. We also use OP to refer to the original poster.",
+      "A persuasive comment (PC) is a comment that directly leads to an opinion change on the part of the OP (i.e., winning a $\\Delta $).",
+      "A top-level comment is a comment that directly replies to an OP, and /r/ChangeMyView requires the top-level comment to \u201cchallenge at least one aspect of OP\u2019s stated view (however minor), unless they are asking a clarifying question.\u201d",
+      "An explanation is a comment where an OP acknowledges a change in his or her view and provides an explanation of the change. As shown in Table TABREF1, the explanation not only provides a rationale, it can also include other discourse acts, such as expressing gratitude.",
+      "Using https://pushshift.io, we collect the posts and comments in /r/ChangeMyView from January 17th, 2013 to January 31st, 2019, and extract tuples of (OP, PC, explanation). We use the tuples from the final six months of our dataset as the test set, those from the six months before that as the validation set, and the remaining tuples as the training set. The sets contain 5,270, 5,831, and 26,617 tuples respectively. Note that there is no overlap in time between the three sets and the test set can therefore be used to assess generalization including potential changes in community norms and world events.",
+      "Preprocessing. We perform a number of preprocessing steps, such as converting blockquotes in Markdown to quotes, filtering explicit edits made by authors, mapping all URLs to a special @url@ token, and replacing hyperlinks with the link text. We ignore all triples that contain any deleted comments or posts. We use spaCy for tokenization and tagging BIBREF35. We also use the NLTK implementation of the Porter stemming algorithm to store the stemmed version of each word, for later use in our prediction task BIBREF36, BIBREF37. Refer to the supplementary material for more information on preprocessing.",
+      "Data statistics. Table TABREF16 provides basic statistics of the training tuples and how they compare to other comments. We highlight the fact that PCs are on average longer than top-level comments, suggesting that PCs contain substantial counterarguments that directly contribute to opinion change. Therefore, we simplify the problem by focusing on the (OP, PC, explanation) tuples and ignore any other exchanges between an OP and a commenter.",
+      "Below, we highlight some notable features of explanations as they appear in our dataset.",
+      "The length of explanations shows stronger correlation with that of OPs and PCs than between OPs and PCs (Figure FIGREF8). This observation indicates that explanations are somehow better related with OPs and PCs than PCs are with OPs in terms of language use. A possible reason is that the explainer combines their natural tendency towards length with accommodating the PC.",
+      "Explanations have a greater fraction of \u201cpointers\u201d than do persuasive comments (Figure FIGREF8). We measure the likelihood of a word in an explanation being copied from either its OP or PC and provide a similar probability for a PC for copying from its OP. As we discussed in Section SECREF1, the words in an explanation are much more likely to come from the existing discussion than are the words in a PC (59.8% vs 39.0%). This phenomenon holds even if we restrict ourselves to considering words outside quotations, which removes the effect of quoting other parts of the discussion, and if we focus only on content words, which removes the effect of \u201creusing\u201d stopwords.",
+      "Relation between a word being echoed and its document frequency (Figure FIGREF8). Finally, as a preview of our main results, the document frequency of a word from the explanandum is related to the probability of being echoed in the explanation. Although the average likelihood declines as the document frequency gets lower, we observe an intriguing U-shape in the scatter plot. In other words, the words that are most likely to be echoed are either unusually frequent or unusually rare, while most words in the middle show a moderate likelihood of being echoed."
+    ],
+    [
+      "To further investigate how explanations select words from the explanandum, we formulate a word-level prediction task to predict whether words in an OP or PC are echoed in its explanation. Formally, given a tuple of (OP, PC, explanation), we extract the unique stemmed words as $\\mathcal {V}_{\\text{OP}}, \\mathcal {V}_{\\text{PC}}, \\mathcal {V}_{\\text{EXP}}$. We then define the label for each word in the OP or PC, $w \\in \\mathcal {V}_{\\text{OP}} \\cup \\mathcal {V}_{\\text{PC}}$, based on the explanation as follows:",
+      "Our prediction task is thus a straightforward binary classification task at the word level. We develop the following five groups of features to capture properties of how a word is used in the explanandum (see Table TABREF18 for the full list):",
+      "[itemsep=0pt,leftmargin=*,topsep=0pt]",
+      "Non-contextual properties of a word. These features are derived directly from the word and capture the general tendency of a word being echoed in explanations.",
+      "Word usage in an OP or PC (two groups). These features capture how a word is used in an OP or PC. As a result, for each feature, we have two values for the OP and PC respectively.",
+      "How a word connects an OP and PC. These features look at the difference between word usage in the OP and PC. We expect this group to be the most important in our task.",
+      "General OP/PC properties. These features capture the general properties of a conversation. They can be used to characterize the background distribution of echoing.",
+      "Table TABREF18 further shows the intuition for including each feature, and condensed $t$-test results after Bonferroni correction. Specifically, we test whether the words that were echoed in explanations have different feature values from those that were not echoed. In addition to considering all words, we also separately consider stopwords and content words in light of Figure FIGREF8. Here, we highlight a few observations:",
+      "[itemsep=0pt,leftmargin=*,topsep=0pt]",
+      "Although we expect more complicated words (#characters) to be echoed more often, this is not the case on average. We also observe an interesting example of Simpson's paradox in the results for Wordnet depth BIBREF38: shallower words are more likely to be echoed across all words, but deeper words are more likely to be echoed in content words and stopwords.",
+      "OPs and PCs generally exhibit similar behavior for most features, except for part-of-speech and grammatical relation (subject, object, and other.) For instance, verbs in an OP are less likely to be echoed, while verbs in a PC are more likely to be echoed.",
+      "Although nouns from both OPs and PCs are less likely to be echoed, within content words, subjects and objects from an OP are more likely to be echoed. Surprisingly, subjects and objects in a PC are less likely to be echoed, which suggests that the original poster tends to refer back to their own subjects and objects, or introduce new ones, when providing explanations.",
+      "Later words in OPs and PCs are more likely to be echoed, especially in OPs. This could relate to OPs summarizing their rationales at the end of their post and PCs putting their strongest points last.",
+      "Although the number of surface forms in an OP or PC is positively correlated with being echoed, the differences in surface forms show reverse trends: the more surface forms of a word that show up only in the PC (i.e., not in the OP), the more likely a word is to be echoed. However, the reverse is true for the number of surface forms in only the OP. Such contrast echoes BIBREF31, in which dissimilarity in word usage between the OP and PC was a predictive feature of successful persuasion."
+    ],
+    [
+      "We further examine the effectiveness of our proposed features in a predictive setting. These features achieve strong performance in the word-level classification task, and can enhance neural models in both the word-level task and generating explanations. However, the word-level task remains challenging, especially for content words."
+    ],
+    [
+      "We consider two classifiers for our word-level classification task: logistic regression and gradient boosting tree (XGBoost) BIBREF39. We hypothesized that XGBoost would outperform logistic regression because our problem is non-linear, as shown in Figure FIGREF8.",
+      "To examine the utility of our features in a neural framework, we further adapt our word-level task as a tagging task, and use LSTM as a baseline. Specifically, we concatenate an OP and PC with a special token as the separator so that an LSTM model can potentially distinguish the OP from PC, and then tag each word based on the label of its stemmed version. We use GloVe embeddings to initialize the word embeddings BIBREF40. We concatenate our proposed features of the corresponding stemmed word to the word embedding; the resulting difference in performance between a vanilla LSTM demonstrates the utility of our proposed features. We scale all features to $[0, 1]$ before fitting the models. As introduced in Section SECREF3, we split our tuples of (OP, PC, explanation) into training, validation, and test sets, and use the validation set for hyperparameter tuning. Refer to the supplementary material for additional details in the experiment.",
+      "Evaluation metric. Since our problem is imbalanced, we use the F1 score as our evaluation metric. For the tagging approach, we average the labels of words with the same stemmed version to obtain a single prediction for the stemmed word. To establish a baseline, we consider a random method that predicts the positive label with 0.15 probability (the base rate of positive instances)."
+    ],
+    [
+      "Overall performance (Figure FIGREF28). Although our word-level task is heavily imbalanced, all of our models outperform the random baseline by a wide margin. As expected, content words are much more difficult to predict than stopwords, but the best F1 score in content words more than doubles that of the random baseline (0.286 vs. 0.116). Notably, although we strongly improve on our random baseline, even our best F1 scores are relatively low, and this holds true regardless of the model used. Despite involving more tokens than standard tagging tasks (e.g., BIBREF41 and BIBREF42), predicting whether a word is going to be echoed in explanations remains a challenging problem.",
+      "Although the vanilla LSTM model incorporates additional knowledge (in the form of word embeddings), the feature-based XGBoost and logistic regression models both outperform the vanilla LSTM model. Concatenating our proposed features with word embeddings leads to improved performance from the LSTM model, which becomes comparable to XGBoost. This suggests that our proposed features can be difficult to learn with an LSTM alone.",
+      "Despite the non-linearity observed in Figure FIGREF8, XGBoost only outperforms logistic regression by a small margin. In the rest of this section, we use XGBoost to further examine the effectiveness of different groups of features, and model performance in different conditions.",
+      "Ablation performance (Table TABREF34). First, if we only consider a single group of features, as we hypothesized, the relation between OP and PC is crucial and leads to almost as strong performance in content words as using all features. To further understand the strong performance of OP-PC relation, Figure FIGREF28 shows the feature importance in the ablated model, measured by the normalized total gain (see the supplementary material for feature importance in the full model). A word's occurrence in both the OP and PC is clearly the most important feature, with distance between its POS tag distributions as the second most important. Recall that in Table TABREF18 we show that words that have similar POS behavior between the OP and PC are more likely to be echoed in the explanation.",
+      "Overall, it seems that word-level properties contribute the most valuable signals for predicting stopwords. If we restrict ourselves to only information in either an OP or PC, how a word is used in a PC is much more predictive of content word echoing (0.233 vs 0.191). This observation suggests that, for content words, the PC captures more valuable information than the OP. This finding is somewhat surprising given that the OP sets the topic of discussion and writes the explanation.",
+      "As for the effects of removing a group of features, we can see that there is little change in the performance on content words. This can be explained by the strong performance of the OP-PC relation on its own, and the possibility of the OP-PC relation being approximated by OP and PC usage. Again, word-level properties are valuable for strong performance in stopwords.",
+      "Performance vs. word source (Figure FIGREF28). We further break down the performance by where a word is from. We can group a word based on whether it shows up only in an OP, a PC, or both OP and PC, as shown in Table TABREF1. There is a striking difference between the performance in the three categories (e.g., for all words, 0.63 in OP & PC vs. 0.271 in PC only). The strong performance on words in both the OP and PC applies to stopwords and content words, even accounting for the shift in the random baseline, and recalls the importance of occurring both in OP and PC as a feature.",
+      "Furthermore, the echoing of words from the PC is harder to predict (0.271) than from the OP (0.347) despite the fact that words only in PCs are more likely to be echoed than words only in OPs (13.5% vs. 8.6%). The performance difference is driven by stopwords, suggesting that our overall model is better at capturing signals for stopwords used in OPs. This might relate to the fact that the OP and the explanation are written by the same author; prior studies have demonstrated the important role of stopwords for authorship attribution BIBREF43.",
+      "Nouns are the most reliably predicted part-of-speech tag within content words (Table TABREF35). Next, we break down the performance by part-of-speech tags. We focus on the part-of-speech tags that are semantically important, namely, nouns, proper nouns, verbs, adverbs, and adjectives.",
+      "Prediction performance can be seen as a proxy for how reliably a part-of-speech tag is reused when providing explanations. Consistent with our expectations for the importance of nouns and verbs, our models achieve the best performance on nouns within content words. Verbs are more challenging, but become the least difficult tag to predict when we consider all words, likely due to stopwords such as \u201chave.\u201d Adjectives turn out to be the most challenging category, suggesting that adjectival choice is perhaps more arbitrary than other parts of speech, and therefore less central to the process of constructing an explanation. The important role of nouns in shaping explanations resonates with the high recall rate of nouns in memory tasks BIBREF44."
+    ],
+    [
+      "One way to measure the ultimate success of understanding pointers in explanations is to be able to generate explanations. We use the pointer generator network with coverage as our starting point BIBREF4, BIBREF46 (see the supplementary material for details). We investigate whether concatenating our proposed features with word embeddings can improve generation performance, as measured by ROUGE scores.",
+      "Consistent with results in sequence tagging for word-level echoing prediction, our proposed features can enhance a neural model with copying mechanisms (see Table TABREF37). Specifically, their use leads to statistically significant improvement in ROUGE-1 and ROUGE-L, while slightly hurting the performance in ROUGE-2 (the difference is not statistically significant). We also find that our features can increase the likelihood of copying: an average of 17.59 unique words get copied to the generated explanation with our features, compared to 14.17 unique words without our features. For comparison, target explanations have an average of 34.81 unique words. We emphasize that generating explanations is a very challenging task (evidenced by the low ROUGE scores and examples in the supplementary material), and that fully solving the generation task requires more work."
+    ],
+    [
+      "In this work, we conduct the first large-scale empirical study of everyday explanations in the context of persuasion. We assemble a novel dataset and formulate a word-level prediction task to understand the selective nature of explanations. Our results suggest that the relation between an OP and PC plays an important role in predicting the echoing of content words, while a word's non-contextual properties matter for stopwords. We show that vanilla LSTMs fail to learn some of the features we develop and that our proposed features can improve the performance in generating explanations using pointer networks. We also demonstrate the important role of nouns in shaping explanations.",
+      "Although our approach strongly outperforms random baselines, the relatively low F1 scores indicate that predicting which word is echoed in explanations is a very challenging task. It follows that we are only able to derive a limited understanding of how people choose to echo words in explanations. The extent to which explanation construction is fundamentally random BIBREF47, or whether there exist other unidentified patterns, is of course an open question. We hope that our study and the resources that we release encourage further work in understanding the pragmatics of explanations.",
+      "There are many promising research directions for future work in advancing the computational understanding of explanations. First, although /r/ChangeMyView has the useful property that its explanations are closely connected to its explananda, it is important to further investigate the extent to which our findings generalize beyond /r/ChangeMyView and Reddit and establish universal properties of explanations. Second, it is important to connect the words in explanations that we investigate here to the structure of explanations in pyschology BIBREF7. Third, in addition to understanding what goes into an explanation, we need to understand what makes an explanation effective. A better understanding of explanations not only helps develop explainable AI, but also informs the process of collecting explanations that machine learning systems learn from BIBREF48, BIBREF49, BIBREF50."
+    ],
+    [
+      "We thank Kimberley Buchan, anonymous reviewers, and members of the NLP+CSS research group at CU Boulder for their insightful comments and discussions; Jason Baumgartner for sharing the dataset that enabled this research."
+    ],
+    [
+      "Before tokenizing, we pass each OP, PC, and explanation through a preprocessing pipeline, with the following steps:",
+      "Occasionally, /r/ChangeMyView's moderators will edit comments, prefixing their edits with \u201cHello, users of CMV\u201d or \u201cThis is a footnote\u201d (see Table TABREF46). We remove this, and any text that follows on the same line.",
+      "We replace URLs with a \u201c@url@\u201d token, defining a URL to be any string which matches the following regular expression: (https?://[^\\s)]*).",
+      "We replace \u201c$\\Delta $\u201d symbols and their analogues\u2014such as \u201c$\\delta $\u201d, \u201c&;#8710;\u201d, and \u201c!delta\u201d\u2014with the word \u201cdelta\u201d. We also remove the word \u201cdelta\u201d from explanations, if the explanation starts with delta.",
+      "Reddit\u2013specific prefixes, such as \u201cu/\u201d (denoting a user) and \u201cr/\u201d (denoting a subreddit) are removed, as we observed that they often interfered with spaCy's ability to correctly parse its inputs.",
+      "We remove any text matching the regular expression EDIT(.*?):.* from the beginning of the match to the end of that line, as well as variations, such as Edit(.*?):.*.",
+      "Reddit allows users to insert blockquoted text. We extract any blockquotes and surround them with standard quotation marks.",
+      "We replace all contiguous whitespace with a single space. We also do this with tab characters and carriage returns, and with two or more hyphens, asterisks, or underscores.",
+      "Tokenizing the data. After passing text through our preprocessing pipeline, we use the default spaCy pipeline to extract part-of-speech tags, dependency tags, and entity details for each token BIBREF35. In addition, we use NLTK to stem words BIBREF36. This is used to compute all word level features discussed in Section 4 of the main paper."
+    ],
+    [
+      "Figure FIGREF49 shows a similar U-shape in the probability of a word being echoed in PC. However, visually, we can see that rare words seem more likely to have high echoing probability in explanations, while that probability is higher for words with moderate frequency in PCs. As PCs tend to be longer than explanations, we also used the echoing probability of the most frequent words to normalize the probability of other words so that they are comparable. We indeed observed a higher likelihood of echoing the rare words, but lower likelihood of echoing words with moderate frequency in explanations than in PCs."
+    ],
+    [
+      "Given an OP, PC, and explanation, we calculate a 66\u2013dimensional vector for each unique stem in the concatenated OP and PC. Here, we describe the process of calculating each feature.",
+      "Inverse document frequency: for a stem $s$, the inverse document frequency is given by $\\log \\frac{N}{\\mathrm {df}_s}$, where $N$ is the total number of documents (here, OPs and PCs) in the training set, and $\\mathrm {df}_s$ is the number of documents in the training data whose set of stemmed words contains $s$.",
+      "Stem length: the number of characters in the stem.",
+      "Wordnet depth (min): starting with the stem, this is the length of the minimum hypernym path to the synset root.",
+      "Wordnet depth (max): similarly, this is the length of the maximum hypernym path.",
+      "Stem transfer probability: the percentage of times in which a stem seen in the explanandum is also seen in the explanation. If, during validation or testing, a stem is encountered for the first time, we set this to be the mean probability of transfer over all stems seen in the training data.",
+      "OP part\u2013of\u2013speech tags: a stem can represent multiple parts of speech. For example, both \u201ctraditions\u201d and \u201ctraditional\u201d will be stemmed to \u201ctradit.\u201d We count the percentage of times the given stem appears as each part\u2013of\u2013speech tag, following the Universal Dependencies scheme BIBREF53. If the stem does not appear in the OP, each part\u2013of\u2013speech feature will be $\\frac{1}{16}$.",
+      "OP subject, object, and other: Given a stem $s$, we calculate the percentage of times that $s$'s surface forms in the OP are classified as subjects, objects, or something else by SpaCy. We follow the CLEAR guidelines, BIBREF51 and use the following tags to indicate a subject: nsubj, nsubjpass, csubj, csubjpass, agent, and expl. Objects are identified using these tags: dobj, dative, attr, oprd. If $s$ does not appear at all in the OP, we let subject, object, and other each equal $\\frac{1}{3}$.",
+      "OP term frequency: the number of times any surface form of a stem appears in the list of tokens that make up the OP.",
+      "OP normalized term frequency: the percentage of the OP's tokens which are a surface form of the given stem.",
+      "OP # of surface forms: the number of different surface forms for the given stem.",
+      "OP location: the average location of each surface form of the given stem which appears in the OP, where the location of a surface form is defined as the percentage of tokens which appear after that surface form. If the stem does not appear at all in the OP, this value is $\\frac{1}{2}$.",
+      "OP is in quotes: the number of times the stem appears in the OP surrounded by quotation marks.",
+      "OP is entity: the percentage of tokens in the OP that are both a surface form for the given stem, and are tagged by SpaCy as one of the following entities: PERSON, NORP, FAC, ORG, GPE, LOC, PRODUCT, EVENT, WORK_OF_ART, LAW, and LANGUAGE.",
+      "PC equivalents of features 6-30.",
+      "In both OP and PC: 1, if one of the stem's surface forms appears in both the OP and PC. 0 otherwise.",
+      "# of unique surface forms in OP: for the given stem, the number of surface forms that appear in the OP, but not in the PC.",
+      "# of unique surface forms in PC: for the given stem, the number of surface forms that appear in the PC, but not in the OP.",
+      "Stem part\u2013of\u2013speech distribution difference: we consider the concatenation of features 6-21, along with the concatenation of features 31-46, as two distributions, and calculate the Jensen\u2013Shannon divergence between them.",
+      "Stem dependency distribution difference: similarly, we consider the concatenation of features 22-24 (OP dependency labels), and the concatenation of features 47-49 (PC dependency labels), as two distributions, and calculate the Jensen\u2013Shannon divergence between them.",
+      "OP length: the number of tokens in the OP.",
+      "PC length: the number of tokens in the PC.",
+      "Length difference: the absolute value of the difference between OP length and PC length.",
+      "Avg. word length difference: the difference between the average number of characters per token in the OP and the average number of characters per token in the PC.",
+      "OP/PC part\u2013of\u2013speech tag distribution difference: the Jensen\u2013Shannon divergence between the part\u2013of\u2013speech tag distributions of the OP on the one hand, and the PC on the other.",
+      "Depth of the PC in the thread: since there can be many back\u2013and\u2013forth replies before a user awards a delta, we number each comment in a thread, starting at 0 for the OP, and incrementing for each new comment before the PC appears."
+    ],
+    [
+      "For each non\u2013LSTM classifier, we train 11 models: one full model, and forward and backward models for each of the five feature groups. To train, we fit on the training set and use the validation set for hyperparameter tuning.",
+      "For the random model, since the echo rate of the training set is 15%, we simply predict 1 with 15% probability, and 0 otherwise.",
+      "For logistic regression, we use the lbfgs solver. To tune hyperparameters, we perform an exhaustive grid search, with $C$ taking values from $\\lbrace 10^{x}:x\\in \\lbrace -1, 0, 1, 2, 3, 4\\rbrace \\rbrace $, and the respective weights of the negative and positive classes taking values from $\\lbrace (x, 1-x): x\\in \\lbrace 0.25, 0.20, 0.15\\rbrace \\rbrace $.",
+      "We also train XGBoost models. Here, we use a learning rate of $0.1$, 1000 estimator trees, and no subsampling. We perform an exhaustive grid search to tune hyperparameters, with the max tree depth equaling 5, 7, or 9, the minimum weight of a child equaling 3, 5, or 7, and the weight of a positive class instance equaling 3, 4, or 5.",
+      "Finally, we train two LSTM models, each with a single 300\u2013dimensional hidden layer. Due to efficiency considerations, we eschewed a full search of the parameter space, but experimented with different values of dropout, learning rate, positive class weight, and batch size. We ultimately trained each model for five epochs with a batch size of 32 and a learning rate of 0.001, using the Adam optimizer BIBREF52. We also weight positive instances four times more highly than negative instances."
+    ],
+    [
+      "We formulate an abstractive summarization task using an OP concatenated with the PC as a source, and the explanation as target. We train two models, one with the features described above, and one without. A shared vocabulary of 50k words is constructed from the training set by setting the maximum encoding length to 500 words. We set the maximum decoding length to 100. We use a pointer generator network with coverage for generating explanations, using a bidirectional LSTM as an encoder and a unidirectional LSTM as a decoder. Both use a 256-dimensional hidden state. The parameters of this network are tuned using a validation set of five thousand instances. We constrain the batch size to 16 and train the network for 20k steps, using the parameters described in Table TABREF82."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0691/instruction.md b/qasper-0691/instruction.md
new file mode 100644
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+Name of Paper: Semantic Role Labeling for Learner Chinese: the Importance of Syntactic Parsing and L2-L1 Parallel Data
+
+Question: Who manually annotated the semantic roles for the set of learner texts?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "An L2-L1 Parallel Corpus",
+    "The Annotation Process",
+    "Inter-annotator Agreement",
+    "Three SRL Systems",
+    "Main Results",
+    "Analysis",
+    "Enhancing SRL with L2-L1 Parallel Data",
+    "The Method",
+    "Experimental Setup",
+    "Conclusion",
+    "Acknowledgement"
+  ],
+  "paragraphs": [
+    [
+      "A learner language (interlanguage) is an idiolect developed by a learner of a second or foreign language which may preserve some features of his/her first language. Previously, encouraging results of automatically building the syntactic analysis of learner languages were reported BIBREF0 , but it is still unknown how semantic processing performs, while parsing a learner language (L2) into semantic representations is the foundation of a variety of deeper analysis of learner languages, e.g., automatic essay scoring. In this paper, we study semantic parsing for interlanguage, taking semantic role labeling (SRL) as a case task and learner Chinese as a case language.",
+      "Before discussing a computation system, we first consider the linguistic competence and performance. Can human robustly understand learner texts? Or to be more precise, to what extent, a native speaker can understand the meaning of a sentence written by a language learner? Intuitively, the answer is towards the positive side. To validate this, we ask two senior students majoring in Applied Linguistics to carefully annotate some L2-L1 parallel sentences with predicate\u2013argument structures according to the specification of Chinese PropBank BIBREF1 , which is developed for L1. A high inter-annotator agreement is achieved, suggesting the robustness of language comprehension for L2. During the course of semantic annotation, we find a non-obvious fact that we can re-use the semantic annotation specification, Chinese PropBank in our case, which is developed for L1. Only modest rules are needed to handle some tricky phenomena. This is quite different from syntactic treebanking for learner sentences, where defining a rich set of new annotation heuristics seems necessary BIBREF2 , BIBREF0 , BIBREF3 .",
+      "Our second concern is to mimic the human's robust semantic processing ability by computer programs. The feasibility of reusing the annotation specification for L1 implies that we can reuse standard CPB data to train an SRL system to process learner texts. To test the robustness of the state-of-the-art SRL algorithms, we evaluate two types of SRL frameworks. The first one is a traditional SRL system that leverages a syntactic parser and heavy feature engineering to obtain explicit information of semantic roles BIBREF4 . Furthermore, we employ two different parsers for comparison: 1) the PCFGLA-based parser, viz. Berkeley parser BIBREF5 , and 2) a minimal span-based neural parser BIBREF6 . The other SRL system uses a stacked BiLSTM to implicitly capture local and non-local information BIBREF7 . and we call it the neural syntax-agnostic system. All systems can achieve state-of-the-art performance on L1 texts but show a significant degradation on L2 texts. This highlights the weakness of applying an L1-sentence-trained system to process learner texts.",
+      "While the neural syntax-agnostic system obtains superior performance on the L1 data, the two syntax-based systems both produce better analyses on the L2 data. Furthermore, as illustrated in the comparison between different parsers, the better the parsing results we get, the better the performance on L2 we achieve. This shows that syntactic parsing is important in semantic construction for learner Chinese. The main reason, according to our analysis, is that the syntax-based system may generate correct syntactic analyses for partial grammatical fragments in L2 texts, which provides crucial information for SRL. Therefore, syntactic parsing helps build more generalizable SRL models that transfer better to new languages, and enhancing syntactic parsing can improve SRL to some extent.",
+      "Our last concern is to explore the potential of a large-scale set of L2-L1 parallel sentences to enhance SRL systems. We find that semantic structures of the L2-L1 parallel sentences are highly consistent. This inspires us to design a novel agreement-based model to explore such semantic coherency information. In particular, we define a metric for comparing predicate\u2013argument structures and searching for relatively good automatic syntactic and semantic annotations to extend the training data for SRL systems. Experiments demonstrate the value of the L2-L1 parallel sentences as well as the effectiveness of our method. We achieve an F-score of 72.06, which is a 2.02 percentage point improvement over the best neural-parser-based baseline.",
+      "To the best of our knowledge, this is the first time that the L2-L1 parallel data is utilized to enhance NLP systems for learner texts.",
+      "For research purpose, we have released our SRL annotations on 600 sentence pairs and the L2-L1 parallel dataset ."
+    ],
+    [
+      "An L2-L1 parallel corpus can greatly facilitate the analysis of a learner language BIBREF9 . Following mizumoto:2011, we collected a large dataset of L2-L1 parallel texts of Mandarin Chinese by exploring \u201clanguage exchange\" social networking services (SNS), i.e., Lang-8, a language-learning website where native speakers can freely correct the sentences written by foreign learners. The proficiency levels of the learners are diverse, but most of the learners, according to our judgment, is of intermediate or lower level.",
+      "Our initial collection consists of 1,108,907 sentence pairs from 135,754 essays. As there is lots of noise in raw sentences, we clean up the data by (1) ruling out redundant content, (2) excluding sentences containing foreign words or Chinese phonetic alphabet by checking the Unicode values, (3) dropping overly simple sentences which may not be informative, and (4) utilizing a rule-based classifier to determine whether to include the sentence into the corpus.",
+      "The final corpus consists of 717,241 learner sentences from writers of 61 different native languages, in which English and Japanese constitute the majority. As for completeness, 82.78% of the Chinese Second Language sentences on Lang-8 are corrected by native human annotators. One sentence gets corrected approximately 1.53 times on average.",
+      "In this paper, we manually annotate the predicate\u2013argument structures for the 600 L2-L1 pairs as the basis for the semantic analysis of learner Chinese. It is from the above corpus that we carefully select 600 pairs of L2-L1 parallel sentences. We would choose the most appropriate one among multiple versions of corrections and recorrect the L1s if necessary. Because word structure is very fundamental for various NLP tasks, our annotation also contains gold word segmentation for both L2 and L1 sentences. Note that there are no natural word boundaries in Chinese text. We first employ a state-of-the-art word segmentation system to produce initial segmentation results and then manually fix segmentation errors.",
+      "The dataset includes four typologically different mother tongues, i.e., English (ENG), Japanese (JPN), Russian (RUS) and Arabic (ARA). Sub-corpus of each language consists of 150 sentence pairs. We take the mother languages of the learners into consideration, which have a great impact on grammatical errors and hence automatic semantic analysis. We hope that four selected mother tongues guarantee a good coverage of typologies. The annotated corpus can be used both for linguistic investigation and as test data for NLP systems."
+    ],
+    [
+      "Semantic role labeling (SRL) is the process of assigning semantic roles to constituents or their head words in a sentence according to their relationship to the predicates expressed in the sentence. Typical semantic roles can be divided into core arguments and adjuncts. The core arguments include Agent, Patient, Source, Goal, etc, while the adjuncts include Location, Time, Manner, Cause, etc.",
+      "To create a standard semantic-role-labeled corpus for learner Chinese, we first annotate a 50-sentence trial set for each native language. Two senior students majoring in Applied Linguistics conducted the annotation. Based on a total of 400 sentences, we adjudicate an initial gold standard, adapting and refining CPB specification as our annotation heuristics. Then the two annotators proceed to annotate a 100-sentence set for each language independently. It is on these larger sets that we report the inter-annotator agreement.",
+      "In the final stage, we also produce an adjudicated gold standard for all 600 annotated sentences. This was achieved by comparing the annotations selected by each annotator, discussing the differences, and either selecting one as fully correct or creating a hybrid representing the consensus decision for each choice point. When we felt that the decisions were not already fully guided by the existing annotation guidelines, we worked to articulate an extension to the guidelines that would support the decision.",
+      "During the annotation, the annotators apply both position labels and semantic role labels. Position labels include S, B, I and E, which are used to mark whether the word is an argument by itself, or at the beginning or in the middle or at the end of a argument. As for role labels, we mainly apply representations defined by CPB BIBREF1 . The predicate in a sentence was labeled as rel, the core semantic roles were labeled as AN and the adjuncts were labeled as AM."
+    ],
+    [
+      "For inter-annotator agreement, we evaluate the precision (P), recall (R), and F1-score (F) of the semantic labels given by the two annotators. Table TABREF5 shows that our inter-annotator agreement is promising. All L1 texts have F-score above 95, and we take this as a reflection that our annotators are qualified. F-scores on L2 sentences are all above 90, just a little bit lower than those of L1, indicating that L2 sentences can be greatly understood by native speakers. Only modest rules are needed to handle some tricky phenomena:",
+      "The labeled argument should be strictly limited to the core roles defined in the frameset of CPB, though the number of arguments in L2 sentences may be more or less than the number defined.",
+      "For the roles in L2 that cannot be labeled as arguments under the specification of CPB, if they provide semantic information such as time, location and reason, we would labeled them as adjuncts though they may not be well-formed adjuncts due to the absence of function words.",
+      "For unnecessary roles in L2 caused by mistakes of verb subcategorization (see examples in Figure FIGREF30 ), we would leave those roles unlabeled.",
+      "Table TABREF10 further reports agreements on each argument (AN) and adjunct (AM) in detail, according to which the high scores are attributed to the high agreement on arguments (AN). The labels of A3 and A4 have no disagreement since they are sparse in CPB and are usually used to label specific semantic roles that have little ambiguity.",
+      "We also conducted in-depth analysis on inter-annotator disagreement. For further details, please refer to duan2018argument."
+    ],
+    [
+      "The work on SRL has included a broad spectrum of machine learning and deep learning approaches to the task. Early work showed that syntactic information is crucial for learning long-range dependencies, syntactic constituency structure and global constraints BIBREF10 , BIBREF11 , while initial studies on neural methods achieved state-of-the-art results with little to no syntactic input BIBREF12 , BIBREF13 , BIBREF14 , BIBREF7 . However, the question whether fully labeled syntactic structures provide an improvement for neural SRL is still unsettled pending further investigation.",
+      "To evaluate the robustness of state-of-the-art SRL algorithms, we evaluate two representative SRL frameworks. One is a traditional syntax-based SRL system that leverages a syntactic parser and manually crafted features to obtain explicit information to find semantic roles BIBREF15 , BIBREF16 In particular, we employ the system introduced in BIBREF4 . This system first collects all c-commanders of a predicate in question from the output of a parser and puts them in order. It then employs a first order linear-chain global linear model to perform semantic tagging. For constituent parsing, we use two parsers for comparison, one is Berkeley parser BIBREF5 , a well-known implementation of the unlexicalized latent variable PCFG model, the other is a minimal span-based neural parser based on independent scoring of labels and spans BIBREF6 . As proposed in BIBREF6 , the second parser is capable of achieving state-of-the-art single-model performance on the Penn Treebank. On the Chinese TreeBank BIBREF17 , it also outperforms the Berkeley parser for the in-domain test. We call the corresponding SRL systems as the PCFGLA-parser-based and neural-parser-based systems.",
+      "The second SRL framework leverages an end-to-end neural model to implicitly capture local and non-local information BIBREF12 , BIBREF7 . In particular, this framework treats SRL as a BIO tagging problem and uses a stacked BiLSTM to find informative embeddings. We apply the system introduced in BIBREF7 for experiments. Because all syntactic information (including POS tags) is excluded, we call this system the neural syntax-agnostic system.",
+      "To train the three SRL systems as well as the supporting parsers, we use the CTB and CPB data . In particular, the sentences selected for the CoNLL 2009 shared task are used here for parameter estimation. Note that, since the Berkeley parser is based on PCFGLA grammar, it may fail to get the syntactic outputs for some sentences, while the other parser does not have that problem. In this case, we have made sure that both parsers can parse all 1,200 sentences successfully."
+    ],
+    [
+      "The overall performances of the three SRL systems on both L1 and L2 data (150 parallel sentences for each mother tongue) are shown in Table TABREF11 . For all systems, significant decreases on different mother languages can be consistently observed, highlighting the weakness of applying L1-sentence-trained systems to process learner texts. Comparing the two syntax-based systems with the neural syntax-agnostic system, we find that the overall INLINEFORM0 F, which denotes the F-score drop from L1 to L2, is smaller in the syntax-based framework than in the syntax-agnostic system. On English, Japanese and Russian L2 sentences, the syntax-based system has better performances though it sometimes works worse on the corresponding L1 sentences, indicating the syntax-based systems are more robust when handling learner texts.",
+      "Furthermore, the neural-parser-based system achieves the best overall performance on the L2 data. Though performing slightly worse than the neural syntax-agnostic one on the L1 data, it has much smaller INLINEFORM0 F, showing that as the syntactic analysis improves, the performances on both the L1 and L2 data grow, while the gap can be maintained. This demonstrates again the importance of syntax in semantic constructions, especially for learner texts.",
+      "Table TABREF45 summarizes the SRL results of the baseline PCFGLA-parser-based model as well as its corresponding retrained models. Since both the syntactic parser and the SRL classifier can be retrained and thus enhanced, we report the individual impact as well as the combined one. We can clearly see that when the PCFGLA parser is retrained with the SRL-consistent sentence pairs, it is able to provide better SRL-oriented syntactic analysis for the L2 sentences as well as their corrections, which are essentially L1 sentences. The outputs of the L1 sentences that are generated by the deep SRL system are also useful for improving the linear SRL classifier. A non-obvious fact is that such a retrained model yields better analysis for not only L1 but also L2 sentences. Fortunately, combining both results in further improvement.",
+      "Table TABREF46 shows the results of the parallel experiments based on the neural parser. Different from the PCFGLA model, the SRL-consistent trees only yield a slight improvement on the L2 data. On the contrary, retraining the SRL classifier is much more effective. This experiment highlights the different strengths of different frameworks for parsing. Though for standard in-domain test, the neural parser performs better and thus is more and more popular, for some other scenarios, the PCFGLA model is stronger.",
+      "Table TABREF47 further shows F-scores for the baseline and the both-retrained model relative to each role type in detail. Given that the F-scores for both models are equal to 0 on A3 and A4, we just omit this part. From the figure we can observe that, all the semantic roles achieve significant improvements in performances."
+    ],
+    [
+      "To better understand the overall results, we further look deep into the output by addressing the questions:",
+      "What types of error negatively impact both systems over learner texts?",
+      "What types of error are more problematic for the neural syntax-agnostic one over the L2 data but can be solved by the syntax-based one to some extent?",
+      "We first carry out a suite of empirical investigations by breaking down error types for more detailed evaluation. To compare two systems, we analyze results on ENG-L2 and JPN-L2 given that they reflect significant advantages of the syntax-based systems over the neural syntax-agnostic system. Note that the syntax-based system here refers to the neural-parser-based one. Finally, a concrete study on the instances in the output is conducted, as to validate conclusions in the previous step.",
+      "We employ 6 oracle transformations designed by he2017deep to fix various prediction errors sequentially (see details in Table TABREF19 ), and observe the relative improvements after each operation, as to obtain fine-grained error types. Figure FIGREF21 compares two systems in terms of different mistakes on ENG-L2 and JPN-L2 respectively. After fixing the boundaries of spans, the neural syntax-agnostic system catches up with the other, illustrating that though both systems handle boundary detection poorly on the L2 sentences, the neural syntax-agnostic one suffers more from this type of errors.",
+      "Excluding boundary errors (after moving, merging, splitting spans and fixing boundaries), we also compare two systems on L2 in terms of detailed label identification, so as to observe which semantic role is more likely to be incorrectly labeled. Figure FIGREF24 shows the confusion matrices. Comparing (a) with (c) and (b) with (d), we can see that the syntax-based and the neural system often overly label A1 when processing learner texts. Besides, the neural syntax-agnostic system predicts the adjunct AM more than necessary on L2 sentences by 54.24% compared with the syntax-based one.",
+      "On the basis of typical error types found in the previous stage, specifically, boundary detection and incorrect labels, we further conduct an on-the-spot investigation on the output sentences.",
+      "Previous work has proposed that the drop in performance of SRL systems mainly occurs in identifying argument boundaries BIBREF18 . According to our results, this problem will be exacerbated when it comes to L2 sentences, while syntactic structure sometimes helps to address this problem.",
+      "Figure FIGREF30 is an example of an output sentence. The Chinese word \u201c\u4e5f\u201d (also) usually serves as an adjunct but is now used for linking the parallel structure \u201c\u7528 \u6c49\u8bed \u4e5f \u8bf4\u8bdd \u5feb\u201d (using Chinese also speaking quickly) in this sentence, which is ill-formed to native speakers and negatively affects the boundary detection of A0 for both systems.",
+      "On the other hand, the neural system incorrectly takes the whole part before \u201c\u5f88 \u96be\u201d (very hard) as A0, regardless of the adjunct \u201c\u5bf9 \u6211 \u6765\u8bf4\u201d (for me), while this can be figured out by exploiting syntactic analysis, as illustrated in Figure FIGREF30 . The constituent \u201c\u5bf9 \u6211 \u6765\u8bf4\u201d (for me) has been recognized as a prepositional phrase (PP) attached to the VP, thus labeled as AM. This shows that by providing information of some well-formed sub-trees associated with correct semantic roles, the syntactic system can perform better than the neural one on SRL for learner texts.",
+      "A second common source of errors is wrong labels, especially for A1. Based on our quantitative analysis, as reported in Table TABREF37 , these phenomena are mainly caused by mistakes of verb subcategorization, where the systems label more arguments than allowed by the predicates. Besides, the deep end-to-end system is also likely to incorrectly attach adjuncts AM to the predicates.",
+      "Figure FIGREF30 is another example. The Chinese verb \u201c\u505a\u996d\u201d (cook-meal) is intransitive while this sentence takes it as a transitive verb, which is very common in L2. Lacking in proper verb subcategorization, both two systems fail to recognize those verbs allowing only one argument and label the A1 incorrectly.",
+      "As for AM, the neural system mistakenly adds the adjunct to the predicate, which can be avoided by syntactic information of the sentence shown in Figure FIGREF30 . The constituent \u201c\u5e38\u5e38\u201d (often) are adjuncts attached to VP structure governed by the verb \u201c\u7ec3\u4e60\u201d(practice), which will not be labeled as AM in terms of the verb \u201c\u505a\u996d\u201d(cook-meal). In other words, the hierarchical structure can help in argument identification and assignment by exploiting local information."
+    ],
+    [
+      "We explore the valuable information about the semantic coherency encoded in the L2-L1 parallel data to improve SRL for learner Chinese. In particular, we introduce an agreement-based model to search for high-quality automatic syntactic and semantic role annotations, and then use these annotations to retrain the two parser-based SRL systems."
+    ],
+    [
+      "For the purpose of harvesting the good automatic syntactic and semantic analysis, we consider the consistency between the automatically produced analysis of a learner sentence and its corresponding well-formed sentence. Determining the measurement metric for comparing predicate\u2013argument structures, however, presents another challenge, because the words of the L2 sentence and its L1 counterpart do not necessarily match. To solve the problem, we use an automatic word aligner. BerkeleyAligner BIBREF19 , a state-of-the-art tool for obtaining a word alignment, is utilized.",
+      "The metric for comparing SRL results of two sentences is based on recall of INLINEFORM0 tuples, where INLINEFORM1 is a predicate, INLINEFORM2 is a word that is in the argument or adjunct of INLINEFORM3 and INLINEFORM4 is the corresponding role. Based on a word alignment, we define the shared tuple as a mutual tuple between two SRL results of an L2-L1 sentence pair, meaning that both the predicate and argument words are aligned respectively, and their role relations are the same. We then have two recall values:",
+      "L2-recall is (# of shared tuples) / (# of tuples of the result in L2)",
+      "L1-recall is (# of shared tuples) / (# of tuples of the result in L1)",
+      "In accordance with the above evaluation method, we select the automatic analysis of highest scoring sentences and use them to expand the training data. Sentences whose L1 and L2 recall are both greater than a threshold INLINEFORM0 are taken as good ones. A parser-based SRL system consists of two essential modules: a syntactic parser and a semantic classifier. To enhance the syntactic parser, the automatically generated syntactic trees of the sentence pairs that exhibit high semantic consistency are directly used to extend training data. To improve a semantic classifier, besides the consistent semantic analysis, we also use the outputs of the L1 but not L2 data which are generated by the neural syntax-agnostic SRL system."
+    ],
+    [
+      "Our SRL corpus contains 1200 sentences in total that can be used as an evaluation for SRL systems. We separate them into three data sets. The first data set is used as development data, which contains 50 L2-L1 sentence pairs for each language and 200 pairs in total. Hyperparameters are tuned using the development set. The second data set contains all other 400 L2 sentences, which is used as test data for L2. Similarly, all other 400 L1 sentences are used as test data for L1.",
+      "The sentence pool for extracting retraining annotations includes all English- and Japanese-native speakers' data along with its corrections. Table TABREF43 presents the basic statistics. Around 8.5 \u2013 11.9% of the sentence can be taken as high L1/L2 recall sentences, which serves as a reflection that argument structure is vital for language acquisition and difficult for learners to master, as proposed in vazquez2004learning and shin2010contribution. The threshold ( INLINEFORM0 ) for selecting sentences is set upon the development data. For example, we use additional 156,520 sentences to enhance the Berkeley parser."
+    ],
+    [
+      "Statistical models of annotating learner texts are making rapid progress. Although there have been some initial studies on defining annotation specification as well as corpora for syntactic analysis, there is almost no work on semantic parsing for interlanguages. This paper discusses this topic, taking Semantic Role Labeling as a case task and learner Chinese as a case language. We reveal three unknown facts that are important towards a deeper analysis of learner languages: (1) the robustness of language comprehension for interlanguage, (2) the weakness of applying L1-sentence-trained systems to process learner texts, and (3) the significance of syntactic parsing and L2-L1 parallel data in building more generalizable SRL models that transfer better to L2. We have successfully provided a better SRL-oriented syntactic parser as well as a semantic classifier for processing the L2 data by exploring L2-L1 parallel data, supported by a significant numeric improvement over a number of state-of-the-art systems. To the best of our knowledge, this is the first work that demonstrates the effectiveness of large-scale L2-L1 parallel data to enhance the NLP system for learner texts."
+    ],
+    [
+      "This work was supported by the National Natural Science Foundation of China (61772036, 61331011) and the Key Laboratory of Science, Technology and Standard in Press Industry (Key Laboratory of Intelligent Press Media Technology). We thank the anonymous reviewers and for their helpful comments. We also thank Nianwen Xue for useful comments on the final version. Weiwei Sun is the corresponding author."
+    ]
+  ]
+}
+```
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+Name of Paper: VAIS Hate Speech Detection System: A Deep Learning based Approach for System Combination
+
+Question: Is the data all in Vietnamese?
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+Name of Paper: Joint Learning of Sentence Embeddings for Relevance and Entailment
+
+Question: what is the size of the introduced dataset?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "The Hypothesis Evaluation Task",
+    "Argus Dataset",
+    "AI2-8grade/CK12 Dataset",
+    "MCTest Dataset",
+    "Related Work",
+    "Neural Model",
+    "Sentence Embeddings",
+    "Evidence Integration",
+    "Experimental Setup",
+    "Evaluation",
+    "Analysis",
+    "Conclusion",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "Let us consider the goal of building machine reasoning systems based on knowledge from fulltext data like encyclopedic articles, scientific papers or news articles. Such machine reasoning systems, like humans researching a problem, must be able to recover evidence from large amounts of retrieved but mostly irrelevant information and judge the evidence to decide the answer to the question at hand.",
+      "A typical approach, used implicitly in information retrieval (and its extensions, like IR-based Question Answering systems BIBREF0 ), is to determine evidence relevancy by a keyword overlap feature (like tf-idf or BM-25 BIBREF1 ) and prune the evidence by the relevancy score. On the other hand, textual entailment systems that seek to confirm hypotheses based on evidence BIBREF2 BIBREF3 BIBREF4 are typically provided with only a single piece of evidence or only evidence pre-determined as relevant, and are often restricted to short and simple sentences without open-domain named entity occurences. In this work, we seek to fuse information retrieval and textual entaiment recognition by defining the Hypothesis Evaluation task as deciding the truth value of a hypothesis by integrating numerous pieces of evidence, not all of it equally relevant.",
+      "As a specific instance, we introduce the Argus Yes/No Question Answering task. The problem is, given a real-world event binary question like Did Donald Trump announce he is running for president? and numerous retrieved news article fragments as evidence, to determine the answer for the question. Our research is motivated by the Argus automatic reporting system for the Augur prediction market platform. BIBREF5 Therefore, we consider the question answering task within the constraints of a practical scenario that has limited available dataset and only minimum supervision. Hence, authentic news sentences are the evidence (with noise like segmentation errors, irrelevant participial phrases, etc.), and whereas we have gold standard for the correct answers, the model must do without explicit supervision on which individual evidence snippets are relevant and what do they entail.",
+      "To this end, we introduce an open dataset of questions and newspaper evidence, and a neural model within the Sentence Pair Scoring framework BIBREF6 that (A) learns sentence embeddings for the question and evidence, (B) the embeddings represent both relevance and entailment characteristics as linear classifier inputs, and (C) the model aggregates all available evidence to produce a binary signal as the answer, which is the only training supervision.",
+      "We also evaluate our model on a related task that concerns ranking answers of multiple-choice questions given a set of evidencing sentences. We consider the MCTest dataset and the AI2-8grade/CK12 dataset that we introduce below.",
+      "The paper is structured as follows. In Sec. SECREF2 , we formally outline the Argus question answering task, describe the question-evidence dataset, and describe the multiple-choice questions task and datasets. In Sec. SECREF3 , we briefly survey the related work on similar problems, whereas in Sec. SECREF4 we propose our neural models for joint learning of sentence relevance and entailment. We present the results in Sec. SECREF5 and conclude with a summary, model usage recommendations and future work directions in Sec. SECREF6 ."
+    ],
+    [
+      "Formally, the Hypothesis Evaluation task is to build a function INLINEFORM0 , where INLINEFORM1 is a binary label (no towards yes) and INLINEFORM2 is a hypothesis instance in the form of question text INLINEFORM3 and a set of INLINEFORM4 evidence texts INLINEFORM5 as extracted from an evidence-carrying corpus."
+    ],
+    [
+      "Our main aim is to propose a solution to the Argus Task, where the Argus system BIBREF7 BIBREF5 is to automatically analyze and answer questions in the context of the Augur prediction market platform. In a prediction market, users pose questions about future events whereas others bet on the yes or no answer, with the assumption that the bet price reflects the real probability of the event. At a specified moment (e.g. after the date of a to-be-predicted sports match), the correct answer is retroactively determined and the bets are paid off. At a larger volume of questions, determining the bet results may present a significant overhead for running of the market. This motivates the Argus system, which should partially automate this determination \u2014 deciding questions related to recent events based on open news sources.",
+      "To train a machine learning model for the INLINEFORM0 function, we have created a dataset of questions with gold labels, and produced sets of evidence texts from a variety of news paper using a pre-existing IR (information retrieval) component of the Argus system. We release this dataset openly.",
+      "To pose a reproducible task for the IR component, the time domain of questions was restricted from September 1, 2014 to September 1, 2015, and topic domain was focused to politics, sports and the stock market. To build the question dataset, we have used several sources:",
+      "We asked Amazon Mechanical Turk users to pose questions, together with a golden label and a news article reference. This seeded the dataset with initial, somewhat redundant 250 questions.",
+      "We manually extended this dataset by derived questions with reversed polarity (to obtain an opposite answer).",
+      "We extended the data with questions autogenerated from 26 templates, pertaining top sporting event winners and US senate or gubernatorial elections.",
+      "To build the evidence dataset, we used the Syphon preprocessing component BIBREF5 of the Argus implementation to identify semantic roles of all question tokens and produce the search keywords if a role was assigned to each token. We then used the IR component to query a corpus of newspaper articles, and kept sentences that contained at least 2/3 of all the keywords. Our corpus of articles contained articles from The Guardian (all articles) and from the New York Times (Sports, Politics and Business sections). Furthermore, we scraped partial archive.org historical data out of 35 RSS feeds from CNN, Reuters, BBC International, CBS News, ABC News, c|net, Financial Times, Skynews and the Washington Post.",
+      "For the final dataset, we kept only questions where at least a single evidence was found (i.e. we successfuly assigned a role to each token, found some news stories and found at least one sentence with 2/3 of question keywords within). The final size of the dataset is outlined in Fig. FIGREF8 and some examples are shown in Fig. FIGREF9 ."
+    ],
+    [
+      "The AI2 Elementary School Science Questions (no-diagrams variant) released by the Allen Institute cover 855 basic four-choice questions regarding high school science and follows up to the Allen AI Science Kaggle challenge. The vocabulary includes scientific jargon and named entities, and many questions are not factoid, requiring real-world reasoning or thought experiments.",
+      "We have combined each answer with the respective question (by substituting the wh-word in the question by each answer) and retrieved evidence sentences for each hypothesis using Solr search in a collection of CK-12 \u201cConcepts B\u201d textbooks. 525 questions attained any supporting evidence, examples are shown in Fig. FIGREF10 .",
+      "We consider this dataset as preliminary since it was not reviewed by a human and many hypotheses are apparently unprovable by the evidence we have gathered (i.e. the theoretical top accuracy is much lower than 1.0). However, we released it to the public and still included it in the comparison as these qualities reflect many realistic datasets of unknown qualities, so we find relative performances of models on such datasets instructive."
+    ],
+    [
+      "The Machine Comprehension Test BIBREF8 dataset has been introduced to provide a challenge for researchers to come up with models that approach human-level reading comprehension, and serve as a higher-level alternative to semantic parsing tasks that enforce a specific knowledge representation. The dataset consists of a set of 660 stories spanning multiple sentences, written in simple and clean language (but with less restricted vocabulary than e.g. the bAbI dataset BIBREF9 ). Each story is accompanied by four questions and each of these lists four possible answers; the questions are tagged as based on just one in-story sentence, or requiring multiple sentence inference. We use an official extension of the dataset for RTE evaluation that again textually merges questions and answers.",
+      "The dataset is split in two parts, MC-160 and MC-500, based on provenance but similar in quality. We train all models on a joined training set.",
+      "The practical setting differs from the Argus task as the MCTest dataset contains relatively restricted vocabulary and well-formed sentences. Furthermore, the goal is to find the single key point in the story to focus on, while in the Argus setting we may have many pieces of evidence supporting an answer; another specific characteristics of MCTest is that it consists of stories where the ordering and proximity of evidence sentences matters."
+    ],
+    [
+      "Our primary concern when integrating natural language query with textual evidence is to find sentence-level representations suitable both for relevance weighing and answer prediction.",
+      "Sentence-level representations in the retrieval + inference context have been popularly proposed within the Memory Network framework BIBREF10 , but explored just in the form of averaged word embeddings; the task includes only very simple sentences and a small vocabulary. Much more realistic setting is introduced in the Answer Sentence Selection context BIBREF11 BIBREF6 , with state-of-art models using complex deep neural architectures with attention BIBREF12 , but the selection task consists of only retrieval and no inference (answer prediction). A more indirect retrieval task regarding news summarization was investigated by BIBREF13 .",
+      "In the entailment context, BIBREF4 introduced a large dataset with single-evidence sentence pairs (Stanford Natural Language Inference, SNLI), but a larger vocabulary and slightly more complicated (but still conservatively formed) sentences. They also proposed baseline recurrent neural model for modeling sentence representations, while word-level attention based models are being studied more recently BIBREF14 BIBREF15 .",
+      "In the MCTest text comprehension challenge BIBREF8 , the leading models use complex engineered features ensembling multiple traditional semantic NLP approaches BIBREF16 . The best deep model so far BIBREF17 uses convolutional neural networks for sentence representations, and attention on multiple levels to pick evidencing sentences."
+    ],
+    [
+      "Our approach is to use a sequence of word embeddings to build sentence embeddings for each hypothesis and respective evidence, then use the sentence embeddings to estimate relevance and entailment of each evidence with regard to the respective hypothesis, and finally integrate the evidence to a single answer."
+    ],
+    [
+      "To produce sentence embeddings, we investigated the neural models proposed in the dataset-sts framework for deep learning of sentence pair scoring functions. BIBREF6 ",
+      "We refer the reader to BIBREF6 and its references for detailed model descriptions. We evaluate an RNN model which uses bidirectionally summed GRU memory cells BIBREF18 and uses the final states as embeddings; a CNN model which uses sentence-max-pooled convolutional filters as embeddings BIBREF19 ; an RNN-CNN model which puts the CNN on top of per-token GRU outputs rather than the word embeddings BIBREF20 ; and an attn1511 model inspired by BIBREF20 that integrates the RNN-CNN model with per-word attention to build hypothesis-specific evidence embeddings. We also report the baseline results of avg mean of word embeddings in the sentence with projection matrix and DAN Deep Averaging Network model that employs word-level dropout and adds multiple nonlinear transformations on top of the averaged embeddings BIBREF21 .",
+      "The original attn1511 model BIBREF6 (as tuned for the Answer Sentence Selection task) used a softmax attention mechanism that would effectively select only a few key words of the evidence to focus on \u2014 for a hypothesis-evidence token INLINEFORM0 scalar attention score INLINEFORM1 , the focus INLINEFORM2 is: INLINEFORM3 ",
+      "A different focus mechanism exhibited better performance in the Hypothesis Evaluation task, modelling per-token attention more independently: INLINEFORM0 ",
+      "We also use relu instead of tanh in the CNNs.",
+      "As model input, we use the standard GloVe embeddings BIBREF22 extended with binary inputs denoting token type and overlap with token or bigram in the paired sentence, as described in BIBREF6 . However, we introduce two changes to the word embedding model \u2014 we use 50-dimensional embeddings instead of 300-dimensional, and rather than building an adaptable embedding matrix from the training set words preinitialized by GloVe, we use only the top 100 most frequent tokens in the adaptable embedding matrix and use fixed GloVe vectors for all other tokens (including tokens not found in the training set). In preliminary experiments, this improved generalization for highly vocabulary-rich tasks like Argus, while still allowing the high-frequency tokens (like interpunction or conjunctions) to learn semantic operator representations.",
+      "As an additional method for producing sentence embeddings, we consider the Ubu. RNN transfer learning method proposed by BIBREF6 where an RNN model (as described above) is trained on the Ubuntu Dialogue task BIBREF23 . The pretrained model weights are used to initialize an RNN model which is then fine-tuned on the Hypothesis Evaluation task. We use the same model as originally proposed (except the aforementioned vocabulary handling modification), with the dot-product scoring used for Ubuntu Dialogue training replaced by MLP point-scores described below."
+    ],
+    [
+      "Our main proposed schema for evidence integration is Evidence Weighing. From each pair of hypothesis and evidence embeddings, we produce two INLINEFORM0 predictions using a pair of MLP point-scorers of dataset-sts BIBREF6 with sigmoid activation function. The predictions are interpreted as INLINEFORM1 entailment (0 to 1 as no to yes) and relevance INLINEFORM2 . To integrate the predictions across multiple pieces of evidence, we propose a weighed average model: INLINEFORM3 ",
+      "We do not have access to any explicit labels for the evidence, but we train the model end-to-end with just INLINEFORM0 labels and the formula for INLINEFORM1 is differentiable, carrying over the gradient to the sentence embedding model. This can be thought of as a simple passage-wide attention model.",
+      "As a baseline strategy, we also consider Evidence Averaging, where we simply produce a single scalar prediction per hypothesis-evidence pair (using the same strategy as above) and decide the hypothesis simply based on the mean prediction across available evidence.",
+      "Finally, following success reported in the Answer Sentence Selection task BIBREF6 , we consider a BM25 Feature combined with Evidence Averaging, where the MLP scorer that produces the pair scalar prediction as above takes an additional BM25 word overlap score input BIBREF1 besides the elementwise embedding comparisons."
+    ],
+    [
+      "We implement the differentiable model in the Keras framework BIBREF24 and train the whole network from word embeddings to output evidence-integrated hypothesis label using the binary cross-entropy loss as an objective and the Adam optimization algorithm BIBREF25 . We apply INLINEFORM0 regularization and a INLINEFORM1 dropout.",
+      "Following the recommendation of BIBREF6 , we report expected test set question accuracy as determined by average accuracy in 16 independent trainings and with 95% confidence intervals based on the Student's t-distribution."
+    ],
+    [
+      "In Fig. FIGREF26 , we report the model performance on the Argus task, showing that the Ubuntu Dialogue transfer RNN outperforms other proposed models by a large margin. However, a comparison of evidence integration approaches in Fig. FIGREF27 shows that evidence integration is not the major deciding factor and there are no staticially meaningful differences between the evaluated approaches. We measured high correlation between classification and relevance scores with Pearson's INLINEFORM0 , showing that our model does not learn a separate evidence weighing function on this task.",
+      "In Fig. FIGREF28 , we look at the model performance on the AI2-8grade/CK12 task, repeating the story of Ubuntu Dialogue transfer RNN dominating other models. However, on this task our proposed evidence weighing scheme improves over simpler approaches \u2014 but just on the best model, as shown in Fig. FIGREF29 . On the other hand, the simplest averaging model benefits from at least BM25 information to select relevant evidence, apparently.",
+      "For the MCTest dataset, Fig. FIGREF30 compares our proposed models with the current state-of-art ensemble of hand-crafted syntactic and frame-semantic features BIBREF16 , as well as past neural models from the literature, all using attention mechanisms \u2014 the Attentive Reader of BIBREF26 , Neural Reasoner of BIBREF27 and the HABCNN model family of BIBREF17 . We see that averaging-based models are surprisingly effective on this task, and in particular on the MC-500 dataset it can beat even the best so far reported model of HABCNN-TE. Our proposed transfer model is statistically equivalent to the best model on both datasets (furthermore, previous work did not include confidence intervals, even though their models should also be stochastically initialized).",
+      "As expected, our models did badly on the multiple-evidence class of questions \u2014 we made no attempt to model information flow across adjacent sentences in our models as this aspect is unique to MCTest in the context of our work.",
+      "Interestingly, evidence weighing does play an important role on the MCTest task as shown in Fig. FIGREF31 , significantly boosting model accuracy. This confirms that a mechanism to allocate attention to different sentences is indeed crucial for this task."
+    ],
+    [
+      "While we can universally proclaim Ubu. RNN as the best model, we observe many aspects of the Hypothesis Evaluation problem that are shared by the AI2-8grade/CK12 and MCTest tasks, but not by the Argus task.",
+      "Our largest surprise lies in the ineffectivity of evidence weighing on the Argus task, since observations of irrelevant passages initially led us to investigate this model. We may also see that non-pretrained RNN does very well on the Argus task while CNN is a better model otherwise.",
+      "An aspect that could explain this rift is that the latter two tasks are primarily retrieval based, where we seek to judge each evidence as irrelevant or essentially a paraphrase of the hypothesis. On the other hand, the Argus task is highly semantic and compositional, with the questions often differing just by a presence of negation \u2014 recurrent model that can capture long-term dependencies and alter sentence representations based on the presence of negation may represent an essential improvement over an n-gram-like convolutional scheme. We might also attribute the lack of success of evidence weighing in the Argus task to a more conservative scheme of passage retrieval employed in the IR pipeline that produced the dataset. Given the large vocabulary and noise levels in the data, we may also simply require more data to train the evidence weighing properly.",
+      "We see from the training vs. test accuracies that RNN-based models (including the word-level attention model) have a strong tendency to overfit on our small datasets, while CNN is much more resilient. While word-level attention seems appealing for such a task, we speculate that we simply might not have enough training data to properly train it. Investigating attention transfer is a point for future work \u2014 by our preliminary experiments on multiple datasets, attention models appear more task specific than the basic text comprehension models of memory based RNNs.",
+      "One concrete limitation of our models in case of the Argus task is a problem of reconciling particular named entity instances. The more obvious form of this issue is Had Roger Federer beat Martin Cilic in US OPEN 2014? versus an opposite Had Martin Cilic beat Roger Federer in US OPEN 2014? \u2014 another form of this problem is reconciling a hypothesis like Will the Royals win the World Series? with evidence Giants Win World Series With Game 7 Victory Over Royals. An abstract embedding of the sentence will not carry over the required information \u2014 it is important to explicitly pass and reconcile the roles of multiple named entities which cannot be meaningfully embedded in a GloVe-like semantic vector space."
+    ],
+    [
+      "We have established a general Hypothesis Evaluation task with three datasets of various properties, and shown that neural models can exhibit strong performance (with less hand-crafting effort than non-neural classifiers). We propose an evidence weighing model that is never harmful and improves performance on some tasks. We also demonstrate that simple models can outperform or closely match performance of complex architectures; all the models we consider are task-independent and were successfully used in different contexts than Hypothesis Evaluation BIBREF6 . Our results empirically show that a basic RNN text comprehension model well trained on a large dataset (even if the task is unrelated and vocabulary characteristics are very different) outperforms or matches more complex architectures trained only on the dataset of the task at hand.",
+      "Finally, on the MCTest dataset, our best proposed model is better or statistically indistinguishable from the best neural model reported so far BIBREF17 , even though it has a simpler architecture and only a naive attention mechanism.",
+      "We would like to draw several recommendations for future research from our findings: (A) encourage usage of basic neural architectures as evaluation baselines; (B) suggest that future research includes models pretrained on large data as baselines; (C) validate complex architectures on tasks with large datasets if they cannot beat baselines on small datasets; and (D) for randomized machine comprehension models (e.g. neural networks with random weight initialization, batch shuffling or probabilistic dropout), report expected test set performance based on multiple independent training runs.",
+      "As a general advice for solving complex tasks with small datasets, besides the point (B) above our analysis suggests convolutional networks as the best models regarding the tendency to overfit, unless semantic composionality plays a crucial role in the task; in this scenario, simple averaging-based models are a great start as well. Preinitializing a model also helps against overfitting.",
+      "We release our implementation of the Argus task, evidence integration models and processing of all the evaluated datasets as open source.",
+      "We believe the next step towards machine comprehension NLP models (based on deep learning but capable of dealing with real-world, large-vocabulary data) will involve research into a better way to deal with entities without available embeddings. When distinguishing specific entities, simple word-level attention mechanisms will not do. A promising approach could extend the flexibility of the final sentence representation, moving from attention mechanism to a memory mechanism by allowing the network to remember a set of \u201cfacts\u201d derived from each sentence; related work has been done for example on end-to-end differentiable shift-reduce parsers with LSTM as stack cells BIBREF28 ."
+    ],
+    [
+      "This work was co-funded by the Augur Project of the Forecast Foundation and financially supported by the Grant Agency of the Czech Technical University in Prague, grant No. SGS16/ 084/OHK3/1T/13. Computational resources were provided by the CESNET LM2015042 and the CERIT Scientific Cloud LM2015085, provided under the programme \u201cProjects of Large Research, Development, and Innovations Infrastructures.\u201d",
+      "We'd like to thank Peronet Despeignes of the Augur Project for his support. Carl Burke has provided instructions for searching CK-12 ebooks within the Kaggle challenge."
+    ]
+  ]
+}
+```
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+Name of Paper: Joint Learning of Sentence Embeddings for Relevance and Entailment
+
+Question: what datasets did they use?
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+++ b/qasper-0711/instruction.md
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+Name of Paper: MUSE: Parallel Multi-Scale Attention for Sequence to Sequence Learning
+
+Question: What evaluation metric is used?
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+++ b/qasper-0716/instruction.md
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+Name of Paper: Aspect Term Extraction with History Attention and Selective Transformation
+
+Question: Do they explore how useful is the detection history and opinion summary?
\ No newline at end of file
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+Name of Paper: Aspect Term Extraction with History Attention and Selective Transformation
+
+Question: Which dataset(s) do they use to train the model?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "The ATE Task",
+    "Model Description",
+    "Joint Training",
+    "Datasets",
+    "Comparisons",
+    "Settings",
+    "Main Results",
+    "Ablation Study",
+    "Attention Visualization and Case Study",
+    "Related Work",
+    "Concluding Discussions"
+  ],
+  "paragraphs": [
+    [
+      "Aspect-Based Sentiment Analysis (ABSA) involves detecting opinion targets and locating opinion indicators in sentences in product review texts BIBREF0 . The first sub-task, called Aspect Term Extraction (ATE), is to identify the phrases targeted by opinion indicators in review sentences. For example, in the sentence \u201cI love the operating system and preloaded software\u201d, the words \u201coperating system\u201d and \u201cpreloaded software\u201d should be extracted as aspect terms, and the sentiment on them is conveyed by the opinion word \u201clove\u201d. According to the task definition, for a term/phrase being regarded as an aspect, it should co-occur with some \u201copinion words\u201d that indicate a sentiment polarity on it BIBREF1 .",
+      "Many researchers formulated ATE as a sequence labeling problem or a token-level classification problem. Traditional sequence models such as Conditional Random Fields (CRFs) BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , Long Short-Term Memory Networks (LSTMs) BIBREF6 and classification models such as Support Vector Machine (SVM) BIBREF7 have been applied to tackle the ATE task, and achieved reasonable performance. One drawback of these existing works is that they do not exploit the fact that, according to the task definition, aspect terms should co-occur with opinion-indicating words. Thus, the above methods tend to output false positives on those frequently used aspect terms in non-opinionated sentences, e.g., the word \u201crestaurant\u201d in \u201cthe restaurant was packed at first, so we waited for 20 minutes\u201d, which should not be extracted because the sentence does not convey any opinion on it.",
+      "There are a few works that consider opinion terms when tackling the ATE task. BIBREF8 proposed Recursive Neural Conditional Random Fields (RNCRF) to explicitly extract aspects and opinions in a single framework. Aspect-opinion relation is modeled via joint extraction and dependency-based representation learning. One assumption of RNCRF is that dependency parsing will capture the relation between aspect terms and opinion words in the same sentence so that the joint extraction can benefit. Such assumption is usually valid for simple sentences, but rather fragile for some complicated structures, such as clauses and parenthesis. Moreover, RNCRF suffers from errors of dependency parsing because its network construction hinges on the dependency tree of inputs. CMLA BIBREF9 models aspect-opinion relation without using syntactic information. Instead, it enables the two tasks to share information via attention mechanism. For example, it exploits the global opinion information by directly computing the association score between the aspect prototype and individual opinion hidden representations and then performing weighted aggregation. However, such aggregation may introduce noise. To some extent, this drawback is inherited from the attention mechanism, as also observed in machine translation BIBREF10 and image captioning BIBREF11 .",
+      "To make better use of opinion information to assist aspect term extraction, we distill the opinion information of the whole input sentence into opinion summary, and such distillation is conditioned on a particular current token for aspect prediction. Then, the opinion summary is employed as part of features for the current aspect prediction. Taking the sentence \u201cthe restaurant is cute but not upscale\u201d as an example, when our model performs the prediction for the word \u201crestaurant\u201d, it first generates an opinion summary of the entire sentence conditioned on \u201crestaurant\u201d. Due to the strong correlation between \u201crestaurant' and \u201cupscale\u201d (an opinion word), the opinion summary will convey more information of \u201cupscale\u201d so that it will help predict \u201crestaurant\u201d as an aspect with high probability. Note that the opinion summary is built on the initial opinion features coming from an auxiliary opinion detection task, and such initial features already distinguish opinion words to some extent. Moreover, we propose a novel transformation network that helps strengthen the favorable correlations, e.g. between \u201crestaurant' and \u201cupscale\u201d, so that the produced opinion summary involves less noise.",
+      "Besides the opinion summary, another useful clue we explore is the aspect prediction history due to the inspiration of two observations: (1) In sequential labeling, the predictions at the previous time steps are useful clues for reducing the error space of the current prediction. For example, in the B-I-O tagging (refer to Section SECREF4 ), if the previous prediction is \u201cO\u201d, then the current prediction cannot be \u201cI\u201d; (2) It is observed that some sentences contain multiple aspect terms. For example, \u201cApple is unmatched in product quality, aesthetics, craftmanship, and customer service\u201d has a coordinate structure of aspects. Under this structure, the previously predicted commonly-used aspect terms (e.g., \u201cproduct quality\u201d) can guide the model to find the infrequent aspect terms (e.g., \u201ccraftmanship\u201d). To capture the above clues, our model distills the information of the previous aspect detection for making a better prediction on the current state.",
+      "Concretely, we propose a framework for more accurate aspect term extraction by exploiting the opinion summary and the aspect detection history. Firstly, we employ two standard Long-Short Term Memory Networks (LSTMs) for building the initial aspect and opinion representations recording the sequential information. To encode the historical information into the initial aspect representations at each time step, we propose truncated history attention to distill useful features from the most recent aspect predictions and generate the history-aware aspect representations. We also design a selective transformation network to obtain the opinion summary at each time step. Specifically, we apply the aspect information to transform the initial opinion representations and apply attention over the transformed representations to generate the opinion summary. Experimental results show that our framework can outperform state-of-the-art methods."
+    ],
+    [
+      "Given a sequence INLINEFORM0 of INLINEFORM1 words, the ATE task can be formulated as a token/word level sequence labeling problem to predict an aspect label sequence INLINEFORM2 , where each INLINEFORM3 comes from a finite label set INLINEFORM4 which describes the possible aspect labels. As shown in the example below:",
+      " ",
+      " INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 denote beginning of, inside and outside of the aspect span respectively. Note that in commonly-used datasets such as BIBREF12 , the gold standard opinions are usually not annotated."
+    ],
+    [
+      "As shown in Figure FIGREF3 , our model contains two key components, namely Truncated History-Attention (THA) and Selective Transformation Network (STN), for capturing aspect detection history and opinion summary respectively. THA and STN are built on two LSTMs that generate the initial word representations for the primary ATE task and the auxiliary opinion detection task respectively. THA is designed to integrate the information of aspect detection history into the current aspect feature to generate a new history-aware aspect representation. STN first calculates a new opinion representation conditioned on the current aspect candidate. Then, we employ a bi-linear attention network to calculate the opinion summary as the weighted sum of the new opinion representations, according to their associations with the current aspect representation. Finally, the history-aware aspect representation and the opinion summary are concatenated as features for aspect prediction of the current time step.",
+      "As Recurrent Neural Networks can record the sequential information BIBREF13 , we employ two vanilla LSTMs to build the initial token-level contextualized representations for sequence labeling of the ATE task and the auxiliary opinion word detection task respectively. For simplicity, let INLINEFORM0 denote an LSTM unit where INLINEFORM1 is the task indicator. In the following sections, without specification, the symbols with superscript INLINEFORM2 and INLINEFORM3 are the notations used in the ATE task and the opinion detection task respectively. We use Bi-Directional LSTM to generate the initial token-level representations INLINEFORM4 ( INLINEFORM5 is the dimension of hidden states): DISPLAYFORM0 ",
+      "",
+      "In principle, RNN can memorize the entire history of the predictions BIBREF13 , but there is no mechanism to exploit the relation between previous predictions and the current prediction. As discussed above, such relation could be useful because of two reasons: (1) reducing the model's error space in predicting the current label by considering the definition of B-I-O schema, (2) improving the prediction accuracy for multiple aspects in one coordinate structure.",
+      "We propose a Truncated History-Attention (THA) component (the THA block in Figure FIGREF3 ) to explicitly model the aspect-aspect relation. Specifically, THA caches the most recent INLINEFORM0 hidden states. At the current prediction time step INLINEFORM1 , THA calculates the normalized importance score INLINEFORM2 of each cached state INLINEFORM3 ( INLINEFORM4 ) as follows: DISPLAYFORM0 ",
+      " DISPLAYFORM0 ",
+      "",
+      " INLINEFORM0 denotes the previous history-aware aspect representation (refer to Eq. EQREF12 ). INLINEFORM1 can be learned during training. INLINEFORM2 are parameters associated with previous aspect representations, current aspect representation and previous history-aware aspect representations respectively. Then, the aspect history INLINEFORM3 is obtained as follows: DISPLAYFORM0 ",
+      "",
+      "To benefit from the previous aspect detection, we consolidate the hidden aspect representation with the distilled aspect history to generate features for the current prediction. Specifically, we adopt a way similar to the residual block BIBREF14 , which is shown to be useful in refining word-level features in Machine Translation BIBREF15 and Part-Of-Speech tagging BIBREF16 , to calculate the history-aware aspect representations INLINEFORM0 at the time step INLINEFORM1 : DISPLAYFORM0 ",
+      "where ReLU is the relu activation function.",
+      "Previous works show that modeling aspect-opinion association is helpful to improve the accuracy of ATE, as exemplified in employing attention mechanism for calculating the opinion information BIBREF9 , BIBREF17 . MIN BIBREF17 focuses on a few surrounding opinion representations and computes their importance scores according to the proximity and the opinion salience derived from a given opinion lexicon. However, it is unable to capture the long-range association between aspects and opinions. Besides, the association is not strong because only the distance information is modeled. Although CMLA BIBREF9 can exploit global opinion information for aspect extraction, it may suffer from the noise brought in by attention-based feature aggregation. Taking the aspect term \u201cfish\u201d in \u201cFurthermore, while the fish is unquestionably fresh, rolls tend to be inexplicably bland.\u201d as an example, it might be enough to tell \u201cfish\u201d is an aspect given the appearance of the strongly related opinion \u201cfresh\u201d. However, CMLA employs conventional attention and does not have a mechanism to suppress the noise caused by other terms such as \u201crolls\u201d. Dependency parsing seems to be a good solution for finding the most related opinion and indeed it was utilized in BIBREF8 , but the parser is prone to generating mistakes when processing the informal online reviews, as discussed in BIBREF17 .",
+      "To make use of opinion information and suppress the possible noise, we propose a novel Selective Transformation Network (STN) (the STN block in Figure FIGREF3 ), and insert it before attending to global opinion features so that more important features with respect to a given aspect candidate will be highlighted. Specifically, STN first calculates a new opinion representation INLINEFORM0 given the current aspect feature INLINEFORM1 as follows: DISPLAYFORM0 ",
+      "where INLINEFORM0 and INLINEFORM1 are parameters for history-aware aspect representations and opinion representations respectively. They map INLINEFORM2 and INLINEFORM3 to the same subspace. Here the aspect feature INLINEFORM4 acts as a \u201cfilter\u201d to keep more important opinion features. Equation EQREF14 also introduces a residual block to obtain a better opinion representation INLINEFORM5 , which is conditioned on the current aspect feature INLINEFORM6 .",
+      "For distilling the global opinion summary, we introduce a bi-linear term to calculate the association score between INLINEFORM0 and each INLINEFORM1 : DISPLAYFORM0 ",
+      "where INLINEFORM0 and INLINEFORM1 are parameters of the Bi-Linear Attention layer. The improved opinion summary INLINEFORM2 at the time INLINEFORM3 is obtained via the weighted sum of the opinion representations: DISPLAYFORM0 ",
+      "Finally, we concatenate the opinion summary INLINEFORM0 and the history-aware aspect representation INLINEFORM1 and feed it into the top-most fully-connected (FC) layer for aspect prediction: DISPLAYFORM0 DISPLAYFORM1 ",
+      "Note that our framework actually performs a multi-task learning, i.e. predicting both aspects and opinions. We regard the initial token-level representations INLINEFORM0 as the features for opinion prediction: DISPLAYFORM0 ",
+      " INLINEFORM0 and INLINEFORM1 are parameters of the FC layers."
+    ],
+    [
+      "All the components in the proposed framework are differentiable. Thus, our framework can be efficiently trained with gradient methods. We use the token-level cross-entropy error between the predicted distribution INLINEFORM0 ( INLINEFORM1 ) and the gold distribution INLINEFORM2 as the loss function: DISPLAYFORM0 ",
+      "Then, the losses from both tasks are combined to form the training objective of the entire model: DISPLAYFORM0 ",
+      "where INLINEFORM0 and INLINEFORM1 represent the loss functions for aspect and opinion extractions respectively."
+    ],
+    [
+      "To evaluate the effectiveness of the proposed framework for the ATE task, we conduct experiments over four benchmark datasets from the SemEval ABSA challenge BIBREF1 , BIBREF18 , BIBREF12 . Table TABREF24 shows their statistics. INLINEFORM0 (SemEval 2014) contains reviews of the laptop domain and those of INLINEFORM1 (SemEval 2014), INLINEFORM2 (SemEval 2015) and INLINEFORM3 (SemEval 2016) are for the restaurant domain. In these datasets, aspect terms have been labeled by the task organizer.",
+      "Gold standard annotations for opinion words are not provided. Thus, we choose words with strong subjectivity from MPQA to provide the distant supervision BIBREF19 . To compare with the best SemEval systems and the current state-of-the-art methods, we use the standard train-test split in SemEval challenge as shown in Table TABREF24 ."
+    ],
+    [
+      "We compare our framework with the following methods:",
+      "CRF-1: Conditional Random Fields with basic feature templates.",
+      "CRF-2: Conditional Random Fields with basic feature templates and word embeddings.",
+      "Semi-CRF: First-order Semi-Markov Conditional Random Fields BIBREF20 and the feature templates in BIBREF21 are adopted.",
+      "LSTM: Vanilla bi-directional LSTM with pre-trained word embeddings.",
+      "IHS_RD BIBREF2 , DLIREC BIBREF3 , EliXa BIBREF22 , NLANGP BIBREF4 : The winning systems in the ATE subtask in SemEval ABSA challenge BIBREF1 , BIBREF18 , BIBREF12 .",
+      "WDEmb BIBREF5 : Enhanced CRF with word embeddings, dependency path embeddings and linear context embeddings.",
+      "MIN BIBREF17 : MIN consists of three LSTMs. Two LSTMs are employed to model the memory interactions between ATE and opinion detection. The last one is a vanilla LSTM used to predict the subjectivity of the sentence as additional guidance.",
+      "RNCRF BIBREF8 : CRF with high-level representations learned from Dependency Tree based Recursive Neural Network.",
+      "CMLA BIBREF9 : CMLA is a multi-layer architecture where each layer consists of two coupled GRUs to model the relation between aspect terms and opinion words.",
+      "To clarify, our framework aims at extracting aspect terms where the opinion information is employed as auxiliary, while RNCRF and CMLA perform joint extraction of aspects and opinions. Nevertheless, the comparison between our framework and RNCRF/CMLA is still fair, because we do not use manually annotated opinions as used by RNCRF and CMLA, instead, we employ an existing opinion lexicon to provide weak opinion supervision."
+    ],
+    [
+      "We pre-processed each dataset by lowercasing all words and replace all punctuations with PUNCT. We use pre-trained GloVe 840B vectors BIBREF23 to initialize the word embeddings and the dimension (i.e., INLINEFORM0 ) is 300. For out-of-vocabulary words, we randomly sample their embeddings from the uniform distribution INLINEFORM1 as done in BIBREF24 . All of the weight matrices except those in LSTMs are initialized from the uniform distribution INLINEFORM2 . For the initialization of the matrices in LSTMs, we adopt Glorot Uniform strategy BIBREF25 . Besides, all biases are initialized as 0's.",
+      "The model is trained with SGD. We apply dropout over the ultimate aspect/opinion features and the input word embeddings of LSTMs. The dropout rates are empirically set as 0.5. With 5-fold cross-validation on the training data of INLINEFORM0 , other hyper-parameters are set as follows: INLINEFORM1 , INLINEFORM2 ; the number of cached historical aspect representations INLINEFORM3 is 5; the learning rate of SGD is 0.07."
+    ],
+    [
+      "As shown in Table TABREF39 , the proposed framework consistently obtains the best scores on all of the four datasets. Compared with the winning systems of SemEval ABSA, our framework achieves 5.0%, 1.6%, 1.4%, 1.3% absolute gains on INLINEFORM0 , INLINEFORM1 , INLINEFORM2 and INLINEFORM3 respectively.",
+      "Our framework can outperform RNCRF, a state-of-the-art model based on dependency parsing, on all datasets. We also notice that RNCRF does not perform well on INLINEFORM0 and INLINEFORM1 (3.7% and 3.9% inferior than ours). We find that INLINEFORM2 and INLINEFORM3 contain many informal reviews, thus RNCRF's performance degradation is probably due to the errors from the dependency parser when processing such informal texts.",
+      "CMLA and MIN do not rely on dependency parsing, instead, they employ attention mechanism to distill opinion information to help aspect extraction. Our framework consistently performs better than them. The gains presumably come from two perspectives: (1) In our model, the opinion summary is exploited after performing the selective transformation conditioned on the current aspect features, thus the summary can to some extent avoid the noise due to directly applying conventional attention. (2) Our model can discover some uncommon aspects under the guidance of some commonly-used aspects in coordinate structures by the history attention.",
+      "CRF with basic feature template is not strong, therefore, we add CRF-2 as another baseline. As shown in Table TABREF39 , CRF-2 with word embeddings achieves much better results than CRF-1 on all datasets. WDEmb, which is also an enhanced CRF-based method using additional dependency context embeddings, obtains superior performances than CRF-2. Therefore, the above comparison shows that word embeddings are useful and the embeddings incorporating structure information can further improve the performance."
+    ],
+    [
+      "To further investigate the efficacy of the key components in our framework, namely, THA and STN, we perform ablation study as shown in the second block of Table TABREF39 . The results show that each of THA and STN is helpful for improving the performance, and the contribution of STN is slightly larger than THA. \u201cOURS w/o THA & STN\u201d only keeps the basic bi-linear attention. Although it performs not bad, it is still less competitive compared with the strongest baseline (i.e., CMLA), suggesting that only using attention mechanism to distill opinion summary is not enough. After inserting the STN component before the bi-linear attention, i.e. \u201cOURS w/o THA\u201d, we get about 1% absolute gains on each dataset, and then the performance is comparable to CMLA. By adding THA, i.e. \u201cOURS\u201d, the performance is further improved, and all state-of-the-art methods are surpassed."
+    ],
+    [
+      "In Figure FIGREF41 , we visualize the opinion attention scores of the words in two example sentences with the candidate aspects \u201cmaitre-D\u201d and \u201cbathroom\u201d. The scores in Figures FIGREF41 and FIGREF41 show that our full model captures the related opinion words very accurately with significantly larger scores, i.e. \u201cincredibly\u201d, \u201cunwelcoming\u201d and \u201carrogant\u201d for \u201cmaitre-D\u201d, and \u201cunfriendly\u201d and \u201cfilthy\u201d for \u201cbathroom\u201d. \u201cOURS w/o STN\u201d directly applies attention over the opinion hidden states INLINEFORM0 's, similar to what CMLA does. As shown in Figure FIGREF41 , it captures some unrelated opinion words (e.g. \u201cfine\u201d) and even some non-opinionated words. As a result, it brings in some noise into the global opinion summary, and consequently the final prediction accuracy will be affected. This example demonstrates that the proposed STN works pretty well to help attend to more related opinion words given a particular aspect.",
+      "Some predictions of our model and those of LSTM and OURS w/o THA & STN are given in Table TABREF43 . The models incorporating attention-based opinion summary (i.e., OURS and OURS w/o THA & STN) can better determine if the commonly-used nouns are aspect terms or not (e.g. \u201cdevice\u201d in the first input), since they make decisions based on the global opinion information. Besides, they are able to extract some infrequent or even misspelled aspect terms (e.g. \u201csurvice\u201d in the second input) based on the indicative clues provided by opinion words. For the last three cases, having aspects in coordinate structures (i.e. the third and the fourth) or long aspects (i.e. the fifth), our model can give precise predictions owing to the previous detection clues captured by THA. Without using these clues, the baseline models fail."
+    ],
+    [
+      "Some initial works BIBREF26 developed a bootstrapping framework for tackling Aspect Term Extraction (ATE) based on the observation that opinion words are usually located around the aspects. BIBREF27 and BIBREF28 performed co-extraction of aspect terms and opinion words based on sophisticated syntactic patterns. However, relying on syntactic patterns suffers from parsing errors when processing informal online reviews. To avoid this drawback, BIBREF29 , BIBREF30 employed word-based translation models. Specifically, these models formulated the ATE task as a monolingual word alignment process and aspect-opinion relation is captured by alignment links rather than word dependencies. The ATE task can also be formulated as a token-level sequence labeling problem. The winning systems BIBREF2 , BIBREF22 , BIBREF4 of SemEval ABSA challenges employed traditional sequence models, such as Conditional Random Fields (CRFs) and Maximum Entropy (ME), to detect aspects. Besides heavy feature engineering, they also ignored the consideration of opinions.",
+      "Recently, neural network based models, such as LSTM-based BIBREF6 and CNN-based BIBREF31 methods, become the mainstream approach. Later on, some neural models jointly extracting aspect and opinion were proposed. BIBREF8 performs the two task in a single Tree-Based Recursive Neural Network. Their network structure depends on dependency parsing, which is prone to error on informal reviews. CMLA BIBREF9 consists of multiple attention layers on top of standard GRUs to extract the aspects and opinion words. Similarly, MIN BIBREF17 employs multiple LSTMs to interactively perform aspect term extraction and opinion word extraction in a multi-task learning framework. Our framework is different from them in two perspectives: (1) It filters the opinion summary by incorporating the aspect features at each time step into the original opinion representations; (2) It exploits history information of aspect detection to capture the coordinate structures and previous aspect features."
+    ],
+    [
+      "For more accurate aspect term extraction, we explored two important types of information, namely aspect detection history, and opinion summary. We design two components, i.e. truncated history attention, and selective transformation network. Experimental results show that our model dominates those joint extraction works such as RNCRF and CMLA on the performance of ATE. It suggests that the joint extraction sacrifices the accuracy of aspect prediction, although the ground-truth opinion words were annotated by these authors. Moreover, one should notice that those joint extraction methods do not care about the correspondence between the extracted aspect terms and opinion words. Therefore, the necessity of such joint extraction should be obelized, given the experimental findings in this paper."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0718/instruction.md b/qasper-0718/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..9681c636dd69a1f283aed217bae69d5900045bbf
--- /dev/null
+++ b/qasper-0718/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Aspect Term Extraction with History Attention and Selective Transformation
+
+Question: By how much do they outperform state-of-the-art methods?
\ No newline at end of file
diff --git a/qasper-0719/instruction.md b/qasper-0719/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..da7715be86b47607d0276a2baab5383f90756490
--- /dev/null
+++ b/qasper-0719/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset
+
+Question: What is the average number of turns per dialog?
\ No newline at end of file
diff --git a/qasper-0720/instruction.md b/qasper-0720/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..2eb5c083cc2d40f3756691c998ba0b9a9735b0f7
--- /dev/null
+++ b/qasper-0720/instruction.md
@@ -0,0 +1,115 @@
+Name of Paper: Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset
+
+Question: What baseline models are offered?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related work ::: Human-machine vs. human-human dialog",
+    "Related work ::: The Wizard of Oz (WOz) Approach and MultiWOZ",
+    "The Taskmaster Corpus ::: Overview",
+    "The Taskmaster Corpus ::: Two-person, spoken dataset",
+    "The Taskmaster Corpus ::: Two-person, spoken dataset ::: WOz platform and data pipeline",
+    "The Taskmaster Corpus ::: Two-person, spoken dataset ::: Agents, workers and training",
+    "The Taskmaster Corpus ::: Self-dialogs (one-person written dataset)",
+    "The Taskmaster Corpus ::: Self-dialogs (one-person written dataset) ::: Task scenarios and instructions",
+    "The Taskmaster Corpus ::: Self-dialogs (one-person written dataset) ::: Pros and cons of self-dialogs",
+    "The Taskmaster Corpus ::: Annotation",
+    "Dataset Analysis ::: Self-dialogs vs MultiWOZ",
+    "Dataset Analysis ::: Self-dialogs vs Two-person",
+    "Dataset Analysis ::: Baseline Experiments: Response Generation",
+    "Dataset Analysis ::: Baseline Experiments: Argument Prediction",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Voice-based \u201cpersonal assistants\" such as Apple's SIRI, Microsoft's Cortana, Amazon Alexa, and the Google Assistant have finally entered the mainstream. This development is generally attributed to major breakthroughs in speech recognition and text-to-speech (TTS) technologies aided by recent progress in deep learning BIBREF0, exponential gains in compute power BIBREF1, BIBREF2, and the ubiquity of powerful mobile devices. The accuracy of machine learned speech recognizers BIBREF3 and speech synthesizers BIBREF4 are good enough to be deployed in real-world products and this progress has been driven by publicly available labeled datasets. However, conspicuously absent from this list is equal progress in machine learned conversational natural language understanding (NLU) and generation (NLG). The NLU and NLG components of dialog systems starting from the early research work BIBREF5 to the present commercially available personal assistants largely rely on rule-based systems. The NLU and NLG systems are often carefully programmed for very narrow and specific cases BIBREF6, BIBREF7. General understanding of natural spoken behaviors across multiple dialog turns, even in single task-oriented situations, is by most accounts still a long way off. In this way, most of these products are very much hand crafted, with inherent constraints on what users can say, how the system responds and the order in which the various subtasks can be completed. They are high precision but relatively low coverage. Not only are such systems unscalable, but they lack the flexibility to engage in truly natural conversation.",
+      "Yet none of this is surprising. Natural language is heavily context dependent and often ambiguous, especially in multi-turn conversations across multiple topics. It is full of subtle discourse cues and pragmatic signals whose patterns have yet to be thoroughly understood. Enabling an automated system to hold a coherent task-based conversation with a human remains one of computer science's most complex and intriguing unsolved problems BIBREF5. In contrast to more traditional NLP efforts, interest in statistical approaches to dialog understanding and generation aided by machine learning has grown considerably in the last couple of years BIBREF8, BIBREF9, BIBREF10. However, the dearth of high quality, goal-oriented dialog data is considered a major hindrance to more significant progress in this area BIBREF9, BIBREF11.",
+      "To help solve the data problem we present Taskmaster-1, a dataset consisting of 13,215 dialogs, including 5,507 spoken and 7,708 written dialogs created with two distinct procedures. Each conversation falls into one of six domains: ordering pizza, creating auto repair appointments, setting up ride service, ordering movie tickets, ordering coffee drinks and making restaurant reservations. For the spoken dialogs, we created a \u201cWizard of Oz\u201d (WOz) system BIBREF12 to collect two-person, spoken conversations. Crowdsourced workers playing the \u201cuser\" interacted with human operators playing the \u201cdigital assistant\u201d using a web-based interface. In this way, users were led to believe they were interacting with an automated system while it was in fact a human, allowing them to express their turns in natural ways but in the context of an automated interface. We refer to this spoken dialog type as \u201ctwo-person dialogs\". For the written dialogs, we engaged crowdsourced workers to write the full conversation themselves based on scenarios outlined for each task, thereby playing roles of both the user and assistant. We refer to this written dialog type as \u201cself-dialogs\". In a departure from traditional annotation techniques BIBREF10, BIBREF8, BIBREF13, dialogs are labeled with simple API calls and arguments. This technique is much easier for annotators to learn and simpler to apply. As such it is more cost effective and, in addition, the same model can be used for multiple service providers.",
+      "Taskmaster-1 has richer and more diverse language than the current popular benchmark in task-oriented dialog, MultiWOZ BIBREF13. Table TABREF2 shows that Taskmaster-1 has more unique words and is more difficult for language models to fit. We also find that Taskmaster-1 is more realistic than MultiWOZ. Specifically, the two-person dialogs in Taskmaster-1 involve more real-word entities than seen in MutliWOZ since we do not restrict conversations to a small knowledge base. Beyond the corpus and the methodologies used to create it, we present several baseline models including state-of-the-art neural seq2seq architectures together with perplexity and BLEU scores. We also provide qualitative human performance evaluations for these models and find that automatic evaluation metrics correlate well with human judgments. We will publicly release our corpus containing conversations, API call and argument annotations, and also the human judgments."
+    ],
+    [
+      "BIBREF14 discuss the major features and differences among the existing offerings in an exhaustive and detailed survey of available corpora for data driven learning of dialog systems. One important distinction covered is that of human-human vs. human-machine dialog data, each having its advantages and disadvantages. Many of the existing task-based datasets have been generated from deployed dialog systems such as the Let\u2019s Go Bus Information System BIBREF15 and the various Dialog State Tracking Challenges (DSTCs) BIBREF16. However, it is doubtful that new data-driven systems built with this type of corpus would show much improvement since they would be biased by the existing system and likely mimic its limitations BIBREF17. Since the ultimate goal is to be able to handle complex human language behaviors, it would seem that human-human conversational data is the better choice for spoken dialog system development BIBREF13. However, learning from purely human-human based corpora presents challenges of its own. In particular, human conversation has a different distribution of understanding errors and exhibits turn-taking idiosyncrasies which may not be well suited for interaction with a dialog system BIBREF17, BIBREF14."
+    ],
+    [
+      "The WOz framework, first introduced by BIBREF12 as a methodology for iterative design of natural language interfaces, presents a more effective approach to human-human dialog collection. In this setup, users are led to believe they are interacting with an automated assistant but in fact it is a human behind the scenes that controls the system responses. Given the human-level natural language understanding, users quickly realize they can comfortably and naturally express their intent rather than having to modify behaviors as is normally the case with a fully automated assistant. At the same time, the machine-oriented context of the interaction, i.e. the use of TTS and slower turn taking cadence, prevents the conversation from becoming fully fledged, overly complex human discourse. This creates an idealized spoken environment, revealing how users would openly and candidly express themselves with an automated assistant that provided superior natural language understanding.",
+      "Perhaps the most relevant work to consider here is the recently released MultiWOZ dataset BIBREF13, since it is similar in size, content and collection methodologies. MultiWOZ has roughly 10,000 dialogs which feature several domains and topics. The dialogs are annotated with both dialog states and dialog acts. MultiWOZ is an entirely written corpus and uses crowdsourced workers for both assistant and user roles. In contrast, Taskmaster-1 has roughly 13,000 dialogs spanning six domains and annotated with API arguments. The two-person spoken dialogs in Taskmaster-1 use crowdsourcing for the user role but trained agents for the assistant role. The assistant's speech is played to the user via TTS. The remaining 7,708 conversations in Taskmaster-1 are self-dialogs, in which crowdsourced workers write the entire conversation themselves. As BIBREF18, BIBREF19 show, self dialogs are surprisingly rich in content."
+    ],
+    [
+      "There are several key attributes that make Taskmaster-1 both unique and effective for data-driven approaches to building dialog systems and for other research.",
+      "",
+      "Spoken and written dialogs: While the spoken sources more closely reflect conversational language BIBREF20, written dialogs are significantly cheaper and easier to gather. This allows for a significant increase in the size of the corpus and in speaker diversity.",
+      "",
+      "Goal-oriented dialogs: All dialogs are based on one of six tasks: ordering pizza, creating auto repair appointments, setting up rides for hire, ordering movie tickets, ordering coffee drinks and making restaurant reservations.",
+      "",
+      "Two collection methods: The two-person dialogs and self-dialogs each have pros and cons, revealing interesting contrasts.",
+      "",
+      "Multiple turns: The average number of utterances per dialog is about 23 which ensures context-rich language behaviors.",
+      "",
+      "API-based annotation: The dataset uses a simple annotation schema providing sufficient grounding for the data while making it easy for workers to apply labels consistently.",
+      "",
+      "Size: The total of 13,215 dialogs in this corpus is on par with similar, recently released datasets such as MultiWOZ BIBREF13."
+    ],
+    [
+      "In order to replicate a two-participant, automated digital assistant experience, we built a WOz platform that pairs agents playing the digital assistant with crowdsourced workers playing the user in task-based conversational scenarios. An example dialog from this dataset is given in Figure FIGREF5."
+    ],
+    [
+      "While it is beyond the scope of this work to describe the entire system in detail, there are several platform features that help illustrate how the process works.",
+      "",
+      "Modality: The agents playing the assistant type their input which is in turn played to the user via text-to-speech (TTS) while the crowdsourced workers playing the user speak aloud to the assistant using their laptop and microphone. We use WebRTC to establish the audio channel. This setup creates a digital assistant-like communication style.",
+      "",
+      "Conversation and user quality control: Once the task is completed, the agents tag each conversation as either successful or problematic depending on whether the session had technical glitches or user behavioral issues. We are also then able to root out problematic users based on this logging.",
+      "",
+      "Agent quality control: Agents are required to login to the system which allows us to monitor performance including the number and length of each session as well as their averages.",
+      "",
+      "User queuing: When there are more users trying to connect to the system than available agents, a queuing mechanism indicates their place in line and connects them automatically once they move to the front of the queue.",
+      "",
+      "Transcription: Once complete, the user's audio-only portion of the dialog is transcribed by a second set of workers and then merged with the assistant's typed input to create a full text version of the dialog. Finally, these conversations are checked for transcription errors and typos and then annotated, as described in Section SECREF48."
+    ],
+    [
+      "Both agents and crowdsourced workers are given written instructions prior to the session. Examples of each are given in Figure FIGREF6 and Figure FIGREF23. The instructions continue to be displayed on screen to the crowdsourced workers while they interact with the assistant. Instructions are modified at times (for either participant or both) to ensure broader coverage of dialog scenarios that are likely to occur in actual user-assistant interactions. For example, in one case users were asked to change their mind after ordering their first item and in another agents were instructed to tell users that a given item was not available. Finally, in their instructions, crowdsourced workers playing the user are told they will be engaging in conversation with \u201ca digital assistant\u201d. However, it is plausible that some suspect human intervention due to the advanced level of natural language understanding from the assistant side.",
+      "Agents playing the assistant role were hired from a pool of dialog analysts and given two hours of training on the system interface as well as on how to handle specific scenarios such as uncooperative users and technical glitches. Uncooperative users typically involve those who either ignored agent input or who rushed through the conversation with short phrases. Technical issues involved dropped sessions (e.g. WebRTC connections failed) or cases in which the user could not hear the agent or vice-versa. In addition, weekly meetings were held with the agents to answer questions and gather feedback on their experiences. Agents typically work four hours per day with dialog types changing every hour. Crowdsourced workers playing the user are accessed using Amazon Mechanical Turk. Payment for a completed dialog session lasting roughly five to seven minutes was typically in the range of $\\$1.00$ to $\\$1.30$. Problematic users are detected either by the agent involved in the specific dialog or by post-session assessment and removed from future requests."
+    ],
+    [
+      "While the two-person approach to data collection creates a realistic scenario for robust, spoken dialog data collection, this technique is time consuming, complex and expensive, requiring considerable technical implementation as well as administrative procedures to train and manage agents and crowdsourced workers. In order to extend the Taskmaster dataset at minimal cost, we use an alternative self-dialog approach in which crowdsourced workers write the full dialogs themselves (i.e. interpreting the roles of both user and assistant)."
+    ],
+    [
+      "Targeting the same six tasks used for the two-person dialogs, we again engaged the Amazon Mechanical Turk worker pool to create self-dialogs, this time as a written exercise. In this case, users are asked to pretend they have a personal assistant who can help them take care of various tasks in real time. They are told to imagine a scenario in which they are speaking to their assistant on the phone while the assistant accesses the services for one of the given tasks. They then write down the entire conversation. Figure FIGREF34 shows a sample set of instructions."
+    ],
+    [
+      "The self-dialog technique renders quality data and avoids some of the challenges seen with the two-person approach. To begin, since the same person is writing both sides of the conversation, we never see misunderstandings that lead to frustration as is sometimes experienced between interlocutors in the two-person approach. In addition, all the self-dialogs follow a reasonable path even when the user is constructing conversations that include understanding errors or other types of dialog glitches such as when a particular choice is not available. As it turns out, crowdsourced workers are quite effective at recreating various types of interactions, both error-free and those containing various forms of linguistic repair. The sample dialog in Figure FIGREF44 shows the result of a self-dialog exercise in which workers were told to write a conversation with various ticket availability issues that is ultimately unsuccessful.",
+      "Two more benefits of the self-dialog approach are its efficiency and cost effectiveness. We were able to gather thousands of dialogs in just days without transcription or trained agents, and spent roughly six times less per dialog. Despite these advantages, the self-dialog written technique cannot recreate the disfluencies and other more complex error patterns that occur in the two-person spoken dialogs which are important for model accuracy and coverage."
+    ],
+    [
+      "We chose a highly simplified annotation approach for Taskmaster-1 as compared to traditional, detailed strategies which require robust agreement among workers and usually include dialog state and slot information, among other possible labels. Instead we focus solely on API arguments for each type of conversation, meaning just the variables required to execute the transaction. For example, in dialogs about setting up UBER rides, we label the \u201cto\" and \u201cfrom\" locations along with the car type (UberX, XL, Pool, etc). For movie tickets, we label the movie name, theater, time, number of tickets, and sometimes screening type (e.g. 3D vs. standard). A complete list of labels is included with the corpus release.",
+      "As discussed in Section SECREF33, to encourage diversity, at times we explicitly ask users to change their mind in the middle of the conversation, and the agents to tell the user that the requested item is not available. This results in conversations having multiple instances of the same argument type. To handle this ambiguity, in addition to the labels mentioned above, the convention of either \u201caccept\u201d or \u201creject\" was added to all labels used to execute the transaction, depending on whether or not that transaction was successful.",
+      "In Figure FIGREF49, both the number of people and the time variables in the assistant utterance would have the \u201c.accept\" label indicating the transaction was completed successfully. If the utterance describing a transaction does not include the variables by name, the whole sentence is marked with the dialog type. For example, a statement such as The table has been booked for you would be labeled as reservation.accept."
+    ],
+    [
+      "We quantitatively compare our self-dialogs (Section SECREF45) with the MultiWOZ dataset in Table TABREF2. Compared to MultiWOZ, we do not ask the users and assistants to stick to detailed scripts and do not restrict them to have conversations surrounding a small knowledge base. Table TABREF2 shows that our dataset has more unique words, and has almost twice the number of utterances per dialog than the MultiWOZ corpus. Finally, when trained with the Transformer BIBREF21 model, we observe significantly higher perplexities and lower BLEU scores for our dataset compared to MultiWOZ suggesting that our dataset conversations are difficult to model. Finally, Table TABREF2 also shows that our dataset contains close to 10 times more real-world named entities than MultiWOZ and thus, could potentially serve as a realistic baseline when designing goal oriented dialog systems. MultiWOZ has only 1338 unique named entities and only 4510 unique values (including date, time etc.) in their datatset."
+    ],
+    [
+      "In this section, we quantitatively compare 5k conversations each of self-dialogs (Section SECREF45) and two-person (Section SECREF31). From Table TABREF50, we find that self-dialogs exhibit higher perplexity ( almost 3 times) compared to the two-person conversations suggesting that self-dialogs are more diverse and contains more non-conventional conversational flows which is inline with the observations in Section-SECREF47. While the number of unique words are higher in the case of self-dialogs, conversations are longer in the two-person conversations. We also report metrics by training a single model on both the datasets together."
+    ],
+    [
+      "We evaluate various seq2seq architectures BIBREF22 on our self-dialog corpus using both automatic evaluation metrics and human judgments. Following the recent line of work on generative dialog systems BIBREF23, we treat the problem of response generation given the dialog history as a conditional language modeling problem. Specifically we want to learn a conditional probability distribution $P_{\\theta }(U_{t}|U_{1:t-1})$ where $U_{t}$ is the next response given dialog history $U_{1:t-1}$. Each utterance $U_i$ itself is comprised of a sequence of words $w_{i_1}, w_{i_2} \\ldots w_{i_k}$. The overall conditional probability is factorized autoregressively as",
+      "$P_{\\theta }$, in this work, is parameterized by a recurrent, convolution or Transformer-based seq2seq model.",
+      "n-gram: We consider 3-gram and 4-gram conditional language model baseline with interpolation. We use random grid search for the best coefficients for the interpolated model.",
+      "Convolution: We use the fconv architecture BIBREF24 and default hyperparameters from the fairseq BIBREF25 framework. We train the network with ADAM optimizer BIBREF26 with learning rate of 0.25 and dropout probability set to 0.2.",
+      "LSTM: We consider LSTM models BIBREF27 with and without attention BIBREF28 and use the tensor2tensor BIBREF29 framework for the LSTM baselines. We use a two-layer LSTM network for both the encoder and the decoder with 128 dimensional hidden vectors.",
+      "Transformer: As with LSTMs, we use the tensor2tensor framework for the Transformer model. Our Transformer BIBREF21 model uses 256 dimensions for both input embedding and hidden state, 2 layers and 4 attention heads. For both LSTMs and Transformer, we train the model with ADAM optimizer ($\\beta _{1} = 0.85$, $\\beta _{2} = 0.997$) and dropout probability set to 0.2.",
+      "GPT-2: Apart from supervised seq2seq models, we also include results from pre-trained GPT-2 BIBREF30 containing 117M parameters.",
+      "We evaluate all the models with perplexity and BLEU scores (Table TABREF55). Additionally, we perform two kinds of human evaluation - Ranking and Rating (LIKERT scale) for the top-3 performing models - Convolution, LSTM-attention and Transformer. For the ranking task, we randomly show 500 partial dialogs and generated responses of the top-3 models from the test set to three different crowdsourced workers and ask them to rank the responses based on their relevance to the dialog history. For the rating task, we show the model responses individually to three different crowdsourced workers and ask them to rate the responses on a 1-5 LIKERT scale based on their appropriateness to the dialog history. From Table-TABREF56, we see that inter-annotator reliability scores (Krippendorf\u2019s Alpha) are higher for the ranking task compared to the rating task. From Table TABREF55, we see that Transformer is the best performing model on automatic evaluation metrics. It is interesting to note that there is a strong correlation between BLEU score and human ranking judgments."
+    ],
+    [
+      "Next, we discuss a set of baseline experiments for the task of argument prediction. API arguments are annotated as spans in the dialog (Section SECREF48). We formulate this problem as mapping text conversation to a sequence of output arguments. Apart from the seq2seq Transformer baseline, we consider an additional model - an enhanced Transformer seq2seq model where the decoder can choose to copy from the input or generate from the vocabulary BIBREF31, BIBREF32. Since all the API arguments are input spans, the copy model having the correct inductive bias achieves the best performance."
+    ],
+    [
+      "To address the lack of quality corpora for data-driven dialog system research and development, this paper introduces Taskmaster-1, a dataset that provides richer and more diverse language as compared to current benchmarks since it is based on unrestricted, task-oriented conversations involving more real-word entities. In addition, we present two data collection methodologies, both spoken and written, that ensure both speaker diversity and conversational accuracy. Our straightforward, API-oriented annotation technique is much easier for annotators to learn and simpler to apply. We give several baseline models including state-of-the-art neural seq2seq architectures, provide qualitative human performance evaluations for these models, and find that automatic evaluation metrics correlate well with human judgments."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0726/instruction.md b/qasper-0726/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..4599921cc00ab6f5a4b0395977183c1c6977c2fe
--- /dev/null
+++ b/qasper-0726/instruction.md
@@ -0,0 +1,155 @@
+Name of Paper: e-SNLI-VE-2.0: Corrected Visual-Textual Entailment with Natural Language Explanations
+
+Question: Is model explanation output evaluated, what metric was used?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "SNLI-VE-2.0",
+    "SNLI-VE-2.0 ::: Re-annotation details",
+    "SNLI-VE-2.0 ::: Re-evaluation of Visual-Textual Entailment",
+    "SNLI-VE-2.0 ::: Re-evaluation of Visual-Textual Entailment ::: Model.",
+    "SNLI-VE-2.0 ::: Re-evaluation of Visual-Textual Entailment ::: Results.",
+    "Visual-Textual Entailment with Natural Language Explanations",
+    "Visual-Textual Entailment with Natural Language Explanations ::: e-SNLI-VE-2.0",
+    "Visual-Textual Entailment with Natural Language Explanations ::: Collecting Explanations",
+    "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations",
+    "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Predict and Explain",
+    "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Predict and Explain ::: Model.",
+    "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Predict and Explain ::: Loss.",
+    "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Predict and Explain ::: Model selection.",
+    "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Predict and Explain ::: Results.",
+    "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Explain Then Predict",
+    "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Explain Then Predict ::: Model.",
+    "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Explain Then Predict ::: Model selection.",
+    "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Explain Then Predict ::: Results.",
+    "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Qualitative Analysis of Generated Explanations",
+    "Conclusion",
+    "Conclusion ::: Acknowledgements.",
+    "Appendix ::: Statistics of e-SNLI-VE-2.0",
+    "Appendix ::: Details of the Mechanical Turk Task",
+    "Appendix ::: Ambiguous Examples from SNLI-VE"
+  ],
+  "paragraphs": [
+    [
+      "Inspired by textual entailment BIBREF0, Xie BIBREF1 introduced the visual-textual entailment (VTE) task, which considers semantic entailment between a premise image and a textual hypothesis. Semantic entailment consists in determining if the hypothesis can be concluded from the premise, and assigning to each pair of (premise image, textual hypothesis) a label among entailment, neutral, and contradiction. In Figure FIGREF3, the label for the first image-sentence pair is entailment, because the hypothesis states that \u201ca bunch of people display different flags\u201d, which can be clearly derived from the image. On the contrary, the second image-sentence pair is labelled as contradiction, because the hypothesis stating that \u201cpeople [are] running a marathon\u201d contradicts the image with static people.",
+      "Xie also propose the SNLI-VE dataset as the first dataset for VTE. SNLI-VE is built from the textual entailment SNLI dataset BIBREF0 by replacing textual premises with the Flickr30k images that they originally described BIBREF2. However, images contain more information than their descriptions, which may entail or contradict the textual hypotheses (see Figure FIGREF3). As a result, the neutral class in SNLI-VE has substantial labelling errors. Vu BIBREF3 estimated ${\\sim }31\\%$ errors in this class, and ${\\sim }1\\%$ for the contradiction and entailment classes.",
+      "Xie BIBREF1 introduced the VTE task under the name of \u201cvisual entailment\u201d, which could imply recognizing entailment between images only. This paper prefers to follow Suzuki BIBREF4 and call it \u201cvisual-textual entailment\u201d instead, as it involves reasoning on image-sentence pairs.",
+      "In this work, we first focus on decreasing the error in the neutral class by collecting new labels for the neutral pairs in the validation and test sets of SNLI-VE, using Amazon Mechanical Turk (MTurk). To ensure high quality annotations, we used a series of quality control measures, such as in-browser checks, inserting trusted examples, and collecting three annotations per instance. Secondly, we re-evaluate current image-text understanding systems, such as the bottom-up top-down attention network (BUTD) BIBREF5 on VTE using our corrected dataset, which we call SNLI-VE-2.0.",
+      "Thirdly, we introduce the e-SNLI-VE-2.0 corpus, which we form by appending human-written natural language explanations to SNLI-VE-2.0. These explanations were collected in e-SNLI BIBREF6 to support textual entailment for SNLI. For the same reasons as above, we re-annotate the explanations for the neutral pairs in the validation and test sets, while keeping the explanations from e-SNLI for all the rest. Finally, we extend a current VTE model with the capacity of learning from these explanations at training time and outputting an explanation for each predicted label at testing time."
+    ],
+    [
+      "The goal of VTE is to determine if a textual hypothesis $H_{text}$ can be concluded, given the information in a premise image $P_{image}$ BIBREF1. There are three possible labels:",
+      "Entailment: if there is enough evidence in $P_{image}$ to conclude that $H_{text}$ is true.",
+      "Contradiction: if there is enough evidence in $P_{image}$ to conclude that $H_{text}$ is false.",
+      "Neutral: if neither of the earlier two are true.",
+      "The SNLI-VE dataset proposed by Xie BIBREF1 is the combination of Flickr30k, a popular image dataset for image captioning BIBREF2 and SNLI, an influential dataset for natural language inference BIBREF0. Textual premises from SNLI are replaced with images from Flickr30k, which is possible, as these premises were originally collected as captions of these images (see Figure FIGREF3).",
+      "However, in practice, a sensible proportion of labels are wrong due to the additional information contained in images. This mostly affects neutral pairs, since images may contain the necessary information to ground a hypothesis for which a simple premise caption was not sufficient. An example is shown in Figure FIGREF3. Vu BIBREF3 report that the label is wrong for ${\\sim }31\\%$ of neutral examples, based on a random subset of 171 neutral points from the test set. We also annotated 150 random neutral examples from the test set and found a similar percentage of 30.6% errors.",
+      "Our annotations are available at https://github.com/virginie-do/e-SNLI-VE/tree/master/annotations/gt_labels.csv"
+    ],
+    [
+      "In this work, we only collect new labels for the neutral pairs in the validation and test sets of SNLI-VE. While the procedure of re-annotation is generic, we limit our re-annotation to these splits as a first step to verify the difference in performance that current models have when evaluated on the corrected test set as well as the effect of model selection on the corrected validation set. We leave for future work re-annotation of the training set, which would likely lead to training better VTE models. We also chose not to re-annotate entailment and contradiction classes, as their error rates are much lower ($<$1% as reported by Vu BIBREF3).",
+      "The main question that we want our dataset to answer is: \u201cWhat is the relationship between the image premise and the sentence hypothesis?\u201d. We provide workers with the definitions of entailment, neutral, and contradiction for image-sentence pairs and one example for each label. As shown in Figure FIGREF8, for each image-sentence pair, workers are required to (a) choose a label, (b) highlight words in the sentence that led to their decision, and (c) explain their decision in a comprehensive and concise manner, using at least half of the words that they highlighted. The collected explanations will be presented in more detail in Section SECREF20, as we focus here on the label correction. We point out that it is likely that requiring an explanation at the same time as requiring a label has a positive effect on the correctness of the label, since having to justify in writing the picked label may make workers pay an increased attention. Moreover, we implemented additional quality control measures for crowdsourced annotations, such as (a) collecting three annotations for every input, (b) injecting trusted annotations into the task for verification BIBREF7, and (c) restricting to workers with at least 90% previous approval rate.",
+      "First, we noticed that some instances in SNLI-VE are ambiguous. We show some examples in Figure FIGREF3 and in Appendix SECREF43. In order to have a better sense of this ambiguity, three authors of this paper independently annotated 100 random examples. All three authors agreed on 54% of the examples, exactly two authors agreed on 45%, and there was only one example on which all three authors disagreed. We identified the following three major sources of ambiguity:",
+      "mapping an emotion in the hypothesis to a facial expression in the image premise, e.g., \u201cpeople enjoy talking\u201d, \u201cangry people\u201d, \u201csad woman\u201d. Even when the face is seen, it may be subjective to infer an emotion from a static image (see Figure FIGREF44 in Appendix SECREF43).",
+      "personal taste, e.g., \u201cthe sign is ugly\u201d.",
+      "lack of consensus on terms such as \u201cmany people\u201d or \u201ccrowded\u201d.",
+      "To account for the ambiguity that the neutral labels seem to present, we considered that an image-sentence pair is too ambiguous and not suitable for a well-defined visual-textual entailment task when three different labels were assigned by the three workers. Hence, we removed these examples from the validation (5.2%) and test (5.5%) sets.",
+      "To ensure that our workers are correctly performing the task, we randomly inserted trusted pairs, i.e., pairs among the 54% on which all three authors agreed on the label. For each set of 10 pairs presented to a worker, one trusted pair was introduced at a random location, so that the worker, while being told that there is such a test pair, cannot figure out which one it is. Via an in-browser check, we only allow workers to submit their answers for each set of 10 instances only if the trusted pair was correctly labelled. Other in-browser checks were done for the collection of explanations, as we will describe in Section SECREF20. More details about the participants and design of the Mechanical Turk task can be found in Appendix SECREF41.",
+      "After collecting new labels for the neutral instances in the validation and testing sets, we randomly select and annotate 150 instances from the validation set that were neutral in SNLI-VE. Based on this sample, the error rate went down from 31% to 12% in SNLI-VE-2.0. Looking at the 18 instances where we disagreed with the label assigned by MTurk workers, we noticed that 12 were due to ambiguity in the examples, and 6 were due to workers' errors. Further investigation into potentially eliminating ambiguous instances would likely be beneficial. However, we leave it as future work, and we proceed in this work with using our corrected labels, since our error rate is significantly lower than that of the original SNLI-VE.",
+      "Finally, we note that only about 62% of the originally neutral pairs remain neutral, while 21% become contradiction and 17% entailment pairs. Therefore, we are now facing an imbalance between the neutral, entailment, and contradiction instances in the validation and testing sets of SNLI-VE-2.0. The neutral class becomes underrepresented and the label distributions in the corrected validation and testing sets both become E / N / C: 39% / 20% / 41%. To account for this, we compute the balanced accuracy, i.e., the average of the three accuracies on each class."
+    ],
+    [
+      "Since we decreased the error rate of labels in the validation and test set, we are interested in the performance of a VTE model when using the corrected sets."
+    ],
+    [
+      "To tackle SNLI-VE, Xie BIBREF1 used EVE (for \u201cExplainable Visual Entailment\u201d), a modified version of the BUTD architecture, the winner of the Visual Question Answering (VQA) challenge in 2017 BIBREF5. Since the EVE implementation is not available at the time of this work, we used the original BUTD architecture, with the same hyperparameters as reported in BIBREF1.",
+      "BUTD contains an image processing module and a text processing module. The image processing module encodes each image region proposed by FasterRCNN BIBREF8 into a feature vector using a bottom-up attention mechanism. In the text processing module, the text hypothesis is encoded into a fixed-length vector, which is the last output of a recurrent neural network with 512-GRU units BIBREF9. To input each token into the recurrent network, we use the pretrained GloVe vectors BIBREF10. Finally, a top-down attention mechanism is used between the hypothesis vector and each of the image region vectors to obtain an attention weight for each region. The weighted sum of these image region vectors is then fused with the text hypothesis vector. The multimodal fusion is fed to a multilayer percetron (MLP) with tanh activations and a final softmax layer to classify the image-sentence relation as entailment, contradiction, or neutral.",
+      "Using the implementation from https://github.com/claudiogreco/coling18-gte.",
+      "We use the original training set from SNLI-VE. To see the impact of correcting the validation and test sets, we do the following three experiments:",
+      "model selection as well as testing are done on the original uncorrected SNLI-VE.",
+      "model selection is done on the uncorrected SNLI-VE validation set, while testing is done on the corrected SNLI-VE-2.0 test set.",
+      "model selection as well as testing are done on the corrected SNLI-VE-2.0.",
+      "Models are trained with cross-entropy loss optimized by the Adam optimizer BIBREF11 with batch size 64. The maximum number of training epochs is set to 100, with early stopping when no improvement is observed on validation accuracy for 3 epochs. The final model checkpoint selected for testing is the one with the highest validation accuracy."
+    ],
+    [
+      "The results of the three experiments enumerated above are reported in Table TABREF18. Surprisingly, we obtained an accuracy of 73.02% on SNLI-VE using BUTD, which is better than the 71.16% reported by Xie BIBREF1 for the EVE system which meant to be an improvement over BUTD. It is also better than their reproduction of BUTD, which gave 68.90%.",
+      "The same BUTD model that achieves 73.02% on the uncorrected SNLI-VE test set, achieves 73.18% balanced accuracy when tested on the corrected test set from SNLI-VE-2.0. Hence, for this model, we do not notice a significant difference in performance. This could be due to randomness. Finally, when we run the training loop again, this time doing the model selection on the corrected validation set from SNLI-VE-2.0, we obtain a slightly worse performance of 72.52%, although the difference is not clearly significant.",
+      "Finally, we recall that the training set has not been re-annotated, and hence approximately 31% image-sentence pairs are wrongly labelled as neutral, which likely affects the performance of the model."
+    ],
+    [
+      "In this work, we also introduce e-SNLI-VE-2.0, a dataset combining SNLI-VE-2.0 with human-written explanations from e-SNLI BIBREF6, which were originally collected to support textual entailment. We replace the explanations for the neutral pairs in the validation and test sets with new ones collected at the same time as the new labels. We extend a current VTE model with an explanation module able to learn from these explanations at training time and generate an explanation for each predicted label at testing time."
+    ],
+    [
+      "e-SNLI BIBREF6 is an extension of the SNLI corpus with human-annotated natural language explanations for the ground-truth labels. The authors use the explanations to train models to also generate natural language justifications for their predictions. They collected one explanation for each instance in the training set of SNLI and three explanations for each instance in the validation and testing sets.",
+      "We randomly selected 100 image-sentence pairs in the validation set of SNLI-VE and their corresponding explanations in e-SNLI and examined how relevant these explanations are for the VTE task. More precisely, we say that an explanation is relevant if it brings information that justifies the relationship between the image and the sentence. We restricted the count to correctly labelled inputs and found that 57% explanations were relevant. For example, the explanation for entailment in Figure FIGREF21 (\u201cCooking in his apartment is cooking\u201d) was counted as irrelevant in our statistics, because it would not be the best explanation for an image-sentence pair, even though it is coherent with the textual pair. We investigate whether these explanations improve a VTE model when enhanced with a component that can process explanations at train time and output them at test time.",
+      "To form e-SNLI-VE-2.0, we append to SNLI-VE-2.0 the explanations from e-SNLI for all except the neutral pairs in the validation and test sets of SNLI-VE, which we replace with newly crowdsourced explanations collected at the same time as the labels for these splits (see Figure FIGREF21). Statistics of e-SNLI-VE-2.0 are shown in Appendix SECREF39, Table TABREF40."
+    ],
+    [
+      "As mentioned before, in order to submit the annotation of an image-sentence pair, three steps must be completed: workers must choose a label, highlight words in the hypothesis, and use at least half of the highlighted words to write an explanation for their decision. The last two steps thus follow the quality control of crowd-sourced explanations introduced by Camburu BIBREF6. We also ensured that workers do not simply use a copy of the given hypothesis as explanation. We ensured all the above via in-browser checks before workers' submission. An example of collected explanations is given in Figure FIGREF21.",
+      "To check the success of our crowdsourcing, we manually assessed the relevance of explanations among a random subset of 100 examples. A marking scale between 0 and 1 was used, assigning a score of $k$/$n$ when $k$ required attributes were given in an explanation out of $n$. We report an 83.5% relevance of explanations from workers. We note that, since our explanations are VTE-specific, they were phrased differently from the ones in e-SNLI, with more specific mentions to the images (e.g., \u201cThere is no labcoat in the picture, just a man wearing a blue shirt.\u201d, \u201cThere are no apples or oranges shown in the picture, only bananas.\u201d). Therefore, it would likely be beneficial to collect new explanations for all SNLI-VE-2.0 (not only for the neutral pairs in the validation and test sets) such that models can learn to output convincing explanations for the task at hand. However, we leave this as future work, and we show in this work the results that one obtains when using the explanations from e-SNLI-VE-2.0."
+    ],
+    [
+      "This section presents two VTE models that generate natural language explanations for their own decisions. We name them PaE-BUTD-VE and EtP-BUTD-VE, where PaE (resp. EtP) is for PredictAndExplain (resp. ExplainThenPredict), two models with similar principles introduced by Camburu BIBREF6. The first system learns to generate an explanation conditioned on the image premise, textual hypothesis, and predicted label. In contrast, the second system learns to first generate an explanation conditioned on the image premise and textual hypothesis, and subsequently makes a prediction solely based on the explanation."
+    ],
+    [
+      "PaE-BUTD-VE is a system for solving VTE and generating natural language explanations for the predicted labels. The explanations are conditioned on the image premise, the text hypothesis, and the predicted label (ground-truth label at train time), as shown in Figure FIGREF24."
+    ],
+    [
+      "As described in Section SECREF12, in the BUTD model, the hypothesis vector and the image vector were fused in a fixed-size feature vector f. The vector f was then given as input to an MLP which outputs a probability distribution over the three labels. In PaE-BUTD-VE, in addition to the classification layer, we add a 512-LSTM BIBREF12 decoder to generate an explanation. The decoder takes the feature vector f as initial state. Following Camburu BIBREF6, we prepend the label as a token at the beginning of the explanation to condition the explanation on the label. The ground truth label is provided at training time, whereas the predicted label is given at test time.",
+      "At test time, we use beam search with a beam width of 3 to decode explanations. For memory and time reduction, we replaced words that appeared less than 15 times among explanations with \u201c#UNK#\u201d. This strategy reduces the output vocabulary size to approximately 8.6k words."
+    ],
+    [
+      "The training loss is a weighted combination of the classification loss and the explanation loss, both computed using softmax cross entropy: $\\mathcal {L} = \\alpha \\mathcal {L}_{label} + (1-\\alpha ) \\mathcal {L}_{explanation} \\; \\textrm {;} \\; \\alpha \\in [0,1]$."
+    ],
+    [
+      "In this experiment, we are first interested in examining if a neural network can generate explanations at no cost for label accuracy. Therefore, only balanced accuracy on label is used for the model selection criterion. However, future work can investigate other selection criteria involving a combination between the label and explanation performances. We performed hyperparameter search on $\\alpha $, considering values between 0.2 and 0.8 with a step of 0.2. We found $\\alpha =0.4$ to produce the best validation balanced accuracy of 72.81%, while BUTD trained without explanations yielded a similar 72.58% validation balanced accuracy."
+    ],
+    [
+      "As summarised in Table TABREF30, we obtain a test balanced accuracy for PaE-BUTD-VE of 73%, while the same model trained without explanations obtains 72.52%. This is encouraging, since it shows that one can obtain additional natural language explanations without sacrificing performance (and eventually even improving the label performance, however, future work is needed to conclude whether the difference $0.48\\%$ improvement in performance is statistically significant).",
+      "Camburu BIBREF6 mentioned that the BLEU score was not an appropriate measure for the quality of explanations and suggested human evaluation instead. We therefore manually scored the relevance of 100 explanations that were generated when the model predicted correct labels. We found that only 20% of explanations were relevant. We highlight that the relevance of explanations is in terms of whether the explanation reflects ground-truth reasons supporting the correct label. This is not to be confused with whether an explanation is correctly illustrating the inner working of the model, which is left as future work. It is also important to note that on a similar experimental setting, Camburu report as low as 34.68% correct explanations, training with explanations that were actually collected for their task. Lastly, the model selection criterion at validation time was the prediction balanced accuracy, which may contribute to the low quality of explanations. While we show that adding an explanation module does not harm prediction performance, more work is necessary to get models that output trustable explanations."
+    ],
+    [
+      "When assigning a label, an explanation is naturally part of the decision-making process. This motivates the design of a system that explains itself before deciding on a label, called EtP-BUTD-VE. For this system, a first neural network is trained to generate an explanation given an image-sentence input. Separately, a second neural network, called ExplToLabel-VE, is trained to predict a label from an explanation (see Figure FIGREF32)."
+    ],
+    [
+      "For the first network, we set $\\alpha =0$ in the training loss of the PaE-BUTD-VE model to obtain a system that only learns to generate an explanation from the image-sentence input, without label prediction. Hence, in this setting, no label is prepended before the explanation.",
+      "For the ExplToLabel-VE model, we use a 512-LSTM followed by an MLP with three 512-layers and ReLU activation, and softmax activation to classify the explanation between entailment, contradiction, and neutral."
+    ],
+    [
+      "For ExplToLabel-VE, the best model is selected on balanced accuracy at validation time. For EtP-BUTD-VE, perplexity is used to select the best model parameters at validation time. It is computed between the explanations produced by the LSTM and ground truth explanations from the validation set."
+    ],
+    [
+      "When we train ExplToLabel-VE on e-SNLI-VE-2.0, we obtain a balanced accuracy of 90.55% on the test set.",
+      "As reported in Table TABREF30, the overall PaE-BUTD-VE system achieves 69.40% balanced accuracy on the test set of e-SNLI-VE-2.0, which is a 3% decrease from the non-explanatory BUTD counterpart (72.52%). However, by setting $\\alpha $ to zero and selecting the model that gives the best perplexity per word at validation, the quality of explanation significantly increased, with 35% relevance, based on manual evaluation. Thus, in our model, generating better explanations involves a small sacrifice in label prediction accuracy, implying a trade-off between explanation generation and accuracy.",
+      "We note that there is room for improvement in our explanation generation method. For example, one can implement an attention mechanism similar to Xu BIBREF13, so that each generated word relates to a relevant part of the multimodal feature representation."
+    ],
+    [
+      "We complement our quantitative results with a qualitative analysis of the explanations generated by our enhanced VTE systems. In Figures FIGREF36 and FIGREF37, we present examples of the predicted labels and generated explanations.",
+      "Figure FIGREF36 shows an example where the EtP-BUTD-VE model produces both a correct label and a relevant explanation. The label is contradiction, because in the image, the students are playing with a soccer ball and not a basketball, thus contradicting the text hypothesis. Given the composition of the generated sentence (\u201cStudents cannot be playing soccer and baseball at the same time.\u201d), ExplToLabel-VE was able to detect a contradiction in the image-sentence input. In comparison, the explanation from e-SNLI-VE-2.0 is not correct, even if it was valid for e-SNLI when the text premise was given. This emphasizes the difficulty that we are facing with generating proper explanations when training on a noisy dataset.",
+      "Even when the generated explanations are irrelevant, we noticed that they are on-topic and that most of the time the mistakes come from repetitions of certain sub-phrases. For example, in Figure FIGREF37, PaE-BUTD-VE predicts the label neutral, which is correct, but the explanation contains an erroneous repetition of the n-gram \u201care in a car\u201d. However, it appears that the system learns to generate a sentence in the form \u201cJust because ...doesn't mean ...\u201d, which is frequently found for the justification of neutral pairs in the training set. The explanation generated by EtP-BUTD-VE adopts the same structure, and the ExplToLabel-VE component correctly classifies the instance as neutral. However, even if the explanation is semantically correct, it is not relevant for the input and fails to explain the classification."
+    ],
+    [
+      "In this paper, we first presented SNLI-VE-2.0, which corrects the neutral instances in the validation and test sets of SNLI-VE. Secondly, we re-evaluated an existing model on the corrected sets in order to update the estimate of its performance on this task. Thirdly, we introduced e-SNLI-VE-2.0, a dataset which extends SNLI-VE-2.0 with natural language explanations. Finally, we trained two types of models that learn from these explanations at training time, and output such explanations at test time, as a stepping stone in explainable artificial intelligence. Our work is a jumping-off point for both the identification and correction of SNLI-VE, as well as in the extension to explainable VTE. We hope that the community will build on our findings to create more robust as well as explainable multimodal systems."
+    ],
+    [
+      "This work was supported by the Oxford Internet Institute, a JP Morgan PhD Fellowship 2019-2020, an Oxford-DeepMind Graduate Scholarship, the Alan Turing Institute under the EPSRC grant EP/N510129/1, and the AXA Research Fund, as well as DFG-EXC-Nummer 2064/1-Projektnummer 390727645 and the ERC under the Horizon 2020 program (grant agreement No. 853489)."
+    ],
+    [
+      "e-SNLI-VE-2.0 is the combination of SNLI-VE-2.0 with explanations from either e-SNLI or our crowdsourced annotations where applicable. The statistics of e-SNLI-VE-2.0 are shown in Table TABREF40.",
+      "Including text hypotheses and explanations."
+    ],
+    [
+      "We used Amazon Mechanical Turk (MTurk) to collect new labels and explanations for SNLI-VE. 2,060 workers participated in the annotation effort, with an average of 1.98 assignments per worker and a standard deviation of 5.54. We required the workers to have a previous approval rate above 90%. No restriction was put on the workers' location.",
+      "Each assignment consisted of a set of 10 image-sentence pairs. For each pair, the participant was asked to (a) choose a label, (b) highlight words in the sentence that led to their decision, and (c) explain their decision in a comprehensive and concise manner, using a subset of the words that they highlighted. The instructions are shown in Figure FIGREF42. Workers were also guided with three annotated examples, one for each label.",
+      "For each assignment of 10 questions, one trusted annotation with gold standard label was inserted at a random position, as a measure to control the quality of label annotation. Each assignment was completed by three different workers. An example of question is shown in Figure FIGREF8 in the core paper."
+    ],
+    [
+      "Some examples in SNLI-VE were ambiguous and could find correct justifications for incompatible labels, as shown in Figures FIGREF44, FIGREF45, and FIGREF46."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0727/instruction.md b/qasper-0727/instruction.md
new file mode 100644
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+++ b/qasper-0727/instruction.md
@@ -0,0 +1,155 @@
+Name of Paper: e-SNLI-VE-2.0: Corrected Visual-Textual Entailment with Natural Language Explanations
+
+Question: How many annotators are used to write natural language explanations to SNLI-VE-2.0?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "SNLI-VE-2.0",
+    "SNLI-VE-2.0 ::: Re-annotation details",
+    "SNLI-VE-2.0 ::: Re-evaluation of Visual-Textual Entailment",
+    "SNLI-VE-2.0 ::: Re-evaluation of Visual-Textual Entailment ::: Model.",
+    "SNLI-VE-2.0 ::: Re-evaluation of Visual-Textual Entailment ::: Results.",
+    "Visual-Textual Entailment with Natural Language Explanations",
+    "Visual-Textual Entailment with Natural Language Explanations ::: e-SNLI-VE-2.0",
+    "Visual-Textual Entailment with Natural Language Explanations ::: Collecting Explanations",
+    "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations",
+    "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Predict and Explain",
+    "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Predict and Explain ::: Model.",
+    "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Predict and Explain ::: Loss.",
+    "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Predict and Explain ::: Model selection.",
+    "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Predict and Explain ::: Results.",
+    "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Explain Then Predict",
+    "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Explain Then Predict ::: Model.",
+    "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Explain Then Predict ::: Model selection.",
+    "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Explain Then Predict ::: Results.",
+    "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Qualitative Analysis of Generated Explanations",
+    "Conclusion",
+    "Conclusion ::: Acknowledgements.",
+    "Appendix ::: Statistics of e-SNLI-VE-2.0",
+    "Appendix ::: Details of the Mechanical Turk Task",
+    "Appendix ::: Ambiguous Examples from SNLI-VE"
+  ],
+  "paragraphs": [
+    [
+      "Inspired by textual entailment BIBREF0, Xie BIBREF1 introduced the visual-textual entailment (VTE) task, which considers semantic entailment between a premise image and a textual hypothesis. Semantic entailment consists in determining if the hypothesis can be concluded from the premise, and assigning to each pair of (premise image, textual hypothesis) a label among entailment, neutral, and contradiction. In Figure FIGREF3, the label for the first image-sentence pair is entailment, because the hypothesis states that \u201ca bunch of people display different flags\u201d, which can be clearly derived from the image. On the contrary, the second image-sentence pair is labelled as contradiction, because the hypothesis stating that \u201cpeople [are] running a marathon\u201d contradicts the image with static people.",
+      "Xie also propose the SNLI-VE dataset as the first dataset for VTE. SNLI-VE is built from the textual entailment SNLI dataset BIBREF0 by replacing textual premises with the Flickr30k images that they originally described BIBREF2. However, images contain more information than their descriptions, which may entail or contradict the textual hypotheses (see Figure FIGREF3). As a result, the neutral class in SNLI-VE has substantial labelling errors. Vu BIBREF3 estimated ${\\sim }31\\%$ errors in this class, and ${\\sim }1\\%$ for the contradiction and entailment classes.",
+      "Xie BIBREF1 introduced the VTE task under the name of \u201cvisual entailment\u201d, which could imply recognizing entailment between images only. This paper prefers to follow Suzuki BIBREF4 and call it \u201cvisual-textual entailment\u201d instead, as it involves reasoning on image-sentence pairs.",
+      "In this work, we first focus on decreasing the error in the neutral class by collecting new labels for the neutral pairs in the validation and test sets of SNLI-VE, using Amazon Mechanical Turk (MTurk). To ensure high quality annotations, we used a series of quality control measures, such as in-browser checks, inserting trusted examples, and collecting three annotations per instance. Secondly, we re-evaluate current image-text understanding systems, such as the bottom-up top-down attention network (BUTD) BIBREF5 on VTE using our corrected dataset, which we call SNLI-VE-2.0.",
+      "Thirdly, we introduce the e-SNLI-VE-2.0 corpus, which we form by appending human-written natural language explanations to SNLI-VE-2.0. These explanations were collected in e-SNLI BIBREF6 to support textual entailment for SNLI. For the same reasons as above, we re-annotate the explanations for the neutral pairs in the validation and test sets, while keeping the explanations from e-SNLI for all the rest. Finally, we extend a current VTE model with the capacity of learning from these explanations at training time and outputting an explanation for each predicted label at testing time."
+    ],
+    [
+      "The goal of VTE is to determine if a textual hypothesis $H_{text}$ can be concluded, given the information in a premise image $P_{image}$ BIBREF1. There are three possible labels:",
+      "Entailment: if there is enough evidence in $P_{image}$ to conclude that $H_{text}$ is true.",
+      "Contradiction: if there is enough evidence in $P_{image}$ to conclude that $H_{text}$ is false.",
+      "Neutral: if neither of the earlier two are true.",
+      "The SNLI-VE dataset proposed by Xie BIBREF1 is the combination of Flickr30k, a popular image dataset for image captioning BIBREF2 and SNLI, an influential dataset for natural language inference BIBREF0. Textual premises from SNLI are replaced with images from Flickr30k, which is possible, as these premises were originally collected as captions of these images (see Figure FIGREF3).",
+      "However, in practice, a sensible proportion of labels are wrong due to the additional information contained in images. This mostly affects neutral pairs, since images may contain the necessary information to ground a hypothesis for which a simple premise caption was not sufficient. An example is shown in Figure FIGREF3. Vu BIBREF3 report that the label is wrong for ${\\sim }31\\%$ of neutral examples, based on a random subset of 171 neutral points from the test set. We also annotated 150 random neutral examples from the test set and found a similar percentage of 30.6% errors.",
+      "Our annotations are available at https://github.com/virginie-do/e-SNLI-VE/tree/master/annotations/gt_labels.csv"
+    ],
+    [
+      "In this work, we only collect new labels for the neutral pairs in the validation and test sets of SNLI-VE. While the procedure of re-annotation is generic, we limit our re-annotation to these splits as a first step to verify the difference in performance that current models have when evaluated on the corrected test set as well as the effect of model selection on the corrected validation set. We leave for future work re-annotation of the training set, which would likely lead to training better VTE models. We also chose not to re-annotate entailment and contradiction classes, as their error rates are much lower ($<$1% as reported by Vu BIBREF3).",
+      "The main question that we want our dataset to answer is: \u201cWhat is the relationship between the image premise and the sentence hypothesis?\u201d. We provide workers with the definitions of entailment, neutral, and contradiction for image-sentence pairs and one example for each label. As shown in Figure FIGREF8, for each image-sentence pair, workers are required to (a) choose a label, (b) highlight words in the sentence that led to their decision, and (c) explain their decision in a comprehensive and concise manner, using at least half of the words that they highlighted. The collected explanations will be presented in more detail in Section SECREF20, as we focus here on the label correction. We point out that it is likely that requiring an explanation at the same time as requiring a label has a positive effect on the correctness of the label, since having to justify in writing the picked label may make workers pay an increased attention. Moreover, we implemented additional quality control measures for crowdsourced annotations, such as (a) collecting three annotations for every input, (b) injecting trusted annotations into the task for verification BIBREF7, and (c) restricting to workers with at least 90% previous approval rate.",
+      "First, we noticed that some instances in SNLI-VE are ambiguous. We show some examples in Figure FIGREF3 and in Appendix SECREF43. In order to have a better sense of this ambiguity, three authors of this paper independently annotated 100 random examples. All three authors agreed on 54% of the examples, exactly two authors agreed on 45%, and there was only one example on which all three authors disagreed. We identified the following three major sources of ambiguity:",
+      "mapping an emotion in the hypothesis to a facial expression in the image premise, e.g., \u201cpeople enjoy talking\u201d, \u201cangry people\u201d, \u201csad woman\u201d. Even when the face is seen, it may be subjective to infer an emotion from a static image (see Figure FIGREF44 in Appendix SECREF43).",
+      "personal taste, e.g., \u201cthe sign is ugly\u201d.",
+      "lack of consensus on terms such as \u201cmany people\u201d or \u201ccrowded\u201d.",
+      "To account for the ambiguity that the neutral labels seem to present, we considered that an image-sentence pair is too ambiguous and not suitable for a well-defined visual-textual entailment task when three different labels were assigned by the three workers. Hence, we removed these examples from the validation (5.2%) and test (5.5%) sets.",
+      "To ensure that our workers are correctly performing the task, we randomly inserted trusted pairs, i.e., pairs among the 54% on which all three authors agreed on the label. For each set of 10 pairs presented to a worker, one trusted pair was introduced at a random location, so that the worker, while being told that there is such a test pair, cannot figure out which one it is. Via an in-browser check, we only allow workers to submit their answers for each set of 10 instances only if the trusted pair was correctly labelled. Other in-browser checks were done for the collection of explanations, as we will describe in Section SECREF20. More details about the participants and design of the Mechanical Turk task can be found in Appendix SECREF41.",
+      "After collecting new labels for the neutral instances in the validation and testing sets, we randomly select and annotate 150 instances from the validation set that were neutral in SNLI-VE. Based on this sample, the error rate went down from 31% to 12% in SNLI-VE-2.0. Looking at the 18 instances where we disagreed with the label assigned by MTurk workers, we noticed that 12 were due to ambiguity in the examples, and 6 were due to workers' errors. Further investigation into potentially eliminating ambiguous instances would likely be beneficial. However, we leave it as future work, and we proceed in this work with using our corrected labels, since our error rate is significantly lower than that of the original SNLI-VE.",
+      "Finally, we note that only about 62% of the originally neutral pairs remain neutral, while 21% become contradiction and 17% entailment pairs. Therefore, we are now facing an imbalance between the neutral, entailment, and contradiction instances in the validation and testing sets of SNLI-VE-2.0. The neutral class becomes underrepresented and the label distributions in the corrected validation and testing sets both become E / N / C: 39% / 20% / 41%. To account for this, we compute the balanced accuracy, i.e., the average of the three accuracies on each class."
+    ],
+    [
+      "Since we decreased the error rate of labels in the validation and test set, we are interested in the performance of a VTE model when using the corrected sets."
+    ],
+    [
+      "To tackle SNLI-VE, Xie BIBREF1 used EVE (for \u201cExplainable Visual Entailment\u201d), a modified version of the BUTD architecture, the winner of the Visual Question Answering (VQA) challenge in 2017 BIBREF5. Since the EVE implementation is not available at the time of this work, we used the original BUTD architecture, with the same hyperparameters as reported in BIBREF1.",
+      "BUTD contains an image processing module and a text processing module. The image processing module encodes each image region proposed by FasterRCNN BIBREF8 into a feature vector using a bottom-up attention mechanism. In the text processing module, the text hypothesis is encoded into a fixed-length vector, which is the last output of a recurrent neural network with 512-GRU units BIBREF9. To input each token into the recurrent network, we use the pretrained GloVe vectors BIBREF10. Finally, a top-down attention mechanism is used between the hypothesis vector and each of the image region vectors to obtain an attention weight for each region. The weighted sum of these image region vectors is then fused with the text hypothesis vector. The multimodal fusion is fed to a multilayer percetron (MLP) with tanh activations and a final softmax layer to classify the image-sentence relation as entailment, contradiction, or neutral.",
+      "Using the implementation from https://github.com/claudiogreco/coling18-gte.",
+      "We use the original training set from SNLI-VE. To see the impact of correcting the validation and test sets, we do the following three experiments:",
+      "model selection as well as testing are done on the original uncorrected SNLI-VE.",
+      "model selection is done on the uncorrected SNLI-VE validation set, while testing is done on the corrected SNLI-VE-2.0 test set.",
+      "model selection as well as testing are done on the corrected SNLI-VE-2.0.",
+      "Models are trained with cross-entropy loss optimized by the Adam optimizer BIBREF11 with batch size 64. The maximum number of training epochs is set to 100, with early stopping when no improvement is observed on validation accuracy for 3 epochs. The final model checkpoint selected for testing is the one with the highest validation accuracy."
+    ],
+    [
+      "The results of the three experiments enumerated above are reported in Table TABREF18. Surprisingly, we obtained an accuracy of 73.02% on SNLI-VE using BUTD, which is better than the 71.16% reported by Xie BIBREF1 for the EVE system which meant to be an improvement over BUTD. It is also better than their reproduction of BUTD, which gave 68.90%.",
+      "The same BUTD model that achieves 73.02% on the uncorrected SNLI-VE test set, achieves 73.18% balanced accuracy when tested on the corrected test set from SNLI-VE-2.0. Hence, for this model, we do not notice a significant difference in performance. This could be due to randomness. Finally, when we run the training loop again, this time doing the model selection on the corrected validation set from SNLI-VE-2.0, we obtain a slightly worse performance of 72.52%, although the difference is not clearly significant.",
+      "Finally, we recall that the training set has not been re-annotated, and hence approximately 31% image-sentence pairs are wrongly labelled as neutral, which likely affects the performance of the model."
+    ],
+    [
+      "In this work, we also introduce e-SNLI-VE-2.0, a dataset combining SNLI-VE-2.0 with human-written explanations from e-SNLI BIBREF6, which were originally collected to support textual entailment. We replace the explanations for the neutral pairs in the validation and test sets with new ones collected at the same time as the new labels. We extend a current VTE model with an explanation module able to learn from these explanations at training time and generate an explanation for each predicted label at testing time."
+    ],
+    [
+      "e-SNLI BIBREF6 is an extension of the SNLI corpus with human-annotated natural language explanations for the ground-truth labels. The authors use the explanations to train models to also generate natural language justifications for their predictions. They collected one explanation for each instance in the training set of SNLI and three explanations for each instance in the validation and testing sets.",
+      "We randomly selected 100 image-sentence pairs in the validation set of SNLI-VE and their corresponding explanations in e-SNLI and examined how relevant these explanations are for the VTE task. More precisely, we say that an explanation is relevant if it brings information that justifies the relationship between the image and the sentence. We restricted the count to correctly labelled inputs and found that 57% explanations were relevant. For example, the explanation for entailment in Figure FIGREF21 (\u201cCooking in his apartment is cooking\u201d) was counted as irrelevant in our statistics, because it would not be the best explanation for an image-sentence pair, even though it is coherent with the textual pair. We investigate whether these explanations improve a VTE model when enhanced with a component that can process explanations at train time and output them at test time.",
+      "To form e-SNLI-VE-2.0, we append to SNLI-VE-2.0 the explanations from e-SNLI for all except the neutral pairs in the validation and test sets of SNLI-VE, which we replace with newly crowdsourced explanations collected at the same time as the labels for these splits (see Figure FIGREF21). Statistics of e-SNLI-VE-2.0 are shown in Appendix SECREF39, Table TABREF40."
+    ],
+    [
+      "As mentioned before, in order to submit the annotation of an image-sentence pair, three steps must be completed: workers must choose a label, highlight words in the hypothesis, and use at least half of the highlighted words to write an explanation for their decision. The last two steps thus follow the quality control of crowd-sourced explanations introduced by Camburu BIBREF6. We also ensured that workers do not simply use a copy of the given hypothesis as explanation. We ensured all the above via in-browser checks before workers' submission. An example of collected explanations is given in Figure FIGREF21.",
+      "To check the success of our crowdsourcing, we manually assessed the relevance of explanations among a random subset of 100 examples. A marking scale between 0 and 1 was used, assigning a score of $k$/$n$ when $k$ required attributes were given in an explanation out of $n$. We report an 83.5% relevance of explanations from workers. We note that, since our explanations are VTE-specific, they were phrased differently from the ones in e-SNLI, with more specific mentions to the images (e.g., \u201cThere is no labcoat in the picture, just a man wearing a blue shirt.\u201d, \u201cThere are no apples or oranges shown in the picture, only bananas.\u201d). Therefore, it would likely be beneficial to collect new explanations for all SNLI-VE-2.0 (not only for the neutral pairs in the validation and test sets) such that models can learn to output convincing explanations for the task at hand. However, we leave this as future work, and we show in this work the results that one obtains when using the explanations from e-SNLI-VE-2.0."
+    ],
+    [
+      "This section presents two VTE models that generate natural language explanations for their own decisions. We name them PaE-BUTD-VE and EtP-BUTD-VE, where PaE (resp. EtP) is for PredictAndExplain (resp. ExplainThenPredict), two models with similar principles introduced by Camburu BIBREF6. The first system learns to generate an explanation conditioned on the image premise, textual hypothesis, and predicted label. In contrast, the second system learns to first generate an explanation conditioned on the image premise and textual hypothesis, and subsequently makes a prediction solely based on the explanation."
+    ],
+    [
+      "PaE-BUTD-VE is a system for solving VTE and generating natural language explanations for the predicted labels. The explanations are conditioned on the image premise, the text hypothesis, and the predicted label (ground-truth label at train time), as shown in Figure FIGREF24."
+    ],
+    [
+      "As described in Section SECREF12, in the BUTD model, the hypothesis vector and the image vector were fused in a fixed-size feature vector f. The vector f was then given as input to an MLP which outputs a probability distribution over the three labels. In PaE-BUTD-VE, in addition to the classification layer, we add a 512-LSTM BIBREF12 decoder to generate an explanation. The decoder takes the feature vector f as initial state. Following Camburu BIBREF6, we prepend the label as a token at the beginning of the explanation to condition the explanation on the label. The ground truth label is provided at training time, whereas the predicted label is given at test time.",
+      "At test time, we use beam search with a beam width of 3 to decode explanations. For memory and time reduction, we replaced words that appeared less than 15 times among explanations with \u201c#UNK#\u201d. This strategy reduces the output vocabulary size to approximately 8.6k words."
+    ],
+    [
+      "The training loss is a weighted combination of the classification loss and the explanation loss, both computed using softmax cross entropy: $\\mathcal {L} = \\alpha \\mathcal {L}_{label} + (1-\\alpha ) \\mathcal {L}_{explanation} \\; \\textrm {;} \\; \\alpha \\in [0,1]$."
+    ],
+    [
+      "In this experiment, we are first interested in examining if a neural network can generate explanations at no cost for label accuracy. Therefore, only balanced accuracy on label is used for the model selection criterion. However, future work can investigate other selection criteria involving a combination between the label and explanation performances. We performed hyperparameter search on $\\alpha $, considering values between 0.2 and 0.8 with a step of 0.2. We found $\\alpha =0.4$ to produce the best validation balanced accuracy of 72.81%, while BUTD trained without explanations yielded a similar 72.58% validation balanced accuracy."
+    ],
+    [
+      "As summarised in Table TABREF30, we obtain a test balanced accuracy for PaE-BUTD-VE of 73%, while the same model trained without explanations obtains 72.52%. This is encouraging, since it shows that one can obtain additional natural language explanations without sacrificing performance (and eventually even improving the label performance, however, future work is needed to conclude whether the difference $0.48\\%$ improvement in performance is statistically significant).",
+      "Camburu BIBREF6 mentioned that the BLEU score was not an appropriate measure for the quality of explanations and suggested human evaluation instead. We therefore manually scored the relevance of 100 explanations that were generated when the model predicted correct labels. We found that only 20% of explanations were relevant. We highlight that the relevance of explanations is in terms of whether the explanation reflects ground-truth reasons supporting the correct label. This is not to be confused with whether an explanation is correctly illustrating the inner working of the model, which is left as future work. It is also important to note that on a similar experimental setting, Camburu report as low as 34.68% correct explanations, training with explanations that were actually collected for their task. Lastly, the model selection criterion at validation time was the prediction balanced accuracy, which may contribute to the low quality of explanations. While we show that adding an explanation module does not harm prediction performance, more work is necessary to get models that output trustable explanations."
+    ],
+    [
+      "When assigning a label, an explanation is naturally part of the decision-making process. This motivates the design of a system that explains itself before deciding on a label, called EtP-BUTD-VE. For this system, a first neural network is trained to generate an explanation given an image-sentence input. Separately, a second neural network, called ExplToLabel-VE, is trained to predict a label from an explanation (see Figure FIGREF32)."
+    ],
+    [
+      "For the first network, we set $\\alpha =0$ in the training loss of the PaE-BUTD-VE model to obtain a system that only learns to generate an explanation from the image-sentence input, without label prediction. Hence, in this setting, no label is prepended before the explanation.",
+      "For the ExplToLabel-VE model, we use a 512-LSTM followed by an MLP with three 512-layers and ReLU activation, and softmax activation to classify the explanation between entailment, contradiction, and neutral."
+    ],
+    [
+      "For ExplToLabel-VE, the best model is selected on balanced accuracy at validation time. For EtP-BUTD-VE, perplexity is used to select the best model parameters at validation time. It is computed between the explanations produced by the LSTM and ground truth explanations from the validation set."
+    ],
+    [
+      "When we train ExplToLabel-VE on e-SNLI-VE-2.0, we obtain a balanced accuracy of 90.55% on the test set.",
+      "As reported in Table TABREF30, the overall PaE-BUTD-VE system achieves 69.40% balanced accuracy on the test set of e-SNLI-VE-2.0, which is a 3% decrease from the non-explanatory BUTD counterpart (72.52%). However, by setting $\\alpha $ to zero and selecting the model that gives the best perplexity per word at validation, the quality of explanation significantly increased, with 35% relevance, based on manual evaluation. Thus, in our model, generating better explanations involves a small sacrifice in label prediction accuracy, implying a trade-off between explanation generation and accuracy.",
+      "We note that there is room for improvement in our explanation generation method. For example, one can implement an attention mechanism similar to Xu BIBREF13, so that each generated word relates to a relevant part of the multimodal feature representation."
+    ],
+    [
+      "We complement our quantitative results with a qualitative analysis of the explanations generated by our enhanced VTE systems. In Figures FIGREF36 and FIGREF37, we present examples of the predicted labels and generated explanations.",
+      "Figure FIGREF36 shows an example where the EtP-BUTD-VE model produces both a correct label and a relevant explanation. The label is contradiction, because in the image, the students are playing with a soccer ball and not a basketball, thus contradicting the text hypothesis. Given the composition of the generated sentence (\u201cStudents cannot be playing soccer and baseball at the same time.\u201d), ExplToLabel-VE was able to detect a contradiction in the image-sentence input. In comparison, the explanation from e-SNLI-VE-2.0 is not correct, even if it was valid for e-SNLI when the text premise was given. This emphasizes the difficulty that we are facing with generating proper explanations when training on a noisy dataset.",
+      "Even when the generated explanations are irrelevant, we noticed that they are on-topic and that most of the time the mistakes come from repetitions of certain sub-phrases. For example, in Figure FIGREF37, PaE-BUTD-VE predicts the label neutral, which is correct, but the explanation contains an erroneous repetition of the n-gram \u201care in a car\u201d. However, it appears that the system learns to generate a sentence in the form \u201cJust because ...doesn't mean ...\u201d, which is frequently found for the justification of neutral pairs in the training set. The explanation generated by EtP-BUTD-VE adopts the same structure, and the ExplToLabel-VE component correctly classifies the instance as neutral. However, even if the explanation is semantically correct, it is not relevant for the input and fails to explain the classification."
+    ],
+    [
+      "In this paper, we first presented SNLI-VE-2.0, which corrects the neutral instances in the validation and test sets of SNLI-VE. Secondly, we re-evaluated an existing model on the corrected sets in order to update the estimate of its performance on this task. Thirdly, we introduced e-SNLI-VE-2.0, a dataset which extends SNLI-VE-2.0 with natural language explanations. Finally, we trained two types of models that learn from these explanations at training time, and output such explanations at test time, as a stepping stone in explainable artificial intelligence. Our work is a jumping-off point for both the identification and correction of SNLI-VE, as well as in the extension to explainable VTE. We hope that the community will build on our findings to create more robust as well as explainable multimodal systems."
+    ],
+    [
+      "This work was supported by the Oxford Internet Institute, a JP Morgan PhD Fellowship 2019-2020, an Oxford-DeepMind Graduate Scholarship, the Alan Turing Institute under the EPSRC grant EP/N510129/1, and the AXA Research Fund, as well as DFG-EXC-Nummer 2064/1-Projektnummer 390727645 and the ERC under the Horizon 2020 program (grant agreement No. 853489)."
+    ],
+    [
+      "e-SNLI-VE-2.0 is the combination of SNLI-VE-2.0 with explanations from either e-SNLI or our crowdsourced annotations where applicable. The statistics of e-SNLI-VE-2.0 are shown in Table TABREF40.",
+      "Including text hypotheses and explanations."
+    ],
+    [
+      "We used Amazon Mechanical Turk (MTurk) to collect new labels and explanations for SNLI-VE. 2,060 workers participated in the annotation effort, with an average of 1.98 assignments per worker and a standard deviation of 5.54. We required the workers to have a previous approval rate above 90%. No restriction was put on the workers' location.",
+      "Each assignment consisted of a set of 10 image-sentence pairs. For each pair, the participant was asked to (a) choose a label, (b) highlight words in the sentence that led to their decision, and (c) explain their decision in a comprehensive and concise manner, using a subset of the words that they highlighted. The instructions are shown in Figure FIGREF42. Workers were also guided with three annotated examples, one for each label.",
+      "For each assignment of 10 questions, one trusted annotation with gold standard label was inserted at a random position, as a measure to control the quality of label annotation. Each assignment was completed by three different workers. An example of question is shown in Figure FIGREF8 in the core paper."
+    ],
+    [
+      "Some examples in SNLI-VE were ambiguous and could find correct justifications for incompatible labels, as shown in Figures FIGREF44, FIGREF45, and FIGREF46."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: e-SNLI-VE-2.0: Corrected Visual-Textual Entailment with Natural Language Explanations
+
+Question: How many natural language explanations are human-written?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "SNLI-VE-2.0",
+    "SNLI-VE-2.0 ::: Re-annotation details",
+    "SNLI-VE-2.0 ::: Re-evaluation of Visual-Textual Entailment",
+    "SNLI-VE-2.0 ::: Re-evaluation of Visual-Textual Entailment ::: Model.",
+    "SNLI-VE-2.0 ::: Re-evaluation of Visual-Textual Entailment ::: Results.",
+    "Visual-Textual Entailment with Natural Language Explanations",
+    "Visual-Textual Entailment with Natural Language Explanations ::: e-SNLI-VE-2.0",
+    "Visual-Textual Entailment with Natural Language Explanations ::: Collecting Explanations",
+    "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations",
+    "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Predict and Explain",
+    "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Predict and Explain ::: Model.",
+    "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Predict and Explain ::: Loss.",
+    "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Predict and Explain ::: Model selection.",
+    "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Predict and Explain ::: Results.",
+    "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Explain Then Predict",
+    "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Explain Then Predict ::: Model.",
+    "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Explain Then Predict ::: Model selection.",
+    "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Explain Then Predict ::: Results.",
+    "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Qualitative Analysis of Generated Explanations",
+    "Conclusion",
+    "Conclusion ::: Acknowledgements.",
+    "Appendix ::: Statistics of e-SNLI-VE-2.0",
+    "Appendix ::: Details of the Mechanical Turk Task",
+    "Appendix ::: Ambiguous Examples from SNLI-VE"
+  ],
+  "paragraphs": [
+    [
+      "Inspired by textual entailment BIBREF0, Xie BIBREF1 introduced the visual-textual entailment (VTE) task, which considers semantic entailment between a premise image and a textual hypothesis. Semantic entailment consists in determining if the hypothesis can be concluded from the premise, and assigning to each pair of (premise image, textual hypothesis) a label among entailment, neutral, and contradiction. In Figure FIGREF3, the label for the first image-sentence pair is entailment, because the hypothesis states that \u201ca bunch of people display different flags\u201d, which can be clearly derived from the image. On the contrary, the second image-sentence pair is labelled as contradiction, because the hypothesis stating that \u201cpeople [are] running a marathon\u201d contradicts the image with static people.",
+      "Xie also propose the SNLI-VE dataset as the first dataset for VTE. SNLI-VE is built from the textual entailment SNLI dataset BIBREF0 by replacing textual premises with the Flickr30k images that they originally described BIBREF2. However, images contain more information than their descriptions, which may entail or contradict the textual hypotheses (see Figure FIGREF3). As a result, the neutral class in SNLI-VE has substantial labelling errors. Vu BIBREF3 estimated ${\\sim }31\\%$ errors in this class, and ${\\sim }1\\%$ for the contradiction and entailment classes.",
+      "Xie BIBREF1 introduced the VTE task under the name of \u201cvisual entailment\u201d, which could imply recognizing entailment between images only. This paper prefers to follow Suzuki BIBREF4 and call it \u201cvisual-textual entailment\u201d instead, as it involves reasoning on image-sentence pairs.",
+      "In this work, we first focus on decreasing the error in the neutral class by collecting new labels for the neutral pairs in the validation and test sets of SNLI-VE, using Amazon Mechanical Turk (MTurk). To ensure high quality annotations, we used a series of quality control measures, such as in-browser checks, inserting trusted examples, and collecting three annotations per instance. Secondly, we re-evaluate current image-text understanding systems, such as the bottom-up top-down attention network (BUTD) BIBREF5 on VTE using our corrected dataset, which we call SNLI-VE-2.0.",
+      "Thirdly, we introduce the e-SNLI-VE-2.0 corpus, which we form by appending human-written natural language explanations to SNLI-VE-2.0. These explanations were collected in e-SNLI BIBREF6 to support textual entailment for SNLI. For the same reasons as above, we re-annotate the explanations for the neutral pairs in the validation and test sets, while keeping the explanations from e-SNLI for all the rest. Finally, we extend a current VTE model with the capacity of learning from these explanations at training time and outputting an explanation for each predicted label at testing time."
+    ],
+    [
+      "The goal of VTE is to determine if a textual hypothesis $H_{text}$ can be concluded, given the information in a premise image $P_{image}$ BIBREF1. There are three possible labels:",
+      "Entailment: if there is enough evidence in $P_{image}$ to conclude that $H_{text}$ is true.",
+      "Contradiction: if there is enough evidence in $P_{image}$ to conclude that $H_{text}$ is false.",
+      "Neutral: if neither of the earlier two are true.",
+      "The SNLI-VE dataset proposed by Xie BIBREF1 is the combination of Flickr30k, a popular image dataset for image captioning BIBREF2 and SNLI, an influential dataset for natural language inference BIBREF0. Textual premises from SNLI are replaced with images from Flickr30k, which is possible, as these premises were originally collected as captions of these images (see Figure FIGREF3).",
+      "However, in practice, a sensible proportion of labels are wrong due to the additional information contained in images. This mostly affects neutral pairs, since images may contain the necessary information to ground a hypothesis for which a simple premise caption was not sufficient. An example is shown in Figure FIGREF3. Vu BIBREF3 report that the label is wrong for ${\\sim }31\\%$ of neutral examples, based on a random subset of 171 neutral points from the test set. We also annotated 150 random neutral examples from the test set and found a similar percentage of 30.6% errors.",
+      "Our annotations are available at https://github.com/virginie-do/e-SNLI-VE/tree/master/annotations/gt_labels.csv"
+    ],
+    [
+      "In this work, we only collect new labels for the neutral pairs in the validation and test sets of SNLI-VE. While the procedure of re-annotation is generic, we limit our re-annotation to these splits as a first step to verify the difference in performance that current models have when evaluated on the corrected test set as well as the effect of model selection on the corrected validation set. We leave for future work re-annotation of the training set, which would likely lead to training better VTE models. We also chose not to re-annotate entailment and contradiction classes, as their error rates are much lower ($<$1% as reported by Vu BIBREF3).",
+      "The main question that we want our dataset to answer is: \u201cWhat is the relationship between the image premise and the sentence hypothesis?\u201d. We provide workers with the definitions of entailment, neutral, and contradiction for image-sentence pairs and one example for each label. As shown in Figure FIGREF8, for each image-sentence pair, workers are required to (a) choose a label, (b) highlight words in the sentence that led to their decision, and (c) explain their decision in a comprehensive and concise manner, using at least half of the words that they highlighted. The collected explanations will be presented in more detail in Section SECREF20, as we focus here on the label correction. We point out that it is likely that requiring an explanation at the same time as requiring a label has a positive effect on the correctness of the label, since having to justify in writing the picked label may make workers pay an increased attention. Moreover, we implemented additional quality control measures for crowdsourced annotations, such as (a) collecting three annotations for every input, (b) injecting trusted annotations into the task for verification BIBREF7, and (c) restricting to workers with at least 90% previous approval rate.",
+      "First, we noticed that some instances in SNLI-VE are ambiguous. We show some examples in Figure FIGREF3 and in Appendix SECREF43. In order to have a better sense of this ambiguity, three authors of this paper independently annotated 100 random examples. All three authors agreed on 54% of the examples, exactly two authors agreed on 45%, and there was only one example on which all three authors disagreed. We identified the following three major sources of ambiguity:",
+      "mapping an emotion in the hypothesis to a facial expression in the image premise, e.g., \u201cpeople enjoy talking\u201d, \u201cangry people\u201d, \u201csad woman\u201d. Even when the face is seen, it may be subjective to infer an emotion from a static image (see Figure FIGREF44 in Appendix SECREF43).",
+      "personal taste, e.g., \u201cthe sign is ugly\u201d.",
+      "lack of consensus on terms such as \u201cmany people\u201d or \u201ccrowded\u201d.",
+      "To account for the ambiguity that the neutral labels seem to present, we considered that an image-sentence pair is too ambiguous and not suitable for a well-defined visual-textual entailment task when three different labels were assigned by the three workers. Hence, we removed these examples from the validation (5.2%) and test (5.5%) sets.",
+      "To ensure that our workers are correctly performing the task, we randomly inserted trusted pairs, i.e., pairs among the 54% on which all three authors agreed on the label. For each set of 10 pairs presented to a worker, one trusted pair was introduced at a random location, so that the worker, while being told that there is such a test pair, cannot figure out which one it is. Via an in-browser check, we only allow workers to submit their answers for each set of 10 instances only if the trusted pair was correctly labelled. Other in-browser checks were done for the collection of explanations, as we will describe in Section SECREF20. More details about the participants and design of the Mechanical Turk task can be found in Appendix SECREF41.",
+      "After collecting new labels for the neutral instances in the validation and testing sets, we randomly select and annotate 150 instances from the validation set that were neutral in SNLI-VE. Based on this sample, the error rate went down from 31% to 12% in SNLI-VE-2.0. Looking at the 18 instances where we disagreed with the label assigned by MTurk workers, we noticed that 12 were due to ambiguity in the examples, and 6 were due to workers' errors. Further investigation into potentially eliminating ambiguous instances would likely be beneficial. However, we leave it as future work, and we proceed in this work with using our corrected labels, since our error rate is significantly lower than that of the original SNLI-VE.",
+      "Finally, we note that only about 62% of the originally neutral pairs remain neutral, while 21% become contradiction and 17% entailment pairs. Therefore, we are now facing an imbalance between the neutral, entailment, and contradiction instances in the validation and testing sets of SNLI-VE-2.0. The neutral class becomes underrepresented and the label distributions in the corrected validation and testing sets both become E / N / C: 39% / 20% / 41%. To account for this, we compute the balanced accuracy, i.e., the average of the three accuracies on each class."
+    ],
+    [
+      "Since we decreased the error rate of labels in the validation and test set, we are interested in the performance of a VTE model when using the corrected sets."
+    ],
+    [
+      "To tackle SNLI-VE, Xie BIBREF1 used EVE (for \u201cExplainable Visual Entailment\u201d), a modified version of the BUTD architecture, the winner of the Visual Question Answering (VQA) challenge in 2017 BIBREF5. Since the EVE implementation is not available at the time of this work, we used the original BUTD architecture, with the same hyperparameters as reported in BIBREF1.",
+      "BUTD contains an image processing module and a text processing module. The image processing module encodes each image region proposed by FasterRCNN BIBREF8 into a feature vector using a bottom-up attention mechanism. In the text processing module, the text hypothesis is encoded into a fixed-length vector, which is the last output of a recurrent neural network with 512-GRU units BIBREF9. To input each token into the recurrent network, we use the pretrained GloVe vectors BIBREF10. Finally, a top-down attention mechanism is used between the hypothesis vector and each of the image region vectors to obtain an attention weight for each region. The weighted sum of these image region vectors is then fused with the text hypothesis vector. The multimodal fusion is fed to a multilayer percetron (MLP) with tanh activations and a final softmax layer to classify the image-sentence relation as entailment, contradiction, or neutral.",
+      "Using the implementation from https://github.com/claudiogreco/coling18-gte.",
+      "We use the original training set from SNLI-VE. To see the impact of correcting the validation and test sets, we do the following three experiments:",
+      "model selection as well as testing are done on the original uncorrected SNLI-VE.",
+      "model selection is done on the uncorrected SNLI-VE validation set, while testing is done on the corrected SNLI-VE-2.0 test set.",
+      "model selection as well as testing are done on the corrected SNLI-VE-2.0.",
+      "Models are trained with cross-entropy loss optimized by the Adam optimizer BIBREF11 with batch size 64. The maximum number of training epochs is set to 100, with early stopping when no improvement is observed on validation accuracy for 3 epochs. The final model checkpoint selected for testing is the one with the highest validation accuracy."
+    ],
+    [
+      "The results of the three experiments enumerated above are reported in Table TABREF18. Surprisingly, we obtained an accuracy of 73.02% on SNLI-VE using BUTD, which is better than the 71.16% reported by Xie BIBREF1 for the EVE system which meant to be an improvement over BUTD. It is also better than their reproduction of BUTD, which gave 68.90%.",
+      "The same BUTD model that achieves 73.02% on the uncorrected SNLI-VE test set, achieves 73.18% balanced accuracy when tested on the corrected test set from SNLI-VE-2.0. Hence, for this model, we do not notice a significant difference in performance. This could be due to randomness. Finally, when we run the training loop again, this time doing the model selection on the corrected validation set from SNLI-VE-2.0, we obtain a slightly worse performance of 72.52%, although the difference is not clearly significant.",
+      "Finally, we recall that the training set has not been re-annotated, and hence approximately 31% image-sentence pairs are wrongly labelled as neutral, which likely affects the performance of the model."
+    ],
+    [
+      "In this work, we also introduce e-SNLI-VE-2.0, a dataset combining SNLI-VE-2.0 with human-written explanations from e-SNLI BIBREF6, which were originally collected to support textual entailment. We replace the explanations for the neutral pairs in the validation and test sets with new ones collected at the same time as the new labels. We extend a current VTE model with an explanation module able to learn from these explanations at training time and generate an explanation for each predicted label at testing time."
+    ],
+    [
+      "e-SNLI BIBREF6 is an extension of the SNLI corpus with human-annotated natural language explanations for the ground-truth labels. The authors use the explanations to train models to also generate natural language justifications for their predictions. They collected one explanation for each instance in the training set of SNLI and three explanations for each instance in the validation and testing sets.",
+      "We randomly selected 100 image-sentence pairs in the validation set of SNLI-VE and their corresponding explanations in e-SNLI and examined how relevant these explanations are for the VTE task. More precisely, we say that an explanation is relevant if it brings information that justifies the relationship between the image and the sentence. We restricted the count to correctly labelled inputs and found that 57% explanations were relevant. For example, the explanation for entailment in Figure FIGREF21 (\u201cCooking in his apartment is cooking\u201d) was counted as irrelevant in our statistics, because it would not be the best explanation for an image-sentence pair, even though it is coherent with the textual pair. We investigate whether these explanations improve a VTE model when enhanced with a component that can process explanations at train time and output them at test time.",
+      "To form e-SNLI-VE-2.0, we append to SNLI-VE-2.0 the explanations from e-SNLI for all except the neutral pairs in the validation and test sets of SNLI-VE, which we replace with newly crowdsourced explanations collected at the same time as the labels for these splits (see Figure FIGREF21). Statistics of e-SNLI-VE-2.0 are shown in Appendix SECREF39, Table TABREF40."
+    ],
+    [
+      "As mentioned before, in order to submit the annotation of an image-sentence pair, three steps must be completed: workers must choose a label, highlight words in the hypothesis, and use at least half of the highlighted words to write an explanation for their decision. The last two steps thus follow the quality control of crowd-sourced explanations introduced by Camburu BIBREF6. We also ensured that workers do not simply use a copy of the given hypothesis as explanation. We ensured all the above via in-browser checks before workers' submission. An example of collected explanations is given in Figure FIGREF21.",
+      "To check the success of our crowdsourcing, we manually assessed the relevance of explanations among a random subset of 100 examples. A marking scale between 0 and 1 was used, assigning a score of $k$/$n$ when $k$ required attributes were given in an explanation out of $n$. We report an 83.5% relevance of explanations from workers. We note that, since our explanations are VTE-specific, they were phrased differently from the ones in e-SNLI, with more specific mentions to the images (e.g., \u201cThere is no labcoat in the picture, just a man wearing a blue shirt.\u201d, \u201cThere are no apples or oranges shown in the picture, only bananas.\u201d). Therefore, it would likely be beneficial to collect new explanations for all SNLI-VE-2.0 (not only for the neutral pairs in the validation and test sets) such that models can learn to output convincing explanations for the task at hand. However, we leave this as future work, and we show in this work the results that one obtains when using the explanations from e-SNLI-VE-2.0."
+    ],
+    [
+      "This section presents two VTE models that generate natural language explanations for their own decisions. We name them PaE-BUTD-VE and EtP-BUTD-VE, where PaE (resp. EtP) is for PredictAndExplain (resp. ExplainThenPredict), two models with similar principles introduced by Camburu BIBREF6. The first system learns to generate an explanation conditioned on the image premise, textual hypothesis, and predicted label. In contrast, the second system learns to first generate an explanation conditioned on the image premise and textual hypothesis, and subsequently makes a prediction solely based on the explanation."
+    ],
+    [
+      "PaE-BUTD-VE is a system for solving VTE and generating natural language explanations for the predicted labels. The explanations are conditioned on the image premise, the text hypothesis, and the predicted label (ground-truth label at train time), as shown in Figure FIGREF24."
+    ],
+    [
+      "As described in Section SECREF12, in the BUTD model, the hypothesis vector and the image vector were fused in a fixed-size feature vector f. The vector f was then given as input to an MLP which outputs a probability distribution over the three labels. In PaE-BUTD-VE, in addition to the classification layer, we add a 512-LSTM BIBREF12 decoder to generate an explanation. The decoder takes the feature vector f as initial state. Following Camburu BIBREF6, we prepend the label as a token at the beginning of the explanation to condition the explanation on the label. The ground truth label is provided at training time, whereas the predicted label is given at test time.",
+      "At test time, we use beam search with a beam width of 3 to decode explanations. For memory and time reduction, we replaced words that appeared less than 15 times among explanations with \u201c#UNK#\u201d. This strategy reduces the output vocabulary size to approximately 8.6k words."
+    ],
+    [
+      "The training loss is a weighted combination of the classification loss and the explanation loss, both computed using softmax cross entropy: $\\mathcal {L} = \\alpha \\mathcal {L}_{label} + (1-\\alpha ) \\mathcal {L}_{explanation} \\; \\textrm {;} \\; \\alpha \\in [0,1]$."
+    ],
+    [
+      "In this experiment, we are first interested in examining if a neural network can generate explanations at no cost for label accuracy. Therefore, only balanced accuracy on label is used for the model selection criterion. However, future work can investigate other selection criteria involving a combination between the label and explanation performances. We performed hyperparameter search on $\\alpha $, considering values between 0.2 and 0.8 with a step of 0.2. We found $\\alpha =0.4$ to produce the best validation balanced accuracy of 72.81%, while BUTD trained without explanations yielded a similar 72.58% validation balanced accuracy."
+    ],
+    [
+      "As summarised in Table TABREF30, we obtain a test balanced accuracy for PaE-BUTD-VE of 73%, while the same model trained without explanations obtains 72.52%. This is encouraging, since it shows that one can obtain additional natural language explanations without sacrificing performance (and eventually even improving the label performance, however, future work is needed to conclude whether the difference $0.48\\%$ improvement in performance is statistically significant).",
+      "Camburu BIBREF6 mentioned that the BLEU score was not an appropriate measure for the quality of explanations and suggested human evaluation instead. We therefore manually scored the relevance of 100 explanations that were generated when the model predicted correct labels. We found that only 20% of explanations were relevant. We highlight that the relevance of explanations is in terms of whether the explanation reflects ground-truth reasons supporting the correct label. This is not to be confused with whether an explanation is correctly illustrating the inner working of the model, which is left as future work. It is also important to note that on a similar experimental setting, Camburu report as low as 34.68% correct explanations, training with explanations that were actually collected for their task. Lastly, the model selection criterion at validation time was the prediction balanced accuracy, which may contribute to the low quality of explanations. While we show that adding an explanation module does not harm prediction performance, more work is necessary to get models that output trustable explanations."
+    ],
+    [
+      "When assigning a label, an explanation is naturally part of the decision-making process. This motivates the design of a system that explains itself before deciding on a label, called EtP-BUTD-VE. For this system, a first neural network is trained to generate an explanation given an image-sentence input. Separately, a second neural network, called ExplToLabel-VE, is trained to predict a label from an explanation (see Figure FIGREF32)."
+    ],
+    [
+      "For the first network, we set $\\alpha =0$ in the training loss of the PaE-BUTD-VE model to obtain a system that only learns to generate an explanation from the image-sentence input, without label prediction. Hence, in this setting, no label is prepended before the explanation.",
+      "For the ExplToLabel-VE model, we use a 512-LSTM followed by an MLP with three 512-layers and ReLU activation, and softmax activation to classify the explanation between entailment, contradiction, and neutral."
+    ],
+    [
+      "For ExplToLabel-VE, the best model is selected on balanced accuracy at validation time. For EtP-BUTD-VE, perplexity is used to select the best model parameters at validation time. It is computed between the explanations produced by the LSTM and ground truth explanations from the validation set."
+    ],
+    [
+      "When we train ExplToLabel-VE on e-SNLI-VE-2.0, we obtain a balanced accuracy of 90.55% on the test set.",
+      "As reported in Table TABREF30, the overall PaE-BUTD-VE system achieves 69.40% balanced accuracy on the test set of e-SNLI-VE-2.0, which is a 3% decrease from the non-explanatory BUTD counterpart (72.52%). However, by setting $\\alpha $ to zero and selecting the model that gives the best perplexity per word at validation, the quality of explanation significantly increased, with 35% relevance, based on manual evaluation. Thus, in our model, generating better explanations involves a small sacrifice in label prediction accuracy, implying a trade-off between explanation generation and accuracy.",
+      "We note that there is room for improvement in our explanation generation method. For example, one can implement an attention mechanism similar to Xu BIBREF13, so that each generated word relates to a relevant part of the multimodal feature representation."
+    ],
+    [
+      "We complement our quantitative results with a qualitative analysis of the explanations generated by our enhanced VTE systems. In Figures FIGREF36 and FIGREF37, we present examples of the predicted labels and generated explanations.",
+      "Figure FIGREF36 shows an example where the EtP-BUTD-VE model produces both a correct label and a relevant explanation. The label is contradiction, because in the image, the students are playing with a soccer ball and not a basketball, thus contradicting the text hypothesis. Given the composition of the generated sentence (\u201cStudents cannot be playing soccer and baseball at the same time.\u201d), ExplToLabel-VE was able to detect a contradiction in the image-sentence input. In comparison, the explanation from e-SNLI-VE-2.0 is not correct, even if it was valid for e-SNLI when the text premise was given. This emphasizes the difficulty that we are facing with generating proper explanations when training on a noisy dataset.",
+      "Even when the generated explanations are irrelevant, we noticed that they are on-topic and that most of the time the mistakes come from repetitions of certain sub-phrases. For example, in Figure FIGREF37, PaE-BUTD-VE predicts the label neutral, which is correct, but the explanation contains an erroneous repetition of the n-gram \u201care in a car\u201d. However, it appears that the system learns to generate a sentence in the form \u201cJust because ...doesn't mean ...\u201d, which is frequently found for the justification of neutral pairs in the training set. The explanation generated by EtP-BUTD-VE adopts the same structure, and the ExplToLabel-VE component correctly classifies the instance as neutral. However, even if the explanation is semantically correct, it is not relevant for the input and fails to explain the classification."
+    ],
+    [
+      "In this paper, we first presented SNLI-VE-2.0, which corrects the neutral instances in the validation and test sets of SNLI-VE. Secondly, we re-evaluated an existing model on the corrected sets in order to update the estimate of its performance on this task. Thirdly, we introduced e-SNLI-VE-2.0, a dataset which extends SNLI-VE-2.0 with natural language explanations. Finally, we trained two types of models that learn from these explanations at training time, and output such explanations at test time, as a stepping stone in explainable artificial intelligence. Our work is a jumping-off point for both the identification and correction of SNLI-VE, as well as in the extension to explainable VTE. We hope that the community will build on our findings to create more robust as well as explainable multimodal systems."
+    ],
+    [
+      "This work was supported by the Oxford Internet Institute, a JP Morgan PhD Fellowship 2019-2020, an Oxford-DeepMind Graduate Scholarship, the Alan Turing Institute under the EPSRC grant EP/N510129/1, and the AXA Research Fund, as well as DFG-EXC-Nummer 2064/1-Projektnummer 390727645 and the ERC under the Horizon 2020 program (grant agreement No. 853489)."
+    ],
+    [
+      "e-SNLI-VE-2.0 is the combination of SNLI-VE-2.0 with explanations from either e-SNLI or our crowdsourced annotations where applicable. The statistics of e-SNLI-VE-2.0 are shown in Table TABREF40.",
+      "Including text hypotheses and explanations."
+    ],
+    [
+      "We used Amazon Mechanical Turk (MTurk) to collect new labels and explanations for SNLI-VE. 2,060 workers participated in the annotation effort, with an average of 1.98 assignments per worker and a standard deviation of 5.54. We required the workers to have a previous approval rate above 90%. No restriction was put on the workers' location.",
+      "Each assignment consisted of a set of 10 image-sentence pairs. For each pair, the participant was asked to (a) choose a label, (b) highlight words in the sentence that led to their decision, and (c) explain their decision in a comprehensive and concise manner, using a subset of the words that they highlighted. The instructions are shown in Figure FIGREF42. Workers were also guided with three annotated examples, one for each label.",
+      "For each assignment of 10 questions, one trusted annotation with gold standard label was inserted at a random position, as a measure to control the quality of label annotation. Each assignment was completed by three different workers. An example of question is shown in Figure FIGREF8 in the core paper."
+    ],
+    [
+      "Some examples in SNLI-VE were ambiguous and could find correct justifications for incompatible labels, as shown in Figures FIGREF44, FIGREF45, and FIGREF46."
+    ]
+  ]
+}
+```
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+Name of Paper: e-SNLI-VE-2.0: Corrected Visual-Textual Entailment with Natural Language Explanations
+
+Question: How much is performance difference of existing model between original and corrected corpus?
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+Name of Paper: An Analysis of Word2Vec for the Italian Language
+
+Question: What is the dataset used as input to the Word2Vec algorithm?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Word2Vec",
+    "Word2Vec ::: Sampling rate",
+    "Word2Vec ::: Negative sampling",
+    "Implementation details",
+    "Results",
+    "Results ::: Analysis of the various models",
+    "Results ::: Comparison with other models",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "In order to make human language comprehensible to a computer, it is obviously essential to provide some word encoding. The simplest approach is the one-hot encoding, where each word is represented by a sparse vector with dimension equal to the vocabulary size. In addition to the storage need, the main problem of this representation is that any concept of word similarity is completely ignored (each vector is orthogonal and equidistant from each other). On the contrary, the understanding of natural language cannot be separated from the semantic knowledge of words, which conditions a different closeness between them. Indeed, the semantic representation of words is the basic problem of Natural Language Processing (NLP). Therefore, there is a necessary need to code words in a space that is linked to their meaning, in order to facilitate a machine in potential task of \u201cunderstanding\" it. In particular, starting from the seminal work BIBREF0, words are usually represented as dense distributed vectors that preserve their uniqueness but, at the same time, are able to encode the similarities.",
+      "These word representations are called Word Embeddings since the words (points in a space of vocabulary size) are mapped in an embedding space of lower dimension. Supported by the distributional hypothesis BIBREF1 BIBREF2, which states that a word can be semantically characterized based on its context (i.e. the words that surround it in the sentence), in recent years many word embedding representations have been proposed (a fairly complete and updated review can be found in BIBREF3 and BIBREF4). These methods can be roughly categorized into two main classes: prediction-based models and count-based models. The former is generally linked to work on Neural Network Language Models (NNLM) and use a training algorithm that predicts the word given its local context, the latter leverage word-context statistics and co-occurrence counts in an entire corpus. The main prediction-based and count-based models are respectively Word2Vec BIBREF5 (W2V) and GloVe BIBREF6.",
+      "Despite the widespread use of these concepts BIBREF7 BIBREF8, few contributions exist regarding the development of a W2V that is not in English. In particular, no detailed analysis on an Italian W2V seems to be present in the literature, except for BIBREF9 and BIBREF10. However, both seem to leave out some elements of fundamental interest in the learning of the neural network, in particular relating to the number of epochs performed during learning, reducing the importance that it may have on the final result. In BIBREF9, this for example leads to the simplistic conclusion that (being able to organize with more freedom in space) the more space is given to the vectors, the better the results may be. However, the problem in complex structures is that large embedding spaces can make training too difficult.",
+      "In this work, by setting the size of the embedding to a commonly used average value, various parameters are analysed as the number of learning epochs changes, depending on the window sizes and the negatively backpropagated samples."
+    ],
+    [
+      "The W2V structure consists of a simple two-level neural network (Figure FIGREF1) with one-hot vectors representing words at the input. It can be trained in two different modes, algorithmically similar, but different in concept: Continuous Bag-of-Words (CBOW) model and Skip-Gram model. While CBOW tries to predict the target words from the context, Skip-Gram instead aims to determine the context for a given target word. The two different approaches therefore modify only the way in which the inputs and outputs are to be managed, but in any case, the network does not change, and the training always takes place between single pairs of words (placed as one-hot in input and output).",
+      "The text is in fact divided into sentences, and for each word of a given sentence a window of words is taken from the right and from the left to define the context. The central word is coupled with each of the words forming the set of pairs for training. Depending on the fact that the central word represents the output or the input in training pairs, the CBOW and Skip-gram models are obtained respectively.",
+      "Regardless of whether W2V is trained to predict the context or the target word, it is used as a word embedding in a substantially different manner from the one for which it has been trained. In particular, the second matrix is totally discarded during use, since the only thing relevant to the representation is the space of the vectors generated in the intermediate level (embedding space)."
+    ],
+    [
+      "The common words (such as \u201cthe\", \u201cof\", etc.) carry very little information on the target word with which they are coupled, and through backpropagation they tend to have extremely small representative vectors in the embedding space. To solve both these problems the W2V algorithm implements a particular \u201csubsampling\" BIBREF11, which acts by eliminating some words from certain sentences. Note that the elimination of a word directly from the text means that it no longer appears in the context of any of the words of the sentence and, at the same time, a number of pairs equal to (at most) twice the size of the window relating to the deleted word will also disappear from the training set.",
+      "In practice, each word is associated with a sort of \u201ckeeping probability\" and, when you meet that word, if this value is greater than a randomly generated value then the word will not be discarded from the text. The W2V implementation assigns this \u201cprobability\" to the generic word $w_i$ through the formula:",
+      "where $f(w_i)$ is the relative frequency of the word $w_i$ (namely $count(w_i)/total$), while $s$ is a sample value, typically set between $10^{-3}$ and $10^{-5}$."
+    ],
+    [
+      "Working with one-hot pairs of words means that the size of the network must be the same at input and output, and must be equal to the size of the vocabulary. So, although very simple, the network has a considerable number of parameters to train, which lead to an excessive computational cost if we are supposed to backpropagate all the elements of the one-hot vector in output.",
+      "The \u201cnegative sampling\" technique BIBREF11 tries to solve this problem by modifying only a small percentage of the net weights every time. In practice, for each pair of words in the training set, the loss function is calculated only for the value 1 and for a few values 0 of the one-hot vector of the desired output. The computational cost is therefore reduced by choosing to backpropagate only $K$ words \u201cnegative\" and one positive, instead of the entire vocabulary. Typical values for negative sampling (the number of negative samples that will be backpropagated and to which therefore the only positive value will always be added), range from 2 to 20, depending on the size of the dataset.",
+      "The probability of selecting a negative word to backpropagate depends on its frequency, in particular through the formula:",
+      "Negative samples are then selected by choosing a sort of \u201cunigram distribution\", so that the most frequent words are also the most often backpropated ones."
+    ],
+    [
+      "The dataset needed to train the W2V was obtained using the information extracted from a dump of the Italian Wikipedia (dated 2019.04.01), from the main categories of Italian Google News (WORLD, NATION, BUSINESS, TECHNOLOGY, ENTERTAINMENT, SPORTS, SCIENCE, HEALTH) and from some anonymized chats between users and a customer care chatbot (Laila). The dataset (composed of 2.6 GB of raw text) includes $421\\,829\\,960$ words divided into $17\\,305\\,401$ sentences.",
+      "The text was previously preprocessed by removing the words whose absolute frequency was less than 5 and eliminating all special characters. Since it is impossible to represent every imaginable numerical value, but not wanting to eliminate the concept of \u201cnumerical representation\" linked to certain words, it was also decided to replace every number present in the text with the particular $\\langle NUM \\rangle $ token; which probably also assumes a better representation in the embedding space (not separating into the various possible values). All the words were then transformed to lowercase (to avoid a double presence) finally producing a vocabulary of $618\\,224$ words.",
+      "Note that among the special characters are also included punctuation marks, which therefore do not appear within the vocabulary. However, some of them (`.', `?' and `!') are later removed, as they are used to separate the sentences.",
+      "The Python implementation provided by Gensim was used for training the various embeddings all with size 300 and sampling parameter ($s$ in Equation DISPLAY_FORM3) set at $0.001$."
+    ],
+    [
+      "To analyse the results we chose to use the test provided by BIBREF10, which consists of $19\\,791$ analogies divided into 19 different categories: 6 related to the \u201csemantic\" macro-area (8915 analogies) and 13 to the \u201csyntactic\" one (10876 analogies). All the analogies are composed by two pairs of words that share a relation, schematized with the equation: $a:a^{*}=b:b^{*}$ (e.g. \u201cman : woman = king : queen\"); where $b^{*}$ is the word to be guessed (\u201cqueen\"), $b$ is the word coupled to it (\u201cking\"), $a$ is the word for the components to be eliminated (\u201cman\"), and $a^{*}$ is the word for the components to be added (\u201cwoman\").",
+      "The determination of the correct response was obtained both through the classical additive cosine distance (3COSADD) BIBREF5:",
+      "and through the multiplicative cosine distance (3COSMUL) BIBREF12:",
+      "where $\\epsilon =10^{-6}$ and $\\cos (x, y) = \\frac{x \\cdot y}{\\left\\Vert x\\right\\Vert \\left\\Vert y\\right\\Vert }$. The extremely low value chosen for the $\\epsilon $ is due to the desire to minimize as much as possible its impact on performance, as during the various testing phases we noticed a strange bound that is still being investigated. As usual, moreover, the representative vectors of the embedding space are previously normalized for the execution of the various tests."
+    ],
+    [
+      "We first analysed 6 different implementations of the Skip-gram model each one trained for 20 epochs. Table TABREF10 shows the accuracy values (only on possible analogies) at the 20th epoch for the six models both using 3COSADD and 3COSMUL. It is interesting to note that the 3COSADD total metric, respect to 3COSMUL, seems to have slightly better results in the two extreme cases of limited learning (W5N5 and W10N20) and under the semantic profile. However, we should keep in mind that the semantic profile is the one best captured by the network in both cases, which is probably due to the nature of the database (mainly composed of articles and news that principally use an impersonal language). In any case, the improvements that are obtained under the syntactic profile lead to the 3COSMUL metric obtaining better overall results.",
+      "Figure FIGREF11 shows the trends of the total accuracy at different epochs for the various models using 3COSMUL (the trend obtained with 3COSADD is very similar). Here we can see how the use of high negative sampling can worsen performance, even causing the network to oscillate (W5N20) in order to better adapt to all the data. The choice of the negative sampling to be used should therefore be strongly linked to the choice of the window size as well as to the number of training epochs.",
+      "Continuing the training of the two worst models up to the 50th epoch, it is observed (Table TABREF12) that they are still able to reach the performances of the other models. The W10N20 model at the 50th epoch even proves to be better than all the other previous models, becoming the reference model for subsequent comparisons. As the various epochs change (Figure FIGREF13.a) it appears to have the same oscillatory pattern observed previously, albeit with only one oscillation given the greater window size. This model is available at: https://mlunicampania.gitlab.io/italian-word2vec/.",
+      "Various tests were also conducted on CBOW models, which however proved to be in general significantly lower than Skip-gram models. Figure FIGREF13.b shows, for example, the accuracy trend for a CBOW model with a window equal to 10 and negative sampling equal to 20, which on 50 epochs reaches only $37.20\\%$ of total accuracy (with 3COSMUL metric)."
+    ],
+    [
+      "Finally, a comparison was made between the Skip-gram model W10N20 obtained at the 50th epoch and the other two W2V in Italian present in the literature (BIBREF9 and BIBREF10). The first test (Table TABREF15) was performed considering all the analogies present, and therefore evaluating as an error any analogy that was not executable (as it related to one or more words absent from the vocabulary).",
+      "As it can be seen, regardless of the metric used, our model has significantly better results than the other two models, both overall and within the two macro-areas. Furthermore, the other two models seem to be more subject to the metric used, perhaps due to a stabilization not yet reached for the few training epochs.",
+      "For a complete comparison, both models were also tested considering only the subset of the analogies in common with our model (i.e. eliminating from the test all those analogies that were not executable by one or the other model). Tables TABREF16 and TABREF17 again highlight the marked increase in performance of our model compared to both."
+    ],
+    [
+      "In this work we have analysed the Word2Vec model for Italian Language obtaining a substantial increase in performance respect to other two models in the literature (and despite the fixed size of the embedding). These results, in addition to the number of learning epochs, are probably also due to the different phase of data pre-processing, very carefully excuted in performing a complete cleaning of the text and above all in substituting the numerical values with a single particular token. We have observed that the number of epochs is an important parameter and its increase leads to results that rank our two worst models almost equal, or even better than others.",
+      "Changing the number of epochs, in some configurations, creates an oscillatory trend, which seems to be linked to a particular interaction between the window size and the negative sampling value. In the future, thanks to the collaboration in the Laila project, we intend to expand the dataset by adding more user chats. The objective will be to verify if the use of a less formal language can improves accuracy in the syntactic macro-area."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0736/instruction.md b/qasper-0736/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..464069ab3c5aa3110b80fe619427967159998a68
--- /dev/null
+++ b/qasper-0736/instruction.md
@@ -0,0 +1,72 @@
+Name of Paper: An Analysis of Word2Vec for the Italian Language
+
+Question: Are the semantic analysis findings for Italian language similar to English language version?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Word2Vec",
+    "Word2Vec ::: Sampling rate",
+    "Word2Vec ::: Negative sampling",
+    "Implementation details",
+    "Results",
+    "Results ::: Analysis of the various models",
+    "Results ::: Comparison with other models",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "In order to make human language comprehensible to a computer, it is obviously essential to provide some word encoding. The simplest approach is the one-hot encoding, where each word is represented by a sparse vector with dimension equal to the vocabulary size. In addition to the storage need, the main problem of this representation is that any concept of word similarity is completely ignored (each vector is orthogonal and equidistant from each other). On the contrary, the understanding of natural language cannot be separated from the semantic knowledge of words, which conditions a different closeness between them. Indeed, the semantic representation of words is the basic problem of Natural Language Processing (NLP). Therefore, there is a necessary need to code words in a space that is linked to their meaning, in order to facilitate a machine in potential task of \u201cunderstanding\" it. In particular, starting from the seminal work BIBREF0, words are usually represented as dense distributed vectors that preserve their uniqueness but, at the same time, are able to encode the similarities.",
+      "These word representations are called Word Embeddings since the words (points in a space of vocabulary size) are mapped in an embedding space of lower dimension. Supported by the distributional hypothesis BIBREF1 BIBREF2, which states that a word can be semantically characterized based on its context (i.e. the words that surround it in the sentence), in recent years many word embedding representations have been proposed (a fairly complete and updated review can be found in BIBREF3 and BIBREF4). These methods can be roughly categorized into two main classes: prediction-based models and count-based models. The former is generally linked to work on Neural Network Language Models (NNLM) and use a training algorithm that predicts the word given its local context, the latter leverage word-context statistics and co-occurrence counts in an entire corpus. The main prediction-based and count-based models are respectively Word2Vec BIBREF5 (W2V) and GloVe BIBREF6.",
+      "Despite the widespread use of these concepts BIBREF7 BIBREF8, few contributions exist regarding the development of a W2V that is not in English. In particular, no detailed analysis on an Italian W2V seems to be present in the literature, except for BIBREF9 and BIBREF10. However, both seem to leave out some elements of fundamental interest in the learning of the neural network, in particular relating to the number of epochs performed during learning, reducing the importance that it may have on the final result. In BIBREF9, this for example leads to the simplistic conclusion that (being able to organize with more freedom in space) the more space is given to the vectors, the better the results may be. However, the problem in complex structures is that large embedding spaces can make training too difficult.",
+      "In this work, by setting the size of the embedding to a commonly used average value, various parameters are analysed as the number of learning epochs changes, depending on the window sizes and the negatively backpropagated samples."
+    ],
+    [
+      "The W2V structure consists of a simple two-level neural network (Figure FIGREF1) with one-hot vectors representing words at the input. It can be trained in two different modes, algorithmically similar, but different in concept: Continuous Bag-of-Words (CBOW) model and Skip-Gram model. While CBOW tries to predict the target words from the context, Skip-Gram instead aims to determine the context for a given target word. The two different approaches therefore modify only the way in which the inputs and outputs are to be managed, but in any case, the network does not change, and the training always takes place between single pairs of words (placed as one-hot in input and output).",
+      "The text is in fact divided into sentences, and for each word of a given sentence a window of words is taken from the right and from the left to define the context. The central word is coupled with each of the words forming the set of pairs for training. Depending on the fact that the central word represents the output or the input in training pairs, the CBOW and Skip-gram models are obtained respectively.",
+      "Regardless of whether W2V is trained to predict the context or the target word, it is used as a word embedding in a substantially different manner from the one for which it has been trained. In particular, the second matrix is totally discarded during use, since the only thing relevant to the representation is the space of the vectors generated in the intermediate level (embedding space)."
+    ],
+    [
+      "The common words (such as \u201cthe\", \u201cof\", etc.) carry very little information on the target word with which they are coupled, and through backpropagation they tend to have extremely small representative vectors in the embedding space. To solve both these problems the W2V algorithm implements a particular \u201csubsampling\" BIBREF11, which acts by eliminating some words from certain sentences. Note that the elimination of a word directly from the text means that it no longer appears in the context of any of the words of the sentence and, at the same time, a number of pairs equal to (at most) twice the size of the window relating to the deleted word will also disappear from the training set.",
+      "In practice, each word is associated with a sort of \u201ckeeping probability\" and, when you meet that word, if this value is greater than a randomly generated value then the word will not be discarded from the text. The W2V implementation assigns this \u201cprobability\" to the generic word $w_i$ through the formula:",
+      "where $f(w_i)$ is the relative frequency of the word $w_i$ (namely $count(w_i)/total$), while $s$ is a sample value, typically set between $10^{-3}$ and $10^{-5}$."
+    ],
+    [
+      "Working with one-hot pairs of words means that the size of the network must be the same at input and output, and must be equal to the size of the vocabulary. So, although very simple, the network has a considerable number of parameters to train, which lead to an excessive computational cost if we are supposed to backpropagate all the elements of the one-hot vector in output.",
+      "The \u201cnegative sampling\" technique BIBREF11 tries to solve this problem by modifying only a small percentage of the net weights every time. In practice, for each pair of words in the training set, the loss function is calculated only for the value 1 and for a few values 0 of the one-hot vector of the desired output. The computational cost is therefore reduced by choosing to backpropagate only $K$ words \u201cnegative\" and one positive, instead of the entire vocabulary. Typical values for negative sampling (the number of negative samples that will be backpropagated and to which therefore the only positive value will always be added), range from 2 to 20, depending on the size of the dataset.",
+      "The probability of selecting a negative word to backpropagate depends on its frequency, in particular through the formula:",
+      "Negative samples are then selected by choosing a sort of \u201cunigram distribution\", so that the most frequent words are also the most often backpropated ones."
+    ],
+    [
+      "The dataset needed to train the W2V was obtained using the information extracted from a dump of the Italian Wikipedia (dated 2019.04.01), from the main categories of Italian Google News (WORLD, NATION, BUSINESS, TECHNOLOGY, ENTERTAINMENT, SPORTS, SCIENCE, HEALTH) and from some anonymized chats between users and a customer care chatbot (Laila). The dataset (composed of 2.6 GB of raw text) includes $421\\,829\\,960$ words divided into $17\\,305\\,401$ sentences.",
+      "The text was previously preprocessed by removing the words whose absolute frequency was less than 5 and eliminating all special characters. Since it is impossible to represent every imaginable numerical value, but not wanting to eliminate the concept of \u201cnumerical representation\" linked to certain words, it was also decided to replace every number present in the text with the particular $\\langle NUM \\rangle $ token; which probably also assumes a better representation in the embedding space (not separating into the various possible values). All the words were then transformed to lowercase (to avoid a double presence) finally producing a vocabulary of $618\\,224$ words.",
+      "Note that among the special characters are also included punctuation marks, which therefore do not appear within the vocabulary. However, some of them (`.', `?' and `!') are later removed, as they are used to separate the sentences.",
+      "The Python implementation provided by Gensim was used for training the various embeddings all with size 300 and sampling parameter ($s$ in Equation DISPLAY_FORM3) set at $0.001$."
+    ],
+    [
+      "To analyse the results we chose to use the test provided by BIBREF10, which consists of $19\\,791$ analogies divided into 19 different categories: 6 related to the \u201csemantic\" macro-area (8915 analogies) and 13 to the \u201csyntactic\" one (10876 analogies). All the analogies are composed by two pairs of words that share a relation, schematized with the equation: $a:a^{*}=b:b^{*}$ (e.g. \u201cman : woman = king : queen\"); where $b^{*}$ is the word to be guessed (\u201cqueen\"), $b$ is the word coupled to it (\u201cking\"), $a$ is the word for the components to be eliminated (\u201cman\"), and $a^{*}$ is the word for the components to be added (\u201cwoman\").",
+      "The determination of the correct response was obtained both through the classical additive cosine distance (3COSADD) BIBREF5:",
+      "and through the multiplicative cosine distance (3COSMUL) BIBREF12:",
+      "where $\\epsilon =10^{-6}$ and $\\cos (x, y) = \\frac{x \\cdot y}{\\left\\Vert x\\right\\Vert \\left\\Vert y\\right\\Vert }$. The extremely low value chosen for the $\\epsilon $ is due to the desire to minimize as much as possible its impact on performance, as during the various testing phases we noticed a strange bound that is still being investigated. As usual, moreover, the representative vectors of the embedding space are previously normalized for the execution of the various tests."
+    ],
+    [
+      "We first analysed 6 different implementations of the Skip-gram model each one trained for 20 epochs. Table TABREF10 shows the accuracy values (only on possible analogies) at the 20th epoch for the six models both using 3COSADD and 3COSMUL. It is interesting to note that the 3COSADD total metric, respect to 3COSMUL, seems to have slightly better results in the two extreme cases of limited learning (W5N5 and W10N20) and under the semantic profile. However, we should keep in mind that the semantic profile is the one best captured by the network in both cases, which is probably due to the nature of the database (mainly composed of articles and news that principally use an impersonal language). In any case, the improvements that are obtained under the syntactic profile lead to the 3COSMUL metric obtaining better overall results.",
+      "Figure FIGREF11 shows the trends of the total accuracy at different epochs for the various models using 3COSMUL (the trend obtained with 3COSADD is very similar). Here we can see how the use of high negative sampling can worsen performance, even causing the network to oscillate (W5N20) in order to better adapt to all the data. The choice of the negative sampling to be used should therefore be strongly linked to the choice of the window size as well as to the number of training epochs.",
+      "Continuing the training of the two worst models up to the 50th epoch, it is observed (Table TABREF12) that they are still able to reach the performances of the other models. The W10N20 model at the 50th epoch even proves to be better than all the other previous models, becoming the reference model for subsequent comparisons. As the various epochs change (Figure FIGREF13.a) it appears to have the same oscillatory pattern observed previously, albeit with only one oscillation given the greater window size. This model is available at: https://mlunicampania.gitlab.io/italian-word2vec/.",
+      "Various tests were also conducted on CBOW models, which however proved to be in general significantly lower than Skip-gram models. Figure FIGREF13.b shows, for example, the accuracy trend for a CBOW model with a window equal to 10 and negative sampling equal to 20, which on 50 epochs reaches only $37.20\\%$ of total accuracy (with 3COSMUL metric)."
+    ],
+    [
+      "Finally, a comparison was made between the Skip-gram model W10N20 obtained at the 50th epoch and the other two W2V in Italian present in the literature (BIBREF9 and BIBREF10). The first test (Table TABREF15) was performed considering all the analogies present, and therefore evaluating as an error any analogy that was not executable (as it related to one or more words absent from the vocabulary).",
+      "As it can be seen, regardless of the metric used, our model has significantly better results than the other two models, both overall and within the two macro-areas. Furthermore, the other two models seem to be more subject to the metric used, perhaps due to a stabilization not yet reached for the few training epochs.",
+      "For a complete comparison, both models were also tested considering only the subset of the analogies in common with our model (i.e. eliminating from the test all those analogies that were not executable by one or the other model). Tables TABREF16 and TABREF17 again highlight the marked increase in performance of our model compared to both."
+    ],
+    [
+      "In this work we have analysed the Word2Vec model for Italian Language obtaining a substantial increase in performance respect to other two models in the literature (and despite the fixed size of the embedding). These results, in addition to the number of learning epochs, are probably also due to the different phase of data pre-processing, very carefully excuted in performing a complete cleaning of the text and above all in substituting the numerical values with a single particular token. We have observed that the number of epochs is an important parameter and its increase leads to results that rank our two worst models almost equal, or even better than others.",
+      "Changing the number of epochs, in some configurations, creates an oscillatory trend, which seems to be linked to a particular interaction between the window size and the negative sampling value. In the future, thanks to the collaboration in the Laila project, we intend to expand the dataset by adding more user chats. The objective will be to verify if the use of a less formal language can improves accuracy in the syntactic macro-area."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0738/instruction.md b/qasper-0738/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..99852c42edd4ff2b041b90dca74134b7c543f45f
--- /dev/null
+++ b/qasper-0738/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Improving Character-based Decoding Using Target-Side Morphological Information for Neural Machine Translation
+
+Question: How are the auxiliary signals from the morphology table incorporated in the decoder?
\ No newline at end of file
diff --git a/qasper-0742/instruction.md b/qasper-0742/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..f9606f172428dc593edaae79eedd2b33f1dc07f7
--- /dev/null
+++ b/qasper-0742/instruction.md
@@ -0,0 +1,43 @@
+Name of Paper: Learning Twitter User Sentiments on Climate Change with Limited Labeled Data
+
+Question: Which machine learning models are used?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Background",
+    "Data",
+    "Labeling Methodology",
+    "Outcome Analysis",
+    "Results & Discussion"
+  ],
+  "paragraphs": [
+    [
+      "Much prior work has been done at the intersection of climate change and Twitter, such as tracking climate change sentiment over time BIBREF2 , finding correlations between Twitter climate change sentiment and seasonal effects BIBREF3 , and clustering Twitter users based on climate mentalities using network analysis BIBREF4 . Throughout, Twitter has been accepted as a powerful tool given the magnitude and reach of samples unattainable from standard surveys. However, the aforementioned studies are not scalable with regards to training data, do not use more recent sentiment analysis tools (such as neural nets), and do not consider unbiased comparisons pre- and post- various climate events (which would allow for a more concrete evaluation of shocks to climate change sentiment). This paper aims to address these three concerns as follows.",
+      "First, we show that machine learning models formed using our labeling technique can accurately predict tweet sentiment (see Section SECREF2 ). We introduce a novel method to intuit binary sentiments of large numbers of tweets for training purposes. Second, we quantify unbiased outcomes from these predicted sentiments (see Section SECREF4 ). We do this by comparing sentiments within the same cohort of Twitter users tweeting both before and after specific natural disasters; this removes bias from over-weighting Twitter users who are only compelled to compose tweets after a disaster."
+    ],
+    [
+      "We henceforth refer to a tweet affirming climate change as a \u201cpositive\" sample (labeled as 1 in the data), and a tweet denying climate change as a \u201cnegative\" sample (labeled as -1 in the data). All data were downloaded from Twitter in two separate batches using the \u201ctwint\" scraping tool BIBREF5 to sample historical tweets for several different search terms; queries always included either \u201cclimate change\" or \u201cglobal warming\", and further included disaster-specific search terms (e.g., \u201cbomb cyclone,\" \u201cblizzard,\" \u201csnowstorm,\" etc.). We refer to the first data batch as \u201cinfluential\" tweets, and the second data batch as \u201cevent-related\" tweets.",
+      "The first data batch consists of tweets relevant to blizzards, hurricanes, and wildfires, under the constraint that they are tweeted by \u201cinfluential\" tweeters, who we define as individuals certain to have a classifiable sentiment regarding the topic at hand. For example, we assume that any tweet composed by Al Gore regarding climate change is a positive sample, whereas any tweet from conspiracy account @ClimateHiJinx is a negative sample. The assumption we make in ensuing methods (confirmed as reasonable in Section SECREF2 ) is that influential tweeters can be used to label tweets in bulk in the absence of manually-labeled tweets. Here, we enforce binary labels for all tweets composed by each of the 133 influential tweeters that we identified on Twitter (87 of whom accept climate change), yielding a total of 16,360 influential tweets.",
+      "The second data batch consists of event-related tweets for five natural disasters occurring in the U.S. in 2018. These are: the East Coast Bomb Cyclone (Jan. 2 - 6); the Mendocino, California wildfires (Jul. 27 - Sept. 18); Hurricane Florence (Aug. 31 - Sept. 19); Hurricane Michael (Oct. 7 - 16); and the California Camp Fires (Nov. 8 - 25). For each disaster, we scraped tweets starting from two weeks prior to the beginning of the event, and continuing through two weeks after the end of the event. Summary statistics on the downloaded event-specific tweets are provided in Table TABREF1 . Note that the number of tweets occurring prior to the two 2018 sets of California fires are relatively small. This is because the magnitudes of these wildfires were relatively unpredictable, whereas blizzards and hurricanes are often forecast weeks in advance alongside public warnings. The first (influential tweet data) and second (event-related tweet data) batches are de-duplicated to be mutually exclusive. In Section SECREF2 , we perform geographic analysis on the event-related tweets from which we can scrape self-reported user city from Twitter user profile header cards; overall this includes 840 pre-event and 5,984 post-event tweets.",
+      "To create a model for predicting sentiments of event-related tweets, we divide the first data batch of influential tweets into training and validation datasets with a 90%/10% split. The training set contains 49.2% positive samples, and the validation set contains 49.0% positive samples. We form our test set by manually labeling a subset of 500 tweets from the the event-related tweets (randomly chosen across all five natural disasters), of which 50.0% are positive samples."
+    ],
+    [
+      "Our first goal is to train a sentiment analysis model (on training and validation datasets) in order to perform classification inference on event-based tweets. We experimented with different feature extraction methods and classification models. Feature extractions examined include Tokenizer, Unigram, Bigram, 5-char-gram, and td-idf methods. Models include both neural nets (e.g. RNNs, CNNs) and standard machine learning tools (e.g. Naive Bayes with Laplace Smoothing, k-clustering, SVM with linear kernel). Model accuracies are reported in Table FIGREF3 .",
+      "The RNN pre-trained using GloVe word embeddings BIBREF6 achieved the higest test accuracy. We pass tokenized features into the embedding layer, followed by an LSTM BIBREF7 with dropout and ReLU activation, and a dense layer with sigmoid activation. We apply an Adam optimizer on the binary crossentropy loss. Implementing this simple, one-layer LSTM allows us to surpass the other traditional machine learning classification methods. Note the 13-point spread between validation and test accuracies achieved. Ideally, the training, validation, and test datasets have the same underlying distribution of tweet sentiments; the assumption made with our labeling technique is that the influential accounts chosen are representative of all Twitter accounts. Critically, when choosing the influential Twitter users who believe in climate change, we highlighted primarily politicians or news sources (i.e., verifiably affirming or denying climate change); these tweets rarely make spelling errors or use sarcasm. Due to this skew, the model yields a high rate of false negatives. It is likely that we could lessen the gap between validation and test accuracies by finding more \u201creal\" Twitter users who are climate change believers, e.g. by using the methodology found in BIBREF4 ."
+    ],
+    [
+      "Our second goal is to compare the mean values of users' binary sentiments both pre- and post- each natural disaster event. Applying our highest-performing RNN to event-related tweets yields the following breakdown of positive tweets: Bomb Cyclone (34.7%), Mendocino Wildfire (80.4%), Hurricane Florence (57.2%), Hurricane Michael (57.6%), and Camp Fire (70.1%). As sanity checks, we examine the predicted sentiments on a subset with geographic user information and compare results to the prior literature.",
+      "In Figure FIGREF3 , we map 4-clustering results on three dimensions: predicted sentiments, latitude, and longitude. The clusters correspond to four major regions of the U.S.: the Northeast (green), Southeast (yellow), Midwest (blue), and West Coast (purple); centroids are designated by crosses. Average sentiments within each cluster confirm prior knowledge BIBREF1 : the Southeast and Midwest have lower average sentiments ( INLINEFORM0 and INLINEFORM1 , respectively) than the West Coast and Northeast (0.22 and 0.09, respectively). In Figure FIGREF5 , we plot predicted sentiment averaged by U.S. city of event-related tweeters. The majority of positive tweets emanate from traditionally liberal hubs (e.g. San Francisco, Los Angeles, Austin), while most negative tweets come from the Philadelphia metropolitan area. These regions aside, rural areas tended to see more negative sentiment tweeters post-event, whereas urban regions saw more positive sentiment tweeters; however, overall average climate change sentiment pre- and post-event was relatively stable geographically. This map further confirms findings that coastal cities tend to be more aware of climate change BIBREF8 .",
+      "From these mapping exercises, we claim that our \u201cinfluential tweet\" labeling is reasonable. We now discuss our final method on outcomes: comparing average Twitter sentiment pre-event to post-event. In Figure FIGREF8 , we display these metrics in two ways: first, as an overall average of tweet binary sentiment, and second, as a within-cohort average of tweet sentiment for the subset of tweets by users who tweeted both before and after the event (hence minimizing awareness bias). We use Student's t-test to calculate the significance of mean sentiment differences pre- and post-event (see Section SECREF4 ). Note that we perform these mean comparisons on all event-related data, since the low number of geo-tagged samples would produce an underpowered study."
+    ],
+    [
+      "In Figure FIGREF8 , we see that overall sentiment averages rarely show movement post-event: that is, only Hurricane Florence shows a significant difference in average tweet sentiment pre- and post-event at the 1% level, corresponding to a 0.12 point decrease in positive climate change sentiment. However, controlling for the same group of users tells a different story: both Hurricane Florence and Hurricane Michael have significant tweet sentiment average differences pre- and post-event at the 1% level. Within-cohort, Hurricane Florence sees an increase in positive climate change sentiment by 0.21 points, which is contrary to the overall average change (the latter being likely biased since an influx of climate change deniers are likely to tweet about hurricanes only after the event). Hurricane Michael sees an increase in average tweet sentiment of 0.11 points, which reverses the direction of tweets from mostly negative pre-event to mostly positive post-event. Likely due to similar bias reasons, the Mendocino wildfires in California see a 0.06 point decrease in overall sentiment post-event, but a 0.09 point increase in within-cohort sentiment. Methodologically, we assert that overall averages are not robust results to use in sentiment analyses.",
+      "We now comment on the two events yielding similar results between overall and within-cohort comparisons. Most tweets regarding the Bomb Cyclone have negative sentiment, though sentiment increases by 0.02 and 0.04 points post-event for overall and within-cohort averages, respectively. Meanwhile, the California Camp Fires yield a 0.11 and 0.27 point sentiment decline in overall and within-cohort averages, respectively. This large difference in sentiment change can be attributed to two factors: first, the number of tweets made regarding wildfires prior to the (usually unexpected) event is quite low, so within-cohort users tend to have more polarized climate change beliefs. Second, the root cause of the Camp Fires was quickly linked to PG&E, bolstering claims that climate change had nothing to do with the rapid spread of fire; hence within-cohort users were less vocally positive regarding climate change post-event.",
+      "There are several caveats in our work: first, tweet sentiment is rarely binary (this work could be extended to a multinomial or continuous model). Second, our results are constrained to Twitter users, who are known to be more negative than the general U.S. population BIBREF9 . Third, we do not take into account the aggregate effects of continued natural disasters over time. Going forward, there is clear demand in discovering whether social networks can indicate environmental metrics in a \u201cnowcasting\" fashion. As climate change becomes more extreme, it remains to be seen what degree of predictive power exists in our current model regarding climate change sentiments with regards to natural disasters."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0743/instruction.md b/qasper-0743/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..c4348637adf6b43c1d77f69f4db23a8cc6151a6d
--- /dev/null
+++ b/qasper-0743/instruction.md
@@ -0,0 +1,43 @@
+Name of Paper: Learning Twitter User Sentiments on Climate Change with Limited Labeled Data
+
+Question: What methodology is used to compensate for limited labelled data?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Background",
+    "Data",
+    "Labeling Methodology",
+    "Outcome Analysis",
+    "Results & Discussion"
+  ],
+  "paragraphs": [
+    [
+      "Much prior work has been done at the intersection of climate change and Twitter, such as tracking climate change sentiment over time BIBREF2 , finding correlations between Twitter climate change sentiment and seasonal effects BIBREF3 , and clustering Twitter users based on climate mentalities using network analysis BIBREF4 . Throughout, Twitter has been accepted as a powerful tool given the magnitude and reach of samples unattainable from standard surveys. However, the aforementioned studies are not scalable with regards to training data, do not use more recent sentiment analysis tools (such as neural nets), and do not consider unbiased comparisons pre- and post- various climate events (which would allow for a more concrete evaluation of shocks to climate change sentiment). This paper aims to address these three concerns as follows.",
+      "First, we show that machine learning models formed using our labeling technique can accurately predict tweet sentiment (see Section SECREF2 ). We introduce a novel method to intuit binary sentiments of large numbers of tweets for training purposes. Second, we quantify unbiased outcomes from these predicted sentiments (see Section SECREF4 ). We do this by comparing sentiments within the same cohort of Twitter users tweeting both before and after specific natural disasters; this removes bias from over-weighting Twitter users who are only compelled to compose tweets after a disaster."
+    ],
+    [
+      "We henceforth refer to a tweet affirming climate change as a \u201cpositive\" sample (labeled as 1 in the data), and a tweet denying climate change as a \u201cnegative\" sample (labeled as -1 in the data). All data were downloaded from Twitter in two separate batches using the \u201ctwint\" scraping tool BIBREF5 to sample historical tweets for several different search terms; queries always included either \u201cclimate change\" or \u201cglobal warming\", and further included disaster-specific search terms (e.g., \u201cbomb cyclone,\" \u201cblizzard,\" \u201csnowstorm,\" etc.). We refer to the first data batch as \u201cinfluential\" tweets, and the second data batch as \u201cevent-related\" tweets.",
+      "The first data batch consists of tweets relevant to blizzards, hurricanes, and wildfires, under the constraint that they are tweeted by \u201cinfluential\" tweeters, who we define as individuals certain to have a classifiable sentiment regarding the topic at hand. For example, we assume that any tweet composed by Al Gore regarding climate change is a positive sample, whereas any tweet from conspiracy account @ClimateHiJinx is a negative sample. The assumption we make in ensuing methods (confirmed as reasonable in Section SECREF2 ) is that influential tweeters can be used to label tweets in bulk in the absence of manually-labeled tweets. Here, we enforce binary labels for all tweets composed by each of the 133 influential tweeters that we identified on Twitter (87 of whom accept climate change), yielding a total of 16,360 influential tweets.",
+      "The second data batch consists of event-related tweets for five natural disasters occurring in the U.S. in 2018. These are: the East Coast Bomb Cyclone (Jan. 2 - 6); the Mendocino, California wildfires (Jul. 27 - Sept. 18); Hurricane Florence (Aug. 31 - Sept. 19); Hurricane Michael (Oct. 7 - 16); and the California Camp Fires (Nov. 8 - 25). For each disaster, we scraped tweets starting from two weeks prior to the beginning of the event, and continuing through two weeks after the end of the event. Summary statistics on the downloaded event-specific tweets are provided in Table TABREF1 . Note that the number of tweets occurring prior to the two 2018 sets of California fires are relatively small. This is because the magnitudes of these wildfires were relatively unpredictable, whereas blizzards and hurricanes are often forecast weeks in advance alongside public warnings. The first (influential tweet data) and second (event-related tweet data) batches are de-duplicated to be mutually exclusive. In Section SECREF2 , we perform geographic analysis on the event-related tweets from which we can scrape self-reported user city from Twitter user profile header cards; overall this includes 840 pre-event and 5,984 post-event tweets.",
+      "To create a model for predicting sentiments of event-related tweets, we divide the first data batch of influential tweets into training and validation datasets with a 90%/10% split. The training set contains 49.2% positive samples, and the validation set contains 49.0% positive samples. We form our test set by manually labeling a subset of 500 tweets from the the event-related tweets (randomly chosen across all five natural disasters), of which 50.0% are positive samples."
+    ],
+    [
+      "Our first goal is to train a sentiment analysis model (on training and validation datasets) in order to perform classification inference on event-based tweets. We experimented with different feature extraction methods and classification models. Feature extractions examined include Tokenizer, Unigram, Bigram, 5-char-gram, and td-idf methods. Models include both neural nets (e.g. RNNs, CNNs) and standard machine learning tools (e.g. Naive Bayes with Laplace Smoothing, k-clustering, SVM with linear kernel). Model accuracies are reported in Table FIGREF3 .",
+      "The RNN pre-trained using GloVe word embeddings BIBREF6 achieved the higest test accuracy. We pass tokenized features into the embedding layer, followed by an LSTM BIBREF7 with dropout and ReLU activation, and a dense layer with sigmoid activation. We apply an Adam optimizer on the binary crossentropy loss. Implementing this simple, one-layer LSTM allows us to surpass the other traditional machine learning classification methods. Note the 13-point spread between validation and test accuracies achieved. Ideally, the training, validation, and test datasets have the same underlying distribution of tweet sentiments; the assumption made with our labeling technique is that the influential accounts chosen are representative of all Twitter accounts. Critically, when choosing the influential Twitter users who believe in climate change, we highlighted primarily politicians or news sources (i.e., verifiably affirming or denying climate change); these tweets rarely make spelling errors or use sarcasm. Due to this skew, the model yields a high rate of false negatives. It is likely that we could lessen the gap between validation and test accuracies by finding more \u201creal\" Twitter users who are climate change believers, e.g. by using the methodology found in BIBREF4 ."
+    ],
+    [
+      "Our second goal is to compare the mean values of users' binary sentiments both pre- and post- each natural disaster event. Applying our highest-performing RNN to event-related tweets yields the following breakdown of positive tweets: Bomb Cyclone (34.7%), Mendocino Wildfire (80.4%), Hurricane Florence (57.2%), Hurricane Michael (57.6%), and Camp Fire (70.1%). As sanity checks, we examine the predicted sentiments on a subset with geographic user information and compare results to the prior literature.",
+      "In Figure FIGREF3 , we map 4-clustering results on three dimensions: predicted sentiments, latitude, and longitude. The clusters correspond to four major regions of the U.S.: the Northeast (green), Southeast (yellow), Midwest (blue), and West Coast (purple); centroids are designated by crosses. Average sentiments within each cluster confirm prior knowledge BIBREF1 : the Southeast and Midwest have lower average sentiments ( INLINEFORM0 and INLINEFORM1 , respectively) than the West Coast and Northeast (0.22 and 0.09, respectively). In Figure FIGREF5 , we plot predicted sentiment averaged by U.S. city of event-related tweeters. The majority of positive tweets emanate from traditionally liberal hubs (e.g. San Francisco, Los Angeles, Austin), while most negative tweets come from the Philadelphia metropolitan area. These regions aside, rural areas tended to see more negative sentiment tweeters post-event, whereas urban regions saw more positive sentiment tweeters; however, overall average climate change sentiment pre- and post-event was relatively stable geographically. This map further confirms findings that coastal cities tend to be more aware of climate change BIBREF8 .",
+      "From these mapping exercises, we claim that our \u201cinfluential tweet\" labeling is reasonable. We now discuss our final method on outcomes: comparing average Twitter sentiment pre-event to post-event. In Figure FIGREF8 , we display these metrics in two ways: first, as an overall average of tweet binary sentiment, and second, as a within-cohort average of tweet sentiment for the subset of tweets by users who tweeted both before and after the event (hence minimizing awareness bias). We use Student's t-test to calculate the significance of mean sentiment differences pre- and post-event (see Section SECREF4 ). Note that we perform these mean comparisons on all event-related data, since the low number of geo-tagged samples would produce an underpowered study."
+    ],
+    [
+      "In Figure FIGREF8 , we see that overall sentiment averages rarely show movement post-event: that is, only Hurricane Florence shows a significant difference in average tweet sentiment pre- and post-event at the 1% level, corresponding to a 0.12 point decrease in positive climate change sentiment. However, controlling for the same group of users tells a different story: both Hurricane Florence and Hurricane Michael have significant tweet sentiment average differences pre- and post-event at the 1% level. Within-cohort, Hurricane Florence sees an increase in positive climate change sentiment by 0.21 points, which is contrary to the overall average change (the latter being likely biased since an influx of climate change deniers are likely to tweet about hurricanes only after the event). Hurricane Michael sees an increase in average tweet sentiment of 0.11 points, which reverses the direction of tweets from mostly negative pre-event to mostly positive post-event. Likely due to similar bias reasons, the Mendocino wildfires in California see a 0.06 point decrease in overall sentiment post-event, but a 0.09 point increase in within-cohort sentiment. Methodologically, we assert that overall averages are not robust results to use in sentiment analyses.",
+      "We now comment on the two events yielding similar results between overall and within-cohort comparisons. Most tweets regarding the Bomb Cyclone have negative sentiment, though sentiment increases by 0.02 and 0.04 points post-event for overall and within-cohort averages, respectively. Meanwhile, the California Camp Fires yield a 0.11 and 0.27 point sentiment decline in overall and within-cohort averages, respectively. This large difference in sentiment change can be attributed to two factors: first, the number of tweets made regarding wildfires prior to the (usually unexpected) event is quite low, so within-cohort users tend to have more polarized climate change beliefs. Second, the root cause of the Camp Fires was quickly linked to PG&E, bolstering claims that climate change had nothing to do with the rapid spread of fire; hence within-cohort users were less vocally positive regarding climate change post-event.",
+      "There are several caveats in our work: first, tweet sentiment is rarely binary (this work could be extended to a multinomial or continuous model). Second, our results are constrained to Twitter users, who are known to be more negative than the general U.S. population BIBREF9 . Third, we do not take into account the aggregate effects of continued natural disasters over time. Going forward, there is clear demand in discovering whether social networks can indicate environmental metrics in a \u201cnowcasting\" fashion. As climate change becomes more extreme, it remains to be seen what degree of predictive power exists in our current model regarding climate change sentiments with regards to natural disasters."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0744/instruction.md b/qasper-0744/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..c2e55977f53e21c6282496c3e2dc1943fba32d16
--- /dev/null
+++ b/qasper-0744/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Learning Twitter User Sentiments on Climate Change with Limited Labeled Data
+
+Question: Which five natural disasters were examined?
\ No newline at end of file
diff --git a/qasper-0745/instruction.md b/qasper-0745/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..b5d205fd513b19b26a23a5618b42a70d7838e98d
--- /dev/null
+++ b/qasper-0745/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: A multimodal deep learning approach for named entity recognition from social media
+
+Question: Which social media platform is explored?
\ No newline at end of file
diff --git a/qasper-0752/instruction.md b/qasper-0752/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..82628ab91bce821350f282d9ea78c446d0f67be2
--- /dev/null
+++ b/qasper-0752/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Domain Adaptation of Recurrent Neural Networks for Natural Language Understanding
+
+Question: Does the performance increase using their method?
\ No newline at end of file
diff --git a/qasper-0755/instruction.md b/qasper-0755/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..c162924e14e202cc09d038f6c2e3aff800e81db7
--- /dev/null
+++ b/qasper-0755/instruction.md
@@ -0,0 +1,111 @@
+Name of Paper: Align, Mask and Select: A Simple Method for Incorporating Commonsense Knowledge into Language Representation Models
+
+Question: How do they select answer candidates for their QA task?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Language Representation Model",
+    "Commonsense Reasoning",
+    "Distant Supervision",
+    "Commonsense Knowledge Base",
+    "Constructing Pre-training Dataset",
+    "Pre-training BERT_CS",
+    "Experiments",
+    "CommonsenseQA",
+    "Winograd Schema Challenge",
+    "GLUE",
+    "Pre-training Strategy",
+    "Performance Curve",
+    "Error Analysis",
+    "Conclusion",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "Pre-trained language representation models, including feature-based methods BIBREF0 , BIBREF1 and fine-tuning methods BIBREF2 , BIBREF3 , BIBREF4 , can capture rich language information from text and then benefit many NLP tasks. Bidirectional Encoder Representations from Transformers (BERT) BIBREF4 , as one of the most recently developed models, has produced the state-of-the-art results by simple fine-tuning on various NLP tasks, including named entity recognition (NER) BIBREF5 , text classification BIBREF6 , natural language inference (NLI) BIBREF7 , question answering (QA) BIBREF8 , BIBREF9 , and has achieved human-level performances on several datasets BIBREF8 , BIBREF9 .",
+      "However, commonsense reasoning is still a challenging task for modern machine learning methods. For example, recently BIBREF10 proposed a commonsense-related task, CommonsenseQA, and showed that the BERT model accuracy remains dozens of points lower than human accuracy on the questions about commonsense knowledge. Some examples from CommonsenseQA are shown in Table 1 part A. As can be seen from the examples, although it is easy for humans to answer the questions based on their knowledge about the world, it is a great challenge for machines when there is limited training data.",
+      "We hypothesize that exploiting knowledge graphs for commonsense in QA modeling can help model choose correct answers. For example, as shown in the part B of Table 1 , some triples from ConceptNet BIBREF11 are quite related to the questions above. Exploiting these triples in the QA modeling may benefit the QA models to make a correct decision.",
+      "In this paper, we propose a pre-training approach that can leverage commmonsense knowledge graphs, such as ConceptNet BIBREF11 , to improve the commonsense reasoning capability of language representation models, such as BERT. And at the same time, the proposed approach targets maintaining comparable performances on other NLP tasks with the original BERT models. It is challenging to incorporate the commonsense knowledge into language representation models since the commonsense knowledge is represented as a structured format, such as (concept $_1$ , relation, concept $_2$ ) in ConceptNet, which is inconsistent with the data used for pre-training language representation models. For example, BERT is pre-trained on the BooksCorpus and English Wikipedia that are composed of unstructured natural language sentences.",
+      "To tackle the challenge mentioned above, inspired by the distant supervision approach BIBREF12 , we propose the \u201calign, mask and select\" (AMS) method that can align the commonsense knowledge graphs with a large text corpus to construct a dataset consisting of sentences with labeled concepts. Different from the pre-training tasks for BERT, the masked language model (MLM) and next sentence prediction (NSP) tasks, we use the generated dataset in a multi-choice question answering task. We then pre-train the BERT model on this dataset with the multi-choice question answering task and fine-tune it on various commonsense-related tasks, such as CommonsenseQA BIBREF10 and Winograd Schema Challenge (WSC) BIBREF13 , and achieve significant improvements. We also fine-tune and evaluate the pre-trained models on other NLP tasks, such as sentence classification and NLI tasks, such as GLUE BIBREF6 , and achieve comparable performance with the original BERT models.",
+      "In summary, the contributions of this paper are threefold. First, we propose a pre-training approach for incorporating commonsense knowledge into language representation models for improving the commonsense reasoning capabilities of these models. Second, We propose an \u201calign, mask and select\" (AMS) method, inspired by the distant supervision approaches, to automatically construct a multi-choice question answering dataset. Third, Experiments demonstrate that the pre-trained model from the proposed approach with fine-tuning achieves significant performance improvements on several commonsense-related tasks, such as CommonsenseQA BIBREF10 and Winograd Schema Challenge BIBREF13 , and still maintains comparable performances on several sentence classification and NLI tasks in GLUE BIBREF6 ."
+    ],
+    [
+      "Language representation models have demonstrated their effectiveness for improving many NLP tasks. These approaches can be categorized into feature-based approaches and fine-tuning approaches. The early Word2Vec BIBREF14 and Glove models BIBREF0 focused on feature-based approaches to transform words into distributed representations. However, these methods suffered from the insufficiency for word disambiguation. BIBREF15 further proposed Embeddings from Language Models (ELMo) that derive context-aware word vectors from a bidirectional LSTM, which is trained with a coupled language model (LM) objective on a large text corpus.",
+      "The fine-tuning approaches are different from the above-mentioned feature-based language approaches which only use the pre-trained language representations as input features. BIBREF2 pre-trained sentence encoders from unlabeled text and fine-tuned for a supervised downstream task. BIBREF3 proposed a generative pre-trained Transformer BIBREF16 (GPT) to learn language representations. BIBREF4 proposed a deep bidirectional model with multi-layer Transformers (BERT), which achieved the state-of-the-art performance for a wide variety of NLP tasks. The advantage of these approaches is that few parameters need to be learned from scratch.",
+      "Though both feature-based and fine-tuning language representation models have achieved great success, they did not incorporate the commonsense knowledge. In this paper, we focus on incorporate commonsense knowledge into pre-training of language representation models."
+    ],
+    [
+      "Commonsense reasoning is a challenging task for modern machine learning methods. As demonstrated in recent work BIBREF17 , incorporating commonsense knowledge into question answering models in a model-integration fashion helped improve commonsense reasoning ability. Instead of ensembling two independent models as in BIBREF17 , an alternative direction is to directly incorporate commonsense knowledge into an unified language representation model. BIBREF18 proposed to directly pre-training BERT on commonsense knowledge triples. For any triple (concept $_1$ , relation, concept $_2$ ), they took the concatenation of concept $_1$ and relation as the question and concept $_2$ as the correct answer. Distractors were formed by randomly picking words or phrases in the ConceptNet. In this work, we also investigate directly incorporating commonsense knowledge into an unified language representation model. However, we hypothesize that the language representations learned in BIBREF18 may be tampered since the inputs to the model constructed this way are not natural language sentences. To address this issue, we propose a pre-training approach for incorporating commonsense knowledge that includes a method to construct large-scale, natural language sentences. BIBREF19 collected the Common Sense Explanations (CoS-E) dataset using Amazon Mechanical Turk and applied a Commonsense Auto-Generated Explanations (CAGE) framework to language representation models, such as GPT and BERT. However, collecting this dataset used a large amount of human efforts. In contrast, in this paper, we propose an \u201calign, mask and select\" (AMS) method, inspired by the distant supervision approaches, to automatically construct a multi-choice question answering dataset."
+    ],
+    [
+      "The distant supervision approach was originally proposed for generating training data for the relation classification task. The distant supervision approach BIBREF12 assumes that if two entities/concepts participate in a relation, all sentences that mention these two entities/concepts express that relation. Note that it is inevitable that there exists noise in the data labeled by distant supervision BIBREF20 . In this paper, instead of employing the relation labels labeled by distant supervision, we focus on the aligned entities/concepts. We propose the AMS method to construct a multi-choice QA dataset that align sentences with commonsense knowledge triples, mask the aligned words (entities/concepts) in sentences and treat the masked sentences as questions, and select several entities/concepts from knowledge graphs as candidate choices."
+    ],
+    [
+      "This section describes the commonsense knowledge base investigated in our experiments. We use the ConceptNet BIBREF11 , one of the most widely used commonsense knowledge bases. ConceptNet is a semantic network that represents the large sets of words and phrases and the commonsense relationships between them. It contains over 21 million edges and over 8 million nodes. Its English vocabulary contains approximately 1,500,000 nodes, and for 83 languages, it contains at least 10,000 nodes for each of them, respectively. ConceptNet contains a core of 36 relations.",
+      "Each instance in ConceptNet can be generally represented as a triple $r_i$ = (concept $_1$ , relation, concept $_2$ ), indicating relation between the two concepts concept $_1$ and concept $_2$ . For example, the triple (semicarbazide, IsA, chemical compound) means that \u201csemicarbazide is a kind of chemical compounds\"; the triple (cooking dinner, Causes, cooked food) means that \u201cthe effect of cooking dinner is cooked food\", etc."
+    ],
+    [
+      "In this section, we describe the details of constructing the commonsense-related multi-choice question answering dataset. Firstly, we filter the triples in ConceptNet with the following steps: (1) Filter triples in which one of the concepts is not English words. (2) Filter triples with the general relations \u201cRelatedTo\" and \u201cIsA\", which hold a large proportion in ConceptNet. (3) Filter triples in which one of the concepts has more than four words or the edit distance between the two concepts is less than four. After filtering, we obtain 606,564 triples.",
+      "Each training sample is generated by three steps: align, mask and select, which we call as AMS method. Each sample in the dataset consists of a question and several candidate answers, which has the same form as the CommonsenseQA dataset. An example of constructing one training sample by masking concept $_2$ is shown in Table 2 .",
+      "Firstly, we align each triple (concept $_1$ , relation, concept $_2$ ) from ConceptNet to the English Wikipedia dataset to extract the sentences with their concepts labeled. Secondly, we mask the concept $_1$ /concept $_2$ in one sentence with a special token [QW] and treat this sentence as a question, where QW is a replacement word of the question words \u201cwhat\", \u201cwhere\", etc. And the masked concept $_1$ /concept $_2$ is the correct answer for this question. Thirdly, for generating the distractors, BIBREF18 proposed a method to form distractors by randomly picking words or phrases in ConceptNet. In this paper, in order to generate more confusing distractors than the random selection approach, we request those distractors and the correct answer share the same concept $_2$ or concept $_1$ and the relation. That is to say, we search ( $\\ast $ , relation, concept $_2$ ) and (concept $_2$0 , relation, $_2$1 ) in ConceptNet to select the distractors instead of random selection, where $_2$2 is a wildcard character that can match any word or phrase. For each question, we reserve four distractors and one correct answer. If there are less than four matched distractors, we discard this question instead of complementing it with random selection. If there are more than four distractors, we randomly select four distractors from them. After applying the AMS method, we create 16,324,846 multi-choice question answering samples."
+    ],
+    [
+      "We investigate a multi-choice question-answering task for pre-training the English BERT base and BERT large models released by Google on our constructed dataset. The resulting models are denoted BERT_CS $_{base}$ and BERT_CS $_{large}$ , respectively. We then investigate the performance of fine-tuning the BERT_CS models on several NLP tasks, including commonsense-related tasks and common NLP tasks, presented in Section \"Experiments\" .",
+      "To reduce the large cost of training BERT_CS models from scratch, we initialize the BERT_CS models (for both BERT $_{base}$ and BERT $_{large}$ models) with the parameter weights released by Google. We concatenate the question with each answer to construct a standard input sequence for BERT_CS (i.e., \u201c[CLS] the largest [QW] by ... ? [SEP] city [SEP]\u201d, where [CLS] and [SEP] are two special tokens), and the hidden representations over the [CLS] token are run through a softmax layer to create the predictions.",
+      "The objective function is defined as follows: ",
+      "$$L = - {\\rm logp}(c_i|s),$$   (Eq. 10) ",
+      "$${\\rm p}(c_i|s) = \\frac{{\\rm exp}(\\mathbf {w}^{T}\\mathbf {c}_{i})}{\\sum _{k=1}^{N}{\\rm exp}(\\mathbf {w}^{T}\\mathbf {c}_{k})},$$   (Eq. 11) ",
+      "where $c_i$ is the correct answer, $\\mathbf {w}$ are the parameters in the softmax layer, N is the total number of all candidates, and $\\mathbf {c}_i$ is the vector representation of the special token [CLS]. We pre-train BERT_CS models with the batch size 160, the initial learning rate $2e^{-5}$ and the max sequence length 128 for 1 epoch. The pre-training is conducted on 16 NVIDIA V100 GPU cards with 32G memory for about 3 days for the BERT_CS $_{large}$ model and 1 day for the BERT_CS $_{base}$ model."
+    ],
+    [
+      "In this section, we investigate the performance of fine-tuning the BERT_CS models on several NLP tasks. Note that when fine tuning on multi-choice QA tasks, e.g., CommonsenseQA and Winograd Schema Challenge (see section 5.3), we fine-tune all parameters in BERT_CS, including the last softmax layer from the token [CLS]; whereas, for other tasks, we randomly initialize the classifier layer and train it from scratch.",
+      "Additionally, as described in BIBREF4 , fine-tuning on BERT sometimes is observed to be unstable on small datasets, so we run experiments with 5 different random seeds and select the best model based on the development set for all of the fine-tuning experiments in this section."
+    ],
+    [
+      "In this subsection, we conduct experiments on a commonsense-related multi-choice question answering benchmark, the CommonsenseQA dataset BIBREF10 . The CommonsenseQA dataset consists of 12,247 questions with one correct answer and four distractor answers. This dataset consists of two splits \u2013 the question token split and the random split. Our experiments are conducted on the more challenging random split, which is the main evaluation split according to BIBREF10 . The statistics of the CommonsenseQA dataset are shown in Table 3 .",
+      "Same as the pre-training stage, the input data for fine-tuning the BERT_CS models is formed by concatenating each question-answer pair as a sequence. The hidden representations over the [CLS] token are run through a softmax layer to create the predictions. The objective function is the same as Equations 10 and 11 . We fine-tune the BERT_CS models on CommonsenseQA for 2 epochs with a learning rate of 1e-5 and a batch size of 16.",
+      "Table 4 shows the accuracies on the CommonsenseQA test set from the baseline BERT models released by Google, the previous state-of-the-art model CoS-E BIBREF19 , and our BERT_CS models. Note that CoS-E model requires a large amount of human effort to collect the Common Sense Explanations (CoS-E) dataset. In comparison, we construct our multi-choice question-answering dataset automatically. The BERT_CS models significantly outperform the baseline BERT model counterparts. BERT_CS $_{large}$ achieves a 5.5% absolute improvement on the CommonsenseQA test set over the baseline BERT $_{large}$ model and a 4% absolute improvement over the previous SOTA CoS-E model."
+    ],
+    [
+      "The Winograd Schema Challenge (WSC) BIBREF13 is introduced for testing AI agents for commonsense knowledge. The WSC consists of 273 instances of the pronoun disambiguation problem (PDP). For example, for sentence \u201cThe delivery truck zoomed by the school bus because it was going so fast.\u201d and a corresponding question \u201cWhat does the word it refers to?\u201d, the machine is expected to answer \u201cdelivery truck\u201d instead of \u201cschool bus\u201d. In this task, we follow BIBREF22 and employ the WSCR dataset BIBREF23 as the extra training data. The WSCR dataset is split into a training set of 1322 examples and a test set of 564 examples. We use these data for fine-tuning and validating BERT_CS models, respectively, and test the fine-tuned BERT_CS models on the WSC dataset.",
+      "We transform the pronoun disambiguation problem into a multi-choice question answering problem. We mask the pronoun word with a special token [QW] to construct a question, and put the two candidate paragraphs as candidate answers. The remaining procedures are the same as QA tasks. We use the same loss function as BIBREF22 , that is, if c $_1$ is correct and c $_2$ is not, the loss is ",
+      "$$\\begin{aligned}\nL = &- {\\rm logp}(c_1|s) + \\\\\n&\\alpha \\cdot max(0, {\\rm logp}(c_2|s)-{\\rm logp}(c_1|s)+\\beta ), \\end{aligned}$$   (Eq. 16) ",
+      "where $p(c_1|s)$ follows Equation 11 with $N=2$ , $\\alpha $ and $\\beta $ are two hyper-parameters. Similar to BIBREF22 , we search $\\alpha \\in \\lbrace 2.5,5,10,20\\rbrace $ and $\\beta \\in \\lbrace 0.05,0.1,0.2,0.4\\rbrace $ by comparing the accuracy on the WSCR test set (i.e., the development set for the WSC data set). We set the batch size 16 and the learning rate $1e^{-5}$ . We evaluate our models on the WSC dataset, as well as the various partitions of the WSC dataset, as described in BIBREF24 . We also evaluate the fine-tuned BERT_CS model (without using the WNLI training data for further fine-tuning) on the WNLI test set, one of the GLUE tasks. We first transform the examples in WNLI from the premise-hypothesis format into the pronoun disambiguation problem format and then transform it into the multi-choice QA format BIBREF22 .",
+      "The results on the WSC dataset and its various partitions and the WNLI test set are shown in Table 5 . Note that the results for BIBREF21 are fine-tuned on the whole WSCR dataset, including the training and test sets. Results for LM ensemble BIBREF25 and Knowledge Hunter BIBREF26 are taken from BIBREF24 . Results for \u201cBERT $_{large}$ + MTP\" is taken from BIBREF22 as the baseline of applying BERT to the WSC task.",
+      "As can be seen from Table 5 , the \u201cBERT $_{large}$ + MCQA\" achieves better performance than \u201cBERT $_{large}$ + MTP\" on four of the seven evaluation criteria and achieves significant improvement on the assoc. and consist. partitions, which demonstrates that MCQA is a better pre-processing method than MTP for the WSC task. Also, the \u201cBERT_CS $_{large}$ + MCQA\" achieves the best performance on all of the evaluation criteria but consist., and achieves a 3.3% absolute improvement on the WSC dataset over the previous SOTA results from BIBREF22 ."
+    ],
+    [
+      "The General Language Understanding Evaluation (GLUE) benchmark BIBREF6 is a collection of diverse natural language understanding tasks, including MNLI, QQP, QNLI, SST-2, CoLA, STS-B, MRPC, of which CoLA and SST-2 are single-sentence tasks, MRPC, STS-B and QQP are similarity and paraphrase tasks, and MNLI, QNLI, RTE and WNLI are natural language inference tasks. To investigate whether our multi-choice QA based pre-training approach degenerates the performance on common sentence classification tasks, we evaluate the BERT_CS $_{base}$ and BERT_CS $_{large}$ models on 8 GLUE datasets and compare the performances with those from the baseline BERT models.",
+      "Following BIBREF4 , we use the batch size 32 and fine-tune for 3 epochs for all GLUE tasks, and select the fine-tuning learning rate (among 1e-5, 2e-5, and 3e-5) based on the performance on the development set. Results are presented in Table 6 . We observe that the BERT_CS $_{large}$ model achieves comparable performance with the BERT $_{large}$ model and the BERT_CS $_{base}$ model achieves slightly better performance than the BERT $_{base}$ model. We hypothesize that the commonsense knowledge may not be required for GLUE tasks. On the other hand, these results demonstrate that our proposed multi-choice QA pre-training task does not degrade the sentence representation capabilities of BERT models."
+    ],
+    [
+      "In this subsection, we conduct several comparison experiments using different data and different pre-training tasks on the BERT $_{base}$ model. For simplicity, we discard the subscript $base$ in this subsection.",
+      "The first set of experiments is to compare the efficacy of our data creation approach versus the data creation approach in BIBREF18 . First, same as BIBREF18 , we collect 606,564 triples from ConceptNet, and construct 1,213,128 questions, each with a correct answer and four distractors. This dataset is denoted the TRIPLES dataset. We pre-train BERT models on the TRIPLES dataset with the same hyper-parameters as the BERT_CS models and the resulting model is denoted BERT_triple. We also create several model counterparts based on our constructed dataset:",
+      "Distractors are formed by randomly picking concept $_1$ /concept $_2$ in ConceptNet instead of those sharing the same concept $_2$ /concept $_1$ and the relation with the correct answers. We denote the resulting model from this dataset BERT_CS_random.",
+      "Instead of pre-training BERT with a multi-choice QA task that chooses the correct answer from several candidate answers, we mask concept $_1$ and concept $_2$ and pre-train BERT with a masked language model (MLM) task. We denote the resulting model from this pre-training task BERT_MLM.",
+      "We randomly mask 15% WordPiece tokens BIBREF27 of the question as in BIBREF4 and then conduct both multi-choice QA task and MLM task simultaneously. The resulting model is denoted BERT_CS_MLM.",
+      "All these BERT models are fine-tuned on the CommonsenseQA dataset with the same hyper-parameters as described in Section \"CommonsenseQA\" and the results are shown in Table 7 . We observe the following from Table 7 .",
+      "Comparing model 1 and model 2, we find that pre-training on ConceptNet benefits the CommonsenseQA task even with the triples as input instead of sentences. Further comparing model 2 and model 6, we find that constructing sentences as input for pre-training BERT performs better on the CommonsenseQA task than using triples for pre-training BERT. We also conduct more detailed comparisons between fine-tuning model 1 and model 2 on GLUE tasks. The results are shown in Table 6 . BERT_triple $_{base}$ yields much worse results than BERT $_{base}$ and BERT_CS $_{base}$ , which demonstrates that pre-training directly on triples may hurt the sentence representation capabilities of BERT.",
+      "Comparing model 3 and model 6, we find that pre-training BERT benefits from a more difficult dataset. In our selection method, all candidate answers share the same (concept $_1$ , relation) or (relation, concept $_2$ ), that is, these candidates have close meanings. These more confusing candidates force BERT_CS to distinguish synonym meanings, resulting in a more powerful BERT_CS model.",
+      "Comparing model 5 and model 6, we find that the multi-choice QA task works better than the masked LM task as the pre-training task for the target multi-choice QA task. We argue that, for the masked LM task, BERT_CS is required to predict each masked wordpieces (in concepts) independently and for the multi-choice QA task, BERT is required to model the whole candidate phrases. In this way, BERT is able to model the whole concepts instead of paying much attention to the single wordpieces in the sentences. Comparing model 4 and model 6, we observe that adding the masked LM task may hurt the performance of BERT_CS. This is probably because the masked words in questions may have a negative influence on the multi-choice QA task. Finally, our proposed model BERT_CS achieves the best performance on the CommonsenseQA development set among these model counterparts."
+    ],
+    [
+      "In this subsection, we plot the performance curve on CommonsenseQA development set from BERT_CS over the pre-training steps. For every 10,000 training steps, we save the model as the initial model for fine-tuning. For every of these models, we run experiments for 10 times repeatedly with random restarts, that is, we use the same pre-trained checkpoint but perform different fine-tuning data shuffling. Due to the unstability of fine-tuning BERT BIBREF4 , we remove the results that are significantly lower than the mean. In our experiments, we remove the accuracy lower than 0.57 for BERT_CS $_{base}$ and 0.60 for BERT_CS $_{large}$ . We plot the mean and standard deviation values in Figure 1 . We observe that the performance of BERT_CS $_{base}$ converges around 50,000 training steps and BERT_CS $_{large}$ converges around the end of the pre-training stage or may not have converged, which demonstrates that the BERT_CS $_{large}$ is more powerful at incorporating commonsense knowledge. We also compare with pre-training BERT_CS models for 2 epochs. However, our model produces worse performance probably due to over-fitting. Pre-training on a larger corpus (with more QA samples) may benefit the BERT_CS models and we leave this to the future work."
+    ],
+    [
+      "Table 8 shows several cases from the Winograd Schema Challenge dataset. Questions 1 and 2 only differ in the words \u201ccompassionate\" and \u201ccruel\". Our model BERT_CS $_{large}$ chooses correct answers for both questions while BERT $_{large}$ chooses the same choice \u201cBill\" for both questions. We speculate that BERT $_{large}$ tends to choosing the closer candidates. We split WSC test set into two parts CLOSE and FAR according as the correct candidate is closer or farther to the pronoun word in the sentence than another candidate. As shown in Table 9 , our model BERT_CS $_{large}$ achieves the same performance on CLOSE set and better performance on FAR set than BERT $_{large}$ . That's to say, BERT_CS $_{large}$ is more robust to the position of the words and focuses more on the semantic of the sentence.",
+      "Questions 3 and 4 only differ in the words \u201clarge\" and \u201csmall\". However, neither BERT_CS $_{large}$ nor BERT $_{large}$ chooses the correct answers. We hypothesize that since \u201csuitcase is large\" and \u201ctrophy is small\" are probably quite frequent for language models, both BERT $_{large}$ and BERT_CS $_{large}$ models make mistakes. In future work, we will investigate other approaches for overcoming the sensitivity of language models and improving commonsense reasoning."
+    ],
+    [
+      "In this paper, we develop a pre-training approach for incorporating commonsense knowledge into language representation models such as BERT. We construct a commonsense-related multi-choice question answering dataset for pre-training BERT. The dataset is created automatically by our proposed \u201calign, mask, and select\" (AMS) method. Experimental results demonstrate that pre-training models using the proposed approach followed by fine-tuning achieves significant improvements on various commonsense-related tasks, such as CommonsenseQA and Winograd Schema Challenge, while maintaining comparable performance on other NLP tasks, such as sentence classification and natural language inference (NLI) tasks, compared to the original BERT models. In future work, we will incorporate the relationship information between two concepts into language representation models. We will also explore other structured knowledge graphs, such as Freebase, to incorporate entity information into language representation models. We also plan to incorporate commonsense knowledge information into other language representation models such as XLNet BIBREF28 ."
+    ],
+    [
+      "The authors would like to thank Lingling Jin, Pengfei Fan, Xiaowei Lu for supporting 16 NVIDIA V100 GPU cards."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0772/instruction.md b/qasper-0772/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..afbeceee8f23d284438fcb55fe5d4cec0a69fda9
--- /dev/null
+++ b/qasper-0772/instruction.md
@@ -0,0 +1,65 @@
+Name of Paper: The First Evaluation of Chinese Human-Computer Dialogue Technology
+
+Question: How many intents were classified?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "The First Evaluation of Chinese Human-Computer Dialogue Technology",
+    "Task 1: User Intent Classification",
+    "Task 2: Online Testing of Task-oriented Dialogue",
+    "Evaluation Data",
+    "Evaluation Results",
+    "Conclusion",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "Recently, human-computer dialogue has been emerged as a hot topic, which has attracted the attention of both academia and industry. In research, the natural language understanding (NLU), dialogue management (DM) and natural language generation (NLG) have been promoted by the technologies of big data and deep learning BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 . Following the development of machine reading comprehension BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 , the NLU technology has made great progress. The development of DM technology is from rule-based approach and supervised learning based approach to reinforcement learning based approach BIBREF15 . The NLG technology is through pattern-based approach, sentence planning approach and end-to-end deep learning approach BIBREF16 , BIBREF17 , BIBREF18 . In application, there are massive products that are based on the technology of human-computer dialogue, such as Apple Siri, Amazon Echo, Microsoft Cortana, Facebook Messenger and Google Allo etc.",
+      "Although the blooming of human-computer dialogue technology in both academia and industry, how to evaluate a dialogue system, especially an open domain chit-chat system, is still an open question. Figure FIGREF6 presents a brief comparison of the open domain chit-chat system and the task-oriented dialogue system.",
+      "From Figure FIGREF6 , we can see that it is quite different between the open domain chit-chat system and the task-oriented dialogue system. For the open domain chit-chat system, as it has no exact goal in a conversation, given an input message, the responses can be various. For example, for the input message \u201cHow is it going today?\u201d, the responses can be \u201cI'm fine!\u201d, \u201cNot bad.\u201d, \u201cI feel so depressed!\u201d, \u201cWhat a bad day!\u201d, etc. There may be infinite number of responses for an open domain messages. Hence, it is difficult to construct a gold standard (usually a reference set) to evaluate a response which is generated by an open domain chit-chat system. For the task-oriented system, although there are some objective evaluation metrics, such as the number of turns in a dialogue, the ratio of task completion, etc., there is no gold standard for automatically evaluating two (or more) dialogue systems when considering the satisfaction of the human and the fluency of the generated dialogue.",
+      "To promote the development of the evaluation technology for dialogue systems, especially considering the language characteristics of Chinese, we organize the first evaluation of Chinese human-computer dialogue technology. In this paper, we will present the evaluation scheme and the released corpus in detail.",
+      "The rest of this paper is as follows. In Section 2, we will briefly introduce the first evaluation of Chinese human-computer dialogue technology, which includes the descriptions and the evaluation metrics of the two tasks. We then present the evaluation data and final results in Section 3 and 4 respectively, following the conclusion and acknowledgements in the last two sections."
+    ],
+    [
+      "The First Evaluation of Chinese Human-Computer Dialogue Technology includes two tasks, namely user intent classification and online testing of task-oriented dialogue."
+    ],
+    [
+      "In using of human-computer dialogue based applications, human may have various intent, for example, chit-chatting, asking questions, booking air tickets, inquiring weather, etc. Therefore, after receiving an input message (text or ASR result) from a user, the first step is to classify the user intent into a specific domain for further processing. Table TABREF7 shows an example of user intent with category information.",
+      "In task 1, there are two top categories, namely, chit-chat and task-oriented dialogue. The task-oriented dialogue also includes 30 sub categories. In this evaluation, we only consider to classify the user intent in single utterance.",
+      "It is worth noting that besides the released data for training and developing, we also allow to collect external data for training and developing. To considering that, the task 1 is indeed includes two sub tasks. One is a closed evaluation, in which only the released data can be used for training and developing. The other is an open evaluation that allow to collect external data for training and developing. For task 1, we use F1-score as evaluation metric."
+    ],
+    [
+      "For the task-oriented dialogue systems, the best way for evaluation is to use the online human-computer dialogue. After finishing an online human-computer dialogue with a dialogue system, the human then manually evaluate the system by using the metrics of user satisfaction degree, dialogue fluency, etc. Therefore, in the task 2, we use an online testing of task-oriented dialogue for dialogue systems. For a human tester, we will give a complete intent with an initial sentence, which is used to start the online human-computer dialogue. Table TABREF12 shows an example of the task-oriented human-computer dialogue. Here \u201cU\u201d and \u201cR\u201d denote user and robot respectively. The complete intent is as following:",
+      "\u201c\u00e6\u009f\u00a5\u00e8\u00af\u00a2\u00e6\u0098\u008e\u00e5\u00a4\u00a9\u00e4\u00bb\u008e\u00e5\u0093\u0088\u00e5\u00b0\u0094\u00e6\u00bb\u00a8\u00e5\u0088\u00b0\u00e5\u008c\u0097\u00e4\u00ba\u00ac\u00e7\u009a\u0084\u00e6\u0099\u009a\u00e9\u0097\u00b4\u00e8\u00bd\u00af\u00e5\u008d\u00a7\u00e7\u0081\u00ab\u00e8\u00bd\u00a6\u00e7\u00a5\u00a8\u00ef\u00bc\u008c\u00e4\u00b8\u008a\u00e4\u00b8\u008b\u00e9\u0093\u00ba\u00e5\u009d\u0087\u00e5\u008f\u00af\u00e3\u0080\u0082",
+      "Inquire the soft berth ticket at tomorrow evening, from Harbin to Beijing, either upper or lower berth is okay.\u201d",
+      "In task 2, there are three categories. They are \u201cair tickets\u201d, \u201ctrain tickets\u201d and \u201chotel\u201d. Correspondingly, there are three type of tasks. All the tasks are in the scope of the three categories. However, a complete user intent may include more than one task. For example, a user may first inquiring the air tickets. However, due to the high price, the user decide to buy a train tickets. Furthermore, the user may also need to book a hotel room at the destination.",
+      "We use manual evaluation for task 2. For each system and each complete user intent, the initial sentence, which is used to start the dialogue, is the same. The tester then begin to converse to each system. A dialogue is finished if the system successfully returns the information which the user inquires or the number of dialogue turns is larger than 30 for a single task. For building the dialogue systems of participants, we release an example set of complete user intent and three data files of flight, train and hotel in JSON format. There are five evaluation metrics for task 2 as following.",
+      "Task completion ratio: The number of completed tasks divided by the number of total tasks.",
+      "User satisfaction degree: There are five scores -2, -1, 0, 1, 2, which denote very dissatisfied, dissatisfied, neutral, satisfied and very satisfied, respectively.",
+      "Response fluency: There are three scores -1, 0, 1, which indicate nonfluency, neutral, fluency.",
+      "Number of dialogue turns: The number of utterances in a task-completed dialogue.",
+      "Guidance ability for out of scope input: There are two scores 0, 1, which represent able to guide or unable to guide.",
+      "For the number of dialogue turns, we have a penalty rule that for a dialogue task, if the system cannot return the result (or accomplish the task) in 30 turns, the dialogue task is end by force. Meanwhile, if a system cannot accomplish a task in less than 30 dialogue turns, the number of dialogue turns is set to 30."
+    ],
+    [
+      "In the evaluation, all the data for training, developing and test is provided by the iFLYTEK Corporation.",
+      "For task 1, as the descriptions in Section SECREF10 , the two top categories are chit-chat (chat in Table TABREF13 ) and task-oriented dialogue. Meanwhile, the task-oriented dialogue also includes 30 sub categories. Actually, the task 1 is a 31 categories classification task. In task 1, besides the data we released for training and developing, we also allow the participants to extend the training and developing corpus. Hence, there are two sub tasks for the task 1. One is closed test, which means the participants can only use the released data for training and developing. The other is open test, which allows the participants to explore external corpus for training and developing. Note that there is a same test set for both the closed test and the open test.",
+      "For task 2, we release 11 examples of the complete user intent and 3 data file, which includes about one month of flight, hotel and train information, for participants to build their dialogue systems. The current date for online test is set to April 18, 2017. If the tester says \u201ctoday\u201d, the systems developed by the participants should understand that he/she indicates the date of April 18, 2017."
+    ],
+    [
+      "There are 74 participants who are signing up the evaluation. The final number of participants is 28 and the number of submitted systems is 43. Table TABREF14 and TABREF15 show the evaluation results of the closed test and open test of the task 1 respectively. Due to the space limitation, we only present the top 5 results of task 1. We will add the complete lists of the evaluation results in the version of full paper.",
+      "Note that for task 2, there are 7 submitted systems. However, only 4 systems can provide correct results or be connected in a right way at the test phase. Therefore, Table TABREF16 shows the complete results of the task 2."
+    ],
+    [
+      "In this paper, we introduce the first evaluation of Chinese human-computer dialogue technology. In detail, we first present the two tasks of the evaluation as well as the evaluation metrics. We then describe the released data for evaluation. Finally, we also show the evaluation results of the two tasks. As the evaluation data is provided by the iFLYTEK Corporation from their real online applications, we believe that the released data will further promote the research of human-computer dialogue and fill the blank of the data on the two tasks."
+    ],
+    [
+      "We would like to thank the Social Media Processing (SMP) committee of Chinese Information Processing Society of China. We thank all the participants of the first evaluation of Chinese human-computer dialogue technology. We also thank the testers from the voice resource department of the iFLYTEK Corporation for their effort to the online real-time human-computer dialogue test and offline dialogue evaluation. We thank Lingzhi Li, Yangzi Zhang, Jiaqi Zhu and Xiaoming Shi from the research center for social computing and information retrieval for their support on the data annotation, establishing the system testing environment and the communication to the participants and help connect their systems to the testing environment."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0773/instruction.md b/qasper-0773/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..f86d5ea865a52822e535de80fa747a2052cc949e
--- /dev/null
+++ b/qasper-0773/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: The First Evaluation of Chinese Human-Computer Dialogue Technology
+
+Question: What was the result of the highest performing system?
\ No newline at end of file
diff --git a/qasper-0774/instruction.md b/qasper-0774/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..8815a33780d791739ca520cfeccc209ef4f682c0
--- /dev/null
+++ b/qasper-0774/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: The First Evaluation of Chinese Human-Computer Dialogue Technology
+
+Question: What metrics are used in the evaluation?
\ No newline at end of file
diff --git a/qasper-0775/instruction.md b/qasper-0775/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..8b744f47aefbce30cd5d294ee7f1b47a43db9fb5
--- /dev/null
+++ b/qasper-0775/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Multi-style Generative Reading Comprehension
+
+Question: How do they measure the quality of summaries?
\ No newline at end of file
diff --git a/qasper-0780/instruction.md b/qasper-0780/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..51631cfccf5da6b05359238854e80efabb161560
--- /dev/null
+++ b/qasper-0780/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Multi-style Generative Reading Comprehension
+
+Question: What is the performance achieved on NarrativeQA?
\ No newline at end of file
diff --git a/qasper-0781/instruction.md b/qasper-0781/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..c127318b6ea46f2a2abb627543bd1a6f9069b62c
--- /dev/null
+++ b/qasper-0781/instruction.md
@@ -0,0 +1,127 @@
+Name of Paper: Multi-style Generative Reading Comprehension
+
+Question: What is an "answer style"?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Problem Formulation",
+    "Proposed Model",
+    "Question-Passages Reader",
+    "Passage Ranker",
+    "Answer Possibility Classifier",
+    "Answer Sentence Decoder",
+    "Loss Function",
+    "Setup",
+    "Results",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Question answering has been a long-standing research problem. Recently, reading comprehension (RC), a challenge to answer a question given textual evidence provided in a document set, has received much attention. Here, current mainstream studies have treated RC as a process of extracting an answer span from one passage BIBREF0 , BIBREF1 or multiple passages BIBREF2 , which is usually done by predicting the start and end positions of the answer BIBREF3 , BIBREF4 .",
+      "The demand for answering questions in natural language is increasing rapidly, and this has led to the development of smart devices such as Siri and Alexa. However, in comparison with answer span extraction, the natural language generation (NLG) ability for RC has been less studied. While datasets such as MS MARCO BIBREF5 have been proposed for providing abstractive answers in natural language, the state-of-the-art methods BIBREF6 , BIBREF7 are based on answer span extraction, even for the datasets. Generative models such as S-Net BIBREF8 suffer from a dearth of training data to cover open-domain questions.",
+      "Moreover, to satisfy various information needs, intelligent agents should be capable of answering one question in multiple styles, such as concise phrases that do not contain the context of the question and well-formed sentences that make sense even without the context of the question. These capabilities complement each other; however, the methods used in previous studies cannot utilize and control different answer styles within a model.",
+      "In this study, we propose a generative model, called Masque, for multi-passage RC. On the MS MARCO 2.1 dataset, Masque achieves state-of-the-art performance on the dataset's two tasks, Q&A and NLG, with different answer styles. The main contributions of this study are that our model enables the following two abilities."
+    ],
+    [
+      "The task considered in this paper, is defined as:",
+      "Problem 1 Given a question with $J$ words $x^q = \\lbrace x^q_1, \\ldots , x^q_J\\rbrace $ , a set of $K$ passages, where each $k$ -th passage is composed of $L$ words $x^{p_k} = \\lbrace x^{p_k}_1, \\ldots , x^{p_k}_{L}\\rbrace $ , and an answer style $s$ , an RC system outputs an answer $y = \\lbrace y_1, \\ldots , y_T \\rbrace $ conditioned on the style.",
+      "In short, for inference, given a set of 3-tuples $(x^q, \\lbrace x^{p_k}\\rbrace , s)$ , the system predicts $P(y)$ . The training data is a set of 6-tuples: $(x^q, \\lbrace x^{p_k}\\rbrace , s, y, a, \\lbrace r^{p_k}\\rbrace )$ , where $a$ is 1 if the question is answerable with the provided passages and 0 otherwise, and $r^{p_k}$ is 1 if the $k$ -th passage is required to formulate the answer and 0 otherwise."
+    ],
+    [
+      "Our proposed model, Masque, is based on multi-source abstractive summarization; the answer our model generates can be viewed as a summary from the question and multiple passages. It is also style-controllable; one model can generate the answer with the target style.",
+      "Masque directly models the conditional probability $p(y|x^q, \\lbrace x^{p_k}\\rbrace , s)$ . In addition to multi-style learning, it considers passage ranking and answer possibility classification together as multi-task learning in order to improve accuracy. Figure 2 shows the model architecture. It consists of the following modules.",
+      " 1 The question-passages reader (\u00a7 \"Question-Passages Reader\" ) models interactions between the question and passages.",
+      " 2 The passage ranker (\u00a7 \"Passage Ranker\" ) finds relevant passages to the question.",
+      " 3 The answer possibility classifier (\u00a7 \"Answer Possibility Classifier\" ) identifies answerable questions.",
+      " 4 The answer sentence decoder (\u00a7 \"Answer Sentence Decoder\" ) outputs a sequence of words conditioned on the style."
+    ],
+    [
+      "Given a question and passages, the question-passages reader matches them so that the interactions among the question (passage) words conditioned on the passages (question) can be captured.",
+      "Let $x^q$ and $x^{p_k}$ represent one-hot vectors of words in the question and $k$ -th passage. First, this layer projects each of the one-hot vectors (of size $V$ ) into a $d_\\mathrm {word}$ -dimensional continuous vector space with a pre-trained weight matrix $W^e \\in \\mathbb {R}^{d_\\mathrm {word} \\times V}$ such as GloVe BIBREF15 . Next, it uses contextualized word representations, ELMo BIBREF16 , which is a character-level two-layer bidirectional language model pre-trained on a large-scale corpus. ELMo representations allow our model to use morphological clues to form robust representations for out-of-vocabulary words unseen in training. Then, the concatenation of the word and contextualized embedding vectors is passed to a two-layer highway network BIBREF17 that is shared for the question and passages.",
+      "This layer uses a stack of Transformer blocks, which are shared for the question and passages, on top of the embeddings provided by the word embedding layer. The input of the first block is immediately mapped to a $d$ -dimensional vector by a linear transformation. The outputs of this layer are sequences of $d$ -dimensional vectors: $E^{p_k} \\in \\mathbb {R}^{d \\times L}$ for the $k$ -th passage and $E^q \\in \\mathbb {R}^{d \\times J}$ for the question.",
+      "It consists of two sub-layers: a self-attention layer and a position-wise feed-forward network. For the self-attention layer, we adopt the multi-head attention mechanism defined in BIBREF12 . The feed-forward network consists of two linear transformations with a GELU BIBREF18 activation in between, following OpenAI GPT BIBREF19 . Each sub-layer is placed inside a residual block BIBREF20 . For an input $x$ and a given sub-layer function $f$ , the output is $\\mathrm {LayerNorm}(f(x)+x)$ , where $\\mathrm {LayerNorm}$ indicates the layer normalization proposed in BIBREF21 . To facilitate these residual connections, all sub-layers produce outputs of dimension $d$ . Note that our model does not use any position embeddings because ELMo gives the positional information of the words in each sequence.",
+      "This layer fuses information from the passages to the question as well as from the question to the passages in a dual mechanism.",
+      "It first computes a similarity matrix $U^{p_k} \\in \\mathbb {R}^{L{\\times }J}$ between the question and $k$ -th passage, as is done in BIBREF22 , where ",
+      "$$U^{p_k}_{lj} = {w^a}^\\top [ E^{p_k}_l; E^q_j; E^{p_k}_l \\odot E^q_j ]$$   (Eq. 15) ",
+      " indicates the similarity between the $l$ -th word of the $k$ -th passage and the $j$ -th question word. $w^a \\in \\mathbb {R}^{3d}$ are learnable parameters. The $\\odot $ operator denotes the Hadamard product, and the $[;]$ operator means vector concatenation across the rows. Next, it obtains the row and column normalized similarity matrices $A^{p_k} = \\mathrm {softmax}_j({U^{p_k}}^\\top ) \\in \\mathbb {R}^{J\\times L}$ and $B^{p_k} = \\mathrm {softmax}_{l}(U^{p_k}) \\in \\mathbb {R}^{L \\times J}$ . We use DCN BIBREF23 as the dual attention mechanism to obtain question-to-passage representations $G^{q \\rightarrow p_k} \\in \\mathbb {R}^{5d \\times L}$ : ",
+      "$$\\nonumber [E^{p_k}; \\bar{A}^{p_k}; \\bar{\\bar{A}}^{p_k}; E^{p_k} \\odot \\bar{A}^{p_k}; E^{p_k} \\odot \\bar{\\bar{A}}^{p_k}]$$   (Eq. 16) ",
+      " and passage-to-question ones $G^{p \\rightarrow q} \\in \\mathbb {R}^{5d \\times J}$ : ",
+      "$$\\begin{split}\n\\nonumber & [ E^{q} ; \\max _k(\\bar{B}^{p_k}); \\max _k(\\bar{\\bar{B}}^{p_k}); \\\\\n&\\hspace{10.0pt} E^{q} \\odot \\max _k(\\bar{B}^{p_k}); E^{q} \\odot \\max _k(\\bar{\\bar{B}}^{p_k}) ] \\mathrm {\\ \\ where}\n\\end{split}\\\\\n\\nonumber &\\bar{A}^{p_k} = E^q A^{p_k}\\in \\mathbb {R}^{d \\times L}, \\ \\bar{B}^{p_k} = E^{p_k} B^{p_k} \\in \\mathbb {R}^{d \\times J} \\\\\n\\nonumber &\\bar{\\bar{A}}^{p_k} = \\bar{B}^{p_k} A^{p_k} \\in \\mathbb {R}^{d \\times L}, \\ \\bar{\\bar{B}}^{p_k} = \\bar{A}^{p_k} B^{p_k} \\in \\mathbb {R}^{d \\times J}.$$   (Eq. 17) ",
+      "This layer uses a stack of Transformer encoder blocks for question representations and obtains $M^q \\in \\mathbb {R}^{d \\times J}$ from $G^{p \\rightarrow q}$ . It also uses an another stack for passage representations and obtains $M^{p_k} \\in \\mathbb {R}^{d \\times L}$ from $G^{q \\rightarrow p_k}$ for each $k$ -th passage. The outputs of this layer, $M^q$ and $\\lbrace M^{p_k}\\rbrace $ , are passed on to the answer sentence decoder; the $\\lbrace M^{p_k}\\rbrace $ are also passed on to the passage ranker and answer possibility classifier."
+    ],
+    [
+      "The passage ranker maps the output of the modeling layer, $\\lbrace M^{p_k}\\rbrace $ , to the relevance score of each passage. To obtain a fixed-dimensional pooled representation of each passage sequence, this layer takes the output for the first passage word, $M^{p_k}_1$ , which corresponds to the beginning-of-sentence token. It calculates the relevance of each $k$ -th passage to the question as: ",
+      "$$\\beta ^{p_k} = \\mathrm {sigmoid}({w^r}^\\top M^{p_k}_1),$$   (Eq. 20) ",
+      " where $w^r \\in \\mathbb {R}^{d}$ are learnable parameters."
+    ],
+    [
+      "The answer possibility classifier maps the output of the modeling layer, $\\lbrace M^{p_k}\\rbrace $ , to the probability of the answer possibility. The classifier takes the output for the first word, $M^{p_k}_1$ , for all passages and concatenates them to obtain a fixed-dimensional representation. It calculates the answer possibility to the question as: ",
+      "$$P(a) = \\mathrm {sigmoid}({w^c}^\\top [M^{p_1}_1; \\ldots ; M^{p_K}_1]),$$   (Eq. 22) ",
+      " where $w^c \\in \\mathbb {R}^{Kd}$ are learnable parameters."
+    ],
+    [
+      "Given the outputs provided by the reader, the decoder generates a sequence of answer words one element at a time. It is auto-regressive BIBREF24 , consuming the previously generated words as additional input at each decoding step.",
+      "Let $y = \\lbrace y_1, \\ldots , y_{T}\\rbrace $ represent one-hot vectors of words in the answer. This layer has the same components as the word embedding layer of the question-passages reader, except that it uses a unidirectional ELMo in order to ensure that the predictions for position $t$ depend only on the known outputs at positions less than $t$ .",
+      "Moreover, to be able to make use of multiple answer styles within a single system, our model introduces an artificial token corresponding to the target style at the beginning of the answer sentence ( $y_1$ ), like BIBREF14 . At test time, the user can specify the first token to control the answer styles. This modification does not require any changes to the model architecture. Note that introducing the tokens on the decoder side prevents the passage ranker and answer possibility classifier from depending on the answer style.",
+      "This layer uses a stack of Transformer decoder blocks on top of the embeddings provided by the word embedding layer. The input is immediately mapped to a $d$ -dimensional vector by a linear transformation, and the output of this layer is a sequence of $d$ -dimensional vectors: $\\lbrace s_1, \\ldots , s_T\\rbrace $ .",
+      "In addition to the encoder block, this block consists of second and third sub-layers after the self-attention block and before the feed-forward network, as shown in Figure 2 . As in BIBREF12 , the self-attention sub-layer uses a sub-sequent mask to prevent positions from attending to subsequent positions. The second and third sub-layers perform the multi-head attention over $M^q$ and $M^{p_\\mathrm {all}}$ , respectively. The $M^{p_\\mathrm {all}}$ is the concatenated outputs of the encoder stack for the passages, ",
+      "$$M^{p_\\mathrm {all}} = [M^{p_1}, \\ldots , M^{p_K}] \\in \\mathbb {R}^{d \\times KL}.$$   (Eq. 27) ",
+      " The $[,]$ operator means vector concatenation across the columns. This attention for the concatenated passages enables our model to produce attention weights that are comparable between passages.",
+      "Our extended mechanism allows both words to be generated from a fixed vocabulary and words to be copied from both the question and multiple passages. Figure 3 shows the overview.",
+      "Let the extended vocabulary, $V_\\mathrm {ext}$ , be the union of the common words (a small subset of the full vocabulary, $V$ , defined by the reader-side word embedding matrix) and all words appearing in the input question and passages. $P^v$ denotes the probability distribution of the $t$ -th answer word, $y_t$ , over the extended vocabulary. It is defined as: ",
+      "$$P^v(y_t) =\\mathrm {softmax}({W^2}^\\top (W^1 s_t + b^1)),$$   (Eq. 31) ",
+      " where the output embedding $W^2 \\in \\mathbb {R}^{d_\\mathrm {word} \\times V_\\mathrm {ext}}$ is tied with the corresponding part of the input embedding BIBREF25 , and $W^1 \\in \\mathbb {R}^{d_\\mathrm {word} \\times d}$ and $b^1 \\in \\mathbb {R}^{d_\\mathrm {word}}$ are learnable parameters. $P^v(y_t)$ is zero if $y_t$ is an out-of-vocabulary word for $V$ .",
+      "The copy mechanism used in the original pointer-generator is based on the attention weights of a single-layer attentional RNN decoder BIBREF9 . The attention weights in our decoder stack are the intermediate outputs in multi-head attentions and are not suitable for the copy mechanism. Therefore, our model also uses additive attentions for the question and multiple passages on top of the decoder stack.",
+      "The layer takes $s_t$ as the query and outputs $\\alpha ^q_t \\in \\mathbb {R}^J$ ( $\\alpha ^p_t \\in \\mathbb {R}^{KL}$ ) as the attention weights and $c^q_t \\in \\mathbb {R}^d$ ( $c^p_t \\in \\mathbb {R}^d$ ) as the context vectors for the question (passages): ",
+      "$$e^q_j &= {w^q}^\\top \\tanh (W^{qm} M_j^q + W^{qs} s_t +b^q), \\\\\n\\alpha ^q_t &= \\mathrm {softmax}(e^q), \\\\\nc^q_t &= \\textstyle \\sum _j \\alpha ^q_{tj} M_j^q, \\\\\ne^{p_k}_l &= {w^p}^\\top \\tanh (W^{pm} M_l^{p_k} + W^{ps} s_t +b^p), \\\\\n\\alpha ^p_t &= \\mathrm {softmax}([e^{p_1}; \\ldots ; e^{p_K}]), \\\\\nc^p_t &= \\textstyle \\sum _{l} \\alpha ^p_{tl} M^{p_\\mathrm {all}}_{l},$$   (Eq. 33) ",
+      " where $w^q$ , $w^p \\in \\mathbb {R}^d$ , $W^{qm}$ , $W^{qs}$ , $W^{pm}$ , $W^{ps} \\in \\mathbb {R}^{d \\times d}$ , and $b^q$ , $b^p \\in \\mathbb {R}^d$ are learnable parameters.",
+      " $P^q$ and $P^p$ are the copy distributions over the extended vocabulary, defined as: ",
+      "$$P^q(y_t) &= \\textstyle \\sum _{j: x^q_j = y_t} \\alpha ^q_{tj}, \\\\\nP^p(y_t) &= \\textstyle \\sum _{l: x^{p_{k(l)}}_{l} = y_t} \\alpha ^p_{tl},$$   (Eq. 34) ",
+      " where $k(l)$ means the passage index corresponding to the $l$ -th word in the concatenated passages.",
+      "The final distribution of the $t$ -th answer word, $y_t$ , is defined as a mixture of the three distributions: ",
+      "$$P(y_t) = \\lambda ^v P^v(y_t) + \\lambda ^q P^q(y_t) + \\lambda ^p P^p(y_t),$$   (Eq. 36) ",
+      " where the mixture weights are given by ",
+      "$$\\lambda ^v, \\lambda ^q, \\lambda ^p = \\mathrm {softmax}(W^m [s_t; c^q_t; c^p_t] + b^m).$$   (Eq. 37) ",
+      " $W^m \\in \\mathbb {R}^{3 \\times 3d}$ , $b^m \\in \\mathbb {R}^3$ are learnable parameters.",
+      "In order not to use words in irrelevant passages, our model introduces the concept of combined attention BIBREF26 . While the original technique combines the word and sentence level attentions, our model combines the passage-level relevance $\\beta ^{p_k}$ and word-level attentions $\\alpha ^p_t$ by using simple scalar multiplication and re-normalization. The updated word attention is: ",
+      "$$\\alpha ^p_{tl} & := \\frac{\\alpha ^p_{tl} \\beta ^{p_{k(l)} }}{\\sum _{l^{\\prime }} \\alpha ^p_{tl^{\\prime }} \\beta ^{p_{k(l^{\\prime })}}}.$$   (Eq. 39) "
+    ],
+    [
+      "We define the training loss as the sum of losses in ",
+      "$$L(\\theta ) = L_\\mathrm {dec} + \\gamma _\\mathrm {rank} L_\\mathrm {rank} + \\gamma _\\mathrm {cls} L_\\mathrm {cls}$$   (Eq. 41) ",
+      " where $\\theta $ is the set of all learnable parameters, and $\\gamma _\\mathrm {rank}$ and $\\gamma _\\mathrm {cls}$ are balancing parameters.",
+      "The loss of the decoder, $L_\\mathrm {dec}$ , is the negative log likelihood of the whole target answer sentence averaged over $N_\\mathrm {able}$ answerable examples: ",
+      "$$L_\\mathrm {dec} = - \\frac{1}{N_\\mathrm {able}}\\sum _{(a,y)\\in \\mathcal {D}} \\frac{a}{T} \\sum _t \\log P(y_{t}),$$   (Eq. 42) ",
+      " where $\\mathcal {D}$ is the training dataset.",
+      "The losses of the passage ranker, $L_\\mathrm {rank}$ , and the answer possibility classifier, $L_\\mathrm {cls}$ , are the binary cross entropy between the true and predicted values averaged over all $N$ examples: ",
+      "$$L_\\mathrm {rank} = - \\frac{1}{NK} \\sum _k \\sum _{r^{p_k}\\in \\mathcal {D}}\n\\biggl (\n\\begin{split}\n&r^{p_k} \\log \\beta ^{p_k} + \\\\\n&(1-r^{p_k}) \\log (1-\\beta ^{p_k})\n\\end{split}\n\\biggr ),\\\\\nL_\\mathrm {cls} = - \\frac{1}{N} \\sum _{a \\in \\mathcal {D}}\n\\biggl (\n\\begin{split}\n&a \\log P(a) + \\\\\n&(1-a) \\log (1-P(a))\n\\end{split}\n\\biggr ).$$   (Eq. 43) "
+    ],
+    [
+      "We conducted experiments on the two tasks of MS MARCO 2.1 BIBREF5 . The answer styles considered in the experiments corresponded to the two tasks. The NLG task requires a well-formed answer that is an abstractive summary of the question and ten passages, averaging 16.6 words. The Q&A task also requires an abstractive answer but prefers a more concise answer than the NLG task, averaging 13.1 words, where many of the answers do not contain the context of the question. For instance, for the question \u201ctablespoon in cup\u201d, the answer in the Q&A task will be \u201c16\u201d, and the answer in the NLG task will be \u201cThere are 16 tablespoons in a cup.\u201d In addition to the ALL dataset, we prepared two subsets (Table 1 ). The ANS set consists of answerable questions, and the WFA set consists of the answerable questions and well-formed answers, where WFA $\\subset $ ANS $\\subset $ ALL.",
+      "We trained our model on a machine with eight NVIDIA P100 GPUs. Our model was jointly trained with the two answer styles in the ALL set for a total of eight epochs with a batch size of 80. The training took roughly six days. The ensemble model consists of six training runs with the identical architecture and hyperparameters. The hidden size $d$ was 304, and the number of attention heads was 8. The inner state size of the feed-forward networks was 256. The numbers of shared encoding blocks, modeling blocks for question, modeling blocks for passages, and decoder blocks were 3, 2, 5, and 8, respectively. We used the pre-trained uncased 300-dimensional GloVe BIBREF15 and the original 512-dimensional ELMo BIBREF16 . We used the spaCy tokenizer, and all words were lowercased except the input for ELMo. The number of common words in $V_\\mathrm {ext}$ was 5,000.",
+      "We used the Adam optimization BIBREF27 with $\\beta _1 = 0.9$ , $\\beta _2 = 0.999$ , and $\\epsilon = 10^{-8}$ . Weights were initialized using $N(0, 0.02)$ , except that the biases of all the linear transformations were initialized with zero vectors. The learning rate was increased linearly from zero to $2.5 \\times 10^{-4}$ in the first 2,000 steps and annealed to 0 using a cosine schedule. All parameter gradients were clipped to a maximum norm of 1. An exponential moving average was applied to all trainable variables with a decay rate 0.9995. The balancing factors of joint learning, $\\lambda _\\mathrm {rank}$ and $\\lambda _\\mathrm {cls}$ , were set to 0.5 and 0.1.",
+      "We used a modified version of the L $_2$ regularization proposed in BIBREF28 , with $w = 0.01$ . We additionally used a dropout BIBREF29 rate of 0.3 for all highway networks and residual and scaled dot-product attention operations in the multi-head attention mechanism. We also used one-sided label smoothing BIBREF30 for the passage relevance and answer possibility labels. We smoothed only the positive labels to 0.9."
+    ],
+    [
+      "Table 2 shows that our ensemble model, controlled with the NLG and Q&A styles, achieved state-of-the-art performance on the NLG and Q&A tasks in terms of Rouge-L. In particular, for the NLG task, our single model outperformed competing models in terms of both Rouge-L and Bleu-1. The capability of creating abstractive summaries from the question and passages contributed to its improvements over the state-of-the-art extractive approaches BIBREF6 , BIBREF7 .",
+      "Table 3 shows the results of the ablation test for our model (controlled with the NLG style) on the well-formed answers of the WFA dev. set. Our model, which was trained with the ALL set consisting of the two styles, outperformed the model trained with the WFA set consisting of the single style. Multi-style learning allowed our model to improve NLG performance by also using non-sentence answers.",
+      "Table 3 shows that our model outperformed the model that used RNNs and self-attentions instead of Transformer blocks as in MCAN BIBREF11 . Our deep Transformer decoder captured the interaction among the question, the passages, and the answer better than a single-layer LSTM decoder.",
+      "Table 3 shows that our model (jointly trained with the passage ranker and answer possibility classifier) outperformed the model that did not use the ranker and classifier. The joint learning has a regularization effect on the question-passages reader.",
+      "We also confirmed that the gold passage ranker, which can predict passage relevances perfectly, improves RC performance significantly. Passage re-ranking will be a key to developing a system that can outperform humans.",
+      "Table 4 shows the passage re-ranking performance for the ten given passages on the ANS dev. set. Our ranker improved the initial ranking provided by Bing by a significant margin. Also, the ranker shares the question-passages reader with the answer decoder, and this sharing contributed to the improvements over the ranker trained without the answer decoder. This result is similar to those reported in BIBREF33 . Moreover, the joint learning with the answer possibility classifier and multiple answer styles, which enables our model to learn from a larger number of data, improved the re-ranking.",
+      "Figure 4 shows the precision-recall curve of answer possibility classification on the ALL dev. set, where the positive class is the answerable data. Our model identified the answerable questions well. The maximum $F_1$ score was 0.7893. This is the first report on answer possibility classification with MS MARCO 2.1.",
+      "Figure 5 shows the lengths of the answers generated by our model, which are broken down by answer style and query type. The generated answers were relatively shorter than the reference answers but well controlled with the target style in every query type.",
+      "Also, we should note that our model does not guarantee the consistency in terms of meaning across the answer styles. We randomly selected 100 questions and compared the answers our model generated with the NLG and Q&A styles. The consistency ratio was 0.81, where major errors were due to copying words from different parts of the passages and generating different words, especially yes/no, from a fixed vocabulary.",
+      "Appendix \"Reading Comprehension Examples generated by Masque from MS MARCO 2.1\" shows examples of generated answers. We found (d) style errors; (e) yes/no classification errors; (f) copy errors with respect to numerical values; and (c,e) grammatical errors that were originally contained in the inputs."
+    ],
+    [
+      "We believe our study makes two contributions to the study of multi-passage RC with NLG. Our model enables 1) multi-source abstractive summarization based RC and 2) style-controllable RC. The key strength of our model is its high accuracy of generating abstractive summaries from the question and passages; our model achieved state-of-the-art performance in terms of Rouge-L on the Q&A and NLG tasks of MS MARCO 2.1 that have different answer styles BIBREF5 .",
+      "The styles considered in this paper are only related to the context of the question in the answer sentence; our model will be promising for controlling other styles such as length and speaking styles. Future work will involve exploring the potential of hybrid models combining extractive and abstractive approaches and improving the passage re-ranking and answerable question identification."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: Dissecting Content and Context in Argumentative Relation Analysis
+
+Question: How do they demonstrate the robustness of their results?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Argumentative Relation Prediction: Models and Features",
+    "Models",
+    "Feature implementation",
+    "Results",
+    "Discussion",
+    "Conclusion",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "In recent years we have witnessed a great surge in activity in the area of computational argument analysis (e.g. BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 ), and the emergence of dedicated venues such as the ACL Argument Mining workshop series starting in 2014 BIBREF4 .",
+      "Argumentative relation classification is a sub-task of argument analysis that aims to determine relations between argumentative units A and B, for example, A supports B; A attacks B. Consider the following argumentative units (1) and (2), given the topic (0) \u201cMarijuana should be legalized\u201d:",
+      "This example is modeled in Figure FIGREF3 .",
+      "It is clear that (1) has a negative stance towards the topic and (2) has a positive stance towards the topic. Moreover, we can say that (2) attacks (1). In discourse, such a relation is often made explicit through discourse markers: (1). However, (2); On the one hand (1), on the other (2); (1), although (2); Admittedly, (2); etc. In the absence of such markers we must determine this relation by assessing the semantics of the individual argumentative units, including (often implicit) world knowledge about how they are related to each other.",
+      "In this work, we show that argumentative relation classifiers \u2013 when provided with textual context surrounding an argumentative unit's span \u2013 are very prone to neglect the actual textual content of the EAU span. Instead they heavily rely on contextual markers, such as conjunctions or adverbials, as a basis for prediction. We argue that a system's capacity of predicting the correct relation based on the argumentative units' content is important in many circumstances, e.g., when an argumentative debate crosses document boundaries. For example, the prohibition of marijuana debate extends across populations and countries \u2013 argumentative units for this debate can be recovered from thousands of documents scattered across the world wide web. As a consequence, argumentative relation classification systems should not be (immensely) dependent on contextual clues \u2013 in the discussed cross-document setting these clues may even be misleading for such a system, since source and target arguments can be embedded in different textual contexts (e.g., when (1) and (2) stem from different documents it is easy to imagine a textual context where (2) is not introduced by however but instead by an `inverse' form such as e.g. moreover)."
+    ],
+    [
+      "It is well-known that the rhetorical and argumentative structure of texts bear great similarities. For example, BIBREF5 , BIBREF6 , BIBREF0 observe that elementary discourse units (EDUs) in RST BIBREF7 share great similarity with elementary argumentative units (EAUs) in argumentation analysis. BIBREF8 experiment with a modified version of the Microtext corpus BIBREF9 , which is an extensively annotated albeit small corpus. Similar to us, they separate argumentative units from discursive contextual markers. While BIBREF8 conduct a human evaluation to investigate the separation of Logos and Pathos aspects of arguments, our work investigates how (de-)contextualization of argumentative units affects automatic argumentative relation classification models."
+    ],
+    [
+      "In this section, we describe different formulations of the argumentative relation classification task and describe features used by our replicated model. In order to test our hypotheses, we propose to group all features into three distinct types."
+    ],
+    [
+      "Now, we introduce a classification of three different prediction models used in the argumentative relation prediction literature. We will inspect all of them and show that all can suffer from severe issues when focusing (too much) on the context.",
+      "The model INLINEFORM0 adopts a discourse parsing view on argumentative relation prediction and predicts one outgoing edge for an argumentative unit (one-outgoing edge). Model INLINEFORM1 assumes a connected graph with argumentative units and is tasked with predicting edge labels for unit tuples (labeling relations in a graph). Finally, a model INLINEFORM2 is given two (possibly) unrelated argumentative units and is tasked with predicting connections as well as edge labels (joint edge prediction and labeling).",
+      " BIBREF13 divide the task into relation prediction INLINEFORM0 and relation class assignment INLINEFORM1 : DISPLAYFORM0 ",
+      " DISPLAYFORM0 ",
+      "which the authors describe as argumentative relation identification ( INLINEFORM0 ) and stance detection ( INLINEFORM1 ). In their experiments, INLINEFORM2 , i.e., no distinction is made between features that access only the argument content (EAU span) or only the EAU's embedding context, and some features also consider both (e.g., discourse features). This model adopts a parsing view on argumentative relation classification: every unit is allowed to have only one type of outgoing relation (this follows trivially from the fact that INLINEFORM3 has only one input). Applying such a model to argumentative attack and support relations might impose unrealistic constraints on the resulting argumentation graph: A given premise might in fact attack or support several other premises. The approach may suffice for the case of student argumentative essays, where EAUs are well-framed in a discourse structure, but seems overly restrictive for many other scenarios.",
+      "Another way of framing the task, is to learn a function DISPLAYFORM0 ",
+      "Here, an argumentative unit is allowed to be in a attack or support relation to multiple other EAUs. Yet, both INLINEFORM0 and INLINEFORM1 assume that inputs are already linked and only the class of the link is unknown.",
+      "Thus, we might also model the task in a three-class classification setting to learn a more general function that performs relation prediction and classification jointly (see also, e.g., BIBREF10 ): DISPLAYFORM0 ",
+      "The model described by Eq. EQREF22 is the most general one: not only does it assume a graph view on argumentative units and their relations (as does Eq. EQREF20 ); in model formulation (Eq. EQREF22 ), an argumentative unit can have no or multiple support or attack relations. It naturally allows for cases where an argumentative unit INLINEFORM0 (supports INLINEFORM1 INLINEFORM2 attacks INLINEFORM3 INLINEFORM4 is-unrelated-to INLINEFORM5 ). Given a set of EAUs mined from different documents, this model enables us to construct a full-fledged argumentation graph."
+    ],
+    [
+      "Our feature implementation follows the feature descriptions for Stance recognition and link identification in BIBREF13 . These features and variations of them have been used successfully in several successive works (cf. BIBREF1 , BIBREF16 , BIBREF15 ).",
+      "For any model the features are indexed by INLINEFORM0 . We create a function INLINEFORM1 which maps from feature indices to feature types. In other words, INLINEFORM2 tells us, for any given feature, whether it is content-based ( INLINEFORM3 ), content-ignorant ( INLINEFORM4 ) or full access ( INLINEFORM5 ). The features for, e.g., the joint prediction model INLINEFORM6 of type INLINEFORM7 ( INLINEFORM8 ) can then simply be described as INLINEFORM9 . Recall that features computed on the basis of the EAU span are content-based ( INLINEFORM10 ), features from the EAU-surrounding text are content-ignorant ( INLINEFORM11 ) and features computed from both are denoted by full-access ( INLINEFORM12 ). Details on the extraction of features are provided below.",
+      "These consist of boolean values indicating whether a certain word appears in the argumentative source or target EAU or both (and separately, their contexts). More precisely, for any classification instance we extract uni-grams from within the span of the EAU (if INLINEFORM0 ) or solely from the sentence-context surrounding the EAUs (if INLINEFORM1 ). Words which occur in both bags are only visible in the full-access setup INLINEFORM2 and are modeled as binary indicators.",
+      "Such features consist of syntactic production rules extracted from constituency trees \u2013 they are modelled analogously to the lexical features as a bag of production rules. To make a clear division between features derived from the EAU embedding context and features derived from within the EAU span, we divide the constituency tree in two parts, as is illustrated in Figure FIGREF26 .",
+      "If the EAU is embedded in a covering sentence, we cut the syntax tree at the corresponding edge ( in Figure FIGREF26 ). In this example, the content-ignorant ( INLINEFORM0 ) bag-of-word production rule representation includes the rules INLINEFORM1 and INLINEFORM2 . Analogously to the lexical features, the production rules are modeled as binary indicator features.",
+      "These features describe shallow statistics such as the ratio of argumentative unit tokens compared to sentence tokens or the position of the argumentative unit in the paragraph. We set these features to zero for the content representation of the argumentative unit and replicate those features that allow us to treat the argumentative unit as a black-box. For example, in the content-based ( INLINEFORM0 ) system that has access only to the EAU, we can compute the #tokens in the EAU, but not the #tokens in EAU divided by #tokens in the sentence. The latter feature is only accessible in the full access system variants. Hence, in the content-based ( INLINEFORM1 ) system most of these statistics are set to zero since they cannot be computed by considering only the EAU span.",
+      "For the content-based representation we retrieve only discourse relations that are confined within the span of the argumentative unit. In the very frequent case that discourse features cross the boundaries of embedding context and EAU span, we only take them into account for INLINEFORM0 .",
+      "We use the element-wise sum of 300-dimensional pre-trained GloVe vectors BIBREF24 corresponding to the words within the EAU span ( INLINEFORM0 ) and the words of the EAU-surrounding context ( INLINEFORM1 ). Additionally, we compute the element-wise subtraction of the source EAU vector from the target EAU vector, with the aim of modelling directions in distributional space, similarly to BIBREF25 . Words with no corresponding pre-trained word vector and empty sequences (e.g., no preceding context available) are treated as a zero-vector.",
+      "Tree-based sentiment annotations are sentiment scores assigned to nodes in constituency parse trees BIBREF26 . We represent these scores by a one-hot vector of dimension 5 (5 is very positive, 1 is very negative). We determine the contextual ( INLINEFORM0 ) sentiment by looking at the highest possible node of the context which does not contain the EAU (ADVP in Figure FIGREF26 ). The sentiment for an EAU span ( INLINEFORM1 ) is assigned to the highest possible node covering the EAU span which does not contain the context sub-tree (S in Figure FIGREF26 ). The full-access ( INLINEFORM2 ) score is assigned to the lowest possible node which covers both the EAU span and its surrounding context (S' in Figure FIGREF26 ). Next to the sentiment scores for the selected tree nodes and analogously to the word embeddings, we also calculate the element-wise subtraction of the one-hot sentiment source vectors from the one-hot sentiment target vectors. This results in three additional vectors corresponding to INLINEFORM3 , INLINEFORM4 and INLINEFORM5 difference vectors."
+    ],
+    [
+      "Our first step towards our main experiments is to replicate the competitive argumentative relation classifier of BIBREF13 , BIBREF1 . Hence, for comparison purposes, we first formulate the task exactly as it was done in this prior work, using the model formulation in Eq. EQREF17 , which determines the type of outgoing edge from a source (i.e., tree-like view).",
+      "The results in Table TABREF38 confirm the results of BIBREF13 and suggest that we successfully replicated a large proportion of their features.",
+      "The results for all three prediction settings (one outgoing edge: INLINEFORM0 , support/attack: INLINEFORM1 and support/attack/neither: INLINEFORM2 ) across all type variables ( INLINEFORM3 , INLINEFORM4 and INLINEFORM5 ) are displayed in Table TABREF39 . All models significantly outperform the majority baseline with respect to macro F1. Intriguingly, the content-ignorant models ( INLINEFORM6 ) always perform significantly better than the models which only have access to the EAUs' content ( INLINEFORM7 , INLINEFORM8 ). In the most general task formulation ( INLINEFORM9 ), we observe that INLINEFORM10 even significantly outperforms the model which has maximum access (seeing both EAU spans and surrounding contexts: INLINEFORM11 ).",
+      "At first glance, the results of the purely EAU focused systems ( INLINEFORM0 ) are disappointing, since they fall far behind their competitors. On the other hand, their F1 scores are not devastatingly bad. The strong most-frequent-class baseline is significantly outperformed by the content-based ( INLINEFORM1 ) system, across all three prediction settings.",
+      "In summary our findings are as follows: (i) models which see the EAU span (content-based, INLINEFORM0 ) are significantly outperformed by models that have no access to the span itself (content-ignorant, INLINEFORM1 ) across all settings; (ii) in two of three prediction settings ( INLINEFORM2 and INLINEFORM3 ), the model which only has access to the context even outperforms the model that has access to all information in the input. The fact that using features derived exclusively from the EAU embedding context ( INLINEFORM4 ) can lead to better results than using a full feature-system ( INLINEFORM5 ) suggests that some information from the EAU can even be harmful. Why this is the case, we cannot answer exactly. A plausible cause might be related to the smaller dimension of the feature space, which makes the SVM less likely to overfit. Still, this finding comes as a surprise and calls for further investigation in future work.",
+      "A system for argumentative relation classification can be applied in one of two settings: single-document or cross-document, as illustrated in Figure FIGREF42 :",
+      "in the first case (top), a system is tasked to classify EAUs that appear linearly in one document \u2013 here contextual clues can often highlight the relationship between two units. This is the setting we have been considering up to now. However, in the second scenario (bottom), we have moved away from the closed single-document setting and ask the system to classify two EAUs extracted from different document contexts. This setting applies, for instance, when we are mining arguments from multiple sources.",
+      "In both cases, however, a system that relies more on contextual clues than on the content expressed in the EAUs is problematic: in the single-document setting, such a system will rely on discourse indicators \u2013 whether or not they are justified by content \u2013 and can thus easily be fooled.",
+      "In the cross-document setting, discourse-based indicators \u2013 being inherently defined with respect to their internal document context \u2013 do not have a defined rhetorical function with respect to EAUs in a separate document and thus a system that has learned to rely on such markers within a single-document setting can be seriously misled.",
+      "We believe that the cross-document setting should be an important goal in argumentation analysis, since it generalizes better to many debates of interest, where EAUs can be found scattered across thousands of documents. For example, for the topic of legalizing marijuana, EAUs may be mined from millions of documents and thus their relations may naturally extend across document boundaries. If a system learns to over-proportionally attend to the EAUs' surrounding contexts it is prone to making many errors.",
+      "In what follows we are simulating the effects that an overly context-sensitive classifier could have in a cross-document setting, by modifying our experimental setting, and study the effects on the different model types: In one setup \u2013 we call it randomized-context \u2013 we systematically distort the context of our testing instances by exchanging the context in a randomized manner; in the other setting \u2013 called no-context, we are deleting the context around the ADUs to be classified. Randomized-context simulates an open world debate where argumentative units may occur in different contexts, sometimes with discourse markers indicating an opposite class. In other words, in this setting we want to examine effects when porting a context-sensitive system to a multi-document setting. For example, as seen in Figure FIGREF42 , the context of an argumentative unit may change from \u201cHowever\u201d to \u201cMoreover\u201d \u2013 which can happen naturally in open debates.",
+      "The results are displayed in Figure FIGREF43 . In the standard setting (Figure FIGREF43 ), the models that have access to the context besides the content ( INLINEFORM0 ) and the models that are only allowed to access the context ( INLINEFORM1 ), always perform better than the content-based models ( INLINEFORM2 ) (bars above zero). However, when we randomly flip contexts of the test instances (Figure FIGREF43 ), or suppress them entirely (Figure FIGREF43 ), the opposite picture emerges: the content-based models always outperform the other models. For some classes (support, INLINEFORM3 ) the difference can exceed 50 F1 percentage points. These two studies, where testing examples are varied regarding their context (randomized-context or no-context) simulates what can be expected if we apply our systems for relation class assignment to EAUs stemming from heterogeneous sources. While the performances of a purely content-based model naturally stays stable, the performance of the other systems decrease notably \u2013 they perform worse than the content-based model.",
+      "We calculate the ANOVA classification F scores of the features with respect to our three task formulations INLINEFORM0 and INLINEFORM1 . The F percentiles of features extracted from the EAU surrounding text ( INLINEFORM2 ) and features extracted from the EAU span ( INLINEFORM3 ), are displayed in Figure FIGREF50 .",
+      "It clearly stands out that features obtained from the EAU surrounding context ( INLINEFORM0 ) are assigned much higher scores compared to features stemming from the EAU span ( INLINEFORM1 ). This holds true for all three task formulations and provides further evidence that models \u2013 when given the option \u2013 put a strong focus on contextual clues while neglecting the information provided by the EAU span itself."
+    ],
+    [
+      "While competitive systems for argumentative relation classification are considered to be robust, our experiments have shown that despite confidence-inspiring scores on unseen testing data, such systems can easily be fooled \u2013 they can deliver strong performance scores although the classifier does not have access to the content of the EAUs. In this respect, we have provided evidence that there is a danger in case models focus too much on rhetorical indicators, in detriment of the context. Thus, the following question arises: How can we prevent argumentation models from modeling arguments or argumentative units and their relations in overly na\u00efve ways? A simple and intuitive way is to dissect EAUs from their surrounding document context. Models trained on data that is restricted to the EAUs' content will be forced to focus on the content of EAUs. We believe that this will enhance the robustness of such models and allows them to generalize to cross-document argument relation classification. The corpus of student essays makes such transformations straightforward: only the EAUs were annotated (e.g., \u201cHowever, INLINEFORM0 A INLINEFORM1 \u201d). If annotations extend over the EAUs (e.g., only full sentences are annotated, \u201c INLINEFORM2 However, A INLINEFORM3 \u201d), such transformations could be performed automatically after a discourse parsing step. When inspecting the student essays corpus, we further observed that an EAU mining step should involve coreference resolution to better capture relations between EAUs that involve anaphors (e.g., \u201cExercising makes you feel better\u201d and \u201cIt INLINEFORM4 increases endorphin levels\u201d).",
+      "Thus, in order to conduct real-world end-to-end argumentation relation mining for a given topic, we envision a system that addresses three steps: (i) mining of EAUs and (ii) replacement of pronouns in EAUs with referenced entities (e.g., INLINEFORM0 ). Finally (iii), given the cross product of mined EAUs we can apply a model of type INLINEFORM1 to construct a full-fledged argumentation graph, possibly spanning multiple documents. We have shown that in order to properly perform step (iii), we need stronger models that are able to better model EAU contents. Hence, we encourage the argumentation community to test their systems on a decontextualized version of the student essays, including the proposed \u2013 and possibly further extended \u2013 testing setups, to challenge the semantic representation and reasoning capacities of argument analysis models. This will lead to more realistic performance estimates and increased robustness of systems when addressing desirable multi-document tasks."
+    ],
+    [
+      "We have shown that systems which put too much focus on discourse information may be easily fooled \u2013 an issue which has severe implications when systems are applied to cross-document argumentative relation classification tasks. The strong reliance on contextual clues is also problematic in single-document contexts, where systems can run a risk of assigning relation labels relying on contextual and rhetorical effects \u2013 instead of focusing on content. Hence, we propose that researchers test their argumentative relation classification systems on two alternative versions of the StudentEssay data that reflect different access levels. (i) EAU-span only, where systems only see the EAU spans and (ii) context-only, where systems can only see the EAU-surrounding context. These complementary settings will (i) challenge the semantic capacities of a system, and (ii) unveil the extent to which a system is focusing on the discourse context when making decisions. We will offer our testing environments to the research community through a platform that provides datasets and scripts and a table to trace the results of content-based systems."
+    ],
+    [
+      "This work has been supported by the German Research Foundation as part of the Research Training Group Adaptive Preparation of Information from Heterogeneous Sources (AIPHES) under grant no. GRK 1994/1 and by the Leibniz ScienceCampus \u201cEmpirical Linguistics and Computational Language Modeling\u201d, supported by the Leibniz Association under grant no. SAS-2015-IDS-LWC and by the Ministry of Science, Research, and Art of Baden-W\u00fcrttemberg. "
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: Dissecting Content and Context in Argumentative Relation Analysis
+
+Question: What baseline and classification systems are used in experiments?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Argumentative Relation Prediction: Models and Features",
+    "Models",
+    "Feature implementation",
+    "Results",
+    "Discussion",
+    "Conclusion",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "In recent years we have witnessed a great surge in activity in the area of computational argument analysis (e.g. BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 ), and the emergence of dedicated venues such as the ACL Argument Mining workshop series starting in 2014 BIBREF4 .",
+      "Argumentative relation classification is a sub-task of argument analysis that aims to determine relations between argumentative units A and B, for example, A supports B; A attacks B. Consider the following argumentative units (1) and (2), given the topic (0) \u201cMarijuana should be legalized\u201d:",
+      "This example is modeled in Figure FIGREF3 .",
+      "It is clear that (1) has a negative stance towards the topic and (2) has a positive stance towards the topic. Moreover, we can say that (2) attacks (1). In discourse, such a relation is often made explicit through discourse markers: (1). However, (2); On the one hand (1), on the other (2); (1), although (2); Admittedly, (2); etc. In the absence of such markers we must determine this relation by assessing the semantics of the individual argumentative units, including (often implicit) world knowledge about how they are related to each other.",
+      "In this work, we show that argumentative relation classifiers \u2013 when provided with textual context surrounding an argumentative unit's span \u2013 are very prone to neglect the actual textual content of the EAU span. Instead they heavily rely on contextual markers, such as conjunctions or adverbials, as a basis for prediction. We argue that a system's capacity of predicting the correct relation based on the argumentative units' content is important in many circumstances, e.g., when an argumentative debate crosses document boundaries. For example, the prohibition of marijuana debate extends across populations and countries \u2013 argumentative units for this debate can be recovered from thousands of documents scattered across the world wide web. As a consequence, argumentative relation classification systems should not be (immensely) dependent on contextual clues \u2013 in the discussed cross-document setting these clues may even be misleading for such a system, since source and target arguments can be embedded in different textual contexts (e.g., when (1) and (2) stem from different documents it is easy to imagine a textual context where (2) is not introduced by however but instead by an `inverse' form such as e.g. moreover)."
+    ],
+    [
+      "It is well-known that the rhetorical and argumentative structure of texts bear great similarities. For example, BIBREF5 , BIBREF6 , BIBREF0 observe that elementary discourse units (EDUs) in RST BIBREF7 share great similarity with elementary argumentative units (EAUs) in argumentation analysis. BIBREF8 experiment with a modified version of the Microtext corpus BIBREF9 , which is an extensively annotated albeit small corpus. Similar to us, they separate argumentative units from discursive contextual markers. While BIBREF8 conduct a human evaluation to investigate the separation of Logos and Pathos aspects of arguments, our work investigates how (de-)contextualization of argumentative units affects automatic argumentative relation classification models."
+    ],
+    [
+      "In this section, we describe different formulations of the argumentative relation classification task and describe features used by our replicated model. In order to test our hypotheses, we propose to group all features into three distinct types."
+    ],
+    [
+      "Now, we introduce a classification of three different prediction models used in the argumentative relation prediction literature. We will inspect all of them and show that all can suffer from severe issues when focusing (too much) on the context.",
+      "The model INLINEFORM0 adopts a discourse parsing view on argumentative relation prediction and predicts one outgoing edge for an argumentative unit (one-outgoing edge). Model INLINEFORM1 assumes a connected graph with argumentative units and is tasked with predicting edge labels for unit tuples (labeling relations in a graph). Finally, a model INLINEFORM2 is given two (possibly) unrelated argumentative units and is tasked with predicting connections as well as edge labels (joint edge prediction and labeling).",
+      " BIBREF13 divide the task into relation prediction INLINEFORM0 and relation class assignment INLINEFORM1 : DISPLAYFORM0 ",
+      " DISPLAYFORM0 ",
+      "which the authors describe as argumentative relation identification ( INLINEFORM0 ) and stance detection ( INLINEFORM1 ). In their experiments, INLINEFORM2 , i.e., no distinction is made between features that access only the argument content (EAU span) or only the EAU's embedding context, and some features also consider both (e.g., discourse features). This model adopts a parsing view on argumentative relation classification: every unit is allowed to have only one type of outgoing relation (this follows trivially from the fact that INLINEFORM3 has only one input). Applying such a model to argumentative attack and support relations might impose unrealistic constraints on the resulting argumentation graph: A given premise might in fact attack or support several other premises. The approach may suffice for the case of student argumentative essays, where EAUs are well-framed in a discourse structure, but seems overly restrictive for many other scenarios.",
+      "Another way of framing the task, is to learn a function DISPLAYFORM0 ",
+      "Here, an argumentative unit is allowed to be in a attack or support relation to multiple other EAUs. Yet, both INLINEFORM0 and INLINEFORM1 assume that inputs are already linked and only the class of the link is unknown.",
+      "Thus, we might also model the task in a three-class classification setting to learn a more general function that performs relation prediction and classification jointly (see also, e.g., BIBREF10 ): DISPLAYFORM0 ",
+      "The model described by Eq. EQREF22 is the most general one: not only does it assume a graph view on argumentative units and their relations (as does Eq. EQREF20 ); in model formulation (Eq. EQREF22 ), an argumentative unit can have no or multiple support or attack relations. It naturally allows for cases where an argumentative unit INLINEFORM0 (supports INLINEFORM1 INLINEFORM2 attacks INLINEFORM3 INLINEFORM4 is-unrelated-to INLINEFORM5 ). Given a set of EAUs mined from different documents, this model enables us to construct a full-fledged argumentation graph."
+    ],
+    [
+      "Our feature implementation follows the feature descriptions for Stance recognition and link identification in BIBREF13 . These features and variations of them have been used successfully in several successive works (cf. BIBREF1 , BIBREF16 , BIBREF15 ).",
+      "For any model the features are indexed by INLINEFORM0 . We create a function INLINEFORM1 which maps from feature indices to feature types. In other words, INLINEFORM2 tells us, for any given feature, whether it is content-based ( INLINEFORM3 ), content-ignorant ( INLINEFORM4 ) or full access ( INLINEFORM5 ). The features for, e.g., the joint prediction model INLINEFORM6 of type INLINEFORM7 ( INLINEFORM8 ) can then simply be described as INLINEFORM9 . Recall that features computed on the basis of the EAU span are content-based ( INLINEFORM10 ), features from the EAU-surrounding text are content-ignorant ( INLINEFORM11 ) and features computed from both are denoted by full-access ( INLINEFORM12 ). Details on the extraction of features are provided below.",
+      "These consist of boolean values indicating whether a certain word appears in the argumentative source or target EAU or both (and separately, their contexts). More precisely, for any classification instance we extract uni-grams from within the span of the EAU (if INLINEFORM0 ) or solely from the sentence-context surrounding the EAUs (if INLINEFORM1 ). Words which occur in both bags are only visible in the full-access setup INLINEFORM2 and are modeled as binary indicators.",
+      "Such features consist of syntactic production rules extracted from constituency trees \u2013 they are modelled analogously to the lexical features as a bag of production rules. To make a clear division between features derived from the EAU embedding context and features derived from within the EAU span, we divide the constituency tree in two parts, as is illustrated in Figure FIGREF26 .",
+      "If the EAU is embedded in a covering sentence, we cut the syntax tree at the corresponding edge ( in Figure FIGREF26 ). In this example, the content-ignorant ( INLINEFORM0 ) bag-of-word production rule representation includes the rules INLINEFORM1 and INLINEFORM2 . Analogously to the lexical features, the production rules are modeled as binary indicator features.",
+      "These features describe shallow statistics such as the ratio of argumentative unit tokens compared to sentence tokens or the position of the argumentative unit in the paragraph. We set these features to zero for the content representation of the argumentative unit and replicate those features that allow us to treat the argumentative unit as a black-box. For example, in the content-based ( INLINEFORM0 ) system that has access only to the EAU, we can compute the #tokens in the EAU, but not the #tokens in EAU divided by #tokens in the sentence. The latter feature is only accessible in the full access system variants. Hence, in the content-based ( INLINEFORM1 ) system most of these statistics are set to zero since they cannot be computed by considering only the EAU span.",
+      "For the content-based representation we retrieve only discourse relations that are confined within the span of the argumentative unit. In the very frequent case that discourse features cross the boundaries of embedding context and EAU span, we only take them into account for INLINEFORM0 .",
+      "We use the element-wise sum of 300-dimensional pre-trained GloVe vectors BIBREF24 corresponding to the words within the EAU span ( INLINEFORM0 ) and the words of the EAU-surrounding context ( INLINEFORM1 ). Additionally, we compute the element-wise subtraction of the source EAU vector from the target EAU vector, with the aim of modelling directions in distributional space, similarly to BIBREF25 . Words with no corresponding pre-trained word vector and empty sequences (e.g., no preceding context available) are treated as a zero-vector.",
+      "Tree-based sentiment annotations are sentiment scores assigned to nodes in constituency parse trees BIBREF26 . We represent these scores by a one-hot vector of dimension 5 (5 is very positive, 1 is very negative). We determine the contextual ( INLINEFORM0 ) sentiment by looking at the highest possible node of the context which does not contain the EAU (ADVP in Figure FIGREF26 ). The sentiment for an EAU span ( INLINEFORM1 ) is assigned to the highest possible node covering the EAU span which does not contain the context sub-tree (S in Figure FIGREF26 ). The full-access ( INLINEFORM2 ) score is assigned to the lowest possible node which covers both the EAU span and its surrounding context (S' in Figure FIGREF26 ). Next to the sentiment scores for the selected tree nodes and analogously to the word embeddings, we also calculate the element-wise subtraction of the one-hot sentiment source vectors from the one-hot sentiment target vectors. This results in three additional vectors corresponding to INLINEFORM3 , INLINEFORM4 and INLINEFORM5 difference vectors."
+    ],
+    [
+      "Our first step towards our main experiments is to replicate the competitive argumentative relation classifier of BIBREF13 , BIBREF1 . Hence, for comparison purposes, we first formulate the task exactly as it was done in this prior work, using the model formulation in Eq. EQREF17 , which determines the type of outgoing edge from a source (i.e., tree-like view).",
+      "The results in Table TABREF38 confirm the results of BIBREF13 and suggest that we successfully replicated a large proportion of their features.",
+      "The results for all three prediction settings (one outgoing edge: INLINEFORM0 , support/attack: INLINEFORM1 and support/attack/neither: INLINEFORM2 ) across all type variables ( INLINEFORM3 , INLINEFORM4 and INLINEFORM5 ) are displayed in Table TABREF39 . All models significantly outperform the majority baseline with respect to macro F1. Intriguingly, the content-ignorant models ( INLINEFORM6 ) always perform significantly better than the models which only have access to the EAUs' content ( INLINEFORM7 , INLINEFORM8 ). In the most general task formulation ( INLINEFORM9 ), we observe that INLINEFORM10 even significantly outperforms the model which has maximum access (seeing both EAU spans and surrounding contexts: INLINEFORM11 ).",
+      "At first glance, the results of the purely EAU focused systems ( INLINEFORM0 ) are disappointing, since they fall far behind their competitors. On the other hand, their F1 scores are not devastatingly bad. The strong most-frequent-class baseline is significantly outperformed by the content-based ( INLINEFORM1 ) system, across all three prediction settings.",
+      "In summary our findings are as follows: (i) models which see the EAU span (content-based, INLINEFORM0 ) are significantly outperformed by models that have no access to the span itself (content-ignorant, INLINEFORM1 ) across all settings; (ii) in two of three prediction settings ( INLINEFORM2 and INLINEFORM3 ), the model which only has access to the context even outperforms the model that has access to all information in the input. The fact that using features derived exclusively from the EAU embedding context ( INLINEFORM4 ) can lead to better results than using a full feature-system ( INLINEFORM5 ) suggests that some information from the EAU can even be harmful. Why this is the case, we cannot answer exactly. A plausible cause might be related to the smaller dimension of the feature space, which makes the SVM less likely to overfit. Still, this finding comes as a surprise and calls for further investigation in future work.",
+      "A system for argumentative relation classification can be applied in one of two settings: single-document or cross-document, as illustrated in Figure FIGREF42 :",
+      "in the first case (top), a system is tasked to classify EAUs that appear linearly in one document \u2013 here contextual clues can often highlight the relationship between two units. This is the setting we have been considering up to now. However, in the second scenario (bottom), we have moved away from the closed single-document setting and ask the system to classify two EAUs extracted from different document contexts. This setting applies, for instance, when we are mining arguments from multiple sources.",
+      "In both cases, however, a system that relies more on contextual clues than on the content expressed in the EAUs is problematic: in the single-document setting, such a system will rely on discourse indicators \u2013 whether or not they are justified by content \u2013 and can thus easily be fooled.",
+      "In the cross-document setting, discourse-based indicators \u2013 being inherently defined with respect to their internal document context \u2013 do not have a defined rhetorical function with respect to EAUs in a separate document and thus a system that has learned to rely on such markers within a single-document setting can be seriously misled.",
+      "We believe that the cross-document setting should be an important goal in argumentation analysis, since it generalizes better to many debates of interest, where EAUs can be found scattered across thousands of documents. For example, for the topic of legalizing marijuana, EAUs may be mined from millions of documents and thus their relations may naturally extend across document boundaries. If a system learns to over-proportionally attend to the EAUs' surrounding contexts it is prone to making many errors.",
+      "In what follows we are simulating the effects that an overly context-sensitive classifier could have in a cross-document setting, by modifying our experimental setting, and study the effects on the different model types: In one setup \u2013 we call it randomized-context \u2013 we systematically distort the context of our testing instances by exchanging the context in a randomized manner; in the other setting \u2013 called no-context, we are deleting the context around the ADUs to be classified. Randomized-context simulates an open world debate where argumentative units may occur in different contexts, sometimes with discourse markers indicating an opposite class. In other words, in this setting we want to examine effects when porting a context-sensitive system to a multi-document setting. For example, as seen in Figure FIGREF42 , the context of an argumentative unit may change from \u201cHowever\u201d to \u201cMoreover\u201d \u2013 which can happen naturally in open debates.",
+      "The results are displayed in Figure FIGREF43 . In the standard setting (Figure FIGREF43 ), the models that have access to the context besides the content ( INLINEFORM0 ) and the models that are only allowed to access the context ( INLINEFORM1 ), always perform better than the content-based models ( INLINEFORM2 ) (bars above zero). However, when we randomly flip contexts of the test instances (Figure FIGREF43 ), or suppress them entirely (Figure FIGREF43 ), the opposite picture emerges: the content-based models always outperform the other models. For some classes (support, INLINEFORM3 ) the difference can exceed 50 F1 percentage points. These two studies, where testing examples are varied regarding their context (randomized-context or no-context) simulates what can be expected if we apply our systems for relation class assignment to EAUs stemming from heterogeneous sources. While the performances of a purely content-based model naturally stays stable, the performance of the other systems decrease notably \u2013 they perform worse than the content-based model.",
+      "We calculate the ANOVA classification F scores of the features with respect to our three task formulations INLINEFORM0 and INLINEFORM1 . The F percentiles of features extracted from the EAU surrounding text ( INLINEFORM2 ) and features extracted from the EAU span ( INLINEFORM3 ), are displayed in Figure FIGREF50 .",
+      "It clearly stands out that features obtained from the EAU surrounding context ( INLINEFORM0 ) are assigned much higher scores compared to features stemming from the EAU span ( INLINEFORM1 ). This holds true for all three task formulations and provides further evidence that models \u2013 when given the option \u2013 put a strong focus on contextual clues while neglecting the information provided by the EAU span itself."
+    ],
+    [
+      "While competitive systems for argumentative relation classification are considered to be robust, our experiments have shown that despite confidence-inspiring scores on unseen testing data, such systems can easily be fooled \u2013 they can deliver strong performance scores although the classifier does not have access to the content of the EAUs. In this respect, we have provided evidence that there is a danger in case models focus too much on rhetorical indicators, in detriment of the context. Thus, the following question arises: How can we prevent argumentation models from modeling arguments or argumentative units and their relations in overly na\u00efve ways? A simple and intuitive way is to dissect EAUs from their surrounding document context. Models trained on data that is restricted to the EAUs' content will be forced to focus on the content of EAUs. We believe that this will enhance the robustness of such models and allows them to generalize to cross-document argument relation classification. The corpus of student essays makes such transformations straightforward: only the EAUs were annotated (e.g., \u201cHowever, INLINEFORM0 A INLINEFORM1 \u201d). If annotations extend over the EAUs (e.g., only full sentences are annotated, \u201c INLINEFORM2 However, A INLINEFORM3 \u201d), such transformations could be performed automatically after a discourse parsing step. When inspecting the student essays corpus, we further observed that an EAU mining step should involve coreference resolution to better capture relations between EAUs that involve anaphors (e.g., \u201cExercising makes you feel better\u201d and \u201cIt INLINEFORM4 increases endorphin levels\u201d).",
+      "Thus, in order to conduct real-world end-to-end argumentation relation mining for a given topic, we envision a system that addresses three steps: (i) mining of EAUs and (ii) replacement of pronouns in EAUs with referenced entities (e.g., INLINEFORM0 ). Finally (iii), given the cross product of mined EAUs we can apply a model of type INLINEFORM1 to construct a full-fledged argumentation graph, possibly spanning multiple documents. We have shown that in order to properly perform step (iii), we need stronger models that are able to better model EAU contents. Hence, we encourage the argumentation community to test their systems on a decontextualized version of the student essays, including the proposed \u2013 and possibly further extended \u2013 testing setups, to challenge the semantic representation and reasoning capacities of argument analysis models. This will lead to more realistic performance estimates and increased robustness of systems when addressing desirable multi-document tasks."
+    ],
+    [
+      "We have shown that systems which put too much focus on discourse information may be easily fooled \u2013 an issue which has severe implications when systems are applied to cross-document argumentative relation classification tasks. The strong reliance on contextual clues is also problematic in single-document contexts, where systems can run a risk of assigning relation labels relying on contextual and rhetorical effects \u2013 instead of focusing on content. Hence, we propose that researchers test their argumentative relation classification systems on two alternative versions of the StudentEssay data that reflect different access levels. (i) EAU-span only, where systems only see the EAU spans and (ii) context-only, where systems can only see the EAU-surrounding context. These complementary settings will (i) challenge the semantic capacities of a system, and (ii) unveil the extent to which a system is focusing on the discourse context when making decisions. We will offer our testing environments to the research community through a platform that provides datasets and scripts and a table to trace the results of content-based systems."
+    ],
+    [
+      "This work has been supported by the German Research Foundation as part of the Research Training Group Adaptive Preparation of Information from Heterogeneous Sources (AIPHES) under grant no. GRK 1994/1 and by the Leibniz ScienceCampus \u201cEmpirical Linguistics and Computational Language Modeling\u201d, supported by the Leibniz Association under grant no. SAS-2015-IDS-LWC and by the Ministry of Science, Research, and Art of Baden-W\u00fcrttemberg. "
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0788/instruction.md b/qasper-0788/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..c93cd07b48e75b29d5b865f5bcf636ed72adaaa2
--- /dev/null
+++ b/qasper-0788/instruction.md
@@ -0,0 +1,84 @@
+Name of Paper: Dissecting Content and Context in Argumentative Relation Analysis
+
+Question: How are the EAU text spans annotated?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Argumentative Relation Prediction: Models and Features",
+    "Models",
+    "Feature implementation",
+    "Results",
+    "Discussion",
+    "Conclusion",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "In recent years we have witnessed a great surge in activity in the area of computational argument analysis (e.g. BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 ), and the emergence of dedicated venues such as the ACL Argument Mining workshop series starting in 2014 BIBREF4 .",
+      "Argumentative relation classification is a sub-task of argument analysis that aims to determine relations between argumentative units A and B, for example, A supports B; A attacks B. Consider the following argumentative units (1) and (2), given the topic (0) \u201cMarijuana should be legalized\u201d:",
+      "This example is modeled in Figure FIGREF3 .",
+      "It is clear that (1) has a negative stance towards the topic and (2) has a positive stance towards the topic. Moreover, we can say that (2) attacks (1). In discourse, such a relation is often made explicit through discourse markers: (1). However, (2); On the one hand (1), on the other (2); (1), although (2); Admittedly, (2); etc. In the absence of such markers we must determine this relation by assessing the semantics of the individual argumentative units, including (often implicit) world knowledge about how they are related to each other.",
+      "In this work, we show that argumentative relation classifiers \u2013 when provided with textual context surrounding an argumentative unit's span \u2013 are very prone to neglect the actual textual content of the EAU span. Instead they heavily rely on contextual markers, such as conjunctions or adverbials, as a basis for prediction. We argue that a system's capacity of predicting the correct relation based on the argumentative units' content is important in many circumstances, e.g., when an argumentative debate crosses document boundaries. For example, the prohibition of marijuana debate extends across populations and countries \u2013 argumentative units for this debate can be recovered from thousands of documents scattered across the world wide web. As a consequence, argumentative relation classification systems should not be (immensely) dependent on contextual clues \u2013 in the discussed cross-document setting these clues may even be misleading for such a system, since source and target arguments can be embedded in different textual contexts (e.g., when (1) and (2) stem from different documents it is easy to imagine a textual context where (2) is not introduced by however but instead by an `inverse' form such as e.g. moreover)."
+    ],
+    [
+      "It is well-known that the rhetorical and argumentative structure of texts bear great similarities. For example, BIBREF5 , BIBREF6 , BIBREF0 observe that elementary discourse units (EDUs) in RST BIBREF7 share great similarity with elementary argumentative units (EAUs) in argumentation analysis. BIBREF8 experiment with a modified version of the Microtext corpus BIBREF9 , which is an extensively annotated albeit small corpus. Similar to us, they separate argumentative units from discursive contextual markers. While BIBREF8 conduct a human evaluation to investigate the separation of Logos and Pathos aspects of arguments, our work investigates how (de-)contextualization of argumentative units affects automatic argumentative relation classification models."
+    ],
+    [
+      "In this section, we describe different formulations of the argumentative relation classification task and describe features used by our replicated model. In order to test our hypotheses, we propose to group all features into three distinct types."
+    ],
+    [
+      "Now, we introduce a classification of three different prediction models used in the argumentative relation prediction literature. We will inspect all of them and show that all can suffer from severe issues when focusing (too much) on the context.",
+      "The model INLINEFORM0 adopts a discourse parsing view on argumentative relation prediction and predicts one outgoing edge for an argumentative unit (one-outgoing edge). Model INLINEFORM1 assumes a connected graph with argumentative units and is tasked with predicting edge labels for unit tuples (labeling relations in a graph). Finally, a model INLINEFORM2 is given two (possibly) unrelated argumentative units and is tasked with predicting connections as well as edge labels (joint edge prediction and labeling).",
+      " BIBREF13 divide the task into relation prediction INLINEFORM0 and relation class assignment INLINEFORM1 : DISPLAYFORM0 ",
+      " DISPLAYFORM0 ",
+      "which the authors describe as argumentative relation identification ( INLINEFORM0 ) and stance detection ( INLINEFORM1 ). In their experiments, INLINEFORM2 , i.e., no distinction is made between features that access only the argument content (EAU span) or only the EAU's embedding context, and some features also consider both (e.g., discourse features). This model adopts a parsing view on argumentative relation classification: every unit is allowed to have only one type of outgoing relation (this follows trivially from the fact that INLINEFORM3 has only one input). Applying such a model to argumentative attack and support relations might impose unrealistic constraints on the resulting argumentation graph: A given premise might in fact attack or support several other premises. The approach may suffice for the case of student argumentative essays, where EAUs are well-framed in a discourse structure, but seems overly restrictive for many other scenarios.",
+      "Another way of framing the task, is to learn a function DISPLAYFORM0 ",
+      "Here, an argumentative unit is allowed to be in a attack or support relation to multiple other EAUs. Yet, both INLINEFORM0 and INLINEFORM1 assume that inputs are already linked and only the class of the link is unknown.",
+      "Thus, we might also model the task in a three-class classification setting to learn a more general function that performs relation prediction and classification jointly (see also, e.g., BIBREF10 ): DISPLAYFORM0 ",
+      "The model described by Eq. EQREF22 is the most general one: not only does it assume a graph view on argumentative units and their relations (as does Eq. EQREF20 ); in model formulation (Eq. EQREF22 ), an argumentative unit can have no or multiple support or attack relations. It naturally allows for cases where an argumentative unit INLINEFORM0 (supports INLINEFORM1 INLINEFORM2 attacks INLINEFORM3 INLINEFORM4 is-unrelated-to INLINEFORM5 ). Given a set of EAUs mined from different documents, this model enables us to construct a full-fledged argumentation graph."
+    ],
+    [
+      "Our feature implementation follows the feature descriptions for Stance recognition and link identification in BIBREF13 . These features and variations of them have been used successfully in several successive works (cf. BIBREF1 , BIBREF16 , BIBREF15 ).",
+      "For any model the features are indexed by INLINEFORM0 . We create a function INLINEFORM1 which maps from feature indices to feature types. In other words, INLINEFORM2 tells us, for any given feature, whether it is content-based ( INLINEFORM3 ), content-ignorant ( INLINEFORM4 ) or full access ( INLINEFORM5 ). The features for, e.g., the joint prediction model INLINEFORM6 of type INLINEFORM7 ( INLINEFORM8 ) can then simply be described as INLINEFORM9 . Recall that features computed on the basis of the EAU span are content-based ( INLINEFORM10 ), features from the EAU-surrounding text are content-ignorant ( INLINEFORM11 ) and features computed from both are denoted by full-access ( INLINEFORM12 ). Details on the extraction of features are provided below.",
+      "These consist of boolean values indicating whether a certain word appears in the argumentative source or target EAU or both (and separately, their contexts). More precisely, for any classification instance we extract uni-grams from within the span of the EAU (if INLINEFORM0 ) or solely from the sentence-context surrounding the EAUs (if INLINEFORM1 ). Words which occur in both bags are only visible in the full-access setup INLINEFORM2 and are modeled as binary indicators.",
+      "Such features consist of syntactic production rules extracted from constituency trees \u2013 they are modelled analogously to the lexical features as a bag of production rules. To make a clear division between features derived from the EAU embedding context and features derived from within the EAU span, we divide the constituency tree in two parts, as is illustrated in Figure FIGREF26 .",
+      "If the EAU is embedded in a covering sentence, we cut the syntax tree at the corresponding edge ( in Figure FIGREF26 ). In this example, the content-ignorant ( INLINEFORM0 ) bag-of-word production rule representation includes the rules INLINEFORM1 and INLINEFORM2 . Analogously to the lexical features, the production rules are modeled as binary indicator features.",
+      "These features describe shallow statistics such as the ratio of argumentative unit tokens compared to sentence tokens or the position of the argumentative unit in the paragraph. We set these features to zero for the content representation of the argumentative unit and replicate those features that allow us to treat the argumentative unit as a black-box. For example, in the content-based ( INLINEFORM0 ) system that has access only to the EAU, we can compute the #tokens in the EAU, but not the #tokens in EAU divided by #tokens in the sentence. The latter feature is only accessible in the full access system variants. Hence, in the content-based ( INLINEFORM1 ) system most of these statistics are set to zero since they cannot be computed by considering only the EAU span.",
+      "For the content-based representation we retrieve only discourse relations that are confined within the span of the argumentative unit. In the very frequent case that discourse features cross the boundaries of embedding context and EAU span, we only take them into account for INLINEFORM0 .",
+      "We use the element-wise sum of 300-dimensional pre-trained GloVe vectors BIBREF24 corresponding to the words within the EAU span ( INLINEFORM0 ) and the words of the EAU-surrounding context ( INLINEFORM1 ). Additionally, we compute the element-wise subtraction of the source EAU vector from the target EAU vector, with the aim of modelling directions in distributional space, similarly to BIBREF25 . Words with no corresponding pre-trained word vector and empty sequences (e.g., no preceding context available) are treated as a zero-vector.",
+      "Tree-based sentiment annotations are sentiment scores assigned to nodes in constituency parse trees BIBREF26 . We represent these scores by a one-hot vector of dimension 5 (5 is very positive, 1 is very negative). We determine the contextual ( INLINEFORM0 ) sentiment by looking at the highest possible node of the context which does not contain the EAU (ADVP in Figure FIGREF26 ). The sentiment for an EAU span ( INLINEFORM1 ) is assigned to the highest possible node covering the EAU span which does not contain the context sub-tree (S in Figure FIGREF26 ). The full-access ( INLINEFORM2 ) score is assigned to the lowest possible node which covers both the EAU span and its surrounding context (S' in Figure FIGREF26 ). Next to the sentiment scores for the selected tree nodes and analogously to the word embeddings, we also calculate the element-wise subtraction of the one-hot sentiment source vectors from the one-hot sentiment target vectors. This results in three additional vectors corresponding to INLINEFORM3 , INLINEFORM4 and INLINEFORM5 difference vectors."
+    ],
+    [
+      "Our first step towards our main experiments is to replicate the competitive argumentative relation classifier of BIBREF13 , BIBREF1 . Hence, for comparison purposes, we first formulate the task exactly as it was done in this prior work, using the model formulation in Eq. EQREF17 , which determines the type of outgoing edge from a source (i.e., tree-like view).",
+      "The results in Table TABREF38 confirm the results of BIBREF13 and suggest that we successfully replicated a large proportion of their features.",
+      "The results for all three prediction settings (one outgoing edge: INLINEFORM0 , support/attack: INLINEFORM1 and support/attack/neither: INLINEFORM2 ) across all type variables ( INLINEFORM3 , INLINEFORM4 and INLINEFORM5 ) are displayed in Table TABREF39 . All models significantly outperform the majority baseline with respect to macro F1. Intriguingly, the content-ignorant models ( INLINEFORM6 ) always perform significantly better than the models which only have access to the EAUs' content ( INLINEFORM7 , INLINEFORM8 ). In the most general task formulation ( INLINEFORM9 ), we observe that INLINEFORM10 even significantly outperforms the model which has maximum access (seeing both EAU spans and surrounding contexts: INLINEFORM11 ).",
+      "At first glance, the results of the purely EAU focused systems ( INLINEFORM0 ) are disappointing, since they fall far behind their competitors. On the other hand, their F1 scores are not devastatingly bad. The strong most-frequent-class baseline is significantly outperformed by the content-based ( INLINEFORM1 ) system, across all three prediction settings.",
+      "In summary our findings are as follows: (i) models which see the EAU span (content-based, INLINEFORM0 ) are significantly outperformed by models that have no access to the span itself (content-ignorant, INLINEFORM1 ) across all settings; (ii) in two of three prediction settings ( INLINEFORM2 and INLINEFORM3 ), the model which only has access to the context even outperforms the model that has access to all information in the input. The fact that using features derived exclusively from the EAU embedding context ( INLINEFORM4 ) can lead to better results than using a full feature-system ( INLINEFORM5 ) suggests that some information from the EAU can even be harmful. Why this is the case, we cannot answer exactly. A plausible cause might be related to the smaller dimension of the feature space, which makes the SVM less likely to overfit. Still, this finding comes as a surprise and calls for further investigation in future work.",
+      "A system for argumentative relation classification can be applied in one of two settings: single-document or cross-document, as illustrated in Figure FIGREF42 :",
+      "in the first case (top), a system is tasked to classify EAUs that appear linearly in one document \u2013 here contextual clues can often highlight the relationship between two units. This is the setting we have been considering up to now. However, in the second scenario (bottom), we have moved away from the closed single-document setting and ask the system to classify two EAUs extracted from different document contexts. This setting applies, for instance, when we are mining arguments from multiple sources.",
+      "In both cases, however, a system that relies more on contextual clues than on the content expressed in the EAUs is problematic: in the single-document setting, such a system will rely on discourse indicators \u2013 whether or not they are justified by content \u2013 and can thus easily be fooled.",
+      "In the cross-document setting, discourse-based indicators \u2013 being inherently defined with respect to their internal document context \u2013 do not have a defined rhetorical function with respect to EAUs in a separate document and thus a system that has learned to rely on such markers within a single-document setting can be seriously misled.",
+      "We believe that the cross-document setting should be an important goal in argumentation analysis, since it generalizes better to many debates of interest, where EAUs can be found scattered across thousands of documents. For example, for the topic of legalizing marijuana, EAUs may be mined from millions of documents and thus their relations may naturally extend across document boundaries. If a system learns to over-proportionally attend to the EAUs' surrounding contexts it is prone to making many errors.",
+      "In what follows we are simulating the effects that an overly context-sensitive classifier could have in a cross-document setting, by modifying our experimental setting, and study the effects on the different model types: In one setup \u2013 we call it randomized-context \u2013 we systematically distort the context of our testing instances by exchanging the context in a randomized manner; in the other setting \u2013 called no-context, we are deleting the context around the ADUs to be classified. Randomized-context simulates an open world debate where argumentative units may occur in different contexts, sometimes with discourse markers indicating an opposite class. In other words, in this setting we want to examine effects when porting a context-sensitive system to a multi-document setting. For example, as seen in Figure FIGREF42 , the context of an argumentative unit may change from \u201cHowever\u201d to \u201cMoreover\u201d \u2013 which can happen naturally in open debates.",
+      "The results are displayed in Figure FIGREF43 . In the standard setting (Figure FIGREF43 ), the models that have access to the context besides the content ( INLINEFORM0 ) and the models that are only allowed to access the context ( INLINEFORM1 ), always perform better than the content-based models ( INLINEFORM2 ) (bars above zero). However, when we randomly flip contexts of the test instances (Figure FIGREF43 ), or suppress them entirely (Figure FIGREF43 ), the opposite picture emerges: the content-based models always outperform the other models. For some classes (support, INLINEFORM3 ) the difference can exceed 50 F1 percentage points. These two studies, where testing examples are varied regarding their context (randomized-context or no-context) simulates what can be expected if we apply our systems for relation class assignment to EAUs stemming from heterogeneous sources. While the performances of a purely content-based model naturally stays stable, the performance of the other systems decrease notably \u2013 they perform worse than the content-based model.",
+      "We calculate the ANOVA classification F scores of the features with respect to our three task formulations INLINEFORM0 and INLINEFORM1 . The F percentiles of features extracted from the EAU surrounding text ( INLINEFORM2 ) and features extracted from the EAU span ( INLINEFORM3 ), are displayed in Figure FIGREF50 .",
+      "It clearly stands out that features obtained from the EAU surrounding context ( INLINEFORM0 ) are assigned much higher scores compared to features stemming from the EAU span ( INLINEFORM1 ). This holds true for all three task formulations and provides further evidence that models \u2013 when given the option \u2013 put a strong focus on contextual clues while neglecting the information provided by the EAU span itself."
+    ],
+    [
+      "While competitive systems for argumentative relation classification are considered to be robust, our experiments have shown that despite confidence-inspiring scores on unseen testing data, such systems can easily be fooled \u2013 they can deliver strong performance scores although the classifier does not have access to the content of the EAUs. In this respect, we have provided evidence that there is a danger in case models focus too much on rhetorical indicators, in detriment of the context. Thus, the following question arises: How can we prevent argumentation models from modeling arguments or argumentative units and their relations in overly na\u00efve ways? A simple and intuitive way is to dissect EAUs from their surrounding document context. Models trained on data that is restricted to the EAUs' content will be forced to focus on the content of EAUs. We believe that this will enhance the robustness of such models and allows them to generalize to cross-document argument relation classification. The corpus of student essays makes such transformations straightforward: only the EAUs were annotated (e.g., \u201cHowever, INLINEFORM0 A INLINEFORM1 \u201d). If annotations extend over the EAUs (e.g., only full sentences are annotated, \u201c INLINEFORM2 However, A INLINEFORM3 \u201d), such transformations could be performed automatically after a discourse parsing step. When inspecting the student essays corpus, we further observed that an EAU mining step should involve coreference resolution to better capture relations between EAUs that involve anaphors (e.g., \u201cExercising makes you feel better\u201d and \u201cIt INLINEFORM4 increases endorphin levels\u201d).",
+      "Thus, in order to conduct real-world end-to-end argumentation relation mining for a given topic, we envision a system that addresses three steps: (i) mining of EAUs and (ii) replacement of pronouns in EAUs with referenced entities (e.g., INLINEFORM0 ). Finally (iii), given the cross product of mined EAUs we can apply a model of type INLINEFORM1 to construct a full-fledged argumentation graph, possibly spanning multiple documents. We have shown that in order to properly perform step (iii), we need stronger models that are able to better model EAU contents. Hence, we encourage the argumentation community to test their systems on a decontextualized version of the student essays, including the proposed \u2013 and possibly further extended \u2013 testing setups, to challenge the semantic representation and reasoning capacities of argument analysis models. This will lead to more realistic performance estimates and increased robustness of systems when addressing desirable multi-document tasks."
+    ],
+    [
+      "We have shown that systems which put too much focus on discourse information may be easily fooled \u2013 an issue which has severe implications when systems are applied to cross-document argumentative relation classification tasks. The strong reliance on contextual clues is also problematic in single-document contexts, where systems can run a risk of assigning relation labels relying on contextual and rhetorical effects \u2013 instead of focusing on content. Hence, we propose that researchers test their argumentative relation classification systems on two alternative versions of the StudentEssay data that reflect different access levels. (i) EAU-span only, where systems only see the EAU spans and (ii) context-only, where systems can only see the EAU-surrounding context. These complementary settings will (i) challenge the semantic capacities of a system, and (ii) unveil the extent to which a system is focusing on the discourse context when making decisions. We will offer our testing environments to the research community through a platform that provides datasets and scripts and a table to trace the results of content-based systems."
+    ],
+    [
+      "This work has been supported by the German Research Foundation as part of the Research Training Group Adaptive Preparation of Information from Heterogeneous Sources (AIPHES) under grant no. GRK 1994/1 and by the Leibniz ScienceCampus \u201cEmpirical Linguistics and Computational Language Modeling\u201d, supported by the Leibniz Association under grant no. SAS-2015-IDS-LWC and by the Ministry of Science, Research, and Art of Baden-W\u00fcrttemberg. "
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0789/instruction.md b/qasper-0789/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..2ef50e8774e5a7a1899ace8a46cd72ad518a1d93
--- /dev/null
+++ b/qasper-0789/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Dissecting Content and Context in Argumentative Relation Analysis
+
+Question: How are elementary argumentative units defined?
\ No newline at end of file
diff --git a/qasper-0799/instruction.md b/qasper-0799/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..41025c54e4a8279cfdedd9726d2e000193c40e06
--- /dev/null
+++ b/qasper-0799/instruction.md
@@ -0,0 +1,102 @@
+Name of Paper: An Annotated Corpus of Emerging Anglicisms in Spanish Newspaper Headlines
+
+Question: What is the performance of the CRF model on the task described?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Anglicism: Scope of the Phenomenon",
+    "Corpus description and annotation ::: Corpus description",
+    "Corpus description and annotation ::: Corpus description ::: Main Corpus",
+    "Corpus description and annotation ::: Corpus description ::: Supplemental Test Set",
+    "Corpus description and annotation ::: Annotation guidelines",
+    "Baseline Model",
+    "Results",
+    "Future Work",
+    "Conclusions",
+    "Acknowledgements",
+    "Language Resource References"
+  ],
+  "paragraphs": [
+    [
+      "The study of English influence in the Spanish language has been a hot topic in Hispanic linguistics for decades, particularly concerning lexical borrowing or anglicisms BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6.",
+      "Lexical borrowing is a phenomenon that affects all languages and constitutes a productive mechanism for word-formation, especially in the press. chesleypaulapredicting2010 estimated that a reader of French newspapers encountered a new lexical borrowing for every 1,000 words. In Chilean newspapers, lexical borrowings account for approximately 30% of neologisms, 80% of those corresponding to English loanwords BIBREF7.",
+      "Detecting lexical borrowings is relevant both for lexicographic purposes and for NLP downstream tasks BIBREF8, BIBREF9. However, strategies to track and register lexical borrowings have traditionally relied on manual review of corpora.",
+      "In this paper we present: (1) a corpus of newspaper headlines in European Spanish annotated with emerging anglicisms and (2) a CRF baseline model for anglicism automatic extraction in Spanish newswire."
+    ],
+    [
+      "Corpus-based studies of English borrowings in Spanish media have traditionally relied on manual evaluation of either previously compiled general corpora such as CREA BIBREF10, BIBREF11, BIBREF12, BIBREF13, either new tailor-made corpora designed to analyze specific genres, varieties or phenomena BIBREF14, BIBREF15, BIBREF16, BIBREF17, BIBREF18, BIBREF19, BIBREF20.",
+      "In terms of automatic detection of anglicisms, previous approaches in different languages have mostly depended on resource lookup (lexicon or corpus frequencies), character n-grams and pattern matching. alex-2008-comparing combined lexicon lookup and a search engine module that used the web as a corpus to detect English inclusions in a corpus of German texts and compared her results with a maxent Markov model. furiassi2007retrieval explored corpora lookup and character n-grams to extract false anglicisms from a corpus of Italian newspapers. andersen2012semi used dictionary lookup, regular expressions and lexicon-derived frequencies of character n-grams to detect anglicism candidates in the Norwegian Newspaper Corpus (NNC) BIBREF21, while losnegaard2012data explored a Machine Learning approach to anglicism detection in Norwegian by using TiMBL (Tilburg Memory-Based Learner, an implementation of a k-nearest neighbor classifier) with character trigrams as features. garley-hockenmaier-2012-beefmoves trained a maxent classifier with character n-gram and morphological features to identify anglicisms in German online communities. In Spanish, serigos2017using extracted anglicisms from a corpus of Argentinian newspapers by combining dictionary lookup (aided by TreeTagger and the NLTK lemmatizer) with automatic filtering of capitalized words and manual inspection. In serigos2017applying, a character n-gram module was added to estimate the probabilities of a word being English or Spanish. moreno2018configuracion used different pattern-matching filters and lexicon lookup to extract anglicism cadidates from a corpus of tweets in US Spanish.",
+      "Work within the code-switching community has also dealt with language identification on multilingual corpora. Due to the nature of code-switching, these models have primarily focused on oral copora and social media datasets BIBREF22, BIBREF23, BIBREF24. In the last shared task of language identification in code-switched data BIBREF23, approaches to English-Spanish included CRFs models BIBREF25, BIBREF26, BIBREF27, BIBREF28, logistic regression BIBREF29 and LSTMs models BIBREF30, BIBREF31.",
+      "The scope and nature of lexical borrowing is, however, somewhat different to that of code-switching. In fact, applying code-switching models to lexical borrowing detection has previously proved to be unsuccessful, as they tend to overestimate the number of anglicisms BIBREF32. In the next section we address the differences between both phenomena and set the scope of this project."
+    ],
+    [
+      "Linguistic borrowing can be defined as the transference of linguistic elements between two languages. Borrowing and code-switching have frequently been described as a continuum BIBREF33, with a fuzzy frontier between the two. As a result, a precise definition of what borrowing is remains elusive BIBREF34 and some authors prefer to talk about code-mixing in general BIBREF35 or \u201clone other-language incorporations\" BIBREF36.",
+      "Lexical borrowing in particular involves the incorporation of single lexical units from one language into another language and is usually accompanied by morphological and phonological modification to conform with the patterns of the recipient language BIBREF37, BIBREF38. By definition, code-switches are not integrated into a recipient language, unlike established loanwords BIBREF39. While code-switches are usually fluent multiword interferences that normally comply with grammatical restrictions in both languages and that are produced by bilingual speakers in bilingual discourses, lexical borrowings are words used by monolingual individuals that eventually become lexicalized and assimilated as part of the recipient language lexicon until the knowledge of \u201cforeign\" origin disappears BIBREF40.",
+      "In terms of approaching the problem, automatic code-switching identification has been framed as a sequence modeling problem where every token receives a language ID label (as in a POS-tagging task). Borrowing detection, on the other hand, while it can also be transformed into a sequence labeling problem, is an extraction task, where only certain spans of texts will be labeled (in the fashion of a NER task).",
+      "Various typologies have been proposed that aim to classify borrowings according to different criteria, both with a cross-linguistic perspective and also specifically aimed to characterize English inclusions in Spanish BIBREF34, BIBREF41, BIBREF42, BIBREF5. In this work, we will be focusing on unassimilated lexical borrowings (sometimes called foreignisms), i.e. words from English origin that are introduced into Spanish without any morphological or orthographic adaptation."
+    ],
+    [
+      "In this subsection we describe the characteristics of the corpus. We first introduce the main corpus, with the usual train/development/test split that was used to train, tune and evaluate the model. We then present an additional test set that was designed to assess the performance of the model on more naturalistic data."
+    ],
+    [
+      "The main corpus consists of a collection of monolingual newspaper headlines written in European Spanish. The corpus contains 16,553 headlines, which amounts to 244,114 tokens. Out of those 16,553 headlines, 1,109 contain at least one anglicism. The total number of anglicisms is 1,176 (most of them are a single word, although some of them were multiword expressions). The corpus was divided into training, development and test set. The proportions of headlines, tokens and anglicisms in each corpus split can be found in Table TABREF6.",
+      "The headlines in this corpus come from the Spanish newspaper eldiario.es, a progressive online newspaper based in Spain. eldiario.es is one of the main national newspapers from Spain and, to the best of our knowledge, the only one that publishes its content under a Creative Commons license, which made it ideal for making the corpus publicly available.",
+      "The headlines were extracted from the newspaper website through web scraping and range from September 2012 to January 2020. Only the following sections were included: economy, technology, lifestyle, music, TV and opinion. These sections were chosen as they were the most likely to contain anglicisms. The proportion of headlines with anglicisms per section can be found in Table TABREF7.",
+      "Using headlines (instead of full articles) was beneficial for several reasons. First of all, annotating a headline is faster and easier than annotating a full article; this helps ensure that a wider variety of topics will be covered in the corpus. Secondly, anglicisms are abundant in headlines, because they are frequently used as a way of calling the attention of the reader BIBREF43. Finally, borrowings that make it to the headline are likely to be particularly salient or relevant, and therefore are good candidates for being extracted and tracked."
+    ],
+    [
+      "In addition to the usual train/development/test split we have just presented, a supplemental test set of 5,017 headlines was collected. The headlines included in this additional test set also belong to eldiario.es. These headlines were retrieved daily through RSS during February 2020 and included all sections from the newspaper. The headlines in the supplemental corpus therefore do not overlap in time with the main corpus and include more sections. The number of headlines, tokens and anglicisms in the supplemental test set can be found in Table TABREF6.",
+      "The motivation behind this supplemental test set is to assess the model performance on more naturalistic data, as the headlines in the supplemental corpus (1) belong to the future of the main corpus and (2) come from a less borrowing-dense sample. This supplemental test set better mimics the real scenario that an actual anglicism extractor would face and can be used to assess how well the model generalizes to detect anglicisms in any section of the daily news, which is ultimately the aim of this project."
+    ],
+    [
+      "The term anglicism covers a wide range of linguistic phenomena. Following the typology proposed by gomez1997towards, we focused on direct, unadapted, emerging Anglicisms, i.e. lexical borrowings from the English language into Spanish that have recently been imported and that have still not been assimilated into Spanish. Other phenomena such as semantic calques, syntactic anglicisms, acronyms and proper names were considered beyond the scope of this annotation project.",
+      "Lexical borrowings can be adapted (the spelling of the word is modified to comply with the phonological and orthographic patterns of the recipient language) or unadapted (the word preserves its original spelling). For this annotation task, adapted borrowings were ignored and only unadapted borrowings were annotated. Therefore, Spanish adaptations of anglicisms like f\u00fatbol (from football), mitin (from meeting) and such were not annotated as borrowings. Similarly, words derived from foreign lexemes that do not comply with Spanish orthotactics but that have been morphologically derived following the Spanish paradigm (hacktivista, hackear, shakespeariano) were not annotated either. However, pseudo-anglicisms (words that are formed as if they were English, but do not exist in English, such as footing or balconing) were annotated.",
+      "Words that were not adapted but whose original spelling complies with graphophonological rules of Spanish (and are therefore unlikely to be ever adapted, such as web, internet, fan, club, videoclip) were annotated or not depending on how recent or emergent they were. After all, a word like club, that has been around in Spanish language for centuries, cannot be considered emergent anymore and, for this project, would not be as interesting to retrieve as real emerging anglicisms. The notion of emergent is, however, time-dependent and quite subjective: in order to determine which unadapted, graphophonologically acceptable borrowings were to be annotated, the online version of the Diccionario de la lengua espa\u00f1ola dle was consulted. This dictionary is compiled by the Royal Spanish Academy, a prescriptive institution on Spanish language. This decision was motivated by the fact that, if a borrowing was already registered by this dictionary (that has conservative approach to language change) and is considered assimilated (that is, the institution recommended no italics or quotation marks to write that word) then it could be inferred that the word was not emergent anymore.",
+      "Although the previous guidelines covered most cases, they proved insufficient. Some anglicisms were unadapted (they preserved their original spelling), unacceptable according to the Spanish graphophonological rules, and yet did not satisfy the condition of being emergent. That was the case of words like jazz or whisky, words that do not comply with Spanish graphophonological rules but that were imported decades ago, cannot be considered emergent anymore and are unlikely to ever be adapted into the Spanish spelling system. To adjudicate these examples on those cases, the criterion of pragmatic markedness proposed by winter2012proposing (that distinguishes between catachrestic and non-catachrestic borrowing) was applied: if a borrowing was not adapted (i.e. its form remained exactly as it came from English) but referred to a particular invention or innovation that came via the English language, that was not perceived as new anymore and that had never competed with a Spanish equivalent, then it was ignored. This criteria proved to be extremely useful to deal with old unadapted anglicisms in the fields of music and food. Figure 1 summarizes the decision steps followed during the annotation process.",
+      "The corpus was annotated by a native speaker of Spanish using Doccano doccano. The annotation tagset includes two labels: ENG, to annotate the English borrowings just described, and OTHER. This OTHER tag was used to tag lexical borrowings from languages other than English. After all, although English is today by far the most prevalent donor of borrowings, there are other languages that also provide new borrowings to Spanish. Furthermore, the tag OTHER allows to annotate borrowings such as premi\u00e8re or tempeh, borrowings that etymologically do not come from English but that have entered the Spanish language via English influence, even when their spelling is very different to English borrowings. In general, we considered that having such a tag could also help assess how successful a classifier is detecting foreign borrowings in general in Spanish newswire (without having to create a label for every possible donor language, as the number of examples would be too sparse). In total, the training set contained 40 entities labeled as OTHER, the development set contained 14 and the test set contained 13. The supplemental test set contained 35 OTHER entities."
+    ],
+    [
+      "A baseline model for automatic extraction of anglicisms was created using the annotated corpus we just presented as training material. As mentioned in Section 3, the task of detecting anglicisms can be approached as a sequence labeling problem where only certain spans of texts will be labeled as anglicism (in a similar way to an NER task). The chosen model was conditional random field model (CRF), which was also the most popular model in both Shared Tasks on Language Identification for Code-Switched Data BIBREF23, BIBREF24.",
+      "The model was built using pycrfsuite korobov2014python, the Python wrapper for crfsuite CRFsuite that implements CRF for labeling sequential data. It also used the Token and Span utilities from spaCy library honnibal2017spacy.",
+      "The following handcrafted features were used for the model:",
+      "Bias feature",
+      "Token feature",
+      "Uppercase feature (y/n)",
+      "Titlecase feature (y/n)",
+      "Character trigram feature",
+      "Quotation feature (y/n)",
+      "Word suffix feature (last three characters)",
+      "POS tag (provided by spaCy utilities)",
+      "Word shape (provided by spaCy utilities)",
+      "Word embedding (see Table TABREF26)",
+      "Given that anglicisms can be multiword expressions (such as best seller, big data) and that those units should be treated as one borrowing and not as two independent borrowings, we used multi-token BIO encoding to denote the boundaries of each span BIBREF44. A window of two tokens in each direction was set for the feature extractor. The algorithm used was gradient descent with the L-BFGS method.",
+      "The model was tuned on the development set doing grid search; the hyperparameters considered were c1 (L1 regularization coefficient: $0.01$, $0.05$, $0.1$, $0.5$, $1.0$), c2 (L2 regularization coefficient: $0.01$, $0.05$, $0.1$, $0.5$, $1.0$), embedding scaling ($0.5$, $1.0$, $2.0$, $4.0$), and embedding type bojanowski2017enriching,josecanete20193255001,cardellinoSBWCE,grave2018learning,honnibal2017spacy,perezfasttext,perezglove (see Table TABREF26). The best results were obtained with c1 = $0.05$, c2 = $0.01$, scaling = $0.5$ and word2vec Spanish embeddings by cardellinoSBWCE. The threshold for the stopping criterion delta was selected through observing the loss during preliminary experiments (delta = $1\\mathrm {e}-3$).",
+      "In order to assess the significance of the the handcrafted features, a feature ablation study was done on the tuned model, ablating one feature at a time and testing on the development set. Due to the scarcity of spans labeled with the OTHER tag on the development set (only 14) and given that the main purpose of the model is to detect anglicisms, the baseline model was run ignoring the OTHER tag both during tuning and the feature ablation experiments. Table TABREF27 displays the results on the development set with all features and for the different feature ablation runs. The results show that all features proposed for the baseline model contribute to the results, with the character trigram feature being the one that has the biggest impact on the feature ablation study."
+    ],
+    [
+      "The baseline model was then run on the test set and the supplemental test set with the set of features and hyperparameters mentioned on Section SECREF5 Table TABREF28 displays the results obtained. The model was run both with and without the OTHER tag. The metrics for ENG display the results obtained only for the spans labeled as anglicisms; the metrics for OTHER display the results obtained for any borrowing other than anglicisms. The metrics for BORROWING discard the type of label and consider correct any labeled span that has correct boundaries, regardless of the label type (so any type of borrowing, regardless if it is ENG or OTHER). In all cases, only full matches were considered correct and no credit was given to partial matching, i.e. if only fake in fake news was retrieved, it was considered wrong and no partial score was given.",
+      "Results on all sets show an important difference between precision and recall, precision being significantly higher than recall. There is also a significant difference between the results obtained on development and test set (F1 = 89.60, F1 = 87.82) and the results on the supplemental test set (F1 = 71.49). The time difference between the supplemental test set and the development and test set (the headlines from the the supplemental test set being from a different time period to the training set) can probably explain these differences.",
+      "Comparing the results with and without the OTHER tag, it seems that including it on the development and test set produces worse results (or they remain roughly the same, at best). However, the best precision result on the supplemental test was obtained when including the OTHER tag and considering both ENG and OTHER spans as BORROWING (precision = 87.62). This is caused by the fact that, while the development and test set were compiled from anglicism-rich newspaper sections (similar to the training set), the supplemental test set contained headlines from all the sections in the newspaper, and therefore included borrowings from other languages such as Catalan, Basque or French. When running the model without the OTHER tag on the supplemental test set, these non-English borrowings were labeled as anglicisms by the model (after all, their spelling does not resemble Spanish spelling), damaging the precision score. When the OTHER tag was included, these non-English borrowings got correctly labeled as OTHER, improving the precision score. This proves that, although the OTHER tag might be irrelevant or even damaging when testing on the development or test set, it can be useful when testing on more naturalistic data, such as the one in the supplemental test set.",
+      "Concerning errors, two types of errors were recurrent among all sets: long titles of songs, films or series written in English were a source of false positives, as the model tended to mistake some of the uncapitalized words in the title for anglicisms (for example, it darker in \u201c`You want it darker', la oscura y brillante despedida de Leonard Cohen\"). On the other hand, anglicisms that appear on the first position of the sentence (and were, therefore, capitalized) were consistently ignored (as the model probably assumed they were named entities) and produced a high number of false negatives (for example, vamping in \u201cVamping: la recurrente leyenda urbana de la luz azul `asesina'\").",
+      "The results on Table TABREF28 cannot, however, be compared to the ones reported by previous work: the metric that we report is span F-measure, as the evaluation was done on span level (instead of token level) and credit was only given to full matches. Secondly, there was no Spanish tag assigned to non-borrowings, that means that no credit was given if a Spanish token was identified as such."
+    ],
+    [
+      "This is an on-going project. The corpus we have just presented is a first step towards the development of an extractor of emerging anglicisms in the Spanish press. Future work includes: assessing whether to keep the OTHER tag, improving the baseline model (particularly to improve recall), assessing the suitability and contribution of different sets of features and exploring different models. In terms of the corpus development, the training set is now closed and stable, but the test set could potentially be increased in order to have more and more diverse anglicisms."
+    ],
+    [
+      "In this paper we have presented a new corpus of 21,570 newspaper headlines written in European Spanish. The corpus is annotated with emergent anglicisms and, up to our very best knowledge, is the first corpus of this type to be released publicly. We have presented the annotation scope, tagset and guidelines, and we have introduced a CRF baseline model for anglicism extraction trained with the described corpus. The results obtained show that the the corpus and baseline model are appropriate for automatic anglicism extraction."
+    ],
+    [
+      "The author would like to thank Constantine Lignos for his feedback and advice on this project."
+    ],
+    [
+      "lrec"
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: Efficient Attention using a Fixed-Size Memory Representation
+
+Question: Which datasets are used in experiments?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Sequence-to-Sequence Model with Attention",
+    "Memory-Based Attention Model",
+    "Model Interpretations",
+    "Position Encodings (PE)",
+    "Toy Copying Experiment",
+    "Machine Translation",
+    "Visualizing Attention",
+    "Related Work",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Sequence-to-sequence models BIBREF0 , BIBREF1 have achieved state of the art results across a wide variety of tasks, including Neural Machine Translation (NMT) BIBREF2 , BIBREF3 , text summarization BIBREF4 , BIBREF5 , speech recognition BIBREF6 , BIBREF7 , image captioning BIBREF8 , and conversational modeling BIBREF9 , BIBREF10 .",
+      "The most popular approaches are based on an encoder-decoder architecture consisting of two recurrent neural networks (RNNs) and an attention mechanism that aligns target to source tokens BIBREF2 , BIBREF11 . The typical attention mechanism used in these architectures computes a new attention context at each decoding step based on the current state of the decoder. Intuitively, this corresponds to looking at the source sequence after the output of every single target token.",
+      "Inspired by how humans process sentences, we believe it may be unnecessary to look back at the entire original source sequence at each step. We thus propose an alternative attention mechanism (section \"Memory-Based Attention Model\" ) that leads to smaller computational time complexity. Our method predicts $K$ attention context vectors while reading the source, and learns to use a weighted average of these vectors at each step of decoding. Thus, we avoid looking back at the source sequence once it has been encoded. We show (section \"Experiments\" ) that this speeds up inference while performing on-par with the standard mechanism on both toy and real-world WMT translation datasets. We also show that our mechanism leads to larger speedups as sequences get longer. Finally, by visualizing the attention scores (section \"Visualizing Attention\" ), we verify that the proposed technique learns meaningful alignments, and that different attention context vectors specialize on different parts of the source."
+    ],
+    [
+      "Our models are based on an encoder-decoder architecture with attention mechanism BIBREF2 , BIBREF11 . An encoder function takes as input a sequence of source tokens $\\mathbf {x} = (x_1, ..., x_m)$ and produces a sequence of states $\\mathbf {s} = (s_1, ..., s_m)$ .The decoder is an RNN that predicts the probability of a target sequence $\\mathbf {y} = (y_1, ..., y_T \\mid \\mathbf {s})$ . The probability of each target token $y_i \\in \\lbrace 1, ... ,|V|\\rbrace $ is predicted based on the recurrent state in the decoder RNN, $h_i$ , the previous words, $y_{<i}$ , and a context vector $c_i$ . The context vector $c_i$ , also referred to as the attention vector, is calculated as a weighted average of the source states. ",
+      "$$c_i & = \\sum _{j}{\\alpha _{ij} s_j} \\\\\n{\\alpha }_{i} & = \\text{softmax}(f_{att}(h_i, \\mathbf {s}))$$   (Eq. 3) ",
+      "Here, $f_{att}(h_i, \\mathbf {s})$ is an attention function that calculates an unnormalized alignment score between the encoder state $s_j$ and the decoder state $h_i$ . Variants of $f_{att}$ used in BIBREF2 and BIBREF11 are: $\nf_{att}(h_i, s_j)=\n{\\left\\lbrace \\begin{array}{ll}\nv_a^T \\text{tanh}(W_a[h_i, s_j]),& \\emph {Bahdanau} \\\\\nh_i^TW_as_j & \\emph {Luong}\n\\end{array}\\right.}\n$ ",
+      "where $W_a$ and $v_a$ are model parameters learned to predict alignment. Let $|S|$ and $|T|$ denote the lengths of the source and target sequences respectively and $D$ denoate the state size of the encoder and decoder RNN. Such content-based attention mechanisms result in inference times of $O(D^2|S||T|)$ , as each context vector depends on the current decoder state $h_i$ and all encoder states, and requires an $O(D^2)$ matrix multiplication.",
+      "The decoder outputs a distribution over a vocabulary of fixed-size $|V|$ : ",
+      "$$P(y_i \\vert y_{<i}, \\mathbf {x}) = \\text{softmax}(W[s_i; c_i] + b)$$   (Eq. 5) ",
+      " The model is trained end-to-end by minimizing the negative log likelihood of the target words using stochastic gradient descent."
+    ],
+    [
+      "Our proposed model is shown in Figure 1 . During encoding, we compute an attention matrix $C \\in \\mathbb {R}^{K \\times D}$ , where $K$ is the number of attention vectors and a hyperparameter of our method, and $D$ is the dimensionality of the top-most encoder state. This matrix is computed by predicting a score vector $\\alpha _t \\in \\mathbb {R}^K$ at each encoding time step $t$ . $C$ is then a linear combination of the encoder states, weighted by $\\alpha _t$ : ",
+      "$$C_k & = \\sum _{t=0}^{|S|}{\\alpha _{tk} s_t} \\\\\n\\alpha _t & = \\text{softmax}(W_\\alpha s_t) ,$$   (Eq. 7) ",
+      " where $W_{\\alpha }$ is a parameter matrix in $\\mathbb {R}^{K\\times D}$ .",
+      "The computational time complexity for this operation is $O(KD|S|)$ . One can think of C as compact fixed-length memory that the decoder will perform attention over. In contrast, standard approaches use a variable-length set of encoder states for attention. At each decoding step, we similarly predict $K$ scores $\\beta \\in \\mathbb {R}^K$ . The final attention context $c$ is a linear combination of the rows in $C$ weighted by the scores. Intuitively, each decoder step predicts how important each of the $K$ attention vectors is. ",
+      "$$c & = \\sum _{i=0}^{K}{\\beta _i C_i} \\\\\n\\beta & = \\text{softmax}(W_\\beta h)$$   (Eq. 8) ",
+      " Here, $h$ is the current state of the decoder, and $W_\\beta $ is a learned parameter matrix. Note that we do not access the encoder states at each decoder step. We simply take a linear combination of the attention matrix $C$ pre-computed during encoding - a much cheaper operation that is independent of the length of the source sequence. The time complexity of this computation is $O(KD|T|)$ as multiplication with the $K$ attention matrices needs to happen at each decoding step.",
+      "Summing $O(KD|S|)$ from encoding and $O(KD|T|)$ from decoding, we have a total linear computational complexity of $O(KD(|S| + |T|)$ . As $D$ is typically very large, 512 or 1024 units in most applications, we expect our model to be faster than the standard attention mechanism running in $O(D^2|S||T|)$ . For long sequences (as in summarization, where |S| is large), we also expect our model to be faster than the cheaper dot-based attention mechanism, which needs $O(D|S||T|)$ computation time and requires encoder and decoder states sizes to match.",
+      "We also experimented with using a sigmoid function instead of the softmax to score the encoder and decoder attention scores, resulting in 4 possible combinations. We call this choice the scoring function. A softmax scoring function calculates normalized scores, while the sigmoid scoring function results in unnormalized scores that can be understood as gates."
+    ],
+    [
+      "Our memory-based attention model can be understood intuitively in two ways. We can interpret it as \"predicting\" the set of attention contexts produced by a standard attention mechanism during encoding. To see this, assume we set $K \\approx |T|$ . In this case, we predict all $|T|$ attention contexts during the encoding stage and learn to choose the right one during decoding. This is cheaper than computing contexts one-by-one based on the decoder and encoder content. In fact, we could enforce this objective by first training a regular attention model and adding a regularization term to force the memory matrix $C$ to be close to the $T\\times D$ vectors computed by the standard attention. We leave it to future work to explore such an objective.",
+      "Alternatively, we can interpret our mechanism as first predicting a compact $K \\times D$ memory matrix, a representation of the source sequence, and then performing location-based attention on the memory by picking which row of the matrix to attend to. Standard location-based attention mechanism, by contrast, predicts a location in the source sequence to focus on BIBREF11 , BIBREF8 ."
+    ],
+    [
+      "In the above formulation, the predictions of attention contexts are symmetric. That is, $C_i$ is not forced to be different from $C_{j\\ne i}$ . While we would hope for the model to learn to generate distinct attention contexts, we now present an extension that pushes the model into this direction. We add position encodings to the score matrix that forces the first few context vector $C_1, C_2, ...$ to focus on the beginning of the sequence and the last few vectors $...,C_{K-1}, C_K$ to focus on the end (thereby encouraging in-between vectors to focus on the middle).",
+      "Explicitly, we multiply the score vector $\\alpha $ with position encodings $l_s\\in \\mathbb {R}^{K}$ : ",
+      "$$C^{PE} & = \\sum _{s=0}^{|S|}{\\alpha ^{PE} h_s} \\\\\n\\alpha ^{PE}_s & = \\text{softmax}(W_\\alpha h_s \\circ l_s)$$   (Eq. 11) ",
+      "To obtain $l_s$ we first calculate a constant matrix $L$ where we define each element as ",
+      "$$L_{ks} & = (1-k/K)(1-s/\\mathcal {S})+\\frac{k}{K}\\frac{s}{\\mathcal {S}},$$   (Eq. 12) ",
+      " adapting a formula from BIBREF13 . Here, $k\\in \\lbrace 1,2,...,K\\rbrace $ is the context vector index and $\\mathcal {S}$ is the maximum sequence length across all source sequences. The manifold is shown graphically in Figure 2 . We can see that earlier encoder states are upweighted in the first context vectors, and later states are upweighted in later vectors. The symmetry of the manifold and its stationary point having value 0.5 both follow from Eq. 12 . The elements of the matrix that fall beyond the sequence lengths are then masked out and the remaining elements are renormalized across the timestep dimension. This results in the jagged array of position encodings $\\lbrace l_{ks}\\rbrace $ ."
+    ],
+    [
+      "Due to the reduction of computational time complexity we expect our method to yield performance gains especially for longer sequences and tasks where the source can be compactly represented in a fixed-size memory matrix. To investigate the trade-off between speed and performance, we compare our technique to standard models with and without attention on a Sequence Copy Task of varying length like in BIBREF14 . We generated 4 training datasets of 100,000 examples and a validation dataset of 1,000 examples. The vocabulary size was 20. For each dataset, the sequences had lengths randomly chosen between 0 to $L$ , for $L\\in \\lbrace 10, 50, 100, 200\\rbrace $ unique to each dataset.",
+      "All models are implemented using TensorFlow based on the seq2seq implementation of BIBREF15 and trained on a single machine with a Nvidia K40m GPU. We use a 2-layer 256-unit, a bidirectional LSTM BIBREF16 encoder, a 2-layer 256-unit LSTM decoder, and 256-dimensional embeddings. For the attention baseline, we use the standard parametrized attention BIBREF2 . Dropout of 0.2 (0.8 keep probability) is applied to the input of each cell and we optimize using Adam BIBREF17 at a learning rate of 0.0001 and batch size 128. We train for at most 200,000 steps (see Figure 3 for sample learning curves). BLEU scores are calculated on tokenized data using the multi-bleu.perl script in Moses. We decode using beam search with a beam",
+      "size of 10 BIBREF18 .",
+      "Table 1 shows the BLEU scores of our model on different sequence lengths while varying $K$ . This is a study of the trade-off between computational time and representational power. A large $K$ allows us to compute complex source representations, while a $K$ of 1 limits the source representation to a single vector. We can see that performance consistently increases with $K$ up to a point that depends on the data length, with longer sequences requiring more complex representations. The results with and without position encodings are almost identical on the toy data. Our technique learns to fit the data as well as the standard attention mechanism despite having less representational power. Both beat the non-attention baseline by a significant margin.",
+      "That we are able to represent the source sequence with a fixed size matrix with fewer than $|S|$ rows suggests that traditional attention mechanisms may be representing the source with redundancies and wasting computational resources. This makes intuitive sense for the toy task, which should require a relatively simple representation.",
+      "The last column shows that our technique significantly speeds up the inference process. The gap in inference speed increases as sequences become longer. We measured inference time on the full validation set of 1,000 examples, not including data loading or model construction times.",
+      "Figure 3 shows the learning curves for sequence length 200. We see that $K=1$ is unable to fit the data distribution, while $K\\in \\lbrace 32, 64\\rbrace $ fits the data almost as quickly as the attention-based model. Figure 3 shows the effect of varying the encoder and decoder scoring functions between softmax and sigmoid. All combinations manage to fit the data, but some converge faster than others. In section \"Visualizing Attention\" we show that distinct alignments are learned by different function combinations."
+    ],
+    [
+      "Next, we explore if the memory-based attention mechanism is able to fit complex real-world datasets. For this purpose we use 4 large machine translation datasets of WMT'17 on the following language pairs: English-Czech (en-cs, 52M examples), English-German (en-de, 5.9M examples), English-Finish (en-fi, 2.6M examples), and English-Turkish (en-tr, 207,373 examples). We used the newly available pre-processed datasets for the WMT'17 task. Note that our scores may not be directly comparable to other work that performs their own data pre-processing. We learn shared vocabularies of 16,000 subword units using the BPE algorithm BIBREF19 . We use newstest2015 as a validation set, and report BLEU on newstest2016.",
+      "We use a similar setup to the Toy Copy task, but use 512 RNN and embedding units, train using 8 distributed workers with 1 GPU each, and train for at most 1M steps. We save checkpoints every 30 minutes during training, and choose the best based on the validation BLEU score.",
+      "Table 2 compares our approach with and without position encodings, and with varying values for hyperparameter $K$ , to baseline models with regular attention mechanism. Learning curves are shown in Figure 4 . We see that our memory attention model with sufficiently high $K$ performs on-par with, or slightly better, than the attention-based baseline model despite its simpler nature. Across the board, models with $K=64$ performed better than corresponding models with $K=32$ , suggesting that using a larger number of attention vectors can capture a richer understanding of source sequences. Position encodings also seem to consistently improve model performance.",
+      "Table 3 shows that our model results in faster decoding time even on a complex dataset with a large vocabulary of 16k. We measured decoding time over the full validation set, not including time used for model setup and data loading, averaged across 10 runs. The average sequence length for examples in this data was 35, and we expect more significant speedups for tasks with longer sequences, as suggested by our experiments on toy data. Note that in our NMT examples/experiments, $K\\approx T$ , but we obtain computational savings from the fact that $K \\ll D$ . We may be able to set $K \\ll T$ , as in toy copying, and still get very good performance in other tasks. For instance, in summarization the source is complex but the representation of the source required to perform the task is \"simple\" (i.e. all that is needed to generate the abstract).",
+      "Figure 5 shows the effect of using sigmoid and softmax function in the encoders and decoders. We found that softmax/softmax consistently performs badly, while all other combinations perform about equally well. We report results for the best combination only (as chosen on the validation set), but we found this choice to only make a minor difference."
+    ],
+    [
+      "A useful property of the standard attention mechanism is that it produces meaningful alignment between source and target sequences. Often, the attention mechanism learns to progressively focus on the next source token as it decodes the target. These visualizations can be an important tool in debugging and evaluating seq2seq models and are often used for unknown token replacement.",
+      "This raises the question of whether or not our proposed memory attention mechanism also learns to generate meaningful alignments. Due to limiting the number of attention contexts to a number that is generally less than the sequence length, it is not immediately obvious what each context would learn to focus on. Our hope was that the model would learn to focus on multiple alignments at the same time, within the same attention vector. For example, if the source sequence is of length 40 and we have $K=10$ attention contexts, we would hope that $C_1$ roughly focuses on tokens 1 to 4, $C_2$ on tokens 5 to 8, and so on. Figures 6 and 7 show that this is indeed the case. To generate this visualization we multiply the attention scores $\\alpha $ and $\\beta $ from the encoder and decoder. Figure 8 shows a sample translation task visualization.",
+      "Figure 6 suggests that our model learns distinct ways to use its memory depending on the encoder and decoder functions. Interestingly, using softmax normalization results in attention maps typical of those derived from using standard attention, i.e. a relatively linear mapping between source and target tokens. Meanwhile, using sigmoid gating results in what seems to be a distributed representation of the source sequences across encoder time steps, with multiple contiguous attention contexts being accessed at each decoding step."
+    ],
+    [
+      "Our contributions build on previous work in making seq2seq models more computationally efficient. BIBREF11 introduce various attention mechanisms that are computationally simpler and perform as well or better than the original one presented in BIBREF2 . However, these typically still require $O(D^2)$ computation complexity, or lack the flexibility to look at the full source sequence. Efficient location-based attention BIBREF8 has also been explored in the image recognition domain.",
+      " BIBREF3 presents several enhancements to the standard seq2seq architecture that allow more efficient computation on GPUs, such as only attending on the bottom layer. BIBREF20 propose a linear time architecture based on stacked convolutional neural networks. BIBREF21 also propose the use of convolutional encoders to speed up NMT. BIBREF22 propose a linear attention mechanism based on covariance matrices applied to information retrieval. BIBREF23 enable online linear time attention calculation by enforcing that the alignment between input and output sequence elements be monotonic. Previously, monotonic attention was proposed for morphological inflection generation by BIBREF24 ."
+    ],
+    [
+      "In this work, we propose a novel memory-based attention mechanism that results in a linear computational time of $O(KD(|S| + |T|))$ during decoding in seq2seq models. Through a series of experiments, we demonstrate that our technique leads to consistent inference speedups as sequences get longer, and can fit complex data distributions such as those found in Neural Machine Translation. We show that our attention mechanism learns meaningful alignments despite being constrained to a fixed representation after encoding. We encourage future work that explores the optimal values of $K$ for various language tasks and examines whether or not it is possible to predict $K$ based on the task at hand. We also encourage evaluating our models on other tasks that must deal with long sequences but have compact representations, such as summarization and question-answering, and further exploration of their effect on memory and training speed."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0830/instruction.md b/qasper-0830/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..309645eb775694f2c202c5a8abb16451ccc8e0d3
--- /dev/null
+++ b/qasper-0830/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Torch-Struct: Deep Structured Prediction Library
+
+Question: Is this library implemented into Torch or is framework agnostic?
\ No newline at end of file
diff --git a/qasper-0837/instruction.md b/qasper-0837/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..b054aa96fe2065af174416389b9c0ebdca1e4341
--- /dev/null
+++ b/qasper-0837/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Combining Adversarial Training and Disentangled Speech Representation for Robust Zero-Resource Subword Modeling
+
+Question: What is the performance difference in performance in unsupervised feature learning between adverserial training and FHVAE-based disentangled speech represenation learning?
\ No newline at end of file
diff --git a/qasper-0852/instruction.md b/qasper-0852/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..2cbfebcfc596521acd64243024875c623aca6222
--- /dev/null
+++ b/qasper-0852/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Plan, Write, and Revise: an Interactive System for Open-Domain Story Generation
+
+Question: What are the baselines?
\ No newline at end of file
diff --git a/qasper-0854/instruction.md b/qasper-0854/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..34dd54317df487c14d2ba6af869cf00d0f18a691
--- /dev/null
+++ b/qasper-0854/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Collecting Indicators of Compromise from Unstructured Text of Cybersecurity Articles using Neural-Based Sequence Labelling
+
+Question: What contextual features are used?
\ No newline at end of file
diff --git a/qasper-0855/instruction.md b/qasper-0855/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..2c785bb9f6010d17654ffb5f7045a37c2ba848c8
--- /dev/null
+++ b/qasper-0855/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Collecting Indicators of Compromise from Unstructured Text of Cybersecurity Articles using Neural-Based Sequence Labelling
+
+Question: Where are the cybersecurity articles used in the model sourced from?
\ No newline at end of file
diff --git a/qasper-0862/instruction.md b/qasper-0862/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..516ef5d3e15ab1add62f7c49fe6fdb1c05ba67eb
--- /dev/null
+++ b/qasper-0862/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Classifying topics in speech when all you have is crummy translations.
+
+Question: What is the architecture of the model?
\ No newline at end of file
diff --git a/qasper-0863/instruction.md b/qasper-0863/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..8940d13c023930eea399751cd288cfd1e314d7d5
--- /dev/null
+++ b/qasper-0863/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Classifying topics in speech when all you have is crummy translations.
+
+Question: What language do they look at?
\ No newline at end of file
diff --git a/qasper-0864/instruction.md b/qasper-0864/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..4bf62bce65aff3bc93fd9df7e5fc67ee0b76b7ca
--- /dev/null
+++ b/qasper-0864/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Word, Subword or Character? An Empirical Study of Granularity in Chinese-English NMT
+
+Question: Where does the vocabulary come from?
\ No newline at end of file
diff --git a/qasper-0865/instruction.md b/qasper-0865/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..702a4438ef36a2164ed5e1f7051114983f1de05b
--- /dev/null
+++ b/qasper-0865/instruction.md
@@ -0,0 +1,109 @@
+Name of Paper: Word, Subword or Character? An Empirical Study of Granularity in Chinese-English NMT
+
+Question: What is the worst performing translation granularity?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Neural Machine Translation",
+    "Description of Different Translation Granularities",
+    "Character Level",
+    "Hybrid Word-Characters Level",
+    "Subword Level",
+    "Dataset",
+    "Training Details",
+    "Data Segmentation",
+    "Results on Chinese-to-English Translation",
+    "Results on English-to-Chinese Translation",
+    "Related Work",
+    "Conclusion",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "Neural machine translation (NMT) proposed by Kalchbrenner and Blunsom BIBREF0 and Sutskever et al. BIBREF1 has achieved significant progress in recent years. Unlike traditional statistical machine translation(SMT) BIBREF2 , BIBREF3 , BIBREF4 which contains multiple separately tuned components, NMT builds an end-to-end framework to model the entire translation process. For several language pairs, NMT has already achieved better translation performance than SMT BIBREF5 , BIBREF6 .",
+      "Conventional NMT system limits the vocabulary to a modest-sized vocabulary in both sides and words out of vocabulary are replaced by a special UNK symbol. However, the process of training and decoding is often conducted on an open vocabulary, in which an obvious problem is that NMT model is incapable of translating rare words. In particular, if a source word is outside the source vocabulary or its translation is outside the target vocabulary, the model is unable to generate proper translation for this word during decoding. Both Sutskever et al. BIBREF1 and Bahdanau et al. BIBREF7 have observed that sentences with many out-of-vocabulary words tend to be translated much more poorly than sentences mainly containing frequent words.",
+      "To address this problem, many researchers propose a broad category of approaches by employing different translation granularities. Most of these are below the word level, e.g. characters BIBREF8 , hybrid word-characters BIBREF9 , BIBREF5 , and more intelligent subwords BIBREF10 , BIBREF5 . Besides, pioneering studies BIBREF5 , BIBREF6 demonstrate that translation tasks involving Chinese are some of the most difficult problems in NMT systems. However, there is no study that shows which translation granularity is suitable for Chinese-to-English and English-to-Chinese translation tasks.",
+      "In this work, we make an empirical comparison of different translation granularities for bidirectional English-Chinese translation tasks. In addition, we analyze the impact of these strategies on the translation results in detail. We demonstrate that Chinese-to-English NMT of 15k and 30k vocabulary size can acquire best results using subword model and with 60k vocabulary size hybrid word-character model obtains the highest performance, while hybrid word-character model is most suitable for English-to-Chinese translation. Our experiment shows that all subword methods are not bounded by the vocabulary size. Furthermore, we carry out the experiments that employ different translation granularities of source side and target side. The translation result shows that when the source granularity is hybrid word-character level and the target sentences are split into subword level by BPE method, it can achieve the best translation performance for Chinese-to-English translation task. As for English-to-Chinese translation task, Hybrid word-character model is most suitable. To the best of our knowledge, this is the first work on an empirical comparison of various translation granularities for bidirectional Chinese-English translations."
+    ],
+    [
+      "Our models are based on an encoder-decoder architecture with attention mechanism proposed by Luong et al. BIBREF11 , which utilizes stacked LSTM layers for both encoder and decoder as illustrated in Figure FIGREF1 . In this section, we make a review of NMT framework.",
+      "First, the NMT encodes the source sentence INLINEFORM0 into a sequence of context vector representation INLINEFORM1 . Then, the NMT decodes from the context vector representation INLINEFORM2 and generates target translation INLINEFORM3 one word each time by maximizing the probability of INLINEFORM4 . Next, We review the encoder and decoder frameworks briefly.",
+      "Encoder: The context vector representation INLINEFORM0 is generated by the encoder using INLINEFORM1 stacked LSTM layers. Bi-directional connections are used for the bottom encoder layer, and INLINEFORM2 is a concatenation vector as shown in Eq. (1): DISPLAYFORM0 ",
+      "All other encoder layers are unidirectional, and INLINEFORM0 is calculated as follows: DISPLAYFORM0 ",
+      "Decoder: The conditional probability INLINEFORM0 is formulated as DISPLAYFORM0 ",
+      "Specifically, we employ a simple concatenation layer to produce an attentional hidden state INLINEFORM0 : DISPLAYFORM0 ",
+      "where INLINEFORM0 denotes the target hidden state at the top layer of a stacking LSTM. The attention model calculates INLINEFORM1 as the weighted sum of the source-side context vector representation, just as illustrated in the upper left corner of Figure FIGREF1 . DISPLAYFORM0 ",
+      "where INLINEFORM0 is a normalized item calculated as follows: DISPLAYFORM0 ",
+      " INLINEFORM0 is computed by using the following formula: DISPLAYFORM0 ",
+      "If INLINEFORM0 , INLINEFORM1 will be calculated by combining INLINEFORM2 as feed input BIBREF11 : DISPLAYFORM0 ",
+      "Given the bilingual training data INLINEFORM0 , all parameters of the attention-based NMT are optimized to maximize the following conditional log-likelihood: DISPLAYFORM0 "
+    ],
+    [
+      "We revisit how the source and target sentences ( INLINEFORM0 and INLINEFORM1 ) are represented in NMT. For the source side of any given training corpus, we scan through the whole corpus to build a vocabulary INLINEFORM2 of unique tokens. A source sentence INLINEFORM3 is then built as a sequence of the integer indices. The target sentence is similarly transformed into a target sequence of integer indices.",
+      "The property of NMT allows us great freedom in the choice of token units, and we can segment sentences in different ways. In this section, we will elaborate on four proposed approaches about the choice of translation granularities."
+    ],
+    [
+      "This translation granularity is easy to implement. For this granularity, what we have to do is split the sentence into a sequence of characters. However, the character-level modeling on the English side is more challenging, as the network has to be able to deal with long and coherent sequence of characters. In this case, the number of characters is often 300 INLINEFORM0 1000 symbols long, where the size of the state space grows exponentially. Therefore, this is a great challenge for us to handle.",
+      "Besides, the alphabet of English is only consist of 26 letters, in which the vocabulary of English side is too small. Considering these facts, we only separate the Chinese side sentences into characters rather than both sides. Figure FIGREF11 shows an example of this translation granularity for character level."
+    ],
+    [
+      "In regular word-based NMT, for all words outside the source vocabulary, one feeds the universal embedding representing UNK as input to the encoder. This is problematic because it discards valuable information about the source word. To address that, hybrid word-character approach will be adopted. In this part, we will introduce this granularity in detail.",
+      "Unlike in the conventional word model where out-of-vocabulary words are collapsed into a single UNK symbol, we convert these words into the sequence of constituent characters. Special prefixes are prepended to the characters. The purpose of the prefixes is to show the location of the characters in a word, and to distinguish them from normal in-vocabulary characters. There are three prefixes: INLINEFORM0 B INLINEFORM1 , INLINEFORM2 M INLINEFORM3 , and INLINEFORM4 E INLINEFORM5 , indicating beginning of the word, middle of the word and end of the word, respectively. During decoding, the output may also contain sequences of special tokens. With the prefixes, it is trivial to reverse the tokenization to the original words as part of a post-processing step. Using this approach, in Figure FIGREF11 , we can see the word \u201c\u9f99\u5e74\u201d is segmented into \u201c INLINEFORM6 B INLINEFORM7 \u9f99 INLINEFORM8 E INLINEFORM9 \u5e74\u201d, and the word \u201c\u7e41\u82b1\u4f3c\u9526\u201d is segmented into \u201c INLINEFORM10 B INLINEFORM11 \u7e41 INLINEFORM12 M INLINEFORM13 \u82b1 INLINEFORM14 M INLINEFORM15 \u4f3c INLINEFORM16 E INLINEFORM17 \u9526\u201d."
+    ],
+    [
+      "Considering languages with productive word formation processes such as agglutination and compounding, translation models require mechanisms that segment the sentence below the word level (In this paper, we call this level of symbols as subword units). In this part, we will introduce the two different methods of translation granularity on subword level.",
+      "Byte pair encoding (BPE) BIBREF12 is a compression algorithm. This simple data compression technique iteratively replaces the most frequent pair of bytes in a sequence with a single, unused byte. This compression method is first introduced into translation granularity by Sennrich et al. BIBREF10 . In this approach, instead of merging frequent pairs of bytes, characters or character sequences will be merged.",
+      "A detailed introduction of algorithm in learning BPE operations is showed in Sennrich et al. BIBREF10 . During decoding time, each word first split into sequences of characters, then learned operation will be applied to merge the characters into larger, known symbols. For BPE method, a special symbol is also needed to indicate the merging position. In Figure FIGREF11 , the word \u201c\u7e41\u82b1\u4f3c\u9526\u201d is segmented into three subword units, and the first three units are appended a special suffix \u201c@@\u201d. In decoding step, the translation results contain the special tokens as well. With these suffixes, we can recover the output easily.",
+      "The wordpiece model (WPM) implementation is initially developed to solve a Japanese/Korean segmentation problem for the speech recognition system BIBREF13 . This approach is completely data-driven and guaranteed to generate a deterministic segmentation for any possible sequence of characters, which is similar to the above method.",
+      "The wordpiece model is generated using a data-driven approach to maximize the language-model likelihood of the training data, given an evolving word definition. The training method of WPM is described in more detail in Schuster and Nakajima BIBREF13 . As shown in Figure FIGREF11 , a special symbol is only prepended at the beginning of the words. In this case, the words \u201c\u9f99\u5e74\u201d, \u201c\u7e41\u82b1\u4f3c\u9526\u201d, \u201c\u6d0b\u6ea2\u201d and \u201c\u7965\u548c\u201d are split into subwords, and the rest words remain the same except for a special prefix \u201c_\u201d."
+    ],
+    [
+      "We perform all these translation granularities on the NIST bidirectional Chinese-English translation tasks. The evaluation metric is BLEU BIBREF14 as calculated by the multi-bleu.perl script.",
+      "Our training data consists of 2.09M sentence pairs extracted from LDC corpus. Table 1 shows the detailed statistics of our training data. To test different approaches on Chinese-to-English translation task, we use NIST 2003(MT03) dataset as the validation set, and NIST 2004(MT04), NIST 2005(MT05), NIST 2006(MT06) datasets as our test sets. For English-to-Chinese translation task, we also use NIST 2003(MT03) dataset as the validation set, and NIST 2008(MT08) will be used as test set."
+    ],
+    [
+      "We build the described models modified from the Zoph_RNN toolkit which is written in C++/CUDA and provides efficient training across multiple GPUs. Our training procedure and hyper parameter choices are similar to those used by Luong et al. BIBREF11 . In the NMT architecture as illustrated in Figure FIGREF1 , the encoder has three stacked LSTM layers including a bidirectional layer, followed by a global attention layer, and the decoder contains two stacked LSTM layers followed by the softmax layer.",
+      "The word embedding dimension and the size of hidden layers are all set to 1000. We limit the maximum length in training corpus to 120. Parameter optimization is performed using both stochastic gradient descent(SGD) method and Adam method BIBREF15 . For the first three epoches, We train using the Adam optimizer and a fixed learning rate of 0.001 without decay. For the remaining six epoches, we train using SGD, and we set learning rate to 0.1 at the beginning and halve the threshold while the perplexity go up on the development set. We set minibatch size to 128. Dropout was also applied on each layer to avoid over-fitting, and the dropout rate is set to 0.2. At test time, we employ beam search with beam size b = 12."
+    ],
+    [
+      "For Chinese word segmentation, we use our in-house segmentation tools. For English corpus, the training data is tokenized with the Moses tokenizer. We carry out Chinese-to-English translation experiment on 30k vocabulary and 15k vocabulary for both sides respectively, and we also conduct English-to-Chinese translation experiment on 30k vocabulary size. The word level translation granularity is set to our baseline method.",
+      "For character level, we only segment the Chinese sentences into characters and the English sentences remain the same. For hybrid word-characters level, we segment training corpus for both sides. We rank the word frequency from greatest to least in training corpus, and in order to prevent the pollution from the very rare word, we have to set a segmentation point relatively higher. For 30k vocabulary, the word frequency below 64 is segmented into characters on Chinese side, and the segmentation point is set to 22 on English side. For 15k vocabulary, we set the segmentation point to 350 and 96 on Chinese side and English side respectively. For 60k vocabulary, the frequency of Chinese words below 14 and that of English words below 6 are split into characters.",
+      "For subword level, two different approaches are used. In BPE method, the number of merge operations is set to 30000 on 30k vocabulary size, 15000 on 15k vocabulary size and 60000 on 60k vocabulary size. For Chinese sentences, we segment the training corpus using our in-house segmentation tools first, and then we can apply the BPE method same as English sentences. Considering the essence of WPM method, we do not have to segment words for Chinese and tokenize sentences for English. That is to say, we can train the WPM without pre-processing step. Hence, for WPM method, we conduct our experiments both on the sentences trained on the raw corpus and the sentences trained on the segmented corpus."
+    ],
+    [
+      "We list the BLEU scores of different translation granularities on 30k vocabulary in Table TABREF27 .",
+      "Row 1 is translation result of the state-of-the-art NMT system with word level. For the character level granularity (Row 2), the translation quality is higher than the word level by only 0.38 BLEU points. The last three lines in Table TABREF27 are subword level translation granularity, which contains BPE method and WPM method. BPE method (Row 4) achieves the best translation performance, which gets an improvement of 1.64 BLEU points over the word level. As for the WPM method (Row 6), the gap between this method and BPE method is narrow. Moreover, hybrid word-character level model (Row 3) outperforms the word level by 1.46 BLEU points, and translation quality of this method is very close to the BPE method. Experiments show that hybrid word-character level granularity and BPE method of subword level granularity are our choices for translation granularity on Chinese-to-English translation task.",
+      "We execute different translation granularities on the training corpus. To make a comparison, We randomly choose 10000 sentences. Table TABREF29 show the average sentence length of different methods on all granularities.",
+      "A well-known flaw of NMT model is the inability to properly translate long sentences. However, most of translation granularities will go below the word level. Therefore, as shown in Table TABREF29 , we can get longer sentences than the word level. We wonder what the translation performance of different lengths are on all translation granularities. We follow Bahdanau et al. BIBREF7 to group sentences of similar lengths together and compute a BLEU score per group, as demonstrated in Figure FIGREF30 .",
+      "In order to make the comparison fair, length refers to the number of tokens split in word level. As above mentioned, hybrid word-character level model is one of suitable granularity choices for Chinese-to-English translation. We can find when the length of sentences is below 20, the translation result of this model outperforms the other models to a great extent. But with the length going up, the advantage over other models is diminishing. The character level granularity performs bad for the sentences whose length are below 20. We think the reason may be that when the sentences are short, the representation of sentence in character level cannot express the sentence meaning well. As for BPE method, we find a strange phenomenon. When the number of words in source sentence is from 60 to 80, the translation performance of BPE method is not so good. However, this method can achieve almost 3 BLEU points higher than next-best approach when the source sentence is longer than 80 words. As shown in Figure FIGREF30 , we can see WPM method does not perform well lower than 60 words in source language. But when the length of sentences is between 60 and 80, this method even outperforms the BPE method by up to 5.51 BLEU points. In this experiment, we conclude that subword model is more effective than other models in handling long sentences.",
+      "We concern what the translation results of different translation granularities are on smaller vocabulary size. We also carry out the experiment on Chinese-to-English task of 15k vocabulary size.",
+      "Compared to 30k vocabulary size, the translation performance of word level (Row 1) on 15k vocabulary size is reduced by 2.14 BLEU points. However, character level (Row 2) and hybrid word-character level (Row 3) achieve 42.09 and 43.12 BLEU points respectively, which is on par with quality of translation on 30k vocabulary. Both these two models exceed word level to a great extent. We infer the reason is that both character level and hybrid word-character level can represent source side and target side sentences better than the word level even if the vocabulary size is small. For subword model, translation performance of these methods remain almost the same as 30k vocabulary, which is beyond our imagination. We can find in Table TABREF32 , WPM method (Row 6) outperforms other models, and to our surprise, translation results of both WPM method and WPM methods with raw corpus (Row 5) obtain a higher BLEU points than 30k vocabulary size. We analyze the reason of this phenomenon is that the subword model is not constrained by the vocabulary size. Although the WPM method achieves the best results for the 15k vocabulary size, this method also belongs to subword level translation granularity. We can conclude that subword translation granularity is more suitable for Chinese-to-English translation task.",
+      "In order to make a comparison of these translation granularities on larger vocabulary size, we perform the our experiment of 60k vocabulary size on Chinese-to-English translation task.",
+      "We can find in Table TABREF34 , the word and character level (Row 1 and Row 2) on 60k vocabulary size are increased by 1.15 and 1.11 BLEU points respectively compared to 30 vocabulary size. However, to our surprise, all the translation results of subword level granularities on 60k vocabulary are below to the 30k vocabulary size. With the increase of vocabulary size, we add more fine-grained subword segmentation units into vocabulary. We infer that large amount of subword units do not have beneficial effect on the translation results. As for hybrid word-character level, this method achieves 43.97 BLEU points, which is highest among all the translation granularities on 60k vocabulary size. Compared with Table TABREF27 , hybrid word-character level outperforms the best translation result on 30k vocabulary size (BPE method) by 0.22 BLEU points.",
+      "We also conduct experiments that we use different translation granularities on source and target side. In order to carry out the experiments easily, we only compare several granularities pairs.",
+      "In Table TABREF36 , we can find that when the source translation granularity is word level (Row 2 and Row 3), the translation performances are relative poor, even worse than the word level of both sides in Table TABREF27 . As for BPE method on source side, the hybrid word-character on target side obtains 43.73 BLEU points (Row 6), which is close to the best translation result in Table TABREF27 . Hybrid_BPE method achieves up to 44.26 BLEU points (Row 4), which is even higher than BPE method by up to 0.51 BLEU points. This method can acquire best translation result for Chinese-to-English translation task."
+    ],
+    [
+      "We evaluate different translation granularities on the English-to-Chinese translation tasks, whose results are presented in Table TABREF39 .",
+      "We find that hybrid word-character level (Row 3) granularity obtains significant accuracy improvements over word level and this granularity is also superior to other granularities on large-scale English-to-Chinese translation. BPE method (Row 4) in this task does not perform well as Chinese-to-English task, the translation quality of it is lower than hybrid word-character model by up to 0.97 BLEU points. However, another subword level translation granularity WPM method (Row 6) achieves 22.14 BLEU points, which is near the hybrid word-character level. Although the vocabulary of character level on Chinese side is only 7.2k, it can also obtain 19.64 BLEU points (Row 2), which is on par with translation performance of word level.",
+      "As Chinese-to-English translation task, we carry out experiments on English-to-Chinese translation for different granularities. According to Table TABREF36 , Hybrid_BPE and BPE_Hybrid methods acquire relative higher translation quality than other methods. Therefore, in this section we only use these two methods to test which is most suitable for English-to-Chinese translation task.",
+      "Table TABREF41 shows that translation performances of both two methods are below to the Hybrid word-character granularity in Table TABREF39 . BPE_Hybrid method (Row 2) achieves 22.12 BLEU points, which is higher than Hybrid_BPE method by 0.39 BLEU points and is near the translation quality of WPM method in Table TABREF39 ."
+    ],
+    [
+      "The recently proposed neural machine translation has drawn more and more attention. Most of existing work in neural machine translation focus on handling rare words BIBREF16 , BIBREF10 , BIBREF17 , integrating SMT strategies BIBREF18 , BIBREF19 , BIBREF20 , BIBREF21 , designing the better framework BIBREF22 , BIBREF11 , BIBREF23 and addressing the low resource scenario BIBREF24 , BIBREF25 , BIBREF26 .",
+      "As for strategies for dealing with rare and unknown words, a number of authors have endeavored to explore methods for addressing them. Luong et al. BIBREF11 and Li et al. BIBREF16 propose simple alignment-based technique that can replace out-of-vocabulary words with similar words. Jean et al. BIBREF27 use a large vocabulary with a method based on importance sampling.",
+      "In addition, another direction to achieve rare words problem in NMT is changing the granularity of segmentation. Chung et al. BIBREF8 focus on handling translation at the level of characters without any word segmentation only on target side. Luong et al. BIBREF9 propose a novel hybrid architecture that combines the strength of both word and character-based models. Sennrich et al. BIBREF10 use BPE method to encode rare and unknown words as sequences of subword units. Wu et al. BIBREF5 use both WPM method and hybrid word-character model in their online translation system. However, there is no study that shows which translation granularity is suitable for translation tasks involving Chinese language. Our goal in this work is to make an empirical comparison of different translation granularities for bidirectional Chinese-English translation tasks."
+    ],
+    [
+      "In this work, we provide an extensive comparison for translation granularities in Chinese-English NMT, such as word, character, subword and hybrid word-character. We have also discussed the advantages and disadvantages of various translation granularities in detail. For the same granularity on both sides, the experiments demonstrate that the subword model best fits Chinese-to-English translation with the vocabulary that is not so big, while the hybrid word-character approach obtains the highest performance on English-to-Chinese translation. In addition, experiments on different granularities show that Hybrid_BPE method can acquire best result for Chinese-to-English translation task."
+    ],
+    [
+      "The research work has been funded by the Natural Science Foundation of China under Grant No. 61333018 and No. 61402478, and it is also supported by the Strategic Priority Research Program of the CAS under Grant No. XDB02070007."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0890/instruction.md b/qasper-0890/instruction.md
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+Name of Paper: MMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension
+
+Question: How big are improvements of MMM over state of the art?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Methods",
+    "Methods ::: Model Architecture",
+    "Methods ::: Multi-step Attention Network",
+    "Methods ::: Two Stage Training",
+    "Methods ::: Two Stage Training ::: Coarse-tuning Stage",
+    "Methods ::: Two Stage Training ::: Multi-task Learning Stage",
+    "Experimental Setup ::: Datasets",
+    "Experimental Setup ::: Speaker Normalization",
+    "Experimental Setup ::: Multi-task Learning",
+    "Experimental Setup ::: Training Details",
+    "Results",
+    "Discussion ::: Why does natural language inference help?",
+    "Discussion ::: Can other tasks help with MCQA?",
+    "Discussion ::: NLI dataset helps with convergence",
+    "Discussion ::: Multi-stage or Multi-task",
+    "Discussion ::: Multi-steps reasoning is important",
+    "Discussion ::: Could the source dataset be benefited?",
+    "Discussion ::: Error Analysis",
+    "Related Work",
+    "Conclusions"
+  ],
+  "paragraphs": [
+    [
+      "Building a system that comprehends text and answers questions is challenging but fascinating, which can be used to test the machine's ability to understand human language BIBREF0, BIBREF1. Many machine reading comprehension (MRC) based question answering (QA) scenarios and datasets have been introduced over the past few years, which differ from each other in various ways, including the source and format of the context documents, whether external knowledge is needed, the format of the answer, to name a few. We can divide these QA tasks into two categories: 1) extractive/abstractive QA such as SQuAD BIBREF2, and HotPotQA BIBREF3. 2) multiple-choice QA (MCQA) tasks such as MultiRC BIBREF4, and MCTest BIBREF5.",
+      "In comparison to extractive/abstractive QA tasks, the answers of the MCQA datasets are in the form of open, natural language sentences and not restricted to spans in text. Various question types exist such as arithmetic, summarization, common sense, logical reasoning, language inference, and sentiment analysis. Therefore it requires more advanced reading skills for the machine to perform well on this task. Table TABREF1 shows one example from one of MCQA datasets, DREAM BIBREF6. To answer the first question in Table TABREF1, the system needs to comprehend the whole dialogue and use some common sense knowledge to infer that such a conversation can only happen between classmates rather than brother and sister. For the second question, the implicit inference relationship between the utterance \u201cYou'll forget your head if you're not careful.\u201d in the passage and the answer option \u201cHe is too careless.\u201d must be figured out by the model to obtain the correct answer. Many MCQA datasets were collected from language or science exams, which were purposely designed by educational experts and consequently require non-trivial reasoning techniques BIBREF7. As a result, the performance of machine readers on these tasks can more accurately gauge comprehension ability of a model.",
+      "Recently large and powerful pre-trained language models such as BERT BIBREF8 have been achieving the state-of-the-art (SOTA) results on various tasks, however, its potency on MCQA datasets has been severely limited by the data insufficiency. For example, the MCTest dataset has two variants: MC160 and MC500, which are curated in a similar way, and MC160 is considered easier than MC500 BIBREF9. However, BERT-based models perform much worse on MC160 compared with MC500 (8\u201310% gap) since the data size of the former is about three times smaller. To tackle this issue, we investigate how to improve the generalization of BERT-based MCQA models with the constraint of limited training data using four representative MCQA datasets: DREAM, MCTest, TOEFL, and SemEval-2018 Task 11.",
+      "We proposed MMM, a Multi-stage Multi-task learning framework for Multi-choice question answering. Our framework involves two sequential stages: coarse-tuning stage using out-of-domain datasets and multi-task learning stage using a larger in-domain dataset. For the first stage, we coarse-tuned our model with natural language inference (NLI) tasks. For the second multi-task fine-tuning stage, we leveraged the current largest MCQA dataset, RACE, as the in-domain source dataset and simultaneously fine-tuned the model on both source and target datasets via multi-task learning. Through extensive experiments, we demonstrate that the two-stage sequential fine-tuning strategy is the optimal choice for BERT-based model on MCQA datasets. Moreover, we also proposed a Multi-step Attention Network (MAN) as the top-level classifier instead of the typical fully-connected neural network for this task and obtained better performance. Our proposed method improves BERT-based baseline models by at least 7% in absolute accuracy for all the MCQA datasets (except the SemEval dataset that already achieves 88.1% for the baseline). As a result, by leveraging BERT and its variant, RoBERTa BIBREF10, our approach advanced the SOTA results for all the MCQA datasets, surpassing the previous SOTA by at least 16% in absolute accuracy (except the SemEval dataset)."
+    ],
+    [
+      "In MCQA, the inputs to the model are a passage, a question, and answer options. The passage, denoted as $P$, consists of a list of sentences. The question and each of the answer options, denoted by $Q$ and $O$, are both single sentences. A MCQA model aims to choose one correct answer from answer options based on $P$ and $Q$."
+    ],
+    [
+      "Figure FIGREF3 illustrates the model architecture. Specifically, we concatenate the passage, question and one of the answer options into a long sequence. For a question with $n$ answer options, we obtain $n$ token sequences of length $l$. Afterwards, each sequence will be encoded by a sentence encoder to get the representation vector $H \\in \\mathbb {R}^{d\\times l}$, which is then projected into a single value $p=C(H)$ ($p\\in \\mathbb {R}^{1}$) via a top-level classifier $C$. In this way, we obtain the logit vector $\\mathbf {p}=[p_1,p_2,...,p_n]$ for all options of a question, which is then transformed into the probability vector through a softmax layer. We choose the option with highest logit value $p$ as the answer. Cross entropy loss is used as the loss function. We used the pre-trained bidirectional transformer encoder, i.e., BERT and RoBERTa as the sentence encoder. The top-level classifier will be detailed in the next subsection."
+    ],
+    [
+      "For the top-level classifier upon the sentence encoder, the simplest choice is a two-layer full-connected neural network (FCNN), which consist of one hidden layer with $tanh$ activation and one output layer without activation. This has been widely adopted when BERT is fine-tuned for the down-streaming classification tasks and performs very well BIBREF8. Inspired from the success of the attention network widely used in the span-based QA task BIBREF11, we propose the multi-step attention network (MAN) as our top-level classifier. Similar to the dynamic or multi-hop memory network BIBREF12, BIBREF13, MAN maintains a state and iteratively refines its prediction via the multi-step reasoning.",
+      "The MAN classifier works as follows. A pair of question and answer option together is considered as a whole segment, denoted as $QO$. Suppose the sequence length of the passage is $p$ and that of the question and option pair is $q$. We first construct the working memory of the passage $H^P\\in \\mathbb {R}^{d\\times p}$ by extracting the hidden state vectors of the tokens that belong to $P$ from $H$ and concatenating them together in the original sequence order. Similarly, we obtain the working memory of the (question, option) pair, denoted as $H^{QO}\\in \\mathbb {R}^{d\\times q}$. Alternatively, we can also encode the passage and (question, option) pair individually to get their representation vectors $H^P$ and $H^{QO}$, but we found that processing them in a pair performs better.",
+      "We then perform $K$-step reasoning over the memory to output the final prediction. Initially, the initial state $\\mathbf {s}^0$ in step 0 is the summary of $H^P$ via self-attention: $\\mathbf {s}^0=\\sum _i \\alpha _i H_i^P$, where $\\alpha _i=\\frac{exp(w_1^TH_i^P)}{\\sum _j exp(w_1^TH_j^P)}$. In the following steps $k \\in {1,2,...,K-1}$, the state is calculated by:",
+      "where $\\mathbf {x}^k=\\sum _i\\beta _iH_i^{QO}$ and $\\beta _i=\\frac{exp(w_2^T[\\mathbf {s}^{k-1};H_i^{QO}])}{\\sum _j exp(w_2^T[\\mathbf {s}^{k-1};H_j^{QO}])}$. Here $[x;y]$ is concatenation of the vectors $x$ and $y$. The final logit value is determined using the last step state:",
+      "Basically, the MAN classifier calculates the attention scores between the passage and (question, option) pair step by step dynamically such that the attention can refine itself through several steps of deliberation. The attention mechanism can help filter out irrelevant information in the passage against (question, option) pair."
+    ],
+    [
+      "We adopt a two-stage procedure to train our model with both in-domain and out-of-domain datasets as shown in Figure FIGREF10."
+    ],
+    [
+      "We first fine-tune the sentence encoder of our model with natural language inference (NLI) tasks. For exploration, we have also tried to fine-tune the sentence encoder on other types of tasks such as sentiment analysis, paraphrasing, and span-based question answering at this stage. However, we found that only NLI task shows robust and significant improvements for our target multi-choice task. See Section SECREF5 for details."
+    ],
+    [
+      "After corase-tuning stage, we simultaneously fine-tune our model on a large in-domain source dataset and the target dataset together via multi-task learning. We share all model parameters including the sentence encoder as well as the top-level classifier for these two datasets."
+    ],
+    [
+      "We use four MCQA datasets as the target datasets: DREAM BIBREF6, MCTest BIBREF9, TOEFL BIBREF5, and SemEval-2018 Task 11 BIBREF14, which are summarized in Table TABREF11. For the first coarse-tuning stage with NLI tasks, we use MultiNLI BIBREF15 and SNLI BIBREF16 as the out-of-domain source datasets. For the second stage, we use the current largest MCQA dataset, i.e., RACE BIBREF7 as in-domain source dataset. For all datasets, we use the official train/dev/test splits."
+    ],
+    [
+      "Passages in DREAM dataset are dialogues between two persons or more. Every utterance in a dialogue starts with the speaker name. For example, in utterance \u201cm: How would he know?\u201d, \u201cm\u201d is the abbreviation of \u201cman\u201d indicating that this utterance is from a man. More than 90% utterances have the speaker names as \u201cw,\u201d \u201cf,\u201d and \u201cm,\u201d which are all abbreviations. However, the speaker names mentioned in the questions are full names such as \u201cwoman\u201d and \u201cman.\u201d In order to make it clear for the model to learn which speaker the question is asking about, we used a speaker normalization strategy by replacing \u201cw\u201d or \u201cf\u201d with \u201cwoman\u201d and \u201cm\u201d with \u201cman\u201d for the speaker names in the utterances. We found this simple strategy is quite effective, providing us with 1% improvement. We will always use this strategy for the DREAM dataset for our method unless explicitly mentioned."
+    ],
+    [
+      "For the multi-task learning stage, at each training step, we randomly selected a dataset from the two datasets (RACE and the target dataset) and then randomly fetched a batch of data from that dataset to train the model. This process was repeated until the predefined maximum number of steps or the early stopping criterion has been met. We adopted the proportional sampling strategy, where the probability of sampling a task is proportional to the relative size of each dataset compared to the cumulative size of all datasets BIBREF17."
+    ],
+    [
+      "We used a linear learning rate decay schedule with warm-up proportion of $0.1$. We set the dropout rate as $0.1$. The maximum sequence length is set to 512. We clipped the gradient norm to 5 for DREAM dataset and 0 for other datasets. The learning rate and number of training epochs vary for different datasets and encoder types, which are summarized in Section 1 of the Supplementary Material.",
+      "More than 90% of passages have more than 512 words in the TOEFL dataset, which exceed the maximum sequence length that BERT supports, thus we cannot process the whole passage within one forward pass. To solve this issue, we propose the sliding window strategy, in which we split the long passage into several snippets of length 512 with overlaps between subsequent snippets and each snippet from the same passage will be assigned with the same label. In training phase, all snippets will be used for training, and in inference phase, we aggregate the logit vectors of all snippets from the same passage and pick the option with highest logit value as the prediction. In experiments, we found the overlap of 256 words is the optimal, which can improve the BERT-Base model from accuracy of 50.0% to 53.2%. We adopted this sliding window strategy only for the TOEFL dataset."
+    ],
+    [
+      "We first evaluate our method on the DREAM dataset. The results are summarized in Table TABREF16. In the table, we first report the accuracy of the SOTA models in the leaderboard. We then report the performance of our re-implementation of fine-tuned models as another set of strong baselines, among which the RoBERTa-Large model has already surpassed the previous SOTA. For these baselines, the top-level classifier is a two-layer FCNN for BERT-based models and a one-layer FCNN for the RoBERTa-Large model. Lastly, we report model performances that use all our proposed method, MMM (MAN classifier + speaker normalization + two stage learning strategies). As direct comparisons, we also list the accuracy increment between MMM and the baseline with the same sentence encoder marked by the parentheses, from which we can see that the performance augmentation is over 9% for BERT-Base and BERT-Large. Although the RoBERTa-Large baseline has already outperformed the BERT-Large baseline by around 18%, MMM gives us another $\\sim $4% improvement, pushing the accuracy closer to the human performance. Overall, MMM has achieved a new SOTA, i.e., test accuracy of 88.9%, which exceeds the previous best by 16.9%.",
+      "We also test our method on three other MCQA datasets: MCTest including MC160 and MC500, TOEFL, and SemEval-2018 Task 11. The results are summarized in Table TABREF17. Similarly, we list the previous SOTA models with their scores for comparison. We compared our method with the baselines that use the same sentence encoder. Except for the SemEval dataset, our method can improve the BERT-Large model by at least 10%. For both MCTest and SemEval datasets, our best scores are very close to the reported human performance. The MC160 and MC500 datasets were curated in almost the same way BIBREF9 with only one difference that MC160 is around three times smaller than MC500. We can see from Table TABREF17 that both the BERT and RoBERTa baselines perform much worse on MC160 than MC500. We think the reason is that the data size of MC160 is not enough to well fine-tune the large models with a huge amount of trainable parameters. However, by leveraging the transfer learning techniques we proposed, we can significantly improve the generalization capability of BERT and RoBERTa models on the small datasets so that the best performance of MC160 can even surpass that of MC500. This demonstrates the effectiveness of our method.",
+      "To better understand why MMM can be successful, we conducted an ablation study be removing one feature at a time on the BERT-Base model. The results are shown in Table TABREF18. We see that the removal of the second stage multi-task learning part hurts our method most significantly, indicating that the majority of improvement is coming from the knowledge transferred from the in-domain dataset. The first stage of coarse-tuning using NLI datasets is also very important, which provides the model with enhanced language inference ability. As for the top-level classifier, i.e., the MAN module, if we replace it with a typical two-layer FCNN as in BIBREF8, we have 1\u20132% performance drop. Lastly, for the DREAM dataset, the speaker normalization strategy gives us another $\\sim $1% improvement."
+    ],
+    [
+      "As shown in Table TABREF18, coarse-tuning on NLI tasks can help improve the performance of MCQA. We conjecture one of the reasons is that, in order to pick the correct answer, we need to rely on the language inference capability in many cases. As an example in Table TABREF1, the utterance highlighted in the bold and italic font in the dialogue is the evidence sentence from which we can obtain the correct answer to Question 2. There is no token overlap between the evidence sentence and the correct answer, indicating that the model cannot solve this question by surface matching. Nevertheless, the correct answer is an entailment to the evidence sentence while the wrong answers are not. Therefore, the capability of language inference enables the model to correctly predict the answer. On the other hand, we can deem the passage and the pair of (question, answer) as a pair of premise and hypothesis. Then the process of choosing the right answer to a certain question is similar to the process of choosing the hypothesis that can best entail the premise. In this sense, the part of MCQA task can be deemed as a NLI task. This also agrees with the argument that NLI is a fundamental ability of a natural language processing model and it can help support other tasks that require higher level of language processing abilities BIBREF21. We provided several more examples that require language inference reading skills in the Section 2 of the Supplementary Material; they are wrongly predicted by the BERT-Base baseline model but can be correctly solved by exposing the model to NLI data with the coarse-tuning stage."
+    ],
+    [
+      "By analyzing the MCQA datasets, we found that some questions ask about the attitude of one person towards something and in some cases, the correct answer is simply a paraphrase of the evidence sentence in the passage. This finding naturally leads to the question: could other kinds of tasks such as sentiment classification, paraphrasing also help with MCQA problems?",
+      "To answer this question, we select several representative datasets for five categories as the up-stream tasks: sentiment analysis, paraphrase, span-based QA, NLI, and MCQA. We conduct experiments where we first train the BERT-Base models on each of the five categories and then further fine-tune our models on the target dataset: DREAM and MC500 (MCTest-MC500). For the sentiment analysis category, we used the Stanford Sentiment Treebank (SST-2) dataset from the GLUE benchmark BIBREF22 (around 60k train examples) and the Yelp dataset (around 430k train examples). For the paraphrase category, three paraphrasing datasets are used from the GLUE benchmark: Microsoft Research Paraphrase Corpus (MRPC), Semantic Textual Similarity Benchmark (STS-B), and Quora Question Pairs (QQP), which are denoted as \u201cGLUE-Para.\u201d. For the span-based QA, we use the SQuAD 1.1, SQuAD 2.0 , and MRQA which is a joint dataset including six popular span-based QA datasets. Table TABREF23 summarizes the results. We see that sentiment analysis datasets do not help much with our target MCQA datasets. But the paraphrase datasets do bring some improvements for MCQA. For span-based QA, only SQuAD 2.0 helps to improve the performance of the target dataset. Interestingly, although MRQA is much larger than other QA datasets (at least six times larger), it makes the performance worst. This suggests that span-based QA might not the appropriate source tasks for transfer learning for MCQA. We hypothesis this could due to the fact that most of the questions are non-extractive (e.g., 84% of questions in DREAM are non-extractive) while all answers are extractive in the span-based QA datasets.",
+      "For the completeness of our experiments, we also used various NLI datasets: MultiNLI, SNLI, Question NLI (QLI), Recognizing Textual Entailment (RTE), and Winograd NLI (WNLI) from the GLUE benchmark. We used them in three kinds of combinations: MultiNLI alone, MultiNLI plus SNLI denoted as \u201cNLI\u201d, and combining all five datasets together, denoted as \u201cGLUE-NLI\u201d. As the results shown in Table TABREF23, NLI and GLUE-NLI are comparable and both can improve the target dataset by a large margin.",
+      "Lastly, among all these tasks, using the MCQA task itself, i.e., pretraining on RACE dataset, can help boost the performance, most. This result agrees with the intuition that the in-domain dataset can be the most ideal data for transfer learning.",
+      "In conclusion, we find that for out-of-domain datasets, the NLI datasets can be most helpful to the MCQA task, indicating that the natural language inference capability should be an important foundation of the MCQA systems. Besides, a larger in-domain dataset, i.e. another MCQA dataset, can also be very useful."
+    ],
+    [
+      "The first stage of coarse-tuning with NLI data can not only improve the accuracy but also help the model converge faster and better. Especially for the BERT-Large and RoBERTa-Large models that have much larger amount of trainable parameters, convergence is very sensitive to the optimization settings. However, with the help of NLI datasets , convergence for large models is no longer an issue, as shown in Figure FIGREF25. Under the same optimization hyper-parameters, compared with the baseline, coarse-tuning can make the training loss of the BERT-Base model decrease much faster. More importantly, for the BERT-Large model, without coarse-tuning, the model does not converge at all at the first several epochs, which can be completely resolved by the help of NLI data."
+    ],
+    [
+      "In a typical scenario where we have one source and one target dataset, we naturally have a question about whether we should simultaneously train a model on them via multi-task learning or first train on the source dataset then on the target sequentially. Many previous works adopted the latter way BIBREF19, BIBREF20, BIBREF23 and BIBREF20 demonstrated that the sequential fine-tuning approach outperforms the multi-task learning setting in their experiments. However, we had contradictory observations in our experiments. Specifically, we conducted a pair of control experiments: one is that we first fine-tune the BERT-Base model on the source dataset RACE and then further fine-tune on the target dataset, and the other is that we simultaneously train the model on RACE and the target dataset via multi-task learning. The comparison results are shown in Table TABREF27. We see that compared with sequential fine-tuning, the multi-task learning achieved better performance. We conjecture that in the sequential fine-tuning setting, while the model is being fine-tuned on the target dataset, some information or knowledge learned from the source dataset may be lost since the model is no longer exposed to the source dataset in this stage. In comparison, this information can be kept in the multi-task learning setting and thus can better help improve the target dataset.",
+      "Now that the multi-task learning approach outperforms the sequential fine-tuning setting, we naturally arrive at another question: what if we merged the coarse-tuning and multi-task learning stages together? That is, what if we simultaneously trained the NLI, source, and target datasets altogether under the multi-task learning framework? We also conducted a pair of control experiments for investigation. The results in Table TABREF27, show that casting the fine-tuning process on three datasets into separate stages performs better, indicating that multi-stage training is also necessary. This verifies our MMM framework with coarse-tuning on out-of-domain datasets and fine-tuning on in-domain datesets."
+    ],
+    [
+      "Previous results show that the MAN classifier shows improvement compared with the FCNN classifier, but we are also interested in how the performance change while varying the number of reasoning steps $K$ as shown in Figure FIGREF29. $K=0$ means that we do not use MAN but FCNN as the classifier. We observe that there is a gradual improvement as we increase $K=1$ to $K=5$, but after 5 steps the improvements have saturated. This verifies that an appropriate number of steps of reasoning is important for the memory network to reflect its benefits."
+    ],
+    [
+      "So far we have been discussing the case where we do multi-task learning with the source dataset RACE and various much smaller target datasets to help improve the targets. We also want to see whether our proposed techniques can also benefit the source dataset itself. Table TABREF31 summarizes the results of BERT-Base model on the RACE dataset obtained by adding the coarse-tuning stage, adding the multi-task training together with DREAM, and adding the MAN module. From this table, we see that all three techniques can bring in improvements over the baseline model for the source dataset RACE, among which NLI coarse-tuning stage can help elevate the scores most.",
+      "Since we found all parts of MMM can work well for the source dataset, we tried to use them to improve the accuracy on RACE. The results are shown in Table TABREF32. We used four kinds of pre-trained sentence encoders: BERT-Base, BERT-Large, XLNet-Large, and RoBERTa-Large. For each encoder, we listed the official report of scores from the leaderboard. Compared with the baselines, MMM leads to improvements ranging from 0.5% to 3.0% in accuracy. Our best result is obtained by the RoBERTa-Large encoder."
+    ],
+    [
+      "In order to investigate how well our model performs for different types of questions, we did an error analysis by first randomly selecting 150 samples that had wrong predictions by the BERT-Base baseline model from the development set of DREAM dataset. We then manually classified them into several question types, as shown in Table TABREF34. The annotation criterion is described in the Section 3 of the Supplementary Material. We see that the BERT-Base baseline model still does not do well on matching problems. We then evaluate our best model on these samples and report the accuracy of each question type in the last column of Table TABREF34. We find that our best model can improve upon every question type significantly especially for the matching problems, and most surprisingly, our best model can even greatly improve its ability on solving the arithmetic problems, achieving the accuracy of 73.7%.",
+      "However, could our model really do math? To investigate this question, we sampled some arithmetic questions that are correctly predicted by our model, made small alterations to the passage or question, and then checked whether our model can still make correct choices. We found our model is very fragile to these minor alterations, implicating that the model is actually not that good at arithmetic problems. We provided one interesting example in the Section 3 of the Supplementary Material."
+    ],
+    [
+      "There are increasing interests in machine reading comprehension (MRC) for question answering (QA). The extractive QA tasks primarily focus on locating text spans from the given document/corpus to answer questions BIBREF2. Answers in abstractive datasets such as MS MARCO BIBREF24, SearchQA BIBREF25, and NarrativeQA BIBREF26 are human-generated and based on source documents or summaries in free text format. However, since annotators tend to copy spans as answers BIBREF27, the majority of answers are still extractive in these datasets. The multi-choice QA datasets are collected either via crowd sourcing, or collected from examinations designed by educational experts BIBREF7. In this type of QA datasets, besides token matching, a significant portion of questions require multi-sentence reasoning and external knowledge BIBREF5.",
+      "Progress of research for MRC first relies on the breakthrough of the sentence encoder, from the basic LSTM to the pre-trained transformer based model BIBREF8, which has elevated the performance of all MRC models by a large margin. Besides, the attention mechanisms between the context and the query can empower the neural models with higher performance BIBREF11. In addition, some techniques such as answer verification BIBREF28, multi-hop reasoning BIBREF29, and synthetic data augmentation can be also helpful.",
+      "Transfer learning has been widely proved to be effective across many domain in NLP. In the QA domain, the most well-known example of transfer learning would be fine-tuning the pre-trained language model such as BERT to the down-streaming QA datasets such as SQuAD BIBREF8. Besides, multi-task learning can also be deemed as a type of transfer learning, since during the training of multiple datasets from different domains for different tasks, knowledge will be shared and transferred from each task to others, which has been used to build a generalized QA model BIBREF30. However, no previous works have investigated that the knowledge from the NLI datasets can also be transferred to improve the MCQA task."
+    ],
+    [
+      "We propose MMM, a multi-stage multi-task transfer learning method on the multiple-choice question answering tasks. Our two-stage training strategy and the multi-step attention network achieved significant improvements for MCQA. We also did detailed analysis to explore the importance of both our training strategies as well as different kinds of in-domain and out-of-domain datasets. We hope our work here can also shed light on new directions for other NLP domains."
+    ]
+  ]
+}
+```
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diff --git a/qasper-0891/instruction.md b/qasper-0891/instruction.md
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+++ b/qasper-0891/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: MMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension
+
+Question: What out of domain datasets authors used for coarse-tuning stage?
\ No newline at end of file
diff --git a/qasper-0896/instruction.md b/qasper-0896/instruction.md
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+++ b/qasper-0896/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: RelNet: End-to-End Modeling of Entities & Relations
+
+Question: How is knowledge retrieved in the memory?
\ No newline at end of file
diff --git a/qasper-0897/instruction.md b/qasper-0897/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..e3e6cd9e4d2b3c0b2bb199be9d92342efb0ab307
--- /dev/null
+++ b/qasper-0897/instruction.md
@@ -0,0 +1,43 @@
+Name of Paper: RelNet: End-to-End Modeling of Entities & Relations
+
+Question: How is knowledge stored in the memory?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "RelNet Model",
+    "Related Work",
+    "Experiments",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Reasoning about entities and their relations is an important problem for achieving general artificial intelligence. Often such problems are formulated as reasoning over graph-structured representation of knowledge. Knowledge graphs, for example, consist of entities and relations between them BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 . Representation learning BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 and reasoning BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 with such structured representations is an important and active area of research.",
+      "Most previous work on knowledge representation and reasoning relies on a pipeline of natural language processing systems, often consisting of named entity extraction BIBREF12 , entity resolution and coreference BIBREF13 , relationship extraction BIBREF4 , and knowledge graph inference BIBREF14 . While this cascaded approach of using NLP systems can be effective at reasoning with knowledge bases at scale, it also leads to a problem of compounding of the error from each component sub-system. The importance of each of these sub-component on a particular downstream application is also not clear.",
+      "For the task of question-answering, we instead make an attempt at an end-to-end approach which directly models the entities and relations in the text as memory slots. While incorporating existing knowledge (from curated knowledge bases) for the purpose of question-answering BIBREF11 , BIBREF8 , BIBREF15 is an important area of research, we consider the simpler setting where all the information is contained within the text itself \u2013 which is the approach taken by many recent memory based neural network models BIBREF16 , BIBREF17 , BIBREF18 , BIBREF19 .",
+      "Recently, BIBREF17 proposed a dynamic memory based neural network for implicitly modeling the state of entities present in the text for question answering. However, this model lacks any module for relational reasoning. In response, we propose RelNet, which extends memory-augmented neural networks with a relational memory to reason about relationships between multiple entities present within the text. Our end-to-end method reads text, and writes to both memory slots and edges between them. Intuitively, the memory slots correspond to entities and the edges correspond to relationships between entities, each represented as a vector. The only supervision signal for our method comes from answering questions on the text.",
+      "We demonstrate the utility of the model through experiments on the bAbI tasks BIBREF18 and find that the model achieves smaller mean error across the tasks than the best previously published result BIBREF17 in the 10k examples regime and achieves 0% error on 11 of the 20 tasks."
+    ],
+    [
+      "We describe the RelNet model in this section. Figure 1 provides a high-level view of the model. The model is sequential in nature, consisting of the following steps: read text, process it into a dynamic relational memory and then attention conditioned on the question generates the answer. We model the dynamic memory in a fashion similar to Recurrent Entity Networks BIBREF17 and then equip it with an additional relational memory.",
+      "There are three main components to the model: 1) input encoder 2) dynamic memory, and 3) output module. We will describe these three modules in details. The input encoder and output module implementations are similar to the Entity Network BIBREF17 and main novelty lies in the dynamic memory. We describe the operations executed by the network for a single example consisting of a document with $T$ sentences, where each sentence consists of a sequence of words represented with $K$ -dimensional word embeddings $\\lbrace e_1, \\ldots , e_N\\rbrace $ , a question on the document represented as another sequence of words and an answer to the question."
+    ],
+    [
+      "There is a long line of work in textual question-answering systems BIBREF21 , BIBREF22 . Recent successful approaches use memory based neural networks for question answering, for example BIBREF23 , BIBREF18 , BIBREF24 , BIBREF19 , BIBREF17 . Our model is also a memory network based model and is also related to the neural turing machine BIBREF25 . As described previously, the model is closely related to the Recurrent Entity Networks model BIBREF17 which describes an end-to-end approach to model entities in text but does not directly model relations. Other approaches to question answering use external knowledge, for instance external knowledge bases BIBREF26 , BIBREF11 , BIBREF27 , BIBREF28 , BIBREF9 or external text like Wikipedia BIBREF29 , BIBREF30 .",
+      "Very recently, and in parallel to this work, a method for relational reasoning called relation networks BIBREF31 was proposed. They demonstrated that simple neural network modules are not as effective at relational reasoning and their proposed module is similar to our model. However, relation network is not a memory-based model and there is no mechanism to read and write relevant information for each pair. Moreover, while their approach scales as the square of the number of sentences, our approach scales as the square of the number of memory slots used per QA pair. The output module in our model can be seen as a type of relation network.",
+      "Representation learning and reasoning over graph structured data is also relevant to this work. Graph based neural network models BIBREF32 , BIBREF33 , BIBREF34 have been proposed which take graph data as an input. The relational memory however does not rely on a specified graph structure and such models can potentially be used for multi-hop reasoning over the relational memory. BIBREF35 proposed a method for learning a graphical representation of the text data for question answering, however the model requires explicit supervision for the graph at every step whereas RelNet does not require explicit supervision for the graph."
+    ],
+    [
+      "We evaluate the model's performance on the bAbI tasks BIBREF18 , a collection of 20 question answering tasks which have become a benchmark for evaluating memory-augmented neural networks. We compare the performance with the Recurrent Entity Networks model (EntNet) BIBREF17 . Performance is measured in terms of mean percentage error on the tasks.",
+      "Training Details: We used Adam and did a grid search for the learning rate in {0.01, 0.005, 0.001} and choose a fixed learning rate of 0.005 based on performance on the validation set, and clip the gradient norm at 2. We keep all other details similar to BIBREF17 for a fair comparison. embedding dimensions were fixed to be 100, models were trained for a maximum of 250 epochs with mini-batches size of 32 for all tasks except 3 for which the batch size was 16. The document sizes were limited to most recent 70 sentences for all tasks, except for task 3 for which it was limited to 130. The RelNet models were run for 5 times with random seed on each task and the model with best validation performance was chosen as the final model. The baseline EntNet model was run for 10 times for each task BIBREF17 .",
+      "The results are shown in Table 1 . The RelNet model achieves a mean error of 0.285% across tasks which is better than the results of the EntNet model BIBREF17 . The RelNet model is able to achieve 0% test error on 11 of the tasks, whereas the EntNet model achieves 0% error on 7 of the tasks."
+    ],
+    [
+      "We demonstrated an end-to-end trained neural network augmented with a structured memory representation which can reason about entities and relations for question answering. Future work will investigate the performance of these models on more real world datasets, interpreting what the models learn, and scaling these models to answer questions about entities and relations from reading massive text corpora."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0898/instruction.md b/qasper-0898/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..5dec2190224781f9f571fcc424a02936f8b24e6e
--- /dev/null
+++ b/qasper-0898/instruction.md
@@ -0,0 +1,43 @@
+Name of Paper: RelNet: End-to-End Modeling of Entities & Relations
+
+Question: What are the relative improvements observed over existing methods?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "RelNet Model",
+    "Related Work",
+    "Experiments",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Reasoning about entities and their relations is an important problem for achieving general artificial intelligence. Often such problems are formulated as reasoning over graph-structured representation of knowledge. Knowledge graphs, for example, consist of entities and relations between them BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 . Representation learning BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 and reasoning BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 with such structured representations is an important and active area of research.",
+      "Most previous work on knowledge representation and reasoning relies on a pipeline of natural language processing systems, often consisting of named entity extraction BIBREF12 , entity resolution and coreference BIBREF13 , relationship extraction BIBREF4 , and knowledge graph inference BIBREF14 . While this cascaded approach of using NLP systems can be effective at reasoning with knowledge bases at scale, it also leads to a problem of compounding of the error from each component sub-system. The importance of each of these sub-component on a particular downstream application is also not clear.",
+      "For the task of question-answering, we instead make an attempt at an end-to-end approach which directly models the entities and relations in the text as memory slots. While incorporating existing knowledge (from curated knowledge bases) for the purpose of question-answering BIBREF11 , BIBREF8 , BIBREF15 is an important area of research, we consider the simpler setting where all the information is contained within the text itself \u2013 which is the approach taken by many recent memory based neural network models BIBREF16 , BIBREF17 , BIBREF18 , BIBREF19 .",
+      "Recently, BIBREF17 proposed a dynamic memory based neural network for implicitly modeling the state of entities present in the text for question answering. However, this model lacks any module for relational reasoning. In response, we propose RelNet, which extends memory-augmented neural networks with a relational memory to reason about relationships between multiple entities present within the text. Our end-to-end method reads text, and writes to both memory slots and edges between them. Intuitively, the memory slots correspond to entities and the edges correspond to relationships between entities, each represented as a vector. The only supervision signal for our method comes from answering questions on the text.",
+      "We demonstrate the utility of the model through experiments on the bAbI tasks BIBREF18 and find that the model achieves smaller mean error across the tasks than the best previously published result BIBREF17 in the 10k examples regime and achieves 0% error on 11 of the 20 tasks."
+    ],
+    [
+      "We describe the RelNet model in this section. Figure 1 provides a high-level view of the model. The model is sequential in nature, consisting of the following steps: read text, process it into a dynamic relational memory and then attention conditioned on the question generates the answer. We model the dynamic memory in a fashion similar to Recurrent Entity Networks BIBREF17 and then equip it with an additional relational memory.",
+      "There are three main components to the model: 1) input encoder 2) dynamic memory, and 3) output module. We will describe these three modules in details. The input encoder and output module implementations are similar to the Entity Network BIBREF17 and main novelty lies in the dynamic memory. We describe the operations executed by the network for a single example consisting of a document with $T$ sentences, where each sentence consists of a sequence of words represented with $K$ -dimensional word embeddings $\\lbrace e_1, \\ldots , e_N\\rbrace $ , a question on the document represented as another sequence of words and an answer to the question."
+    ],
+    [
+      "There is a long line of work in textual question-answering systems BIBREF21 , BIBREF22 . Recent successful approaches use memory based neural networks for question answering, for example BIBREF23 , BIBREF18 , BIBREF24 , BIBREF19 , BIBREF17 . Our model is also a memory network based model and is also related to the neural turing machine BIBREF25 . As described previously, the model is closely related to the Recurrent Entity Networks model BIBREF17 which describes an end-to-end approach to model entities in text but does not directly model relations. Other approaches to question answering use external knowledge, for instance external knowledge bases BIBREF26 , BIBREF11 , BIBREF27 , BIBREF28 , BIBREF9 or external text like Wikipedia BIBREF29 , BIBREF30 .",
+      "Very recently, and in parallel to this work, a method for relational reasoning called relation networks BIBREF31 was proposed. They demonstrated that simple neural network modules are not as effective at relational reasoning and their proposed module is similar to our model. However, relation network is not a memory-based model and there is no mechanism to read and write relevant information for each pair. Moreover, while their approach scales as the square of the number of sentences, our approach scales as the square of the number of memory slots used per QA pair. The output module in our model can be seen as a type of relation network.",
+      "Representation learning and reasoning over graph structured data is also relevant to this work. Graph based neural network models BIBREF32 , BIBREF33 , BIBREF34 have been proposed which take graph data as an input. The relational memory however does not rely on a specified graph structure and such models can potentially be used for multi-hop reasoning over the relational memory. BIBREF35 proposed a method for learning a graphical representation of the text data for question answering, however the model requires explicit supervision for the graph at every step whereas RelNet does not require explicit supervision for the graph."
+    ],
+    [
+      "We evaluate the model's performance on the bAbI tasks BIBREF18 , a collection of 20 question answering tasks which have become a benchmark for evaluating memory-augmented neural networks. We compare the performance with the Recurrent Entity Networks model (EntNet) BIBREF17 . Performance is measured in terms of mean percentage error on the tasks.",
+      "Training Details: We used Adam and did a grid search for the learning rate in {0.01, 0.005, 0.001} and choose a fixed learning rate of 0.005 based on performance on the validation set, and clip the gradient norm at 2. We keep all other details similar to BIBREF17 for a fair comparison. embedding dimensions were fixed to be 100, models were trained for a maximum of 250 epochs with mini-batches size of 32 for all tasks except 3 for which the batch size was 16. The document sizes were limited to most recent 70 sentences for all tasks, except for task 3 for which it was limited to 130. The RelNet models were run for 5 times with random seed on each task and the model with best validation performance was chosen as the final model. The baseline EntNet model was run for 10 times for each task BIBREF17 .",
+      "The results are shown in Table 1 . The RelNet model achieves a mean error of 0.285% across tasks which is better than the results of the EntNet model BIBREF17 . The RelNet model is able to achieve 0% test error on 11 of the tasks, whereas the EntNet model achieves 0% error on 7 of the tasks."
+    ],
+    [
+      "We demonstrated an end-to-end trained neural network augmented with a structured memory representation which can reason about entities and relations for question answering. Future work will investigate the performance of these models on more real world datasets, interpreting what the models learn, and scaling these models to answer questions about entities and relations from reading massive text corpora."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0900/instruction.md b/qasper-0900/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..b0f90c4bc2a8c352ea675078d682e629da610da1
--- /dev/null
+++ b/qasper-0900/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: RelNet: End-to-End Modeling of Entities & Relations
+
+Question: What methods is RelNet compared to?
\ No newline at end of file
diff --git a/qasper-0907/instruction.md b/qasper-0907/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..edab0922bc07d7dc6ce1c5a2d0027418f6207f5f
--- /dev/null
+++ b/qasper-0907/instruction.md
@@ -0,0 +1,78 @@
+Name of Paper: An Empirical Comparison of Simple Domain Adaptation Methods for Neural Machine Translation
+
+Question: How much improvement does their method get over the fine tuning baseline?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Methods for Comparison",
+    "Fine Tuning",
+    "Multi Domain",
+    "Mixed Fine Tuning",
+    "Experimental Settings",
+    "High Quality In-domain Corpus Setting",
+    "Low Quality In-domain Corpus Setting",
+    "MT Systems",
+    "Results",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "One of the most attractive features of neural machine translation (NMT) BIBREF0 , BIBREF1 , BIBREF2 is that it is possible to train an end to end system without the need to deal with word alignments, translation rules and complicated decoding algorithms, which are a characteristic of statistical machine translation (SMT) systems. However, it is reported that NMT works better than SMT only when there is an abundance of parallel corpora. In the case of low resource domains, vanilla NMT is either worse than or comparable to SMT BIBREF3 .",
+      "Domain adaptation has been shown to be effective for low resource NMT. The conventional domain adaptation method is fine tuning, in which an out-of-domain model is further trained on in-domain data BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 . However, fine tuning tends to overfit quickly due to the small size of the in-domain data. On the other hand, multi domain NMT BIBREF8 involves training a single NMT model for multiple domains. This method adds tags \u201c<2domain>\" by modifying the parallel corpora to indicate domains without any modifications to the NMT system architecture. However, this method has not been studied for domain adaptation in particular.",
+      "Motivated by these two lines of studies, we propose a new domain adaptation method called \u201cmixed fine tuning,\" where we first train an NMT model on an out-of-domain parallel corpus, and then fine tune it on a parallel corpus that is a mix of the in-domain and out-of-domain corpora. Fine tuning on the mixed corpus instead of the in-domain corpus can address the overfitting problem. All corpora are augmented with artificial tags to indicate specific domains. We tried two different corpora settings:",
+      "We observed that \u201cmixed fine tuning\" works significantly better than methods that use fine tuning and domain tag based approaches separately. Our contributions are twofold:"
+    ],
+    [
+      "Besides fine tuning and multi domian NMT using tags, another direction for domain adaptation is using in-domain monolingual data. Either training an in-domain recurrent neural language (RNN) language model for the NMT decoder BIBREF13 or generating synthetic data by back translating target in-domain monolingual data BIBREF5 have been studied."
+    ],
+    [
+      "All the methods that we compare are simple and do not need any modifications to the NMT system."
+    ],
+    [
+      "Fine tuning is the conventional way for domain adaptation, and thus serves as a baseline in this study. In this method, we first train an NMT system on a resource rich out-of-domain corpus till convergence, and then fine tune its parameters on a resource poor in-domain corpus (Figure 1 )."
+    ],
+    [
+      "The multi domain method is originally motivated by BIBREF14 , which uses tags to control the politeness of NMT translations. The overview of this method is shown in the dotted section in Figure 2 . In this method, we simply concatenate the corpora of multiple domains with two small modifications: a. Appending the domain tag \u201c<2domain>\" to the source sentences of the respective corpora. This primes the NMT decoder to generate sentences for the specific domain. b. Oversampling the smaller corpus so that the training procedure pays equal attention to each domain.",
+      "We can further fine tune the multi domain model on the in-domain data, which is named as \u201cmulti domain + fine tuning.\u201d"
+    ],
+    [
+      "The proposed mixed fine tuning method is a combination of the above methods (shown in Figure 2 ). The training procedure is as follows:",
+      "Train an NMT model on out-of-domain data till convergence.",
+      "Resume training the NMT model from step 1 on a mix of in-domain and out-of-domain data (by oversampling the in-domain data) till convergence.",
+      "By default, we utilize domain tags, but we also consider settings where we do not use them (i.e., \u201cw/o tags\u201d). We can further fine tune the model from step 2 on the in-domain data, which is named as \u201cmixed fine tuning + fine tuning.\u201d",
+      "Note that in the \u201cfine tuning\u201d method, the vocabulary obtained from the out-of-domain data is used for the in-domain data; while for the \u201cmulti domain\u201d and \u201cmixed fine tuning\u201d methods, we use a vocabulary obtained from the mixed in-domain and out-of-domain data for all the training stages."
+    ],
+    [
+      "We conducted NMT domain adaptation experiments in two different settings as follows:"
+    ],
+    [
+      "Chinese-to-English translation was the focus of the high quality in-domain corpus setting. We utilized the resource rich patent out-of-domain data to augment the resource poor spoken language in-domain data. The patent domain MT was conducted on the Chinese-English subtask (NTCIR-CE) of the patent MT task at the NTCIR-10 workshop BIBREF9 . The NTCIR-CE task uses 1000000, 2000, and 2000 sentences for training, development, and testing, respectively. The spoken domain MT was conducted on the Chinese-English subtask (IWSLT-CE) of the TED talk MT task at the IWSLT 2015 workshop BIBREF10 . The IWSLT-CE task contains 209,491 sentences for training. We used the dev 2010 set for development, containing 887 sentences. We evaluated all methods on the 2010, 2011, 2012, and 2013 test sets, containing 1570, 1245, 1397, and 1261 sentences, respectively."
+    ],
+    [
+      "Chinese-to-Japanese translation was the focus of the low quality in-domain corpus setting. We utilized the resource rich scientific out-of-domain data to augment the resource poor Wikipedia (essentially open) in-domain data. The scientific domain MT was conducted on the Chinese-Japanese paper excerpt corpus (ASPEC-CJ) BIBREF11 , which is one subtask of the workshop on Asian translation (WAT) BIBREF15 . The ASPEC-CJ task uses 672315, 2090, and 2107 sentences for training, development, and testing, respectively. The Wikipedia domain task was conducted on a Chinese-Japanese corpus automatically extracted from Wikipedia (WIKI-CJ) BIBREF12 using the ASPEC-CJ corpus as a seed. The WIKI-CJ task contains 136013, 198, and 198 sentences for training, development, and testing, respectively."
+    ],
+    [
+      "For NMT, we used the KyotoNMT system BIBREF16 . The NMT training settings are the same as those of the best systems that participated in WAT 2016. The sizes of the source and target vocabularies, the source and target side embeddings, the hidden states, the attention mechanism hidden states, and the deep softmax output with a 2-maxout layer were set to 32,000, 620, 1000, 1000, and 500, respectively. We used 2-layer LSTMs for both the source and target sides. ADAM was used as the learning algorithm, with a dropout rate of 20% for the inter-layer dropout, and L2 regularization with a weight decay coefficient of 1e-6. The mini batch size was 64, and sentences longer than 80 tokens were discarded. We early stopped the training process when we observed that the BLEU score of the development set converges. For testing, we self ensembled the three parameters of the best development loss, the best development BLEU, and the final parameters. Beam size was set to 100.",
+      "For performance comparison, we also conducted experiments on phrase based SMT (PBSMT). We used the Moses PBSMT system BIBREF17 for all of our MT experiments. For the respective tasks, we trained 5-gram language models on the target side of the training data using the KenLM toolkit with interpolated Kneser-Ney discounting, respectively. In all of our experiments, we used the GIZA++ toolkit for word alignment; tuning was performed by minimum error rate training BIBREF18 , and it was re-run for every experiment.",
+      "For both MT systems, we preprocessed the data as follows. For Chinese, we used KyotoMorph for segmentation, which was trained on the CTB version 5 (CTB5) and SCTB BIBREF19 . For English, we tokenized and lowercased the sentences using the tokenizer.perl script in Moses. Japanese was segmented using JUMAN BIBREF20 .",
+      "For NMT, we further split the words into sub-words using byte pair encoding (BPE) BIBREF21 , which has been shown to be effective for the rare word problem in NMT. Another motivation of using sub-words is making the different domains share more vocabulary, which is important especially for the resource poor domain. For the Chinese-to-English tasks, we trained two BPE models on the Chinese and English vocabularies, respectively. For the Chinese-to-Japanese tasks, we trained a joint BPE model on both of the Chinese and Japanese vocabularies, because Chinese and Japanese could share some vocabularies of Chinese characters. The number of merge operations was set to 30,000 for all the tasks."
+    ],
+    [
+      "Tables 1 and 2 show the translation results on the Chinese-to-English and Chinese-to-Japanese tasks, respectively. The entries with SMT and NMT are the PBSMT and NMT systems, respectively; others are the different methods described in Section \"Methods for Comparison\" . In both tables, the numbers in bold indicate the best system and all systems that were not significantly different from the best system. The significance tests were performed using the bootstrap resampling method BIBREF22 at $p < 0.05$ .",
+      "We can see that without domain adaptation, the SMT systems perform significantly better than the NMT system on the resource poor domains, i.e., IWSLT-CE and WIKI-CJ; while on the resource rich domains, i.e., NTCIR-CE and ASPEC-CJ, NMT outperforms SMT. Directly using the SMT/NMT models trained on the out-of-domain data to translate the in-domain data shows bad performance. With our proposed \u201cMixed fine tuning\" domain adaptation method, NMT significantly outperforms SMT on the in-domain tasks.",
+      "Comparing different domain adaptation methods, \u201cMixed fine tuning\u201d shows the best performance. We believe the reason for this is that \u201cMixed fine tuning\u201d can address the over-fitting problem of \u201cFine tuning.\u201d We observed that while \u201cFine tuning\u201d overfits quickly after only 1 epoch of training, \u201cMixed fine tuning\u201d only slightly overfits until covergence. In addition, \u201cMixed fine tuning\u201d does not worsen the quality of out-of-domain translations, while \u201cFine tuning\u201d and \u201cMulti domain\u201d do. One shortcoming of \u201cMixed fine tuning\u201d is that compared to \u201cfine tuning,\u201d it took a longer time for the fine tuning process, as the time until convergence is essentially proportional to the size of the data used for fine tuning.",
+      "\u201cMulti domain\u201d performs either as well as (IWSLT-CE) or worse than (WIKI-CJ) \u201cFine tuning,\u201d but \u201cMixed fine tuning\u201d performs either significantly better than (IWSLT-CE) or is comparable to (WIKI-CJ) \u201cFine tuning.\u201d We believe the performance difference between the two tasks is due to their unique characteristics. As WIKI-CJ data is of relatively poorer quality, mixing it with out-of-domain data does not have the same level of positive effects as those obtained by the IWSLT-CE data.",
+      "The domain tags are helpful for both \u201cMulti domain\u201d and \u201cMixed fine tuning.\u201d Essentially, further fine tuning on in-domain data does not help for both \u201cMulti domain\u201d and \u201cMixed fine tuning.\u201d We believe the reason for this is that the \u201cMulti domain\u201d and \u201cMixed fine tuning\u201d methods already utilize the in-domain data used for fine tuning."
+    ],
+    [
+      "In this paper, we proposed a novel domain adaptation method named \u201cmixed fine tuning\u201d for NMT. We empirically compared our proposed method against fine tuning and multi domain methods, and have shown that it is effective but is sensitive to the quality of the in-domain data used.",
+      "In the future, we plan to incorporate an RNN model into our current architecture to leverage abundant in-domain monolingual corpora. We also plan on exploring the effects of synthetic data by back translating large in-domain monolingual corpora. "
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0910/instruction.md b/qasper-0910/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..36462cff22e1e0f11122d7acaa97c79d5e53f7e6
--- /dev/null
+++ b/qasper-0910/instruction.md
@@ -0,0 +1,56 @@
+Name of Paper: Combining Search with Structured Data to Create a More Engaging User Experience in Open Domain Dialogue
+
+Question: Why mixed initiative multi-turn dialogs are the greatest challenge in building open-domain conversational agents?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Modeling Discourse Coherence",
+    "Mixed Initiative Dialogue",
+    "Natural Language Generation",
+    "Conclusions"
+  ],
+  "paragraphs": [
+    [
+      "The Alexa Prize funded 12 international teams to compete to create a conversational agent that can discuss any topic for at least 20 minutes. UCSC's Slugbot was one of these funded teams. The greatest challenges with the competition arise directly from the potential for ongoing mixed-initiative multi-turn dialogues, which do not follow a particular plan or pursue a particular fixed information need. This paper describes some of the lessons we learned building SlugBot for the 2017 Alexa Prize, particularly focusing on the challenges of integrating content found via search with content from structured data in order to carry on an ongoing, coherent, open-domain, mixed-initiative conversation. SlugBot's conversations over the semi-finals user evaluation averaged 8:17 minutes.",
+      "Unlike much previous work on conversational AI, SlugBot could not and did not assume that the user had an \u201cinformation need\u201d BIBREF0 , BIBREF1 , BIBREF2 . Rather, the design of the Alexa Prize was aimed at open conversations that could engage the user, through any type of dialogue or chitchat, discussing films and books, gossiping about celebrities, playing verbal games, telling stories or sharing experiences, or any other of many different types of activities that conversation is often used for.",
+      "This open design foregrounds many longstanding challenges that have not been solved even for task-oriented dialogue systems. These include:",
+      "This paper is structured around the \u201clessons learned\u201d with respect to these challenges from our experience building SlugBot. To be clear, we are not offering a solution to these problems: instead our intention is simply to highlight the difficulties with developing adequate computational models of these phenomena that particularly arise in the context of open-domain conversations, where users cannot be assumed to be pursuing a particular task or information need. We will attempt to motivate our hypothesis that a comprehensive solution to these challenges for open-domain dialogue requires a much deeper understanding and utilization of the semantic relations that underly dialogue coherence.",
+      "For example, consider dialogue focused on content related to the movie domain. This should be one of the easiest domains because it is well-structured, and there are existing systems handling conversations where there is a specified user information need or task, such as finding films with particular properties, finding out what is playing and where, or booking a movie ticket BIBREF3 , BIBREF4 , BIBREF5 . Moreover, the Internet Movie Database (IMDB) BIBREF6 provides information on plot, rating, and actors that can be leveraged to support conversations. IMDB also makes use of the Schema.org BIBREF7 structure to connect common entities to their related attribute types (such as Actor $\\rightarrow $ Person $\\rightarrow $ birthDate), allowing the system to retrieve a large set of possible next topics and related facts and entities.",
+      "However, remember that SlugBot is based on the assumption that the user might simply enjoy talking about films and related entities and therefore may freely move the conversational focus among different movie entities, along with the vast array of semantically-associated movie attributes: movies have actors, genres, plots, and awards; actors have names, affiliations, other movies they were in, awards, etc. Actors are people, who have spouses, families and friends, and engage in other life activities besides acting, such as political advocacy.",
+      "A potential dialogue is shown in Table 1 . The interaction might appear to be simple enough: the user chooses to discuss movies, and selects Jason Bourne as the specific movie she is interested in, the system finds the movie in IMDB, and then provides information on its rating, lead actor, and plot. The user then changes the topic to other movies with the same actor, and the conversation continues.",
+      "Even with the availability of IMDB, however, the interaction is not totally straightforward. The RHS of Table 1 describes some of the required competencies and decisions SlugBot must make. First, Slugbot must be able to perform coreference resolution and recognize that the movie and it in turns U6 and U8 are coreferential. We estimate the accuracy of noun-phrase coreference resolution to only be about 70% for off-the-shelf tools applied to dialogue, since most of them are targeted to text BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 , BIBREF15 .",
+      "More challenging is that at each system turn, there are a large number of conversational moves that are possible. Making good decisions about what to say next requires balancing a dialogue policy as to what dialogue acts might be good in this context, with real-time information as to what types of content might be possible to use in this context. Slugbot could offer an opinion as in turn S3, ask a follow-on question as in S3, take the initiative to provide unasked for information, as in S5, or decide, e.g. in the case of the user's request for plot information, to use search to retrieve some relevant content. Search cannot be used effectively here without constructing an appropriate query, or knowing in advance where plot information might be available. In a real-time system, live search may not be able to achieve the required speed and efficiency, so preprocessing or caching of relevant information may be necessary. Finally, most other domains do not have such high quality structured data available, leaving us to develop or try to rely on more general models of discourse coherence."
+    ],
+    [
+      "In open-domain conversation, dialogue coherence between related turns must be maintained. What underlies dialogue coherence goes beyond simple word overlap or similarity, and its clear that neural models of open-domain conversational dialogue do not yet capture it. Theories of discourse posit that there are a small number of semantic relations that can hold between adjacent turns: at the most general level these are contingency, comparison, expansion, and temporal order BIBREF16 , BIBREF17 , BIBREF18 . We posit that one way to allow SlugBot to take the initiative and produce a turn that maintains discourse coherence is to find content to use in Slugbot's next turn that instantiates a valid semantic relation between the current user turn and SlugBot's next turn. One of the strongest bases for such semantic relations are the relations captured by ontologies or frames, which give us related entities, e.g. movies have actors and directors BIBREF4 , BIBREF21 . These types of relations can be used to instantiate the expansion relation, which basically captures moving to strongly related subtopics, often by chaining off a particular discourse entity. To find content to instantiate the expansion relation to use in Slugbot's next turn (taking the initiative), we carry out the following pipeline:",
+      "In the case of movies, the structure of IMDB, as discussed above, allows us to link between related entities and attributes using various DB keys. However other conversational domains do not have freely available richly structured information such as this. It is rare for a single resource to aggregate all the information that might be useful, so SlugBot must be able to leverage information and integrate information from multiple sources. But state-of-the-art knowledge bases and ontologies are still limited. Table 2 lists some of the resources that we have found to be most useful for search and structured information.",
+      "Like movies, sports is another domain that has rich structure, and in which there is broad user interest. Search results for a query about \"Madison Bumgarner\" are in Figure 1 , showcasing a sample of the different information retrievable from each source (Step 2 of the pipeline).",
+      "From the Google Knowledge Graph (Figure 1 result we are able to ascertain the entity type, a brief description, and a relevant Wikipedia page (Figure 1 ) which we can use to find accurate structured information. We may further augment our knowledge by using the information returned by the Google Knowledge Graph as parameters to our YAGO or DBpedia query which can more easily extract specific relationships between an entity-attribute. For example, the results returned by YAGO for the \"Madison Bumgarner\" query contains a connection to the headline Struggling MadBum might not garner next start, which is contextually relevant data not encapsulated anywhere in the previously examined results.",
+      "There, however, exists a disconnect between the resources, i.e. some entities are available in one resource and not another, or there may be inconsistent information across resources. While it would be nice not to have to anticipate the types of integration that are needed, our take-away from this, is that at present, it appears we have to accomplish the steps in our pipeline by integrating knowledge from different resources in advance, even though projects such as YAGO have already been working on such integration for at least ten years.",
+      "Other discourse coherence relations besides expansion are also viable candidates for selecting content for next turns, but finding content that instantiates these relations can be a challenging problem in itself. For example, in casual conversation, it is common to provide opinions and then perhaps further take the initiative and justify them. The justification of an opinion is a type of contingency relation: we describe how we curate content to provide justifications in Section \"Mixed Initiative Dialogue\" .",
+      "We have also been able to use the temporal relation in a limited way by drawing on narratively structured sources, such as personal stories in blogs. Since these stories are told in temporal order, we can repurpose the content of these blogs to tell stories, maintaining pre-existing narrative coherence when the system produces a sequence of turns BIBREF33 . However, we posit that there is much more that could be done to make better use of deep semantic discourse relations for recognizing discourse relations and generating coherent conversational turns."
+    ],
+    [
+      "Mixed Initiative dialogue is key to a natural conversational interaction BIBREF34 , BIBREF35 , BIBREF36 , BIBREF37 , BIBREF38 , BIBREF2 , and this is even more important for open domain dialogue than it is for task-oriented or information seeking dialogue. One of our primary hypotheses, as described above, is that good models of discourse coherence will help SlugBot identify content that can be used to take the initiative. However, models of discourse coherence have been rarely applied to conversation BIBREF39 , BIBREF40 , BIBREF41 and thus there is considerable work to be done simply in understanding how these relations can be instantiated in dialogue.",
+      "In addition, a further challenge arises from the fact that both system and user options for dialogue acts are extremely varied at each turn, e.g. user intents can be to provide opinions, give or solicit information, contrast two possibilities, request the system to perform an action, and more. One reasonable taxonomy for the types of dialogue acts that might be available to SlugBot could be based for example on the dialogue act annotations in the Switchboard corpus BIBREF42 .",
+      "Here, we consider a simple case combining discourse relations and dialogue acts that we have implemented in Slugbot in order to take the initiative in a way that we hoped the user would find interesting. Our aim was to utilize the contingency discourse relation to connect a statement of opinion and its justification. We designed a template containing both arguments of the contingency relation, namely I think $\\lbrace entity\\rbrace $ is $\\lbrace sentiment\\rbrace $ because $\\lbrace justification\\rbrace $ . We construct a table of argument pairs that can instantiate this relation, as shown in Table 3 . This table can be populated by crowd-sourcing or by using search as a pre-processing step.",
+      "Table 4 illustrates how this is used in our conversations about comics. At Line 6, when the user asks Who is your favorite character?, it is most appropriate to provide an opinion. It is difficult to imagine retrieving search-based data which contains a contextually relevant opinion, but it is even more difficult to imagine that if search had returned such an opinion, that search could be used a second time in order to retrieve a justification for the provided opinion and answer the user's follow-up question in Line 8, Okay why?. The source text for the search would have to be annotated for the type of content that could be used to provide justifications, and search would have to support these types of semantic relations."
+    ],
+    [
+      "The current challenges for natural language generation, in our view, arise from the need to combine information from structured and unstructured sources when producing conversational utterances. SlugBot currently uses a combination of pre-written templates, sentence selection, and techniques for telling stories that are based on converting monologic stories to dialogic sequences BIBREF33 .",
+      "Structured data, when available, can do more than structure a search result: it can also be easier to use within a conversation because it provides the necessary structure needed for high precision natural language generation BIBREF22 , BIBREF43 . More precisely, a small set of generic templates with various slots can be filled with information from structured data sources to insure high quality, accurate responses. These generic templates can be hand crafted, or prepared in advance by learning natural language generation templates automatically from appropriate conversational domain sources such as different types of user-generated content BIBREF44 , BIBREF23 , as illustrated in our justification initiatives above in Section \"Mixed Initiative Dialogue\" .",
+      "For general fact-based questions, on the other hand, search content can be used directly. For example, at line 14 in Table 5 when the user asks What was the first movie to feature a vampire?, search provides us with a good response. This introduces however the challenge of updating the discourse context with the right representation of the two movies under discussion, so that they can then be available for follow-on coreference. This is an open problem.",
+      "It is clear that in order to use a semi-structured approach, we need to determine when to utilize each source. Structured data can be easier to formulate into system responses and can often more easily handle on-topic follow-up questions, but is more limited in scope. An obvious approach, also used in the Watson Jeopardy system BIBREF45 , is to pool responses from both sources and rank them. We have not, to date, collected enough data to build a ranker.",
+      "Our plan is to apply a combination of reinforcement learning and learning of ranking functions for utterance variants in a particular context to SlugBot conversations as we move forward with our own data collection, outside of the Alexa Prize competition BIBREF46 , BIBREF47 , BIBREF48 , BIBREF49 , BIBREF50 . The first step however is to use the Alexa Prize competition data to learn a Paradise-Open-Domain evaluation function, with additional metrics relevant to open-domain dialogue, e.g. independent variable metrics that predict overall dialogue quality such as response delay, vocabulary diversity, dialogue act sequence n-grams BIBREF51 , conversational depth, number of reprompts BIBREF52 , and other measures that can be automatically logged. Many of the required measures have been used over the last 20 years in Paradise to evaluate task-oriented dialogue systems and they remain highly relevant to overall dialogue quality in open-domain dialogue systems BIBREF53 , BIBREF54 , BIBREF55 . We predict this can potentially improve the overall performance of the system as demonstrated in Table 6 . Here, the structured data is sparse, resulting in an uninteresting response, while search returns a very robust answer. Our Paradise-Open-Domain evaluation function would need to learn to place priority on the result returned by search, through ranking, despite having structured data.",
+      "For open domain NLG, we have also conducted experiments with neural sequence to sequence approaches using open domain corpora such as film dialogue, Big Bang theory scripts, and open subtitles. These approaches to date do not produce interesting utterances that maintain discourse coherence. It is possible that further curation and semantic annotation of these resources, e.g. by labelling semantic roles and identifying dialogue acts and discourse relations might be helpful, but this could also introduce data sparsity. For example in Switchboard the dialogue act distribution is highly skewed. Integrating information across multiple sources could also be further explored BIBREF33 . Recent work on hybrid neural generation approaches that use knowledge of sentence and discourse planning structures also seem promising BIBREF24 , BIBREF48 , BIBREF56 ."
+    ],
+    [
+      "In this paper, we describe some of the challenges we encountered building SlugBot, an open domain conversational agent funded by the Amazon Alexa Prize. We have introduced more problems than we have solved, and we have attempted to support our hypothesis that we need richer models of discourse coherence and discourse semantics to allow a conversational agent to take the initiative in open domain conversations. We illustrated how search and structured information can be combined in order for SlugBot to find content to use to take the initiative and respond to the user's utterances. We propose a hybrid approach for language generation that which combines templates to generate responses with sentence selection from search, and we show examples in different domains to demonstrate real-world use cases that make use of our approach. For future work, we plan to bring together resources that provide structured data from different sources into a single, accessible framework, to supply personal assistants with scalable knowledge bases that will power more natural, mixed initiative, and engaging conversations. We believe that it will be possible in the next few years to build conversational agents that can carry on a conversation for 20 minutes about many different topics."
+    ]
+  ]
+}
+```
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diff --git a/qasper-0911/instruction.md b/qasper-0911/instruction.md
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+++ b/qasper-0911/instruction.md
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+Name of Paper: Identifying and Understanding User Reactions to Deceptive and Trusted Social News Sources
+
+Question: How is speed measured?
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diff --git a/qasper-0916/instruction.md b/qasper-0916/instruction.md
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+++ b/qasper-0916/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Discriminative Acoustic Word Embeddings: Recurrent Neural Network-Based Approaches
+
+Question: Which dataset do they use?
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+++ b/qasper-0917/instruction.md
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+Name of Paper: Discriminative Acoustic Word Embeddings: Recurrent Neural Network-Based Approaches
+
+Question: By how much do they outpeform previous results on the word discrimination task?
\ No newline at end of file
diff --git a/qasper-0918/instruction.md b/qasper-0918/instruction.md
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+Name of Paper: Semantic Holism and Word Representations in Artificial Neural Networks
+
+Question: How does Frege's holistic and functional approach to meaning relates to general distributional hypothesis?
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diff --git a/qasper-0919/instruction.md b/qasper-0919/instruction.md
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+Name of Paper: Semantic Holism and Word Representations in Artificial Neural Networks
+
+Question: What does Frege's holistic and functional approach to meaning states?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "INTRODUCTION",
+    "INTRODUCTION ::: Related work",
+    "SEMANTIC HOLISM AND ATOMISM",
+    "SEMANTIC HOLISM AND ATOMISM ::: Atomism",
+    "SEMANTIC HOLISM AND ATOMISM ::: Holism",
+    "WORD REPRESENTATIONS IN AI",
+    "WORD REPRESENTATIONS IN AI ::: Semantic properties of the Skip-Gram model",
+    "RELEVANT THEORIES OF MEANING",
+    "RELEVANT THEORIES OF MEANING ::: The distributional hypothesis",
+    "RELEVANT THEORIES OF MEANING ::: The use theory of meaning",
+    "RELEVANT THEORIES OF MEANING ::: Structuralism",
+    "SKIP-GRAM AND TRUTH-VALUE POTENTIAL",
+    "SKIP-GRAM AND TRUTH-VALUE POTENTIAL ::: The truth-value potential",
+    "SKIP-GRAM AND TRUTH-VALUE POTENTIAL ::: Word2vec models and semantic holism",
+    "CONCLUSION AND FUTURE WORK"
+  ],
+  "paragraphs": [
+    [
+      "\u201cMeaning is, therefore, something that words have in sentences; and it's something that sentences have in a language.\u201d BIBREF0 On the other hand, meaning could also be something that words have on their own, with sentences being compositions and language a collection of words. This is the question of semantic holism versus atomism, which was important in the philosophy of language in the second half of the 20th century and has not been satisfyingly answered yet.",
+      "Artificial neural networks are the state-of-the-art solution for many problems in natural language processing (and machine learning in general). They produce word representation with interesting properties, but the way they work is little understood from the perspective of linguistics or the philosophy of language.",
+      "We believe that by finding parallels between concepts in AI and the philosophy of language, we can better understand both areas.",
+      "In this paper, we present an analogy between meaning defined as truth-value potential (a reformulation of Fregean holistic and functional approach) and a variant of language representation model, therefore pointing out a possibility that its \u201cstriking syntactic and semantic properties\u201d BIBREF1 are formed due to adhering to holistic principles."
+    ],
+    [
+      "We have found only one work concerning the philosophical aspects of neural language models BIBREF2. It is, however, concentrating on Self-Organizing Maps and Quine's version of semantic holism.",
+      "There are papers showing that Skip-gram with negative sampling is implicitly a factorization of a word-context matrix (e.g. BIBREF3, although this result was later contested by various authors, such as BIBREF4 and BIBREF5), or deriving the equations in an alternative way BIBREF6 (discussed more in Section SECREF3). This may tell us something about the model, but it does not answer the principal question: why should the matrix factorized in a certain way contain semantic information?"
+    ],
+    [
+      "Semantic holism (or meaning holism) is \u201cthe thesis that what a linguistic expression means depends on its relations to many or all other expressions within the same totality. [...] The totality in question may be the language to which the expressions belong, or a theory formulation in that language.\u201d BIBREF7 The opposing view is called semantic atomism, and it claims that there are expressions (typically words), whose meaning does not depend on the meaning of other expressions. The meaning of these expressions is given by something outside language (e.g. their relation to physical or mental objects).",
+      "In the following sections, we will specify the implications of both alternatives for semantics. The question also plays a role in cognitive science (content identity and similarity), epistemology (commensurability of theories) and seems to be strongly connected with the analytic/synthetic distinction BIBREF0. There are other positions in between these two, such as semantic molecularism or the belief that neither relations external nor internal are primary in forming meaning. However, to keep this text simple, we will only concentrate on extreme positions. We will also only talk about words, although the same argument can be used with smaller meaningful language units (e.g. parts of a compound word).",
+      "Our goal is not to asses whether the truth lies with holism, atomism or neither of them. We will only show that holism is a useful perspective when understanding neural language models is concerned.",
+      "Before we get into details of the two perspectives, let us point out two critical aspects of their difference: holism proclaims interdependence of meanings of words, contrary to their independence in atomism. And holism favours decomposition over composition."
+    ],
+    [
+      "\u201cIt is a widely held view that much of the history of the philosophy of language consists of a failed attempt to make semantic atomism work.\u201d BIBREF0",
+      "Atomism played an important role in analytic philosophy, starting with Bertrand Russell's logical atomism and continuing with logical positivism, as exemplified in this quote by Carnap BIBREF8:",
+      "A language consists of a vocabulary and a syntax, i.e. a set of words which have meanings and rules of sentence formation. These rules indicate how sentences may be formed out of the various sorts of words.",
+      "For logical positivists, words have meaning, because they refer to objects (be it physical, sensual, logical, mathematical or other). The rules of composition determine the meaning of sentences (and rule out senseless sequences of words).",
+      "Under this (or similar) view, the fact that words refer to the outside world is presupposed. Their references are independent of each other (that \u201cdog\u201d refers to dog is independent of that \u201chorse\u201d refers to horse). There is strong emphasis on compositionality, that reached its peak in Chomskian linguistics and is still relevant today.",
+      "Crucially, this means that a word can have meaning on its own (e.g. by referring to something). The meaning of larger units, such as sentences, is derived by the rules of composition from the meaning of words."
+    ],
+    [
+      "Semantic holism accents the interdependence of meaning. The whole (language, theory, ...) is the primary vehicle of meaning. The meaning of smaller units is derived by decomposition.",
+      "This view is motivated by the same word having a different meaning in a different context. Gottlob Frege has shown BIBREF9 that even such seemingly unambiguous words as numbers play distinct roles in different situations: \u201c5 is a prime number\u201d and \u201cthere are 5 cows on the meadow\u201d are different at least in that the first \u201c5\u201d signifies a complete (abstract) object, while the second one needs to be supplemented with information that it is cattle of which there are 5 specimens, otherwise the expression would not be grammatical.",
+      "Frege promoted what we could call sentence holism: \u201cOnly in the context of a sentence does a word have a meaning.\u201d BIBREF10 We will later use its modern reformulation to show an analogy with certain neural language models and therefore their holistic character.",
+      "Another group of arguments for holism consist of variations on the theme of impossibility of knowing or using a word without being able to use other words. For example, it could be argued that a person could not correctly use the word \u201cmammal\u201d, without also knowing (at least some of) \u201cbird\u201d, \u201canimal\u201d and kinds of animals. Therefore the meaning of words cannot be formed in isolation.",
+      "Something that is harder to explain under holism than under atomism is the fact that words refer to objects. If the meaning of words is given by other words, how is it connected to the world around us? However, not all words refer to something. And even if subscribing to holism makes explaining reference harder, it may be because it is a hard problem to explain.",
+      "Another thing that is simpler under atomism is compositionality. While in atomism it plays a central role as one of the presupposed properties of language, holism may not need it. But it does not claim that words do not have meanining at all, only that it is derived (by some sort of decomposition) from the meaning of the whole."
+    ],
+    [
+      "Although all artificial neural networks that work with language must have some way of representing it, the most interesting representations come from neural language models. Language modelling is a task of predicting a missing word from a sequence or generating text. There is also a similar class of models that are designed specifically to produce representations of language units, which we will call neural language representation models.",
+      "The representations (also called embeddings) are high dimensional vectors of real numbers. They are either learned together with the rest of the network for the particular task or pretrained by a general language representation model (typically on a larger dataset not specific for the task).",
+      "Some neural language (representation) models produce representation with semantic properties, although the task of language modeling itself is not (at least at the first sight) directly connected with semantics and no explicit semantic annotation is given to the neural network.",
+      "These semantic properties became popular with the invention of the word2vec software and the Skip-gram model, whose author said about it BIBREF1:",
+      "The model itself has no knowledge of syntax or morphology or semantics. Remarkably, training such a purely lexical model to maximize likelihood will induce word representations with striking syntactic and semantic properties.",
+      "However, they did not present any explanation of the phenomenon.",
+      "Goldberg and Levy BIBREF6 present a detailed derivation of the central equation of the Skip-gram model. In the last section they say:",
+      "Why does this produce good word representations?",
+      "Good question. We don't really know.",
+      "The distributional hypothesis states that words in similar contexts have similar meanings. The objective [of the Skip-gram model] clearly tries to increase the [dot product of the context and the word representations] for good word-context pairs, and decrease it for bad ones. Intuitively, this means that words that share many contexts will be similar to each other (note also that contexts sharing many words will also be similar to each other). This is, however, very hand-wavy. Can we make this intuition more precise? We'd really like to see something more formal.",
+      "We believe that the implicit holistic component of this \u201chand-wavy\u201d approach is central to the quality of Skip-gram representations and we can make the intuition more precise by analogy with the definition of the truth-value potential."
+    ],
+    [
+      "The Skip-gram model was introduced by Tom\u00e1\u0161 Mikolov et al. BIBREF11 as a method to efficiently train word embeddings. It exceeded state-of-the-art in various semantic tasks. The embeddings have interesting semantic properties, most notably the vector arithmetic illustrated by Figure FIGREF4 and the following equation BIBREF1:",
+      "meaning that starting with the word \u201cking\u201d, if we subtract the vector for the word \u201cman\u201d and add the vector for the word \u201cwoman\u201d, the nearest vector in the embedding space will be the one that corresponds to the word \u201cqueen\u201d. This means that queen is to woman as king is to man.",
+      "Hollis et al. BIBREF12 show that it is possible to infer various psycholinguistic and semantic properties of words from the Skip-gram embeddings. Mikolov et al. BIBREF13 also trained the Skip-gram model with phrases, resulting in even simpler and more elegant equations, such as",
+      "Mikolov et al. BIBREF11 proposed another shallow neural language model, Continuous Bag of Words (CBOW). The main difference between CBOW and Skip-gram (see Figure FIGREF6) is that while Skip-gram predicts context words from a given word, CBOW predicts a word from a given context."
+    ],
+    [
+      "In this section, we discuss theories of meaning that are relevant to word representations in artificial neural networks. Notice that even though they strictly speaking do not require meaning holism, they all lean towards it quite strongly."
+    ],
+    [
+      "Holism is generally a better alternative in cases where there is nothing beside language itself to anchor meaning to. This is the case of neural language (representation) models. If they represent meaning at all, it must be derived from the training corpus. This may be the reason behind the popularity of the distributional hypothesis in neural language model literature. The famous saying by Firth BIBREF14, \u201cYou shall know a word by the company it keeps!\u201d, is quoted in majority of papers concerned with vector space models of language.",
+      "The general distributional hypothesis states that the meaning of a word is given by the contexts in which it occurs. It is, however, worth noticing that in Firth's theory, collocation is just one among multiple levels of meaning and his text does not support the idea of meaning based on context alone.",
+      "A more suitable formulation of the distributional hypothesis (referenced in connection to Skip-gram in BIBREF15) is found in Distributional structure BIBREF16, where it is suggested that distribution may be used for comparing meanings and that \u201cdifference of meaning correlates with difference of distribution\u201d.",
+      "Although this certainly describes a basic principle of neural language models, it is still rather vague."
+    ],
+    [
+      "The use theory of meaning can be summed up as \u201cthe meaning of a word is its use in the language\u201d BIBREF17. It is associated with late Wittgenstein's concept of language game. In Philosophical Investigations BIBREF17, he writes:",
+      "To say \u201cThis combination of words makes no sense\u201d excludes it from the sphere of language and thereby bounds the domain of language. [...] When a sentence is called senseless, it is not as it were its sense that is senseless. But a combination of words is being excluded from the language, withdrawn from circulation.",
+      "This \u201cbounding of the domain of language\u201d is precisely what language model does, therefore the use theory may be one way to connect language modelling and semantics.",
+      "That \u201cknowledge of language emerges from language use\u201d is also one of main hypotheses of cognitive linguistics BIBREF18."
+    ],
+    [
+      "In structuralism BIBREF19, the meaning of a word is given by its relation to the other words of the language:",
+      "The elements of a structure have neither extrinsic designation, nor intrinsic signification. Then what is left? [...] [N]othing other than a sense [...]: a sense which is necessarily and uniquely \u201cpositional.\u201d BIBREF20",
+      "This holds for word representations in artificial neural networks as well. The vectors representing the words do not have any other meaning than their position among the rest of the vectors and a single vector does not have any significance outside the model. This is also demonstrated by the vectors being different every time the model is trained because of random initialization."
+    ],
+    [
+      "In this section, we introduce the truth-value potential and show that Skip-gram corresponds to it better than CBOW."
+    ],
+    [
+      "Tugendhat's compact reformulation of Frege's sentence holism, the definition of meaning as truth-value potential is BIBREF21:",
+      "[T]wo expressions $\\phi $ and $\\psi $ have the same truth-value potential if and only if, whenever each is completed by the same expression to form a sentence, the two sentences have the same truth-value.",
+      "We can also express this definition in the following form:",
+      "where $M$ is the truth-value potential (meaning), $T$ is the truth-value of the sentence and $x(\\omega )$ is the result of completing the expression $\\omega $ by the expression $x$ to form a sentence.",
+      "One important aspect of this definition is that, following Frege BIBREF10, it is based on an assumption that the sentence (or rather the corresponding judgement) is the basic unit of meaning."
+    ],
+    [
+      "The definition of meaning as truth-value potential is analogous to the process of training a model for word representations. One difference is that when we are training a model, we do not have the whole of language at our disposal. Even after approximating the language with a finite corpus, it still is not practical to compare all the contexts for a given word at the same time, therefore the universal quantifier has to be replaced by an iterative process of examining the contexts one by one (or actually batch by batch, which is a step back towards the totality that is being estimated). And we have no means to asses whether the sentences from the corpus are true or false. We can either assume that they are mostly true, or try to replace the concept of truth with something else (maybe language use). Even the first option seems to be enough\u2014imagine a corpus full of false sentences about cats, e.g. \u201cCats can fly.\u201d, \u201cCats are cetaceans.\u201d etc. We cannot expect the representation of the word \u201ccats\u201d in a model trained on this corpus to be any good, therefore the requirement for the corpus to consist mostly of true sentences is not excessive.",
+      "The simplest model that corresponds to this analogy is the Skip-gram model. It does just what is described in the definition \u2013 it fixes a word and goes through all the possible contexts. It compares the words based on the context. The context words are predicted and their representations are fixed (in a single training step), while the representation of a single word is learned. By learning the representation of a word from the representation of the context, Skip-gram complies to the principles of semantic holism. The analogy between the definition of truth-value potential and the process of training the Skip-gram model is one possible explanation for its semantic properties and its performance in semantic tasks.",
+      "The complementary CBOW architecture (see Figure FIGREF6) performs much worse in the evaluation of the semantic tasks BIBREF11. In CBOW, a missing word is predicted from its context. Therefore, in a single learning step, the representation of the missing word is fixed. What changes (and is learned) is the representation of the context words. By learning the representation of the context from the representation of the word, CBOW is implicitly conforming to semantic atomism: words are the basic units of meaning and the meaning of the broader context is derived from the atomic meaning of words. This may be the reason why CBOW does not exhibit the same semantic properties as Skip-gram and it performs worse in semantic tasks."
+    ],
+    [
+      "The distributional hypothesis as an explanation for the semantic properties of neural language models should be expanded into a more detailed account. We show one possible way to do that via a Fregean approach to meaning.",
+      "Both the distributional hypothesis itself and Tugendhat's interpretation of Frege's work are examples of holistic approaches to meaning, where the meaning of the whole determines the meaning of parts. As we demonstrated on the opposition between Skip-gram and CBOW models, the distinction between semantic holism and atomism may play an essential role in semantic properties of neural language representations models.",
+      "We have demonstrated the connection between the Skip-gram model and the definition of meaning as truth-value potential. Although this is an isolated observation of an analogy between a specific model and a specific theory about meaning, it is a crucial step towards finding a theory of meaning that would correspond to the current results of NLP research, increasing our understanding of NLP and ultimately the language itself.",
+      "The direction of research from successful language technologies to properties of language itself offers many opportunities for inquiry, with very few being explored so far.",
+      "Many state-of-the-art models for natural language processing use smaller units than words for their input and output. This analysis could be extended to take this into account.",
+      "It might also be interesting to think about the philosophy of science in technical fields dominated by machine learning, but that is far beyond the scope of this paper.",
+      "This work has been supported by the grant 18-02196S of the Czech Science Foundation. This research was partially supported by SVV project number 260 575."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0920/instruction.md b/qasper-0920/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..74005ce4d0335e1f2cbb2ce0591ac3099d891105
--- /dev/null
+++ b/qasper-0920/instruction.md
@@ -0,0 +1,105 @@
+Name of Paper: Paraphrase Generation from Latent-Variable PCFGs for Semantic Parsing
+
+Question: Do they evaluate the quality of the paraphrasing model?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Paraphrase Generation Using Grammars",
+    "Paraphrases Generation Algorithm",
+    "Bi-Layered L-PCFGs",
+    "Paraphrase Classification",
+    "Semantic Parsing using Paraphrasing",
+    "Ungrounded Graphs from Paraphrases",
+    "Grounded Graphs from Ungrounded Graphs",
+    "Learning",
+    "Experimental Setup",
+    "Evaluation Data and Metric",
+    "Baselines",
+    "Implementation Details",
+    "Results and Discussion",
+    "Conclusion",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "Semantic parsers map sentences onto logical forms that can be used to query databases BIBREF0 , BIBREF1 , instruct robots BIBREF2 , extract information BIBREF3 , or describe visual scenes BIBREF4 . In this paper we consider the problem of semantically parsing questions into Freebase logical forms for the goal of question answering. Current systems accomplish this by learning task-specific grammars BIBREF5 , strongly-typed CCG grammars BIBREF6 , BIBREF7 , or neural networks without requiring any grammar BIBREF8 . These methods are sensitive to the words used in a question and their word order, making them vulnerable to unseen words and phrases. Furthermore, mismatch between natural language and Freebase makes the problem even harder. For example, Freebase expresses the fact that \u201cCzech is the official language of Czech Republic\u201d (encoded as a graph), whereas to answer a question like \u201cWhat do people in Czech Republic speak?\u201d one should infer people in Czech Republic refers to Czech Republic and What refers to the language and speak refers to the predicate official language.",
+      "We address the above problems by using paraphrases of the original question. Paraphrasing has shown to be promising for semantic parsing BIBREF9 , BIBREF10 , BIBREF11 . We propose a novel framework for paraphrasing using latent-variable PCFGs (L-PCFGs). Earlier approaches to paraphrasing used phrase-based machine translation for text-based QA BIBREF12 , BIBREF13 , or hand annotated grammars for KB-based QA BIBREF10 . We find that phrase-based statistical machine translation (MT) approaches mainly produce lexical paraphrases without much syntactic diversity, whereas our grammar-based approach is capable of producing both lexically and syntactically diverse paraphrases. Unlike MT based approaches, our system does not require aligned parallel paraphrase corpora. In addition we do not require hand annotated grammars for paraphrase generation but instead learn the grammar directly from a large scale question corpus.",
+      "The main contributions of this paper are two fold. First, we present an algorithm (\u00a7 \"Paraphrase Generation Using Grammars\" ) to generate paraphrases using latent-variable PCFGs. We use the spectral method of narayan-15 to estimate L-PCFGs on a large scale question treebank. Our grammar model leads to a robust and an efficient system for paraphrase generation in open-domain question answering. While CFGs have been explored for paraphrasing using bilingual parallel corpus BIBREF14 , ours is the first implementation of CFG that uses only monolingual data. Second, we show that generated paraphrases can be used to improve semantic parsing of questions into Freebase logical forms (\u00a7 \"Semantic Parsing using Paraphrasing\" ). We build on a strong baseline of reddylargescale2014 and show that our grammar model competes with MT baseline even without using any parallel paraphrase resources."
+    ],
+    [
+      "Our paraphrase generation algorithm is based on a model in the form of an L-PCFG. L-PCFGs are PCFGs where the nonterminals are refined with latent states that provide some contextual information about each node in a given derivation. L-PCFGs have been used in various ways, most commonly for syntactic parsing BIBREF15 , BIBREF16 , BIBREF17 , BIBREF18 , BIBREF19 , BIBREF20 .",
+      "In our estimation of L-PCFGs, we use the spectral method of narayan-15, instead of using EM, as has been used in the past by matsuzaki-2005 and petrov-2006. The spectral method we use enables the choice of a set of feature functions that indicate the latent states, which proves to be useful in our case. It also leads to sparse grammar estimates and compact models.",
+      "The spectral method works by identifying feature functions for \u201cinside\u201d and \u201coutside\u201d trees, and then clusters them into latent states. Then it follows with a maximum likelihood estimation step, that assumes the latent states are represented by clusters obtained through the feature function clustering. For more details about these constructions, we refer the reader to cohen-13 and narayan-15.",
+      "The rest of this section describes our paraphrase generation algorithm."
+    ],
+    [
+      "We define our paraphrase generation task as a sampling problem from an L-PCFG $G_{\\mathrm {syn}}$ , which is estimated from a large corpus of parsed questions. Once this grammar is estimated, our algorithm follows a pipeline with two major steps.",
+      "We first build a word lattice $W_q$ for the input question $q$ . We use the lattice to constrain our paraphrases to a specific choice of words and phrases that can be used. Once this lattice is created, a grammar $G_{\\mathrm {syn}}^{\\prime }$ is then extracted from $G_{\\mathrm {syn}}$ . This grammar is constrained to the lattice.",
+      "We experiment with three ways of constructing word lattices: na\u00efve word lattices representing the words from the input question only, word lattices constructed with the Paraphrase Database BIBREF14 and word lattices constructed with a bi-layered L-PCFG, described in \u00a7 \"Bi-Layered L-PCFGs\" . For example, Figure 1 shows an example word lattice for the question What language do people in Czech Republic speak? using the lexical and phrasal rules from the PPDB.",
+      "Once $G_{\\mathrm {syn}}^{\\prime }$ is generated, we sample paraphrases of the input question $q$ . These paraphrases are further filtered with a classifier to improve the precision of the generated paraphrases.",
+      "We train the L-PCFG $G_{\\mathrm {syn}}$ on the Paralex corpus BIBREF9 . Paralex is a large monolingual parallel corpus, containing 18 million pairs of question paraphrases with 2.4M distinct questions in the corpus. It is suitable for our task of generating paraphrases since its large scale makes our model robust for open-domain questions. We construct a treebank by parsing 2.4M distinct questions from Paralex using the BLLIP parser BIBREF25 .",
+      "Given the treebank, we use the spectral algorithm of narayan-15 to learn an L-PCFG for constituency parsing to learn $G_{\\mathrm {syn}}$ . We follow narayan-15 and use the same feature functions for the inside and outside trees as they use, capturing contextual syntactic information about nonterminals. We refer the reader to narayan-15 for more detailed description of these features. In our experiments, we set the number of latent states to 24.",
+      "Once we estimate $G_{\\mathrm {syn}}$ from the Paralex corpus, we restrict it for each question to a grammar $G_{\\mathrm {syn}}^{\\prime }$ by keeping only the rules that could lead to a derivation over the lattice. This step is similar to lexical pruning in standard grammar-based generation process to avoid an intermediate derivation which can never lead to a successful derivation BIBREF26 , BIBREF27 .",
+      "Sampling a question from the grammar $G_{\\mathrm {syn}}^{\\prime }$ is done by recursively sampling nodes in the derivation tree, together with their latent states, in a top-down breadth-first fashion. Sampling from the pruned grammar $G_{\\mathrm {syn}}^{\\prime }$ raises an issue of oversampling words that are more frequent in the training data. To lessen this problem, we follow a controlled sampling approach where sampling is guided by the word lattice $W_q$ . Once a word $w$ from a path $e$ in $W_q$ is sampled, all other parallel or conflicting paths to $e$ are removed from $W_q$ . For example, generating for the word lattice in Figure 1 , when we sample the word citizens, we drop out the paths \u201chuman beings\u201d, \u201cpeople's\u201d, \u201cthe population\u201d, \u201cpeople\u201d and \u201cmembers of the public\u201d from $W_q$ and accordingly update the grammar. The controlled sampling ensures that each sampled question uses words from a single start-to-end path in $W_q$ . For example, we could sample a question what is Czech Republic 's language? by sampling words from the path (what, language, do, people 's, in, Czech, Republic, is speaking, ?) in Figure 1 . We repeat this sampling process to generate multiple potential paraphrases.",
+      "The resulting generation algorithm has multiple advantages over existing grammar generation methods. First, the sampling from an L-PCFG grammar lessens the lexical ambiguity problem evident in lexicalized grammars such as tree adjoining grammars BIBREF27 and combinatory categorial grammars BIBREF28 . Our grammar is not lexicalized, only unary context-free rules are lexicalized. Second, the top-down sampling restricts the combinatorics inherent to bottom-up search BIBREF29 . Third, we do not restrict the generation by the order information in the input. The lack of order information in the input often raises the high combinatorics in lexicalist approaches BIBREF30 . In our case, however, we use sampling to reduce this problem, and it allows us to produce syntactically diverse questions. And fourth, we impose no constraints on the grammar thereby making it easier to maintain bi-directional (recursive) grammars that can be used both for parsing and for generation BIBREF31 ."
+    ],
+    [
+      "As mentioned earlier, one of our lattice types is based on bi-layered PCFGs introduced here.",
+      "In their traditional use, the latent states in L-PCFGs aim to capture syntactic information. We introduce here the use of an L-PCFG with two layers of latent states: one layer is intended to capture the usual syntactic information, and the other aims to capture semantic and topical information by using a large set of states with specific feature functions.",
+      "To create the bi-layered L-PCFG, we again use the spectral algorithm of narayan-15 to estimate a grammar $G_{\\mathrm {par}}$ from the Paralex corpus. We use the word alignment of paraphrase question pairs in Paralex to map inside and outside trees of each nonterminals in the treebank to bag of word features. The number of latent states we use is 1,000.",
+      "Once the two feature functions (syntactic in $G_{\\mathrm {syn}}$ and semantic in $G_{\\mathrm {par}}$ ) are created, each nonterminal in the training treebank is assigned two latent states (cluster identifiers). Figure 2 shows an example annotation of trees for three paraphrase questions from the Paralex corpus. We compute the parameters of the bi-layered L-PCFG $G_{\\mathrm {layered}}$ with a simple frequency count maximum likelihood estimate over this annotated treebank. As such, $G_{\\mathrm {layered}}$ is a combination of $G_{\\mathrm {syn}}$ and $G_{\\mathrm {par}}$ , resulting in 24,000 latent states (24 syntactic x 1000 semantic).",
+      "Consider an example where we want to generate paraphrases for the question what day is nochebuena. Parsing it with $G_{\\mathrm {layered}}$ will lead to the leftmost hybrid structure as shown in Figure 2 . The assignment of the first latent states for each nonterminals ensures that we retrieve the correct syntactic representation of the sentence. Here, however, we are more interested in the second latent states assigned to each nonterminals which capture the paraphrase information of the sentence at various levels. For example, we have a unary lexical rule (NN-*-142 day) indicating that we observe day with NN of the paraphrase type 142. We could use this information to extract unary rules of the form (NN-*-142 $w$ ) in the treebank that will generate words $w$ which are paraphrases to day. Similarly, any node WHNP-*-291 in the treebank will generate paraphrases for what day, SBARQ-*-403, for what day is nochebuena. This way we will be able to generate paraphrases when is nochebuena and when is nochebuena celebrated as they both have SBARQ-*-403 as their roots.",
+      "To generate a word lattice $W_q$ for a given question $q$ , we parse $q$ with the bi-layered grammar $G_{\\mathrm {layered}}$ . For each rule of the form $X$ - $m_1$ - $m_2 \\rightarrow w$ in the bi-layered tree with $X \\in {\\cal P}$ , $m_1 \\in \\lbrace  1, \\ldots , 24 \\rbrace $ , $m_2 \\in \\lbrace  1, \\ldots , 1000 \\rbrace $ and $q$0 a word in $q$1 , we extract rules of the form $q$2 - $q$3 - $q$4 from $q$5 such that $q$6 . For each such $q$7 , we add a path $q$8 parallel to $q$9 in the word lattice."
+    ],
+    [
+      "Our sampling algorithm overgenerates paraphrases which are incorrect. To improve its precision, we build a binary classifier to filter the generated paraphrases. We randomly select 100 distinct questions from the Paralex corpus and generate paraphrases using our generation algorithm with various lattice settings. We randomly select 1,000 pairs of input-sampled sentences and manually annotate them as \u201ccorrect\u201d or \u201cincorrect\u201d paraphrases. We train our classifier on this manually created training data. We follow madnani2012, who used MT metrics for paraphrase identification, and experiment with 8 MT metrics as features for our binary classifier. In addition, we experiment with a binary feature which checks if the sampled paraphrase preserves named entities from the input sentence. We use WEKA BIBREF32 to replicate the classifier of madnani2012 with our new feature. We tune the feature set for our classifier on the development data."
+    ],
+    [
+      "In this section we describe how the paraphrase algorithm is used for converting natural language to Freebase queries. Following reddylargescale2014, we formalize the semantic parsing problem as a graph matching problem, i.e., finding the Freebase subgraph (grounded graph) that is isomorphic to the input question semantic structure (ungrounded graph).",
+      "This formulation has a major limitation that can be alleviated by using our paraphrase generation algorithm. Consider the question What language do people in Czech Republic speak?. The ungrounded graph corresponding to this question is shown in Figure 3 . The Freebase grounded graph which results in correct answer is shown in Figure 3 . Note that these two graphs are non-isomorphic making it impossible to derive the correct grounding from the ungrounded graph. In fact, at least 15% of the examples in our development set fail to satisfy isomorphic assumption. In order to address this problem, we use paraphrases of the input question to generate additional ungrounded graphs, with the aim that one of those paraphrases will have a structure isomorphic to the correct grounding. Figure 3 and Figure 3 are two such paraphrases which can be converted to Figure 3 as described in sec:groundedGraphs.",
+      "For a given input question, first we build ungrounded graphs from its paraphrases. We convert these graphs to Freebase graphs. To learn this mapping, we rely on manually assembled question-answer pairs. For each training question, we first find the set of oracle grounded graphs\u2014Freebase subgraphs which when executed yield the correct answer\u2014derivable from the question's ungrounded graphs. These oracle graphs are then used to train a structured perceptron model. These steps are discussed in detail below."
+    ],
+    [
+      "We use GraphParser BIBREF7 to convert paraphrases to ungrounded graphs. This conversion involves three steps: 1) parsing the paraphrase using a CCG parser to extract syntactic derivations BIBREF33 , 2) extracting logical forms from the CCG derivations BIBREF34 , and 3) converting the logical forms to an ungrounded graph. The ungrounded graph for the example question and its paraphrases are shown in Figure 3 , Figure 3 and Figure 3 , respectively."
+    ],
+    [
+      "The ungrounded graphs are grounded to Freebase subgraphs by mapping entity nodes, entity-entity edges and entity type nodes in the ungrounded graph to Freebase entities, relations and types, respectively. For example, the graph in Figure 3 can be converted to a Freebase graph in Figure 3 by replacing the entity node Czech Republic with the Freebase entity CzechRepublic, the edge (speak.arg $_2$ , speak.in) between $x$ and Czech Republic with the Freebase relation (location.country.official_language.2, location.country.official_language.1), the type node language with the Freebase type language.human_language, and the target node remains intact. The rest of the nodes, edges and types are grounded to null. In a similar fashion, Figure 3 can be grounded to Figure 3 , but not Figure 3 to Figure 3 . If no paraphrase is isomorphic to the target grounded grounded graph, our grounding fails."
+    ],
+    [
+      "We use a linear model to map ungrounded graphs to grounded ones. The parameters of the model are learned from question-answer pairs. For example, the question What language do people in Czech Republic speak? paired with its answer $\\lbrace \\textsc {CzechLanguage}\\rbrace $ . In line with most work on question answering against Freebase, we do not rely on annotated logical forms associated with the question for training and treat the mapping of a question to its grounded graph as latent.",
+      "Let $q$ be a question, let $p$ be a paraphrase, let $u$ be an ungrounded graph for $p$ , and let $g$ be a grounded graph formed by grounding the nodes and edges of $u$ to the knowledge base $\\mathcal {K}$ (throughout we use Freebase as the knowledge base). Following reddylargescale2014, we use beam search to find the highest scoring tuple of paraphrase, ungrounded and grounded graphs $(\\hat{p}, \\hat{u}, \\hat{g})$ under the model $\\theta \\in \\mathbb {R}^n$ : $\n({\\hat{p},\\hat{u},\\hat{g}}) = \\operatornamewithlimits{arg\\,max}_{(p,u,g)} \\theta \\cdot \\Phi (p,u,g,q,\\mathcal {K})\\,,\n$ ",
+      "where $\\Phi (p, u, g, q, \\mathcal {K}) \\in \\mathbb {R}^n$ denotes the features for the tuple of paraphrase, ungrounded and grounded graphs. The feature function has access to the paraphrase, ungrounded and grounded graphs, the original question, as well as to the content of the knowledge base and the denotation $|g|_\\mathcal {K}$ (the denotation of a grounded graph is defined as the set of entities or attributes reachable at its target node). See sec:details for the features employed. The model parameters are estimated with the averaged structured perceptron BIBREF35 . Given a training question-answer pair $(q,\\mathcal {A})$ , the update is: $\n\\theta ^{t+1} \\leftarrow \\theta ^{t} + \\Phi (p^+, u^+, g^+, q,\n\\mathcal {K}) - \\Phi (\\hat{p}, \\hat{u}, \\hat{g}, q, \\mathcal {K})\\,,\n$ ",
+      "where $({p^+,u^+,g^+})$ denotes the tuple of gold paraphrase, gold ungrounded and grounded graphs for $q$ . Since we do not have direct access to the gold paraphrase and graphs, we instead rely on the set of oracle tuples, $\\mathcal {O}_{\\mathcal {K}, \\mathcal {A}}(q)$ , as a proxy: $\n(p^{+},u^{+},{g^{+}}) = \\operatornamewithlimits{arg\\,max}_{(p,u,g) \\in \\mathcal {O}_{\\mathcal {K},\\mathcal {A}}(q)} \\theta \\cdot \\Phi ({p,u,g,q,\\mathcal {K}})\\,,\n$ ",
+      "where $\\mathcal {O}_{\\mathcal {K}, \\mathcal {A}}(q)$ is defined as the set of tuples ( $p$ , $u$ , $g$ ) derivable from the question $q$ , whose denotation $|g|_\\mathcal {K}$ has minimal $F_1$ -loss against the gold answer $\\mathcal {A}$ . We find the oracle graphs for each question a priori by performing beam-search with a very large beam."
+    ],
+    [
+      "Below, we give details on the evaluation dataset and baselines used for comparison. We also describe the model features and provide implementation details."
+    ],
+    [
+      "We evaluate our approach on the WebQuestions dataset BIBREF5 . WebQuestions consists of 5,810 question-answer pairs where questions represents real Google search queries. We use the standard train/test splits, with 3,778 train and 2,032 test questions. For our development experiments we tune the models on held-out data consisting of 30% training questions, while for final testing we use the complete training data. We use average precision (avg P.), average recall (avg R.) and average F $_1$ (avg F $_1$ ) proposed by berantsemantic2013 as evaluation metrics."
+    ],
+    [
+      "We use GraphParser without paraphrases as our baseline. This gives an idea about the impact of using paraphrases.",
+      "We compare our paraphrasing models with monolingual machine translation based model for paraphrase generation BIBREF24 , BIBREF36 . In particular, we use Moses BIBREF37 to train a monolingual phrase-based MT system on the Paralex corpus. Finally, we use Moses decoder to generate 10-best distinct paraphrases for the test questions."
+    ],
+    [
+      "For WebQuestions, we use 8 handcrafted part-of-speech patterns (e.g., the pattern (DT)?(JJ.? $\\mid $ NN.?){0,2}NN.? matches the noun phrase the big lebowski) to identify candidate named entity mention spans. We use the Stanford CoreNLP caseless tagger for part-of-speech tagging BIBREF38 . For each candidate mention span, we retrieve the top 10 entities according to the Freebase API. We then create a lattice in which the nodes correspond to mention-entity pairs, scored by their Freebase API scores, and the edges encode the fact that no joint assignment of entities to mentions can contain overlapping spans. We take the top 10 paths through the lattice as possible entity disambiguations. For each possibility, we generate $n$ -best paraphrases that contains the entity mention spans. In the end, this process creates a total of $10n$ paraphrases. We generate ungrounded graphs for these paraphrases and treat the final entity disambiguation and paraphrase selection as part of the semantic parsing problem.",
+      "We use the features from reddylargescale2014. These include edge alignments and stem overlaps between ungrounded and grounded graphs, and contextual features such as word and grounded relation pairs. In addition to these features, we add two new real-valued features \u2013 the paraphrase classifier's score and the entity disambiguation lattice score.",
+      "We use beam search to infer the highest scoring graph pair for a question. The search operates over entity-entity edges and entity type nodes of each ungrounded graph. For an entity-entity edge, there are two operations: ground the edge to a Freebase relation, or skip the edge. Similarly, for an entity type node, there are two operations: ground the node to a Freebase type, or skip the node. We use a beam size of 100 in all our experiments."
+    ],
+    [
+      "In this section, we present results from five different systems for our question-answering experiments: original, mt, naive, ppdb and bilayered. First two are baseline systems. Other three systems use paraphrases generated from an L-PCFG grammar. naive uses a word lattice with a single start-to-end path representing the input question itself, ppdb uses a word lattice constructed using the PPDB rules, and bilayered uses bi-layered L-PCFG to build word lattices. Note that naive does not require any parallel resource to train, ppdb requires an external paraphrase database, and bilayered, like mt, needs a parallel corpus with paraphrase pairs. We tune our classifier features and GraphParser features on the development data. We use the best setting from tuning for evaluation on the test data."
+    ],
+    [
+      "We described a grammar method to generate paraphrases for questions, and applied it to a question answering system based on semantic parsing. We showed that using paraphrases for a question answering system is a useful way to improve its performance. Our method is rather generic and can be applied to any question answering system."
+    ],
+    [
+      "The authors would like to thank Nitin Madnani for his help with the implementation of the paraphrase classifier. We would like to thank our anonymous reviewers for their insightful comments. This research was supported by an EPSRC grant (EP/L02411X/1), the H2020 project SUMMA (under grant agreement 688139), and a Google PhD Fellowship for the second author."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0921/instruction.md b/qasper-0921/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..f2b1cf85c609e04936234f6efad473d8ba496697
--- /dev/null
+++ b/qasper-0921/instruction.md
@@ -0,0 +1,105 @@
+Name of Paper: Paraphrase Generation from Latent-Variable PCFGs for Semantic Parsing
+
+Question: How many paraphrases are generated per question?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Paraphrase Generation Using Grammars",
+    "Paraphrases Generation Algorithm",
+    "Bi-Layered L-PCFGs",
+    "Paraphrase Classification",
+    "Semantic Parsing using Paraphrasing",
+    "Ungrounded Graphs from Paraphrases",
+    "Grounded Graphs from Ungrounded Graphs",
+    "Learning",
+    "Experimental Setup",
+    "Evaluation Data and Metric",
+    "Baselines",
+    "Implementation Details",
+    "Results and Discussion",
+    "Conclusion",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "Semantic parsers map sentences onto logical forms that can be used to query databases BIBREF0 , BIBREF1 , instruct robots BIBREF2 , extract information BIBREF3 , or describe visual scenes BIBREF4 . In this paper we consider the problem of semantically parsing questions into Freebase logical forms for the goal of question answering. Current systems accomplish this by learning task-specific grammars BIBREF5 , strongly-typed CCG grammars BIBREF6 , BIBREF7 , or neural networks without requiring any grammar BIBREF8 . These methods are sensitive to the words used in a question and their word order, making them vulnerable to unseen words and phrases. Furthermore, mismatch between natural language and Freebase makes the problem even harder. For example, Freebase expresses the fact that \u201cCzech is the official language of Czech Republic\u201d (encoded as a graph), whereas to answer a question like \u201cWhat do people in Czech Republic speak?\u201d one should infer people in Czech Republic refers to Czech Republic and What refers to the language and speak refers to the predicate official language.",
+      "We address the above problems by using paraphrases of the original question. Paraphrasing has shown to be promising for semantic parsing BIBREF9 , BIBREF10 , BIBREF11 . We propose a novel framework for paraphrasing using latent-variable PCFGs (L-PCFGs). Earlier approaches to paraphrasing used phrase-based machine translation for text-based QA BIBREF12 , BIBREF13 , or hand annotated grammars for KB-based QA BIBREF10 . We find that phrase-based statistical machine translation (MT) approaches mainly produce lexical paraphrases without much syntactic diversity, whereas our grammar-based approach is capable of producing both lexically and syntactically diverse paraphrases. Unlike MT based approaches, our system does not require aligned parallel paraphrase corpora. In addition we do not require hand annotated grammars for paraphrase generation but instead learn the grammar directly from a large scale question corpus.",
+      "The main contributions of this paper are two fold. First, we present an algorithm (\u00a7 \"Paraphrase Generation Using Grammars\" ) to generate paraphrases using latent-variable PCFGs. We use the spectral method of narayan-15 to estimate L-PCFGs on a large scale question treebank. Our grammar model leads to a robust and an efficient system for paraphrase generation in open-domain question answering. While CFGs have been explored for paraphrasing using bilingual parallel corpus BIBREF14 , ours is the first implementation of CFG that uses only monolingual data. Second, we show that generated paraphrases can be used to improve semantic parsing of questions into Freebase logical forms (\u00a7 \"Semantic Parsing using Paraphrasing\" ). We build on a strong baseline of reddylargescale2014 and show that our grammar model competes with MT baseline even without using any parallel paraphrase resources."
+    ],
+    [
+      "Our paraphrase generation algorithm is based on a model in the form of an L-PCFG. L-PCFGs are PCFGs where the nonterminals are refined with latent states that provide some contextual information about each node in a given derivation. L-PCFGs have been used in various ways, most commonly for syntactic parsing BIBREF15 , BIBREF16 , BIBREF17 , BIBREF18 , BIBREF19 , BIBREF20 .",
+      "In our estimation of L-PCFGs, we use the spectral method of narayan-15, instead of using EM, as has been used in the past by matsuzaki-2005 and petrov-2006. The spectral method we use enables the choice of a set of feature functions that indicate the latent states, which proves to be useful in our case. It also leads to sparse grammar estimates and compact models.",
+      "The spectral method works by identifying feature functions for \u201cinside\u201d and \u201coutside\u201d trees, and then clusters them into latent states. Then it follows with a maximum likelihood estimation step, that assumes the latent states are represented by clusters obtained through the feature function clustering. For more details about these constructions, we refer the reader to cohen-13 and narayan-15.",
+      "The rest of this section describes our paraphrase generation algorithm."
+    ],
+    [
+      "We define our paraphrase generation task as a sampling problem from an L-PCFG $G_{\\mathrm {syn}}$ , which is estimated from a large corpus of parsed questions. Once this grammar is estimated, our algorithm follows a pipeline with two major steps.",
+      "We first build a word lattice $W_q$ for the input question $q$ . We use the lattice to constrain our paraphrases to a specific choice of words and phrases that can be used. Once this lattice is created, a grammar $G_{\\mathrm {syn}}^{\\prime }$ is then extracted from $G_{\\mathrm {syn}}$ . This grammar is constrained to the lattice.",
+      "We experiment with three ways of constructing word lattices: na\u00efve word lattices representing the words from the input question only, word lattices constructed with the Paraphrase Database BIBREF14 and word lattices constructed with a bi-layered L-PCFG, described in \u00a7 \"Bi-Layered L-PCFGs\" . For example, Figure 1 shows an example word lattice for the question What language do people in Czech Republic speak? using the lexical and phrasal rules from the PPDB.",
+      "Once $G_{\\mathrm {syn}}^{\\prime }$ is generated, we sample paraphrases of the input question $q$ . These paraphrases are further filtered with a classifier to improve the precision of the generated paraphrases.",
+      "We train the L-PCFG $G_{\\mathrm {syn}}$ on the Paralex corpus BIBREF9 . Paralex is a large monolingual parallel corpus, containing 18 million pairs of question paraphrases with 2.4M distinct questions in the corpus. It is suitable for our task of generating paraphrases since its large scale makes our model robust for open-domain questions. We construct a treebank by parsing 2.4M distinct questions from Paralex using the BLLIP parser BIBREF25 .",
+      "Given the treebank, we use the spectral algorithm of narayan-15 to learn an L-PCFG for constituency parsing to learn $G_{\\mathrm {syn}}$ . We follow narayan-15 and use the same feature functions for the inside and outside trees as they use, capturing contextual syntactic information about nonterminals. We refer the reader to narayan-15 for more detailed description of these features. In our experiments, we set the number of latent states to 24.",
+      "Once we estimate $G_{\\mathrm {syn}}$ from the Paralex corpus, we restrict it for each question to a grammar $G_{\\mathrm {syn}}^{\\prime }$ by keeping only the rules that could lead to a derivation over the lattice. This step is similar to lexical pruning in standard grammar-based generation process to avoid an intermediate derivation which can never lead to a successful derivation BIBREF26 , BIBREF27 .",
+      "Sampling a question from the grammar $G_{\\mathrm {syn}}^{\\prime }$ is done by recursively sampling nodes in the derivation tree, together with their latent states, in a top-down breadth-first fashion. Sampling from the pruned grammar $G_{\\mathrm {syn}}^{\\prime }$ raises an issue of oversampling words that are more frequent in the training data. To lessen this problem, we follow a controlled sampling approach where sampling is guided by the word lattice $W_q$ . Once a word $w$ from a path $e$ in $W_q$ is sampled, all other parallel or conflicting paths to $e$ are removed from $W_q$ . For example, generating for the word lattice in Figure 1 , when we sample the word citizens, we drop out the paths \u201chuman beings\u201d, \u201cpeople's\u201d, \u201cthe population\u201d, \u201cpeople\u201d and \u201cmembers of the public\u201d from $W_q$ and accordingly update the grammar. The controlled sampling ensures that each sampled question uses words from a single start-to-end path in $W_q$ . For example, we could sample a question what is Czech Republic 's language? by sampling words from the path (what, language, do, people 's, in, Czech, Republic, is speaking, ?) in Figure 1 . We repeat this sampling process to generate multiple potential paraphrases.",
+      "The resulting generation algorithm has multiple advantages over existing grammar generation methods. First, the sampling from an L-PCFG grammar lessens the lexical ambiguity problem evident in lexicalized grammars such as tree adjoining grammars BIBREF27 and combinatory categorial grammars BIBREF28 . Our grammar is not lexicalized, only unary context-free rules are lexicalized. Second, the top-down sampling restricts the combinatorics inherent to bottom-up search BIBREF29 . Third, we do not restrict the generation by the order information in the input. The lack of order information in the input often raises the high combinatorics in lexicalist approaches BIBREF30 . In our case, however, we use sampling to reduce this problem, and it allows us to produce syntactically diverse questions. And fourth, we impose no constraints on the grammar thereby making it easier to maintain bi-directional (recursive) grammars that can be used both for parsing and for generation BIBREF31 ."
+    ],
+    [
+      "As mentioned earlier, one of our lattice types is based on bi-layered PCFGs introduced here.",
+      "In their traditional use, the latent states in L-PCFGs aim to capture syntactic information. We introduce here the use of an L-PCFG with two layers of latent states: one layer is intended to capture the usual syntactic information, and the other aims to capture semantic and topical information by using a large set of states with specific feature functions.",
+      "To create the bi-layered L-PCFG, we again use the spectral algorithm of narayan-15 to estimate a grammar $G_{\\mathrm {par}}$ from the Paralex corpus. We use the word alignment of paraphrase question pairs in Paralex to map inside and outside trees of each nonterminals in the treebank to bag of word features. The number of latent states we use is 1,000.",
+      "Once the two feature functions (syntactic in $G_{\\mathrm {syn}}$ and semantic in $G_{\\mathrm {par}}$ ) are created, each nonterminal in the training treebank is assigned two latent states (cluster identifiers). Figure 2 shows an example annotation of trees for three paraphrase questions from the Paralex corpus. We compute the parameters of the bi-layered L-PCFG $G_{\\mathrm {layered}}$ with a simple frequency count maximum likelihood estimate over this annotated treebank. As such, $G_{\\mathrm {layered}}$ is a combination of $G_{\\mathrm {syn}}$ and $G_{\\mathrm {par}}$ , resulting in 24,000 latent states (24 syntactic x 1000 semantic).",
+      "Consider an example where we want to generate paraphrases for the question what day is nochebuena. Parsing it with $G_{\\mathrm {layered}}$ will lead to the leftmost hybrid structure as shown in Figure 2 . The assignment of the first latent states for each nonterminals ensures that we retrieve the correct syntactic representation of the sentence. Here, however, we are more interested in the second latent states assigned to each nonterminals which capture the paraphrase information of the sentence at various levels. For example, we have a unary lexical rule (NN-*-142 day) indicating that we observe day with NN of the paraphrase type 142. We could use this information to extract unary rules of the form (NN-*-142 $w$ ) in the treebank that will generate words $w$ which are paraphrases to day. Similarly, any node WHNP-*-291 in the treebank will generate paraphrases for what day, SBARQ-*-403, for what day is nochebuena. This way we will be able to generate paraphrases when is nochebuena and when is nochebuena celebrated as they both have SBARQ-*-403 as their roots.",
+      "To generate a word lattice $W_q$ for a given question $q$ , we parse $q$ with the bi-layered grammar $G_{\\mathrm {layered}}$ . For each rule of the form $X$ - $m_1$ - $m_2 \\rightarrow w$ in the bi-layered tree with $X \\in {\\cal P}$ , $m_1 \\in \\lbrace  1, \\ldots , 24 \\rbrace $ , $m_2 \\in \\lbrace  1, \\ldots , 1000 \\rbrace $ and $q$0 a word in $q$1 , we extract rules of the form $q$2 - $q$3 - $q$4 from $q$5 such that $q$6 . For each such $q$7 , we add a path $q$8 parallel to $q$9 in the word lattice."
+    ],
+    [
+      "Our sampling algorithm overgenerates paraphrases which are incorrect. To improve its precision, we build a binary classifier to filter the generated paraphrases. We randomly select 100 distinct questions from the Paralex corpus and generate paraphrases using our generation algorithm with various lattice settings. We randomly select 1,000 pairs of input-sampled sentences and manually annotate them as \u201ccorrect\u201d or \u201cincorrect\u201d paraphrases. We train our classifier on this manually created training data. We follow madnani2012, who used MT metrics for paraphrase identification, and experiment with 8 MT metrics as features for our binary classifier. In addition, we experiment with a binary feature which checks if the sampled paraphrase preserves named entities from the input sentence. We use WEKA BIBREF32 to replicate the classifier of madnani2012 with our new feature. We tune the feature set for our classifier on the development data."
+    ],
+    [
+      "In this section we describe how the paraphrase algorithm is used for converting natural language to Freebase queries. Following reddylargescale2014, we formalize the semantic parsing problem as a graph matching problem, i.e., finding the Freebase subgraph (grounded graph) that is isomorphic to the input question semantic structure (ungrounded graph).",
+      "This formulation has a major limitation that can be alleviated by using our paraphrase generation algorithm. Consider the question What language do people in Czech Republic speak?. The ungrounded graph corresponding to this question is shown in Figure 3 . The Freebase grounded graph which results in correct answer is shown in Figure 3 . Note that these two graphs are non-isomorphic making it impossible to derive the correct grounding from the ungrounded graph. In fact, at least 15% of the examples in our development set fail to satisfy isomorphic assumption. In order to address this problem, we use paraphrases of the input question to generate additional ungrounded graphs, with the aim that one of those paraphrases will have a structure isomorphic to the correct grounding. Figure 3 and Figure 3 are two such paraphrases which can be converted to Figure 3 as described in sec:groundedGraphs.",
+      "For a given input question, first we build ungrounded graphs from its paraphrases. We convert these graphs to Freebase graphs. To learn this mapping, we rely on manually assembled question-answer pairs. For each training question, we first find the set of oracle grounded graphs\u2014Freebase subgraphs which when executed yield the correct answer\u2014derivable from the question's ungrounded graphs. These oracle graphs are then used to train a structured perceptron model. These steps are discussed in detail below."
+    ],
+    [
+      "We use GraphParser BIBREF7 to convert paraphrases to ungrounded graphs. This conversion involves three steps: 1) parsing the paraphrase using a CCG parser to extract syntactic derivations BIBREF33 , 2) extracting logical forms from the CCG derivations BIBREF34 , and 3) converting the logical forms to an ungrounded graph. The ungrounded graph for the example question and its paraphrases are shown in Figure 3 , Figure 3 and Figure 3 , respectively."
+    ],
+    [
+      "The ungrounded graphs are grounded to Freebase subgraphs by mapping entity nodes, entity-entity edges and entity type nodes in the ungrounded graph to Freebase entities, relations and types, respectively. For example, the graph in Figure 3 can be converted to a Freebase graph in Figure 3 by replacing the entity node Czech Republic with the Freebase entity CzechRepublic, the edge (speak.arg $_2$ , speak.in) between $x$ and Czech Republic with the Freebase relation (location.country.official_language.2, location.country.official_language.1), the type node language with the Freebase type language.human_language, and the target node remains intact. The rest of the nodes, edges and types are grounded to null. In a similar fashion, Figure 3 can be grounded to Figure 3 , but not Figure 3 to Figure 3 . If no paraphrase is isomorphic to the target grounded grounded graph, our grounding fails."
+    ],
+    [
+      "We use a linear model to map ungrounded graphs to grounded ones. The parameters of the model are learned from question-answer pairs. For example, the question What language do people in Czech Republic speak? paired with its answer $\\lbrace \\textsc {CzechLanguage}\\rbrace $ . In line with most work on question answering against Freebase, we do not rely on annotated logical forms associated with the question for training and treat the mapping of a question to its grounded graph as latent.",
+      "Let $q$ be a question, let $p$ be a paraphrase, let $u$ be an ungrounded graph for $p$ , and let $g$ be a grounded graph formed by grounding the nodes and edges of $u$ to the knowledge base $\\mathcal {K}$ (throughout we use Freebase as the knowledge base). Following reddylargescale2014, we use beam search to find the highest scoring tuple of paraphrase, ungrounded and grounded graphs $(\\hat{p}, \\hat{u}, \\hat{g})$ under the model $\\theta \\in \\mathbb {R}^n$ : $\n({\\hat{p},\\hat{u},\\hat{g}}) = \\operatornamewithlimits{arg\\,max}_{(p,u,g)} \\theta \\cdot \\Phi (p,u,g,q,\\mathcal {K})\\,,\n$ ",
+      "where $\\Phi (p, u, g, q, \\mathcal {K}) \\in \\mathbb {R}^n$ denotes the features for the tuple of paraphrase, ungrounded and grounded graphs. The feature function has access to the paraphrase, ungrounded and grounded graphs, the original question, as well as to the content of the knowledge base and the denotation $|g|_\\mathcal {K}$ (the denotation of a grounded graph is defined as the set of entities or attributes reachable at its target node). See sec:details for the features employed. The model parameters are estimated with the averaged structured perceptron BIBREF35 . Given a training question-answer pair $(q,\\mathcal {A})$ , the update is: $\n\\theta ^{t+1} \\leftarrow \\theta ^{t} + \\Phi (p^+, u^+, g^+, q,\n\\mathcal {K}) - \\Phi (\\hat{p}, \\hat{u}, \\hat{g}, q, \\mathcal {K})\\,,\n$ ",
+      "where $({p^+,u^+,g^+})$ denotes the tuple of gold paraphrase, gold ungrounded and grounded graphs for $q$ . Since we do not have direct access to the gold paraphrase and graphs, we instead rely on the set of oracle tuples, $\\mathcal {O}_{\\mathcal {K}, \\mathcal {A}}(q)$ , as a proxy: $\n(p^{+},u^{+},{g^{+}}) = \\operatornamewithlimits{arg\\,max}_{(p,u,g) \\in \\mathcal {O}_{\\mathcal {K},\\mathcal {A}}(q)} \\theta \\cdot \\Phi ({p,u,g,q,\\mathcal {K}})\\,,\n$ ",
+      "where $\\mathcal {O}_{\\mathcal {K}, \\mathcal {A}}(q)$ is defined as the set of tuples ( $p$ , $u$ , $g$ ) derivable from the question $q$ , whose denotation $|g|_\\mathcal {K}$ has minimal $F_1$ -loss against the gold answer $\\mathcal {A}$ . We find the oracle graphs for each question a priori by performing beam-search with a very large beam."
+    ],
+    [
+      "Below, we give details on the evaluation dataset and baselines used for comparison. We also describe the model features and provide implementation details."
+    ],
+    [
+      "We evaluate our approach on the WebQuestions dataset BIBREF5 . WebQuestions consists of 5,810 question-answer pairs where questions represents real Google search queries. We use the standard train/test splits, with 3,778 train and 2,032 test questions. For our development experiments we tune the models on held-out data consisting of 30% training questions, while for final testing we use the complete training data. We use average precision (avg P.), average recall (avg R.) and average F $_1$ (avg F $_1$ ) proposed by berantsemantic2013 as evaluation metrics."
+    ],
+    [
+      "We use GraphParser without paraphrases as our baseline. This gives an idea about the impact of using paraphrases.",
+      "We compare our paraphrasing models with monolingual machine translation based model for paraphrase generation BIBREF24 , BIBREF36 . In particular, we use Moses BIBREF37 to train a monolingual phrase-based MT system on the Paralex corpus. Finally, we use Moses decoder to generate 10-best distinct paraphrases for the test questions."
+    ],
+    [
+      "For WebQuestions, we use 8 handcrafted part-of-speech patterns (e.g., the pattern (DT)?(JJ.? $\\mid $ NN.?){0,2}NN.? matches the noun phrase the big lebowski) to identify candidate named entity mention spans. We use the Stanford CoreNLP caseless tagger for part-of-speech tagging BIBREF38 . For each candidate mention span, we retrieve the top 10 entities according to the Freebase API. We then create a lattice in which the nodes correspond to mention-entity pairs, scored by their Freebase API scores, and the edges encode the fact that no joint assignment of entities to mentions can contain overlapping spans. We take the top 10 paths through the lattice as possible entity disambiguations. For each possibility, we generate $n$ -best paraphrases that contains the entity mention spans. In the end, this process creates a total of $10n$ paraphrases. We generate ungrounded graphs for these paraphrases and treat the final entity disambiguation and paraphrase selection as part of the semantic parsing problem.",
+      "We use the features from reddylargescale2014. These include edge alignments and stem overlaps between ungrounded and grounded graphs, and contextual features such as word and grounded relation pairs. In addition to these features, we add two new real-valued features \u2013 the paraphrase classifier's score and the entity disambiguation lattice score.",
+      "We use beam search to infer the highest scoring graph pair for a question. The search operates over entity-entity edges and entity type nodes of each ungrounded graph. For an entity-entity edge, there are two operations: ground the edge to a Freebase relation, or skip the edge. Similarly, for an entity type node, there are two operations: ground the node to a Freebase type, or skip the node. We use a beam size of 100 in all our experiments."
+    ],
+    [
+      "In this section, we present results from five different systems for our question-answering experiments: original, mt, naive, ppdb and bilayered. First two are baseline systems. Other three systems use paraphrases generated from an L-PCFG grammar. naive uses a word lattice with a single start-to-end path representing the input question itself, ppdb uses a word lattice constructed using the PPDB rules, and bilayered uses bi-layered L-PCFG to build word lattices. Note that naive does not require any parallel resource to train, ppdb requires an external paraphrase database, and bilayered, like mt, needs a parallel corpus with paraphrase pairs. We tune our classifier features and GraphParser features on the development data. We use the best setting from tuning for evaluation on the test data."
+    ],
+    [
+      "We described a grammar method to generate paraphrases for questions, and applied it to a question answering system based on semantic parsing. We showed that using paraphrases for a question answering system is a useful way to improve its performance. Our method is rather generic and can be applied to any question answering system."
+    ],
+    [
+      "The authors would like to thank Nitin Madnani for his help with the implementation of the paraphrase classifier. We would like to thank our anonymous reviewers for their insightful comments. This research was supported by an EPSRC grant (EP/L02411X/1), the H2020 project SUMMA (under grant agreement 688139), and a Google PhD Fellowship for the second author."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0926/instruction.md b/qasper-0926/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..9660d2df5d02b2ec711d0ff9556c08e2957c38bb
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@@ -0,0 +1,75 @@
+Name of Paper: Characterizing Diabetes, Diet, Exercise, and Obesity Comments on Twitter
+
+Question: How were topics of interest about DDEO identified?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Methods",
+    "Data Collection",
+    "Topic Discovery",
+    "Topic Content Analysis",
+    "Results",
+    "Discussion",
+    "Conclusion",
+    "Conflict of interest",
+    "Acknowledgement"
+  ],
+  "paragraphs": [
+    [
+      "The global prevalence of obesity has doubled between 1980 and 2014, with more than 1.9 billion adults considered as overweight and over 600 million adults considered as obese in 2014 BIBREF0 . Since the 1970s, obesity has risen 37 percent affecting 25 percent of the U.S. adults BIBREF1 . Similar upward trends of obesity have been found in youth populations, with a 60% increase in preschool aged children between 1990 and 2010 BIBREF2 . Overweight and obesity are the fifth leading risk for global deaths according to the European Association for the Study of Obesity BIBREF0 . Excess energy intake and inadequate energy expenditure both contribute to weight gain and diabetes BIBREF3 , BIBREF4 .",
+      "Obesity can be reduced through modifiable lifestyle behaviors such as diet and exercise BIBREF4 . There are several comorbidities associated with being overweight or obese, such as diabetes BIBREF5 . The prevalence of diabetes in adults has risen globally from 4.7% in 1980 to 8.5% in 2014. Current projections estimate that by 2050, 29 million Americans will be diagnosed with type 2 diabetes, which is a 165% increase from the 11 million diagnosed in 2002 BIBREF6 . Studies show that there are strong relations among diabetes, diet, exercise, and obesity (DDEO) BIBREF7 , BIBREF4 , BIBREF8 , BIBREF9 ; however, the general public's perception of DDEO remains limited to survey-based studies BIBREF10 .",
+      "The growth of social media has provided a research opportunity to track public behaviors, information, and opinions about common health issues. It is estimated that the number of social media users will increase from 2.34 billion in 2016 to 2.95 billion in 2020 BIBREF11 . Twitter has 316 million users worldwide BIBREF12 providing a unique opportunity to understand users' opinions with respect to the most common health issues BIBREF13 . Publicly available Twitter posts have facilitated data collection and leveraged the research at the intersection of public health and data science; thus, informing the research community of major opinions and topics of interest among the general population BIBREF14 , BIBREF15 , BIBREF16 that cannot otherwise be collected through traditional means of research (e.g., surveys, interviews, focus groups) BIBREF17 , BIBREF18 . Furthermore, analyzing Twitter data can help health organizations such as state health departments and large healthcare systems to provide health advice and track health opinions of their populations and provide effective health advice when needed BIBREF13 .",
+      "Among computational methods to analyze tweets, computational linguistics is a well-known developed approach to gain insight into a population, track health issues, and discover new knowledge BIBREF19 , BIBREF20 , BIBREF21 , BIBREF22 . Twitter data has been used for a wide range of health and non-health related applications, such as stock market BIBREF23 and election analysis BIBREF24 . Some examples of Twitter data analysis for health-related topics include: flu BIBREF25 , BIBREF26 , BIBREF27 , BIBREF28 , BIBREF29 , BIBREF30 , mental health BIBREF31 , Ebola BIBREF32 , BIBREF33 , Zika BIBREF34 , medication use BIBREF35 , BIBREF36 , BIBREF37 , diabetes BIBREF38 , and weight loss and obesity BIBREF39 , BIBREF40 , BIBREF41 , BIBREF42 , BIBREF21 .",
+      "The previous Twitter studies have dealt with extracting common topics of one health issue discussed by the users to better understand common themes; however, this study utilizes an innovative approach to computationally analyze unstructured health related text data exchanged via Twitter to characterize health opinions regarding four common health issues, including diabetes, diet, exercise and obesity (DDEO) on a population level. This study identifies the characteristics of the most common health opinions with respect to DDEO and discloses public perception of the relationship among diabetes, diet, exercise and obesity. These common public opinions/topics and perceptions can be used by providers and public health agencies to better understand the common opinions of their population denominators in regard to DDEO, and reflect upon those opinions accordingly."
+    ],
+    [
+      "Our approach uses semantic and linguistics analyses for disclosing health characteristics of opinions in tweets containing DDEO words. The present study included three phases: data collection, topic discovery, and topic-content analysis."
+    ],
+    [
+      "This phase collected tweets using Twitter's Application Programming Interfaces (API) BIBREF43 . Within the Twitter API, diabetes, diet, exercise, and obesity were selected as the related words BIBREF4 and the related health areas BIBREF19 . Twitter's APIs provides both historic and real-time data collections. The latter method randomly collects 1% of publicly available tweets. This paper used the real-time method to randomly collect 10% of publicly available English tweets using several pre-defined DDEO-related queries (Table TABREF6 ) within a specific time frame. We used the queries to collect approximately 4.5 million related tweets between 06/01/2016 and 06/30/2016. The data will be available in the first author's website. Figure FIGREF3 shows a sample of collected tweets in this research."
+    ],
+    [
+      "To discover topics from the collected tweets, we used a topic modeling approach that fuzzy clusters the semantically related words such as assigning \u201cdiabetes\", \u201ccancer\", and \u201cinfluenza\" into a topic that has an overall \u201cdisease\" theme BIBREF44 , BIBREF45 . Topic modeling has a wide range of applications in health and medical domains such as predicting protein-protein relationships based on the literature knowledge BIBREF46 , discovering relevant clinical concepts and structures in patients' health records BIBREF47 , and identifying patterns of clinical events in a cohort of brain cancer patients BIBREF48 .",
+      "Among topic models, Latent Dirichlet Allocation (LDA) BIBREF49 is the most popular effective model BIBREF50 , BIBREF19 as studies have shown that LDA is an effective computational linguistics model for discovering topics in a corpus BIBREF51 , BIBREF52 . LDA assumes that a corpus contains topics such that each word in each document can be assigned to the topics with different degrees of membership BIBREF53 , BIBREF54 , BIBREF55 .",
+      "Twitter users can post their opinions or share information about a subject to the public. Identifying the main topics of users' tweets provides an interesting point of reference, but conceptualizing larger subtopics of millions of tweets can reveal valuable insight to users' opinions. The topic discovery component of the study approach uses LDA to find main topics, themes, and opinions in the collected tweets.",
+      "We used the Mallet implementation of LDA BIBREF49 , BIBREF56 with its default settings to explore opinions in the tweets. Before identifying the opinions, two pre-processing steps were implemented: (1) using a standard list for removing stop words, that do not have semantic value for analysis (such as \u201cthe\"); and, (2) finding the optimum number of topics. To determine a proper number of topics, log-likelihood estimation with 80% of tweets for training and 20% of tweets for testing was used to find the highest log-likelihood, as it is the optimum number of topics BIBREF57 . The highest log-likelihood was determined 425 topics."
+    ],
+    [
+      "The topic content analysis component used an objective interpretation approach with a lexicon-based approach to analyze the content of topics. The lexicon-based approach uses dictionaries to disclose the semantic orientation of words in a topic. Linguistic Inquiry and Word Count (LIWC) is a linguistics analysis tool that reveals thoughts, feelings, personality, and motivations in a corpus BIBREF58 , BIBREF59 , BIBREF60 . LIWC has accepted rate of sensitivity, specificity, and English proficiency measures BIBREF61 . LIWC has a health related dictionary that can help to find whether a topic contains words associated with health. In this analysis, we used LIWC to find health related topics."
+    ],
+    [
+      "Obesity and Diabetes showed the highest and the lowest number of tweets (51.7% and 8.0%). Diet and Exercise formed 23.7% and 16.6% of the tweets (Table TABREF6 ).",
+      "Out of all 4.5 million DDEO-related tweets returned by Tweeter's API, the LDA found 425 topics. We used LIWC to filter the detected 425 topics and found 222 health-related topics. Additionally, we labeled topics based on the availability of DDEO words. For example, if a topic had \u201cdiet\", we labeled it as a diet-related topic. As expected and driven by the initial Tweeter API's query, common topics were Diabetes, Diet, Exercise, and Obesity (DDEO). (Table TABREF7 ) shows that the highest and the lowest number of topics were related to exercise and diabetes (80 and 21 out of 222). Diet and Obesity had almost similar rates (58 and 63 out of 222).",
+      "Each of the DDEO topics included several common subtopics including both DDEO and non-DDEO terms discovered by the LDA algorithm (Table TABREF7 ). Common subtopics for \u201cDiabetes\", in order of frequency, included type 2 diabetes, obesity, diet, exercise, blood pressure, heart attack, yoga, and Alzheimer. Common subtopics for \u201cDiet\" included obesity, exercise, weight loss [medicine], celebrities, vegetarian, diabetes, religious diet, pregnancy, and mental health. Frequent subtopics for \u201cExercise\" included fitness, obesity, daily plan, diet, brain, diabetes, and computer games. And finally, the most common subtopics for \u201cObesity\" included diet, exercise, children, diabetes, Alzheimer, and cancer (Table TABREF7 ). Table TABREF8 provides illustrative examples for each of the topics and subtopics.",
+      "Further exploration of the subtopics revealed additional patterns of interest (Tables TABREF7 and TABREF8 ). We found 21 diabetes-related topics with 8 subtopics. While type 2 diabetes was the most frequent of the sub-topics, heart attack, Yoga, and Alzheimer are the least frequent subtopics for diabetes. Diet had a wide variety of emerging themes ranging from celebrity diet (e.g., Beyonce) to religious diet (e.g., Ramadan). Diet was detected in 63 topics with 10 subtopics; obesity, and pregnancy and mental health were the most and the least discussed obesity-related topics, respectively. Exploring the themes for Exercise subtopics revealed subjects such as computer games (e.g., Pokemon-Go) and brain exercises (e.g., memory improvement). Exercise had 7 subtopics with fitness as the most discussed subtopic and computer games as the least discussed subtopic. Finally, Obesity themes showed topics such as Alzheimer (e.g., research studies) and cancer (e.g., breast cancer). Obesity had the lowest diversity of subtopics: six with diet as the most discussed subtopic, and Alzheimer and cancer as the least discussed subtopics.",
+      "Diabetes subtopics show the relation between diabetes and exercise, diet, and obesity. Subtopics of diabetes revealed that users post about the relationship between diabetes and other diseases such as heart attack (Tables TABREF7 and TABREF8 ). The subtopic Alzheimer is also shown in the obesity subtopics. This overlap between categories prompts the discussion of research and linkages among obesity, diabetes, and Alzheimer's disease. Type 2 diabetes was another subtopic expressed by users and scientifically documented in the literature.",
+      "The main DDEO topics showed some level of interrelationship by appearing as subtopics of other DDEO topics. The words with italic and underline styles in Table 2 demonstrate the relation among the four DDEO areas. Our results show users' interest about posting their opinions, sharing information, and conversing about exercise & diabetes, exercise & diet, diabetes & diet, diabetes & obesity, and diet & obesity (Figure FIGREF9 ). The strongest correlation among the topics was determined to be between exercise and obesity ( INLINEFORM0 ). Other notable correlations were: diabetes and obesity ( INLINEFORM1 ), and diet and obesity ( INLINEFORM2 )."
+    ],
+    [
+      "Diabetes, diet, exercise, and obesity are common public health related opinions. Analyzing individual- level opinions by automated algorithmic techniques can be a useful approach to better characterize health opinions of a population. Traditional public health polls and surveys are limited by a small sample size; however, Twitter provides a platform to capture an array of opinions and shared information a expressed in the words of the tweeter. Studies show that Twitter data can be used to discover trending topics, and that there is a strong correlation between Twitter health conversations and Centers for Disease Control and Prevention (CDC) statistics BIBREF62 .",
+      "This research provides a computational content analysis approach to conduct a deep analysis using a large data set of tweets. Our framework decodes public health opinions in DDEO related tweets, which can be applied to other public health issues. Among health-related subtopics, there are a wide range of topics from diseases to personal experiences such as participating in religious activities or vegetarian diets.",
+      "Diabetes subtopics showed the relationship between diabetes and exercise, diet, and obesity (Tables TABREF7 and TABREF8 ). Subtopics of diabetes revealed that users posted about the relation between diabetes and other diseases such as heart attack. The subtopic Alzheimer is also shown in the obesity subtopics. This overlap between categories prompts the discussion of research and linkages among obesity, diabetes, and Alzheimer's disease. Type 2 diabetes was another subtopic that was also expressed by users and scientifically documented in the literature. The inclusion of Yoga in posts about diabetes is interesting. While yoga would certainly be labeled as a form of fitness, when considering the post, it was insightful to see discussion on the mental health benefits that yoga offers to those living with diabetes BIBREF63 .",
+      "Diet had the highest number of subtopics. For example, religious diet activities such as fasting during the month of Ramadan for Muslims incorporated two subtopics categorized under the diet topic (Tables TABREF7 and TABREF8 ). This information has implications for the type of diets that are being practiced in the religious community, but may help inform religious scholars who focus on health and psychological conditions during fasting. Other religions such as Judaism, Christianity, and Taoism have periods of fasting that were not captured in our data collection, which may have been due to lack of posts or the timeframe in which we collected data. The diet plans of celebrities were also considered influential to explaining and informing diet opinions of Twitter users BIBREF64 .",
+      "Exercise themes show the Twitter users' association of exercise with \u201cbrain\" benefits such as increased memory and cognitive performance (Tables TABREF7 and TABREF8 ) BIBREF65 . The topics also confirm that exercising is associated with controlling diabetes and assisting with meal planning BIBREF66 , BIBREF9 , and obesity BIBREF67 . Additionally, we found the Twitter users mentioned exercise topics about the use of computer games that assist with exercising. The recent mobile gaming phenomenon Pokeman-Go game BIBREF68 was highly associated with the exercise topic. Pokemon-Go allows users to operate in a virtual environment while simultaneously functioning in the real word. Capturing Pokemons, battling characters, and finding physical locations for meeting other users required physically activity to reach predefined locations. These themes reflect on the potential of augmented reality in increasing patients' physical activity levels BIBREF69 .",
+      "Obesity had the lowest number of subtopics in our study. Three of the subtopics were related to other diseases such as diabetes (Tables TABREF7 and TABREF8 ). The scholarly literature has well documented the possible linkages between obesity and chronic diseases such as diabetes BIBREF1 as supported by the study results. The topic of children is another prominent subtopic associated with obesity. There has been an increasing number of opinions in regard to child obesity and national health campaigns that have been developed to encourage physical activity among children BIBREF70 . Alzheimer was also identified as a topic under obesity. Although considered a perplexing finding, recent studies have been conducted to identify possible correlation between obesity and Alzheimer's disease BIBREF71 , BIBREF72 , BIBREF73 . Indeed, Twitter users have expressed opinions about the study of Alzheimer's disease and the linkage between these two topics.",
+      "This paper addresses a need for clinical providers, public health experts, and social scientists to utilize a large conversational dataset to collect and utilize population level opinions and information needs. Although our framework is applied to Twitter, the applications from this study can be used in patient communication devices monitored by physicians or weight management interventions with social media accounts, and support large scale population-wide initiatives to promote healthy behaviors and preventative measures for diabetes, diet, exercise, and obesity.",
+      "This research has some limitations. First, our DDEO analysis does not take geographical location of the Twitter users into consideration and thus does not reveal if certain geographical differences exists. Second, we used a limited number of queries to select the initial pool of tweets, thus perhaps missing tweets that may have been relevant to DDEO but have used unusual terms referenced. Third, our analysis only included tweets generated in one month; however, as our previous work has demonstrated BIBREF42 , public opinion can change during a year. Additionally, we did not track individuals across time to detect changes in common themes discussed. Our future research plans includes introducing a dynamic framework to collect and analyze DDEO related tweets during extended time periods (multiple months) and incorporating spatial analysis of DDEO-related tweets."
+    ],
+    [
+      "This study represents the first step in developing routine processes to collect, analyze, and interpret DDEO-related posts to social media around health-related topics and presents a transdisciplinary approach to analyzing public discussions around health topics. With 2.34 billion social media users in 2016, the ability to collect and synthesize social media data will continue to grow. Developing methods to make this process more streamlined and robust will allow for more rapid identification of public health trends in real time.",
+      "Note: Amir Karami will handle correspondence at all stages of refereeing and publication."
+    ],
+    [
+      "The authors state that they have no conflict of interest."
+    ],
+    [
+      "This research was partially supported by the first author's startup research funding provided by the University of South Carolina, School of Library and Information Science. We thank Jill Chappell-Fail and Jeff Salter at the University of South Carolina College of Information and Communications for assistance with technical support.",
+      "References"
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: Rethinking travel behavior modeling representations through embeddings
+
+Question: What datasets are used for evaluation?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Representing categorical variables",
+    "The concept of text embeddings",
+    "Travel behaviour embeddings",
+    "Travel behaviour embeddings ::: The general idea",
+    "Travel behaviour embeddings ::: Methodology",
+    "An experiment with mode choice",
+    "An experiment with mode choice ::: The Swissmetro dataset",
+    "An experiment with mode choice ::: Principles for the model specification"
+  ],
+  "paragraphs": [
+    [
+      "Since their early days, representation in random utility behavior models has followed generally quite clear principles. For example, numeric quantities like travel time and cost may be directly used or transformed depending on observed non-linear efects (e.g. using log). Numeric variables that are not \u201cquantities\" per se, such as age or even geographic coordinates tend to be discretized and then transformed into vectors of dummy variables. Similarly, categorical variables such as education level or trip purpose are already discrete, and thus are also usually \u201cdummyfied\". Then, we may interact any subset of the above by combining (typically, multiplying) them, as long as we get in the end a vector of numeric values that can be incorporated in a statistical model, a linear one in the case of the most common logit model.",
+      "There are however phenomena that are hard to represent, and modelers end up struggling to find the right representation. For example, influence of social interactions between different persons, hierarchical decision making, autocorrelated nature of time and space, or abstract concepts such as accessibility, attitudes, personality traits and so on. The point here, is that the nature of our models seems to enforce a compromise between the true semantics of a variable (i.e. the \u201cmeaning\" of a certain information for the decision making process) and its realisation in practice. And that further research should be done to find new representation paradigms.",
+      "Historically speaking, the natural language processing (NLP) field has had similar dilemmas for decades, and for a while two general trends were competing: the statistical modeling approaches, and the linguistic theory based approaches. The former relied on simple representations, such as vector frequencies, or dummy variables, to become practical, while the latter used domain knowledge such as grammars or logic. Until recently, neither had considerable success in making machines able to understand or generate human language, but developments in deep neural networks together with overwhelmingly massive amounts of data (i.e. the World Wide Web) brought them to a new area, where the two are approaching each other and achieving hitherto results considered extremely hard, such as question answering, translation, next word prediction. One of the key concepts in this revolution is that of embeddings, which will be further explained in this paper.",
+      "Our focus here is on the representation of categorical variables. The default paradigm is dummy variables (also known as \u201cone-hot-encoding\" in machine learning literature), which have well-known limitations, namely the explosion of dimensionality and enforced ortogonality. The former happens because we assign one new \u201cdummy\" variable to each of D-1 categories, and easily go from a small original variable specification to one with hundreds of variables, bringing problems in model estimation and analysis. This often affects the data collection process itself. Since one doesn't want to end up with too many categories, we might as well give less options in a survey, or decrease the resolution of a sensor. The problem of enforced ortogonality relates to the fact that, in a dummy encoding, all categories become equidistant. The similarity between \u201cstudent\" and \u201cemployed\" is the same as between \u201cstudent\" and \u201cretired\", which in many cases (e.g. mode choice, departure time choice) goes against intuition. Other encoding methods exist, such as contrasted encoding or principal components analysis (PCA). The former ends up being a subtle variation on the dummy approach, but the latter already provides an interesting answer to the problem: categories are no longer forcibly equidistant, and the number of variables can be much reduced. However, it is a non-supervised approach. The distance between \u201cstudent\" and \u201cemployed\" will always be the same, regardless of the problem we are solving, but this may be intuitively illogical if we consider car ownership versus departure time choice models for example.",
+      "The key idea in this paper is to introduce a method, called Travel Behavior embeddings, that borrows much from the NLP concept. This method serves to encode categorical variables, and is dependent on the problem at hand. We will focus on mode choice, and test on a well-known dataset, by comparing with both dummy and PCA encoding. All the dataset and code are made openly available, and the reader can follow and generate results him/herself using an iPython notebook included. Our ultimate goal is certainly that the reader reuses our PyTre package for own purposes.",
+      "This paper presents some results and conclusions, after a relatively long exploration and analysis process, including other datasets and code variations not mentioned here for interest of clarity and replicability. While we show these concepts to be promising and innovative in this paper, one should be wary of over-hyping yet another Machine Learning/Artificial Intelligence concept: after all, Machine Learning is still essentially based on statistics. In NLP, the number of different words in consideration at a given moment can be in order of tens of thousands, while our categorical variables rarely go beyond a few dozens. This means that, for example, it becomes clear later that the least number of original categories, the less the benefit of embeddings (in the limit, a binary variable like gender, is useless to do embeddings with), and also that if we do get a significantly large and statistically representative dataset, a dummy variables representation is sufficient. We will quickly see, however, that complexity can grow quick enough to justify an embeddings based method even if without the shockingly better performance observed in NLP applications."
+    ],
+    [
+      "We are generally concerned with random utility maximization (RUM) models, for they have a dominant role in travel behavior modeling. The nature of such models is predominantly numeric, linear, and quite often strictly flat (notwithstanding hierarchical variations, such as nested models BIBREF1, hierarchical Bayes BIBREF2, or non-linear transformations). As a consequence, while numerical variables (e.g. travel time, cost, or income) can be directly used as available, perhaps subject to transformations or segmentation, nominal ones bring about a greater challenge. We tend to enforce a limited set of treatments such as:",
+      "Dummy variables, or one-hot encoding - for each categorical variable $v$ with D categories, we get D-1 binary variables (the \u201cdummies\"). At each input vector $x_n$, with categorical value $v=d$, the value \u201c1\" will be assigned to the corresponding dummy, while \u201c0\" to all others. If $v$ corresponds to the \u201cdefault\" category, all dummies are \u201c0\".",
+      "Contrast encoding BIBREF3 - same as dummy encoding, but instead of \u201c1\" for each category, we have a value that results from a contrasting formula. There are many different formulas (e.g. Helmert, Sum, Backward Difference), but all consist of subtracting the mean of the target variable, for a given category, with a general stastic (e.g. the mean of the dependent variable for all categories; the mean of the dependent variable in the previous category in an ordered list).",
+      "Principal Components Analysis (PCA) - run the PCA algorithm on the data matrix obtained by dummy representation of the categorical variable, then re-represent it with the corresponding projected eigenvector coefficients. One selects K eigenvectors (e.g. according to a variance explained rule), and thus each category is mapped to a vector of K real values.",
+      "Segmenting models, mixture models - A general alternative to categorical data representation is in fact to avoid it in the first place. One obvious method would be through creating hierarchical disaggregate methods (e.g. one per category). This is not in itself a representation paradigm, but an alternative way to see this problem. It certainly raises scalability and inference concerns.",
+      "In datasets where behavior heterogeneity is high, and number of observations is significantly smaller than population size, increasing dimensionality by adding a variable per each category is very risky because the amount of data that is in practice usable to estimate each new coefficient becomes insufficient. A simple intuition here is by considering that, for a dummy variable that is only \u201c1\" for a few observations in the dataset, its coefficient will be \u201cactivated\" only that small number of times. If there is a lot of variance in the associated behavior, the variance of the coefficient will also be large, and the coefficient will be considered statistically insignificant.",
+      "The benefit of representations that map into a latent space, like embeddings and PCA, is that such a space is inevitably shared, and thus every observation contributes indirectly to all category variables. This comes with no interpretability cost, because one can always map to the \u201cdummy\" space and analyse the individual coefficients, as will be shown in our experiments."
+    ],
+    [
+      "The idea of text embeddings comes from a simple re-representation necessity. A natural-language processing system is itself also a numeric machine, therefore it requires each individual word in a dictionary to match its own numeric representation. Just as in our travel models, a possible solution has been to use dummy variables, and it is quite obvious that the dimensionality of such a one-hot encoding vector, quickly becomes overwhelming. Think for example next word prediction algorithm, like the one we have in our smartphones. It is essentially a skip-gram BIBREF4 model that predicts the next word, given the n words before. The English dictionary has about 300000 words, and if we have about 5 words before for context, the number of independent variables of the model would become 1.5 million!",
+      "The goal of text embeddings algorithms (e.g. Word2Vec BIBREF5) is to a) reduce the representation of each word to a computationally acceptable dimension, while simultaneously b) learning the semantic distance between different words. In other words, the euclidean distance of semantically related words (e.g. \u201cdog\" and \u201ccat\") in this new space should be smaller than unrelated words (e.g. \u201cdog\" and \u201coptimize\"). As mentioned before, in a dummy (or one-hot) encoding, all distances between words are equal by definition.",
+      "The word embeddings methodology is very well explained in several webpages such as BIBREF6, so the reader is strongly encouraged to visit them first. However, for the sake of completeness, we summarize here the general idea.",
+      "Imagine the following task: given a word $w_i$ in a text, predict the next word $w_o$. If we solve it with a neural network model, we could have the architecture in Figure FIGREF8, where the input consists simply of the one-hot-encoding representation of the word (i.e. one dummy variable for each word in a dictionary of dimensionality $D$), and the output corresponds to the probability of each word in the dictionary being the next one (also a vector with dimensionality $D$).",
+      "The output layer thus consists simply of a softmax function. In other words, exactly the classical multinomial logit formulation that we would have in an RUM, in which each different word corresponds to an \u201calternative\".",
+      "The concept of embeddings is directly associated to the hidden layer, which is a set of linear activation neurons, typically with a dimensionality $K<<D$. Each such neuron is simply an identity function: it sums all inputs; then propagates this sum to the output layer. Since only one input neuron is activated at a time (remember that the input is a one-hot-encoding vector, with one \u201c1\" and the rest with \u201c0\"), each hidden layer neuron just propagates the (single) weight that links to that input neuron. If we have enough data for training this model, we will eventually land on a situation where, for each input word, there is a fixed vector of weights that are directly used in the output (softmax) function, to generate the prediction. With more data, this weight vector will not change (down to some small delta threshold). These stable vectors are what we call embeddings, and the dimensionality of these vectors is called embedding size.",
+      "Formally, we have a dataset $\\mathcal {D}=\\lbrace x_n, y_n\\rbrace , n=1\\ldots N$, where each $x_n$ and $y_n$ are one-hot (dummy) encodings of categorical variables. The dimensionality of $x_n$ is $D\\times 1$, with $D$ being the number of different categories in $x_n$, while the dimensionality of $y_n$ is $C\\times 1$, with $C$ being the number of categories (alternatives) in $y_n$. The full expression for the embeddings model as described is:",
+      "where $W$ is the embeddings matrix of size $K\\times D$, where $K$ is called the embeddings size. $B$ is a matrix of coefficients ($C\\times K$) for the softmax layer, so $B_c$ is simply the coefficients (row) vector for output class (alternative) $c$, and $\\alpha _c$ is the corresponding intercept. The typical loss function used in such models is called the categorical cross entropy:",
+      "Where $\\delta _{i}$ is the kronecker delta ($\\delta _{true}=1; \\delta _{false}=0$), and $\\mathcal {L}(n)$ is the cumulative loss for an individual data point. This formalization is the simplest version, without loss of generality. In practice, as seen below, we will model multiple embeddings matrices simultaneously, and will add regularization terms to the loss function, so the models tested in this paper consist of compositions of the above.",
+      "So these so called embeddings are in fact a relatively shallow data representation in a simple neural network. What is their added value? Obviously, the first practical benefit is dimensionality reduction, because now there is a mapping between each of the $C$ words to a unique vector of size $K$. The second aspect is that this new representation is the one that maximizes the performance towards a specific task (in our example, prediction of the next word), therefore it is a supervised process, as opposed for example to PCA. The third and more interesting aspect relates with semantic similarity. A natural consequence of the mentioned algorithm is that words that have similar output distributions (i.e. next words) will tend to be close to each other. Figure FIGREF10 shows a 2D visualization (t-SNE) with a subset of english words. In such a visualization, data is projected in 2D space by maintaining the same vector-to-vector distances as in the original ($K$ order space). Therefore the X and Y axes have no specific meaning, only distances between every pair of points are relevant.",
+      "We can see that semantically similar concepts, more specifically concepts that tend to have the same distribution of \u201cnext words\", are placed closer. Another intriguing consequence is that, since the words are now in the $K$ dimensional, embeddings space, we can also do some linear algebra on them. A well known formulation is $King-Man+Woman=Queen$. Essentially, the vector $King-Man$ corresponds to the concept of \u201ccrowning\" (therefore $Woman+crowning=Queen$). The same could be done with many other concept pairs. Figure FIGREF11 show also an alternative interpretation of \u201cman-female\", as well as examples with cities and verb tense.",
+      "Finally, another relevant note on the embeddings representation is that, just like the PCA encoding, one can always project back into the original space and use this for interpretability. In other words, since there is a 1-to-1 mapping from each category to its encoding, there is also a 1-to-1 mapping between a model that uses dummy variables and a model using such encodings. This may be useful for interpretability, since in the case of dummy variables we have a direct interpretation (e.g. a beta coefficient value in a logit model) for the effect of a given category, while the same doesn't happen for an encoded variable (i.e. there is no meaning for the value of a single beta coefficient in an embeddings encoding when K>1). In order to preserve statistical significance information (e.g. p-values) we only need to follow the well known rules of normal random variables.",
+      "There are open databases available (e.g. GLoVe BIBREF9, FastText BIBREF7) that provide word embedding tables for the entire English language (Glove provides several embedding tables, up to embedding size between 100 and 300). In our next word application example, we now talk about models with 500-1500 variables, which is very manageable for our machines today.",
+      "Summarizing, the general idea of word embeddings is to re-represent a categorical variable into a lower dimensional representation with continuous values . Whenever such a variable is to be used in a model, one can simply replace it with the corresponding embeddings vector. We have previously demonstrated the value of such word embeddings in demand prediction in special events BIBREF10, where we collected event textual descriptions, and used Glove embedding vectors to incorporate such information in a neural network model.",
+      "Finally, an interesting point to mention relates to the typical difference in dataset size between the original embeddings training model (Glove, approximately 6 billion input word vectors from 37 million texts) and the model one implements to solve a particular problem (in our special events case, less than 1000 short event descriptions, with at most few hundred words each). Instead of creating ourselves a new embeddings model using the events dataset, we reused the pre-trained GloVe dataset. The benefit is significant because in practice we trained our model to deal with all words in the dictionary, much beyond the limited vocabulary that we obtained in our 1000 short texts. In practice we have used a very small percentage of the english dictionary. When, in an out-of-sample test, our model finds words that were not in the training set, it still works perfectly well."
+    ],
+    [
+      "Differently to textual data, our goal in this paper is to explore the large amount of categorical data that is often collected in travel surveys. This includes trip purpose, education level, or family type. We also consider other variables that are not necessarily of categorical nature, but typically end up as dummy encoding, due to segmentation, such as age, income, or even origin/destination pair.",
+      "Our hypothesis is that, given the limitations of dummy variables that are commonly used and the unsupervised nature of PCA, using instead an embeddings mechanism should improve significantly the quality of our models, both in terms of loglikelihood but also in terms of allowing for lower complexity (i.e. less variables). Ultimately, one could think of a framework such as GLoVe, where embeddings for such variables could be trivially shared with the community. For example, we could have a \u201cTravel behavior embeddings\" database, incrementally built from travel surveys from around the world. Such database could have embeddings for mode choice target variables, but also for departure time, destination choice, car ownership, and so on. Whenever a modeler wanted to estimate a new model, she could just download the right encodings and use them directly. This is particularly relevant if one considers the complicated challenges for opening or sharing travel survey datasets in our field. Of course, a major question arises: are behaviors that consistent across the world? There are certainly nuances across the world, but we believe that general patterns would emerge (e.g. a \u201cbusiness\" trip purpose will be closer to \u201cwork\" than \u201cleisure\", in a departure time choice model; \u201cstudent\" will be closer to \u201cunemployed\" than to \u201cretired\" in a car ownership model)."
+    ],
+    [
+      "We believe that, as with word embeddings, a mapping that preserves semantic distance relative to a certain choice problem, should be useful for modeling. As with a PCA encoding, another benefit is that by sharing parameters in the learning process, the model can generalize better, as opposed to a dummy encoding, where each categorical value has its own parameter, that is only active when observed.",
+      "The general idea is thus to create a mapping between a variable for which we want to find an embeddings representation, and a target variable, as in Figure FIGREF15. We call the mapping function \u201cPyTre Embeddings\", because that is the name of the object in our proposed Python \u201cTravel Embeddings\" package.",
+      "From an experimental design and application perspective, the approach followed in this paper is the following:",
+      "Create list of categorical variables to encode (the encoding set)",
+      "Split dataset into train, development and test sets",
+      "For each variable in encoding set, learn the new embeddings using the embeddings train set . This should be done simultaneously (all variable embeddings estimated at once, as explained in the next section).",
+      "Encode choice models for train, development and test sets using the learned embeddings",
+      "Estimate choice model accordingly using its train set",
+      "Evaluate the new model using the test set",
+      "Since there is stochasticity in the embeddings training model, we will repeat the above multiple times, for the different experiments in the paper (and report the respective mean and standard deviation statistics). Whenever we want to analyse a particular model (e.g. to check the coefficients of a choice model), we select the one with the highest likelihood at the development set (i.e. in practice, its out-of-sample generalization performance), and report its performance on the test set."
+    ],
+    [
+      "Since a choice model will typically involve other variables than the categorical ones that we learn the embeddings for, it is important to take into account their effects. Figure FIGREF24 shows the simplest travel embeddings model. As an example, the categorical variable is trip purpose, and there are a few other variables such as gender, cost of the alternatives, distance, and so on. Notice that they are directly fed into the softmax output layer, together with the embeddings output.",
+      "The dataset sizes in transportation behavior modeling are substantially smaller than typical word embeddings ones, and the risk of overfitting is therefore higher. To mitigate this problem, besides adding regularization penalties in the objective function, we add what we call a regularizer layer for each embedding, which is no more than a softmax layer that penalizes whenever it cannot recover the original one-hot-encoding vectors (Figure FIGREF25, left). We call the combination of embeddings and its regularizer network, a Travel Embeddings layer. Finally, it is obviously better to train all embeddings simultaneously, so that they accommodate each other's effects (Figure FIGREF25, right)."
+    ],
+    [
+      "The goal of this paper is to test the potential of embeddings in a simple and well-known choice model context, comparing it to well-known baseline techniques. Therefore, the general model specification follows quite simple assumptions. We expect that in future work from us or others, more elaborate derivations can take advantage of embeddings such as nested, mixed logit or latent class choice models (LCCM), for example.",
+      "We will apply the methodology to the well-known \u201cSwissmetro\" dataset. We will compare it with a dummy variables and PCA baselines. We will follow the 3-way experimental design mentioned before: split the dataset into train, development and test sets, so that the embeddings, PCA eigenvectors and the choice model are estimated from the same train and development sets, and validate it out-of-sample. For the sake of interpretability, we will also project back coefficients from the embeddings as well as PCA models into the dummy variable space.",
+      "All experiment code is available as a jupyter notebook in a package we created for this work (to which we called PyTre). For estimating the multinomial logit model (MNL) we used the PyLogit BIBREF11 package."
+    ],
+    [
+      "The Swissmetro dataset consists of survey data collected on the trains between St. Gallen and Geneva, Switzerland, during March 1998. According to its description BIBREF0, the respondents provided information in order to analyze the impact of the modal innovation in transportation, represented by the Swissmetro, a revolutionary mag-lev underground system, against the usual transport modes represented by car and train. After discarding respondents for which some variables were not available (e.g. age, purpose), a total of 10469 responses from 1188 individuals were used for the experiments.",
+      "We split the dataset into 3 different parts:",
+      "Embeddings train set: 60% of the dataset (6373 vectors)",
+      "Development set: 20% of the dataset (2003 vectors)",
+      "Test set: 20% of the dataset (2003 vectors)"
+    ],
+    [
+      "The PyLogit package BIBREF11 also uses Swissmetro as an example. Therefore, our model specifications will extend the default one from this package. We re-estimated this model with the train set and validated with testset. The results are shown in tables TABREF31 and TABREF32. Since we are comparing the models at the test set, the key indicators should be pseudo R-square and log-likelihood. Indicators that consider model complexity (robust r-square and AIC) are less important on the test set in our view because the overfitting effect (i.e. improving fit just by adding more variables) will no longer be verifiable in this way. Instead, one sees overfitting if test set performance is considerably inferior to the training set."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0928/instruction.md b/qasper-0928/instruction.md
new file mode 100644
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+Name of Paper: Rethinking travel behavior modeling representations through embeddings
+
+Question: How do their train their embeddings?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Representing categorical variables",
+    "The concept of text embeddings",
+    "Travel behaviour embeddings",
+    "Travel behaviour embeddings ::: The general idea",
+    "Travel behaviour embeddings ::: Methodology",
+    "An experiment with mode choice",
+    "An experiment with mode choice ::: The Swissmetro dataset",
+    "An experiment with mode choice ::: Principles for the model specification"
+  ],
+  "paragraphs": [
+    [
+      "Since their early days, representation in random utility behavior models has followed generally quite clear principles. For example, numeric quantities like travel time and cost may be directly used or transformed depending on observed non-linear efects (e.g. using log). Numeric variables that are not \u201cquantities\" per se, such as age or even geographic coordinates tend to be discretized and then transformed into vectors of dummy variables. Similarly, categorical variables such as education level or trip purpose are already discrete, and thus are also usually \u201cdummyfied\". Then, we may interact any subset of the above by combining (typically, multiplying) them, as long as we get in the end a vector of numeric values that can be incorporated in a statistical model, a linear one in the case of the most common logit model.",
+      "There are however phenomena that are hard to represent, and modelers end up struggling to find the right representation. For example, influence of social interactions between different persons, hierarchical decision making, autocorrelated nature of time and space, or abstract concepts such as accessibility, attitudes, personality traits and so on. The point here, is that the nature of our models seems to enforce a compromise between the true semantics of a variable (i.e. the \u201cmeaning\" of a certain information for the decision making process) and its realisation in practice. And that further research should be done to find new representation paradigms.",
+      "Historically speaking, the natural language processing (NLP) field has had similar dilemmas for decades, and for a while two general trends were competing: the statistical modeling approaches, and the linguistic theory based approaches. The former relied on simple representations, such as vector frequencies, or dummy variables, to become practical, while the latter used domain knowledge such as grammars or logic. Until recently, neither had considerable success in making machines able to understand or generate human language, but developments in deep neural networks together with overwhelmingly massive amounts of data (i.e. the World Wide Web) brought them to a new area, where the two are approaching each other and achieving hitherto results considered extremely hard, such as question answering, translation, next word prediction. One of the key concepts in this revolution is that of embeddings, which will be further explained in this paper.",
+      "Our focus here is on the representation of categorical variables. The default paradigm is dummy variables (also known as \u201cone-hot-encoding\" in machine learning literature), which have well-known limitations, namely the explosion of dimensionality and enforced ortogonality. The former happens because we assign one new \u201cdummy\" variable to each of D-1 categories, and easily go from a small original variable specification to one with hundreds of variables, bringing problems in model estimation and analysis. This often affects the data collection process itself. Since one doesn't want to end up with too many categories, we might as well give less options in a survey, or decrease the resolution of a sensor. The problem of enforced ortogonality relates to the fact that, in a dummy encoding, all categories become equidistant. The similarity between \u201cstudent\" and \u201cemployed\" is the same as between \u201cstudent\" and \u201cretired\", which in many cases (e.g. mode choice, departure time choice) goes against intuition. Other encoding methods exist, such as contrasted encoding or principal components analysis (PCA). The former ends up being a subtle variation on the dummy approach, but the latter already provides an interesting answer to the problem: categories are no longer forcibly equidistant, and the number of variables can be much reduced. However, it is a non-supervised approach. The distance between \u201cstudent\" and \u201cemployed\" will always be the same, regardless of the problem we are solving, but this may be intuitively illogical if we consider car ownership versus departure time choice models for example.",
+      "The key idea in this paper is to introduce a method, called Travel Behavior embeddings, that borrows much from the NLP concept. This method serves to encode categorical variables, and is dependent on the problem at hand. We will focus on mode choice, and test on a well-known dataset, by comparing with both dummy and PCA encoding. All the dataset and code are made openly available, and the reader can follow and generate results him/herself using an iPython notebook included. Our ultimate goal is certainly that the reader reuses our PyTre package for own purposes.",
+      "This paper presents some results and conclusions, after a relatively long exploration and analysis process, including other datasets and code variations not mentioned here for interest of clarity and replicability. While we show these concepts to be promising and innovative in this paper, one should be wary of over-hyping yet another Machine Learning/Artificial Intelligence concept: after all, Machine Learning is still essentially based on statistics. In NLP, the number of different words in consideration at a given moment can be in order of tens of thousands, while our categorical variables rarely go beyond a few dozens. This means that, for example, it becomes clear later that the least number of original categories, the less the benefit of embeddings (in the limit, a binary variable like gender, is useless to do embeddings with), and also that if we do get a significantly large and statistically representative dataset, a dummy variables representation is sufficient. We will quickly see, however, that complexity can grow quick enough to justify an embeddings based method even if without the shockingly better performance observed in NLP applications."
+    ],
+    [
+      "We are generally concerned with random utility maximization (RUM) models, for they have a dominant role in travel behavior modeling. The nature of such models is predominantly numeric, linear, and quite often strictly flat (notwithstanding hierarchical variations, such as nested models BIBREF1, hierarchical Bayes BIBREF2, or non-linear transformations). As a consequence, while numerical variables (e.g. travel time, cost, or income) can be directly used as available, perhaps subject to transformations or segmentation, nominal ones bring about a greater challenge. We tend to enforce a limited set of treatments such as:",
+      "Dummy variables, or one-hot encoding - for each categorical variable $v$ with D categories, we get D-1 binary variables (the \u201cdummies\"). At each input vector $x_n$, with categorical value $v=d$, the value \u201c1\" will be assigned to the corresponding dummy, while \u201c0\" to all others. If $v$ corresponds to the \u201cdefault\" category, all dummies are \u201c0\".",
+      "Contrast encoding BIBREF3 - same as dummy encoding, but instead of \u201c1\" for each category, we have a value that results from a contrasting formula. There are many different formulas (e.g. Helmert, Sum, Backward Difference), but all consist of subtracting the mean of the target variable, for a given category, with a general stastic (e.g. the mean of the dependent variable for all categories; the mean of the dependent variable in the previous category in an ordered list).",
+      "Principal Components Analysis (PCA) - run the PCA algorithm on the data matrix obtained by dummy representation of the categorical variable, then re-represent it with the corresponding projected eigenvector coefficients. One selects K eigenvectors (e.g. according to a variance explained rule), and thus each category is mapped to a vector of K real values.",
+      "Segmenting models, mixture models - A general alternative to categorical data representation is in fact to avoid it in the first place. One obvious method would be through creating hierarchical disaggregate methods (e.g. one per category). This is not in itself a representation paradigm, but an alternative way to see this problem. It certainly raises scalability and inference concerns.",
+      "In datasets where behavior heterogeneity is high, and number of observations is significantly smaller than population size, increasing dimensionality by adding a variable per each category is very risky because the amount of data that is in practice usable to estimate each new coefficient becomes insufficient. A simple intuition here is by considering that, for a dummy variable that is only \u201c1\" for a few observations in the dataset, its coefficient will be \u201cactivated\" only that small number of times. If there is a lot of variance in the associated behavior, the variance of the coefficient will also be large, and the coefficient will be considered statistically insignificant.",
+      "The benefit of representations that map into a latent space, like embeddings and PCA, is that such a space is inevitably shared, and thus every observation contributes indirectly to all category variables. This comes with no interpretability cost, because one can always map to the \u201cdummy\" space and analyse the individual coefficients, as will be shown in our experiments."
+    ],
+    [
+      "The idea of text embeddings comes from a simple re-representation necessity. A natural-language processing system is itself also a numeric machine, therefore it requires each individual word in a dictionary to match its own numeric representation. Just as in our travel models, a possible solution has been to use dummy variables, and it is quite obvious that the dimensionality of such a one-hot encoding vector, quickly becomes overwhelming. Think for example next word prediction algorithm, like the one we have in our smartphones. It is essentially a skip-gram BIBREF4 model that predicts the next word, given the n words before. The English dictionary has about 300000 words, and if we have about 5 words before for context, the number of independent variables of the model would become 1.5 million!",
+      "The goal of text embeddings algorithms (e.g. Word2Vec BIBREF5) is to a) reduce the representation of each word to a computationally acceptable dimension, while simultaneously b) learning the semantic distance between different words. In other words, the euclidean distance of semantically related words (e.g. \u201cdog\" and \u201ccat\") in this new space should be smaller than unrelated words (e.g. \u201cdog\" and \u201coptimize\"). As mentioned before, in a dummy (or one-hot) encoding, all distances between words are equal by definition.",
+      "The word embeddings methodology is very well explained in several webpages such as BIBREF6, so the reader is strongly encouraged to visit them first. However, for the sake of completeness, we summarize here the general idea.",
+      "Imagine the following task: given a word $w_i$ in a text, predict the next word $w_o$. If we solve it with a neural network model, we could have the architecture in Figure FIGREF8, where the input consists simply of the one-hot-encoding representation of the word (i.e. one dummy variable for each word in a dictionary of dimensionality $D$), and the output corresponds to the probability of each word in the dictionary being the next one (also a vector with dimensionality $D$).",
+      "The output layer thus consists simply of a softmax function. In other words, exactly the classical multinomial logit formulation that we would have in an RUM, in which each different word corresponds to an \u201calternative\".",
+      "The concept of embeddings is directly associated to the hidden layer, which is a set of linear activation neurons, typically with a dimensionality $K<<D$. Each such neuron is simply an identity function: it sums all inputs; then propagates this sum to the output layer. Since only one input neuron is activated at a time (remember that the input is a one-hot-encoding vector, with one \u201c1\" and the rest with \u201c0\"), each hidden layer neuron just propagates the (single) weight that links to that input neuron. If we have enough data for training this model, we will eventually land on a situation where, for each input word, there is a fixed vector of weights that are directly used in the output (softmax) function, to generate the prediction. With more data, this weight vector will not change (down to some small delta threshold). These stable vectors are what we call embeddings, and the dimensionality of these vectors is called embedding size.",
+      "Formally, we have a dataset $\\mathcal {D}=\\lbrace x_n, y_n\\rbrace , n=1\\ldots N$, where each $x_n$ and $y_n$ are one-hot (dummy) encodings of categorical variables. The dimensionality of $x_n$ is $D\\times 1$, with $D$ being the number of different categories in $x_n$, while the dimensionality of $y_n$ is $C\\times 1$, with $C$ being the number of categories (alternatives) in $y_n$. The full expression for the embeddings model as described is:",
+      "where $W$ is the embeddings matrix of size $K\\times D$, where $K$ is called the embeddings size. $B$ is a matrix of coefficients ($C\\times K$) for the softmax layer, so $B_c$ is simply the coefficients (row) vector for output class (alternative) $c$, and $\\alpha _c$ is the corresponding intercept. The typical loss function used in such models is called the categorical cross entropy:",
+      "Where $\\delta _{i}$ is the kronecker delta ($\\delta _{true}=1; \\delta _{false}=0$), and $\\mathcal {L}(n)$ is the cumulative loss for an individual data point. This formalization is the simplest version, without loss of generality. In practice, as seen below, we will model multiple embeddings matrices simultaneously, and will add regularization terms to the loss function, so the models tested in this paper consist of compositions of the above.",
+      "So these so called embeddings are in fact a relatively shallow data representation in a simple neural network. What is their added value? Obviously, the first practical benefit is dimensionality reduction, because now there is a mapping between each of the $C$ words to a unique vector of size $K$. The second aspect is that this new representation is the one that maximizes the performance towards a specific task (in our example, prediction of the next word), therefore it is a supervised process, as opposed for example to PCA. The third and more interesting aspect relates with semantic similarity. A natural consequence of the mentioned algorithm is that words that have similar output distributions (i.e. next words) will tend to be close to each other. Figure FIGREF10 shows a 2D visualization (t-SNE) with a subset of english words. In such a visualization, data is projected in 2D space by maintaining the same vector-to-vector distances as in the original ($K$ order space). Therefore the X and Y axes have no specific meaning, only distances between every pair of points are relevant.",
+      "We can see that semantically similar concepts, more specifically concepts that tend to have the same distribution of \u201cnext words\", are placed closer. Another intriguing consequence is that, since the words are now in the $K$ dimensional, embeddings space, we can also do some linear algebra on them. A well known formulation is $King-Man+Woman=Queen$. Essentially, the vector $King-Man$ corresponds to the concept of \u201ccrowning\" (therefore $Woman+crowning=Queen$). The same could be done with many other concept pairs. Figure FIGREF11 show also an alternative interpretation of \u201cman-female\", as well as examples with cities and verb tense.",
+      "Finally, another relevant note on the embeddings representation is that, just like the PCA encoding, one can always project back into the original space and use this for interpretability. In other words, since there is a 1-to-1 mapping from each category to its encoding, there is also a 1-to-1 mapping between a model that uses dummy variables and a model using such encodings. This may be useful for interpretability, since in the case of dummy variables we have a direct interpretation (e.g. a beta coefficient value in a logit model) for the effect of a given category, while the same doesn't happen for an encoded variable (i.e. there is no meaning for the value of a single beta coefficient in an embeddings encoding when K>1). In order to preserve statistical significance information (e.g. p-values) we only need to follow the well known rules of normal random variables.",
+      "There are open databases available (e.g. GLoVe BIBREF9, FastText BIBREF7) that provide word embedding tables for the entire English language (Glove provides several embedding tables, up to embedding size between 100 and 300). In our next word application example, we now talk about models with 500-1500 variables, which is very manageable for our machines today.",
+      "Summarizing, the general idea of word embeddings is to re-represent a categorical variable into a lower dimensional representation with continuous values . Whenever such a variable is to be used in a model, one can simply replace it with the corresponding embeddings vector. We have previously demonstrated the value of such word embeddings in demand prediction in special events BIBREF10, where we collected event textual descriptions, and used Glove embedding vectors to incorporate such information in a neural network model.",
+      "Finally, an interesting point to mention relates to the typical difference in dataset size between the original embeddings training model (Glove, approximately 6 billion input word vectors from 37 million texts) and the model one implements to solve a particular problem (in our special events case, less than 1000 short event descriptions, with at most few hundred words each). Instead of creating ourselves a new embeddings model using the events dataset, we reused the pre-trained GloVe dataset. The benefit is significant because in practice we trained our model to deal with all words in the dictionary, much beyond the limited vocabulary that we obtained in our 1000 short texts. In practice we have used a very small percentage of the english dictionary. When, in an out-of-sample test, our model finds words that were not in the training set, it still works perfectly well."
+    ],
+    [
+      "Differently to textual data, our goal in this paper is to explore the large amount of categorical data that is often collected in travel surveys. This includes trip purpose, education level, or family type. We also consider other variables that are not necessarily of categorical nature, but typically end up as dummy encoding, due to segmentation, such as age, income, or even origin/destination pair.",
+      "Our hypothesis is that, given the limitations of dummy variables that are commonly used and the unsupervised nature of PCA, using instead an embeddings mechanism should improve significantly the quality of our models, both in terms of loglikelihood but also in terms of allowing for lower complexity (i.e. less variables). Ultimately, one could think of a framework such as GLoVe, where embeddings for such variables could be trivially shared with the community. For example, we could have a \u201cTravel behavior embeddings\" database, incrementally built from travel surveys from around the world. Such database could have embeddings for mode choice target variables, but also for departure time, destination choice, car ownership, and so on. Whenever a modeler wanted to estimate a new model, she could just download the right encodings and use them directly. This is particularly relevant if one considers the complicated challenges for opening or sharing travel survey datasets in our field. Of course, a major question arises: are behaviors that consistent across the world? There are certainly nuances across the world, but we believe that general patterns would emerge (e.g. a \u201cbusiness\" trip purpose will be closer to \u201cwork\" than \u201cleisure\", in a departure time choice model; \u201cstudent\" will be closer to \u201cunemployed\" than to \u201cretired\" in a car ownership model)."
+    ],
+    [
+      "We believe that, as with word embeddings, a mapping that preserves semantic distance relative to a certain choice problem, should be useful for modeling. As with a PCA encoding, another benefit is that by sharing parameters in the learning process, the model can generalize better, as opposed to a dummy encoding, where each categorical value has its own parameter, that is only active when observed.",
+      "The general idea is thus to create a mapping between a variable for which we want to find an embeddings representation, and a target variable, as in Figure FIGREF15. We call the mapping function \u201cPyTre Embeddings\", because that is the name of the object in our proposed Python \u201cTravel Embeddings\" package.",
+      "From an experimental design and application perspective, the approach followed in this paper is the following:",
+      "Create list of categorical variables to encode (the encoding set)",
+      "Split dataset into train, development and test sets",
+      "For each variable in encoding set, learn the new embeddings using the embeddings train set . This should be done simultaneously (all variable embeddings estimated at once, as explained in the next section).",
+      "Encode choice models for train, development and test sets using the learned embeddings",
+      "Estimate choice model accordingly using its train set",
+      "Evaluate the new model using the test set",
+      "Since there is stochasticity in the embeddings training model, we will repeat the above multiple times, for the different experiments in the paper (and report the respective mean and standard deviation statistics). Whenever we want to analyse a particular model (e.g. to check the coefficients of a choice model), we select the one with the highest likelihood at the development set (i.e. in practice, its out-of-sample generalization performance), and report its performance on the test set."
+    ],
+    [
+      "Since a choice model will typically involve other variables than the categorical ones that we learn the embeddings for, it is important to take into account their effects. Figure FIGREF24 shows the simplest travel embeddings model. As an example, the categorical variable is trip purpose, and there are a few other variables such as gender, cost of the alternatives, distance, and so on. Notice that they are directly fed into the softmax output layer, together with the embeddings output.",
+      "The dataset sizes in transportation behavior modeling are substantially smaller than typical word embeddings ones, and the risk of overfitting is therefore higher. To mitigate this problem, besides adding regularization penalties in the objective function, we add what we call a regularizer layer for each embedding, which is no more than a softmax layer that penalizes whenever it cannot recover the original one-hot-encoding vectors (Figure FIGREF25, left). We call the combination of embeddings and its regularizer network, a Travel Embeddings layer. Finally, it is obviously better to train all embeddings simultaneously, so that they accommodate each other's effects (Figure FIGREF25, right)."
+    ],
+    [
+      "The goal of this paper is to test the potential of embeddings in a simple and well-known choice model context, comparing it to well-known baseline techniques. Therefore, the general model specification follows quite simple assumptions. We expect that in future work from us or others, more elaborate derivations can take advantage of embeddings such as nested, mixed logit or latent class choice models (LCCM), for example.",
+      "We will apply the methodology to the well-known \u201cSwissmetro\" dataset. We will compare it with a dummy variables and PCA baselines. We will follow the 3-way experimental design mentioned before: split the dataset into train, development and test sets, so that the embeddings, PCA eigenvectors and the choice model are estimated from the same train and development sets, and validate it out-of-sample. For the sake of interpretability, we will also project back coefficients from the embeddings as well as PCA models into the dummy variable space.",
+      "All experiment code is available as a jupyter notebook in a package we created for this work (to which we called PyTre). For estimating the multinomial logit model (MNL) we used the PyLogit BIBREF11 package."
+    ],
+    [
+      "The Swissmetro dataset consists of survey data collected on the trains between St. Gallen and Geneva, Switzerland, during March 1998. According to its description BIBREF0, the respondents provided information in order to analyze the impact of the modal innovation in transportation, represented by the Swissmetro, a revolutionary mag-lev underground system, against the usual transport modes represented by car and train. After discarding respondents for which some variables were not available (e.g. age, purpose), a total of 10469 responses from 1188 individuals were used for the experiments.",
+      "We split the dataset into 3 different parts:",
+      "Embeddings train set: 60% of the dataset (6373 vectors)",
+      "Development set: 20% of the dataset (2003 vectors)",
+      "Test set: 20% of the dataset (2003 vectors)"
+    ],
+    [
+      "The PyLogit package BIBREF11 also uses Swissmetro as an example. Therefore, our model specifications will extend the default one from this package. We re-estimated this model with the train set and validated with testset. The results are shown in tables TABREF31 and TABREF32. Since we are comparing the models at the test set, the key indicators should be pseudo R-square and log-likelihood. Indicators that consider model complexity (robust r-square and AIC) are less important on the test set in our view because the overfitting effect (i.e. improving fit just by adding more variables) will no longer be verifiable in this way. Instead, one sees overfitting if test set performance is considerably inferior to the training set."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0929/instruction.md b/qasper-0929/instruction.md
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@@ -0,0 +1,3 @@
+Name of Paper: Rethinking travel behavior modeling representations through embeddings
+
+Question: How do they model travel behavior?
\ No newline at end of file
diff --git a/qasper-0938/instruction.md b/qasper-0938/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..8ccf92b3dfe4a04951934c68b00f21bfb6a768b6
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+++ b/qasper-0938/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Modeling Trolling in Social Media Conversations
+
+Question: What is the size of the dataset?
\ No newline at end of file
diff --git a/qasper-0942/instruction.md b/qasper-0942/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..971e77536498d853dfe1fee485d23bffd4f2c0b9
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+++ b/qasper-0942/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Measuring Compositional Generalization: A Comprehensive Method on Realistic Data
+
+Question: How authors justify that question answering dataset presented is realistic?
\ No newline at end of file
diff --git a/qasper-0943/instruction.md b/qasper-0943/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..753b6a7ae9a75cc10acbadc69d7160b77a729f26
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+++ b/qasper-0943/instruction.md
@@ -0,0 +1,598 @@
+Name of Paper: Measuring Compositional Generalization: A Comprehensive Method on Realistic Data
+
+Question: What three machine architectures are analyzed?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Distribution-Based Compositionality Assessment (DBCA)",
+    "Distribution-Based Compositionality Assessment (DBCA) ::: Principles for measuring compositionality",
+    "The CFQ Dataset",
+    "The CFQ Dataset ::: Automatic, rule-based generation",
+    "The CFQ Dataset ::: Dataset details and statistics",
+    "Compositionality Experiments for CFQ and scan",
+    "Experimental Results and Analysis ::: Experiment Setup",
+    "Experimental Results and Analysis ::: Results and analysis for CFQ",
+    "Experimental Results and Analysis ::: Results and analysis for scan",
+    "Related Work",
+    "Conclusion and Outlook",
+    "Example Dataset Item",
+    "Data Quality Analysis",
+    "Data Distribution Analysis ::: Answer frequencies",
+    "Data Distribution Analysis ::: Impact of subsampling on the distribution of complexity levels",
+    "Data Distribution Analysis ::: Impact of subsampling on the frequency of rules and rule combinations",
+    "Divergence-Based Split Analysis ::: Qualitative analysis of MCD@!START@$_{1}$@!END@",
+    "Divergence-Based Split Analysis ::: Quantitative analysis of MCD@!START@$_{1}$@!END@",
+    "Hyperparameters",
+    "Detailed error analysis ::: Breakdown of error types",
+    "Detailed error analysis ::: Qualitative error analysis",
+    "Additional experimental results on scan",
+    "Analysis of relations between accuracy, compound divergence, and training size",
+    "Logical Form",
+    "Rule Format",
+    "Rule Format ::: Grammar rule format",
+    "Rule Format ::: Knowledge rule format",
+    "Rule Format ::: Inference rule format",
+    "Rule Format ::: Resolution rule format",
+    "Generation Algorithm",
+    "Generation Algorithm ::: Join by Logical Form",
+    "Generation Algorithm ::: Relationship between Generation and Parsing",
+    "Generation Algorithm ::: Selecting an appropriate sample set",
+    "Example of a rule application DAG",
+    "Example of a rule application DAG ::: DAG normalization",
+    "Example of a rule application DAG ::: Concept abbreviations",
+    "Example of a rule application DAG ::: Entity placeholders",
+    "Example of a rule application DAG ::: Subgraphs and their weights",
+    "Rules Index",
+    "Rules Index ::: Grammar rules",
+    "Rules Index ::: Inference rules",
+    "Rules Index ::: Resolution rules",
+    "Rules Index ::: Knowledge rules"
+  ],
+  "paragraphs": [
+    [
+      "Human intelligence exhibits systematic compositionality BIBREF0, the capacity to understand and produce a potentially infinite number of novel combinations of known components, i.e., to make \u201cinfinite use of finite means\u201d BIBREF1. In the context of learning from a set of training examples, we can observe compositionality as compositional generalization, which we take to mean the ability to systematically generalize to composed test examples of a certain distribution after being exposed to the necessary components during training on a different distribution.",
+      "Humans demonstrate this ability in many different domains, such as natural language understanding (NLU) and visual scene understanding. For example, we can learn the meaning of a new word and then apply it to other language contexts. As BIBREF2 put it: \u201cOnce a person learns the meaning of a new verb `dax', he or she can immediately understand the meaning of `dax twice' and `sing and dax'.\u201d Similarly, we can learn a new object shape and then understand its compositions with previously learned colors or materials BIBREF3, BIBREF4.",
+      "In contrast, state-of-the-art machine learning (ML) methods often fail to capture the compositional structure that is underlying the problem domain and thus fail to generalize compositionally BIBREF2, BIBREF5, BIBREF6, BIBREF7, BIBREF3. We believe that part of the reason for this shortcoming is a lack of realistic benchmarks that comprehensively measure this aspect of learning in realistic scenarios.",
+      "As others have proposed, compositional generalization can be assessed using a train-test split based on observable properties of the examples that intuitively correlate with their underlying compositional structure. BIBREF8, for example, propose to test on different output patterns than are in the train set, while BIBREF2 propose, among others, to split examples by output length or to test on examples containing primitives that are rarely shown during training. In this paper, we formalize and generalize this intuition and make these contributions:",
+      "We introduce distribution-based compositionality assessment (DBCA), which is a novel method to quantitatively assess the adequacy of a particular dataset split for measuring compositional generalization and to construct splits that are ideally suited for this purpose (Section SECREF2).",
+      "We present the Compositional Freebase Questions (CFQ) , a simple yet realistic and large NLU dataset that is specifically designed to measure compositional generalization using the DBCA method, and we describe how to construct such a dataset (Section SECREF3).",
+      "We use the DBCA method to construct a series of experiments for measuring compositionality on CFQ and scan BIBREF2 and to quantitatively compare these experiments to other compositionality experiments (Section SECREF4).",
+      "We analyze the performance of three baseline ML architectures on these experiments and show that these architectures fail to generalize compositionally, and perhaps more surprisingly, that compound divergence between train and test sets is a good predictor of the test accuracy (Section SECREF5).",
+      ""
+    ],
+    [
+      "",
+      "Like other authors, we propose to measure a learner's ability to generalize compositionally by using a setup where the train and test sets come from different distributions. More specifically, we propose a setup where each example is obtained by composing primitive elements (atoms), and where these atoms are similarly represented in the train and test sets while the test set contains novel compounds, i.e., new ways of composing the atoms of the train set.",
+      "As a simple illustrative scenario, consider the task of answering simple questions such as \u201cWho directed Inception?\u201d and \u201cDid Christopher Nolan produce Goldfinger?\u201d. In this scenario, the atoms intuitively correspond to the primitive elements that are used to compose those questions, such as the predicates \u201cdirect(ed)\u201d and \u201cproduce(d)\u201d, the question patterns \u201cWho [predicate] [entity]\u201d and \u201cDid [entity1] [predicate] [entity2]\u201d, and the entities \u201cInception\u201d, \u201cChristopher Nolan\u201d, etc. The compounds on the other hand correspond to the combinations of these atoms that appear in the various examples: \"Who directed [entity]?\", \"Did Christopher Nolan [predicate] Inception?\", etc.",
+      "To measure compositional generalization on such a task, one might therefore use the questions \u201cWho directed Inception?\u201d and \u201cDid Christopher Nolan produce Goldfinger?\u201d as training examples while testing on questions such as \u201cDid Christopher Nolan direct Goldfinger?\u201d and \"Who produced Inception?\" because the atoms are identically represented in the train and test sets while the compounds differ.",
+      "To make this intuition more precise, we focus on datasets such as CFQ (introduced in Section SECREF3) and scan BIBREF2, where each example can be created from a formal set of rules by successively applying a number of these rules. In this case, the atoms are the individual rules, while the compounds are the subgraphs of the directed acyclic graphs (DAGs) that correspond to the rule applications. (See Sections SECREF3 and SECREF4 for more details.)",
+      ""
+    ],
+    [
+      "We use the term compositionality experiment to mean a particular way of splitting the data into train and test sets with the goal of measuring compositional generalization. Based on the notions of atoms and compounds described above, we say that an ideal compositionality experiment should adhere to the following two principles:",
+      "",
+      "Similar atom distribution: All atoms present in the test set are also present in the train set, and the distribution of atoms in the train set is as similar as possible to their distribution in the test set.",
+      "",
+      "Different compound distribution: The distribution of compounds in the train set is as different as possible from the distribution in the test set.",
+      "",
+      "The second principle guarantees that the experiment is compositionally challenging in the sense that it tests the learner on compounds that are as different as possible from the compounds used during training. The first principle aims to guarantee that the experiment is exclusively measuring the effect of the difference in the way atoms are composed to form compounds (rather than some related but different property such as domain adaptation on the distribution of the atoms).",
+      "To determine to which degree a certain experiment adheres to these principles, we use the following formalization. For a sample set $T$, we use $\\mathcal {F}_A(T)$ to denote the frequency distribution of atoms in $T$ and $\\mathcal {F}_C(T)$ for the weighted frequency distribution of compounds in $T$, which correspond to the subgraphs of the rule application DAGs. For practicality, we do not consider all subgraphs of rule application DAGs when computing the compound divergence. Instead, we first generate a large subset $\\mathbb {G}$ of subgraphs, then weight them in context of their occurrence, and keep only the ones with highest sum of weights. The purpose of the weighting is to avoid double-counting compounds that are highly correlated with some of their super-compounds. We achieve this by calculating the weight of $G \\in \\mathbb {G}$ in a sample as $w(G) = \\max _{g \\in \\text{occ}(G)} (1 - \\max _{G^{\\prime }: g \\prec g^{\\prime } \\in \\text{occ}(G^{\\prime })} P(G^{\\prime }| G))$, where $\\text{occ}(G)$ is the set of all occurrences of $G$ in the sample, $\\prec $ denotes the strict subgraph relation, and $P(G^{\\prime }| G)$ is the empirical probability of $G^{\\prime }$ occurring as a supergraph of $G$ over the full sample set. See Appendix SECREF202 for example subgraphs and more details on the weighting.",
+      "We measure divergence (or similarity) of the weighted distributions using the Chernoff coefficient $C_\\alpha (P \\Vert Q) = \\sum _{k} p_k^\\alpha \\, q_k^{1-\\alpha } \\in [0, 1]$ BIBREF9. For the atom divergence, we use $\\alpha =0.5$, which corresponds to the Bhattacharyya coefficient and reflects the desire of making the atom distributions in train and test as similar as possible. For the compound divergence, we use $\\alpha = 0.1$, which reflects the intuition that it is more important whether a certain compound occurs in $P$ (train) than whether the probabilities in $P$ (train) and $Q$ (test) match exactly. This allows us to formally define as follows the notions of compound divergence $\\mathcal {D}_C$ and atom divergence $\\mathcal {D}_A$ of a compositionality experiment consisting of a train set $V$ and a test set $W$:",
+      "Based on these principles, we suggest to use as a preferred compositionality benchmark for a given dataset the accuracy obtained by a learner on splits with maximum compound divergence and low atom divergence (we use $\\mathcal {D}_A \\le 0.02$). See Section SECREF4 for details about how to construct such splits."
+    ],
+    [
+      "We present the Compositional Freebase Questions (CFQ) as an example of how to construct a dataset that is specifically designed to measure compositional generalization using the DBCA method introduced above. CFQ is a simple yet realistic, large dataset of natural language questions and answers that also provides for each question a corresponding sparql query against the Freebase knowledge base BIBREF10. This means that CFQ can be used for semantic parsing BIBREF11, BIBREF12, which is the task that we focus on in this paper."
+    ],
+    [
+      "BIBREF13 describe a number of benefits for automated rule-based dataset generation, including scalability, control of scope, and avoidance of human errors. Beyond these benefits, however, such an approach is particularly attractive in the context of measuring compositional generalization using the DBCA method, as it allows us to precisely track the atoms (rules) and compounds (rule applications) of each example by recording the sequence of rule applications used to generate it.",
+      "Since the way we measure compositionality depends on how the examples can be broken down into atoms and compounds, we design the generation rules so as to have few and meaningful atoms. More precisely, we aim to have as few rules as possible so that the richness of the examples comes from composing them, which yields a large variety of compounds (enabling a large range of different compound divergences) while making it easy to obtain similar distributions of atoms. Also, we aim to make our rules truly \u201catomic\u201d in the sense that the behavior of any rule is independent of the context where it is applied (e.g., rules may not contain \u201cif-then-else\u201d constructs).",
+      "In order to minimize the number of rules, we use an intermediate logical form that serves as a uniform semantic representation with relatively direct mappings to natural language and sparql. Our rules thus fall into the following four categories (a selection of rules is provided in Appendix SECREF20):",
+      "Grammar rules that generate natural language constructs and corresponding logical forms.",
+      "Inference rules that describe transformations on logical forms, allowing us to factor out transformations that are independent of specific linguistic and sparql constructs.",
+      "Resolution rules that map constructs of the logical form to sparql constructs.",
+      "Knowledge rules that supply logical form expressions that are universally applicable. Other rules can be kept more generic by parameterizing them on knowledge.",
+      "These rules define a language of triples of the form $\\langle \\text{question, logical form, \\textsc {sparql}{} query} \\rangle $. Our generation algorithm produces such triples in a mixed top-down and bottom-up fashion. We first apply grammar rules and inference rules to produce the natural language questions and their semantics in our logical form. Then we apply resolution rules to obtain the sparql query. See Figure FIGREF14 for an illustration. In addition, the generator produces a normalized, directed acyclic graph (DAG) of rule applications that corresponds to the normalized program that generated the triple. (Appendix SECREF19 shows an example.) Edges of this DAG represent dependencies among the rule applications, and the normalization ensures that a certain rule combination is represented using the same DAG across all the examples where it occurs.",
+      "The described approach can generate a potentially infinite set of questions, from which we first sample randomly and then subsample (to maximize the overall diversity of rule combinations while keeping a uniform distribution over complexity). We measure the diversity of rule combinations using the empirical entropy of a weighted subset of the rule application DAGs, and we use the number of rule applications as a measure of the complexity of an example. We also limit the maximum example complexity such that the questions remain relatively natural. Table TABREF15 shows examples of generated questions at varying levels of complexity. An example of a complete data item is shown in Appendix SECREF8, a more detailed data quality analysis is presented in Appendix SECREF9, and the generation algorithm is discussed in more detail in Appendix SECREF18."
+    ],
+    [
+      "Input and output. While the primary focus of the dataset is semantic parsing (natural language question to sparql query), we also provide natural language answers for each question. This allows the dataset to be used in a text-in-text-out scenario as well (see Appendix SECREF8).",
+      "Ambiguity. We largely avoid ambiguity in the questions. In particular, we make sure each name is used to refer to exactly one entity, and we avoid different possible parse trees, different interpretations of plurals, and the need for disambiguation that requires semantic knowledge.",
+      "Scope. We select the following language features as compositional building blocks: open questions and closed questions; subordinate clauses; active and passive voice; conjunctions of verb phrases and of noun phrases; possessives with roles (\u201cX's parent\u201d); adjectives; and type restrictions. For knowledge base features, we select roles, verbs, types, and adjectives from domains that are well-represented in Freebase and that can be combined easily. We start from the popular movie domain (e.g., directing, producing, editor, sequel) and extend this with personal relations (e.g., parent, spouse, sibling), companies (e.g., founding, employer), and adjectives (e.g., gender, nationality).",
+      "Logical form and grammar. For the internal logical form, we adopt a variation of the description logic $\\mathcal {EL}$ BIBREF14, BIBREF15, augmented with additional constructors (see Appendix SECREF16) to more easily map to certain linguistic structures. For the grammar rules, we use a unification-based grammar syntax similar to that used in the Prolog extension GULP 3.1 BIBREF16, with addition of support for disjunction, negation, absence, and default inheritance of features for compactness of representation.",
+      "Grounding in Freebase. Once an example is generated by the CFQ rules, it still contains entity placeholders instead of Freebase machine ids (MIDs). For the task of semantic parsing, the examples could theoretically be used as-is, as our avoidance of semantic ambiguity means that a learner should not need knowledge of the specific entity in order to parse the question. To make the questions natural, however, we apply an additional step of replacing the placeholders with appropriate specific entities. To do this we first execute the generated sparql query against Freebase. This returns a set of candidate MID combinations that satisfy the query and can be used as substitutes. If the set is empty, we abandon the generated question candidate as unnatural. Otherwise, we pick one combination at random to yield a question with positive answer. In the case of a closed question, we also generate a variation that yields the answer \u201cNo\u201d, which we do by mixing in MIDs from another substitution (or a more generic replacement if that fails) to keep the question as plausible-sounding as possible. We then randomly choose either the question with positive or with negative answer, to avoid spurious correlations between question structure and yes/no answer.",
+      "Semantic and structural filtering. Even among the questions that can be satisfied in Freebase, there are some that are meaningful but somewhat unnatural, such as \u201cWas Strange Days directed by a female person whose gender is female?\u201d. We automatically filter out such unnatural questions using semantic and structural rules. Note that since we do not require a learner to identify such questions, we do not track these filtering rules.",
+      "Release and statistics.",
+      "CFQ contains 239,357 English question-answer pairs that are answerable using the public Freebase data. (The data URL is not yet provided for anonymous review.) We include a list of MIDs such that their English names map unambiguously to a MID. Table TABREF17(a) summarizes the overall statistics of CFQ. Table TABREF17(b) uses numbers from BIBREF8 and from an analysis of WebQuestionsSP BIBREF17 and ComplexWebQuestions BIBREF18 to compare three key statistics of CFQ to other semantic parsing datasets (none of which provide annotations of their compositional structure). CFQ contains the most query patterns by an order of magnitude and also contains significantly more queries and questions than the other datasets. Note that it would be easy to boost the raw number of questions in CFQ almost arbitrarily by repeating the same question pattern with varying entities, but we use at most one entity substitution per question pattern. Appendix SECREF10 contains more detailed analyses of the data distribution."
+    ],
+    [
+      "The DBCA principles described in Section SECREF6 enable a generic and task-independent method for constructing compositionality experiments. To construct such an experiment for a dataset $U$ and a desired combination of atom and compound divergences, we use an iterative greedy algorithm that starts with empty sets $V$ (train) and $W$ (test), and then alternates between adding an example $u \\in U$ to $V$ or $W$ (while maintaining the desired train/test ratio). At each iteration, the element $u$ is selected such that $\\mathcal {D}_C(V \\Vert W)$ and $\\mathcal {D}_A(V \\Vert W)$ are kept as closely as possible to the desired values. To reduce the risk of being stuck in a local optimum, we also allow removing examples at certain iterations.",
+      "In general, there are many different splits that satisfy a desired compound and atom divergence. This reflects the fact that a certain compound may either occur exclusively in the train set or the test set, or it may occur in both of them because the split may have achieved the desired compound divergence by separating other (possibly orthogonal) compounds. Our greedy algorithm addresses this by making random choices along the way, starting with picking the first example randomly.",
+      "For the goal of measuring compositional generalization as accurately as possible, it is particularly interesting to construct maximum compound divergence (MCD) splits, which aim for a maximum compound divergence at a low atom divergence (we use $\\mathcal {D}_A \\le 0.02$). Table TABREF18 compares the compound divergence $\\mathcal {D}_C$ and atom divergence $\\mathcal {D}_A$ of three MCD splits to a random split baseline as well as to several previously suggested compositionality experiments for both CFQ and the existing scan dataset (cf. Section SECREF30). The split methods (beyond random split) are the following:",
+      "Output length: Variation of the setup described by BIBREF2 where the train set consists of examples with output (sparql query or action sequence) length $\\le \\hspace{-2.5pt} N$, while the test set consists of examples with output length $> \\hspace{-2.5pt} N$. For CFQ, we use $N = 7$ constraints. For scan, we use $N = 22$ actions.",
+      "Input length: Variation of the above setup, in which the train set consists of examples with input (question or command) length $\\le N$, while test set consists of examples with input length $> N$. For CFQ, we use $N=19$ grammar leaves. For SCAN, we use $N=8$ tokens.",
+      "Output pattern: Variation of setup described by BIBREF8, in which the split is based on randomly assigning clusters of examples sharing the same output (query or action sequence) pattern. Query patterns are determined by anonymizing entities and properties; action sequence patterns collapse primitive actions and directions.",
+      "Input pattern: Variation of the previous setup in which the split is based on randomly assigning clusters of examples sharing the same input (question or command) pattern. Question patterns are determined by anonymizing entity and property names ; command patterns collapse verbs and the interchangeable pairs left/right, around/opposite, twice/thrice.",
+      "All of these experiments are based on the same train and validation/test sizes of 40% and 10% of the whole set, respectively. For CFQ, this corresponds to about 96k train and 12k validation and test examples, whereas for scan, it corresponds to about 8k train and 1k validation and test examples. We chose to use half of the full dataset for the train-test splits, as it led to an appropriate balance between high compound divergence and high train set size in informal experiments.",
+      "The MCD splits achieve a significantly higher compound divergence at a similar atom divergence when compared to the other experiments. The reason for this is that, instead of focusing on only one intuitive but rather arbitrary aspect of compositional generalization, the MCD splits aim to optimize divergence across all compounds directly.",
+      "Interestingly, the MCD splits still correlate with the aspects of compositional generalization that are targeted by the other experiments in this table. As shown in the four right columns of Table TABREF18, for each MCD split, the train set $V$ contains on average shorter examples than the test set $W$ (measured by the ratio of average lengths), and $V$ also contains only a small fraction of the input and output patterns used in $W$ (measured by the fraction of patterns covered). However, these correlations are less pronounced than for the experiments that specifically target these aspects, and they vary significantly across the different MCD splits.",
+      "This illustrates that MCD splits are comprehensive in the sense that they cover many different aspects of compositional generalization, especially when looking at multiple of them. It also means that whether a certain example ends up in train or test is not determined solely by a single criterion that is immediately observable when looking at the input and output (such as length). As we show in Appendix SECREF91, this generally makes the examples in train and test look fairly similar."
+    ],
+    [
+      "We use three encoder-decoder neural architectures as baselines: (1) LSTM+attention as an LSTM BIBREF19 with attention mechanism BIBREF20; (2) Transformer BIBREF21 and (3) Universal Transformer BIBREF22.",
+      "We tune the hyperparameters using a CFQ random split, and we keep the hyperparameters fixed for both CFQ and scan (listed in Appendix SECREF12). In particular the number of training steps is kept constant to remove this factor of variation. We train a fresh model for each experiment, and we replicate each experiment 5 times and report the resulting mean accuracy with 95% confidence intervals.",
+      "Note that while we construct test and validation sets from the same distribution, we suggest that hyperparameter tuning should be done on a random split (or random subset of the train set) if one wants to measure compositional generalization of a model with respect to an unknown test distribution as opposed to an architecture with respect to a known test distribution. Tuning on a validation set that has the same distribution as the test set would amount to optimizing for a particular type of compound divergence and thus measure the ability for a particular architecture to yield models that can be made to generalize in one particular way (through leaking information about the test set in the hyperparameters).",
+      "Similarly to BIBREF8, we anonymize the Freebase names and MIDs in the textual input and the SPARQL output, respectively, by replacing them with a placeholder (e.g., \u201cM0\u201d for the first MID). This removes the need for two learning sub-tasks that are orthogonal to our focus: named entity recognition and learning that the MIDs are patterns that need to be copied. An example input-output (question-query) pair then looks like the following: `Was M0 a screenwriter' $\\mapsto $ `select count(*) where {M0 a ns:film.writer}'.",
+      "The main relation we are interested in is the one between compound divergence of the data split and accuracy. Specifically, we compute the accuracy of each model configuration on a series of divergence-based splits that we produce with target compound divergences that span the range between zero and the maximum achievable in 0.1 increments (while ensuring that atom divergence does not exceed the value of 0.02). For each target divergence, we produce at least 3 different splits with different randomization parameters (compare Section SECREF4). For comparison, we also compute accuracies on the other splits shown in Table TABREF18."
+    ],
+    [
+      "The mean accuracies of the three architectures on CFQ are shown in Figure FIGREF28(a) and Table TABREF29. We make three main observations:",
+      "All models achieve an accuracy larger than 95% on a random split, and this is true even if they are trained on 10 times fewer training instances (see Appendix SECREF15 for a more detailed analysis on the performance with varying training size).",
+      "The mean accuracy on the MCD splits is below 20% for all architectures, which means that even a large train set (about 96k instances) with a similar distribution of atoms between train and test is not sufficient for these architectures to perform well on the test distribution.",
+      "For all architectures, there is a strong negative correlation between the compound divergence and the mean accuracy.",
+      "This suggests that the baseline models are able to capture the superficial structure of the dataset, but fail to capture the compositional structure. We find it surprising that varying the compound divergence gives direct control of the (mean) accuracy, even though the examples in train and test look similar (see Appendix SECREF91). This means that compound divergence seems to capture the core difficulty for these ML architectures to generalize compositionally.",
+      "Note that the experiment based on output-length exhibits a worse accuracy than what we would expect based on its compositional divergence. One explanation for this is that the test distribution varies from the training distribution in other ways than compound divergence (namely in output length and a slightly higher atom divergence), which seems to make this split particularly difficult for the baseline architectures. To analyze the influence of the length ratio further, we compute the correlation between length ratios and accuracy of the baseline systems and compare it to the correlation between compound divergence and accuracy. We observe $R^2$ correlation coefficients between 0.11 and 0.22 for the input and output length ratios and between 0.81 and 0.88 for the compound divergence. This shows that despite the known phenomenon that the baseline systems struggle to generalize to longer lengths, the compound divergence seems to be a stronger explanation for the accuracy on different splits than the lengths ratios.",
+      "Error analysis. We perform an analysis of the errors for the split MCD$_{1}$ (the first MCD split that we constructed, with more details provided in Appendix SECREF13). We observe accuracies between 29% and 37% on the test set of this particular split. Qualitatively, all three systems seem to make similar errors at this point (68% of errors are on the same samples). They make more errors for longer sequences and predict about 20% too short output when they make an error. The most common category of error is the omission of a clause in the output (present in 43%-49% of the test samples), e.g.: (1) Omitted conjunctions: for the input \u201cWhat spouse of a film producer executive produced and edited M0, M1, and M2?\u201d the best system ignores \u201cexecutive produced\u201d in the output. (2) Omitted adjectives: for the input \u201cWhich female Spanish film producer was M3' s spouse?\u201d the best system ignores the adjective \u201cfemale\u201d."
+    ],
+    [
+      "To demonstrate the use of our analysis method on another dataset, we re-create the scan dataset BIBREF2, which consists of compositional navigation commands (e.g, `turn left twice and jump') mapped to corresponding action sequences (e.g., `lturn lturn jump'). We use the original grammar while tracking the rule applications used for the construction of each input-output pair. This enables us to compare the compositional generalization abilities of the baseline systems on this dataset in a novel way.",
+      "Figure FIGREF28(b) shows the graphs for the scan data set in the same setup as Figure FIGREF28(a) does for CFQ. We observe that the compound divergence again is a good predictor for the mean accuracy for all three architectures. One difference is that for scan the systems are able to attain accuracies close to 100% for compound divergences up to around 0.2, which is not the case for CFQ. This seems to be in line with the fact that overall CFQ is a more complex task than scan: the total number of rules used in generating scan is only 38 in comparison to 443 rules in the construction of CFQ.",
+      "Appendix SECREF14 provides a comparison to other experiments presented in previous work, including experiments that have significantly different atom distributions. We observe that this generally causes lower accuracies but does not break the correlation between accuracy and compound divergence."
+    ],
+    [
+      "To measure compositional generalization for semantic parsing to SQL, BIBREF8 propose to ensure that no SQL query pattern occurs in both the train and the test set (\u201cquery split\u201d), and they provide such splits for several data sets. By evaluating several ML architectures the authors confirm that this query-pattern split is harder to learn than a conventional split.",
+      "BIBREF2 introduce the scan dataset, and several publications provide interesting analyses of compositional generalization using it BIBREF5, BIBREF6. BIBREF7 discuss a particular extension of a seq2seq model that is effective in handling difficult scan sub-tasks by separating semantic and syntactic information during learning. Our contributions extend the analyses on the scan data in several ways: CFQ provides richer annotations and covers a broader subset of English than the scan dataset, and we propose a comprehensive score for assessing aggregate compositionality of a system on a given task.",
+      "The mathematics dataset BIBREF13 is a large, automatically generated set of 112M samples in 56 separated sub-tasks. The authors present data and experiments that share common goals with our approach, but focus on mathematical reasoning instead of natural language. Our breakdown of generation rules per train sample is more fine-grained, which allows a more precise compositional generalization analysis. Being automatically generated also links our approach to datasets such as the bAbI tasks BIBREF23, which however do not focus on compositional generalization.",
+      "A dataset related to CFQ is ComplexWebQuestions BIBREF18, which consists of complex questions that are automatically generated from simpler sub-questions in WebQuestionsSP BIBREF17 and then reworded manually. While these datasets can be used for semantic parsing, we did not find them suitable for a thorough compositionality analysis because a consistent annotation with the compositional structure would be hard to obtain. Other approaches to semi-automatic dataset creation also use paraphrasing BIBREF24, BIBREF25.",
+      "BIBREF3 introduce the generated clevr dataset, which shares common goals with our work applied in the area of visual reasoning. The dataset's functional programs capture some of the structural information of the questions and are linked one-to-many to the 423 question patterns used. The authors specifically investigate generalization to new combinations of visual attributes in one experiment which uses a particular train-test split based on the colors used. BIBREF26 propose a neural-symbolic architecture and discuss promising results on additional specific splits of the clevr data, e.g. based on object counts and program depth. BIBREF27 describe how the application of compositional attention networks to the clevr data leads to structured and data-efficient learning. BIBREF28 present a large, compositional, generated visual question answering data set with functional programs, on which neural state machines achieve good performance BIBREF29. The use of specific splits between train and test data also occurs in the context of visual data. E.g., BIBREF30 propose a greedy split algorithm to maximize the coverage of test concepts in the train set while keeping question-type/answer pairs disjoint and observe performance degradation of existing approaches. BIBREF31 introduce a synthetic visual question answering dataset called sqoop, which is used to test whether a learner can answer questions about all possible object pairs after being trained on a subset.",
+      "While these datasets are very interesting, the additional annotation that we provide in CFQ indicating the exact rule trees needed to link input and output makes additional analyses regarding compositionality possible. Our analyses go beyond many of the presented discussions (that mostly focus on accuracy regarding particular holdouts) in formalizing an approach that uses the atom and compound divergences to measure compositionality.",
+      "A number of ML approaches have been developed for semantic parsing. BIBREF32 propose Key-Value Memory Networks \u2013 neural network-based architectures that internalize a knowledge base into the network \u2013 and introduce the WikiMovies dataset. BIBREF33 develop an end-to-end architecture that can handle noise in questions and learn multi-hop reasoning simultaneously. They introduce the MetaQA benchmark that is based on WikiMovies but uses a set of only 511 question patterns (mod entities) shared between train and test.",
+      "With regards to studying compositionality in ML, BIBREF34 argue that combinatorial generalization should be a top priority to achieve human-like abilities. BIBREF35 discusses measuring the compositionality of a trained representation, e.g. of a learned embedding. The author suggests to use a tree reconstruction error that is based on how well the oracle derivation of the input matches the structure that can be derived on the representations. BIBREF4 discuss an architecture that enables the learning of compositional concept operators on top of learned visual abstractions. BIBREF36 introduce the compositional recursive learner that \u201ccan generalize to more complex problems than the learner has previously encountered\u201d."
+    ],
+    [
+      "In this paper we presented what is (to the best of our knowledge) the largest and most comprehensive benchmark for compositional generalization on a realistic NLU task. It is based on a new dataset generated via a principled rule-based approach and a new method of splitting the dataset by optimizing the divergence of atom and compound distributions between train and test sets. The performance of three baselines indicates that in a simple but realistic NLU scenario, state-of-the-art learning systems fail to generalize compositionally even if they are provided with large amounts of training data and that the mean accuracy is strongly correlated with the compound divergence.",
+      "We hope our work will inspire others to use this benchmark as a yardstick to advance the compositional generalization capabilities of learning systems and achieve high accuracy at high compound divergence. Some specific directions that we consider promising include applying unsupervised pretraining on the input language or output queries and the use of more diverse or more targeted learning architectures, such as syntactic attention BIBREF7. We also believe it would be interesting to apply the DBCA approach to other domains such as visual reasoning, e.g. based on clevr BIBREF3.",
+      "In the area of compositionality benchmarks, we are interested in determining the performance of current architectures on the end-to-end task that expects a natural language answer given a natural language question in CFQ. We would like also to extend our approach to broader subsets of language understanding, including use of ambiguous constructs, negations, quantification, comparatives, additional languages, and other vertical domains."
+    ],
+    [
+      "The following shows an example data item including the question text in various forms, the answer, the sparql query in various forms, some tracked statistics, and the set of used rules (atoms) and the applied rule tree (compound). Some details are omitted, indicated by ellipses (`...')."
+    ],
+    [
+      "During the development of our data generation pipeline, we manually checked the generated examples for quality. Below is a random selection of 50 examples of the final CFQ dataset (no cherry-picking was used). Brackets around [entity names] are provided just for ease of human reading. Manual checking also indicated that all questions are associated with the semantically correct sparql queries. However, because we rely on the data present in Freebase, there are three debatable questions which sound somewhat unnatural (UNKREF33, UNKREF51, and UNKREF59, see further discussion below the list).",
+      "Who was a writer, star, and cinematographer of [Tetsuo: The Bullet Man], [Nightmare Detective], and [Bullet Ballet]?",
+      "Which male person was a sibling of [Andrew Klavan]?",
+      "Did [Wallace Stevens] influence [Levi Seeley]'s spouse and parent?",
+      "Did a producer, writer, and art director of [Thelma & Luis] produce, direct, and write [Light Girls]?",
+      "Were [Hangover Square], [Zack and Miri Make a Porno], and [Clerks II] edited by a founder and employee of a film producer?",
+      "What American parent of [Charlie Sistovaris] was a British screenwriter's sibling?",
+      "Did [Anne Williams Rubinstein] marry a person that influenced a screenwriter and influenced [John Most]?",
+      "Was [Cach\u00fan cach\u00fan ra ra!]'s director a film director's American child?",
+      "Did [Maisy's Garden]'s executive producer write, edit, and executive produce [Pakalppooram], [It's Not About the Shawerma], [Rick's Canoe], and [The Fifth Wall]?",
+      "Was [Holly Ellenson]'s child [Wally Ellenson]?",
+      "Did [Emerald Cities]'s cinematographer, writer, and editor edit, executive produce, and direct [Blues for the Avatar] and [White Stork Is Coming]?",
+      "Was a film producer [Lilies of the Ghetto]'s distributor and producer?",
+      "Which child of [Mimi Iger] did a film producer employ and [The Walt Disney Company] employ?",
+      "What Japanese spouse of [Hong Kong Paradise]'s star did [Ineko Arima] and [Nishiki K\u00f4] marry?",
+      "Who influenced and was influenced by [Black Dynamite]'s star?",
+      "What was written by, edited by, directed by, produced by, and executive produced by [Pauline Collins]'s child's sibling?",
+      "Which Swedish film director that [Th\u00e9o Van Horn]'s actor influenced did [Egen ing\u0227ng] star?",
+      "Who was influenced by [Golden Yeggs]'s star, was influenced by [Richard Pryor], was influenced by [Bill Murray], and married [Elaine Chappelle]?",
+      "What did [This Is My Show]'s director, cinematographer, and star direct, edit, produce, and executive produce?",
+      "Who was a male costume designer and director of [Ene... due... like... fake...] and [The Windmill Bar]?",
+      "Was [Kumudu Munasinghe] a Dutch film producer's country of nationality's employee?",
+      "Did an art director, editor, director, writer, cinematographer, and star of [Tetsuo II: Body Hammer] produce [Nightmare Detective], [Tetsuo: The Iron Man], and [A Snake of June]?",
+      "Was [Alexandra Naoum] [Monsieur Verdoux]'s producer, writer, and star?",
+      "What film director founded [THX], was employed by [American Zoetrope], [LucasArts], [Skywalker Sound], and [Lucasfilm], and founded [Industrial Light & Magic]?",
+      "What male employee of [Weta Workshop] was [Bad Taste]'s editor?",
+      "Were [Weta Digital] and [Weta Workshop] founded by a cinematographer and founded by a film editor?",
+      "What art director influenced [DreamWorks Animation]'s founder?",
+      "Did [Daisies] star [Fruit of Paradise]'s costume designer and writer, star [Jarom\u00edr Vom\u00e1cka], and star [Jirina Myskova]?",
+      "What character was influenced by a costume designer, influenced by [Pedro Calder\u00f3n de la Barca], influenced by [William Shakespeare] and [Luis Bu\u00f1uel], and influenced by [Miguel de Unamuno]?",
+      "What British costume designer of [The Love Letter] and [The Chamber] was a screenwriter's child?",
+      "Was [Eric Massa] a cinematographer's parent's sibling's American sibling?",
+      "What art director of [Stepping Sisters 1932] was a parent of [Imre S\u00e1ndorh\u00e1zi]?",
+      "What was executive produced by, written by, produced by, and edited by a director of [V/H/S/2]'s sequel?",
+      "What did an editor and cinematographer of [Tongue Twister Variations] direct?",
+      "Who was a Canadian screenwriter that produced [Her Painted Hero] and [The Nick of Time Baby]?",
+      "Which American parent of [Janet Friedman] did [Rose Friedman] influence and marry?",
+      "Did [George Carlin] influence [Louis C.K.: Shameless]'s executive producer and influence [Joan Rivers]?",
+      "Who was a male writer, star, director, and costume designer of [The Wizard of Speed and Time]?",
+      "Who was [Lost Boys: The Thirst]'s prequel's sequel's art director?",
+      "Did a cinematographer's female parent executive produce, direct, and write [Hit Dat Shit 5]?",
+      "Who married [Siri von Essen], influenced [A Lesson in Love]'s director and art director, influenced [Tennessee Williams], and influenced [Maxim Gorky]?",
+      "What Italian film director directed [Children of Hannibal]?",
+      "What film producer directed, wrote, edited, and produced [la estrella], [la ardilla], and [el valiente]?",
+      "Were [Flames: The Movie] and [Soltera] directed by a male person and executive produced by [Hilda Russoff]'s spouse?",
+      "Was a sibling of [Fawwaz bin Abdulaziz Al Saud] [Badr bin Abdulaziz Al Saud]'s sibling?",
+      "What did a sibling of [Louise Rohr] executive produce, produce, and edit?",
+      "Did a French cinematographer of [Le Volcan interdit] edit [The Last Bolshevik] and direct [A.K.] and [Statues Also Die]?",
+      "Was [Mannai Thottu Kumbidanum] directed by and written by a Dutch male cinematographer?",
+      "Was a director, art director, executive producer, and costume designer of [But I'm a Genderqueer] [Lauren Soldano]?",
+      "Was [When We Were Kings] produced by a film editor whose spouse was employed by [Royal Academy of Dramatic Art] and distributed by [PolyGram Filmed Entertainment]?",
+      "Further discussion of the debatable questions:",
+      "Did [Wallace Stevens] influence [Levi Seeley]'s spouse and parent?",
+      "The occurrence of the seemingly implausible combination of roles \u201cspouse and parent\u201d is due to incorrect data in Freebase, in which there are 502 entities asserted to be both the spouse and parent of other entities. For instance, \u201cAnne Dacre\u201d is both the spouse and parent of \u201cChristopher Conyers\u201d. We can also find occasional occurrences in CFQ of other implausible role combinations, such as \u201cparent and child\u201d, \u201cspouse and sibling\u201d etc., triggered by similar Freebase data issues.",
+      "Was [Kumudu Munasinghe] a Dutch film producer's country of nationality's employee?",
+      "The somewhat unnatural phrasing of \u201ccountry's employee\u201d occurs due to a modeling choice in Freebase, in which the same entity is used to represent both a country and the government of that country. This makes it possible for a country to employ a person.",
+      "What character was influenced by a costume designer, influenced by [Pedro Calder\u00f3n de la Barca], influenced by [William Shakespeare] and [Luis Bu\u00f1uel], and influenced by [Miguel de Unamuno]?",
+      "The somewhat unnatural phrasing of \u201ca character was influenced by\u201d occurs due to a modeling choice in Freebase, in which when a film character is based on a real person, Freebase commonly uses the same entity to represent both. This makes \u201cperson\u201d and \u201ccharacter\u201d exchangeable in the questions where the person is also a film character."
+    ],
+    [
+      "Table TABREF85 shows the most frequently occurring answers in CFQ. Not surprisingly, after the answers \u201cYes\u201d and \u201cNo\u201d, entities related in Freebase to the domain of movies have highest frequency."
+    ],
+    [
+      "Figure FIGREF87 illustrates how subsampling changes the distribution of questions in CFQ with different levels of complexity to become more even."
+    ],
+    [
+      "Subsampling increases the frequency of rarely used rules and rule combinations and decreases the frequency of commonly used ones. For rules, this is illustrated by Figure FIGREF89 which shows the ratio of examples each rule appears in, before and after subsampling, in the order of their frequency. Figure FIGREF90 shows the same comparison for rule combinations."
+    ],
+    [
+      "Traditional compositionality experiments often use train-test splits based on observable properties of the input and output (e.g., input/output complexity, input/output patterns, and input/output feature holdouts). One consequence of this is that the difference between train and test examples is relatively easily observable \u201cwith the naked eye\u201d. The lists below illustrate that this is not usually the case for divergence-based splits. Similar to the random sample of the general data in Appendix SECREF9 we provide a random sample of size 20 from both the train and test set here. Indeed, even for the MCD$_{1}$ split with a high divergence of 0.694, the 20 random samples of train and test questions shown below cannot easily be distinguished as they both contain the same kind of questions of different sizes.",
+      "Train samples from MCD$_{1}$:",
+      "What was founded by a costume designer, founded by [Forgotten Silver]'s star, and founded by [Jamie Selkirk]?",
+      "Which male person influenced and was influenced by [William Dean Howells]?",
+      "Did [Marco Bellocchio] produce, write, and direct [Greek Pete]?",
+      "What did [Rick Schmidt] edit, [Philip Rashkovetsky] edit, and a cinematographer edit?",
+      "Were [The Living Playing Cards] and [The Haunted Castle] edited by, directed by, and produced by a French writer of [Le cauchemar de M\u00e9li\u00e8s]?",
+      "What did a spouse of [Shorts]'s producer's spouse executive produce and direct?",
+      "Did [P. G. Wodehouse], [Raymond Chandler], [Edward Bunker], [Pauline Kael], and [Michael Cimino] influence [Grindhouse]'s cinematographer and star?",
+      "What Mexican person did a film producer employ?",
+      "Did [The Midnight After]'s Chinese executive producer edit [Perfect Life] and [Dumplings]?",
+      "Who did [For the Secret Service]'s director's female spouse influence?",
+      "Who married, was influenced by, and influenced a company's founder?",
+      "Was [MAN SE]'s French male German employee's employer [Sulzer]?",
+      "Who influenced an actor that [Robin Santana] was influenced by and [K. J. Stevens] was influenced by and was influenced by [Virgil]?",
+      "Did [Pirates of Malaysia] star [Giuseppe Addobbati] and star a Spanish screenwriter?",
+      "Was [The Silence of the Sea] written by, produced by, executive produced by, directed by, and edited by [The Red Circle]'s French editor?",
+      "Did [Chanel] employ a German costume designer, employ [Gaspard Ulliel] and [Maureen Chiquet], and employ [Jacques Polge]?",
+      "Who was influenced by [Adam Sandler] and married a film producer?",
+      "Did a Spanish screenwriter's child direct and edit [Bakuchi-uchi: Nagaremono]?",
+      "Was a founder of [IG Port] employed by a film producer?",
+      "Was [Orizzonti Orizzonti!] executive produced by and written by an art director's sibling?",
+      "Test samples from MCD$_{1}$:",
+      "What sequel of [Paranormal Activity 2] was edited by and written by a film director?",
+      "What spouse of a film producer founded [Grand Hustle Records] and was employed by [40/40 Club], [Roc-A-Fella Records], and [Def Jam Recordings]?",
+      "Did [Pixar] employ an art director and employ [Susham Bedi]?",
+      "Was a sibling of [David Lindbland] [Dynamit Nobel]'s Swedish founder?",
+      "What prequel of [Charlie the Unicorn 2] starred, was edited by, was produced by, was written by, and was directed by [Jason Steele]?",
+      "Did [Rick Schmidt] direct, produce, executive produce, and edit [Blues for the Avatar], [White Stork Is Coming], [The Fifth Wall], and [It's Not About the Shawerma]?",
+      "Was [Luke Larkin Music] an art director's employer?",
+      "What prequel of [Goat Story 2] was executive produced, written, directed, edited, and produced by [Jan Tom\u00e1nek]?",
+      "Was [Bullet Ballet]'s editor, star, director, and cinematographer [Promises Written in Water]'s star, director, writer, executive producer, and art director?",
+      "What was edited by, produced by, directed by, and written by [Ellis Kaan Ozen], [Thaw Bwe], [Jeffrey Malkofsky-Berger], and [Leslie Berkley]?",
+      "Was a person's female sibling [Reggae in a Babylon]'s producer?",
+      "Who was a director, cinematographer, executive producer, art director, producer, star, and writer of [The Man Who Killed God]?",
+      "Was [My Sweet Home]'s director, editor, writer, art director, producer, cinematographer, and costume designer a person?",
+      "Which art director, star, and editor of [The Brown Bunny] and [Promises Written in Water] did [Cord] star?",
+      "Did an employee and founder of [Virgin Mobile Australia], [Virgin Mobile USA], and [Virgin Mobile France] found [Virgin America] and found [V2 Records]?",
+      "Was a Chinese executive producer and star of [Happy Ghost II] and [All's Well, Ends Well 2010] a film director?",
+      "Was [The Voyeur]'s executive producer an actor's parent?",
+      "Did [Erasable Cities]'s writer, producer, editor, art director, cinematographer, and director produce and executive produce [Promises Written in Water]?",
+      "Who was an editor, star, and cinematographer of [Tetsuo: The Iron Man], [A Snake of June], and [Bullet Ballet]?",
+      "Was a costume designer's employer [Philips High School]?"
+    ],
+    [
+      "Figure FIGREF133 shows the frequency of atoms (upper graph) and compounds (lower graph) in the train and test sets of the maximum compound divergence split for the CFQ data. As the frequency of an atom resp. compound we use the fraction of examples it appears in. Both atoms and compounds are indexed primarily by their frequency in the train set, secondarily by their frequency in the test set, in decreasing order. For practical reasons we only look at a small subset of compounds here but we believe the analysis is representative.",
+      "We can see that the frequency of atoms in the two sets is very aligned and that all atoms from the test set appear in the train set. The frequency of compounds however is wildly different: While some invariably occur in both sets, the frequencies are often not aligned and most compounds appear only in either the train or the test set."
+    ],
+    [
+      "The experiments were run using the tensor2tensor framework BIBREF39 with some of the hyperparameters tuned using a random split of a previous, smaller version of the data set during development. We use the default hyperparameter sets publicly available in the tensor2tensor implementation (obtained from https://github.com/tensorflow/tensor2tensor) and override the tuned hyperparameters. The hyperparameters used are summarized in Table TABREF134."
+    ],
+    [
+      "Table TABREF136 shows a more detailed analysis of the errors that the baseline models make on CFQ for MCD$_{1}$ (compare Section SECREF24). The reported errors are bucketized into three main types: sparql property clause error, sparql filter clause error and malformed sparql query in the model's output. The total number of test set examples exhibiting any clause or filter error is reported (sum column), as well as the number of insertions (ins), deletions (del), and substitutions (sub) in the model's output with respect to the correct query. Property clause substitution errors are further subdivided into those where only the property itself is wrong while subject and object are correct (prop), those where the property is correct but either subject or object is wrong (node) and those where both the property and the subject or the object are wrong (both).",
+      "The accuracy metric requires the model response and the golden (correct) answer to be exactly equal to each other. Thus, a sparql query with the same clauses as the golden answer but in a different order or with some of the clauses appearing multiple times is also considered to be an error despite being equivalent to the golden answer in its meaning. The amount of such errors is relatively small though, accounting for 1.8%, 0.6% and 1.5% of total test set size for LSTM+Attention, Transformer and Universal Transformer respectively."
+    ],
+    [
+      "Below we qualitatively analyze a number of instances the models fail on. We anonymize the MIDs in the same way as the data is provided to the models (see Section SECREF5). We first select queries on which all machine learning systems fail in all replicated runs (about 5k instances out of a total of about 12k), and then randomly select queries from this list. In the following we discuss a few cases in more detail. Note that, for readability, we use the following abbreviations for the sparql properties in Query 1:",
+      "ns:people.person.child = ns:people.person.children|",
+      "ns:fictional_universe.fictional_character.children|",
+      "ns:organization.organization.child/",
+      "ns:organization.organization_relationship.child",
+      "ns:people.person.sibling = ns:people.person.siblings/",
+      "ns:people.siblingrelationship.sibling|",
+      "ns:fictionaluniverse.fictionalcharacter.siblings/",
+      "ns:fictionaluniverse.",
+      "siblingrelationshipoffictionalcharacters.siblings",
+      "Query 1: \u201cWhat sibling of M0 was M1' s parent?\u201d",
+      "Golden (correct) sparql query:",
+      "SELECT DISTINCT ?x0 WHERE {",
+      "?x0 ns:people.person.child M1 .",
+      "?x0 ns:people.person.sibling M0 .",
+      "FILTER ( ?x0 != M0 )",
+      "}",
+      "Inferred (system) sparql query:",
+      "SELECT DISTINCT ?x0 WHERE {",
+      "?x0 ns:people.person.sibling ?x1 .",
+      "?x0 ns:people.person.sibling M0 .",
+      "?x1 ns:people.person.child M1 .",
+      "FILTER ( ?x0 != ?x1 )",
+      "}",
+      "Analysis. The meaning of the sparql query generated by the system is \u201cWhat sibling of M0 was a sibling of M1's parent?\u201d, which is incorrect. We next analyze the train set, in order to show that we believe enough information has been provided in the train set for the question to be answered correctly.",
+      "Some subqueries of the query and their occurrences are shown in Table TABREF140. While the exact subquery \u201cWhat sibling\u201d does not occur at training, the two words have been shown separately in many instances: the subqueries \u201csibling of Mx\u201d, and \u201cMx's parent\u201d occur 2,331 and 1,222 times, respectively. We can analyze this example in more detail by comparing parts of the rule tree of this example with those shown at training. As can be read from the table, similar sentences have been shown during training. Some examples are:",
+      "What was executive produced by and written by a sibling of M0?",
+      "What costume designer did M1's parent employ?",
+      "What cinematographer was a film editor that M2 and M3 married?",
+      "What film director was a character influenced by M2?",
+      "Query 2: \u201cDid a male film director edit and direct M0 and M1?\u201d",
+      "Golden (correct) sparql query:",
+      "SELECT count ( * ) WHERE {",
+      "?x0 ns:film.director.film M0 .",
+      "?x0 ns:film.director.film M1 .",
+      "?x0 ns:film.editor.film M0 .",
+      "?x0 ns:film.editor.film M1 .",
+      "?x0 ns:people.person.gender m_05zppz",
+      "}",
+      "Inferred (system) sparql query:",
+      "SELECT count ( * ) WHERE {",
+      "?x0 ns:film.director.film M0 .",
+      "?x0 ns:film.director.film M1 .",
+      "?x0 ns:film.editor.film M0 .",
+      "?x0 ns:people.person.gender m_05zppz",
+      "}",
+      "Analysis. The meaning of the inferred sparql query is \u201cDid a male film director edit M0 and direct M0 and M1?\u201d. It thus seems the model `forgets' to include the relation between the director and movie M1.",
+      "Looking at subqueries and their occurrence count (Table TABREF145), we see again that various subqueries occur often during training. However, \u201cedit and direct\u201d have not been shown often together. When looking at the rule trees, we see that both conjunctions in the query occur often at training separately: \u201cDid [DetNP] [VP] and [VP] [DetNP]\u201d occurs 1,432 times, and \u201cDid [DetNP] [VP] [Entity] and [Entity]\u201d occurs 909 times. However, they never occur together: \u201cDid [DetNP] [VP] and [VP] [DetNP] and [DetNP]\u201d does not occur at training. This may be the reason why all systems fail on this example, but at the same time we believe a compositional learner should be able to generalize correctly given the training instances. Some examples are:",
+      "Did a male film director that M3's parent married influence an art director?",
+      "Did a film producer that played M2 edit and direct M1?",
+      "Did a screenwriter edit and direct a sequel of M1",
+      "Did a Chinese male film director edit M1 and M2?"
+    ],
+    [
+      "",
+      "Figure FIGREF150 shows a scatter plot of accuracy vs. compound divergence for the three baseline architectures (see Section SECREF5) on existing splits of the scan data. These splits are discussed in BIBREF2 and BIBREF6, and the exact split data is available. (Data splits obtained from https://github.com/brendenlake/SCAN). We map these splits onto the re-created scan data, which enables us to measure the atom and compound divergences. The authors present a total of six split experiments (some with several sub-experiments):",
+      "",
+      "BIBREF2:",
+      "simple (random)",
+      "by action sequence length",
+      "adding a primitive and adding a primitive along with complex combinations",
+      "BIBREF6:",
+      "adding a template",
+      "adding template fillers",
+      "adding more training examples of fillers (fewshot)",
+      "",
+      "In the plot, we omit some data points that are too close to be distinguished easily. The point labels have the form `(abbreviated experiment name)<(parameter)>@(number of samples) (baseline system abbreviation) [(train set size fraction), (split atom divergence)]'. The train set size fraction is given as a percentage of the overall data size. The baseline system abbreviations are LSTM, T for Transformer, UT for Universal Transformer, T/UT where both transformer models are indistinguishable, and empty where all three systems perform indistinguishably. The abbreviated experiment name is one of the names in italics above.",
+      "We can observe a strong dependency of the accuracies on the compound divergence of the data split. Again, this seems to indicate that the compound divergence is correlated with accuracy for these baseline architectures. One difference to the data shown in Figure FIGREF28(b) is that for this set of experiments the accuracy drops faster with increasing compound divergence. One explanation for this effect is that the experiments are directly aimed at highlighting one specific potentially problematic scenario for learning. E.g. in the experiment `primitive<jump>' (with very low accuracies for all three systems) the jump command is shown exactly in one combination (namely alone) in the training data while it occurs in all test examples in arbitrary combinations.",
+      "This is reflected in the higher atom divergence value of 0.08 for this split, as well as in all other splits that exhibit a low accuracy at a low compound divergence in Figure FIGREF150. Note that BIBREF2 already compare the experiment `primitive<jump>' to the experiment `primitive<turn left>' for which all three systems achieve a much higher accuracy. In their interpretation of this phenomenon, they mainly focus on the fact that in contrast to 'jump', the action 'turn left' is also generated by other inputs. We additionally observe that the latter experiment also has a slightly lower atom divergence of 0.07, a lower compound divergence, and it covers a much larger part of the data in the train set (94% vs. 63%).",
+      "While the accuracies we observe for the `primitive' experiments are very much in line with the results reported by BIBREF2, we noticed a few interesting differences for other experiments: All three systems go to 100% accuracy on the fewshot task even for one example (while BIBREF6 report a slowly increasing accuracy for the architecture they evaluate). On the other hand, both transformer models only reach 0% accuracy on the length split, while the LSTM obtains around 14% (which is in line with what previous work reports)."
+    ],
+    [
+      "Figure FIGREF28 shows for all baseline systems a strong correlation between accuracy and compound divergence for the chosen training sizes (96k for CFQ and 8k for scan). One interesting question is whether and how this correlation is changed for different training sizes. Figures FIGREF159 and FIGREF159 show that this correlation holds also for smaller training sizes but that the accuracy is generally somewhat lower for smaller training sizes.",
+      "At the same time, we observe that the difference between accuracies of various training sizes gets smaller as the training size increases. This can be seen even more clearly in Figures FIGREF159 and FIGREF159, which plot the training size rather than the compound divergence on the x-axis. These figures show that the increase in accuracy flattens out significantly as we reach training size of about 80k for CFQ and about 6k for SCAN. This indicates that further increasing train set size may not be sufficient to do well on these compositionality experiments."
+    ],
+    [
+      "To represent our logical form we use syntax of the description logic $\\mathcal {EL}$ BIBREF14, BIBREF15 with additional concept and role constructors. These constructors do not have description logic semantics; instead, their meaning is completely determined by the set of generation rules of the CFQ dataset.",
+      "Let $A$ be a concept name, $C, C_1, C_2$ be concepts, $R, R_1, R_2$ be roles, and $v$ be a raw string. Then the following would be concepts:",
+      "and the following would be roles:",
+      "Note that our logical form does not have roles other than those in a form of RolePair($C_1$, $C_2$).",
+      "New strings are generated by using a special function new_var($\\$S$). This function generates a unique string of the form ?x<N>, where N is a unique number, and assigns that string to variable $\\$S$. This string can later be used as a variable in a sparql constraint."
+    ],
+    [
+      "This section describes the format of each of the rule types we use for generating the CFQ dataset, in the form in which they appear in the rules index in Appendix SECREF20.",
+      "General formatting conventions shared across all rule types:",
+      "Variable names are prefixed by `$'. Example: $X.",
+      "(Exception: In grammar rules, while variables standing for constants are prefixed by `$', variables standing for logical forms are prefixed by `_'. Example: _action.)",
+      "Concept names are written in camel case. Example: FilmProducer.",
+      "Names of functions that output logical forms (concepts, roles, or knowledge) are also written in camel case. Examples: DropDependency, BoundRolePairs, RolePair.",
+      "Names of functions that output string literals or which are used for converting logical forms to sparql are written in lowercase with underscores. Examples: def2sparql, get_specializations, new_var.",
+      "String literals are enclosed in single quotes. Example: 'ns:film:director'."
+    ],
+    [
+      "The CFQ grammar is a unification-based grammar of recursive rewriting rules used to generate pairs of strings and their corresponding logical form. For an introductory overview of unification-based grammars including several popular variations, see BIBREF38. The rules in the CFQ grammar follow a similar syntax in particular to that used in the Prolog extension GULP 3.1 BIBREF16, with the addition of support for disjunction, negation, absence, and default inheritance of features, and with minor differences in formatting described below.",
+      "Properties shared between the CFQ grammar syntax and that of BIBREF16 include the following:",
+      "Grammar rules are notated as variations of context-free phrase-structure rules of the form $T_{0} \\rightarrow T_{1}$ ... $T_{n}$, where each of the syntactic non-terminals and terminals $T_{0}$ ... $T_{n}$ are augmented with feature lists in parentheses.",
+      "Each grammar rule can be interpreted as specifying how a feature structure (with logical form) that is unifiable with the lefthand side can be re-written to the sequence of features structures (with logical form) indicated on the righthand side.",
+      "Features are represented as attribute-value pairs separated by a colon (i.e., $attribute$:$value$).",
+      "Shared values in feature structures are represented through the use of variables.",
+      "Specifically, in the rules index, CFQ grammar rules are described in the format",
+      "$T_{0}(F_{0})[H]/L_{0} \\rightarrow T_{1}(F_{1})/L_{1}$ ... $T_{n}(F_{n})/L_{n}$",
+      "where:",
+      "Each $T_{i}$ is a syntactic category (syntactic nonterminal) or a string literal (syntactic terminal).",
+      "Each $L_{i}$ for $i \\in [1, n]$ is either a variable representing a logical form or an empty string. In the case when $L_{i}$ is an empty string, we allow dropping the trailing slash from the $T_{i}(F_{i})/L_{i}$ expression, resulting in just $T_{i}(F_{i})$.",
+      "$L_{0}$ is a logical form expressed in terms of $L_{1}...L_{n}$.",
+      "Each $F_{i}$ is a comma-separated feature list of the form $(attribute_{1}$:$value_{1}$, ..., $attribute_{k}$:$value_{k})$. In the case where $F_{i}$ is empty, we allow dropping the parentheses from the $T_{i}(F_{i})$ expression, resulting in just $T_{i}$.",
+      "$H$ is either an empty string or one of the variables $L_{i}$ for $i \\in [1, n]$, indicating that $F_{0}$ default inherits the features of $F_{i}$ (the syntactic \u201chead\u201d). In the case where $H$ is an empty string, we allow dropping the brackets from the $T_{0}(F_{0})[H]$ expression, resulting in just $T_{0}(F_{0})$.",
+      "Note that while the above notation adopts the convention of splitting out the syntactic category and logical form from the feature list for visual prominence and to highlight the relationship to its context-free phrase-structure rule core, behaviorally it is identical to adding two more features to the feature list (we can call them, for example, $cat$ and $sem$) to represent the syntactic category and logical form.",
+      "This means that, for example, the rule",
+      "ACTIVE_VP[_head]/_head",
+      "$\\rightarrow $ VP_SIMPLE(form:infinitive)/_head",
+      "can be considered a notational shorthand for the following rule expressed purely using feature lists:",
+      "(cat:ACTIVE_VP, sem:_head)[_head]",
+      "$\\rightarrow $ (cat:VP_SIMPLE, sem:_head, form:infinitive)",
+      "Disjunction of features. Similarly to BIBREF37, we allow disjunctive feature specifications, which we denote by separating the alternative values with a pipe (`$|$'). The feature specification (form:gerund|infinitive) would thus unify with either (form:gerund) or (form:infinitive), but not with (form:pastparticiple).",
+      "Absence of features. We use a special atomic value _none_ to indicate that a given feature must either be absent or else explicitly set to the value _none_. The feature specification (subject:_none_, object:yes) would thus unify with either (object:yes) or (subject:_none_, object:yes), but not with (subject:yes, object:yes).",
+      "Negation of features. Similarly to BIBREF37, we allow negated feature specifications, which we denote by prefixing the attribute with a minus sign (`-'). The feature specification (-form:gerund|infinitive) would thus unify with (form:pastparticiple) or (form:_none_), but not with (form:gerund) or (form:infinitive). In general, a feature specification of the form (-attribute:v$_{1}$|...|v$_{j}$) can be considered a notational shorthand for (attribute:v$_{j+1}$|...|v$_{k}$|_none_), where v$_{j+1}$|...|v$_{k}$ is an enumeration of all possible values of the feature attribute other than v$_{1}$|...|v$_{j}$.",
+      "Default inheritance of features. If the lefthand side term is notated as $T_{0}(F_{0})[H]$, with $H$ equal to one of the variables $L_{i}$ for $i \\in [1, n]$, then this is interpreted as a notational shorthand for augmenting both $F_{0}$ and $F_{i}$ with an additional list of attribute-value pairs $(a_{1}$:$\\$v_{1}, ..., a_{k}$:$\\$v_{k})$, where $a_{1} ... a_{k}$ are all of the attributes listed in $F_{i}$ that were not originally listed in $F_{0}$.",
+      "Unification of logical forms. As described in Appendix SECREF16, we represent logical forms using a variation of description logic, rather than using feature structures. In the context of unification, we consider logical forms to unify if and only they achieve structural concept equality after variable replacement (using the same variable replacements applied during unification of the corresponding feature lists), while taking into account the commutativity and associativity of $\\sqcap $. For example, under this criterion, the logical form GenderRel $\\sqcap $ $\\exists $RolePair(Predicate, Gender)._head would unify with either GenderRel $\\sqcap $ $\\exists $RolePair(Predicate, Gender).Male or with ($\\exists $RolePair(Predicate, Gender).Male) $\\sqcap $ GenderRel under a variable replacement mapping _head to Male, but would not unify with GenderRel $\\sqcap $ $\\exists $RolePair(Predicate, Gender).Male $\\sqcap $ $\\exists $RolePair(Predicate, GenderHaver).FilmProducer."
+    ],
+    [
+      "CFQ knowledge rules output expressions representing facts that are known to be true. They have no direct effect on text, logical forms, or sparql, but the generated knowledge can be used as preconditions to other rules. In the rules index, they are described in the following format:",
+      "$\\rightarrow K$, where $K$ is knowledge that is output.",
+      "By convention, we define the rule name of a knowledge rule to be simply the string representing the knowledge that the rule outputs, and we omit the rule name in the rules index for brevity.",
+      "The union of those rules defines a knowledge base which we denote with $KB^{CFQ}$.",
+      "All knowledge in CFQ is represented in the form $P(X_1,...,X_n)$, where $P$ is a predicate from the list below, and $X_1, ..., X_n$ are either logical forms or else raw strings. Knowledge rules do not use variable-based expressions.",
+      "Supported knowledge predicates:",
+      "BoundRolePairs",
+      "ExclusiveRolePair",
+      "FreebaseEntityMapping",
+      "FreebasePropertyMapping",
+      "FreebaseTypeMapping",
+      "NonExclusiveRolePair",
+      "Role"
+    ],
+    [
+      "CFQ inference rules transform logical forms and may be conditioned on knowledge. In the rules index, they are described in the following format:",
+      "$K: L_0 \\rightarrow L_1$",
+      "where $K$ represents a comma-separated list of knowledge preconditions, and $L_0$ and $L_1$ represent the input and output logical forms, all expressed in terms of a shared set of variables $v_1,...,v_m$.",
+      "These rules are interpreted as stating that if there exists a variable replacement $r()$ replacing $v_1,...,v_m$ with some logical forms $l_1,...,l_m$ respectively, such that $r(K) \\subseteq KB^{CFQ}$, then we can apply the inference rule by rewriting $r(L_0)$ to $r(L_1)$."
+    ],
+    [
+      "CFQ resolution rules transform sparql expressions and may be conditioned on knowledge. They do not affect text or logical forms.",
+      "In the rules index, they are described in the following format:",
+      "$K: S_0 \\rightarrow S_1~...~S_n$",
+      "where $K$ represents a comma-separated list of knowledge preconditions, $S_0$ is a variable-based expression and $S_1~...~S_n$ are either raw sparql strings or else expressions described in terms of the same variables used in $S_0$ and $K$.",
+      "These rules are interpreted as stating that if there exists a variable replacement $r()$ replacing $v_1,...,v_m$ with some logical forms, strings, or expressions $l_1,...,l_m$ respectively, such that $r(K) \\subseteq KB^{CFQ}$, then we can apply the resolution rule by rewriting $r(S_0)$ to the sequence of terms $r(S_1)~...~r(S_n)$."
+    ],
+    [
+      "Our generation algorithm produces triples of the form $\\langle \\text{question, logical form, \\textsc {sparql}{} query} \\rangle $ in a mixed top-down and bottom-up fashion, with the final program of rule applications output alongside each triple in the form of a rule application DAG. The top-down portion of generation is responsible for efficiently searching for rules that can be applied to produce a meaningful example, while the bottom-up portion is responsible for actually applying the rules (i.e., performing the composition) and for producing the DAG.",
+      "The generation process proceeds in two phases, each involving a top-down as well as bottom-up aspect. In the first phase, we apply grammar rules interleaved with inference rules to produce a pair of $\\langle \\text{question, logical form} \\rangle $. Specifically, we apply a recursive top-down algorithm which starts with the $S$ nonterminal and at every step performs a random search over the rules in the grammar which could produce the target nonterminal with accompanying feature structure. This top-down process proceeds until a candidate syntactic parse tree is attained whose leaves consist purely of syntactic terminals (i.e., string literals or entity placeholders). The grammar rules from this candidate parse tree are then applied in a bottom-up fashion beginning with the syntactic terminals to yield a tree of $\\langle \\text{text, logical form} \\rangle $ pairs. After each such bottom-up grammar rule application, we then greedily apply all possible inference rules on the resulting logical forms, applying an arbitrary deterministic ordering to the inference rules in cases where rules could be applied in multiple valid orderings. This ensures that inference rules and grammar rules are executed in an interleaved manner and each inference rule is applied at the earliest possible occasion.",
+      "When a $\\langle \\text{question, logical form} \\rangle $ pair is generated for the $S$ nonterminal, we proceed to the second phase of the algorithm, in which resolution rules are applied to generate a corresponding sparql query to make up the third element of the desired $\\langle \\text{question, logical form, \\textsc {sparql}{} query} \\rangle $ triple. In practice, the bulk of the work in this phase is performed in a top-down fashion, in which resolution rules are recursively applied to transform a starting expression of the form get_specializations($L) (where $L represents the logical form output from the grammar phase) into a sequence of text literals representing the sparql query. This is followed nominally by a bottom-up process to construct the rule application DAG, yielding a tree of resolution rule applications of a similar form to the tree of interleaved grammar and inference rules output from the grammar phase. Note that while the grammar phase involves a large degree of random choice, the resolution phase proceeds much more deterministically, as the CFQ resolution rules have been designed such that any given question can yield only one possible sparql query, modulo commutativity and associativity of $\\sqcap $. In cases where resolution rules could be applied in multiple valid orderings, we again apply an arbitrary deterministic ordering to the resolution rules so as to yield as consistent as possible a rule application DAG and $\\langle \\text{question, logical form, \\textsc {sparql}{} query} \\rangle $ triple for any given question.",
+      "Finally, to ease the task of tracking unique query patterns and to minimize the impact on the learning task of implementation details regarding choice of variable names or ordering of clauses, we normalize the final sparql query by alphabetically sorting the query clauses and re-numbering the variables to follow a standard increasing order.",
+      "The resulting $\\langle \\text{question, logical form, \\textsc {sparql}{} query} \\rangle $ triple is then appended to the CFQ dataset."
+    ],
+    [
+      "In general, we do not explicitly track rules to represent the example-independent behaviors of the generation algorithm, as the universal applicability of these rules mean that the complete behavior of the generator should be observable on any reasonably-sized train set. The same applies to certain core behaviors of the description logic $\\mathcal {EL}$, such as commutativity and associativity of $\\sqcap $, which we omit tracking as explicit rules due to their similar ubiquity of application.",
+      "One example-independent rule, however, that we do explicitly track is the rule that describes the handover process between the grammar phase and the resolution phase \u2013 or in terms of the rule application DAG, the rule that joins the tree of interleaved grammar and inference rule applications with the tree of resolution rule applications. We call this rule JOIN_BY_LOGICAL_FORM. It is included in the rules list for every example in CFQ and appears as the head of the rule application tree for each example."
+    ],
+    [
+      "Note that conceptually a similar approach for combining the different rule types could be applied to the semantic parsing task. The main difference would be that, instead of performing random search over the grammar, the semantic parsing task would need to find the set of rules which produce the desired input text."
+    ],
+    [
+      "For many domains, the set of examples generated by exhaustively combining rules is infinite or prohibitively large. For example, the CFQ grammar generates an infinite set of questions, and even when restricted to a reasonable complexity, the set is still too large for practical use. This means that we need to choose which subset of examples we want to include in our dataset. Given our goal of comprehensively measuring compositional generalization, we do this by:",
+      "maximizing the overall diversity of rule combinations (allowing us to test as many rule combinations as possible)",
+      "while using a uniform distribution from simple examples to increasingly more complex examples.",
+      "We measure the diversity of rule combinations of a dataset using the empirical entropy over the frequency distribution of the subgraphs of the rule application DAGs, and we measure the complexity of an example using the number of rule applications used to generate it.",
+      "For CFQ, we choose the following practical trade-off between these two criteria. We first generate a sufficiently large sample set by performing random rule applications. We then subsample from it to select a subset that maximizes the entropy of the subgraph distribution (while only taking into account subgraphs with a limited number of nodes for practicality). We use a greedy algorithm that incrementally assigns elements to the subsampled set while maximizing entropy at each step.",
+      "The subsampling is initially limited to examples with the smallest complexity level and continues with increasingly larger complexity levels. We cap the maximum number of examples per level to achieve a uniform distribution across levels, and we limit the maximum complexity level such that the questions remain relatively natural. Table TABREF15 shows examples of generated questions at varying levels of complexity."
+    ],
+    [
+      "Figures FIGREF190 through FIGREF192 show the rule application DAG that was produced when generating the question \u201cWho directed [entity]?\u201d. They illustrate how grammar, inference, and knowledge rules are combined to generate a pair of text and logical form, and how resolution rules are used to generate the sparql query for the resulting logical form."
+    ],
+    [
+      "As discussed in Section SECREF3, nodes of this DAG represent rule applications while edges represent dependencies among the rules; i.e., an edge $A \\rightarrow B$ means that rule $B$ strictly depends on rule $A$ in the sense that the generator cannot apply rule $B$ before applying rule $A$. The DAG is normalized to ensure that a certain rule combination is represented using the same DAG across all the examples where it occurs. This is important for meaningfully comparing measures such as entropy and divergence across subgraphs of different examples.",
+      "Specifically, together with adopting the measures described above to ensure that rules are applied in a deterministic order, we achieve the normalization of the DAG by only producing edges that represent \u201cminimal dependencies\u201d. This means that if a rule $A$ can be applied after rule $B$, but it could also be applied after rule $B^{\\prime }$ with $B \\rightarrow B^{\\prime }$ (i.e., $B^{\\prime }$ depends on $B$), we don't produce the edge $B^{\\prime } \\rightarrow A$."
+    ],
+    [
+      "For brevity, in the rule application DAG figures we have applied the following abbreviations for several lengthy concept names:",
+      "Director = FilmDirector",
+      "Directee = DirectedFilm",
+      "Directing = DirectingAFilm",
+      "SubjectAgentVerb = PredicateWithBoundRolePairs(RolePair( SubjectHaver, Subject), RolePair(Predicate, Agent))",
+      "ObjectUndergoerVerb = PredicateWithBoundRolePairs(RolePair( ObjectHaver, Object), RolePair(Predicate, Undergoer))",
+      "E1 = Entity('?E1')"
+    ],
+    [
+      "As described in Section SECREF16, during generation we initially generate a $\\langle \\text{question, logical form, \\textsc {sparql}{} query} \\rangle $ triple containing entity placeholders, and then replace those placeholders with specific entities as a post-processing step. Conceptually, one could construct a rule application DAG describing either the process by which the original $\\langle \\text{question, logical form, \\textsc {sparql}{} query} \\rangle $ triple with entity placeholders was generated, or alternatively the rules that would need to be applied if constructing the $\\langle \\text{question, logical form, \\textsc {sparql}{} query} \\rangle $ triple containing the final entity MIDs directly. Structurally, these two DAGs are identical, differing only in the definition of two entity-related rules described below. The rule application DAG shown in the accompanying figures is the version using entity placeholders.",
+      "Versions of entity rules applicable when using entity placeholders:",
+      "ENTITY=[ENTITY]_HSz7QrdGdsX:",
+      "ENTITY(number:singular)/Entity(new_var(V1))",
+      "$\\rightarrow $ '[entity]'",
+      "ENTITY_MID:",
+      "ent2sparql(Entity($X)) $\\rightarrow $ $X",
+      "Versions of entity rules applicable when using actual entity MIDs:",
+      "ENTITY=[ENTITY]_HSz7QrdGdsX:",
+      "ENTITY(number:singular)/'m.'$X",
+      "$\\rightarrow $ 'm.'$X",
+      "ENTITY_MID:",
+      "ent2sparql('m.'$X) $\\rightarrow $ 'ns:m.'$X"
+    ],
+    [
+      "Figure FIGREF203 shows an example of subgraphs in order to provide more details on the sampling and weighting of compounds. An example non-linear subgraph is highlighted by the red area, and two linear subgraphs are highlighted by the blue and the yellow areas, respectively.",
+      "As described in Section SECREF6, given a large subset $\\mathbb {G}$ of subgraphs from the sample set as a whole, we calculate for each sample the weight of each subgraph $G \\in \\mathbb {G}$ that occurs in that sample as:",
+      "where $\\text{occ}(G)$ is the set of all occurrences of $G$ in the sample, $\\prec $ denotes the strict subgraph relation, and $P(G^{\\prime }| G)$ is the empirical probability of $G^{\\prime }$ occurring as a supergraph of $G$ over the full sample set.",
+      "Intuitively, we are trying to estimate how interesting the subgraph $G$ is in the sample. First, for every occurrence $g$ of a subgraph $G$, we look for the supergraph $G^{\\prime }$ of $g$ that co-occurs most often with $G$ in the full sample set. The empirical probability of having $G^{\\prime }$ as a supergraph of $G$ determines how interesting the occurrence $g$ is \u2013 the higher this probability, the less interesting the occurrence. Thus we compute the weight of the occurrence as the complement of this maximum empirical probability. Then we take the weight of $G$ to be the weight of the most interesting occurrence $g$ of $G$ in the sample.",
+      "E.g. in the extreme case that $G$ only occurs within the context $G^{\\prime }$, the weight of $G$ will be 0 in all samples. Conversely, if $G$ occurs in many different contexts, such that there is no single other subgraph $G^{\\prime }$ that subsumes it in many cases, then $w(G)$ will be high in all samples in which it occurs. This ensures that when calculating compound divergence based on a weighted subset of compounds, the most representative compounds are taken into account, while avoiding double-counting compounds whose frequency of occurrence is already largely explainable by the frequency of occurrence of one of its super-compounds.",
+      "Returning to our example in Figure FIGREF203, suppose that $G$ represents the smallest linear subgraph (yellow area), and suppose that the weight of $G$ in this sample is 0.4. Then this means that there exists some other subgraph $G^{\\prime }$ (for instance, the linear subgraph highlighted by the blue area) that is a supergraph of $G$ in 60% of the occurrences of $G$ across the sample set."
+    ],
+    [
+      "Below is a selection of the rules used in the generation of CFQ. Specifically, this includes all rules involved in generating the question \u201cWho directed [entity]?\u201d (the same example illustrated in the rule application DAG in Appendix SECREF19). The format of the rules is discussed in Appendix SECREF17."
+    ],
+    [
+      "S=WHQ_F6E9egkQqxj:",
+      "S/_x",
+      "$\\rightarrow $ WHQ/_x",
+      "WHQ=NPQ_INDIRECT_VP_INDIRECT_TXCca9URgVm:",
+      "WHQ[_subject]/DropDependency(_subject) $\\sqcap $ DropDependency($\\exists $RolePair(Subject, SubjectHaver)._action)",
+      "$\\rightarrow $ NPQ_INDIRECT(is_what:_none_, number:$n)/_subject",
+      "VP_INDIRECT(form:past, number:$n, object:yes, subject:_none_)/_action",
+      "NPQ_INDIRECT=WHO_5ptbPXXbuLZ:",
+      "NPQ_INDIRECT(number:singular)/Person",
+      "$\\rightarrow $ 'who'",
+      "VP_INDIRECT=VP_INDIRECT_DP_ZJH4NhRkByc:",
+      "VP_INDIRECT(object:yes)[_action]/_action $\\sqcap $ $\\exists $RolePair(ObjectHaver, Object)._object",
+      "$\\rightarrow $ VP_INDIRECT(object:_none_, subject:_none_)/_action",
+      "DP/_object",
+      "VP_INDIRECT=ACTIVE_VP_RX51Tm7RXPe:",
+      "VP_INDIRECT(object_type:$ut, subject_type:$at)[_head]/_head $\\sqcap $ PredicateWithBoundRolePairs(RolePair(SubjectHaver, Subject), RolePair(Predicate, Agent)) $\\sqcap $ PredicateWithBoundRolePairs(RolePair(ObjectHaver, Object), RolePair(Predicate, Undergoer))",
+      "$\\rightarrow $ ACTIVE_VP(agent_type:$at, undergoer_type:$ut)/_head",
+      "ACTIVE_VP=VP_SIMPLE_hJqAyjRUYJp:",
+      "ACTIVE_VP(number:singular)[_head]/_head",
+      "$\\rightarrow $ VP_SIMPLE(form:past)/_head",
+      "VP_SIMPLE=VP_GHWf3fcVRZg:",
+      "VP_SIMPLE(agent_type:person, undergoer_type:movie)[_head]/_head",
+      "$\\rightarrow $ VP(concept_id:DirectingAFilm)/_head",
+      "VP=DIRECTED_JkYzNbQyXtv:",
+      "VP(concept_id:DirectingAFilm, form:past)/DirectingAFilm",
+      "$\\rightarrow $ 'directed'",
+      "DP=ENTITY_M6fSP5GvRaN:",
+      "DP(is_proper_noun:yes, number:singular)[_head]/_head",
+      "$\\rightarrow $ ENTITY/_head",
+      "ENTITY=[ENTITY]_HSz7QrdGdsX:",
+      "ENTITY(number:singular)/Entity(new_var(V1))",
+      "$\\rightarrow $ '[entity]'",
+      "... (211 grammar rules total)"
+    ],
+    [
+      "BOUND_ROLES_WITH_PREDICATE_OBJECT:",
+      "BoundRolePairs($A, RolePair($R, $Q), RolePair($T, $S)):",
+      "$\\exists $RolePair($Q, $R).($A $\\sqcap $ $B) $\\rightarrow $ $\\exists $RolePair($S, $T).($A $\\sqcap $ $B)",
+      "BOUND_ROLES_WITH_PREDICATE_SUBJECT:",
+      "BoundRolePairs($B, RolePair($Q, $R), RolePair($S, $T)):",
+      "$B $\\sqcap $ $\\exists $RolePair($Q, $R).$A $\\rightarrow $ $B $\\sqcap $ $\\exists $RolePair($S, $T).$A",
+      "IGNORE_BOUND_ROLE_PAIRS:",
+      "$A $\\sqcap $ PredicateWithBoundRolePairs($X, $Y) $\\rightarrow $ $A",
+      "IGNORE_DEPENDENCY_DROPPING:",
+      "DropDependency($X) $\\rightarrow $ $X",
+      "PREDICATE_UNREIFICATION:",
+      "Role($Q, $P), Role($R, $P):",
+      "$\\exists $RolePair($Q, Predicate).($P $\\sqcap $ $\\exists $RolePair(Predicate, $R).$A) $\\rightarrow $ $\\exists $RolePair($Q, $R).$A",
+      "... (17 inference rules total)"
+    ],
+    [
+      "CONJUNCTION_WITHOUT_ENTITY:",
+      "def2sparql($X $\\sqcap $ $Y, $V1) $\\rightarrow $ def2sparql($X, $V1) ' . ' def2sparql($Y, $V1)",
+      "ENTITY_MID:",
+      "ent2sparql(Entity($X)) $\\rightarrow $ $X",
+      "GET_SPECIALIZATIONS:",
+      "get_specializations($X) $\\rightarrow $ 'SELECT DISTINCT ' get_var($X, new_var($V0)) ' WHERE { ' def2sparql($X, get_var($X, $V0)) '}'",
+      "GET_VAR_CONJUNCTION:",
+      "get_var($X $\\sqcap $ $Y, $V1) $\\rightarrow $ shared_var(get_var($X, get_var($Y, $V1)), get_var($Y, get_var($X, $V1)))",
+      "GET_VAR_RELATION:",
+      "get_var($\\exists $$R.$X, $V1) $\\rightarrow $ $V1",
+      "GET_VAR_TYPE:",
+      "FreebaseTypeMapping($X, $F):",
+      "get_var($X, $V1) $\\rightarrow $ $V1",
+      "PROPERTY_MAPPING:",
+      "FreebasePropertyMapping($R, $F):",
+      "role2sparql($R) $\\rightarrow $ $F",
+      "RELATION_MAPPING_WITHOUT_EXCLUSION:",
+      "NonExclusiveRolePair($R):",
+      "rel2sparql($X, $R, $Y) $\\rightarrow $ $X role2sparql($R) $Y",
+      "RELATION_TO_ENTITY:",
+      "def2sparql($\\exists $$R.$X, $V1) $\\rightarrow $ rel2sparql($V1, $R, ent2sparql($X))",
+      "SHARED_VAR:",
+      "shared_var($X, $X) $\\rightarrow $ $X",
+      "SPECIALIZATION_OF_TYPE:",
+      "def2sparql($X, $V1) $\\rightarrow $ $V1 ' a ' type2sparql($X)",
+      "TYPE_MAPPING:",
+      "FreebaseTypeMapping($X, $F):",
+      "type2sparql($X) $\\rightarrow $ $F",
+      "... (21 resolution rules total)"
+    ],
+    [
+      "$\\rightarrow $ BoundRolePairs(DirectingFilm, RolePair(Predicate, Agent), RolePair(Predicate, FilmDirector))",
+      "$\\rightarrow $ BoundRolePairs(DirectingFilm, RolePair(Predicate, Undergoer), RolePair(Predicate, DirectedFilm))",
+      "$\\rightarrow $ BoundRolePairs(PredicateWithBoundRolePairs(RolePair(ObjectHaver, Object), RolePair(Predicate, Undergoer)), RolePair(ObjectHaver, Object), RolePair(Predicate, Undergoer))",
+      "$\\rightarrow $ BoundRolePairs(PredicateWithBoundRolePairs(RolePair(Subject, SubjectHaver), RolePair(Agent, Predicate)), RolePair(Subject, SubjectHaver), RolePair(Agent, Predicate))",
+      "$\\rightarrow $ FreebasePropertyMapping(RolePair(FilmDirector, DirectedFilm), 'ns:film.director.film')",
+      "$\\rightarrow $ FreebaseTypeMapping(Person, 'ns:people.person')",
+      "$\\rightarrow $ NonExclusiveRolePair(FilmDirector, DirectedFilm)",
+      "$\\rightarrow $ Role(DirectedFilm, DirectingFilm)",
+      "$\\rightarrow $ Role(FilmDirector, DirectingFilm)",
+      "",
+      "... (194 knowledge rules total)"
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0944/instruction.md b/qasper-0944/instruction.md
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+Name of Paper: Measuring Compositional Generalization: A Comprehensive Method on Realistic Data
+
+Question: How big is new question answering dataset?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Distribution-Based Compositionality Assessment (DBCA)",
+    "Distribution-Based Compositionality Assessment (DBCA) ::: Principles for measuring compositionality",
+    "The CFQ Dataset",
+    "The CFQ Dataset ::: Automatic, rule-based generation",
+    "The CFQ Dataset ::: Dataset details and statistics",
+    "Compositionality Experiments for CFQ and scan",
+    "Experimental Results and Analysis ::: Experiment Setup",
+    "Experimental Results and Analysis ::: Results and analysis for CFQ",
+    "Experimental Results and Analysis ::: Results and analysis for scan",
+    "Related Work",
+    "Conclusion and Outlook",
+    "Example Dataset Item",
+    "Data Quality Analysis",
+    "Data Distribution Analysis ::: Answer frequencies",
+    "Data Distribution Analysis ::: Impact of subsampling on the distribution of complexity levels",
+    "Data Distribution Analysis ::: Impact of subsampling on the frequency of rules and rule combinations",
+    "Divergence-Based Split Analysis ::: Qualitative analysis of MCD@!START@$_{1}$@!END@",
+    "Divergence-Based Split Analysis ::: Quantitative analysis of MCD@!START@$_{1}$@!END@",
+    "Hyperparameters",
+    "Detailed error analysis ::: Breakdown of error types",
+    "Detailed error analysis ::: Qualitative error analysis",
+    "Additional experimental results on scan",
+    "Analysis of relations between accuracy, compound divergence, and training size",
+    "Logical Form",
+    "Rule Format",
+    "Rule Format ::: Grammar rule format",
+    "Rule Format ::: Knowledge rule format",
+    "Rule Format ::: Inference rule format",
+    "Rule Format ::: Resolution rule format",
+    "Generation Algorithm",
+    "Generation Algorithm ::: Join by Logical Form",
+    "Generation Algorithm ::: Relationship between Generation and Parsing",
+    "Generation Algorithm ::: Selecting an appropriate sample set",
+    "Example of a rule application DAG",
+    "Example of a rule application DAG ::: DAG normalization",
+    "Example of a rule application DAG ::: Concept abbreviations",
+    "Example of a rule application DAG ::: Entity placeholders",
+    "Example of a rule application DAG ::: Subgraphs and their weights",
+    "Rules Index",
+    "Rules Index ::: Grammar rules",
+    "Rules Index ::: Inference rules",
+    "Rules Index ::: Resolution rules",
+    "Rules Index ::: Knowledge rules"
+  ],
+  "paragraphs": [
+    [
+      "Human intelligence exhibits systematic compositionality BIBREF0, the capacity to understand and produce a potentially infinite number of novel combinations of known components, i.e., to make \u201cinfinite use of finite means\u201d BIBREF1. In the context of learning from a set of training examples, we can observe compositionality as compositional generalization, which we take to mean the ability to systematically generalize to composed test examples of a certain distribution after being exposed to the necessary components during training on a different distribution.",
+      "Humans demonstrate this ability in many different domains, such as natural language understanding (NLU) and visual scene understanding. For example, we can learn the meaning of a new word and then apply it to other language contexts. As BIBREF2 put it: \u201cOnce a person learns the meaning of a new verb `dax', he or she can immediately understand the meaning of `dax twice' and `sing and dax'.\u201d Similarly, we can learn a new object shape and then understand its compositions with previously learned colors or materials BIBREF3, BIBREF4.",
+      "In contrast, state-of-the-art machine learning (ML) methods often fail to capture the compositional structure that is underlying the problem domain and thus fail to generalize compositionally BIBREF2, BIBREF5, BIBREF6, BIBREF7, BIBREF3. We believe that part of the reason for this shortcoming is a lack of realistic benchmarks that comprehensively measure this aspect of learning in realistic scenarios.",
+      "As others have proposed, compositional generalization can be assessed using a train-test split based on observable properties of the examples that intuitively correlate with their underlying compositional structure. BIBREF8, for example, propose to test on different output patterns than are in the train set, while BIBREF2 propose, among others, to split examples by output length or to test on examples containing primitives that are rarely shown during training. In this paper, we formalize and generalize this intuition and make these contributions:",
+      "We introduce distribution-based compositionality assessment (DBCA), which is a novel method to quantitatively assess the adequacy of a particular dataset split for measuring compositional generalization and to construct splits that are ideally suited for this purpose (Section SECREF2).",
+      "We present the Compositional Freebase Questions (CFQ) , a simple yet realistic and large NLU dataset that is specifically designed to measure compositional generalization using the DBCA method, and we describe how to construct such a dataset (Section SECREF3).",
+      "We use the DBCA method to construct a series of experiments for measuring compositionality on CFQ and scan BIBREF2 and to quantitatively compare these experiments to other compositionality experiments (Section SECREF4).",
+      "We analyze the performance of three baseline ML architectures on these experiments and show that these architectures fail to generalize compositionally, and perhaps more surprisingly, that compound divergence between train and test sets is a good predictor of the test accuracy (Section SECREF5).",
+      ""
+    ],
+    [
+      "",
+      "Like other authors, we propose to measure a learner's ability to generalize compositionally by using a setup where the train and test sets come from different distributions. More specifically, we propose a setup where each example is obtained by composing primitive elements (atoms), and where these atoms are similarly represented in the train and test sets while the test set contains novel compounds, i.e., new ways of composing the atoms of the train set.",
+      "As a simple illustrative scenario, consider the task of answering simple questions such as \u201cWho directed Inception?\u201d and \u201cDid Christopher Nolan produce Goldfinger?\u201d. In this scenario, the atoms intuitively correspond to the primitive elements that are used to compose those questions, such as the predicates \u201cdirect(ed)\u201d and \u201cproduce(d)\u201d, the question patterns \u201cWho [predicate] [entity]\u201d and \u201cDid [entity1] [predicate] [entity2]\u201d, and the entities \u201cInception\u201d, \u201cChristopher Nolan\u201d, etc. The compounds on the other hand correspond to the combinations of these atoms that appear in the various examples: \"Who directed [entity]?\", \"Did Christopher Nolan [predicate] Inception?\", etc.",
+      "To measure compositional generalization on such a task, one might therefore use the questions \u201cWho directed Inception?\u201d and \u201cDid Christopher Nolan produce Goldfinger?\u201d as training examples while testing on questions such as \u201cDid Christopher Nolan direct Goldfinger?\u201d and \"Who produced Inception?\" because the atoms are identically represented in the train and test sets while the compounds differ.",
+      "To make this intuition more precise, we focus on datasets such as CFQ (introduced in Section SECREF3) and scan BIBREF2, where each example can be created from a formal set of rules by successively applying a number of these rules. In this case, the atoms are the individual rules, while the compounds are the subgraphs of the directed acyclic graphs (DAGs) that correspond to the rule applications. (See Sections SECREF3 and SECREF4 for more details.)",
+      ""
+    ],
+    [
+      "We use the term compositionality experiment to mean a particular way of splitting the data into train and test sets with the goal of measuring compositional generalization. Based on the notions of atoms and compounds described above, we say that an ideal compositionality experiment should adhere to the following two principles:",
+      "",
+      "Similar atom distribution: All atoms present in the test set are also present in the train set, and the distribution of atoms in the train set is as similar as possible to their distribution in the test set.",
+      "",
+      "Different compound distribution: The distribution of compounds in the train set is as different as possible from the distribution in the test set.",
+      "",
+      "The second principle guarantees that the experiment is compositionally challenging in the sense that it tests the learner on compounds that are as different as possible from the compounds used during training. The first principle aims to guarantee that the experiment is exclusively measuring the effect of the difference in the way atoms are composed to form compounds (rather than some related but different property such as domain adaptation on the distribution of the atoms).",
+      "To determine to which degree a certain experiment adheres to these principles, we use the following formalization. For a sample set $T$, we use $\\mathcal {F}_A(T)$ to denote the frequency distribution of atoms in $T$ and $\\mathcal {F}_C(T)$ for the weighted frequency distribution of compounds in $T$, which correspond to the subgraphs of the rule application DAGs. For practicality, we do not consider all subgraphs of rule application DAGs when computing the compound divergence. Instead, we first generate a large subset $\\mathbb {G}$ of subgraphs, then weight them in context of their occurrence, and keep only the ones with highest sum of weights. The purpose of the weighting is to avoid double-counting compounds that are highly correlated with some of their super-compounds. We achieve this by calculating the weight of $G \\in \\mathbb {G}$ in a sample as $w(G) = \\max _{g \\in \\text{occ}(G)} (1 - \\max _{G^{\\prime }: g \\prec g^{\\prime } \\in \\text{occ}(G^{\\prime })} P(G^{\\prime }| G))$, where $\\text{occ}(G)$ is the set of all occurrences of $G$ in the sample, $\\prec $ denotes the strict subgraph relation, and $P(G^{\\prime }| G)$ is the empirical probability of $G^{\\prime }$ occurring as a supergraph of $G$ over the full sample set. See Appendix SECREF202 for example subgraphs and more details on the weighting.",
+      "We measure divergence (or similarity) of the weighted distributions using the Chernoff coefficient $C_\\alpha (P \\Vert Q) = \\sum _{k} p_k^\\alpha \\, q_k^{1-\\alpha } \\in [0, 1]$ BIBREF9. For the atom divergence, we use $\\alpha =0.5$, which corresponds to the Bhattacharyya coefficient and reflects the desire of making the atom distributions in train and test as similar as possible. For the compound divergence, we use $\\alpha = 0.1$, which reflects the intuition that it is more important whether a certain compound occurs in $P$ (train) than whether the probabilities in $P$ (train) and $Q$ (test) match exactly. This allows us to formally define as follows the notions of compound divergence $\\mathcal {D}_C$ and atom divergence $\\mathcal {D}_A$ of a compositionality experiment consisting of a train set $V$ and a test set $W$:",
+      "Based on these principles, we suggest to use as a preferred compositionality benchmark for a given dataset the accuracy obtained by a learner on splits with maximum compound divergence and low atom divergence (we use $\\mathcal {D}_A \\le 0.02$). See Section SECREF4 for details about how to construct such splits."
+    ],
+    [
+      "We present the Compositional Freebase Questions (CFQ) as an example of how to construct a dataset that is specifically designed to measure compositional generalization using the DBCA method introduced above. CFQ is a simple yet realistic, large dataset of natural language questions and answers that also provides for each question a corresponding sparql query against the Freebase knowledge base BIBREF10. This means that CFQ can be used for semantic parsing BIBREF11, BIBREF12, which is the task that we focus on in this paper."
+    ],
+    [
+      "BIBREF13 describe a number of benefits for automated rule-based dataset generation, including scalability, control of scope, and avoidance of human errors. Beyond these benefits, however, such an approach is particularly attractive in the context of measuring compositional generalization using the DBCA method, as it allows us to precisely track the atoms (rules) and compounds (rule applications) of each example by recording the sequence of rule applications used to generate it.",
+      "Since the way we measure compositionality depends on how the examples can be broken down into atoms and compounds, we design the generation rules so as to have few and meaningful atoms. More precisely, we aim to have as few rules as possible so that the richness of the examples comes from composing them, which yields a large variety of compounds (enabling a large range of different compound divergences) while making it easy to obtain similar distributions of atoms. Also, we aim to make our rules truly \u201catomic\u201d in the sense that the behavior of any rule is independent of the context where it is applied (e.g., rules may not contain \u201cif-then-else\u201d constructs).",
+      "In order to minimize the number of rules, we use an intermediate logical form that serves as a uniform semantic representation with relatively direct mappings to natural language and sparql. Our rules thus fall into the following four categories (a selection of rules is provided in Appendix SECREF20):",
+      "Grammar rules that generate natural language constructs and corresponding logical forms.",
+      "Inference rules that describe transformations on logical forms, allowing us to factor out transformations that are independent of specific linguistic and sparql constructs.",
+      "Resolution rules that map constructs of the logical form to sparql constructs.",
+      "Knowledge rules that supply logical form expressions that are universally applicable. Other rules can be kept more generic by parameterizing them on knowledge.",
+      "These rules define a language of triples of the form $\\langle \\text{question, logical form, \\textsc {sparql}{} query} \\rangle $. Our generation algorithm produces such triples in a mixed top-down and bottom-up fashion. We first apply grammar rules and inference rules to produce the natural language questions and their semantics in our logical form. Then we apply resolution rules to obtain the sparql query. See Figure FIGREF14 for an illustration. In addition, the generator produces a normalized, directed acyclic graph (DAG) of rule applications that corresponds to the normalized program that generated the triple. (Appendix SECREF19 shows an example.) Edges of this DAG represent dependencies among the rule applications, and the normalization ensures that a certain rule combination is represented using the same DAG across all the examples where it occurs.",
+      "The described approach can generate a potentially infinite set of questions, from which we first sample randomly and then subsample (to maximize the overall diversity of rule combinations while keeping a uniform distribution over complexity). We measure the diversity of rule combinations using the empirical entropy of a weighted subset of the rule application DAGs, and we use the number of rule applications as a measure of the complexity of an example. We also limit the maximum example complexity such that the questions remain relatively natural. Table TABREF15 shows examples of generated questions at varying levels of complexity. An example of a complete data item is shown in Appendix SECREF8, a more detailed data quality analysis is presented in Appendix SECREF9, and the generation algorithm is discussed in more detail in Appendix SECREF18."
+    ],
+    [
+      "Input and output. While the primary focus of the dataset is semantic parsing (natural language question to sparql query), we also provide natural language answers for each question. This allows the dataset to be used in a text-in-text-out scenario as well (see Appendix SECREF8).",
+      "Ambiguity. We largely avoid ambiguity in the questions. In particular, we make sure each name is used to refer to exactly one entity, and we avoid different possible parse trees, different interpretations of plurals, and the need for disambiguation that requires semantic knowledge.",
+      "Scope. We select the following language features as compositional building blocks: open questions and closed questions; subordinate clauses; active and passive voice; conjunctions of verb phrases and of noun phrases; possessives with roles (\u201cX's parent\u201d); adjectives; and type restrictions. For knowledge base features, we select roles, verbs, types, and adjectives from domains that are well-represented in Freebase and that can be combined easily. We start from the popular movie domain (e.g., directing, producing, editor, sequel) and extend this with personal relations (e.g., parent, spouse, sibling), companies (e.g., founding, employer), and adjectives (e.g., gender, nationality).",
+      "Logical form and grammar. For the internal logical form, we adopt a variation of the description logic $\\mathcal {EL}$ BIBREF14, BIBREF15, augmented with additional constructors (see Appendix SECREF16) to more easily map to certain linguistic structures. For the grammar rules, we use a unification-based grammar syntax similar to that used in the Prolog extension GULP 3.1 BIBREF16, with addition of support for disjunction, negation, absence, and default inheritance of features for compactness of representation.",
+      "Grounding in Freebase. Once an example is generated by the CFQ rules, it still contains entity placeholders instead of Freebase machine ids (MIDs). For the task of semantic parsing, the examples could theoretically be used as-is, as our avoidance of semantic ambiguity means that a learner should not need knowledge of the specific entity in order to parse the question. To make the questions natural, however, we apply an additional step of replacing the placeholders with appropriate specific entities. To do this we first execute the generated sparql query against Freebase. This returns a set of candidate MID combinations that satisfy the query and can be used as substitutes. If the set is empty, we abandon the generated question candidate as unnatural. Otherwise, we pick one combination at random to yield a question with positive answer. In the case of a closed question, we also generate a variation that yields the answer \u201cNo\u201d, which we do by mixing in MIDs from another substitution (or a more generic replacement if that fails) to keep the question as plausible-sounding as possible. We then randomly choose either the question with positive or with negative answer, to avoid spurious correlations between question structure and yes/no answer.",
+      "Semantic and structural filtering. Even among the questions that can be satisfied in Freebase, there are some that are meaningful but somewhat unnatural, such as \u201cWas Strange Days directed by a female person whose gender is female?\u201d. We automatically filter out such unnatural questions using semantic and structural rules. Note that since we do not require a learner to identify such questions, we do not track these filtering rules.",
+      "Release and statistics.",
+      "CFQ contains 239,357 English question-answer pairs that are answerable using the public Freebase data. (The data URL is not yet provided for anonymous review.) We include a list of MIDs such that their English names map unambiguously to a MID. Table TABREF17(a) summarizes the overall statistics of CFQ. Table TABREF17(b) uses numbers from BIBREF8 and from an analysis of WebQuestionsSP BIBREF17 and ComplexWebQuestions BIBREF18 to compare three key statistics of CFQ to other semantic parsing datasets (none of which provide annotations of their compositional structure). CFQ contains the most query patterns by an order of magnitude and also contains significantly more queries and questions than the other datasets. Note that it would be easy to boost the raw number of questions in CFQ almost arbitrarily by repeating the same question pattern with varying entities, but we use at most one entity substitution per question pattern. Appendix SECREF10 contains more detailed analyses of the data distribution."
+    ],
+    [
+      "The DBCA principles described in Section SECREF6 enable a generic and task-independent method for constructing compositionality experiments. To construct such an experiment for a dataset $U$ and a desired combination of atom and compound divergences, we use an iterative greedy algorithm that starts with empty sets $V$ (train) and $W$ (test), and then alternates between adding an example $u \\in U$ to $V$ or $W$ (while maintaining the desired train/test ratio). At each iteration, the element $u$ is selected such that $\\mathcal {D}_C(V \\Vert W)$ and $\\mathcal {D}_A(V \\Vert W)$ are kept as closely as possible to the desired values. To reduce the risk of being stuck in a local optimum, we also allow removing examples at certain iterations.",
+      "In general, there are many different splits that satisfy a desired compound and atom divergence. This reflects the fact that a certain compound may either occur exclusively in the train set or the test set, or it may occur in both of them because the split may have achieved the desired compound divergence by separating other (possibly orthogonal) compounds. Our greedy algorithm addresses this by making random choices along the way, starting with picking the first example randomly.",
+      "For the goal of measuring compositional generalization as accurately as possible, it is particularly interesting to construct maximum compound divergence (MCD) splits, which aim for a maximum compound divergence at a low atom divergence (we use $\\mathcal {D}_A \\le 0.02$). Table TABREF18 compares the compound divergence $\\mathcal {D}_C$ and atom divergence $\\mathcal {D}_A$ of three MCD splits to a random split baseline as well as to several previously suggested compositionality experiments for both CFQ and the existing scan dataset (cf. Section SECREF30). The split methods (beyond random split) are the following:",
+      "Output length: Variation of the setup described by BIBREF2 where the train set consists of examples with output (sparql query or action sequence) length $\\le \\hspace{-2.5pt} N$, while the test set consists of examples with output length $> \\hspace{-2.5pt} N$. For CFQ, we use $N = 7$ constraints. For scan, we use $N = 22$ actions.",
+      "Input length: Variation of the above setup, in which the train set consists of examples with input (question or command) length $\\le N$, while test set consists of examples with input length $> N$. For CFQ, we use $N=19$ grammar leaves. For SCAN, we use $N=8$ tokens.",
+      "Output pattern: Variation of setup described by BIBREF8, in which the split is based on randomly assigning clusters of examples sharing the same output (query or action sequence) pattern. Query patterns are determined by anonymizing entities and properties; action sequence patterns collapse primitive actions and directions.",
+      "Input pattern: Variation of the previous setup in which the split is based on randomly assigning clusters of examples sharing the same input (question or command) pattern. Question patterns are determined by anonymizing entity and property names ; command patterns collapse verbs and the interchangeable pairs left/right, around/opposite, twice/thrice.",
+      "All of these experiments are based on the same train and validation/test sizes of 40% and 10% of the whole set, respectively. For CFQ, this corresponds to about 96k train and 12k validation and test examples, whereas for scan, it corresponds to about 8k train and 1k validation and test examples. We chose to use half of the full dataset for the train-test splits, as it led to an appropriate balance between high compound divergence and high train set size in informal experiments.",
+      "The MCD splits achieve a significantly higher compound divergence at a similar atom divergence when compared to the other experiments. The reason for this is that, instead of focusing on only one intuitive but rather arbitrary aspect of compositional generalization, the MCD splits aim to optimize divergence across all compounds directly.",
+      "Interestingly, the MCD splits still correlate with the aspects of compositional generalization that are targeted by the other experiments in this table. As shown in the four right columns of Table TABREF18, for each MCD split, the train set $V$ contains on average shorter examples than the test set $W$ (measured by the ratio of average lengths), and $V$ also contains only a small fraction of the input and output patterns used in $W$ (measured by the fraction of patterns covered). However, these correlations are less pronounced than for the experiments that specifically target these aspects, and they vary significantly across the different MCD splits.",
+      "This illustrates that MCD splits are comprehensive in the sense that they cover many different aspects of compositional generalization, especially when looking at multiple of them. It also means that whether a certain example ends up in train or test is not determined solely by a single criterion that is immediately observable when looking at the input and output (such as length). As we show in Appendix SECREF91, this generally makes the examples in train and test look fairly similar."
+    ],
+    [
+      "We use three encoder-decoder neural architectures as baselines: (1) LSTM+attention as an LSTM BIBREF19 with attention mechanism BIBREF20; (2) Transformer BIBREF21 and (3) Universal Transformer BIBREF22.",
+      "We tune the hyperparameters using a CFQ random split, and we keep the hyperparameters fixed for both CFQ and scan (listed in Appendix SECREF12). In particular the number of training steps is kept constant to remove this factor of variation. We train a fresh model for each experiment, and we replicate each experiment 5 times and report the resulting mean accuracy with 95% confidence intervals.",
+      "Note that while we construct test and validation sets from the same distribution, we suggest that hyperparameter tuning should be done on a random split (or random subset of the train set) if one wants to measure compositional generalization of a model with respect to an unknown test distribution as opposed to an architecture with respect to a known test distribution. Tuning on a validation set that has the same distribution as the test set would amount to optimizing for a particular type of compound divergence and thus measure the ability for a particular architecture to yield models that can be made to generalize in one particular way (through leaking information about the test set in the hyperparameters).",
+      "Similarly to BIBREF8, we anonymize the Freebase names and MIDs in the textual input and the SPARQL output, respectively, by replacing them with a placeholder (e.g., \u201cM0\u201d for the first MID). This removes the need for two learning sub-tasks that are orthogonal to our focus: named entity recognition and learning that the MIDs are patterns that need to be copied. An example input-output (question-query) pair then looks like the following: `Was M0 a screenwriter' $\\mapsto $ `select count(*) where {M0 a ns:film.writer}'.",
+      "The main relation we are interested in is the one between compound divergence of the data split and accuracy. Specifically, we compute the accuracy of each model configuration on a series of divergence-based splits that we produce with target compound divergences that span the range between zero and the maximum achievable in 0.1 increments (while ensuring that atom divergence does not exceed the value of 0.02). For each target divergence, we produce at least 3 different splits with different randomization parameters (compare Section SECREF4). For comparison, we also compute accuracies on the other splits shown in Table TABREF18."
+    ],
+    [
+      "The mean accuracies of the three architectures on CFQ are shown in Figure FIGREF28(a) and Table TABREF29. We make three main observations:",
+      "All models achieve an accuracy larger than 95% on a random split, and this is true even if they are trained on 10 times fewer training instances (see Appendix SECREF15 for a more detailed analysis on the performance with varying training size).",
+      "The mean accuracy on the MCD splits is below 20% for all architectures, which means that even a large train set (about 96k instances) with a similar distribution of atoms between train and test is not sufficient for these architectures to perform well on the test distribution.",
+      "For all architectures, there is a strong negative correlation between the compound divergence and the mean accuracy.",
+      "This suggests that the baseline models are able to capture the superficial structure of the dataset, but fail to capture the compositional structure. We find it surprising that varying the compound divergence gives direct control of the (mean) accuracy, even though the examples in train and test look similar (see Appendix SECREF91). This means that compound divergence seems to capture the core difficulty for these ML architectures to generalize compositionally.",
+      "Note that the experiment based on output-length exhibits a worse accuracy than what we would expect based on its compositional divergence. One explanation for this is that the test distribution varies from the training distribution in other ways than compound divergence (namely in output length and a slightly higher atom divergence), which seems to make this split particularly difficult for the baseline architectures. To analyze the influence of the length ratio further, we compute the correlation between length ratios and accuracy of the baseline systems and compare it to the correlation between compound divergence and accuracy. We observe $R^2$ correlation coefficients between 0.11 and 0.22 for the input and output length ratios and between 0.81 and 0.88 for the compound divergence. This shows that despite the known phenomenon that the baseline systems struggle to generalize to longer lengths, the compound divergence seems to be a stronger explanation for the accuracy on different splits than the lengths ratios.",
+      "Error analysis. We perform an analysis of the errors for the split MCD$_{1}$ (the first MCD split that we constructed, with more details provided in Appendix SECREF13). We observe accuracies between 29% and 37% on the test set of this particular split. Qualitatively, all three systems seem to make similar errors at this point (68% of errors are on the same samples). They make more errors for longer sequences and predict about 20% too short output when they make an error. The most common category of error is the omission of a clause in the output (present in 43%-49% of the test samples), e.g.: (1) Omitted conjunctions: for the input \u201cWhat spouse of a film producer executive produced and edited M0, M1, and M2?\u201d the best system ignores \u201cexecutive produced\u201d in the output. (2) Omitted adjectives: for the input \u201cWhich female Spanish film producer was M3' s spouse?\u201d the best system ignores the adjective \u201cfemale\u201d."
+    ],
+    [
+      "To demonstrate the use of our analysis method on another dataset, we re-create the scan dataset BIBREF2, which consists of compositional navigation commands (e.g, `turn left twice and jump') mapped to corresponding action sequences (e.g., `lturn lturn jump'). We use the original grammar while tracking the rule applications used for the construction of each input-output pair. This enables us to compare the compositional generalization abilities of the baseline systems on this dataset in a novel way.",
+      "Figure FIGREF28(b) shows the graphs for the scan data set in the same setup as Figure FIGREF28(a) does for CFQ. We observe that the compound divergence again is a good predictor for the mean accuracy for all three architectures. One difference is that for scan the systems are able to attain accuracies close to 100% for compound divergences up to around 0.2, which is not the case for CFQ. This seems to be in line with the fact that overall CFQ is a more complex task than scan: the total number of rules used in generating scan is only 38 in comparison to 443 rules in the construction of CFQ.",
+      "Appendix SECREF14 provides a comparison to other experiments presented in previous work, including experiments that have significantly different atom distributions. We observe that this generally causes lower accuracies but does not break the correlation between accuracy and compound divergence."
+    ],
+    [
+      "To measure compositional generalization for semantic parsing to SQL, BIBREF8 propose to ensure that no SQL query pattern occurs in both the train and the test set (\u201cquery split\u201d), and they provide such splits for several data sets. By evaluating several ML architectures the authors confirm that this query-pattern split is harder to learn than a conventional split.",
+      "BIBREF2 introduce the scan dataset, and several publications provide interesting analyses of compositional generalization using it BIBREF5, BIBREF6. BIBREF7 discuss a particular extension of a seq2seq model that is effective in handling difficult scan sub-tasks by separating semantic and syntactic information during learning. Our contributions extend the analyses on the scan data in several ways: CFQ provides richer annotations and covers a broader subset of English than the scan dataset, and we propose a comprehensive score for assessing aggregate compositionality of a system on a given task.",
+      "The mathematics dataset BIBREF13 is a large, automatically generated set of 112M samples in 56 separated sub-tasks. The authors present data and experiments that share common goals with our approach, but focus on mathematical reasoning instead of natural language. Our breakdown of generation rules per train sample is more fine-grained, which allows a more precise compositional generalization analysis. Being automatically generated also links our approach to datasets such as the bAbI tasks BIBREF23, which however do not focus on compositional generalization.",
+      "A dataset related to CFQ is ComplexWebQuestions BIBREF18, which consists of complex questions that are automatically generated from simpler sub-questions in WebQuestionsSP BIBREF17 and then reworded manually. While these datasets can be used for semantic parsing, we did not find them suitable for a thorough compositionality analysis because a consistent annotation with the compositional structure would be hard to obtain. Other approaches to semi-automatic dataset creation also use paraphrasing BIBREF24, BIBREF25.",
+      "BIBREF3 introduce the generated clevr dataset, which shares common goals with our work applied in the area of visual reasoning. The dataset's functional programs capture some of the structural information of the questions and are linked one-to-many to the 423 question patterns used. The authors specifically investigate generalization to new combinations of visual attributes in one experiment which uses a particular train-test split based on the colors used. BIBREF26 propose a neural-symbolic architecture and discuss promising results on additional specific splits of the clevr data, e.g. based on object counts and program depth. BIBREF27 describe how the application of compositional attention networks to the clevr data leads to structured and data-efficient learning. BIBREF28 present a large, compositional, generated visual question answering data set with functional programs, on which neural state machines achieve good performance BIBREF29. The use of specific splits between train and test data also occurs in the context of visual data. E.g., BIBREF30 propose a greedy split algorithm to maximize the coverage of test concepts in the train set while keeping question-type/answer pairs disjoint and observe performance degradation of existing approaches. BIBREF31 introduce a synthetic visual question answering dataset called sqoop, which is used to test whether a learner can answer questions about all possible object pairs after being trained on a subset.",
+      "While these datasets are very interesting, the additional annotation that we provide in CFQ indicating the exact rule trees needed to link input and output makes additional analyses regarding compositionality possible. Our analyses go beyond many of the presented discussions (that mostly focus on accuracy regarding particular holdouts) in formalizing an approach that uses the atom and compound divergences to measure compositionality.",
+      "A number of ML approaches have been developed for semantic parsing. BIBREF32 propose Key-Value Memory Networks \u2013 neural network-based architectures that internalize a knowledge base into the network \u2013 and introduce the WikiMovies dataset. BIBREF33 develop an end-to-end architecture that can handle noise in questions and learn multi-hop reasoning simultaneously. They introduce the MetaQA benchmark that is based on WikiMovies but uses a set of only 511 question patterns (mod entities) shared between train and test.",
+      "With regards to studying compositionality in ML, BIBREF34 argue that combinatorial generalization should be a top priority to achieve human-like abilities. BIBREF35 discusses measuring the compositionality of a trained representation, e.g. of a learned embedding. The author suggests to use a tree reconstruction error that is based on how well the oracle derivation of the input matches the structure that can be derived on the representations. BIBREF4 discuss an architecture that enables the learning of compositional concept operators on top of learned visual abstractions. BIBREF36 introduce the compositional recursive learner that \u201ccan generalize to more complex problems than the learner has previously encountered\u201d."
+    ],
+    [
+      "In this paper we presented what is (to the best of our knowledge) the largest and most comprehensive benchmark for compositional generalization on a realistic NLU task. It is based on a new dataset generated via a principled rule-based approach and a new method of splitting the dataset by optimizing the divergence of atom and compound distributions between train and test sets. The performance of three baselines indicates that in a simple but realistic NLU scenario, state-of-the-art learning systems fail to generalize compositionally even if they are provided with large amounts of training data and that the mean accuracy is strongly correlated with the compound divergence.",
+      "We hope our work will inspire others to use this benchmark as a yardstick to advance the compositional generalization capabilities of learning systems and achieve high accuracy at high compound divergence. Some specific directions that we consider promising include applying unsupervised pretraining on the input language or output queries and the use of more diverse or more targeted learning architectures, such as syntactic attention BIBREF7. We also believe it would be interesting to apply the DBCA approach to other domains such as visual reasoning, e.g. based on clevr BIBREF3.",
+      "In the area of compositionality benchmarks, we are interested in determining the performance of current architectures on the end-to-end task that expects a natural language answer given a natural language question in CFQ. We would like also to extend our approach to broader subsets of language understanding, including use of ambiguous constructs, negations, quantification, comparatives, additional languages, and other vertical domains."
+    ],
+    [
+      "The following shows an example data item including the question text in various forms, the answer, the sparql query in various forms, some tracked statistics, and the set of used rules (atoms) and the applied rule tree (compound). Some details are omitted, indicated by ellipses (`...')."
+    ],
+    [
+      "During the development of our data generation pipeline, we manually checked the generated examples for quality. Below is a random selection of 50 examples of the final CFQ dataset (no cherry-picking was used). Brackets around [entity names] are provided just for ease of human reading. Manual checking also indicated that all questions are associated with the semantically correct sparql queries. However, because we rely on the data present in Freebase, there are three debatable questions which sound somewhat unnatural (UNKREF33, UNKREF51, and UNKREF59, see further discussion below the list).",
+      "Who was a writer, star, and cinematographer of [Tetsuo: The Bullet Man], [Nightmare Detective], and [Bullet Ballet]?",
+      "Which male person was a sibling of [Andrew Klavan]?",
+      "Did [Wallace Stevens] influence [Levi Seeley]'s spouse and parent?",
+      "Did a producer, writer, and art director of [Thelma & Luis] produce, direct, and write [Light Girls]?",
+      "Were [Hangover Square], [Zack and Miri Make a Porno], and [Clerks II] edited by a founder and employee of a film producer?",
+      "What American parent of [Charlie Sistovaris] was a British screenwriter's sibling?",
+      "Did [Anne Williams Rubinstein] marry a person that influenced a screenwriter and influenced [John Most]?",
+      "Was [Cach\u00fan cach\u00fan ra ra!]'s director a film director's American child?",
+      "Did [Maisy's Garden]'s executive producer write, edit, and executive produce [Pakalppooram], [It's Not About the Shawerma], [Rick's Canoe], and [The Fifth Wall]?",
+      "Was [Holly Ellenson]'s child [Wally Ellenson]?",
+      "Did [Emerald Cities]'s cinematographer, writer, and editor edit, executive produce, and direct [Blues for the Avatar] and [White Stork Is Coming]?",
+      "Was a film producer [Lilies of the Ghetto]'s distributor and producer?",
+      "Which child of [Mimi Iger] did a film producer employ and [The Walt Disney Company] employ?",
+      "What Japanese spouse of [Hong Kong Paradise]'s star did [Ineko Arima] and [Nishiki K\u00f4] marry?",
+      "Who influenced and was influenced by [Black Dynamite]'s star?",
+      "What was written by, edited by, directed by, produced by, and executive produced by [Pauline Collins]'s child's sibling?",
+      "Which Swedish film director that [Th\u00e9o Van Horn]'s actor influenced did [Egen ing\u0227ng] star?",
+      "Who was influenced by [Golden Yeggs]'s star, was influenced by [Richard Pryor], was influenced by [Bill Murray], and married [Elaine Chappelle]?",
+      "What did [This Is My Show]'s director, cinematographer, and star direct, edit, produce, and executive produce?",
+      "Who was a male costume designer and director of [Ene... due... like... fake...] and [The Windmill Bar]?",
+      "Was [Kumudu Munasinghe] a Dutch film producer's country of nationality's employee?",
+      "Did an art director, editor, director, writer, cinematographer, and star of [Tetsuo II: Body Hammer] produce [Nightmare Detective], [Tetsuo: The Iron Man], and [A Snake of June]?",
+      "Was [Alexandra Naoum] [Monsieur Verdoux]'s producer, writer, and star?",
+      "What film director founded [THX], was employed by [American Zoetrope], [LucasArts], [Skywalker Sound], and [Lucasfilm], and founded [Industrial Light & Magic]?",
+      "What male employee of [Weta Workshop] was [Bad Taste]'s editor?",
+      "Were [Weta Digital] and [Weta Workshop] founded by a cinematographer and founded by a film editor?",
+      "What art director influenced [DreamWorks Animation]'s founder?",
+      "Did [Daisies] star [Fruit of Paradise]'s costume designer and writer, star [Jarom\u00edr Vom\u00e1cka], and star [Jirina Myskova]?",
+      "What character was influenced by a costume designer, influenced by [Pedro Calder\u00f3n de la Barca], influenced by [William Shakespeare] and [Luis Bu\u00f1uel], and influenced by [Miguel de Unamuno]?",
+      "What British costume designer of [The Love Letter] and [The Chamber] was a screenwriter's child?",
+      "Was [Eric Massa] a cinematographer's parent's sibling's American sibling?",
+      "What art director of [Stepping Sisters 1932] was a parent of [Imre S\u00e1ndorh\u00e1zi]?",
+      "What was executive produced by, written by, produced by, and edited by a director of [V/H/S/2]'s sequel?",
+      "What did an editor and cinematographer of [Tongue Twister Variations] direct?",
+      "Who was a Canadian screenwriter that produced [Her Painted Hero] and [The Nick of Time Baby]?",
+      "Which American parent of [Janet Friedman] did [Rose Friedman] influence and marry?",
+      "Did [George Carlin] influence [Louis C.K.: Shameless]'s executive producer and influence [Joan Rivers]?",
+      "Who was a male writer, star, director, and costume designer of [The Wizard of Speed and Time]?",
+      "Who was [Lost Boys: The Thirst]'s prequel's sequel's art director?",
+      "Did a cinematographer's female parent executive produce, direct, and write [Hit Dat Shit 5]?",
+      "Who married [Siri von Essen], influenced [A Lesson in Love]'s director and art director, influenced [Tennessee Williams], and influenced [Maxim Gorky]?",
+      "What Italian film director directed [Children of Hannibal]?",
+      "What film producer directed, wrote, edited, and produced [la estrella], [la ardilla], and [el valiente]?",
+      "Were [Flames: The Movie] and [Soltera] directed by a male person and executive produced by [Hilda Russoff]'s spouse?",
+      "Was a sibling of [Fawwaz bin Abdulaziz Al Saud] [Badr bin Abdulaziz Al Saud]'s sibling?",
+      "What did a sibling of [Louise Rohr] executive produce, produce, and edit?",
+      "Did a French cinematographer of [Le Volcan interdit] edit [The Last Bolshevik] and direct [A.K.] and [Statues Also Die]?",
+      "Was [Mannai Thottu Kumbidanum] directed by and written by a Dutch male cinematographer?",
+      "Was a director, art director, executive producer, and costume designer of [But I'm a Genderqueer] [Lauren Soldano]?",
+      "Was [When We Were Kings] produced by a film editor whose spouse was employed by [Royal Academy of Dramatic Art] and distributed by [PolyGram Filmed Entertainment]?",
+      "Further discussion of the debatable questions:",
+      "Did [Wallace Stevens] influence [Levi Seeley]'s spouse and parent?",
+      "The occurrence of the seemingly implausible combination of roles \u201cspouse and parent\u201d is due to incorrect data in Freebase, in which there are 502 entities asserted to be both the spouse and parent of other entities. For instance, \u201cAnne Dacre\u201d is both the spouse and parent of \u201cChristopher Conyers\u201d. We can also find occasional occurrences in CFQ of other implausible role combinations, such as \u201cparent and child\u201d, \u201cspouse and sibling\u201d etc., triggered by similar Freebase data issues.",
+      "Was [Kumudu Munasinghe] a Dutch film producer's country of nationality's employee?",
+      "The somewhat unnatural phrasing of \u201ccountry's employee\u201d occurs due to a modeling choice in Freebase, in which the same entity is used to represent both a country and the government of that country. This makes it possible for a country to employ a person.",
+      "What character was influenced by a costume designer, influenced by [Pedro Calder\u00f3n de la Barca], influenced by [William Shakespeare] and [Luis Bu\u00f1uel], and influenced by [Miguel de Unamuno]?",
+      "The somewhat unnatural phrasing of \u201ca character was influenced by\u201d occurs due to a modeling choice in Freebase, in which when a film character is based on a real person, Freebase commonly uses the same entity to represent both. This makes \u201cperson\u201d and \u201ccharacter\u201d exchangeable in the questions where the person is also a film character."
+    ],
+    [
+      "Table TABREF85 shows the most frequently occurring answers in CFQ. Not surprisingly, after the answers \u201cYes\u201d and \u201cNo\u201d, entities related in Freebase to the domain of movies have highest frequency."
+    ],
+    [
+      "Figure FIGREF87 illustrates how subsampling changes the distribution of questions in CFQ with different levels of complexity to become more even."
+    ],
+    [
+      "Subsampling increases the frequency of rarely used rules and rule combinations and decreases the frequency of commonly used ones. For rules, this is illustrated by Figure FIGREF89 which shows the ratio of examples each rule appears in, before and after subsampling, in the order of their frequency. Figure FIGREF90 shows the same comparison for rule combinations."
+    ],
+    [
+      "Traditional compositionality experiments often use train-test splits based on observable properties of the input and output (e.g., input/output complexity, input/output patterns, and input/output feature holdouts). One consequence of this is that the difference between train and test examples is relatively easily observable \u201cwith the naked eye\u201d. The lists below illustrate that this is not usually the case for divergence-based splits. Similar to the random sample of the general data in Appendix SECREF9 we provide a random sample of size 20 from both the train and test set here. Indeed, even for the MCD$_{1}$ split with a high divergence of 0.694, the 20 random samples of train and test questions shown below cannot easily be distinguished as they both contain the same kind of questions of different sizes.",
+      "Train samples from MCD$_{1}$:",
+      "What was founded by a costume designer, founded by [Forgotten Silver]'s star, and founded by [Jamie Selkirk]?",
+      "Which male person influenced and was influenced by [William Dean Howells]?",
+      "Did [Marco Bellocchio] produce, write, and direct [Greek Pete]?",
+      "What did [Rick Schmidt] edit, [Philip Rashkovetsky] edit, and a cinematographer edit?",
+      "Were [The Living Playing Cards] and [The Haunted Castle] edited by, directed by, and produced by a French writer of [Le cauchemar de M\u00e9li\u00e8s]?",
+      "What did a spouse of [Shorts]'s producer's spouse executive produce and direct?",
+      "Did [P. G. Wodehouse], [Raymond Chandler], [Edward Bunker], [Pauline Kael], and [Michael Cimino] influence [Grindhouse]'s cinematographer and star?",
+      "What Mexican person did a film producer employ?",
+      "Did [The Midnight After]'s Chinese executive producer edit [Perfect Life] and [Dumplings]?",
+      "Who did [For the Secret Service]'s director's female spouse influence?",
+      "Who married, was influenced by, and influenced a company's founder?",
+      "Was [MAN SE]'s French male German employee's employer [Sulzer]?",
+      "Who influenced an actor that [Robin Santana] was influenced by and [K. J. Stevens] was influenced by and was influenced by [Virgil]?",
+      "Did [Pirates of Malaysia] star [Giuseppe Addobbati] and star a Spanish screenwriter?",
+      "Was [The Silence of the Sea] written by, produced by, executive produced by, directed by, and edited by [The Red Circle]'s French editor?",
+      "Did [Chanel] employ a German costume designer, employ [Gaspard Ulliel] and [Maureen Chiquet], and employ [Jacques Polge]?",
+      "Who was influenced by [Adam Sandler] and married a film producer?",
+      "Did a Spanish screenwriter's child direct and edit [Bakuchi-uchi: Nagaremono]?",
+      "Was a founder of [IG Port] employed by a film producer?",
+      "Was [Orizzonti Orizzonti!] executive produced by and written by an art director's sibling?",
+      "Test samples from MCD$_{1}$:",
+      "What sequel of [Paranormal Activity 2] was edited by and written by a film director?",
+      "What spouse of a film producer founded [Grand Hustle Records] and was employed by [40/40 Club], [Roc-A-Fella Records], and [Def Jam Recordings]?",
+      "Did [Pixar] employ an art director and employ [Susham Bedi]?",
+      "Was a sibling of [David Lindbland] [Dynamit Nobel]'s Swedish founder?",
+      "What prequel of [Charlie the Unicorn 2] starred, was edited by, was produced by, was written by, and was directed by [Jason Steele]?",
+      "Did [Rick Schmidt] direct, produce, executive produce, and edit [Blues for the Avatar], [White Stork Is Coming], [The Fifth Wall], and [It's Not About the Shawerma]?",
+      "Was [Luke Larkin Music] an art director's employer?",
+      "What prequel of [Goat Story 2] was executive produced, written, directed, edited, and produced by [Jan Tom\u00e1nek]?",
+      "Was [Bullet Ballet]'s editor, star, director, and cinematographer [Promises Written in Water]'s star, director, writer, executive producer, and art director?",
+      "What was edited by, produced by, directed by, and written by [Ellis Kaan Ozen], [Thaw Bwe], [Jeffrey Malkofsky-Berger], and [Leslie Berkley]?",
+      "Was a person's female sibling [Reggae in a Babylon]'s producer?",
+      "Who was a director, cinematographer, executive producer, art director, producer, star, and writer of [The Man Who Killed God]?",
+      "Was [My Sweet Home]'s director, editor, writer, art director, producer, cinematographer, and costume designer a person?",
+      "Which art director, star, and editor of [The Brown Bunny] and [Promises Written in Water] did [Cord] star?",
+      "Did an employee and founder of [Virgin Mobile Australia], [Virgin Mobile USA], and [Virgin Mobile France] found [Virgin America] and found [V2 Records]?",
+      "Was a Chinese executive producer and star of [Happy Ghost II] and [All's Well, Ends Well 2010] a film director?",
+      "Was [The Voyeur]'s executive producer an actor's parent?",
+      "Did [Erasable Cities]'s writer, producer, editor, art director, cinematographer, and director produce and executive produce [Promises Written in Water]?",
+      "Who was an editor, star, and cinematographer of [Tetsuo: The Iron Man], [A Snake of June], and [Bullet Ballet]?",
+      "Was a costume designer's employer [Philips High School]?"
+    ],
+    [
+      "Figure FIGREF133 shows the frequency of atoms (upper graph) and compounds (lower graph) in the train and test sets of the maximum compound divergence split for the CFQ data. As the frequency of an atom resp. compound we use the fraction of examples it appears in. Both atoms and compounds are indexed primarily by their frequency in the train set, secondarily by their frequency in the test set, in decreasing order. For practical reasons we only look at a small subset of compounds here but we believe the analysis is representative.",
+      "We can see that the frequency of atoms in the two sets is very aligned and that all atoms from the test set appear in the train set. The frequency of compounds however is wildly different: While some invariably occur in both sets, the frequencies are often not aligned and most compounds appear only in either the train or the test set."
+    ],
+    [
+      "The experiments were run using the tensor2tensor framework BIBREF39 with some of the hyperparameters tuned using a random split of a previous, smaller version of the data set during development. We use the default hyperparameter sets publicly available in the tensor2tensor implementation (obtained from https://github.com/tensorflow/tensor2tensor) and override the tuned hyperparameters. The hyperparameters used are summarized in Table TABREF134."
+    ],
+    [
+      "Table TABREF136 shows a more detailed analysis of the errors that the baseline models make on CFQ for MCD$_{1}$ (compare Section SECREF24). The reported errors are bucketized into three main types: sparql property clause error, sparql filter clause error and malformed sparql query in the model's output. The total number of test set examples exhibiting any clause or filter error is reported (sum column), as well as the number of insertions (ins), deletions (del), and substitutions (sub) in the model's output with respect to the correct query. Property clause substitution errors are further subdivided into those where only the property itself is wrong while subject and object are correct (prop), those where the property is correct but either subject or object is wrong (node) and those where both the property and the subject or the object are wrong (both).",
+      "The accuracy metric requires the model response and the golden (correct) answer to be exactly equal to each other. Thus, a sparql query with the same clauses as the golden answer but in a different order or with some of the clauses appearing multiple times is also considered to be an error despite being equivalent to the golden answer in its meaning. The amount of such errors is relatively small though, accounting for 1.8%, 0.6% and 1.5% of total test set size for LSTM+Attention, Transformer and Universal Transformer respectively."
+    ],
+    [
+      "Below we qualitatively analyze a number of instances the models fail on. We anonymize the MIDs in the same way as the data is provided to the models (see Section SECREF5). We first select queries on which all machine learning systems fail in all replicated runs (about 5k instances out of a total of about 12k), and then randomly select queries from this list. In the following we discuss a few cases in more detail. Note that, for readability, we use the following abbreviations for the sparql properties in Query 1:",
+      "ns:people.person.child = ns:people.person.children|",
+      "ns:fictional_universe.fictional_character.children|",
+      "ns:organization.organization.child/",
+      "ns:organization.organization_relationship.child",
+      "ns:people.person.sibling = ns:people.person.siblings/",
+      "ns:people.siblingrelationship.sibling|",
+      "ns:fictionaluniverse.fictionalcharacter.siblings/",
+      "ns:fictionaluniverse.",
+      "siblingrelationshipoffictionalcharacters.siblings",
+      "Query 1: \u201cWhat sibling of M0 was M1' s parent?\u201d",
+      "Golden (correct) sparql query:",
+      "SELECT DISTINCT ?x0 WHERE {",
+      "?x0 ns:people.person.child M1 .",
+      "?x0 ns:people.person.sibling M0 .",
+      "FILTER ( ?x0 != M0 )",
+      "}",
+      "Inferred (system) sparql query:",
+      "SELECT DISTINCT ?x0 WHERE {",
+      "?x0 ns:people.person.sibling ?x1 .",
+      "?x0 ns:people.person.sibling M0 .",
+      "?x1 ns:people.person.child M1 .",
+      "FILTER ( ?x0 != ?x1 )",
+      "}",
+      "Analysis. The meaning of the sparql query generated by the system is \u201cWhat sibling of M0 was a sibling of M1's parent?\u201d, which is incorrect. We next analyze the train set, in order to show that we believe enough information has been provided in the train set for the question to be answered correctly.",
+      "Some subqueries of the query and their occurrences are shown in Table TABREF140. While the exact subquery \u201cWhat sibling\u201d does not occur at training, the two words have been shown separately in many instances: the subqueries \u201csibling of Mx\u201d, and \u201cMx's parent\u201d occur 2,331 and 1,222 times, respectively. We can analyze this example in more detail by comparing parts of the rule tree of this example with those shown at training. As can be read from the table, similar sentences have been shown during training. Some examples are:",
+      "What was executive produced by and written by a sibling of M0?",
+      "What costume designer did M1's parent employ?",
+      "What cinematographer was a film editor that M2 and M3 married?",
+      "What film director was a character influenced by M2?",
+      "Query 2: \u201cDid a male film director edit and direct M0 and M1?\u201d",
+      "Golden (correct) sparql query:",
+      "SELECT count ( * ) WHERE {",
+      "?x0 ns:film.director.film M0 .",
+      "?x0 ns:film.director.film M1 .",
+      "?x0 ns:film.editor.film M0 .",
+      "?x0 ns:film.editor.film M1 .",
+      "?x0 ns:people.person.gender m_05zppz",
+      "}",
+      "Inferred (system) sparql query:",
+      "SELECT count ( * ) WHERE {",
+      "?x0 ns:film.director.film M0 .",
+      "?x0 ns:film.director.film M1 .",
+      "?x0 ns:film.editor.film M0 .",
+      "?x0 ns:people.person.gender m_05zppz",
+      "}",
+      "Analysis. The meaning of the inferred sparql query is \u201cDid a male film director edit M0 and direct M0 and M1?\u201d. It thus seems the model `forgets' to include the relation between the director and movie M1.",
+      "Looking at subqueries and their occurrence count (Table TABREF145), we see again that various subqueries occur often during training. However, \u201cedit and direct\u201d have not been shown often together. When looking at the rule trees, we see that both conjunctions in the query occur often at training separately: \u201cDid [DetNP] [VP] and [VP] [DetNP]\u201d occurs 1,432 times, and \u201cDid [DetNP] [VP] [Entity] and [Entity]\u201d occurs 909 times. However, they never occur together: \u201cDid [DetNP] [VP] and [VP] [DetNP] and [DetNP]\u201d does not occur at training. This may be the reason why all systems fail on this example, but at the same time we believe a compositional learner should be able to generalize correctly given the training instances. Some examples are:",
+      "Did a male film director that M3's parent married influence an art director?",
+      "Did a film producer that played M2 edit and direct M1?",
+      "Did a screenwriter edit and direct a sequel of M1",
+      "Did a Chinese male film director edit M1 and M2?"
+    ],
+    [
+      "",
+      "Figure FIGREF150 shows a scatter plot of accuracy vs. compound divergence for the three baseline architectures (see Section SECREF5) on existing splits of the scan data. These splits are discussed in BIBREF2 and BIBREF6, and the exact split data is available. (Data splits obtained from https://github.com/brendenlake/SCAN). We map these splits onto the re-created scan data, which enables us to measure the atom and compound divergences. The authors present a total of six split experiments (some with several sub-experiments):",
+      "",
+      "BIBREF2:",
+      "simple (random)",
+      "by action sequence length",
+      "adding a primitive and adding a primitive along with complex combinations",
+      "BIBREF6:",
+      "adding a template",
+      "adding template fillers",
+      "adding more training examples of fillers (fewshot)",
+      "",
+      "In the plot, we omit some data points that are too close to be distinguished easily. The point labels have the form `(abbreviated experiment name)<(parameter)>@(number of samples) (baseline system abbreviation) [(train set size fraction), (split atom divergence)]'. The train set size fraction is given as a percentage of the overall data size. The baseline system abbreviations are LSTM, T for Transformer, UT for Universal Transformer, T/UT where both transformer models are indistinguishable, and empty where all three systems perform indistinguishably. The abbreviated experiment name is one of the names in italics above.",
+      "We can observe a strong dependency of the accuracies on the compound divergence of the data split. Again, this seems to indicate that the compound divergence is correlated with accuracy for these baseline architectures. One difference to the data shown in Figure FIGREF28(b) is that for this set of experiments the accuracy drops faster with increasing compound divergence. One explanation for this effect is that the experiments are directly aimed at highlighting one specific potentially problematic scenario for learning. E.g. in the experiment `primitive<jump>' (with very low accuracies for all three systems) the jump command is shown exactly in one combination (namely alone) in the training data while it occurs in all test examples in arbitrary combinations.",
+      "This is reflected in the higher atom divergence value of 0.08 for this split, as well as in all other splits that exhibit a low accuracy at a low compound divergence in Figure FIGREF150. Note that BIBREF2 already compare the experiment `primitive<jump>' to the experiment `primitive<turn left>' for which all three systems achieve a much higher accuracy. In their interpretation of this phenomenon, they mainly focus on the fact that in contrast to 'jump', the action 'turn left' is also generated by other inputs. We additionally observe that the latter experiment also has a slightly lower atom divergence of 0.07, a lower compound divergence, and it covers a much larger part of the data in the train set (94% vs. 63%).",
+      "While the accuracies we observe for the `primitive' experiments are very much in line with the results reported by BIBREF2, we noticed a few interesting differences for other experiments: All three systems go to 100% accuracy on the fewshot task even for one example (while BIBREF6 report a slowly increasing accuracy for the architecture they evaluate). On the other hand, both transformer models only reach 0% accuracy on the length split, while the LSTM obtains around 14% (which is in line with what previous work reports)."
+    ],
+    [
+      "Figure FIGREF28 shows for all baseline systems a strong correlation between accuracy and compound divergence for the chosen training sizes (96k for CFQ and 8k for scan). One interesting question is whether and how this correlation is changed for different training sizes. Figures FIGREF159 and FIGREF159 show that this correlation holds also for smaller training sizes but that the accuracy is generally somewhat lower for smaller training sizes.",
+      "At the same time, we observe that the difference between accuracies of various training sizes gets smaller as the training size increases. This can be seen even more clearly in Figures FIGREF159 and FIGREF159, which plot the training size rather than the compound divergence on the x-axis. These figures show that the increase in accuracy flattens out significantly as we reach training size of about 80k for CFQ and about 6k for SCAN. This indicates that further increasing train set size may not be sufficient to do well on these compositionality experiments."
+    ],
+    [
+      "To represent our logical form we use syntax of the description logic $\\mathcal {EL}$ BIBREF14, BIBREF15 with additional concept and role constructors. These constructors do not have description logic semantics; instead, their meaning is completely determined by the set of generation rules of the CFQ dataset.",
+      "Let $A$ be a concept name, $C, C_1, C_2$ be concepts, $R, R_1, R_2$ be roles, and $v$ be a raw string. Then the following would be concepts:",
+      "and the following would be roles:",
+      "Note that our logical form does not have roles other than those in a form of RolePair($C_1$, $C_2$).",
+      "New strings are generated by using a special function new_var($\\$S$). This function generates a unique string of the form ?x<N>, where N is a unique number, and assigns that string to variable $\\$S$. This string can later be used as a variable in a sparql constraint."
+    ],
+    [
+      "This section describes the format of each of the rule types we use for generating the CFQ dataset, in the form in which they appear in the rules index in Appendix SECREF20.",
+      "General formatting conventions shared across all rule types:",
+      "Variable names are prefixed by `$'. Example: $X.",
+      "(Exception: In grammar rules, while variables standing for constants are prefixed by `$', variables standing for logical forms are prefixed by `_'. Example: _action.)",
+      "Concept names are written in camel case. Example: FilmProducer.",
+      "Names of functions that output logical forms (concepts, roles, or knowledge) are also written in camel case. Examples: DropDependency, BoundRolePairs, RolePair.",
+      "Names of functions that output string literals or which are used for converting logical forms to sparql are written in lowercase with underscores. Examples: def2sparql, get_specializations, new_var.",
+      "String literals are enclosed in single quotes. Example: 'ns:film:director'."
+    ],
+    [
+      "The CFQ grammar is a unification-based grammar of recursive rewriting rules used to generate pairs of strings and their corresponding logical form. For an introductory overview of unification-based grammars including several popular variations, see BIBREF38. The rules in the CFQ grammar follow a similar syntax in particular to that used in the Prolog extension GULP 3.1 BIBREF16, with the addition of support for disjunction, negation, absence, and default inheritance of features, and with minor differences in formatting described below.",
+      "Properties shared between the CFQ grammar syntax and that of BIBREF16 include the following:",
+      "Grammar rules are notated as variations of context-free phrase-structure rules of the form $T_{0} \\rightarrow T_{1}$ ... $T_{n}$, where each of the syntactic non-terminals and terminals $T_{0}$ ... $T_{n}$ are augmented with feature lists in parentheses.",
+      "Each grammar rule can be interpreted as specifying how a feature structure (with logical form) that is unifiable with the lefthand side can be re-written to the sequence of features structures (with logical form) indicated on the righthand side.",
+      "Features are represented as attribute-value pairs separated by a colon (i.e., $attribute$:$value$).",
+      "Shared values in feature structures are represented through the use of variables.",
+      "Specifically, in the rules index, CFQ grammar rules are described in the format",
+      "$T_{0}(F_{0})[H]/L_{0} \\rightarrow T_{1}(F_{1})/L_{1}$ ... $T_{n}(F_{n})/L_{n}$",
+      "where:",
+      "Each $T_{i}$ is a syntactic category (syntactic nonterminal) or a string literal (syntactic terminal).",
+      "Each $L_{i}$ for $i \\in [1, n]$ is either a variable representing a logical form or an empty string. In the case when $L_{i}$ is an empty string, we allow dropping the trailing slash from the $T_{i}(F_{i})/L_{i}$ expression, resulting in just $T_{i}(F_{i})$.",
+      "$L_{0}$ is a logical form expressed in terms of $L_{1}...L_{n}$.",
+      "Each $F_{i}$ is a comma-separated feature list of the form $(attribute_{1}$:$value_{1}$, ..., $attribute_{k}$:$value_{k})$. In the case where $F_{i}$ is empty, we allow dropping the parentheses from the $T_{i}(F_{i})$ expression, resulting in just $T_{i}$.",
+      "$H$ is either an empty string or one of the variables $L_{i}$ for $i \\in [1, n]$, indicating that $F_{0}$ default inherits the features of $F_{i}$ (the syntactic \u201chead\u201d). In the case where $H$ is an empty string, we allow dropping the brackets from the $T_{0}(F_{0})[H]$ expression, resulting in just $T_{0}(F_{0})$.",
+      "Note that while the above notation adopts the convention of splitting out the syntactic category and logical form from the feature list for visual prominence and to highlight the relationship to its context-free phrase-structure rule core, behaviorally it is identical to adding two more features to the feature list (we can call them, for example, $cat$ and $sem$) to represent the syntactic category and logical form.",
+      "This means that, for example, the rule",
+      "ACTIVE_VP[_head]/_head",
+      "$\\rightarrow $ VP_SIMPLE(form:infinitive)/_head",
+      "can be considered a notational shorthand for the following rule expressed purely using feature lists:",
+      "(cat:ACTIVE_VP, sem:_head)[_head]",
+      "$\\rightarrow $ (cat:VP_SIMPLE, sem:_head, form:infinitive)",
+      "Disjunction of features. Similarly to BIBREF37, we allow disjunctive feature specifications, which we denote by separating the alternative values with a pipe (`$|$'). The feature specification (form:gerund|infinitive) would thus unify with either (form:gerund) or (form:infinitive), but not with (form:pastparticiple).",
+      "Absence of features. We use a special atomic value _none_ to indicate that a given feature must either be absent or else explicitly set to the value _none_. The feature specification (subject:_none_, object:yes) would thus unify with either (object:yes) or (subject:_none_, object:yes), but not with (subject:yes, object:yes).",
+      "Negation of features. Similarly to BIBREF37, we allow negated feature specifications, which we denote by prefixing the attribute with a minus sign (`-'). The feature specification (-form:gerund|infinitive) would thus unify with (form:pastparticiple) or (form:_none_), but not with (form:gerund) or (form:infinitive). In general, a feature specification of the form (-attribute:v$_{1}$|...|v$_{j}$) can be considered a notational shorthand for (attribute:v$_{j+1}$|...|v$_{k}$|_none_), where v$_{j+1}$|...|v$_{k}$ is an enumeration of all possible values of the feature attribute other than v$_{1}$|...|v$_{j}$.",
+      "Default inheritance of features. If the lefthand side term is notated as $T_{0}(F_{0})[H]$, with $H$ equal to one of the variables $L_{i}$ for $i \\in [1, n]$, then this is interpreted as a notational shorthand for augmenting both $F_{0}$ and $F_{i}$ with an additional list of attribute-value pairs $(a_{1}$:$\\$v_{1}, ..., a_{k}$:$\\$v_{k})$, where $a_{1} ... a_{k}$ are all of the attributes listed in $F_{i}$ that were not originally listed in $F_{0}$.",
+      "Unification of logical forms. As described in Appendix SECREF16, we represent logical forms using a variation of description logic, rather than using feature structures. In the context of unification, we consider logical forms to unify if and only they achieve structural concept equality after variable replacement (using the same variable replacements applied during unification of the corresponding feature lists), while taking into account the commutativity and associativity of $\\sqcap $. For example, under this criterion, the logical form GenderRel $\\sqcap $ $\\exists $RolePair(Predicate, Gender)._head would unify with either GenderRel $\\sqcap $ $\\exists $RolePair(Predicate, Gender).Male or with ($\\exists $RolePair(Predicate, Gender).Male) $\\sqcap $ GenderRel under a variable replacement mapping _head to Male, but would not unify with GenderRel $\\sqcap $ $\\exists $RolePair(Predicate, Gender).Male $\\sqcap $ $\\exists $RolePair(Predicate, GenderHaver).FilmProducer."
+    ],
+    [
+      "CFQ knowledge rules output expressions representing facts that are known to be true. They have no direct effect on text, logical forms, or sparql, but the generated knowledge can be used as preconditions to other rules. In the rules index, they are described in the following format:",
+      "$\\rightarrow K$, where $K$ is knowledge that is output.",
+      "By convention, we define the rule name of a knowledge rule to be simply the string representing the knowledge that the rule outputs, and we omit the rule name in the rules index for brevity.",
+      "The union of those rules defines a knowledge base which we denote with $KB^{CFQ}$.",
+      "All knowledge in CFQ is represented in the form $P(X_1,...,X_n)$, where $P$ is a predicate from the list below, and $X_1, ..., X_n$ are either logical forms or else raw strings. Knowledge rules do not use variable-based expressions.",
+      "Supported knowledge predicates:",
+      "BoundRolePairs",
+      "ExclusiveRolePair",
+      "FreebaseEntityMapping",
+      "FreebasePropertyMapping",
+      "FreebaseTypeMapping",
+      "NonExclusiveRolePair",
+      "Role"
+    ],
+    [
+      "CFQ inference rules transform logical forms and may be conditioned on knowledge. In the rules index, they are described in the following format:",
+      "$K: L_0 \\rightarrow L_1$",
+      "where $K$ represents a comma-separated list of knowledge preconditions, and $L_0$ and $L_1$ represent the input and output logical forms, all expressed in terms of a shared set of variables $v_1,...,v_m$.",
+      "These rules are interpreted as stating that if there exists a variable replacement $r()$ replacing $v_1,...,v_m$ with some logical forms $l_1,...,l_m$ respectively, such that $r(K) \\subseteq KB^{CFQ}$, then we can apply the inference rule by rewriting $r(L_0)$ to $r(L_1)$."
+    ],
+    [
+      "CFQ resolution rules transform sparql expressions and may be conditioned on knowledge. They do not affect text or logical forms.",
+      "In the rules index, they are described in the following format:",
+      "$K: S_0 \\rightarrow S_1~...~S_n$",
+      "where $K$ represents a comma-separated list of knowledge preconditions, $S_0$ is a variable-based expression and $S_1~...~S_n$ are either raw sparql strings or else expressions described in terms of the same variables used in $S_0$ and $K$.",
+      "These rules are interpreted as stating that if there exists a variable replacement $r()$ replacing $v_1,...,v_m$ with some logical forms, strings, or expressions $l_1,...,l_m$ respectively, such that $r(K) \\subseteq KB^{CFQ}$, then we can apply the resolution rule by rewriting $r(S_0)$ to the sequence of terms $r(S_1)~...~r(S_n)$."
+    ],
+    [
+      "Our generation algorithm produces triples of the form $\\langle \\text{question, logical form, \\textsc {sparql}{} query} \\rangle $ in a mixed top-down and bottom-up fashion, with the final program of rule applications output alongside each triple in the form of a rule application DAG. The top-down portion of generation is responsible for efficiently searching for rules that can be applied to produce a meaningful example, while the bottom-up portion is responsible for actually applying the rules (i.e., performing the composition) and for producing the DAG.",
+      "The generation process proceeds in two phases, each involving a top-down as well as bottom-up aspect. In the first phase, we apply grammar rules interleaved with inference rules to produce a pair of $\\langle \\text{question, logical form} \\rangle $. Specifically, we apply a recursive top-down algorithm which starts with the $S$ nonterminal and at every step performs a random search over the rules in the grammar which could produce the target nonterminal with accompanying feature structure. This top-down process proceeds until a candidate syntactic parse tree is attained whose leaves consist purely of syntactic terminals (i.e., string literals or entity placeholders). The grammar rules from this candidate parse tree are then applied in a bottom-up fashion beginning with the syntactic terminals to yield a tree of $\\langle \\text{text, logical form} \\rangle $ pairs. After each such bottom-up grammar rule application, we then greedily apply all possible inference rules on the resulting logical forms, applying an arbitrary deterministic ordering to the inference rules in cases where rules could be applied in multiple valid orderings. This ensures that inference rules and grammar rules are executed in an interleaved manner and each inference rule is applied at the earliest possible occasion.",
+      "When a $\\langle \\text{question, logical form} \\rangle $ pair is generated for the $S$ nonterminal, we proceed to the second phase of the algorithm, in which resolution rules are applied to generate a corresponding sparql query to make up the third element of the desired $\\langle \\text{question, logical form, \\textsc {sparql}{} query} \\rangle $ triple. In practice, the bulk of the work in this phase is performed in a top-down fashion, in which resolution rules are recursively applied to transform a starting expression of the form get_specializations($L) (where $L represents the logical form output from the grammar phase) into a sequence of text literals representing the sparql query. This is followed nominally by a bottom-up process to construct the rule application DAG, yielding a tree of resolution rule applications of a similar form to the tree of interleaved grammar and inference rules output from the grammar phase. Note that while the grammar phase involves a large degree of random choice, the resolution phase proceeds much more deterministically, as the CFQ resolution rules have been designed such that any given question can yield only one possible sparql query, modulo commutativity and associativity of $\\sqcap $. In cases where resolution rules could be applied in multiple valid orderings, we again apply an arbitrary deterministic ordering to the resolution rules so as to yield as consistent as possible a rule application DAG and $\\langle \\text{question, logical form, \\textsc {sparql}{} query} \\rangle $ triple for any given question.",
+      "Finally, to ease the task of tracking unique query patterns and to minimize the impact on the learning task of implementation details regarding choice of variable names or ordering of clauses, we normalize the final sparql query by alphabetically sorting the query clauses and re-numbering the variables to follow a standard increasing order.",
+      "The resulting $\\langle \\text{question, logical form, \\textsc {sparql}{} query} \\rangle $ triple is then appended to the CFQ dataset."
+    ],
+    [
+      "In general, we do not explicitly track rules to represent the example-independent behaviors of the generation algorithm, as the universal applicability of these rules mean that the complete behavior of the generator should be observable on any reasonably-sized train set. The same applies to certain core behaviors of the description logic $\\mathcal {EL}$, such as commutativity and associativity of $\\sqcap $, which we omit tracking as explicit rules due to their similar ubiquity of application.",
+      "One example-independent rule, however, that we do explicitly track is the rule that describes the handover process between the grammar phase and the resolution phase \u2013 or in terms of the rule application DAG, the rule that joins the tree of interleaved grammar and inference rule applications with the tree of resolution rule applications. We call this rule JOIN_BY_LOGICAL_FORM. It is included in the rules list for every example in CFQ and appears as the head of the rule application tree for each example."
+    ],
+    [
+      "Note that conceptually a similar approach for combining the different rule types could be applied to the semantic parsing task. The main difference would be that, instead of performing random search over the grammar, the semantic parsing task would need to find the set of rules which produce the desired input text."
+    ],
+    [
+      "For many domains, the set of examples generated by exhaustively combining rules is infinite or prohibitively large. For example, the CFQ grammar generates an infinite set of questions, and even when restricted to a reasonable complexity, the set is still too large for practical use. This means that we need to choose which subset of examples we want to include in our dataset. Given our goal of comprehensively measuring compositional generalization, we do this by:",
+      "maximizing the overall diversity of rule combinations (allowing us to test as many rule combinations as possible)",
+      "while using a uniform distribution from simple examples to increasingly more complex examples.",
+      "We measure the diversity of rule combinations of a dataset using the empirical entropy over the frequency distribution of the subgraphs of the rule application DAGs, and we measure the complexity of an example using the number of rule applications used to generate it.",
+      "For CFQ, we choose the following practical trade-off between these two criteria. We first generate a sufficiently large sample set by performing random rule applications. We then subsample from it to select a subset that maximizes the entropy of the subgraph distribution (while only taking into account subgraphs with a limited number of nodes for practicality). We use a greedy algorithm that incrementally assigns elements to the subsampled set while maximizing entropy at each step.",
+      "The subsampling is initially limited to examples with the smallest complexity level and continues with increasingly larger complexity levels. We cap the maximum number of examples per level to achieve a uniform distribution across levels, and we limit the maximum complexity level such that the questions remain relatively natural. Table TABREF15 shows examples of generated questions at varying levels of complexity."
+    ],
+    [
+      "Figures FIGREF190 through FIGREF192 show the rule application DAG that was produced when generating the question \u201cWho directed [entity]?\u201d. They illustrate how grammar, inference, and knowledge rules are combined to generate a pair of text and logical form, and how resolution rules are used to generate the sparql query for the resulting logical form."
+    ],
+    [
+      "As discussed in Section SECREF3, nodes of this DAG represent rule applications while edges represent dependencies among the rules; i.e., an edge $A \\rightarrow B$ means that rule $B$ strictly depends on rule $A$ in the sense that the generator cannot apply rule $B$ before applying rule $A$. The DAG is normalized to ensure that a certain rule combination is represented using the same DAG across all the examples where it occurs. This is important for meaningfully comparing measures such as entropy and divergence across subgraphs of different examples.",
+      "Specifically, together with adopting the measures described above to ensure that rules are applied in a deterministic order, we achieve the normalization of the DAG by only producing edges that represent \u201cminimal dependencies\u201d. This means that if a rule $A$ can be applied after rule $B$, but it could also be applied after rule $B^{\\prime }$ with $B \\rightarrow B^{\\prime }$ (i.e., $B^{\\prime }$ depends on $B$), we don't produce the edge $B^{\\prime } \\rightarrow A$."
+    ],
+    [
+      "For brevity, in the rule application DAG figures we have applied the following abbreviations for several lengthy concept names:",
+      "Director = FilmDirector",
+      "Directee = DirectedFilm",
+      "Directing = DirectingAFilm",
+      "SubjectAgentVerb = PredicateWithBoundRolePairs(RolePair( SubjectHaver, Subject), RolePair(Predicate, Agent))",
+      "ObjectUndergoerVerb = PredicateWithBoundRolePairs(RolePair( ObjectHaver, Object), RolePair(Predicate, Undergoer))",
+      "E1 = Entity('?E1')"
+    ],
+    [
+      "As described in Section SECREF16, during generation we initially generate a $\\langle \\text{question, logical form, \\textsc {sparql}{} query} \\rangle $ triple containing entity placeholders, and then replace those placeholders with specific entities as a post-processing step. Conceptually, one could construct a rule application DAG describing either the process by which the original $\\langle \\text{question, logical form, \\textsc {sparql}{} query} \\rangle $ triple with entity placeholders was generated, or alternatively the rules that would need to be applied if constructing the $\\langle \\text{question, logical form, \\textsc {sparql}{} query} \\rangle $ triple containing the final entity MIDs directly. Structurally, these two DAGs are identical, differing only in the definition of two entity-related rules described below. The rule application DAG shown in the accompanying figures is the version using entity placeholders.",
+      "Versions of entity rules applicable when using entity placeholders:",
+      "ENTITY=[ENTITY]_HSz7QrdGdsX:",
+      "ENTITY(number:singular)/Entity(new_var(V1))",
+      "$\\rightarrow $ '[entity]'",
+      "ENTITY_MID:",
+      "ent2sparql(Entity($X)) $\\rightarrow $ $X",
+      "Versions of entity rules applicable when using actual entity MIDs:",
+      "ENTITY=[ENTITY]_HSz7QrdGdsX:",
+      "ENTITY(number:singular)/'m.'$X",
+      "$\\rightarrow $ 'm.'$X",
+      "ENTITY_MID:",
+      "ent2sparql('m.'$X) $\\rightarrow $ 'ns:m.'$X"
+    ],
+    [
+      "Figure FIGREF203 shows an example of subgraphs in order to provide more details on the sampling and weighting of compounds. An example non-linear subgraph is highlighted by the red area, and two linear subgraphs are highlighted by the blue and the yellow areas, respectively.",
+      "As described in Section SECREF6, given a large subset $\\mathbb {G}$ of subgraphs from the sample set as a whole, we calculate for each sample the weight of each subgraph $G \\in \\mathbb {G}$ that occurs in that sample as:",
+      "where $\\text{occ}(G)$ is the set of all occurrences of $G$ in the sample, $\\prec $ denotes the strict subgraph relation, and $P(G^{\\prime }| G)$ is the empirical probability of $G^{\\prime }$ occurring as a supergraph of $G$ over the full sample set.",
+      "Intuitively, we are trying to estimate how interesting the subgraph $G$ is in the sample. First, for every occurrence $g$ of a subgraph $G$, we look for the supergraph $G^{\\prime }$ of $g$ that co-occurs most often with $G$ in the full sample set. The empirical probability of having $G^{\\prime }$ as a supergraph of $G$ determines how interesting the occurrence $g$ is \u2013 the higher this probability, the less interesting the occurrence. Thus we compute the weight of the occurrence as the complement of this maximum empirical probability. Then we take the weight of $G$ to be the weight of the most interesting occurrence $g$ of $G$ in the sample.",
+      "E.g. in the extreme case that $G$ only occurs within the context $G^{\\prime }$, the weight of $G$ will be 0 in all samples. Conversely, if $G$ occurs in many different contexts, such that there is no single other subgraph $G^{\\prime }$ that subsumes it in many cases, then $w(G)$ will be high in all samples in which it occurs. This ensures that when calculating compound divergence based on a weighted subset of compounds, the most representative compounds are taken into account, while avoiding double-counting compounds whose frequency of occurrence is already largely explainable by the frequency of occurrence of one of its super-compounds.",
+      "Returning to our example in Figure FIGREF203, suppose that $G$ represents the smallest linear subgraph (yellow area), and suppose that the weight of $G$ in this sample is 0.4. Then this means that there exists some other subgraph $G^{\\prime }$ (for instance, the linear subgraph highlighted by the blue area) that is a supergraph of $G$ in 60% of the occurrences of $G$ across the sample set."
+    ],
+    [
+      "Below is a selection of the rules used in the generation of CFQ. Specifically, this includes all rules involved in generating the question \u201cWho directed [entity]?\u201d (the same example illustrated in the rule application DAG in Appendix SECREF19). The format of the rules is discussed in Appendix SECREF17."
+    ],
+    [
+      "S=WHQ_F6E9egkQqxj:",
+      "S/_x",
+      "$\\rightarrow $ WHQ/_x",
+      "WHQ=NPQ_INDIRECT_VP_INDIRECT_TXCca9URgVm:",
+      "WHQ[_subject]/DropDependency(_subject) $\\sqcap $ DropDependency($\\exists $RolePair(Subject, SubjectHaver)._action)",
+      "$\\rightarrow $ NPQ_INDIRECT(is_what:_none_, number:$n)/_subject",
+      "VP_INDIRECT(form:past, number:$n, object:yes, subject:_none_)/_action",
+      "NPQ_INDIRECT=WHO_5ptbPXXbuLZ:",
+      "NPQ_INDIRECT(number:singular)/Person",
+      "$\\rightarrow $ 'who'",
+      "VP_INDIRECT=VP_INDIRECT_DP_ZJH4NhRkByc:",
+      "VP_INDIRECT(object:yes)[_action]/_action $\\sqcap $ $\\exists $RolePair(ObjectHaver, Object)._object",
+      "$\\rightarrow $ VP_INDIRECT(object:_none_, subject:_none_)/_action",
+      "DP/_object",
+      "VP_INDIRECT=ACTIVE_VP_RX51Tm7RXPe:",
+      "VP_INDIRECT(object_type:$ut, subject_type:$at)[_head]/_head $\\sqcap $ PredicateWithBoundRolePairs(RolePair(SubjectHaver, Subject), RolePair(Predicate, Agent)) $\\sqcap $ PredicateWithBoundRolePairs(RolePair(ObjectHaver, Object), RolePair(Predicate, Undergoer))",
+      "$\\rightarrow $ ACTIVE_VP(agent_type:$at, undergoer_type:$ut)/_head",
+      "ACTIVE_VP=VP_SIMPLE_hJqAyjRUYJp:",
+      "ACTIVE_VP(number:singular)[_head]/_head",
+      "$\\rightarrow $ VP_SIMPLE(form:past)/_head",
+      "VP_SIMPLE=VP_GHWf3fcVRZg:",
+      "VP_SIMPLE(agent_type:person, undergoer_type:movie)[_head]/_head",
+      "$\\rightarrow $ VP(concept_id:DirectingAFilm)/_head",
+      "VP=DIRECTED_JkYzNbQyXtv:",
+      "VP(concept_id:DirectingAFilm, form:past)/DirectingAFilm",
+      "$\\rightarrow $ 'directed'",
+      "DP=ENTITY_M6fSP5GvRaN:",
+      "DP(is_proper_noun:yes, number:singular)[_head]/_head",
+      "$\\rightarrow $ ENTITY/_head",
+      "ENTITY=[ENTITY]_HSz7QrdGdsX:",
+      "ENTITY(number:singular)/Entity(new_var(V1))",
+      "$\\rightarrow $ '[entity]'",
+      "... (211 grammar rules total)"
+    ],
+    [
+      "BOUND_ROLES_WITH_PREDICATE_OBJECT:",
+      "BoundRolePairs($A, RolePair($R, $Q), RolePair($T, $S)):",
+      "$\\exists $RolePair($Q, $R).($A $\\sqcap $ $B) $\\rightarrow $ $\\exists $RolePair($S, $T).($A $\\sqcap $ $B)",
+      "BOUND_ROLES_WITH_PREDICATE_SUBJECT:",
+      "BoundRolePairs($B, RolePair($Q, $R), RolePair($S, $T)):",
+      "$B $\\sqcap $ $\\exists $RolePair($Q, $R).$A $\\rightarrow $ $B $\\sqcap $ $\\exists $RolePair($S, $T).$A",
+      "IGNORE_BOUND_ROLE_PAIRS:",
+      "$A $\\sqcap $ PredicateWithBoundRolePairs($X, $Y) $\\rightarrow $ $A",
+      "IGNORE_DEPENDENCY_DROPPING:",
+      "DropDependency($X) $\\rightarrow $ $X",
+      "PREDICATE_UNREIFICATION:",
+      "Role($Q, $P), Role($R, $P):",
+      "$\\exists $RolePair($Q, Predicate).($P $\\sqcap $ $\\exists $RolePair(Predicate, $R).$A) $\\rightarrow $ $\\exists $RolePair($Q, $R).$A",
+      "... (17 inference rules total)"
+    ],
+    [
+      "CONJUNCTION_WITHOUT_ENTITY:",
+      "def2sparql($X $\\sqcap $ $Y, $V1) $\\rightarrow $ def2sparql($X, $V1) ' . ' def2sparql($Y, $V1)",
+      "ENTITY_MID:",
+      "ent2sparql(Entity($X)) $\\rightarrow $ $X",
+      "GET_SPECIALIZATIONS:",
+      "get_specializations($X) $\\rightarrow $ 'SELECT DISTINCT ' get_var($X, new_var($V0)) ' WHERE { ' def2sparql($X, get_var($X, $V0)) '}'",
+      "GET_VAR_CONJUNCTION:",
+      "get_var($X $\\sqcap $ $Y, $V1) $\\rightarrow $ shared_var(get_var($X, get_var($Y, $V1)), get_var($Y, get_var($X, $V1)))",
+      "GET_VAR_RELATION:",
+      "get_var($\\exists $$R.$X, $V1) $\\rightarrow $ $V1",
+      "GET_VAR_TYPE:",
+      "FreebaseTypeMapping($X, $F):",
+      "get_var($X, $V1) $\\rightarrow $ $V1",
+      "PROPERTY_MAPPING:",
+      "FreebasePropertyMapping($R, $F):",
+      "role2sparql($R) $\\rightarrow $ $F",
+      "RELATION_MAPPING_WITHOUT_EXCLUSION:",
+      "NonExclusiveRolePair($R):",
+      "rel2sparql($X, $R, $Y) $\\rightarrow $ $X role2sparql($R) $Y",
+      "RELATION_TO_ENTITY:",
+      "def2sparql($\\exists $$R.$X, $V1) $\\rightarrow $ rel2sparql($V1, $R, ent2sparql($X))",
+      "SHARED_VAR:",
+      "shared_var($X, $X) $\\rightarrow $ $X",
+      "SPECIALIZATION_OF_TYPE:",
+      "def2sparql($X, $V1) $\\rightarrow $ $V1 ' a ' type2sparql($X)",
+      "TYPE_MAPPING:",
+      "FreebaseTypeMapping($X, $F):",
+      "type2sparql($X) $\\rightarrow $ $F",
+      "... (21 resolution rules total)"
+    ],
+    [
+      "$\\rightarrow $ BoundRolePairs(DirectingFilm, RolePair(Predicate, Agent), RolePair(Predicate, FilmDirector))",
+      "$\\rightarrow $ BoundRolePairs(DirectingFilm, RolePair(Predicate, Undergoer), RolePair(Predicate, DirectedFilm))",
+      "$\\rightarrow $ BoundRolePairs(PredicateWithBoundRolePairs(RolePair(ObjectHaver, Object), RolePair(Predicate, Undergoer)), RolePair(ObjectHaver, Object), RolePair(Predicate, Undergoer))",
+      "$\\rightarrow $ BoundRolePairs(PredicateWithBoundRolePairs(RolePair(Subject, SubjectHaver), RolePair(Agent, Predicate)), RolePair(Subject, SubjectHaver), RolePair(Agent, Predicate))",
+      "$\\rightarrow $ FreebasePropertyMapping(RolePair(FilmDirector, DirectedFilm), 'ns:film.director.film')",
+      "$\\rightarrow $ FreebaseTypeMapping(Person, 'ns:people.person')",
+      "$\\rightarrow $ NonExclusiveRolePair(FilmDirector, DirectedFilm)",
+      "$\\rightarrow $ Role(DirectedFilm, DirectingFilm)",
+      "$\\rightarrow $ Role(FilmDirector, DirectingFilm)",
+      "",
+      "... (194 knowledge rules total)"
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0945/instruction.md b/qasper-0945/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..08ef45c7e2e179f94c25b609b769f69ddeba2eb1
--- /dev/null
+++ b/qasper-0945/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Measuring Compositional Generalization: A Comprehensive Method on Realistic Data
+
+Question: What are other approaches into creating compositional generalization benchmarks?
\ No newline at end of file
diff --git a/qasper-0952/instruction.md b/qasper-0952/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..cd704252e1b968471c457028c75c3f52bfb25406
--- /dev/null
+++ b/qasper-0952/instruction.md
@@ -0,0 +1,209 @@
+Name of Paper: FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow
+
+Question: What are three neural machine translation (NMT) benchmark datasets used for evaluation?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Background",
+    "Background ::: Flow-based Generative Models",
+    "Background ::: Variational Inference and Training",
+    "FlowSeq",
+    "FlowSeq ::: Source Encoder",
+    "FlowSeq ::: Posterior ::: Generation of Latent Variables.",
+    "FlowSeq ::: Posterior ::: Zero initialization.",
+    "FlowSeq ::: Posterior ::: Token Dropout.",
+    "FlowSeq ::: Decoder",
+    "FlowSeq ::: Flow Architecture for Prior",
+    "FlowSeq ::: Flow Architecture for Prior ::: Actnorm.",
+    "FlowSeq ::: Flow Architecture for Prior ::: Invertible Multi-head Linear Layers.",
+    "FlowSeq ::: Flow Architecture for Prior ::: Affine Coupling Layers.",
+    "FlowSeq ::: Flow Architecture for Prior ::: Multi-scale Architecture.",
+    "FlowSeq ::: Predicting Target Sequence Length",
+    "FlowSeq ::: Decoding Process",
+    "FlowSeq ::: Decoding Process ::: Argmax Decoding.",
+    "FlowSeq ::: Decoding Process ::: Noisy Parallel Decoding (NPD).",
+    "FlowSeq ::: Decoding Process ::: Importance Weighted Decoding (IWD)",
+    "FlowSeq ::: Discussion",
+    "Experiments ::: Experimental Setups ::: Translation Datasets",
+    "Experiments ::: Experimental Setups ::: Modules and Hyperparameters",
+    "Experiments ::: Experimental Setups ::: Optimization",
+    "Experiments ::: Experimental Setups ::: Knowledge Distillation",
+    "Experiments ::: Main Results",
+    "Experiments ::: Analysis on Decoding Speed",
+    "Experiments ::: Analysis on Decoding Speed ::: How does batch size affect the decoding speed?",
+    "Experiments ::: Analysis on Decoding Speed ::: How does sentence length affect the decoding speed?",
+    "Experiments ::: Analysis of Rescoring Candidates",
+    "Experiments ::: Analysis of Translation Diversity",
+    "Conclusion",
+    "Acknowledgments",
+    "Flow Layers ::: ActNorm",
+    "Flow Layers ::: Invertible Linear",
+    "Flow Layers ::: Affine Coupling",
+    "Analysis of training dynamics",
+    "Analysis of Translation Results",
+    "Results of Translation Diversity"
+  ],
+  "paragraphs": [
+    [
+      "Neural sequence-to-sequence (seq2seq) models BIBREF0, BIBREF1, BIBREF2, BIBREF3 generate an output sequence $\\mathbf {y} = \\lbrace y_1, \\ldots , y_T\\rbrace $ given an input sequence $\\mathbf {x} = \\lbrace x_1, \\ldots , x_{T^{\\prime }}\\rbrace $ using conditional probabilities $P_\\theta (\\mathbf {y}|\\mathbf {x})$ predicted by neural networks (parameterized by $\\theta $).",
+      "Most seq2seq models are autoregressive, meaning that they factorize the joint probability of the output sequence given the input sequence $P_\\theta (\\mathbf {y}|\\mathbf {x})$ into the product of probabilities over the next token in the sequence given the input sequence and previously generated tokens:",
+      "Each factor, $P_\\theta (y_{t} | y_{<t}, \\mathbf {x})$, can be implemented by function approximators such as RNNs BIBREF0 and Transformers BIBREF3. This factorization takes the complicated problem of joint estimation over an exponentially large output space of outputs $\\mathbf {y}$, and turns it into a sequence of tractable multi-class classification problems predicting $y_t$ given the previous words, allowing for simple maximum log-likelihood training. However, this assumption of left-to-right factorization may be sub-optimal from a modeling perspective BIBREF4, BIBREF5, and generation of outputs must be done through a linear left-to-right pass through the output tokens using beam search, which is not easily parallelizable on hardware such as GPUs.",
+      "Recently, there has been work on non-autoregressive sequence generation for neural machine translation (NMT; BIBREF6, BIBREF7, BIBREF8) and language modeling BIBREF9. Non-autoregressive models attempt to model the joint distribution $P_\\theta (\\mathbf {y}|\\mathbf {x})$ directly, decoupling the dependencies of decoding history during generation. A na\u00efve solution is to assume that each token of the target sequence is independent given the input:",
+      "Unfortunately, the performance of this simple model falls far behind autoregressive models, as seq2seq tasks usually do have strong conditional dependencies between output variables BIBREF6. This problem can be mitigated by introducing a latent variable $\\mathbf {z}$ to model these conditional dependencies:",
+      "where $p_{\\theta }(\\mathbf {z}|\\mathbf {x})$ is the prior distribution over latent $\\mathbf {z}$ and $P_{\\theta }(\\mathbf {y}|\\mathbf {z}, \\mathbf {x})$ is the \u201cgenerative\u201d distribution (a.k.a decoder). Non-autoregressive generation can be achieved by the following independence assumption in the decoding process:",
+      "BIBREF6 proposed a $\\mathbf {z}$ representing fertility scores specifying the number of output words each input word generates, significantly improving the performance over Eq. (DISPLAY_FORM4). But the performance still falls behind state-of-the-art autoregressive models due to the limited expressiveness of fertility to model the interdependence between words in $\\textbf {y}$.",
+      "In this paper, we propose a simple, effective, and efficient model, FlowSeq, which models expressive prior distribution $p_{\\theta }(\\mathbf {z}|\\mathbf {x})$ using a powerful mathematical framework called generative flow BIBREF10. This framework can elegantly model complex distributions, and has obtained remarkable success in modeling continuous data such as images and speech through efficient density estimation and sampling BIBREF11, BIBREF12, BIBREF13. Based on this, we posit that generative flow also has potential to introduce more meaningful latent variables $\\mathbf {z}$ in the non-autoregressive generation in Eq. (DISPLAY_FORM5).",
+      "FlowSeq is a flow-based sequence-to-sequence model, which is (to our knowledge) the first non-autoregressive seq2seq model utilizing generative flows. It allows for efficient parallel decoding while modeling the joint distribution of the output sequence. Experimentally, on three benchmark datasets for machine translation \u2013 WMT2014, WMT2016 and IWSLT-2014, FlowSeq achieves comparable performance with state-of-the-art non-autoregressive models, and almost constant decoding time w.r.t. the sequence length compared to a typical left-to-right Transformer model, which is super-linear."
+    ],
+    [
+      "As noted above, incorporating expressive latent variables $\\mathbf {z}$ is essential to decouple the dependencies between tokens in the target sequence in non-autoregressive models. However, in order to model all of the complexities of sequence generation to the point that we can read off all of the words in the output in an independent fashion (as in Eq. (DISPLAY_FORM6)), the prior distribution $p_{\\theta }(\\mathbf {z}|\\mathbf {x})$ will necessarily be quite complex. In this section, we describe generative flows BIBREF10, an effective method for arbitrary modeling of complicated distributions, before describing how we apply them to sequence-to-sequence generation in \u00a7SECREF3."
+    ],
+    [
+      "Put simply, flow-based generative models work by transforming a simple distribution (e.g. a simple Gaussian) into a complex one (e.g. the complex prior distribution over $\\mathbf {z}$ that we want to model) through a chain of invertible transformations.",
+      "Formally, a set of latent variables $\\mathbf {\\upsilon } \\in \\Upsilon $ are introduced with a simple prior distribution $p_{\\Upsilon }(\\upsilon )$. We then define a bijection function $f: \\mathcal {Z} \\rightarrow \\Upsilon $ (with $g = f^{-1}$), whereby we can define a generative process over variables $\\mathbf {z}$:",
+      "An important insight behind flow-based models is that given this bijection function, the change of variable formula defines the model distribution on $\\mathbf {z}\\in \\mathcal {Z}$ by:",
+      "Here $\\frac{\\partial f_{\\theta }(\\mathbf {z})}{\\partial \\mathbf {z}}$ is the Jacobian matrix of $f_{\\theta }$ at $\\mathbf {z}$.",
+      "Eq. (DISPLAY_FORM9) provides a way to calculate the (complex) density of $\\mathbf {z}$ by calculating the (simple) density of $\\upsilon $ and the Jacobian of the transformation from $\\mathbf {z}$ to $\\upsilon $. For efficiency purposes, flow-based models generally use certain types of transformations $f_{\\theta }$ where both the inverse functions $g_{\\theta }$ and the Jacobian determinants are tractable to compute. A stacked sequence of such invertible transformations is also called a (normalizing) flow BIBREF10:",
+      "where $f = f_1 \\circ f_2 \\circ \\cdots \\circ f_K$ is a flow of $K$ transformations (omitting $\\theta $s for brevity)."
+    ],
+    [
+      "In the context of maximal likelihood estimation (MLE), we wish to minimize the negative log-likelihood of the parameters:",
+      "where $D=\\lbrace (\\mathbf {x}^i, \\mathbf {y}^i)\\rbrace _{i=1}^{N}$ is the set of training data. However, the likelihood $P_{\\theta }(\\mathbf {y}| \\mathbf {x})$ after marginalizing out latent variables $\\mathbf {z}$ (LHS in Eq. (DISPLAY_FORM5)) is intractable to compute or differentiate directly. Variational inference BIBREF14 provides a solution by introducing a parametric inference model $q_{\\phi }(\\mathbf {z}|\\mathbf {y}, \\mathbf {x})$ (a.k.a posterior) which is then used to approximate this integral by sampling individual examples of $\\mathbf {z}$. These models then optimize the evidence lower bound (ELBO), which considers both the \u201creconstruction error\u201d $\\log P_\\theta (\\mathbf {y}|\\mathbf {z},\\mathbf {x})$ and KL-divergence between the posterior and the prior:",
+      "Both inference model $\\phi $ and decoder $\\theta $ parameters are optimized according to this objective."
+    ],
+    [
+      "We first overview FlowSeq's architecture (shown in Figure FIGREF13) and training process here before detailing each component in following sections. Similarly to classic seq2seq models, at both training and test time FlowSeq first reads the whole input sequence $\\mathbf {x}$ and calculates a vector for each word in the sequence, the source encoding.",
+      "At training time, FlowSeq's parameters are learned using a variational training paradigm overviewed in \u00a7SECREF10. First, we draw samples of latent codes $\\mathbf {z}$ from the current posterior $q_{\\phi } (\\mathbf {z}|\\mathbf {y}, \\mathbf {x})$. Next, we feed $\\mathbf {z}$ together with source encodings into the decoder network and the prior flow to compute the probabilities of $P_{\\theta }(\\mathbf {y}|\\mathbf {z}, \\mathbf {x})$ and $p_{\\theta }(\\mathbf {z}|\\mathbf {x})$ for optimizing the ELBO (Eq. (DISPLAY_FORM12)).",
+      "At test time, generation is performed by first sampling a latent code $\\mathbf {z}$ from the prior flow by executing the generative process defined in Eq. (DISPLAY_FORM8). In this step, the source encodings produced from the encoder are used as conditional inputs. Then the decoder receives both the sampled latent code $\\mathbf {z}$ and the source encoder outputs to generate the target sequence $\\mathbf {y}$ from $P_{\\theta }(\\mathbf {y}|\\mathbf {z}, \\mathbf {x})$."
+    ],
+    [
+      "The source encoder encodes the source sequences into hidden representations, which are used in computing attention when generating latent variables in the posterior network and prior network as well as the cross-attention with decoder. Any standard neural sequence model can be used as its encoder, including RNNs BIBREF0 or Transformers BIBREF3."
+    ],
+    [
+      "The latent variables $\\mathbf {z}$ are represented as a sequence of continuous random vectors $\\mathbf {z}=\\lbrace \\mathbf {z}_1, \\ldots , \\mathbf {z}_T\\rbrace $ with the same length as the target sequence $\\mathbf {y}$. Each $\\mathbf {z}_t$ is a $d_{\\mathrm {z}}$-dimensional vector, where $d_{\\mathrm {z}}$ is the dimension of the latent space. The posterior distribution $q_{\\phi } (\\mathbf {z}|\\mathbf {y}, \\mathbf {x})$ models each $\\mathbf {z}_t$ as a diagonal Gaussian with learned mean and variance:",
+      "where $\\mu _{t}(\\cdot )$ and $\\sigma _{t}(\\cdot )$ are neural networks such as RNNs or Transformers."
+    ],
+    [
+      "While we perform standard random initialization for most layers of the network, we initialize the last linear transforms that generate the $\\mu $ and $\\log \\sigma ^2$ values with zeros. This ensures that the posterior distribution as a simple normal distribution, which we found helps train very deep generative flows more stably."
+    ],
+    [
+      "The motivation of introducing the latent variable $\\mathbf {z}$ into the model is to model the uncertainty in the generative process. Thus, it is preferable that $\\mathbf {z}$ capture contextual interdependence between tokens in $\\mathbf {y}$. However, there is an obvious local optimum where the posterior network generates a latent vector $\\mathbf {z}_t$ that only encodes the information about the corresponding target token $y_t$, and the decoder simply generates the \u201ccorrect\u201d token at each step $t$ with $\\mathbf {z}_t$ as input. In this case, FlowSeq reduces to the baseline model in Eq. (DISPLAY_FORM4). To escape this undesired local optimum, we apply token-level dropout to randomly drop an entire token when calculating the posterior, to ensure the model also has to learn how to use contextual information. This technique is similar to the \u201cmasked language model\u201d in previous studies BIBREF15, BIBREF16, BIBREF17.",
+      ""
+    ],
+    [
+      "As the decoder, we take the latent sequence $\\mathbf {z}$ as input, run it through several layers of a neural sequence model such as a Transformer, then directly predict the output tokens in $\\mathbf {y}$ individually and independently. Notably, unlike standard seq2seq decoders, we do not perform causal masking to prevent attending to future tokens, making the model fully non-autoregressive."
+    ],
+    [
+      "The flow architecture is based on Glow BIBREF11. It consists of a series of steps of flow, combined in a multi-scale architecture (see Figure FIGREF13.) Each step of flow consists three types of elementary flows \u2013 actnorm, invertible multi-head linear, and coupling. Note that all three functions are invertible and conducive to calculation of log determinants (details in Appendix SECREF6)."
+    ],
+    [
+      "The activation normalization layer (actnorm; BIBREF11) is an alternative for batch normalization BIBREF18, that has mainly been used in the context of image data to alleviate problems in model training. Actnorm performs an affine transformation of the activations using a scale and bias parameter per feature for sequences:",
+      "Both $\\mathbf {z}$ and $\\mathbf {z}^{\\prime }$ are tensors of shape $[T\\times d_{\\mathrm {z}}]$ with time dimension $t$ and feature dimension $d_{\\mathrm {z}}$. The parameters are initialized such that over each feature $\\mathbf {z}_{t}^{\\prime }$ has zero mean and unit variance given an initial mini-batch of data."
+    ],
+    [
+      "To incorporate general permutations of variables along the feature dimension to ensure that each dimension can affect every other ones after a sufficient number of steps of flow, BIBREF11 proposed a trainable invertible $1\\times 1$ convolution layer for 2D images. It is straightforward to apply similar transformations to sequential data:",
+      "",
+      "where $\\mathbf {W}$ is the weight matrix of shape $[d_{\\mathrm {z}} \\times d_{\\mathrm {z}}]$. The log-determinant of this transformation is:",
+      "",
+      "The cost of computing $\\mathrm {det}(\\mathbf {W})$ is $O(d_{\\mathrm {z}}^3)$.",
+      "Unfortunately, $d_{\\mathrm {z}}$ in Seq2Seq generation is commonly large, e.g. 512, significantly slowing down the model for computing $\\mathrm {det}(\\mathbf {W})$. To apply this to sequence generation, we propose a multi-head invertible linear layer, which first splits each $d_{\\mathrm {z}}$-dimensional feature vector into $h$ heads with dimension $d_h = d_{\\mathrm {z}}/h$. Then the linear transformation in (DISPLAY_FORM26) is applied to each head, with $d_h\\times d_h$ weight matrix $\\mathbf {W}$, significantly reducing the dimension. For splitting of heads, one step of flow contains one linear layer with either row-major or column-major splitting format, and these steps with different linear layers are composed in an alternating pattern."
+    ],
+    [
+      "To model interdependence across time steps, we use affine coupling layers BIBREF19:",
+      "where $\\mathrm {s}(\\mathbf {z}_a, \\mathbf {x})$ and $\\mathrm {b}(\\mathbf {z}_a, \\mathbf {x})$ are outputs of two neural networks with $\\mathbf {z}_a$ and $\\mathbf {x}$ as input. These are shown in Figure FIGREF21 (c). In experiments, we implement $\\mathrm {s}(\\cdot )$ and $\\mathrm {b}(\\cdot )$ with one Transformer decoder layer BIBREF3: multi-head self-attention over $\\mathbf {z}_a$, followed by multi-head inter-attention over $\\mathbf {x}$, followed by a position-wise feed-forward network. The input $\\mathbf {z}_a$ is fed into this layer in one pass, without causal masking.",
+      "As in BIBREF19, the $\\mathrm {split}()$ function splits $\\mathbf {z}$ the input tensor into two halves, while the $\\mathrm {concat}$ operation performs the corresponding reverse concatenation operation. In our architecture, three types of split functions are used, based on the split dimension and pattern. Figure FIGREF21 (b) illustrates the three splitting types. The first type of split groups $\\mathbf {z}$ along the time dimension on alternate indices. In this case, FlowSeq mainly models the interactions between time-steps. The second and third types of splits perform on the feature dimension, with continuous and alternate patterns, respectively. For each type of split, we alternate $\\mathbf {z}_a$ and $\\mathbf {z}_b$ to increase the flexibility of the split function. Different types of affine coupling layers alternate in the flow, similar to the linear layers."
+    ],
+    [
+      "We follow BIBREF19 in implementing a multi-scale architecture using the squeezing operation on the feature dimension, which has been demonstrated helpful for training deep flows. Formally, each scale is a combination of several steps of the flow (see Figure FIGREF21 (a)). After each scale, the model drops half of the dimensions with the third type of split in Figure FIGREF21 (b) to reduce computational and memory cost, outputting the tensor with shape $[T \\times \\frac{d}{2}]$. Then the squeezing operation transforms the $T \\times \\frac{d}{2}$ tensor into an $\\frac{T}{2} \\times d$ one as the input of the next scale. We pad each sentence with EOS tokens to ensure $T$ is divisible by 2. The right component of Figure FIGREF13 illustrates the multi-scale architecture."
+    ],
+    [
+      "In autoregressive seq2seq models, it is natural to determine the length of the sequence dynamically by simply predicting a special EOS token. However, for FlowSeq to predict the entire sequence in parallel, it needs to know its length in advance to generate the latent sequence $\\mathbf {z}$. Instead of predicting the absolute length of the target sequence, we predict the length difference between source and target sequences using a classifier with a range of $[-20, 20]$. Numbers in this range are predicted by max-pooling the source encodings into a single vector, running this through a linear layer, and taking a softmax. This classifier is learned jointly with the rest of the model.",
+      ""
+    ],
+    [
+      "At inference time, the model needs to identify the sequence with the highest conditional probability by marginalizing over all possible latent variables (see Eq. (DISPLAY_FORM5)), which is intractable in practice. We propose three approximating decoding algorithms to reduce the search space.",
+      ""
+    ],
+    [
+      "Following BIBREF6, one simple and effective method is to select the best sequence by choosing the highest-probability latent sequence $\\mathbf {z}$:",
+      "where identifying $\\mathbf {y}^*$ only requires independently maximizing the local probability for each output position (see Eq. DISPLAY_FORM6)."
+    ],
+    [
+      "A more accurate approximation of decoding, proposed in BIBREF6, is to draw samples from the latent space and compute the best output for each latent sequence. Then, a pre-trained autoregressive model is adopted to rank these sequences. In FlowSeq, different candidates can be generated by sampling different target lengths or different samples from the prior, and both of the strategies can be batched via masks during decoding. In our experiments, we first select the top $l$ length candidates from the length predictor in \u00a7SECREF29. Then, for each length candidate we use $r$ random samples from the prior network to generate output sequences, yielding a total of $l\\times r$ candidates."
+    ],
+    [
+      "The third approximating method is based on the lower bound of importance weighted estimation BIBREF20. Similarly to NPD, IWD first draws samples from the latent space and computes the best output for each latent sequence. Then, IWD ranks these candidate sequences with $K$ importance samples:",
+      "IWD does not rely on a separate pre-trained model, though it significantly slows down the decoding speed. The detailed comparison of these three decoding methods is provided in \u00a7SECREF45."
+    ],
+    [
+      "Different from the architecture proposed in BIBREF9, the architecture of FlowSeq is not using any autoregressive flow BIBREF21, BIBREF22, yielding a truly non-autoregressive model with efficient generation. Note that the FlowSeq remains non-autoregressive even if we use an RNN in the architecture because RNN is only used to encode a complete sequence of codes and all the input tokens can be fed into the RNN in parallel. This makes it possible to use highly-optimized implementations of RNNs such as those provided by cuDNN. Thus while RNNs do experience some drop in speed, it is less extreme than that experienced when using autoregressive models."
+    ],
+    [
+      "We evaluate FlowSeq on three machine translation benchmark datasets: WMT2014 DE-EN (around 4.5M sentence pairs), WMT2016 RO-EN (around 610K sentence pairs) and a smaller dataset IWSLT2014 DE-EN (around 150K sentence pairs). We use scripts from fairseq BIBREF23 to preprocess WMT2014 and IWSLT2014, where the preprocessing steps follow BIBREF3 for WMT2014. We use the data provided in BIBREF7 for WMT2016. For both WMT datasets, the source and target languages share the same set of BPE embeddings while for IWSLT2014 we use separate embeddings. During training, we filter out sentences longer than 80 for WMT dataset and 60 for IWSLT, respectively."
+    ],
+    [
+      "We implement the encoder, decoder and posterior networks with standard (unmasked) Transformer layers BIBREF3. For WMT datasets, the encoder consists of 6 layers, and the decoder and posterior are composed of 4 layers, and 8 attention heads. and for IWSLT, the encoder has 5 layers, and decoder and posterior have 3 layers, and 4 attention heads. The prior flow consists of 3 scales with the number of steps $[48, 48, 16]$ from bottom to top. To dissect the impact of model dimension on translation quality and speed, we perform experiments on two versions of FlowSeq with $d_{model}/d_{hidden} = 256/512$ (base) and $d_{model}/d_{hidden} = 512/1024$ (large). More model details are provided in Appendix SECREF7."
+    ],
+    [
+      "Parameter optimization is performed with the Adam optimizer BIBREF24 with $\\beta =(0.9, 0.999)$ and $\\epsilon =1e-6$. Each mini-batch consist of 2048 sentences. The learning rate is initialized to $5e-4$, and exponentially decays with rate $0.999995$. The gradient clipping cutoff is $1.0$. For all the FlowSeq models, we apply $0.1$ label smoothing and averaged the 5 best checkpoints to create the final model.",
+      "At the beginning of training, the posterior network is randomly initialized, producing noisy supervision to the prior. To mitigate this issue, we first set the weight of the $\\mathrm {KL}$ term in ELBO to zero for 30,000 updates to train the encoder, decoder and posterior networks. Then the $\\mathrm {KL}$ weight linearly increases to one for another 10,000 updates, which we found essential to accelerate training and achieve stable performance."
+    ],
+    [
+      "Previous work on non-autoregressive generation BIBREF6, BIBREF8 has used translations produced by a pre-trained autoregressive NMT model as the training data, noting that this can significantly improve the performance. We analyze the impact of distillation in \u00a7 SECREF45."
+    ],
+    [
+      "We first conduct experiments to compare the performance of FlowSeq with strong baseline models, including NAT w/ Fertility BIBREF6, NAT-IR BIBREF7, NAT-REG BIBREF25, LV NAR BIBREF26, CTC Loss BIBREF27, and CMLM BIBREF8.",
+      "Table TABREF39 provides the BLEU scores of FlowSeq with argmax decoding, together with baselines with purely non-autoregressive decoding methods that generate output sequence in one parallel pass. The first block lists results of models trained on raw data, while the second block are results using knowledge distillation. Without using knowledge distillation, FlowSeq base model achieves significant improvement (more than 9 BLEU points) over CMLM-base and LV NAR. It demonstrates the effectiveness of FlowSeq on modeling the complex interdependence in target languages.",
+      "Towards the effect of knowledge distillation, we can mainly obtain two observations: i) Similar to the findings in previous work, knowledge distillation still benefits the translation quality of FlowSeq. ii) Compared to previous models, the benefit of knowledge distillation on FlowSeq is less significant, yielding less than 3 BLEU improvement on WMT2014 DE-EN corpus, and even no improvement on WMT2016 RO-EN corpus. The reason might be that FlowSeq does not rely much on knowledge distillation to alleviate the multi-modality problem.",
+      "Table TABREF40 illustrates the BLEU scores of FlowSeq and baselines with advanced decoding methods such as iterative refinement, IWD and NPD rescoring. The first block in Table TABREF40 includes the baseline results from autoregressive Transformer. For the sampling procedure in IWD and NPD, we sampled from a reduced-temperature model BIBREF11 to obtain high-quality samples. We vary the temperature within $\\lbrace 0.1, 0.2, 0.3, 0.4, 0.5, 1.0\\rbrace $ and select the best temperature based on the performance on development sets. The analysis of the impact of sampling temperature and other hyper-parameters on samples is in \u00a7 SECREF50. For FlowSeq, NPD obtains better results than IWD, showing that FlowSeq still falls behind auto-regressive Transformer on model data distributions. Comparing with CMLM BIBREF8 with 10 iterations of refinement, which is a contemporaneous work that achieves state-of-the-art translation performance, FlowSeq obtains competitive performance on both WMT2014 and WMT2016 corpora, with only slight degradation in translation quality. Leveraging iterative refinement to further improve the performance of FlowSeq has been left to future work."
+    ],
+    [
+      "In this section, we compare the decoding speed (measured in average time in seconds required to decode one sentence) of FlowSeq at test time with that of the autoregressive Transformer model. We use the test set of WMT14 EN-DE for evaluation and all experiments are conducted on a single NVIDIA TITAN X GPU."
+    ],
+    [
+      "First, we investigate how different decoding batch size can affect the decoding speed. We vary the decoding batch size within $\\lbrace 1, 4, 8, 32, 64, 128\\rbrace $. Figure. FIGREF44 shows that for both FlowSeq and Transformer decoding is faster when using a larger batch size. However, FlowSeq has much larger gains in the decoding speed w.r.t. the increase in batch size, gaining a speed up of 594% of base model and 403% of large model when using a batch size of 128. We hypothesize that this is because the operations in FlowSeq are more friendly to batching while the Transformer model with beam search at test time is less efficient in benefiting from batching."
+    ],
+    [
+      "Next, we examine if sentence length is a major factor affecting the decoding speed. We bucket the test data by the target sentence length. From Fig. FIGREF44, we can see that as the sentence length increases, FlowSeq achieves almost constant decoding time while Transformer has a linearly increasing decoding time. The relative decoding speed up of FlowSeq versus Transformer linearly increases as the sequence length increases. The potential of decoding long sequences with constant time is an attractive property of FlowSeq."
+    ],
+    [
+      "In Fig. FIGREF49, we analyze how different sampling hyperparameters affect the performance of rescoring. First, we observe that the number of samples $r$ for each length is the most important factor. The performance is always improved with a larger sample size. Second, a larger number of length candidates does not necessarily increase the rescoring performance. Third, we find that a larger sampling temperature (0.3 - 0.5) can increase the diversity of translations and leads to better rescoring BLEU. However, the latent samples become noisy when a large temperature (1.0) is used."
+    ],
+    [
+      "Following BIBREF28, we analyze the output diversity of FlowSeq. BIBREF28 proposed pairwise-BLEU and BLEU computed in a leave-one-out manner to calibrate the diversity and quality of translation hypotheses. A lower pairwise-BLEU score implies a more diverse hypothesis set. And a higher BLEU score implies a better translation quality. We experiment on a subset of test set of WMT14-ENDE with ten references each sentence BIBREF29. In Fig. FIGREF52, we compare FlowSeq with other multi-hypothesis generation methods (ten hypotheses each sentence) to analyze how well the generation outputs of FlowSeq are in terms of diversity and quality. The right corner area of the figure indicates the ideal generations: high diversity and high quality. While FlowSeq still lags behind the autoregressive generations, by increasing the sampling temperature it provides a way of generating more diverse outputs while keeping the translation quality almost unchanged. More analysis of translation outputs and detailed results are provided in the Appendix SECREF9 and SECREF10.",
+      ""
+    ],
+    [
+      "We propose FlowSeq, an efficient and effective model for non-autoregressive sequence generation by using generative flows. One potential direction for future work is to leverage iterative refinement techniques such as masked language models to further improve translation quality. Another exciting direction is to, theoretically and empirically, investigate the latent space in FlowSeq, hence providing deep insights of the model, even enhancing controllable text generation.",
+      ""
+    ],
+    [
+      "This work was supported in part by DARPA grant FA8750-18-2-0018 funded under the AIDA program and grant HR0011-15-C-0114 funded under the LORELEI program. Any opinions, findings, and conclusions expressed in this material are those of the authors and do not necessarily reflect the views of DARPA. The authors thank Amazon for their gift of AWS cloud credits and anonymous reviewers for their helpful suggestions.",
+      "Appendix: FlowSeq"
+    ],
+    [
+      "Log-determinant:"
+    ],
+    [
+      "Log-determinant:",
+      "where $h$ is the number of heads."
+    ],
+    [
+      "Log-determinant:"
+    ],
+    [
+      "In Fig. FIGREF57, we plot the train and dev loss together with dev BLEU scores for the first 50 epochs. We can see that the reconstruction loss is increasing at the initial stage of training, then start to decrease when training with full KL loss. In addition, we observed that FlowSeq does not suffer the KL collapse problem BIBREF30, BIBREF31. This is because the decoder of FlowSeq is non-autogressive, with latent variable $\\mathbf {z}$ as the only input."
+    ],
+    [
+      "In Tab. TABREF58, we present randomly picked translation outputs from the test set of WMT14-DEEN. For each German input sentence, we pick three hypotheses from 30 samples. We have the following observations: First, in most cases, it can accurately express the meaning of the source sentence, sometimes in a different way from the reference sentence, which cannot be precisely reflected by the BLEU score. Second, by controlling the sampling hyper-parameters such as the length candidates $l$, the sampling temperature $\\tau $ and the number of samples $r$ under each length, FlowSeq is able to generate diverse translations expressing the same meaning. Third, repetition and broken translations also exist in some cases due to the lack of language model dependencies in the decoder."
+    ],
+    [
+      "Table TABREF59 shows the detailed results of translation deversity."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0955/instruction.md b/qasper-0955/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..c9658ad63213dc62b5a11f6b20c34ccee141a0b4
--- /dev/null
+++ b/qasper-0955/instruction.md
@@ -0,0 +1,67 @@
+Name of Paper: On Leveraging the Visual Modality for Neural Machine Translation
+
+Question: What are 3 novel fusion techniques that are proposed?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Proposed Fusion Techniques",
+    "Proposed Fusion Techniques ::: Step-Wise Decoder Fusion",
+    "Proposed Fusion Techniques ::: Multimodal Attention Modulation",
+    "Proposed Fusion Techniques ::: Visual-Semantic (VS) Regularizer",
+    "Results and Analysis",
+    "Results and Analysis ::: Experimental Results",
+    "Results and Analysis ::: Discussion",
+    "Results and Analysis ::: Discussion ::: PCA of Visual Features",
+    "Results and Analysis ::: Discussion ::: Comparison of Attention Components",
+    "Conclusions and Future Work"
+  ],
+  "paragraphs": [
+    [
+      "A number of works have explored integrating the visual modality for Neural Machine Translation (NMT) models, though, there has been relatively modest gains or no gains at all by incorporating the visual modality in the translation pipeline BIBREF0. In particular, BIBREF1 leverage multi-task learning, BIBREF2 use visual adaptive training, while BIBREF3, BIBREF4, BIBREF5 use a number of fusion techniques to incorporate features obtained from the visual modality.",
+      "Regarding the seemingly low utility of visual modality in machine translation, BIBREF6 hypothesize that the highly relevant visual properties are often not represented by linguistic models because they are too obvious to be explicitly mentioned in text (e.g., birds have wings, violins are brown). Similarly, BIBREF7 argue that perceptual information is already sufficiently encoded in textual cues. However, recently BIBREF0 have demonstrated that neural models are capable of leveraging the visual modality for translations, and posit that it is the nature of the Multi30k dataset (the only multimodal machine translation dataset at the time) which is inhibiting gains from the visual modality to emerge, due to the presence of short, simple and repetitive sentences, which renders the source text as sufficient context for translation. In this work, we further investigate this hypothesis on a large-scale multimodal machine translation (MMT) dataset, named How2 BIBREF2, which has 1.57 times longer sentences, in terms of the mean sentence length, when compared to Multi30k .",
+      "To this end, we restrict ourselves to the Sequence-to-Sequence (Seq2Seq) framework and propose three simple but novel fusion techniques to ensure the utilization of visual context during different stages (Input Context Encoding, Attention and Supervision) of the Sequence-to-Sequence transduction pipeline. We then evaluate and analyze the results for further insights, with the goal of testing the utility of visual modality for NMT under full source-side linguistic context."
+    ],
+    [
+      "In this section, we describe three additions to the Seq2Seq model to ensure that the visual context is utilized at different stages, namely when computing context during each step of the decoder, during attention as well as when computing the supervision signal in the Sequence-to-Sequence pipeline. This is done to encourage the Seq2Seq NMT model to make use of the visual features under full linguistic context. In each case, we assume that the visual features are fine-tuned using a visual encoder, which is trained jointly alongside the Seq2Seq model."
+    ],
+    [
+      "Our first proposed technique is the step-wise decoder fusion of visual features during every prediction step i.e. we concatenate the visual encoding as context at each step of the decoding process. This differs from the usual practice of passing the visual feature only at the beginning of the decoding process BIBREF5."
+    ],
+    [
+      "Similar to general attention BIBREF8, wherein a variable-length alignment vector $a_{th}(s)$, whose size equals the number of time steps on the source side, is derived by comparing the current target hidden state $h_{t}$ with each source hidden state $\\overline{h_{s}}$; we consider a variant wherein the visual encoding $v_{t}$ is used to calculate an attention distribution $a_{tv}(s)$ over the source encodings as well. Then, the true attention distribution $a_{t}(s)$ is computed as an interpolation between the visual and text based attention scores. The score function is a content based scoring mechanism as usual.",
+      "This formulation differs from BIBREF3 in that we use both the natural language as well as the visual modality to compute attention over the source sentence, rather than having attention over images. Since attention is computed over the same source embeddings (arising from a single encoder) using two different modalities, our approach also differs from BIBREF4, which focuses on combining the attention scores of multiple source encoders."
+    ],
+    [
+      "In terms of leveraging the visual modality for supervision, BIBREF1 use multi-task learning to learn grounded representations through image representation prediction. However, to our knowledge, visual-semantic supervision hasn't been much explored for multimodal translation in terms of loss functions.",
+      "Our proposed technique is the inclusion of visual-semantic supervision to the machine translation model. Recently, BIBREF9 proposed an optimal transport based loss function which computes the distance between the word embeddings of the predicted sentence and the target sentence and uses it as a regularizer $L_{\\text{ot}}^{\\text{tgt}}$. The purpose of this term is to provide the model with sequence level supervision. We leverage this idea by including a Cosine distance term, $L_{\\text{cosine}}^{\\text{visual}}$, between the visual encoding (which is at the sentence level) and the target/predicted sentence embeddings (computed as the average of the target/predicted word embeddings). The purpose of this distance term is to provide sequence level supervision by aligning the visual and text embeddings. In practice, as in BIBREF9, we introduce a hyperparameter in the loss function:",
+      "where $\\gamma $ is a hyper-parameter balancing the effect of loss components (a separate hyperparameter than in Section 2.2)."
+    ],
+    [
+      "Throughout our experiments, we use the 300 hours subset of How2 dataset BIBREF10, which contains 300 hours of videos, sentence-level time alignments to the ground-truth English subtitles, and Portuguese translations of English subtitles. The How2 dataset has 2048 dimensional pre-trained ResNeXt embeddings BIBREF11 available for each of the video clips aligned to the sentences.",
+      "Further, our baseline model is the canonical Seq2Seq model BIBREF12 consisting of bidirectional LSTM as encoder and decoder, general attention BIBREF8 and length normalization BIBREF13. In all cases, we use the embedding size of 300 and the hidden size of 512. Whenever the visual modality is used, we encode each of the visual features to 300 dimensional vectors through an encoder (consisting of a Linear layer followed by Batch Normalization and ReLU non-linearity) which is also trained end-to-end with the Seq2Seq model. Further, to integrate sequence level supervision as in BIBREF9, we utilize the Geomloss library , which provides a batched implementation of the Sinkhorn algorithm for the Optimal Transport computation. For all the translation experiments, we preprocess the data by lowercasing and removing the punctuations BIBREF2, and construct vocabulary at word level. Adam optimizer with a learning rate of 0.001 and a learning rate decay of 0.5 is used to throughout to train our models."
+    ],
+    [
+      "The performances of the models are summarized in Table TABREF9, along with the gains in BLEU points. From Table TABREF9, we can make a few observations:",
+      "The visual modality leads to modest gains in BLEU scores. The proposed VS regularizer leads to slightly higher gain when compared to Decoder-Fusion and Attention modulation techniques for the En-Pt language pair.",
+      "Further, the gains from incorporating the visual modality are less for Multimodal Attention and VS Regularization in the case of the reversed language pair of Pt-En (Table TABREF10), even though the visual modality is common to both the languages. This can possibly be attributed to the How2 dataset creation process wherein first the videos were aligned with English sentences and then the Portuguese translations were created, implying a reduction in correspondence with the visual modality due to errors introduced in the translation process."
+    ],
+    [
+      "To analyze the reasons for modest gains, despite incorporating multiple techniques to effectively leverage the visual modality for machine translation, we inspect the dataset as well as the proposed mechanisms."
+    ],
+    [
+      "We first investigate and compare the visual feature quality of the How2 dataset with respect to that of the Multi30k dataset . To analyze the discriminativeness of the visual features for both of these datasets, we leverage an analysis mechanism used in BIBREF14 in the context of analyzing word embedding discriminativeness. We analyze the variance of the visual features corresponding to each sentence in the training set. Since the visual features semantically represent the sentence as well, we could analyze how well the features are able to discriminate between the sentences and consequently between the individual words, as a measure of their utility for NMT.",
+      "Figure FIGREF14 (Top) shows the variance explained by the Top 100 principal components, obtained by applying PCA on the How2 and Multi30k training set visual features. The original feature dimensions are 2048 in both the cases. It is clear from the Figure FIGREF14 that most of the energy of the visual feature space resides in a low-dimensional subspace BIBREF14. In other words, there exist a few directions in the embedding space which disproportionately explain the variance. These \"common\" directions affect all of the embeddings in the same way, rendering them less discriminative. Figure FIGREF14 also shows the cumulative variance explained by Top 10, 20, 50 and 100 principal components respectively. It is clear that the visual features in the case of How2 dataset are much more dominated by the \"common\" dimensions, when compared to the Multi30k dataset. Further, this analysis is still at the sentence level, i.e. the visual features are much less discriminative among individual sentences, further aggravating the problem at the token level. This suggests that the existing visual features aren't sufficient enough to expect benefits from the visual modality in NMT, since they won't provide discriminativeness among the vocabulary elements at the token level during prediction. Further, this also indicates that under subword vocabulary such as BPE BIBREF15 or Sentence-Piece BIBREF16, the utility of such visual embeddings will only aggravate."
+    ],
+    [
+      "In this section, we analyze the visual and text based attention mechanisms. We find that the visual attention is very sparse, in that just one source encoding is attended to (the maximum visual attention over source encodings, across the test set, has mean 0.99 and standard deviation 0.015), thereby limiting the use of modulation. Thus, in practice, we find that a small weight ($\\gamma =0.1$) is necessary to prevent degradation due to this sparse visual attention component. Figure FIGREF18 & FIGREF19 shows the comparison of visual and text based attention for two sentences, one long source sentence of length 21 and one short source sentence of length 7. In both cases, we find that the visual component of the attention hasn't learnt any variation over the source encodings, again suggesting that the visual embeddings do not lend themselves to enhancing token-level discriminativess during prediction. We find this to be consistent across sentences of different lengths."
+    ],
+    [
+      "To conclude, we investigated the utility of visual modality for NMT, under full linguistic context on a new large-scale MMT dataset named How2. Our results on the How2 dataset confirm the general consensus that the visual modality does not lead to any significant gains for NMT, however, unlike BIBREF0 we attribute the relatively modest gains to the limited discriminativeness offered by the existing visual features, rather than the length of the sentences in the dataset. We validate this hypothesis quantitatively through a PCA based analysis of the visual features as well as qualitatively by analyzing attention components. We hope that our work would lead to more useful techniques and better visual features for MMT. An immediate future direction to explore would be to construct more discriminative features for utilizing the visual modality in NMT."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+++ b/qasper-0963/instruction.md
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+Name of Paper: A Set of Recommendations for Assessing Human-Machine Parity in Language Translation
+
+Question: What percentage fewer errors did professional translations make?
\ No newline at end of file
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+++ b/qasper-0964/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: A Set of Recommendations for Assessing Human-Machine Parity in Language Translation
+
+Question: What was the weakness in Hassan et al's evaluation design?
\ No newline at end of file
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+Name of Paper: Recognizing Musical Entities in User-generated Content
+
+Question: What kind of corpus-based features are taken into account?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Methodology",
+    "Dataset",
+    "NER system",
+    "Schedule matching",
+    "Results",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "The increasing use of social media and microblogging services has broken new ground in the field of Information Extraction (IE) from user-generated content (UGC). Understanding the information contained in users' content has become one of the main goal for many applications, due to the uniqueness and the variety of this data BIBREF0 . However, the highly informal and noisy status of these sources makes it difficult to apply techniques proposed by the NLP community for dealing with formal and structured content BIBREF1 .",
+      "In this work, we analyze a set of tweets related to a specific classical music radio channel, BBC Radio 3, interested in detecting two types of musical named entities, Contributor and Musical Work.",
+      "The method proposed makes use of the information extracted from the radio schedule for creating links between users' tweets and tracks broadcasted. Thanks to this linking, we aim to detect when users refer to entities included into the schedule. Apart from that, we consider a series of linguistic features, partly taken from the NLP literature and partly specifically designed for this task, for building statistical models able to recognize the musical entities. To that aim, we perform several experiments with a supervised learning model, Support Vector Machine (SVM), and a recurrent neural network architecture, a bidirectional LSTM with a CRF layer (biLSTM-CRF).",
+      "The contributions in this work are summarized as follows:",
+      "The paper is structured as follows. In Section 2, we present a review of the previous works related to Named Entity Recognition, focusing on its application on UGC and MIR. Afterwards, in Section 3 it is presented the methodology of this work, describing the dataset and the method proposed. In Section 4, the results obtained are shown. Finally, in Section 5 conclusions are discussed."
+    ],
+    [
+      "Named Entity Recognition (NER), or alternatively Named Entity Recognition and Classification (NERC), is the task of detecting entities in an input text and to assign them to a specific class. It starts to be defined in the early '80, and over the years several approaches have been proposed BIBREF2 . Early systems were based on handcrafted rule-based algorithms, while recently several contributions by Machine Learning scientists have helped in integrating probabilistic models into NER systems.",
+      "In particular, new developments in neural architectures have become an important resource for this task. Their main advantages are that they do not need language-specific knowledge resources BIBREF3 , and they are robust to the noisy and short nature of social media messages BIBREF4 . Indeed, according to a performance analysis of several Named Entity Recognition and Linking systems presented in BIBREF5 , it has been found that poor capitalization is one of the main issues when dealing with microblog content. Apart from that, typographic errors and the ubiquitous occurrence of out-of-vocabulary (OOV) words also cause drops in NER recall and precision, together with shortenings and slang, particularly pronounced in tweets.",
+      "Music Information Retrieval (MIR) is an interdisciplinary field which borrows tools of several disciplines, such as signal processing, musicology, machine learning, psychology and many others, for extracting knowledge from musical objects (be them audio, texts, etc.) BIBREF6 . In the last decade, several MIR tasks have benefited from NLP, such as sound and music recommendation BIBREF7 , automatic summary of song review BIBREF8 , artist similarity BIBREF9 and genre classification BIBREF10 .",
+      "In the field of IE, a first approach for detecting musical named entities from raw text, based on Hidden Markov Models, has been proposed in BIBREF11 . In BIBREF12 , the authors combine state-of-the-art Entity Linking (EL) systems to tackle the problem of detecting musical entities from raw texts. The method proposed relies on the argumentum ad populum intuition, so if two or more different EL systems perform the same prediction in linking a named entity mention, the more likely this prediction is to be correct. In detail, the off-the-shelf systems used are: DBpedia Spotlight BIBREF13 , TagMe BIBREF14 , Babelfy BIBREF15 . Moreover, a first Musical Entity Linking, MEL has been presented in BIBREF16 which combines different state-of-the-art NLP libraries and SimpleBrainz, an RDF knowledge base created from MusicBrainz after a simplification process.",
+      "Furthermore, Twitter has also been at the center of many studies done by the MIR community. As example, for building a music recommender system BIBREF17 analyzes tweets containing keywords like nowplaying or listeningto. In BIBREF9 , a similar dataset it is used for discovering cultural listening patterns. Publicly available Twitter corpora built for MIR investigations have been created, among others the Million Musical Tweets dataset BIBREF18 and the #nowplaying dataset BIBREF19 ."
+    ],
+    [
+      "We propose a hybrid method which recognizes musical entities in UGC using both contextual and linguistic information. We focus on detecting two types of entities: Contributor: person who is related to a musical work (composer, performer, conductor, etc). Musical Work: musical composition or recording (symphony, concerto, overture, etc).",
+      "As case study, we have chosen to analyze tweets extracted from the channel of a classical music radio, BBC Radio 3. The choice to focus on classical music has been mostly motivated by the particular discrepancy between the informal language used in the social platform and the formal nomenclature of contributors and musical works. Indeed, users when referring to a musician or to a classical piece in a tweet, rarely use the full name of the person or of the work, as shown in Table 2.",
+      "We extract information from the radio schedule for recreating the musical context to analyze user-generated tweets, detecting when they are referring to a specific work or contributor recently played. We manage to associate to every track broadcasted a list of entities, thanks to the tweets automatically posted by the BBC Radio3 Music Bot, where it is described the track actually on air in the radio. In Table 3, examples of bot-generated tweets are shown.",
+      "Afterwards, we detect the entities on the user-generated content by means of two methods: on one side, we use the entities extracted from the radio schedule for generating candidates entities in the user-generated tweets, thanks to a matching algorithm based on time proximity and string similarity. On the other side, we create a statistical model capable of detecting entities directly from the UGC, aimed to model the informal language of the raw texts. In Figure 1, an overview of the system proposed is presented."
+    ],
+    [
+      "In May 2018, we crawled Twitter using the Python library Tweepy, creating two datasets on which Contributor and Musical Work entities have been manually annotated, using IOB tags.",
+      "The first set contains user-generated tweets related to the BBC Radio 3 channel. It represents the source of user-generated content on which we aim to predict the named entities. We create it filtering the messages containing hashtags related to BBC Radio 3, such as #BBCRadio3 or #BBCR3. We obtain a set of 2,225 unique user-generated tweets. The second set consists of the messages automatically generated by the BBC Radio 3 Music Bot. This set contains 5,093 automatically generated tweets, thanks to which we have recreated the schedule.",
+      "In Table 4, the amount of tokens and relative entities annotated are reported for the two datasets. For evaluation purposes, both sets are split in a training part (80%) and two test sets (10% each one) randomly chosen. Within the user-generated corpora, entities annotated are only about 5% of the whole amount of tokens. In the case of the automatically generated tweets, the percentage is significantly greater and entities represent about the 50%."
+    ],
+    [
+      "According to the literature reviewed, state-of-the-art NER systems proposed by the NLP community are not tailored to detect musical entities in user-generated content. Consequently, our first objective has been to understand how to adapt existing systems for achieving significant results in this task.",
+      "In the following sections, we describe separately the features, the word embeddings and the models considered. All the resources used are publicy available.",
+      "We define a set of features for characterizing the text at the token level. We mix standard linguistic features, such as Part-Of-Speech (POS) and chunk tag, together with several gazetteers specifically built for classical music, and a series of features representing tokens' left and right context. For extracting the POS and the chunk tag we use the Python library twitter_nlp, presented in BIBREF1 .",
+      "In total, we define 26 features for describing each token: 1)POS tag; 2)Chunk tag; 3)Position of the token within the text, normalized between 0 and 1; 4)If the token starts with a capital letter; 5)If the token is a digit. Gazetteers: 6)Contributor first names; 7)Contributor last names; 8)Contributor types (\"soprano\", \"violinist\", etc.); 9)Classical work types (\"symphony\", \"overture\", etc.); 10)Musical instruments; 11)Opus forms (\"op\", \"opus\"); 12)Work number forms (\"no\", \"number\"); 13)Work keys (\"C\", \"D\", \"E\", \"F\" , \"G\" , \"A\", \"B\", \"flat\", \"sharp\"); 14)Work Modes (\"major\", \"minor\", \"m\"). Finally, we complete the tokens' description including as token's features the surface form, the POS and the chunk tag of the previous and the following two tokens (12 features).",
+      "We consider two sets of GloVe word embeddings BIBREF20 for training the neural architecture, one pre-trained with 2B of tweets, publicy downloadable, one trained with a corpora of 300K tweets collected during the 2014-2017 BBC Proms Festivals and disjoint from the data used in our experiments.",
+      "The first model considered for this task has been the John Platt's sequential minimal optimization algorithm for training a support vector classifier BIBREF21 , implemented in WEKA BIBREF22 . Indeed, in BIBREF23 results shown that SVM outperforms other machine learning models, such as Decision Trees and Naive Bayes, obtaining the best accuracy when detecting named entities from the user-generated tweets.",
+      "However, recent advances in Deep Learning techniques have shown that the NER task can benefit from the use of neural architectures, such as biLSTM-networks BIBREF3 , BIBREF4 . We use the implementation proposed in BIBREF24 for conducting three different experiments. In the first, we train the model using only the word embeddings as feature. In the second, together with the word embeddings we use the POS and chunk tag. In the third, all the features previously defined are included, in addition to the word embeddings. For every experiment, we use both the pre-trained embeddings and the ones that we created with our Twitter corpora. In section 4, results obtained from the several experiments are reported."
+    ],
+    [
+      "The bot-generated tweets present a predefined structure and a formal language, which facilitates the entities detection. In this dataset, our goal is to assign to each track played on the radio, represented by a tweet, a list of entities extracted from the tweet raw text. For achieving that, we experiment with the algorithms and features presented previously, obtaining an high level of accuracy, as presented in section 4. The hypothesis considered is that when a radio listener posts a tweet, it is possible that she is referring to a track which has been played a relatively short time before. In this cases, we want to show that knowing the radio schedule can help improving the results when detecting entities.",
+      "Once assigned a list of entities to each track, we perform two types of matching. Firstly, within the tracks we identify the ones which have been played in a fixed range of time (t) before and after the generation of the user's tweet. Using the resulting tracks, we create a list of candidates entities on which performing string similarity. The score of the matching based on string similarity is computed as the ratio of the number of tokens in common between an entity and the input tweet, and the total number of token of the entity: DISPLAYFORM0 ",
+      "In order to exclude trivial matches, tokens within a list of stop words are not considered while performing string matching. The final score is a weighted combination of the string matching score and the time proximity of the track, aimed to enhance matches from tracks played closer to the time when the user is posting the tweet.",
+      "The performance of the algorithm depends, apart from the time proximity threshold t, also on other two thresholds related to the string matching, one for the Musical Work (w) and one for the Contributor (c) entities. It has been necessary for avoiding to include candidate entities matched against the schedule with a low score, often source of false positives or negatives. Consequently, as last step Contributor and Musical Work candidates entities with respectively a string matching score lower than c and w, are filtered out. In Figure 2, an example of Musical Work entity recognized in an user-generated tweet using the schedule information is presented.",
+      "The entities recognized from the schedule matching are joined with the ones obtained directly from the statistical models. In the joined results, the criteria is to give priority to the entities recognized from the machine learning techniques. If they do not return any entities, the entities predicted by the schedule matching are considered. Our strategy is justified by the poorer results obtained by the NER based only on the schedule matching, compared to the other models used in the experiments, to be presented in the next section."
+    ],
+    [
+      "The performances of the NER experiments are reported separately for three different parts of the system proposed.",
+      "Table 6 presents the comparison of the various methods while performing NER on the bot-generated corpora and the user-generated corpora. Results shown that, in the first case, in the training set the F1 score is always greater than 97%, with a maximum of 99.65%. With both test sets performances decrease, varying between 94-97%. In the case of UGC, comparing the F1 score we can observe how performances significantly decrease. It can be considered a natural consequence of the complex nature of the users' informal language in comparison to the structured message created by the bot.",
+      "In Table 7, results of the schedule matching are reported. We can observe how the quality of the linking performed by the algorithm is correlated to the choice of the three thresholds. Indeed, the Precision score increase when the time threshold decrease, admitting less candidates as entities during the matching, and when the string similarity thresholds increase, accepting only candidates with an higher degree of similarity. The behaviour of the Recall score is inverted.",
+      "Finally, we test the impact of using the schedule matching together with a biLSTM-CRF network. In this experiment, we consider the network trained using all the features proposed, and the embeddings not pre-trained. Table 8 reports the results obtained. We can observe how generally the system benefits from the use of the schedule information. Especially in the testing part, where the neural network recognizes with less accuracy, the explicit information contained in the schedule can be exploited for identifying the entities at which users are referring while listening to the radio and posting the tweets."
+    ],
+    [
+      "We have presented in this work a novel method for detecting musical entities from user-generated content, modelling linguistic features with statistical models and extracting contextual information from a radio schedule. We analyzed tweets related to a classical music radio station, integrating its schedule to connect users' messages to tracks broadcasted. We focus on the recognition of two kinds of entities related to the music field, Contributor and Musical Work.",
+      "According to the results obtained, we have seen a pronounced difference between the system performances when dealing with the Contributor instead of the Musical Work entities. Indeed, the former type of entity has been shown to be more easily detected in comparison to the latter, and we identify several reasons behind this fact. Firstly, Contributor entities are less prone to be shorten or modified, while due to their longness, Musical Work entities often represent only a part of the complete title of a musical piece. Furthermore, Musical Work titles are typically composed by more tokens, including common words which can be easily misclassified. The low performances obtained in the case of Musical Work entities can be a consequences of these observations. On the other hand, when referring to a Contributor users often use only the surname, but in most of the cases it is enough for the system to recognizing the entities.",
+      "From the experiments we have seen that generally the biLSTM-CRF architecture outperforms the SVM model. The benefit of using the whole set of features is evident in the training part, but while testing the inclusion of the features not always leads to better results. In addition, some of the features designed in our experiments are tailored to the case of classical music, hence they might not be representative if applied to other fields. We do not exclude that our method can be adapted for detecting other kinds of entity, but it might be needed to redefine the features according to the case considered. Similarly, it has not been found a particular advantage of using the pre-trained embeddings instead of the one trained with our corpora. Furthermore, we verified the statistical significance of our experiment by using Wilcoxon Rank-Sum Test, obtaining that there have been not significant difference between the various model considered while testing.",
+      "The information extracted from the schedule also present several limitations. In fact, the hypothesis that a tweet is referring to a track broadcasted is not always verified. Even if it is common that radios listeners do comments about tracks played, or give suggestion to the radio host about what they would like to listen, it is also true that they might refer to a Contributor or Musical Work unrelated to the radio schedule."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0973/instruction.md b/qasper-0973/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..2221266dca5de54931d9256d7f8a2634287fac69
--- /dev/null
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@@ -0,0 +1,75 @@
+Name of Paper: Recognizing Musical Entities in User-generated Content
+
+Question: Which machine learning algorithms did the explore?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Methodology",
+    "Dataset",
+    "NER system",
+    "Schedule matching",
+    "Results",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "The increasing use of social media and microblogging services has broken new ground in the field of Information Extraction (IE) from user-generated content (UGC). Understanding the information contained in users' content has become one of the main goal for many applications, due to the uniqueness and the variety of this data BIBREF0 . However, the highly informal and noisy status of these sources makes it difficult to apply techniques proposed by the NLP community for dealing with formal and structured content BIBREF1 .",
+      "In this work, we analyze a set of tweets related to a specific classical music radio channel, BBC Radio 3, interested in detecting two types of musical named entities, Contributor and Musical Work.",
+      "The method proposed makes use of the information extracted from the radio schedule for creating links between users' tweets and tracks broadcasted. Thanks to this linking, we aim to detect when users refer to entities included into the schedule. Apart from that, we consider a series of linguistic features, partly taken from the NLP literature and partly specifically designed for this task, for building statistical models able to recognize the musical entities. To that aim, we perform several experiments with a supervised learning model, Support Vector Machine (SVM), and a recurrent neural network architecture, a bidirectional LSTM with a CRF layer (biLSTM-CRF).",
+      "The contributions in this work are summarized as follows:",
+      "The paper is structured as follows. In Section 2, we present a review of the previous works related to Named Entity Recognition, focusing on its application on UGC and MIR. Afterwards, in Section 3 it is presented the methodology of this work, describing the dataset and the method proposed. In Section 4, the results obtained are shown. Finally, in Section 5 conclusions are discussed."
+    ],
+    [
+      "Named Entity Recognition (NER), or alternatively Named Entity Recognition and Classification (NERC), is the task of detecting entities in an input text and to assign them to a specific class. It starts to be defined in the early '80, and over the years several approaches have been proposed BIBREF2 . Early systems were based on handcrafted rule-based algorithms, while recently several contributions by Machine Learning scientists have helped in integrating probabilistic models into NER systems.",
+      "In particular, new developments in neural architectures have become an important resource for this task. Their main advantages are that they do not need language-specific knowledge resources BIBREF3 , and they are robust to the noisy and short nature of social media messages BIBREF4 . Indeed, according to a performance analysis of several Named Entity Recognition and Linking systems presented in BIBREF5 , it has been found that poor capitalization is one of the main issues when dealing with microblog content. Apart from that, typographic errors and the ubiquitous occurrence of out-of-vocabulary (OOV) words also cause drops in NER recall and precision, together with shortenings and slang, particularly pronounced in tweets.",
+      "Music Information Retrieval (MIR) is an interdisciplinary field which borrows tools of several disciplines, such as signal processing, musicology, machine learning, psychology and many others, for extracting knowledge from musical objects (be them audio, texts, etc.) BIBREF6 . In the last decade, several MIR tasks have benefited from NLP, such as sound and music recommendation BIBREF7 , automatic summary of song review BIBREF8 , artist similarity BIBREF9 and genre classification BIBREF10 .",
+      "In the field of IE, a first approach for detecting musical named entities from raw text, based on Hidden Markov Models, has been proposed in BIBREF11 . In BIBREF12 , the authors combine state-of-the-art Entity Linking (EL) systems to tackle the problem of detecting musical entities from raw texts. The method proposed relies on the argumentum ad populum intuition, so if two or more different EL systems perform the same prediction in linking a named entity mention, the more likely this prediction is to be correct. In detail, the off-the-shelf systems used are: DBpedia Spotlight BIBREF13 , TagMe BIBREF14 , Babelfy BIBREF15 . Moreover, a first Musical Entity Linking, MEL has been presented in BIBREF16 which combines different state-of-the-art NLP libraries and SimpleBrainz, an RDF knowledge base created from MusicBrainz after a simplification process.",
+      "Furthermore, Twitter has also been at the center of many studies done by the MIR community. As example, for building a music recommender system BIBREF17 analyzes tweets containing keywords like nowplaying or listeningto. In BIBREF9 , a similar dataset it is used for discovering cultural listening patterns. Publicly available Twitter corpora built for MIR investigations have been created, among others the Million Musical Tweets dataset BIBREF18 and the #nowplaying dataset BIBREF19 ."
+    ],
+    [
+      "We propose a hybrid method which recognizes musical entities in UGC using both contextual and linguistic information. We focus on detecting two types of entities: Contributor: person who is related to a musical work (composer, performer, conductor, etc). Musical Work: musical composition or recording (symphony, concerto, overture, etc).",
+      "As case study, we have chosen to analyze tweets extracted from the channel of a classical music radio, BBC Radio 3. The choice to focus on classical music has been mostly motivated by the particular discrepancy between the informal language used in the social platform and the formal nomenclature of contributors and musical works. Indeed, users when referring to a musician or to a classical piece in a tweet, rarely use the full name of the person or of the work, as shown in Table 2.",
+      "We extract information from the radio schedule for recreating the musical context to analyze user-generated tweets, detecting when they are referring to a specific work or contributor recently played. We manage to associate to every track broadcasted a list of entities, thanks to the tweets automatically posted by the BBC Radio3 Music Bot, where it is described the track actually on air in the radio. In Table 3, examples of bot-generated tweets are shown.",
+      "Afterwards, we detect the entities on the user-generated content by means of two methods: on one side, we use the entities extracted from the radio schedule for generating candidates entities in the user-generated tweets, thanks to a matching algorithm based on time proximity and string similarity. On the other side, we create a statistical model capable of detecting entities directly from the UGC, aimed to model the informal language of the raw texts. In Figure 1, an overview of the system proposed is presented."
+    ],
+    [
+      "In May 2018, we crawled Twitter using the Python library Tweepy, creating two datasets on which Contributor and Musical Work entities have been manually annotated, using IOB tags.",
+      "The first set contains user-generated tweets related to the BBC Radio 3 channel. It represents the source of user-generated content on which we aim to predict the named entities. We create it filtering the messages containing hashtags related to BBC Radio 3, such as #BBCRadio3 or #BBCR3. We obtain a set of 2,225 unique user-generated tweets. The second set consists of the messages automatically generated by the BBC Radio 3 Music Bot. This set contains 5,093 automatically generated tweets, thanks to which we have recreated the schedule.",
+      "In Table 4, the amount of tokens and relative entities annotated are reported for the two datasets. For evaluation purposes, both sets are split in a training part (80%) and two test sets (10% each one) randomly chosen. Within the user-generated corpora, entities annotated are only about 5% of the whole amount of tokens. In the case of the automatically generated tweets, the percentage is significantly greater and entities represent about the 50%."
+    ],
+    [
+      "According to the literature reviewed, state-of-the-art NER systems proposed by the NLP community are not tailored to detect musical entities in user-generated content. Consequently, our first objective has been to understand how to adapt existing systems for achieving significant results in this task.",
+      "In the following sections, we describe separately the features, the word embeddings and the models considered. All the resources used are publicy available.",
+      "We define a set of features for characterizing the text at the token level. We mix standard linguistic features, such as Part-Of-Speech (POS) and chunk tag, together with several gazetteers specifically built for classical music, and a series of features representing tokens' left and right context. For extracting the POS and the chunk tag we use the Python library twitter_nlp, presented in BIBREF1 .",
+      "In total, we define 26 features for describing each token: 1)POS tag; 2)Chunk tag; 3)Position of the token within the text, normalized between 0 and 1; 4)If the token starts with a capital letter; 5)If the token is a digit. Gazetteers: 6)Contributor first names; 7)Contributor last names; 8)Contributor types (\"soprano\", \"violinist\", etc.); 9)Classical work types (\"symphony\", \"overture\", etc.); 10)Musical instruments; 11)Opus forms (\"op\", \"opus\"); 12)Work number forms (\"no\", \"number\"); 13)Work keys (\"C\", \"D\", \"E\", \"F\" , \"G\" , \"A\", \"B\", \"flat\", \"sharp\"); 14)Work Modes (\"major\", \"minor\", \"m\"). Finally, we complete the tokens' description including as token's features the surface form, the POS and the chunk tag of the previous and the following two tokens (12 features).",
+      "We consider two sets of GloVe word embeddings BIBREF20 for training the neural architecture, one pre-trained with 2B of tweets, publicy downloadable, one trained with a corpora of 300K tweets collected during the 2014-2017 BBC Proms Festivals and disjoint from the data used in our experiments.",
+      "The first model considered for this task has been the John Platt's sequential minimal optimization algorithm for training a support vector classifier BIBREF21 , implemented in WEKA BIBREF22 . Indeed, in BIBREF23 results shown that SVM outperforms other machine learning models, such as Decision Trees and Naive Bayes, obtaining the best accuracy when detecting named entities from the user-generated tweets.",
+      "However, recent advances in Deep Learning techniques have shown that the NER task can benefit from the use of neural architectures, such as biLSTM-networks BIBREF3 , BIBREF4 . We use the implementation proposed in BIBREF24 for conducting three different experiments. In the first, we train the model using only the word embeddings as feature. In the second, together with the word embeddings we use the POS and chunk tag. In the third, all the features previously defined are included, in addition to the word embeddings. For every experiment, we use both the pre-trained embeddings and the ones that we created with our Twitter corpora. In section 4, results obtained from the several experiments are reported."
+    ],
+    [
+      "The bot-generated tweets present a predefined structure and a formal language, which facilitates the entities detection. In this dataset, our goal is to assign to each track played on the radio, represented by a tweet, a list of entities extracted from the tweet raw text. For achieving that, we experiment with the algorithms and features presented previously, obtaining an high level of accuracy, as presented in section 4. The hypothesis considered is that when a radio listener posts a tweet, it is possible that she is referring to a track which has been played a relatively short time before. In this cases, we want to show that knowing the radio schedule can help improving the results when detecting entities.",
+      "Once assigned a list of entities to each track, we perform two types of matching. Firstly, within the tracks we identify the ones which have been played in a fixed range of time (t) before and after the generation of the user's tweet. Using the resulting tracks, we create a list of candidates entities on which performing string similarity. The score of the matching based on string similarity is computed as the ratio of the number of tokens in common between an entity and the input tweet, and the total number of token of the entity: DISPLAYFORM0 ",
+      "In order to exclude trivial matches, tokens within a list of stop words are not considered while performing string matching. The final score is a weighted combination of the string matching score and the time proximity of the track, aimed to enhance matches from tracks played closer to the time when the user is posting the tweet.",
+      "The performance of the algorithm depends, apart from the time proximity threshold t, also on other two thresholds related to the string matching, one for the Musical Work (w) and one for the Contributor (c) entities. It has been necessary for avoiding to include candidate entities matched against the schedule with a low score, often source of false positives or negatives. Consequently, as last step Contributor and Musical Work candidates entities with respectively a string matching score lower than c and w, are filtered out. In Figure 2, an example of Musical Work entity recognized in an user-generated tweet using the schedule information is presented.",
+      "The entities recognized from the schedule matching are joined with the ones obtained directly from the statistical models. In the joined results, the criteria is to give priority to the entities recognized from the machine learning techniques. If they do not return any entities, the entities predicted by the schedule matching are considered. Our strategy is justified by the poorer results obtained by the NER based only on the schedule matching, compared to the other models used in the experiments, to be presented in the next section."
+    ],
+    [
+      "The performances of the NER experiments are reported separately for three different parts of the system proposed.",
+      "Table 6 presents the comparison of the various methods while performing NER on the bot-generated corpora and the user-generated corpora. Results shown that, in the first case, in the training set the F1 score is always greater than 97%, with a maximum of 99.65%. With both test sets performances decrease, varying between 94-97%. In the case of UGC, comparing the F1 score we can observe how performances significantly decrease. It can be considered a natural consequence of the complex nature of the users' informal language in comparison to the structured message created by the bot.",
+      "In Table 7, results of the schedule matching are reported. We can observe how the quality of the linking performed by the algorithm is correlated to the choice of the three thresholds. Indeed, the Precision score increase when the time threshold decrease, admitting less candidates as entities during the matching, and when the string similarity thresholds increase, accepting only candidates with an higher degree of similarity. The behaviour of the Recall score is inverted.",
+      "Finally, we test the impact of using the schedule matching together with a biLSTM-CRF network. In this experiment, we consider the network trained using all the features proposed, and the embeddings not pre-trained. Table 8 reports the results obtained. We can observe how generally the system benefits from the use of the schedule information. Especially in the testing part, where the neural network recognizes with less accuracy, the explicit information contained in the schedule can be exploited for identifying the entities at which users are referring while listening to the radio and posting the tweets."
+    ],
+    [
+      "We have presented in this work a novel method for detecting musical entities from user-generated content, modelling linguistic features with statistical models and extracting contextual information from a radio schedule. We analyzed tweets related to a classical music radio station, integrating its schedule to connect users' messages to tracks broadcasted. We focus on the recognition of two kinds of entities related to the music field, Contributor and Musical Work.",
+      "According to the results obtained, we have seen a pronounced difference between the system performances when dealing with the Contributor instead of the Musical Work entities. Indeed, the former type of entity has been shown to be more easily detected in comparison to the latter, and we identify several reasons behind this fact. Firstly, Contributor entities are less prone to be shorten or modified, while due to their longness, Musical Work entities often represent only a part of the complete title of a musical piece. Furthermore, Musical Work titles are typically composed by more tokens, including common words which can be easily misclassified. The low performances obtained in the case of Musical Work entities can be a consequences of these observations. On the other hand, when referring to a Contributor users often use only the surname, but in most of the cases it is enough for the system to recognizing the entities.",
+      "From the experiments we have seen that generally the biLSTM-CRF architecture outperforms the SVM model. The benefit of using the whole set of features is evident in the training part, but while testing the inclusion of the features not always leads to better results. In addition, some of the features designed in our experiments are tailored to the case of classical music, hence they might not be representative if applied to other fields. We do not exclude that our method can be adapted for detecting other kinds of entity, but it might be needed to redefine the features according to the case considered. Similarly, it has not been found a particular advantage of using the pre-trained embeddings instead of the one trained with our corpora. Furthermore, we verified the statistical significance of our experiment by using Wilcoxon Rank-Sum Test, obtaining that there have been not significant difference between the various model considered while testing.",
+      "The information extracted from the schedule also present several limitations. In fact, the hypothesis that a tweet is referring to a track broadcasted is not always verified. Even if it is common that radios listeners do comments about tracks played, or give suggestion to the radio host about what they would like to listen, it is also true that they might refer to a Contributor or Musical Work unrelated to the radio schedule."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: Recognizing Musical Entities in User-generated Content
+
+Question: What language is the Twitter content in?
\ No newline at end of file
diff --git a/qasper-0975/instruction.md b/qasper-0975/instruction.md
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index 0000000000000000000000000000000000000000..eeeb1abd106608f1c4552674ffdefa81c1493b04
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+Name of Paper: MIT-QCRI Arabic Dialect Identification System for the 2017 Multi-Genre Broadcast Challenge
+
+Question: What is the architecture of the siamese neural network?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "MGB-3 Arabic Dialect Identification",
+    "Dialect Identification Task & System",
+    "Baseline ADI System",
+    "Siamese Neural Network-based ADI",
+    "i-vector Post-Processing",
+    "Phoneme Features",
+    "Character Features",
+    "Score Calibration",
+    "ADI Experiments",
+    "Using Training Data for Training",
+    "Using Training and Development Data for Training",
+    "Performance Evaluation of Submission",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "One of the challenges of processing real-world spoken content, such as media broadcasts, is the potential presence of different dialects of a language in the material. Dialect identification can be a useful capability to identify which dialect is being spoken during a recording. Dialect identification can be regarded as a special case of language recognition, requiring an ability to discriminate between different members within the same language family, as opposed to across language families (i.e., for language recognition). The dominant approach, based on i-vector extraction, has proven to be very effective for both language and speaker recognition BIBREF0 . Recently, phonetically aware deep neural models have also been found to be effective in combination with i-vectors BIBREF1 , BIBREF2 , BIBREF3 . Phonetically aware models could be beneficial for dialect identification, since they provide a mechanism to focus attention on small phonetic differences between dialects with predominantly common phonetic inventories.",
+      "Since 2015, the Arabic Multi-Genre Broadcast (MGB) Challenge tasks have provided a valuable resource for researchers interested in processing multi-dialectal Arabic speech. For the ASRU 2017 MGB-3 Challenge, there were two possible tasks. The first task was aimed at developing an automatic speech recognition system for Arabic dialectal speech based on a multi-genre broadcast audio dataset. The second task was aimed at developing an Arabic Dialect Identification (ADI) capability for five major Arabic dialects. This paper reports our experimentation efforts for the ADI task.",
+      "While the MGB-3 Arabic ASR task included seven different genres from the broadcast domain, the ADI task focused solely on broadcast news. Participants were provided high-quality Aljazeera news broadcasts as well as transcriptions generated by a multi-dialect ASR system created from the MGB-2 dataset BIBREF4 . The biggest difference from previous MGB challenges is that only a relatively small development set of in-domain data is provided for adaptation to the test set (i.e., the training data is mismatched with the test data). For the ADI baseline, participants were also provided with i-vector features from the audio dataset, and lexical features from the transcripts. Evaluation software was shared with all participants using baseline features available via Github.",
+      "The evaluation scenario for the MGB-3 ADI task can be viewed as channel and domain mismatch because the recording environment of the training data is different from the development and test data. In general, channel or domain mismatch between training and test data can be a significant factor affecting system performance. Differences in channel, genre, language, topic etc. produce shifts in low-dimensional projections of the corresponding speech and ultimately cause performance degradations on evaluation data.",
+      "In order to address performance degradation of speaker and language recognition systems due to domain mismatches, researchers have proposed various approaches to compensate for, and to adapt to the mismatch BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 , BIBREF15 , BIBREF16 , BIBREF17 . For the MGB-3 ADI task, we utilized the development data to adapt to the test data recording domain, and investigated approaches to improve ADI performance both on the domain mismatched scenario, and the matching scenario, by using a recursive whitening transformation, a weighted dialect i-vector model, and a Siamese Neural Network.",
+      "In contrast to the language recognition scenario, where there are different linguistic units across languages, language dialects typically share a common phonetic inventory and written language. Thus, we can potentially use ASR outputs such as phones, characters, and lexicons as features. N-gram histograms of phonemes, characters and lexicons can be used as feature vectors directly, and indeed, a lexicon-based n-gram feature vector was provided for the MGB-3 ADI baseline. The linguistic feature space is, naturally, completely different to the audio feature space, so a fusion of the results from both feature representations has been previously shown to be beneficial BIBREF18 , BIBREF19 , BIBREF20 , BIBREF21 , BIBREF22 . Moreover, the linguistic feature has an advantage in channel domain mismatch situations because the transcription itself does not reflect the recording environment, and only contains linguistic information.",
+      "In this paper, we describe our work for the MGB-3 ADI Challenge. The final MIT-QCRI submitted system is a combination of audio and linguistic feature-based systems, and includes multiple approaches to address the challenging mismatched conditions. From the official results, this system achieved the best performance among all participants. The following sections describe our research in greater detail."
+    ],
+    [
+      "For the MGB-3 ADI task, the challenge organizers provided 13,825 utterances (53.6 hours) for the training (TRN) set, 1,524 utterances (10 hours) for a development (DEV) set, and 1,492 utterances (10.1 hours) for a test (TST) set. Each dataset consisted of five Arabic dialects: Egyptian (EGY), Levantine (LEV), Gulf (GLF), North African (NOR), and Modern Standard Arabic (MSA). Detailed statistics of the ADI dataset can be found in BIBREF23 . Table TABREF3 shows some facts about the evaluation conditions and data properties. Note that the development set is relatively small compared to the training set. However, it is matched with the test set channel domain. Thus, the development set provides valuable information to adapt or compensate the channel (recording) domain mismatch between the train and test sets."
+    ],
+    [
+      "The MGB-3 ADI task asks participants to classify speech as one of five dialects, by specifying one dialect for each audio file for their submission. Performance is evaluated via three indices: overall accuracy, average precision, and average recall for the five dialects."
+    ],
+    [
+      "The challenge organizers provided features and code for a baseline ADI system. The features consisted of 400 dimensional i-vector features for each audio file (based on bottleneck feature inputs for their frame-level acoustic representation), as well as lexical features using bigrams generated from transcriptions BIBREF23 . For baseline dialect identification, a multi-class Support Vector Machine (SVM) was used. The baseline i-vector performance was 57.3%, 60.8%, and 58.0% for accuracy, precision and recall respectively. Lexical features achieved 48.4%, 51.0%, and 49.3%, respectively. While the audio-based features achieved better performance than the lexical features, both systems only obtained approximately 50% accuracy, indicating that this ADI task is difficult, considering that there are only five classes to choose from."
+    ],
+    [
+      "To further distinguish speech from different Arabic dialects, while making speech from the same dialect more similar, we adopted a Siamese neural network architecture BIBREF24 based on an i-vector feature space. The Siamese neural network has two parallel convolutional networks, INLINEFORM0 , that share the same set of weights, INLINEFORM1 , as shown in Figure FIGREF5 (a). Let INLINEFORM2 and INLINEFORM3 be a pair of i-vectors for which we wish to compute a distance. Let INLINEFORM4 be the label for the pair, where INLINEFORM5 = 1 if the i-vectors INLINEFORM6 and INLINEFORM7 belong to same dialect, and INLINEFORM8 otherwise. To optimize the network, we use a Euclidean distance loss function between the label and the cosine distance, INLINEFORM9 , where INLINEFORM10 ",
+      "For training, i-vector pairs and their corresponding labels can be processed by combinations of i-vectors from the training dataset. The trained convolutional network INLINEFORM0 transforms an i-vector INLINEFORM1 to a low-dimensional subspace that is more robust for distinguishing dialects. A detailed illustration of the convolutional network INLINEFORM2 is shown in Figure FIGREF5 (b). The final transformed i-vector, INLINEFORM3 , is a 200-dimensional vector. No nonlinear activation function was used on the fully connected layer. A cosine distance is used for scoring."
+    ],
+    [
+      "In this section we describe the domain adaptation techniques we investigated using the development set to help adapt our models to the test set.",
+      "Although the baseline system used an SVM classifier, Cosine Distance Scoring (CDS) is a fast, simple, and effective method to measure the similarity between an enrolled i-vector dialect model, and a test utterance i-vector. Under CDS, ZT-norm or S-norm can be also applied for score normalization BIBREF25 . Dialect enrollment can be obtained by means of i-vectors for each dialect, and is called the i-vector dialect model: INLINEFORM0 , where INLINEFORM1 is the number of utterances for each dialect INLINEFORM2 . Since we have two datasets for dialect enrollment, INLINEFORM3 for the training set, and INLINEFORM4 for the development set, we use an interpolation approach with parameter INLINEFORM5 , where INLINEFORM6 ",
+      "We observed that the mismatched training set is useful when combined with matched development set. Figure FIGREF7 shows the performance evaluation by parameter INLINEFORM0 on the same experimental conditions of System 2 in Section 4.3. This approach can be thought of as exactly the same as score fusion for different system. However, score fusion is usually performed at the system score level, while this approach uses a combination of knowledge of in-domain and out-of-domain i-vectors with a gamma weight on a single system.",
+      "For i-vector-based speaker and language recognition approaches, a whitening transformation and length normalization is considered essential BIBREF26 . Since length normalization is inherently a nonlinear, non-whitening operation, recently, a recursive whitening transformation has been proposed to reduce residual un-whitened components in the i-vector space, as illustrated in Figure FIGREF10 BIBREF14 . In this approach, the data subset that best matches the test data is used at each iteration to calculate the whitening transformation. In our ADI experiments, we applied 1 to 3 levels of recursive whitening transformation using the training and development data."
+    ],
+    [
+      "Phoneme feature extraction consists of extracting the phone sequence, and phone duration statistics using four different speech recognizers: Czech, Hungarian, and Russian using narrowband model, and English using a broadband model BIBREF27 . We evaluated the four systems using a Support Vector Machine (SVM). The hyper-parameters for the SVM are distance from the hyperplane (C is 0.01), and penalty l2. We used the training data for training the SVM and the development data for testing. Table TABREF13 shows the results for the four phoneme recognizers. The Hungarian phoneme recognition obtained the best results, so we used it for the final system combination."
+    ],
+    [
+      "Word sequences are extracted using a state-of-the-art Arabic speech-to-text transcription system built as part of the MGB-2 BIBREF28 . The system is a combination of a Time Delayed Neural Network (TDNN), a Long Short-Term Memory Recurrent Neural Network (LSTM) and Bidirectional LSTM acoustic models, followed by 4-gram and Recurrent Neural Network (RNN) language model rescoring. Our system uses a grapheme lexicon during both training and decoding. The acoustic models are trained on 1,200 hours of Arabic broadcast speech. We also perform data augmentation (speed and volume perturbation) which gives us three times the original training data. For more details see the system description paper BIBREF4 . We kept the <UNK> from the ASR system, which indicates out-of-vocabulary (OOV) words, we replaced it with special symbol. Space was inserted between all characters including the word boundaries. An SVM classifier was trained similarly to the one used for the phoneme ASR systems, and we achieved 52% accuracy, 51.2% precision and 51.8% recall. The confusion matrix is different between the phoneme classifier and the character classifier systems, which motivates us to use both of them in the final system combination."
+    ],
+    [
+      "All scores are calibrated to be between 0 and 1. A linear calibration is done by the Bosaris toolkit BIBREF29 . Fusion is also done in a linear manner."
+    ],
+    [
+      "For experiments and evaluation, we use i-vectors and transcriptions that are provided by the challenge organizers. Please refer to BIBREF23 for descriptions of i-vector extraction and Arabic speech-to-text configuration."
+    ],
+    [
+      "The first experiment we conducted used only the training data for developing the ADI system. Thus, the interpolated i-vector dialect model cannot be used for this experimental condition. Table TABREF14 shows the performance on dimension reduced i-vectors using the Siamese network (Siam i-vector), and Linear Discriminant Analysis (LDA i-vector), as compared to the baseline i-vector system. LDA reduces the 400-dimension i-vector to 4, while the Siamese network reduces it from 400 to 200. Since the Siamese network used a cosine distance for the loss function, the Siam i-vector showed better performance with the CDS scoring method, while others achieved better performance with an SVM. The best system using Siam i-vector showed overall 10% better performance accuracy, as compared to the baseline."
+    ],
+    [
+      "For our second experiment, both the training and development data were used for training. For phoneme and character features, we show development set experimental results in Table TABREF15 . For i-vector experiments, we show results in Table TABREF16 . In the table we see that the interpolated dialect model gave significant improvements in all three metrics. The recursive whitening transformation gave slight improvements on the original i-vector, but not after LDA and the Siamese network. The best system is the original i-vector with recursive whitening, and an interpolated i-vector dialect model, which achieves over 20% accuracy improvement over the baseline.",
+      "While the Siamese i-vector network helped in the training data only experiments, it does not show any advantage over the baseline i-vector for this condition. We suspect this result is due to the composition of the data used for training the Siamese network. To train the network, i-vector pairs are chosen from from training dataset. We selected the pairs using both the training and development datasets. However, if we could put more emphasis on the development data, we suspect the Siamese i-vector network would be more robust on the test data. We plan to further examine the performances due to different compositions of data in the future."
+    ],
+    [
+      "Tables TABREF21 and TABREF22 show detailed performance evaluations of our three submitted systems. System 1 was trained using only the training data as shown in Table TABREF21 . Systems 2 and 3 were trained using both the training and development sets as shown in Table TABREF22 . We found the best linear fusion weight based on System 1 to prevent over-fitting was 0.7, 0.2 and 0.1 for i-vector, character, and phonetic based scores respectively. We applied the same weights to Systems 2 and 3 for fusion.",
+      "From Table TABREF21 , we see that the Siamese network demonstrates its effectiveness on both the development and test sets without using any information of the test domain. The interpolated i-vector dialect model also demonstrates that it reflects test domain information well as shown by Systems 2 and 3 in Table TABREF22 . Although we expected that the linguistic features would not affected by the domain mismatch, character and phoneme features show useful contributions for all systems. We believe the reason for the performance degradation of Systems 2 and 3 after fusion on the development data can be seen in the fusion rule. We applied the fusion rule derived from System 1 which was not optimal for Systems 2 and 3, considering the development set evaluation. By including the development data as part of their training, Systems 2 and 3 are subsequently overfit on the development data, which was why we used the fusion rule of System 1. From the excellent fusion performance on the test data for Systems 2 and 3, we believe that the fusion rule from System 1 prevented an over-fitted result."
+    ],
+    [
+      "In this paper, we describe the MIT-QCRI ADI system using both audio and linguistic features for the MGB-3 challenge. We studied several approaches to address dialect variability and domain mismatches between the training and test sets. Without knowledge of the test domain where the system will be applied, i-vector dimensionality reduction using a Siamese network was found to be useful, while an interpolated i-vector dialect model showed effectiveness with relatively small amounts of test domain information from the development data. On both conditions, fusion of audio and linguistic feature guarantees substantial improvements on dialect identification. As these approaches are not limited to dialect identification, we plan to explore their utility on other speaker and language recognition problems in the future."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0980/instruction.md b/qasper-0980/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..2a6424ee0e4950e1ce27e449e635212d670f0abc
--- /dev/null
+++ b/qasper-0980/instruction.md
@@ -0,0 +1,193 @@
+Name of Paper: Bias in Semantic and Discourse Interpretation
+
+Question: Which interpretative biases are analyzed in this paper?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Objective of the paper",
+    "Some examples of bias",
+    "Organization of the paper",
+    "The model of interpretive bias",
+    "Epistemic ME games",
+    "Generalizing from the case study",
+    "ME persuasion games",
+    "ME truth games",
+    "Looking ahead",
+    "Conclusions"
+  ],
+  "paragraphs": [
+    [
+      "Bias is generally considered to be a negative term: a biased story is seen as one that perverts or subverts the truth by offering a partial or incomplete perspective on the facts. But bias is in fact essential to understanding: one cannot interpret a set of facts\u2014something humans are disposed to try to do even in the presence of data that is nothing but noise [38]\u2014without relying on a bias or hypothesis to guide that interpretation. Suppose someone presents you with the sequence INLINEFORM0 and tells you to guess the next number. To make an educated guess, you must understand this sequence as instantiating a particular pattern; otherwise, every possible continuation of the sequence will be equally probable for you. Formulating a hypothesis about what pattern is at work will allow you to predict how the sequence will play out, putting you in a position to make a reasonable guess as to what comes after 3. Formulating the hypothesis that this sequence is structured by the Fibonacci function (even if you don't know its name), for example, will lead you to guess that the next number is 5; formulating the hypothesis that the sequence is structured by the successor function but that every odd successor is repeated once will lead you to guess that it is 3. Detecting a certain pattern allows you to determine what we will call a history: a set of given entities or eventualities and a set of relations linking those entities together. The sequence of numbers INLINEFORM1 and the set of relation instances that the Fibonacci sequence entails as holding between them is one example of a history. Bias, then, is the set of features, constraints, and assumptions that lead an interpreter to select one history\u2014one way of stitching together a set of observed data\u2014over another.",
+      "Bias is also operative in linguistic interpretation. An interpreter's bias surfaces, for example, when the interpreter connects bits of information content together to resolve ambiguities. Consider: . Julie isn't coming. The meeting has been cancelled.",
+      "While these clauses are not explicitly connected, an interpreter will typically have antecedent biases that lead her to interpret eventualities described by the two clauses as figuring in one of two histories: one in which the eventuality described by the first clause caused the second, or one in which the second caused the first. Any time that structural connections are left implicit by speakers\u2014and this is much if not most of the time in text\u2014 interpreters will be left to infer these connections and thereby potentially create their own history or version of events.",
+      "Every model of data, every history over that data, comes with a bias that allows us to use observed facts to make predictions; bias even determines what kind of predictions the model is meant to make. Bayesian inference, which underlies many powerful models of inference and machine learning, likewise relies on bias in several ways: the estimate of a state given evidence depends upon a prior probability distribution over states, on assumptions about what parameters are probabilistically independent, and on assumptions about the kind of conditional probability distribution that each parameter abides by (e.g., normal distribution, noisy-or, bimodal). Each of these generates a (potentially different) history."
+    ],
+    [
+      "In this paper, we propose a program for research on bias. We will show how to model various types of bias as well as the way in which bias leads to the selection of a history for a set of data, where the data might be a set of nonlinguistic entities or a set of linguistically expressed contents. In particular, we'll look at what people call \u201cunbiased\u201d histories. For us these also involve a bias, what we call a \u201ctruth seeking bias\u201d. This is a bias that gets at the truth or acceptably close to it. Our model can show us what such a bias looks like. And we will examine the question of whether it is possible to find such a truth oriented bias for a set of facts, and if so, under what conditions. Can we detect and avoid biases that don't get at the truth but are devised for some other purpose?",
+      "Our study of interpretive bias relies on three key premises. The first premise is that histories are discursive interpretations of a set of data in the sense that like discourse interpretations, they link together a set of entities with semantically meaningful relations. As such they are amenable to an analysis using the tools used to model a discourse's content and structure. The second is that a bias consists of a purpose or goal that the histories it generates are built to achieve and that agents build histories for many different purposes\u2014to discover the truth or to understand, but also to conceal the truth, to praise or disparage, to persuade or to dissuade. To properly model histories and the role of biases in creating them, we need a model of the discourse purposes to whose end histories are constructed and of the way that they, together with prior assumptions, shape and determine histories. The third key premise of our approach is that bias is manifested in and conveyed through histories, and so studying histories is crucial for a better understanding of bias."
+    ],
+    [
+      "Let's consider the following example of biased interpretation of a conversation. Here is an example analyzed in BIBREF0 to which we will return in the course of the paper. . Sa Reporter: On a different subject is there a reason that the Senator won't say whether or not someone else bought some suits for him? Sheehan: Rachel, the Senator has reported every gift he has ever received. Reporter: That wasn't my question, Cullen. Sheehan: (i) The Senator has reported every gift he has ever received. (ii) We are not going to respond to unnamed sources on a blog. . Reporter: So Senator Coleman's friend has not bought these suits for him? Is that correct? Sheehan: The Senator has reported every gift he has ever received.",
+      "Sheehan continues to repeat, \u201cThe Senator has reported every gift he has ever received\u201d seven more times in two minutes to every follow up question by the reporter corps. http://www.youtube.com/watch?v=VySnpLoaUrI. For convenience, we denote this sentence uttered by Sheehan (which is an EDU in the languare of SDRT as we shall see presently) as INLINEFORM0 .",
+      "Now imagine two \u201cjuries,\u201d onlookers or judges who interpret what was said and evaluate the exchange, yielding differing interpretations. The interpretations differ principally in how the different contributions of Sheehan and the reporter hang together. In other words, the different interpretations provide different discourse structures that we show schematically in the graphs below. The first is one in which Sheehan's response INLINEFORM0 in SECREF3 b is somewhat puzzling and not taken as an answer to the reporter's question in SECREF3 a. In effect this \u201cjury\u201d could be the reporter herself. This Jury then interprets the move in SECREF3 c as a correction of the prior exchange. The repetition of INLINEFORM1 in SECREF3 d.ii is taken tentatively as a correction of the prior exchange (that is, the moves SECREF3 a, SECREF3 b and SECREF3 c together), which the Jury then takes the reporter to try to establish with SECREF3 e. When Sheehan repeats SECREF3 a again in SECREF3 f, this jury might very well take Sheehan to be evading all questions on the subject.",
+      "A different Jury, however, might have a different take on the conversation as depicted in the discourse structure below. Such a jury might take INLINEFORM0 to be at least an indirect answer to the question posed in SECREF3 a, and as a correction to the Reporter's evidently not taking INLINEFORM1 as an answer. The same interpretation of INLINEFORM2 would hold for this Jury when it is repeated in SECREF3 f. Such a Jury would be a supporter of Sheehan or even Sheehan himself. What accounts for these divergent discourse structures? We will argue that it is the biases of the two Juries that create these different interpretations. And these biases are revealed at least implicitly in how they interpret the story: Jury 1 is at the outset at least guarded, if not skeptical, in its appraisal of Sheehan's interest in answering the reporter's questions. On the other hand, Jury 2 is fully convinced of Sheehan's position and thus interprets his responses much more charitably. BIBREF0 shows formally that there is a co-dependence between biases and interpretations; a certain interpretation created because of a certain bias can in turn strengthen that bias, and we will sketch some of the details of this story below.",
+      "The situation of our two juries applies to a set of nonlinguistic facts. In such a case we take our \u201cjury\u201d to be the author of a history over that set of facts. The jury in this case evaluates and interprets the facts just as our juries did above concerning linguistic messages. To tell a history about a set of facts is to connect them together just as discourse constituents are connected together. And these connections affect and may even determine the way the facts are conceptualized BIBREF1 . Facts typically do not wear their connections to other facts on their sleeves and so how one takes those connections to be is often subject to bias. Even if their characterization and their connections to other facts are \u201cintuitively clear\u201d, our jury may choose to pick only certain connections to convey a particular history or even to make up connections that might be different. One jury might build a history over the set of facts that conveys one set of ideas, while the other might build a quite different history with a different message. Such histories reflect the purposes and assumptions that were exploited to create that structure.",
+      "As an example of this, consider the lead paragraphs of articles from the New York Times, Townhall and Newsbusters concerning the March for Science held in April, 2017.",
+      "The March for Science on April 22 may or may not accomplish the goals set out by its organizers. But it has required many people who work in a variety of scientific fields \u2014 as well as Americans who are passionate about science \u2014 to grapple with the proper role of science in our civic life. The discussion was evident in thousands of responses submitted to NYTimes.com ahead of the march, both from those who will attend and those who are sitting it out.",
+      "",
+      "\u2013New York Times",
+      "Do you have march fatigue yet? The left, apparently, does not, so we're in for some street theater on Earth Day, April 22, with the so-called March for Science. It's hard to think of a better way to undermine the public's faith in science than to stage demonstrations in Washington, D.C., and around the country modeled on the Women's March on Washington that took place in January. The Women's March was an anti-Donald Trump festival. Science, however, to be respected, must be purely the search for truth. The organizers of this \u201cMarch for Science\" \u2013 by acknowledging that their demonstration is modeled on the Women's March \u2013 are contributing to the politicization of science, exactly what true upholders of science should be at pains to avoid.",
+      "",
+      "\u2013Townhall",
+      "Thousands of people have expressed interest in attending the \u201cMarch for Science\u201d this Earth Day, but internally the event was fraught with conflict and many actual scientists rejected the march and refused to participate.",
+      "",
+      "\u2013Newsbusters",
+      "These different articles begin with some of the same basic facts: the date and purpose of the march, and the fact that the march's import for the science community is controversial, for example. But bias led the reporters to stitch together very different histories. The New York Times, for instance, interprets the controversy as generating a serious discussion about \u201cthe proper role of science in our civic life,\u201d while Townhall interprets the march as a political stunt that does nothing but undermine science.",
+      "While the choice of wording helps to convey bias, just as crucial is the way that the reporters portray the march as being related to other events. Which events authors choose to include in their history, which they leave out, and the way the events chosen relate to the march are crucial factors in conveying bias. Townhall's bias against the March of Science expressed in the argument that it politicizes science cannot be traced back to negative opinion words; it relies on a comparison between the March for Science and the Women's March, which is portrayed as a political, anti-Trump event. Newsbusters takes a different track: the opening paragraph conveys an overall negative perspective on the March for Science, despite its neutral language, but it achieves this by contrasting general interest in the march with a claimed negative view of the march by many \u201cactual scientists.\u201d On the other hand, the New York Times points to an important and presumably positive outcome of the march, despite its controversiality: a renewed look into the role of science in public life and politics. Like Newsbusters, it lacks any explicit evaluative language and relies on the structural relations between events to convey an overall positive perspective; it contrasts the controversy surrounding the march with a claim that the march has triggered an important discussion, which is in turn buttressed by the reporter's mentioning of the responses of the Times' readership.",
+      "A formally precise account of interpretive bias will thus require an analysis of histories and their structure and to this end, we exploit Segmented Discourse Representation Theory or SDRT BIBREF2 , BIBREF3 . As the most precise and well-studied formal model of discourse structure and interpretation to date, SDRT enables us to characterize and to compare histories in terms of their structure and content. But neither SDRT nor any other, extant theoretical or computational approach to discourse interpretation can adequately deal with the inherent subjectivity and interest relativity of interpretation, which our study of bias will illuminate. Message Exchange (ME) Games, a theory of games that builds on SDRT, supplements SDRT with an analysis of the purposes and assumptions that figure in bias. While epistemic game theory in principle can supply an analysis of these assumptions, it lacks linguistic constraints and fails to reflect the basic structure of conversations BIBREF4 . ME games will enable us not only to model the purposes and assumptions behind histories but also to evaluate their complexity and feasibility in terms of the existence of winning strategies.",
+      "Bias has been studied in cognitive psychology and empirical economics BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF5 , BIBREF13 . Since the seminal work of Kahneman and Tversky and the economist Allais, psychologists and empirical economists have provided valuable insights into cognitive biases in simple decision problems and simple mathematical tasks BIBREF14 . Some of this work, for example the bias of framing effects BIBREF7 , is directly relevant to our theory of interpretive bias. A situation is presented using certain lexical choices that lead to different \u201cframes\u201d: INLINEFORM0 of the people will live if you do INLINEFORM1 (frame 1) versus INLINEFORM2 of the people will die if you do INLINEFORM3 (frame 2). In fact, INLINEFORM4 , the total population in question; so the two consequents of the conditionals are equivalent. Each frame elaborates or \u201ccolors\u201d INLINEFORM5 in a way that affects an interpreter's evaluation of INLINEFORM6 . These frames are in effect short histories whose discourse structure explains their coloring effect. Psychologists, empirical economists and statisticians have also investigated cases of cognitive bias in which subjects deviate from prescriptively rational or independently given objective outcomes in quantitative decision making and frequency estimation, even though they arguably have the goal of seeking an optimal or \u201ctrue\u201d solution. In a general analysis of interpretive bias like ours, however, it is an open question whether there is an objective norm or not, whether it is attainable and, if so, under what conditions, and whether an agent builds a history for attaining that norm or for some other purpose."
+    ],
+    [
+      "Our paper is organized as follows. Section SECREF2 introduces our model of interpretive bias. Section SECREF3 looks forward towards some consequences of our model for learning and interpretation. We then draw some conclusions in Section SECREF4 . A detailed and formal analysis of interpretive bias has important social implications. Questions of bias are not only timely but also pressing for democracies that are having a difficult time dealing with campaigns of disinformation and a society whose information sources are increasingly fragmented and whose biases are often concealed. Understanding linguistic and cognitive mechanisms for bias precisely and algorithmically can yield valuable tools for navigating in an informationally bewildering world."
+    ],
+    [
+      "As mentioned in Section SECREF1 , understanding interpretive bias requires two ingredients. First, we need to know what it is to interpret a text or to build a history over a set of facts. Our answer comes from analyzing discourse structure and interpretation in SDRT BIBREF2 , BIBREF3 . A history for a text connects its elementary information units, units that convey propositions or describe events, using semantic relations that we call discourse relations to construct a coherent and connected whole. Among such relations are logical, causal, evidential, sequential and resemblance relations as well as relations that link one unit with an elaboration of its content. It has been shown in the literature that discourse structure is an important factor in accurately extracting sentiments and opinions from text BIBREF15 , BIBREF16 , BIBREF17 , and our examples show that this is the case for interpretive bias as well."
+    ],
+    [
+      "The second ingredient needed to understand interpretive bias is the connection between on the one hand the purpose and assumption behind telling a story and on the other the particular way in which that story is told. A history puts the entities to be understood into a structure that serves certain purposes or conversational goals BIBREF18 . Sometimes the history attempts to get at the \u201ctruth\u201d, the true causal and taxonomic structure of a set of events. But a history may also serve other purposes\u2014e.g., to persuade, or to dupe an audience. Over the past five years, BIBREF4 , BIBREF19 , BIBREF20 , BIBREF21 have developed an account of conversational purposes or goals and how they guide strategic reasoning in a framework called Message Exchange (ME) Games. ME games provide a general and formally precise framework for not only the analysis of conversational purposes and conversational strategies, but also for the typology of dialogue games from BIBREF22 and finally for the analysis of strategies for achieving what we would intuitively call \u201cunbiased interpretation\u201d, as we shall see in the next section. In fact in ME Games, conversational goals are analyzed as properties, and hence sets, of conversations; these are the conversations that \u201cgo well\u201d for the player. ME games bring together the linguistic analysis of SDRT with a game theoretic approach to strategic reasoning; in an ME game, players alternate making sequences of discourse moves such as those described in SDRT, and a player wins if the conversation constructed belongs to her winning condition, which is a subset of the set of all possible conversational plays. ME games are designed to analyze the interaction between conversational structure, purposes and assumptions, in the absence of assumptions about cooperativity or other cognitive hypotheses, which can cause problems of interpretability in other frameworks BIBREF23 . ME games also assume a Jury that sets the winning conditions and thus evaluates whether the conversational moves made by players or conversationalists are successful or not. The Jury can be one or both of the players themselves or some exogenous body.",
+      "To define an ME game, we first fix a finite set of players INLINEFORM0 and let INLINEFORM1 range over INLINEFORM2 . For simplicity, we consider here the case where there are only two players, that is INLINEFORM3 , but the notions can be easily lifted to the case where there are more than two players. Here, Player INLINEFORM4 will denote the opponent of Player INLINEFORM5 . We need a vocabulary INLINEFORM6 of moves or actions; these are the discourse moves as defined by the language of SDRT. The intuitive idea behind an ME game is that a conversation proceeds in turns where in each turn one of the players `speaks' or plays a string of elements from INLINEFORM7 . In addition, in the case of conversations, it is essential to keep track of \u201cwho says what\u201d. To model this, each player INLINEFORM8 was assigned a copy INLINEFORM9 of the vocabulary INLINEFORM10 which is simply given as INLINEFORM11 . As BIBREF4 argues, a conversation may proceed indefinitely, and so conversations correspond to plays of ME games, typically denoted as INLINEFORM12 , which are the union of finite or infinite sequences in INLINEFORM13 , denoted as INLINEFORM14 and INLINEFORM15 respectively. The set of all possible conversations is thus INLINEFORM16 and is denoted as INLINEFORM17 . [ME game BIBREF4 ] A Message Exchange game (ME game), INLINEFORM18 , is a tuple INLINEFORM19 where INLINEFORM20 is a Jury. Due to the ambiguities in language, discourse moves in SDRT are underspecified formulas that may yield more than one fully specified discourse structure or histories for the conversation; a resulting play in an ME game thus forms one or more histories or complete discourse structures for the entire conversation.",
+      "To make ME games into a truly realistic model of conversation requires taking account of the limited information available to conversational participants. BIBREF0 imported the notion of a type space from epistemic game theory BIBREF24 to take account of this. The type of a player INLINEFORM0 or the Jury is an abstract object that is used to code-up anything and everything about INLINEFORM1 or the Jury, including her behavior, the way she strategizes, her personal biases, etc. BIBREF24 . Let INLINEFORM2 denote the set of strategies for Player INLINEFORM3 in an ME game; let INLINEFORM4 ; and let INLINEFORM5 be the set of strategies of INLINEFORM6 given play INLINEFORM7 . [Harsanyi type space BIBREF24 ] A Harsanyi type space for INLINEFORM8 is a tuple INLINEFORM9 such that INLINEFORM10 and INLINEFORM11 , for each INLINEFORM12 , are non-empty (at-most countable) sets called the Jury-types and INLINEFORM13 -types respectively and INLINEFORM14 and INLINEFORM15 are the beliefs of Player INLINEFORM16 and the Jury respectively at play INLINEFORM17 . BIBREF0 defines the beliefs of the players and Jury using the following functions. [Belief function] For every play INLINEFORM18 the (first order) belief INLINEFORM19 of player INLINEFORM20 at INLINEFORM21 is a pair of measurable functions INLINEFORM22 where INLINEFORM23 is the belief function and INLINEFORM24 is the interpretation function defined as: INLINEFORM25 INLINEFORM26 ",
+      "where INLINEFORM0 is the set of probability distributions over the corresponding set. Similarly the (first order) belief INLINEFORM1 of the Jury is a pair of measurable functions INLINEFORM2 where the belief function INLINEFORM3 and the interpretation function INLINEFORM4 are defined as: INLINEFORM5 INLINEFORM6 ",
+      " Composing INLINEFORM0 and INLINEFORM1 together over their respective outputs reveals a correspondence between interpretations of plays and types for a fixed Jury type INLINEFORM2 : every history yields a distribution over types for the players and every tuple of types for the players and the Jury fixes a distribution over histories. We'll call this the types/history correspondence.",
+      "An epistemic ME game is an ME game with a Harsanyi type space and a type/history correspondence as we've defined it. By adding types to an ME game, we provide the beginnings of a game theoretic model of interpretive bias that we believe is completely new. Our definition of bias is now: [Interpretive Bias] An interpretive bias in an epistemic ME game is the probability distribution over types given by the belief function of the conversationalists or players, or the Jury. Note that in an ME game there are typically several interpretive biases at work: each player has her own bias, as does the Jury.",
+      "Outside of language, statisticians study bias; and sample bias is currently an important topic. To do so, they exploit statistical models with a set of parameters and random variables, which play the role of our types in interpretive bias. But for us, the interpretive process is already well underway once the model, with its constraints, features and explanatory hypotheses, is posited; at least a partial history, or set of histories, has already been created.",
+      "The ME model in BIBREF0 not only makes histories dependent on biases but also conditionally updates an agent's bias, the probability distribution, given the interpretation of the conversation or more generally a course of events as it has so far unfolded and crucially as the agent has so far interpreted it. This means that certain biases are reinforced as a history develops, and in turn strengthen the probability of histories generated by such biases in virtue of the types/histories correspondence. We now turn to an analysis of SECREF3 discussed in BIBREF4 , BIBREF0 where arguably this happens."
+    ],
+    [
+      "The Sheehan case study in BIBREF0 shows the interactions of interpretation and probability distributions over types. We'll refer to content that exploit assumptions about types' epistemic content. SECREF3 also offers a case of a self-confirming bias with Jury INLINEFORM0 . But the analysis proposed by BIBREF0 leaves open an important open question about what types are relevant to constructing a particular history and only examines one out of many other cases of biased interpretation. In epistemic game models, the relevant types are typically given exogenously and Harsanyi's type space construction is silent on this question. The question seems a priori very hard to answer, because anything and everything might be relevant to constructing a history.",
+      "In SECREF3 , the relevant types have to do with the interpreters' or Jurys' attitudes towards the commitments of the spokesman and Coleman. These attitudes might reinforce or be a product of other beliefs like beliefs about the spokesman's political affiliations. But we will put forward the following simplifying hypothesis:",
+      "Hypothesis 1: epistemic content is based on assumptions about types defined by different attitudes to commitments by the players and or the Jury to the contents of a discourse move or sequence of discourse moves.",
+      "Hypothesis 2: These assumptions can be represented as probability distributions over types.",
+      "In SECREF3 , we've only looked at epistemic content from the point of view of the interpreter, which involves types for the Jury defined in terms of probability distributions over types for the speaker. But we can look at subjective interpretations from the perspective of the speaker as well. In other words, we look at how the speaker might conceptualize the discourse situation, in particular her audience. We illustrate this with another type of content based on types. Consider the following move by Marion Le Pen, a leader of the French nationalist, right-wing party le Front National in which she recently said: . La France \u00e9tait la fille a\u00een\u00e9e de l'\u00e9glise. Elle est en passe de devenir la petite ni\u00e8ce de l'Islam. (France was once the eldest daughter of the Catholic church. It is now becoming the little niece of Islam.)",
+      " SECREF8 appeals to what the speaker takes to be her intended audience's beliefs about Islam, Catholicism and France. In virtue of these beliefs, this discourse move takes on a loaded racist meaning, conveying an assault on France and its once proud status by people of North African descent. Without those background beliefs, however, Le Pen's statement might merely be considered a somewhat curious description of a recent shift in religious majorities. This is known as a \u201cdog whistle,\u201d in which a discourse move communicates a content other than its grammatically determined content to a particular audience BIBREF25 . While BIBREF26 proposes that such messages are conventional implicatures, BIBREF25 , BIBREF27 show that dog whistle content doesn't behave like other conventional implicatures; in terms of tests about \u201cat issue content\u201d, dog whistle content patterns with other at issue content, not with the content associated with conventional implicatures in the sense of BIBREF28 . This also holds of content that resolves ambiguities as in SECREF3 .",
+      "The dogwhistle content seems to be driven by the hearer's type in SECREF8 or the speaker's beliefs about the interpreter's or hearer's type. Generalizing from BIBREF29 , the use of the historical expression la fille ain\u00e9e de l'\u00e9glise contrasted with la petite ni\u00e8ce has come to encode a type, in much the same way that dropping the final g in present participles and gerunds has come to signify a type BIBREF29 , for the speaker INLINEFORM0 about hearer INLINEFORM1 ; e.g., INLINEFORM2 will believe that INLINEFORM3 has the strategy of using just this language to access the loaded interpretation and moreover will identify with its content. Because this meaning comes about in virtue of the hearer's type, the speaker is in a position to plausibly deny that they committed to conveying a racist meaning, which is a feature of such dog whistles. In fact, we might say that all dogwhistle content is so determined.",
+      "We can complicate the analysis by considering the speaker's types, the interlocutor's types and types for the Jury when these three components of an ME game are distinct (i.e. the Jury is distinct from the interlocutors). A case like this is the Bronston example discussed in BIBREF0 .",
+      "By looking at dogwhistles, we've now distinguished two kinds of epistemic content that depends on an interpreters' type. The epistemic content may as in SECREF3 fill out the meaning of an underspecified play to produce a determinate history. Dog whistles add content to a specific discourse unit that goes beyond its grammatically determined meaning. More formally, we can define these two kinds of epistemic content using the machinery of ME games. Given that plays in an ME game are sequences of discourse moves, we can appeal to the semantics of these moves and a background consequence relation INLINEFORM0 defined as usual. In addition, a play INLINEFORM1 in an ME game may itself be a fully specified history or a sequence of discourse moves that is compatible with several fully specified histories given a particular interpreter's or Jury's type INLINEFORM2 . Let INLINEFORM3 be the set of histories (FLFs) compatible with a play INLINEFORM4 given an interpreter or Jury type INLINEFORM5 . INLINEFORM6 will be ambiguous and open to epistemic content supplementation just in case: (i) INLINEFORM7 for any type INLINEFORM8 for a linguistically competent jury, and (ii) there are INLINEFORM9 , such that INLINEFORM10 and INLINEFORM11 are semantically distinct (neither one entails the other). Now suppose that a play INLINEFORM12 gives rise through the grammar to a history, INLINEFORM13 . Then INLINEFORM14 is a dog whistle for INLINEFORM15 just in case: (i) INLINEFORM16 , (ii) INLINEFORM17 and (iii) there is a INLINEFORM18 that can positively affect some jury perhaps distinct from INLINEFORM19 and such that INLINEFORM20 . On this definition, a player who utters such a play INLINEFORM21 always has the excuse that what he/she actually meant was INLINEFORM22 when challenged\u2014which seems to be one essential feature of a dog whistle.",
+      "Plays with such semantic features may not be a pervasive feature of conversation; not every element is underspecified or is given a content over and above its linguistically determined one. But in interpreting a set of nonlinguistic facts INLINEFORM0 or data not already connected together in a history, that is in constructing a history over INLINEFORM1 , an interpreter INLINEFORM2 , who in this case is a speaker or writer, must appeal to her beliefs, which includes her beliefs about the Jury to whom her discourse actions are directed. So certainly the type of INLINEFORM3 , which includes beliefs about the Jury for the text, is relevant to what history emerges. The facts in INLINEFORM4 don't wear their relational properties to other facts on their sleeves so to speak, and so INLINEFORM5 has to supply the connections to construct the history. In effect for a set of non linguistically given facts, \u201cambiguities of attachment,\u201d whose specification determines how the facts in INLINEFORM6 are related to each other, are ubiquitous and must be resolved in constructing a history. The speaker or \u201chistory creator\u201d INLINEFORM7 's background beliefs determine the play and the history an interpreter INLINEFORM8 takes away.",
+      "In the case of constructing a history over a set of nonlinguistic facts INLINEFORM0 , the interpreter INLINEFORM1 's task of getting the history INLINEFORM2 has constructed will not reliably succeed unless one of two conditions are met: either INLINEFORM3 and INLINEFORM4 just happen to share the relevant beliefs (have close enough types) so that they construct the same histories from INLINEFORM5 , or INLINEFORM6 uses linguistic devices to signal the history. ME games require winning conversations, and by extension texts, to be (mostly) coherent, which means that the discourse connections between the elements in the history must be largely determined in any successful play, or can be effectively determined by INLINEFORM14 . This means that INLINEFORM15 will usually reveal relevant information about her type through her play, in virtue of the type/history correspondence, enough to reconstruct the history or much of it. In the stories on the March for Science, for example, the reporters evoke very different connections between the march and other facts. The Townhall reporter, for instance, connects the March for Science to the Women's march and \u201cleftwing\u201d political manifestations and manifests a negative attitude toward the March. But he does so so unambiguously that little subjective interpretation on the part of the interpreter or Jury is needed to construct the history or assign a high probability to a type for INLINEFORM16 that drives the story.",
+      "This discussion leads to the following observations. To construct a history over a set of disconnected nonlinguistic facts INLINEFORM0 , in general a Jury needs to exploit linguistic pointers to the connections between elements of INLINEFORM1 , if the speaker is to achieve the goal of imparting a (discourse) coherent story, unless the speaker knows that the Jury or interpreter has detailed knowledge of her type. The speaker may choose to leave certain elements underspecified or ambiguous, or use a specified construction, to invoke epistemic content for a particular type that she is confident the Jury instantiates. How much so depends on her confidence in the type of the Jury. This distribution or confidence level opens a panoply of options about the uses of epistemic content: at one end there are histories constructed from linguistic cues with standard, grammatically encoded meanings; at the other end there are histories generated by a code shared with only a few people whose types are mutually known. As the conversation proceeds as we have seen, probabilities about types are updated and so the model should predict that a speaker may resort to more code-like messages in the face of feedback confirming her hypotheses about the Jury's type (if such feedback can be given) and that the speaker may revert to a more message exploiting grammatical cues in the face of feedback disconfirming her hypotheses about the Jury's type. Thus, the epistemic ME model predicts a possible change in register as the speaker receives more information about the Jury's type, though this change is subject to other conversational goals coded in the speaker's victory condition for the ME game."
+    ],
+    [
+      "We've now seen how histories in ME games bring an interpretive bias, the bias of the history's creator, to the understanding of a certain set of facts. We've also seen how epistemic ME games allow for the introduction of epistemic content in the interpretation of plays. Each such epistemic interpretation is an instance of a bias that goes beyond the grammatically determined meaning of the play and is dependent upon the Jury's or interpreter's type. We now make explicit another crucial component of ME games and their relation to bias: the players' winning conditions or discourse goals. Why is this relevant to a study of bias? The short answer is that players' goals tells us whether two players' biases on a certain subject are compatible or resolvable or not. Imagine that our two Juries in SECREF3 shared the same goal\u2014of getting at the truth behind the Senator's refusal to comment about the suits. They might still have come up with the opposing interpretations that they did in our discussion above. But they could have discussed their differences, and eventually would have come to agreement, as we show below in Proposition SECREF19 .",
+      "However, our two Juries might have different purposes too. One Jury might have the purpose of finding out about the suits, like the reporters; the other might have the purpose just to see Senator Coleman defended, a potentially quite different winning condition and collection of histories. In so doing we would identify Jury 1 with the reporters or at least Rachel, and Jury 2 with Sheehan. Such different discourse purposes have to be taken into account in attempting to make a distinction between good and bad biases. From the perspective of subjective rationality or rationalizability (an important criterion in epistemic game theory BIBREF33 ), good biases for a particular conversation should be those that lead to histories in the winning condition, histories that fulfill the discourse purpose; bad biases lead to histories that do not achieve the winning condition. The goals that a Jury or interpreter INLINEFORM0 adopts and her biases go together; INLINEFORM1 's interpretive bias is good for speaker INLINEFORM2 , if it helps INLINEFORM3 achieve her winning condition. Hence, INLINEFORM4 's beliefs about INLINEFORM5 are crucial to her success and rationalizable behavior. Based on those beliefs INLINEFORM6 's behavior is rationalizable in the sense we have just discussed. If she believes Jury 2 is the one whose winning condition she should satisfy, there is no reason for her to change that behavior. Furthermore, suppose Jury 1 and Jury 2 discuss their evaluations; given that they have different goals, there is no reason for them to come to an agreement with the other's point of view either. Both interpretations are rationalizable as well, if the respective Juries have the goals they do above. A similar story applies to constructing histories over a set of facts, in so far as they had different conceptions of winning conditions set by their respective Juries. In contrast to Aumann's dictum BIBREF32 , in our scenario there is every reason to agree to disagree!",
+      "Understanding such discourse goals is crucial to understanding bias for at least two reasons. The first is that together with the types that are conventionally coded in discourse moves, they fix the space of relevant types. In SECREF3 , Jury 1 is sensitive to a winning condition in which the truth about the suits is revealed, what we call a truth oriented goal. The goal of Jury 2, on the other hand, is to see that Coleman is successfully defended, what we call a persuasion goal. In fact, we show below that a truth oriented goal is a kind of persuasion goal. Crucial to the accomplishment of either of these goals is for the Jury INLINEFORM0 to decide whether the speaker INLINEFORM1 is committing to a definite answer that she will defend (or better yet an answer that she believes) on a given move to a question from her interlocutor or is INLINEFORM2 trying to avoid any such commitments. If it's the latter, then INLINEFORM3 would be epistemically rash to be persuaded. But the two possibilities are just the two types for Sheehan that are relevant to the interpretation of the ambiguous moves in SECREF3 . Because persuasive goals are almost ubiquitous at least as parts of speaker goals, not only in conversation but also for texts (think of how the reporters in the examples on the March for Science are seeking to convince us of a particular view of the event), we claim that these two types are relevant to the interpretation of many, if not all, conversations. In general we conjecture that the relevant types for interpretation may all rely on epistemic requirements for meeting various kinds of conversational goals.",
+      "The second reason that discourse goals are key to understanding bias is that by analyzing persuasion goals in more detail we get to the heart of what bias is. Imagine a kind of ME game played between two players, E(lo\u00efse) and A(belard), where E proposes and tries to defend a particular interpretation of some set of facts INLINEFORM0 , and A tries to show the interpretation is incorrect, misguided, based on prejudice or whatever will convince the Jury to be dissuaded from adopting E's interpretation of INLINEFORM1 . As in all ME games, E's victory condition in an ME persuasion game is a set of histories determined by the Jury, but but it crucially depends on E's and A's beliefs about the Jury: E has to provide a history INLINEFORM2 over INLINEFORM3 ; A has to attack that history in ways that accord with her beliefs about the Jury; and E has to defend INLINEFORM4 in ways that will, given her beliefs, dispose the Jury favorably to it.",
+      "An ME persuasion game is one where E and A each present elements of INLINEFORM0 and may also make argumentative or attack moves in their conversation. At each turn of the game, A can argue about the history constructed by E over the facts given so far, challenge it with new facts or attack its assumptions, with the result that E may rethink and redo portions her history over INLINEFORM1 (though not abandon the original history entirely) in order to render A's attack moot. E wins if the history she finally settles on for the facts in INLINEFORM2 allows her to rebut every attack by A; A wins otherwise. A reasonable precisification of this victory condition is that the proportions of good unanswered attacks on the latest version of E's history with respect to the total number of attacks at some point continues to diminish and eventually goes to 0. This is a sort of limit condition: if we think of the initial segments INLINEFORM3 E's play as producing an \u201cinitial\u201d history INLINEFORM4 over INLINEFORM5 , as INLINEFORM6 , INLINEFORM7 has no unanswered counterattacks by A that affect the Jury. Such winning histories are extremely difficult to construct; as one can see from inspection, no finite segment of an infinite play guarantees such a winning condition. We shall call a history segment that is part of a history in INLINEFORM8 's winning condition as we have just characterized it, E-defensible.",
+      "The notion of an ME persuasion game opens the door to a study of attacks, a study that can draw on work in argumentation and game theory BIBREF34 , BIBREF35 , BIBREF36 . ME games and ME persuasion games in particular go beyond the work just cited, however, because our notion of an effective attack involves the type of the Jury as a crucial parameter; the effectiveness of an attack for a Jury relies on its prejudices, technically its priors about the game's players' types (and hence their beliefs and motives). For instance, an uncovering of an agent's racist bias when confronted with a dog whistle like that in SECREF8 is an effective attack technique if the respondent's type for the Jury is such that it is sensitive to such accusations, while it will fail if the Jury is insensitive to such accusations. ME games make plain the importance in a persuasion game of accurately gauging the beliefs of the Jury!"
+    ],
+    [
+      "We now turn to a special kind of ME persuasion game with what we call a disinterested Jury. The intuition behind a disinterested Jury is simple: such a Jury judges the persuasion game based only on the public commitments that follow from the discourse moves that the players make. It is not predisposed to either player in the game. While it is difficult to define such a disinterested Jury in terms of its credences, its probability distribution over types, we can establish some necessary conditions. We first define the notion of the dual of a play of an ME game. Let INLINEFORM0 be an element of the labeled vocabulary with player INLINEFORM1 . Define its dual as: INLINEFORM2 ",
+      "The dual of a play INLINEFORM0 then is simply the lifting of this operator over the entire sequence of INLINEFORM1 . That is, if INLINEFORM2 , where INLINEFORM3 then INLINEFORM4 ",
+      "Then, a disinterested Jury must necessarily satisfy:",
+      "Indifference towards player identity: A Jury INLINEFORM0 is unbiased only if for every INLINEFORM1 , INLINEFORM2 iff INLINEFORM3 .",
+      "Symmetry of prior belief: A Jury is unbiased only if it has symmetrical prior beliefs about the player types.",
+      "Clearly, the Jury INLINEFORM0 does not have symmetrical prior beliefs nor is it indifferent to player identity, while Jury INLINEFORM1 arguably has symmetrical beliefs about the participants in SECREF3 . Note also that while Symmetry of prior beliefs is satisfied by a uniform distribution over all types, but it does not entail such a uniform distribution. Symmetry is closely related to the principle of maximum entropy used in fields as diverse as physics and computational linguistics BIBREF37 , according to which in the absence of any information about the players would entail a uniform probability distribution over types.",
+      "A distinterested Jury should evaluate a conversation based solely on the strength of the points put forth by the participants. But also crucially it should evaluate the conversation in light of the right points. So for instance, appeals to ad hominem attacks by A or colorful insults should not sway the Jury in favor of A. They should evaluate only based on how the points brought forward affect their credences under conditionalization. A distinterested Jury is impressed only by certain attacks from A, ones based on evidence (E's claims aren't supported by the facts) and on formal properties of coherence, consistency and explanatory or predictive power. In such a game it is common knowledge that attacks based on information about E's type that is not relevant either to the evidential support or formal properties of her history are ignored by the Jury and the participants know this. The same goes for E; counterattacks by her on A that are not based on evidence or the formal properties mentioned above.",
+      " BIBREF4 discusses the formal properties of coherence and consistency in detail, and we say more about explanatory and predictive power below. The evidential criterion, however, is also particularly important, and it is one that a disinterested Jury must attend to. Luckily for us, formal epistemologists have formulated constraints like cognitive skill and safety or anti-luck on beliefs that are relevant to characterizing this evidential criterion BIBREF38 , BIBREF39 . Cognitive skill is a factor that affects the success (accuracy) of an agent's beliefs: the success of an agent's beliefs is the result of her cognitive skill, exactly to the extent that the reasoning process that produces them makes evidential factors (how weighty, specific, misleading, etc., the agent's evidence is) comparatively important for explaining that success, and makes non-evidential factors comparatively unimportant. In addition, we will require that the relevant evidential factors are those that have been demonstrated to be effective in the relevant areas of inquiry. So if a Jury measures the success of a persuasion game in virtue of a criterion of cognitive ability on the part of the participants and this is common knowledge among the participants (something we will assume throughout here), then, for instance, A's attacks have to be about the particular evidence adduced to support E's history, the way it was collected or verifiable errors in measurements etc., and preclude general skeptical claims from credible attacks in such a game. These epistemic components thus engender more relevant types for interpretation: are the players using cognitive skill and anti-luck conditions or not? More particularly, most climate skeptics' attacks on climate change science, using general doubts about the evidence without using any credible scientific criteria attacking specific evidential bases, would consequently be ruled as irrelevant in virtue of a property like cognitive skill. But this criterion may also affect the Jury's interpretation of the conversation. A Jury whose beliefs are constrained by cognitive ability will adjust its beliefs about player types and about interpretation only in the light of relevant evidential factors.",
+      "Safety is a feature of beliefs that says that conditionalizing on circumstances that could have been otherwise without one's evidence changing should not affect the strength of one's beliefs. Safety rules out out belief profiles in which luck or mere hunches play a role.",
+      "The notion of a disinterested jury is formally a complicated one. Consider an interpretation of a conversation between two players E and A. Bias can be understood as a sort of modal operator over an agent's first order and higher order beliefs. So a disinterested Jury in an ME game means that neither its beliefs about A nor about E involve an interested bias; nor do its beliefs about A's beliefs about E's beliefs or E's beliefs about the A's beliefs about E's beliefs, and so on up the epistemic hierarchy. Thus, a disinterested Jury in this setting involves an infinitary conjunction of modal statements, which is intuitively (and mathematically) a complex condition on beliefs. And since this disinterestedness must be common knowledge amongst the players, E and A have equally complex beliefs.",
+      "We are interested in ME persuasion games in which the truth may emerge. Is an ME persuasion game with a disinterested Jury sufficient to ensure such an outcome? No. there may be a fatal flaw in E's history that INLINEFORM0 does not uncover and that the Jury does not see. We have to suppose certain abilities on the part of INLINEFORM1 and/or the Jury\u2014namely, that if E has covered up some evidence or falsely constructed evidence or has introduced an inconsistency in her history, that eventually A will uncover it. Further, if there is an unexplained leap, an incoherence in the history, then INLINEFORM2 will eventually find it. Endowing INLINEFORM3 with such capacities would suffice to ensure a history that is in E's winning condition to be the best possible approximation to the truth, a sort of Peircean ideal. Even if we assume only that INLINEFORM4 is a competent and skilled practitioner of her art, we have something like a good approximation of the truth for any history in E's winning condition. We call a persuasion game with such a disinterested Jury and such a winning condition for INLINEFORM5 an ME truth game.",
+      "In an ME truth game, a player or a Jury may not be completely disinterested because of skewed priors. But she may still be interested in finding out the truth and thus adjusting her priors in the face of evidence. We put some constraints on the revision of beliefs of a truth interested player. Suppose such a player INLINEFORM0 has a prior INLINEFORM1 on INLINEFORM2 such that INLINEFORM5 , but in a play INLINEFORM6 of an ME truth game it is revealed that INLINEFORM7 has no confirming evidence for INLINEFORM8 that the opponent INLINEFORM9 cannot attack without convincing rebuttal. Then a truth interested player INLINEFORM10 should update her beliefs INLINEFORM11 after INLINEFORM12 so that INLINEFORM13 . On the other hand, if INLINEFORM14 cannot rebut the confirming evidence that INLINEFORM15 has for INLINEFORM16 , then INLINEFORM17 . Where INLINEFORM18 is infinite, we put a condition on the prefixes INLINEFORM19 of INLINEFORM20 : INLINEFORM21 . Given our concepts of truth interested players and an ME truth game, we can show the following. If the two players of a 2 history ME truth game INLINEFORM22 , have access to all the facts in INLINEFORM23 , and are truth interested but have incompatible histories for INLINEFORM24 based on distinct priors, they will eventually agree to a common history for INLINEFORM25 . To prove this, we note that our players will note the disagreement and try to overcome it since they have a common interest, in the truth about INLINEFORM26 . Then it suffices to look at two cases: in case one, one player INLINEFORM27 converges to the INLINEFORM28 's beliefs in the ME game because INLINEFORM29 successfully attacks the grounds on which INLINEFORM30 's incompatible interpretation is based; in case two, neither INLINEFORM31 nor INLINEFORM32 is revealed to have good evidential grounds for their conflicting beliefs and so they converge to common revised beliefs that assign an equal probability to the prior beliefs that were in conflict. Note that the difference with BIBREF32 is that we need to assume that players interested in the truth conditionalize upon outcomes of discussion in an ME game in the same way. Players who do not do this need not ever agree.",
+      "There are interesting variants of an ME truth game where one has to do with approximations. ME truth games are infinitary games, in which getting a winning history is something E may or may not achieve in the limit. But typically we want the right, or \u201cgood enough\u201d interpretation sooner rather than later. We can also appeal to discounted ME games developed in BIBREF21 , in which the scores are assigned to individual discourse moves in context which diminish as the game progresses, to investigate cases where getting things right, or right enough, early on in an ME truth game is crucial.",
+      "In another variant of an ME truth game, which we call a 2-history ME truth game, we pit two biases one for E and one for A, and the two competing histories they engender, about a set of facts against each other. Note that such a game is not necessarily win-lose as is the original ME truth game, because neither history the conversationalists develop and defend may satisfy the disinterested Jury. That is, both E and A may lose in such a game. Is it also possible that they both win? Can both E and A revise their histories so that their opponents have in the end no telling attacks against their histories? We think not at least in the case where the histories make or entail contradictory claims: in such a case they should both lose because they cannot defeat the opposing possibility.",
+      "Suppose INLINEFORM0 wants to win an ME truth game and to construct a truthful history. Let's assume that the set of facts INLINEFORM1 over which the history is constructed is finite. What should she do? Is it possible for her to win? How hard is it for her to win? Does INLINEFORM2 have a winning strategy? As an ME truth game is win-lose, if the winning condition is Borel definable, it will be determined BIBREF4 ; either INLINEFORM3 has a winning strategy or INLINEFORM4 does. Whether INLINEFORM5 has a winning strategy or not is important: if she does, there is a method for finding an optimal history in the winning set; if she doesn't, an optimal history from the point of view of a truth-seeking goal in the ME truth game is not always attainable.",
+      "To construct a history from ambiguous signals for a history over INLINEFORM0 , the interpreter must rely on her beliefs about the situation and her interlocutors to estimate the right history. So the question of getting at truthful interpretations of histories depends at least in part on the right answer to the question, what are the right beliefs about the situation and the participants that should be invoked in interpretation? Given that beliefs are probabilistic, the space of possible beliefs is vast. The right set of beliefs will typically form a very small set with respect to the set of all possible beliefs about a typical conversational setting. Assuming that one will be in such a position \u201cby default\u201d without any further argumentation is highly implausible, as a simple measure theoretic argument ensures that the set of possible interpretations are almost always biased away from a winning history in an ME truth game.",
+      "What is needed for E-defensibility and a winning strategy in an ME truth game? BIBREF4 argued that consistency and coherence (roughly, the elements of the history have to be semantically connected in relevant ways BIBREF3 ) are necessary conditions on all winning conditions and would thus apply to such histories. A necessary additional property is completeness, an accounting of all or sufficiently many of the facts the history is claimed to cover. We've also mentioned the care that has to be paid to the evidence and how it supports the history. Finally, it became apparent when we considered a variant of an ME truth game in which two competing histories were pitted against each other that a winning condition for each player is that they must be able to defeat the opposing view or at least cast doubt on it.",
+      "More particularly, truth seeking biases should provide predictive and explanatory power, which are difficult to define. But we offer the following encoding of predictiveness and explanatory power as constraints on continuations of a given history in an ME truth game. [Predictiveness] A history INLINEFORM0 developed in an ME game for a set of facts INLINEFORM1 is predictive just in case when INLINEFORM2 is presented with a set of facts INLINEFORM3 relevantly similar to INLINEFORM4 , INLINEFORM5 implies a E-defensible extension INLINEFORM6 of INLINEFORM7 to all the facts in INLINEFORM8 . A similar definition can be given for the explanatory power of a history.",
+      "Does INLINEFORM0 have a strategy for constructing a truthful history that can guarantee all of these things? Well, if the facts INLINEFORM1 it is supposed to relate are sufficiently simple or sufficiently unambiguous in the sense that they determine just one history and E is effectively able to build and defend such a history, then yes she does. So very simple cases like establishing whether your daughter has a snack for after school in the morning or not are easy to determine, and the history is equally simple, once you have the right evidence: yes she has a snack, or no she doesn't. A text which is unambiguous similarly determines only one history, and linguistic competence should suffice to determine what that history is. On the other hand, it is also possible that INLINEFORM2 may determine the right history INLINEFORM3 from a play INLINEFORM4 when INLINEFORM5 depends on the type of the relevant players of INLINEFORM6 . For INLINEFORM7 can have a true \u201ctype\u201d for the players relevant to INLINEFORM8 . In general whether or not a player has a winning strategy will depend on the structure of the optimal history targeted, as well as on the resources and constraints on the players in an ME truth game.",
+      "In the more general case, however, whether INLINEFORM0 has a winning strategy in an ME truth game become in general non trivial. At least in a relative sort of way, E can construct a model satisfying her putative history at each stage to show consistency (relative to ZF or some other background theory); coherence can be verified by inspection over the finite discourse graph of the relevant history at each stage and ensuing attacks. Finally completeness and evidential support can be guaranteed at each stage in the history's construction, if E has the right sort of beliefs. If all this can be guaranteed at each stage, von Neumann's minimax theorem or its extension in BIBREF40 guarantees that E has a winning strategy for E-defensibility.",
+      "In future work, we plan to analyze in detail some complicated examples like the ongoing debate about climate, change where there is large scale scientific agreement but where disagreement exists because of distinct winning conditions."
+    ],
+    [
+      "An ME truth game suggests a certain notion of truth: the truth is a winning history in an ME persuasion game with a disinterested Jury. This is a Peircean \u201cbest attainable\u201d approximation of the truth, an \u201dinternal\u201d notion of truth based on consistency, coherence with the available evidence and explanatory and predictive power. But we could investigate also a more external view of truth. Such a view would suppose that the Jury has in its possession the \u201ctrue history over a set of facts INLINEFORM0 , that the history eventually constructed by E should converge to within a certain margin of error in the limit.",
+      "We think ME games are a promising tool for investigating bias, and in this section we mention some possible applications and open questions that ME games might help us answer. ME truth games allow us to analyze extant strategies for eliminating bias. For instance, given two histories for a given set of facts, it is a common opinion that one finds a less biased history by splitting the difference between them. This is a strategy perhaps distantly inspired by the idea that the truth lies in the golden mean between extremes. But is this really true? ME games should allow us to encode this strategy and find out.",
+      "Another connection that our approach can exploit is the one between games and reinforcement learning BIBREF44 , BIBREF45 , BIBREF46 . While reinforcement learning is traditionally understood as a problem involving a single agent and is not powerful enough to understand the dynamics of competing biases of agents with different winning conditions, there is a direct connection made in BIBREF45 between evolutionary games with replicator dynamics and the stochastic learning theory of BIBREF47 with links to multiagent reinforcement learning. BIBREF44 , BIBREF46 provide a foundation for multiagent reinforcement learning in stochastic games. The connection between ME games and stochastic and evolutionary games has not been explored but some victory conditions in ME games can be an objective that a replicator dynamics converges to, and epistemic ME games already encompass a stochastic component. Thus, our research will be able to draw on relevant results in these areas.",
+      "A typical assumption we make as scientists is that rationality would lead us to always prefer to have a more complete and more accurate history for our world. But bias isn't so simple, as an analysis of ME games can show. ME games are played for many purposes with non truth-seeking biases that lead to histories that are not a best approximation to the truth may be the rational or optimal choice, if the winning condition in the game is other than that defined in an ME truth game. This has real political and social relevance; for example, a plausible hypothesis is that those who argue that climate change is a hoax are building an alternative history, not to get at the truth but for other political purposes. Even being a truth interested player can at least initially fail to generate histories that are in the winning condition of an ME truth game. Suppose E, motivated by truth interest, has constructed for facts INLINEFORM0 a history INLINEFORM1 that meets constraints including coherence, consistency, and completeness, and it provides explanatory and predictive power for at least a large subset INLINEFORM2 of INLINEFORM3 . E's conceptualization of INLINEFORM4 can still go wrong, and E may fail to have a winning strategy in interesting ways. First, INLINEFORM5 can mischaracterize INLINEFORM6 with high confidence in virtue of evidence only from INLINEFORM7 BIBREF48 ; Especially if INLINEFORM8 is large and hence INLINEFORM9 is just simply very \u201clong\u201d, it is intuitively more difficult even for truth seeking players to come to accept that an alternative history is the correct one. Second, INLINEFORM10 may lack or be incompatible with concepts that would be needed to be aware of facts in INLINEFORM11 . BIBREF55 , BIBREF23 investigate a special case of this, a case of unawareness. To succeed E would have to learn the requisite concepts first.",
+      "All of this has important implications for learning. We can represent learning as the following ME games. It is common to represent making a prediction Y from data X as a zero sum game between our player E and Nature: E wins if for data X provided by Nature, E makes a prediction that the Jury judges to be correct. More generally, an iterated learning process is a repeated zero sum game, in which E makes predictions in virtue of some history, which one might also call a model or a set of hypotheses; if she makes a correct prediction at round n, she reinforces her beliefs in her current history; if she makes a wrong prediction, she adjusts it. The winning condition may be defined in terms of some function of the scores at each learning round or in terms of some global convergence property. Learning conceived in this way is a variant of a simple ME truth game in which costs are assigned to individual discourse moves as in discounted ME games.",
+      "In an ME truth game, where E develops a history INLINEFORM0 over a set of facts INLINEFORM1 while A argues for an alternative history INLINEFORM2 over INLINEFORM3 , A can successfully defend history INLINEFORM4 as long as either the true history INLINEFORM5 is (a) not learnable or (b) not uniquely learnable. In case (a), E cannot convince the Jury that INLINEFORM6 is the right history; in case (b) A can justify INLINEFORM7 as an alternative interpretation. Consider the bias of a hardened climate change skeptic: the ME model predicts that simply presenting new facts to the agent will not induce him to change his history, even if to a disinterested Jury his history is clearly not in his winning condition. He may either simply refuse to be convinced because he is not truth interested, or because he thinks his alternative history INLINEFORM8 can explain all of the data in INLINEFORM9 just as well as E's climate science history INLINEFORM10 . Thus, ME games open up an unexplored research area of unlearnable histories for certain agents."
+    ],
+    [
+      "In this paper, we have put forward the foundations of a formal model of interpretive bias. Our approach differs from philosophical and AI work on dialogue that links dialogue understanding to the recovery of speaker intentions and beliefs BIBREF56 , BIBREF57 . Studies of multimodal interactions in Human Robot Interaction (HRI) have also followed the Gricean tradition BIBREF58 , BIBREF59 , BIBREF60 . BIBREF61 , BIBREF4 , BIBREF62 ), offer many reasons why a Gricean program for dialogue understanding is difficult for dialogues in which there is not a shared task and a strong notion of co-operativity. Our model is not in the business of intention and belief recovery, but rather works from what contents agents explicitly commit to with their actions, linguistic and otherwise, to determine a rational reconstruction of an underlying interpretive bias and what goals a bias would satisfy. In this we also go beyond what current theories of discourse structure like SDRT can accomplish. Our theoretical work also requires an empirical component on exactly how bias is manifested to be complete. This has links to the recent interest in fake news. Modeling interpretive bias can help in detecting fake news by providing relevant types to check in interpretation and by providing an epistemic foundation for fake news detection by exploiting ME truth games where one can draw from various sources to check the credibility of a story. In a future paper, we intend to investigate these connections thoroughly.",
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+    ]
+  ]
+}
+```
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diff --git a/qasper-0981/instruction.md b/qasper-0981/instruction.md
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+Name of Paper: A Swiss German Dictionary: Variation in Speech and Writing
+
+Question: How many words are coded in the dictionary?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Dictionary Content and access",
+    "Construction of the dictionary",
+    "Construction of the dictionary ::: Discretising continuous variation",
+    "Construction of the dictionary ::: Manual annotation ::: SAMPAs",
+    "Construction of the dictionary ::: Manual annotation ::: GSWs",
+    "Construction of the dictionary ::: Automatic annotation",
+    "Construction of the dictionary ::: Automatic annotation ::: Transformer-based Phoneme to Grapheme (p2g)",
+    "Construction of the dictionary ::: Automatic annotation ::: Test set and evaluation",
+    "Construction of the dictionary ::: Automatic annotation ::: Grapheme to Phoneme (g2p) and its benefits for ASR",
+    "Discussion",
+    "Conclusion",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "Swiss German refers to any of the German varieties that are spoken in about two thirds of Switzerland BIBREF0. Besides at least one of those dialectal varieties, Swiss German people also master standard (or 'High') German which is taught in school as the official language of communication.",
+      "Swiss German is varies strongly. Many differences exist in the dialectal continuum of the German speaking part of Switzerland. Besides pronunciation, it also varies a lot in writing. Standard German used to be the exclusive language for writing in Switzerland. Writing in Swiss German has only come up rather recently (notably in text messaging). Because of this, there are no orthographic conventions for Swiss German varieties. Even people speaking the same dialect can, and often do, write phonetically identical words differently.",
+      "In this paper, we present a dictionary of written standard German words paired with their pronunciation in Swiss German words. Additionally Swiss German spontaneous writings, i.e. writings as they may be used in text messages by native speakers, are paired with Swiss German pronunciations.",
+      "The primary motivation for building this dictionary is rendering Swiss German accessible for technologies such as Automatic Speech Recognition (ASR).",
+      "This is the first publicly described Swiss German dictionary shared for research purposes. Furthermore, this is the first dictionary that combines pronunciations of Swiss German with spontaneous writings."
+    ],
+    [
+      "This dictionary complements previously developed resources for Swiss German, which share some common information. Spontaneous noisy writing has already been recorded in text corpora BIBREF1, BIBREF2, BIBREF3, some of which are also normalized. These resources contain relatively large lexicons of words used in context, but they do not contain any information about pronunciation. The features of speech are represented in other resources, such as BIBREF4, BIBREF5, BIBREF6, which, on the other hand, contain relatively small lexicons (small set of words known to vary across dialects). The ArchiMob corpus does contain a large lexicon of speech and writing (Dieth transcription), but the spoken part is available in audio sources only, without phonetic transcription.",
+      "This dictionary is the first resource to combine all the relevant information together. A relatively large lexicon has been constructed in which phonetic transcriptions (in the SAMPA alphabet) are mapped to various spontaneous writings controlling for the regional distribution. Some of the representations in this dictionary are produced manually, while others are added using automatic processing.",
+      "Automatic word-level conversion between various writings in Swiss German has been addressed in several projects, mostly for the purpose of writing normalization BIBREF7, BIBREF2, BIBREF8, BIBREF9, BIBREF10, BIBREF11, BIBREF0, BIBREF12. The task of normalization consist of mapping multiple variants of a single lexical item into a single writing usually identical to standard German (an example would be the Swiss German words aarbet and arb\u00e4it which both map to standard German arbeit ('work')). Early data sets were processed manually (SMS). This was followed by an implementation of character-level statistical machine translation models BIBREF13, BIBREF14 and, more recently, with neural sequence-to-sequence technology. The solution by lusettietal18 employes soft-attention encoder-decoder recurrent networks enhanced with synchronous multilevel decoding. ruzsicsetal19 develop these models further to integrate linguistic (PoS) features.",
+      "A slightly different task of translating between standard German and Swiss dialects was first addressed with finite state technology BIBREF15. More recently, honnet-etal17 test convolutional neural networks on several data sets.",
+      "We continue the work on using neural networks for modeling word-level conversion. Unlike previous work, which dealt with written forms only, we train models for mapping phonetic representations to various possible writings. The proposed solution relies on the latest framework for sequence-to-sequence tasks \u2014 transformer networks BIBREF16."
+    ],
+    [
+      "We pair 11'248 standard German written words with their phonetical representations in six different Swiss dialects: Z\u00fcrich, St. Gallen, Basel, Bern, Visp, and Stans (Figure FIGREF1). The phonetic words were written in a modified version of the Speech Assessment Methods Phonetic Alphabet (SAMPA). The Swiss German phonetic words are also paired with Swiss German writings in the latin alphabet. (From here onwards, a phonetic representation of a Swiss German word will be called a SAMPA and a written Swiss German word will be called a GSW.)",
+      "This dictionary comes in two versions as we used two differently sized sets of SAMPA characters. Our extended set including 137 phones allows for a detailed and adequate representation of the diverse pronunciation in Switzerland. The smaller set of 59 phones is easier to compute. The phone reduction was mainly done by splitting up combined SAMPA-characters such as diphthongs. UI s t r $ \\lbrace $ tt @ and U I s t r $ \\lbrace $ t t @ for example are both representations of the Stans pronunciation of the standard German word austreten ('step out'). The latter representation belongs to the dictionary based on the smaller phoneset. Table TABREF2 shows an example of five dictionary entries based on the bigger phoneset.",
+      "For a subset of 9000 of 11'248 standard German words, we have manually annotated GSWs for Visp (9000) and for Zurich (2 x 9000, done by two different annotators). For a subsubset of 600 of those standard German words we have manually annotated GSWs for the four other dialects of St. Gallen, Basel, Bern, and Stans. The remaining writing variants are generated using automatic methods described below.",
+      "The dictionary is freely available for research purposes under the creative commons share-alike non-commercial licence via this website http://tiny.uzh.ch/11X."
+    ],
+    [
+      "In the following we present the steps of construction of our dictionary, also detailing how we chose the six dialects to represent Swiss German and how, starting with a list of standard German words, we retrieved the mapping SAMPAs and GSWs."
+    ],
+    [
+      "To be able to represent Swiss German by only a few dialects which differ considerably it is necessary to discretize linguistic varieties. Because, as mentioned earlier, regional language variation in Switzerland is continuous. For this identification of different varieties we used a dialectometric analysis BIBREF17. This analysis is based on lexical, phonological, morphological data of the German speaking areas of Switzerland BIBREF4. As we worked with word-lists and not sentences, we discounted syntactical influences on area boundaries that are also described in that analysis. We represent six differentiated linguistic varieties. We considered working with ten linguistic varieties because this number of areas was the 'best-cut'-analysis in the dialectometric analysis BIBREF17. Yet, due to time restraints and considerable overlap between some of the linguistic varieties, we reduced this number to six. We also made some adjustements to the chosen varieties in order to correspond better to the perception of speakers and in favor of more densely populated areas.",
+      "One way to represent the six individualized linguistic varieties would have been to annotate the dialectal centers, i.e. those places that have the average values of dialectal properties within the area where the variety is spoken. However, we chose to represent the linguistic varieties by the most convenient urban places. Those were the dialects of the Cities Zurich, St. Gallen, Basel, Bern, and Visp, and Stans."
+    ],
+    [
+      "For each standard German word in our dictionary we manually annotated its phonetic representation in the six chosen dialects. The information about the pronunciation of Swiss German words is partially available also from other sources but not fully accessible BIBREF4 BIBREF7.",
+      "To help us with pronunciation our annotators first used their knowledge as native speakers (for Zurich and Visp). Secondly, they consulted dialect specific grammars BIBREF18 BIBREF19 BIBREF20 BIBREF21 BIBREF22 as well as dialect specific lexica BIBREF23 BIBREF24 BIBREF25. They also considered existing Swiss German dictionaries BIBREF7 BIBREF4, listened to recordings BIBREF0 and conferred with friends and acquaintances originating from the respective locations."
+    ],
+    [
+      "9000 GSWs for Visp German and 2 x 9000 GSWs for Zurich German were annotated by native speakers of the respective dialect. Our annotators created the GSWs while looking at standard German words and without looking at the corresponding SAMPAs for Visp and Zurich. Through this independence from SAMPAs we are able to avoid biases concerning the phonetics as well as the meaning of the word in generating GSWs.",
+      "At a later stage of our work, we added each 600 GSWs for the four dialects of St. Gallen, Basel, Bern, and Stans in order to improve our phoneme-to-grapheme(p2g) model (see next section). For the manual annotation of these dialects we had no native speakers. Therefore, when writing the GSWs, our annotators relied on the corresponding SAMPAs of these dialects, which they had made an effort to create before."
+    ],
+    [
+      "In order to account for the mentioned variety of everyday Swiss German writing, we aimed for more than one GSW per SAMPA. The heterogeneous writing style makes the SAMPA$\\,\\rightarrow \\,$GSW a one to many relation instead of the regular one to one that speakers of standard languages are accustomed to. To save time in generating the many GSWs, we opted for an automatic process.",
+      "We first tried to automatize the generation of GSWs with a rule-based program. Via SAMPAs together with phoneme-to-grapheme mappings we tried to obtain all possible GSWs. Yet, this yielded mostly impossible writings and also not all the writings we had already done manually. We then set up a phoneme-to-grapheme(p2g) model to generate the most likely spellings."
+    ],
+    [
+      "The process of generating written forms from a given SAMPA can be viewed as a sequence-to-sequence problem, where the input is a sequence of phonemes and the output is a sequence of graphemes.",
+      "We decided to use a Transformer-based model for the phoneme-to-grapheme (p2g) task. The reason for this is twofold. First, the Transformer has shown great success in seq2seq tasks and it has outperformed LSTM and CNN-based models. Second, it is computationally more efficient than LSTM and CNN networks.",
+      "The Transformer consists of an encoder and a decoder part. The encoder generates a contextual representation for each input SAMPA that is then fed into the decoder together with the previously decoded grapheme. They both have N identical layers. In the encoder, each layer has a multi-head self-attention layer and a position-wise fully-connected feed-forward layer. While in the decoder, in addition to these two layers, we also have an additional multi-headed attention layer that uses the output of the encoder BIBREF16.",
+      "We are using a Pytorch implementation of the Transformer. As a result of the small size of the dataset, we are using a smaller model with only 2 layers and 2 heads. The dimension of the key (d_k) and value (d_v) is 32, the dimension of the model (d_model) and the word vectors (d_word_vec) is 50 and the hidden inner dimension (d_inner_hid) is 400. The model is trained for 55 epochs with a batch size of 64 and a dropout of 0.2. For decoding the output of the model, we are using beam search with beam size 10. We experimented with different beam sizes, but we saw that it does not have significant influence on the result.",
+      "The training set is made of 24'000 phonemes-to-graphemes pairs, which are the result of transcribing 8'000 High German words into two Zurich forms and one Visp form. Those transcriptions were made independently by three native speakers. Due to the scarcity of data, we decided not to distinguish between dialects. Hence, a single model receives a sequence of SAMPA symbols and learns to generate a matching sequence of characters."
+    ],
+    [
+      "Our team of Swiss German annotators evaluated a test-set of 1000 words. We aimed to exclude only very far-off forms (tagged '0'), such that they are very probably to be seen as false by Swiss German speakers. The accepted writings (tagged '1') might include some that seem off to the Swiss German reader.",
+      "In order to consistently rate the output, the criteria shown in table TABREF4 were followed. A GSW was tagged '0' if there was at least one letter added, missing, or changed without comprehensible phonetic reason. GSWs were also tagged '0' if there were at least two mistakes that our annotators saw as minor. 'Minor mistakes' are substitutions of related sounds or spellings, added or omitted geminates, and changes in vowel length.",
+      "For each of the 1000 words in the test-set, five GSW-predictions in all six dialects were given to our annotators. For Visp and Zurich they tagged each 1000x5 GSW predictions with 1 or 0. For St. Gallen, Basel, Bern, and Stans, they evaluated 200x5.",
+      "In Table TABREF13 we show the result from this evaluation. We count the number of correct GSWs (labeled as '1') among the top 5 candidates generated by the p2g model, where the first candidate is the most relevant, then the second one and so on.",
+      "The evaluation was done at a stage where our model was trained only on GSW for Zurich and Visp (see sec. SECREF8). The amount of correct predictions are lower for the dialects of St. Gallen, Basel, Bern, and Stans, mainly because there were some special SAMPA characters we used for those dialects and the model did not have the correlating latin character strings. After the evaluation, we added each 600 GSWs for the four dialects of St. Gallen, Basel, Bern, and Stans to improve the model."
+    ],
+    [
+      "Automatic speech recognition (ASR) systems are the main use cases for our dictionary. ASR systems convert spoken language into text. Today, they are widely used in different domains from customer and help centers to voice-controlled assistants and devices. The main resources needed for an ASR system are audio, transcriptions and a phonetic dictionary. The quality of the ASR system is highly dependant of the quality of the dictionary. With our resource we provide such a phonetic dictionary.",
+      "To increase the benefits of our data for ASR systems, we also trained a grapheme-to-phoneme (g2p) model: Out-of-vocabulary words can be a problem for ASR system. For those out-of-vocabulary words we need a model that can generate pronunciations from a written form, in real time. This is why we train a grapheme-to-phoneme (g2p) model that generates a sequence of phonemes for a given word. We train the g2p model using our dictionary and compare its performance with a widely used joint-sequence g2p model, Sequitur BIBREF26. For the g2p model we are using the same architecture as for the p2g model. The only difference is input and output vocabulary. The Sequitur and our model are using the dictionary with the same train (19'898 samples), test (2'412 samples) and validation (2'212 samples) split. Additionally, we also test their performance only on the items from the Zurich and Visp dialect, because most of the samples are from this two dialects. In Table TABREF15 we show the result of the comparison of the two models. We compute the edit distance between the predicted and the true pronunciation and report the number of exact matches. In the first columns we have the result using the whole test set with all the dialects, and in the 2nd and 3rd columns we show the number of exact matches only on the samples from the test set that are from the Zurich and Visp dialect. For here we can clearly see that our model performs better than the Sequitur model. The reason why we have less matches in the Visp dialect compared to Zurich is because most of the our data is from the Zurich dialect."
+    ],
+    [
+      "One of our objectives was to map phonetic words with their writings. There are some mismatches between SAMPA and GSWs in our dictionary, especially when the GSWs were done manually and independently from the SAMPA. Those mismatches occur where there is no straightforward correspondence of a standard German and Swiss German word.",
+      "Two kinds of such a missing correspondence can be distinguished. First, there are ambiguous standard German words. And that is necessarily so, as our dictionary is based on a list of standard German words without sentential or any other context. An example for a (morphologically) ambiguous word is standard German liebe. As we did not differentiate upper- and lower-case, it can both mean (a) 'I love' or (b) 'the love'. As evident from table 1, liebe (a) and liebi (b) were mixed in our dictionary. The same is the case for standard German frage which means either (a) 'I ask' or (b) 'the question'. Swiss German fr\u00f6ge, froge, fregu (a) and or (b) fraag, froog were mixed. (For both examples, see table 1.)",
+      "The second case of missing straightforward correspondence is distance between standard German and Swiss German. For one, lexical preferences in Swiss German differ from those in standard German. To express that food is 'tasty' in standard German, the word lecker is used. This is also possible in Swiss German, yet the word fein is much more common. Another example is that the standard German word rasch ('swiftly') is uncommon in Swiss German \u2013 synonyms of the word are preferred. Both of this shows in the variety of options our annotators chose for those words (see table 1). Also, the same standard German word may have several dialectal versions in Swiss German. For example there is a short and long version for the standard German word grossvater, namely grospi and grossvatter.",
+      "A second aim was to represent the way Swiss German speaking people write spontaneously. However, as our annotators wrote the spontaneous GSWs mostly while looking at standard German words, our GSWs might be biased towards standard German orthography. Yet, there is potentially also a standard German influence in the way Swiss German is actually written.",
+      "We partly revised our dictionary in order to adapt to everyday writing: We introduced explicit boundary marking into our SAMPAs. We inserted an _ in the SAMPA where there would usually be a space in writing. An example where people would conventionally add a space are corresponding forms to standard German preterite forms, for example 'ging'. The Swiss German corresponding past participles \u2013 here isch gange \u2013 would (most often) be written separately. So entries like b i n k a N @ in table 1 were changed to b i n _ k a N @."
+    ],
+    [
+      "In this work we introduced the first Swiss German dictionary. Through its dual nature - both spontaneous written forms in multiple dialects and accompanying phonetic representations - we believe it will become a valuable resource for multiple tasks, including automated speech recognition (ASR). This resource was created using a combination of manual and automated work, in a collaboration between linguists and data scientists that leverages the best of two worlds - domain knowledge and data-driven focus on likely character combinations.",
+      "Through the combination of complementary skills we overcame the difficulty posed by the important variations in written Swiss German and generated a resource that adds value to downstream tasks. We show that the SAMPA to written Swiss German is useful in speech recognition and can replace the previous state of the art. Moreover the written form to SAMPA is promising and has applications in areas like text-to-speech.",
+      "We make the dictionary freely available for researchers to expand and use."
+    ],
+    [
+      "We would like to thank our collaborators Alina M\u00e4chler and Raphael Tandler for their valueable contribution."
+    ]
+  ]
+}
+```
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+Name of Paper: QuaRel: A Dataset and Models for Answering Questions about Qualitative Relationships
+
+Question: Do all questions in the dataset allow the answers to pick from 2 options?
\ No newline at end of file
diff --git a/qasper-0987/instruction.md b/qasper-0987/instruction.md
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@@ -0,0 +1,3 @@
+Name of Paper: Natural Language Interactions in Autonomous Vehicles: Intent Detection and Slot Filling from Passenger Utterances
+
+Question: What is shared in the joint model?
\ No newline at end of file
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+++ b/qasper-0988/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Natural Language Interactions in Autonomous Vehicles: Intent Detection and Slot Filling from Passenger Utterances
+
+Question: Are the intent labels imbalanced in the dataset?
\ No newline at end of file
diff --git a/qasper-0989/instruction.md b/qasper-0989/instruction.md
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+Name of Paper: Sentiment Analysis of Citations Using Word2vec
+
+Question: What kernels are used in the support vector machines?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Pre-processing",
+    "Overall Sent2vec Training",
+    "Polarity-Specific Word Representation Training",
+    "Training Dataset",
+    "Test Dataset",
+    "Evaluation Strategy",
+    "Results",
+    "Discussion and Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "The evolution of scientific ideas happens when old ideas are replaced by new ones. Researchers usually conduct scientific experiments based on the previous publications. They either take use of others work as a solution to solve their specific problem, or they improve the results documented in the previous publications by introducing new solutions. I refer to the former as positive citation and the later negative citation. Citation sentence examples with different sentiment polarity are shown in Table TABREF2 .",
+      "Sentiment analysis of citations plays an important role in plotting scientific idea flow. I can see from Table TABREF2 , one of the ideas introduced in paper A0 is Hidden Markov Model (HMM) based part-of-speech (POS) tagging, which has been referenced positively in paper A1. In paper A2, however, a better approach was brought up making the idea (HMM based POS) in paper A0 negative. This citation sentiment analysis could lead to future-works in such a way that new approaches (mentioned in paper A2) are recommended to other papers which cited A0 positively . Analyzing citation sentences during literature review is time consuming. Recently, researchers developed algorithms to automatically analyze citation sentiment. For example, BIBREF0 extracted several features for citation purpose and polarity classification, such as reference count, contrary expression and dependency relations. Jochim et al. tried to improve the result by using unigram and bigram features BIBREF1 . BIBREF2 used word level features, contextual polarity features, and sentence structure based features to detect sentiment citations. Although they generated good results using the combination of features, it required a lot of engineering work and big amount of annotated data to obtain the features. Further more, capturing accurate features relies on other NLP techniques, such as part-of-speech tagging (POS) and sentence parsing. Therefore, it is necessary to explore other techniques that are free from hand-crafted features. With the development of neural networks and deep learning, it is possible to learn the representations of concepts from unlabeled text corpus automatically. These representations can be treated as concept features for classification. An important advance in this area is the development of the word2vec technique BIBREF3 , which has proved to be an effective approach in Twitter sentiment classification BIBREF4 .",
+      "In this work, the word2vec technique on sentiment analysis of citations was explored. Word embeddings trained from different corpora were compared."
+    ],
+    [
+      "Mikolov et al. introduced word2vec technique BIBREF3 that can obtain word vectors by training text corpus. The idea of word2vec (word embeddings) originated from the concept of distributed representation of words BIBREF5 . The common method to derive the vectors is using neural probabilistic language model BIBREF6 . Word embeddings proved to be effective representations in the tasks of sentiment analysis BIBREF4 , BIBREF7 , BIBREF8 and text classification BIBREF9 . Sadeghian and Sharafat BIBREF10 extended word embeddings to sentence embeddings by averaging the word vectors in a sentiment review statement. Their results showed that word embeddings outperformed the bag-of-words model in sentiment classification. In this work, I are aiming at evaluating word embeddings for sentiment analysis of citations. The research questions are:"
+    ],
+    [
+      "The SentenceModel provided by LingPipe was used to segment raw text into its constituent sentences . The data I used to train the vectors has noise. For example, there are incomplete sentences mistakenly detected (e.g. Publication Year.). To address this issue, I eliminated sentences with less than three words.",
+      "",
+      ""
+    ],
+    [
+      "In the work, I constructed sentence embeddings based on word embeddings. I simply averaged the vectors of the words in one sentence to obtain sentence embeddings (sent2vec). The main process in this step is to learn the word embedding matrix INLINEFORM0 :",
+      " INLINEFORM0 INLINEFORM1 INLINEFORM2 INLINEFORM3 (1)",
+      "where INLINEFORM0 ( INLINEFORM1 ) is the word embedding for word INLINEFORM2 , which could be learned by the classical word2vec algorithm BIBREF3 . The parameters that I used to train the word embeddings are the same as in the work of Sadeghian and Sharafat"
+    ],
+    [
+      "To improve sentiment citation classification results, I trained polarity specific word embeddings (PS-Embeddings), which were inspired by the Sentiment-Specific Word Embedding BIBREF4 . After obtaining the PS-Embeddings, I used the same scheme to average the vectors in one sentence according to the sent2vec model."
+    ],
+    [
+      "The ACL-Embeddings (300 and 100 dimensions) from ACL collection were trained . ACL Anthology Reference Corpus contains the canonical 10,921 computational linguistics papers, from which I have generated 622,144 sentences after filtering out sentences with lower quality.",
+      "For training polarity specific word embeddings (PS-Embeddings, 100 dimensions), I selected 17,538 sentences (8,769 positive and 8,769 negative) from ACL collection, by comparing sentences with the polar phrases .",
+      "",
+      "",
+      "The pre-trained Brown-Embeddings (100 dimensions) learned from Brown corpus was also used as a comparison."
+    ],
+    [
+      "To evaluate the sent2vec performance on citation sentiment detection, I conducted experiments on three datasets. The first one (dataset-basic) was originally taken from ACL Anthology BIBREF11 . Athar and Awais BIBREF2 manually annotated 8,736 citations from 310 publications in the ACL Anthology. I used all of the labeled sentences (830 positive, 280 negative and 7,626 objective) for testing. ",
+      "The second dataset (dataset-implicit) was used for evaluating implicit citation classification, containing 200,222 excluded (x), 282 positive (p), 419 negative (n) and 2,880 objective (o) annotated sentences. Every sentence which does not contain any direct or indirect mention of the citation is labeled as being excluded (x) . The third dataset (dataset-pn) is a subset of dataset-basic, containing 828 positive and 280 negative citations. Dataset-pn was used for the purposes of (1) evaluating binary classification (positive versus negative) performance using sent2vec; (2) Comparing the sentiment classification ability of PS-Embeddings with other embeddings."
+    ],
+    [
+      "One-Vs-The-Rest strategy was adopted for the task of multi-class classification and I reported F-score, micro-F, macro-F and weighted-F scores using 10-fold cross-validation. The F1 score is a weighted average of the precision and recall. In the multi-class case, this is the weighted average of the F1 score of each class. There are several types of averaging performed on the data: Micro-F calculates metrics globally by counting the total true positives, false negatives and false positives. Macro-F calculates metrics for each label, and find their unweighted mean. Macro-F does not take label imbalance into account. Weighted-F calculates metrics for each label, and find their average, weighted by support (the number of true instances for each label). Weighted-F alters macro-F to account for label imbalance."
+    ],
+    [
+      "The performances of citation sentiment classification on dataset-basic and dataset-implicit were shown in Table TABREF25 and Table TABREF26 respectively. The result of classifying positive and negative citations was shown in Table TABREF27 . To compare with the outcomes in the work of BIBREF2 , I selected two records from their results: the best one (based on features n-gram + dependencies + negation) and the baseline (based on 1-3 grams). From Table TABREF25 I can see that the features extracted by BIBREF2 performed far better than word embeddings, in terms of macro-F (their best macro-F is 0.90, the one in this work is 0.33). However, the higher micro-F score (The highest micro-F in this work is 0.88, theirs is 0.78) and the weighted-F scores indicated that this method may achieve better performances if the evaluations are conducted on a balanced dataset. Among the embeddings, ACL-Embeddings performed better than Brown corpus in terms of macro-F and weighted-F measurements . To compare the dimensionality of word embeddings, ACL300 gave a higher micro-F score than ACL100, but there is no difference between 300 and 100 dimensional ACL-embeddings when look at the macro-F and weighted-F scores.",
+      "Table TABREF26 showed the sent2vec performance on classifying implicit citations with four categories: objective, negative, positive and excluded. The method in this experiment had a poor performance on detecting positive citations, but it was comparable with both the baseline and sentence structure method BIBREF12 for the category of objective citations. With respect to classifying negative citations, this method was not as good as sentence structure features but it outperformed the baseline. The results of classifying category X from the rest showed that the performances of this method and the sentence structure method are fairly equal.",
+      "Table TABREF27 showed the results of classifying positive and negative citations using different word embeddings. The macro-F score 0.85 and the weighted-F score 0.86 proved that word2vec is effective on classifying positive and negative citations. However, unlike the outcomes in the paper of BIBREF4 , where they concluded that sentiment specific word embeddings performed best, integrating polarity information did not improve the result in this experiment."
+    ],
+    [
+      "In this paper, I reported the citation sentiment classification results based on word embeddings. The binary classification results in Table TABREF27 showed that word2vec is a promising tool for distinguishing positive and negative citations. From Table TABREF27 I can see that there are no big differences among the scores generated by ACL100 and Brown100, despite they have different vocabulary sizes (ACL100 has 14,325 words, Brown100 has 56,057 words). The polarity specific word embeddings did not show its strength in the task of binary classification. For the task of classifying implicit citations (Table TABREF26 ), in general, sent2vec (macro-F 0.44) was comparable with the baseline (macro-F 0.47) and it was effective for detecting objective sentences (F-score 0.84) as well as separating X sentences from the rest (F-score 0.997), but it did not work well on distinguishing positive citations from the rest. For the overall classification (Table TABREF25 ), however, this method was not as good as hand-crafted features, such as n-grams and sentence structure features. I may conclude from this experiment that word2vec technique has the potential to capture sentiment information in the citations, but hand-crafted features have better performance. "
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-0990/instruction.md b/qasper-0990/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..fba613fedacf5413ad074827afa5e3223667baf4
--- /dev/null
+++ b/qasper-0990/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Sentiment Analysis of Citations Using Word2vec
+
+Question: What dataset is used?
\ No newline at end of file
diff --git a/qasper-0997/instruction.md b/qasper-0997/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..6fb0740ef00e0756293ba0a8cfca4d9e755c64a7
--- /dev/null
+++ b/qasper-0997/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Semantic Document Distance Measures and Unsupervised Document Revision Detection
+
+Question: What metrics are used to evaluation revision detection?
\ No newline at end of file
diff --git a/qasper-1002/instruction.md b/qasper-1002/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..203d48d92a1c833210690b0ca179770aa427385d
--- /dev/null
+++ b/qasper-1002/instruction.md
@@ -0,0 +1,84 @@
+Name of Paper: Multi-Task Bidirectional Transformer Representations for Irony Detection
+
+Question: Where did this model place in the final evaluation of the shared task?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Methods",
+    "Methods ::: GRU",
+    "Methods ::: BERT",
+    "Methods ::: Multi-task Learning",
+    "Data",
+    "Models ::: GRU",
+    "Models ::: Single-Task BERT",
+    "Models ::: Multi-Task BERT",
+    "Models ::: In-Domain Pre-Training",
+    "Models ::: IDAT@FIRE2019 Submission",
+    "Related Work",
+    "Conclusion",
+    "Acknowledgement"
+  ],
+  "paragraphs": [
+    [
+      "The proliferation of social media has provided a locus for use, and thereby collection, of figurative and creative language data, including irony BIBREF0. According to the Merriam-Webster online dictionary, irony refers to \u201cthe use of word to express something other than and especially the opposite of the literal meaning.\" A complex, controversial, and intriguing linguistic phenomenon, irony has been studied in disciplines such as linguistics, philosophy, and rhetoric. Irony detection also has implications for several NLP tasks such as sentiment analysis, hate speech detection, fake news detection, etc BIBREF0. Hence, automatic irony detection can potentially improve systems designed for each of these tasks. In this paper, we focus on learning irony. More specifically, we report our work submitted to the FIRE 2019 Arabic irony detection task (IDAT@FIRE2019). We focus our energy on an important angle of the problem\u2013the small size of training data.",
+      "Deep learning is the most successful under supervised conditions with large amounts of training data (tens-to-hundreds of thousands of examples). For most real-world tasks, we hard to obtain labeled data. Hence, it is highly desirable to eliminate, or at least reduce, dependence on supervision. In NLP, pre-training language models on unlabeled data has emerged as a successful approach for improving model performance. In particular, the pre-trained multilingual Bidirectional Encoder Representations from Transformers (BERT) BIBREF1 was introduced to learn language regularities from unlabeled data. Multi-task learning (MTL) is another approach that helps achieve inductive transfer between various tasks. More specifically, MTL leverages information from one or more source tasks to improve a target task BIBREF2, BIBREF3. In this work, we introduce Transformer representations (BERT) in an MTL setting to address the data bottleneck in IDAT@FIRE2019. To show the utility of BERT, we compare to a simpler model with gated recurrent units (GRU) in a single task setting. To identify the utility, or lack thereof, of MTL BERT, we compare to a single task BERT model. For MTL BERT, we train on a number of tasks simultaneously. Tasks we train on are sentiment analysis, gender detection, age detection, dialect identification, and emotion detection.",
+      "Another problem we face is that the BERT model released by Google is trained only on Arabic Wikipedia, which is almost exclusively Modern Standard Arabic (MSA). This introduces a language variety mismatch due to the irony data involving a number of dialects that come from the Twitter domain. To mitigate this issue, we further pre-train BERT on an in-house dialectal Twitter dataset, showing the utility of this measure. To summarize, we make the following contributions:",
+      "In the context of the Arabic irony task, we show how a small-sized labeled data setting can be mitigated by training models in a multi-task learning setup.",
+      "We view different varieties of Arabic as different domains, and hence introduce a simple, yet effective, `in-domain' training measure where we further pre-train BERT on a dataset closer to task domain (in that it involves dialectal tweet data)."
+    ],
+    [
+      ""
+    ],
+    [
+      "For our baseline, we use gated recurrent units (GRU) BIBREF4, a simplification of long-short term memory (LSTM) BIBREF5, which in turn is a variation of recurrent neural networks (RNNs). A GRU learns based on the following:",
+      "where the update state $\\textbf {\\textit {z}}^{(t)}$ decides how much the unit updates its content:",
+      "where W and U are weight matrices. The candidate activation makes use of a reset gate $\\textbf {\\textit {r}}^{(t)}$:",
+      "where $\\odot $ is a Hadamard product (element-wise multiplication). When its value is close to zero, the reset gate allows the unit to forget the previously computed state. The reset gate $\\textbf {\\textit {r}}^{(t)}$ is computed as follows:"
+    ],
+    [
+      "BERT BIBREF1 is based on the Transformer BIBREF6, a network architecture that depends solely on encoder-decoder attention. The Transformer attention employs a function operating on queries, keys, and values. This attention function maps a query and a set of key-value pairs to an output, where the output is a weighted sum of the values. Encoder of the Transformer in BIBREF6 has 6 attention layers, each of which is composed of two sub-layers: (1) multi-head attention where queries, keys, and values are projected h times into linear, learned projections and ultimately concatenated; and (2) fully-connected feed-forward network (FFN) that is applied to each position separately and identically. Decoder of the Transformer also employs 6 identical layers, yet with an extra sub-layer that performs multi-head attention over the encoder stack. The architecture of BERT BIBREF1 is a multi-layer bidirectional Transformer encoder BIBREF6. It uses masked language models to enable pre-trained deep bidirectional representations, in addition to a binary next sentence prediction task captures context (i.e., sentence relationships). More information about BERT can be found in BIBREF1."
+    ],
+    [
+      "In multi-task learning (MTL), a learner uses a number of (usually relevant) tasks to improve performance on a target task BIBREF2, BIBREF3. The MTL setup enables the learner to use cues from various tasks to improve the performance on the target task. MTL also usually helps regularize the model since the learner needs to find representations that are not specific to a single task, but rather more general. Supervised learning with deep neural networks requires large amounts of labeled data, which is not always available. By employing data from additional tasks, MTL thus practically augments training data to alleviate need for large labeled data. Many researchers achieve state-of-the-art results by employing MTL in supervised learning settings BIBREF7, BIBREF8. In specific, BERT was successfully used with MTL. Hence, we employ multi-task BERT (following BIBREF8). For our training, we use the same pre-trained BERT-Base Multilingual Cased model as the initial checkpoint. For this MTL pre-training of BERT, we use the same afore-mentioned single-task BERT parameters. We now describe our data."
+    ],
+    [
+      "The shared task dataset contains 5,030 tweets related to different political issues and events in the Middle East taking place between 2011 and 2018. Tweets are collected using pre-defined keywords (i.e. targeted political figures or events) and the positive class involves ironic hashtags such as #sokhria, #tahakoum, and #maskhara (Arabic variants for \u201cirony\"). Duplicates, retweets, and non-intelligible tweets are removed by organizers. Tweets involve both MSA as well as dialects at various degrees of granularity such as Egyptian, Gulf, and Levantine.",
+      "IDAT@FIRE2019 is set up as a binary classification task where tweets are assigned labels from the set {ironic, non-ironic}. A total of 4,024 tweets were released by organizers as training data. In addition, 1,006 tweets were used by organizers as as test data. Test labels were not release; and teams were expected to submit the predictions produced by their systems on the test split. For our models, we split the 4,024 released training data into 90% TRAIN ($n$=3,621 tweets; `ironic'=1,882 and `non-ironic'=1,739) and 10% DEV ($n$=403 tweets; `ironic'=209 and `non-ironic'=194). We train our models on TRAIN, and evaluate on DEV.",
+      "Our multi-task BERT models involve six different Arabic classification tasks. We briefly introduce the data for these tasks here:",
+      "Author profiling and deception detection in Arabic (APDA). BIBREF9 . From APDA, we only use the corpus of author profiling (which includes the three profiling tasks of age, gender, and variety). The organizers of APDA provide 225,000 tweets as training data. Each tweet is labelled with three tags (one for each task). To develop our models, we split the training data into 90% training set ($n$=202,500 tweets) and 10% development set ($n$=22,500 tweets). With regard to age, authors consider tweets of three classes: {Under 25, Between 25 and 34, and Above 35}. For the Arabic varieties, they consider the following fifteen classes: {Algeria, Egypt, Iraq, Kuwait, Lebanon-Syria, Lybia, Morocco, Oman, Palestine-Jordan, Qatar, Saudi Arabia, Sudan, Tunisia, UAE, Yemen}. Gender is labeled as a binary task with {male,female} tags.",
+      "LAMA+DINA Emotion detection. Alhuzali et al. BIBREF10 introduce LAMA, a dataset for Arabic emotion detection. They use a first-person seed phrase approach and extend work by Abdul-Mageed et al. BIBREF11 for emotion data collection from 6 to 8 emotion categories (i.e. anger, anticipation, disgust, fear, joy, sadness, surprise and trust). We use the combined LAMA+DINA corpus. It is split by the authors as 189,902 tweets training set, 910 as development, and 941 as test. In our experiment, we use only the training set for out MTL experiments.",
+      "Sentiment analysis in Arabic tweets. This dataset is a shared task on Kaggle by Motaz Saad . The corpus contains 58,751 Arabic tweets (46,940 training, and 11,811 test). The tweets are annotated with positive and negative labels based on an emoji lexicon."
+    ],
+    [
+      "We train a baseline GRU network with our iorny TRAIN data. This network has only one layer unidirectional GRU, with 500 unites and a linear, output layer. The input word tokens are embedded by the trainable word vectors which are initialized with a standard normal distribution, with $\\mu =0$, and $\\sigma =1$, i.e., $W \\sim N(0,1)$. We use Adam BIBREF12 with a fixed learning rate of $1e-3$ for optimization. For regularization, we use dropout BIBREF13 with a rate of 0.5 on the hidden layer. We set the maximum sequence sequence in our GRU model to 50 words, and use all 22,000 words of training set as vocabulary. We employ batch training with a batch size of 64 for this model. We run the network for 20 epochs and save the model at the end of each epoch, choosing the model that performs highest on DEV as our best model. We report our best result on DEV in Table TABREF22. Our best result is acquired with 12 epochs. As Table TABREF22 shows, the baseline obtains $accuracy=73.70\\%$ and $F_1=73.47$."
+    ],
+    [
+      "We use the BERT-Base Multilingual Cased model released by the authors BIBREF1 . The model is trained on 104 languages (including Arabic) with 12 layers, 768 hidden units each, 12 attention heads. The entire model has 110M parameters. The model has 119,547 shared WordPieces vocabulary, and was pre-trained on the entire Wikipedia for each language. For fine-tuning, we use a maximum sequence size of 50 tokens and a batch size of 32. We set the learning rate to $2e-5$ and train for 20 epochs. For single-task learning, we fine-tune BERT on the training set (i.e., TRAIN) of the irony task exclusively. We refer to this model as BERT-ST, ST standing for `single task.' As Table TABREF22 shows, BERT-ST unsurprisingly acquires better performance than the baseline GRU model. On accuracy, BERT-ST is 7.94% better than the baseline. BERT-ST obtains 81.62 $F_1$ which is 7.35 better than the baseline."
+    ],
+    [
+      "We follow the work of Liu et al. BIBREF8 for training an MTL BERT in that we fine-tune the afore-mentioned BERT-Base Multilingual Cased model with different tasks jointly. First, we fine-tune with the three tasks of author profiling and the irony task simultaneously. We refer to this model trained on the 4 tasks simply as BERT-MT4. BERT-MT5 refers to the model fine-tuned on the 3 author profiling tasks, the emotion task, and the irony task. We also refer to the model fine-tuned on all six tasks (adding the sentiment task mentioned earlier) as BERT-MT6. For MTL BERT, we use the same parameters as the single task BERT listed in the previous sub-section (i.e., Single-Task BERT). In Table TABREF22, we present the performance on the DEV set of only the irony detection task. We note that all the results of multitask learning with BERT are better than those with the single task BERT. The model trained on all six tasks obtains the best result, which is 2.23% accuracy and 2.25% $F_1$ higher than the single task BERT model."
+    ],
+    [
+      "Our irony data involves dialects such as Egyptian, Gulf, and Levantine, as we explained earlier. The BERT-Base Multilingual Cased model we used, however, was trained on Arabic Wikipedia, which is mostly MSA. We believe this dialect mismatch is sub-optimal. As Sun et al. BIBREF14 show, further pre-training with domain specific data can improve performance of a learner. Viewing dialects as constituting different domains, we turn to dialectal data to further pre-train BERT. Namely, we use 1M tweets randomly sampled from an in-house Twitter dataset to resume pre-training BERT before we fine-tune on the irony data. We use BERT-Base Multilingual Cased model as an initial checkpoint and pre-train on this 1M dataset with a learning rate of $2e-5$, for 10 epochs. Then, we fine-tune on MT5 (and then on MT6) with the new further-pre-trained BERT model. We refer to the new models as BERT-1M-MT5 and BERT-1M-MT6, respectively. As Table TABREF22 shows, BERT-1M-MT5 performs best: BERT-1M-MT5 obtains 84.37% accuracy (0.5% less than BERT-MT6) and 83.34 $F_1$ (0.47% less than BERT-MT6)."
+    ],
+    [
+      "For the shared task submission, we use the predictions of BERT-1M-MT5 as our first submitted system. Then, we concatenate our DEV and TRAIN data to compose a new training set (thus using all the training data released by organizers) to re-train BERT-1M-MT5 and BERT-MT6 with the same parameters. We use the predictions of these two models as our second and third submissions. Our second submission obtains 82.4 $F_1$ on the official test set, and ranks $4th$ on this shared task."
+    ],
+    [
+      "Multi-Task Learning. MTL has been effectively used to model several NLP problems. These include, for example, syntactic parsing BIBREF15, sequence labeling BIBREF16, BIBREF17, and text classification BIBREF18.",
+      "Irony in different languages. Irony detection has been investigated in various languages. For example, Hee et al. BIBREF19 propose two irony detection tasks in English tweets. Task A is a binary classification task (irony vs. non-irony), and Task B is multi-class identification of a specific type of irony from the set {verbal, situational, other-irony, non-ironic}. They use hashtags to automatically collect tweets that they manually annotate using a fine-grained annotation scheme. Participants in this competition construct models based on logistic regression and support vector machine (SVM) BIBREF20, XGBoost BIBREF21, convolutional neural networks (CNNs) BIBREF21, long short-term memory networks (LSTMs) BIBREF22, etc. For the Italian language, Cignarella et al. propose the IronTA shared task BIBREF23, and the best system BIBREF24 is a combination of bi-directional LSTMs, word $n$-grams, and affective lexicons. For Spanish, Ortega-Bueno1 et al. BIBREF25 introduce the IroSvA shared task, a binary classification task for tweets and news comments. The best-performing model on the task, BIBREF26, employs pre-trained Word2Vec, multi-head Transformer encoder and a global average pooling mechanism.",
+      "Irony in Arabic. Although Arabic is a widely spoken collection of languages ($\\sim $ 300 million native speakers) BIBREF27, BIBREF28, there has not been works on irony that we know of on the language. IDAT@FIRE2019 aims at bridging this gap. The closest works in Arabic are those focusing on other text classification tasks such as sentiment analysis BIBREF29, BIBREF30, BIBREF31, BIBREF32, emotion BIBREF10, and dialect identification BIBREF28, BIBREF33, BIBREF34, BIBREF35."
+    ],
+    [
+      "In this paper, we described our submissions to the Irony Detection in Arabic shared task (IDAT@FIRE2019). We presented how we acquire effective models using pre-trained BERT in a multi-task learning setting. We also showed the utility of viewing different varieties of Arabic as different domains by reporting better performance with models pre-trained with dialectal data rather than exclusively on MSA. Our multi-task model with domain-specific BERT ranks $4th$ in the official IDAT@FIRE2019 evaluation. The model has the advantage of being exclusively based on deep learning. In the future, we will investigate other multi-task learning architectures, and extend our work with semi-supervised methods."
+    ],
+    [
+      "We acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC), the Social Sciences Research Council of Canada (SSHRC), and Compute Canada (www.computecanada.ca)."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1003/instruction.md b/qasper-1003/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..0956d4ad384c2113197e09103152acc171113531
--- /dev/null
+++ b/qasper-1003/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Multi-Task Bidirectional Transformer Representations for Irony Detection
+
+Question: What in-domain data is used to continue pre-training?
\ No newline at end of file
diff --git a/qasper-1004/instruction.md b/qasper-1004/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..027f9b6850366d0275dccf9df58290a17c193ade
--- /dev/null
+++ b/qasper-1004/instruction.md
@@ -0,0 +1,84 @@
+Name of Paper: Multi-Task Bidirectional Transformer Representations for Irony Detection
+
+Question: What dialect is used in the Google BERT model and what is used in the task data?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Methods",
+    "Methods ::: GRU",
+    "Methods ::: BERT",
+    "Methods ::: Multi-task Learning",
+    "Data",
+    "Models ::: GRU",
+    "Models ::: Single-Task BERT",
+    "Models ::: Multi-Task BERT",
+    "Models ::: In-Domain Pre-Training",
+    "Models ::: IDAT@FIRE2019 Submission",
+    "Related Work",
+    "Conclusion",
+    "Acknowledgement"
+  ],
+  "paragraphs": [
+    [
+      "The proliferation of social media has provided a locus for use, and thereby collection, of figurative and creative language data, including irony BIBREF0. According to the Merriam-Webster online dictionary, irony refers to \u201cthe use of word to express something other than and especially the opposite of the literal meaning.\" A complex, controversial, and intriguing linguistic phenomenon, irony has been studied in disciplines such as linguistics, philosophy, and rhetoric. Irony detection also has implications for several NLP tasks such as sentiment analysis, hate speech detection, fake news detection, etc BIBREF0. Hence, automatic irony detection can potentially improve systems designed for each of these tasks. In this paper, we focus on learning irony. More specifically, we report our work submitted to the FIRE 2019 Arabic irony detection task (IDAT@FIRE2019). We focus our energy on an important angle of the problem\u2013the small size of training data.",
+      "Deep learning is the most successful under supervised conditions with large amounts of training data (tens-to-hundreds of thousands of examples). For most real-world tasks, we hard to obtain labeled data. Hence, it is highly desirable to eliminate, or at least reduce, dependence on supervision. In NLP, pre-training language models on unlabeled data has emerged as a successful approach for improving model performance. In particular, the pre-trained multilingual Bidirectional Encoder Representations from Transformers (BERT) BIBREF1 was introduced to learn language regularities from unlabeled data. Multi-task learning (MTL) is another approach that helps achieve inductive transfer between various tasks. More specifically, MTL leverages information from one or more source tasks to improve a target task BIBREF2, BIBREF3. In this work, we introduce Transformer representations (BERT) in an MTL setting to address the data bottleneck in IDAT@FIRE2019. To show the utility of BERT, we compare to a simpler model with gated recurrent units (GRU) in a single task setting. To identify the utility, or lack thereof, of MTL BERT, we compare to a single task BERT model. For MTL BERT, we train on a number of tasks simultaneously. Tasks we train on are sentiment analysis, gender detection, age detection, dialect identification, and emotion detection.",
+      "Another problem we face is that the BERT model released by Google is trained only on Arabic Wikipedia, which is almost exclusively Modern Standard Arabic (MSA). This introduces a language variety mismatch due to the irony data involving a number of dialects that come from the Twitter domain. To mitigate this issue, we further pre-train BERT on an in-house dialectal Twitter dataset, showing the utility of this measure. To summarize, we make the following contributions:",
+      "In the context of the Arabic irony task, we show how a small-sized labeled data setting can be mitigated by training models in a multi-task learning setup.",
+      "We view different varieties of Arabic as different domains, and hence introduce a simple, yet effective, `in-domain' training measure where we further pre-train BERT on a dataset closer to task domain (in that it involves dialectal tweet data)."
+    ],
+    [
+      ""
+    ],
+    [
+      "For our baseline, we use gated recurrent units (GRU) BIBREF4, a simplification of long-short term memory (LSTM) BIBREF5, which in turn is a variation of recurrent neural networks (RNNs). A GRU learns based on the following:",
+      "where the update state $\\textbf {\\textit {z}}^{(t)}$ decides how much the unit updates its content:",
+      "where W and U are weight matrices. The candidate activation makes use of a reset gate $\\textbf {\\textit {r}}^{(t)}$:",
+      "where $\\odot $ is a Hadamard product (element-wise multiplication). When its value is close to zero, the reset gate allows the unit to forget the previously computed state. The reset gate $\\textbf {\\textit {r}}^{(t)}$ is computed as follows:"
+    ],
+    [
+      "BERT BIBREF1 is based on the Transformer BIBREF6, a network architecture that depends solely on encoder-decoder attention. The Transformer attention employs a function operating on queries, keys, and values. This attention function maps a query and a set of key-value pairs to an output, where the output is a weighted sum of the values. Encoder of the Transformer in BIBREF6 has 6 attention layers, each of which is composed of two sub-layers: (1) multi-head attention where queries, keys, and values are projected h times into linear, learned projections and ultimately concatenated; and (2) fully-connected feed-forward network (FFN) that is applied to each position separately and identically. Decoder of the Transformer also employs 6 identical layers, yet with an extra sub-layer that performs multi-head attention over the encoder stack. The architecture of BERT BIBREF1 is a multi-layer bidirectional Transformer encoder BIBREF6. It uses masked language models to enable pre-trained deep bidirectional representations, in addition to a binary next sentence prediction task captures context (i.e., sentence relationships). More information about BERT can be found in BIBREF1."
+    ],
+    [
+      "In multi-task learning (MTL), a learner uses a number of (usually relevant) tasks to improve performance on a target task BIBREF2, BIBREF3. The MTL setup enables the learner to use cues from various tasks to improve the performance on the target task. MTL also usually helps regularize the model since the learner needs to find representations that are not specific to a single task, but rather more general. Supervised learning with deep neural networks requires large amounts of labeled data, which is not always available. By employing data from additional tasks, MTL thus practically augments training data to alleviate need for large labeled data. Many researchers achieve state-of-the-art results by employing MTL in supervised learning settings BIBREF7, BIBREF8. In specific, BERT was successfully used with MTL. Hence, we employ multi-task BERT (following BIBREF8). For our training, we use the same pre-trained BERT-Base Multilingual Cased model as the initial checkpoint. For this MTL pre-training of BERT, we use the same afore-mentioned single-task BERT parameters. We now describe our data."
+    ],
+    [
+      "The shared task dataset contains 5,030 tweets related to different political issues and events in the Middle East taking place between 2011 and 2018. Tweets are collected using pre-defined keywords (i.e. targeted political figures or events) and the positive class involves ironic hashtags such as #sokhria, #tahakoum, and #maskhara (Arabic variants for \u201cirony\"). Duplicates, retweets, and non-intelligible tweets are removed by organizers. Tweets involve both MSA as well as dialects at various degrees of granularity such as Egyptian, Gulf, and Levantine.",
+      "IDAT@FIRE2019 is set up as a binary classification task where tweets are assigned labels from the set {ironic, non-ironic}. A total of 4,024 tweets were released by organizers as training data. In addition, 1,006 tweets were used by organizers as as test data. Test labels were not release; and teams were expected to submit the predictions produced by their systems on the test split. For our models, we split the 4,024 released training data into 90% TRAIN ($n$=3,621 tweets; `ironic'=1,882 and `non-ironic'=1,739) and 10% DEV ($n$=403 tweets; `ironic'=209 and `non-ironic'=194). We train our models on TRAIN, and evaluate on DEV.",
+      "Our multi-task BERT models involve six different Arabic classification tasks. We briefly introduce the data for these tasks here:",
+      "Author profiling and deception detection in Arabic (APDA). BIBREF9 . From APDA, we only use the corpus of author profiling (which includes the three profiling tasks of age, gender, and variety). The organizers of APDA provide 225,000 tweets as training data. Each tweet is labelled with three tags (one for each task). To develop our models, we split the training data into 90% training set ($n$=202,500 tweets) and 10% development set ($n$=22,500 tweets). With regard to age, authors consider tweets of three classes: {Under 25, Between 25 and 34, and Above 35}. For the Arabic varieties, they consider the following fifteen classes: {Algeria, Egypt, Iraq, Kuwait, Lebanon-Syria, Lybia, Morocco, Oman, Palestine-Jordan, Qatar, Saudi Arabia, Sudan, Tunisia, UAE, Yemen}. Gender is labeled as a binary task with {male,female} tags.",
+      "LAMA+DINA Emotion detection. Alhuzali et al. BIBREF10 introduce LAMA, a dataset for Arabic emotion detection. They use a first-person seed phrase approach and extend work by Abdul-Mageed et al. BIBREF11 for emotion data collection from 6 to 8 emotion categories (i.e. anger, anticipation, disgust, fear, joy, sadness, surprise and trust). We use the combined LAMA+DINA corpus. It is split by the authors as 189,902 tweets training set, 910 as development, and 941 as test. In our experiment, we use only the training set for out MTL experiments.",
+      "Sentiment analysis in Arabic tweets. This dataset is a shared task on Kaggle by Motaz Saad . The corpus contains 58,751 Arabic tweets (46,940 training, and 11,811 test). The tweets are annotated with positive and negative labels based on an emoji lexicon."
+    ],
+    [
+      "We train a baseline GRU network with our iorny TRAIN data. This network has only one layer unidirectional GRU, with 500 unites and a linear, output layer. The input word tokens are embedded by the trainable word vectors which are initialized with a standard normal distribution, with $\\mu =0$, and $\\sigma =1$, i.e., $W \\sim N(0,1)$. We use Adam BIBREF12 with a fixed learning rate of $1e-3$ for optimization. For regularization, we use dropout BIBREF13 with a rate of 0.5 on the hidden layer. We set the maximum sequence sequence in our GRU model to 50 words, and use all 22,000 words of training set as vocabulary. We employ batch training with a batch size of 64 for this model. We run the network for 20 epochs and save the model at the end of each epoch, choosing the model that performs highest on DEV as our best model. We report our best result on DEV in Table TABREF22. Our best result is acquired with 12 epochs. As Table TABREF22 shows, the baseline obtains $accuracy=73.70\\%$ and $F_1=73.47$."
+    ],
+    [
+      "We use the BERT-Base Multilingual Cased model released by the authors BIBREF1 . The model is trained on 104 languages (including Arabic) with 12 layers, 768 hidden units each, 12 attention heads. The entire model has 110M parameters. The model has 119,547 shared WordPieces vocabulary, and was pre-trained on the entire Wikipedia for each language. For fine-tuning, we use a maximum sequence size of 50 tokens and a batch size of 32. We set the learning rate to $2e-5$ and train for 20 epochs. For single-task learning, we fine-tune BERT on the training set (i.e., TRAIN) of the irony task exclusively. We refer to this model as BERT-ST, ST standing for `single task.' As Table TABREF22 shows, BERT-ST unsurprisingly acquires better performance than the baseline GRU model. On accuracy, BERT-ST is 7.94% better than the baseline. BERT-ST obtains 81.62 $F_1$ which is 7.35 better than the baseline."
+    ],
+    [
+      "We follow the work of Liu et al. BIBREF8 for training an MTL BERT in that we fine-tune the afore-mentioned BERT-Base Multilingual Cased model with different tasks jointly. First, we fine-tune with the three tasks of author profiling and the irony task simultaneously. We refer to this model trained on the 4 tasks simply as BERT-MT4. BERT-MT5 refers to the model fine-tuned on the 3 author profiling tasks, the emotion task, and the irony task. We also refer to the model fine-tuned on all six tasks (adding the sentiment task mentioned earlier) as BERT-MT6. For MTL BERT, we use the same parameters as the single task BERT listed in the previous sub-section (i.e., Single-Task BERT). In Table TABREF22, we present the performance on the DEV set of only the irony detection task. We note that all the results of multitask learning with BERT are better than those with the single task BERT. The model trained on all six tasks obtains the best result, which is 2.23% accuracy and 2.25% $F_1$ higher than the single task BERT model."
+    ],
+    [
+      "Our irony data involves dialects such as Egyptian, Gulf, and Levantine, as we explained earlier. The BERT-Base Multilingual Cased model we used, however, was trained on Arabic Wikipedia, which is mostly MSA. We believe this dialect mismatch is sub-optimal. As Sun et al. BIBREF14 show, further pre-training with domain specific data can improve performance of a learner. Viewing dialects as constituting different domains, we turn to dialectal data to further pre-train BERT. Namely, we use 1M tweets randomly sampled from an in-house Twitter dataset to resume pre-training BERT before we fine-tune on the irony data. We use BERT-Base Multilingual Cased model as an initial checkpoint and pre-train on this 1M dataset with a learning rate of $2e-5$, for 10 epochs. Then, we fine-tune on MT5 (and then on MT6) with the new further-pre-trained BERT model. We refer to the new models as BERT-1M-MT5 and BERT-1M-MT6, respectively. As Table TABREF22 shows, BERT-1M-MT5 performs best: BERT-1M-MT5 obtains 84.37% accuracy (0.5% less than BERT-MT6) and 83.34 $F_1$ (0.47% less than BERT-MT6)."
+    ],
+    [
+      "For the shared task submission, we use the predictions of BERT-1M-MT5 as our first submitted system. Then, we concatenate our DEV and TRAIN data to compose a new training set (thus using all the training data released by organizers) to re-train BERT-1M-MT5 and BERT-MT6 with the same parameters. We use the predictions of these two models as our second and third submissions. Our second submission obtains 82.4 $F_1$ on the official test set, and ranks $4th$ on this shared task."
+    ],
+    [
+      "Multi-Task Learning. MTL has been effectively used to model several NLP problems. These include, for example, syntactic parsing BIBREF15, sequence labeling BIBREF16, BIBREF17, and text classification BIBREF18.",
+      "Irony in different languages. Irony detection has been investigated in various languages. For example, Hee et al. BIBREF19 propose two irony detection tasks in English tweets. Task A is a binary classification task (irony vs. non-irony), and Task B is multi-class identification of a specific type of irony from the set {verbal, situational, other-irony, non-ironic}. They use hashtags to automatically collect tweets that they manually annotate using a fine-grained annotation scheme. Participants in this competition construct models based on logistic regression and support vector machine (SVM) BIBREF20, XGBoost BIBREF21, convolutional neural networks (CNNs) BIBREF21, long short-term memory networks (LSTMs) BIBREF22, etc. For the Italian language, Cignarella et al. propose the IronTA shared task BIBREF23, and the best system BIBREF24 is a combination of bi-directional LSTMs, word $n$-grams, and affective lexicons. For Spanish, Ortega-Bueno1 et al. BIBREF25 introduce the IroSvA shared task, a binary classification task for tweets and news comments. The best-performing model on the task, BIBREF26, employs pre-trained Word2Vec, multi-head Transformer encoder and a global average pooling mechanism.",
+      "Irony in Arabic. Although Arabic is a widely spoken collection of languages ($\\sim $ 300 million native speakers) BIBREF27, BIBREF28, there has not been works on irony that we know of on the language. IDAT@FIRE2019 aims at bridging this gap. The closest works in Arabic are those focusing on other text classification tasks such as sentiment analysis BIBREF29, BIBREF30, BIBREF31, BIBREF32, emotion BIBREF10, and dialect identification BIBREF28, BIBREF33, BIBREF34, BIBREF35."
+    ],
+    [
+      "In this paper, we described our submissions to the Irony Detection in Arabic shared task (IDAT@FIRE2019). We presented how we acquire effective models using pre-trained BERT in a multi-task learning setting. We also showed the utility of viewing different varieties of Arabic as different domains by reporting better performance with models pre-trained with dialectal data rather than exclusively on MSA. Our multi-task model with domain-specific BERT ranks $4th$ in the official IDAT@FIRE2019 evaluation. The model has the advantage of being exclusively based on deep learning. In the future, we will investigate other multi-task learning architectures, and extend our work with semi-supervised methods."
+    ],
+    [
+      "We acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC), the Social Sciences Research Council of Canada (SSHRC), and Compute Canada (www.computecanada.ca)."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1005/instruction.md b/qasper-1005/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..db70c1bf33cba72fa7ef31a24ab918c65545282e
--- /dev/null
+++ b/qasper-1005/instruction.md
@@ -0,0 +1,84 @@
+Name of Paper: Multi-Task Bidirectional Transformer Representations for Irony Detection
+
+Question: What are the tasks used in the mulit-task learning setup?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Methods",
+    "Methods ::: GRU",
+    "Methods ::: BERT",
+    "Methods ::: Multi-task Learning",
+    "Data",
+    "Models ::: GRU",
+    "Models ::: Single-Task BERT",
+    "Models ::: Multi-Task BERT",
+    "Models ::: In-Domain Pre-Training",
+    "Models ::: IDAT@FIRE2019 Submission",
+    "Related Work",
+    "Conclusion",
+    "Acknowledgement"
+  ],
+  "paragraphs": [
+    [
+      "The proliferation of social media has provided a locus for use, and thereby collection, of figurative and creative language data, including irony BIBREF0. According to the Merriam-Webster online dictionary, irony refers to \u201cthe use of word to express something other than and especially the opposite of the literal meaning.\" A complex, controversial, and intriguing linguistic phenomenon, irony has been studied in disciplines such as linguistics, philosophy, and rhetoric. Irony detection also has implications for several NLP tasks such as sentiment analysis, hate speech detection, fake news detection, etc BIBREF0. Hence, automatic irony detection can potentially improve systems designed for each of these tasks. In this paper, we focus on learning irony. More specifically, we report our work submitted to the FIRE 2019 Arabic irony detection task (IDAT@FIRE2019). We focus our energy on an important angle of the problem\u2013the small size of training data.",
+      "Deep learning is the most successful under supervised conditions with large amounts of training data (tens-to-hundreds of thousands of examples). For most real-world tasks, we hard to obtain labeled data. Hence, it is highly desirable to eliminate, or at least reduce, dependence on supervision. In NLP, pre-training language models on unlabeled data has emerged as a successful approach for improving model performance. In particular, the pre-trained multilingual Bidirectional Encoder Representations from Transformers (BERT) BIBREF1 was introduced to learn language regularities from unlabeled data. Multi-task learning (MTL) is another approach that helps achieve inductive transfer between various tasks. More specifically, MTL leverages information from one or more source tasks to improve a target task BIBREF2, BIBREF3. In this work, we introduce Transformer representations (BERT) in an MTL setting to address the data bottleneck in IDAT@FIRE2019. To show the utility of BERT, we compare to a simpler model with gated recurrent units (GRU) in a single task setting. To identify the utility, or lack thereof, of MTL BERT, we compare to a single task BERT model. For MTL BERT, we train on a number of tasks simultaneously. Tasks we train on are sentiment analysis, gender detection, age detection, dialect identification, and emotion detection.",
+      "Another problem we face is that the BERT model released by Google is trained only on Arabic Wikipedia, which is almost exclusively Modern Standard Arabic (MSA). This introduces a language variety mismatch due to the irony data involving a number of dialects that come from the Twitter domain. To mitigate this issue, we further pre-train BERT on an in-house dialectal Twitter dataset, showing the utility of this measure. To summarize, we make the following contributions:",
+      "In the context of the Arabic irony task, we show how a small-sized labeled data setting can be mitigated by training models in a multi-task learning setup.",
+      "We view different varieties of Arabic as different domains, and hence introduce a simple, yet effective, `in-domain' training measure where we further pre-train BERT on a dataset closer to task domain (in that it involves dialectal tweet data)."
+    ],
+    [
+      ""
+    ],
+    [
+      "For our baseline, we use gated recurrent units (GRU) BIBREF4, a simplification of long-short term memory (LSTM) BIBREF5, which in turn is a variation of recurrent neural networks (RNNs). A GRU learns based on the following:",
+      "where the update state $\\textbf {\\textit {z}}^{(t)}$ decides how much the unit updates its content:",
+      "where W and U are weight matrices. The candidate activation makes use of a reset gate $\\textbf {\\textit {r}}^{(t)}$:",
+      "where $\\odot $ is a Hadamard product (element-wise multiplication). When its value is close to zero, the reset gate allows the unit to forget the previously computed state. The reset gate $\\textbf {\\textit {r}}^{(t)}$ is computed as follows:"
+    ],
+    [
+      "BERT BIBREF1 is based on the Transformer BIBREF6, a network architecture that depends solely on encoder-decoder attention. The Transformer attention employs a function operating on queries, keys, and values. This attention function maps a query and a set of key-value pairs to an output, where the output is a weighted sum of the values. Encoder of the Transformer in BIBREF6 has 6 attention layers, each of which is composed of two sub-layers: (1) multi-head attention where queries, keys, and values are projected h times into linear, learned projections and ultimately concatenated; and (2) fully-connected feed-forward network (FFN) that is applied to each position separately and identically. Decoder of the Transformer also employs 6 identical layers, yet with an extra sub-layer that performs multi-head attention over the encoder stack. The architecture of BERT BIBREF1 is a multi-layer bidirectional Transformer encoder BIBREF6. It uses masked language models to enable pre-trained deep bidirectional representations, in addition to a binary next sentence prediction task captures context (i.e., sentence relationships). More information about BERT can be found in BIBREF1."
+    ],
+    [
+      "In multi-task learning (MTL), a learner uses a number of (usually relevant) tasks to improve performance on a target task BIBREF2, BIBREF3. The MTL setup enables the learner to use cues from various tasks to improve the performance on the target task. MTL also usually helps regularize the model since the learner needs to find representations that are not specific to a single task, but rather more general. Supervised learning with deep neural networks requires large amounts of labeled data, which is not always available. By employing data from additional tasks, MTL thus practically augments training data to alleviate need for large labeled data. Many researchers achieve state-of-the-art results by employing MTL in supervised learning settings BIBREF7, BIBREF8. In specific, BERT was successfully used with MTL. Hence, we employ multi-task BERT (following BIBREF8). For our training, we use the same pre-trained BERT-Base Multilingual Cased model as the initial checkpoint. For this MTL pre-training of BERT, we use the same afore-mentioned single-task BERT parameters. We now describe our data."
+    ],
+    [
+      "The shared task dataset contains 5,030 tweets related to different political issues and events in the Middle East taking place between 2011 and 2018. Tweets are collected using pre-defined keywords (i.e. targeted political figures or events) and the positive class involves ironic hashtags such as #sokhria, #tahakoum, and #maskhara (Arabic variants for \u201cirony\"). Duplicates, retweets, and non-intelligible tweets are removed by organizers. Tweets involve both MSA as well as dialects at various degrees of granularity such as Egyptian, Gulf, and Levantine.",
+      "IDAT@FIRE2019 is set up as a binary classification task where tweets are assigned labels from the set {ironic, non-ironic}. A total of 4,024 tweets were released by organizers as training data. In addition, 1,006 tweets were used by organizers as as test data. Test labels were not release; and teams were expected to submit the predictions produced by their systems on the test split. For our models, we split the 4,024 released training data into 90% TRAIN ($n$=3,621 tweets; `ironic'=1,882 and `non-ironic'=1,739) and 10% DEV ($n$=403 tweets; `ironic'=209 and `non-ironic'=194). We train our models on TRAIN, and evaluate on DEV.",
+      "Our multi-task BERT models involve six different Arabic classification tasks. We briefly introduce the data for these tasks here:",
+      "Author profiling and deception detection in Arabic (APDA). BIBREF9 . From APDA, we only use the corpus of author profiling (which includes the three profiling tasks of age, gender, and variety). The organizers of APDA provide 225,000 tweets as training data. Each tweet is labelled with three tags (one for each task). To develop our models, we split the training data into 90% training set ($n$=202,500 tweets) and 10% development set ($n$=22,500 tweets). With regard to age, authors consider tweets of three classes: {Under 25, Between 25 and 34, and Above 35}. For the Arabic varieties, they consider the following fifteen classes: {Algeria, Egypt, Iraq, Kuwait, Lebanon-Syria, Lybia, Morocco, Oman, Palestine-Jordan, Qatar, Saudi Arabia, Sudan, Tunisia, UAE, Yemen}. Gender is labeled as a binary task with {male,female} tags.",
+      "LAMA+DINA Emotion detection. Alhuzali et al. BIBREF10 introduce LAMA, a dataset for Arabic emotion detection. They use a first-person seed phrase approach and extend work by Abdul-Mageed et al. BIBREF11 for emotion data collection from 6 to 8 emotion categories (i.e. anger, anticipation, disgust, fear, joy, sadness, surprise and trust). We use the combined LAMA+DINA corpus. It is split by the authors as 189,902 tweets training set, 910 as development, and 941 as test. In our experiment, we use only the training set for out MTL experiments.",
+      "Sentiment analysis in Arabic tweets. This dataset is a shared task on Kaggle by Motaz Saad . The corpus contains 58,751 Arabic tweets (46,940 training, and 11,811 test). The tweets are annotated with positive and negative labels based on an emoji lexicon."
+    ],
+    [
+      "We train a baseline GRU network with our iorny TRAIN data. This network has only one layer unidirectional GRU, with 500 unites and a linear, output layer. The input word tokens are embedded by the trainable word vectors which are initialized with a standard normal distribution, with $\\mu =0$, and $\\sigma =1$, i.e., $W \\sim N(0,1)$. We use Adam BIBREF12 with a fixed learning rate of $1e-3$ for optimization. For regularization, we use dropout BIBREF13 with a rate of 0.5 on the hidden layer. We set the maximum sequence sequence in our GRU model to 50 words, and use all 22,000 words of training set as vocabulary. We employ batch training with a batch size of 64 for this model. We run the network for 20 epochs and save the model at the end of each epoch, choosing the model that performs highest on DEV as our best model. We report our best result on DEV in Table TABREF22. Our best result is acquired with 12 epochs. As Table TABREF22 shows, the baseline obtains $accuracy=73.70\\%$ and $F_1=73.47$."
+    ],
+    [
+      "We use the BERT-Base Multilingual Cased model released by the authors BIBREF1 . The model is trained on 104 languages (including Arabic) with 12 layers, 768 hidden units each, 12 attention heads. The entire model has 110M parameters. The model has 119,547 shared WordPieces vocabulary, and was pre-trained on the entire Wikipedia for each language. For fine-tuning, we use a maximum sequence size of 50 tokens and a batch size of 32. We set the learning rate to $2e-5$ and train for 20 epochs. For single-task learning, we fine-tune BERT on the training set (i.e., TRAIN) of the irony task exclusively. We refer to this model as BERT-ST, ST standing for `single task.' As Table TABREF22 shows, BERT-ST unsurprisingly acquires better performance than the baseline GRU model. On accuracy, BERT-ST is 7.94% better than the baseline. BERT-ST obtains 81.62 $F_1$ which is 7.35 better than the baseline."
+    ],
+    [
+      "We follow the work of Liu et al. BIBREF8 for training an MTL BERT in that we fine-tune the afore-mentioned BERT-Base Multilingual Cased model with different tasks jointly. First, we fine-tune with the three tasks of author profiling and the irony task simultaneously. We refer to this model trained on the 4 tasks simply as BERT-MT4. BERT-MT5 refers to the model fine-tuned on the 3 author profiling tasks, the emotion task, and the irony task. We also refer to the model fine-tuned on all six tasks (adding the sentiment task mentioned earlier) as BERT-MT6. For MTL BERT, we use the same parameters as the single task BERT listed in the previous sub-section (i.e., Single-Task BERT). In Table TABREF22, we present the performance on the DEV set of only the irony detection task. We note that all the results of multitask learning with BERT are better than those with the single task BERT. The model trained on all six tasks obtains the best result, which is 2.23% accuracy and 2.25% $F_1$ higher than the single task BERT model."
+    ],
+    [
+      "Our irony data involves dialects such as Egyptian, Gulf, and Levantine, as we explained earlier. The BERT-Base Multilingual Cased model we used, however, was trained on Arabic Wikipedia, which is mostly MSA. We believe this dialect mismatch is sub-optimal. As Sun et al. BIBREF14 show, further pre-training with domain specific data can improve performance of a learner. Viewing dialects as constituting different domains, we turn to dialectal data to further pre-train BERT. Namely, we use 1M tweets randomly sampled from an in-house Twitter dataset to resume pre-training BERT before we fine-tune on the irony data. We use BERT-Base Multilingual Cased model as an initial checkpoint and pre-train on this 1M dataset with a learning rate of $2e-5$, for 10 epochs. Then, we fine-tune on MT5 (and then on MT6) with the new further-pre-trained BERT model. We refer to the new models as BERT-1M-MT5 and BERT-1M-MT6, respectively. As Table TABREF22 shows, BERT-1M-MT5 performs best: BERT-1M-MT5 obtains 84.37% accuracy (0.5% less than BERT-MT6) and 83.34 $F_1$ (0.47% less than BERT-MT6)."
+    ],
+    [
+      "For the shared task submission, we use the predictions of BERT-1M-MT5 as our first submitted system. Then, we concatenate our DEV and TRAIN data to compose a new training set (thus using all the training data released by organizers) to re-train BERT-1M-MT5 and BERT-MT6 with the same parameters. We use the predictions of these two models as our second and third submissions. Our second submission obtains 82.4 $F_1$ on the official test set, and ranks $4th$ on this shared task."
+    ],
+    [
+      "Multi-Task Learning. MTL has been effectively used to model several NLP problems. These include, for example, syntactic parsing BIBREF15, sequence labeling BIBREF16, BIBREF17, and text classification BIBREF18.",
+      "Irony in different languages. Irony detection has been investigated in various languages. For example, Hee et al. BIBREF19 propose two irony detection tasks in English tweets. Task A is a binary classification task (irony vs. non-irony), and Task B is multi-class identification of a specific type of irony from the set {verbal, situational, other-irony, non-ironic}. They use hashtags to automatically collect tweets that they manually annotate using a fine-grained annotation scheme. Participants in this competition construct models based on logistic regression and support vector machine (SVM) BIBREF20, XGBoost BIBREF21, convolutional neural networks (CNNs) BIBREF21, long short-term memory networks (LSTMs) BIBREF22, etc. For the Italian language, Cignarella et al. propose the IronTA shared task BIBREF23, and the best system BIBREF24 is a combination of bi-directional LSTMs, word $n$-grams, and affective lexicons. For Spanish, Ortega-Bueno1 et al. BIBREF25 introduce the IroSvA shared task, a binary classification task for tweets and news comments. The best-performing model on the task, BIBREF26, employs pre-trained Word2Vec, multi-head Transformer encoder and a global average pooling mechanism.",
+      "Irony in Arabic. Although Arabic is a widely spoken collection of languages ($\\sim $ 300 million native speakers) BIBREF27, BIBREF28, there has not been works on irony that we know of on the language. IDAT@FIRE2019 aims at bridging this gap. The closest works in Arabic are those focusing on other text classification tasks such as sentiment analysis BIBREF29, BIBREF30, BIBREF31, BIBREF32, emotion BIBREF10, and dialect identification BIBREF28, BIBREF33, BIBREF34, BIBREF35."
+    ],
+    [
+      "In this paper, we described our submissions to the Irony Detection in Arabic shared task (IDAT@FIRE2019). We presented how we acquire effective models using pre-trained BERT in a multi-task learning setting. We also showed the utility of viewing different varieties of Arabic as different domains by reporting better performance with models pre-trained with dialectal data rather than exclusively on MSA. Our multi-task model with domain-specific BERT ranks $4th$ in the official IDAT@FIRE2019 evaluation. The model has the advantage of being exclusively based on deep learning. In the future, we will investigate other multi-task learning architectures, and extend our work with semi-supervised methods."
+    ],
+    [
+      "We acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC), the Social Sciences Research Council of Canada (SSHRC), and Compute Canada (www.computecanada.ca)."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1022/instruction.md b/qasper-1022/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..4da83cfd56db4e391c1c5c9429419f7571952c18
--- /dev/null
+++ b/qasper-1022/instruction.md
@@ -0,0 +1,70 @@
+Name of Paper: Assessing the Efficacy of Clinical Sentiment Analysis and Topic Extraction in Psychiatric Readmission Risk Prediction
+
+Question: How do they incorporate sentiment analysis?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction and Related Work",
+    "Data",
+    "Feature Extraction",
+    "Feature Extraction ::: Structured Features",
+    "Feature Extraction ::: Unstructured Features",
+    "Experiments and Results",
+    "Conclusions",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "Psychotic disorders affect approximately 2.5-4% of the population BIBREF0 BIBREF1. They are one of the leading causes of disability worldwide BIBREF2 and are a frequent cause of inpatient readmission after discharge BIBREF3. Readmissions are disruptive for patients and families, and are a key driver of rising healthcare costs BIBREF4 BIBREF5. Assessing readmission risk is therefore critically needed, as it can help inform the selection of treatment interventions and implement preventive measures.",
+      "Predicting hospital readmission risk is, however, a complex endeavour across all medical fields. Prior work in readmission risk prediction has used structured data (such as medical comorbidity, prior hospitalizations, sociodemographic factors, functional status, physiological variables, etc) extracted from patients' charts BIBREF6. NLP-based prediction models that extract unstructured data from EHR have also been developed with some success in other medical fields BIBREF7. In Psychiatry, due to the unique characteristics of medical record content (highly varied and context-sensitive vocabulary, abundance of multiword expressions, etc), NLP-based approaches have seldom been applied BIBREF8, BIBREF9, BIBREF10 and strategies to study readmission risk factors primarily rely on clinical observation and manual review BIBREF11 BIBREF12, which is effort-intensive, and does not scale well.",
+      "In this paper we aim to assess the suitability of using NLP-based features like clinical sentiment analysis and topic extraction to predict 30-day readmission risk in psychiatry patients. We begin by describing the EHR corpus that was created using in-house data to train and evaluate our models. We then present the NLP pipeline for feature extraction that was used to parse the EHRs in our corpus. Finally, we compare the performances of our model when using only structured clinical variables and when incorporating features derived from free-text narratives."
+    ],
+    [
+      "The corpus consists of a collection of 2,346 clinical notes (admission notes, progress notes, and discharge summaries), which amounts to 2,372,323 tokens in total (an average of 1,011 tokens per note). All the notes were written in English and extracted from the EHRs of 183 psychosis patients from McLean Psychiatric Hospital in Belmont, MA, all of whom had in their history at least one instance of 30-day readmission.",
+      "The age of the patients ranged from 20 to 67 (mean = 26.65, standard deviation = 8.73). 51% of the patients were male. The number of admissions per patient ranged from 2 to 21 (mean = 4, standard deviation = 2.85). Each admission contained on average 4.25 notes and 4,298 tokens. In total, the corpus contains 552 admissions, and 280 of those (50%) resulted in early readmissions."
+    ],
+    [
+      "The readmission risk prediction task was performed at the admission level. An admission consists of a collection of all the clinical notes for a given patient written by medical personnel between inpatient admission and discharge. Every admission was labeled as either `readmitted' (i.e. the patient was readmitted within the next 30 days of discharge) or `not readmitted'. Therefore, the classification task consists of creating a single feature representation of all the clinical notes belonging to one admission, plus the past medical history and demographic information of the patient, and establishing whether that admission will be followed by a 30-day readmission or not.",
+      "45 clinically interpretable features per admission were extracted as inputs to the readmission risk classifier. These features can be grouped into three categories (See Table TABREF5 for complete list of features):",
+      "Sociodemographics: gender, age, marital status, etc.",
+      "Past medical history: number of previous admissions, history of suicidality, average length of stay (up until that admission), etc.",
+      "Information from the current admission: length of stay (LOS), suicidal risk, number and length of notes, time of discharge, evaluation scores, etc.",
+      "The Current Admission feature group has the most number of features, with 29 features included in this group alone. These features can be further stratified into two groups: `structured' clinical features and `unstructured' clinical features."
+    ],
+    [
+      "Structure features are features that were identified on the EHR using regular expression matching and include rating scores that have been reported in the psychiatric literature as correlated with increased readmission risk, such as Global Assessment of Functioning, Insight and Compliance:",
+      "Global Assessment of Functioning (GAF): The psychosocial functioning of the patient ranging from 100 (extremely high functioning) to 1 (severely impaired) BIBREF13.",
+      "Insight: The degree to which the patient recognizes and accepts his/her illness (either Good, Fair or Poor).",
+      "Compliance: The ability of the patient to comply with medication and to follow medical advice (either Yes, Partial, or None).",
+      "These features are widely-used in clinical practice and evaluate the general state and prognosis of the patient during the patient's evaluation."
+    ],
+    [
+      "Unstructured features aim to capture the state of the patient in relation to seven risk factor domains (Appearance, Thought Process, Thought Content, Interpersonal, Substance Use, Occupation, and Mood) from the free-text narratives on the EHR. These seven domains have been identified as associated with readmission risk in prior work BIBREF14.",
+      "These unstructured features include: 1) the relative number of sentences in the admission notes that involve each risk factor domain (out of total number of sentences within the admission) and 2) clinical sentiment scores for each of these risk factor domains, i.e. sentiment scores that evaluate the patient\u2019s psychosocial functioning level (positive, negative, or neutral) with respect to each of these risk factor domain.",
+      "These sentiment scores were automatically obtained through the topic extraction and sentiment analysis pipeline introduced in our prior work BIBREF15 and pretrained on in-house psychiatric EHR text. In our paper we also showed that this automatic pipeline achieves reasonably strong F-scores, with an overall performance of 0.828 F1 for the topic extraction component and 0.5 F1 on the clinical sentiment component.",
+      "The clinical sentiment scores are computed for every note in the admission. Figure FIGREF4 details the data analysis pipeline that is employed for the feature extraction.",
+      "First, a multilayer perceptron (MLP) classifier is trained on EHR sentences (8,000,000 sentences consisting of 340,000,000 tokens) that are extracted from the Research Patient Data Registry (RPDR), a centralized regional data repository of clinical data from all institutions in the Partners HealthCare network. These sentences are automatically identified and labeled for their respective risk factor domain(s) by using a lexicon of clinician identified domain-related keywords and multiword expressions, and thus require no manual annotation. The sentences are vectorized using the Universal Sentence Encoder (USE), a transformer attention network pretrained on a large volume of general-domain web data and optimized for greater-than-word length sequences.",
+      "Sentences that are marked for one or more of the seven risk factor domains are then passed to a suite of seven clinical sentiment MLP classifiers (one for each risk factor domain) that are trained on a corpus of 3,500 EHR sentences (63,127 tokens) labeled by a team of three clinicians involved in this project. To prevent overfitting to this small amount of training data, the models are designed to be more generalizable through the use of two hidden layers and a dropout rate BIBREF16 of 0.75.",
+      "The outputs of each clinical sentiment model are then averaged across notes to create a single value for each risk factor domain that corresponds to the patient's level of functioning on a -1 to 1 scale (see Figure 2)."
+    ],
+    [
+      "We tested six different classification models: Stochastic Gradient Descent, Logistic Regression, C-Support Vector, Decision Tree, Random Forest, and MLP. All of them were implemented and fine-tuned using the scikit-learn machine learning toolkit BIBREF17. Because an accurate readmission risk prediction model is designed to be used to inform treatment decisions, it is important in adopting a model architecture that is clinically interpretable and allows for an analysis of the specific contribution of each feature in the input. As such, we include a Random Forest classifier, which we also found to have the best performance out of the six models.",
+      "To systematically evaluate the importance of the clinical sentiment values extracted from the free text in EHRs, we first build a baseline model using the structured features, which are similar to prior studies on readmission risk prediction BIBREF6. We then compare two models incorporating the unstructured features. In the \"Baseline+Domain Sentences\" model, we consider whether adding the counts of sentences per EHR that involve each of the seven risk factor domains as identified by our topic extraction model improved the model performance. In the \"Baseline+Clinical Sentiment\" model, we evaluate whether adding clinical sentiment scores for each risk factor domain improved the model performance. We also experimented with combining both sets of features and found no additional improvement.",
+      "Each model configuration was trained and evaluated 100 times and the features with the highest importance for each iteration were recorded. To further fine-tune our models, we also perform three-fold cross-validated recursive feature elimination 30 times on each of the three configurations and report the performances of the models with the best performing feature sets. These can be found in Table TABREF9.",
+      "Our baseline results show that the model trained using only the structured features produce equivalent performances as reported by prior models for readmission risk prediction across all healthcare fields BIBREF18. The two models that were trained using unstructured features produced better results and both outperform the baseline results. The \"Baseline+Clinical Sentiment\" model produced the best results, resulting in an F1 of 0.72, an improvement of 14.3% over the baseline.",
+      "In order to establish what features were not relevant in the classification task, we performed recursive feature elimination. We identified 13 feature values as being not predictive of readmission (they were eliminated from at least two of the three feature sets without producing a drop in performance) including: all values for marital status (Single, Married, Other, and Unknown), missing values for GAF at admission, GAF score difference between admission & discharge, GAF at discharge, Veteran status, Race, and Insight & Mode of Past Insight values reflecting a clinically positive change (Good and Improving). Poor Insight values, however, are predictive of readmission."
+    ],
+    [
+      "We have introduced and assessed the efficacy of adding NLP-based features like topic extraction and clinical sentiment features to traditional structured-feature based classification models for early readmission prediction in psychiatry patients. The approach we have introduced is a hybrid machine learning approach that combines deep learning techniques with linear methods to ensure clinical interpretability of the prediction model.",
+      "Results show not only that both the number of sentences per risk domain and the clinical sentiment analysis scores outperform the structured-feature baseline and contribute significantly to better classification results, but also that the clinical sentiment features produce the highest results in all evaluation metrics (F1 = 0.72).",
+      "These results suggest that clinical sentiment features for each of seven risk domains extracted from free-text narratives further enhance early readmission prediction. In addition, combining state-of-art MLP methods has a potential utility in generating clinical meaningful features that can be be used in downstream linear models with interpretable and transparent results. In future work, we intend to increase the size of the EHR corpus, increase the demographic spread of patients, and extract new features based on clinical expertise to increase our model performances. Additionally, we intend to continue our clinical sentiment annotation project from BIBREF15 to increase the accuracy of that portion of our NLP pipeline."
+    ],
+    [
+      "This work was supported by a grant from the National Institute of Mental Health (grant no. 5R01MH109687 to Mei-Hua Hall). We would also like to thank the LOUHI 2019 Workshop reviewers for their constructive and helpful comments."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: Exploring Scholarly Data by Semantic Query on Knowledge Graph Embedding Space
+
+Question: What new interesting tasks can be solved based on the uncanny semantic structures of the embedding space?
\ No newline at end of file
diff --git a/qasper-1033/instruction.md b/qasper-1033/instruction.md
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+Name of Paper: Exploring Scholarly Data by Semantic Query on Knowledge Graph Embedding Space
+
+Question: What are the uncanny semantic structures of the embedding space?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work ::: Knowledge graph for scholarly data",
+    "Related Work ::: Knowledge graph embedding",
+    "Related Work ::: Word embedding",
+    "Theoretical analysis",
+    "Theoretical analysis ::: The semantic structures of CP@!START@$ _h $@!END@",
+    "Theoretical analysis ::: Semantic query",
+    "Semantic query framework",
+    "Exploration tasks and semantic queries conversion",
+    "Exploration tasks and semantic queries conversion ::: Similar entities",
+    "Exploration tasks and semantic queries conversion ::: Similar entities with bias",
+    "Exploration tasks and semantic queries conversion ::: Analogy query",
+    "Exploration tasks and semantic queries conversion ::: Analogy browsing",
+    "Conclusion",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "In recent years, digital libraries have moved towards open science and open access with several large scholarly datasets being constructed. Most popular datasets include millions of papers, authors, venues, and other information. Their large size and heterogeneous contents make it very challenging to effectively manage, explore, and utilize these datasets. The knowledge graph has emerged as a universal data format for representing knowledge about entities and their relationships in such complicated data. The main part of a knowledge graph is a collection of triples, with each triple $ (h, t, r) $ denoting the fact that relation $ r $ exists between head entity $ h $ and tail entity $ t $. This can also be formalized as a labeled directed multigraph where each triple $ (h, t, r) $ represents a directed edge from node $ h $ to node $ t $ with label $ r $. Therefore, it is straightforward to build knowledge graphs for scholarly data by representing natural connections between scholarly entities with triples such as (AuthorA, Paper1, write) and (Paper1, Paper2, cite).",
+      "Notably, instead of using knowledge graphs directly in some tasks, we can model them by knowledge graph embedding methods, which represent entities and relations as embedding vectors in semantic space, then model the interactions between them to solve the knowledge graph completion task. There are many approaches BIBREF0 to modeling the interactions between embedding vectors resulting in many knowledge graph embedding methods such as ComplEx BIBREF1 and CP$ _h $ BIBREF2. In the case of word embedding methods such as word2vec, embedding vectors are known to contain rich semantic information that enables them to be used in many semantic applications BIBREF3. However, the semantic structures in the knowledge graph embedding space are not well-studied, thus knowledge graph embeddings are only used for knowledge graph completion but remain absent in the toolbox for data analysis of heterogeneous data in general and scholarly data in particular, although they have the potential to be highly effective and efficient. In this paper, we address these issues by providing a theoretical understanding of their semantic structures and designing a general semantic query framework to support data exploration.",
+      "For theoretical analysis, we first analyze the state-of-the-art knowledge graph embedding model CP$ _h $ BIBREF2 in comparison to the popular word embedding model word2vec skipgram BIBREF3 to explain its components and provide understandings to its semantic structures. We then define the semantic queries on the knowledge graph embedding spaces, which are algebraic operations between the embedding vectors in the knowledge graph embedding space to solve queries such as similarity and analogy between the entities on the original datasets.",
+      "Based on our theoretical results, we design a general framework for data exploration on scholarly data by semantic queries on knowledge graph embedding space. The main component in this framework is the conversion between the data exploration tasks and the semantic queries. We first outline the semantic query solutions to some traditional data exploration tasks, such as similar paper prediction and similar author prediction. We then propose a group of new interesting tasks, such as analogy query and analogy browsing, and discuss how they can be used in modern digital libraries."
+    ],
+    [
+      "Knowledge graph has gradually become the standard data format for heterogeneous and complicated datasets BIBREF4. There have been several attempts to build knowledge graph for scholarly data, either adopting the scholarly network directly BIBREF5, or deriving the knowledge graph from some similarity measures BIBREF6 BIBREF7, or constructing the knowledge graph from survey papers BIBREF8. However, they mostly focus on the data format or graph inference aspects of knowledge graph. In this paper, we instead focus on the knowledge graph embedding methods and especially the application of embedding vectors in data exploration."
+    ],
+    [
+      "For a more in depth survey of knowledge graph embedding methods, please refer to BIBREF0, which defines their architecture, categorization, and interaction mechanisms. In this paper, we only focus on the semantic structures of the state-of-the-art model CP$ _h $ BIBREF2, which is an extension of CP BIBREF9.",
+      "In CP, each entity $ e $ has two embedding vectors $ $ and $ ^{(2)} $ depending on its role in a triple as head or as tail, respectively. CP$ _h $ augments the data by making an inverse triple $ (t, h, r^{(a)}) $ for each existing triple $ (h, t, r) $, where $ r^{(a)} $ is the augmented relation corresponding to $ r $. When maximizing the likelihood by stochastic gradient descent, its score function is the sum:",
+      "where $ , ^{(2)}, , ^{(2)}, , ^{(a)} \\in ^{D} $ are the embedding vectors of $ h $, $ t $, and $ r $, respectively, and the trilinear-product $ \\langle \\cdot , \\cdot , \\cdot \\rangle $ is defined as:",
+      "where $ D $ is the embedding size and $ d $ is the dimension for which $ h_d $, $ t_d $, and $ r_d $ are the scalar entries.",
+      "The validity of each triple is modeled as a Bernoulli distribution and its validity probability is computed by the standard logistic function $ \\sigma (\\cdot ) $ as:"
+    ],
+    [
+      "The most popular word embedding models in recent years are word2vec variants such as word2vec skipgram BIBREF3, which predicts the context-words $ c_i $ independently given the target-word $ w $, that is:",
+      "In practice, the expensive softmax functions in these multinoulli distributions are avoided by approximating them with negative sampling and solve for the Bernoulli distributions by using the standard logistic function $ \\sigma (\\cdot ) $:",
+      "where $ _{c_i} $ is the context-embedding vector of context-word $ c_i $ and $ _w $ is the word-embedding vector of target-word $ w $."
+    ],
+    [
+      "Word2vec skipgram and its semantic structures are well-studied both theoretically and empirically BIBREF3. CP$ _h $ is a new state of the art among many knowledge graph embedding models. We first ground the theoretical basis of CP$ _h $ on word2vec skipgram to explain its components and understand its semantic structures. We then define semantic queries on knowledge graph embedding space."
+    ],
+    [
+      "We first look at Eq. DISPLAY_FORM8 of word2vec skipgram and consider only one context-word $ c $ for simplicity. We can write the probability in proportional format as:",
+      "Note that the context-word $ c $ and target-word $ w $ are ordered and in word2vec skipgram, the target-word is the central word in a sliding window, e.g., $ w_i $ is the target-word and $ w_{i-k}, \\dots , w_{i-1}, w_{i+1}, \\dots , w_{i+k} $ are context-words. Therefore, the roles in each word pair are symmetric over the whole dataset. When maximizing the likelihood by stochastic gradient descent, we can write the approximate probability of unordered word pair and expand the dot products as:",
+      "where $ _c $ and $ _c $ are the context-embedding and word-embedding vectors of $ c $, respectively, $ _w $ and $ _w $ are the context-embedding and word-embedding vectors of $ w $, respectively, and $ {u_c}_d, {v_c}_d, {u_w}_d $, and $ {v_w}_d $ are their scalar entries, respectively.",
+      "We now return to Eq. DISPLAY_FORM3 of CP$ _h $ to also write the probability in Eq. DISPLAY_FORM5 in proportional format and expand the trilinear products according to Eq. DISPLAY_FORM4 as:",
+      "where $ , ^{(2)} $, $ , ^{(2)} $, $ , ^{(a)} $ are knowledge graph embedding vectors and $ h_d, h^{(2)}_d $, $ t_d, t^{(2)}_d $, $ r_d, r^{(a)}_d $ are the scalar entries.",
+      "Comparing Eq. of word2vec skipgram and Eq. of CP$ _h $, we can see they have essentially the same form and mechanism. Note that the embedding vectors in word2vec skipgram are learned by aligning each target-word to different context-words and vice versa, which is essentially the same for CP$ _h $ by aligning each head entity to different tail entities in different triples and vice versa, with regards to the dimensions weighted by each relation. This result suggests that the semantic structures of CP$ _h $ are similar to those in word2vec skipgram and we can use the head-role-based entity embedding vectors, such as $ $, for semantic applications similarly to word embedding vectors. The tail-role-based entity embedding vectors, such as $ ^{(2)} $, contain almost the same information due to their symmetric roles, thus can be discarded in semantic tasks, which justifies this common practices in word embedding applications BIBREF3."
+    ],
+    [
+      "We mainly concern with the two following structures of the embedding space.",
+      "Semantic similarity structure: Semantically similar entities are close to each other in the embedding space, and vice versa. This structure can be identified by a vector similarity measure, such as the dot product between two embedding vectors. The similarity between two embedding vectors is computed as:",
+      "Semantic direction structure: There exist semantic directions in the embedding space, by which only one semantic aspect changes while all other aspects stay the same. It can be identified by a vector difference, such as the subtraction between two embedding vectors. The semantic direction between two embedding vectors is computed as:",
+      "The algebraic operations, which include the above dot product and vector subtraction, or their combinations, can be used to approximate some important tasks on the original data. To do this, we first need to convert the data exploration task to the appropriate operations. We then conduct the operations on the embedding vectors and obtain the results. This process is defined as following.",
+      "Definition 1 Semantic queries on knowledge graph embedding space are defined as the algebraic operations between the knowledge graph embedding vectors to approximate a given data exploration task on the original dataset."
+    ],
+    [
+      "Given the theoretical results, here we design a general framework for scholarly data exploration by using semantic queries on knowledge graph embedding space. Figure FIGREF19 shows the architecture of the proposed framework. There are three main components, namely data processing, task processing, and query processing.",
+      "Data processing: with two steps, (1) constructing the knowledge graph from scholarly data by using the scholarly graph directly with entities such as authors, papers, venues, and relations such as author-write-paper, paper-cite-paper, paper-in-venue, and (2) learning the knowledge graph embeddings as in BIBREF0.",
+      "Task processing: converting data exploration tasks to algebraic operations on the embedding space by following task-specific conversion templates. Some important tasks and their conversion templates are discussed in Section SECREF5.",
+      "Query processing: executing semantic query on the embedding space and return results. Note that the algebraic operations on embedding vectors are linear and can be performed in parallel. Therefore, the semantic query is efficient.",
+      "Note that the proposed semantic query framework makes no assumption on the specific knowledge graph embedding models and the induced embedding spaces. Any embedding space that contains rich semantic information such as the listed semantic structures can be applied in this framework."
+    ],
+    [
+      "Here we present and discuss the semantic queries for some traditional and newly proposed data exploration tasks on scholarly data."
+    ],
+    [
+      "Tasks Given an entity $ e \\in $, find entities that are similar to $ e $. For example, given AuthorA, find authors, papers, and venues that are similar to AuthorA. Note that we can restrict to find specific entity types. This is a traditional tasks in scholarly data exploration, whereas other below tasks are new.",
+      "Semantic query We can solve this task by looking for the entities with highest similarity to $ e $. For example, the first result is:"
+    ],
+    [
+      "Tasks Given an entity $ e \\in $ and some positive bias entities $ A = \\lbrace a_1, \\dots , a_k\\rbrace $ known as expected results, find entities that are similar to $ e $ following the bias in $ A $. For example, given AuthorA and some successfully collaborating authors, find other similar authors that may also result in good collaborations with AuthorA.",
+      "Semantic query We can solve this task by looking for the entities with highest similarity to both $ e $ and $ A $. For example, denoting the arithmetic mean of embedding vectors in $ A $ as $ \\bar{A} $, the first result is:"
+    ],
+    [
+      "Tasks Given an entity $ e \\in $, positive bias $ A = \\lbrace a_1, \\dots , a_k\\rbrace $, and negative bias $ B = \\lbrace b_1, \\dots , b_k\\rbrace $, find entities that are similar to $ e $ following the biases in $ A $ and $ B $. The essence of this task is tracing along a semantic direction defined by the positive and negative biases. For example, start with AuthorA, we can trace along the expertise direction to find authors that are similar to AuthorA but with higher or lower expertise.",
+      "Semantic query We can solve this task by looking for the entities with highest similarity to $ e $ and $ A $ but not $ B $. For example, denoting the arithmetic mean of embedding vectors in $ A $ and $ B $ as $ \\bar{A} $ and $ \\bar{B} $, respectively, note that $ \\bar{A} - \\bar{B} $ defines the semantic direction along the positive and negative biases, the first result is:"
+    ],
+    [
+      "Tasks This task is an extension of the above analogy query task, by tracing along multiple semantic directions defined by multiple pairs of positive and negative biases. This task can be implemented as an interactive data analysis tool. For example, start with AuthorA, we can trace to authors with higher expertise, then continue tracing to new domains to find all authors similar to AuthorA with high expertise in the new domain. For another example, start with Paper1, we can trace to papers with higher quality, then continue tracing to new domain to look for papers similar to Paper1 with high quality in the new domain.",
+      "Semantic query We can solve this task by simply repeating the semantic query for analogy query with each pair of positive and negative bias. Note that we can also combine different operations in different order to support flexible browsing."
+    ],
+    [
+      "In this paper, we studied the application of knowledge graph embedding in exploratory data analysis. We analyzed the CP$ _h $ model and provided understandings to its semantic structures. We then defined the semantic queries on knowledge graph embedding space to efficiently approximate some operations on heterogeneous data such as scholarly data. We designed a general framework to systematically apply semantic queries to solve scholarly data exploration tasks. Finally, we outlined and discussed the solutions to some traditional and pioneering exploration tasks emerged from the semantic structures of the knowledge graph embedding space.",
+      "This paper is dedicated to the theoretical foundation of a new approach and discussions of emerging tasks, whereas experiments and evaluations are left for the future work. There are several other promising directions for future research. One direction is to explore new tasks or new solutions of traditional tasks using the proposed method. Another direction is to implement the proposed exploration tasks on real-life digital libraries for online evaluation."
+    ],
+    [
+      "This work was supported by \u201cCross-ministerial Strategic Innovation Promotion Program (SIP) Second Phase, Big-data and AI-enabled Cyberspace Technologies\u201d by New Energy and Industrial Technology Development Organization (NEDO).",
+      "1.0"
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1034/instruction.md b/qasper-1034/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..af49b18dbfa13a9933dc457478fd8524fc683e74
--- /dev/null
+++ b/qasper-1034/instruction.md
@@ -0,0 +1,102 @@
+Name of Paper: Exploring Scholarly Data by Semantic Query on Knowledge Graph Embedding Space
+
+Question: What is the general framework for data exploration by semantic queries?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work ::: Knowledge graph for scholarly data",
+    "Related Work ::: Knowledge graph embedding",
+    "Related Work ::: Word embedding",
+    "Theoretical analysis",
+    "Theoretical analysis ::: The semantic structures of CP@!START@$ _h $@!END@",
+    "Theoretical analysis ::: Semantic query",
+    "Semantic query framework",
+    "Exploration tasks and semantic queries conversion",
+    "Exploration tasks and semantic queries conversion ::: Similar entities",
+    "Exploration tasks and semantic queries conversion ::: Similar entities with bias",
+    "Exploration tasks and semantic queries conversion ::: Analogy query",
+    "Exploration tasks and semantic queries conversion ::: Analogy browsing",
+    "Conclusion",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "In recent years, digital libraries have moved towards open science and open access with several large scholarly datasets being constructed. Most popular datasets include millions of papers, authors, venues, and other information. Their large size and heterogeneous contents make it very challenging to effectively manage, explore, and utilize these datasets. The knowledge graph has emerged as a universal data format for representing knowledge about entities and their relationships in such complicated data. The main part of a knowledge graph is a collection of triples, with each triple $ (h, t, r) $ denoting the fact that relation $ r $ exists between head entity $ h $ and tail entity $ t $. This can also be formalized as a labeled directed multigraph where each triple $ (h, t, r) $ represents a directed edge from node $ h $ to node $ t $ with label $ r $. Therefore, it is straightforward to build knowledge graphs for scholarly data by representing natural connections between scholarly entities with triples such as (AuthorA, Paper1, write) and (Paper1, Paper2, cite).",
+      "Notably, instead of using knowledge graphs directly in some tasks, we can model them by knowledge graph embedding methods, which represent entities and relations as embedding vectors in semantic space, then model the interactions between them to solve the knowledge graph completion task. There are many approaches BIBREF0 to modeling the interactions between embedding vectors resulting in many knowledge graph embedding methods such as ComplEx BIBREF1 and CP$ _h $ BIBREF2. In the case of word embedding methods such as word2vec, embedding vectors are known to contain rich semantic information that enables them to be used in many semantic applications BIBREF3. However, the semantic structures in the knowledge graph embedding space are not well-studied, thus knowledge graph embeddings are only used for knowledge graph completion but remain absent in the toolbox for data analysis of heterogeneous data in general and scholarly data in particular, although they have the potential to be highly effective and efficient. In this paper, we address these issues by providing a theoretical understanding of their semantic structures and designing a general semantic query framework to support data exploration.",
+      "For theoretical analysis, we first analyze the state-of-the-art knowledge graph embedding model CP$ _h $ BIBREF2 in comparison to the popular word embedding model word2vec skipgram BIBREF3 to explain its components and provide understandings to its semantic structures. We then define the semantic queries on the knowledge graph embedding spaces, which are algebraic operations between the embedding vectors in the knowledge graph embedding space to solve queries such as similarity and analogy between the entities on the original datasets.",
+      "Based on our theoretical results, we design a general framework for data exploration on scholarly data by semantic queries on knowledge graph embedding space. The main component in this framework is the conversion between the data exploration tasks and the semantic queries. We first outline the semantic query solutions to some traditional data exploration tasks, such as similar paper prediction and similar author prediction. We then propose a group of new interesting tasks, such as analogy query and analogy browsing, and discuss how they can be used in modern digital libraries."
+    ],
+    [
+      "Knowledge graph has gradually become the standard data format for heterogeneous and complicated datasets BIBREF4. There have been several attempts to build knowledge graph for scholarly data, either adopting the scholarly network directly BIBREF5, or deriving the knowledge graph from some similarity measures BIBREF6 BIBREF7, or constructing the knowledge graph from survey papers BIBREF8. However, they mostly focus on the data format or graph inference aspects of knowledge graph. In this paper, we instead focus on the knowledge graph embedding methods and especially the application of embedding vectors in data exploration."
+    ],
+    [
+      "For a more in depth survey of knowledge graph embedding methods, please refer to BIBREF0, which defines their architecture, categorization, and interaction mechanisms. In this paper, we only focus on the semantic structures of the state-of-the-art model CP$ _h $ BIBREF2, which is an extension of CP BIBREF9.",
+      "In CP, each entity $ e $ has two embedding vectors $ $ and $ ^{(2)} $ depending on its role in a triple as head or as tail, respectively. CP$ _h $ augments the data by making an inverse triple $ (t, h, r^{(a)}) $ for each existing triple $ (h, t, r) $, where $ r^{(a)} $ is the augmented relation corresponding to $ r $. When maximizing the likelihood by stochastic gradient descent, its score function is the sum:",
+      "where $ , ^{(2)}, , ^{(2)}, , ^{(a)} \\in ^{D} $ are the embedding vectors of $ h $, $ t $, and $ r $, respectively, and the trilinear-product $ \\langle \\cdot , \\cdot , \\cdot \\rangle $ is defined as:",
+      "where $ D $ is the embedding size and $ d $ is the dimension for which $ h_d $, $ t_d $, and $ r_d $ are the scalar entries.",
+      "The validity of each triple is modeled as a Bernoulli distribution and its validity probability is computed by the standard logistic function $ \\sigma (\\cdot ) $ as:"
+    ],
+    [
+      "The most popular word embedding models in recent years are word2vec variants such as word2vec skipgram BIBREF3, which predicts the context-words $ c_i $ independently given the target-word $ w $, that is:",
+      "In practice, the expensive softmax functions in these multinoulli distributions are avoided by approximating them with negative sampling and solve for the Bernoulli distributions by using the standard logistic function $ \\sigma (\\cdot ) $:",
+      "where $ _{c_i} $ is the context-embedding vector of context-word $ c_i $ and $ _w $ is the word-embedding vector of target-word $ w $."
+    ],
+    [
+      "Word2vec skipgram and its semantic structures are well-studied both theoretically and empirically BIBREF3. CP$ _h $ is a new state of the art among many knowledge graph embedding models. We first ground the theoretical basis of CP$ _h $ on word2vec skipgram to explain its components and understand its semantic structures. We then define semantic queries on knowledge graph embedding space."
+    ],
+    [
+      "We first look at Eq. DISPLAY_FORM8 of word2vec skipgram and consider only one context-word $ c $ for simplicity. We can write the probability in proportional format as:",
+      "Note that the context-word $ c $ and target-word $ w $ are ordered and in word2vec skipgram, the target-word is the central word in a sliding window, e.g., $ w_i $ is the target-word and $ w_{i-k}, \\dots , w_{i-1}, w_{i+1}, \\dots , w_{i+k} $ are context-words. Therefore, the roles in each word pair are symmetric over the whole dataset. When maximizing the likelihood by stochastic gradient descent, we can write the approximate probability of unordered word pair and expand the dot products as:",
+      "where $ _c $ and $ _c $ are the context-embedding and word-embedding vectors of $ c $, respectively, $ _w $ and $ _w $ are the context-embedding and word-embedding vectors of $ w $, respectively, and $ {u_c}_d, {v_c}_d, {u_w}_d $, and $ {v_w}_d $ are their scalar entries, respectively.",
+      "We now return to Eq. DISPLAY_FORM3 of CP$ _h $ to also write the probability in Eq. DISPLAY_FORM5 in proportional format and expand the trilinear products according to Eq. DISPLAY_FORM4 as:",
+      "where $ , ^{(2)} $, $ , ^{(2)} $, $ , ^{(a)} $ are knowledge graph embedding vectors and $ h_d, h^{(2)}_d $, $ t_d, t^{(2)}_d $, $ r_d, r^{(a)}_d $ are the scalar entries.",
+      "Comparing Eq. of word2vec skipgram and Eq. of CP$ _h $, we can see they have essentially the same form and mechanism. Note that the embedding vectors in word2vec skipgram are learned by aligning each target-word to different context-words and vice versa, which is essentially the same for CP$ _h $ by aligning each head entity to different tail entities in different triples and vice versa, with regards to the dimensions weighted by each relation. This result suggests that the semantic structures of CP$ _h $ are similar to those in word2vec skipgram and we can use the head-role-based entity embedding vectors, such as $ $, for semantic applications similarly to word embedding vectors. The tail-role-based entity embedding vectors, such as $ ^{(2)} $, contain almost the same information due to their symmetric roles, thus can be discarded in semantic tasks, which justifies this common practices in word embedding applications BIBREF3."
+    ],
+    [
+      "We mainly concern with the two following structures of the embedding space.",
+      "Semantic similarity structure: Semantically similar entities are close to each other in the embedding space, and vice versa. This structure can be identified by a vector similarity measure, such as the dot product between two embedding vectors. The similarity between two embedding vectors is computed as:",
+      "Semantic direction structure: There exist semantic directions in the embedding space, by which only one semantic aspect changes while all other aspects stay the same. It can be identified by a vector difference, such as the subtraction between two embedding vectors. The semantic direction between two embedding vectors is computed as:",
+      "The algebraic operations, which include the above dot product and vector subtraction, or their combinations, can be used to approximate some important tasks on the original data. To do this, we first need to convert the data exploration task to the appropriate operations. We then conduct the operations on the embedding vectors and obtain the results. This process is defined as following.",
+      "Definition 1 Semantic queries on knowledge graph embedding space are defined as the algebraic operations between the knowledge graph embedding vectors to approximate a given data exploration task on the original dataset."
+    ],
+    [
+      "Given the theoretical results, here we design a general framework for scholarly data exploration by using semantic queries on knowledge graph embedding space. Figure FIGREF19 shows the architecture of the proposed framework. There are three main components, namely data processing, task processing, and query processing.",
+      "Data processing: with two steps, (1) constructing the knowledge graph from scholarly data by using the scholarly graph directly with entities such as authors, papers, venues, and relations such as author-write-paper, paper-cite-paper, paper-in-venue, and (2) learning the knowledge graph embeddings as in BIBREF0.",
+      "Task processing: converting data exploration tasks to algebraic operations on the embedding space by following task-specific conversion templates. Some important tasks and their conversion templates are discussed in Section SECREF5.",
+      "Query processing: executing semantic query on the embedding space and return results. Note that the algebraic operations on embedding vectors are linear and can be performed in parallel. Therefore, the semantic query is efficient.",
+      "Note that the proposed semantic query framework makes no assumption on the specific knowledge graph embedding models and the induced embedding spaces. Any embedding space that contains rich semantic information such as the listed semantic structures can be applied in this framework."
+    ],
+    [
+      "Here we present and discuss the semantic queries for some traditional and newly proposed data exploration tasks on scholarly data."
+    ],
+    [
+      "Tasks Given an entity $ e \\in $, find entities that are similar to $ e $. For example, given AuthorA, find authors, papers, and venues that are similar to AuthorA. Note that we can restrict to find specific entity types. This is a traditional tasks in scholarly data exploration, whereas other below tasks are new.",
+      "Semantic query We can solve this task by looking for the entities with highest similarity to $ e $. For example, the first result is:"
+    ],
+    [
+      "Tasks Given an entity $ e \\in $ and some positive bias entities $ A = \\lbrace a_1, \\dots , a_k\\rbrace $ known as expected results, find entities that are similar to $ e $ following the bias in $ A $. For example, given AuthorA and some successfully collaborating authors, find other similar authors that may also result in good collaborations with AuthorA.",
+      "Semantic query We can solve this task by looking for the entities with highest similarity to both $ e $ and $ A $. For example, denoting the arithmetic mean of embedding vectors in $ A $ as $ \\bar{A} $, the first result is:"
+    ],
+    [
+      "Tasks Given an entity $ e \\in $, positive bias $ A = \\lbrace a_1, \\dots , a_k\\rbrace $, and negative bias $ B = \\lbrace b_1, \\dots , b_k\\rbrace $, find entities that are similar to $ e $ following the biases in $ A $ and $ B $. The essence of this task is tracing along a semantic direction defined by the positive and negative biases. For example, start with AuthorA, we can trace along the expertise direction to find authors that are similar to AuthorA but with higher or lower expertise.",
+      "Semantic query We can solve this task by looking for the entities with highest similarity to $ e $ and $ A $ but not $ B $. For example, denoting the arithmetic mean of embedding vectors in $ A $ and $ B $ as $ \\bar{A} $ and $ \\bar{B} $, respectively, note that $ \\bar{A} - \\bar{B} $ defines the semantic direction along the positive and negative biases, the first result is:"
+    ],
+    [
+      "Tasks This task is an extension of the above analogy query task, by tracing along multiple semantic directions defined by multiple pairs of positive and negative biases. This task can be implemented as an interactive data analysis tool. For example, start with AuthorA, we can trace to authors with higher expertise, then continue tracing to new domains to find all authors similar to AuthorA with high expertise in the new domain. For another example, start with Paper1, we can trace to papers with higher quality, then continue tracing to new domain to look for papers similar to Paper1 with high quality in the new domain.",
+      "Semantic query We can solve this task by simply repeating the semantic query for analogy query with each pair of positive and negative bias. Note that we can also combine different operations in different order to support flexible browsing."
+    ],
+    [
+      "In this paper, we studied the application of knowledge graph embedding in exploratory data analysis. We analyzed the CP$ _h $ model and provided understandings to its semantic structures. We then defined the semantic queries on knowledge graph embedding space to efficiently approximate some operations on heterogeneous data such as scholarly data. We designed a general framework to systematically apply semantic queries to solve scholarly data exploration tasks. Finally, we outlined and discussed the solutions to some traditional and pioneering exploration tasks emerged from the semantic structures of the knowledge graph embedding space.",
+      "This paper is dedicated to the theoretical foundation of a new approach and discussions of emerging tasks, whereas experiments and evaluations are left for the future work. There are several other promising directions for future research. One direction is to explore new tasks or new solutions of traditional tasks using the proposed method. Another direction is to implement the proposed exploration tasks on real-life digital libraries for online evaluation."
+    ],
+    [
+      "This work was supported by \u201cCross-ministerial Strategic Innovation Promotion Program (SIP) Second Phase, Big-data and AI-enabled Cyberspace Technologies\u201d by New Energy and Industrial Technology Development Organization (NEDO).",
+      "1.0"
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1035/instruction.md b/qasper-1035/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..f54893403e7eb2284b9a47ffbbfa6c59d8acf84a
--- /dev/null
+++ b/qasper-1035/instruction.md
@@ -0,0 +1,102 @@
+Name of Paper: Exploring Scholarly Data by Semantic Query on Knowledge Graph Embedding Space
+
+Question: What data exploration is supported by the analysis of these semantic structures?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work ::: Knowledge graph for scholarly data",
+    "Related Work ::: Knowledge graph embedding",
+    "Related Work ::: Word embedding",
+    "Theoretical analysis",
+    "Theoretical analysis ::: The semantic structures of CP@!START@$ _h $@!END@",
+    "Theoretical analysis ::: Semantic query",
+    "Semantic query framework",
+    "Exploration tasks and semantic queries conversion",
+    "Exploration tasks and semantic queries conversion ::: Similar entities",
+    "Exploration tasks and semantic queries conversion ::: Similar entities with bias",
+    "Exploration tasks and semantic queries conversion ::: Analogy query",
+    "Exploration tasks and semantic queries conversion ::: Analogy browsing",
+    "Conclusion",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "In recent years, digital libraries have moved towards open science and open access with several large scholarly datasets being constructed. Most popular datasets include millions of papers, authors, venues, and other information. Their large size and heterogeneous contents make it very challenging to effectively manage, explore, and utilize these datasets. The knowledge graph has emerged as a universal data format for representing knowledge about entities and their relationships in such complicated data. The main part of a knowledge graph is a collection of triples, with each triple $ (h, t, r) $ denoting the fact that relation $ r $ exists between head entity $ h $ and tail entity $ t $. This can also be formalized as a labeled directed multigraph where each triple $ (h, t, r) $ represents a directed edge from node $ h $ to node $ t $ with label $ r $. Therefore, it is straightforward to build knowledge graphs for scholarly data by representing natural connections between scholarly entities with triples such as (AuthorA, Paper1, write) and (Paper1, Paper2, cite).",
+      "Notably, instead of using knowledge graphs directly in some tasks, we can model them by knowledge graph embedding methods, which represent entities and relations as embedding vectors in semantic space, then model the interactions between them to solve the knowledge graph completion task. There are many approaches BIBREF0 to modeling the interactions between embedding vectors resulting in many knowledge graph embedding methods such as ComplEx BIBREF1 and CP$ _h $ BIBREF2. In the case of word embedding methods such as word2vec, embedding vectors are known to contain rich semantic information that enables them to be used in many semantic applications BIBREF3. However, the semantic structures in the knowledge graph embedding space are not well-studied, thus knowledge graph embeddings are only used for knowledge graph completion but remain absent in the toolbox for data analysis of heterogeneous data in general and scholarly data in particular, although they have the potential to be highly effective and efficient. In this paper, we address these issues by providing a theoretical understanding of their semantic structures and designing a general semantic query framework to support data exploration.",
+      "For theoretical analysis, we first analyze the state-of-the-art knowledge graph embedding model CP$ _h $ BIBREF2 in comparison to the popular word embedding model word2vec skipgram BIBREF3 to explain its components and provide understandings to its semantic structures. We then define the semantic queries on the knowledge graph embedding spaces, which are algebraic operations between the embedding vectors in the knowledge graph embedding space to solve queries such as similarity and analogy between the entities on the original datasets.",
+      "Based on our theoretical results, we design a general framework for data exploration on scholarly data by semantic queries on knowledge graph embedding space. The main component in this framework is the conversion between the data exploration tasks and the semantic queries. We first outline the semantic query solutions to some traditional data exploration tasks, such as similar paper prediction and similar author prediction. We then propose a group of new interesting tasks, such as analogy query and analogy browsing, and discuss how they can be used in modern digital libraries."
+    ],
+    [
+      "Knowledge graph has gradually become the standard data format for heterogeneous and complicated datasets BIBREF4. There have been several attempts to build knowledge graph for scholarly data, either adopting the scholarly network directly BIBREF5, or deriving the knowledge graph from some similarity measures BIBREF6 BIBREF7, or constructing the knowledge graph from survey papers BIBREF8. However, they mostly focus on the data format or graph inference aspects of knowledge graph. In this paper, we instead focus on the knowledge graph embedding methods and especially the application of embedding vectors in data exploration."
+    ],
+    [
+      "For a more in depth survey of knowledge graph embedding methods, please refer to BIBREF0, which defines their architecture, categorization, and interaction mechanisms. In this paper, we only focus on the semantic structures of the state-of-the-art model CP$ _h $ BIBREF2, which is an extension of CP BIBREF9.",
+      "In CP, each entity $ e $ has two embedding vectors $ $ and $ ^{(2)} $ depending on its role in a triple as head or as tail, respectively. CP$ _h $ augments the data by making an inverse triple $ (t, h, r^{(a)}) $ for each existing triple $ (h, t, r) $, where $ r^{(a)} $ is the augmented relation corresponding to $ r $. When maximizing the likelihood by stochastic gradient descent, its score function is the sum:",
+      "where $ , ^{(2)}, , ^{(2)}, , ^{(a)} \\in ^{D} $ are the embedding vectors of $ h $, $ t $, and $ r $, respectively, and the trilinear-product $ \\langle \\cdot , \\cdot , \\cdot \\rangle $ is defined as:",
+      "where $ D $ is the embedding size and $ d $ is the dimension for which $ h_d $, $ t_d $, and $ r_d $ are the scalar entries.",
+      "The validity of each triple is modeled as a Bernoulli distribution and its validity probability is computed by the standard logistic function $ \\sigma (\\cdot ) $ as:"
+    ],
+    [
+      "The most popular word embedding models in recent years are word2vec variants such as word2vec skipgram BIBREF3, which predicts the context-words $ c_i $ independently given the target-word $ w $, that is:",
+      "In practice, the expensive softmax functions in these multinoulli distributions are avoided by approximating them with negative sampling and solve for the Bernoulli distributions by using the standard logistic function $ \\sigma (\\cdot ) $:",
+      "where $ _{c_i} $ is the context-embedding vector of context-word $ c_i $ and $ _w $ is the word-embedding vector of target-word $ w $."
+    ],
+    [
+      "Word2vec skipgram and its semantic structures are well-studied both theoretically and empirically BIBREF3. CP$ _h $ is a new state of the art among many knowledge graph embedding models. We first ground the theoretical basis of CP$ _h $ on word2vec skipgram to explain its components and understand its semantic structures. We then define semantic queries on knowledge graph embedding space."
+    ],
+    [
+      "We first look at Eq. DISPLAY_FORM8 of word2vec skipgram and consider only one context-word $ c $ for simplicity. We can write the probability in proportional format as:",
+      "Note that the context-word $ c $ and target-word $ w $ are ordered and in word2vec skipgram, the target-word is the central word in a sliding window, e.g., $ w_i $ is the target-word and $ w_{i-k}, \\dots , w_{i-1}, w_{i+1}, \\dots , w_{i+k} $ are context-words. Therefore, the roles in each word pair are symmetric over the whole dataset. When maximizing the likelihood by stochastic gradient descent, we can write the approximate probability of unordered word pair and expand the dot products as:",
+      "where $ _c $ and $ _c $ are the context-embedding and word-embedding vectors of $ c $, respectively, $ _w $ and $ _w $ are the context-embedding and word-embedding vectors of $ w $, respectively, and $ {u_c}_d, {v_c}_d, {u_w}_d $, and $ {v_w}_d $ are their scalar entries, respectively.",
+      "We now return to Eq. DISPLAY_FORM3 of CP$ _h $ to also write the probability in Eq. DISPLAY_FORM5 in proportional format and expand the trilinear products according to Eq. DISPLAY_FORM4 as:",
+      "where $ , ^{(2)} $, $ , ^{(2)} $, $ , ^{(a)} $ are knowledge graph embedding vectors and $ h_d, h^{(2)}_d $, $ t_d, t^{(2)}_d $, $ r_d, r^{(a)}_d $ are the scalar entries.",
+      "Comparing Eq. of word2vec skipgram and Eq. of CP$ _h $, we can see they have essentially the same form and mechanism. Note that the embedding vectors in word2vec skipgram are learned by aligning each target-word to different context-words and vice versa, which is essentially the same for CP$ _h $ by aligning each head entity to different tail entities in different triples and vice versa, with regards to the dimensions weighted by each relation. This result suggests that the semantic structures of CP$ _h $ are similar to those in word2vec skipgram and we can use the head-role-based entity embedding vectors, such as $ $, for semantic applications similarly to word embedding vectors. The tail-role-based entity embedding vectors, such as $ ^{(2)} $, contain almost the same information due to their symmetric roles, thus can be discarded in semantic tasks, which justifies this common practices in word embedding applications BIBREF3."
+    ],
+    [
+      "We mainly concern with the two following structures of the embedding space.",
+      "Semantic similarity structure: Semantically similar entities are close to each other in the embedding space, and vice versa. This structure can be identified by a vector similarity measure, such as the dot product between two embedding vectors. The similarity between two embedding vectors is computed as:",
+      "Semantic direction structure: There exist semantic directions in the embedding space, by which only one semantic aspect changes while all other aspects stay the same. It can be identified by a vector difference, such as the subtraction between two embedding vectors. The semantic direction between two embedding vectors is computed as:",
+      "The algebraic operations, which include the above dot product and vector subtraction, or their combinations, can be used to approximate some important tasks on the original data. To do this, we first need to convert the data exploration task to the appropriate operations. We then conduct the operations on the embedding vectors and obtain the results. This process is defined as following.",
+      "Definition 1 Semantic queries on knowledge graph embedding space are defined as the algebraic operations between the knowledge graph embedding vectors to approximate a given data exploration task on the original dataset."
+    ],
+    [
+      "Given the theoretical results, here we design a general framework for scholarly data exploration by using semantic queries on knowledge graph embedding space. Figure FIGREF19 shows the architecture of the proposed framework. There are three main components, namely data processing, task processing, and query processing.",
+      "Data processing: with two steps, (1) constructing the knowledge graph from scholarly data by using the scholarly graph directly with entities such as authors, papers, venues, and relations such as author-write-paper, paper-cite-paper, paper-in-venue, and (2) learning the knowledge graph embeddings as in BIBREF0.",
+      "Task processing: converting data exploration tasks to algebraic operations on the embedding space by following task-specific conversion templates. Some important tasks and their conversion templates are discussed in Section SECREF5.",
+      "Query processing: executing semantic query on the embedding space and return results. Note that the algebraic operations on embedding vectors are linear and can be performed in parallel. Therefore, the semantic query is efficient.",
+      "Note that the proposed semantic query framework makes no assumption on the specific knowledge graph embedding models and the induced embedding spaces. Any embedding space that contains rich semantic information such as the listed semantic structures can be applied in this framework."
+    ],
+    [
+      "Here we present and discuss the semantic queries for some traditional and newly proposed data exploration tasks on scholarly data."
+    ],
+    [
+      "Tasks Given an entity $ e \\in $, find entities that are similar to $ e $. For example, given AuthorA, find authors, papers, and venues that are similar to AuthorA. Note that we can restrict to find specific entity types. This is a traditional tasks in scholarly data exploration, whereas other below tasks are new.",
+      "Semantic query We can solve this task by looking for the entities with highest similarity to $ e $. For example, the first result is:"
+    ],
+    [
+      "Tasks Given an entity $ e \\in $ and some positive bias entities $ A = \\lbrace a_1, \\dots , a_k\\rbrace $ known as expected results, find entities that are similar to $ e $ following the bias in $ A $. For example, given AuthorA and some successfully collaborating authors, find other similar authors that may also result in good collaborations with AuthorA.",
+      "Semantic query We can solve this task by looking for the entities with highest similarity to both $ e $ and $ A $. For example, denoting the arithmetic mean of embedding vectors in $ A $ as $ \\bar{A} $, the first result is:"
+    ],
+    [
+      "Tasks Given an entity $ e \\in $, positive bias $ A = \\lbrace a_1, \\dots , a_k\\rbrace $, and negative bias $ B = \\lbrace b_1, \\dots , b_k\\rbrace $, find entities that are similar to $ e $ following the biases in $ A $ and $ B $. The essence of this task is tracing along a semantic direction defined by the positive and negative biases. For example, start with AuthorA, we can trace along the expertise direction to find authors that are similar to AuthorA but with higher or lower expertise.",
+      "Semantic query We can solve this task by looking for the entities with highest similarity to $ e $ and $ A $ but not $ B $. For example, denoting the arithmetic mean of embedding vectors in $ A $ and $ B $ as $ \\bar{A} $ and $ \\bar{B} $, respectively, note that $ \\bar{A} - \\bar{B} $ defines the semantic direction along the positive and negative biases, the first result is:"
+    ],
+    [
+      "Tasks This task is an extension of the above analogy query task, by tracing along multiple semantic directions defined by multiple pairs of positive and negative biases. This task can be implemented as an interactive data analysis tool. For example, start with AuthorA, we can trace to authors with higher expertise, then continue tracing to new domains to find all authors similar to AuthorA with high expertise in the new domain. For another example, start with Paper1, we can trace to papers with higher quality, then continue tracing to new domain to look for papers similar to Paper1 with high quality in the new domain.",
+      "Semantic query We can solve this task by simply repeating the semantic query for analogy query with each pair of positive and negative bias. Note that we can also combine different operations in different order to support flexible browsing."
+    ],
+    [
+      "In this paper, we studied the application of knowledge graph embedding in exploratory data analysis. We analyzed the CP$ _h $ model and provided understandings to its semantic structures. We then defined the semantic queries on knowledge graph embedding space to efficiently approximate some operations on heterogeneous data such as scholarly data. We designed a general framework to systematically apply semantic queries to solve scholarly data exploration tasks. Finally, we outlined and discussed the solutions to some traditional and pioneering exploration tasks emerged from the semantic structures of the knowledge graph embedding space.",
+      "This paper is dedicated to the theoretical foundation of a new approach and discussions of emerging tasks, whereas experiments and evaluations are left for the future work. There are several other promising directions for future research. One direction is to explore new tasks or new solutions of traditional tasks using the proposed method. Another direction is to implement the proposed exploration tasks on real-life digital libraries for online evaluation."
+    ],
+    [
+      "This work was supported by \u201cCross-ministerial Strategic Innovation Promotion Program (SIP) Second Phase, Big-data and AI-enabled Cyberspace Technologies\u201d by New Energy and Industrial Technology Development Organization (NEDO).",
+      "1.0"
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1041/instruction.md b/qasper-1041/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..ab9cdd064c79a8836d55ba9ed99955cde4a9c95c
--- /dev/null
+++ b/qasper-1041/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Contextualized Word Embeddings Enhanced Event Temporal Relation Extraction for Story Understanding
+
+Question: What type of baseline are established for the two datasets?
\ No newline at end of file
diff --git a/qasper-1046/instruction.md b/qasper-1046/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..d2dbb898afe87e3829b697546dae06330fba9e1d
--- /dev/null
+++ b/qasper-1046/instruction.md
@@ -0,0 +1,99 @@
+Name of Paper: Subword-augmented Embedding for Cloze Reading Comprehension
+
+Question: how are rare words defined?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "The Subword-augmented Word Embedding",
+    "BPE Subword Segmentation",
+    "Subword-augmented Word Embedding",
+    "Attention Module",
+    "Dataset and Settings",
+    "Main Results",
+    "Merging Times of BPE",
+    "Filter Mechanism",
+    "Subword-Augmented Representations",
+    "Machine Reading Comprehension",
+    "Augmented Word Embedding",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "This work is licensed under a Creative Commons Attribution 4.0 International License. License details: http://creativecommons.org/licenses/by/4.0/",
+      "A recent hot challenge is to train machines to read and comprehend human languages. Towards this end, various machine reading comprehension datasets have been released, including cloze-style BIBREF0 , BIBREF1 , BIBREF2 and user-query types BIBREF3 , BIBREF4 . Meanwhile, a number of deep learning models are designed to take up the challenges, most of which focus on attention mechanism BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 . However, how to represent word in an effective way remains an open problem for diverse natural language processing tasks, including machine reading comprehension for different languages. Particularly, for a language like Chinese with a large set of characters (typically, thousands of), lots of which are semantically ambiguous, using either word-level or character-level embedding alone to build the word representations would not be accurate enough. This work especially focuses on a cloze-style reading comprehension task over fairy stories, which is highly challenging due to diverse semantic patterns with personified expressions and reference.",
+      "In real practice, a reading comprehension model or system which is often called reader in literatures easily suffers from out-of-vocabulary (OOV) word issues, especially for the cloze-style reading comprehension tasks when the ground-truth answers tend to include rare words or named entities (NE), which are hardly fully recorded in the vocabulary. This is more challenging in Chinese. There are over 13,000 characters in Chinese while there are only 26 letters in English without regard to punctuation marks. If a reading comprehension system cannot effectively manage the OOV issues, the performance will not be semantically accurate for the task.",
+      "Commonly, words are represented as vectors using either word embedding or character embedding. For the former, each word is mapped into low dimensional dense vectors from a lookup table. Character representations are usually obtained by applying neural networks on the character sequence of the word, and their hidden states are obtained to form the representation. Intuitively, word-level representation is good at catching global context and dependency relationships between words, while character embedding helps for dealing with rare word representation.",
+      "However, the minimal meaningful unit below word usually is not character, which motivates researchers to explore the potential unit (subword) between character and word to model sub-word morphologies or lexical semantics. In fact, morphological compounding (e.g. sunshine or playground) is one of the most common and productive methods of word formation across human languages, which inspires us to represent word by meaningful sub-word units. Recently, researchers have started to work on morphologically informed word embeddings BIBREF11 , BIBREF12 , aiming at better capturing syntactic, lexical and morphological information. With ready subwords, we do not have to work with characters, and segmentation could be stopped at the subword-level to reach a meaningful representation.",
+      "In this paper, we present various simple yet accurate subword-augmented embedding (SAW) strategies and propose SAW Reader as an instance. Specifically, we adopt subword information to enrich word embedding and survey different SAW operations to integrate word-level and subword-level embedding for a fine-grained representation. To ensure adequate training of OOV and low-frequency words, we employ a short list mechanism. Our evaluation will be performed on three public Chinese reading comprehension datasets and one English benchmark dataset for showing our method is also effective in multi-lingual case."
+    ],
+    [
+      "The concerned reading comprehension task can be roughly categorized as user-query type and cloze-style according to the answer form. Answers in the former are usually a span of texts while in the cloze-style task, the answers are words or phrases which lets the latter be the harder-hit area of OOV issues, inspiring us to select the cloze-style as our testbed for SAW strategies. Our preliminary study shows even for the advanced word-character based GA reader, OOV answers still account for nearly 1/5 in the error results. This also motivates us to explore better representations to further performance improvement.",
+      "The cloze-style task in this work can be described as a triple INLINEFORM0 , where INLINEFORM1 is a document (context), INLINEFORM2 is a query over the contents of INLINEFORM3 , in which a word or phrase is the right answer INLINEFORM4 . This section will introduce the proposed SAW Reader in the context of cloze-style reading comprehension. Given the triple INLINEFORM5 , the SAW Reader will be built in the following steps."
+    ],
+    [
+      "Word in most languages usually can be split into meaningful subword units despite of the writing form. For example, \u201cindispensable\" could be split into the following subwords: INLINEFORM0 .",
+      "In our implementation, we adopt Byte Pair Encoding (BPE) BIBREF13 which is a simple data compression technique that iteratively replaces the most frequent pair of bytes in a sequence by a single, unused byte. BPE allows for the representation of an open vocabulary through a fixed-size vocabulary of variable-length character sequences, making it a very suitable word segmentation strategy for neural network models.",
+      "The generalized framework can be described as follows. Firstly, all the input sequences (strings) are tokenized into a sequence of single-character subwords, then we repeat,",
+      "Count all bigrams under the current segmentation status of all sequences.",
+      "Find the bigram with the highest frequency and merge them in all the sequences. Note the segmentation status is updating now.",
+      "If the merging times do not reach the specified number, go back to 1, otherwise the algorithm ends.",
+      "In BIBREF14 , BPE is adopted to segment infrequent words into sub-word units for machine translation. However, there is a key difference between the motivations for subword segmentation. We aim to refine the word representations by using subwords, for both frequent and infrequent words, which is more generally motivated. To this end, we adaptively tokenize words in multi-granularity by controlling the merging times."
+    ],
+    [
+      "Our subwords are also formed as character n-grams, do not cross word boundaries. After using unsupervised segmentation methods to split each word into a subword sequence, an augmented embedding (AE) is to straightforwardly integrate word embedding INLINEFORM0 and subword embedding INLINEFORM1 for a given word INLINEFORM2 . INLINEFORM3 ",
+      " where INLINEFORM0 denotes the detailed integration operation. In this work, we investigate concatenation (concat), element-wise summation (sum) and element-wise multiplication (mul). Thus, each document INLINEFORM1 and query INLINEFORM2 is represented as INLINEFORM3 matrix where INLINEFORM4 denotes the dimension of word embedding and INLINEFORM5 is the number of words in the input.",
+      "Subword embedding could be useful to refine the word embedding in a finer-grained way, we also consider improving word representation from itself. For quite a lot of words, especially those rare ones, their word embedding is extremely hard to learn due to the data sparse issue. Actually, if all the words in the dataset are used to build the vocabulary, the OOV words from the test set will not obtain adequate training. If they are initiated inappropriately, either with relatively high or low weights, they will harm the answer prediction. To alleviate the OOV issues, we keep a short list INLINEFORM0 for specific words. INLINEFORM1 ",
+      "If INLINEFORM0 is in INLINEFORM1 , the immediate word embedding INLINEFORM2 is indexed from word lookup table INLINEFORM3 where INLINEFORM4 denotes the size (recorded words) of lookup table. Otherwise, it will be represented as the randomly initialized default word (denoted by a specific mark INLINEFORM5 ). Note that, this is intuitively like \u201cguessing\u201d the possible unknown words (which will appear during test) from the vocabulary during training and only the word embedding of the OOV words will be replaced by INLINEFORM6 while their subword embedding INLINEFORM7 will still be processed using the original word. In this way, the OOV words could be tuned sufficiently with expressive meaning after training. During test, the word embedding of unknown words would not severely bias its final representation. Thus, INLINEFORM8 ( INLINEFORM9 ) can be rewritten as INLINEFORM10 ",
+      "In our experiments, the short list is determined according to the word frequency. Concretely, we sort the vocabulary according to the word frequency from high to low. A frequency filter ratio INLINEFORM0 is set to filter out the low-frequency words (rare words) from the lookup table. For example, INLINEFORM1 =0.9 means the least frequent 10% words are replaced with the default UNK notation.",
+      "The subword embedding INLINEFORM0 is generated by taking the final outputs of a bidirectional gated recurrent unit (GRU) BIBREF15 applied to the embeddings from a lookup table of subwords. The structure of GRU used in this paper are described as follows. INLINEFORM1 ",
+      " where INLINEFORM0 denotes the element-wise multiplication. INLINEFORM1 and INLINEFORM2 are the reset and update gates respectively, and INLINEFORM3 are the hidden states. A bi-directional GRU (BiGRU) processes the sequence in both forward and backward directions. Subwords of each word are successively fed to forward GRU and backward GRU to obtain the internal features of two directions. The output for each input is the concatenation of the two vectors from both directions: INLINEFORM4 . Then, the output of BiGRUs is passed to a fully connected layer to obtain the final subword embedding INLINEFORM5 . INLINEFORM6 "
+    ],
+    [
+      "Our attention module is based on the Gated attention Reader (GA Reader) proposed by BIBREF9 . We choose this model due to its simplicity with comparable performance so that we can focus on the effectiveness of SAW strategies. This module can be described in the following two steps. After augmented embedding, we use two BiGRUs to get contextual representations of the document and query respectively, where the representation of each word is formed by concatenating the forward and backward hidden states. INLINEFORM0 ",
+      " For each word INLINEFORM0 in INLINEFORM1 , we form a word-specific representation of the query INLINEFORM2 using soft attention, and then adopt element-wise product to multiply the query representation with the document word representation. INLINEFORM3 ",
+      " where INLINEFORM0 denotes the multiplication operator to model the interactions between INLINEFORM1 and INLINEFORM2 . Then, the document contextual representation INLINEFORM3 is gated by query representation.",
+      "Suppose the network has INLINEFORM0 layers. At each layer, the document representation INLINEFORM1 is updated through above attention learning. After going through all the layers, our model comes to answer prediction phase. We use all the words in the document to form the candidate set INLINEFORM2 . Let INLINEFORM3 denote the INLINEFORM4 -th intermediate output of query representation INLINEFORM5 and INLINEFORM6 represent the full output of document representation INLINEFORM7 . The probability of each candidate word INLINEFORM8 as being the answer is predicted using a softmax layer over the inner-product between INLINEFORM9 and INLINEFORM10 . INLINEFORM11 ",
+      " where vector INLINEFORM0 denotes the probability distribution over all the words in the document. Note that each word may occur several times in the document. Thus, the probabilities of each candidate word occurring in different positions of the document are summed up for final prediction. INLINEFORM1 ",
+      " where INLINEFORM0 denotes the set of positions that a particular word INLINEFORM1 occurs in the document INLINEFORM2 . The training objective is to maximize INLINEFORM3 where INLINEFORM4 is the correct answer.",
+      "Finally, the candidate word with the highest probability will be chosen as the predicted answer. INLINEFORM0 ",
+      "Different from recent work employing complex attention mechanisms BIBREF5 , BIBREF7 , BIBREF16 , our attention mechanism is much more simple with comparable performance so that we can focus on the effectiveness of SAW strategies."
+    ],
+    [
+      "To verify the effectiveness of our proposed model, we conduct multiple experiments on three Chinese Machine Reading Comprehension datasets, namely CMRC-2017 BIBREF17 , People's Daily (PD) and Children Fairy Tales (CFT) BIBREF2 . In these datasets, a story containing consecutive sentences is formed as the Document and one of the sentences is either automatically or manually selected as the Query where one token is replaced by a placeholder to indicate the answer to fill in. Table TABREF8 gives data statistics. Different from the current cloze-style datasets for English reading comprehension, such as CBT, Daily Mail and CNN BIBREF0 , the three Chinese datasets do not provide candidate answers. Thus, the model has to find the correct answer from the entire document.",
+      "Besides, we also use the Children's Book Test (CBT) dataset BIBREF1 to test the generalization ability in multi-lingual case. We only focus on subsets where the answer is either a common noun (CN) or NE which is more challenging since the answer is likely to be rare words. We evaluate all the models in terms of accuracy, which is the standard evaluation metric for this task.",
+      "Throughout this paper, we use the same model setting to make fair comparisons. According to our preliminary experiments, we report the results based on the following settings. The default integration strategy is element-wise product. Word embeddings were 200 INLINEFORM0 and pre-trained by word2vec BIBREF18 toolkit on Wikipedia corpus. Subword embedding were 100 INLINEFORM1 and randomly initialized with the uniformed distribution in the interval [-0:05; 0:05]. Our model was implemented using the Theano and Lasagne Python libraries. We used stochastic gradient descent with ADAM updates for optimization BIBREF19 . The batch size was 64 and the initial learning rate was 0.001 which was halved every epoch after the second epoch. We also used gradient clipping with a threshold of 10 to stabilize GRU training BIBREF20 . We use three attention layers for all experiments. The GRU hidden units for both the word and subword representation were 128. The default frequency filter proportion was 0.9 and the default merging times of BPE was 1,000. We also apply dropout between layers with a dropout rate of 0.5 ."
+    ],
+    [
+      "[7]http://www.hfl-tek.com/cmrc2017/leaderboard.html",
+      "Table TABREF17 shows our results on CMRC-2017 dataset, which shows that our SAW Reader (mul) outperforms all other single models on the test set, with 7.57% improvements compared with Attention Sum Reader (AS Reader) baseline. Although WHU's model achieves the best besides our model on the valid set with only 0.75% below ours, their result on the test set is lower than ours by 2.27%, indicating our model has a satisfactory generalization ability.",
+      "We also list different integration operations for word and subword embeddings. Table TABREF19 shows the comparisons. From the results, we can see that Word + BPE outperforms Word + Char which indicates subword embedding works essentially. We also observe that mul outperforms the other two operations, concat and sum. This reveals that mul might be more informative than concat and sum operations. The superiority might be due to element-wise product being capable of modeling the interactions and eliminating distribution differences between word and subword embedding. Intuitively, this is also similar to endow subword-aware \u201cattention\u201d over the word embedding. In contrast, concatenation operation may cause too high dimension, which leads to serious over-fitting issues, and sum operation is too simple to prevent from detailed information losing.",
+      "Since there is no training set for CFT dataset, our model is trained on PD training set. Note that the CFT dataset is harder for the machine to answer because the test set is further processed by human evaluation, and may not be accordance with the pattern of PD dataset. The results on PD and CFT datasets are listed in Table TABREF20 . As we see that, our SAW Reader significantly outperforms the CAS Reader in all types of testing, with improvements of 7.0% on PD and 8.8% on CFT test sets, respectively. Although the domain and topic of PD and CFT datasets are quite different, the results indicate that our model also works effectively for out-of-domain learning.",
+      "To verify if our method can only work for Chinese, we also evaluate the effectiveness of the proposed method on benchmark English dataset. We use CBT dataset as our testbed to evaluate the performance. For a fair comparison, we simply set the same parameters as before. Table TABREF22 shows the results. We observe that our model outperforms most of the previously public works, with 2.4 % gains on the CBT-NE test set compared with GA Reader which adopts word and character embedding concatenation. Our SAW Reader also achieves comparable performance with FG Reader who adopts neural gates to combine word-level and character-level representations with assistance of extra features including NE, POS and word frequency while our model is much simpler and faster. This result shows our SAW Reader is not restricted to Chinese reading comprehension, but also for other languages."
+    ],
+    [
+      "The vocabulary size could seriously involve the segmentation granularity. For BPE segmentation, the resulted subword vocabulary size is equal to the merging times plus the number of single-character types. To have an insight of the influence, we adopt merge times from 0 to 20 INLINEFORM0 , and conduct quantitative study on CMRC-2017 for BPE segmentation. Figure FIGREF25 shows the results. We observe that when the vocabulary size is 1 INLINEFORM1 , the models could obtain the best performance. The results indicate that for a task like reading comprehension the subwords, being a highly flexible grained representation between character and word, tends to be more like characters instead of words. However, when the subwords completely fall into characters, the model performs the worst. This indicates that the balance between word and character is quite critical and an appropriate grain of character-word segmentation could essentially improve the word representation."
+    ],
+    [
+      "To investigate the impact of the short list to the model performance, we conduct quantitative study on the filter ratio from [0.1, 0.2, ..., 1]. The results on the CMRC-2017 dataset are depicted in Figure FIGREF25 . As we can see that when INLINEFORM0 our SAW reader can obtain the best performance, showing that building the vocabulary among all the training set is not optimal and properly reducing the frequency filter ratio can boost the accuracy. This is partially attributed to training the model from the full vocabulary would cause serious over-fitting as the rare words representations can not obtain sufficient tuning. If the rare words are not initialized properly, they would also bias the whole word representations. Thus a model without OOV mechanism will fail to precisely represent those inevitable OOV words from test sets."
+    ],
+    [
+      "In text understanding tasks, if the ground-truth answer is OOV word or contains OOV word(s), the performance of deep neural networks would severely drop due to the incomplete representation, especially for cloze-style reading comprehension task where the answer is only one word or phrase. In CMRC-2017, we observe questions with OOV answers (denoted as \u201cOOV questions\") account for 17.22% in the error results of the best Word + Char embedding based model. With BPE subword embedding, 12.17% of these \u201cOOV questions\" could be correctly answered. This shows the subword representations could be essentially useful for modeling rare and unseen words.",
+      "To analyze the reading process of SAW Reader, we draw the attention distributions at intermediate layers as shown in Figure FIGREF28 . We observe the salient candidates in the document can be focused after the pair-wise matching of document and query and the right answer (\u201cThe mole\") could obtain a high weight at the very beginning. After attention learning, the key evidence of the answer would be collected and irrelevant parts would be ignored. This shows our SAW Reader is effective at selecting the vital points at the fundamental embedding layer, guiding the attention layers to collect more relevant pieces."
+    ],
+    [
+      "Recently, many deep learning models have been proposed for reading comprehension BIBREF16 , BIBREF22 , BIBREF23 , BIBREF24 , BIBREF25 , BIBREF9 , BIBREF26 , BIBREF27 . Notably, Chen2016A conducted an in-depth and thoughtful examination on the comprehension task based on an attentive neural network and an entity-centric classifier with a careful analysis based on handful features. kadlec2016text proposed the Attention Sum Reader (AS Reader) that uses attention to directly pick the answer from the context, which is motivated by the Pointer Network BIBREF28 . Instead of summing the attention of query-to-document, GA Reader BIBREF9 defined an element-wise product to endowing attention on each word of the document using the entire query representation to build query-specific representations of words in the document for accurate answer selection. Wang2017Gated employed gated self-matching networks (R-net) on passage against passage itself to refine passage representation with information from the whole passage. Cui2016Attention introduced an \u201cattended attention\" mechanism (AoA) where query-to-document and document-to-query are mutually attentive and interactive to each other."
+    ],
+    [
+      "Distributed word representation plays a fundamental role in neural models BIBREF29 , BIBREF30 , BIBREF31 , BIBREF32 , BIBREF33 , BIBREF34 , BIBREF35 , BIBREF36 . Recently, character embeddings are widely used to enrich word representations BIBREF37 , BIBREF21 , BIBREF38 , BIBREF39 . Yang2016Words explored a fine-grained gating mechanism (FG Reader) to dynamically combine word-level and character-level representations based on properties of the words. However, this method is computationally complex and it is not end-to-end, requiring extra labels such as NE and POS tags. Seo2016Bidirectional concatenated the character and word embedding to feed a two-layer Highway Network.",
+      "Not only for machine reading comprehension tasks, character embedding has also benefit other natural language process tasks, such as word segmentation BIBREF40 , machine translation BIBREF38 , tagging BIBREF41 , BIBREF42 and language modeling BIBREF43 , BIBREF44 . However, character embedding only shows marginal improvement due to a lack internal semantics. Lexical, syntactic and morphological information are also considered to improve word representation BIBREF12 , BIBREF45 . Bojanowski2016Enriching proposed to learn representations for character INLINEFORM0 -gram vectors and represent words as the sum of the INLINEFORM1 -gram vectors. Avraham2017The built a model inspired by BIBREF46 , who used morphological tags instead of INLINEFORM2 -grams. They jointly trained their morphological and semantic embeddings, implicitly assuming that morphological and semantic information should live in the same space. However, the linguistic knowledge resulting subwords, typically, morphological suffix, prefix or stem, may not be suitable for different kinds of languages and tasks. Sennrich2015Neural introduced the byte pair encoding (BPE) compression algorithm into neural machine translation for being capable of open-vocabulary translation by encoding rare and unknown words as subword units. Instead, we consider refining the word representations for both frequent and infrequent words from a computational perspective. Our proposed subword-augmented embedding approach is more general, which can be adopted to enhance the representation for each word by adaptively altering the segmentation granularity in multiple NLP tasks."
+    ],
+    [
+      "This paper presents an effective neural architecture, called subword-augmented word embedding to enhance the model performance for the cloze-style reading comprehension task. The proposed SAW Reader uses subword embedding to enhance the word representation and limit the word frequency spectrum to train rare words efficiently. With the help of the short list, the model size will also be reduced together with training speedup. Unlike most existing works, which introduce either complex attentive architectures or many manual features, our model is much more simple yet effective. Giving state-of-the-art performance on multiple benchmarks, the proposed reader has been proved effective for learning joint representation at both word and subword level and alleviating OOV difficulties."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1048/instruction.md b/qasper-1048/instruction.md
new file mode 100644
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@@ -0,0 +1,99 @@
+Name of Paper: Subword-augmented Embedding for Cloze Reading Comprehension
+
+Question: what are the baselines?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "The Subword-augmented Word Embedding",
+    "BPE Subword Segmentation",
+    "Subword-augmented Word Embedding",
+    "Attention Module",
+    "Dataset and Settings",
+    "Main Results",
+    "Merging Times of BPE",
+    "Filter Mechanism",
+    "Subword-Augmented Representations",
+    "Machine Reading Comprehension",
+    "Augmented Word Embedding",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "This work is licensed under a Creative Commons Attribution 4.0 International License. License details: http://creativecommons.org/licenses/by/4.0/",
+      "A recent hot challenge is to train machines to read and comprehend human languages. Towards this end, various machine reading comprehension datasets have been released, including cloze-style BIBREF0 , BIBREF1 , BIBREF2 and user-query types BIBREF3 , BIBREF4 . Meanwhile, a number of deep learning models are designed to take up the challenges, most of which focus on attention mechanism BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 . However, how to represent word in an effective way remains an open problem for diverse natural language processing tasks, including machine reading comprehension for different languages. Particularly, for a language like Chinese with a large set of characters (typically, thousands of), lots of which are semantically ambiguous, using either word-level or character-level embedding alone to build the word representations would not be accurate enough. This work especially focuses on a cloze-style reading comprehension task over fairy stories, which is highly challenging due to diverse semantic patterns with personified expressions and reference.",
+      "In real practice, a reading comprehension model or system which is often called reader in literatures easily suffers from out-of-vocabulary (OOV) word issues, especially for the cloze-style reading comprehension tasks when the ground-truth answers tend to include rare words or named entities (NE), which are hardly fully recorded in the vocabulary. This is more challenging in Chinese. There are over 13,000 characters in Chinese while there are only 26 letters in English without regard to punctuation marks. If a reading comprehension system cannot effectively manage the OOV issues, the performance will not be semantically accurate for the task.",
+      "Commonly, words are represented as vectors using either word embedding or character embedding. For the former, each word is mapped into low dimensional dense vectors from a lookup table. Character representations are usually obtained by applying neural networks on the character sequence of the word, and their hidden states are obtained to form the representation. Intuitively, word-level representation is good at catching global context and dependency relationships between words, while character embedding helps for dealing with rare word representation.",
+      "However, the minimal meaningful unit below word usually is not character, which motivates researchers to explore the potential unit (subword) between character and word to model sub-word morphologies or lexical semantics. In fact, morphological compounding (e.g. sunshine or playground) is one of the most common and productive methods of word formation across human languages, which inspires us to represent word by meaningful sub-word units. Recently, researchers have started to work on morphologically informed word embeddings BIBREF11 , BIBREF12 , aiming at better capturing syntactic, lexical and morphological information. With ready subwords, we do not have to work with characters, and segmentation could be stopped at the subword-level to reach a meaningful representation.",
+      "In this paper, we present various simple yet accurate subword-augmented embedding (SAW) strategies and propose SAW Reader as an instance. Specifically, we adopt subword information to enrich word embedding and survey different SAW operations to integrate word-level and subword-level embedding for a fine-grained representation. To ensure adequate training of OOV and low-frequency words, we employ a short list mechanism. Our evaluation will be performed on three public Chinese reading comprehension datasets and one English benchmark dataset for showing our method is also effective in multi-lingual case."
+    ],
+    [
+      "The concerned reading comprehension task can be roughly categorized as user-query type and cloze-style according to the answer form. Answers in the former are usually a span of texts while in the cloze-style task, the answers are words or phrases which lets the latter be the harder-hit area of OOV issues, inspiring us to select the cloze-style as our testbed for SAW strategies. Our preliminary study shows even for the advanced word-character based GA reader, OOV answers still account for nearly 1/5 in the error results. This also motivates us to explore better representations to further performance improvement.",
+      "The cloze-style task in this work can be described as a triple INLINEFORM0 , where INLINEFORM1 is a document (context), INLINEFORM2 is a query over the contents of INLINEFORM3 , in which a word or phrase is the right answer INLINEFORM4 . This section will introduce the proposed SAW Reader in the context of cloze-style reading comprehension. Given the triple INLINEFORM5 , the SAW Reader will be built in the following steps."
+    ],
+    [
+      "Word in most languages usually can be split into meaningful subword units despite of the writing form. For example, \u201cindispensable\" could be split into the following subwords: INLINEFORM0 .",
+      "In our implementation, we adopt Byte Pair Encoding (BPE) BIBREF13 which is a simple data compression technique that iteratively replaces the most frequent pair of bytes in a sequence by a single, unused byte. BPE allows for the representation of an open vocabulary through a fixed-size vocabulary of variable-length character sequences, making it a very suitable word segmentation strategy for neural network models.",
+      "The generalized framework can be described as follows. Firstly, all the input sequences (strings) are tokenized into a sequence of single-character subwords, then we repeat,",
+      "Count all bigrams under the current segmentation status of all sequences.",
+      "Find the bigram with the highest frequency and merge them in all the sequences. Note the segmentation status is updating now.",
+      "If the merging times do not reach the specified number, go back to 1, otherwise the algorithm ends.",
+      "In BIBREF14 , BPE is adopted to segment infrequent words into sub-word units for machine translation. However, there is a key difference between the motivations for subword segmentation. We aim to refine the word representations by using subwords, for both frequent and infrequent words, which is more generally motivated. To this end, we adaptively tokenize words in multi-granularity by controlling the merging times."
+    ],
+    [
+      "Our subwords are also formed as character n-grams, do not cross word boundaries. After using unsupervised segmentation methods to split each word into a subword sequence, an augmented embedding (AE) is to straightforwardly integrate word embedding INLINEFORM0 and subword embedding INLINEFORM1 for a given word INLINEFORM2 . INLINEFORM3 ",
+      " where INLINEFORM0 denotes the detailed integration operation. In this work, we investigate concatenation (concat), element-wise summation (sum) and element-wise multiplication (mul). Thus, each document INLINEFORM1 and query INLINEFORM2 is represented as INLINEFORM3 matrix where INLINEFORM4 denotes the dimension of word embedding and INLINEFORM5 is the number of words in the input.",
+      "Subword embedding could be useful to refine the word embedding in a finer-grained way, we also consider improving word representation from itself. For quite a lot of words, especially those rare ones, their word embedding is extremely hard to learn due to the data sparse issue. Actually, if all the words in the dataset are used to build the vocabulary, the OOV words from the test set will not obtain adequate training. If they are initiated inappropriately, either with relatively high or low weights, they will harm the answer prediction. To alleviate the OOV issues, we keep a short list INLINEFORM0 for specific words. INLINEFORM1 ",
+      "If INLINEFORM0 is in INLINEFORM1 , the immediate word embedding INLINEFORM2 is indexed from word lookup table INLINEFORM3 where INLINEFORM4 denotes the size (recorded words) of lookup table. Otherwise, it will be represented as the randomly initialized default word (denoted by a specific mark INLINEFORM5 ). Note that, this is intuitively like \u201cguessing\u201d the possible unknown words (which will appear during test) from the vocabulary during training and only the word embedding of the OOV words will be replaced by INLINEFORM6 while their subword embedding INLINEFORM7 will still be processed using the original word. In this way, the OOV words could be tuned sufficiently with expressive meaning after training. During test, the word embedding of unknown words would not severely bias its final representation. Thus, INLINEFORM8 ( INLINEFORM9 ) can be rewritten as INLINEFORM10 ",
+      "In our experiments, the short list is determined according to the word frequency. Concretely, we sort the vocabulary according to the word frequency from high to low. A frequency filter ratio INLINEFORM0 is set to filter out the low-frequency words (rare words) from the lookup table. For example, INLINEFORM1 =0.9 means the least frequent 10% words are replaced with the default UNK notation.",
+      "The subword embedding INLINEFORM0 is generated by taking the final outputs of a bidirectional gated recurrent unit (GRU) BIBREF15 applied to the embeddings from a lookup table of subwords. The structure of GRU used in this paper are described as follows. INLINEFORM1 ",
+      " where INLINEFORM0 denotes the element-wise multiplication. INLINEFORM1 and INLINEFORM2 are the reset and update gates respectively, and INLINEFORM3 are the hidden states. A bi-directional GRU (BiGRU) processes the sequence in both forward and backward directions. Subwords of each word are successively fed to forward GRU and backward GRU to obtain the internal features of two directions. The output for each input is the concatenation of the two vectors from both directions: INLINEFORM4 . Then, the output of BiGRUs is passed to a fully connected layer to obtain the final subword embedding INLINEFORM5 . INLINEFORM6 "
+    ],
+    [
+      "Our attention module is based on the Gated attention Reader (GA Reader) proposed by BIBREF9 . We choose this model due to its simplicity with comparable performance so that we can focus on the effectiveness of SAW strategies. This module can be described in the following two steps. After augmented embedding, we use two BiGRUs to get contextual representations of the document and query respectively, where the representation of each word is formed by concatenating the forward and backward hidden states. INLINEFORM0 ",
+      " For each word INLINEFORM0 in INLINEFORM1 , we form a word-specific representation of the query INLINEFORM2 using soft attention, and then adopt element-wise product to multiply the query representation with the document word representation. INLINEFORM3 ",
+      " where INLINEFORM0 denotes the multiplication operator to model the interactions between INLINEFORM1 and INLINEFORM2 . Then, the document contextual representation INLINEFORM3 is gated by query representation.",
+      "Suppose the network has INLINEFORM0 layers. At each layer, the document representation INLINEFORM1 is updated through above attention learning. After going through all the layers, our model comes to answer prediction phase. We use all the words in the document to form the candidate set INLINEFORM2 . Let INLINEFORM3 denote the INLINEFORM4 -th intermediate output of query representation INLINEFORM5 and INLINEFORM6 represent the full output of document representation INLINEFORM7 . The probability of each candidate word INLINEFORM8 as being the answer is predicted using a softmax layer over the inner-product between INLINEFORM9 and INLINEFORM10 . INLINEFORM11 ",
+      " where vector INLINEFORM0 denotes the probability distribution over all the words in the document. Note that each word may occur several times in the document. Thus, the probabilities of each candidate word occurring in different positions of the document are summed up for final prediction. INLINEFORM1 ",
+      " where INLINEFORM0 denotes the set of positions that a particular word INLINEFORM1 occurs in the document INLINEFORM2 . The training objective is to maximize INLINEFORM3 where INLINEFORM4 is the correct answer.",
+      "Finally, the candidate word with the highest probability will be chosen as the predicted answer. INLINEFORM0 ",
+      "Different from recent work employing complex attention mechanisms BIBREF5 , BIBREF7 , BIBREF16 , our attention mechanism is much more simple with comparable performance so that we can focus on the effectiveness of SAW strategies."
+    ],
+    [
+      "To verify the effectiveness of our proposed model, we conduct multiple experiments on three Chinese Machine Reading Comprehension datasets, namely CMRC-2017 BIBREF17 , People's Daily (PD) and Children Fairy Tales (CFT) BIBREF2 . In these datasets, a story containing consecutive sentences is formed as the Document and one of the sentences is either automatically or manually selected as the Query where one token is replaced by a placeholder to indicate the answer to fill in. Table TABREF8 gives data statistics. Different from the current cloze-style datasets for English reading comprehension, such as CBT, Daily Mail and CNN BIBREF0 , the three Chinese datasets do not provide candidate answers. Thus, the model has to find the correct answer from the entire document.",
+      "Besides, we also use the Children's Book Test (CBT) dataset BIBREF1 to test the generalization ability in multi-lingual case. We only focus on subsets where the answer is either a common noun (CN) or NE which is more challenging since the answer is likely to be rare words. We evaluate all the models in terms of accuracy, which is the standard evaluation metric for this task.",
+      "Throughout this paper, we use the same model setting to make fair comparisons. According to our preliminary experiments, we report the results based on the following settings. The default integration strategy is element-wise product. Word embeddings were 200 INLINEFORM0 and pre-trained by word2vec BIBREF18 toolkit on Wikipedia corpus. Subword embedding were 100 INLINEFORM1 and randomly initialized with the uniformed distribution in the interval [-0:05; 0:05]. Our model was implemented using the Theano and Lasagne Python libraries. We used stochastic gradient descent with ADAM updates for optimization BIBREF19 . The batch size was 64 and the initial learning rate was 0.001 which was halved every epoch after the second epoch. We also used gradient clipping with a threshold of 10 to stabilize GRU training BIBREF20 . We use three attention layers for all experiments. The GRU hidden units for both the word and subword representation were 128. The default frequency filter proportion was 0.9 and the default merging times of BPE was 1,000. We also apply dropout between layers with a dropout rate of 0.5 ."
+    ],
+    [
+      "[7]http://www.hfl-tek.com/cmrc2017/leaderboard.html",
+      "Table TABREF17 shows our results on CMRC-2017 dataset, which shows that our SAW Reader (mul) outperforms all other single models on the test set, with 7.57% improvements compared with Attention Sum Reader (AS Reader) baseline. Although WHU's model achieves the best besides our model on the valid set with only 0.75% below ours, their result on the test set is lower than ours by 2.27%, indicating our model has a satisfactory generalization ability.",
+      "We also list different integration operations for word and subword embeddings. Table TABREF19 shows the comparisons. From the results, we can see that Word + BPE outperforms Word + Char which indicates subword embedding works essentially. We also observe that mul outperforms the other two operations, concat and sum. This reveals that mul might be more informative than concat and sum operations. The superiority might be due to element-wise product being capable of modeling the interactions and eliminating distribution differences between word and subword embedding. Intuitively, this is also similar to endow subword-aware \u201cattention\u201d over the word embedding. In contrast, concatenation operation may cause too high dimension, which leads to serious over-fitting issues, and sum operation is too simple to prevent from detailed information losing.",
+      "Since there is no training set for CFT dataset, our model is trained on PD training set. Note that the CFT dataset is harder for the machine to answer because the test set is further processed by human evaluation, and may not be accordance with the pattern of PD dataset. The results on PD and CFT datasets are listed in Table TABREF20 . As we see that, our SAW Reader significantly outperforms the CAS Reader in all types of testing, with improvements of 7.0% on PD and 8.8% on CFT test sets, respectively. Although the domain and topic of PD and CFT datasets are quite different, the results indicate that our model also works effectively for out-of-domain learning.",
+      "To verify if our method can only work for Chinese, we also evaluate the effectiveness of the proposed method on benchmark English dataset. We use CBT dataset as our testbed to evaluate the performance. For a fair comparison, we simply set the same parameters as before. Table TABREF22 shows the results. We observe that our model outperforms most of the previously public works, with 2.4 % gains on the CBT-NE test set compared with GA Reader which adopts word and character embedding concatenation. Our SAW Reader also achieves comparable performance with FG Reader who adopts neural gates to combine word-level and character-level representations with assistance of extra features including NE, POS and word frequency while our model is much simpler and faster. This result shows our SAW Reader is not restricted to Chinese reading comprehension, but also for other languages."
+    ],
+    [
+      "The vocabulary size could seriously involve the segmentation granularity. For BPE segmentation, the resulted subword vocabulary size is equal to the merging times plus the number of single-character types. To have an insight of the influence, we adopt merge times from 0 to 20 INLINEFORM0 , and conduct quantitative study on CMRC-2017 for BPE segmentation. Figure FIGREF25 shows the results. We observe that when the vocabulary size is 1 INLINEFORM1 , the models could obtain the best performance. The results indicate that for a task like reading comprehension the subwords, being a highly flexible grained representation between character and word, tends to be more like characters instead of words. However, when the subwords completely fall into characters, the model performs the worst. This indicates that the balance between word and character is quite critical and an appropriate grain of character-word segmentation could essentially improve the word representation."
+    ],
+    [
+      "To investigate the impact of the short list to the model performance, we conduct quantitative study on the filter ratio from [0.1, 0.2, ..., 1]. The results on the CMRC-2017 dataset are depicted in Figure FIGREF25 . As we can see that when INLINEFORM0 our SAW reader can obtain the best performance, showing that building the vocabulary among all the training set is not optimal and properly reducing the frequency filter ratio can boost the accuracy. This is partially attributed to training the model from the full vocabulary would cause serious over-fitting as the rare words representations can not obtain sufficient tuning. If the rare words are not initialized properly, they would also bias the whole word representations. Thus a model without OOV mechanism will fail to precisely represent those inevitable OOV words from test sets."
+    ],
+    [
+      "In text understanding tasks, if the ground-truth answer is OOV word or contains OOV word(s), the performance of deep neural networks would severely drop due to the incomplete representation, especially for cloze-style reading comprehension task where the answer is only one word or phrase. In CMRC-2017, we observe questions with OOV answers (denoted as \u201cOOV questions\") account for 17.22% in the error results of the best Word + Char embedding based model. With BPE subword embedding, 12.17% of these \u201cOOV questions\" could be correctly answered. This shows the subword representations could be essentially useful for modeling rare and unseen words.",
+      "To analyze the reading process of SAW Reader, we draw the attention distributions at intermediate layers as shown in Figure FIGREF28 . We observe the salient candidates in the document can be focused after the pair-wise matching of document and query and the right answer (\u201cThe mole\") could obtain a high weight at the very beginning. After attention learning, the key evidence of the answer would be collected and irrelevant parts would be ignored. This shows our SAW Reader is effective at selecting the vital points at the fundamental embedding layer, guiding the attention layers to collect more relevant pieces."
+    ],
+    [
+      "Recently, many deep learning models have been proposed for reading comprehension BIBREF16 , BIBREF22 , BIBREF23 , BIBREF24 , BIBREF25 , BIBREF9 , BIBREF26 , BIBREF27 . Notably, Chen2016A conducted an in-depth and thoughtful examination on the comprehension task based on an attentive neural network and an entity-centric classifier with a careful analysis based on handful features. kadlec2016text proposed the Attention Sum Reader (AS Reader) that uses attention to directly pick the answer from the context, which is motivated by the Pointer Network BIBREF28 . Instead of summing the attention of query-to-document, GA Reader BIBREF9 defined an element-wise product to endowing attention on each word of the document using the entire query representation to build query-specific representations of words in the document for accurate answer selection. Wang2017Gated employed gated self-matching networks (R-net) on passage against passage itself to refine passage representation with information from the whole passage. Cui2016Attention introduced an \u201cattended attention\" mechanism (AoA) where query-to-document and document-to-query are mutually attentive and interactive to each other."
+    ],
+    [
+      "Distributed word representation plays a fundamental role in neural models BIBREF29 , BIBREF30 , BIBREF31 , BIBREF32 , BIBREF33 , BIBREF34 , BIBREF35 , BIBREF36 . Recently, character embeddings are widely used to enrich word representations BIBREF37 , BIBREF21 , BIBREF38 , BIBREF39 . Yang2016Words explored a fine-grained gating mechanism (FG Reader) to dynamically combine word-level and character-level representations based on properties of the words. However, this method is computationally complex and it is not end-to-end, requiring extra labels such as NE and POS tags. Seo2016Bidirectional concatenated the character and word embedding to feed a two-layer Highway Network.",
+      "Not only for machine reading comprehension tasks, character embedding has also benefit other natural language process tasks, such as word segmentation BIBREF40 , machine translation BIBREF38 , tagging BIBREF41 , BIBREF42 and language modeling BIBREF43 , BIBREF44 . However, character embedding only shows marginal improvement due to a lack internal semantics. Lexical, syntactic and morphological information are also considered to improve word representation BIBREF12 , BIBREF45 . Bojanowski2016Enriching proposed to learn representations for character INLINEFORM0 -gram vectors and represent words as the sum of the INLINEFORM1 -gram vectors. Avraham2017The built a model inspired by BIBREF46 , who used morphological tags instead of INLINEFORM2 -grams. They jointly trained their morphological and semantic embeddings, implicitly assuming that morphological and semantic information should live in the same space. However, the linguistic knowledge resulting subwords, typically, morphological suffix, prefix or stem, may not be suitable for different kinds of languages and tasks. Sennrich2015Neural introduced the byte pair encoding (BPE) compression algorithm into neural machine translation for being capable of open-vocabulary translation by encoding rare and unknown words as subword units. Instead, we consider refining the word representations for both frequent and infrequent words from a computational perspective. Our proposed subword-augmented embedding approach is more general, which can be adopted to enhance the representation for each word by adaptively altering the segmentation granularity in multiple NLP tasks."
+    ],
+    [
+      "This paper presents an effective neural architecture, called subword-augmented word embedding to enhance the model performance for the cloze-style reading comprehension task. The proposed SAW Reader uses subword embedding to enhance the word representation and limit the word frequency spectrum to train rare words efficiently. With the help of the short list, the model size will also be reduced together with training speedup. Unlike most existing works, which introduce either complex attentive architectures or many manual features, our model is much more simple yet effective. Giving state-of-the-art performance on multiple benchmarks, the proposed reader has been proved effective for learning joint representation at both word and subword level and alleviating OOV difficulties."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1050/instruction.md b/qasper-1050/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..0d35acd1ac48aad0d10525568e9503f4ba3f7989
--- /dev/null
+++ b/qasper-1050/instruction.md
@@ -0,0 +1,90 @@
+Name of Paper: Kurdish (Sorani) Speech to Text: Presenting an Experimental Dataset
+
+Question: How was the dataset collected?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related work",
+    "The BD-4SK-ASR Dataset",
+    "The BD-4SK-ASR Dataset ::: Phoeset",
+    "The BD-4SK-ASR Dataset ::: Filler phones",
+    "The BD-4SK-ASR Dataset ::: The File IDs",
+    "The BD-4SK-ASR Dataset ::: The Transcription",
+    "The BD-4SK-ASR Dataset ::: The Corpus",
+    "The BD-4SK-ASR Dataset ::: The Narration Files",
+    "The BD-4SK-ASR Dataset ::: The Language Model",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Kurdish language processing requires endeavor by interested researchers and scholars to overcome with a large gap which it has regarding the resource scarcity. The areas that need attention and the efforts required have been addressed in BIBREF0.",
+      "The Kurdish speech recognition is an area which has not been studied so far. We were not able to retrieve any resources in the literature regarding this subject.",
+      "In this paper, we present a dataset based on CMUShpinx BIBREF1 for Sorani Kurdish. We call it a Dataset for Sorani Kurdish Automatic Speech Recognition (BD-4SK-ASR). Although other technologies are emerging, CMUShpinx could still be used for experimental studies.",
+      "The rest of this paper is organized as follows. Section SECREF2 reviews the related work. Section SECREF3 presents different parts of the dataset, such as the dictionary, phoneset, transcriptions, corpus, and language model. Finally, Section SECREF4 concludes the paper and suggests some areas for future work."
+    ],
+    [
+      "The work on Automatic Speech Recognition (ASR) has a long history, but we could not retrieve any literature on Kurdish ASR at the time of compiling this article. However, the literature on ASR for different languages is resourceful. Also, researchers have widely used CMUSphinx for ASR though other technologies have been emerging in recent years BIBREF1.",
+      "We decided to use CMUSphinx because we found it a proper and well-established environment to start Kurdish ASR."
+    ],
+    [
+      "To develop the dataset, we extracted 200 sentences from Sorani Kurdish books of grades one to three of the primary school in the Kurdistan Region of Iraq. We randomly created 2000 sentences from the extracted sentences.",
+      "In the following sections, we present the available items in the dataset. The dataset ia available on https://github.com/KurdishBLARK/BD-4SK-ASR."
+    ],
+    [
+      "The phoneset includes 34 phones for Sorani Kurdish. A sample of the file content is given below.",
+      "R",
+      "RR",
+      "S",
+      "SIL",
+      "SH",
+      "T",
+      "V",
+      "W",
+      "WW",
+      "Figure FIGREF3 shows the Sorani letters in Persian-Arabic script, the suggested phoneme (capital English letters), and an example of the transformation of words in the developed corpus."
+    ],
+    [
+      "The filler phone file usually contains fillers in spoken sentences. In our basic sentences, we have only considered silence. Therefore it only includes three lines to indicate the possible pauses at the beginning and end of the sentences and also after each word."
+    ],
+    [
+      "This file includes the list of files in which the narrated sentences have been recorded. The recorded files are in wav formats. However, in the file IDs, the extension is omitted. A sample of the file content is given below. The test directory is the directory in which the files are located.",
+      "test/T1-1-50-01",
+      "test/T1-1-50-02",
+      "test/T1-1-50-03",
+      "test/T1-1-50-04",
+      "test/T1-1-50-05",
+      "test/T1-1-50-06"
+    ],
+    [
+      "This file contains the transcription of each sentence based on the phoneset along with the file ID in which the equivalent narration has been saved. The following is a sample of the content of the file.",
+      "<s> BYR RRAAMAAN DAARISTAANA AMAANAY </s> (T1-1-50-18)",
+      "<s> DWWRA HAWLER CHIRAAYA SARDAAN NABWW </s> (T1-1-50-19)",
+      "<s> SAALL DYWAAR QWTAABXAANA NACHIN </s> (T1-1-50-20)",
+      "<s> XWENDIN ANDAAMAANY GASHA </s> (T1-1-50-21)",
+      "<s> NAMAAM WRYAA KIRD PSHWWDAA </s> (T1-1-50-22)",
+      "<s> DARCHWWY DAKAN DAKAWET </s> (T1-1-50-23)",
+      "<s> CHAND BIRAAT MAQAST </s> (T1-1-50-24)",
+      "<s> BAAXCHAKAY DAAYK DARCHWWY </s> (T1-1-50-25)",
+      "<s> RROZH JWAAN DAKAWET ZYAANYAAN </s> (T1-1-50-26)",
+      ""
+    ],
+    [
+      "The corpus includes 2000 sentences. Theses sentence are random renderings of 200 sentences, which we have taken from Sorani Kurdish books of the grades one to three of the primary school in the Kurdistan Region of Iraq. The reason that we have taken only 200 sentences is to have a smaller dictionary and also to increase the repetition of each word in the narrated speech. We transformed the corpus sentences, which are in Persian-Arabic script, into the format which complies with the suggested phones for the related Sorani letters (see Section SECREF6)."
+    ],
+    [
+      "Two thousand narration files were created. We used Audacity to record the narrations. We used a normal laptop in a quiet room and minimized the background noise. However, we could not manage to avoid the noise of the fan of the laptop. A single speaker narrated the 2000 sentences, which took several days. We set the Audacity software to have a sampling rate of 16, 16-bit bit rate, and a mono (single) channel. The noise reduction db was set to 6, the sensitivity to 4.00, and the frequency smoothing to 0."
+    ],
+    [
+      "We created the language from the transcriptions. The model was created using CMUSphinx in which (fixed) discount mass is 0.5, and backoffs are computed using the ratio method. The model includes 283 unigrams, 5337 bigrams, and 6935 trigrams."
+    ],
+    [
+      "We presented a dataset, BD-4SK-ASR, that could be used in training and developing an acoustic model for Automatic Speech Recognition in CMUSphinx environment for Sorani Kurdish. The Kurdish books of grades one to three of primary schools in the Kurdistan Region of Iraq were used to extract 200 sample sentences. The dataset includes the dictionary, the phoneset, the transcriptions of the corpus sentences using the suggested phones, the recorded narrations of the sentences, and the acoustic model. The dataset could be used to start experiments on Sorani Kurdish ASR.",
+      "As it was mentioned before, research and development on Kurdish ASR require a huge amount of effort. A variety of areas must be explored, and various resources must be collected and developed. The multi-dialect characteristic of Kurdish makes these tasks rather demanding. To participate in these efforts, we are interested in the expansion of Kurdish ASR by developing a larger dataset based on larger Sorani corpora, working on the other Kurdish dialects, and using new environments for ASR such as Kaldi."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1051/instruction.md b/qasper-1051/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..e8aeed9ef2fb33fac5a8579129ebb25a169b3ba4
--- /dev/null
+++ b/qasper-1051/instruction.md
@@ -0,0 +1,90 @@
+Name of Paper: Kurdish (Sorani) Speech to Text: Presenting an Experimental Dataset
+
+Question: What is the size of the dataset?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related work",
+    "The BD-4SK-ASR Dataset",
+    "The BD-4SK-ASR Dataset ::: Phoeset",
+    "The BD-4SK-ASR Dataset ::: Filler phones",
+    "The BD-4SK-ASR Dataset ::: The File IDs",
+    "The BD-4SK-ASR Dataset ::: The Transcription",
+    "The BD-4SK-ASR Dataset ::: The Corpus",
+    "The BD-4SK-ASR Dataset ::: The Narration Files",
+    "The BD-4SK-ASR Dataset ::: The Language Model",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Kurdish language processing requires endeavor by interested researchers and scholars to overcome with a large gap which it has regarding the resource scarcity. The areas that need attention and the efforts required have been addressed in BIBREF0.",
+      "The Kurdish speech recognition is an area which has not been studied so far. We were not able to retrieve any resources in the literature regarding this subject.",
+      "In this paper, we present a dataset based on CMUShpinx BIBREF1 for Sorani Kurdish. We call it a Dataset for Sorani Kurdish Automatic Speech Recognition (BD-4SK-ASR). Although other technologies are emerging, CMUShpinx could still be used for experimental studies.",
+      "The rest of this paper is organized as follows. Section SECREF2 reviews the related work. Section SECREF3 presents different parts of the dataset, such as the dictionary, phoneset, transcriptions, corpus, and language model. Finally, Section SECREF4 concludes the paper and suggests some areas for future work."
+    ],
+    [
+      "The work on Automatic Speech Recognition (ASR) has a long history, but we could not retrieve any literature on Kurdish ASR at the time of compiling this article. However, the literature on ASR for different languages is resourceful. Also, researchers have widely used CMUSphinx for ASR though other technologies have been emerging in recent years BIBREF1.",
+      "We decided to use CMUSphinx because we found it a proper and well-established environment to start Kurdish ASR."
+    ],
+    [
+      "To develop the dataset, we extracted 200 sentences from Sorani Kurdish books of grades one to three of the primary school in the Kurdistan Region of Iraq. We randomly created 2000 sentences from the extracted sentences.",
+      "In the following sections, we present the available items in the dataset. The dataset ia available on https://github.com/KurdishBLARK/BD-4SK-ASR."
+    ],
+    [
+      "The phoneset includes 34 phones for Sorani Kurdish. A sample of the file content is given below.",
+      "R",
+      "RR",
+      "S",
+      "SIL",
+      "SH",
+      "T",
+      "V",
+      "W",
+      "WW",
+      "Figure FIGREF3 shows the Sorani letters in Persian-Arabic script, the suggested phoneme (capital English letters), and an example of the transformation of words in the developed corpus."
+    ],
+    [
+      "The filler phone file usually contains fillers in spoken sentences. In our basic sentences, we have only considered silence. Therefore it only includes three lines to indicate the possible pauses at the beginning and end of the sentences and also after each word."
+    ],
+    [
+      "This file includes the list of files in which the narrated sentences have been recorded. The recorded files are in wav formats. However, in the file IDs, the extension is omitted. A sample of the file content is given below. The test directory is the directory in which the files are located.",
+      "test/T1-1-50-01",
+      "test/T1-1-50-02",
+      "test/T1-1-50-03",
+      "test/T1-1-50-04",
+      "test/T1-1-50-05",
+      "test/T1-1-50-06"
+    ],
+    [
+      "This file contains the transcription of each sentence based on the phoneset along with the file ID in which the equivalent narration has been saved. The following is a sample of the content of the file.",
+      "<s> BYR RRAAMAAN DAARISTAANA AMAANAY </s> (T1-1-50-18)",
+      "<s> DWWRA HAWLER CHIRAAYA SARDAAN NABWW </s> (T1-1-50-19)",
+      "<s> SAALL DYWAAR QWTAABXAANA NACHIN </s> (T1-1-50-20)",
+      "<s> XWENDIN ANDAAMAANY GASHA </s> (T1-1-50-21)",
+      "<s> NAMAAM WRYAA KIRD PSHWWDAA </s> (T1-1-50-22)",
+      "<s> DARCHWWY DAKAN DAKAWET </s> (T1-1-50-23)",
+      "<s> CHAND BIRAAT MAQAST </s> (T1-1-50-24)",
+      "<s> BAAXCHAKAY DAAYK DARCHWWY </s> (T1-1-50-25)",
+      "<s> RROZH JWAAN DAKAWET ZYAANYAAN </s> (T1-1-50-26)",
+      ""
+    ],
+    [
+      "The corpus includes 2000 sentences. Theses sentence are random renderings of 200 sentences, which we have taken from Sorani Kurdish books of the grades one to three of the primary school in the Kurdistan Region of Iraq. The reason that we have taken only 200 sentences is to have a smaller dictionary and also to increase the repetition of each word in the narrated speech. We transformed the corpus sentences, which are in Persian-Arabic script, into the format which complies with the suggested phones for the related Sorani letters (see Section SECREF6)."
+    ],
+    [
+      "Two thousand narration files were created. We used Audacity to record the narrations. We used a normal laptop in a quiet room and minimized the background noise. However, we could not manage to avoid the noise of the fan of the laptop. A single speaker narrated the 2000 sentences, which took several days. We set the Audacity software to have a sampling rate of 16, 16-bit bit rate, and a mono (single) channel. The noise reduction db was set to 6, the sensitivity to 4.00, and the frequency smoothing to 0."
+    ],
+    [
+      "We created the language from the transcriptions. The model was created using CMUSphinx in which (fixed) discount mass is 0.5, and backoffs are computed using the ratio method. The model includes 283 unigrams, 5337 bigrams, and 6935 trigrams."
+    ],
+    [
+      "We presented a dataset, BD-4SK-ASR, that could be used in training and developing an acoustic model for Automatic Speech Recognition in CMUSphinx environment for Sorani Kurdish. The Kurdish books of grades one to three of primary schools in the Kurdistan Region of Iraq were used to extract 200 sample sentences. The dataset includes the dictionary, the phoneset, the transcriptions of the corpus sentences using the suggested phones, the recorded narrations of the sentences, and the acoustic model. The dataset could be used to start experiments on Sorani Kurdish ASR.",
+      "As it was mentioned before, research and development on Kurdish ASR require a huge amount of effort. A variety of areas must be explored, and various resources must be collected and developed. The multi-dialect characteristic of Kurdish makes these tasks rather demanding. To participate in these efforts, we are interested in the expansion of Kurdish ASR by developing a larger dataset based on larger Sorani corpora, working on the other Kurdish dialects, and using new environments for ASR such as Kaldi."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1056/instruction.md b/qasper-1056/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..eb966c0466b7d747f1fa8253fecba586d514d538
--- /dev/null
+++ b/qasper-1056/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Cohesion and Coalition Formation in the European Parliament: Roll-Call Votes and Twitter Activities
+
+Question: What is the relationship between the co-voting and retweeting patterns?
\ No newline at end of file
diff --git a/qasper-1057/instruction.md b/qasper-1057/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..6161f1d30b796a52ba4ad319afabe5843bc51ecb
--- /dev/null
+++ b/qasper-1057/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Cohesion and Coalition Formation in the European Parliament: Roll-Call Votes and Twitter Activities
+
+Question: Does the analysis find that coalitions are formed in the same way for different policy areas?
\ No newline at end of file
diff --git a/qasper-1058/instruction.md b/qasper-1058/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..90ee31208f3e93235707407f370be19809d7e9ab
--- /dev/null
+++ b/qasper-1058/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Cohesion and Coalition Formation in the European Parliament: Roll-Call Votes and Twitter Activities
+
+Question: What insights does the analysis give about the cohesion of political groups in the European parliament?
\ No newline at end of file
diff --git a/qasper-1059/instruction.md b/qasper-1059/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..1612986865dde649094dcbbcdfab77cdd82f65dc
--- /dev/null
+++ b/qasper-1059/instruction.md
@@ -0,0 +1,155 @@
+Name of Paper: Cohesion and Coalition Formation in the European Parliament: Roll-Call Votes and Twitter Activities
+
+Question: Do they authors account for differences in usage of Twitter amongst MPs into their model?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Abstract",
+    "Introduction",
+    "Related work",
+    "Methods",
+    "Co-voting measured by agreement",
+    "A network-based measure of co-voting",
+    "Measuring cohesion and coalitions on Twitter",
+    "Cohesion of political groups",
+    "Coalitions in the European Parliament",
+    "Discussion",
+    "Conclusions",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "We study the cohesion within and the coalitions between political groups in the Eighth European Parliament (2014\u20132019) by analyzing two entirely different aspects of the behavior of the Members of the European Parliament (MEPs) in the policy-making processes. On one hand, we analyze their co-voting patterns and, on the other, their retweeting behavior. We make use of two diverse datasets in the analysis. The first one is the roll-call vote dataset, where cohesion is regarded as the tendency to co-vote within a group, and a coalition is formed when the members of several groups exhibit a high degree of co-voting agreement on a subject. The second dataset comes from Twitter; it captures the retweeting (i.e., endorsing) behavior of the MEPs and implies cohesion (retweets within the same group) and coalitions (retweets between groups) from a completely different perspective.",
+      "We employ two different methodologies to analyze the cohesion and coalitions. The first one is based on Krippendorff's Alpha reliability, used to measure the agreement between raters in data-analysis scenarios, and the second one is based on Exponential Random Graph Models, often used in social-network analysis. We give general insights into the cohesion of political groups in the European Parliament, explore whether coalitions are formed in the same way for different policy areas, and examine to what degree the retweeting behavior of MEPs corresponds to their co-voting patterns. A novel and interesting aspect of our work is the relationship between the co-voting and retweeting patterns."
+    ],
+    [
+      "Social-media activities often reflect phenomena that occur in other complex systems. By observing social networks and the content propagated through these networks, we can describe or even predict the interplay between the observed social-media activities and another complex system that is more difficult, if not impossible, to monitor. There are numerous studies reported in the literature that successfully correlate social-media activities to phenomena like election outcomes BIBREF0 , BIBREF1 or stock-price movements BIBREF2 , BIBREF3 .",
+      "In this paper we study the cohesion and coalitions exhibited by political groups in the Eighth European Parliament (2014\u20132019). We analyze two entirely different aspects of how the Members of the European Parliament (MEPs) behave in policy-making processes. On one hand, we analyze their co-voting patterns and, on the other, their retweeting (i.e., endorsing) behavior.",
+      "We use two diverse datasets in the analysis: the roll-call votes and the Twitter data. A roll-call vote (RCV) is a vote in the parliament in which the names of the MEPs are recorded along with their votes. The RCV data is available as part of the minutes of the parliament's plenary sessions. From this perspective, cohesion is seen as the tendency to co-vote (i.e., cast the same vote) within a group, and a coalition is formed when members of two or more groups exhibit a high degree of co-voting on a subject. The second dataset comes from Twitter. It captures the retweeting behavior of MEPs and implies cohesion (retweets within the same group) and coalitions (retweets between the groups) from a completely different perspective.",
+      "With over 300 million monthly active users and 500 million tweets posted daily, Twitter is one of the most popular social networks. Twitter allows its users to post short messages (tweets) and to follow other users. A user who follows another user is able to read his/her public tweets. Twitter also supports other types of interaction, such as user mentions, replies, and retweets. Of these, retweeting is the most important activity as it is used to share and endorse content created by other users. When a user retweets a tweet, the information about the original author as well as the tweet's content are preserved, and the tweet is shared with the user's followers. Typically, users retweet content that they agree with and thus endorse the views expressed by the original tweeter.",
+      "We apply two different methodologies to analyze the cohesion and coalitions. The first one is based on Krippendorff's INLINEFORM0 BIBREF4 which measures the agreement among observers, or voters in our case. The second one is based on Exponential Random Graph Models (ERGM) BIBREF5 . In contrast to the former, ERGM is a network-based approach and is often used in social-network analyses. Even though these two methodologies come with two different sets of techniques and are based on different assumptions, they provide consistent results.",
+      "The main contributions of this paper are as follows:",
+      "(i) We give general insights into the cohesion of political groups in the Eighth European Parliament, both overall and across different policy areas.",
+      "(ii) We explore whether coalitions are formed in the same way for different policy areas.",
+      "(iii) We explore to what degree the retweeting behavior of MEPs corresponds to their co-voting patterns.",
+      "(iv) We employ two statistically sound methodologies and examine the extent to which the results are sensitive to the choice of methodology. While the results are mostly consistent, we show that the difference are due to the different treatment of non-attending and abstaining MEPs by INLINEFORM0 and ERGM.",
+      "The most novel and interesting aspect of our work is the relationship between the co-voting and the retweeting patterns. The increased use of Twitter by MEPs on days with a roll-call vote session (see Fig FIGREF1 ) is an indicator that these two processes are related. In addition, the force-based layouts of the co-voting network and the retweet network reveal a very similar structure on the left-to-center side of the political spectrum (see Fig FIGREF2 ). They also show a discrepancy on the far-right side of the spectrum, which calls for a more detailed analysis."
+    ],
+    [
+      "In this paper we study and relate two very different aspects of how MEPs behave in policy-making processes. First, we look at their co-voting behavior, and second, we examine their retweeting patterns. Thus, we draw related work from two different fields of science. On one hand, we look at how co-voting behavior is analyzed in the political-science literature and, on the other, we explore how Twitter is used to better understand political and policy-making processes. The latter has been more thoroughly explored in the field of data mining (specifically, text mining and network analysis).",
+      "To the best of our knowledge, this is the first paper that studies legislative behavior in the Eighth European Parliament. The legislative behavior of the previous parliaments was thoroughly studied by Hix, Attina, and others BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 . These studies found that voting behavior is determined to a large extent\u2014and when viewed over time, increasingly so\u2014by affiliation to a political group, as an organizational reflection of the ideological position. The authors found that the cohesion of political groups in the parliament has increased, while nationality has been less and less of a decisive factor BIBREF12 . The literature also reports that a split into political camps on the left and right of the political spectrum has recently replaced the `grand coalition' between the two big blocks of Christian Conservatives (EPP) and Social Democrats (S&D) as the dominant form of finding majorities in the parliament. The authors conclude that coalitions are to a large extent formed along the left-to-right axis BIBREF12 .",
+      "In this paper we analyze the roll-call vote data published in the minutes of the parliament's plenary sessions. For a given subject, the data contains the vote of each MEP present at the respective sitting. Roll-call vote data from the European Parliament has already been extensively studied by other authors, most notably by Hix et al. BIBREF10 , BIBREF13 , BIBREF11 . To be able to study the cohesion and coalitions, authors like Hix, Attina, and Rice BIBREF6 , BIBREF13 , BIBREF14 defined and employed a variety of agreement measures. The most prominent measure is the Agreement Index proposed by Hix et al. BIBREF13 . This measure computes the agreement score from the size of the majority class for a particular vote. The Agreement Index, however, exhibits two drawbacks: (i) it does not account for co-voting by chance, and (ii) without a proper adaptation, it does not accommodate the scenario in which the agreement is to be measured between two different political groups.",
+      "We employ two statistically sound methodologies developed in two different fields of science. The first one is based on Krippendorff's INLINEFORM0 BIBREF4 . INLINEFORM1 is a measure of the agreement among observers, coders, or measuring instruments that assign values to items or phenomena. It compares the observed agreement to the agreement expected by chance. INLINEFORM2 is used to measure the inter- and self-annotator agreement of human experts when labeling data, and the performance of classification models in machine learning scenarios BIBREF15 . In addition to INLINEFORM3 , we employ Exponential Random Graph Models (ERGM) BIBREF5 . In contrast to the former, ERGM is a network-based approach, often used in social-network analyses. ERGM can be employed to investigate how different network statistics (e.g., number of edges and triangles) or external factors (e.g., political group membership) govern the network-formation process.",
+      "The second important aspect of our study is related to analyzing the behavior of participants in social networks, specifically Twitter. Twitter is studied by researchers to better understand different political processes, and in some cases to predict their outcomes. Eom et al. BIBREF1 consider the number of tweets by a party as a proxy for the collective attention to the party, explore the dynamics of the volume, and show that this quantity contains information about an election's outcome. Other studies BIBREF16 reach similar conclusions. Conover et al. BIBREF17 predicted the political alignment of Twitter users in the run-up to the 2010 US elections based on content and network structure. They analyzed the polarization of the retweet and mention networks for the same elections BIBREF18 . Borondo et al. BIBREF19 analyzed user activity during the Spanish presidential elections. They additionally analyzed the 2012 Catalan elections, focusing on the interplay between the language and the community structure of the network BIBREF20 . Most existing research, as Larsson points out BIBREF21 , focuses on the online behavior of leading political figures during election campaigns.",
+      "This paper continues our research on communities that MEPs (and their followers) form on Twitter BIBREF22 . The goal of our research was to evaluate the role of Twitter in identifying communities of influence when the actual communities are known. We represent the influence on Twitter by the number of retweets that MEPs \u201creceive\u201d. We construct two networks of influence: (i) core, which consists only of MEPs, and (ii) extended, which also involves their followers. We compare the detected communities in both networks to the groups formed by the political, country, and language membership of MEPs. The results show that the detected communities in the core network closely match the political groups, while the communities in the extended network correspond to the countries of residence. This provides empirical evidence that analyzing retweet networks can reveal real-world relationships and can be used to uncover hidden properties of the networks.",
+      "Lazer BIBREF23 highlights the importance of network-based approaches in political science in general by arguing that politics is a relational phenomenon at its core. Some researchers have adopted the network-based approach to investigate the structure of legislative work in the US Congress, including committee and sub-committee membership BIBREF24 , bill co-sponsoring BIBREF25 , and roll-call votes BIBREF26 . More recently, Dal Maso et al. BIBREF27 examined the community structure with respect to political coalitions and government structure in the Italian Parliament. Scherpereel et al. BIBREF28 examined the constituency, personal, and strategic characteristics of MEPs that influence their tweeting behavior. They suggested that Twitter's characteristics, like immediacy, interactivity, spontaneity, personality, and informality, are likely to resonate with political parties across Europe. By fitting regression models, the authors find that MEPs from incohesive groups have a greater tendency to retweet.",
+      "In contrast to most of these studies, we focus on the Eighth European Parliament, and more importantly, we study and relate two entirely different behavioral aspects, co-voting and retweeting. The goal of this research is to better understand the cohesion and coalition formation processes in the European Parliament by quantifying and comparing the co-voting patterns and social behavior."
+    ],
+    [
+      "In this section we present the methods to quantify cohesion and coalitions from the roll-call votes and Twitter activities."
+    ],
+    [
+      "We first show how the co-voting behaviour of MEPs can be quantified by a measure of the agreement between them. We treat individual RCVs as observations, and MEPs as independent observers or raters. When they cast the same vote, there is a high level of agreement, and when they vote differently, there is a high level of disagreement. We define cohesion as the level of agreement within a political group, a coalition as a voting agreement between political groups, and opposition as a disagreement between different groups.",
+      "There are many well-known measures of agreement in the literature. We selected Krippendorff's Alpha-reliability ( INLINEFORM0 ) BIBREF4 , which is a generalization of several specialized measures. It works for any number of observers, and is applicable to different variable types and metrics (e.g., nominal, ordered, interval, etc.). In general, INLINEFORM1 is defined as follows: INLINEFORM2 ",
+      "where INLINEFORM0 is the actual disagreement between observers (MEPs), and INLINEFORM1 is disagreement expected by chance. When observers agree perfectly, INLINEFORM2 INLINEFORM3 , when the agreement equals the agreement by chance, INLINEFORM4 INLINEFORM5 , and when the observers disagree systematically, INLINEFORM6 INLINEFORM7 .",
+      "The two disagreement measures are defined as follows: INLINEFORM0 ",
+      " INLINEFORM0 ",
+      "The arguments INLINEFORM0 , and INLINEFORM1 are defined below and refer to the values in the coincidence matrix that is constructed from the RCVs data. In roll-call votes, INLINEFORM2 (and INLINEFORM3 ) is a nominal variable with two possible values: yes and no. INLINEFORM4 is a difference function between the values of INLINEFORM5 and INLINEFORM6 , defined as: INLINEFORM7 ",
+      "The RCVs data has the form of a reliability data matrix: INLINEFORM0 ",
+      "where INLINEFORM0 is the number of RCVs, INLINEFORM1 is the number of MEPs, INLINEFORM2 is the number of votes cast in the voting INLINEFORM3 , and INLINEFORM4 is the actual vote of an MEP INLINEFORM5 in voting INLINEFORM6 (yes or no).",
+      "A coincidence matrix is constructed from the reliability data matrix, and is in general a INLINEFORM0 -by- INLINEFORM1 square matrix, where INLINEFORM2 is the number of possible values of INLINEFORM3 . In our case, where only yes/no votes are relevant, the coincidence matrix is a 2-by-2 matrix of the following form: INLINEFORM4 ",
+      "A cell INLINEFORM0 accounts for all coincidences from all pairs of MEPs in all RCVs where one MEP has voted INLINEFORM1 and the other INLINEFORM2 . INLINEFORM3 and INLINEFORM4 are the totals for each vote outcome, and INLINEFORM5 is the grand total. The coincidences INLINEFORM6 are computed as: INLINEFORM7 ",
+      "where INLINEFORM0 is the number of INLINEFORM1 pairs in vote INLINEFORM2 , and INLINEFORM3 is the number of MEPs that voted in INLINEFORM4 . When computing INLINEFORM5 , each pair of votes is considered twice, once as a INLINEFORM6 pair, and once as a INLINEFORM7 pair. The coincidence matrix is therefore symmetrical around the diagonal, and the diagonal contains all the equal votes.",
+      "The INLINEFORM0 agreement is used to measure the agreement between two MEPs or within a group of MEPs. When applied to a political group, INLINEFORM1 corresponds to the cohesion of the group. The closer INLINEFORM2 is to 1, the higher the agreement of the MEPs in the group, and hence the higher the cohesion of the group.",
+      "We propose a modified version of INLINEFORM0 to measure the agreement between two different groups, INLINEFORM1 and INLINEFORM2 . In the case of a voting agreement between political groups, high INLINEFORM3 is interpreted as a coalition between the groups, whereas negative INLINEFORM4 indicates political opposition.",
+      "Suppose INLINEFORM0 and INLINEFORM1 are disjoint subsets of all the MEPs, INLINEFORM2 , INLINEFORM3 . The respective number of votes cast by both group members in vote INLINEFORM4 is INLINEFORM5 and INLINEFORM6 . The coincidences are then computed as: INLINEFORM7 ",
+      "where the INLINEFORM0 pairs come from different groups, INLINEFORM1 and INLINEFORM2 . The total number of such pairs in vote INLINEFORM3 is INLINEFORM4 . The actual number INLINEFORM5 of the pairs is multiplied by INLINEFORM6 so that the total contribution of vote INLINEFORM7 to the coincidence matrix is INLINEFORM8 ."
+    ],
+    [
+      "In this section we describe a network-based approach to analyzing the co-voting behavior of MEPs. For each roll-call vote we form a network, where the nodes in the network are MEPs, and an undirected edge between two MEPs is formed when they cast the same vote.",
+      "We are interested in the factors that determine the cohesion within political groups and coalition formation between political groups. Furthermore, we investigate to what extent communication in a different social context, i.e., the retweeting behavior of MEPs, can explain the co-voting of MEPs. For this purpose we apply an Exponential Random Graph Model BIBREF5 to individual roll-call vote networks, and aggregate the results by means of the meta-analysis.",
+      "ERGMs allow us to investigate the factors relevant for the network-formation process. Network metrics, as described in the abundant literature, serve to gain information about the structural properties of the observed network. A model investigating the processes driving the network formation, however, has to take into account that there can be a multitude of alternative networks. If we are interested in the parameters influencing the network formation we have to consider all possible networks and measure their similarity to the originally observed network. The family of ERGMs builds upon this idea.",
+      "Assume a random graph INLINEFORM0 , in the form of a binary adjacency matrix, made up of a set of INLINEFORM1 nodes and INLINEFORM2 edges INLINEFORM3 where, similar to a binary choice model, INLINEFORM4 if the nodes INLINEFORM5 are connected and INLINEFORM6 if not. Since network data is by definition relational and thus violates assumptions of independence, classical binary choice models, like logistic regression, cannot be applied in this context. Within an ERGM, the probability for a given network is modelled by DISPLAYFORM0 ",
+      " where INLINEFORM0 is the vector of parameters and INLINEFORM1 is the vector of network statistics (counts of network substructures), which are a function of the adjacency matrix INLINEFORM2 . INLINEFORM3 is a normalization constant corresponding to the sample of all possible networks, which ensures a proper probability distribution. Evaluating the above expression allows us to make assertions if and how specific nodal attributes influence the network formation process. These nodal attributes can be endogenous (dyad-dependent parameters) to the network, like the in- and out-degrees of a node, or exogenous (dyad-independent parameters), as the party affiliation, or the country of origin in our case.",
+      "An alternative formulation of the ERGM provides the interpretation of the coefficients. We introduce the change statistic, which is defined as the change in the network statistics when an edge between nodes INLINEFORM0 and INLINEFORM1 is added or not. If INLINEFORM2 and INLINEFORM3 denote the vectors of counts of network substructures when the edge is added or not, the change statistics is defined as follows: INLINEFORM4 ",
+      "With this at hand it can be shown that the distribution of the variable INLINEFORM0 , conditional on the rest of the graph INLINEFORM1 , corresponds to: INLINEFORM2 ",
+      "This implies on the one hand that the probability depends on INLINEFORM0 via the change statistic INLINEFORM1 , and on the other hand, that each coefficient within the vector INLINEFORM2 represents an increase in the conditional log-odds ( INLINEFORM3 ) of the graph when the corresponding element in the vector INLINEFORM4 increases by one. The need to condition the probability on the rest of the network can be illustrated by a simple example. The addition (removal) of a single edge alters the network statistics. If a network has only edges INLINEFORM5 and INLINEFORM6 , the creation of an edge INLINEFORM7 would not only add an additional edge but would also alter the count for other network substructures included in the model. In this example, the creation of the edge INLINEFORM8 also increases the number of triangles by one. The coefficients are transformed into probabilities with the logistic function: INLINEFORM9 ",
+      "For example, in the context of roll-call votes, the probability that an additional co-voting edge is formed between two nodes (MEPs) of the same political group is computed with that equation. In this context, the nodematch (nodemix) coefficients of the ERGM (described in detail bellow) therefore refer to the degree of homophilous (heterophilous) matching of MEPs with regard to their political affiliation, or, expressed differently, the propensity of MEPs to co-vote with other MEPs of their respective political group or another group.",
+      "A positive coefficient reflects an increased chance that an edge between two nodes with respective properties, like group affiliation, given all other parameters unchanged, is formed. Or, put differently, a positive coefficient implies that the probability of observing a network with a higher number of corresponding pairs relative to the hypothetical baseline network, is higher than to observe the baseline network itself BIBREF31 . For an intuitive interpretation, log-odds value of 0 corresponds to the even chance probability of INLINEFORM0 . Log-odds of INLINEFORM1 correspond to an increase of probability by INLINEFORM2 , whereas log-odds of INLINEFORM3 correspond to a decrease of probability by INLINEFORM4 .",
+      "The computational challenges of estimating ERGMs is to a large degree due to the estimation of the normalizing constant. The number of possible networks is already extremely large for very small networks and the computation is simply not feasible. Therefore, an appropriate sample has to be found, ideally covering the most probable areas of the probability distribution. For this we make use of a method from the Markov Chain Monte Carlo (MCMC) family, namely the Metropolis-Hastings algorithm.",
+      "The idea behind this algorithm is to generate and sample highly weighted random networks departing from the observed network. The Metropolis-Hastings algorithm is an iterative algorithm which samples from the space of possible networks by randomly adding or removing edges from the starting network conditional on its density. If the likelihood, in the ERGM context also denoted as weights, of the newly generated network is higher than that of the departure network it is retained, otherwise it is discarded. In the former case, the algorithm starts anew from the newly generated network. Otherwise departure network is used again. Repeating this procedure sufficiently often and summing the weights associated to the stored (sampled) networks allows to compute an approximation of the denominator in equation EQREF18 (normalizing constant).",
+      "The algorithm starts sampling from the originally observed network INLINEFORM0 . The optimization of the coefficients is done simultaneously, equivalently with the Metropolis-Hastings algorithm. At the beginning starting values have to be supplied. For the study at hand we used the \u201cergm\u201d library from the statistical R software package BIBREF5 implementing the Gibbs-Sampling algorithm BIBREF32 which is a special case of the Metropolis-Hastings algorithm outlined.",
+      "In order to answer our question of the importance of the factors which drive the network formation process in the roll-call co-voting network, the ERGM is specified with the following parameters:",
+      "nodematch country: This parameter adds one network statistic to the model, i.e., the number of edges INLINEFORM0 where INLINEFORM1 . The coefficient indicates the homophilious mixing behavior of MEPs with respect to their country of origin. In other words, this coefficient indicates how relevant nationality is in the formation of edges in the co-voting network.",
+      "nodematch national party: This parameter adds one network statistic to the model: the number of edges INLINEFORM0 with INLINEFORM1 . The coefficient indicates the homophilious mixing behavior of the MEPs with regard to their party affiliation at the national level. In the context of this study, this coefficient can be interpreted as an indicator for within-party cohesion at the national level.",
+      "nodemix EP group: This parameter adds one network statistic for each pair of European political groups. These coefficients shed light on the degree of coalitions between different groups as well as the within group cohesion . Given that there are nine groups in the European Parliament, this coefficient adds in total 81 statistics to the model.",
+      "edge covariate Twitter: This parameter corresponds to a square matrix with the dimension of the adjacency matrix of the network, which corresponds to the number of mutual retweets between the MEPs. It provides an insight about the extent to which communication in one social context (Twitter), can explain cooperation in another social context (co-voting in RCVs).",
+      "An ERGM as specified above is estimated for each of the 2535 roll-call votes. Each roll-call vote is thereby interpreted as a binary network and as an independent study. It is assumed that a priori each MEP could possibly form an edge with each other MEP in the context of a roll-call vote. Assumptions over the presence or absence of individual MEPs in a voting session are not made. In other words the dimensions of the adjacency matrix (the node set), and therefore the distribution from which new networks are drawn, is kept constant over all RCVs and therefore for every ERGM. The ERGM results therefore implicitly generalize to the case where potentially all MEPs are present and could be voting. Not voting is incorporated implicitly by the disconnectedness of a node.",
+      "The coefficients of the 2535 roll-call vote studies are aggregated by means of a meta-analysis approach proposed by Lubbers BIBREF33 and Snijders et al. BIBREF34 . We are interested in average effect sizes of different matching patterns over different topics and overall. Considering the number of RCVs, it seems straightforward to interpret the different RCV networks as multiplex networks and collapse them into one weighted network, which could then be analysed by means of a valued ERGM BIBREF35 . There are, however, two reasons why we chose the meta-analysis approach instead. First, aggregating the RCV data results into an extremely dense network, leading to severe convergence (degeneracy) problems for the ERGM. Second, the RCV data contains information about the different policy areas the individual votes were about. Since we are interested in how the coalition formation in the European Parliament differs over different areas, a method is needed that allows for an ex-post analysis of the corresponding results. We therefore opted for the meta-analysis approach by Lubbers and Snijders et al. This approach allows us to summarize the results by decomposing the coefficients into average effects and (class) subject-specific deviations. The different ERGM runs for each RCV are thereby regarded as different studies with identical samples that are combined to obtain a general overview of effect sizes. The meta-regression model is defined as: INLINEFORM0 ",
+      "Here INLINEFORM0 is a parameter estimate for class INLINEFORM1 , and INLINEFORM2 is the average coefficient. INLINEFORM3 denotes the normally distributed deviation of the class INLINEFORM4 with a mean of 0 and a variance of INLINEFORM5 . INLINEFORM6 is the estimation error of the parameter value INLINEFORM7 from the ERGM. The meta-analysis model is fitted by an iterated, weighted, least-squares model in which the observations are weighted by the inverse of their variances. For the overall nodematch between political groups, we weighted the coefficients by group sizes. The results from the meta analysis can be interpreted as if they stemmed from an individual ERGM run.",
+      "In our study, the meta-analysis was performed using the RSiena library BIBREF36 , which implements the method proposed by Lubbers and Snijders et al. BIBREF33 , BIBREF34 ."
+    ],
+    [
+      "The retweeting behavior of MEPs is captured by their retweet network. Each MEP active on Twitter is a node in this network. An edge in the network between two MEPs exists when one MEP retweeted the other. The weight of the edge is the number of retweets between the two MEPs. The resulting retweet network is an undirected, weighted network.",
+      "We measure the cohesion of a political group INLINEFORM0 as the average retweets, i.e., the ratio of the number of retweets between the MEPs in the group INLINEFORM1 to the number of MEPs in the group INLINEFORM2 . The higher the ratio, the more each MEP (on average) retweets the MEPs from the same political group, hence, the higher the cohesion of the political group. The definition of the average retweeets ( INLINEFORM3 ) of a group INLINEFORM4 is: INLINEFORM5 ",
+      "This measure of cohesion captures the aggregate retweeting behavior of the group. If we consider retweets as endorsements, a larger number of retweets within the group is an indicator of agreement between the MEPs in the group. It does not take into account the patterns of retweeting within the group, thus ignoring the social sub-structure of the group. This is a potentially interesting direction and we leave it for future work.",
+      "We employ an analogous measure for the strength of coalitions in the retweet network. The coalition strength between two groups INLINEFORM0 and INLINEFORM1 is the ratio of the number of retweets from one group to the other (but not within groups) INLINEFORM2 to the total number of MEPs in both groups, INLINEFORM3 . The definition of the average retweeets ( INLINEFORM4 ) between groups INLINEFORM5 and INLINEFORM6 is: INLINEFORM7 "
+    ],
+    [
+      "In this section we first report on the level of cohesion of the European Parliament's groups by analyzing the co-voting through the agreement and ERGM measures. Next, we explore two important policy areas, namely Economic and monetary system and State and evolution of the Union. Finally, we analyze the cohesion of the European Parliament's groups on Twitter.",
+      "Existing research by Hix et al. BIBREF10 , BIBREF13 , BIBREF11 shows that the cohesion of the European political groups has been rising since the 1990s, and the level of cohesion remained high even after the EU's enlargement in 2004, when the number of MEPs increased from 626 to 732.",
+      "We measure the co-voting cohesion of the political groups in the Eighth European Parliament using Krippendorff's Alpha\u2014the results are shown in Fig FIGREF30 (panel Overall). The Greens-EFA have the highest cohesion of all the groups. This finding is in line with an analysis of previous compositions of the Fifth and Sixth European Parliaments by Hix and Noury BIBREF11 , and the Seventh by VoteWatch BIBREF37 . They are closely followed by the S&D and EPP. Hix and Noury reported on the high cohesion of S&D in the Fifth and Sixth European Parliaments, and we also observe this in the current composition. They also reported a slightly less cohesive EPP-ED. This group split in 2009 into EPP and ECR. VoteWatch reports EPP to have cohesion on a par with Greens-EFA and S&D in the Seventh European Parliament. The cohesion level we observe in the current European Parliament is also similar to the level of Greens-EFA and S&D.",
+      "The catch-all group of the non-aligned (NI) comes out as the group with the lowest cohesion. In addition, among the least cohesive groups in the European Parliament are the Eurosceptics EFDD, which include the British UKIP led by Nigel Farage, and the ENL whose largest party are the French National Front, led by Marine Le Pen. Similarly, Hix and Noury found that the least cohesive groups in the Seventh European Parliament are the nationalists and Eurosceptics. The Eurosceptic IND/DEM, which participated in the Sixth European Parliament, transformed into the current EFDD, while the nationalistic UEN was dissolved in 2009.",
+      "We also measure the voting cohesion of the European Parliament groups using an ERGM, a network-based method\u2014the results are shown in Fig FIGREF31 (panel Overall). The cohesion results obtained with ERGM are comparable to the results based on agreement. In this context, the parameters estimated by the ERGM refer to the matching of MEPs who belong to the same political group (one parameter per group). The parameters measure the homophilous matching between MEPs who have the same political affiliation. A positive value for the estimated parameter indicates that the co-voting of MEPs from that group is greater than what is expected by chance, where the expected number of co-voting links by chance in a group is taken to be uniformly random. A negative value indicates that there are fewer co-voting links within a group than expected by chance.",
+      "Even though INLINEFORM0 and ERGM compute scores relative to what is expected by chance, they refer to different interpretations of chance. INLINEFORM1 's concept of chance is based on the number of expected pair-wise co-votes between MEPs belonging to a group, knowing the votes of these MEPs on all RCVs. ERGM's concept of chance is based on the number of expected pair-wise co-votes between MEPs belonging to a group on a given RCV, knowing the network-related properties of the co-voting network on that particular RCV. The main difference between INLINEFORM2 and ERGM, though, is the treatment of non-voting and abstained MEPs. INLINEFORM3 considers only the yes/no votes, and consequently, agreements by the voting MEPs of the same groups are considerably higher than co-voting by chance. ERGM, on the other hand, always considers all MEPs, and non-voting and abstained MEPs are treated as disconnected nodes. The level of co-voting by chance is therefore considerably lower, since there is often a large fraction of MEPs that do not attand or abstain.",
+      "As with INLINEFORM0 , Greens-EFA, S&D, and EPP exhibit the highest cohesion, even though their ranking is permuted when compared to the ranking obtained with INLINEFORM1 . At the other end of the scale, we observe the same situation as with INLINEFORM2 . The non-aligned members NI have the lowest cohesion, followed by EFDD and ENL.",
+      "The only place where the two methods disagree is the level of cohesion of GUE-NGL. The Alpha attributes GUE-NGL a rather high level of cohesion, on a par with ALDE, whereas the ERGM attributes them a much lower cohesion. The reason for this difference is the relatively high abstention rate of GUE-NGL. Whereas the overall fraction of non-attending and abstaining MEPs across all RCVs and all political groups is 25%, the GUE-NGL abstention rate is 34%. This is reflected in an above average cohesion by INLINEFORM0 where only yes/no votes are considered, and in a relatively lower, below average cohesion by ERGM. In the later case, the non-attendance is interpreted as a non-cohesive voting of a political groups as a whole.",
+      "In addition to the overall cohesion, we also focus on two selected policy areas. The cohesion of the political groups related to these two policy areas is shown in the first two panels in Fig FIGREF30 ( INLINEFORM0 ) and Fig FIGREF31 (ERGM).",
+      "The most important observation is that the level of cohesion of the political groups is very stable across different policy areas. These results are corroborated by both methodologies. Similar to the overall cohesion, the most cohesive political groups are the S&D, Greens-EFA, and EPP. The least cohesive group is the NI, followed by the ENL and EFDD. The two methodologies agree on the level of cohesion for all the political groups, except for GUE-NGL, due to a lower attendance rate.",
+      "We determine the cohesion of political groups on Twitter by using the average number of retweets between MEPs within the same group. The results are shown in Fig FIGREF33 . The right-wing ENL and EFDD come out as the most cohesive groups, while all the other groups have a far lower average number of retweets. MEPs from ENL and EFDD post by far the largest number of retweets (over 240), and at the same time over 94% of their retweets are directed to MEPs from the same group. Moreover, these two groups stand out in the way the retweets are distributed within the group. A large portion of the retweets of EFDD (1755) go to Nigel Farage, the leader of the group. Likewise, a very large portion of retweets of ENL (2324) go to Marine Le Pen, the leader of the group. Farage and Le Pen are by far the two most retweeted MEPs, with the third one having only 666 retweets."
+    ],
+    [
+      "Coalition formation in the European Parliament is largely determined by ideological positions, reflected in the degree of cooperation of parties at the national and European levels. The observation of ideological inclinations in the coalition formation within the European Parliament was already made by other authors BIBREF11 and is confirmed in this study. The basic patterns of coalition formation in the European Parliament can already be seen in the co-voting network in Fig FIGREF2 A. It is remarkable that the degree of attachment between the political groups, which indicates the degree of cooperation in the European Parliament, nearly exactly corresponds to the left-to-right seating order.",
+      "The liberal ALDE seems to have an intermediator role between the left and right parts of the spectrum in the parliament. Between the extreme (GUE-NGL) and center left (S&D) groups, this function seems to be occupied by Greens-EFA. The non-aligned members NI, as well as the Eurosceptic EFFD and ENL, seem to alternately tip the balance on both poles of the political spectrum. Being ideologically more inclined to vote with other conservative and right-wing groups (EPP, ECR), they sometimes also cooperate with the extreme left-wing group (GUE-NGL) with which they share their Euroscepticism as a common denominator.",
+      "Figs FIGREF36 and FIGREF37 give a more detailed understanding of the coalition formation in the European Parliament. Fig FIGREF36 displays the degree of agreement or cooperation between political groups measured by Krippendorff's INLINEFORM0 , whereas Fig FIGREF37 is based on the result from the ERGM. We first focus on the overall results displayed in the right-hand plots of Figs FIGREF36 and FIGREF37 .",
+      "The strongest degrees of cooperation are observed, with both methods, between the two major parties (EPP and S&D) on the one hand, and the liberal ALDE on the other. Furthermore, we see a strong propensity for Greens-EFA to vote with the Social Democrats (5th strongest coalition by INLINEFORM0 , and 3rd by ERGM) and the GUE-NGL (3rd strongest coalition by INLINEFORM1 , and 5th by ERGM). These results underline the role of ALDE and Greens-EFA as intermediaries for the larger groups to achieve a majority. Although the two largest groups together have 405 seats and thus significantly more than the 376 votes needed for a simple majority, the degree of cooperation between the two major groups is ranked only as the fourth strongest by both methods. This suggests that these two political groups find it easier to negotiate deals with smaller counterparts than with the other large group. This observation was also made by Hix et al. BIBREF12 , who noted that alignments on the left and right of the political spectrum have in recent years replaced the \u201cGrand Coalition\u201d between the two large blocks of Christian Conservatives (EPP) and Social Democrats (S&D) as the dominant form of finding majorities in the parliament.",
+      "Next, we focus on the coalition formation within the two selected policy areas. The area State and Evolution of the Union is dominated by cooperation between the two major groups, S&D and EPP, as well as ALDE. We also observe a high degree of cooperation between groups that are generally regarded as integration friendly, like Greens-EFA and GUE-NGL. We see, particularly in Fig FIGREF36 , a relatively high degree of cooperation between groups considered as Eurosceptic, like ECR, EFFD, ENL, and the group of non-aligned members.",
+      "The dichotomy between supporters and opponents of European integration is even more pronounced within the policy area Economic and Monetary System. In fact, we are taking a closer look specifically at these two areas as they are, at the same time, both contentious and important. Both methods rank the cooperation between S&D and EPP on the one hand, and ALDE on the other, as the strongest.",
+      "We also observe a certain degree of unanimity among the Eurosceptic and right-wing groups (EFDD, ENL, and NI) in this policy area. This seems plausible, as these groups were (especially in the aftermath of the global financial crisis and the subsequent European debt crisis) in fierce opposition to further payments to financially troubled member states. However, we also observe a number of strong coalitions that might, at first glance, seem unusual, specifically involving the left-wing group GUE-NGL on the one hand, and the right-wing EFDD, ENL, and NI on the other. These links also show up in the network plot in Fig FIGREF2 A. This might be attributable to a certain degree of Euroscepticism on both sides: rooted in criticism of capitalism on the left, and at least partly a raison d'\u00eatre on the right. Hix et al. BIBREF11 discovered this pattern as well, and proposed an additional explanation\u2014these coalitions also relate to a form of government-opposition dynamic that is rooted at the national level, but is reflected in voting patterns at the European level.",
+      "In general, we observe two main differences between the INLINEFORM0 and ERGM results: the baseline cooperation as estimated by INLINEFORM1 is higher, and the ordering of coalitions from the strongest to the weakest is not exactly the same. The reason is the same as for the cohesion, namely different treatment of non-voting and abstaining MEPs. When they are ignored, as by INLINEFORM2 , the baseline level of inter-group co-voting is higher. When non-attending and abstaining is treated as voting differently, as by ERGM, it is considerably more difficult to achieve co-voting coalitions, specially when there are on average 25% MEPs that do not attend or abstain. Groups with higher non-attendance rates, such as GUE-NGL (34%) and NI (40%) are less likely to form coalitions, and therefore have relatively lower ERGM coefficients (Fig FIGREF37 ) than INLINEFORM3 scores (Fig FIGREF36 ).",
+      "The first insight into coalition formation on Twitter can be observed in the retweet network in Fig FIGREF2 B. The ideological left to right alignment of the political groups is reflected in the retweet network. Fig FIGREF40 shows the strength of the coalitions on Twitter, as estimated by the number of retweets between MEPs from different groups. The strongest coalitions are formed between the right-wing groups EFDD and ECR, as well as ENL and NI. At first, this might come as a surprise, since these groups do not form strong coalitions in the European Parliament, as can be seen in Figs FIGREF36 and FIGREF37 . On the other hand, the MEPs from these groups are very active Twitter users. As previously stated, MEPs from ENL and EFDD post the largest number of retweets. Moreover, 63% of the retweets outside of ENL are retweets of NI. This effect is even more pronounced with MEPs from EFDD, whose retweets of ECR account for 74% of their retweets from other groups.",
+      "In addition to these strong coalitions on the right wing, we find coalition patterns to be very similar to the voting coalitions observed in the European Parliament, seen in Figs FIGREF36 and FIGREF37 . The strongest coalitions, which come immediately after the right-wing coalitions, are between Greens-EFA on the one hand, and GUE-NGL and S&D on the other, as well as ALDE on the one hand, and EPP and S&D on the other. These results corroborate the role of ALDE and Greens-EFA as intermediaries in the European Parliament, not only in the legislative process, but also in the debate on social media.",
+      "To better understand the formation of coalitions in the European Parliament and on Twitter, we examine the strongest cooperation between political groups at three different thresholds. For co-voting coalitions in the European Parliament we choose a high threshold of INLINEFORM0 , a medium threshold of INLINEFORM1 , and a negative threshold of INLINEFORM2 (which corresponds to strong oppositions). In this way we observe the overall patterns of coalition and opposition formation in the European Parliament and in the two specific policy areas. For cooperation on Twitter, we choose a high threshold of INLINEFORM3 , a medium threshold of INLINEFORM4 , and a very low threshold of INLINEFORM5 .",
+      "The strongest cooperations in the European Parliament over all policy areas are shown in Fig FIGREF42 G. It comes as no surprise that the strongest cooperations are within the groups (in the diagonal). Moreover, we again observe GUE-NGL, S&D, Greens-EFA, ALDE, and EPP as the most cohesive groups. In Fig FIGREF42 H, we observe coalitions forming along the diagonal, which represents the seating order in the European Parliament. Within this pattern, we observe four blocks of coalitions: on the left, between GUE-NGL, S&D, and Greens-EFA; in the center, between S&D, Greens-EFA, ALDE, and EPP; on the right-center between ALDE, EPP, and ECR; and finally, on the far-right between ECR, EFDD, ENL, and NI. Fig FIGREF42 I shows the strongest opposition between groups that systematically disagree in voting. The strongest disagreements are between left- and right-aligned groups, but not between the left-most and right-most groups, in particular, between GUE-NGL and ECR, but also between S&D and Greens-EFA on one side, and ENL and NI on the other.",
+      "In the area of Economic and monetary system we see a strong cooperation between EPP and S&D (Fig FIGREF42 A), which is on a par with the cohesion of the most cohesive groups (GUE-NGL, S&D, Greens-EFA, ALDE, and EPP), and is above the cohesion of the other groups. As pointed out in the section \u201csec:coalitionpolicy\u201d, there is a strong separation in two blocks between supporters and opponents of European integration, which is even more clearly observed in Fig FIGREF42 B. On one hand, we observe cooperation between S&D, ALDE, EPP, and ECR, and on the other, cooperation between GUE-NGL, Greens-EFA, EFDD, ENL, and NI. This division in blocks is seen again in Fig FIGREF42 C, which shows the strongest disagreements. Here, we observe two blocks composed of S&D, EPP, and ALDE on one hand, and GUE-NGL, EFDD, ENL, and NI on the other, which are in strong opposition to each other.",
+      "In the area of State and Evolution of the Union we again observe a strong division in two blocks (see Fig FIGREF42 E). This is different to the Economic and monetary system, however, where we observe a far-left and far-right cooperation, where the division is along the traditional left-right axis.",
+      "The patterns of coalitions forming on Twitter closely resemble those in the European Parliament. In Fig FIGREF42 J we see that the strongest degrees of cooperation on Twitter are within the groups. The only group with low cohesion is the NI, whose members have only seven retweets between them. The coalitions on Twitter follow the seating order in the European Parliament remarkably well (see Fig FIGREF42 K). What is striking is that the same blocks form on the left, center, and on the center-right, both in the European Parliament and on Twitter. The largest difference between the coalitions in the European Parliament and on Twitter is on the far-right, where we observe ENL and NI as isolated blocks.",
+      "The results shown in Fig FIGREF44 quantify the extent to which communication in one social context (Twitter) can explain cooperation in another social context (co-voting in the European Parliament). A positive value indicates that the matching behavior in the retweet network is similar to the one in the co-voting network, specific for an individual policy area. On the other hand, a negative value implies a negative \u201ccorrelation\u201d between the retweeting and co-voting of MEPs in the two different contexts.",
+      "The bars in Fig FIGREF44 correspond to the coefficients from the edge covariate terms of the ERGM, describing the relationship between the retweeting and co-voting behavior of MEPs. The coefficients are aggregated for individual policy areas by means of a meta-analysis.",
+      "Overall, we observe a positive correlation between retweeting and co-voting, which is significantly different from zero. The strongest positive correlations are in the areas Area of freedom, security and justice, External relations of the Union, and Internal markets. Weaker, but still positive, correlations are observed in the areas Economic, social and territorial cohesion, European citizenship, and State and evolution of the Union. The only exception, with a significantly negative coefficient, is the area Economic and monetary system. This implies that in the area Economic and monetary system we observe a significant deviation from the usual co-voting patterns. Results from section \u201csec:coalitionpolicy\u201d, confirm that this is indeed the case. Especially noteworthy are the coalitions between GUE-NGL and Greens-EFA on the left wing, and EFDD and ENL on the right wing. In the section \u201csec:coalitionpolicy\u201d we interpret these results as a combination of Euroscepticism on both sides, motivated on the left by a skeptical attitude towards the market orientation of the EU, and on the right by a reluctance to give up national sovereignty."
+    ],
+    [
+      "We study cohesion and coalitions in the Eighth European Parliament by analyzing, on one hand, MEPs' co-voting tendencies and, on the other, their retweeting behavior.",
+      "We reveal that the most cohesive political group in the European Parliament, when it comes to co-voting, is Greens-EFA, closely followed by S&D and EPP. This is consistent with what VoteWatch BIBREF37 reported for the Seventh European Parliament. The non-aligned (NI) come out as the least cohesive group, followed by the Eurosceptic EFDD. Hix and Noury BIBREF11 also report that nationalists and Eurosceptics form the least cohesive groups in the Sixth European Parliament. We reaffirm most of these results with both of the two employed methodologies. The only point where the two methodologies disagree is in the level of cohesion for the left-wing GUE-NGL, which is portrayed by ERGM as a much less cohesive group, due to their relatively lower attendance rate. The level of cohesion of the political groups is quite stable across different policy areas and similar conclusions apply.",
+      "On Twitter we can see results that are consistent with the RCV results for the left-to-center political spectrum. The exception, which clearly stands out, is the right-wing groups ENL and EFDD that seem to be the most cohesive ones. This is the direct opposite of what was observed in the RCV data. We speculate that this phenomenon can be attributed to the fact that European right-wing groups, on a European but also on a national level, rely to a large degree on social media to spread their narratives critical of European integration. We observed the same phenomenon recently during the Brexit campaign BIBREF38 . Along our interpretation the Brexit was \u201cwon\u201d to some extent due to these social media activities, which are practically non-existent among the pro-EU political groups. The fact that ENL and EFDD are the least cohesive groups in the European Parliament can be attributed to their political focus. It seems more important for the group to agree on its anti-EU stance and to call for independence and sovereignty, and much less important to agree on other issues put forward in the parliament.",
+      "The basic pattern of coalition formation, with respect to co-voting, can already be seen in Fig FIGREF2 A: the force-based layout almost completely corresponds to the seating order in the European Parliament (from the left- to the right-wing groups). A more thorough examination shows that the strongest cooperation can be observed, for both methodologies, between EPP, S&D, and ALDE, where EPP and S&D are the two largest groups, while the liberal ALDE plays the role of an intermediary in this context. On the other hand, the role of an intermediary between the far-left GUE-NGL and its center-left neighbor, S&D, is played by the Greens-EFA. These three parties also form a strong coalition in the European Parliament. On the far right of the spectrum, the non-aligned, EFDD, and ENL form another coalition. This behavior was also observed by Hix et al. BIBREF12 , stating that alignments on the left and right have in recent years replaced the \u201cGrand Coalition\u201d between the two large blocks of Christian Conservatives (EPP) and Social Democrats (S&D) as the dominant form of finding majorities in the European Parliament. When looking at the policy area Economic and monetary system, we see the same coalitions. However, interestingly, EFDD, ENL, and NI often co-vote with the far-left GUE-NGL. This can be attributed to a certain degree of Euroscepticism on both sides: as a criticism of capitalism, on one hand, and as the main political agenda, on the other. This pattern was also discovered by Hix et al. BIBREF12 , who argued that these coalitions emerge from a form of government-opposition dynamics, rooted at the national level, but also reflected at the European level.",
+      "When studying coalitions on Twitter, the strongest coalitions can be observed on the right of the spectrum (between EFDD, ECR, ENL, and NI). This is, yet again, in contrast to what was observed in the RCV data. The reason lies in the anti-EU messages they tend to collectively spread (retweet) across the network. This behavior forms strong retweet ties, not only within, but also between, these groups. For example, MEPs of EFDD mainly retweet MEPs from ECR (with the exception of MEPs from their own group). In contrast to these right-wing coalitions, we find the other coalitions to be consistent with what is observed in the RCV data. The strongest coalitions on the left-to-center part of the axis are those between GUE-NGL, Greens-EFA, and S&D, and between S&D, ALDE, and EPP. These results reaffirm the role of Greens-EFA and ALDE as intermediaries, not only in the European Parliament but also in the debates on social media.",
+      "Last, but not least, with the ERGM methodology we measure the extent to which the retweet network can explain the co-voting activities in the European Parliament. We compute this for each policy area separately and also over all RCVs. We conclude that the retweet network indeed matches the co-voting behavior, with the exception of one specific policy area. In the area Economic and monetary system, the links in the (overall) retweet network do not match the links in the co-voting network. Moreover, the negative coefficients imply a radically different formation of coalitions in the European Parliament. This is consistent with the results in Figs FIGREF36 and FIGREF37 (the left-hand panels), and is also observed in Fig FIGREF42 (the top charts). From these figures we see that in this particular case, the coalitions are also formed between the right-wing groups and the far-left GUE-NGL. As already explained, we attribute this to the degree of Euroscepticism that these groups share on this particular policy issue."
+    ],
+    [
+      "In this paper we analyze (co-)voting patterns and social behavior of members of the European Parliament, as well as the interaction between these two systems. More precisely, we analyze a set of 2535 roll-call votes as well as the tweets and retweets of members of the MEPs in the period from October 2014 to February 2016. The results indicate a considerable level of correlation between these two complex systems. This is consistent with previous findings of Cherepnalkoski et al. BIBREF22 , who reconstructed the adherence of MEPs to their respective political or national group solely from their retweeting behavior.",
+      "We employ two different methodologies to quantify the co-voting patterns: Krippendorff's INLINEFORM0 and ERGM. They were developed in different fields of research, use different techniques, and are based on different assumptions, but in general they yield consistent results. However, there are some differences which have consequences for the interpretation of the results.",
+      " INLINEFORM0 is a measure of agreement, designed as a generalization of several specialized measures, that can compare different numbers of observations, in our case roll-call votes. It only considers yes/no votes. Absence and abstention by MEPs is ignored. Its baseline ( INLINEFORM1 ), i.e., co-voting by chance, is computed from the yes/no votes of all MEPs on all RCVs.",
+      "ERGMs are used in social-network analyses to determine factors influencing the edge formation process. In our case an edge between two MEPs is formed when they cast the same yes/no vote within a RCV. It is assumed that a priori each MEP can form a link with any other MEP. No assumptions about the presence or absence of individual MEPs in a voting session are made. Each RCV is analyzed as a separate binary network. The node set is thereby kept constant for each RCV network. While the ERGM departs from the originally observed network, where MEPs who didn't vote or abstained appear as isolated nodes, links between these nodes are possible within the network sampling process which is part of the ERGM optimization process. The results of several RCVs are aggregated by means of the meta-analysis approach. The baseline (ERGM coefficients INLINEFORM0 ), i.e., co-voting by chance, is computed from a large sample of randomly generated networks.",
+      "These two different baselines have to be taken into account when interpreting the results of INLINEFORM0 and ERGM. In a typical voting session, 25% of the MEPs are missing or abstaining. When assessing cohesion of political groups, all INLINEFORM1 values are well above the baseline, and the average INLINEFORM2 . The average ERGM cohesion coefficients, on the other hand, are around the baseline. The difference is even more pronounced for groups with higher non-attendance/abstention rates like GUE-NGL (34%) and NI (40%). When assessing strength of coalitions between pairs of groups, INLINEFORM3 values are balanced around the baseline, while the ERGM coefficients are mostly negative. The ordering of coalitions from the strongest to the weakest is therefor different when groups with high non-attendance/abstention rates are involved.",
+      "The choice of the methodology to asses cohesion and coalitions is not obvious. Roll-call voting is used for decisions which demand a simple majority only. One might however argue that non-attendance/abstention corresponds to a no vote, or that absence is used strategically. Also, the importance of individual votes, i.e., how high on the agenda of a political group is the subject, affects their attendance, and consequently the perception of their cohesion and the potential to act as a reliable coalition partner."
+    ],
+    [
+      "This work was supported in part by the EC projects SIMPOL (no. 610704) and DOLFINS (no. 640772), and by the Slovenian ARRS programme Knowledge Technologies (no. P2-103)."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1060/instruction.md b/qasper-1060/instruction.md
new file mode 100644
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@@ -0,0 +1,155 @@
+Name of Paper: Cohesion and Coalition Formation in the European Parliament: Roll-Call Votes and Twitter Activities
+
+Question: Did the authors examine if any of the MEPs used the disclaimer that retweeting does not imply endorsement on their twitter profile?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Abstract",
+    "Introduction",
+    "Related work",
+    "Methods",
+    "Co-voting measured by agreement",
+    "A network-based measure of co-voting",
+    "Measuring cohesion and coalitions on Twitter",
+    "Cohesion of political groups",
+    "Coalitions in the European Parliament",
+    "Discussion",
+    "Conclusions",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "We study the cohesion within and the coalitions between political groups in the Eighth European Parliament (2014\u20132019) by analyzing two entirely different aspects of the behavior of the Members of the European Parliament (MEPs) in the policy-making processes. On one hand, we analyze their co-voting patterns and, on the other, their retweeting behavior. We make use of two diverse datasets in the analysis. The first one is the roll-call vote dataset, where cohesion is regarded as the tendency to co-vote within a group, and a coalition is formed when the members of several groups exhibit a high degree of co-voting agreement on a subject. The second dataset comes from Twitter; it captures the retweeting (i.e., endorsing) behavior of the MEPs and implies cohesion (retweets within the same group) and coalitions (retweets between groups) from a completely different perspective.",
+      "We employ two different methodologies to analyze the cohesion and coalitions. The first one is based on Krippendorff's Alpha reliability, used to measure the agreement between raters in data-analysis scenarios, and the second one is based on Exponential Random Graph Models, often used in social-network analysis. We give general insights into the cohesion of political groups in the European Parliament, explore whether coalitions are formed in the same way for different policy areas, and examine to what degree the retweeting behavior of MEPs corresponds to their co-voting patterns. A novel and interesting aspect of our work is the relationship between the co-voting and retweeting patterns."
+    ],
+    [
+      "Social-media activities often reflect phenomena that occur in other complex systems. By observing social networks and the content propagated through these networks, we can describe or even predict the interplay between the observed social-media activities and another complex system that is more difficult, if not impossible, to monitor. There are numerous studies reported in the literature that successfully correlate social-media activities to phenomena like election outcomes BIBREF0 , BIBREF1 or stock-price movements BIBREF2 , BIBREF3 .",
+      "In this paper we study the cohesion and coalitions exhibited by political groups in the Eighth European Parliament (2014\u20132019). We analyze two entirely different aspects of how the Members of the European Parliament (MEPs) behave in policy-making processes. On one hand, we analyze their co-voting patterns and, on the other, their retweeting (i.e., endorsing) behavior.",
+      "We use two diverse datasets in the analysis: the roll-call votes and the Twitter data. A roll-call vote (RCV) is a vote in the parliament in which the names of the MEPs are recorded along with their votes. The RCV data is available as part of the minutes of the parliament's plenary sessions. From this perspective, cohesion is seen as the tendency to co-vote (i.e., cast the same vote) within a group, and a coalition is formed when members of two or more groups exhibit a high degree of co-voting on a subject. The second dataset comes from Twitter. It captures the retweeting behavior of MEPs and implies cohesion (retweets within the same group) and coalitions (retweets between the groups) from a completely different perspective.",
+      "With over 300 million monthly active users and 500 million tweets posted daily, Twitter is one of the most popular social networks. Twitter allows its users to post short messages (tweets) and to follow other users. A user who follows another user is able to read his/her public tweets. Twitter also supports other types of interaction, such as user mentions, replies, and retweets. Of these, retweeting is the most important activity as it is used to share and endorse content created by other users. When a user retweets a tweet, the information about the original author as well as the tweet's content are preserved, and the tweet is shared with the user's followers. Typically, users retweet content that they agree with and thus endorse the views expressed by the original tweeter.",
+      "We apply two different methodologies to analyze the cohesion and coalitions. The first one is based on Krippendorff's INLINEFORM0 BIBREF4 which measures the agreement among observers, or voters in our case. The second one is based on Exponential Random Graph Models (ERGM) BIBREF5 . In contrast to the former, ERGM is a network-based approach and is often used in social-network analyses. Even though these two methodologies come with two different sets of techniques and are based on different assumptions, they provide consistent results.",
+      "The main contributions of this paper are as follows:",
+      "(i) We give general insights into the cohesion of political groups in the Eighth European Parliament, both overall and across different policy areas.",
+      "(ii) We explore whether coalitions are formed in the same way for different policy areas.",
+      "(iii) We explore to what degree the retweeting behavior of MEPs corresponds to their co-voting patterns.",
+      "(iv) We employ two statistically sound methodologies and examine the extent to which the results are sensitive to the choice of methodology. While the results are mostly consistent, we show that the difference are due to the different treatment of non-attending and abstaining MEPs by INLINEFORM0 and ERGM.",
+      "The most novel and interesting aspect of our work is the relationship between the co-voting and the retweeting patterns. The increased use of Twitter by MEPs on days with a roll-call vote session (see Fig FIGREF1 ) is an indicator that these two processes are related. In addition, the force-based layouts of the co-voting network and the retweet network reveal a very similar structure on the left-to-center side of the political spectrum (see Fig FIGREF2 ). They also show a discrepancy on the far-right side of the spectrum, which calls for a more detailed analysis."
+    ],
+    [
+      "In this paper we study and relate two very different aspects of how MEPs behave in policy-making processes. First, we look at their co-voting behavior, and second, we examine their retweeting patterns. Thus, we draw related work from two different fields of science. On one hand, we look at how co-voting behavior is analyzed in the political-science literature and, on the other, we explore how Twitter is used to better understand political and policy-making processes. The latter has been more thoroughly explored in the field of data mining (specifically, text mining and network analysis).",
+      "To the best of our knowledge, this is the first paper that studies legislative behavior in the Eighth European Parliament. The legislative behavior of the previous parliaments was thoroughly studied by Hix, Attina, and others BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 . These studies found that voting behavior is determined to a large extent\u2014and when viewed over time, increasingly so\u2014by affiliation to a political group, as an organizational reflection of the ideological position. The authors found that the cohesion of political groups in the parliament has increased, while nationality has been less and less of a decisive factor BIBREF12 . The literature also reports that a split into political camps on the left and right of the political spectrum has recently replaced the `grand coalition' between the two big blocks of Christian Conservatives (EPP) and Social Democrats (S&D) as the dominant form of finding majorities in the parliament. The authors conclude that coalitions are to a large extent formed along the left-to-right axis BIBREF12 .",
+      "In this paper we analyze the roll-call vote data published in the minutes of the parliament's plenary sessions. For a given subject, the data contains the vote of each MEP present at the respective sitting. Roll-call vote data from the European Parliament has already been extensively studied by other authors, most notably by Hix et al. BIBREF10 , BIBREF13 , BIBREF11 . To be able to study the cohesion and coalitions, authors like Hix, Attina, and Rice BIBREF6 , BIBREF13 , BIBREF14 defined and employed a variety of agreement measures. The most prominent measure is the Agreement Index proposed by Hix et al. BIBREF13 . This measure computes the agreement score from the size of the majority class for a particular vote. The Agreement Index, however, exhibits two drawbacks: (i) it does not account for co-voting by chance, and (ii) without a proper adaptation, it does not accommodate the scenario in which the agreement is to be measured between two different political groups.",
+      "We employ two statistically sound methodologies developed in two different fields of science. The first one is based on Krippendorff's INLINEFORM0 BIBREF4 . INLINEFORM1 is a measure of the agreement among observers, coders, or measuring instruments that assign values to items or phenomena. It compares the observed agreement to the agreement expected by chance. INLINEFORM2 is used to measure the inter- and self-annotator agreement of human experts when labeling data, and the performance of classification models in machine learning scenarios BIBREF15 . In addition to INLINEFORM3 , we employ Exponential Random Graph Models (ERGM) BIBREF5 . In contrast to the former, ERGM is a network-based approach, often used in social-network analyses. ERGM can be employed to investigate how different network statistics (e.g., number of edges and triangles) or external factors (e.g., political group membership) govern the network-formation process.",
+      "The second important aspect of our study is related to analyzing the behavior of participants in social networks, specifically Twitter. Twitter is studied by researchers to better understand different political processes, and in some cases to predict their outcomes. Eom et al. BIBREF1 consider the number of tweets by a party as a proxy for the collective attention to the party, explore the dynamics of the volume, and show that this quantity contains information about an election's outcome. Other studies BIBREF16 reach similar conclusions. Conover et al. BIBREF17 predicted the political alignment of Twitter users in the run-up to the 2010 US elections based on content and network structure. They analyzed the polarization of the retweet and mention networks for the same elections BIBREF18 . Borondo et al. BIBREF19 analyzed user activity during the Spanish presidential elections. They additionally analyzed the 2012 Catalan elections, focusing on the interplay between the language and the community structure of the network BIBREF20 . Most existing research, as Larsson points out BIBREF21 , focuses on the online behavior of leading political figures during election campaigns.",
+      "This paper continues our research on communities that MEPs (and their followers) form on Twitter BIBREF22 . The goal of our research was to evaluate the role of Twitter in identifying communities of influence when the actual communities are known. We represent the influence on Twitter by the number of retweets that MEPs \u201creceive\u201d. We construct two networks of influence: (i) core, which consists only of MEPs, and (ii) extended, which also involves their followers. We compare the detected communities in both networks to the groups formed by the political, country, and language membership of MEPs. The results show that the detected communities in the core network closely match the political groups, while the communities in the extended network correspond to the countries of residence. This provides empirical evidence that analyzing retweet networks can reveal real-world relationships and can be used to uncover hidden properties of the networks.",
+      "Lazer BIBREF23 highlights the importance of network-based approaches in political science in general by arguing that politics is a relational phenomenon at its core. Some researchers have adopted the network-based approach to investigate the structure of legislative work in the US Congress, including committee and sub-committee membership BIBREF24 , bill co-sponsoring BIBREF25 , and roll-call votes BIBREF26 . More recently, Dal Maso et al. BIBREF27 examined the community structure with respect to political coalitions and government structure in the Italian Parliament. Scherpereel et al. BIBREF28 examined the constituency, personal, and strategic characteristics of MEPs that influence their tweeting behavior. They suggested that Twitter's characteristics, like immediacy, interactivity, spontaneity, personality, and informality, are likely to resonate with political parties across Europe. By fitting regression models, the authors find that MEPs from incohesive groups have a greater tendency to retweet.",
+      "In contrast to most of these studies, we focus on the Eighth European Parliament, and more importantly, we study and relate two entirely different behavioral aspects, co-voting and retweeting. The goal of this research is to better understand the cohesion and coalition formation processes in the European Parliament by quantifying and comparing the co-voting patterns and social behavior."
+    ],
+    [
+      "In this section we present the methods to quantify cohesion and coalitions from the roll-call votes and Twitter activities."
+    ],
+    [
+      "We first show how the co-voting behaviour of MEPs can be quantified by a measure of the agreement between them. We treat individual RCVs as observations, and MEPs as independent observers or raters. When they cast the same vote, there is a high level of agreement, and when they vote differently, there is a high level of disagreement. We define cohesion as the level of agreement within a political group, a coalition as a voting agreement between political groups, and opposition as a disagreement between different groups.",
+      "There are many well-known measures of agreement in the literature. We selected Krippendorff's Alpha-reliability ( INLINEFORM0 ) BIBREF4 , which is a generalization of several specialized measures. It works for any number of observers, and is applicable to different variable types and metrics (e.g., nominal, ordered, interval, etc.). In general, INLINEFORM1 is defined as follows: INLINEFORM2 ",
+      "where INLINEFORM0 is the actual disagreement between observers (MEPs), and INLINEFORM1 is disagreement expected by chance. When observers agree perfectly, INLINEFORM2 INLINEFORM3 , when the agreement equals the agreement by chance, INLINEFORM4 INLINEFORM5 , and when the observers disagree systematically, INLINEFORM6 INLINEFORM7 .",
+      "The two disagreement measures are defined as follows: INLINEFORM0 ",
+      " INLINEFORM0 ",
+      "The arguments INLINEFORM0 , and INLINEFORM1 are defined below and refer to the values in the coincidence matrix that is constructed from the RCVs data. In roll-call votes, INLINEFORM2 (and INLINEFORM3 ) is a nominal variable with two possible values: yes and no. INLINEFORM4 is a difference function between the values of INLINEFORM5 and INLINEFORM6 , defined as: INLINEFORM7 ",
+      "The RCVs data has the form of a reliability data matrix: INLINEFORM0 ",
+      "where INLINEFORM0 is the number of RCVs, INLINEFORM1 is the number of MEPs, INLINEFORM2 is the number of votes cast in the voting INLINEFORM3 , and INLINEFORM4 is the actual vote of an MEP INLINEFORM5 in voting INLINEFORM6 (yes or no).",
+      "A coincidence matrix is constructed from the reliability data matrix, and is in general a INLINEFORM0 -by- INLINEFORM1 square matrix, where INLINEFORM2 is the number of possible values of INLINEFORM3 . In our case, where only yes/no votes are relevant, the coincidence matrix is a 2-by-2 matrix of the following form: INLINEFORM4 ",
+      "A cell INLINEFORM0 accounts for all coincidences from all pairs of MEPs in all RCVs where one MEP has voted INLINEFORM1 and the other INLINEFORM2 . INLINEFORM3 and INLINEFORM4 are the totals for each vote outcome, and INLINEFORM5 is the grand total. The coincidences INLINEFORM6 are computed as: INLINEFORM7 ",
+      "where INLINEFORM0 is the number of INLINEFORM1 pairs in vote INLINEFORM2 , and INLINEFORM3 is the number of MEPs that voted in INLINEFORM4 . When computing INLINEFORM5 , each pair of votes is considered twice, once as a INLINEFORM6 pair, and once as a INLINEFORM7 pair. The coincidence matrix is therefore symmetrical around the diagonal, and the diagonal contains all the equal votes.",
+      "The INLINEFORM0 agreement is used to measure the agreement between two MEPs or within a group of MEPs. When applied to a political group, INLINEFORM1 corresponds to the cohesion of the group. The closer INLINEFORM2 is to 1, the higher the agreement of the MEPs in the group, and hence the higher the cohesion of the group.",
+      "We propose a modified version of INLINEFORM0 to measure the agreement between two different groups, INLINEFORM1 and INLINEFORM2 . In the case of a voting agreement between political groups, high INLINEFORM3 is interpreted as a coalition between the groups, whereas negative INLINEFORM4 indicates political opposition.",
+      "Suppose INLINEFORM0 and INLINEFORM1 are disjoint subsets of all the MEPs, INLINEFORM2 , INLINEFORM3 . The respective number of votes cast by both group members in vote INLINEFORM4 is INLINEFORM5 and INLINEFORM6 . The coincidences are then computed as: INLINEFORM7 ",
+      "where the INLINEFORM0 pairs come from different groups, INLINEFORM1 and INLINEFORM2 . The total number of such pairs in vote INLINEFORM3 is INLINEFORM4 . The actual number INLINEFORM5 of the pairs is multiplied by INLINEFORM6 so that the total contribution of vote INLINEFORM7 to the coincidence matrix is INLINEFORM8 ."
+    ],
+    [
+      "In this section we describe a network-based approach to analyzing the co-voting behavior of MEPs. For each roll-call vote we form a network, where the nodes in the network are MEPs, and an undirected edge between two MEPs is formed when they cast the same vote.",
+      "We are interested in the factors that determine the cohesion within political groups and coalition formation between political groups. Furthermore, we investigate to what extent communication in a different social context, i.e., the retweeting behavior of MEPs, can explain the co-voting of MEPs. For this purpose we apply an Exponential Random Graph Model BIBREF5 to individual roll-call vote networks, and aggregate the results by means of the meta-analysis.",
+      "ERGMs allow us to investigate the factors relevant for the network-formation process. Network metrics, as described in the abundant literature, serve to gain information about the structural properties of the observed network. A model investigating the processes driving the network formation, however, has to take into account that there can be a multitude of alternative networks. If we are interested in the parameters influencing the network formation we have to consider all possible networks and measure their similarity to the originally observed network. The family of ERGMs builds upon this idea.",
+      "Assume a random graph INLINEFORM0 , in the form of a binary adjacency matrix, made up of a set of INLINEFORM1 nodes and INLINEFORM2 edges INLINEFORM3 where, similar to a binary choice model, INLINEFORM4 if the nodes INLINEFORM5 are connected and INLINEFORM6 if not. Since network data is by definition relational and thus violates assumptions of independence, classical binary choice models, like logistic regression, cannot be applied in this context. Within an ERGM, the probability for a given network is modelled by DISPLAYFORM0 ",
+      " where INLINEFORM0 is the vector of parameters and INLINEFORM1 is the vector of network statistics (counts of network substructures), which are a function of the adjacency matrix INLINEFORM2 . INLINEFORM3 is a normalization constant corresponding to the sample of all possible networks, which ensures a proper probability distribution. Evaluating the above expression allows us to make assertions if and how specific nodal attributes influence the network formation process. These nodal attributes can be endogenous (dyad-dependent parameters) to the network, like the in- and out-degrees of a node, or exogenous (dyad-independent parameters), as the party affiliation, or the country of origin in our case.",
+      "An alternative formulation of the ERGM provides the interpretation of the coefficients. We introduce the change statistic, which is defined as the change in the network statistics when an edge between nodes INLINEFORM0 and INLINEFORM1 is added or not. If INLINEFORM2 and INLINEFORM3 denote the vectors of counts of network substructures when the edge is added or not, the change statistics is defined as follows: INLINEFORM4 ",
+      "With this at hand it can be shown that the distribution of the variable INLINEFORM0 , conditional on the rest of the graph INLINEFORM1 , corresponds to: INLINEFORM2 ",
+      "This implies on the one hand that the probability depends on INLINEFORM0 via the change statistic INLINEFORM1 , and on the other hand, that each coefficient within the vector INLINEFORM2 represents an increase in the conditional log-odds ( INLINEFORM3 ) of the graph when the corresponding element in the vector INLINEFORM4 increases by one. The need to condition the probability on the rest of the network can be illustrated by a simple example. The addition (removal) of a single edge alters the network statistics. If a network has only edges INLINEFORM5 and INLINEFORM6 , the creation of an edge INLINEFORM7 would not only add an additional edge but would also alter the count for other network substructures included in the model. In this example, the creation of the edge INLINEFORM8 also increases the number of triangles by one. The coefficients are transformed into probabilities with the logistic function: INLINEFORM9 ",
+      "For example, in the context of roll-call votes, the probability that an additional co-voting edge is formed between two nodes (MEPs) of the same political group is computed with that equation. In this context, the nodematch (nodemix) coefficients of the ERGM (described in detail bellow) therefore refer to the degree of homophilous (heterophilous) matching of MEPs with regard to their political affiliation, or, expressed differently, the propensity of MEPs to co-vote with other MEPs of their respective political group or another group.",
+      "A positive coefficient reflects an increased chance that an edge between two nodes with respective properties, like group affiliation, given all other parameters unchanged, is formed. Or, put differently, a positive coefficient implies that the probability of observing a network with a higher number of corresponding pairs relative to the hypothetical baseline network, is higher than to observe the baseline network itself BIBREF31 . For an intuitive interpretation, log-odds value of 0 corresponds to the even chance probability of INLINEFORM0 . Log-odds of INLINEFORM1 correspond to an increase of probability by INLINEFORM2 , whereas log-odds of INLINEFORM3 correspond to a decrease of probability by INLINEFORM4 .",
+      "The computational challenges of estimating ERGMs is to a large degree due to the estimation of the normalizing constant. The number of possible networks is already extremely large for very small networks and the computation is simply not feasible. Therefore, an appropriate sample has to be found, ideally covering the most probable areas of the probability distribution. For this we make use of a method from the Markov Chain Monte Carlo (MCMC) family, namely the Metropolis-Hastings algorithm.",
+      "The idea behind this algorithm is to generate and sample highly weighted random networks departing from the observed network. The Metropolis-Hastings algorithm is an iterative algorithm which samples from the space of possible networks by randomly adding or removing edges from the starting network conditional on its density. If the likelihood, in the ERGM context also denoted as weights, of the newly generated network is higher than that of the departure network it is retained, otherwise it is discarded. In the former case, the algorithm starts anew from the newly generated network. Otherwise departure network is used again. Repeating this procedure sufficiently often and summing the weights associated to the stored (sampled) networks allows to compute an approximation of the denominator in equation EQREF18 (normalizing constant).",
+      "The algorithm starts sampling from the originally observed network INLINEFORM0 . The optimization of the coefficients is done simultaneously, equivalently with the Metropolis-Hastings algorithm. At the beginning starting values have to be supplied. For the study at hand we used the \u201cergm\u201d library from the statistical R software package BIBREF5 implementing the Gibbs-Sampling algorithm BIBREF32 which is a special case of the Metropolis-Hastings algorithm outlined.",
+      "In order to answer our question of the importance of the factors which drive the network formation process in the roll-call co-voting network, the ERGM is specified with the following parameters:",
+      "nodematch country: This parameter adds one network statistic to the model, i.e., the number of edges INLINEFORM0 where INLINEFORM1 . The coefficient indicates the homophilious mixing behavior of MEPs with respect to their country of origin. In other words, this coefficient indicates how relevant nationality is in the formation of edges in the co-voting network.",
+      "nodematch national party: This parameter adds one network statistic to the model: the number of edges INLINEFORM0 with INLINEFORM1 . The coefficient indicates the homophilious mixing behavior of the MEPs with regard to their party affiliation at the national level. In the context of this study, this coefficient can be interpreted as an indicator for within-party cohesion at the national level.",
+      "nodemix EP group: This parameter adds one network statistic for each pair of European political groups. These coefficients shed light on the degree of coalitions between different groups as well as the within group cohesion . Given that there are nine groups in the European Parliament, this coefficient adds in total 81 statistics to the model.",
+      "edge covariate Twitter: This parameter corresponds to a square matrix with the dimension of the adjacency matrix of the network, which corresponds to the number of mutual retweets between the MEPs. It provides an insight about the extent to which communication in one social context (Twitter), can explain cooperation in another social context (co-voting in RCVs).",
+      "An ERGM as specified above is estimated for each of the 2535 roll-call votes. Each roll-call vote is thereby interpreted as a binary network and as an independent study. It is assumed that a priori each MEP could possibly form an edge with each other MEP in the context of a roll-call vote. Assumptions over the presence or absence of individual MEPs in a voting session are not made. In other words the dimensions of the adjacency matrix (the node set), and therefore the distribution from which new networks are drawn, is kept constant over all RCVs and therefore for every ERGM. The ERGM results therefore implicitly generalize to the case where potentially all MEPs are present and could be voting. Not voting is incorporated implicitly by the disconnectedness of a node.",
+      "The coefficients of the 2535 roll-call vote studies are aggregated by means of a meta-analysis approach proposed by Lubbers BIBREF33 and Snijders et al. BIBREF34 . We are interested in average effect sizes of different matching patterns over different topics and overall. Considering the number of RCVs, it seems straightforward to interpret the different RCV networks as multiplex networks and collapse them into one weighted network, which could then be analysed by means of a valued ERGM BIBREF35 . There are, however, two reasons why we chose the meta-analysis approach instead. First, aggregating the RCV data results into an extremely dense network, leading to severe convergence (degeneracy) problems for the ERGM. Second, the RCV data contains information about the different policy areas the individual votes were about. Since we are interested in how the coalition formation in the European Parliament differs over different areas, a method is needed that allows for an ex-post analysis of the corresponding results. We therefore opted for the meta-analysis approach by Lubbers and Snijders et al. This approach allows us to summarize the results by decomposing the coefficients into average effects and (class) subject-specific deviations. The different ERGM runs for each RCV are thereby regarded as different studies with identical samples that are combined to obtain a general overview of effect sizes. The meta-regression model is defined as: INLINEFORM0 ",
+      "Here INLINEFORM0 is a parameter estimate for class INLINEFORM1 , and INLINEFORM2 is the average coefficient. INLINEFORM3 denotes the normally distributed deviation of the class INLINEFORM4 with a mean of 0 and a variance of INLINEFORM5 . INLINEFORM6 is the estimation error of the parameter value INLINEFORM7 from the ERGM. The meta-analysis model is fitted by an iterated, weighted, least-squares model in which the observations are weighted by the inverse of their variances. For the overall nodematch between political groups, we weighted the coefficients by group sizes. The results from the meta analysis can be interpreted as if they stemmed from an individual ERGM run.",
+      "In our study, the meta-analysis was performed using the RSiena library BIBREF36 , which implements the method proposed by Lubbers and Snijders et al. BIBREF33 , BIBREF34 ."
+    ],
+    [
+      "The retweeting behavior of MEPs is captured by their retweet network. Each MEP active on Twitter is a node in this network. An edge in the network between two MEPs exists when one MEP retweeted the other. The weight of the edge is the number of retweets between the two MEPs. The resulting retweet network is an undirected, weighted network.",
+      "We measure the cohesion of a political group INLINEFORM0 as the average retweets, i.e., the ratio of the number of retweets between the MEPs in the group INLINEFORM1 to the number of MEPs in the group INLINEFORM2 . The higher the ratio, the more each MEP (on average) retweets the MEPs from the same political group, hence, the higher the cohesion of the political group. The definition of the average retweeets ( INLINEFORM3 ) of a group INLINEFORM4 is: INLINEFORM5 ",
+      "This measure of cohesion captures the aggregate retweeting behavior of the group. If we consider retweets as endorsements, a larger number of retweets within the group is an indicator of agreement between the MEPs in the group. It does not take into account the patterns of retweeting within the group, thus ignoring the social sub-structure of the group. This is a potentially interesting direction and we leave it for future work.",
+      "We employ an analogous measure for the strength of coalitions in the retweet network. The coalition strength between two groups INLINEFORM0 and INLINEFORM1 is the ratio of the number of retweets from one group to the other (but not within groups) INLINEFORM2 to the total number of MEPs in both groups, INLINEFORM3 . The definition of the average retweeets ( INLINEFORM4 ) between groups INLINEFORM5 and INLINEFORM6 is: INLINEFORM7 "
+    ],
+    [
+      "In this section we first report on the level of cohesion of the European Parliament's groups by analyzing the co-voting through the agreement and ERGM measures. Next, we explore two important policy areas, namely Economic and monetary system and State and evolution of the Union. Finally, we analyze the cohesion of the European Parliament's groups on Twitter.",
+      "Existing research by Hix et al. BIBREF10 , BIBREF13 , BIBREF11 shows that the cohesion of the European political groups has been rising since the 1990s, and the level of cohesion remained high even after the EU's enlargement in 2004, when the number of MEPs increased from 626 to 732.",
+      "We measure the co-voting cohesion of the political groups in the Eighth European Parliament using Krippendorff's Alpha\u2014the results are shown in Fig FIGREF30 (panel Overall). The Greens-EFA have the highest cohesion of all the groups. This finding is in line with an analysis of previous compositions of the Fifth and Sixth European Parliaments by Hix and Noury BIBREF11 , and the Seventh by VoteWatch BIBREF37 . They are closely followed by the S&D and EPP. Hix and Noury reported on the high cohesion of S&D in the Fifth and Sixth European Parliaments, and we also observe this in the current composition. They also reported a slightly less cohesive EPP-ED. This group split in 2009 into EPP and ECR. VoteWatch reports EPP to have cohesion on a par with Greens-EFA and S&D in the Seventh European Parliament. The cohesion level we observe in the current European Parliament is also similar to the level of Greens-EFA and S&D.",
+      "The catch-all group of the non-aligned (NI) comes out as the group with the lowest cohesion. In addition, among the least cohesive groups in the European Parliament are the Eurosceptics EFDD, which include the British UKIP led by Nigel Farage, and the ENL whose largest party are the French National Front, led by Marine Le Pen. Similarly, Hix and Noury found that the least cohesive groups in the Seventh European Parliament are the nationalists and Eurosceptics. The Eurosceptic IND/DEM, which participated in the Sixth European Parliament, transformed into the current EFDD, while the nationalistic UEN was dissolved in 2009.",
+      "We also measure the voting cohesion of the European Parliament groups using an ERGM, a network-based method\u2014the results are shown in Fig FIGREF31 (panel Overall). The cohesion results obtained with ERGM are comparable to the results based on agreement. In this context, the parameters estimated by the ERGM refer to the matching of MEPs who belong to the same political group (one parameter per group). The parameters measure the homophilous matching between MEPs who have the same political affiliation. A positive value for the estimated parameter indicates that the co-voting of MEPs from that group is greater than what is expected by chance, where the expected number of co-voting links by chance in a group is taken to be uniformly random. A negative value indicates that there are fewer co-voting links within a group than expected by chance.",
+      "Even though INLINEFORM0 and ERGM compute scores relative to what is expected by chance, they refer to different interpretations of chance. INLINEFORM1 's concept of chance is based on the number of expected pair-wise co-votes between MEPs belonging to a group, knowing the votes of these MEPs on all RCVs. ERGM's concept of chance is based on the number of expected pair-wise co-votes between MEPs belonging to a group on a given RCV, knowing the network-related properties of the co-voting network on that particular RCV. The main difference between INLINEFORM2 and ERGM, though, is the treatment of non-voting and abstained MEPs. INLINEFORM3 considers only the yes/no votes, and consequently, agreements by the voting MEPs of the same groups are considerably higher than co-voting by chance. ERGM, on the other hand, always considers all MEPs, and non-voting and abstained MEPs are treated as disconnected nodes. The level of co-voting by chance is therefore considerably lower, since there is often a large fraction of MEPs that do not attand or abstain.",
+      "As with INLINEFORM0 , Greens-EFA, S&D, and EPP exhibit the highest cohesion, even though their ranking is permuted when compared to the ranking obtained with INLINEFORM1 . At the other end of the scale, we observe the same situation as with INLINEFORM2 . The non-aligned members NI have the lowest cohesion, followed by EFDD and ENL.",
+      "The only place where the two methods disagree is the level of cohesion of GUE-NGL. The Alpha attributes GUE-NGL a rather high level of cohesion, on a par with ALDE, whereas the ERGM attributes them a much lower cohesion. The reason for this difference is the relatively high abstention rate of GUE-NGL. Whereas the overall fraction of non-attending and abstaining MEPs across all RCVs and all political groups is 25%, the GUE-NGL abstention rate is 34%. This is reflected in an above average cohesion by INLINEFORM0 where only yes/no votes are considered, and in a relatively lower, below average cohesion by ERGM. In the later case, the non-attendance is interpreted as a non-cohesive voting of a political groups as a whole.",
+      "In addition to the overall cohesion, we also focus on two selected policy areas. The cohesion of the political groups related to these two policy areas is shown in the first two panels in Fig FIGREF30 ( INLINEFORM0 ) and Fig FIGREF31 (ERGM).",
+      "The most important observation is that the level of cohesion of the political groups is very stable across different policy areas. These results are corroborated by both methodologies. Similar to the overall cohesion, the most cohesive political groups are the S&D, Greens-EFA, and EPP. The least cohesive group is the NI, followed by the ENL and EFDD. The two methodologies agree on the level of cohesion for all the political groups, except for GUE-NGL, due to a lower attendance rate.",
+      "We determine the cohesion of political groups on Twitter by using the average number of retweets between MEPs within the same group. The results are shown in Fig FIGREF33 . The right-wing ENL and EFDD come out as the most cohesive groups, while all the other groups have a far lower average number of retweets. MEPs from ENL and EFDD post by far the largest number of retweets (over 240), and at the same time over 94% of their retweets are directed to MEPs from the same group. Moreover, these two groups stand out in the way the retweets are distributed within the group. A large portion of the retweets of EFDD (1755) go to Nigel Farage, the leader of the group. Likewise, a very large portion of retweets of ENL (2324) go to Marine Le Pen, the leader of the group. Farage and Le Pen are by far the two most retweeted MEPs, with the third one having only 666 retweets."
+    ],
+    [
+      "Coalition formation in the European Parliament is largely determined by ideological positions, reflected in the degree of cooperation of parties at the national and European levels. The observation of ideological inclinations in the coalition formation within the European Parliament was already made by other authors BIBREF11 and is confirmed in this study. The basic patterns of coalition formation in the European Parliament can already be seen in the co-voting network in Fig FIGREF2 A. It is remarkable that the degree of attachment between the political groups, which indicates the degree of cooperation in the European Parliament, nearly exactly corresponds to the left-to-right seating order.",
+      "The liberal ALDE seems to have an intermediator role between the left and right parts of the spectrum in the parliament. Between the extreme (GUE-NGL) and center left (S&D) groups, this function seems to be occupied by Greens-EFA. The non-aligned members NI, as well as the Eurosceptic EFFD and ENL, seem to alternately tip the balance on both poles of the political spectrum. Being ideologically more inclined to vote with other conservative and right-wing groups (EPP, ECR), they sometimes also cooperate with the extreme left-wing group (GUE-NGL) with which they share their Euroscepticism as a common denominator.",
+      "Figs FIGREF36 and FIGREF37 give a more detailed understanding of the coalition formation in the European Parliament. Fig FIGREF36 displays the degree of agreement or cooperation between political groups measured by Krippendorff's INLINEFORM0 , whereas Fig FIGREF37 is based on the result from the ERGM. We first focus on the overall results displayed in the right-hand plots of Figs FIGREF36 and FIGREF37 .",
+      "The strongest degrees of cooperation are observed, with both methods, between the two major parties (EPP and S&D) on the one hand, and the liberal ALDE on the other. Furthermore, we see a strong propensity for Greens-EFA to vote with the Social Democrats (5th strongest coalition by INLINEFORM0 , and 3rd by ERGM) and the GUE-NGL (3rd strongest coalition by INLINEFORM1 , and 5th by ERGM). These results underline the role of ALDE and Greens-EFA as intermediaries for the larger groups to achieve a majority. Although the two largest groups together have 405 seats and thus significantly more than the 376 votes needed for a simple majority, the degree of cooperation between the two major groups is ranked only as the fourth strongest by both methods. This suggests that these two political groups find it easier to negotiate deals with smaller counterparts than with the other large group. This observation was also made by Hix et al. BIBREF12 , who noted that alignments on the left and right of the political spectrum have in recent years replaced the \u201cGrand Coalition\u201d between the two large blocks of Christian Conservatives (EPP) and Social Democrats (S&D) as the dominant form of finding majorities in the parliament.",
+      "Next, we focus on the coalition formation within the two selected policy areas. The area State and Evolution of the Union is dominated by cooperation between the two major groups, S&D and EPP, as well as ALDE. We also observe a high degree of cooperation between groups that are generally regarded as integration friendly, like Greens-EFA and GUE-NGL. We see, particularly in Fig FIGREF36 , a relatively high degree of cooperation between groups considered as Eurosceptic, like ECR, EFFD, ENL, and the group of non-aligned members.",
+      "The dichotomy between supporters and opponents of European integration is even more pronounced within the policy area Economic and Monetary System. In fact, we are taking a closer look specifically at these two areas as they are, at the same time, both contentious and important. Both methods rank the cooperation between S&D and EPP on the one hand, and ALDE on the other, as the strongest.",
+      "We also observe a certain degree of unanimity among the Eurosceptic and right-wing groups (EFDD, ENL, and NI) in this policy area. This seems plausible, as these groups were (especially in the aftermath of the global financial crisis and the subsequent European debt crisis) in fierce opposition to further payments to financially troubled member states. However, we also observe a number of strong coalitions that might, at first glance, seem unusual, specifically involving the left-wing group GUE-NGL on the one hand, and the right-wing EFDD, ENL, and NI on the other. These links also show up in the network plot in Fig FIGREF2 A. This might be attributable to a certain degree of Euroscepticism on both sides: rooted in criticism of capitalism on the left, and at least partly a raison d'\u00eatre on the right. Hix et al. BIBREF11 discovered this pattern as well, and proposed an additional explanation\u2014these coalitions also relate to a form of government-opposition dynamic that is rooted at the national level, but is reflected in voting patterns at the European level.",
+      "In general, we observe two main differences between the INLINEFORM0 and ERGM results: the baseline cooperation as estimated by INLINEFORM1 is higher, and the ordering of coalitions from the strongest to the weakest is not exactly the same. The reason is the same as for the cohesion, namely different treatment of non-voting and abstaining MEPs. When they are ignored, as by INLINEFORM2 , the baseline level of inter-group co-voting is higher. When non-attending and abstaining is treated as voting differently, as by ERGM, it is considerably more difficult to achieve co-voting coalitions, specially when there are on average 25% MEPs that do not attend or abstain. Groups with higher non-attendance rates, such as GUE-NGL (34%) and NI (40%) are less likely to form coalitions, and therefore have relatively lower ERGM coefficients (Fig FIGREF37 ) than INLINEFORM3 scores (Fig FIGREF36 ).",
+      "The first insight into coalition formation on Twitter can be observed in the retweet network in Fig FIGREF2 B. The ideological left to right alignment of the political groups is reflected in the retweet network. Fig FIGREF40 shows the strength of the coalitions on Twitter, as estimated by the number of retweets between MEPs from different groups. The strongest coalitions are formed between the right-wing groups EFDD and ECR, as well as ENL and NI. At first, this might come as a surprise, since these groups do not form strong coalitions in the European Parliament, as can be seen in Figs FIGREF36 and FIGREF37 . On the other hand, the MEPs from these groups are very active Twitter users. As previously stated, MEPs from ENL and EFDD post the largest number of retweets. Moreover, 63% of the retweets outside of ENL are retweets of NI. This effect is even more pronounced with MEPs from EFDD, whose retweets of ECR account for 74% of their retweets from other groups.",
+      "In addition to these strong coalitions on the right wing, we find coalition patterns to be very similar to the voting coalitions observed in the European Parliament, seen in Figs FIGREF36 and FIGREF37 . The strongest coalitions, which come immediately after the right-wing coalitions, are between Greens-EFA on the one hand, and GUE-NGL and S&D on the other, as well as ALDE on the one hand, and EPP and S&D on the other. These results corroborate the role of ALDE and Greens-EFA as intermediaries in the European Parliament, not only in the legislative process, but also in the debate on social media.",
+      "To better understand the formation of coalitions in the European Parliament and on Twitter, we examine the strongest cooperation between political groups at three different thresholds. For co-voting coalitions in the European Parliament we choose a high threshold of INLINEFORM0 , a medium threshold of INLINEFORM1 , and a negative threshold of INLINEFORM2 (which corresponds to strong oppositions). In this way we observe the overall patterns of coalition and opposition formation in the European Parliament and in the two specific policy areas. For cooperation on Twitter, we choose a high threshold of INLINEFORM3 , a medium threshold of INLINEFORM4 , and a very low threshold of INLINEFORM5 .",
+      "The strongest cooperations in the European Parliament over all policy areas are shown in Fig FIGREF42 G. It comes as no surprise that the strongest cooperations are within the groups (in the diagonal). Moreover, we again observe GUE-NGL, S&D, Greens-EFA, ALDE, and EPP as the most cohesive groups. In Fig FIGREF42 H, we observe coalitions forming along the diagonal, which represents the seating order in the European Parliament. Within this pattern, we observe four blocks of coalitions: on the left, between GUE-NGL, S&D, and Greens-EFA; in the center, between S&D, Greens-EFA, ALDE, and EPP; on the right-center between ALDE, EPP, and ECR; and finally, on the far-right between ECR, EFDD, ENL, and NI. Fig FIGREF42 I shows the strongest opposition between groups that systematically disagree in voting. The strongest disagreements are between left- and right-aligned groups, but not between the left-most and right-most groups, in particular, between GUE-NGL and ECR, but also between S&D and Greens-EFA on one side, and ENL and NI on the other.",
+      "In the area of Economic and monetary system we see a strong cooperation between EPP and S&D (Fig FIGREF42 A), which is on a par with the cohesion of the most cohesive groups (GUE-NGL, S&D, Greens-EFA, ALDE, and EPP), and is above the cohesion of the other groups. As pointed out in the section \u201csec:coalitionpolicy\u201d, there is a strong separation in two blocks between supporters and opponents of European integration, which is even more clearly observed in Fig FIGREF42 B. On one hand, we observe cooperation between S&D, ALDE, EPP, and ECR, and on the other, cooperation between GUE-NGL, Greens-EFA, EFDD, ENL, and NI. This division in blocks is seen again in Fig FIGREF42 C, which shows the strongest disagreements. Here, we observe two blocks composed of S&D, EPP, and ALDE on one hand, and GUE-NGL, EFDD, ENL, and NI on the other, which are in strong opposition to each other.",
+      "In the area of State and Evolution of the Union we again observe a strong division in two blocks (see Fig FIGREF42 E). This is different to the Economic and monetary system, however, where we observe a far-left and far-right cooperation, where the division is along the traditional left-right axis.",
+      "The patterns of coalitions forming on Twitter closely resemble those in the European Parliament. In Fig FIGREF42 J we see that the strongest degrees of cooperation on Twitter are within the groups. The only group with low cohesion is the NI, whose members have only seven retweets between them. The coalitions on Twitter follow the seating order in the European Parliament remarkably well (see Fig FIGREF42 K). What is striking is that the same blocks form on the left, center, and on the center-right, both in the European Parliament and on Twitter. The largest difference between the coalitions in the European Parliament and on Twitter is on the far-right, where we observe ENL and NI as isolated blocks.",
+      "The results shown in Fig FIGREF44 quantify the extent to which communication in one social context (Twitter) can explain cooperation in another social context (co-voting in the European Parliament). A positive value indicates that the matching behavior in the retweet network is similar to the one in the co-voting network, specific for an individual policy area. On the other hand, a negative value implies a negative \u201ccorrelation\u201d between the retweeting and co-voting of MEPs in the two different contexts.",
+      "The bars in Fig FIGREF44 correspond to the coefficients from the edge covariate terms of the ERGM, describing the relationship between the retweeting and co-voting behavior of MEPs. The coefficients are aggregated for individual policy areas by means of a meta-analysis.",
+      "Overall, we observe a positive correlation between retweeting and co-voting, which is significantly different from zero. The strongest positive correlations are in the areas Area of freedom, security and justice, External relations of the Union, and Internal markets. Weaker, but still positive, correlations are observed in the areas Economic, social and territorial cohesion, European citizenship, and State and evolution of the Union. The only exception, with a significantly negative coefficient, is the area Economic and monetary system. This implies that in the area Economic and monetary system we observe a significant deviation from the usual co-voting patterns. Results from section \u201csec:coalitionpolicy\u201d, confirm that this is indeed the case. Especially noteworthy are the coalitions between GUE-NGL and Greens-EFA on the left wing, and EFDD and ENL on the right wing. In the section \u201csec:coalitionpolicy\u201d we interpret these results as a combination of Euroscepticism on both sides, motivated on the left by a skeptical attitude towards the market orientation of the EU, and on the right by a reluctance to give up national sovereignty."
+    ],
+    [
+      "We study cohesion and coalitions in the Eighth European Parliament by analyzing, on one hand, MEPs' co-voting tendencies and, on the other, their retweeting behavior.",
+      "We reveal that the most cohesive political group in the European Parliament, when it comes to co-voting, is Greens-EFA, closely followed by S&D and EPP. This is consistent with what VoteWatch BIBREF37 reported for the Seventh European Parliament. The non-aligned (NI) come out as the least cohesive group, followed by the Eurosceptic EFDD. Hix and Noury BIBREF11 also report that nationalists and Eurosceptics form the least cohesive groups in the Sixth European Parliament. We reaffirm most of these results with both of the two employed methodologies. The only point where the two methodologies disagree is in the level of cohesion for the left-wing GUE-NGL, which is portrayed by ERGM as a much less cohesive group, due to their relatively lower attendance rate. The level of cohesion of the political groups is quite stable across different policy areas and similar conclusions apply.",
+      "On Twitter we can see results that are consistent with the RCV results for the left-to-center political spectrum. The exception, which clearly stands out, is the right-wing groups ENL and EFDD that seem to be the most cohesive ones. This is the direct opposite of what was observed in the RCV data. We speculate that this phenomenon can be attributed to the fact that European right-wing groups, on a European but also on a national level, rely to a large degree on social media to spread their narratives critical of European integration. We observed the same phenomenon recently during the Brexit campaign BIBREF38 . Along our interpretation the Brexit was \u201cwon\u201d to some extent due to these social media activities, which are practically non-existent among the pro-EU political groups. The fact that ENL and EFDD are the least cohesive groups in the European Parliament can be attributed to their political focus. It seems more important for the group to agree on its anti-EU stance and to call for independence and sovereignty, and much less important to agree on other issues put forward in the parliament.",
+      "The basic pattern of coalition formation, with respect to co-voting, can already be seen in Fig FIGREF2 A: the force-based layout almost completely corresponds to the seating order in the European Parliament (from the left- to the right-wing groups). A more thorough examination shows that the strongest cooperation can be observed, for both methodologies, between EPP, S&D, and ALDE, where EPP and S&D are the two largest groups, while the liberal ALDE plays the role of an intermediary in this context. On the other hand, the role of an intermediary between the far-left GUE-NGL and its center-left neighbor, S&D, is played by the Greens-EFA. These three parties also form a strong coalition in the European Parliament. On the far right of the spectrum, the non-aligned, EFDD, and ENL form another coalition. This behavior was also observed by Hix et al. BIBREF12 , stating that alignments on the left and right have in recent years replaced the \u201cGrand Coalition\u201d between the two large blocks of Christian Conservatives (EPP) and Social Democrats (S&D) as the dominant form of finding majorities in the European Parliament. When looking at the policy area Economic and monetary system, we see the same coalitions. However, interestingly, EFDD, ENL, and NI often co-vote with the far-left GUE-NGL. This can be attributed to a certain degree of Euroscepticism on both sides: as a criticism of capitalism, on one hand, and as the main political agenda, on the other. This pattern was also discovered by Hix et al. BIBREF12 , who argued that these coalitions emerge from a form of government-opposition dynamics, rooted at the national level, but also reflected at the European level.",
+      "When studying coalitions on Twitter, the strongest coalitions can be observed on the right of the spectrum (between EFDD, ECR, ENL, and NI). This is, yet again, in contrast to what was observed in the RCV data. The reason lies in the anti-EU messages they tend to collectively spread (retweet) across the network. This behavior forms strong retweet ties, not only within, but also between, these groups. For example, MEPs of EFDD mainly retweet MEPs from ECR (with the exception of MEPs from their own group). In contrast to these right-wing coalitions, we find the other coalitions to be consistent with what is observed in the RCV data. The strongest coalitions on the left-to-center part of the axis are those between GUE-NGL, Greens-EFA, and S&D, and between S&D, ALDE, and EPP. These results reaffirm the role of Greens-EFA and ALDE as intermediaries, not only in the European Parliament but also in the debates on social media.",
+      "Last, but not least, with the ERGM methodology we measure the extent to which the retweet network can explain the co-voting activities in the European Parliament. We compute this for each policy area separately and also over all RCVs. We conclude that the retweet network indeed matches the co-voting behavior, with the exception of one specific policy area. In the area Economic and monetary system, the links in the (overall) retweet network do not match the links in the co-voting network. Moreover, the negative coefficients imply a radically different formation of coalitions in the European Parliament. This is consistent with the results in Figs FIGREF36 and FIGREF37 (the left-hand panels), and is also observed in Fig FIGREF42 (the top charts). From these figures we see that in this particular case, the coalitions are also formed between the right-wing groups and the far-left GUE-NGL. As already explained, we attribute this to the degree of Euroscepticism that these groups share on this particular policy issue."
+    ],
+    [
+      "In this paper we analyze (co-)voting patterns and social behavior of members of the European Parliament, as well as the interaction between these two systems. More precisely, we analyze a set of 2535 roll-call votes as well as the tweets and retweets of members of the MEPs in the period from October 2014 to February 2016. The results indicate a considerable level of correlation between these two complex systems. This is consistent with previous findings of Cherepnalkoski et al. BIBREF22 , who reconstructed the adherence of MEPs to their respective political or national group solely from their retweeting behavior.",
+      "We employ two different methodologies to quantify the co-voting patterns: Krippendorff's INLINEFORM0 and ERGM. They were developed in different fields of research, use different techniques, and are based on different assumptions, but in general they yield consistent results. However, there are some differences which have consequences for the interpretation of the results.",
+      " INLINEFORM0 is a measure of agreement, designed as a generalization of several specialized measures, that can compare different numbers of observations, in our case roll-call votes. It only considers yes/no votes. Absence and abstention by MEPs is ignored. Its baseline ( INLINEFORM1 ), i.e., co-voting by chance, is computed from the yes/no votes of all MEPs on all RCVs.",
+      "ERGMs are used in social-network analyses to determine factors influencing the edge formation process. In our case an edge between two MEPs is formed when they cast the same yes/no vote within a RCV. It is assumed that a priori each MEP can form a link with any other MEP. No assumptions about the presence or absence of individual MEPs in a voting session are made. Each RCV is analyzed as a separate binary network. The node set is thereby kept constant for each RCV network. While the ERGM departs from the originally observed network, where MEPs who didn't vote or abstained appear as isolated nodes, links between these nodes are possible within the network sampling process which is part of the ERGM optimization process. The results of several RCVs are aggregated by means of the meta-analysis approach. The baseline (ERGM coefficients INLINEFORM0 ), i.e., co-voting by chance, is computed from a large sample of randomly generated networks.",
+      "These two different baselines have to be taken into account when interpreting the results of INLINEFORM0 and ERGM. In a typical voting session, 25% of the MEPs are missing or abstaining. When assessing cohesion of political groups, all INLINEFORM1 values are well above the baseline, and the average INLINEFORM2 . The average ERGM cohesion coefficients, on the other hand, are around the baseline. The difference is even more pronounced for groups with higher non-attendance/abstention rates like GUE-NGL (34%) and NI (40%). When assessing strength of coalitions between pairs of groups, INLINEFORM3 values are balanced around the baseline, while the ERGM coefficients are mostly negative. The ordering of coalitions from the strongest to the weakest is therefor different when groups with high non-attendance/abstention rates are involved.",
+      "The choice of the methodology to asses cohesion and coalitions is not obvious. Roll-call voting is used for decisions which demand a simple majority only. One might however argue that non-attendance/abstention corresponds to a no vote, or that absence is used strategically. Also, the importance of individual votes, i.e., how high on the agenda of a political group is the subject, affects their attendance, and consequently the perception of their cohesion and the potential to act as a reliable coalition partner."
+    ],
+    [
+      "This work was supported in part by the EC projects SIMPOL (no. 610704) and DOLFINS (no. 640772), and by the Slovenian ARRS programme Knowledge Technologies (no. P2-103)."
+    ]
+  ]
+}
+```
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+Name of Paper: Neural Language Modeling by Jointly Learning Syntax and Lexicon
+
+Question: How do they show their model discovers underlying syntactic structure?
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+Name of Paper: Question Answering from Unstructured Text by Retrieval and Comprehension
+
+Question: How can a neural model be used for a retrieval if the input is the entire Wikipedia?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "WikiMovies Dataset",
+    "Comprehension Model",
+    "Comprehension model detail",
+    "Retrieval Model",
+    "Hand-Crafted Model (r1)",
+    "Learning Model (R2)",
+    "Experiments",
+    "Performance of Retrieval Models",
+    "Benefit of training methods",
+    "Visualization",
+    "Performance in each category",
+    "Analysis of the mixture gate",
+    "Related Work",
+    "Conclusion and Future Work"
+  ],
+  "paragraphs": [
+    [
+      "Natural language based consumer products, such as Apple Siri and Amazon Alexa, have found wide spread use in the last few years. A key requirement for these conversational systems is the ability to answer factual questions from the users, such as those about movies, music, and artists.",
+      "Most of the current approaches for Question Answering (QA) are based on structured Knowledge Bases (KB) such as Freebase BIBREF0 and Wikidata BIBREF1 . In this setting the question is converted to a logical form using semantic parsing, which is queried against the KB to obtain the answer BIBREF2 , BIBREF3 . However, recent studies have shown that even large curated KBs, such as Freebase, are incomplete BIBREF4 . Further, KBs support only certain types of answer schemas, and constructing and maintaining them is expensive.",
+      "On the other hand, there is a vast amount of unstructured knowledge available in textual form from web pages such as Wikipedia, and hence an alternative is to directly answer questions from these documents. In this approach, shown in Figure 1 , articles relevant to the question are first selected (retrieval step). Then, the retrieved articles and question are jointly processed to extract the answer (comprehension step). This retrieval based approach has a longer history than the KB based approach BIBREF5 . It can potentially provide a much wider coverage over questions, and is not limited to specific answer schemas. However, there are still gaps in its performance compared to the KB-based approach BIBREF6 . The comprehension step, which requires parsing information from natural language, is the main bottleneck, though suboptimal retrieval can also lead to lower performance.",
+      "Several large-scale datasets introduced recently BIBREF7 , BIBREF8 have facilitated the development of powerful neural models for reading comprehension. These models fall into one of two categories: (1) those which extract answers as a span of text from the document BIBREF9 , BIBREF10 , BIBREF11 (Figure 2 top); (2) those which select the answer from a fixed vocabulary BIBREF12 , BIBREF6 (Figure 2 bottom). Here we argue that depending on the type of question, either (1) or (2) may be more appropriate, and introduce a latent variable mixture model to combine the two in a single end-to-end framework.",
+      "We incorporate the above mixture model in a simple Recurrent Neural Network (RNN) architecture with an attention mechanism BIBREF13 for comprehension. In the second part of the paper we focus on the retrieval step for the QA system, and introduce a neural network based ranking model to select the articles to feed the comprehension model. We evaluate our model on WikiMovies dataset, which consists of 200K questions about movies, along with 18K Wikipedia articles for extracting the answers. KV:16 applied Key-Value Memory Neural Networks (KV-MemNN) to the dataset, achieving 76.2% accuracy. Adding the mixture model for answer selection improves the performance to 85.4%. Further, the ranking model improves both precision and recall of the retrieved articles, and leads to an overall performance of 85.8%."
+    ],
+    [
+      "We focus on the WikiMovies dataset, proposed by BIBREF6 . The dataset consists of pairs of questions and answers about movies. Some examples are shown in Table 1 .",
+      "As a knowledge source approximately 18K articles from Wikipedia are also provided, where each article is about a movie. Since movie articles can be very long, we only use the first paragraph of the article, which typically provides a summary of the movie. Formally, the dataset consists of question-answer pairs $\\lbrace (q_j, A_j)\\rbrace _{j=1}^J$ and movie articles $\\lbrace d_k\\rbrace _{k=1}^K$ . Additionally, the dataset includes a list of entities: movie titles, actor names, genres etc. Answers to all the questions are in the entity list. The questions are created by human annotators using SimpleQuestions BIBREF14 , an existing open-domain question answering dataset, and the annotated answers come from facts in two structured KBs: OMDb and MovieLens.",
+      "There are two splits of the dataset. The \u201cFull\u201d dataset consists of 200K pairs of questions and answers. In this dataset, some questions are difficult to answer from Wikipedia articles alone. A second version of the dataset, \u201cWiki Entity\u201d is constructed by removing those QA pairs where the entities in QAs are not found in corresponding Wikipedia articles. We call these splits WikiMovies-FL and WikiMovies-WE, respectively. The questions are divided into train, dev and test such that the same question template does not appear in different splits. Further, they can be categorized into 13 categories, including movie_to_actors, director_to_movies, etc. The basic statistics of the dataset are summarized in Table 2 .",
+      "We also note that more than 50% of the entities appear less than 5 times in the training set. This makes it very difficult to learn the global statistics of each entity, necessitating the need to use an external knowledge source."
+    ],
+    [
+      "Our QA system answers questions in two steps, as shown in Figure 1 . The first step is retrieval, where articles relevant to the question are retrieved. The second step is comprehension, where the question and retrieved articles are processed to derive answers.",
+      "In this section we focus on the comprehension model, assuming that relevant articles have already been retrieved and merged into a context document. In the next section, we will discuss approaches for retrieving the articles.",
+      " BIBREF6 , who introduced WikiMovies dataset, used an improved variant of Memory Networks called Key-Value Memory Networks. Instead, we use RNN based network, which has been successfully used in many reading comprehension tasks BIBREF10 , BIBREF9 , BIBREF12 .",
+      "WikiMovies dataset has two notable differences from many of the existing comprehension datasets, such as CNN and SQuAD BIBREF10 , BIBREF9 , BIBREF12 . First, with imperfect retrieval, the answer may not be present in the context. We handle this case by using the proposed mixture model. Second, there may be multiple answers to a question, such as a list of actors. We handle this by optimizing a sum of the cross-entropy loss over all possible answers.",
+      "We also use attention sum architecture proposed by BIBREF10 , which has been shown to give high performance for comprehension tasks. In this approach, attention scores over the context entities are used as the output. We term this the attention distribution $p_{att}$ , defined over the entities in the context. The mixture model combines this distribution with another output probability distribution $p_{vocab}$ over all the entities in the vocabulary. The intuition behind this is that named entities (such as actors and directors) can be better handled by the attention part, since there are few global statistics available for these, and other entities (such as languages and genres) can be captured by vocabulary part, for which global statistics can be leveraged."
+    ],
+    [
+      "Let $\\mathcal {V}$ be the vocabulary consisting of all tokens in the corpus, and $\\mathcal {E}$ be the set of entities in the corpus The question is converted to a sequence of lower cased word ids, $(w_i) \\in \\mathcal {V}$ and a sequence of 0-1 flags for word capitalization, $(c_i) \\in \\lbrace 0,1\\rbrace $ . For each word position $i$ , we also associate an entity id if the i-th word is part of an entity, $e_i \\in \\mathcal {E}$ (see Figure 3 ). Then, the combined embedding of the i-th position is given by ",
+      "$$x_i = W_w(w_i) + W_c(c_i) \\Vert W_e(e_i), \\hspace{7.22743pt} (i=1,\\ldots ,L_q), $$   (Eq. 12) ",
+      "where $\\Vert $ is the concatenation of two vectors, $L_q$ is the number of words in a question $q$ , and $W_w, W_c$ and $W_e$ are embedding matrices. Note that if there are no entities at i-th position, $W_e(e_i)$ is set to zero. The context is composed of up to $M$ movie articles concatenated with a special separation symbol. The contexts are embedded in exactly the same way as questions, sharing the embedding matrices.",
+      "To avoid overfitting, we use another technique called anonymization. We limit the number of columns of $W_e$ to a relatively small number, $n_e$ , and entity ids are mapped to one of $n_e$ columns randomly (without collision). The map is common for each question/context pair but randomized across pairs. The method is similar to the anonymization method used in CNN / Daily Mail datasets BIBREF8 . emergent:16 showed that such a procedure actually helps readers since it adds coreference information to the system.",
+      "Next, the question embedding sequence $(x_i)$ is fed into a bidirectional GRU (BiGRU) BIBREF15 to obtain a fixed length vector $v$ ",
+      "$$v = \\overrightarrow{h}_{q}(L_q) \\Vert \\overleftarrow{h}_{q}(0), $$   (Eq. 13) ",
+      "where $\\overrightarrow{h}_{q}$ and $\\overleftarrow{h}_{q}$ are the final hidden states of forward and backward GRUs respectively.",
+      "The context embedding sequence is fed into another BiGRU, to produce the output $H_c = [h_{c,1}, h_{c,2}, \\ldots h_{c,L_c}]$ , where $L_c$ is the length of the context. An attention score for each word position $i$ is given by ",
+      "$$s_i \\propto \\exp ( v^T h_{c,i} ).$$   (Eq. 14) ",
+      "The probability over the entities in the context is then given by ",
+      "$$p_{att}(e) \\propto \\sum _{i \\in I(e, c)} s_i,$$   (Eq. 15) ",
+      "where $I(e,c)$ is the set of word positions in the entity $e$ within the context $c$ .",
+      "We next define the probability $p_{vocab}$ to be the probability over the complete set of entities in the corpus, given by ",
+      "$$p_{vocab}(e) = {\\rm Softmax}(V u), $$   (Eq. 16) ",
+      "where the vector $u$ is given by $u = \\sum _{i} s_i h_{c, i}$ . Each row of the matrix $V$ is the coefficient vector for an entity in the vocabulary. It is computed similar to Eq. ( 12 ). ",
+      "$$V(e) = \\sum _{w \\in e} W_w(w) + \\sum _{c \\in e} W_c(c) \\Vert W_e(e). $$   (Eq. 17) ",
+      "The embedding matrices are shared between question and context.",
+      "The final probability that an entity $e$ answers the question is given by the mixture $p(e) = (1-g) p_{att}(e) + g p_{vocab}(e)$ , with the mixture coefficient $g$ defined as ",
+      "$$g = \\sigma (W_g g_0), \\hspace{7.22743pt} g_0 = v^T u \\Vert \\max V u.$$   (Eq. 18) ",
+      "The two components of $g_0$ correspond to the attention part and vocabulary part respectively. Depending on the strength of each, the value of $g$ may be high or low.",
+      "Since there may be multiple answers for a question, we optimize the sum of the probabilities: ",
+      "$$\\textrm {loss} = - \\log \\Big ( \\sum _{a \\in A_j} p(a|q_j,c_j) \\Big ) $$   (Eq. 19) ",
+      "Our overall model is displayed in Figure 4 .",
+      "We note that KV-MemNN BIBREF6 employs \u201cTitle encoding\u201d technique, which uses the prior knowledge that movie titles are often in answers. BIBREF6 showed that this technique substantially improves model performance by over 7% for WikiMovies-WE dataset. In our work, on the other hand, we do not use any data specific feature engineering."
+    ],
+    [
+      "Our QA system answers questions by two steps as in Figure 1 . Accurate retrieval of relevant articles is essential for good performance of the comprehension model, and in this section we discuss three approaches for it. We use up to $M$ articles as context. A baseline approach for retrieval is to select articles which contain at least one entity also present in the question. We identify maximal intervals of words that match entities in questions and articles. Capitalization of words is ignored in this step because some words in the questions are not properly capitalized. Out of these (say $N$ ) articles we can randomly select $M$ . We call this approach (r0). For some movie titles, however, this method retrieves too many articles that are actually not related to questions. For example, there is a movie titled \u201cLove Story\u201d which accidentally picks up the words \u201clove story\u201d. This degrades the performance of the comprehension step. Hence, we describe two more retrieval models \u2013 (1) a dataset specific hand-crafted approach, and (2) a general learning based approach."
+    ],
+    [
+      "In this approach, the $N$ articles retrieved using entity matching are assigned scores based on certain heuristics. If the movie title matches an entity in the question, the article is given a high score, since it is very likely to be relevant. A similar heuristic was also employed in BIBREF6 . In addition, the number of matching entities is also used to score each article. The top $M$ articles based on these scores are selected for comprehension. This hand-crafted approach already gives strong performance for the WikiMovies dataset, however the heuristic for matching article titles may not be appropriate for other QA tasks. Hence we also study a general learning based approach for retrieval."
+    ],
+    [
+      "The learning model for retrieval is trained by an oracle constructed using distant supervision. Using the answer labels in the training set, we can find appropriate articles that include the information requested in the question. For example, for x_to_movie question type, the answer movie articles are the correct articles to be retrieved. On the other hand, for questions in movie_to_x type, the movie in the question should be retrieved. Having collected the labels, we train a retrieval model for classifying a question and article pair as relevant or not relevant.",
+      "Figure 5 gives an overview of the model, which uses a Word Level Attention (WLA) mechanism. First, the question and article are embedded into vector sequences, using the same method as the comprehension model. We do not use anonymization here, to retain simplicity. Otherwise, the anonymization procedure would have to be repeated several times for a potentially large collection of documents. These vector sequences are next fed to a Bi-GRU, to produce the outputs $v$ (for the question) and $H_c$ (for the document) similar to the previous section.",
+      "To classify the article as relevant or not, we introduce a novel attention mechanism to compute the score, ",
+      "$$s = \\sum _{i} ((w \\tilde{v} + b)^T \\tilde{h}_{c,i})^4$$   (Eq. 25) ",
+      "Each term in the sum above corresponds to the match between the query representation and a token in the context. This is passed through a 4-th order non-linearity so that relevant tokens are emphasized more. Next, we compute the probability that the article is relevant using a sigmoid: ",
+      "$$o = \\sigma (w^{\\prime } s + b^{\\prime })$$   (Eq. 27) ",
+      "In the above, $\\tilde{x}$ is the normalized version (by L2-norm) of vector $x$ , $w, b, w^{\\prime }, b^{\\prime }$ are scalar learnable parameters to control scales."
+    ],
+    [
+      "We evaluate the comprehension model on both WikiMovies-FL and WikiMovies-WE datasets. The performance is evaluated using the accuracy of the top hit (single answer) over all possible answers (all entities). This is called hits@1 metric.",
+      "For the comprehension model, we use embedding dimension 100, and GRU dimension 128. We use up to $M=10$ retrieved articles as context. The order of the articles are randomly shuffled for each training instance to prevent over-fitting. The size of the anonymized entity set $n_e$ is 600, since in most of the cases, number of entities in a question and context pair is less than 600.",
+      "For training the comprehension model, the Adam BIBREF16 optimization rule is used with batch size 32. We stop the optimization based on dev-set performance, and training takes around 10 epochs. For WikiMovies-FL (resp. WikiMovies-WE) dataset, each epoch took approximately 4 (resp. 2) hours on an Nvidia GTX1080 GPU.",
+      "For training the retrieval model R2, we use a binary cross entropy objective. Since most articles are not relevant to a question, the ration of positive and negative samples is tuned to $1:10$ . Each epoch for training the retrieval model takes about 40 minutes on an Nvidia GTX1080 GPU."
+    ],
+    [
+      "We evaluate the retrieval models based on precision and recall of the oracle articles. The evaluation is done on the test set. R@k is the ratio of cases where the highest ranked oracle article is in the top k retrieved articles. P@k is the ratio of oracle articles which are in the top k retrieved results. These numbers are summarized in Table 3 . We can see that both (r1) and (R2) significantly outperform (r0), with (R2) doing slightly better. We emphasize that (R2) uses no domain specific knowledge, and can be readily applied to other datasets where articles may not be about specific types of entities.",
+      "We have also tested simpler models based on inner product of question and article vectors. In these models, a question $q_j$ and article $d_k$ are converted to vectors $\\Phi (q_j), \\Psi (d_k)$ , and the relevance score is given by their inner product: ",
+      "$${\\rm score}(j,k) = \\Phi (q_j)^T \\Psi (d_k).$$   (Eq. 32) ",
+      "In the view of computation, those models are attractive because we can compute the article vectors offline, and do not need to compute the attention over words in the article. Maximum Inner Product Search algorithms may also be utilized here BIBREF17 , BIBREF18 . However, as shown in upper block of Table 4 , those models perform much worse in terms of scoring. The \u201cSum of Hidden State\u201d and \u201cQuery Free Attention\u201d models are similar to WLA model, using BiGRUs for question and article. In both of those models, $\\Phi (q)$ is defined the same way as WLA model, Eq ( 13 ). For the \u201cSum of Hidden States\u201d model, $\\Psi (d)$ is given by the sum of BiGRU hidden states. This is the same as the proposed model by replacing the fourth order of WLA to one. For the \u201cQuery Free Attention\u201d model, $\\Psi (d)$ is given by the sum of BiGRU hidden states.",
+      "We compare our model and several ablations with the KV-MemNN model. Table 5 shows the average performance across three evaluations. The (V) \u201cVocabulary Model\u201d and (A) \u201cAttention Model\u201d are simplified versions of the full (AV) \u201cAttention and Vocabulary Model\u201d, using only $p_{vocab}$ and $p_{att}$ , respectively. Using a mixture of $p_{att}$ and $p_{vocab}$ gives the best performance.",
+      "Interestingly, for WE dataset the Attention model works better. For FL dataset, on the other hand, it is often impossible to select answer from the context, and hence the Vocab model works better.",
+      "The number of entities in the full vocabulary is 71K, and some of these are rare. Our intuition to use the Vocab model was to only use it for common entities, and hence we next constructed a smaller vocabulary consisting of all entities which appear at least 10 times in the corpus. This results in a subset vocabulary $\\mathcal {V}_S$ of 2400 entities. Using this vocabulary in the mixture model (AsV) further improves the performance.",
+      "Table 5 also shows a comparison between (r0), (r1), and (R2) in terms of the overall task performance. We can see that improving the quality of retrieved articles benefits the downstream comprehension performance. In line with the results of the previous section, (r1) and (R2) significantly outperform (r0). Among (r1) and (R2), (R2) performs slightly better."
+    ],
+    [
+      "Table 6 shows the impact of anonymization of entities and shuffling of training articles before the comprehension step, described in Section \"Comprehension Model\" .",
+      "Shuffling the context article before concatenating them, works as a data augmentation technique. Entity anonymization helps because without it each entity has one embedding. Since most of the entities appear only a few times in the articles, these embeddings may not be properly trained. Instead, the anonymous embedding vectors are trained to distinguish different entities. This technique is motivated by a similar procedure used in the construction of CNN / Daily Mail BIBREF8 , and discussed in detail in BIBREF19 ."
+    ],
+    [
+      "Figure 6 shows a test example from the WikiMovies-FL test data. In this case, even though the answers \u201cHindi\u201d and \u201cEnglish\u201d are not in the context, they are correctly estimated from $p_{vocab}$ . Note the high value of $g$ in this case. Figure 7 shows another example of how the mixture model works. Here the the answer is successfully selected from the document instead of the vocabulary. Note the low value of $g$ in this case."
+    ],
+    [
+      "Table 7 shows the comparison for each category of questions between our model and KV-MemNN for the WikiMovies-WE dataset . We can see that performance improvements in the movie_to_x category is relatively large. The KV-MemNN model has a dataset specific \u201cTitle encoding\u201d feature which helps the model x_to_movie question types. However without this feature performance in other categories is poor."
+    ],
+    [
+      "The benefit of the mixture model comes from the fact that $p_{pointer}$ works well for some question types, while $p_{vocab}$ works well for others. Table 8 shows how often for each category $p_{vocab}$ is used ( $g > 0.5$ ) in AsV model. For question types \u201cMovie to Language\u201d and \u201cMovie to Genre\u201d (the so called \u201cchoice questions\u201d) the number of possible answers is small. For this case, even if the answer can be found in the context, it is easier for the model to select answer from an external vocabulary which encodes global statistics about the entities. For other \u201cfree questions\u201d, depending on the question type, one approach is better than the other. Our model is able to successfully estimate the latent category and switch the model type by controlling the coefficient $g$ ."
+    ],
+    [
+      "hierarchical:16 solve the QA problem by selecting a sentence in the document. They show that joint training of selection and comprehension slightly improves the performance. In our case, joint training is much harder because of the large number of movie articles. Hence we introduce a two-step retrieval and comprehension approach.",
+      "Recently architecture:16 proposed a framework to use the performance on a downstream task (e.g. comprehension) as a signal to guide the learning of neural network which determines the input to the downstream task (e.g. retrieval). This motivates us to introduce neural network based approach for both retrieval and comprehension, since in this case the retrieval step can be directly trained to maximize the downstream performance.",
+      "In the context of language modeling, the idea of combining of two output probabilities is given in BIBREF20 , however, our equation to compute the mixture coefficient is slightly different. More recently, ahn2016neural used a mixture model to predict the next word from either the entire vocabulary, or a set of Knowledge Base facts associated with the text. In this work, we present the first application of such a mixture model to reading comprehension."
+    ],
+    [
+      "We have developed QA system using a two-step retrieval and comprehension approach. The comprehension step uses a mixture model to achieve state of the art performance on WikiMovies dataset, improving previous work by a significant margin.",
+      "We would like to emphasize that our approach has minimal heuristics and does not use dataset specific feature engineering. Efficient retrieval while maintaining representation variation is a challenging problem. While there has been a lot of research on comprehension, little focus has been given to designing neural network based retrieval models. We present a simple such model, and emphasize the importance of this direction of research."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1067/instruction.md b/qasper-1067/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..1b2f8879c76a40ec064b9284792b51d41066518c
--- /dev/null
+++ b/qasper-1067/instruction.md
@@ -0,0 +1,82 @@
+Name of Paper: UDS--DFKI Submission to the WMT2019 Similar Language Translation Shared Task
+
+Question: Which algorithm is used in the UDS-DFKI system?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Data",
+    "Data ::: Pre-processing",
+    "System Architecture - The Transference Model",
+    "Experiments",
+    "Experiments ::: Experiment Setup",
+    "Experiments ::: Hyper-parameter Setup",
+    "Results",
+    "Conclusion",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "The shared tasks organized annually at WMT provide important benchmarks used in the MT community. Most of these shared tasks include English data, which contributes to make English the most resource-rich language in MT and NLP. In the most popular WMT shared task for example, the News task, MT systems have been trained to translate texts from and to English BIBREF0, BIBREF1.",
+      "This year, we have observed a shift on the dominant role that English on the WMT shared tasks. The News task featured for the first time two language pairs which did not include English: German-Czech and French-German. In addition to that, the Similar Language Translation was organized for the first time at WMT 2019 with the purpose of evaluating the performance of MT systems on three pairs of similar languages from three different language families: Ibero-Romance, Indo-Aryan, and Slavic.",
+      "The Similar Language Translation BIBREF2 task provided participants with training, development, and testing data from the following language pairs: Spanish - Portuguese (Romance languages), Czech - Polish (Slavic languages), and Hindi - Nepali (Indo-Aryan languages). Participant could submit system outputs to any of the three language pairs in any direction. The shared task attracted a good number of participants and the performance of all entries was evaluated using popular MT automatic evaluation metrics, namely BLEU BIBREF3 and TER BIBREF4.",
+      "In this paper we describe the UDS-DFKI system to the WMT 2019 Similar Language Translation task. The system achieved competitive performance and ranked second among ten entries in Czech to Polish translation in terms of BLEU score."
+    ],
+    [
+      "With the widespread use of MT technology and the commercial and academic success of NMT, there has been more interest in training systems to translate between languages other than English BIBREF5. One reason for this is the growing need of direct translation between pairs of similar languages, and to a lesser extent language varieties, without the use of English as a pivot language. The main challenge is to overcome the limitation of available parallel data taking advantage of the similarity between languages. Studies have been published on translating between similar languages (e.g. Catalan - Spanish BIBREF5) and language varieties such as European and Brazilian Portuguese BIBREF6, BIBREF7. The study by lakew2018neural tackles both training MT systems to translate between European\u2013Brazilian Portuguese and European\u2013Canadian French, and two pairs of similar languages Croatian\u2013Serbian and Indonesian\u2013Malay.",
+      "Processing similar languages and language varieties has attracted attention not only in the MT community but in NLP in general. This is evidenced by a number of research papers published in the last few years and the recent iterations of the VarDial evaluation campaign which featured multiple shared tasks on topics such as dialect detection, morphosyntactic tagging, cross-lingual parsing, cross-lingual morphological analysis BIBREF8, BIBREF9."
+    ],
+    [
+      "We used the Czech\u2013Polish dataset provided by the WMT 2019 Similar Language Translation task organizers for our experiments. The released parallel dataset consists of out-of-domain (or general-domain) data only and it differs substantially from the released development set which is part of a TED corpus. The parallel data includes Europarl v9, Wiki-titles v1, and JRC-Acquis. We combine all the released data and prepare a large out-domain dataset."
+    ],
+    [
+      "The out-domain data is noisy for our purposes, so we apply methods for cleaning. We performed the following two steps: (i) we use the cleaning process described in Pal:2015:WMT, and (ii) we execute the Moses BIBREF10 corpus cleaning scripts with minimum and maximum number of tokens set to 1 and 100, respectively. After cleaning, we perform punctuation normalization, and then we use the Moses tokenizer to tokenize the out-domain corpus with `no-escape' option. Finally, we apply true-casing.",
+      "The cleaned version of the released data, i.e., the General corpus containing 1,394,319 sentences, is sorted based on the score in Equation DISPLAY_FORM2. Thereafter, We split the entire data (1,394,319) into two sets; we use the first 1,000 for validation and the remaining as training data. The released development set (Dev) is used as test data for our experiment. It should be noted noted that, we exclude 1,000 sentences from the General corpus which are scored as top (i.e., more in-domain like) during the data selection process.",
+      "We prepare two parallel training sets from the aforementioned training data: (i) transference500K(presented next), collected 500,000 parallel data through data selection method BIBREF11, which are very similar to the in-domain data (for our case the development set), and (ii) transferenceALL, utilizing all the released out-domain data sorted by Equation DISPLAY_FORM2.",
+      "The transference500Ktraining set is prepared using in-domain (development set) bilingual cross-entropy difference for data selection as was described in Axelrod:2011. The difference in cross-entropy is computed based on two language models (LM): a domain-specific LM is estimated from the in-domain (containing 2050 sentences) corpus ($lm_{i}$) and the out-domain LM ($lm_{o}$) is estimated from the eScape corpus. We rank the eScape corpus by assigning a score to each of the individual sentences which is the sum of the three cross-entropy ($H$) differences. For a $j^{th}$ sentence pair ${src}_j$\u2013${trg}_j$, the score is calculated based on Equation DISPLAY_FORM2.",
+      ""
+    ],
+    [
+      "Our transference model extends the original transformer model to multi-encoder based transformer architecture. The transformer architecture BIBREF12 is built solely upon such attention mechanisms completely replacing recurrence and convolutions. The transformer uses positional encoding to encode the input and output sequences, and computes both self- and cross-attention through so-called multi-head attentions, which are facilitated by parallelization. We use multi-head attention to jointly attend to information at different positions from different representation subspaces.",
+      "The first encoder ($enc_1$) of our model encodes word form information of the source ($f_w$), and a second sub-encoder ($enc_2$) to encode sub-word (byte-pair-encoding) information of the source ($f_s$). Additionally, a second encoder ($enc_{src \\rightarrow mt}$) which takes the encoded representation from the $enc_1$, combines this with the self-attention-based encoding of $f_s$ ($enc_2$), and prepares a representation for the decoder ($dec_{e}$) via cross-attention. Our second encoder ($enc_{1 \\rightarrow 2}$) can be viewed as a transformer based NMT's decoding block, however, without masking. The intuition behind our architecture is to generate better representations via both self- and cross-attention and to further facilitate the learning capacity of the feed-forward layer in the decoder block. In our transference model, one self-attended encoder for $f_w$, $\\mathbf {f_w}$ = $(w_1, w_2, \\ldots , w_k)$, returns a sequence of continuous representations, $enc_{2}$, and a second self-attended sub-encoder for $f_s$, $\\mathbf {f_s}$ = $(s_1, s_2, \\ldots , s_l)$, returns another sequence of continuous representations, $enc_{2}$. Self-attention at this point provides the advantage of aggregating information from all of the words, including $f_w$ and $f_s$, and successively generates a new representation per word informed by the entire $f_w$ and $f_s$ context. The internal $enc_{2}$ representation performs cross-attention over $enc_{1}$ and prepares a final representation ($enc_{1 \\rightarrow 2}$) for the decoder ($dec_{e}$). The decoder generates the $e$ output in sequence, $\\mathbf {e}$ = $(e_1, e_2, \\ldots , e_n)$, one word at a time from left to right by attending to previously generated words as well as the final representations ($enc_{1 \\rightarrow 2}$) generated by the encoder.",
+      "We use the scale-dot attention mechanism (like Vaswani:NIPS2017) for both self- and cross-attention, as defined in Equation DISPLAY_FORM3, where $Q$, $K$ and $V$ are query, key and value, respectively, and $d_k$ is the dimension of $K$.",
+      "The multi-head attention mechanism in the transformer network maps the Q, K, and V matrices by using different linear projections. Then $h$ parallel heads are employed to focus on different parts in V. The $i^{th}$ multi-head attention is denoted by $head_i$ in Equation DISPLAY_FORM4. $head_i$ is linearly learned by three projection parameter matrices: $W_i^Q,W_i^K \\in R^{d_{model} \\times d_k}$, $W_i^V \\in R^{d_{model} \\times d_v}$; where $d_k = d_v = d_{model}/h$, and $d_{model}$ is the number of hidden units of our network.",
+      "Finally, all the vectors produced by parallel heads are linearly projected using concatenation and form a single vector, called a multi-head attention ($M_{att}$) (cf. Equation DISPLAY_FORM5). Here the dimension of the learned weight matrix $W^O$ is $R^{d_{model} \\times d_{model}}$.",
+      ""
+    ],
+    [
+      "We explore our transference model \u2013a two-encoder based transformer architecture, in CS-PL similar language translation task."
+    ],
+    [
+      "For transferenceALL, we initially train on the complete out-of-domain dataset (General). The General data is sorted based on their in-domain similarities as described in Equation DISPLAY_FORM2.",
+      "transferenceALLmodels are then fine-tuned towards the 500K (in-domain-like) data. Finally, we perform checkpoint averaging using the 8 best checkpoints. We report the results on the provided development set, which we use as a test set before the submission. Additionally we also report the official test set result.",
+      "To handle out-of-vocabulary words and to reduce the vocabulary size, instead of considering words, we consider subword units BIBREF13 by using byte-pair encoding (BPE). In the preprocessing step, instead of learning an explicit mapping between BPEs in the Czech (CS) and Polish (PL), we define BPE tokens by jointly processing all parallel data. Thus, CS and PL derive a single BPE vocabulary. Since CS and PL belong to the similar language, they naturally share a good fraction of BPE tokens, which reduces the vocabulary size.",
+      "We pass word level information on the first encoder and the BPE information to the second one. On the decoder side of the transference model we pass only BPE text.",
+      "We evaluate our approach with development data which is used as test case before submission. We use BLEU BIBREF3 and TER BIBREF4."
+    ],
+    [
+      "We follow a similar hyper-parameter setup for all reported systems. All encoders, and the decoder, are composed of a stack of $N_{fw} = N_{fs} = N_{es} = 6$ identical layers followed by layer normalization. Each layer again consists of two sub-layers and a residual connection BIBREF14 around each of the two sub-layers. We apply dropout BIBREF15 to the output of each sub-layer, before it is added to the sub-layer input and normalized. Furthermore, dropout is applied to the sums of the word embeddings and the corresponding positional encodings in both encoders as well as the decoder stacks.",
+      "We set all dropout values in the network to 0.1. During training, we employ label smoothing with value $\\epsilon _{ls}$ = 0.1. The output dimension produced by all sub-layers and embedding layers is $d_{model} = 512$. Each encoder and decoder layer contains a fully connected feed-forward network ($FFN$) having dimensionality of $d_{model} = 512$ for the input and output and dimensionality of $d_{ff} = 2048$ for the inner layers. For the scaled dot-product attention, the input consists of queries and keys of dimension $d_k$, and values of dimension $d_v$. As multi-head attention parameters, we employ $h = 8$ for parallel attention layers, or heads. For each of these we use a dimensionality of $d_k = d_v = d_{model}/h = 64$. For optimization, we use the Adam optimizer BIBREF16 with $\\beta _1 = 0.9$, $\\beta _2 = 0.98$ and $\\epsilon = 10^{-9}$.",
+      "The learning rate is varied throughout the training process, and increasing for the first training steps $warmup_{steps} = 8000$ and afterwards decreasing as described in BIBREF12. All remaining hyper-parameters are set analogously to those of the transformer's base model. At training time, the batch size is set to 25K tokens, with a maximum sentence length of 256 subwords, and a vocabulary size of 28K. After each epoch, the training data is shuffled. After finishing training, we save the 8 best checkpoints which are written at each epoch. Finally, we use a single model obtained by averaging the last 8 checkpoints. During decoding, we perform beam search with a beam size of 4. We use shared embeddings between CS and PL in all our experiments."
+    ],
+    [
+      "We present the results obtained by our system in Table TABREF8.",
+      "Our fine-tuned system on development set provides significant performance improvement over the generic model. We found +12.9 absolute BLEU points improvement over the generic model. Similar improvement is also observed in terms of TER (-16.9 absolute). It is to be noted that our generic model is trained solely on the clean version of training data.",
+      "Before submission, we performed punctuation normalization, unicode normalization, and detokenization for the run.",
+      "In Table TABREF9 we present the ranking of the competition provided by the shared task organizers. Ten entries were submitted by five teams and are ordered by BLEU score. TER is reported for all submissions which achieved BLEU score greater than 5.0. The type column specifies the type of system, whether it is a Primary (P) or Constrastive (C) entry.",
+      "Our system was ranked second in the competition only 0.3 BLEU points behind the winning team UPC-TALP. The relative low BLEU and high TER scores obtained by all teams are due to out-of-domain data provided in the competition which made the task equally challenging to all participants."
+    ],
+    [
+      "This paper presented the UDS-DFKI system submitted to the Similar Language Translation shared task at WMT 2019. We presented the results obtained by our system in translating from Czech to Polish. Our system achieved competitive performance ranking second among ten teams in the competition in terms of BLEU score. The fact that out-of-domain data was provided by the organizers resulted in a challenging but interesting scenario for all participants.",
+      "In future work, we would like to investigate how effective is the proposed hypothesis (i.e., word-BPE level information) in similar language translation. Furthermore, we would like to explore the similarity between these two languages (and the other two language pairs in the competition) in more detail by training models that can best capture morphological differences between them. During such competitions, this is not always possible due to time constraints."
+    ],
+    [
+      "This research was funded in part by the German research foundation (DFG) under grant number GE 2819/2-1 (project MMPE) and the German Federal Ministry of Education and Research (BMBF) under funding code 01IW17001 (project Deeplee). The responsibility for this publication lies with the authors. We would like to thank the anonymous WMT reviewers for their valuable input, and the organizers of the shared task."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1068/instruction.md b/qasper-1068/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..9744a878b12b03536c28396241e512c5524a6c3d
--- /dev/null
+++ b/qasper-1068/instruction.md
@@ -0,0 +1,82 @@
+Name of Paper: UDS--DFKI Submission to the WMT2019 Similar Language Translation Shared Task
+
+Question: Does the use of out-of-domain data improve the performance of the method?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Data",
+    "Data ::: Pre-processing",
+    "System Architecture - The Transference Model",
+    "Experiments",
+    "Experiments ::: Experiment Setup",
+    "Experiments ::: Hyper-parameter Setup",
+    "Results",
+    "Conclusion",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "The shared tasks organized annually at WMT provide important benchmarks used in the MT community. Most of these shared tasks include English data, which contributes to make English the most resource-rich language in MT and NLP. In the most popular WMT shared task for example, the News task, MT systems have been trained to translate texts from and to English BIBREF0, BIBREF1.",
+      "This year, we have observed a shift on the dominant role that English on the WMT shared tasks. The News task featured for the first time two language pairs which did not include English: German-Czech and French-German. In addition to that, the Similar Language Translation was organized for the first time at WMT 2019 with the purpose of evaluating the performance of MT systems on three pairs of similar languages from three different language families: Ibero-Romance, Indo-Aryan, and Slavic.",
+      "The Similar Language Translation BIBREF2 task provided participants with training, development, and testing data from the following language pairs: Spanish - Portuguese (Romance languages), Czech - Polish (Slavic languages), and Hindi - Nepali (Indo-Aryan languages). Participant could submit system outputs to any of the three language pairs in any direction. The shared task attracted a good number of participants and the performance of all entries was evaluated using popular MT automatic evaluation metrics, namely BLEU BIBREF3 and TER BIBREF4.",
+      "In this paper we describe the UDS-DFKI system to the WMT 2019 Similar Language Translation task. The system achieved competitive performance and ranked second among ten entries in Czech to Polish translation in terms of BLEU score."
+    ],
+    [
+      "With the widespread use of MT technology and the commercial and academic success of NMT, there has been more interest in training systems to translate between languages other than English BIBREF5. One reason for this is the growing need of direct translation between pairs of similar languages, and to a lesser extent language varieties, without the use of English as a pivot language. The main challenge is to overcome the limitation of available parallel data taking advantage of the similarity between languages. Studies have been published on translating between similar languages (e.g. Catalan - Spanish BIBREF5) and language varieties such as European and Brazilian Portuguese BIBREF6, BIBREF7. The study by lakew2018neural tackles both training MT systems to translate between European\u2013Brazilian Portuguese and European\u2013Canadian French, and two pairs of similar languages Croatian\u2013Serbian and Indonesian\u2013Malay.",
+      "Processing similar languages and language varieties has attracted attention not only in the MT community but in NLP in general. This is evidenced by a number of research papers published in the last few years and the recent iterations of the VarDial evaluation campaign which featured multiple shared tasks on topics such as dialect detection, morphosyntactic tagging, cross-lingual parsing, cross-lingual morphological analysis BIBREF8, BIBREF9."
+    ],
+    [
+      "We used the Czech\u2013Polish dataset provided by the WMT 2019 Similar Language Translation task organizers for our experiments. The released parallel dataset consists of out-of-domain (or general-domain) data only and it differs substantially from the released development set which is part of a TED corpus. The parallel data includes Europarl v9, Wiki-titles v1, and JRC-Acquis. We combine all the released data and prepare a large out-domain dataset."
+    ],
+    [
+      "The out-domain data is noisy for our purposes, so we apply methods for cleaning. We performed the following two steps: (i) we use the cleaning process described in Pal:2015:WMT, and (ii) we execute the Moses BIBREF10 corpus cleaning scripts with minimum and maximum number of tokens set to 1 and 100, respectively. After cleaning, we perform punctuation normalization, and then we use the Moses tokenizer to tokenize the out-domain corpus with `no-escape' option. Finally, we apply true-casing.",
+      "The cleaned version of the released data, i.e., the General corpus containing 1,394,319 sentences, is sorted based on the score in Equation DISPLAY_FORM2. Thereafter, We split the entire data (1,394,319) into two sets; we use the first 1,000 for validation and the remaining as training data. The released development set (Dev) is used as test data for our experiment. It should be noted noted that, we exclude 1,000 sentences from the General corpus which are scored as top (i.e., more in-domain like) during the data selection process.",
+      "We prepare two parallel training sets from the aforementioned training data: (i) transference500K(presented next), collected 500,000 parallel data through data selection method BIBREF11, which are very similar to the in-domain data (for our case the development set), and (ii) transferenceALL, utilizing all the released out-domain data sorted by Equation DISPLAY_FORM2.",
+      "The transference500Ktraining set is prepared using in-domain (development set) bilingual cross-entropy difference for data selection as was described in Axelrod:2011. The difference in cross-entropy is computed based on two language models (LM): a domain-specific LM is estimated from the in-domain (containing 2050 sentences) corpus ($lm_{i}$) and the out-domain LM ($lm_{o}$) is estimated from the eScape corpus. We rank the eScape corpus by assigning a score to each of the individual sentences which is the sum of the three cross-entropy ($H$) differences. For a $j^{th}$ sentence pair ${src}_j$\u2013${trg}_j$, the score is calculated based on Equation DISPLAY_FORM2.",
+      ""
+    ],
+    [
+      "Our transference model extends the original transformer model to multi-encoder based transformer architecture. The transformer architecture BIBREF12 is built solely upon such attention mechanisms completely replacing recurrence and convolutions. The transformer uses positional encoding to encode the input and output sequences, and computes both self- and cross-attention through so-called multi-head attentions, which are facilitated by parallelization. We use multi-head attention to jointly attend to information at different positions from different representation subspaces.",
+      "The first encoder ($enc_1$) of our model encodes word form information of the source ($f_w$), and a second sub-encoder ($enc_2$) to encode sub-word (byte-pair-encoding) information of the source ($f_s$). Additionally, a second encoder ($enc_{src \\rightarrow mt}$) which takes the encoded representation from the $enc_1$, combines this with the self-attention-based encoding of $f_s$ ($enc_2$), and prepares a representation for the decoder ($dec_{e}$) via cross-attention. Our second encoder ($enc_{1 \\rightarrow 2}$) can be viewed as a transformer based NMT's decoding block, however, without masking. The intuition behind our architecture is to generate better representations via both self- and cross-attention and to further facilitate the learning capacity of the feed-forward layer in the decoder block. In our transference model, one self-attended encoder for $f_w$, $\\mathbf {f_w}$ = $(w_1, w_2, \\ldots , w_k)$, returns a sequence of continuous representations, $enc_{2}$, and a second self-attended sub-encoder for $f_s$, $\\mathbf {f_s}$ = $(s_1, s_2, \\ldots , s_l)$, returns another sequence of continuous representations, $enc_{2}$. Self-attention at this point provides the advantage of aggregating information from all of the words, including $f_w$ and $f_s$, and successively generates a new representation per word informed by the entire $f_w$ and $f_s$ context. The internal $enc_{2}$ representation performs cross-attention over $enc_{1}$ and prepares a final representation ($enc_{1 \\rightarrow 2}$) for the decoder ($dec_{e}$). The decoder generates the $e$ output in sequence, $\\mathbf {e}$ = $(e_1, e_2, \\ldots , e_n)$, one word at a time from left to right by attending to previously generated words as well as the final representations ($enc_{1 \\rightarrow 2}$) generated by the encoder.",
+      "We use the scale-dot attention mechanism (like Vaswani:NIPS2017) for both self- and cross-attention, as defined in Equation DISPLAY_FORM3, where $Q$, $K$ and $V$ are query, key and value, respectively, and $d_k$ is the dimension of $K$.",
+      "The multi-head attention mechanism in the transformer network maps the Q, K, and V matrices by using different linear projections. Then $h$ parallel heads are employed to focus on different parts in V. The $i^{th}$ multi-head attention is denoted by $head_i$ in Equation DISPLAY_FORM4. $head_i$ is linearly learned by three projection parameter matrices: $W_i^Q,W_i^K \\in R^{d_{model} \\times d_k}$, $W_i^V \\in R^{d_{model} \\times d_v}$; where $d_k = d_v = d_{model}/h$, and $d_{model}$ is the number of hidden units of our network.",
+      "Finally, all the vectors produced by parallel heads are linearly projected using concatenation and form a single vector, called a multi-head attention ($M_{att}$) (cf. Equation DISPLAY_FORM5). Here the dimension of the learned weight matrix $W^O$ is $R^{d_{model} \\times d_{model}}$.",
+      ""
+    ],
+    [
+      "We explore our transference model \u2013a two-encoder based transformer architecture, in CS-PL similar language translation task."
+    ],
+    [
+      "For transferenceALL, we initially train on the complete out-of-domain dataset (General). The General data is sorted based on their in-domain similarities as described in Equation DISPLAY_FORM2.",
+      "transferenceALLmodels are then fine-tuned towards the 500K (in-domain-like) data. Finally, we perform checkpoint averaging using the 8 best checkpoints. We report the results on the provided development set, which we use as a test set before the submission. Additionally we also report the official test set result.",
+      "To handle out-of-vocabulary words and to reduce the vocabulary size, instead of considering words, we consider subword units BIBREF13 by using byte-pair encoding (BPE). In the preprocessing step, instead of learning an explicit mapping between BPEs in the Czech (CS) and Polish (PL), we define BPE tokens by jointly processing all parallel data. Thus, CS and PL derive a single BPE vocabulary. Since CS and PL belong to the similar language, they naturally share a good fraction of BPE tokens, which reduces the vocabulary size.",
+      "We pass word level information on the first encoder and the BPE information to the second one. On the decoder side of the transference model we pass only BPE text.",
+      "We evaluate our approach with development data which is used as test case before submission. We use BLEU BIBREF3 and TER BIBREF4."
+    ],
+    [
+      "We follow a similar hyper-parameter setup for all reported systems. All encoders, and the decoder, are composed of a stack of $N_{fw} = N_{fs} = N_{es} = 6$ identical layers followed by layer normalization. Each layer again consists of two sub-layers and a residual connection BIBREF14 around each of the two sub-layers. We apply dropout BIBREF15 to the output of each sub-layer, before it is added to the sub-layer input and normalized. Furthermore, dropout is applied to the sums of the word embeddings and the corresponding positional encodings in both encoders as well as the decoder stacks.",
+      "We set all dropout values in the network to 0.1. During training, we employ label smoothing with value $\\epsilon _{ls}$ = 0.1. The output dimension produced by all sub-layers and embedding layers is $d_{model} = 512$. Each encoder and decoder layer contains a fully connected feed-forward network ($FFN$) having dimensionality of $d_{model} = 512$ for the input and output and dimensionality of $d_{ff} = 2048$ for the inner layers. For the scaled dot-product attention, the input consists of queries and keys of dimension $d_k$, and values of dimension $d_v$. As multi-head attention parameters, we employ $h = 8$ for parallel attention layers, or heads. For each of these we use a dimensionality of $d_k = d_v = d_{model}/h = 64$. For optimization, we use the Adam optimizer BIBREF16 with $\\beta _1 = 0.9$, $\\beta _2 = 0.98$ and $\\epsilon = 10^{-9}$.",
+      "The learning rate is varied throughout the training process, and increasing for the first training steps $warmup_{steps} = 8000$ and afterwards decreasing as described in BIBREF12. All remaining hyper-parameters are set analogously to those of the transformer's base model. At training time, the batch size is set to 25K tokens, with a maximum sentence length of 256 subwords, and a vocabulary size of 28K. After each epoch, the training data is shuffled. After finishing training, we save the 8 best checkpoints which are written at each epoch. Finally, we use a single model obtained by averaging the last 8 checkpoints. During decoding, we perform beam search with a beam size of 4. We use shared embeddings between CS and PL in all our experiments."
+    ],
+    [
+      "We present the results obtained by our system in Table TABREF8.",
+      "Our fine-tuned system on development set provides significant performance improvement over the generic model. We found +12.9 absolute BLEU points improvement over the generic model. Similar improvement is also observed in terms of TER (-16.9 absolute). It is to be noted that our generic model is trained solely on the clean version of training data.",
+      "Before submission, we performed punctuation normalization, unicode normalization, and detokenization for the run.",
+      "In Table TABREF9 we present the ranking of the competition provided by the shared task organizers. Ten entries were submitted by five teams and are ordered by BLEU score. TER is reported for all submissions which achieved BLEU score greater than 5.0. The type column specifies the type of system, whether it is a Primary (P) or Constrastive (C) entry.",
+      "Our system was ranked second in the competition only 0.3 BLEU points behind the winning team UPC-TALP. The relative low BLEU and high TER scores obtained by all teams are due to out-of-domain data provided in the competition which made the task equally challenging to all participants."
+    ],
+    [
+      "This paper presented the UDS-DFKI system submitted to the Similar Language Translation shared task at WMT 2019. We presented the results obtained by our system in translating from Czech to Polish. Our system achieved competitive performance ranking second among ten teams in the competition in terms of BLEU score. The fact that out-of-domain data was provided by the organizers resulted in a challenging but interesting scenario for all participants.",
+      "In future work, we would like to investigate how effective is the proposed hypothesis (i.e., word-BPE level information) in similar language translation. Furthermore, we would like to explore the similarity between these two languages (and the other two language pairs in the competition) in more detail by training models that can best capture morphological differences between them. During such competitions, this is not always possible due to time constraints."
+    ],
+    [
+      "This research was funded in part by the German research foundation (DFG) under grant number GE 2819/2-1 (project MMPE) and the German Federal Ministry of Education and Research (BMBF) under funding code 01IW17001 (project Deeplee). The responsibility for this publication lies with the authors. We would like to thank the anonymous WMT reviewers for their valuable input, and the organizers of the shared task."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: Exploration on Generating Traditional Chinese Medicine Prescriptions from Symptoms with an End-to-End Approach
+
+Question: Do they impose any grammatical constraints over the generated output?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Methodology",
+    "Task Definition",
+    "Basic Encoder-Decoder Model",
+    "Coverage Mechanism",
+    "Soft Loss Function",
+    "Dataset Construction",
+    "Experiment Settings",
+    "Proposed Baseline",
+    "Human Evaluation",
+    "Automatic Evaluation Results",
+    "Case Study",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Traditional Chinese Medicine (TCM) is one of the most important forms of medical treatment in China and the surrounding areas. TCM has accumulated large quantities of documentation and therapy records in the long history of development. Prescriptions consisting of herbal medication are the most important form of TCM treatment. TCM practitioners prescribe according to a patient's symptoms that are observed and analyzed by the practitioners themselves instead of using medical equipment, e.g., the CT. The patient takes the decoction made out of the herbal medication in the prescription. A complete prescription includes the composition of herbs, the proportion of herbs, the preparation method and the doses of the decoction. In this work, we focus on the composition part of the prescription, which is the most essential part of the prescription.",
+      "During the long history of TCM, there has been a number of therapy records or treatment guidelines in the TCM classics composed by outstanding TCM researchers and practitioners. In real life, TCM practitioners often take these classical records for reference when prescribing for the patient, which inspires us to design a model that can automatically generate prescriptions by learning from these classics. It also needs to be noted that due to the issues in actual practice, the objective of this work is to generate candidate prescriptions to facilitate the prescribing procedure instead of substituting the human practitioners completely. An example of TCM prescription is shown in Table 1 . The herbs in the prescription are organized in a weak order. By \u201cweak order\u201d, we mean that the effect of the herbs are not influenced by the order. However, the order of the herbs reflects the way of thinking when constructing the prescription. Therefore, the herbs are connected to each other, and the most important ones are usually listed first.",
+      "Due to the lack of digitalization and formalization, TCM has not attracted sufficient attention in the artificial intelligence community. To facilitate the studies on automatic TCM prescription generation, we collect and clean a large number of prescriptions as well as their corresponding symptom descriptions from the Internet.",
+      "Inspired by the great success of natural language generation tasks like neural machine translation (NMT) BIBREF0 , BIBREF1 , BIBREF2 , abstractive summarization BIBREF3 , generative question answering BIBREF4 , and neural dialogue response generation BIBREF5 , BIBREF6 , we propose to adopt the end-to-end paradigm, mainly the sequence to sequence model, to tackle the task of generating TCM prescriptions based on textual symptom descriptions.",
+      "The sequence to sequence model (seq2seq) consists of an encoder that encodes the input sequence and a decoder that generates the output sequence. The success in the language generation tasks indicates that the seq2seq model can learn the semantic relation between the output sequence and the input sequence quite well. It is also a desirable characteristic for generating prescriptions according to the textual symptom description.",
+      "The prescription generation task is similar to the generative question answering (QA). In such task settings, the encoder part of the model takes in the question, and encodes the sequence of tokens into a set of hidden states, which embody the information of the question. The decoder part then iteratively generates tokens based on the information encoded in the hidden states of the encoder. The model would learn how to generate response after training on the corresponding question-answer pairs.",
+      "In the TCM prescription generation task, the textual symptom descriptions can be seen as the question and the aim of the task is to produce a set of TCM herbs that form a prescription as the answer to the question. However, the set of herbs is different from the textual answers to a question in the QA task. A difference that is most evident is that there will not be any duplication of herbs in the prescription. However, the basic seq2seq model sometimes produces the same herb tokens repeatedly when applied to the TCM prescription generation task. This phenomenon can hurt the performance of recall rate even after applying a post-process to eliminate repetitions. Because in a limited length of the prescription , the model would produce the same token over and over again, rather than real and novel ones. Furthermore, the basic seq2seq assumes a strict order between generated tokens, but in reality, we should not severely punish the model when it predicts the correct tokens in the wrong order. In this paper, we explore to automatically generate TCM prescriptions based on textual symptoms. We propose a soft seq2seq model with coverage mechanism and a novel soft loss function. The coverage mechanism is designed to make the model aware of the herbs that have already been generated while the soft loss function is to relieve the side effect of strict order assumption. In the experiment results, our proposed model beats all the baselines in professional evaluations, and we observe a large increase in both the recall rate and the F1 score compared with the basic seq2seq model.",
+      "The main contributions of this paper lie in the following three folds:"
+    ],
+    [
+      "There has not been much work concerning computational TCM. zhou2010development attempted to build a TCM clinical data warehouse so that the TCM knowledge can be analyzed and used. This is a typical way of collecting data, since the number of prescriptions given by the practitioners in the clinics is very large. However, in reality, most of the TCM doctors do not refer to the constructed digital systems, because the quality of the input data tends to be poor. Therefore, we choose prescriptions in the classics (books or documentation) of TCM. Although the available data can be fewer than the clinical data, it guarantees the quality of the prescriptions.",
+      "wang2004self attempted to construct a self-learning expert system with several simple classifiers to facilitate the TCM diagnosis procedure, Wang2013TCM proposed to use shallow neural networks and CRF based multi-labeling learning methods to model TCM inquiry process, but they only considered the disease of chronic gastritis and its taxonomy is very simple. These methods either utilize traditional data mining methods or are highly involved with expert crafted systems. Zhang2011Topic,Zhu2017TCM proposed to use LDA to model the herbs. li2017distributed proposed to learn the distributed embedding for TCM herbs with recurrent neural networks."
+    ],
+    [
+      "Neural sequence to sequence model has proven to be very effective in a wide range of natural language generation tasks, including neural machine translation and abstractive text summarization. In this section, we first describe the definition of the TCM prescription generation task. Then, we introduce how to apply seq2seq model in the prescription composition task. Next, we show how to guide the model to generate more fruitful herbs in the setting of this task by introducing coverage mechanism. Finally, we introduce our novel soft loss function that relieves the strict assumption of order between tokens. An overview of the our final model is shown in Figure 1 ."
+    ],
+    [
+      "Given a TCM herbal treatment dataset that consists of $N$ data samples, the $i$ -th data sample ( $x^{(i)}, p^{(i)}$ ) contains one piece of source text $x^{(i)}$ that describes the symptoms, and $M_{i}$ TCM herbs $(p_{1}^{i},p_{2}^{i}, ..., p_{M_{i}}^{i})$ that make up the herb prescription $p^{(i)}$ .",
+      "We view the symptoms as a sequence of characters $x^{(i)} = (x^{(i)}_{1}, x^{(i)}_{2}, ..., x^{(i)}_{T})$ . We do not segment the characters into words because they are mostly in traditional Chinese that uses characters as basic semantic units. The herbs $p_{1}^{i},p_{2}^{i}, ..., p_{M_{i}}^{i}$ are all different from each other."
+    ],
+    [
+      "Sequence-to-sequence model was first proposed to solve the machine translation problem. The model consists of two parts, an encoder and a decoder. The encoder is bound to take in the source sequence and compress the sequence into a series of hidden states. The decoder is used to generate a sequence of target tokens based on the information embodied in the hidden states given by the encoder. Typically, both the encoder and the decoder are implemented with recurrent neural networks (RNN).",
+      "In our TCM prescription generation task, the encoder RNN converts the variable-length symptoms in character sequence $x = (x_{1},x_{2},...,x_{T})$ into a set of hidden representations $h = (h_{1},h_{2},...,h_{T})$ , by iterating the following equations along time $t$ : ",
+      "$$h_{t} = f(x_{t},h_{t-1})$$   (Eq. 8) ",
+      "where $f$ is a RNN family function. In our implementation, we choose gated recurrent unit (GRU BIBREF1 ) as $f$ , as the gating mechanism is expected to model long distance dependency better. Furthermore, we choose the bidirectional version of recurrent neural networks as the encoder to solve the problem that the later words get more emphasis in the unidirectional version. We concatenate both the $h_{t}$ in the forward and backward pass and get $\\widehat{h_{t}}$ as the final representation of the hidden state at time step $t$ .",
+      "We get the context vector $c$ representing the whole source $x$ at the $t$ -th time through a non-linear function $q$ , normally known as the attention mechanism: ",
+      "$$c_{t} = \\sum _{j=1}^{T}\\alpha _{tj}h_{j} \\\\\n\\alpha _{tj} = \\frac{\\text{exp}\\left( a\\left(s_{t-1},h_{j}\\right)\\right)}{\\sum _{k=1}^{T}\\text{exp}\\left( a\\left(s_{t-1},h_{k}\\right)\\right)}$$   (Eq. 9) ",
+      "The context vector $c_{t}$ is calculated as a weighted sum of hidden representation produced by the encoder $\\textbf {h} = (h_{1},...,h_{T})$ . $a(s_{t-1},h_{j})$ is a soft alignment function that measures the relevance between $s_{t-1}$ and $h_{j}$ . It computes how much $h_j$ is needed for the $t$ -th output word based on the previous hidden state of the decoder $s_{t-1}$ . The decoder is another RNN. It generates a variable-length sequence $y = (y_{1},y_{2}, ..., y_{T^{\\prime }})$ token by token (herb), through a conditional language model: ",
+      "$$s_{t} = f(s_{t-1},c_{t},Ey_{t-1})  \\\\\np(y_{t}|y_{1,...,t},x) = g(s_{t})$$   (Eq. 10) ",
+      "where $s_{t}$ is the hidden state of the decoder RNN at time step $t$ . $f$ is also a gated recurrent unit. The non-linear function $g$ is a $softmax$ layer, which outputs the probabilities of all the herbs in the herb vocabulary. $E \\in (V\\times d)$ is the embedding matrix of the target tokens, $V$ is the number of herb vocabulary, $d$ is the embedding dimension. $y_{t-1}$ is the last predicted token.",
+      "In the decoder, the context vector $c_{t}$ is calculated based on the hidden state $s_{t-1}$ of the decoder at time step $t-1$ and all the hidden states in the encoder. The procedure is known as the attention mechanism. The attention mechanism is expected to supplement the information from the source sequence that is more connected to the current hidden state of the decoder instead of only depending on a fixed vector produced by the encoder.",
+      "The encoder and decoder networks are trained jointly to maximize the conditional probability of the target sequence. A soft version of cross entropy loss is applied to maximize the conditional probability, which we will describe in detail."
+    ],
+    [
+      "Different from natural language generation tasks, there is no duplicate herb in the TCM prescription generation task. When directly applying seq2seq model in this task, the decoder tends to generate some frequently observed herbs over and over again. Although we can prune the repeated herbs through post processing by eliminating the repeated ones, it still hurts the recall performance as the maximum length of a prescription is limited. This situation is still true when we use a $<EOS>$ label to indicate where the generation should stop.",
+      "To encourage the decoder to generate more diverse and reasonable herb tokens, we propose to apply coverage mechanism to make the model aware of the already generated herbs. Coverage mechanism BIBREF7 , BIBREF8 , BIBREF9 was first proposed to help the decoder focus on the part that has not been paid much attention by feeding a fertility vector to the attention calculation, indicating how much information of the input is used.",
+      "In our model, we do not use the fertility vector to tune the attention weights. The reason is that the symptoms are related to others and altogether describe the whole disease, which is explained in Section \"Introduction\" . Still, inspired by its motivation, we adapt the coverage mechanism to the decoder where a coverage vector is fed to the GRU cell together with the context vector. Equation 10 is then replaced by the following ones. ",
+      "$$a_{t} = \\tanh (WD_{t}+b) \\\\\ns_{t} = f(s_{t-1}, c_{t}, Ey_{t-1}, a_{t})$$   (Eq. 12) ",
+      "where $a_{t}$ is the coverage vector at the $t$ -th time step in decoding. $D_{t}$ is the one-hot representation of the generated tokens until the $t$ -th time step. $W\\in \\mathbb {R}^{V\\times H}$ is a learnable parameter matrix, where $V$ is the size of the herb vocabulary and $H$ is the size of the hidden state. By feeding the coverage vector, which is also a sketch of the generated herbs, to the GRU as part of the input, our model can softly switch more probability to the herbs that have not been predicted. This way, the model is encouraged to produce novel herbs rather than repeatedly predicting the frequently observed ones, thus increasing the recall rate."
+    ],
+    [
+      "We argue that even though the order of the herbs matters when generating the prescription BIBREF10 , BIBREF11 , we should not strictly restrict the order. However, the traditional cross entropy loss function applied to the basic seq2seq model puts a strict assumption on the order of the labels. To deal with the task of predicting weakly ordered labels (or even unordered labels), we propose a soft loss function instead of the original hard cross entropy loss function: ",
+      "$$loss = -\\sum _{t}\\ q^{\\prime }_{t}\\ log(p_t)$$   (Eq. 14) ",
+      "Instead of using the original hard one-hot target probability $q_t$ , we use a soft target probability distribution $q^{\\prime }_{t}$ , which is calculated according to $q_t$ and the target sequence $\\mathbf {q}$ of this sample. Let $\\mathbf {q_v}$ denote the bag of words representation of $\\mathbf {q}$ , where only slots of the target herbs in $\\mathbf {q}$ are filled with $1s$ . We use a function $\\xi $ to project the original target label probability $q_t$ into a new probability distribution $q^{\\prime }_{t}$0 . ",
+      "$$q^{\\prime }_t = \\xi (q_t, \\mathbf {q_v})$$   (Eq. 15) ",
+      "This function $\\xi $ is designed so as to decrease the harsh punishment when the model predicts the labels in the wrong order. In this paper, we apply a simple yet effective projection function as Equation 16 . This is an example implementation, and one can design more sophisticated projection functions if needed. ",
+      "$$\\xi (y_t,\\mathbf {s}) = ((\\mathbf {q_v}/M) + y_t) / 2 $$   (Eq. 16) ",
+      "where $M$ is the length of $q$ . This function means that at the $t$ -th time of decoding, for each target herb token $p_i$ , we first split a probability density of $1.0$ equally across all the $l$ herbs into $1/M$ . Then, we take the average of this probability distribution and the original probability $q_t$ to be the final probability distribution at time $t$ ."
+    ],
+    [
+      "We crawl the data from UTF8gbsnTCM Prescription Knowledge Base (\u4e2d\u533b\u65b9\u5242\u77e5\u8bc6\u5e93) . This knowledge base includes comprehensive TCM documentation in the history. The database includes 710 TCM historic books or documents as well as some modern ones, consisting of 85,166 prescriptions in total. Each item in the database provides the name, the origin, the composition, the effect, the contraindications, and the preparation method. We clean and formalize the database and get 82,044 usable symptom-prescription pairs",
+      "In the process of formalization, we temporarily omit the dose information and the preparation method description, as we are mainly concerned with the composition. Because the names of the herbs have evolved a lot, we conclude heuristic rules as well as specific projection rules to project some rarely seen herbs to their similar forms that are normally referred to. There are also prescriptions that refer to the name of other prescriptions. We simply substitute these names with their constituents.",
+      "To make the experiment result more robust, we conduct our experiments on two separate test datasets. The first one is a subset of the data described above. We randomly split the whole data into three parts, the training data (90%), the development data (5%) and the test data (5%). The second one is a set of symptom-prescription pairs we manually extracted from the modern text book of the course Formulaology of TCM (UTF8gbsn\u4e2d\u533b\u65b9\u5242\u5b66) that is popularly adopted by many TCM colleges in China.",
+      "There are more cases in the first sampled test dataset (4,102 examples), but it suffers from lower quality, as this dataset was parsed with simple rules, which may not cover all exceptions. The second test dataset has been proofread and all of the prescriptions are the most classical and influential ones in the history. So the quality is much better than the first one. However, the number of the cases is limited. There are 141 symptom-prescription pairs in the second dataset. Thus we use two test sets to do evaluation to take the advantages of both data magnitude and quality."
+    ],
+    [
+      "In our experiments, we implement our models with the PyTorch toolkit . We set the embedding size of both Chinese characters in the symptoms and the herb tokens to 100. We set the hidden state size to 300, and the batch size to 20. We set the maximum length of the herb sequence to 20 because the length of nearly all the prescriptions are within this range (see Table 2 for the statistics of the length of prescriptions). Unless specifically stated, we use bidirectional gated recurrent neural networks (BiGRNN) to encode the symptoms. Adam BIBREF12 , and use the model parameters that generate the best F1 score on the development set in testing"
+    ],
+    [
+      "In this sub-section, we present the Multi-label baseline we apply. In this model, we use a BiGRNN as the encoder, which encodes symptoms in the same way as it is described in Section \"Methodology\" . Because the position of the herbs does not matter in the results, for the generation part, we implement a multi-label classification method to predict the herbs. We use the multi-label max-margin loss (MultiLabelMarginLoss in pytorch) as the optimization objective, because this loss function is more insensitive to the threshold, thus making the model more robust. We set the threshold to be 0.5, that is, if the probability given by the model is above 0.5 and within the top $k$ range (we set k to 20 in our experiment, same to seq2seq model), we take the tokens as answers. The way to calculate probability is shown below. ",
+      "$$p(i) = \\sigma (W_{o}h_{T})$$   (Eq. 23) ",
+      "where $\\sigma $ indicates the non-linear function $sigmoid$ , $W_{o} \\in \\mathbb {R}^{H \\times V}$ , $H$ is the size of the hidden state produced by the encoder and $V$ is the size of the herb vocabulary. $h_{T}$ is the last hidden state produced by the encoder.",
+      "During evaluation, we choose the herbs satisfying two conditions:",
+      "The predicted probability of the herb is within top $k$ among all the herbs, where $k$ is a hyper-parameter. We set $k$ to be the same as the maximum length of seq2seq based models (20).",
+      "The predicted probability is above a threshold 0.5 (related to the max-margin)."
+    ],
+    [
+      "Since medical treatment is a very complex task, we invite two professors from Beijing University of Chinese Medicine, which is one of the best Traditional Chinese Medicine academies in China. Both of the professors enjoy over five years of practicing traditional Chinese medical treatment. The evaluators are asked to evaluate the prescriptions with scores between 0 and 10. Both the textual symptoms and the standard reference are given, which is similar to the form of evaluation in a normal TCM examination. Different from the automatic evaluation method, the human evaluators focus on the potential curative effect of the candidate answers, rather than merely the literal similarity. We believe this way of evaluation is much more reasonable and close to reality.",
+      "Because the evaluation procedure is very time consuming (each item requires more than 1 minute), we only ask the evaluators to judge the results from test set 2.",
+      "As shown in Table 3 , both of the basic seq2seq model and our proposed modification are much better than the multi-label baseline. Our proposed model gets a high score of 7.3, which can be of real help to TCM practitioners when prescribing in the real life treatment."
+    ],
+    [
+      "We use micro Precision, Recall, and F1 score as the automatic metrics to evaluate the results, because the internal order between the herbs does not matter when we do not consider the prescribing process.",
+      "In Table 4 , we show the results of our proposed models as well as the baseline models. One thing that should be noted is that since the data in Test set 2 (extracted from text book) have much better quality than Test set 1, the performance on Test set 2 is much higher than it is on Test set 1, which is consistent with our instinct.",
+      "From the experiment results we can see that the baseline model multi-label has higher micro recall rate 29.72, 40.49 but much lower micro precision 10.83, 13.51. This is because unlike the seq2seq model that dynamically determines the length of the generated sequence, the output length is rigid and can only be determined by thresholds. We take the tokens within the top 20 as the answer for the multi-label model.",
+      "As to the basic seq2seq model, although it beats the multi-label model overall, the recall rate drops substantially. This problem is partly caused by the repetition problem, the basic seq2seq model sometimes predicts high frequent tokens instead of more meaningful ones. Apart from this, although the seq2seq based model is better able to model the correlation between target labels, it makes a strong assumption on the order of the target sequence. In the prescription generation task, the order between herb tokens are helpful for generating the sequence. However, since the order between the herbs does not affect the effect of the prescription, we do not consider the order when evaluating the generated sequence. We call the phenomenon that the herbs are under the \u201cweak order\u201d. The much too strong assumption on order can hurt the performance of the model when the correct tokens are placed in the wrong order.",
+      "In Table 5 we show the effect of applying coverage mechanism and soft loss function.",
+      "Coverage mechanism gives a sketch on the prescription. The mechanism not only encourages the model to generate novel herbs but also enables the model to generate tokens based on the already predicted ones. This can be proved by the improvement on Test set 2, where both the precision and the recall are improved over the basic seq2seq model.",
+      "The most significant improvement comes from applying the soft loss function. The soft loss function can relieve the strong assumption of order made by seq2seq model. Because predicting a correct token in the wrong position is not as harmful as predicting a completely wrong token. This simple modification gives a big improvement on both test sets for all the three evaluation metrics."
+    ],
+    [
+      "In this subsection, we show an example generated by various models in Table 6 in test set 2 because the quality of test set 2 is much more satisfactory. The multi-label model produces too many herbs that lower the precision, we do not go deep into its results, already we report its results in the table.",
+      "For the basic seq2seq model, the result is better than multi-label baseline in this case. UTF8gbsn\u201c\u67f4\u80e1\u201d (radix bupleuri)\u3001\u201c\u845b\u6839\u201d (the root of kudzu vine) can be roughly matched with \u201c\u6076\u98ce\u53d1\u70ed\uff0c\u6c57\u51fa\u5934\u75bc\u201d (Aversion to wind, fever, sweating, headache), \u201c\u7518\u8349\u201d (Glycyrrhiza)\u3001\u201c\u9648\u76ae\u201d (dried tangerine or orange peel)\u3001\u201c\u6854\u6897\u201d (Platycodon grandiflorum) can be roughly matched with \u201c\u9f3b\u9e23\u54bd\u5e72\uff0c\u82d4\u767d\u4e0d\u6e34\u201d (nasal obstruction, dry throat, white tongue coating, not thirsty), \u201c\u5ddd\u828e\u201d (Ligusticum wallichii) can be used to treat the symptom of \u201c\u5934\u75bc\u201d (headache). In this case, most of the herbs can be matched with certain symptoms in the textual description. However, the problem is that unlike the reference, the composition of herbs lacks the overall design. The symptoms should not be treated independently, as they are connected to other symptoms. For example, the appearance of symptom UTF8gbsn\u201c\u5934\u75bc\u201d (headache) must be treated together with UTF8gbsn\u201c\u6c57\u51fa\u201d (sweat). When there is simply headache without sweat, UTF8gbsn\u201c\u5ddd\u828e\u201d (Ligusticum wallichii) may be suitable. However, since there is already sweat, this herb is not suitable in this situation. This drawback results from the fact that this model heavily relies on the attention mechanism that tries to match the current hidden state in the decoder to a part of the context in the encoder every time it predicts a token.",
+      "Translation: UTF8gbsn\u6842\u679d - cassia twig, \u828d\u836f - Chinese herbaceous peony \u5927\u9ec4 - Rhubarb, \u539a\u6734 - Magnolia officinalis, \u67b3\u5b9e - Fructus Aurantii Immaturus, \u8292\u785d - Mirabilite, \u6800\u5b50 - Cape Jasmine Fruit, \u67b3\u58f3 - Fructus Aurantii, \u5f53\u5f52 - Angelica Sinensis, \u7518\u8349 - Glycyrrhiza, \u9ec4\u82a9 - Scutellaria, \u751f\u59dc - ginger, \u5927\u67a3 - Chinese date, \u67f4\u80e1 - radix bupleuri, \u845b\u6839 - the root of kudzu vine, \u9648\u76ae - dried tangerine or orange peel, \u6854\u6897 - Platycodon grandiflorum, \u5ddd\u828e - Ligusticum wallichii, \u9ebb\u9ec4 - Chinese ephedra",
+      "For our proposed model, the results are much more satisfactory. UTF8gbsn\u201c\u5916\u611f\u98ce\u5bd2\u201d (Exogenous wind-cold exterior deficiency syndrome) is the reason of the disease, the symptoms UTF8gbsn\u201c\u6076\u98ce\u53d1\u70ed\uff0c\u6c57\u51fa\u5934\u75bc\uff0c\u9f3b\u9e23\u54bd\u5e72\uff0c\u82d4\u767d\u4e0d\u6e34\uff0c\u8109\u6d6e\u7f13\u6216\u6d6e\u5f31\u201d (Aversion to wind, fever, sweating, headache, nasal obstruction, dry throat, white tongue coating, not thirsty, floating slow pulse or floating weak pulse) are the corresponding results. The prescription generated by our proposed model can also be used to cure UTF8gbsn\u201c\u5916\u611f\u98ce\u5bd2\u201d (Exogenous wind-cold exterior deficiency syndrome), in fact UTF8gbsn\u201c\u9ebb\u9ec4\u201d (Chinese ephedra) and \u201c\u6842\u679d\u201d (cassia twig) together is a common combination to cure cold. However, UTF8gbsn\u201c\u9ebb\u9ec4\u201d (Chinese ephedra) is not suitable here because there is already sweat. One of the most common effect of UTF8gbsn\u201c\u9ebb\u9ec4\u201d (Chinese ephedra) is to make the patient sweat. Since there is already sweat, it should not be used. Compared with the basic seq2seq model, our proposed model have a sense of overall disease, rather than merely discretely focusing on individual symptoms.",
+      "From the above analysis, we can see that compared with the basic seq2seq model, our proposed soft seq2seq model is aware more of the connections between symptoms, and has a better overall view on the disease. This advantage is correspondent to the principle of prescribing in TCM that the prescription should be focusing on the UTF8gbsn\u201c\u8fa9\u8bc1\u201d (the reason behind the symptoms) rather than the superficial UTF8gbsn\u201c\u75c7\u201d (symptoms)."
+    ],
+    [
+      "In this paper, we propose a TCM prescription generation task that automatically predicts the herbs in a prescription based on the textual symptom descriptions. To our knowledge, this is the first time that this critical and practicable task has been considered. To advance the research in this task, we construct a dataset of 82,044 symptom-prescription pairs based on the TCM Prescription Knowledge Base.",
+      "Besides the automatic evaluation, we also invite professionals to evaluate the prescriptions given by various models, the results of which show that our model reaches the score of 7.3 out of 10, demonstrating the effectiveness. In the experiments, we observe that directly applying seq2seq model would lead to the repetition problem that lowers the recall rate and the strong assumption of the order between herb tokens can hurt the performance. We propose to apply the coverage mechanism and the soft loss function to solve this problem. From the experimental results, we can see that this approach alleviates the repetition problem and results in an improved recall rate."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1070/instruction.md b/qasper-1070/instruction.md
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index 0000000000000000000000000000000000000000..9dcf31915528e83c08459ac3c97cb5404f3a6a04
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@@ -0,0 +1,3 @@
+Name of Paper: Exploration on Generating Traditional Chinese Medicine Prescriptions from Symptoms with an End-to-End Approach
+
+Question: Why did they think this was a good idea?
\ No newline at end of file
diff --git a/qasper-1079/instruction.md b/qasper-1079/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..9b766fea8afac57691f54a04bdc39a06db85657a
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@@ -0,0 +1,74 @@
+Name of Paper: A Resource for Studying Chatino Verbal Morphology
+
+Question: What are the architectures used for the three tasks?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "The Chatino Language",
+    "The Chatino Language ::: Typology and Writing System",
+    "The Chatino Language ::: Verb Morphology",
+    "The Resource",
+    "Baseline Results ::: Inflectional realization",
+    "Baseline Results ::: Morphological Analysis",
+    "Baseline Results ::: Lemmatization",
+    "Related Work",
+    "Conclusion",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "The recent years have seen unprecedented forward steps for Natural Language Processing (NLP) over almost every NLP subtask, relying on the advent of large data collections that can be leveraged to train deep neural networks. However, this progress has solely been observed in languages with significant data resources, while low-resource languages are left behind.",
+      "The situation for endangered languages is usually even worse, as the focus of the scientific community mostly relies in language documentation. The typical endangered language documentation process typically includes the creation of language resources in the form of word lists, audio and video recordings, notes, or grammar fragments, with the created resources then stored into large online linguistics archives. This process is often hindered by the so-called Transcription Bottleneck, but recent advances BIBREF0, BIBREF1 provide promising directions towards a solution for this issue.",
+      "However, language documentation and linguistic description, although extremely important itself, does not meaningfully contribute to language conservation, which aims to ensure that the language stays in use. We believe that a major avenue towards continual language use is by further creating language technologies for endangered languages, essentially elevating them to the same level as high-resource, economically or politically stronger languages.",
+      "The majority of the world's languages are categorized as synthetic, meaning that they have rich morphology, be it fusional, agglutinative, polysynthetic, or a mixture thereof. As Natural Language Processing (NLP) keeps expanding its frontiers to encompass more and more languages, modeling of the grammatical functions that guide language generation is of utmost importance. It follows, then, that the next crucial step for expanding NLP research on endangered languages is creating benchmarks for standard NLP tasks in such languages.",
+      "With this work we take a small first step towards this direction. We present a resource that allows for benchmarking two NLP tasks in San Juan Quiahije Chatino, an endangered language spoken in southern Mexico: morphological analysis and morphological inflection, with a focus on the verb morphology of the language.",
+      "We first briefly discuss the Chatino language and the intricacies of its verb morphology (\u00a7SECREF2), then describe the resource (\u00a7SECREF3), and finally present baseline results on both the morphological analysis and the inflection tasks using state-of-the-art neural models (\u00a7SECREF4). We make our resource publicly available online."
+    ],
+    [
+      "Chatino is a group of languages spoken in Oaxaca, Mexico. Together with the Zapotec language group, the Chatino languages form the Zapotecan branch of the Otomanguean language family. There are three main Chatino languages: Zenzontepec Chatino (ZEN, ISO 639-2 code czn), Tataltepec Chatino (TAT, cta), and Eastern Chatino (ISO 639-2 ctp, cya, ctz, and cly) (E.Cruz 2011 and Campbell 2011). San Juan Quiahije Chatino (SJQ), the language of the focus of this study, belongs to Eastern Chatino, and is used by about 3000 speakers."
+    ],
+    [
+      "Eastern Chatino languages , including SJQ Chatino, are intensively tonal BIBREF2, BIBREF3. Tones mark both lexical and grammatical distinctions in Eastern Chatino languages.",
+      "In SJQ Chatino, there are eleven tones. Three different systems for representing tone distinctions are employed in the literature: the S-H-M-L system of BIBREF2; the numeral system of BIBREF4; and the alphabetic system of BIBREF3. The correspondences among these three systems are given in Table . For present purposes, we will use numeral representations of the second sort. The number 1 represents a high pitch, 4 represents a low pitch, and double digits represent contour tones."
+    ],
+    [
+      "SJQ Chatino verb inflection distinguishes four aspect/mood categories: completive (`I did'), progressive (`I am doing'), habitual (`I habitually do') and potential (`I might do'). In each of these categories, verbs inflect for three persons (first, second, third) and two numbers (singular, plural) and distinguish inclusive and exclusive categories of the first person plural (`we including you' vs `we excluding you'). Verbs can be classified into dozens of different conjugation classes. Each conjugation class involves its own tone pattern; each tone pattern is based on a series of three person/number (PN) triplets. A PN triplet [X, Y, Z] consists of three tones: tone X is employed in the third person singular as well as in all plural forms; tone Y is employed in the second person singular, and tone Z, in the third person singular. Thus, a verb's membership in a particular conjugation class entails the assignment of one tone triplet to completive forms, another to progressive forms, and a third to habitual and potential forms. The paradigm of the verb lyu1 `fall' in Table illustrates: the conjugation class to which this verb belongs entails the assignment of the triplet [1, 42, 20] to the completive, [1, 42, 32] to the progressive, and [20, 42, 32] to the habitual and potential. Verbs in other conjugation classes exhibit other triplet series."
+    ],
+    [
+      "We provide a hand-curated collection of complete inflection tables for 198 lemmata. The morphological tags follow the guidelines of the UniMorph schema BIBREF6, BIBREF7, in order to allow for the potential of cross-lingual transfer learning, and they are tagged with respect to:",
+      "Person: first (1), second (2), and third (3)",
+      "Number: singular (SG) ad plural (PL)",
+      "Inclusivity (only applicable to first person plural verbs: inclusive (INCL) and exclusive (EXCL)",
+      "Aspect/mood: completive (CPL), progressive (PROG), potential (POT), and habitual (HAB).",
+      "Two examples of complete inflection tables for the verbs ndyu2 `fell from above' and lyu1 `fall' are shown in Table . Note how the first verb has the same PN triplet for all four aspect/mood categories, while the second paradigm is more representative in that it involves three different triplets (one for the completive, another for the progressive, and another for the habitual/potential). This variety is at the core of why the SJQ verb morphology is particularly interesting, and a challenging testcase for modern NLP systems.",
+      "In total, we end up with 4716 groupings (triplets) of a lemma, a tag-set, and a form; we split these groupings randomly into a training set (3774 groupings), a development set (471 groupings), and test set (471 groupings). Basic statistics of the corpus are outlined in Table 1 . Compared to all the other languages from the Unimorph project, this puts SJQ Chatino in the low- to mid-resource category, but nonetheless it is more than enough for benchmarking purposes."
+    ],
+    [
+      "Inflectional realization defines the inflected forms of a lexeme/lemma. As a computational task, often referred to as simply \u201cmorphological inflection,\" inflectional realization is framed as a mapping from the pairing of a lemma with a set of morphological tags to the corresponding word form. For example, the inflectional realization of SJQ Chatino verb forms entails a mapping of the pairing of the lemma lyu1 `fall' with the tag-set 1;SG;PROG to the word form nlyon32.",
+      "Morphological inflection has been thoroughly studied in monolingual high resource settings, especially through the recent SIGMORPHON challenges BIBREF8, BIBREF9, BIBREF10, with the latest iteration focusing more on low-resource settings, utilizing cross-lingual transfer BIBREF11. We use the guidelines of the state-of-the-art approach of BIBREF12 that achieved the highest inflection accuracy in the latest SIGMORPHON 2019 morphological inflection shared task. Our models are implemented in DyNet BIBREF13.",
+      "Inflection results are outlined in Table . In the `standard' setting we simply train on the pre-defined training set, achieving an exact-match accuracy of 60% over the test set. Interestingly, the data augmentation approach of BIBREF12 that hallucinates new training paradigms based on character level alignments does not heed significant improvements in accuracy (only 2 percentage points increase, cf. with more than 15 percentage points increases in other languages). These results indicate that automatic morphological inflection for low-resource tonal languages like SJQ Chatino poses a particularly challenging setting, which perhaps requires explicit handling of tone information by the model."
+    ],
+    [
+      "Morphological analysis is the task of creating a morphosyntactic description for a given word. It can be framed in a context-agnostic manner (as in our case) or within a given context, as for instance for the SIGMORPHON 2019 second shared task BIBREF11. We trained an encoder-decoder model that receives the form as character-level input, encodes it with a BiLSTM encoder, and then an attention enhanced decoder BIBREF14 outputs the corresponding sequence of morphological tags, implemented in DyNet. The baseline results are shown in Table . The exact-match accuracy of 67% is lower than the average accuracy that context-aware systems can achieve, and it highlights the challenge that the complexity of the tonal system of SJQ Chatino can pose."
+    ],
+    [
+      "Lemmatization is the task of retrieving the underlying lemma from which an inflected form was derived. Although in some languages the lemma is distinct from all forms, in SJQ Chatino the lemma is defined as the completive third-person singular form. As a computational task, lemmatization entails producing the lemma given an inflected form (and possibly, given a set of morphological tags describing the input form). Popular approaches tackle it as a character-level edit sequence generation task BIBREF15, or as a character-level sequence-to-sequence task BIBREF16. For our baseline lemmatization systems we follow the latter approach. We trained a character level encoder-decoder model, similar to the above-mentioned inflection system, implemented in DyNet.",
+      "The baseline results, with and without providing gold morphological tags along with the inflected form as input, are outlined in Table . We find that automatic lemmatization in SJQ Chatino achieves fairly high accuracy even with our simple baseline models (89% accuracy, $0.27$ average Levenshtein distance) and that providing the gold morphological tags provides a performance boost indicated by small improvements on both metrics. It it worth noting, though, that these results are also well below the $94--95\\%$ average accuracy and $0.13$ average Levenshtein distance that lemmatization models achieved over 107 treebanks in 66 languages for the SIGMORPHON 2019 shared task BIBREF11."
+    ],
+    [
+      "Our work builds and expands upon previous work on Indigenous languages of the Americas specifically focusing on the complexity of their morphology. Among other works similar to ours, BIBREF17 focused on the morphology of Dene verbs, BIBREF18 on Arapaho verbs, BIBREF19 on Shipibo-Konibo, and BIBREF20 on Saint Lawrence Island and Central Siberian Yupik. BIBREF21 describe an approach for elicit complete inflection paradigms, with experiments in languages like Nahuatl. Our resource is the first one for SJQ Chatino, but it also provides an exciting new data point in the computational study of morphological analysis, lemmatization, and inflection, as it is the first one in a tonal language with explicit tonal markings in the writing system. In a similar vein, the Oto-Manguean Inflectional Class Database BIBREF22 provides a valuable resource for studying the verbal morphology of Oto-Manguean languages (including a couple of other Chatino variants: Yaitepec and Zenzotepec Chatino) but not in a format suitable for computational experiments."
+    ],
+    [
+      "We presented a resource of 198 complete inflectional paradigms in San Juan Quiahije Chatino, which will facilitate research in computational morphological analysis and inflection for low-resource tonal languages and languages of Mesoamerica. We also provide strong baseline results on computational morphological analysis, lemmatization, and inflection realization, using character-level neural encoder-decoder systems.",
+      "For future work, while we will keep expanding our resource to include more paradigms, we will also follow the community guidelines in extending our resource to include morphological analysis and inflection examples in context."
+    ],
+    [
+      "Part of this work was done during the Workshop on Language Technology for Language Documentation and Revitalization. This material is based upon work generously supported by the National Science Foundation under grant 1761548."
+    ]
+  ]
+}
+```
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diff --git a/qasper-1084/instruction.md b/qasper-1084/instruction.md
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index 0000000000000000000000000000000000000000..8dd23b2f60499cb0314a438a80a6d549a77611bb
--- /dev/null
+++ b/qasper-1084/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: N-GrAM: New Groningen Author-profiling Model
+
+Question: How do their results compare against other competitors in the PAN 2017 shared task on Author Profiling?
\ No newline at end of file
diff --git a/qasper-1092/instruction.md b/qasper-1092/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..7237d0e95b522b20c241a82b0187fa8d4559d013
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+++ b/qasper-1092/instruction.md
@@ -0,0 +1,112 @@
+Name of Paper: Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation
+
+Question: What baseline is used to compare the experimental results against?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Sources of Bias in Dialogue Datasets ::: Bias in Character Personas",
+    "Sources of Bias in Dialogue Datasets ::: Bias in Character Personas ::: Qualitative Examination.",
+    "Sources of Bias in Dialogue Datasets ::: Bias in Character Personas ::: Quantitative Examination.",
+    "Sources of Bias in Dialogue Datasets ::: Bias in Dialogue Utterances",
+    "Sources of Bias in Dialogue Datasets ::: Bias in Dialogue Utterances ::: Qualitative Examination.",
+    "Sources of Bias in Dialogue Datasets ::: Bias in Dialogue Utterances ::: Measuring Bias.",
+    "Methodology: Mitigating Bias in Generative Dialogue",
+    "Methodology: Mitigating Bias in Generative Dialogue ::: Models",
+    "Methodology: Mitigating Bias in Generative Dialogue ::: Counterfactual Data Augmentation",
+    "Methodology: Mitigating Bias in Generative Dialogue ::: Positive-Bias Data Collection",
+    "Methodology: Mitigating Bias in Generative Dialogue ::: Positive-Bias Data Collection ::: Gender-swapping Existing Personas",
+    "Methodology: Mitigating Bias in Generative Dialogue ::: Positive-Bias Data Collection ::: New and Diverse characters",
+    "Methodology: Mitigating Bias in Generative Dialogue ::: Positive-Bias Data Collection ::: New dialogues",
+    "Methodology: Mitigating Bias in Generative Dialogue ::: Conditional Training",
+    "Results",
+    "Results ::: Bias is Amplified in Generation",
+    "Results ::: Genderedness of Generated Text",
+    "Results ::: Conditional Training Controls Gendered Words",
+    "Results ::: Safety of Generated Text",
+    "Results ::: Human Evaluation",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Since machine learning algorithms learn to model patterns present in training datasets, what they learn is affected by data quality. Analysis has found that model predictions directly reflect the biases found in training datasets, such as image classifiers learning to associate ethnicity with specific activities BIBREF1. Recent work in natural language processing has found similar biases, such as in word embeddings BIBREF2, BIBREF3, BIBREF4, object classification BIBREF5, natural language inference BIBREF6, and coreference resolution BIBREF7. Less work has focused on the biases present in dialogue utterances BIBREF8, BIBREF9, despite bias being clearly present in human interactions, and the rapid development of dialogue agents for real-world use-cases, such as interactive assistants. In this work we aim to address this by focusing on mitigating gender bias.",
+      "We use the dialogue dataset from the LIGHT text adventure world BIBREF0 as a testbed for our investigation into de-biasing dialogues. The dataset consists of a set of crowd-sourced locations, characters, and objects, which form the backdrop for the dialogues between characters. In the dialogue creation phase, crowdworkers are presented with personas for characters\u2014which themselves were written by other crowdworkers\u2014that they should enact; the dialogues the crowdworkers generate from these personas form the dialogue dataset. Dialogue datasets are susceptible to reflecting the biases of the crowdworkers as they are often collected solely via crowdsourcing. Further, the game's medieval setting may encourage crowdworkers to generate text which accentuates the historical biases and inequalities of that time period BIBREF10, BIBREF11. However, despite the fact that the dialogues take place in a fantasy adventure world, LIGHT is a game and thus we are under no obligation to recreate historical biases in this environment, and can instead use creative license to shape it into a fun world with gender parity.",
+      "We use the dialogues in LIGHT because we find that it is highly imbalanced with respect to gender: there are over 60% more male-gendered characters than female. We primarily address the discrepancy in the representation of male and female genders, although there are many characters that are gender neutral (like \u201ctrees\") or for which the gender could not be determined. We did not find any explicitly identified non-binary characters. We note that this is a bias in and of itself, and should be addressed in future work. We show that training on gender biased data leads existing generative dialogue models to amplify gender bias further. To offset this, we collect additional in-domain personas and dialogues to balance gender and increase the diversity of personas in the dataset. Next, we combine this approach with Counterfactual Data Augmentation and methods for controllable text generation to mitigate the bias in dialogue generation. Our proposed techniques create models that produce engaging responses with less gender bias."
+    ],
+    [
+      "Recent work in dialogue incorporates personas, or personality descriptions that ground speaker's chat, such as I love fishing BIBREF12, BIBREF13, BIBREF14, BIBREF15, BIBREF16. Personas have been shown to increase engagingness and improve consistency. However, they can be a starting point for bias BIBREF17, BIBREF18, BIBREF9, as bias in the personas propagates to subsequent conversations."
+    ],
+    [
+      "Analyzing the personas in LIGHT qualitatively, we find many examples of bias. For example, the character girl contains the line I regularly clean and cook dinner. Further examples are given in Table TABREF1."
+    ],
+    [
+      "We quantitatively analyze bias by first examining whether the existing personas are offensive, and second, evaluating their gender balance. To assess the pervasiveness of unsafe content present in personas, we asked three independent annotators to examine each character's persona for potentially offensive content. If annotators selected that the content was offensive or maybe offensive, they were asked to place it in one of four categories \u2013 racist, sexist, classist, other \u2013 and to provide a reason for their response. Just over 2% of personas were flagged by at least one annotator, and these personas are removed from the dataset.",
+      "We further examined gender bias in personas. Annotators were asked to label the gender of each character based on their persona description (choosing \u201cneutral\" if it was not explicit in the persona). This annotation is possible because some personas include lines such as I am a young woman, although the majority of personas do not mention an explicit gender. Annotators found nearly 50% more male-gendered characters than female-gendered characters (Table TABREF5).",
+      "While annotators labeled personas as explicitly male, female, or gender-neutral, gender bias may still exist in personas beyond explicit sentences such as I am a young man. For example, personas can contain gendered references such as I want to follow in my father's footsteps rather than mother's footsteps. These relational nouns BIBREF19, BIBREF20 such as father encode a specific relationship that can be gender biased. In this example, that relationship would be between the character and a man, rather than a woman. We analyzed the frequency of references to other gendered characters in the personas by counting the appearance of gendered words using the list compiled by BIBREF21 (for example he vs. she), and find that men are disproportionately referred to in the personas: there are nearly 3x as many mentions of men than women."
+    ],
+    [
+      "After analyzing the bias in LIGHT personas, we go on to analyze the bias in dialogues created from those personas and how to quantify it."
+    ],
+    [
+      "In our analysis, we found many examples of biased utterances in the data used to train dialogue agents. For example, the character with a queen persona utters the line I spend my days embroidery and having a talk with the ladies. Another character in a dialogue admires a sultry wench with fire in her eyes. An example of persona bias propagating to the dialogue can be found in Table TABREF2."
+    ],
+    [
+      "Sexism is clearly present in many datasets BIBREF9, but finding a good way to measure sexism, especially at scale, can be challenging. A simple answer would be to rely on crowdworkers operating under their own notions of \u201csexism\u201d to annotate the dialogues. However, in our experience, crowdworkers hold a range of views, often different from ours, as to what counts as sexism, making mere human evaluation far from sufficient. Note that the original LIGHT personas and dialogues were generated by crowdworkers, leaving little reason to believe that crowdworkers will be proficient at spotting the sexism that they themselves embued the dataset with in the first place. Therefore, we supplement our crowdworker-collected human annotations of gender bias with additional quantitative measurements: we measure the ratio of gendered words (taken from the union of several existing gendered word lists that were each created through either automatic means, or by experts BIBREF21, BIBREF22, BIBREF23), and we run an existing dialogue safety classifier BIBREF24 to measure offensiveness of the dialogues."
+    ],
+    [
+      "We explore both data augmentation and algorithmic methods to mitigate bias in generative Transformer dialogue models. We describe first our modeling setting and then the three proposed techniques for mitigating bias. Using (i) counterfactual data augmentation BIBREF25 to swap gendered words and (ii) additional data collection with crowdworkers, we create a gender-balanced dataset. Further, (iii) we describe a controllable generation method which moderates the male and female gendered words it produces."
+    ],
+    [
+      "Following BIBREF0, in all of our experiments we fine-tune a large, pre-trained Transformer encoder-decoder neural network on the dialogues in the LIGHT dataset. The model was pre-trained on Reddit conversations, using a previously existing Reddit dataset extracted and obtained by a third party and made available on pushshift.io. During pre-training, models were trained to generate a comment conditioned on the full thread leading up to the comment. Comments containing URLs or that were under 5 characters in length were removed from the corpus, as were all child comments, resulting in approximately $2,200$ million training examples. The model is a 8 layer encoder, 8 layer decoder with 512 dimensional embeddings and 16 attention heads, and is based on the ParlAI implementation of BIBREF26. For generation, we decode sequences with beam search with beam size 5."
+    ],
+    [
+      "One of the solutions that has been proposed for mitigating gender bias on the word embedding level is Counterfactual Data Augmentation (CDA) BIBREF25. We apply this method by augmenting our dataset with a copy of every dialogue with gendered words swapped using the gendered word pair list provided by BIBREF21. For example, all instances of grandmother are swapped with grandfather."
+    ],
+    [
+      "To create a more gender-balanced dataset, we collect additional data using a Positive-Bias Data Collection (Pos. Data) strategy."
+    ],
+    [
+      "There are a larger number of male-gendered character personas than female-gendered character personas (see Section SECREF2), so we balance existing personas using gender-swapping. For every gendered character in the dataset, we ask annotators to create a new character with a persona of the opposite gender that is otherwise identical except for referring nouns or pronouns. Additionally, we ask annotators to swap the gender of any characters that are referred to in the persona text for a given character."
+    ],
+    [
+      "As discussed in Section SECREF2, it is insufficient to simply balance references to men and women in the dataset, as there may be bias in the form of sexism. While it is challenging to detect sexism, we attempt to offset this type of bias by collecting a set of interesting and independent characters. We do this by seeding workers with examples like adventurer with the persona I am an woman passionate about exploring a world I have not yet seen. I embark on ambitious adventures. We give the additional instruction to attempt to create diverse characters. Even with this instruction, crowdworkers still created roughly 3x as many male-gendered characters as female-gendered characters. We exclude male-gendered characters created in this fashion.",
+      "In combination with the gender swapped personas above, this yields a new set of 2,676 character personas (compared to 1,877 from the original dataset), for which the number of men and women and the number of references to male or female gendered words is roughly balanced: see Table TABREF5."
+    ],
+    [
+      "Finally, we collect additional dialogues with these newly created gender balanced character personas, favoring conversations that feature female gendered characters to offset the imbalance in the original data. We added further instructions for annotators to be mindful of gender bias during their conversations, and in particular to assume equality between genders \u2013 social, economic, political, or otherwise \u2013 in this fantasy setting. In total, we collect 507 new dialogues containing 6,658 new dialogue utterances in total (about 6% of the size of the full LIGHT dataset)."
+    ],
+    [
+      "Bias in dialogue can manifest itself in various forms, but one form is the imbalanced use of gendered words. For example, LIGHT contains far more male-gendered words than female-gendered words rather than an even split between words of both genders. To create models that can generate a gender-balanced number of gendered words, we propose Conditional Training (CT) for controlling generative model output BIBREF27, BIBREF28, BIBREF29, BIBREF30. Previous work proposed a mechanism to train models with specific control tokens so models learn to associate the control token with the desired text properties BIBREF28, then modifying the control tokens during inference to produce the desired result.",
+      "Prior to training, each dialogue response is binned into one of four bins \u2013 $\\text{F}^{0/+}\\text{M}^{0/+}$ \u2013 where $\\text{F}^{0}$ indicates that there are zero female gendered words in the response and $\\text{F}^{+}$ indicates the presence of at least one female gendered word. The gendered words are determined via an aggregation of existing lists of gendered nouns and adjectives from BIBREF21, BIBREF22, BIBREF23. The bins are used to train a conditional model by appending a special token (indicating the bin for the target response) to the end of the input which is given to the encoder. At inference time, the bins can be manipulated to produce dialogue outputs with various quantities of gendered words."
+    ],
+    [
+      "We train generative Transformer models using each of these methods \u2013 Counterfactual Data Augmentation that augments with swaps of gendered words (CDA, \u00a7SECREF19), adding new dialogues (Positive-Bias Data Collection, \u00a7SECREF20), and controllable generation to control the quantity of gendered words (CT, \u00a7SECREF24) \u2013 and finally combine all of these methods together (ALL)."
+    ],
+    [
+      "Existing Transformer generative dialogue models BIBREF31, BIBREF32, BIBREF0 are trained to take as input the dialogue context and generate the next utterance. Previous work has shown that machine learning models reflect the biases present in data BIBREF4, BIBREF3, and that these biases can be easy to learn compared to more challenging reasoning BIBREF2, BIBREF33. Generative models often use beam search or top-k sampling BIBREF34 to decode, and these methods are well-known to produce generic text BIBREF35, which makes them susceptible statistical biases present in datasets.",
+      "As shown in Table TABREF11, we find that existing models actually amplify bias. When the trained model generates gendered words (i.e., words from our gendered word list), it generates male-gendered words the vast majority of the time \u2013 even on utterances for which it is supposed to generate only female-gendered words (i.e., the gold label only contains female-gendered words), it generates male-gendered words nearly $78\\%$ of the time.",
+      "Additionally, following BIBREF8, we run an offensive language classifier on the gold responses and the model generated utterances (Table TABREF16) and find that the model produces more offensive utterances than exist in the dataset."
+    ],
+    [
+      "We analyze the performance of the various techniques by dividing the test set using the four genderedness bins \u2013 $\\text{F}^{0}\\text{M}^{0}$, $\\text{F}^{0}\\text{M}^{+}$, $\\text{F}^{+}\\text{M}^{0}$, and $\\text{F}^{+}\\text{M}^{+}$ \u2013 and calculate the F1 word overlap with the gold response, the percentage of gendered words generated (% gend. words), and the percentage of male-gendered words generated (relative to the sum total of gendered words generated by the model). We compare to the gold labels from the test set and a baseline model that does not use any of the bias mitigation techniques. Results for all methods are displayed in Table TABREF11.",
+      "Each of the methods we explore improve in % gendered words, % male bias, and F1 over the baseline Transformer generation model, but we find combining all methods in one \u2013 the ALL model is the most advantageous. While ALL has more data than CDA and CT, more data alone is not enough \u2014 the Positive-Bias Data Collection model does not achieve as good results. Both the CT and ALL models benefit from knowing the data split ($\\text{F}^{0}\\text{M}^{0}$, for example), and both models yield a genderedness ratio closest to ground truth."
+    ],
+    [
+      "Our proposed CT method can be used to control the use of gendered words in generated dialogues. We examine the effect of such training by generating responses on the test set by conditioning the ALL model on a singular bin for all examples. Results are shown in Figure FIGREF12. Changing the bin radically changes the genderedness of generated text without significant changes to F1.",
+      "Examples of generated text from both the baseline and the ALL model are shown in Table TABREF31. The baseline model generates male-gendered words even when the gold response contains no gendered words or only female-gendered words, even generating unlikely sequences such as \u201cmy name is abigail. i am the king of this kingdom.\"."
+    ],
+    [
+      "Using a dialogue safety classifier BIBREF24, we find that our proposed de-biased models are rated as less offensive compared to the baseline generative Transformer and the LIGHT data (see Table TABREF16)."
+    ],
+    [
+      "Finally, we use human evaluation to compare the quality of our de-biasing methods. We use the dialogue evaluation system Acute-Eval BIBREF36 to ask human evaluators to compare two conversations from different models and decide which model is more biased and which model is more engaging. Following Acute-Eval, we collect 100 human and model paired chats. Conversations from a human and baseline model are compared to conversations from a human and the ALL model with all generations set to the $\\text{F}^{0}\\text{M}^{0}$ gender-neutral control bin. Evaluators are asked which model is more engaging and for which model they find it more difficult to predict the gender of the speaker. We found that asking about difficulty of predicting a speaker's gender was much more effective than asking evaluators to evaluate sexism or gender bias. Figure FIGREF17 shows that evaluators rate the ALL model harder to predict the gender of (statistically significant at $p < 0.01$) while engagingness does not change. Our proposed methods are able to mitigate gender bias without degrading dialogue quality."
+    ],
+    [
+      "We analyze gender bias in dialogue and propose a general purpose method for understanding and mitigating bias in character personas and their associated dialogues. We present techniques using data augmentation and controllable generation to reduce gender bias in neural language generation for dialogue. We use the dataset LIGHT as a testbed for this work. By integrating these methods together, our models provide control over how gendered dialogue is and decrease the offensiveness of the generated utterances. Overall, our proposed methodology reduces the effect of bias while maintaining dialogue engagingness."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1093/instruction.md b/qasper-1093/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..adfadbf9810701baab890a6054e5faca896d69eb
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@@ -0,0 +1,112 @@
+Name of Paper: Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation
+
+Question: How does counterfactual data augmentation aim to tackle bias?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Sources of Bias in Dialogue Datasets ::: Bias in Character Personas",
+    "Sources of Bias in Dialogue Datasets ::: Bias in Character Personas ::: Qualitative Examination.",
+    "Sources of Bias in Dialogue Datasets ::: Bias in Character Personas ::: Quantitative Examination.",
+    "Sources of Bias in Dialogue Datasets ::: Bias in Dialogue Utterances",
+    "Sources of Bias in Dialogue Datasets ::: Bias in Dialogue Utterances ::: Qualitative Examination.",
+    "Sources of Bias in Dialogue Datasets ::: Bias in Dialogue Utterances ::: Measuring Bias.",
+    "Methodology: Mitigating Bias in Generative Dialogue",
+    "Methodology: Mitigating Bias in Generative Dialogue ::: Models",
+    "Methodology: Mitigating Bias in Generative Dialogue ::: Counterfactual Data Augmentation",
+    "Methodology: Mitigating Bias in Generative Dialogue ::: Positive-Bias Data Collection",
+    "Methodology: Mitigating Bias in Generative Dialogue ::: Positive-Bias Data Collection ::: Gender-swapping Existing Personas",
+    "Methodology: Mitigating Bias in Generative Dialogue ::: Positive-Bias Data Collection ::: New and Diverse characters",
+    "Methodology: Mitigating Bias in Generative Dialogue ::: Positive-Bias Data Collection ::: New dialogues",
+    "Methodology: Mitigating Bias in Generative Dialogue ::: Conditional Training",
+    "Results",
+    "Results ::: Bias is Amplified in Generation",
+    "Results ::: Genderedness of Generated Text",
+    "Results ::: Conditional Training Controls Gendered Words",
+    "Results ::: Safety of Generated Text",
+    "Results ::: Human Evaluation",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Since machine learning algorithms learn to model patterns present in training datasets, what they learn is affected by data quality. Analysis has found that model predictions directly reflect the biases found in training datasets, such as image classifiers learning to associate ethnicity with specific activities BIBREF1. Recent work in natural language processing has found similar biases, such as in word embeddings BIBREF2, BIBREF3, BIBREF4, object classification BIBREF5, natural language inference BIBREF6, and coreference resolution BIBREF7. Less work has focused on the biases present in dialogue utterances BIBREF8, BIBREF9, despite bias being clearly present in human interactions, and the rapid development of dialogue agents for real-world use-cases, such as interactive assistants. In this work we aim to address this by focusing on mitigating gender bias.",
+      "We use the dialogue dataset from the LIGHT text adventure world BIBREF0 as a testbed for our investigation into de-biasing dialogues. The dataset consists of a set of crowd-sourced locations, characters, and objects, which form the backdrop for the dialogues between characters. In the dialogue creation phase, crowdworkers are presented with personas for characters\u2014which themselves were written by other crowdworkers\u2014that they should enact; the dialogues the crowdworkers generate from these personas form the dialogue dataset. Dialogue datasets are susceptible to reflecting the biases of the crowdworkers as they are often collected solely via crowdsourcing. Further, the game's medieval setting may encourage crowdworkers to generate text which accentuates the historical biases and inequalities of that time period BIBREF10, BIBREF11. However, despite the fact that the dialogues take place in a fantasy adventure world, LIGHT is a game and thus we are under no obligation to recreate historical biases in this environment, and can instead use creative license to shape it into a fun world with gender parity.",
+      "We use the dialogues in LIGHT because we find that it is highly imbalanced with respect to gender: there are over 60% more male-gendered characters than female. We primarily address the discrepancy in the representation of male and female genders, although there are many characters that are gender neutral (like \u201ctrees\") or for which the gender could not be determined. We did not find any explicitly identified non-binary characters. We note that this is a bias in and of itself, and should be addressed in future work. We show that training on gender biased data leads existing generative dialogue models to amplify gender bias further. To offset this, we collect additional in-domain personas and dialogues to balance gender and increase the diversity of personas in the dataset. Next, we combine this approach with Counterfactual Data Augmentation and methods for controllable text generation to mitigate the bias in dialogue generation. Our proposed techniques create models that produce engaging responses with less gender bias."
+    ],
+    [
+      "Recent work in dialogue incorporates personas, or personality descriptions that ground speaker's chat, such as I love fishing BIBREF12, BIBREF13, BIBREF14, BIBREF15, BIBREF16. Personas have been shown to increase engagingness and improve consistency. However, they can be a starting point for bias BIBREF17, BIBREF18, BIBREF9, as bias in the personas propagates to subsequent conversations."
+    ],
+    [
+      "Analyzing the personas in LIGHT qualitatively, we find many examples of bias. For example, the character girl contains the line I regularly clean and cook dinner. Further examples are given in Table TABREF1."
+    ],
+    [
+      "We quantitatively analyze bias by first examining whether the existing personas are offensive, and second, evaluating their gender balance. To assess the pervasiveness of unsafe content present in personas, we asked three independent annotators to examine each character's persona for potentially offensive content. If annotators selected that the content was offensive or maybe offensive, they were asked to place it in one of four categories \u2013 racist, sexist, classist, other \u2013 and to provide a reason for their response. Just over 2% of personas were flagged by at least one annotator, and these personas are removed from the dataset.",
+      "We further examined gender bias in personas. Annotators were asked to label the gender of each character based on their persona description (choosing \u201cneutral\" if it was not explicit in the persona). This annotation is possible because some personas include lines such as I am a young woman, although the majority of personas do not mention an explicit gender. Annotators found nearly 50% more male-gendered characters than female-gendered characters (Table TABREF5).",
+      "While annotators labeled personas as explicitly male, female, or gender-neutral, gender bias may still exist in personas beyond explicit sentences such as I am a young man. For example, personas can contain gendered references such as I want to follow in my father's footsteps rather than mother's footsteps. These relational nouns BIBREF19, BIBREF20 such as father encode a specific relationship that can be gender biased. In this example, that relationship would be between the character and a man, rather than a woman. We analyzed the frequency of references to other gendered characters in the personas by counting the appearance of gendered words using the list compiled by BIBREF21 (for example he vs. she), and find that men are disproportionately referred to in the personas: there are nearly 3x as many mentions of men than women."
+    ],
+    [
+      "After analyzing the bias in LIGHT personas, we go on to analyze the bias in dialogues created from those personas and how to quantify it."
+    ],
+    [
+      "In our analysis, we found many examples of biased utterances in the data used to train dialogue agents. For example, the character with a queen persona utters the line I spend my days embroidery and having a talk with the ladies. Another character in a dialogue admires a sultry wench with fire in her eyes. An example of persona bias propagating to the dialogue can be found in Table TABREF2."
+    ],
+    [
+      "Sexism is clearly present in many datasets BIBREF9, but finding a good way to measure sexism, especially at scale, can be challenging. A simple answer would be to rely on crowdworkers operating under their own notions of \u201csexism\u201d to annotate the dialogues. However, in our experience, crowdworkers hold a range of views, often different from ours, as to what counts as sexism, making mere human evaluation far from sufficient. Note that the original LIGHT personas and dialogues were generated by crowdworkers, leaving little reason to believe that crowdworkers will be proficient at spotting the sexism that they themselves embued the dataset with in the first place. Therefore, we supplement our crowdworker-collected human annotations of gender bias with additional quantitative measurements: we measure the ratio of gendered words (taken from the union of several existing gendered word lists that were each created through either automatic means, or by experts BIBREF21, BIBREF22, BIBREF23), and we run an existing dialogue safety classifier BIBREF24 to measure offensiveness of the dialogues."
+    ],
+    [
+      "We explore both data augmentation and algorithmic methods to mitigate bias in generative Transformer dialogue models. We describe first our modeling setting and then the three proposed techniques for mitigating bias. Using (i) counterfactual data augmentation BIBREF25 to swap gendered words and (ii) additional data collection with crowdworkers, we create a gender-balanced dataset. Further, (iii) we describe a controllable generation method which moderates the male and female gendered words it produces."
+    ],
+    [
+      "Following BIBREF0, in all of our experiments we fine-tune a large, pre-trained Transformer encoder-decoder neural network on the dialogues in the LIGHT dataset. The model was pre-trained on Reddit conversations, using a previously existing Reddit dataset extracted and obtained by a third party and made available on pushshift.io. During pre-training, models were trained to generate a comment conditioned on the full thread leading up to the comment. Comments containing URLs or that were under 5 characters in length were removed from the corpus, as were all child comments, resulting in approximately $2,200$ million training examples. The model is a 8 layer encoder, 8 layer decoder with 512 dimensional embeddings and 16 attention heads, and is based on the ParlAI implementation of BIBREF26. For generation, we decode sequences with beam search with beam size 5."
+    ],
+    [
+      "One of the solutions that has been proposed for mitigating gender bias on the word embedding level is Counterfactual Data Augmentation (CDA) BIBREF25. We apply this method by augmenting our dataset with a copy of every dialogue with gendered words swapped using the gendered word pair list provided by BIBREF21. For example, all instances of grandmother are swapped with grandfather."
+    ],
+    [
+      "To create a more gender-balanced dataset, we collect additional data using a Positive-Bias Data Collection (Pos. Data) strategy."
+    ],
+    [
+      "There are a larger number of male-gendered character personas than female-gendered character personas (see Section SECREF2), so we balance existing personas using gender-swapping. For every gendered character in the dataset, we ask annotators to create a new character with a persona of the opposite gender that is otherwise identical except for referring nouns or pronouns. Additionally, we ask annotators to swap the gender of any characters that are referred to in the persona text for a given character."
+    ],
+    [
+      "As discussed in Section SECREF2, it is insufficient to simply balance references to men and women in the dataset, as there may be bias in the form of sexism. While it is challenging to detect sexism, we attempt to offset this type of bias by collecting a set of interesting and independent characters. We do this by seeding workers with examples like adventurer with the persona I am an woman passionate about exploring a world I have not yet seen. I embark on ambitious adventures. We give the additional instruction to attempt to create diverse characters. Even with this instruction, crowdworkers still created roughly 3x as many male-gendered characters as female-gendered characters. We exclude male-gendered characters created in this fashion.",
+      "In combination with the gender swapped personas above, this yields a new set of 2,676 character personas (compared to 1,877 from the original dataset), for which the number of men and women and the number of references to male or female gendered words is roughly balanced: see Table TABREF5."
+    ],
+    [
+      "Finally, we collect additional dialogues with these newly created gender balanced character personas, favoring conversations that feature female gendered characters to offset the imbalance in the original data. We added further instructions for annotators to be mindful of gender bias during their conversations, and in particular to assume equality between genders \u2013 social, economic, political, or otherwise \u2013 in this fantasy setting. In total, we collect 507 new dialogues containing 6,658 new dialogue utterances in total (about 6% of the size of the full LIGHT dataset)."
+    ],
+    [
+      "Bias in dialogue can manifest itself in various forms, but one form is the imbalanced use of gendered words. For example, LIGHT contains far more male-gendered words than female-gendered words rather than an even split between words of both genders. To create models that can generate a gender-balanced number of gendered words, we propose Conditional Training (CT) for controlling generative model output BIBREF27, BIBREF28, BIBREF29, BIBREF30. Previous work proposed a mechanism to train models with specific control tokens so models learn to associate the control token with the desired text properties BIBREF28, then modifying the control tokens during inference to produce the desired result.",
+      "Prior to training, each dialogue response is binned into one of four bins \u2013 $\\text{F}^{0/+}\\text{M}^{0/+}$ \u2013 where $\\text{F}^{0}$ indicates that there are zero female gendered words in the response and $\\text{F}^{+}$ indicates the presence of at least one female gendered word. The gendered words are determined via an aggregation of existing lists of gendered nouns and adjectives from BIBREF21, BIBREF22, BIBREF23. The bins are used to train a conditional model by appending a special token (indicating the bin for the target response) to the end of the input which is given to the encoder. At inference time, the bins can be manipulated to produce dialogue outputs with various quantities of gendered words."
+    ],
+    [
+      "We train generative Transformer models using each of these methods \u2013 Counterfactual Data Augmentation that augments with swaps of gendered words (CDA, \u00a7SECREF19), adding new dialogues (Positive-Bias Data Collection, \u00a7SECREF20), and controllable generation to control the quantity of gendered words (CT, \u00a7SECREF24) \u2013 and finally combine all of these methods together (ALL)."
+    ],
+    [
+      "Existing Transformer generative dialogue models BIBREF31, BIBREF32, BIBREF0 are trained to take as input the dialogue context and generate the next utterance. Previous work has shown that machine learning models reflect the biases present in data BIBREF4, BIBREF3, and that these biases can be easy to learn compared to more challenging reasoning BIBREF2, BIBREF33. Generative models often use beam search or top-k sampling BIBREF34 to decode, and these methods are well-known to produce generic text BIBREF35, which makes them susceptible statistical biases present in datasets.",
+      "As shown in Table TABREF11, we find that existing models actually amplify bias. When the trained model generates gendered words (i.e., words from our gendered word list), it generates male-gendered words the vast majority of the time \u2013 even on utterances for which it is supposed to generate only female-gendered words (i.e., the gold label only contains female-gendered words), it generates male-gendered words nearly $78\\%$ of the time.",
+      "Additionally, following BIBREF8, we run an offensive language classifier on the gold responses and the model generated utterances (Table TABREF16) and find that the model produces more offensive utterances than exist in the dataset."
+    ],
+    [
+      "We analyze the performance of the various techniques by dividing the test set using the four genderedness bins \u2013 $\\text{F}^{0}\\text{M}^{0}$, $\\text{F}^{0}\\text{M}^{+}$, $\\text{F}^{+}\\text{M}^{0}$, and $\\text{F}^{+}\\text{M}^{+}$ \u2013 and calculate the F1 word overlap with the gold response, the percentage of gendered words generated (% gend. words), and the percentage of male-gendered words generated (relative to the sum total of gendered words generated by the model). We compare to the gold labels from the test set and a baseline model that does not use any of the bias mitigation techniques. Results for all methods are displayed in Table TABREF11.",
+      "Each of the methods we explore improve in % gendered words, % male bias, and F1 over the baseline Transformer generation model, but we find combining all methods in one \u2013 the ALL model is the most advantageous. While ALL has more data than CDA and CT, more data alone is not enough \u2014 the Positive-Bias Data Collection model does not achieve as good results. Both the CT and ALL models benefit from knowing the data split ($\\text{F}^{0}\\text{M}^{0}$, for example), and both models yield a genderedness ratio closest to ground truth."
+    ],
+    [
+      "Our proposed CT method can be used to control the use of gendered words in generated dialogues. We examine the effect of such training by generating responses on the test set by conditioning the ALL model on a singular bin for all examples. Results are shown in Figure FIGREF12. Changing the bin radically changes the genderedness of generated text without significant changes to F1.",
+      "Examples of generated text from both the baseline and the ALL model are shown in Table TABREF31. The baseline model generates male-gendered words even when the gold response contains no gendered words or only female-gendered words, even generating unlikely sequences such as \u201cmy name is abigail. i am the king of this kingdom.\"."
+    ],
+    [
+      "Using a dialogue safety classifier BIBREF24, we find that our proposed de-biased models are rated as less offensive compared to the baseline generative Transformer and the LIGHT data (see Table TABREF16)."
+    ],
+    [
+      "Finally, we use human evaluation to compare the quality of our de-biasing methods. We use the dialogue evaluation system Acute-Eval BIBREF36 to ask human evaluators to compare two conversations from different models and decide which model is more biased and which model is more engaging. Following Acute-Eval, we collect 100 human and model paired chats. Conversations from a human and baseline model are compared to conversations from a human and the ALL model with all generations set to the $\\text{F}^{0}\\text{M}^{0}$ gender-neutral control bin. Evaluators are asked which model is more engaging and for which model they find it more difficult to predict the gender of the speaker. We found that asking about difficulty of predicting a speaker's gender was much more effective than asking evaluators to evaluate sexism or gender bias. Figure FIGREF17 shows that evaluators rate the ALL model harder to predict the gender of (statistically significant at $p < 0.01$) while engagingness does not change. Our proposed methods are able to mitigate gender bias without degrading dialogue quality."
+    ],
+    [
+      "We analyze gender bias in dialogue and propose a general purpose method for understanding and mitigating bias in character personas and their associated dialogues. We present techniques using data augmentation and controllable generation to reduce gender bias in neural language generation for dialogue. We use the dataset LIGHT as a testbed for this work. By integrating these methods together, our models provide control over how gendered dialogue is and decrease the offensiveness of the generated utterances. Overall, our proposed methodology reduces the effect of bias while maintaining dialogue engagingness."
+    ]
+  ]
+}
+```
\ No newline at end of file
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new file mode 100644
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@@ -0,0 +1,3 @@
+Name of Paper: Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation
+
+Question: In the targeted data collection approach, what type of data is targetted?
\ No newline at end of file
diff --git a/qasper-1095/instruction.md b/qasper-1095/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..2a20f6890c575c9f2a7e75ec8f8b126da11d2cff
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@@ -0,0 +1,91 @@
+Name of Paper: Efficient Vector Representation for Documents through Corruption
+
+Question: Which language models do they compare against?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Works and Notations",
+    "Method",
+    "Corruption as data-dependent regularization",
+    "Experiments",
+    "Baselines",
+    "Sentiment analysis",
+    "Word analogy",
+    "Document Classification",
+    "Semantic relatedness",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Text understanding starts with the challenge of finding machine-understandable representation that captures the semantics of texts. Bag-of-words (BoW) and its N-gram extensions are arguably the most commonly used document representations. Despite its simplicity, BoW works surprisingly well for many tasks BIBREF0 . However, by treating words and phrases as unique and discrete symbols, BoW often fails to capture the similarity between words or phrases and also suffers from sparsity and high dimensionality.",
+      "Recent works on using neural networks to learn distributed vector representations of words have gained great popularity. The well celebrated Word2Vec BIBREF1 , by learning to predict the target word using its neighboring words, maps words of similar meanings to nearby points in the continuous vector space. The surprisingly simple model has succeeded in generating high-quality word embeddings for tasks such as language modeling, text understanding and machine translation. Word2Vec naturally scales to large datasets thanks to its simple model architecture. It can be trained on billions of words per hour on a single machine.",
+      "Paragraph Vectors BIBREF2 generalize the idea to learn vector representation for documents. A target word is predicted by the word embeddings of its neighbors in together with a unique document vector learned for each document. It outperforms established document representations, such as BoW and Latent Dirichlet Allocation BIBREF3 , on various text understanding tasks BIBREF4 . However, two caveats come with this approach: 1) the number of parameters grows with the size of the training corpus, which can easily go to billions; and 2) it is expensive to generate vector representations for unseen documents at test time.",
+      "We propose an efficient model architecture, referred to as Document Vector through Corruption (Doc2VecC), to learn vector representations for documents. It is motivated by the observation that linear operations on the word embeddings learned by Word2Vec can sustain substantial amount of syntactic and semantic meanings of a phrase or a sentence BIBREF5 . For example, vec(\u201cRussia\u201d) + vec(\u201criver\u201d) is close to vec(\u201cVolga River\u201d) BIBREF6 , and vec(\u201cking\u201d) - vec(\u201cman\u201d) + vec(\u201cwomen\u201d) is close to vec(\u201cqueen\u201d) BIBREF5 . In Doc2VecC, we represent each document as a simple average of the word embeddings of all the words in the document. In contrast to existing approaches which post-process learned word embeddings to form document representation BIBREF7 , BIBREF8 , Doc2VecC enforces a meaningful document representation can be formed by averaging the word embeddings during learning. Furthermore, we include a corruption model that randomly remove words from a document during learning, a mechanism that is critical to the performance and learning speed of our algorithm.",
+      "Doc2VecC has several desirable properties: 1. The model complexity of Doc2VecC is decoupled from the size of the training corpus, depending only on the size of the vocabulary; 2. The model architecture of Doc2VecC resembles that of Word2Vec, and can be trained very efficiently; 3. The new framework implicitly introduces a data-dependent regularization, which favors rare or informative words and suppresses words that are common but not discriminative; 4. Vector representation of a document can be generated by simply averaging the learned word embeddings of all the words in the document, which significantly boost test efficiency; 5. The vector representation generated by Doc2VecC matches or beats the state-of-the-art for sentiment analysis, document classification as well as semantic relatedness tasks."
+    ],
+    [
+      "Text representation learning has been extensively studied. Popular representations range from the simplest BoW and its term-frequency based variants BIBREF9 , language model based methods BIBREF10 , BIBREF11 , BIBREF12 , topic models BIBREF13 , BIBREF3 , Denoising Autoencoders and its variants BIBREF14 , BIBREF15 , and distributed vector representations BIBREF8 , BIBREF2 , BIBREF16 . Another prominent line of work includes learning task-specific document representation with deep neural networks, such as CNN BIBREF17 or LSTM based approaches BIBREF18 , BIBREF19 .",
+      "In this section, we briefly introduce Word2Vec and Paragraph Vectors, the two approaches that are most similar to ours. There are two well-know model architectures used for both methods, referred to as Continuous Bag-of-Words (CBoW) and Skipgram models BIBREF1 . In this work, we focus on CBoW. Extending to Skipgram is straightforward. Here are the notations we are going to use throughout the paper:"
+    ],
+    [
+      "Several works BIBREF6 , BIBREF5 showcased that syntactic and semantic regularities of phrases and sentences are reasonably well preserved by adding or subtracting word embeddings learned through Word2Vec. It prompts us to explore the option of simply representing a document as an average of word embeddings. Figure FIGREF9 illustrates the new model architecture.",
+      "Similar to Word2Vec or Paragraph Vectors, Doc2VecC consists of an input layer, a projection layer as well as an output layer to predict the target word, \u201cceremony\u201d in this example. The embeddings of neighboring words (\u201copening\u201d, \u201cfor\u201d, \u201cthe\u201d) provide local context while the vector representation of the entire document (shown in grey) serves as the global context. In contrast to Paragraph Vectors, which directly learns a unique vector for each document, Doc2VecC represents each document as an average of the embeddings of words randomly sampled from the document (\u201cperformance\u201d at position INLINEFORM0 , \u201cpraised\u201d at position INLINEFORM1 , and \u201cbrazil\u201d at position INLINEFORM2 ). BIBREF25 also proposed the idea of using average of word embeddings to represent the global context of a document. Different from their work, we choose to corrupt the original document by randomly removing significant portion of words, and represent the document using only the embeddings of the words remained. This corruption mechanism offers us great speedup during training as it significantly reduces the number of parameters to update in back propagation. At the same time, as we are going to detail in the next section, it introduces a special form of regularization, which brings great performance improvement.",
+      "Here we describe the stochastic process we used to generate a global context at each update. The global context, which we denote as INLINEFORM0 , is generated through a unbiased mask-out/drop-out corruption, in which we randomly overwrites each dimension of the original document INLINEFORM1 with probability INLINEFORM2 . To make the corruption unbiased, we set the uncorrupted dimensions to INLINEFORM3 times its original value. Formally, DISPLAYFORM0 ",
+      "Doc2VecC then defines the probability of observing a target word INLINEFORM0 given its local context INLINEFORM1 as well as the global context INLINEFORM2 as DISPLAYFORM0 ",
+      "Here INLINEFORM0 is the length of the document. Exactly computing the probability is impractical, instead we approximate it with negative sampling BIBREF1 . DISPLAYFORM0 ",
+      "here INLINEFORM0 stands for a uniform distribution over the terms in the vocabulary. The two projection matrices INLINEFORM1 and INLINEFORM2 are then learned to minimize the loss: DISPLAYFORM0 ",
+      "Given the learned projection matrix INLINEFORM0 , we then represent each document simply as an average of the embeddings of the words in the document, DISPLAYFORM0 ",
+      "We are going to elaborate next why we choose to corrupt the original document with the corruption model in eq.( EQREF10 ) during learning, and how it enables us to simply use the average word embeddings as the vector representation for documents at test time."
+    ],
+    [
+      "We approximate the log likelihood for each instance INLINEFORM0 in eq.( EQREF13 ) with its Taylor expansion with respect to INLINEFORM1 up to the second-order BIBREF26 , BIBREF27 , BIBREF28 . Concretely, we choose to expand at the mean of the corruption INLINEFORM2 : INLINEFORM3 ",
+      "where INLINEFORM0 and INLINEFORM1 are the first-order (i.e., gradient) and second-order (i.e., Hessian) of the log likelihood with respect to INLINEFORM2 . Expansion at the mean INLINEFORM3 is crucial as shown in the following steps. Let us assume that for each instance, we are going to sample the global context INLINEFORM4 infinitely many times, and thus compute the expected log likelihood with respect to the corrupted INLINEFORM5 . INLINEFORM6 ",
+      "The linear term disappears as INLINEFORM0 . We substitute in INLINEFORM1 for the mean INLINEFORM2 of the corrupting distribution (unbiased corruption) and the matrix INLINEFORM3 for the variance, and obtain DISPLAYFORM0 ",
+      "As each word in a document is corrupted independently of others, the variance matrix INLINEFORM0 is simplified to a diagonal matrix with INLINEFORM1 element equals INLINEFORM2 . As a result, we only need to compute the diagonal terms of the Hessian matrix INLINEFORM3 .",
+      "The INLINEFORM0 dimension of the Hessian's diagonal evaluated at the mean INLINEFORM1 is given by INLINEFORM2 ",
+      "Plug the Hessian matrix and the variance matrix back into eq.( EQREF16 ), and then back to the loss defined in eq.( EQREF13 ), we can see that Doc2VecC intrinsically minimizes DISPLAYFORM0 ",
+      "Each INLINEFORM0 in the first term measures the log likelihood of observing the target word INLINEFORM1 given its local context INLINEFORM2 and the document vector INLINEFORM3 . As such, Doc2VecC enforces that a document vector generated by averaging word embeddings can capture the global semantics of the document, and fill in information missed in the local context. The second term here is a data-dependent regularization. The regularization on the embedding INLINEFORM4 of each word INLINEFORM5 takes the following form, INLINEFORM6 ",
+      "where INLINEFORM0 prescribes the confidence of predicting the target word INLINEFORM1 given its neighboring context INLINEFORM2 as well as the document vector INLINEFORM3 .",
+      "Closely examining INLINEFORM0 leads to several interesting findings: 1. the regularizer penalizes more on the embeddings of common words. A word INLINEFORM1 that frequently appears across the training corpus, i.e, INLINEFORM2 often, will have a bigger regularization than a rare word; 2. on the other hand, the regularization is modulated by INLINEFORM3 , which is small if INLINEFORM4 . In other words, if INLINEFORM5 is critical to a confident prediction INLINEFORM6 when it is active, then the regularization is diminished. Similar effect was observed for dropout training for logistic regression model BIBREF27 and denoising autoencoders BIBREF28 ."
+    ],
+    [
+      "We evaluate Doc2VecC on a sentiment analysis task, a document classification task and a semantic relatedness task, along with several document representation learning algorithms. All experiments can be reproduced using the code available at https://github.com/mchen24/iclr2017"
+    ],
+    [
+      "We compare against the following document representation baselines: bag-of-words (BoW); Denoising Autoencoders (DEA) BIBREF14 , a representation learned from reconstructing original document INLINEFORM0 using corrupted one INLINEFORM1 . SDAs have been shown to be the state-of-the-art for sentiment analysis tasks BIBREF29 . We used Kullback-Liebler divergence as the reconstruction error and an affine encoder. To scale up the algorithm to large vocabulary, we only take into account the non-zero elements of INLINEFORM2 in the reconstruction error and employed negative sampling for the remainings; Word2Vec BIBREF1 +IDF, a representation generated through weighted average of word vectors learned using Word2Vec; Doc2Vec BIBREF2 ; Skip-thought Vectors BIBREF16 , a generic, distributed sentence encoder that extends the Word2Vec skip-gram model to sentence level. It has been shown to produce highly generic sentence representations that apply to various natural language processing tasks. We also include RNNLM BIBREF11 , a recurrent neural network based language model in the comparison. In the semantic relatedness task, we further compare to LSTM-based methods BIBREF18 that have been reported on this dataset."
+    ],
+    [
+      "For sentiment analysis, we use the IMDB movie review dataset. It contains 100,000 movies reviews categorized as either positive or negative. It comes with predefined train/test split BIBREF30 : 25,000 reviews are used for training, 25,000 for testing, and the rest as unlabeled data. The two classes are balanced in the training and testing sets. We remove words that appear less than 10 times in the training set, resulting in a vocabulary of 43,375 distinct words and symbols.",
+      "Setup. We test the various representation learning algorithms under two settings: one follows the same protocol proposed in BIBREF8 , where representation is learned using all the available data, including the test set; another one where the representation is learned using training and unlabeled set only. For both settings, a linear support vector machine (SVM) BIBREF31 is trained afterwards on the learned representation for classification. For Skip-thought Vectors, we used the generic model trained on a much bigger book corpus to encode the documents. A vector of 4800 dimensions, first 2400 from the uni-skip model, and the last 2400 from the bi-skip model, are generated for each document. In comparison, all the other algorithms produce a vector representation of size 100. The supervised RNN-LM is learned on the training set only. The hyper-parameters are tuned on a validation set subsampled from the training set.",
+      "Accuracy. Comparing the two columns in Table TABREF20 , we can see that all the representation learning algorithms benefits from including the testing data during the representation learning phrase. Doc2VecC achieved similar or even better performance than Paragraph Vectors. Both methods outperforms the other baselines, beating the BOW representation by 15%. In comparison with Word2Vec+IDF, which applies post-processing on learned word embeddings to form document representation, Doc2VecC naturally enforces document semantics to be captured by averaged word embeddings during training. This leads to better performance. Doc2VecC reduces to Denoising Autoencoders (DEA) if the local context words are removed from the paradigm shown in Figure FIGREF9 . By including the context words, Doc2VecC allows the document vector to focus more on capturing the global context. Skip-thought vectors perform surprisingly poor on this dataset comparing to other methods. We hypothesized that it is due to the length of paragraphs in this dataset. The average length of paragraphs in the IMDB movie review dataset is INLINEFORM0 , much longer than the ones used for training and testing in the original paper, which is in the order of 10. As noted in BIBREF18 , the performance of LSTM based method (similarly, the gated RNN used in Skip-thought vectors) drops significantly with increasing paragraph length, as it is hard to preserve state over long sequences of words.",
+      "Time. Table TABREF22 summarizes the time required by these algorithms to learn and generate the document representation. Word2Vec is the fastest one to train. Denoising Autoencoders and Doc2VecC second that. The number of parameters that needs to be back-propagated in each update was increased by the number of surviving words in INLINEFORM0 . We found that both models are not sensitive to the corruption rate INLINEFORM1 in the noise model. Since the learning time decreases with higher corruption rate, we used INLINEFORM2 throughout the experiments. Paragraph Vectors takes longer time to train as there are more parameters (linear to the number of document in the learning set) to learn. At test time, Word2Vec+IDF, DEA and Doc2VecC all use (weighted) averaging of word embeddings as document representation. Paragraph Vectors, on the other hand, requires another round of inference to produce the vector representation of unseen test documents. It takes Paragraph Vectors 4 minutes and 17 seconds to infer the vector representations for the 25,000 test documents, in comparison to 7 seconds for the other methods. As we did not re-train the Skip-thought vector models on this dataset, the training time reported in the table is the time it takes to generate the embeddings for the 25,000 training documents. Due to repeated high-dimensional matrix operations required for encoding long paragraphs, it takes fairly long time to generate the representations for these documents. Similarly for testing. The experiments were conducted on a desktop with Intel i7 2.2Ghz cpu.",
+      "Data dependent regularization. As explained in Section SECREF15 , the corruption introduced in Doc2VecC acts as a data-dependent regularization that suppresses the embeddings of frequent but uninformative words. Here we conduct an experiment to exam the effect. We used a cutoff of 100 in this experiment. Table TABREF24 lists the words having the smallest INLINEFORM0 norm of embeddings found by different algorithms. The number inside the parenthesis after each word is the number of times this word appears in the learning set. In word2Vec or Paragraph Vectors, the least frequent words have embeddings that are close to zero, despite some of them being indicative of sentiment such as debacle, bliss and shabby. In contrast, Doc2VecC manages to clamp down the representation of words frequently appear in the training set, but are uninformative, such as symbols and stop words.",
+      "Subsampling frequent words. Note that for all the numbers reported, we applied the trick of subsampling of frequent words introduced in BIBREF6 to counter the imbalance between frequent and rare words. It is critical to the performance of simple Word2Vec+AVG as the sole remedy to diminish the contribution of common words in the final document representation. If we were to remove this step, the error rate of Word2Vec+AVG will increases from INLINEFORM0 to INLINEFORM1 . Doc2VecC on the other hand naturally exerts a stronger regularization toward embeddings of words that are frequent but uninformative, therefore does not rely on this trick."
+    ],
+    [
+      "In table TABREF24 , we demonstrated that the corruption model introduced in Doc2VecC dampens the embeddings of words which are common and non-discriminative (stop words). In this experiment, we are going to quantatively compare the word embeddings generated by Doc2VecC to the ones generated by Word2Vec, or Paragraph Vectors on the word analogy task introduced by BIBREF1 . The dataset contains five types of semantic questions, and nine types of syntactic questions, with a total of 8,869 semantic and 10,675 syntactic questions. The questions are answered through simple linear algebraic operations on the word embeddings generated by different methods. Please refer to the original paper for more details on the evaluation protocol.",
+      "We trained the word embeddings of different methods using the English news dataset released under the ACL workshop on statistical machine translation. The training set includes close to 15M paragraphs with 355M tokens. We compare the performance of word embeddings trained by different methods with increasing embedding dimensionality as well as increasing training data.",
+      "We observe similar trends as in BIBREF1 . Increasing embedding dimensionality as well as training data size improves performance of the word embeddings on this task. However, the improvement is diminishing. Doc2VecC produces word embeddings which performs significantly better than the ones generated by Word2Vec. We observe close to INLINEFORM0 uplift when we train on the full training corpus. Paragraph vectors on the other hand performs surprisingly bad on this dataset. Our hypothesis is that due to the large capacity of the model architecture, Paragraph Vectors relies mostly on the unique document vectors to capture the information in a text document instead of learning the word semantic or syntactic similarities. This also explains why the PV-DBOW BIBREF2 model architecture proposed in the original work, which completely removes word embedding layers, performs comparable to the distributed memory version.",
+      "In table 5, we list a detailed comparison of the performance of word embeddings generated by Word2Vec and Doc2VecC on the 14 subtasks, when trained on the full dataset with embedding of size 100. We can see that Doc2VecC significantly outperforms the word embeddings produced by Word2Vec across almost all the subtasks."
+    ],
+    [
+      "For the document classification task, we use a subset of the wikipedia dump, which contains over 300,000 wikipedia pages in 100 categories. The 100 categories includes categories under sports, entertainment, literature, and politics etc. Examples of categories include American drama films, Directorial debut films, Major League Baseball pitchers and Sydney Swans players. Body texts (the second paragraph) were extracted for each page as a document. For each category, we select 1,000 documents with unique category label, and 100 documents were used for training and 900 documents for testing. The remaining documents are used as unlabeled data. The 100 classes are balanced in the training and testing sets. For this data set, we learn the word embedding and document representation for all the algorithms using all the available data. We apply a cutoff of 10, resulting in a vocabulary of size INLINEFORM0 .",
+      "Table TABREF29 summarizes the classification error of a linear SVM trained on representations of different sizes. We can see that most of the algorithms are not sensitive to the size of the vector representation. Doc2Vec benefits most from increasing representation size. Across all sizes of representations, Doc2VecC outperform the existing algorithms by a significant margin. In fact, Doc2VecC can achieve same or better performance with a much smaller representation vector.",
+      "Figure FIGREF30 visualizes the document representations learned by Doc2Vec (left) and Doc2VecC (right) using t-SNE BIBREF32 . We can see that documents from the same category are nicely clustered using the representation generated by Doc2VecC. Doc2Vec, on the other hand, does not produce a clear separation between different categories, which explains its worse performance reported in Table TABREF29 .",
+      "Figure FIGREF31 visualizes the vector representation generated by Doc2VecC w.r.t. coarser categorization. we manually grouped the 100 categories into 7 coarse categories, television, albums, writers, musicians, athletes, species and actors. Categories that do no belong to any of these 7 groups are not included in the figure. We can see that documents belonging to a coarser category are grouped together. This subset includes is a wide range of sports descriptions, ranging from football, crickets, baseball, and cycling etc., which explains why the athletes category are less concentrated. In the projection, we can see documents belonging to the musician category are closer to those belonging to albums category than those of athletes or species."
+    ],
+    [
+      "We test Doc2VecC on the SemEval 2014 Task 1: semantic relatedness SICK dataset BIBREF33 . Given two sentences, the task is to determine how closely they are semantically related. The set contains 9,927 pairs of sentences with human annotated relatedness score, ranging from 1 to 5. A score of 1 indicates that the two sentences are not related, while 5 indicates high relatedness. The set is splitted into a training set of 4,500 instances, a validation set of 500, and a test set of 4,927.",
+      "We compare Doc2VecC with several winning solutions of the competition as well as several more recent techniques reported on this dataset, including bi-directional LSTM and Tree-LSTM trained from scratch on this dataset, Skip-thought vectors learned a large book corpus BIBREF34 and produced sentence embeddings of 4,800 dimensions on this dataset. We follow the same protocol as in skip-thought vectors, and train Doc2VecC on the larger book corpus dataset. Contrary to the vocabulary expansion technique used in BIBREF16 to handle out-of-vocabulary words, we extend the vocabulary of the learned model directly on the target dataset in the following way: we use the pre-trained word embedding as an initialization, and fine-tune the word and sentence representation on the SICK dataset. Notice that the fine-tuning is done for sentence representation learning only, and we did not use the relatedness score in the learning. This step brings small improvement to the performance of our algorithm. Given the sentence embeddings, we used the exact same training and testing protocol as in BIBREF16 to score each pair of sentences: with two sentence embedding INLINEFORM0 and INLINEFORM1 , we concatenate their component-wise product, INLINEFORM2 and their absolute difference, INLINEFORM3 as the feature representation.",
+      "Table TABREF35 summarizes the performance of various algorithms on this dataset. Despite its simplicity, Doc2VecC significantly out-performs the winning solutions of the competition, which are heavily feature engineered toward this dataset and several baseline methods, noticeably the dependency-tree RNNs introduced in BIBREF35 , which relies on expensive dependency parsers to compose sentence vectors from word embeddings. The performance of Doc2VecC is slightly worse than the LSTM based methods or skip-thought vectors on this dataset, while it significantly outperforms skip-thought vectors on the IMDB movie review dataset ( INLINEFORM0 error rate vs INLINEFORM1 ). As we hypothesized in previous section, while Doc2VecC is better at handling longer paragraphs, LSTM-based methods are superior for relatively short sentences (of length in the order of 10s). We would like to point out that Doc2VecC is much faster to train and test comparing to skip-thought vectors. It takes less than 2 hours to learn the embeddings on the large book corpus for Doc2VecC on a desktop with Intel i7 2.2Ghz cpu, in comparison to the 2 weeks on GPU required by skip-thought vectors."
+    ],
+    [
+      "We introduce a new model architecture Doc2VecC for document representation learning. It is very efficient to train and test thanks to its simple model architecture. Doc2VecC intrinsically makes sure document representation generated by averaging word embeddings capture semantics of document during learning. It also introduces a data-dependent regularization which favors informative or rare words while dampening the embeddings of common and non-discriminative words. As such, each document can be efficiently represented as a simple average of the learned word embeddings. In comparison to several existing document representation learning algorithms, Doc2VecC outperforms not only in testing efficiency, but also in the expressiveness of the generated representations."
+    ]
+  ]
+}
+```
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diff --git a/qasper-1115/instruction.md b/qasper-1115/instruction.md
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+++ b/qasper-1115/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Using Whole Document Context in Neural Machine Translation
+
+Question: Which language-pair had the better performance?
\ No newline at end of file
diff --git a/qasper-1140/instruction.md b/qasper-1140/instruction.md
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+++ b/qasper-1140/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Identifying Clickbait: A Multi-Strategy Approach Using Neural Networks
+
+Question: What are the differences with previous applications of neural networks for this task?
\ No newline at end of file
diff --git a/qasper-1141/instruction.md b/qasper-1141/instruction.md
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+Name of Paper: Consistency of a Recurrent Language Model With Respect to Incomplete Decoding
+
+Question: How much improvement is gained from the proposed approaches?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Background",
+    "Background ::: Recurrent Language Models",
+    "Background ::: Decoding Algorithms",
+    "Background ::: Decoding Algorithms ::: Stochastic decoding.",
+    "Background ::: Decoding Algorithms ::: Deterministic decoding.",
+    "Background ::: Decoding Algorithms ::: Incompleteness.",
+    "Consistency of a Decoding Algorithm ::: Definition of consistency.",
+    "Consistency of a Decoding Algorithm ::: Inconsistency of incomplete decoding.",
+    "Fixing the inconsistency",
+    "Fixing the inconsistency ::: Consistent Sampling Algorithms",
+    "Fixing the inconsistency ::: A Self-Terminating Recurrent Language Model",
+    "Empirical Validation",
+    "Empirical Validation ::: Sequence completion.",
+    "Empirical Validation ::: Dataset.",
+    "Empirical Validation ::: Context distribution.",
+    "Empirical Validation ::: Evaluation metrics.",
+    "Empirical Validation ::: Training.",
+    "Empirical Validation ::: Models.",
+    "Empirical Validation ::: Inconsistency of Recurrent Language Models",
+    "Empirical Validation ::: Consistency of the Proposed Methods",
+    "Empirical Validation ::: Consistency of the Proposed Methods ::: Consistent sampling.",
+    "Empirical Validation ::: Consistency of the Proposed Methods ::: Self-terminating RNN.",
+    "Future Directions",
+    "Conclusion",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "Neural sequence models trained with maximum likelihood estimation (MLE) have become a standard approach to modeling sequences in a variety of natural language applications such as machine translation BIBREF0, dialogue modeling BIBREF1, and language modeling BIBREF2. Despite this success, MLE-trained neural sequence models have been shown to exhibit issues such as length bias BIBREF3, BIBREF4 and degenerate repetition BIBREF5. These issues are suspected to be related to the maximum likelihood objective's local normalization, which results in a discrepancy between the learned model's distribution and the distribution induced by the decoding algorithm used to generate sequences BIBREF6, BIBREF7. This has prompted the development of alternative decoding methods BIBREF8, BIBREF5 and training objectives BIBREF9, BIBREF10. In this paper, we formalize and study this discrepancy between the model and the decoding algorithm.",
+      "We begin by formally defining recurrent neural language models, a family that encompasses neural models used in practice, such as recurrent neural networks BIBREF11, BIBREF12, BIBREF13, and transformers BIBREF14. Next, we formally define a decoding algorithm \u2013 a function that induces a distribution over sequences given a recurrent language model and a context distribution \u2013 which is used to obtain probable sequences from a model. In this paper, we show that the distribution induced by a decoding algorithm can contradict this intended use; instead, the decoding algorithm may return improbable, infinite-length sequences.",
+      "Our main finding is that a sequence which receives zero probability under a recurrent language model's distribution can receive nonzero probability under the distribution induced by a decoding algorithm. This occurs when the recurrent language model always ranks the sequence termination token outside of the set of tokens considered at each decoding step, yielding an infinite-length, zero probability sequence. This holds whenever the decoding algorithm is incomplete, in the sense that the algorithm excludes tokens from consideration at each step of decoding, which is the case for common methods such as greedy search, beam search, top-$k$ sampling BIBREF15, and nucleus sampling BIBREF5. We formalize our main finding using the notion of consistency BIBREF16 \u2013 whether a distribution assigns probability mass only to finite sequences \u2013 and prove that a consistent recurrent language model paired with an incomplete decoding algorithm can induce an inconsistent sequence distribution.",
+      "Based on the insight that inconsistency occurs due to the behavior of the termination token under incomplete decoding, we develop two methods for addressing inconsistency. First, we propose consistent sampling methods which guarantee that the termination token is not excluded from selection during decoding. Second, we introduce a self-terminating recurrent language model which ensures that the termination token is eventually ranked above all others, guaranteeing consistency under incomplete decoding.",
+      "To empirically measure inconsistency, we decode sequences from trained recurrent language models and measure the proportion of sequences with lengths far exceeding the maximum training sequence length. Our experiments on the Wikitext2 dataset BIBREF17 suggest that inconsistency occurs in practice when using incomplete decoding methods, while the proposed consistent sampling methods and self-terminating model parameterization prevent inconsistency and maintain language modeling quality.",
+      "The theoretical analysis reveals defects of existing decoding algorithms, providing a way to develop future models, inference procedures, and learning algorithms. We present methods related to sampling and model parameterization, but there are more directions which we leave to the future; we close with directions related to sequence-level learning."
+    ],
+    [
+      "We begin our discussion by establishing background definitions. First, we define a sequence which is the main object of our investigation.",
+      "Definition 2.1 (Sequence) A sequence $Y$ is an ordered collection of items from a predefined finite vocabulary $V$. A sequence of finite length always ends with a special token $\\left<\\text{eos}\\right>\\in V$ that only appears at the end of a sequence.",
+      "Each model we consider generates a sequence conditioned on context information, such as a prefix in sentence completion. To consider this, we define a context distribution.",
+      "Definition 2.2 (Context distribution) A context distribution $p(C)$ is a probability distribution defined over a set $\\mathcal {C}$. An element $C\\in \\mathcal {C}$ is called a context."
+    ],
+    [
+      "A recurrent language model is an autoregressive model of a sequence distribution, where each conditional probability is parameterized with a neural network. Importantly, we assume that all tokens in a sequence are dependent on each other under a recurrent language model. This allows us to avoid cases in which the model degenerates to a Markovian language model, such as an $n$-gram model with a finite $n$.",
+      "Definition 2.3 (Recurrent language model) A recurrent language model $p_\\theta $ is a neural network that computes the following conditional probability at each time step",
+      "where $h_t = f_{\\theta }(y_t, h_{t-1})$ and $h_0 = g_{\\theta }(C)$, and $u,c,\\theta $ are parameters. A recurrent language model thereby computes the probability of a sequence $Y=(y_1, \\ldots , y_T)$ by",
+      "where $y_{<t}=(y_1,\\ldots ,y_{t-1})$. This distribution satisfies",
+      "Practical variants of the recurrent language model differ by the choice of transition function $f_{\\theta }$ BIBREF11, BIBREF13, BIBREF12, BIBREF14. The use of softmax BIBREF18 implies that every unique token in the vocabulary is considered at every location of a sequence.",
+      "Remark 2.1 Under the conditional distribution of a recurrent language model, every token $v\\in V$ is assigned a positive probability. This implies that $0 < p_\\theta (v\\,|\\,y_{<t}, C) < 1.$ In addition, it follows that any finite sequence is probable by a recurrent language model under any context, i.e., $p_{\\theta }(Y\\,|\\,C) > 0$ for any sequence $Y$ of finite length."
+    ],
+    [
+      "Because it is intractable to decode the most probable sequence, it is necessary in practice to use an approximate decoding algorithm.",
+      "Definition 2.4 (Decoding algorithm) A decoding algorithm $\\mathcal {F}(p_{\\theta }, C)$ is a function that generates a sequence $\\tilde{Y}$ given a recurrent language model $p_{\\theta }$ and context $C$. Let $q_{\\mathcal {F}}$ denote the distribution induced by the decoding algorithm $\\mathcal {F}$.",
+      "We consider two families of decoding algorithms. In our analysis we only consider decoding algorithms that decode in a single pass, forward in time, without modifying previously selected tokens."
+    ],
+    [
+      "The first family consists of stochastic algorithms. Among them, ancestral sampling is asymptotically unbiased and can be used for finding the most probable sequence, although it requires a substantial number of samples to achieve a low-variance estimate.",
+      "Definition 2.5 (Ancestral sampling) Ancestral sampling $\\mathcal {F}_{\\text{anc}}$ generates a sequence from a recurrent language model $p_{\\theta }$ given context $C$ by recursively sampling from $p_{\\theta }(y_t\\,|\\,\\tilde{y}_{<t}, C)$ until $\\tilde{y}_t = \\left<\\text{eos}\\right>$:",
+      "In order to avoid the high variance, two approximate stochastic decoding algorithms have recently been proposed and tested with recurrent language models. Top-$k$ sampling considers only a subset of the $k$ most probable tokens from the vocabulary at a time, while nucleus sampling considers only the minimal subset of most probable tokens whose total probability is higher than a predefined threshold.",
+      "Definition 2.6 (Top-$k$ sampling BIBREF15) Top-$k$ sampling $\\mathcal {F}_{\\text{top-k}}$ generates a sequence from a recurrent language model $p_{\\theta }$ given context $C$ by recursively sampling from the following proposal distribution:",
+      "Definition 2.7 (Nucleus sampling BIBREF5) Nucleus sampling $\\mathcal {F}_{\\text{nuc-}\\mu }$ generates a sequence from a recurrent language model $p_{\\theta }$ given context $C$ by recursively sampling from the following proposal distribution. Let $v_1,\\ldots ,v_{|V|}$ denote tokens in $V$ such that $p_{\\theta }(v_i\\,|\\,y_{<t},C) \\ge p_{\\theta }(v_j\\,|\\,y_{<t},C)$ for all $i < j$, and define",
+      "where $V_{\\mu } = \\left\\lbrace v_1, \\cdots , v_{k_\\mu } \\right\\rbrace $ with"
+    ],
+    [
+      "The other family consists of deterministic decoding algorithms, where a token is selected deterministically according to a rule at each decoding step. The most naive algorithm, called greedy decoding, simply takes the most probable token at each step.",
+      "Definition 2.8 (Greedy decoding) Greedy decoding $\\mathcal {F}_{\\text{greedy}}$ generates a sequence from a recurrent language model $p_{\\theta }$ given context $C$ by recursively selecting the most likely token from $p_{\\theta }(y_t | \\tilde{y}_{<t}, C)$ until $\\tilde{y}_t = \\left<\\text{eos}\\right>$:",
+      "In contrast to greedy decoding, beam search operates on the level of partial sequences or prefixes.",
+      "Definition 2.9 (Prefix) A prefix $\\rho _t$ is an ordered collection of items from $V$. The score of a prefix is",
+      "where $\\rho _t[\\tau ]$ is a token at time $\\tau $ from $\\rho _t$.",
+      "Starting from a set of empty prefixes, at each iteration a new prefix set is formed by expanding each prefix, then choosing the highest scoring expanded prefixes.",
+      "Definition 2.10 (Beam search) Beam search with width $k$, $\\mathcal {F}_{\\text{beam}-k}$, generates a sequence from a recurrent language model $p_{\\theta }$ by maintaining a size-$k$ prefix set $\\mathrm {P}_t^{\\text{top}}$. Starting with $P_0^{top}=\\varnothing $, at each iteration $t\\in \\lbrace 1,2,\\ldots \\rbrace $ beam search forms a new prefix set $\\mathrm {P}_t^{\\text{top}}$ by expanding the current set, $\\mathrm {P}_t = \\bigcup _{\\rho \\in \\mathrm {P}_{t-1}^{\\text{top}}} \\lbrace \\rho \\circ v\\, |\\, v\\in V\\rbrace $ (where $\\rho \\circ v$ is concatenation), then choosing the $k$ highest scoring elements,",
+      "Any $\\rho \\in \\mathrm {P}_t^{\\text{top}}$ ending with $\\left<\\text{eos}\\right>$ is restricted from being expanded further, and is added to a set $S$. Beam search ends when $S$ contains $k$ sequences, and returns the highest scoring sequence in $S$."
+    ],
+    [
+      "Other than ancestral sampling, the decoding algorithms above are incomplete in that they only consider a strict subset of the the full vocabulary $V$ at each time step, aside from the trivial case of $k=|V|$.",
+      "Definition 2.11 (Incomplete Decoding) A decoding algorithm $\\mathcal {F}$ is incomplete when for each context $C$ and prefix $y_{<t}$, there is a strict subset $V^{\\prime }_t\\subsetneq V$ such that"
+    ],
+    [
+      "A recurrent language model $p_{\\theta }$ may assign a positive probability to an infinitely long sequence, in which case we call the model inconsistent. This notion of consistency was raised and analyzed earlier, for instance by BIBREF19 and BIBREF16, in terms of whether the distribution induced by $p_{\\theta }$ is concentrated on finite sequences. We extend their definition to account for the context $C$.",
+      "Definition 3.1 (Consistency of a recurrent language model) A recurrent language model is consistent under a context distribution $p(C)$ if $p_{\\theta }(|Y|=\\infty ) = 0$. Otherwise, the recurrent language model is said to be inconsistent.",
+      "Any sequence decoded from a consistent model for a given probable context is guaranteed to terminate.",
+      "Lemma 3.1 If a recurrent language model $p_{\\theta }$ is consistent, $p_{\\theta }(|Y|=\\infty \\,|\\,C)=0$ for any probable context $C$.",
+      "Next, we establish a practical condition under which a recurrent language model is consistent.",
+      "Lemma 3.2 A recurrent language model $p_{\\theta }$ is consistent if $\\Vert h_t\\Vert _p$ is uniformly bounded for some $p\\ge 1$.",
+      "[Proof sketch] If $\\Vert h_t\\Vert _p$ is bounded, then each $u_v^\\top h_t$ is bounded, hence $p_{\\theta }(\\left<\\text{eos}\\right>| y_{<t}, C)>\\xi >0$ for a constant $\\xi $. Thus $p_{\\theta }(|Y|=\\infty ) \\le \\lim _{t\\rightarrow \\infty } (1 - \\xi )^t = 0$, meaning that $p_{\\theta }$ is consistent.",
+      "Although this condition is practical because layer normalization or bounded activation functions BIBREF11, BIBREF12, BIBREF14 result in bounded $h_t$, we show that even if a recurrent language model is consistent, a decoding algorithm may produce an infinite-length sequence. We formalize this discrepancy using the consistency of a decoding algorithm.",
+      "Definition 3.2 (Consistency of a decoding algorithm) A decoding algorithm $\\mathcal {F}$ is consistent with respect to a consistent recurrent language model $p_{\\theta }$ under a context distribution $p(C)$ if the decoding algorithm $\\mathcal {F}$ preserves the consistency of the model $p_{\\theta }$, that is, $q_{\\mathcal {F}}(|Y|=\\infty )=0$.",
+      "When a consistent recurrent language model $p_{\\theta }$ and a decoding algorithm $\\mathcal {F}$ induce a consistent distribution $q_{\\mathcal {F}}$, we say that $p_{\\theta }$ paired with $\\mathcal {F}$ is consistent. For instance, any consistent recurrent language model paired with ancestral sampling is consistent, because the induced distribution $q_{\\mathcal {F}_{\\text{anc}}}$ is the same as the distribution of the original model. We also have an analogue of Lemma UNKREF21.",
+      "Lemma 3.3 A consistent decoding algorithm with respect to a consistent recurrent language model decodes only probable sequences. That is, if $q_{\\mathcal {F}}(Y\\,|\\,C)>0$, then $p_{\\theta }(Y\\,|\\,C)>0$ for any probable context $C$."
+    ],
+    [
+      "Any incomplete decoding algorithm (Definition UNKREF18) can be inconsistent regardless of the context distribution, because there is a recurrent language model that places $\\left<\\text{eos}\\right>$ outside of $V^{\\prime }_t$ at every step of decoding. To show this, we construct a consistent recurrent language model whose distribution induced by an incomplete decoding algorithm is inconsistent.",
+      "Theorem 3.4 (Inconsistency of an incomplete decoding algorithm) There exists a consistent recurrent language model $p_{\\theta }$ from which an incomplete decoding algorithm $\\mathcal {F}$, that considers only up to $(|V|-1)$-most likely tokens according to $p_{\\theta }(y_t\\,|\\,y_{<t},C)$ at each step $t$, finds a sequence $\\tilde{Y}$ whose probability under $p_{\\theta }$ is 0 for any context distribution.",
+      "We prove this theorem by constructing a $\\tanh $ recurrent network. We define the recurrent function $f_{\\theta }$ as",
+      "where $e(y_{t}) \\in \\mathbb {R}^{|V|}$ is a one-hot representation of $y_t$, $W_h \\in \\mathbb {R}^{d \\times d}$ where every entry is positive, and $I$ is an identity matrix of size $|V| \\times |V|$. $h_0 = g_{\\theta }(C)$ is constructed to consist of positive values only. Because each element of $|h_t|$ is bounded by 1, the constructed recurrent language model $p_{\\theta }$ is consistent by Lemma UNKREF23.",
+      "For $v \\ne \\left<\\text{eos}\\right>$, we set $u_v$ (see Definition UNKREF4) to be",
+      "where all elements of $\\bar{u}_v$ are positive and $e(v)$ is a one-hot representation of $v$. $c_v$ is set to zero. Next, let",
+      "where all elements of $\\bar{u}_{\\left<\\text{eos}\\right>}$ are negative.",
+      "This defines a valid recurrent language model (Definition UNKREF4), since the conditional distribution at each time $t$ is influenced by all the previous tokens. More specifically, the logit of a token $v$ depends on $\\sum _{t^{\\prime }=1}^t {1}(y_{t^{\\prime }} = v)$, where 1 is an indicator function.",
+      "This recurrent language model always outputs positive logits for non-$\\left<\\text{eos}\\right>$ tokens, and outputs negative logits for the $\\left<\\text{eos}\\right>$ token. This implies $p(\\left<\\text{eos}\\right>|\\,y_{<t}, C) < p(v\\,|\\,y_{<t}, C)$ for all $v \\in V \\backslash \\left\\lbrace \\left<\\text{eos}\\right>\\right\\rbrace $. This means that $\\left<\\text{eos}\\right>$ is always ranked last at each time step, so an incomplete decoding algorithm that considers at most $(|V|-1)$ most probable tokens at each time step from $p_{\\theta }(y_t\\,|\\,y_{<t}, C)$ cannot decode $\\left<\\text{eos}\\right>$ and thus always decodes an infinitely long sequence.",
+      "The log-probability of this infinitely long sequence $\\hat{Y}$ is",
+      "For any $v\\in V$,",
+      "where $b_v = \\sum _{v^{\\prime }\\ne v} \\exp (-\\Vert u_{v^{\\prime }}\\Vert _1)$. The last inequality holds because $x/(x+b_v)$ is increasing in $x>0$. Therefore, the log-probability $\\log p_{\\theta }(\\hat{Y}\\,|\\,C)$ diverges as $|\\hat{Y}| \\rightarrow \\infty $, and thus $p_{\\theta }(\\hat{Y}\\,|\\,C) = 0$, which implies the decoding algorithm $\\mathcal {F}$ is inconsistent by Lemma UNKREF25. Greedy decoding, beam search, top-$k$ sampling, and nucleus sampling are all inconsistent according to this theorem; there are consistent models $p_{\\theta }$ that induce inconsistent distributions when paired with these decoding algorithms."
+    ],
+    [
+      "In this section, we consider two ways to prevent inconsistency arising from incomplete decoding algorithms. First, we introduce consistent versions of top-$k$ and nucleus sampling. Second, we introduce the self-terminating recurrent language model, which is consistent when paired with any of the decoding algorithms considered in this paper."
+    ],
+    [
+      "The proof of Theorem UNKREF27 suggests that inconsistency of incomplete decoding algorithms arises from the fact that $\\left<\\text{eos}\\right>$ may be excluded indefinitely from the set of top-ranked tokens. We propose a simple modification to top-$k$ and nucleus sampling that forces $\\left<\\text{eos}\\right>$ to be included at each step of decoding. First, we give a condition for when a particular model $p_{\\theta }$ paired with a decoding algorithm $\\mathcal {F}$ is consistent.",
+      "Theorem 4.1 Let $p_{\\theta }$ be a consistent recurrent language model. If a decoding algorithm $\\mathcal {F}$ satisfies $q_{\\mathcal {F}}(\\left<\\text{eos}\\right>|\\,y_{<t}, C) \\ge p_{\\theta }(\\left<\\text{eos}\\right>|\\,y_{<t}, C)$ for every prefix $y_{<t}$ and context $C$, then the decoding algorithm $\\mathcal {F}$ is consistent with respect to the model $p_{\\theta }$.",
+      "Let $P^{\\prime }_{t-1}$ denote a set of all prefixes $y_{<t}$ of length $t-1$. For $t\\ge 1$,",
+      "Taking the limit $t\\rightarrow \\infty $ and expectation over $C$ on both sides, we have",
+      "from which the decoding algorithm is consistent.",
+      "We define consistent variants of top-$k$ and nucleus sampling which satisfy this condition.",
+      "Definition 4.1 (Consistent top-$k$ sampling) Consistent top-$k$ sampling is top-$k$ sampling with the following modified proposal distribution:",
+      "where $V^{\\prime } = \\left\\lbrace \\left<\\text{eos}\\right>\\right\\rbrace \\cup \\underset{v^{\\prime }}{\\arg \\text{top-k}}\\ p_{\\theta }(v^{\\prime }\\,|\\,y_{<t}, C)$.",
+      "Definition 4.2 (Consistent nucleus sampling) Consistent nucleus sampling is nucleus sampling with the following modified proposal distribution:",
+      "The induced probability of $\\left<\\text{eos}\\right>$ under these two algorithms is always equal to or larger than the model's probability. By Theorem UNKREF29, these algorithms are consistent with respect to any consistent recurrent language model."
+    ],
+    [
+      "Although these consistent sampling algorithms can be used with any recurrent language model, their stochastic nature may not be suitable for finding a single, highly probable sequence. To avoid this limitation, we propose the self-terminating recurrent language model (STRLM).",
+      "Definition 4.3 (Self-terminating recurrent language model) A self-terminating recurrent language model computes the following conditional probability at each time step:",
+      "where",
+      "with $\\sigma : \\mathbb {R} \\rightarrow [0,1-\\epsilon ]$ and $\\epsilon \\in (0,1)$. $h_t$ is computed as in the original recurrent language model.",
+      "The underlying idea is that the probability of $\\left<\\text{eos}\\right>$ increases monotonically. The model is consistent when paired with greedy decoding.",
+      "Theorem 4.2 Greedy decoding is consistent with respect to any self-terminating recurrent language model.",
+      "Let $p_{t}^{\\left<\\text{eos}\\right>}$ denote $p_{\\theta }(\\left<\\text{eos}\\right>|\\,y_{<t}, C)$ and $a_{t}^{\\left<\\text{eos}\\right>}$ denote $u_{\\left<\\text{eos}\\right>}^\\top h_t + c_{\\left<\\text{eos}\\right>}$. By Definition UNKREF33 we have",
+      "Take $B=-\\log 2 / \\log (1-\\epsilon )$. We then have $p_{t}^{\\left<\\text{eos}\\right>}>1/2$ for all $t > B$, which implies that $\\left<\\text{eos}\\right>$ is always the most probable token after time step $B$. Hence, the sequence length is less than $B$ with probability 1. Beam search is also consistent with respect to any self-terminating recurrent language model according to a similar argument; see Appendix for the proof."
+    ],
+    [
+      "The theoretical results rely on the existence of a model that results in inconsistency; it remains to be shown that inconsistency with respect to incomplete decoding occurs with recurrent language models encountered in practice. Moreover, while the proposed consistent sampling methods and self-terminating recurrent language model carry theoretical guarantees in terms of consistency, we must check whether they retain language modeling quality. To do so, we perform two experiments using a sequence completion task. In each experiment, we use the beginning of a sequence as context, then decode continuations from a trained recurrent language model and measure the proportion of non-terminated sequences in order to approximately measure inconsistency. The first experiment (\u00a7SECREF45) shows that inconsistency occurs in practice, and the second experiment (\u00a7SECREF47) shows the effectiveness of the proposed approaches."
+    ],
+    [
+      "We evaluate recurrent language models on a sequence completion task, which has previously been used to evaluate the effectiveness of sequence models, e.g. BIBREF20, BIBREF21, BIBREF2, BIBREF5, BIBREF10. Sequence completion is a general setting for studying the behavior of language models, encompassing machine translation BIBREF0, story generation BIBREF15, and dialogue modeling BIBREF1. The task consists of decoding a continuation $\\hat{Y}\\sim \\mathcal {F}(p_{\\theta }, C)$ given a length-$k$ prefix $C=(c_1,\\ldots ,c_k)$, resulting in a completion $(c_1,\\ldots ,c_k,\\hat{y}_1\\ldots ,\\hat{y}_T)$."
+    ],
+    [
+      "We use the Wikitext2 dataset BIBREF17 consisting of paragraphs from Wikipedia, since it has frequently been used to evaluate language models BIBREF22, BIBREF23, BIBREF24. We split each paragraph into sentences using Spacy, resulting in roughly 100k sequences (78,274 train, 8,464 valid, 9,708 test). We split each sequence, using the first $k$ tokens as a context and the remaining tokens as a continuation. To ensure that each sequence contains a prefix, we prepend padding tokens to make it length $k$. Special $\\left<\\text{bos}\\right>$ and $\\left<\\text{eos}\\right>$ tokens are then inserted at the beginning and end of every sequence. Our experiments use $k=10$. We model sequences at the word level with a vocabulary size of 33,182. The average training sequence length is 24 tokens, with a maximum of 137."
+    ],
+    [
+      "We define empirical context distributions with prefixes from the train, valid, and test sets,",
+      "where $\\mathcal {D}=\\lbrace (C^{(n)},Y^{(n)})\\rbrace _{n=1}^{N}$ is a dataset split."
+    ],
+    [
+      "We use finite sequences to approximately measure the consistency of a model paired with a decoding algorithm, since decoding an infinite-length sequence is impossible. We use the proportion of decoded continuations that are longer than a predefined limit,",
+      "where $\\hat{Y}^{(n)}\\sim \\mathcal {F}(p_{\\theta }, C^{(n)})$ for each context $C^{(n)}$ in $\\mathcal {D}$. We call $r_L$ the non-termination ratio of the decoding algorithm $\\mathcal {F}$ for an underlying model and context distribution. A value of $r_L$ greater than zero means that some sequences did not terminate within $L$ steps. When $L$ is infinity, this implies that the model paired with the decoding algorithm is inconsistent. In practice, we use a finite $L$ that is substantially larger than the maximum training sequence length, and we interpret a non-zero $r_L$ as evidence that the model paired with the decoding algorithm is inconsistent. We use $L=1500$, which is more than 10 times the maximum training sequence length.",
+      "In each experiment, we report the mean and standard deviation of metrics across 10 independent initializations. Unless specified otherwise, we report metrics using the test context distribution, since the train, valid, and randomly generated context distributions had similar results."
+    ],
+    [
+      "We train recurrent language models for sequence completion with maximum likelihood, using the following loss on each sequence $Y=(c_1,\\ldots ,c_k,y_1,\\ldots ,y_T)$:",
+      "This amounts to running the full training sequence through a recurrent model and zeroing the loss for the first $k$ tokens, so that the first $k$ steps correspond to learning a $g_{\\theta }$ that encodes the context. Each model is trained on a single Nvidia P40 GPU for up to 100 epochs, stopping early when validation perplexity does not decrease for 10 consecutive epochs."
+    ],
+    [
+      "We consider recurrent neural networks with hyperbolic tangent activations ($\\tanh $-RNN) BIBREF11 and LSTM units (LSTM-RNN) BIBREF13. We perform an initial hyper-parameter sweep and select the best set of hyper-parameters for each of $\\tanh $-RNN and LSTM-RNN based on the validation perplexities. With this best set of hyperparameters, we train each of these models with 10 different initializations. The choice of $\\tanh $ and LSTM RNNs implies that all of the recurrent language models that we train are consistent according to Lemma UNKREF23. Our LSTM models achieve similar test perplexity ($91.86 \\pm 0.4$) to those reported in previous work BIBREF24; see Appendix for further details.",
+      "Additionally, we train self-terminating $\\tanh $-RNN and LSTM-RNN variants (Definition UNKREF33) at various values of $\\epsilon $, which controls a lower bound on the termination probability at each step. We use $\\sigma (x)=(1-\\epsilon )\\text{sigmoid}(x)$. We use the hyper-parameters selected in the preceding grid search."
+    ],
+    [
+      "In this experiment, we demonstrate evidence of inconsistency with incomplete decoding methods (Theorem UNKREF27).",
+      "Table TABREF43 shows non-termination ratios for the recurrent language models using the incomplete decoding algorithms considered in this work, along with ancestral sampling. Decoding with ancestral sampling always resulted in sequences that terminated within $L$ steps, since the induced distribution is the same as that of the consistent model. On the other hand, the non-zero non-termination ratios for the incomplete decoding algorithms suggest inconsistency with respect to each algorithm, providing evidence for Theorem UNKREF27.",
+      "In particular, greedy search, beam search, and nucleus sampling yielded non-terminating sequences with both the $\\tanh $ and LSTM RNNs. Using greedy decoding, roughly 6% of all contexts resulted in a non-terminating continuation with the $\\tanh $-RNN, and roughly 1% with the LSTM-RNN. Nucleus sampling also produced non-terminating sequences with the $\\tanh $-RNN (2.49%, nuc-0.2) and LSTM-RNN (0.76%, nuc-0.2), with the amount of non-termination decreasing as $\\mu $ increased (see Definition UNKREF11), likely due to $\\left<\\text{eos}\\right>$ having a higher chance of being included in $V_{\\mu }$. Top-$k$ sampling resulted in non-terminating sequences with the $\\tanh $-RNN, but not with the LSTM, implying that $\\left<\\text{eos}\\right>$ was ranked within the top $k$ positions on at least one timestep during each decoding. Beam search produced non-terminating sequences with both the $\\tanh $-RNN (beam-2,4) and LSTM-RNN (beam-2) models. This means that $\\left<\\text{eos}\\right>$ was outside of the top tokens (determined by the beam width) considered at each step, since in our experiments we terminated the beam search when a single beam prefix contained $\\left<\\text{eos}\\right>$. With the LSTM-RNN, a larger beam width (beam-4) prevented non-termination."
+    ],
+    [
+      "In this experiment, we evaluate the consistent variants of top-$k$ and nucleus sampling (\u00a7SECREF28) as well as the self-terminating recurrent language model (\u00a7SECREF32) in terms of consistency and language modeling quality."
+    ],
+    [
+      "Table TABREF44 shows that consistent nucleus and top-$k$ sampling (\u00a7SECREF28) resulted in only terminating sequences, except for a few cases that we attribute to the finite limit $L$ used to measure the non-termination ratio. The example continuations in Table TABREF46 show that the sampling tends to preserve language modeling quality on prefixes that led to termination with the baseline (first row). On prefixes that led to non-termination with the baseline (second & third rows), the quality tends to improve since the continuation now terminates. Since the model's non-$\\left<\\text{eos}\\right>$ token probabilities at each step are only modified by a multiplicative constant, the sampling process can still enter a repetitive cycle (e.g. when the constant is close to 1), though the cycle is guaranteed to eventually terminate."
+    ],
+    [
+      "As seen in Table TABREF50, the self-terminating recurrent language models with $\\epsilon \\in \\lbrace 10^{-2},10^{-3}\\rbrace $ are consistent with respect to greedy decoding, at the expense of perplexity compared to the vanilla model. The value of $\\epsilon $ from Definition UNKREF33, which controls a lower-bound on termination probability at each step, influences both $r_L$ and perplexity. When $\\epsilon $ is too large ($\\epsilon =10^{-2}$), perplexity degrades. When $\\epsilon $ is too small ($\\epsilon =10^{-4}$), the lower-bound grows slowly, so $\\left<\\text{eos}\\right>$ is not guaranteed to be top-ranked within $L$ steps, and the metrics resemble the baseline's. An $\\epsilon $ of $10^{-3}$ balanced consistency and language modeling quality, with a zero non-termination ratio and perplexity within 3 points of the baseline.",
+      "For the example decoded sequences in Table TABREF46, generation quality is similar when both the self-terminating and baseline models terminate (first row). For prefixes that led to non-termination with the baseline, the self-terminating variant can yield a finite sequence with reasonable quality (second row). This suggests that some cases of degenerate repetition BIBREF5, BIBREF10 may be attributed to inconsistency. However, in other cases the self-terminating model enters a repetitive (but finite) cycle that resembles the baseline (third row), showing that consistency does not necessarily eliminate degenerate repetition."
+    ],
+    [
+      "The methods we proposed in this paper have focused on how to resolve inconsistency from the viewpoint of decoding algorithms or model parameterization. Another approach is to address the issue of inconsistency in the learning phase.",
+      "One interesting direction is to investigate whether maximum likelihood learning is a cause of inconsistency. Given a training set $\\left\\lbrace (C^{(n)}, Y^{(n)}) \\right\\rbrace _{n=1}^N$ drawn from a data distribution, maximum likelihood learning solves:",
+      "where $\\Omega (\\theta )$ is a regularizer and $\\lambda $ is a regularization weight.",
+      "Inconsistency may arise from the lack of decoding in solving this optimization problem. Maximum likelihood learning fits the model $p_{\\theta }$ using the data distribution, whereas a decoded sequence from the trained model follows the distribution $q_{\\mathcal {F}}$ induced by a decoding algorithm. Based on this discrepancy, we make a strong conjecture: we cannot be guaranteed to obtain a good consistent sequence generator using maximum likelihood learning and greedy decoding. Sequence-level learning, however, uses a decoding algorithm during training BIBREF25, BIBREF26. We hypothesize that sequence-level learning can result in a good sequence generator that is consistent with respect to incomplete decoding."
+    ],
+    [
+      "We extended the notion of consistency of a recurrent language model put forward by BIBREF16 to incorporate a decoding algorithm, and used it to analyze the discrepancy between a model and the distribution induced by a decoding algorithm. We proved that incomplete decoding is inconsistent, and proposed two methods to prevent this: consistent decoding and the self-terminating recurrent language model. Using a sequence completion task, we confirmed that empirical inconsistency occurs in practice, and that each method prevents inconsistency while maintaining the quality of generated sequences. We suspect the absence of decoding in maximum likelihood estimation as a cause behind this inconsistency, and suggest investigating sequence-level learning as an alternative in the future."
+    ],
+    [
+      "We thank Chris Dyer, Noah Smith and Kevin Knight for valuable discussions. This work was supported by Samsung Advanced Institute of Technology (Next Generation Deep Learning: from pattern recognition to AI) and Samsung Research (Improving Deep Learning using Latent Structure). KC thanks eBay and NVIDIA for their support."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1146/instruction.md b/qasper-1146/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..1c4470b75085d13842c423d0e9f9665045057c2a
--- /dev/null
+++ b/qasper-1146/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Modality-Balanced Models for Visual Dialogue
+
+Question: What model was winner of the Visual Dialog challenge 2018?
\ No newline at end of file
diff --git a/qasper-1147/instruction.md b/qasper-1147/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..b0ad604460f1d0f6c5f4193bef9fc1938dcdab5d
--- /dev/null
+++ b/qasper-1147/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Modality-Balanced Models for Visual Dialogue
+
+Question: Which method for integration peforms better ensemble or consensus dropout fusion with shared parameters?
\ No newline at end of file
diff --git a/qasper-1170/instruction.md b/qasper-1170/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..ac2ef2aea453333e6905b743e71abed03598bc31
--- /dev/null
+++ b/qasper-1170/instruction.md
@@ -0,0 +1,126 @@
+Name of Paper: Revealing the Dark Secrets of BERT
+
+Question: What subset of GLUE tasks is used?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related work",
+    "Methodology",
+    "Experiments",
+    "Experiments ::: BERT's self-attention patterns",
+    "Experiments ::: BERT's self-attention patterns ::: Results",
+    "Experiments ::: Relation-specific heads in BERT",
+    "Experiments ::: Relation-specific heads in BERT ::: Results",
+    "Experiments ::: Change in self-attention patterns after fine-tuning",
+    "Experiments ::: Change in self-attention patterns after fine-tuning ::: Results",
+    "Experiments ::: Attention to linguistic features",
+    "Experiments ::: Attention to linguistic features ::: Results",
+    "Experiments ::: Token-to-token attention",
+    "Experiments ::: Token-to-token attention ::: Results",
+    "Experiments ::: Disabling self-attention heads",
+    "Experiments ::: Disabling self-attention heads ::: Results",
+    "Discussion",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Over the past year, models based on the Transformer architecture BIBREF0 have become the de-facto standard for state-of-the-art performance on many natural language processing (NLP) tasks BIBREF1, BIBREF2. Their key feature is the self-attention mechanism that provides an alternative to conventionally used recurrent neural networks (RNN).",
+      "One of the most popular Transformer-based models is BERT, which learns text representations using a bi-directional Transformer encoder pre-trained on the language modeling task BIBREF2. BERT-based architectures have produced new state-of-the-art performance on a range of NLP tasks of different nature, domain, and complexity, including question answering, sequence tagging, sentiment analysis, and inference. State-of-the-art performance is usually obtained by fine-tuning the pre-trained model on the specific task. In particular, BERT-based models are currently dominating the leaderboards for SQuAD BIBREF3 and GLUE benchmarks BIBREF4.",
+      "However, the exact mechanisms that contribute to the BERT's outstanding performance still remain unclear. We address this problem through selecting a set of linguistic features of interest and conducting a series of experiments that aim to provide insights about how well these features are captured by BERT. This paper makes the following contributions:",
+      "We propose the methodology and offer the first detailed analysis of BERT's capacity to capture different kinds of linguistic information by encoding it in its self-attention weights.",
+      "We present the evidence of BERT's overparametrization and suggest a counter-intuitive yet frustratingly simple way of improving its performance, showing absolute gains of up to 3.2%."
+    ],
+    [
+      "There have been several recent attempts to assess BERT's ability to capture structural properties of language. BIBREF5 demonstrated that BERT consistently assigns higher scores to the correct verb forms as opposed to the incorrect one in a masked language modeling task, suggesting some ability to model subject-verb agreement. BIBREF6 extended this work to using multiple layers and tasks, supporting the claim that BERT's intermediate layers capture rich linguistic information. On the other hand, BIBREF7 concluded that LSTMs generalize to longer sequences better, and are more robust with respect to agreement distractors, compared to Transformers.",
+      "BIBREF8 investigated the transferability of contextualized word representations to a number of probing tasks requiring linguistic knowledge. Their findings suggest that (a) the middle layers of Transformer-based architectures are the most transferable to other tasks, and (b) higher layers of Transformers are not as task specific as the ones of RNNs. BIBREF9 argued that models using self-attention outperform CNN- and RNN-based models on a word sense disambiguation task due to their ability to extract semantic features from text.",
+      "Our work contributes to the above discussion, but rather than examining representations extracted from different layers, we focus on the understanding of the self-attention mechanism itself, since it is the key feature of Transformer-based models.",
+      "Another research direction that is relevant to our work is neural network pruning. BIBREF10 showed that widely used complex architectures suffer from overparameterization, and can be significantly reduced in size without a loss in performance. BIBREF5 observed that the smaller version of BERT achieves better scores on a number of syntax-testing experiments than the larger one. BIBREF11 questioned the necessity of computation-heavy neural networks, proving that a simple yet carefully tuned BiLSTM without attention achieves the best or at least competitive results compared to more complex architectures on the document classification task. BIBREF12 presented more evidence of unnecessary complexity of the self-attention mechanism, and proposed a more lightweight and scalable dynamic convolution-based architecture that outperforms the self-attention baseline. These studies suggest a potential direction for future research, and are in good accordance with our observations."
+    ],
+    [
+      "We pose the following research questions:",
+      "What are the common attention patterns, how do they change during fine-tuning, and how does that impact the performance on a given task? (Sec. SECREF17, SECREF30)",
+      "What linguistic knowledge is encoded in self-attention weights of the fine-tuned models and what portion of it comes from the pre-trained BERT? (Sec. SECREF25, SECREF34, SECREF36)",
+      "How different are the self-attention patterns of different heads, and how important are they for a given task? (Sec. SECREF39)",
+      "The answers to these questions come from a series of experiments with the basic pre-trained or the fine-tuned BERT models, as will be discussed below. All the experiments with the pre-trained BERT were conducted using the model provided with the PyTorch implementation of BERT (bert-base-uncased, 12-layer, 768-hidden, 12-heads, 110M parameters). We chose this smaller version of BERT because it shows competitive, if not better, performance while having fewer layers and heads, which makes it more interpretable.",
+      "We use the following subset of GLUE tasks BIBREF4 for fine-tuning:",
+      "MRPC: the Microsoft Research Paraphrase Corpus BIBREF13",
+      "STS-B: the Semantic Textual Similarity Benchmark BIBREF14",
+      "SST-2: the Stanford Sentiment Treebank, two-way classification BIBREF15",
+      "QQP: the Quora Question Pairs dataset",
+      "RTE: the Recognizing Textual Entailment datasets",
+      "QNLI: Question-answering NLI based on the Stanford Question Answering Dataset BIBREF3",
+      "MNLI: the Multi-Genre Natural Language Inference Corpus, matched section BIBREF16",
+      "Please refer to the original GLUE paper for details on the QQP and RTE datasets BIBREF4. We excluded two tasks: CoLa and the Winograd Schema Challenge. The latter is excluded due to the small size of the dataset. As for CoLa (the task of predicting linguistic acceptability judgments), GLUE authors report that the human performance is only 66.4, which is explained by the problems with the underlying methodology BIBREF17. Note also that CoLa is not included in the upcoming version of GLUE BIBREF18. All fine-tuning experiments follow the parameters reported in the original study (a batch size of 32 and 3 epochs, see devlin2018bert).",
+      "In all these experiments, for a given input, we extract self-attention weights for each head in every layer. This results in a 2D float array of shape $L\\times L$, where $L$ is the length of an input sequence. We will refer to such arrays as self-attention maps. Analysis of individual self-attention maps allows us to determine which target tokens are attended to the most as the input is processed token by token. We use these experiments to analyze how BERT processes different kinds of linguistic information, including the processing of different parts of speech (nouns, pronouns, and verbs), syntactic roles (objects, subjects), semantic relations, and negation tokens."
+    ],
+    [
+      "In this section, we present the experiments conducted to address the above research questions."
+    ],
+    [
+      "Manual inspection of self-attention maps for both basic pre-trained and fine-tuned BERT models suggested that there is a limited set of self-attention maps types that are repeatedly encoded across different heads. Consistently with previous observations, we identified five frequently occurring patterns, examples of which are shown in fig:atttypes:",
+      "Vertical: mainly corresponds to attention to special BERT tokens [CLS] and [SEP];",
+      "Diagonal: formed by the attention to the previous/following tokens;",
+      "Vertical+Diagonal: a mix of the previous two types,",
+      "Block: intra-sentence attention for the tasks with two distinct sentences (such as, for example, RTE or MRPC),",
+      "Heterogeneous: highly variable depending on the specific input and cannot be characterized by a distinct structure.",
+      "Whereas the attention to the special tokens is important for cross-sentence reasoning, and the attention to the previous/following token comes from language model pre-training, we hypothesize that the last of the listed types is more likely to capture interpretable linguistic features, necessary for language understanding.",
+      "To get a rough estimate of the percentage of attention heads that may capture linguistically interpretable information, we manually annotated around 400 sample self-attention maps as belonging to one of the five classes. The self-attention maps were obtained by feeding random input examples from selected tasks into the corresponding fine-tuned BERT model. This produced a somewhat unbalanced dataset, in which the \u201cVertical\u201d class accounted for 30% of all samples. We then trained a convolutional neural network with 8 convolutional layers and ReLU activation functions to classify input maps into one of these classes. This model achieved the F1 score of 0.86 on the annotated dataset. We used this classifier to estimate the proportion of different self-attention patterns for the target GLUE tasks using up to 1000 examples (where available) from each validation set."
+    ],
+    [
+      "fig:attentionbydataset shows that the self-attention map types described above are consistently repeated across different heads and tasks. While a large portion of encoded information corresponds to attention to the previous/following token, to the special tokens, or a mixture of the two (the first three classes), the estimated upper bound on all heads in the \u201cHeterogeneous\u201d category (i.e. the ones that could be informative) varies from 32% (MRPC) to 61% (QQP) depending on the task.",
+      "We would like to emphasize that this only gives the upper bound on the percentage of attention heads that could potentially capture meaningful structural information beyond adjacency and separator tokens."
+    ],
+    [
+      "In this experiment, our goal was to understand whether different syntactic and semantic relations are captured by self-attention patterns. While a large number of such relations could be investigated, we chose to examine semantic role relations defined in frame semantics, since they can be viewed as being at the intersection of syntax and semantics. Specifically, we focused on whether BERT captures FrameNet's relations between frame-evoking lexical units (predicates) and core frame elements BIBREF19, and whether the links between them produce higher attention weights in certain specific heads. We used pre-trained BERT in these experiments.",
+      "The data for this experiment comes from FrameNet BIBREF19, a database that contains frame annotations for example sentences for different lexical units. Frame elements correspond to semantic roles for a given frame, for example, \u201cbuyer\", \u201cseller\", and \u201cgoods\u201d for the \u201cCommercial_transaction\" frame evoked by the words \u201csell\u201d and \u201cspend\u201d or \u201ctopic\u201d and \u201ctext\u201d for the \u201cScrutiny\u201d semantic frame evoked by the verb \u201caddress\u201d. fig:framenet shows an example of such annotation.",
+      "We extracted sample sentences for every lexical unit in the database and identified the corresponding core frame elements. Annotated elements in FrameNet may be rather long, so we considered only the sentences with frame elements of 3 tokens or less. Since each sentences is annotated only for one frame, semantic links from other frames can exist between unmarked elements. We therefore filter out all the sentences longer than 12 tokens, since shorter sentences are less likely to evoke multiple frames.",
+      "To establish whether BERT attention captures semantic relations that do not simply correspond to the previous/following token, we exclude sentences where the linked objects are less than two tokens apart. This leaves us with 473 annotated sentences.",
+      "For each of these sentences, we obtain pre-trained BERT's attention weights for each of the 144 heads. For every head, we return the maximum absolute attention weight among those token pairs that correspond to the annotated semantic link contained within a given sentence. We then average the derived scores over all the collected examples. This strategy allows us to identify the heads that prioritize the features correlated with frame-semantic relations within a sentence."
+    ],
+    [
+      "The heatmap of averaged attention scores over all collected examples (fig:framenetresults) suggests that 2 out of 144 heads tend to attend to the parts of the sentence that FrameNet annotators identified as core elements of the same frame. fig:framenetresults shows an example of this attention pattern for these two heads. Both show high attention weight for \u201che\u201d while processing \u201cagitated\u201d in the sentence \u201cHe was becoming agitated\" (the frame \u201cEmotion_directed\u201d)."
+    ],
+    [
+      "Fine-tuning has a huge effect on performance, and this section attempts to find out why. To study how attention per head changes on average for each of the target GLUE tasks, we calculate cosine similarity between pre-trained and fine-tuned BERT's flattened arrays of attention weights. We average the derived similarities over all the development set examples. To evaluate contribution of pre-trained BERT to overall performance on the tasks, we consider two configurations of weights initialization, namely, pre-trained BERT weights and weights randomly sampled from normal distribution."
+    ],
+    [
+      "fig:cosine shows that for all the tasks except QQP, it is the last two layers that undergo the largest changes compared to the pre-trained BERT model. At the same time, tab:glue-results shows that fine-tuned BERT outperforms pre-trained BERT by a significant margin on all the tasks (with an average of 35.9 points of absolute difference). This leads us to conclude that the last two layers encode task-specific features that are attributed to the gain of scores, while earlier layers capture more fundamental and low-level information used in fine-tuned models. Randomly initialized BERT consistently produces lower scores than the ones achieved with pre-trained BERT. In fact, for some tasks (STS-B and QNLI), initialization with random weights gives worse performance that that of pre-trained BERT alone without fine-tuning. This suggests that pre-trained BERT does indeed contain linguistic knowledge that is helpful for solving these GLUE tasks. These results are consistent with similar studies, e.g., BIBREF20's results on fine-tuning a convolutional neural network pre-trained on ImageNet or BIBREF21's results on transfer learning for medical natural language inference."
+    ],
+    [
+      "In this experiment, we investigate whether fine-tuning BERT for a given task creates self-attention patterns which emphasize specific linguistic features. In this case, certain kinds of tokens may get high attention weights from all the other tokens in the sentence, producing vertical stripes on the corresponding attention maps (fig:atttypes).",
+      "We tested this hypothesis by checking whether there are vertical stripe patterns corresponding to certain linguistically interpretable features, and to what extent such features are relevant for solving a given task. In particular, we investigated attention to nouns, verbs, pronouns, subjects, objects, and negation words, and special BERT tokens across the tasks.",
+      "For every head, we compute the sum of self-attention weights assigned to the token of interest from each input token. Since the weights depend on the number of tokens in the input sequence, this sum is normalized by sequence length. This allows us to aggregate the weights for this feature across different examples. If there are multiple tokens of the same type (e.g. several nouns or negations), we take the maximum value. We disregard input sentences that do not contain a given feature.",
+      "For each investigated feature, we calculate this aggregated attention score for each head in every layer and build a map in order to detect the heads potentially responsible for this feature. We then compare the obtained maps to the ones derived using the pre-trained BERT model. This comparison enables us to determine if a particular feature is important for a specific task and whether it contributes to some tasks more than to others."
+    ],
+    [
+      "Contrary to our initial hypothesis that the vertical attention pattern may be motivated by linguistically meaningful features, we found that it is associated predominantly, if not exclusively, with attention to [CLS] and [SEP] tokens (see Figure FIGREF32. Note that the absolute [SEP] weights for the SST-2 sentiment analysis task are greater than for other tasks, which is explained by the fact that there is only one sentence in the model inputs, i.e. only one [SEP] token instead of two. There is also a clear tendency for earlier layers to pay attention to [CLS] and for later layers to [SEP], and this trend is consistent across all the tasks. We did detect heads that paid increased (compared to the pre-trained BERT) attention to nouns and direct objects of the main predicates (on the MRPC, RTE and QQP tasks), and negation tokens (on the QNLI task), but the attention weights of such tokens were negligible compared to [CLS] and [SEP]. Therefore, we believe that the striped attention maps generally come from BERT pre-training tasks rather than from task-specific linguistic reasoning."
+    ],
+    [
+      "To complement the experiments in Sec. SECREF34 and SECREF25, in this section, we investigate the attention patterns between tokens in the same sentence, i.e. whether any of the tokens are particularly important while a given token is being processed. We were interested specifically in the verb-subject relation and the noun-pronoun relation. Also, since BERT uses the representation of the [CLS] token in the last layer to make the prediction, we used the features from the experiment in Sec. SECREF34 in order to check if they get higher attention weights while the model is processing the [CLS] token."
+    ],
+    [
+      "Our token-to-token attention experiments for detecting heads that prioritize noun-pronoun and verb-subject links resulted in a set of potential head candidates that coincided with diagonally structured attention maps. We believe that this happened due to the inherent property of English syntax where the dependent elements frequently appear close to each other, so it is difficult to distinguish such relations from the previous/following token attention coming from language model pre-training.",
+      "Our investigation of attention distribution for the [CLS] token in the output layer suggests that for most tasks, with the exception of STS-B, RTE and QNLI, the [SEP] gets attended the most, as shown in fig:cls. Based on manual inspection, for the mentioned remaining tasks, the greatest attention weights correspond to the punctuation tokens, which are in a sense similar to [SEP]."
+    ],
+    [
+      "Since there does seem to be a certain degree of specialization for different heads, we investigated the effects of disabling different heads in BERT and the resulting effects on task performance. Since BERT relies heavily on the learned attention weights, we define disabling a head as modifying the attention values of a head to be constant $a = \\frac{1}{L}$ for every token in the input sentence, where $L$ is the length of the sentence. Thus, every token receives the same attention, effectively disabling the learned attention patterns while maintaining the information flow of the original model. Note that by using this framework, we can disable an arbitrary number of heads, ranging from a single head per model to the whole layer or multiple layers."
+    ],
+    [
+      "Our experiments suggest that certain heads have a detrimental effect on the overall performance of BERT, and this trend holds for all the chosen tasks. Unexpectedly, disabling some heads leads not to a drop in accuracy, as one would expect, but to an increase in performance. This is effect is different across tasks and datasets. While disabling some heads improves the results, disabling the others hurts the results. However, it is important to note that across all tasks and datasets, disabling some heads leads to an increase in performance. The gain from disabling a single head is different for different tasks, ranging from the minimum absolute gain of 0.1% for STS-B, to the maximum of 1.2% for MRPC (see fig:disableheadsall). In fact, for some tasks, such as MRPC and RTE, disabling a random head gives, on average, an increase in performance. Furthermore, disabling a whole layer, that is, all 12 heads in a given layer, also improves the results. fig:disablelayers shows the resulting model performance on the target GLUE tasks when different layers are disabled. Notably, disabling the first layer in the RTE task gives a significant boost, resulting in an absolute performance gain of 3.2%. However, effects of this operation vary across tasks, and for QNLI and MNLI, it produces a performance drop of up to -0.2%."
+    ],
+    [
+      "In general, our results suggest that even the smaller base BERT model is significantly overparametrized. This is supported by the discovery of repeated self-attention patterns in different heads, as well as the fact that disabling both single and multiple heads is not detrimental to model performance and in some cases even improves it.",
+      "We found no evidence that attention patterns that are mappable onto core frame-semantic relations actually improve BERT's performance. 2 out of 144 heads that seem to be \u201cresponsible\" for these relations (see Section SECREF25) do not appear to be important in any of the GLUE tasks: disabling of either one does not lead to a drop of accuracy. This implies that fine-tuned BERT does not rely on this piece of semantic information and prioritizes other features instead. For instance, we noticed that both STS-B and RTE fine-tuned models rely on attention in the same pair of heads (head 1 in the fourth layer, and head 12 in the second layer), as shown in Figure FIGREF37. We manually checked the attention maps in those heads for a set of random inputs, and established that both of them have high weights for words that appear in both sentences of the input examples. This most likely means that word-by-word comparison of the two sentences provides a solid strategy of making a classification prediction for STS-B and RTE. Unfortunately, we were not able to provide a conceptually similar interpretation of heads important for other tasks."
+    ],
+    [
+      "In this work, we proposed a set of methods for analyzing self-attention mechanisms of BERT, comparing attention patterns for the pre-trained and fine-tuned versions of BERT.",
+      "Our most surprising finding is that, although attention is the key BERT's underlying mechanism, the model can benefit from attention \"disabling\". Moreover, we demonstrated that there is redundancy in the information encoded by different heads and the same patterns get consistently repeated regardless of the target task. We believe that these two findings together suggest a further direction for research on BERT interpretation, namely, model pruning and finding an optimal sub-architecture reducing data repetition.",
+      "Another direction for future work is to study self-attention patterns in a different language. We think that it would allow to disentangle attention maps potentially encoding linguistic information and heads that use simple heuristics like attending to the following/previous tokens."
+    ]
+  ]
+}
+```
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+Name of Paper: Exploring Hierarchical Interaction Between Review and Summary for Better Sentiment Analysis
+
+Question: Do they predict the sentiment of the review summary?
\ No newline at end of file
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+Name of Paper: Language-Agnostic Syllabification with Neural Sequence Labeling
+
+Question: What are the datasets used for the task?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Method",
+    "Method ::: Bidirectional LSTM",
+    "Method ::: CNN",
+    "Method ::: Output: Conditional Random Field",
+    "Method ::: Training",
+    "Materials",
+    "Materials ::: Software",
+    "Materials ::: Datasets",
+    "Experiments",
+    "Experiments ::: Data Cleaning",
+    "Experiments ::: Hyperparameter Specification",
+    "Experiments ::: Results",
+    "Discussion",
+    "Conclusion",
+    "Conclusion ::: Future Work",
+    "Acknowledgment"
+  ],
+  "paragraphs": [
+    [
+      "Words can be considered compositions of syllables, which in turn are compositions of phones. Phones are units of sound producible by the human vocal apparatus. Syllables play an important role in prosody and are influential components of natural language understanding, speech production, and speech recognition systems. Text-to-speech (TTS) systems can rely heavily on automatically syllabified phone sequences BIBREF0. One prominent example is Festival, an open source TTS system that relies on a syllabification algorithm to organize speech production BIBREF1.",
+      "Linguists have recognized since the late 1940s that the syllable is a hierarchical structure, present in most, if not all, languages (though there is some disagreement on this score. See, for example, BIBREF2). An optional consonant onset is followed by a rime, which may be further decomposed into a high sonority vowel nucleus followed by an optional consonant coda. All languages appear to have at least the single syllable vowel ($V$) and the two syllable vowel-consonant ($VC$) forms in their syllable inventories. For example, oh and so in English. Most languages supplement these with codas to form the $\\lbrace V, CV, VC, CVC\\rbrace $ syllable inventory. Sonority rises from the consonant onset to the vowel nucleus and falls toward the consonant coda, as in the English pig.",
+      "The components of the syllable obey the phonotactic constraints of the language in which they occur, and therein lies the question that motivates this research. Phonologists agree that the human vocal apparatus produces speech sounds that form a sonority hierarchy, from highest to lowest: vowels, glides, liquids, nasals, and obstruents. Examples are, come, twist, lack, ring, and cat, respectively. English, and other languages with complex syllable inventories, supplement the basic forms in ways that are usually consistent with the sonority hierarchy, where usually is the operative word. Thus, English permits double consonant onsets, as in twist with a consonant lower in the hierarchy (t, an obstruent) followed by a consonant one higher in the hierarchy (w, a glide). So sonority rises to the vowel, i, falls to the fricative, s, an obstruent, and falls further to another obstruent, t, still lower in the hierarchy. Yet p and w do not form a double consonant onset in English, probably because English avoids grouping sounds that use the same articulators, the lips, in this instance. Constructing an automatic syllabifier could be the process of encoding all rules such as these in the language under investigation. Another approach, one more congenial to the rising tide of so-called usage-based linguists (e.g, BIBREF3), is to recognize that the regularities of language formulated as rules can be usefully expressed as probabilities BIBREF4, BIBREF5, BIBREF6.",
+      "An automatic syllabifier is a computer program that, given a word as a sequence of phones, divides the word into its component syllables, where the syllables are legal in the language under investigation. Approaches take the form of dictionary-based look-up procedures, rule-based systems, data-driven systems, and hybrids thereof BIBREF7. Dictionary look-ups are limited to phone sequences previously seen and thus cannot handle new vocabulary BIBREF8. Rule-based approaches can process previously unseen phone sequences by encoding linguistic knowledge. Formalized language-specific rules are developed by hand, necessarily accompanied by many exceptions, such as the one noted in the previous paragraph. An important example is the syllabification package tsylb, developed at the National Institute of Standards and Technology (NIST), which is based on Daniel Kahn's 1979 MIT dissertation BIBREF9, BIBREF10. Language particularity is a stumbling block for rule-based and other formal approaches to language such as Optimality Theory (OT), however much they strive for universality. Thus, T.A. Hall argues that the OT approach to syllabification found in BIBREF11 is superior to previous OT research as well as to Kahn's rule-based work, because both postulate language-specific structures without cross-linguistic motivation. From Hall's perspective, previous systems do not capture important cross-linguistic features of the syllable. In a word, the earlier systems require kludges, an issue for both builders of automatic, language-agnostic syllabifiers and theoretical linguists like Hall.",
+      "Data-driven syllabification methods, like the one to be presented in this paper, have the potential to function across languages and to process new, out of dictionary words. For languages that have transcribed syllable data, data-driven approaches often outperform rule-based ones. BIBREF12 used a combined support vector machine (SVM) and hidden Markov model (HMM) to maximize the classification margin between a correct and incorrect syllable boundary. BIBREF13 used segmental conditional random fields (SCRF). The SCRF hybrid method statistically leveraged general principles of syllabification such as legality, sonority and maximal onset. Many other HMM-based labeling structures exist, such as evolved phonetic categorization and high order n-gram models with back-off BIBREF14, BIBREF15.",
+      "Data-driven models are evaluated by word accuracy against transcribed datasets. Commonly, only one language or languages of the same family are used. The CELEX lexical database from BIBREF16 contains syllabifications of phone sequences for English, Dutch, and German. These three languages fall into the West Germanic language family, so the phonologies of each are closely related. Evaluating a model solely on these three languages, the approach taken in BIBREF13 and others, does not adequately test a model's generalized ability to learn diverse syllable structures.",
+      "In this paper, we present a neural network that can syllabify phone sequences without introducing any fixed principles or rules of syllabification. We show that this novel approach to syllabification is language-agnostic by evaluating it on datasets of six languages, five from two major language families, and one that appears to be unrelated to any existing language."
+    ],
+    [
+      "Syllabification can be considered a sequence labeling task where each label delineates the existence or absence of a syllable boundary. As such, syllabification has much in common with well-researched topics such as part-of-speech tagging, named-entity recognition, and chunking BIBREF17. Neural networks have recently outpaced more traditional methods in sequence labeling tasks. These neural-based approaches are taking the place of HMMs, maximum entropy Markov models (MEMM), and conditional random fields (CRF) BIBREF18.",
+      "In the following section and in Fig. FIGREF1, we present a neural network architecture that leverages both recurrence and one-dimensional convolutions. Recurrence enables our model to read a sequence much like a human would; a sequence with elements $abcd$ would be read one element at a time, updating a latent understanding after reading each $a$, $b$, $c$, and finally $d$. One-dimensional convolutions extract a spatial relationship between sequential elements. The $abcd$ example sequence may then be read as $ab$, $bc$, $cd$. Explicitly recognizing this spatial relationship is beneficial in syllabification because a syllable is a local sub-sequence of phones within a word. The input to the model is a sequence of phones that together represent a word. We pad each phone sequence to a length of $n$ where $n$ is the length of the longest phone sequence. All inputs then take the form",
+      "Each phone $p_i$ is mapped to a $d$-dimensional embedding vector $x_i$ resulting in",
+      "where $x$ has a dimension of $d\\times n$. Taken together, the phone embeddings represent the relationships between phones in a real-valued vector space. The embedding dimension $d$ is optimized as a model hyperparameter and has a large impact on overall model performance BIBREF19. As such, we carefully tune $d$ for the proposed Base model and reduce it for our Small model as described in Section SECREF24.",
+      "The vector values of the phone embeddings are learned during each model training. Using learned embeddings enables the model to have a custom embedding space for each language that it is trained on. This is desirable because phonetic patterns differ from language to language. Also, learned embeddings allow the model to be trained using the input of any phonetic transcription. For example, one training of the model can use IPA and one can use SAMPA without needing to specify a mapping of one alphabet to another."
+    ],
+    [
+      "Recurrent neural networks (RNNs) differ from standard feed-forward neural networks in their treatment of input order; each element is processed given the context of the input that came before. RNNs operate on sequential data and can take many forms. Our network leverages the long short-term memory (LSTM) cell which is a prominent RNN variant capable of capturing long-term sequential dependencies BIBREF20. The gated memory cells of LSTM are an improvement over the standard RNN because the standard RNN is often biased toward short-term dependencies BIBREF21, BIBREF22. At each time step, the LSTM cell determines what information is important to introduce, to keep, and to output. This is done using an input gate, a forget gate, and an output gate shown in Fig. FIGREF5. LSTM operates in a single direction through time. This can be a limitation when a time step has both past dependency and future dependency. For example, a consonant sound may be the coda of a syllable earlier in the sequence or the onset of a syllable later in the sequence. Thus, processing a phonetic sequence in both the forward and backwards directions provides an improved context for assigning syllable boundaries. A bidirectional LSTM (BiLSTM) is formed when an LSTM moving forward through time is concatenated with an LSTM moving backward through time BIBREF23.",
+      "We use the LSTM network as follows. The $x$ vector is fed through the LSTM network which outputs a vector $\\overrightarrow{h_i}$ for each time step $i$ from 0 to $n-1$. This is the forward LSTM. As we have access to the complete vector $x$, we can process a backward LSTM as well. This is done by computing a vector $\\overleftarrow{h_i}$ for each time step $i$ from $n-1$ to 0. Finally, we concatenate the backward LSTM with the forward LSTM:",
+      "Both $\\overrightarrow{h_i}$ and $\\overleftarrow{h_i}$ have a dimension of $l$, which is an optimized hyperparameter. The BiLSTM output $h$ thus has dimension $2l\\times n$."
+    ],
+    [
+      "Convolutional neural networks (CNNs) are traditionally used in computer vision, but perform well in many text processing tasks that benefit from position-invariant abstractions BIBREF24, BIBREF25. These abstractions depend exclusively on local neighboring features rather than the position of features in a global structure. According to a comparative study by BIBREF26, BiLSTMs tend to outperform CNNs in sequential tasks such as POS tagging, but CNNs tend to outperform BiLSTMs in global relation detection tasks such as keyphrase matching for question answering. We use both the BiLSTM and the CNN in our network so that the strengths of each are incorporated. CNNs have been combined with BiLSTMs to perform state-of-the-art sequence tagging in both POS tagging and NER. BIBREF27 used BiLSTMs to process the word sequence while each word's character sequence was processed with CNNs to provide a second representation. In textual syllabification, the only input is the phone sequence.",
+      "Both our BiLSTM and CNN components process the same input: the $x$ vector. We pad $x$ with $w-1$ $d$-dimensional zero vectors before $x_0$. A 1-dimensional convolutional filter of width $w$ processes a window $x_{i-w+1},...,x_i$ for all $i$ from 0 to $n-1$. To determine the output vector $c$, the convolutional filter performs a nonlinear weight and bias computation. Due to the padding of $x$, the resulting dimension of $c$ is $f\\times n$ where $f$ is the number of filters used. A 1-dimensional max pooling is performed over $c$ with a stride of 1 which keeps the dimensionality unaltered. The pool size is an optimized hyperparameter that determines how many adjacent elements are used in the $max$ operation. The convolutional and max pooling components can be repeated to compute higher-level abstractions. As the convolutional and max pooling output is conformant to the BiLSTM output, we can concatenate them to create a combined vector with dimension $(2l+f)\\times n$:"
+    ],
+    [
+      "We introduce a time-distributed fully connected layer over vector $o$, taking $o$ from a dimension of $(2l+f)\\times n$ down to a dimension of $2\\times n$. We do this because there are two class labels: either a syllable boundary or no syllable boundary. The output of the model is a sequence",
+      "When $y_i\\equiv 0$, there is no syllable boundary predicted to follow the phone $p_i$. When $y_i\\equiv 1$, there is a syllable boundary predicted to follow $p_i$. Intuitively, we seek an output sequence $y$ that gives the highest $p(y|o)$. One approach calculates the softmax for each $o_i$:",
+      "The softmax normalizes each $o_i$ to a probability distribution over the two discrete class labels. We can then model $p(y|o)$ by multiplying the maximum of each $s_i$ together:",
+      "When using the softmax, $p(y|o)$ is calculated under the limiting assumption that each $o_i$ is independent. To more accurately model $p(y|o)$, we replace the softmax classifier with a conditional random field (CRF) BIBREF28. Specifically, we use a linear-chain CRF which is a sequential model that leverages both past and future output tags to model the output probability. The linear-chain CRF can be considered a sequential generalization of logistic regression classifiers as well as a discriminative analogue of hidden Markov models because it models $p(y|o)$ directly instead of modeling $p(o|y)$ BIBREF29. Using sequence-level tag information with a CRF has been shown to improve tag accuracy in the related tasks of POS tagging, chunking, and NER BIBREF30, BIBREF31. We use a linear-chain CRF to model the conditional distribution directly:",
+      "where $Z(o)$ is the normalization function",
+      "and $\\theta $ is a learned parameter vector scaled by the set of transition feature functions $f$."
+    ],
+    [
+      "Training of the network parameters is performed using backpropagation. Using Keras, the backpropagation is automatically defined given the forward definition of the network. The defined loss function is sparse categorical cross entropy, in accordance with the real-valued probabilities given by the CRF output layer. Loss optimization is performed with the Adam optimizer BIBREF32. Adam was chosen because it adapts the learning rate on a parameter-to-parameter basis; strong convergence occurs at the end of optimization. Training is performed to a set number of epochs. Early stopping allows the network to conclude training if convergence is reached prior to reaching the epoch training limit BIBREF33."
+    ],
+    [
+      "The materials for this research comprises the software described above and several syllabified datasets."
+    ],
+    [
+      "The implementation of our model was adapted from an open source code library designed for general-purpose sequence tagging and made available by BIBREF37. The modifications to this code include adding data preparation scripts and changing the model architecture to reflect the network architecture described above. Our code is made publicly available for future research at https://github.com/jacobkrantz/lstm-syllabify."
+    ],
+    [
+      "To produce a language-agnostic syllabifier, it is crucial to test syllabification accuracy across different language families and language groupings within families. We selected six evaluation languages: English, Dutch, Italian, French, Basque, and Manipuri. These represent two language families (Indo-European, Sino-Tibetan), a language isolate thought to be unrelated to any existing language (Basque), and two different subfamilies within the Indo-European family (West Germanic, Romance). The primary constraint was the availability of syllabified datasets for training and testing. Table TABREF17 presents details of each dataset.",
+      "Among the six languages we evaluate with, both English and Dutch are notable for the availability of rich datasets of phonetic and syllabic transcriptions. These are found in the CELEX (Dutch Centre for Lexical Information) database BIBREF16. CELEX was built jointly by the University of Nijmegen, the Institute for Dutch Lexicology in Leiden, the Max Planck Institute for Psycholinguistics in Nijmegen, and the Institute for Perception Research in Eindhoven. CELEX is maintained by the Max Planck Institute for Psycholinguistics. The CELEX database contains information on orthography, phonology, morphology, syntax and word frequency. It also contains syllabified words in Dutch and English transcribed using SAM-PA, CELEX, CPA, and DISC notations. The first three are variations of the International Phonetic Alphabet (IPA), in that each uses a standard ASCII character to represent each IPA character. DISC is different than the other three in that it maps a distinct ASCII character to each phone in the sound systems of Dutch, English, and German BIBREF38. Different phonetic transcriptions are used in different datasets. Part of the strength of our proposed syllabifier is that every transcription can be used as-is without any additional modification to the syllabifier or the input sequences. The other datasets were hand-syllabified by linguists with the exception of the IIT-Guwahat dataset and the Festival dataset. Both IIT-Guwahat and Festival were initially syllabified with a naive algorithm and then each entry was confirmed or corrected by hand.",
+      "For each dataset used to evaluate the proposed model, we compare our results with published accuracies of existing syllabification systems. Table TABREF21 shows the performance of well known and state of the art syllabifiers for each dataset. Liang's hyphenation algorithm is commonly known for its usage in . The patgen program was used to learn the rules of syllable boundaries BIBREF39. What we call Entropy CRF is a method particular to Manipuri; a rule-based component estimates the entropy of phones and phone clusters while a data-driven CRF component treats syllabification as a sequence modeling task BIBREF35."
+    ],
+    [
+      "Each dataset used to evaluate the model was split into three groups: training, development, and test. Each training epoch iterated over the training set to optimize the model parameters. The development set was used to tune the hyperparameters of the model, such as the batch size and the phone embedding dimension. The test set was exclusively used for reporting the model accuracy. The datasets were split randomly by percentages 80 (training), 10 (development), and 10 (test). For the English CELEX dataset of $89,402$ words, this resulted in $71,522$ words for training and $8,940$ words for each development and training.",
+      "For each experiment, models were initialized with a random set of parameter weights. BIBREF37 showed that differences in random number generation produce statistically significant variances in the accuracy of LSTM-based models. Due to the stochastic nature of neural network training, we performed each experiment 20 times. We report model accuracy as a mean and standard deviation of these experiment repetitions."
+    ],
+    [
+      "Prior to splitting each dataset, a simple cleaning process had to be performed to remove unwanted entries. This cleaning involved removing all entries that had at least one other entry with the same word. It is important to note that two words being different does not necessitate a different pronunciation or syllabification. These entries with different words but same pronunciations were kept in the dataset. No other cleaning was needed for the datasets other than mapping the syllabified phone sequence to an input-target pair usable by our model for training and evaluation. This cleaning process contributes to the language-agnostic nature of this research. The simplicity of the cleaning process is enabled by the fact that the model is end to end; no external phonetic features are gathered, and any phonetic transcription can be accommodated in the training process."
+    ],
+    [
+      "For all experiments, models were trained with a batch size of 64. A limit of 120 epochs was imposed with early stopping after 10 unimproved epochs. Dropout was used for the input connection to the BiLSTM layer at $25\\%$ BIBREF41. The learned embeddings layer had dimension $d=300$. The LSTM outputs, $\\overrightarrow{h_i}$ and $\\overleftarrow{h_i}$, both had dimension $l=300$. The convolutional to max pooling component was repeated twice before concatenation with the BiLSTM output. 200 convolutional filters were used and each had a dimension of 3. Finally, when using the Adam optimizer, we scaled the gradient norm when it exceeded $1.0$ using the Keras clipnorm parameter. All training was performed on single GPU machines on Amazon Web Services (AWS) servers which provided more than enough compute power. The average training of a model on the English CELEX dataset took approximately 45 minutes to reach convergence."
+    ],
+    [
+      "We tested three model versions against all datasets. The model we call Base is the BiLSTM-CNN-CRF model described in Section SECREF2 with the associated hyperparameters. Another model, Small, uses the same architecture as Base but reduces the number of convolutional layers to 1, the convolutional filters to 40, the LSTM dimension $l$ to 50, and the phone embedding size $d$ to 100. We also tested a Base-Softmax model, which replaces the CRF output of the Base model with a softmax. A comparison of the results of these three models can be seen in Table TABREF25. This comparison empirically motivates the CRF output because Base almost always outperforms Base-Softmax. Of these three models, the Base model performed the best with the exception of the French and Manipuri datasets. The differences in the French results can be considered negligible because the accuracies are all near $100\\%$. The Small model performed best on Manipuri, which may suggest that reducing the number of parameters of the Base model leads to better accuracy on smaller datasets.",
+      "When comparing our model with previous syllabifiers, we consider the Base model exclusively. In Table TABREF26, a side-by-side comparison of our Base model to a selection of published syllabifiers shows that Base is near state-of-the art performance on English CELEX. For the Dutch dataset, we report an accuracy of $99.47 \\pm 0.04\\%$, which improves on the previously best-known accuracy of $99.16\\%$ from the HMM-SVM of BIBREF12. Best-known results are also obtained on the Italian, French, and Basque datasets. Our reported accuracy of $94.9 \\pm 0.3\\%$ on the Manipuri dataset is furthest from state of the art. We suspect this to be due to having limited amounts of training data; the $97.5\\%$ accurate system from BIBREF35 supplemented their data-driven approach with rules of syllabification."
+    ],
+    [
+      "Examples from the outputs of the Base model can give us insight into what the model does well and what types of words it struggles with. The total number of sounds across languages is vast, but not infinite, as Ladefoged and Maddieson's The Sounds of the the World's Languages demonstrates BIBREF42. Different languages choose different inventories from the total producible by the human vocal apparatus. Within a language, sounds and patterns of sound vary widely in frequency, though with considerable regularity. This regularity has led a generation of linguists to attempt to uncover rules that describe not only syntax, but sound as well. Chomsky and Halle's The Sound Pattern of English is the classic effort, first appearing in 1968 BIBREF43. It is not surprising that the earliest attempts to produce automatic syllabifiers were based on just such rule collections. Nor is it surprising that the best-known rule-based syllabifier was inspired by a doctoral dissertation at MIT, Noam Chomsky's home institution for five decades. An alternative approach is to recognize that 1) rules can be reconceptualized as probabilities and 2) native speakers of a language have internalized those very probabilities. Nevertheless, where there is probability, there is ambiguity. With all of these caveats in mind, a few examples have been selected from our results to showcase the model as shown in Table TABREF27.",
+      "The syllabification of misinterpretation illustrates the model's ability to process longer words. Containing 14 phones and 5 syllables, this word demonstrates that the model's pattern finding technique works well regardless of the location of phonetic and syllabic patterns in the word. The model can accurately handle prefixes, correctly syllabifying mis- as Table TABREF27 shows. Another word is achieved. Inflected languages, such as English, use morphemes to distinguish mood, tense, case, and number, among others. Thus, the verb achieve has several forms, or conjugates. The syllabifier correctly detected the stem and the past tense morpheme, ed. An odd aspect of the English CELEX dataset is the occurrence of entries, $22,393$ of which, that either have hyphens or are multiple entirely separate words, such as public-address systems. Because the phonetic representation does not denote hyphens or whitespace, the model has difficulties processing these words."
+    ],
+    [
+      "We proposed a sequential neural network model that is capable of syllabifying phonetic sequences. This model is independent of any hand-crafted linguistic knowledge. We showed that this model performs at or near state of the art levels on a variety of datasets sampled from two Indo-European, one Sino-Tibetan, and an apparently family-less language. Specifically, the proposed model achieved accuracies higher than any other we could find on datasets from Dutch, Italian, French, and Basque languages and close to the best-reported accuracy for English and Manipuri. Evaluating the performance of the syllabifier across diverse languages provides strong evidence that the proposed model is language-agnostic."
+    ],
+    [
+      "With a language-agnostic syllabification system, any language can be syllabified given enough labeled training data. A problem is that many languages do not have large, labeled syllabification datasets. For example, we failed to find available and sufficient datasets in the Slavic languages of Russian and Serbian. This problem can be addressed either in a concentrated effort to create more labeled data or in the development of systems that require limited data."
+    ],
+    [
+      "This research was supported in part by a Gonzaga University McDonald Work Award by Robert and Claire McDonald and an Amazon Web Services (AWS) grant through the Cloud Credits for Research program."
+    ]
+  ]
+}
+```
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+Name of Paper: Language-Agnostic Syllabification with Neural Sequence Labeling
+
+Question: Which models achieve state-of-the-art performances?
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+Name of Paper: Language-Agnostic Syllabification with Neural Sequence Labeling
+
+Question: Is the LSTM bidirectional?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Method",
+    "Method ::: Bidirectional LSTM",
+    "Method ::: CNN",
+    "Method ::: Output: Conditional Random Field",
+    "Method ::: Training",
+    "Materials",
+    "Materials ::: Software",
+    "Materials ::: Datasets",
+    "Experiments",
+    "Experiments ::: Data Cleaning",
+    "Experiments ::: Hyperparameter Specification",
+    "Experiments ::: Results",
+    "Discussion",
+    "Conclusion",
+    "Conclusion ::: Future Work",
+    "Acknowledgment"
+  ],
+  "paragraphs": [
+    [
+      "Words can be considered compositions of syllables, which in turn are compositions of phones. Phones are units of sound producible by the human vocal apparatus. Syllables play an important role in prosody and are influential components of natural language understanding, speech production, and speech recognition systems. Text-to-speech (TTS) systems can rely heavily on automatically syllabified phone sequences BIBREF0. One prominent example is Festival, an open source TTS system that relies on a syllabification algorithm to organize speech production BIBREF1.",
+      "Linguists have recognized since the late 1940s that the syllable is a hierarchical structure, present in most, if not all, languages (though there is some disagreement on this score. See, for example, BIBREF2). An optional consonant onset is followed by a rime, which may be further decomposed into a high sonority vowel nucleus followed by an optional consonant coda. All languages appear to have at least the single syllable vowel ($V$) and the two syllable vowel-consonant ($VC$) forms in their syllable inventories. For example, oh and so in English. Most languages supplement these with codas to form the $\\lbrace V, CV, VC, CVC\\rbrace $ syllable inventory. Sonority rises from the consonant onset to the vowel nucleus and falls toward the consonant coda, as in the English pig.",
+      "The components of the syllable obey the phonotactic constraints of the language in which they occur, and therein lies the question that motivates this research. Phonologists agree that the human vocal apparatus produces speech sounds that form a sonority hierarchy, from highest to lowest: vowels, glides, liquids, nasals, and obstruents. Examples are, come, twist, lack, ring, and cat, respectively. English, and other languages with complex syllable inventories, supplement the basic forms in ways that are usually consistent with the sonority hierarchy, where usually is the operative word. Thus, English permits double consonant onsets, as in twist with a consonant lower in the hierarchy (t, an obstruent) followed by a consonant one higher in the hierarchy (w, a glide). So sonority rises to the vowel, i, falls to the fricative, s, an obstruent, and falls further to another obstruent, t, still lower in the hierarchy. Yet p and w do not form a double consonant onset in English, probably because English avoids grouping sounds that use the same articulators, the lips, in this instance. Constructing an automatic syllabifier could be the process of encoding all rules such as these in the language under investigation. Another approach, one more congenial to the rising tide of so-called usage-based linguists (e.g, BIBREF3), is to recognize that the regularities of language formulated as rules can be usefully expressed as probabilities BIBREF4, BIBREF5, BIBREF6.",
+      "An automatic syllabifier is a computer program that, given a word as a sequence of phones, divides the word into its component syllables, where the syllables are legal in the language under investigation. Approaches take the form of dictionary-based look-up procedures, rule-based systems, data-driven systems, and hybrids thereof BIBREF7. Dictionary look-ups are limited to phone sequences previously seen and thus cannot handle new vocabulary BIBREF8. Rule-based approaches can process previously unseen phone sequences by encoding linguistic knowledge. Formalized language-specific rules are developed by hand, necessarily accompanied by many exceptions, such as the one noted in the previous paragraph. An important example is the syllabification package tsylb, developed at the National Institute of Standards and Technology (NIST), which is based on Daniel Kahn's 1979 MIT dissertation BIBREF9, BIBREF10. Language particularity is a stumbling block for rule-based and other formal approaches to language such as Optimality Theory (OT), however much they strive for universality. Thus, T.A. Hall argues that the OT approach to syllabification found in BIBREF11 is superior to previous OT research as well as to Kahn's rule-based work, because both postulate language-specific structures without cross-linguistic motivation. From Hall's perspective, previous systems do not capture important cross-linguistic features of the syllable. In a word, the earlier systems require kludges, an issue for both builders of automatic, language-agnostic syllabifiers and theoretical linguists like Hall.",
+      "Data-driven syllabification methods, like the one to be presented in this paper, have the potential to function across languages and to process new, out of dictionary words. For languages that have transcribed syllable data, data-driven approaches often outperform rule-based ones. BIBREF12 used a combined support vector machine (SVM) and hidden Markov model (HMM) to maximize the classification margin between a correct and incorrect syllable boundary. BIBREF13 used segmental conditional random fields (SCRF). The SCRF hybrid method statistically leveraged general principles of syllabification such as legality, sonority and maximal onset. Many other HMM-based labeling structures exist, such as evolved phonetic categorization and high order n-gram models with back-off BIBREF14, BIBREF15.",
+      "Data-driven models are evaluated by word accuracy against transcribed datasets. Commonly, only one language or languages of the same family are used. The CELEX lexical database from BIBREF16 contains syllabifications of phone sequences for English, Dutch, and German. These three languages fall into the West Germanic language family, so the phonologies of each are closely related. Evaluating a model solely on these three languages, the approach taken in BIBREF13 and others, does not adequately test a model's generalized ability to learn diverse syllable structures.",
+      "In this paper, we present a neural network that can syllabify phone sequences without introducing any fixed principles or rules of syllabification. We show that this novel approach to syllabification is language-agnostic by evaluating it on datasets of six languages, five from two major language families, and one that appears to be unrelated to any existing language."
+    ],
+    [
+      "Syllabification can be considered a sequence labeling task where each label delineates the existence or absence of a syllable boundary. As such, syllabification has much in common with well-researched topics such as part-of-speech tagging, named-entity recognition, and chunking BIBREF17. Neural networks have recently outpaced more traditional methods in sequence labeling tasks. These neural-based approaches are taking the place of HMMs, maximum entropy Markov models (MEMM), and conditional random fields (CRF) BIBREF18.",
+      "In the following section and in Fig. FIGREF1, we present a neural network architecture that leverages both recurrence and one-dimensional convolutions. Recurrence enables our model to read a sequence much like a human would; a sequence with elements $abcd$ would be read one element at a time, updating a latent understanding after reading each $a$, $b$, $c$, and finally $d$. One-dimensional convolutions extract a spatial relationship between sequential elements. The $abcd$ example sequence may then be read as $ab$, $bc$, $cd$. Explicitly recognizing this spatial relationship is beneficial in syllabification because a syllable is a local sub-sequence of phones within a word. The input to the model is a sequence of phones that together represent a word. We pad each phone sequence to a length of $n$ where $n$ is the length of the longest phone sequence. All inputs then take the form",
+      "Each phone $p_i$ is mapped to a $d$-dimensional embedding vector $x_i$ resulting in",
+      "where $x$ has a dimension of $d\\times n$. Taken together, the phone embeddings represent the relationships between phones in a real-valued vector space. The embedding dimension $d$ is optimized as a model hyperparameter and has a large impact on overall model performance BIBREF19. As such, we carefully tune $d$ for the proposed Base model and reduce it for our Small model as described in Section SECREF24.",
+      "The vector values of the phone embeddings are learned during each model training. Using learned embeddings enables the model to have a custom embedding space for each language that it is trained on. This is desirable because phonetic patterns differ from language to language. Also, learned embeddings allow the model to be trained using the input of any phonetic transcription. For example, one training of the model can use IPA and one can use SAMPA without needing to specify a mapping of one alphabet to another."
+    ],
+    [
+      "Recurrent neural networks (RNNs) differ from standard feed-forward neural networks in their treatment of input order; each element is processed given the context of the input that came before. RNNs operate on sequential data and can take many forms. Our network leverages the long short-term memory (LSTM) cell which is a prominent RNN variant capable of capturing long-term sequential dependencies BIBREF20. The gated memory cells of LSTM are an improvement over the standard RNN because the standard RNN is often biased toward short-term dependencies BIBREF21, BIBREF22. At each time step, the LSTM cell determines what information is important to introduce, to keep, and to output. This is done using an input gate, a forget gate, and an output gate shown in Fig. FIGREF5. LSTM operates in a single direction through time. This can be a limitation when a time step has both past dependency and future dependency. For example, a consonant sound may be the coda of a syllable earlier in the sequence or the onset of a syllable later in the sequence. Thus, processing a phonetic sequence in both the forward and backwards directions provides an improved context for assigning syllable boundaries. A bidirectional LSTM (BiLSTM) is formed when an LSTM moving forward through time is concatenated with an LSTM moving backward through time BIBREF23.",
+      "We use the LSTM network as follows. The $x$ vector is fed through the LSTM network which outputs a vector $\\overrightarrow{h_i}$ for each time step $i$ from 0 to $n-1$. This is the forward LSTM. As we have access to the complete vector $x$, we can process a backward LSTM as well. This is done by computing a vector $\\overleftarrow{h_i}$ for each time step $i$ from $n-1$ to 0. Finally, we concatenate the backward LSTM with the forward LSTM:",
+      "Both $\\overrightarrow{h_i}$ and $\\overleftarrow{h_i}$ have a dimension of $l$, which is an optimized hyperparameter. The BiLSTM output $h$ thus has dimension $2l\\times n$."
+    ],
+    [
+      "Convolutional neural networks (CNNs) are traditionally used in computer vision, but perform well in many text processing tasks that benefit from position-invariant abstractions BIBREF24, BIBREF25. These abstractions depend exclusively on local neighboring features rather than the position of features in a global structure. According to a comparative study by BIBREF26, BiLSTMs tend to outperform CNNs in sequential tasks such as POS tagging, but CNNs tend to outperform BiLSTMs in global relation detection tasks such as keyphrase matching for question answering. We use both the BiLSTM and the CNN in our network so that the strengths of each are incorporated. CNNs have been combined with BiLSTMs to perform state-of-the-art sequence tagging in both POS tagging and NER. BIBREF27 used BiLSTMs to process the word sequence while each word's character sequence was processed with CNNs to provide a second representation. In textual syllabification, the only input is the phone sequence.",
+      "Both our BiLSTM and CNN components process the same input: the $x$ vector. We pad $x$ with $w-1$ $d$-dimensional zero vectors before $x_0$. A 1-dimensional convolutional filter of width $w$ processes a window $x_{i-w+1},...,x_i$ for all $i$ from 0 to $n-1$. To determine the output vector $c$, the convolutional filter performs a nonlinear weight and bias computation. Due to the padding of $x$, the resulting dimension of $c$ is $f\\times n$ where $f$ is the number of filters used. A 1-dimensional max pooling is performed over $c$ with a stride of 1 which keeps the dimensionality unaltered. The pool size is an optimized hyperparameter that determines how many adjacent elements are used in the $max$ operation. The convolutional and max pooling components can be repeated to compute higher-level abstractions. As the convolutional and max pooling output is conformant to the BiLSTM output, we can concatenate them to create a combined vector with dimension $(2l+f)\\times n$:"
+    ],
+    [
+      "We introduce a time-distributed fully connected layer over vector $o$, taking $o$ from a dimension of $(2l+f)\\times n$ down to a dimension of $2\\times n$. We do this because there are two class labels: either a syllable boundary or no syllable boundary. The output of the model is a sequence",
+      "When $y_i\\equiv 0$, there is no syllable boundary predicted to follow the phone $p_i$. When $y_i\\equiv 1$, there is a syllable boundary predicted to follow $p_i$. Intuitively, we seek an output sequence $y$ that gives the highest $p(y|o)$. One approach calculates the softmax for each $o_i$:",
+      "The softmax normalizes each $o_i$ to a probability distribution over the two discrete class labels. We can then model $p(y|o)$ by multiplying the maximum of each $s_i$ together:",
+      "When using the softmax, $p(y|o)$ is calculated under the limiting assumption that each $o_i$ is independent. To more accurately model $p(y|o)$, we replace the softmax classifier with a conditional random field (CRF) BIBREF28. Specifically, we use a linear-chain CRF which is a sequential model that leverages both past and future output tags to model the output probability. The linear-chain CRF can be considered a sequential generalization of logistic regression classifiers as well as a discriminative analogue of hidden Markov models because it models $p(y|o)$ directly instead of modeling $p(o|y)$ BIBREF29. Using sequence-level tag information with a CRF has been shown to improve tag accuracy in the related tasks of POS tagging, chunking, and NER BIBREF30, BIBREF31. We use a linear-chain CRF to model the conditional distribution directly:",
+      "where $Z(o)$ is the normalization function",
+      "and $\\theta $ is a learned parameter vector scaled by the set of transition feature functions $f$."
+    ],
+    [
+      "Training of the network parameters is performed using backpropagation. Using Keras, the backpropagation is automatically defined given the forward definition of the network. The defined loss function is sparse categorical cross entropy, in accordance with the real-valued probabilities given by the CRF output layer. Loss optimization is performed with the Adam optimizer BIBREF32. Adam was chosen because it adapts the learning rate on a parameter-to-parameter basis; strong convergence occurs at the end of optimization. Training is performed to a set number of epochs. Early stopping allows the network to conclude training if convergence is reached prior to reaching the epoch training limit BIBREF33."
+    ],
+    [
+      "The materials for this research comprises the software described above and several syllabified datasets."
+    ],
+    [
+      "The implementation of our model was adapted from an open source code library designed for general-purpose sequence tagging and made available by BIBREF37. The modifications to this code include adding data preparation scripts and changing the model architecture to reflect the network architecture described above. Our code is made publicly available for future research at https://github.com/jacobkrantz/lstm-syllabify."
+    ],
+    [
+      "To produce a language-agnostic syllabifier, it is crucial to test syllabification accuracy across different language families and language groupings within families. We selected six evaluation languages: English, Dutch, Italian, French, Basque, and Manipuri. These represent two language families (Indo-European, Sino-Tibetan), a language isolate thought to be unrelated to any existing language (Basque), and two different subfamilies within the Indo-European family (West Germanic, Romance). The primary constraint was the availability of syllabified datasets for training and testing. Table TABREF17 presents details of each dataset.",
+      "Among the six languages we evaluate with, both English and Dutch are notable for the availability of rich datasets of phonetic and syllabic transcriptions. These are found in the CELEX (Dutch Centre for Lexical Information) database BIBREF16. CELEX was built jointly by the University of Nijmegen, the Institute for Dutch Lexicology in Leiden, the Max Planck Institute for Psycholinguistics in Nijmegen, and the Institute for Perception Research in Eindhoven. CELEX is maintained by the Max Planck Institute for Psycholinguistics. The CELEX database contains information on orthography, phonology, morphology, syntax and word frequency. It also contains syllabified words in Dutch and English transcribed using SAM-PA, CELEX, CPA, and DISC notations. The first three are variations of the International Phonetic Alphabet (IPA), in that each uses a standard ASCII character to represent each IPA character. DISC is different than the other three in that it maps a distinct ASCII character to each phone in the sound systems of Dutch, English, and German BIBREF38. Different phonetic transcriptions are used in different datasets. Part of the strength of our proposed syllabifier is that every transcription can be used as-is without any additional modification to the syllabifier or the input sequences. The other datasets were hand-syllabified by linguists with the exception of the IIT-Guwahat dataset and the Festival dataset. Both IIT-Guwahat and Festival were initially syllabified with a naive algorithm and then each entry was confirmed or corrected by hand.",
+      "For each dataset used to evaluate the proposed model, we compare our results with published accuracies of existing syllabification systems. Table TABREF21 shows the performance of well known and state of the art syllabifiers for each dataset. Liang's hyphenation algorithm is commonly known for its usage in . The patgen program was used to learn the rules of syllable boundaries BIBREF39. What we call Entropy CRF is a method particular to Manipuri; a rule-based component estimates the entropy of phones and phone clusters while a data-driven CRF component treats syllabification as a sequence modeling task BIBREF35."
+    ],
+    [
+      "Each dataset used to evaluate the model was split into three groups: training, development, and test. Each training epoch iterated over the training set to optimize the model parameters. The development set was used to tune the hyperparameters of the model, such as the batch size and the phone embedding dimension. The test set was exclusively used for reporting the model accuracy. The datasets were split randomly by percentages 80 (training), 10 (development), and 10 (test). For the English CELEX dataset of $89,402$ words, this resulted in $71,522$ words for training and $8,940$ words for each development and training.",
+      "For each experiment, models were initialized with a random set of parameter weights. BIBREF37 showed that differences in random number generation produce statistically significant variances in the accuracy of LSTM-based models. Due to the stochastic nature of neural network training, we performed each experiment 20 times. We report model accuracy as a mean and standard deviation of these experiment repetitions."
+    ],
+    [
+      "Prior to splitting each dataset, a simple cleaning process had to be performed to remove unwanted entries. This cleaning involved removing all entries that had at least one other entry with the same word. It is important to note that two words being different does not necessitate a different pronunciation or syllabification. These entries with different words but same pronunciations were kept in the dataset. No other cleaning was needed for the datasets other than mapping the syllabified phone sequence to an input-target pair usable by our model for training and evaluation. This cleaning process contributes to the language-agnostic nature of this research. The simplicity of the cleaning process is enabled by the fact that the model is end to end; no external phonetic features are gathered, and any phonetic transcription can be accommodated in the training process."
+    ],
+    [
+      "For all experiments, models were trained with a batch size of 64. A limit of 120 epochs was imposed with early stopping after 10 unimproved epochs. Dropout was used for the input connection to the BiLSTM layer at $25\\%$ BIBREF41. The learned embeddings layer had dimension $d=300$. The LSTM outputs, $\\overrightarrow{h_i}$ and $\\overleftarrow{h_i}$, both had dimension $l=300$. The convolutional to max pooling component was repeated twice before concatenation with the BiLSTM output. 200 convolutional filters were used and each had a dimension of 3. Finally, when using the Adam optimizer, we scaled the gradient norm when it exceeded $1.0$ using the Keras clipnorm parameter. All training was performed on single GPU machines on Amazon Web Services (AWS) servers which provided more than enough compute power. The average training of a model on the English CELEX dataset took approximately 45 minutes to reach convergence."
+    ],
+    [
+      "We tested three model versions against all datasets. The model we call Base is the BiLSTM-CNN-CRF model described in Section SECREF2 with the associated hyperparameters. Another model, Small, uses the same architecture as Base but reduces the number of convolutional layers to 1, the convolutional filters to 40, the LSTM dimension $l$ to 50, and the phone embedding size $d$ to 100. We also tested a Base-Softmax model, which replaces the CRF output of the Base model with a softmax. A comparison of the results of these three models can be seen in Table TABREF25. This comparison empirically motivates the CRF output because Base almost always outperforms Base-Softmax. Of these three models, the Base model performed the best with the exception of the French and Manipuri datasets. The differences in the French results can be considered negligible because the accuracies are all near $100\\%$. The Small model performed best on Manipuri, which may suggest that reducing the number of parameters of the Base model leads to better accuracy on smaller datasets.",
+      "When comparing our model with previous syllabifiers, we consider the Base model exclusively. In Table TABREF26, a side-by-side comparison of our Base model to a selection of published syllabifiers shows that Base is near state-of-the art performance on English CELEX. For the Dutch dataset, we report an accuracy of $99.47 \\pm 0.04\\%$, which improves on the previously best-known accuracy of $99.16\\%$ from the HMM-SVM of BIBREF12. Best-known results are also obtained on the Italian, French, and Basque datasets. Our reported accuracy of $94.9 \\pm 0.3\\%$ on the Manipuri dataset is furthest from state of the art. We suspect this to be due to having limited amounts of training data; the $97.5\\%$ accurate system from BIBREF35 supplemented their data-driven approach with rules of syllabification."
+    ],
+    [
+      "Examples from the outputs of the Base model can give us insight into what the model does well and what types of words it struggles with. The total number of sounds across languages is vast, but not infinite, as Ladefoged and Maddieson's The Sounds of the the World's Languages demonstrates BIBREF42. Different languages choose different inventories from the total producible by the human vocal apparatus. Within a language, sounds and patterns of sound vary widely in frequency, though with considerable regularity. This regularity has led a generation of linguists to attempt to uncover rules that describe not only syntax, but sound as well. Chomsky and Halle's The Sound Pattern of English is the classic effort, first appearing in 1968 BIBREF43. It is not surprising that the earliest attempts to produce automatic syllabifiers were based on just such rule collections. Nor is it surprising that the best-known rule-based syllabifier was inspired by a doctoral dissertation at MIT, Noam Chomsky's home institution for five decades. An alternative approach is to recognize that 1) rules can be reconceptualized as probabilities and 2) native speakers of a language have internalized those very probabilities. Nevertheless, where there is probability, there is ambiguity. With all of these caveats in mind, a few examples have been selected from our results to showcase the model as shown in Table TABREF27.",
+      "The syllabification of misinterpretation illustrates the model's ability to process longer words. Containing 14 phones and 5 syllables, this word demonstrates that the model's pattern finding technique works well regardless of the location of phonetic and syllabic patterns in the word. The model can accurately handle prefixes, correctly syllabifying mis- as Table TABREF27 shows. Another word is achieved. Inflected languages, such as English, use morphemes to distinguish mood, tense, case, and number, among others. Thus, the verb achieve has several forms, or conjugates. The syllabifier correctly detected the stem and the past tense morpheme, ed. An odd aspect of the English CELEX dataset is the occurrence of entries, $22,393$ of which, that either have hyphens or are multiple entirely separate words, such as public-address systems. Because the phonetic representation does not denote hyphens or whitespace, the model has difficulties processing these words."
+    ],
+    [
+      "We proposed a sequential neural network model that is capable of syllabifying phonetic sequences. This model is independent of any hand-crafted linguistic knowledge. We showed that this model performs at or near state of the art levels on a variety of datasets sampled from two Indo-European, one Sino-Tibetan, and an apparently family-less language. Specifically, the proposed model achieved accuracies higher than any other we could find on datasets from Dutch, Italian, French, and Basque languages and close to the best-reported accuracy for English and Manipuri. Evaluating the performance of the syllabifier across diverse languages provides strong evidence that the proposed model is language-agnostic."
+    ],
+    [
+      "With a language-agnostic syllabification system, any language can be syllabified given enough labeled training data. A problem is that many languages do not have large, labeled syllabification datasets. For example, we failed to find available and sufficient datasets in the Slavic languages of Russian and Serbian. This problem can be addressed either in a concentrated effort to create more labeled data or in the development of systems that require limited data."
+    ],
+    [
+      "This research was supported in part by a Gonzaga University McDonald Work Award by Robert and Claire McDonald and an Amazon Web Services (AWS) grant through the Cloud Credits for Research program."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: An Incremental Parser for Abstract Meaning Representation
+
+Question: Which subtasks do they evaluate on?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Transition-Based AMR Parsing",
+    "Transition system for AMR Parsing",
+    "Training the System",
+    "Oracle",
+    "Transition Classifier",
+    "Concept Identification",
+    "Edge Labeling",
+    "Fine-grained Evaluation",
+    "Experiments",
+    "Related Work",
+    "Conclusion",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "Semantic parsing aims to solve the problem of canonicalizing language and representing its meaning: given an input sentence, it aims to extract a semantic representation of that sentence. Abstract meaning representation BIBREF0 , or AMR for short, allows us to do that with the inclusion of most of the shallow-semantic natural language processing (NLP) tasks that are usually addressed separately, such as named entity recognition, semantic role labeling and co-reference resolution. AMR is partially motivated by the need to provide the NLP community with a single dataset that includes basic disambiguation information, instead of having to rely on different datasets for each disambiguation problem. The annotation process is straightforward, enabling the development of large datasets. Alternative semantic representations have been developed and studied, such as CCG BIBREF1 , BIBREF2 and UCCA BIBREF3 .",
+      "Several parsers for AMR have been recently developed BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 . This line of research is new and current results suggest a large room for improvement. Greedy transition-based methods BIBREF14 are one of the most popular choices for dependency parsing, because of their good balance between efficiency and accuracy. These methods seem promising also for AMR, due to the similarity between dependency trees and AMR structures, i.e., both representations use graphs with nodes that have lexical content and edges that represent linguistic relations.",
+      "A transition system is an abstract machine characterized by a set of configurations and transitions between them. The basic components of a configuration are a stack of partially processed words and a buffer of unseen input words. Starting from an initial configuration, the system applies transitions until a terminal configuration is reached. The sentence is scanned left to right, with linear time complexity for dependency parsing. This is made possible by the use of a greedy classifier that chooses the transition to be applied at each step.",
+      "In this paper we introduce a parser for AMR that is inspired by the ArcEager dependency transition system of nivre2004. The main difference between our system and ArcEager is that we need to account for the mapping from word tokens to AMR nodes, non-projectivity of AMR structures and reentrant nodes (multiple incoming edges). Our AMR parser brings closer dependency parsing and AMR parsing by showing that dependency parsing algorithms, with some modifications, can be used for AMR. Key properties such as working left-to-right, incrementality and linear complexity further strengthen its relevance.",
+      "The AMR parser of wang2boosting, called CAMR, also defines a transition system. It differs from ours because we process the sentence left-to-right while they first acquire the entire dependency tree and then process it bottom-up. More recently emnlp2016 presented a non-greedy transition system for AMR parsing, based on ArcStandard BIBREF15 . Our transition system is also related to an adaptation of ArcEager for directed acyclic graphs (DAGs), introduced by sagae2008shift. This is also the basis for ribeyre2015because, a transition system used to parse dependency graphs. Similarly, du2014peking also address dependency graph parsing by means of transition systems. Analogously to dependency trees, dependency graphs have the property that their nodes consist of the word tokens, which is not true for AMR. As such, these transition systems are more closely related to traditional transition systems for dependency parsing.",
+      "Our contributions in this paper are as follows:"
+    ],
+    [
+      "Similarly to dependency parsing, AMR parsing is partially based on the identification of predicate-argument structures. Much of the dependency parsing literature focuses on transition-based dependency parsing\u2014an approach to parsing that scans the sentence from left to right in linear time and updates an intermediate structure that eventually ends up being a dependency tree.",
+      "The two most common transition systems for greedy dependency parsing are ArcStandard and ArcEager. With ArcStandard, a stack is maintained along with a buffer on which the left-to-right scan is performed. At each step, the parser chooses to scan a word in the buffer and shift it onto the stack, or else to create an arc between the two top-most elements in the stack and pop the dependent. ArcStandard parses a sentence in a pure bottom-up, left-to-right fashion (similarly to shift-reduce context-free grammar parsers), and must delay the construction of right arcs until all the dependent node has been completed. This imposes strong limitations on the degree of incrementality of the parser. The ArcEager system was designed to improve on ArcStandard by mixing bottom up and top-down strategies. More precisely, in the ArcEager parser left arcs are constructed bottom-up and right arcs are constructed top-down, so that right dependents can be attached to their heads even if some of their own dependents are not identified yet. In this way arcs are constructed as soon as the head and the dependent are available in the stack.",
+      "Because of the similarity of AMR structures to dependency structures, transition systems are also helpful for AMR parsing. Starting from the ArcEager system, we develop here a novel transition system, called AmrEager that parses sentences into AMR structures. There are three key differences between AMRs and dependency trees that require further adjustments for dependency parsers to be used with AMRs.",
+      "A key difference between English dependency trees and AMR structures is projectivity. Dependency trees in English are usually projective, roughly meaning that there are no crossing arcs if the edges are drawn in the semi-plane above the words. While this restriction is empirically motivated in syntactic theories for English, it is no longer motivated for AMR structures.",
+      "The notion of projectivity can be generalized to AMR graphs as follows. The intuition is that we can use the alignment INLINEFORM0 to map AMR edges back to the sentence INLINEFORM1 , and test whether there exist pairs of crossing edges. Figure FIGREF13 shows this mapping for the AMR of Figure FIGREF7 , where the edge connecting excuse to I crosses another edge. More formally, consider an AMR edge INLINEFORM2 . Let INLINEFORM3 and INLINEFORM4 , so that INLINEFORM5 is aligned with INLINEFORM6 and INLINEFORM7 is aligned with INLINEFORM8 . The spanning set for INLINEFORM9 , written INLINEFORM10 , is the set of all nodes INLINEFORM11 such that INLINEFORM12 and INLINEFORM13 if INLINEFORM14 or INLINEFORM15 if INLINEFORM16 . We say that INLINEFORM17 is projective if, for every node INLINEFORM18 , all of its parent and child nodes are in INLINEFORM19 ; otherwise, we say that INLINEFORM20 is non-projective. An AMR is projective if all of its edges are projective, and is non-projective otherwise. This corresponds to the intuitive definition of projectivity for DAGs introduced in sagae2008shift and is closely related to the definition of non-crossing graphs of kuhlmann2015parsing.",
+      "Table TABREF15 demonstrates that a relatively small percentage of all AMR edges are non-projective. Yet, 35% of the sentences contain at least one non-projective edge.",
+      "https://github.com/jflanigan/jamr/blob/master/docs/Hand_Alignments.md",
+      "AMRs are graphs rather than trees because they can have nodes with multiple parents, called reentrant nodes, as in the node you for the AMR of Figure FIGREF7 . There are two phenomena that cause reentrancies in AMR: control, where a reentrant edge appears between siblings of a control verb, and co-reference, where multiple mentions correspond to the same concept.",
+      "In contrast, dependency trees do not have nodes with multiple parents. Therefore, when creating a new arc, transition systems for dependency parsing check that the dependent does not already have a head node, preventing the node from having additional parents. To handle reentrancy, which is not uncommon in AMR structures as shown in Table TABREF15 , we drop this constraint.",
+      "Another main difference with dependency parsing is that in AMR there is no straightforward mapping between a word in the sentence and a node in the graph: words may generate no nodes, one node or multiple nodes. In addition, the labels at the nodes are often not easily determined by the word in the sentence. For instance expectation translates to expect-01 and teacher translates to the two nodes teach-01 and person, connected through an :ARG0 edge, expressing that a teacher is a person who teaches. A mechanism of concept identification is therefore required to map each token INLINEFORM0 to a subgraph with the correct labels at its nodes and edges: if INLINEFORM1 is the gold alignment, this should be the subgraph INLINEFORM2 defined in Equation ( EQREF11 ). To obtain alignments between the tokens in the sentence and the nodes in the AMR graph of our training data, we run the JAMR aligner."
+    ],
+    [
+      "A stack INLINEFORM0 is a list of nodes of the partially constructed AMR graph, with the top element INLINEFORM1 at the right. We use the symbol ` INLINEFORM2 ' as the concatenation operator. A buffer INLINEFORM3 is a list of indices from INLINEFORM4 , with the first element INLINEFORM5 at the left, representing the word tokens from the input still to be processed. A configuration of our parser is a triple INLINEFORM6 , where INLINEFORM7 is the set of AMR edges that have been constructed up to this point.",
+      "In order to introduce the transition actions of our parser we need some additional notation. We use a function INLINEFORM0 that maps indices from INLINEFORM1 to AMR graph fragments. For each INLINEFORM2 , INLINEFORM3 is a graph INLINEFORM4 , with single root INLINEFORM5 , representing the semantic contribution of word INLINEFORM6 to the AMR for INLINEFORM7 . As already mentioned, INLINEFORM8 can have a single node representing the concept associated with INLINEFORM9 , or it can have several nodes in case INLINEFORM10 denotes a complex concept, or it can be empty.",
+      "The transition Shift is used to decide if and what to push on the stack after consuming a token from the buffer. Intuitively, the graph fragment INLINEFORM0 obtained from the token INLINEFORM1 , if not empty, is \u201cmerged\u201d with the graph we have constructed so far. We then push onto the stack the node INLINEFORM2 for further processing. LArc INLINEFORM3 creates an edge with label INLINEFORM4 between the top-most node and the second top-most node in the stack, and pops the latter. RArc INLINEFORM5 is the symmetric operation, but does not pop any node from the stack.",
+      "Finally, Reduce pops the top-most node from the stack, and it also recovers reentrant edges between its sibling nodes, capturing for instance several control verb patterns. To accomplish this, Reduce decides whether to create an additional edge between the node being removed and the previously created sibling in the partial graph. This way of handling control verbs is similar to the REENTRANCE transition of wang2boosting.",
+      "The choice of popping the dependent in the LArc transition is inspired by ArcEager, where left-arcs are constructed bottom-up to increase the incrementality of the transition system BIBREF15 . This affects our ability to recover some reentrant edges: consider a node INLINEFORM0 with two parents INLINEFORM1 and INLINEFORM2 , where the arc INLINEFORM3 is a left-arc and INLINEFORM4 is any arc. If the first arc to be processed is INLINEFORM5 , we use LArc that pops INLINEFORM6 , hence making it impossible to create the second arc INLINEFORM7 . Nevertheless, we discovered that this approach works better than a completely unrestricted allowance of reentrancy. The reason is that if we do not remove dependents at all when first attached to a node, the stack becomes larger, and nodes which should be connected end up being distant from each other, and as such, are never connected.",
+      "The initial configuration of the system has a INLINEFORM0 node (representing the root) in the stack and the entire sentence in the buffer. The terminal configuration consists of an empty buffer and a stack with only the INLINEFORM1 node. The transitions required to parse the sentence The boy and the girl are shown in Table TABREF20 , where the first line shows the initial configuration and the last line shows the terminal configuration.",
+      "Similarly to the transitions of the ArcEager, the above transitions construct edges as soon as the head and the dependent are available in the stack, with the aim of maximizing the parser incrementality. We now show that our greedy transition-based AMR parser is linear-time in INLINEFORM0 , the length of the input sentence INLINEFORM1 . We first claim that the output graph has size INLINEFORM2 . Each token in INLINEFORM3 is mapped to a constant number of nodes in the graph by Shift. Thus the number of nodes is INLINEFORM4 . Furthermore, each node can have at most three parent nodes, created by transitions RArc, LArc and Reduce, respectively. Thus the number of edges is also INLINEFORM5 . It is possible to bound the maximum number of transitions required to parse INLINEFORM6 : the number of Shift is bounded by INLINEFORM7 , and the number of Reduce, LArc and RArc is bounded by the size of the graph, which is INLINEFORM8 . Since each transition can be carried out in constant time, we conclude that our parser runs in linear time."
+    ],
+    [
+      "Several components have to be learned: (1) a transition classifier that predicts the next transition given the current configuration, (2) a binary classifier that decides whether or not to create a reentrancy after a Reduce, (3) a concept identification step for each Shift to compute INLINEFORM0 , and 3) another classifier to label edges after each LArc or RArc."
+    ],
+    [
+      "Training our system from data requires an oracle\u2014an algorithm that given a gold-standard AMR graph and a sentence returns transition sequences that maximize the overlap between the gold-standard graph and the graph dictated by the sequence of transitions.",
+      "We adopt a shortest stack, static oracle similar to manningfast. Informally, static means that if the actual configuration of the parser has no mistakes, the oracle provides a transition that does not introduce any mistake. Shortest stack means that the oracle prefers transitions where the number of items in the stack is minimized. Given the current configuration INLINEFORM0 and the gold-standard graph INLINEFORM1 , the oracle is defined as follows, where we test the conditions in the given order and apply the action associated with the first match:",
+      "if INLINEFORM0 then LArc( INLINEFORM1 );",
+      "if INLINEFORM0 then RArc( INLINEFORM1 );",
+      "if INLINEFORM0 then Reduce;",
+      "Shift otherwise.",
+      "The oracle first checks whether some gold-standard edge can be constructed from the two elements at the top of the stack (conditions 1 and 2). If LArc or RArc are not possible, the oracle checks whether all possible edges in the gold graph involving INLINEFORM0 have already been processed, in which case it chooses Reduce (conditions 3). To this end, it suffices to check the buffer, since LArc and RArc have already been excluded and elements in the stack deeper than position two can no longer be accessed by the parser. If Reduce is not possible, Shift is chosen.",
+      "Besides deciding on the next transition, the oracle also needs the alignments, which we generate with JAMR, in order to know how to map the next token in the sentence to its AMR subgraph INLINEFORM0 defined in ( EQREF11 )."
+    ],
+    [
+      "Like all other transition systems of this kind, our transition system has a \u201ccontroller\u201d that predicts a transition given the current configuration (among Shift, LArc, RArc and Reduce). The examples from which we learn this controller are based on features extracted from the oracle transition sequences, where the oracle is applied on the training data.",
+      "As a classifier, we use a feed-forward neural network with two hidden layers of 200 tanh units and learning rate set to 0.1, with linear decaying. The input to the network consists of the concatenation of embeddings for words, POS tags and Stanford parser dependencies, one-hot vectors for named entities and additional sparse features, extracted from the current configuration of the transition system; this is reported in more details in Table TABREF27 . The embeddings for words and POS tags were pre-trained on a large unannotated corpus consisting of the first 1 billion characters from Wikipedia. For lexical information, we also extract the leftmost (in the order of the aligned words) child (c), leftmost parent (p) and leftmost grandchild (cc). Leftmost and rightmost items are common features for transition-based parsers BIBREF17 , BIBREF18 but we found only leftmost to be helpful in our case. All POS tags, dependencies and named entities are generated using Stanford CoreNLP BIBREF19 . The accuracy of this classifier on the development set is 84%.",
+      "Similarly, we train a binary classifier for deciding whether or not to create a reentrant edge after a Reduce: in this case we use word and POS embeddings for the two nodes being connected and their parent as well as dependency label embeddings for the arcs between them."
+    ],
+    [
+      "This routine is called every time the transition classifier decides to do a Shift; it is denoted by INLINEFORM0 in \u00a7 SECREF3 . This component could be learned in a supervised manner, but we were not able to improve on a simple heuristic, which works as follows: during training, for each Shift decided by the oracle, we store the pair INLINEFORM1 in a phrase-table. During parsing, the most frequent graph INLINEFORM2 for the given token is then chosen. In other words, INLINEFORM3 approximates INLINEFORM4 by means of the graph most frequently seen among all occurrences of token INLINEFORM5 in the training set.",
+      "An obvious problem with the phrase-table approach is that it does not generalize to unseen words. In addition, our heuristic relies on the fact that the mappings observed in the data are correct, which is not the case when the JAMR-generated alignments contain a mistake. In order to alleviate this problem we observe that there are classes of words such as named entities and numeric quantities that can be disambiguated in a deterministic manner. We therefore implement a set of \u201chooks\u201d that are triggered by the named entity tag of the next token in the sentence. These hooks override the normal Shift mechanism and apply a fixed rule instead. For instance, when we see the token New York (the two tokens are collapsed in a single one at preprocessing) we generate the subgraph of Figure FIGREF30 and push its root onto the stack. Similar subgraphs are generated for all states, cities, countries and people. We also use hooks for ordinal numbers, percentages, money and dates."
+    ],
+    [
+      "Edge labeling determines the labels for the edges being created. Every time the transition classifier decides to take an LArc or RArc operation, the edge labeler needs to decide on a label for it. There are more than 100 possible labels such as :ARG0, :ARG0-of, :ARG1, :location, :time and :polarity. We use a feed-forward neural network similar to the one we trained for the transition classier, with features shown in Table TABREF32 . The accuracy of this classifier on the development set is 77%.",
+      "We constrain the labels predicted by the neural network in order to satisfy requirements of AMR. For instance, the label :top can only be applied when the node from which the edge starts is the special INLINEFORM0 node. Other constraints are used for the :polarity label and for edges attaching to numeric quantities.",
+      "Sometimes the label predicted by the neural network is not a label that satisfies the requirements of AMR. For instance, the label :top can only be applied when the node from which the edge starts is the special INLINEFORM0 node. In order to avoid generating such erroneous labels, we use a set of rules, shown in Table TABREF34 . These rules determine which labels are allowed for the newly created edge so that we only consider those during prediction. Also ARG roles cannot always be applied: each Propbank frame allows a limited number of arguments. For example, while add-01 and add-02 allow for :ARG1 and :ARG2 (and their inverse :ARG1-of and :ARG2-of), add-03 and add-04 only allow :ARG2 (and :ARG2-of)."
+    ],
+    [
+      "Until now, AMR parsers were evaluated using the Smatch score. Given the candidate graphs and the gold graphs in the form of AMR annotations, Smatch first tries to find the best alignments between the variable names for each pair of graphs and it then computes precision, recall and F1 of the concepts and relations. We note that the Smatch score has two flaws: (1) while AMR parsing involves a large number of subtasks, the Smatch score consists of a single number that does not assess the quality of each subtasks separately; (2) the Smatch score weighs different types of errors in a way which is not necessarily useful for solving a specific NLP problem. For example, for a specific problem concept detection might be deemed more important than edge detection, or guessing the wrong sense for a concept might be considered less severe than guessing the wrong verb altogether.",
+      "Consider the two parses for the sentence Silvio Berlusconi gave Lucio Stanca his current role of modernizing Italy's bureaucracy in Figure FIGREF36 . At the top, we show the output of a parser (Parse 1) that is not able to deal with named entities. At the bottom, we show the output of a parser (Parse 2) which, except for :name, :op and :wiki, always uses the edge label :ARG0. The Smatch scores for the two parses are 56 and 78 respectively. Both parses make obvious mistakes but the three named entity errors in Parse 1 are considered more important than the six wrong labels in Parse 2. However, without further analysis, it is not advisable to conclude that Parse 2 is better than Parse 1. In order to better understand the limitations of the different parsers, find their strengths and gain insight in which downstream tasks they may be helpful, we compute a set of metrics on the test set.",
+      "Unlabeled is the Smatch score computed on the predicted graphs after removing all edge labels. In this way, we only assess the node labels and the graph topology, which may be enough to benefit several NLP tasks because it identifies basic predicate-argument structure. For instance, we may be interested in knowing whether two events or entities are related to each other, while not being concerned with the precise type of relation holding between them.",
+      "No WSD gives a score that does not take into account word sense disambiguation errors. By ignoring the sense specified by the Propbank frame used (e.g., duck-01 vs duck-02) we have a score that does not take into account this additional complexity in the parsing procedure. To compute this score, we simply strip off the suffixes from all Propbank frames and calculate the Smatch score.",
+      "Following sawai, we also evaluate the parsers using the Smatch score on noun phrases only (NP-only), by extracting from the AMR dataset all noun phrases that do not include further NPs.",
+      "As we previously discussed, reentrancy is a very important characteristic of AMR graphs and it is not trivial to handle. We therefore implement a test for it (Reentrancy), where we compute the Smatch score only on reentrant edges.",
+      "Concept identification is another critical component of the parsing process and we therefore compute the F-score on the list of predicted concepts (Concepts) too. Identifying the correct concepts is fundamental: if a concept is not identified, it will not be possible to retrieve any edge involving that concept, with likely significant consequences on accuracy. This metric is therefore quite important to score highly on.",
+      "Similarly to our score for concepts, we further compute an F-score on the named entities (Named Ent.) and wiki roles for named entities (Wikification) that consider edges labeled with :name and :wiki respectively. These two metrics are strictly related to the concept score. However, since named entity recognition is the focus of dedicated research, we believe it is important to define a metric that specifically assesses this problem. Negation detection is another task which has received some attention. An F-score for this (Negations) is also defined, where we find all negated concepts by looking for the :polarity role. The reason we can compute a simple F-score instead of using Smatch for these metrics is that there are no variable names involved.",
+      "Finally we compute the Smatch score on :ARG edges only, in order to have a score for semantic role labeling (SRL), which is another extremely important subtask of AMR, as it is based on the identification of predicate-argument structures.",
+      "Using this evaluation suite we can evaluate AMRs on a wide range of metrics that can help us find strengths and weakness of each parser, hence speeding up the research in this area. Table TABREF37 reports the scores for the two parses of Figure FIGREF36 , where we see that Parse 1 gets a high score for semantic role labeling while Parse 2 is optimal for named entity recognition. Moreover, we can make additional observations such as that Parse 2 is optimal with respect to unlabeled score and that Parse 1 recovers more reentrancies."
+    ],
+    [
+      "We compare our parser against two available parsers: JAMR BIBREF4 and CAMR BIBREF20 , BIBREF5 , using the LDC2015E86 dataset for evaluation. Both parsers are available online and were recently updated for SemEval-2016 Task 8 BIBREF21 , BIBREF22 . However, CAMR's SemEval system, which reports a Smatch score of 67, is not publicly available. CAMR has a quadratic worst-case complexity (although linear in practice). In JAMR, the concept identification step is quadratic and the relation identification step is INLINEFORM0 , with INLINEFORM1 being the set of nodes in the AMR graph.",
+      "Table TABREF40 shows the results obtained by the parsers on all metrics previously introduced. On Smatch, our system does not give state-of-the-art results. However, we do obtain the best results for Unlabeled and Concept and outperform the other parses for Named Ent. and Negations. Our score of Reentrancy is also close the best scoring system, which is particularly relevant given the importance of reentrancies in AMR. The use of the Reduce transition, which targets reentrancies caused by control verbs, is critical in order to achieve this result.",
+      "The relatively high results we obtain for the unlabeled case suggests that our parser has difficulty in labeling the arcs. Our score for concept identification, which is on par with the best result from the other parsers, demonstrates that there is a relatively low level of token ambiguity. State-of-the-art results for this problem can be obtained by choosing the most frequent subgraph for a given token based on a phrase-table constructed from JAMR alignments on the training data. The scores for named entities and wikification are heavily dependent on the hooks mentioned in \u00a7 SECREF29 , which in turn relies on the named entity recognizer to make the correct predictions. In order to alleviate the problem of wrong automatic alignments with respect to polarity and better detect negation, we performed a post-processing step on the aligner output where we align the AMR constant - (minus) with words bearing negative polarity such as not, illegitimate and asymmetry.",
+      "Our experiments demonstrate that there is no parser for AMR yet that conclusively does better than all other parsers on all metrics. Advantages of our parser are the worst-case linear complexity and the fact that is possible to perform incremental AMR parsing, which is both helpful for real-time applications and to investigate how meaning of English sentences can be built incrementally left-to-right."
+    ],
+    [
+      "The first data-driven AMR parser is due to carbonell2014discriminative. The problem is addressed in two separate stages: concept identification and relation identification. They use a sequence labeling algorithm to identify concepts and frame the relation prediction task as a constrained combinatorial optimization problem. werling2015robust notice that the difficult bit is the concept identification and propose a better way to handle that task: an action classifier to generate concepts by applying predetermined actions. Other proposals involve a synchronous hyperedge replacement grammar solution BIBREF6 , a syntax-based machine translation approach BIBREF7 where a grammar of string-to-tree rules is created after reducing AMR graphs to trees by removing all reentrancies, a CCG system that first parses sentences into lambda-calculus representations BIBREF11 . A systematic translation from AMR to first order logic formulas, with a special treatment for quantification, reentrancy and negation, is discussed in bos2016expressive. In microsoft, a pre-existing logical form parser is used and the output is then converted into AMR graphs. Yet another solution is proposed by searnamr who discuss a parser that uses SEARN BIBREF23 , a \u201clearning to search\u201d algorithm.",
+      "Transition-based algorithms for AMR parsing are compelling because traditional graph-based techniques are computationally expensive. wang and wang2boosting propose a framework that parses a sentence into its AMR structure through a two-stage process: a dependency tree is generated from the input sentence through a transition-based parser and then another transition-based parser is used to generate the AMR. The main benefit of this approach is that the dependency parser can be trained on a training set much larger than the training set for the tree-to-graph algorithm. Others further built on this parser: goodman2016noise use imitation learning to alleviate the probem of error propagation in the greedy parser, while barzdins2016riga create a wrapper around it to fix frequent mistakes and investigate ensembles with a character level neural parser. More recently emnlp2016 presented a non-greedy transition system for AMR parsing, based on ArcStandard BIBREF15 .",
+      "AMR parsing as a whole is a complex task because it involves many subtasks including named entity recognition, co-reference resolution and semantic role labeling. sawai do not attempt at parsing AMR graphs for entire sentences but they instead handle simple noun phrases (NPs). They extract NPs from the AMR dataset only when they do not include further NPs, do not include pronouns nor named entities. Due to these restrictions, the AMRs are mostly trees and easier to handle than the original AMR graphs. They approach this task using a transition based system inspired by ArcStandard.",
+      "AMR is not the only way to represent meaning in natural language sentences. Alternative semantic representations have been developed and studied, such as Boxer BIBREF24 , CCG BIBREF1 , BIBREF2 and UCCA BIBREF3 ."
+    ],
+    [
+      "We presented a transition system that builds AMR graphs in linear time by processing the sentences left-to-right, trained with feed-forward neural networks. The parser demonstrates that it is possible to perform AMR parsing using techniques inspired by dependency parsing.",
+      "We also noted that it is less informative to evaluate the entire parsing process with Smatch than to use a collection of metrics aimed at evaluating the various subproblems in the parsing process. We further showed that our left-to-right transition system is competitive with publicly available state-of-the-art parsers. Although we do not outperform the best baseline in terms of Smatch score, we show on par or better results for several of the metrics proposed. We hope that moving away from a single-metric evaluation will further speed up progress in AMR parsing."
+    ],
+    [
+      "The authors would like to thank the three anonymous reviewers and Sameer Bansal, Jeff Flanigan, Sorcha Gilroy, Adam Lopez, Nikos Papasarantopoulos, Nathan Schneider, Mark Steedman, Sam Thomson, Clara Vania and Chuan Wang for their help and comments. This research was supported by a grant from Bloomberg and by the H2020 project SUMMA, under grant agreement 688139."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1184/instruction.md b/qasper-1184/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..1731e69604439de96883ecc9adf9507570171f4a
--- /dev/null
+++ b/qasper-1184/instruction.md
@@ -0,0 +1,75 @@
+Name of Paper: Multilingual Speech Recognition with Corpus Relatedness Sampling
+
+Question: By how much do they, on average, outperform the baseline multilingual model on 16 low-resource tasks?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Approach",
+    "Corpus Embedding",
+    "Corpus Relatedness Sampling",
+    "Experiments",
+    "Datasets",
+    "Experiment Settings",
+    "Results",
+    "Conclusion",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "In recent years, Deep Neural Networks (DNNs) have been successfully applied to Automatic Speech Recognition (ASR) for many well-resourced languages including Mandarin and English BIBREF0 , BIBREF1 . However, only a small portion of languages have clean speech labeled corpus. As a result, there is an increasing interest in building speech recognition systems for low-resource languages. To address this issue, researchers have successfully exploited multilingual speech recognition models by taking advantage of labeled corpora in other languages BIBREF2 , BIBREF3 . Multilingual speech recognition enables acoustic models to share parameters across multiple languages, therefore low-resource acoustic models can benefit from rich resources.",
+      "While low-resource multilingual works have proposed various acoustic models, those works tend to combine several low-resource corpora together without paying attention to the variety of corpora themselves. One common training approach here is to first pretrain a multilingual model by combining all training corpora, then the pretrained model is fine-tuned on the target corpus BIBREF4 . During the training process, each corpus in the training set is treated equally and sampled uniformly. We argue, however, this approach does not take account of the characteristics of each corpus, therefore it fails to take advantage of the relations between them. For example, a conversation corpus might be more beneficial to another conversation corpus rather than an audio book corpus.",
+      "In this work, we propose an effective sampling strategy (Corpus Relatedness Sampling) to take advantage of relations among corpora. Firstly, we introduce the corpus-level embedding which can be used to compute the similarity between corpora. The embedding can be estimated by being jointly trained with the acoustic model. Next, we compute the similarity between each corpus and the target corpus, the similarity is then used to optimize the model with respect to the target corpus. During the training process, we start by uniformly sampling from each corpus, then the sampling distribution is gradually updated so that more related corpora would be sampled more frequently. Eventually, only the target corpus would be sampled from the training set as the target corpus is the most related corpus to itself. While our approach differs from the pretrained model and the fine-tuned model, we can prove that those models are special cases of our sampling strategy.",
+      "To evaluate our sampling strategy, we compare it with the pretrained model and fine-tuned model on 16 different corpora. The results show that our approach outperforms those baselines on all corpora: it achieves 1.6% lower phone error rate on average. Additionally, we demonstrate that our corpus-level embeddings are able to capture the characteristics of each corpus, especially the language and domain information. The main contributions of this paper are as follows:"
+    ],
+    [
+      "Multilingual speech recognition has explored various models to share parameters across languages in different ways. For example, parameters can be shared by using posterior features from other languages BIBREF5 , applying the same GMM components across different HMM states BIBREF6 , training shared hidden layers in DNNs BIBREF2 , BIBREF3 or LSTM BIBREF4 , using language independent bottleneck features BIBREF7 , BIBREF8 . Some models only share their hidden layers, but use separate output layers to predict their phones BIBREF2 , BIBREF3 . Other models have only one shared output layer to predict the universal phone set shared by all languages BIBREF9 , BIBREF10 , BIBREF11 . While those works proposed the multilingual models in different ways, few of them have explicitly exploited the relatedness across various languages and corpora. In contrast, our work computes the relatedness between different corpora using the embedding representations and exploits them efficiently.",
+      "The embedding representations have been heavily used in multiple fields. In particular, embeddings of multiple granularities have been explored in many NLP tasks. To name a few, character embedding BIBREF12 , subword embedding BIBREF13 , sentence embedding BIBREF14 and document embedding BIBREF15 . However, there are few works exploring the corpus level embeddings. The main reason is that the number of corpora involved in most experiments is usually limited and it is not useful to compute corpus embeddings. The only exception is the multitask learning where many tasks and corpora are combined together. For instance, the language level (corpus level) embedding can be generated along with the model in machine translation BIBREF16 and speech recognition BIBREF17 . However, those embeddings are only used as an auxiliary feature to the model, few works continue to exploit those embeddings themselves.",
+      "Another important aspect of our work is that we focused on the sampling strategy for speech recognition. While most of the previous speech works mainly emphasized the acoustic modeling side, there are also some attempts focusing on the sampling strategies. For instance, curriculum learning would train the acoustic model by starting from easy training samples and increasingly adapt it to more difficult samples BIBREF0 , BIBREF18 . Active learning is an approach trying to minimize human costs to collect transcribed speech data BIBREF19 . Furthermore, sampling strategies can also be helpful to speed up the training process BIBREF20 . However, the goals of most strategies are to improve the acoustic model by modifying the sampling distribution within a single speech corpus for a single language. On the contrary, our approach aims to optimize the multilingual acoustic model by modifying distributions across all the training corpora."
+    ],
+    [
+      "In this section, we describe our approach to compute the corpus embedding and our Corpus Relatedness Sampling strategy."
+    ],
+    [
+      "Suppose that INLINEFORM0 is the target low-resource corpus, we are interested in optimizing the acoustic model with a much larger training corpora set INLINEFORM1 where INLINEFORM2 is the number of corpora and INLINEFORM3 . Each corpus INLINEFORM4 is a collection of INLINEFORM5 pairs where INLINEFORM6 is the input features and INLINEFORM7 is its target.",
+      "Our purpose here is to compute the embedding INLINEFORM0 for each corpus INLINEFORM1 where INLINEFORM2 is expected to encode information about its corpus INLINEFORM3 . Those embeddings can be jointly trained with the standard multilingual model BIBREF4 . First, the embedding matrix INLINEFORM4 for all corpora is initialized, the INLINEFORM5 -th row of INLINEFORM6 is corresponding to the embedding INLINEFORM7 of the corpus INLINEFORM8 . Next, during the training phase, INLINEFORM9 can be used to bias the input feature INLINEFORM10 as follows. DISPLAYFORM0 ",
+      "where INLINEFORM0 is an utterance sampled randomly from INLINEFORM1 , INLINEFORM2 is its hidden features, INLINEFORM3 is the parameter of the acoustic model and Encoder is the stacked bidirectional LSTM as shown in Figure. FIGREF5 . Next, we apply the language specific softmax to compute logits INLINEFORM4 and optimize them with the CTC objective BIBREF29 . The embedding matrix INLINEFORM5 can be optimized together with the model during the training process."
+    ],
+    [
+      "With the embedding INLINEFORM0 of each corpus INLINEFORM1 , we can compute the similarity score between any two corpora using the cosine similarity. DISPLAYFORM0 ",
+      "As the similarity reflects the relatedness between corpora in the training set, we would like to sample the training set based on this similarity: those corpora which have a higher similarity with the target corpus INLINEFORM0 should be sampled more frequently. Therefore, we assume those similarity scores to be the sampling logits and they should be normalized with softmax. DISPLAYFORM0 ",
+      "where INLINEFORM0 is the probability to sample INLINEFORM1 from INLINEFORM2 , and INLINEFORM3 is the temperature to normalize the distribution during the training phase. We argue that different temperatures could create different training conditions. The model with a lower temperature tends to sample each corpus equally like uniform sampling. In contrast, a higher temperature means that the sampling distribution should be biased toward the target corpus like the fine-tuning.",
+      "Next, we prove that both the pretrained model and the fine-tuned model can be realized with specific temperatures. In the case of the pretrained model, each corpus should be sampled equally. This can be implemented by setting INLINEFORM0 to be 0. DISPLAYFORM0 ",
+      "On the other hand, the fine-tuned model should only consider samples from the target corpus INLINEFORM0 , while ignoring all other corpora. We argue that this condition can be approximated by setting INLINEFORM1 to a very large number. As INLINEFORM2 and INLINEFORM3 if INLINEFORM4 , we can prove the statement as follows: DISPLAYFORM0 ",
+      "While both the pretrained model and the fine-tuned model are special cases of our approach, we note that our approach is more flexible to sample from related corpora by interpolating between those two extreme temperatures. In practice, we would like to start with a low temperature to sample broadly in the early training phase. Then we gradually increase the temperature so that it can focus more on the related corpora. Eventually, the temperature would be high enough so that the model is automatically fine-tuned on the target corpus. Specifically, in our experiment, we start training with a very low temperature INLINEFORM0 , and increase its value every epoch INLINEFORM1 as follows. DISPLAYFORM0 ",
+      " where INLINEFORM0 is the temperature of epoch INLINEFORM1 and INLINEFORM2 is a hyperparameter to control the growth rate of the temperature."
+    ],
+    [
+      "To demonstrate that our sampling approach could improve the multilingual model, we conduct experiments on 16 corpora to compare our approach with the pretrained model and fine-tuned model."
+    ],
+    [
+      "We first describe our corpus collection. Table. TABREF3 lists all corpora we used in the experiments. There are 16 corpora from 10 languages. To increase the variety of corpus, we selected 4 English corpora and 4 Mandarin corpora in addition to the low resource language corpora. As the target of this experiment is low resource speech recognition, we only randomly select 100,000 utterances even if there are more in each corpus. All corpora are available in LDC, voxforge, openSLR or other public websites. Each corpus is manually assigned one domain based on its speech style. Specifically, the domain candidates are telephone, read and broadcast."
+    ],
+    [
+      "We use EESEN BIBREF30 for the acoustic modeling and epitran BIBREF31 as the g2p tool in this work. Every utterance in the corpora is firstly re-sampled into 8000Hz, and then we extract 40 dimension MFCCs features from each audio. We use a recent multilingual CTC model as our acoustic architecture BIBREF4 : The architecture is a 6 layer bidirectional LSTM model with 320 cells in each layer. We use this architecture for both the baseline models and the proposed model.",
+      "Our baseline model is the fine-tuned model: we first pretrained a model by uniformly sampling from all corpora. After the loss converges, we fine-tune the model on each of our target corpora. To compare it with our sampling approach, we first train an acoustic model to compute the embeddings of all corpora, then the embeddings are used to estimate the similarity as described in the previous section. The initial temperature INLINEFORM0 is set to 0.01, and the growth rate is INLINEFORM1 . We evaluated all models using the phone error rate (PER) instead of the word error rate (WER). The reason is that we mainly focus on the acoustic model in this experiment. Additionally, some corpora (e.g.: Dutch voxforge) in this experiment have very few amounts of texts, therefore it is difficult to create a reasonable language model without augmenting texts using other corpora, which is beyond the scope of this work."
+    ],
+    [
+      "Table. TABREF16 shows the results of our evaluation. We compare our approach with the baseline using all corpora. The left-most column of Table. TABREF16 shows the corpus we used for each experiment, the remaining columns are corresponding to the phone error rate of the pretrained model, the fine-tuned model and our proposed model. First, we can easily confirm that the fine-tuned model outperforms the pretrained model on all corpora. For instance, the fine-tuned model outperforms the pretrained model by 4.7% on the Amharic corpus. The result is reasonable as the pretrained model is optimized with the entire training set, while the fine-tuned model is further adapted to the target corpus. Next, the table suggests our Corpus Relatedness Sampling approach achieves better results than the fine-tuned model on all test corpora. For instance, the phone error rate is improved from 40.7% to 36.9% on Amharic and is improved from 41.9% to 40.0% on Bengali. On average, our approach outperforms the fine-tuned model by 1.6% phone error rate. The results demonstrate that our sampling approach is more effective at optimizing the acoustic model on the target corpus. We also train baseline models by appending corpus embeddings to input features, but the proposed model outperforms those baselines similarly.",
+      "One interesting trend we observed in the table is that the improvements differ across the target corpora. For instance, the improvement on the Dutch corpus is 3.4%, on the other hand, its improvement of 0.6% is relatively smaller on the Zulu dataset. We believe the difference in improvements can be explained by the size of each corpus. The size of Dutch corpus is very small as shown in Table. TABREF3 , therefore the fine-tuned model is prone to overfit to the dataset very quickly. In contrast, it is less likely for a larger corpus to overfit. Compared with the fine-tuned model, our approach optimizes the model by gradually changing the temperature without quick overfitting. This mechanism could be interpreted as a built-in regularization. As a result, our model can achieve much better performance in small corpora by preventing the overfitting effect.",
+      "To understand how our corpus embeddings contribute to our approach, we rank those embeddings and show the top-2 similar corpora for each corpus in Table. TABREF17 . We note that the target corpus itself is removed from the rankings because it is the most related corpus to itself. The results of the top half show very clearly that our embeddings can capture the language level information: For most English and Mandarin corpora, the most related corpus is another English or Mandarin corpus. Additionally, the bottom half of the table indicates that our embeddings are able to capture domain level information as well. For instance, the top 2 related corpus for Amharic is Bengali and Swahili. According to Table. TABREF3 , those three corpora belong to the telephone domain. In addition, Dutch is a read corpus, its top 2 related corpora are also from the same domain. This also explains why the 1st related corpus of Mandarin (hk) is Bengali: because both of them are from the same telephone domain.",
+      "To further investigate the domain information contained in the corpus embeddings, we train the corpus embeddings with an even larger corpora collection (36 corpora) and plot all of them in Figure. FIGREF18 . To create the plot, the dimension of each corpus embedding is reduced to 2 with t-SNE BIBREF32 . The figure demonstrates clearly that our corpus embeddings are capable of capturing the domain information: all corpora with the same domain are clustered together. This result also means that our approach improves the model by sampling more frequently from the corpora of the same speech domain."
+    ],
+    [
+      "In this work, we propose an approach to compute corpus-level embeddings. We also introduce Corpus Relatedness Sampling approach to train multilingual speech recognition models based on those corpus embeddings. Our experiment shows that our approach outperforms the fine-tuned multilingual models in all 16 test corpora by 1.6 phone error rate on average. Additionally, we demonstrate that our corpus embeddings can capture both language and domain information of each corpus."
+    ],
+    [
+      "This project was sponsored by the Defense Advanced Research Projects Agency (DARPA) Information Innovation Office (I2O), program: Low Resource Languages for Emergent Incidents (LORELEI), issued by DARPA/I2O under Contract No. HR0011-15-C-0114."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1185/instruction.md b/qasper-1185/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..42f6ed8e58843074598671aa94bdabe67dddd6bf
--- /dev/null
+++ b/qasper-1185/instruction.md
@@ -0,0 +1,75 @@
+Name of Paper: Multilingual Speech Recognition with Corpus Relatedness Sampling
+
+Question: How do they compute corpus-level embeddings?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Approach",
+    "Corpus Embedding",
+    "Corpus Relatedness Sampling",
+    "Experiments",
+    "Datasets",
+    "Experiment Settings",
+    "Results",
+    "Conclusion",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "In recent years, Deep Neural Networks (DNNs) have been successfully applied to Automatic Speech Recognition (ASR) for many well-resourced languages including Mandarin and English BIBREF0 , BIBREF1 . However, only a small portion of languages have clean speech labeled corpus. As a result, there is an increasing interest in building speech recognition systems for low-resource languages. To address this issue, researchers have successfully exploited multilingual speech recognition models by taking advantage of labeled corpora in other languages BIBREF2 , BIBREF3 . Multilingual speech recognition enables acoustic models to share parameters across multiple languages, therefore low-resource acoustic models can benefit from rich resources.",
+      "While low-resource multilingual works have proposed various acoustic models, those works tend to combine several low-resource corpora together without paying attention to the variety of corpora themselves. One common training approach here is to first pretrain a multilingual model by combining all training corpora, then the pretrained model is fine-tuned on the target corpus BIBREF4 . During the training process, each corpus in the training set is treated equally and sampled uniformly. We argue, however, this approach does not take account of the characteristics of each corpus, therefore it fails to take advantage of the relations between them. For example, a conversation corpus might be more beneficial to another conversation corpus rather than an audio book corpus.",
+      "In this work, we propose an effective sampling strategy (Corpus Relatedness Sampling) to take advantage of relations among corpora. Firstly, we introduce the corpus-level embedding which can be used to compute the similarity between corpora. The embedding can be estimated by being jointly trained with the acoustic model. Next, we compute the similarity between each corpus and the target corpus, the similarity is then used to optimize the model with respect to the target corpus. During the training process, we start by uniformly sampling from each corpus, then the sampling distribution is gradually updated so that more related corpora would be sampled more frequently. Eventually, only the target corpus would be sampled from the training set as the target corpus is the most related corpus to itself. While our approach differs from the pretrained model and the fine-tuned model, we can prove that those models are special cases of our sampling strategy.",
+      "To evaluate our sampling strategy, we compare it with the pretrained model and fine-tuned model on 16 different corpora. The results show that our approach outperforms those baselines on all corpora: it achieves 1.6% lower phone error rate on average. Additionally, we demonstrate that our corpus-level embeddings are able to capture the characteristics of each corpus, especially the language and domain information. The main contributions of this paper are as follows:"
+    ],
+    [
+      "Multilingual speech recognition has explored various models to share parameters across languages in different ways. For example, parameters can be shared by using posterior features from other languages BIBREF5 , applying the same GMM components across different HMM states BIBREF6 , training shared hidden layers in DNNs BIBREF2 , BIBREF3 or LSTM BIBREF4 , using language independent bottleneck features BIBREF7 , BIBREF8 . Some models only share their hidden layers, but use separate output layers to predict their phones BIBREF2 , BIBREF3 . Other models have only one shared output layer to predict the universal phone set shared by all languages BIBREF9 , BIBREF10 , BIBREF11 . While those works proposed the multilingual models in different ways, few of them have explicitly exploited the relatedness across various languages and corpora. In contrast, our work computes the relatedness between different corpora using the embedding representations and exploits them efficiently.",
+      "The embedding representations have been heavily used in multiple fields. In particular, embeddings of multiple granularities have been explored in many NLP tasks. To name a few, character embedding BIBREF12 , subword embedding BIBREF13 , sentence embedding BIBREF14 and document embedding BIBREF15 . However, there are few works exploring the corpus level embeddings. The main reason is that the number of corpora involved in most experiments is usually limited and it is not useful to compute corpus embeddings. The only exception is the multitask learning where many tasks and corpora are combined together. For instance, the language level (corpus level) embedding can be generated along with the model in machine translation BIBREF16 and speech recognition BIBREF17 . However, those embeddings are only used as an auxiliary feature to the model, few works continue to exploit those embeddings themselves.",
+      "Another important aspect of our work is that we focused on the sampling strategy for speech recognition. While most of the previous speech works mainly emphasized the acoustic modeling side, there are also some attempts focusing on the sampling strategies. For instance, curriculum learning would train the acoustic model by starting from easy training samples and increasingly adapt it to more difficult samples BIBREF0 , BIBREF18 . Active learning is an approach trying to minimize human costs to collect transcribed speech data BIBREF19 . Furthermore, sampling strategies can also be helpful to speed up the training process BIBREF20 . However, the goals of most strategies are to improve the acoustic model by modifying the sampling distribution within a single speech corpus for a single language. On the contrary, our approach aims to optimize the multilingual acoustic model by modifying distributions across all the training corpora."
+    ],
+    [
+      "In this section, we describe our approach to compute the corpus embedding and our Corpus Relatedness Sampling strategy."
+    ],
+    [
+      "Suppose that INLINEFORM0 is the target low-resource corpus, we are interested in optimizing the acoustic model with a much larger training corpora set INLINEFORM1 where INLINEFORM2 is the number of corpora and INLINEFORM3 . Each corpus INLINEFORM4 is a collection of INLINEFORM5 pairs where INLINEFORM6 is the input features and INLINEFORM7 is its target.",
+      "Our purpose here is to compute the embedding INLINEFORM0 for each corpus INLINEFORM1 where INLINEFORM2 is expected to encode information about its corpus INLINEFORM3 . Those embeddings can be jointly trained with the standard multilingual model BIBREF4 . First, the embedding matrix INLINEFORM4 for all corpora is initialized, the INLINEFORM5 -th row of INLINEFORM6 is corresponding to the embedding INLINEFORM7 of the corpus INLINEFORM8 . Next, during the training phase, INLINEFORM9 can be used to bias the input feature INLINEFORM10 as follows. DISPLAYFORM0 ",
+      "where INLINEFORM0 is an utterance sampled randomly from INLINEFORM1 , INLINEFORM2 is its hidden features, INLINEFORM3 is the parameter of the acoustic model and Encoder is the stacked bidirectional LSTM as shown in Figure. FIGREF5 . Next, we apply the language specific softmax to compute logits INLINEFORM4 and optimize them with the CTC objective BIBREF29 . The embedding matrix INLINEFORM5 can be optimized together with the model during the training process."
+    ],
+    [
+      "With the embedding INLINEFORM0 of each corpus INLINEFORM1 , we can compute the similarity score between any two corpora using the cosine similarity. DISPLAYFORM0 ",
+      "As the similarity reflects the relatedness between corpora in the training set, we would like to sample the training set based on this similarity: those corpora which have a higher similarity with the target corpus INLINEFORM0 should be sampled more frequently. Therefore, we assume those similarity scores to be the sampling logits and they should be normalized with softmax. DISPLAYFORM0 ",
+      "where INLINEFORM0 is the probability to sample INLINEFORM1 from INLINEFORM2 , and INLINEFORM3 is the temperature to normalize the distribution during the training phase. We argue that different temperatures could create different training conditions. The model with a lower temperature tends to sample each corpus equally like uniform sampling. In contrast, a higher temperature means that the sampling distribution should be biased toward the target corpus like the fine-tuning.",
+      "Next, we prove that both the pretrained model and the fine-tuned model can be realized with specific temperatures. In the case of the pretrained model, each corpus should be sampled equally. This can be implemented by setting INLINEFORM0 to be 0. DISPLAYFORM0 ",
+      "On the other hand, the fine-tuned model should only consider samples from the target corpus INLINEFORM0 , while ignoring all other corpora. We argue that this condition can be approximated by setting INLINEFORM1 to a very large number. As INLINEFORM2 and INLINEFORM3 if INLINEFORM4 , we can prove the statement as follows: DISPLAYFORM0 ",
+      "While both the pretrained model and the fine-tuned model are special cases of our approach, we note that our approach is more flexible to sample from related corpora by interpolating between those two extreme temperatures. In practice, we would like to start with a low temperature to sample broadly in the early training phase. Then we gradually increase the temperature so that it can focus more on the related corpora. Eventually, the temperature would be high enough so that the model is automatically fine-tuned on the target corpus. Specifically, in our experiment, we start training with a very low temperature INLINEFORM0 , and increase its value every epoch INLINEFORM1 as follows. DISPLAYFORM0 ",
+      " where INLINEFORM0 is the temperature of epoch INLINEFORM1 and INLINEFORM2 is a hyperparameter to control the growth rate of the temperature."
+    ],
+    [
+      "To demonstrate that our sampling approach could improve the multilingual model, we conduct experiments on 16 corpora to compare our approach with the pretrained model and fine-tuned model."
+    ],
+    [
+      "We first describe our corpus collection. Table. TABREF3 lists all corpora we used in the experiments. There are 16 corpora from 10 languages. To increase the variety of corpus, we selected 4 English corpora and 4 Mandarin corpora in addition to the low resource language corpora. As the target of this experiment is low resource speech recognition, we only randomly select 100,000 utterances even if there are more in each corpus. All corpora are available in LDC, voxforge, openSLR or other public websites. Each corpus is manually assigned one domain based on its speech style. Specifically, the domain candidates are telephone, read and broadcast."
+    ],
+    [
+      "We use EESEN BIBREF30 for the acoustic modeling and epitran BIBREF31 as the g2p tool in this work. Every utterance in the corpora is firstly re-sampled into 8000Hz, and then we extract 40 dimension MFCCs features from each audio. We use a recent multilingual CTC model as our acoustic architecture BIBREF4 : The architecture is a 6 layer bidirectional LSTM model with 320 cells in each layer. We use this architecture for both the baseline models and the proposed model.",
+      "Our baseline model is the fine-tuned model: we first pretrained a model by uniformly sampling from all corpora. After the loss converges, we fine-tune the model on each of our target corpora. To compare it with our sampling approach, we first train an acoustic model to compute the embeddings of all corpora, then the embeddings are used to estimate the similarity as described in the previous section. The initial temperature INLINEFORM0 is set to 0.01, and the growth rate is INLINEFORM1 . We evaluated all models using the phone error rate (PER) instead of the word error rate (WER). The reason is that we mainly focus on the acoustic model in this experiment. Additionally, some corpora (e.g.: Dutch voxforge) in this experiment have very few amounts of texts, therefore it is difficult to create a reasonable language model without augmenting texts using other corpora, which is beyond the scope of this work."
+    ],
+    [
+      "Table. TABREF16 shows the results of our evaluation. We compare our approach with the baseline using all corpora. The left-most column of Table. TABREF16 shows the corpus we used for each experiment, the remaining columns are corresponding to the phone error rate of the pretrained model, the fine-tuned model and our proposed model. First, we can easily confirm that the fine-tuned model outperforms the pretrained model on all corpora. For instance, the fine-tuned model outperforms the pretrained model by 4.7% on the Amharic corpus. The result is reasonable as the pretrained model is optimized with the entire training set, while the fine-tuned model is further adapted to the target corpus. Next, the table suggests our Corpus Relatedness Sampling approach achieves better results than the fine-tuned model on all test corpora. For instance, the phone error rate is improved from 40.7% to 36.9% on Amharic and is improved from 41.9% to 40.0% on Bengali. On average, our approach outperforms the fine-tuned model by 1.6% phone error rate. The results demonstrate that our sampling approach is more effective at optimizing the acoustic model on the target corpus. We also train baseline models by appending corpus embeddings to input features, but the proposed model outperforms those baselines similarly.",
+      "One interesting trend we observed in the table is that the improvements differ across the target corpora. For instance, the improvement on the Dutch corpus is 3.4%, on the other hand, its improvement of 0.6% is relatively smaller on the Zulu dataset. We believe the difference in improvements can be explained by the size of each corpus. The size of Dutch corpus is very small as shown in Table. TABREF3 , therefore the fine-tuned model is prone to overfit to the dataset very quickly. In contrast, it is less likely for a larger corpus to overfit. Compared with the fine-tuned model, our approach optimizes the model by gradually changing the temperature without quick overfitting. This mechanism could be interpreted as a built-in regularization. As a result, our model can achieve much better performance in small corpora by preventing the overfitting effect.",
+      "To understand how our corpus embeddings contribute to our approach, we rank those embeddings and show the top-2 similar corpora for each corpus in Table. TABREF17 . We note that the target corpus itself is removed from the rankings because it is the most related corpus to itself. The results of the top half show very clearly that our embeddings can capture the language level information: For most English and Mandarin corpora, the most related corpus is another English or Mandarin corpus. Additionally, the bottom half of the table indicates that our embeddings are able to capture domain level information as well. For instance, the top 2 related corpus for Amharic is Bengali and Swahili. According to Table. TABREF3 , those three corpora belong to the telephone domain. In addition, Dutch is a read corpus, its top 2 related corpora are also from the same domain. This also explains why the 1st related corpus of Mandarin (hk) is Bengali: because both of them are from the same telephone domain.",
+      "To further investigate the domain information contained in the corpus embeddings, we train the corpus embeddings with an even larger corpora collection (36 corpora) and plot all of them in Figure. FIGREF18 . To create the plot, the dimension of each corpus embedding is reduced to 2 with t-SNE BIBREF32 . The figure demonstrates clearly that our corpus embeddings are capable of capturing the domain information: all corpora with the same domain are clustered together. This result also means that our approach improves the model by sampling more frequently from the corpora of the same speech domain."
+    ],
+    [
+      "In this work, we propose an approach to compute corpus-level embeddings. We also introduce Corpus Relatedness Sampling approach to train multilingual speech recognition models based on those corpus embeddings. Our experiment shows that our approach outperforms the fine-tuned multilingual models in all 16 test corpora by 1.6 phone error rate on average. Additionally, we demonstrate that our corpus embeddings can capture both language and domain information of each corpus."
+    ],
+    [
+      "This project was sponsored by the Defense Advanced Research Projects Agency (DARPA) Information Innovation Office (I2O), program: Low Resource Languages for Emergent Incidents (LORELEI), issued by DARPA/I2O under Contract No. HR0011-15-C-0114."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: Weakly Supervised Domain Detection
+
+Question: What is the performance of their model?
\ No newline at end of file
diff --git a/qasper-1201/instruction.md b/qasper-1201/instruction.md
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+Name of Paper: Weakly Supervised Domain Detection
+
+Question: Which text genres did they experiment with?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Problem Formulation",
+    "The Encoder Module",
+    "The Detector Module",
+    "Experimental Setup",
+    "Automatic Evaluation",
+    "Human Evaluation",
+    "Domain-Specific Summarization",
+    "Conclusions",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "Text classification is a fundamental task in Natural Language processing which has been found useful in a wide spectrum of applications ranging from search engines enabling users to identify content on websites, sentiment and social media analysis, customer relationship management systems, and spam detection. Over the past several years, text classification has been predominantly modeled as a supervised learning problem (e.g., BIBREF0 , BIBREF1 , BIBREF2 ) for which appropriately labeled data must be collected. Such data is often domain-dependent (i.e., covering specific topics such as those relating to \u201cBusiness\u201d or \u201cMedicine\u201d) and a classifier trained using data from one domain is likely to perform poorly on another. For example, the phrase \u201cthe mouse died quickly\u201d may indicate negative sentiment in a customer review describing the hand-held pointing device or positive sentiment when describing a laboratory experiment performed on a rodent. The ability to handle a wide variety of domains has become more pertinent with the rise of data-hungry machine learning techniques like neural networks and their application to a plethora of textual media ranging from news articles to twitter, blog posts, medical journals, Reddit comments, and parliamentary debates BIBREF0 , BIBREF3 , BIBREF4 , BIBREF5 .",
+      "The question of how to best deal with multiple domains when training data is available for one or few of them has met with much interest in the literature. The field of domain adaptation BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 aims at improving the learning of a predictive function in a target domain where there is little or no labeled data, using knowledge transferred from a source domain where sufficient labeled data is available. Another line of work BIBREF11 , BIBREF12 , BIBREF13 assumes that labeled data may exist for multiple domains, but in insufficient amounts to train classifiers for one or more of them. The aim of multi-domain text classification is to leverage all the available resources in order to improve system performance across domains simultaneously.",
+      "In this paper we investigate the question of how domain-specific data might be obtained in order to enable the development of text classification tools as well as more domain aware applications such as summarization, question answering, and information extraction. We refer to this task as domain detection and assume a fairly common setting where the domains of a corpus collection are known and the aim is to identify textual segments which are domain-heavy, i.e., documents, sentences, or phrases providing evidence for a given domain.",
+      "Domain detection can be formulated as a multilabel classification problem, where a model is trained to recognize domain evidence at the sentence-, phrase-, or word-level. By definition then, domain detection would require training data with fine-grained domain labels, thereby increasing the annotation burden; we must provide labels for training domain detectors and for modeling the task we care about in the first place. In this paper we consider the problem of fine-grained domain detection from the perspective of Multiple Instance Learning (MIL; BIBREF14 ) and develop domain models with very little human involvement. Instead of learning from individually labeled segments, our model only requires document-level supervision and optionally prior domain knowledge and learns to introspectively judge the domain of constituent segments. Importantly, we do not require document-level domain annotations either since we obtain these via distant supervision by leveraging information drawn from Wikipedia.",
+      "Our domain detection framework comprises two neural network modules; an encoder learns representations for words and sentences together with prior domain information if the latter is available (e.g., domain definitions), while a detector generates domain-specific scores for words, sentences, and documents. We obtain a segment-level domain predictor which is trained end-to-end on document-level labels using a hierarchical, attention-based neural architecture BIBREF15 . We conduct domain detection experiments on English and Chinese and measure system performance using both automatic and human-based evaluation. Experimental results show that our model outperforms several strong baselines and is robust across languages and text genres, despite learning from weak supervision. We also showcase our model's application potential for text summarization.",
+      "Our contributions in this work are threefold; we propose domain detection, as a new fine-grained multilabel learning problem which we argue would benefit the development of domain aware NLP tools; we introduce a weakly supervised encoder-detector model within the context of multiple instance learning; and demonstrate that it can be applied across languages and text genres without modification."
+    ],
+    [
+      "Our work lies at the intersection of multiple research areas, including domain adaptation, representation learning, multiple instance learning, and topic modeling. We review related work below."
+    ],
+    [
+      "We formulate domain detection as a multilabel learning problem. Our model is trained on samples of document-label pairs. Each document consists of INLINEFORM0 sentences INLINEFORM1 and is associated with discrete labels INLINEFORM2 . In this work, domain labels are not annotated manually but extrapolated from Wikipedia (see Section SECREF6 for details). In a non-MIL framework, a model typically learns to predict document labels by directly conditioning on its sentence representations INLINEFORM3 or their aggregate. In contrast, INLINEFORM4 under MIL is a learned function INLINEFORM5 of latent instance-level labels, i.e., INLINEFORM6 . A MIL classifier will therefore first produce domain scores for all instances (aka sentences), and then learn to integrate instance scores into a bag (aka document) prediction.",
+      "In this paper we further assume that the instance-bag relation applies to sentences and documents but also to words and sentences. In addition, we incorporate prior domain information to facilitate learning in a weakly supervised setting: each domain is associated with a definition INLINEFORM0 , i.e., a few sentences providing a high-level description of the domain at hand. For example, the definition of the \u201cLifestyle\u201d domain is \u201cthe interests, opinions, behaviors, and behavioral orientations of an individual, group, or culture\u201d.",
+      "Figure FIGREF5 provides an overview of our Domain Detection Network, which we call DetNet. The model comprises two modules; an encoder learns representations for words and sentences whilst incorporating prior domain information; a detector generates domain scores for words, sentences, and documents by selectively attending to previously encoded information. We describe the two modules in more detail below."
+    ],
+    [
+      "We learn representations for words and sentences using identical encoders with separate learning parameters. Given a document, the two encoders implement the following steps: INLINEFORM0 ",
+      " For each sentence INLINEFORM0 , the word-level encoder yields contextualized word representations INLINEFORM1 and their attention weights INLINEFORM2 . Sentence embeddings INLINEFORM3 are obtained via weighted averaging and then provided as input to the sentence-level encoder which outputs contextualized representations INLINEFORM4 and their attention weights INLINEFORM5 .",
+      "In this work we aim to model fairly long documents (e.g., Wikipedia articles; see Section SECREF6 for details). For this reason, our encoder builds on the Transformer architecture BIBREF15 , a recently proposed highly efficient model which has achieved state-of-the-art performance in machine translation BIBREF15 and question answering BIBREF35 . The Transformer aims at reducing the fundamental constraint of sequential computation which underlies most architectures based on recurrent neural networks. It eliminates recurrence in favor of applying a self-attention mechanism which directly models relationships between all words in a sentence, regardless of their position."
+    ],
+    [
+      "DetNet adopts three detectors corresponding to words, sentences, and documents: INLINEFORM0 ",
+      " WordDet first produces word domain scores using both lexical semantic information INLINEFORM0 and prior (domain) knowledge INLINEFORM1 ; SentDet yields domain scores for sentences while integrating downstream instance signals INLINEFORM2 and sentence semantics INLINEFORM3 ; finally, DocDet makes the final document-level predictions based on sentence scores."
+    ],
+    [
+      "[t] Document Generation Input: INLINEFORM0 : Label combinations",
+      " INLINEFORM0 : Sentence subcorpora",
+      " INLINEFORM0 : Maximum document length",
+      "Output: A synthetic document [0] Generate INLINEFORM0 Generate a document domain set INLINEFORM1 INLINEFORM2 ",
+      " INLINEFORM0 Number of domain labels Generate a noisy domain INLINEFORM1 INLINEFORM2 ",
+      " INLINEFORM0 ; A set of candidate domain sets",
+      " INLINEFORM0 INLINEFORM1 INLINEFORM2 ",
+      " INLINEFORM0 Number of unused labels INLINEFORM1 Number of sentence blocks INLINEFORM2 For generated sentences",
+      " INLINEFORM0 INLINEFORM1 Generate INLINEFORM2 Generate INLINEFORM3 sentences INLINEFORM4 INLINEFORM5 INLINEFORM6 INLINEFORM7 INLINEFORM8 Shuffle INLINEFORM9 INLINEFORM10 "
+    ],
+    [
+      "In this section we present the results of our automatic evaluation for sentence and document predictions. Problematically, for sentence predictions we do not have gold-standard domain labels (we have only extrapolated these from Wikipedia for documents). We therefore developed an automatic approach for creating silver standard domain labels which we describe below."
+    ],
+    [
+      "Aside from automatic evaluation, we also assessed model performance against human elicited domain labels for sentences and words. The purpose of this experiment was threefold: (a) to validate the results obtained from automatic evaluation; (b) to evaluate finer-grained model performance at the word level; and (c) to examine whether our model generalizes to non-Wikipedia articles. For this, we created a third test set from the New York Times, in addition to our Wikipedia-based English and Chinese datasets. For all three corpora, we randomly sampled two documents for each domain, and then from each document, we sampled one long paragraph or a few consecutive short paragraphs containing 8\u201312 sentences. Amazon Mechanical Turkers were asked to read these sentences and assign a domain based on the seven labels used in this paper (multiple labels were allowed). Participants were provided with domain definitions. We obtained five annotations per sentence and adopted the majority label as the sentence's domain label. We obtained two annotated datasets for English (Wiki-en and NYT-en) and one for Chinese (Wiki-zh), consisting of 122/14, 111/11, and 117/12 sentences/documents each.",
+      "Word-level domain evaluation is more challenging; taken out-of-context, individual words might be uninformative or carry meanings compatible with multiple domains. Expecting crowdworkers to annotate domain labels word-by-word with high confidence, might be therefore problematic. In order to reduce annotation complexity, we opted for a retrieval-style task for word evaluation. Specifically, AMT workers were given a sentence and its domain label (obtained from the sentence-level elicitation study described above), and asked to highlight which words they considered consistent with the domain of the sentence. We used the same corpora/sentences as in our first AMT study. Analogously, words in each sentence were annotated by five participants and their labels were determined by majority agreement.",
+      "Fully hierarchical variants of our model (i.e., DetNet INLINEFORM0 , DetNet INLINEFORM1 ) and L-LDA are able to produce word-level predictions; we thus retrieved the words within a sentence whose domain score was above the threshold of 0 and compared them against the labels provided by crowdworkers. MilNet and DetNet INLINEFORM2 can only make sentence-level predictions. In this case, we assume that the sentence domain applies to all words therein. HierNet can only produce document-level predictions based on which we generate sentence labels and further assume that these apply to sentence words too. Again, we report INLINEFORM3 2prp+r INLINEFORM4 p INLINEFORM5 r INLINEFORM6 ",
+      "We show model performance against AMT domain labels in Table TABREF42 . Consistent with the automatic evaluation results, DetNet variants are the best performing models on the sentence-level task. On the Wikipedia datasets, DetNet INLINEFORM0 or DetNet INLINEFORM1 outperform all baselines and DetNet INLINEFORM2 by a large margin, showing that word-level signals can indeed help detect sentence domains. Although statistical models are typically less accurate when they are applied to data that has a different distribution from the training data, DetNet INLINEFORM3 works surprisingly well on NYT, substantially outperforming all other systems. We also notice that prior information is useful in making domain predictions for NYT sentences: since our models are trained on Wikipedia, prior domain definitions largely alleviate the genre shift to non-Wikipedia sentences. Table TABREF43 provides a breakdown of the performance of DetNet INLINEFORM4 across domains. Overall, the model performs worst on LIF and GEN domains (which are very broad) and best on BUS and MIL (which are very narrow).",
+      "With regard to word-level evaluation, DetNet INLINEFORM0 and DetNet INLINEFORM1 are the best systems and are significantly better against all comparison models by a wide margin, except L-LDA. The latter is a strong domain detection system at the word-level since it is able to directly associate words with domain labels (see Equation ( EQREF34 )) without resorting to document- or sentence-level predictions. However, our two-level hierarchical model is superior considering all-around performance across sentences and documents. The results here accord with our intuition from previous experiments: hierarchical models outperform simpler variants (including MilNet) since they are able to capture and exploit fine-grained domain signals relatively accurately. Interestingly, prior information does not seem to have an effect on the Wikipedia datasets, but is useful when transferring to NYT. We also observe that models trained on the Chinese datasets perform consistently better than English. Analysis of the annotations provided by crowdworkers revealed that the ratio of domain words in Chinese is higher compared to English (27.47 INLINEFORM2 vs. 13.86 INLINEFORM3 in Wikipedia and 16.42 INLINEFORM4 in NYT), possibly rendering word retrieval in Chinese an easier task.",
+      "Table TABREF44 shows the 15 most representative domain words identified by our model (DetNet INLINEFORM0 ) on Wiki-en for our seven domains. We obtained this list by weighting word domain scores INLINEFORM1 with their attention scores: DISPLAYFORM0 ",
+      "and ranking all words in the development set according to INLINEFORM0 , separately for each domain. Since words appearing in different contexts are usually associated with multiple domains, we determine a word's ranking for a given domain based on the highest score. As shown in Table TABREF44 , biosecurity and authoritarianism are prevalent in both GOV and LAW domains. Interestingly, with contextualized word representations, fairly general English words are recognized as domain heavy. For example, technique is a strong domain word in HEA and 420 in GOV (the latter is slang for the consumption of cannabis and highly associated with government regulations).",
+      "For comparison, we also show the top domain words identified by L-LDA via matrix INLINEFORM0 (see Equation ( EQREF34 )). To produce meaningful output, we have removed stop words and punctuation tokens, which are given very high domain scores by L-LDA (this is not entirely surprising since INLINEFORM1 is based on simple co-occurrence). Notice that no such post-processing is necessary for our model. As shown in Table TABREF44 , the top domain words identified by L-LDA (on the right) are more general and less informative, compared to those from DetNet INLINEFORM2 (on the left)."
+    ],
+    [
+      "In this section we illustrate how fine-grained domain scores can be used to produce domain summaries, following an extractive, unsupervised approach. We assume the user specifies the domains they are interested in a priori (e.g., LAW, HEA) and the system returns summaries targeting the semantics of these domains.",
+      "Specifically, we introduce DetRank, an extension of the well-known TextRank algorithm BIBREF42 , which incorporates domain signals acquired by DetNet INLINEFORM0 . For each document, TextRank builds a directed graph INLINEFORM1 with nodes INLINEFORM2 corresponding to sentences, and undirected edges INLINEFORM3 whose weights are computed based on sentence similarity. Specifically, edge weights are represented with matrix INLINEFORM4 where each element INLINEFORM5 corresponds to the transition probability from vertex INLINEFORM6 to vertex INLINEFORM7 . Following barrios2016variations, INLINEFORM8 is computed with the Okapi BM25 algorithm BIBREF43 , a probabilistic version of TF-IDF, and small weights ( INLINEFORM9 ) are set to zeros. Unreachable nodes are further pruned to acquire the final vertex set INLINEFORM10 .",
+      "To enhance TextRank with domain information, we first multiply sentence-level domain scores INLINEFORM0 with their corresponding attention scores: DISPLAYFORM0 ",
+      "and for a given domain INLINEFORM0 , we can extract a (domain) sentence score vector INLINEFORM1 . Then, from INLINEFORM2 , we produce vector INLINEFORM3 representing a distribution of domain signals over sentences: DISPLAYFORM0 ",
+      "In order to render domain signals in different sentences more discernible, we scale all elements in INLINEFORM0 to INLINEFORM1 before obtaining a legitimate distribution with the INLINEFORM2 function. Finally, we integrate the domain component into the original transition matrix as: DISPLAYFORM0 ",
+      "where INLINEFORM0 controls the extent to which domain-specific information influences sentence selection for the summarization task; higher INLINEFORM1 will lead to summaries which are more domain-relevant. Here, we empirically set INLINEFORM2 . The main difference between DetRank and TextRank is that TextRank treats INLINEFORM3 as a damping factor and a uniform probability distribution is applied to INLINEFORM4 .",
+      "In order to decide which sentence to include in the summary, a node\u2019s centrality is measured using a graph-based ranking algorithm BIBREF42 . Specifically, we run a Markov chain with INLINEFORM0 on INLINEFORM1 until it converges to the stationary distribution INLINEFORM2 where each element denotes the salience of a sentence. In the proposed DetRank algorithm, INLINEFORM3 jointly expresses the importance of a sentence in the document and its relevance to the given domain (controlled by INLINEFORM4 ). We rank sentences according to INLINEFORM5 and select the top INLINEFORM6 ones, subject to a budget (e.g., 100 words).",
+      "We ran a judgment elicitation study on summaries produced by TextRank and DetRank. Participants were provided with domain definitions and asked to decide which summary was best according to the criteria of: Informativeness (does the summary contain more information about a specific domain, e.g., \u201cGovernment and Politics\u201d?), Succinctness (does the summary avoid unnecessary detail and redundant information?), and Coherence (does the summary make logical sense?). Amazon Mechanical Turk (AMT) workers were allowed to answer \u201cBoth\u201d or \u201cNeither\u201d in cases where they could not discriminate between summaries. We sampled 50 summary pairs from the English Wikipedia development set. We collected three responses per summary pair and determined which system participants preferred based on majority agreement.",
+      "Table TABREF51 shows the proportion of times AMT workers preferred each system according to the criteria of Informativeness, Succinctness, Coherence, and overall. As can be seen, participants find DetRank summaries more informative and coherent. While it is perhaps not surprising for DetRank to produce summaries which are domain informative since it explicitly takes domain signals into account, it is interesting to note that focusing on a specific domain also helps discard irrelevant information and produce more coherent summaries. This, on the other hand, possibly renders DetRank's summaries more verbose (see the Succinctness ratings in Table TABREF51 ).",
+      "Figure FIGREF46 shows example summaries for the Wikipedia article Arms Industry for domains MIL and BUS. Both summaries begin with a sentence which introduces the arms industry to the reader. When MIL is the domain of interest, the summary focuses on military products such as guns and missiles. When the domain changes to BUS, the summary puts more emphasis on trade, e.g., market competition and companies doing military business, such as Boeing and Eurofighter."
+    ],
+    [
+      "In this work, we proposed an encoder-detector framework for domain detection. Leveraging only weak domain supervision, our model achieves results superior to competitive baselines across different languages, segment granularities, and text genres. Aside from identifying domain specific training data, we also show that our model holds promise for other natural language tasks, such as text summarization. Beyond domain detection, we hope that some of the work described here might be of relevance to other multilabel classification problems such as sentiment analysis BIBREF29 , relation extraction BIBREF44 , and named entity recognition BIBREF45 . More generally, our experiments show that the proposed framework can be applied to textual data using minimal supervision, significantly alleviating the annotation bottleneck for text classification problems.",
+      "A key feature in achieving performance superior to competitive baselines is the hierarchical nature of our model, where representations are encoded step-by-step, first for words, then for sentences, and finally for documents. The framework flexibly integrates prior information which can be used to enhance the otherwise weak supervision signal or to render the model more robust across genres. In the future, we would like to investigate semi-supervised instantiations of MIL, where aside from bag labels, small amounts of instance labels are also available BIBREF23 . It would also be interesting to examine how the label space influences model performance, especially since in our scenario the labels are extrapolated from Wikipedia and might be naturally noisy and/or ambiguous."
+    ],
+    [
+      "The authors would like to thank the anonymous reviewers and the action editor, Yusuke Miyao, for their valuable feedback. We acknowledge the financial support of the European Research Council (Lapata; award number 681760). This research is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via contract FA8650-17-C-9118. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation therein."
+    ]
+  ]
+}
+```
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+Name of Paper: Harry Potter and the Action Prediction Challenge from Natural Language
+
+Question: How well does a simple bag-of-words baseline do?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "HPAC: The Harry Potter's Action prediction Corpus",
+    "Domain motivation",
+    "Data crawling",
+    "Models",
+    "Machine learning models",
+    "Sequential models",
+    "Conclusion",
+    "Acknowlegments",
+    "Corpus distribution"
+  ],
+  "paragraphs": [
+    [
+      "Natural language processing (nlp) has achieved significant advances in reading comprehension tasks BIBREF0 , BIBREF1 . These are partially due to embedding methods BIBREF2 , BIBREF3 and neural networks BIBREF4 , BIBREF5 , BIBREF6 , but also to the availability of new resources and challenges. For instance, in cloze-form tasks BIBREF7 , BIBREF8 , the goal is to predict the missing word given a short context. weston2015towards presented baBI, a set of proxy tasks for reading comprenhension. In the SQuAD corpus BIBREF9 , the aim is to answer questions given a Wikipedia passage. 2017arXiv171207040K introduce NarrativeQA, where answering the questions requires to process entire stories. In a related line, 2017arXiv171011601F use fictional crime scene investigation data, from the CSI series, to define a task where the models try to answer the question: \u2018who committed the crime?\u2019.",
+      "In an alternative line of work, script induction BIBREF10 has been also a useful approach to evaluate inference and semantic capabilities of nlp systems. Here, a model processes a document to infer new sequences that reflect events that are statistically probable (e.g. go to a restaurant, be seated, check the menu, ...). For example, chambers2008unsupervised introduce narrative event chains, a representation of structured knowledge of a set of events occurring around a protagonist. They then propose a method to learn statistical scripts, and also introduce two different evaluation strategies. With a related aim, Pichotta2014Statistical propose a multi-event representation of statistical scripts to be able to consider multiple entities. These same authors BIBREF11 have also studied the abilities of recurrent neural networks for learning scripts, generating upcoming events given a raw sequence of tokens, using bleu BIBREF12 for evaluation.",
+      "This paper explores instead a new task: action prediction from natural language descriptions of scenes. The challenge is addressed as follows: given a natural language input sequence describing the scene, such as a piece of a story coming from a transcript, the goal is to infer which action is most likely to happen next."
+    ],
+    [
+      "To build an action prediction corpus, we need to: (1) consider the set of actions, and (2) collect data where these occur. Data should come from different users, to approximate a real natural language task. Also, it needs to be annotated, determining that a piece of text ends up triggering an action. These tasks are however time consuming, as they require annotators to read vast amounts of large texts. In this context, machine comprehension resources usually establish a compromise between their complexity and the costs of building them BIBREF7 , BIBREF13 ."
+    ],
+    [
+      "We rely on an intuitive idea that uses transcripts from the Harry Potter world to build up a corpus for textual action prediction. The domain has a set of desirable properties to evaluate reading comprehension systems, which we now review.",
+      "Harry Potter novels define a variety of spells. These are keywords cast by witches and wizards to achieve purposes, such as turning on a light (\u2018Lumos\u2019), unlocking a door (\u2018Alohomora\u2019) or killing (\u2018Avada Kedavra\u2019). They abstract complex and non-ambiguous actions. Their use also makes it possible to build an automatic and self-annotated corpus for action prediction. The moment a spell occurs in a text represents a response to the environment, and hence, it can be used to label the preceding text fragment as a scene description that ends up triggering that action. Table 1 illustrates it with some examples from the original books.",
+      "This makes it possible to consider texts from the magic world of Harry Potter as the domain for the action prediction corpus, and the spells as the set of eligible actions. Determining the length of the preceding context, namely snippet, that has to be considered as the scene description is however not trivial. This paper considers experiments (\u00a7 \"Experiments\" ) using snippets with the 32, 64, 96 and 128 previous tokens to an action. We provide the needed scripts to rebuild the corpus using arbitrary lengths."
+    ],
+    [
+      "The number of occurrences of spells in the original Harry Potter books is small (432 occurrences), which makes it difficult to train and test a machine learning model. However, the amount of available fan fiction for this saga allows to create a large corpus. For hpac, we used fan fiction (and only fan fiction texts) from https://www.fanfiction.net/book/Harry-Potter/ and a version of the crawler by milli2016beyond. We collected Harry Potter stories written in English and marked with the status \u2018completed\u2019. From these we extracted a total of 82 836 spell occurrences, that we used to obtain the scene descriptions. Table 2 details the statistics of the corpus (see also Appendix \"Corpus distribution\" ). Note that similar to Twitter corpora, fan fiction stories can be deleted over time by users or admins, causing losses in the dataset.",
+      "We tokenized the samples with BIBREF14 and merged the occurrences of multi-word spells into a single token."
+    ],
+    [
+      "This work addresses the task as a classification problem, and in particular as a sequence to label classification problem. For this reason, we rely on standard models used for this type of task: multinomial logistic regression, a multi-layered perceptron, convolutional neural networks and long short-term memory networks. We outline the essentials of each of these models, but will treat them as black boxes. In a related line, kaushik2018much discuss the need of providing rigorous baselines that help better understand the improvement coming from future and complex models, and also the need of not demanding architectural novelty when introducing new datasets.",
+      "Although not done in this work, an alternative (but also natural) way to address the task is as a special case of language modelling, where the output vocabulary is restricted to the size of the `action' vocabulary. Also, note that the performance for this task is not expected to achieve a perfect accuracy, as there may be situations where more than one action is reasonable, and also because writers tell a story playing with elements such as surprise or uncertainty.",
+      "The source code for the models can be found in the GitHub repository mentioned above."
+    ],
+    [
+      "The input sentence $w_{1:n}$ is encoded as a one-hot vector, $\\mathbf {v}$ (total occurrence weighting scheme).",
+      "Let mlr $_\\theta (\\mathbf {v})$ be an abstraction of a multinomial logistic regression parametrized by $\\theta $ , the output for an input $\\mathbf {v}$ is computed as the $\\operatornamewithlimits{arg\\,max}_{a \\in A}$ $P(y=a|\\mathbf {v})$ , where $P(y=a|\\mathbf {v})$ is a $softmax$ function, i.e, $P(y=a|\\mathbf {v}) = \\frac{e^{W_{a} \\cdot \\mathbf {v}}}{\\sum _{a^{\\prime }}^{A} e^{W_{a^{\\prime }} \\cdot \\mathbf {v}}}$ .",
+      "We use one hidden layer with a rectifier activation function ( $relu(x)$ = $max(0,x)$ ). The output is computed as mlp $_\\theta (\\mathbf {v})$ = $softmax(W_2 \\cdot relu(W \\cdot \\mathbf {v} + \\mathbf {b}) + \\mathbf {b_2})$ ."
+    ],
+    [
+      "The input sequence is represented as a sequence of word embeddings, $\\mathbf {w}_{1:n}$ , where $\\mathbf {w}_i$ is a concatenation of an internal embedding learned during the training process for the word $w_i$ , and a pre-trained embedding extracted from GloVe BIBREF15 , that is further fine-tuned.",
+      " BIBREF5 : The output for an element $\\mathbf {w}_i$ also depends on the output of $\\mathbf {w}_{i-1}$ . The lstm $_\\theta (\\mathbf {w}_{1:n})$ takes as input a sequence of word embeddings and produces a sequence of hidden outputs, $\\mathbf {h}_{1:n}$ ( $\\mathbf {h}_{i}$ size set to 128). The last output of the lstm $_\\theta $ , $\\mathbf {h}_n$ , is fed to a mlp $_\\theta $ .",
+      " BIBREF16 , BIBREF17 . It captures local properties over continuous slices of text by applying a convolution layer made of different filters. We use a wide convolution, with a window slice size of length 3 and 250 different filters. The convolutional layer uses a $\\mathit {relu}$ as the activation function. The output is fed to a max pooling layer, whose output vector is passed again as input to a mlp $_\\theta $ ."
+    ],
+    [
+      "We explored action prediction from written stories. We first introduced a corpus set in the world of Harry Potter's literature. Spells in these novels act as keywords that abstract actions. This idea was used to label a collection of fan fiction. We then evaluated standard nlp approaches, from logistic regression to sequential models such as lstms. The latter performed better in general, although vanilla models achieved a higher performance for actions that occurred a few times in the training set. An analysis over the output of the lstm approach also revealed difficulties to discriminate among semantically related actions.",
+      "The challenge here proposed corresponded to a fictional domain. A future line of work we are interested in is to test whether the knowledge learned with this dataset could be transferred to real-word actions (i.e. real-domain setups), or if such transfer is not possible and a model needs to be trained from scratch."
+    ],
+    [
+      "This work has received support from the TELEPARES-UDC project (FFI2014-51978-C2-2-R) and the ANSWER-ASAP project (TIN2017-85160-C2-1-R) from MINECO, and from Xunta de Galicia (ED431B 2017/01), and from the European Research Council (ERC), under the European Union's Horizon 2020 research and innovation programme (FASTPARSE, grant agreement No 714150)."
+    ],
+    [
+      "Table 6 summarizes the label distribution across the training, development and test sets of the hpac corpus."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1207/instruction.md b/qasper-1207/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..3ba60907d7e1fd2f737d478a489344b71db35350
--- /dev/null
+++ b/qasper-1207/instruction.md
@@ -0,0 +1,103 @@
+Name of Paper: Finding Dominant User Utterances And System Responses in Conversations
+
+Question: Do they study frequent user responses to help automate modelling of those?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "The Proposed Approach",
+    " Model",
+    "SimCluster Algorithm",
+    "Alignment",
+    "Experiments on Synthetic Dataset",
+    "Evaluation and Results",
+    "Description and preprocessing of dataset",
+    "Results",
+    "Conclusions",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "There are several existing works that focus on modelling conversation using prior human to human conversational data BIBREF0 , BIBREF1 , BIBREF2 . BIBREF3 models the conversation from pairs of consecutive tweets. Deep learning based approaches have also been used to model the dialog in an end to end manner BIBREF4 , BIBREF5 . Memory networks have been used by Bordes et al Bor16 to model goal based dialog conversations. More recently, deep reinforcement learning models have been used for generating interactive and coherent dialogs BIBREF6 and negotiation dialogs BIBREF7 .",
+      "Industry on the other hand has focused on building frameworks that allow manual specification of dialog models such as api.ai, Watson Conversational Services, and Microsoft Bot framework. These frameworks provide ways to specify intents, and a dialog flow. The user utterances are mapped to intents that are passed to a dialog flow manager. The dialog manager generates a response and updates the dialog state. See Figure FIGREF4 for an example of some intents and a dialog flow in a technical support domain. The dialog flow shows that when a user expresses an intent of # laptop_heat, then the system should respond with an utterance \u201cCould you let me know the serial number of your machine \u201d. The designer needs to specify intents (for example # laptop_heat, # email_not_opening) and also provide corresponding system responses in the dialog flow. This way of specifying a dialog model using intents and corresponding system responses manually is more popular in industry than a data driven approach as it makes dialog model easy to interpret and debug as well as provides a better control to a dialog designer. However, this is very time consuming and laborious and thus involves huge costs.",
+      "One approach to reduce the task of a dialog designer is to provide her with frequent user intents and possible corresponding system responses in a given domain. This can be done by analysing prior human to human conversations in the domain. Figure FIGREF5 (a) provides some example conversations in the technical support domain between users and agents.",
+      "In order to identify frequent user intents, one can use existing clustering algorithms to group together all the utterances from the users. Here each cluster would correspond to a new intent and each utterance in the cluster would correspond to an example for the intent. Similarly the agents utterances can be clustered to identify system responses. However, we argue that rather than treating user utterances and agents responses in an isolated manner, there is merit in jointly clustering them. There is adjacency information of these utterances that can be utilized to identify better user intents and system responses. As an example, consider agent utterances A.2 in box A and A.2 in box C in Figure FIGREF5 (a). The utterances \u201cWhich operating system do you use?\" and \u201cWhat OS is installed in your machine\" have no syntactic similarity and therefore may not be grouped together. However the fact that these utterances are adjacent to the similar user utterances \u201cI am unable to start notes email client\" and \u201cUnable to start my email client\" provides some evidence that the agent utterances might be similar. Similarly the user utterances \u201cMy system keeps getting rebooted\" and \u201cMachine is booting time and again\" ( box B and D in Figure FIGREF5 (a))- that are syntactically not similar - could be grouped together since the adjacent agent utterances, \u201cIs your machine heating up?\" and \u201cIs the machine heating?\" are similar.",
+      "Joint clustering of user utterances and agent utterances allow us to align the user utterance clusters with agent utterance clusters. Figure FIGREF5 (b) shows some examples of user utterance clusters and agent utterance clusters along with their alignments. Note that the user utterance clusters can be used by a dialog designer to specify intents, the agent utterance clusters can be used to create system responses and their alignment can be used to create part of the dialog flow.",
+      "We propose two ways to take adjacency information into account. Firstly we propose a method called SimCluster for jointly or simultaneously clustering user utterances and agent utterances. SimCluster extends the K-means clustering method by incorporating additional penalty terms in the objective function that try to align the clusters together (described in Section SECREF3 ). The algorithm creates initial user utterance clusters as well as agent utterance clusters and then use bi-partite matching to get the best alignment across these clusters. Minimizing the objective function pushes the cluster centroids to move towards the centroids of the aligned clusters. The process implicitly ensures that the similarity of adjacent agent utterances affect the grouping of user utterances and conversely similarity of adjacent user utterances affect the grouping of agent utterances. In our second approach we use the information about neighbouring utterances for creating the vector representation of an utterance. For this we train a sequence to sequence model BIBREF8 to create the vectors (described in Section SECREF5 ).",
+      "Our experiments described in section SECREF5 show that we achieve upto 10% absolute improvement in F1 scores over standard K-means using SimCluster. Also we observe that clustering of customer utterances gains significantly by using the adjacency information of agent utterances whereas the gain in clustering quality of agent utterances is moderate. This is because the agent utterances typically follow similar syntactic constructs whereas customer utterances are more varied. Considering the agent utterances into account while clustering users utterances is thus helpful. The organization of the rest of the paper is as follows. In Section SECREF2 we describe the related work. In Section SECREF3 we describe our problem formulation for clustering and the associated algorithm. Finally in sections SECREF4 and SECREF5 we discuss our experiments on synthetic and real datasets respectively."
+    ],
+    [
+      "The notion of adjacency pairs was introduced by Sacks et al SSE74 to formalize the structure of a dialog. Adjacency pairs have been used to analyze the semantics of the dialog in computational linguistics community BIBREF9 . Clustering has been used for different tasks related to conversation. BIBREF10 considers the task of discovering dialog acts by clustering the raw utterances. We aim to obtain the frequent adjacency pairs through clustering.",
+      "There have been several works regarding extensions of clustering to different scenarios such as:-"
+    ],
+    [
+      "In this section we describe our approach SimCluster that performs clustering in the two domains simultaneously and ensures that the generated clusters can be aligned with each other. We will describe the model in section SECREF9 and the algorithm in Section SECREF11 ."
+    ],
+    [
+      "We consider a problem setting where we are given a collection of pairs of consecutive utterances, with vector representations INLINEFORM0 where INLINEFORM1 s are in speaker 1's domain and INLINEFORM2 s are in speaker 2's domain. We need to simultaneously cluster the utterances in their respective domains to minimize the variations within each domain and also ensure that the clusters for both domains are close together.",
+      "We denote the clusters for speaker 1's domain by INLINEFORM0 with their respective means INLINEFORM1 . We denote the clusters assignments for INLINEFORM2 by INLINEFORM3 .",
+      "We denote the clusters for second speaker by INLINEFORM0 with their respective means INLINEFORM1 . We denote the clusters assignments for INLINEFORM2 by INLINEFORM3 . The usual energy function has the terms for distance of points from their corresponding cluster centroids. To be able to ensure that the clusters in each domain are similar, we also consider an alignment between the centroids of the two domains. Since the semantic representations in the two domains are not comparable we consider a notion of induced centroids.",
+      "We define the induced centroids INLINEFORM0 as the arithmetic means of the points INLINEFORM1 s such that INLINEFORM2 's have the same cluster assigned to them. Similarly, we define INLINEFORM3 as the arithmetic means of INLINEFORM4 s such that INLINEFORM5 s have the same cluster assigned to them. More formally, we define these induced centroids as:- INLINEFORM6 ",
+      "and INLINEFORM0 ",
+      "The alignment between these clusters given by the function INLINEFORM0 , which is a bijective mapping from the cluster indices in speaker 1's domain to those in speaker 2's domain. Though there can be several choices for this alignment function, we consider this alignment to be a matching which maximizes the sum of number of common indices in the aligned clusters. More formally we define INLINEFORM1 ",
+      "Then the matching INLINEFORM0 is defined to be the bijective function which maximizes INLINEFORM1 . We consider a term in the cost function corresponding to the sum of distances between the original centroids and the matched induced centroids. Our overall cost function is now given by:- INLINEFORM2 ",
+      "We explain the above definition via an example. Consider the clusters shown in Figure FIGREF10 . Here the INLINEFORM0 would match INLINEFORM1 to INLINEFORM2 , INLINEFORM3 to INLINEFORM4 and INLINEFORM5 to INLINEFORM6 , giving a match score of 6. Since INLINEFORM7 , INLINEFORM8 and INLINEFORM9 are present in the cluster INLINEFORM10 , INLINEFORM11 is given by INLINEFORM12 . Similarly INLINEFORM13 ",
+      " In a similar manner, INLINEFORM0 s can also be defined. Now the alignment terms are given by:- INLINEFORM1 ",
+      " "
+    ],
+    [
+      "[] SimCluster [1] SimClusterInput: INLINEFORM0 ,k (No. of cluster) Output: A cluster assignment INLINEFORM1 for INLINEFORM2 s and a cluster assignment INLINEFORM3 for INLINEFORM4 s Initialize a set of centroids INLINEFORM5 , and INLINEFORM6 Perform simple clustering for a few iterations For each i, compute INLINEFORM7 as the index j among 1 to k which minimizes INLINEFORM8 . Similarly , compute INLINEFORM9 as the index j' among 1 to k which minimizes INLINEFORM10 . Update the centroids, INLINEFORM11 and INLINEFORM12 as:- INLINEFORM13 ",
+      "and INLINEFORM0 ",
+      " Perform a Hungarian matching between the cluster indices in the two domains with weights",
+      "N(j,j') on edges from index j to index j'. convergence To minimize the above energy term we adopt an approach similar to Lloyd's clustering algorithm Llo82 . We assume that we are given a set of initial seeds for the cluster centroids INLINEFORM0 and INLINEFORM1 . We repeat the following steps iteratively:-",
+      "Minimize the energy with respect to cluster assignment keeping centroids unchanged. As in standard K-means algorithm, this is achieved by updating the cluster assignment, INLINEFORM0 for each index i to be the cluster index j which minimizes INLINEFORM1 . Correspondingly for INLINEFORM2 , we pick the cluster index j' which minimizes INLINEFORM3 .",
+      " Minimize the energy with respect to the centroids keeping cluster assignment unchanged. To achieve this step we need to minimize the energy function with respect to the centroids INLINEFORM0 and INLINEFORM1 . This is achieved by setting INLINEFORM2 for each j and INLINEFORM3 for each j.",
+      "Setting INLINEFORM0 , we obtain INLINEFORM1 ",
+      "or equivalently INLINEFORM0 ",
+      " Similarly, setting INLINEFORM0 , we obtain INLINEFORM1 ",
+      "Finally we update the matching between the clusters. To do so, we need to find a bipartite matching match on the cluster indices so as to maximize INLINEFORM0 . We use Hungarian algorithm BIBREF13 to perform the same i.e. we define a bipartite graph with vertices consisting of cluster indices in the two domains. There is an edge from vertex representing cluster indices j (in domain 1) and j' in domain 2, with weight N(j,j'). We find a maximum weight bipartite matching in this graph.",
+      "Similar to Lloyd's algorithm, each step of the above algorithm decreases the cost function. This ensures that the algorithm achieves a local minima of the cost function if it converges. See Algorithm SECREF11 for a formal description of the approach. The centroid update step of the above algorithm also has an intuitive explanation i.e. we are slightly moving away the centroid towards the matched induced centroid. This is consistent with our goal of aligning the clusters together in the two domains."
+    ],
+    [
+      "The algorithm above maintains a mapping between the clusters in each speaker's domain. This mapping serves to give us the alignment between the clusters required to provide a corresponding response for a given user intent."
+    ],
+    [
+      "We performed experiments on synthetically generated dataset since it gives us a better control over the distribution of the data. Specifically we compared the gains obtained using our approach versus the variance of the distribution. We created dataset from the following generative process. [H] Generative Process [1] Generate data",
+      "Pick k points INLINEFORM0 as domain -1 means and a corresponding set of k points INLINEFORM1 as domain-2 means, and covariance matrices INLINEFORM2 ",
+      "iter INLINEFORM0 upto num INLINEFORM1 samples Sample class INLINEFORM2 Sample INLINEFORM3 Sample INLINEFORM4 Add q and a so sampled to the list of q,a pairs We generated the dataset from the above sampling process with means selected on a 2 dimensional grid of size INLINEFORM5 with variance set as INLINEFORM6 in each dimension.10000 sample points were generated. The parameter INLINEFORM7 of the above algorithm was set to 0.5 and k was set to 9 (since the points could be generated from one of the 9 gaussians with centroids on a INLINEFORM8 grid).",
+      "We compared the results with simple K-means clustering with k set to 9. For each of these, the initialization of means was done using INLINEFORM0 sampling approach BIBREF14 ."
+    ],
+    [
+      "To evaluate the clusters we computed the following metrics",
+      "ARI (Adjusted Rand Index): Standard Rand Index is a metric used to check the clustering quality against a given standard set of clusters by comparing the pairwise clustering decisions. It is defined as INLINEFORM0 , where a is the number of true positive pairs, b is the number of true negative pairs, c is the number of false positive pairs and d is the number of false negative pairs. Adjusted rand index corrects the standard rand index for chance and is defined as INLINEFORM1 BIBREF15 .",
+      "We compute ARI score for both the source clusters as well as the target clusters.",
+      "F1 scores: We also report F1 scores for the pairwise clustering decisions. In the above notation we considered the pair-precision as INLINEFORM0 and recall as INLINEFORM1 . The F1 measure is the Harmonic mean given as INLINEFORM2 .",
+      "We used the gaussian index from which an utterance pair was generated as the ground truth label, which served to provide ground truth clusters for computation of the above evaluation metrics. Table TABREF15 shows a comparison of the results on SimCluster versus K-means algorithm. Here our SimCluster algorithm improves the F1-scores from 0.412 and 0.417 in the two domains to 0.442 and 0.441. The ARI scores also improve from 0.176 and 0.180 to 0.203 and 0.204.",
+      "We also performed experiments to see how the performance of SimCluster is affected by the variance in the cluster (controlled by the generative process in Algorithm SECREF11 ). Intuitively we expect SimCluster to obtain an advantage over simple K-means when variance is larger. This is because at larger variance, the data points are more likely to be generated away from the centroid due to which they might be clustered incorrectly with the points from neighbouring cluster. However if the corresponding point from the other domain is generated closer to the centroid, it might help in clustering the given data point correctly. We performed these experiments with points generated from Algorithm SECREF11 at differet values of variance. We generated the points with centroids located on a grid of size INLINEFORM0 in each domain. The value of k was set to 9. The experiment was repeated for each value of variance between 0.1 to 1.0 in the intervals of 0.1. Figures FIGREF22 and FIGREF23 show the percentage improvement on ARI score and F1 score respectively achieved by SimCluster (over K-means) versus variance."
+    ],
+    [
+      "We have experimented on a dataset containing Twitter conversations between customers and Amazon help. The dataset consisted of 92130 conversations between customers and amazon help. We considered the conversations with exactly two speakers Amazon Help and a customer. Consecutive utterances by the same speaker were concatenated and considered as a single utterance. From these we extracted adjacency pairs of the form of a customer utterance followed by an agent (Amazon Help) utterance. We then selected the utterance pairs from 8 different categories, like late delivery of item, refund, unable to sign into the account, replacement of item, claim of warranty, tracking delivery information etc. A total of 1944 utterance pairs were selected.",
+      "To create the vector representation we had used two distinct approaches:-",
+      "Paragraph to vector approach (Doc2Vec) by Le and Mikolov LM14. Here we trained the vectors using distributed memory algorithm and trained for 40 iterations. A window size of 4 was used.",
+      "We also trained the vectors using sequence to sequence approach BIBREF8 , on the Twitter dataset where we considered the task of predicting the reply of Amazon Help for customer's query and vice versa.",
+      "The encoded vector from the input sequence forms the corresponding vector representation. For the task of generating the agent's response for customer utterance the encoding from the input sequence (in the trained model) forms the vector representation for the customer utterance. Similarly for the task of generating the previous customer utterance from the agent's response, the intermediate encoding forms the vector representation for the agent utterance. We used an LSTM based 3-layered sequence to sequence model with attention for this task.",
+      "We ran the K-means clustering algorithm for 5 iterations followed by our SimCluster algorithm for 30 iterations to form clusters in both the (customer and agent) domains. The hyper parameter( INLINEFORM0 ) is chosen based on a validation set. We varied the value of INLINEFORM1 from 0.5 to 1.0 at intervals of 0.025. The initialization of centroids was performed using INLINEFORM2 sampling approach BIBREF14 ."
+    ],
+    [
+      "For the clusters so obtained we have computed F1 and ARI measures as before and compared with the K-means approach. We used the partitioning formed by the 8 categories (from which the utterance pairs were selected) as the ground truth clustering.",
+      "Table TABREF20 summarizes the results. We observe that for K-means algorithm, the vectors generated from sequence to sequence model perform better than the vectors generated using paragraph to vector for both the domains. This is expected as the vectors generated from sequence to sequence model encode some adjacency information as well. We further observe that the SimCluster approach performs better than the K-means approach for both the vector representations. It improves the F1-scores for Doc2Vec representation from 0.787 and 0.783 to 0.88 and 0.887 in the two domains. Also the F1-scores on Seq2Seq based representation improve from 0.83 and 0.9 to 0.86 and 0.916 using SimCluster. However the gains are much more in case of Doc2Vec representations than Seq2Seq representations since Doc2Vec did not have any information from the other domain where as some amount of this information is already captured by Seq2Seq representation. Moreover it is the clustering of customer utterances which is likely to see an improvement. This is because agent utterances tends to follow a generic pattern while customer utterances tend to be more varied. Considering agent utterances while generating clusters in the user domain thus tends to be more helpful than the other way round.",
+      "Table TABREF25 shows qualitative results on the same dataset. Column 1 and 2 consists of clusters of utterances in customer domain and agent domain respectively. The utterances with usual font are representative utterances from clusters obtained through K-means clustering. The utterances in bold face indicate the similar utterances which were incorrectly classified in different clusters using K-means but were correctly classified together with the utterances by SimCluster algorithm."
+    ],
+    [
+      "One of the first steps to automate the construction of conversational systems could be to identify the frequent user utterances and their corresponding system responses. In this paper we proposed an approach to compute these groups of utterances by clustering the utterances in both the domains using our novel SimCluster algorithm which seeks to simultaneously cluster the utterances and align the utterances in two domains. Through our experiments on synthetically generated datset we have shown that SimCluster has more advantage over K-means on datasets with larger variance. Our technique improves upon the ARI and F1 scores on a real dataset containing Twitter conversations."
+    ],
+    [
+      "We thank Dr. David Nahamoo (CTO, Speech Technology and Fellow IBM Research ) for his valuable guidance and feedback. We also acknowledge the anonymous reviewers of IJCNLP 2017 for their comments."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1208/instruction.md b/qasper-1208/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..0198eaa48e8431f871fb8199ab8bb2d32ea6fedc
--- /dev/null
+++ b/qasper-1208/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Finding Dominant User Utterances And System Responses in Conversations
+
+Question: How do they divide text into utterances?
\ No newline at end of file
diff --git a/qasper-1209/instruction.md b/qasper-1209/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..b7da9644b1be080471cb1790f77a5241db919019
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+++ b/qasper-1209/instruction.md
@@ -0,0 +1,103 @@
+Name of Paper: Finding Dominant User Utterances And System Responses in Conversations
+
+Question: Do they use the same distance metric for both the SimCluster and K-means algorithm?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "The Proposed Approach",
+    " Model",
+    "SimCluster Algorithm",
+    "Alignment",
+    "Experiments on Synthetic Dataset",
+    "Evaluation and Results",
+    "Description and preprocessing of dataset",
+    "Results",
+    "Conclusions",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "There are several existing works that focus on modelling conversation using prior human to human conversational data BIBREF0 , BIBREF1 , BIBREF2 . BIBREF3 models the conversation from pairs of consecutive tweets. Deep learning based approaches have also been used to model the dialog in an end to end manner BIBREF4 , BIBREF5 . Memory networks have been used by Bordes et al Bor16 to model goal based dialog conversations. More recently, deep reinforcement learning models have been used for generating interactive and coherent dialogs BIBREF6 and negotiation dialogs BIBREF7 .",
+      "Industry on the other hand has focused on building frameworks that allow manual specification of dialog models such as api.ai, Watson Conversational Services, and Microsoft Bot framework. These frameworks provide ways to specify intents, and a dialog flow. The user utterances are mapped to intents that are passed to a dialog flow manager. The dialog manager generates a response and updates the dialog state. See Figure FIGREF4 for an example of some intents and a dialog flow in a technical support domain. The dialog flow shows that when a user expresses an intent of # laptop_heat, then the system should respond with an utterance \u201cCould you let me know the serial number of your machine \u201d. The designer needs to specify intents (for example # laptop_heat, # email_not_opening) and also provide corresponding system responses in the dialog flow. This way of specifying a dialog model using intents and corresponding system responses manually is more popular in industry than a data driven approach as it makes dialog model easy to interpret and debug as well as provides a better control to a dialog designer. However, this is very time consuming and laborious and thus involves huge costs.",
+      "One approach to reduce the task of a dialog designer is to provide her with frequent user intents and possible corresponding system responses in a given domain. This can be done by analysing prior human to human conversations in the domain. Figure FIGREF5 (a) provides some example conversations in the technical support domain between users and agents.",
+      "In order to identify frequent user intents, one can use existing clustering algorithms to group together all the utterances from the users. Here each cluster would correspond to a new intent and each utterance in the cluster would correspond to an example for the intent. Similarly the agents utterances can be clustered to identify system responses. However, we argue that rather than treating user utterances and agents responses in an isolated manner, there is merit in jointly clustering them. There is adjacency information of these utterances that can be utilized to identify better user intents and system responses. As an example, consider agent utterances A.2 in box A and A.2 in box C in Figure FIGREF5 (a). The utterances \u201cWhich operating system do you use?\" and \u201cWhat OS is installed in your machine\" have no syntactic similarity and therefore may not be grouped together. However the fact that these utterances are adjacent to the similar user utterances \u201cI am unable to start notes email client\" and \u201cUnable to start my email client\" provides some evidence that the agent utterances might be similar. Similarly the user utterances \u201cMy system keeps getting rebooted\" and \u201cMachine is booting time and again\" ( box B and D in Figure FIGREF5 (a))- that are syntactically not similar - could be grouped together since the adjacent agent utterances, \u201cIs your machine heating up?\" and \u201cIs the machine heating?\" are similar.",
+      "Joint clustering of user utterances and agent utterances allow us to align the user utterance clusters with agent utterance clusters. Figure FIGREF5 (b) shows some examples of user utterance clusters and agent utterance clusters along with their alignments. Note that the user utterance clusters can be used by a dialog designer to specify intents, the agent utterance clusters can be used to create system responses and their alignment can be used to create part of the dialog flow.",
+      "We propose two ways to take adjacency information into account. Firstly we propose a method called SimCluster for jointly or simultaneously clustering user utterances and agent utterances. SimCluster extends the K-means clustering method by incorporating additional penalty terms in the objective function that try to align the clusters together (described in Section SECREF3 ). The algorithm creates initial user utterance clusters as well as agent utterance clusters and then use bi-partite matching to get the best alignment across these clusters. Minimizing the objective function pushes the cluster centroids to move towards the centroids of the aligned clusters. The process implicitly ensures that the similarity of adjacent agent utterances affect the grouping of user utterances and conversely similarity of adjacent user utterances affect the grouping of agent utterances. In our second approach we use the information about neighbouring utterances for creating the vector representation of an utterance. For this we train a sequence to sequence model BIBREF8 to create the vectors (described in Section SECREF5 ).",
+      "Our experiments described in section SECREF5 show that we achieve upto 10% absolute improvement in F1 scores over standard K-means using SimCluster. Also we observe that clustering of customer utterances gains significantly by using the adjacency information of agent utterances whereas the gain in clustering quality of agent utterances is moderate. This is because the agent utterances typically follow similar syntactic constructs whereas customer utterances are more varied. Considering the agent utterances into account while clustering users utterances is thus helpful. The organization of the rest of the paper is as follows. In Section SECREF2 we describe the related work. In Section SECREF3 we describe our problem formulation for clustering and the associated algorithm. Finally in sections SECREF4 and SECREF5 we discuss our experiments on synthetic and real datasets respectively."
+    ],
+    [
+      "The notion of adjacency pairs was introduced by Sacks et al SSE74 to formalize the structure of a dialog. Adjacency pairs have been used to analyze the semantics of the dialog in computational linguistics community BIBREF9 . Clustering has been used for different tasks related to conversation. BIBREF10 considers the task of discovering dialog acts by clustering the raw utterances. We aim to obtain the frequent adjacency pairs through clustering.",
+      "There have been several works regarding extensions of clustering to different scenarios such as:-"
+    ],
+    [
+      "In this section we describe our approach SimCluster that performs clustering in the two domains simultaneously and ensures that the generated clusters can be aligned with each other. We will describe the model in section SECREF9 and the algorithm in Section SECREF11 ."
+    ],
+    [
+      "We consider a problem setting where we are given a collection of pairs of consecutive utterances, with vector representations INLINEFORM0 where INLINEFORM1 s are in speaker 1's domain and INLINEFORM2 s are in speaker 2's domain. We need to simultaneously cluster the utterances in their respective domains to minimize the variations within each domain and also ensure that the clusters for both domains are close together.",
+      "We denote the clusters for speaker 1's domain by INLINEFORM0 with their respective means INLINEFORM1 . We denote the clusters assignments for INLINEFORM2 by INLINEFORM3 .",
+      "We denote the clusters for second speaker by INLINEFORM0 with their respective means INLINEFORM1 . We denote the clusters assignments for INLINEFORM2 by INLINEFORM3 . The usual energy function has the terms for distance of points from their corresponding cluster centroids. To be able to ensure that the clusters in each domain are similar, we also consider an alignment between the centroids of the two domains. Since the semantic representations in the two domains are not comparable we consider a notion of induced centroids.",
+      "We define the induced centroids INLINEFORM0 as the arithmetic means of the points INLINEFORM1 s such that INLINEFORM2 's have the same cluster assigned to them. Similarly, we define INLINEFORM3 as the arithmetic means of INLINEFORM4 s such that INLINEFORM5 s have the same cluster assigned to them. More formally, we define these induced centroids as:- INLINEFORM6 ",
+      "and INLINEFORM0 ",
+      "The alignment between these clusters given by the function INLINEFORM0 , which is a bijective mapping from the cluster indices in speaker 1's domain to those in speaker 2's domain. Though there can be several choices for this alignment function, we consider this alignment to be a matching which maximizes the sum of number of common indices in the aligned clusters. More formally we define INLINEFORM1 ",
+      "Then the matching INLINEFORM0 is defined to be the bijective function which maximizes INLINEFORM1 . We consider a term in the cost function corresponding to the sum of distances between the original centroids and the matched induced centroids. Our overall cost function is now given by:- INLINEFORM2 ",
+      "We explain the above definition via an example. Consider the clusters shown in Figure FIGREF10 . Here the INLINEFORM0 would match INLINEFORM1 to INLINEFORM2 , INLINEFORM3 to INLINEFORM4 and INLINEFORM5 to INLINEFORM6 , giving a match score of 6. Since INLINEFORM7 , INLINEFORM8 and INLINEFORM9 are present in the cluster INLINEFORM10 , INLINEFORM11 is given by INLINEFORM12 . Similarly INLINEFORM13 ",
+      " In a similar manner, INLINEFORM0 s can also be defined. Now the alignment terms are given by:- INLINEFORM1 ",
+      " "
+    ],
+    [
+      "[] SimCluster [1] SimClusterInput: INLINEFORM0 ,k (No. of cluster) Output: A cluster assignment INLINEFORM1 for INLINEFORM2 s and a cluster assignment INLINEFORM3 for INLINEFORM4 s Initialize a set of centroids INLINEFORM5 , and INLINEFORM6 Perform simple clustering for a few iterations For each i, compute INLINEFORM7 as the index j among 1 to k which minimizes INLINEFORM8 . Similarly , compute INLINEFORM9 as the index j' among 1 to k which minimizes INLINEFORM10 . Update the centroids, INLINEFORM11 and INLINEFORM12 as:- INLINEFORM13 ",
+      "and INLINEFORM0 ",
+      " Perform a Hungarian matching between the cluster indices in the two domains with weights",
+      "N(j,j') on edges from index j to index j'. convergence To minimize the above energy term we adopt an approach similar to Lloyd's clustering algorithm Llo82 . We assume that we are given a set of initial seeds for the cluster centroids INLINEFORM0 and INLINEFORM1 . We repeat the following steps iteratively:-",
+      "Minimize the energy with respect to cluster assignment keeping centroids unchanged. As in standard K-means algorithm, this is achieved by updating the cluster assignment, INLINEFORM0 for each index i to be the cluster index j which minimizes INLINEFORM1 . Correspondingly for INLINEFORM2 , we pick the cluster index j' which minimizes INLINEFORM3 .",
+      " Minimize the energy with respect to the centroids keeping cluster assignment unchanged. To achieve this step we need to minimize the energy function with respect to the centroids INLINEFORM0 and INLINEFORM1 . This is achieved by setting INLINEFORM2 for each j and INLINEFORM3 for each j.",
+      "Setting INLINEFORM0 , we obtain INLINEFORM1 ",
+      "or equivalently INLINEFORM0 ",
+      " Similarly, setting INLINEFORM0 , we obtain INLINEFORM1 ",
+      "Finally we update the matching between the clusters. To do so, we need to find a bipartite matching match on the cluster indices so as to maximize INLINEFORM0 . We use Hungarian algorithm BIBREF13 to perform the same i.e. we define a bipartite graph with vertices consisting of cluster indices in the two domains. There is an edge from vertex representing cluster indices j (in domain 1) and j' in domain 2, with weight N(j,j'). We find a maximum weight bipartite matching in this graph.",
+      "Similar to Lloyd's algorithm, each step of the above algorithm decreases the cost function. This ensures that the algorithm achieves a local minima of the cost function if it converges. See Algorithm SECREF11 for a formal description of the approach. The centroid update step of the above algorithm also has an intuitive explanation i.e. we are slightly moving away the centroid towards the matched induced centroid. This is consistent with our goal of aligning the clusters together in the two domains."
+    ],
+    [
+      "The algorithm above maintains a mapping between the clusters in each speaker's domain. This mapping serves to give us the alignment between the clusters required to provide a corresponding response for a given user intent."
+    ],
+    [
+      "We performed experiments on synthetically generated dataset since it gives us a better control over the distribution of the data. Specifically we compared the gains obtained using our approach versus the variance of the distribution. We created dataset from the following generative process. [H] Generative Process [1] Generate data",
+      "Pick k points INLINEFORM0 as domain -1 means and a corresponding set of k points INLINEFORM1 as domain-2 means, and covariance matrices INLINEFORM2 ",
+      "iter INLINEFORM0 upto num INLINEFORM1 samples Sample class INLINEFORM2 Sample INLINEFORM3 Sample INLINEFORM4 Add q and a so sampled to the list of q,a pairs We generated the dataset from the above sampling process with means selected on a 2 dimensional grid of size INLINEFORM5 with variance set as INLINEFORM6 in each dimension.10000 sample points were generated. The parameter INLINEFORM7 of the above algorithm was set to 0.5 and k was set to 9 (since the points could be generated from one of the 9 gaussians with centroids on a INLINEFORM8 grid).",
+      "We compared the results with simple K-means clustering with k set to 9. For each of these, the initialization of means was done using INLINEFORM0 sampling approach BIBREF14 ."
+    ],
+    [
+      "To evaluate the clusters we computed the following metrics",
+      "ARI (Adjusted Rand Index): Standard Rand Index is a metric used to check the clustering quality against a given standard set of clusters by comparing the pairwise clustering decisions. It is defined as INLINEFORM0 , where a is the number of true positive pairs, b is the number of true negative pairs, c is the number of false positive pairs and d is the number of false negative pairs. Adjusted rand index corrects the standard rand index for chance and is defined as INLINEFORM1 BIBREF15 .",
+      "We compute ARI score for both the source clusters as well as the target clusters.",
+      "F1 scores: We also report F1 scores for the pairwise clustering decisions. In the above notation we considered the pair-precision as INLINEFORM0 and recall as INLINEFORM1 . The F1 measure is the Harmonic mean given as INLINEFORM2 .",
+      "We used the gaussian index from which an utterance pair was generated as the ground truth label, which served to provide ground truth clusters for computation of the above evaluation metrics. Table TABREF15 shows a comparison of the results on SimCluster versus K-means algorithm. Here our SimCluster algorithm improves the F1-scores from 0.412 and 0.417 in the two domains to 0.442 and 0.441. The ARI scores also improve from 0.176 and 0.180 to 0.203 and 0.204.",
+      "We also performed experiments to see how the performance of SimCluster is affected by the variance in the cluster (controlled by the generative process in Algorithm SECREF11 ). Intuitively we expect SimCluster to obtain an advantage over simple K-means when variance is larger. This is because at larger variance, the data points are more likely to be generated away from the centroid due to which they might be clustered incorrectly with the points from neighbouring cluster. However if the corresponding point from the other domain is generated closer to the centroid, it might help in clustering the given data point correctly. We performed these experiments with points generated from Algorithm SECREF11 at differet values of variance. We generated the points with centroids located on a grid of size INLINEFORM0 in each domain. The value of k was set to 9. The experiment was repeated for each value of variance between 0.1 to 1.0 in the intervals of 0.1. Figures FIGREF22 and FIGREF23 show the percentage improvement on ARI score and F1 score respectively achieved by SimCluster (over K-means) versus variance."
+    ],
+    [
+      "We have experimented on a dataset containing Twitter conversations between customers and Amazon help. The dataset consisted of 92130 conversations between customers and amazon help. We considered the conversations with exactly two speakers Amazon Help and a customer. Consecutive utterances by the same speaker were concatenated and considered as a single utterance. From these we extracted adjacency pairs of the form of a customer utterance followed by an agent (Amazon Help) utterance. We then selected the utterance pairs from 8 different categories, like late delivery of item, refund, unable to sign into the account, replacement of item, claim of warranty, tracking delivery information etc. A total of 1944 utterance pairs were selected.",
+      "To create the vector representation we had used two distinct approaches:-",
+      "Paragraph to vector approach (Doc2Vec) by Le and Mikolov LM14. Here we trained the vectors using distributed memory algorithm and trained for 40 iterations. A window size of 4 was used.",
+      "We also trained the vectors using sequence to sequence approach BIBREF8 , on the Twitter dataset where we considered the task of predicting the reply of Amazon Help for customer's query and vice versa.",
+      "The encoded vector from the input sequence forms the corresponding vector representation. For the task of generating the agent's response for customer utterance the encoding from the input sequence (in the trained model) forms the vector representation for the customer utterance. Similarly for the task of generating the previous customer utterance from the agent's response, the intermediate encoding forms the vector representation for the agent utterance. We used an LSTM based 3-layered sequence to sequence model with attention for this task.",
+      "We ran the K-means clustering algorithm for 5 iterations followed by our SimCluster algorithm for 30 iterations to form clusters in both the (customer and agent) domains. The hyper parameter( INLINEFORM0 ) is chosen based on a validation set. We varied the value of INLINEFORM1 from 0.5 to 1.0 at intervals of 0.025. The initialization of centroids was performed using INLINEFORM2 sampling approach BIBREF14 ."
+    ],
+    [
+      "For the clusters so obtained we have computed F1 and ARI measures as before and compared with the K-means approach. We used the partitioning formed by the 8 categories (from which the utterance pairs were selected) as the ground truth clustering.",
+      "Table TABREF20 summarizes the results. We observe that for K-means algorithm, the vectors generated from sequence to sequence model perform better than the vectors generated using paragraph to vector for both the domains. This is expected as the vectors generated from sequence to sequence model encode some adjacency information as well. We further observe that the SimCluster approach performs better than the K-means approach for both the vector representations. It improves the F1-scores for Doc2Vec representation from 0.787 and 0.783 to 0.88 and 0.887 in the two domains. Also the F1-scores on Seq2Seq based representation improve from 0.83 and 0.9 to 0.86 and 0.916 using SimCluster. However the gains are much more in case of Doc2Vec representations than Seq2Seq representations since Doc2Vec did not have any information from the other domain where as some amount of this information is already captured by Seq2Seq representation. Moreover it is the clustering of customer utterances which is likely to see an improvement. This is because agent utterances tends to follow a generic pattern while customer utterances tend to be more varied. Considering agent utterances while generating clusters in the user domain thus tends to be more helpful than the other way round.",
+      "Table TABREF25 shows qualitative results on the same dataset. Column 1 and 2 consists of clusters of utterances in customer domain and agent domain respectively. The utterances with usual font are representative utterances from clusters obtained through K-means clustering. The utterances in bold face indicate the similar utterances which were incorrectly classified in different clusters using K-means but were correctly classified together with the utterances by SimCluster algorithm."
+    ],
+    [
+      "One of the first steps to automate the construction of conversational systems could be to identify the frequent user utterances and their corresponding system responses. In this paper we proposed an approach to compute these groups of utterances by clustering the utterances in both the domains using our novel SimCluster algorithm which seeks to simultaneously cluster the utterances and align the utterances in two domains. Through our experiments on synthetically generated datset we have shown that SimCluster has more advantage over K-means on datasets with larger variance. Our technique improves upon the ARI and F1 scores on a real dataset containing Twitter conversations."
+    ],
+    [
+      "We thank Dr. David Nahamoo (CTO, Speech Technology and Fellow IBM Research ) for his valuable guidance and feedback. We also acknowledge the anonymous reviewers of IJCNLP 2017 for their comments."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1210/instruction.md b/qasper-1210/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..6a5075f0d2bfae9601c6fbb6f883d998264fbbb6
--- /dev/null
+++ b/qasper-1210/instruction.md
@@ -0,0 +1,103 @@
+Name of Paper: Finding Dominant User Utterances And System Responses in Conversations
+
+Question: How do they generate the synthetic dataset?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "The Proposed Approach",
+    " Model",
+    "SimCluster Algorithm",
+    "Alignment",
+    "Experiments on Synthetic Dataset",
+    "Evaluation and Results",
+    "Description and preprocessing of dataset",
+    "Results",
+    "Conclusions",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "There are several existing works that focus on modelling conversation using prior human to human conversational data BIBREF0 , BIBREF1 , BIBREF2 . BIBREF3 models the conversation from pairs of consecutive tweets. Deep learning based approaches have also been used to model the dialog in an end to end manner BIBREF4 , BIBREF5 . Memory networks have been used by Bordes et al Bor16 to model goal based dialog conversations. More recently, deep reinforcement learning models have been used for generating interactive and coherent dialogs BIBREF6 and negotiation dialogs BIBREF7 .",
+      "Industry on the other hand has focused on building frameworks that allow manual specification of dialog models such as api.ai, Watson Conversational Services, and Microsoft Bot framework. These frameworks provide ways to specify intents, and a dialog flow. The user utterances are mapped to intents that are passed to a dialog flow manager. The dialog manager generates a response and updates the dialog state. See Figure FIGREF4 for an example of some intents and a dialog flow in a technical support domain. The dialog flow shows that when a user expresses an intent of # laptop_heat, then the system should respond with an utterance \u201cCould you let me know the serial number of your machine \u201d. The designer needs to specify intents (for example # laptop_heat, # email_not_opening) and also provide corresponding system responses in the dialog flow. This way of specifying a dialog model using intents and corresponding system responses manually is more popular in industry than a data driven approach as it makes dialog model easy to interpret and debug as well as provides a better control to a dialog designer. However, this is very time consuming and laborious and thus involves huge costs.",
+      "One approach to reduce the task of a dialog designer is to provide her with frequent user intents and possible corresponding system responses in a given domain. This can be done by analysing prior human to human conversations in the domain. Figure FIGREF5 (a) provides some example conversations in the technical support domain between users and agents.",
+      "In order to identify frequent user intents, one can use existing clustering algorithms to group together all the utterances from the users. Here each cluster would correspond to a new intent and each utterance in the cluster would correspond to an example for the intent. Similarly the agents utterances can be clustered to identify system responses. However, we argue that rather than treating user utterances and agents responses in an isolated manner, there is merit in jointly clustering them. There is adjacency information of these utterances that can be utilized to identify better user intents and system responses. As an example, consider agent utterances A.2 in box A and A.2 in box C in Figure FIGREF5 (a). The utterances \u201cWhich operating system do you use?\" and \u201cWhat OS is installed in your machine\" have no syntactic similarity and therefore may not be grouped together. However the fact that these utterances are adjacent to the similar user utterances \u201cI am unable to start notes email client\" and \u201cUnable to start my email client\" provides some evidence that the agent utterances might be similar. Similarly the user utterances \u201cMy system keeps getting rebooted\" and \u201cMachine is booting time and again\" ( box B and D in Figure FIGREF5 (a))- that are syntactically not similar - could be grouped together since the adjacent agent utterances, \u201cIs your machine heating up?\" and \u201cIs the machine heating?\" are similar.",
+      "Joint clustering of user utterances and agent utterances allow us to align the user utterance clusters with agent utterance clusters. Figure FIGREF5 (b) shows some examples of user utterance clusters and agent utterance clusters along with their alignments. Note that the user utterance clusters can be used by a dialog designer to specify intents, the agent utterance clusters can be used to create system responses and their alignment can be used to create part of the dialog flow.",
+      "We propose two ways to take adjacency information into account. Firstly we propose a method called SimCluster for jointly or simultaneously clustering user utterances and agent utterances. SimCluster extends the K-means clustering method by incorporating additional penalty terms in the objective function that try to align the clusters together (described in Section SECREF3 ). The algorithm creates initial user utterance clusters as well as agent utterance clusters and then use bi-partite matching to get the best alignment across these clusters. Minimizing the objective function pushes the cluster centroids to move towards the centroids of the aligned clusters. The process implicitly ensures that the similarity of adjacent agent utterances affect the grouping of user utterances and conversely similarity of adjacent user utterances affect the grouping of agent utterances. In our second approach we use the information about neighbouring utterances for creating the vector representation of an utterance. For this we train a sequence to sequence model BIBREF8 to create the vectors (described in Section SECREF5 ).",
+      "Our experiments described in section SECREF5 show that we achieve upto 10% absolute improvement in F1 scores over standard K-means using SimCluster. Also we observe that clustering of customer utterances gains significantly by using the adjacency information of agent utterances whereas the gain in clustering quality of agent utterances is moderate. This is because the agent utterances typically follow similar syntactic constructs whereas customer utterances are more varied. Considering the agent utterances into account while clustering users utterances is thus helpful. The organization of the rest of the paper is as follows. In Section SECREF2 we describe the related work. In Section SECREF3 we describe our problem formulation for clustering and the associated algorithm. Finally in sections SECREF4 and SECREF5 we discuss our experiments on synthetic and real datasets respectively."
+    ],
+    [
+      "The notion of adjacency pairs was introduced by Sacks et al SSE74 to formalize the structure of a dialog. Adjacency pairs have been used to analyze the semantics of the dialog in computational linguistics community BIBREF9 . Clustering has been used for different tasks related to conversation. BIBREF10 considers the task of discovering dialog acts by clustering the raw utterances. We aim to obtain the frequent adjacency pairs through clustering.",
+      "There have been several works regarding extensions of clustering to different scenarios such as:-"
+    ],
+    [
+      "In this section we describe our approach SimCluster that performs clustering in the two domains simultaneously and ensures that the generated clusters can be aligned with each other. We will describe the model in section SECREF9 and the algorithm in Section SECREF11 ."
+    ],
+    [
+      "We consider a problem setting where we are given a collection of pairs of consecutive utterances, with vector representations INLINEFORM0 where INLINEFORM1 s are in speaker 1's domain and INLINEFORM2 s are in speaker 2's domain. We need to simultaneously cluster the utterances in their respective domains to minimize the variations within each domain and also ensure that the clusters for both domains are close together.",
+      "We denote the clusters for speaker 1's domain by INLINEFORM0 with their respective means INLINEFORM1 . We denote the clusters assignments for INLINEFORM2 by INLINEFORM3 .",
+      "We denote the clusters for second speaker by INLINEFORM0 with their respective means INLINEFORM1 . We denote the clusters assignments for INLINEFORM2 by INLINEFORM3 . The usual energy function has the terms for distance of points from their corresponding cluster centroids. To be able to ensure that the clusters in each domain are similar, we also consider an alignment between the centroids of the two domains. Since the semantic representations in the two domains are not comparable we consider a notion of induced centroids.",
+      "We define the induced centroids INLINEFORM0 as the arithmetic means of the points INLINEFORM1 s such that INLINEFORM2 's have the same cluster assigned to them. Similarly, we define INLINEFORM3 as the arithmetic means of INLINEFORM4 s such that INLINEFORM5 s have the same cluster assigned to them. More formally, we define these induced centroids as:- INLINEFORM6 ",
+      "and INLINEFORM0 ",
+      "The alignment between these clusters given by the function INLINEFORM0 , which is a bijective mapping from the cluster indices in speaker 1's domain to those in speaker 2's domain. Though there can be several choices for this alignment function, we consider this alignment to be a matching which maximizes the sum of number of common indices in the aligned clusters. More formally we define INLINEFORM1 ",
+      "Then the matching INLINEFORM0 is defined to be the bijective function which maximizes INLINEFORM1 . We consider a term in the cost function corresponding to the sum of distances between the original centroids and the matched induced centroids. Our overall cost function is now given by:- INLINEFORM2 ",
+      "We explain the above definition via an example. Consider the clusters shown in Figure FIGREF10 . Here the INLINEFORM0 would match INLINEFORM1 to INLINEFORM2 , INLINEFORM3 to INLINEFORM4 and INLINEFORM5 to INLINEFORM6 , giving a match score of 6. Since INLINEFORM7 , INLINEFORM8 and INLINEFORM9 are present in the cluster INLINEFORM10 , INLINEFORM11 is given by INLINEFORM12 . Similarly INLINEFORM13 ",
+      " In a similar manner, INLINEFORM0 s can also be defined. Now the alignment terms are given by:- INLINEFORM1 ",
+      " "
+    ],
+    [
+      "[] SimCluster [1] SimClusterInput: INLINEFORM0 ,k (No. of cluster) Output: A cluster assignment INLINEFORM1 for INLINEFORM2 s and a cluster assignment INLINEFORM3 for INLINEFORM4 s Initialize a set of centroids INLINEFORM5 , and INLINEFORM6 Perform simple clustering for a few iterations For each i, compute INLINEFORM7 as the index j among 1 to k which minimizes INLINEFORM8 . Similarly , compute INLINEFORM9 as the index j' among 1 to k which minimizes INLINEFORM10 . Update the centroids, INLINEFORM11 and INLINEFORM12 as:- INLINEFORM13 ",
+      "and INLINEFORM0 ",
+      " Perform a Hungarian matching between the cluster indices in the two domains with weights",
+      "N(j,j') on edges from index j to index j'. convergence To minimize the above energy term we adopt an approach similar to Lloyd's clustering algorithm Llo82 . We assume that we are given a set of initial seeds for the cluster centroids INLINEFORM0 and INLINEFORM1 . We repeat the following steps iteratively:-",
+      "Minimize the energy with respect to cluster assignment keeping centroids unchanged. As in standard K-means algorithm, this is achieved by updating the cluster assignment, INLINEFORM0 for each index i to be the cluster index j which minimizes INLINEFORM1 . Correspondingly for INLINEFORM2 , we pick the cluster index j' which minimizes INLINEFORM3 .",
+      " Minimize the energy with respect to the centroids keeping cluster assignment unchanged. To achieve this step we need to minimize the energy function with respect to the centroids INLINEFORM0 and INLINEFORM1 . This is achieved by setting INLINEFORM2 for each j and INLINEFORM3 for each j.",
+      "Setting INLINEFORM0 , we obtain INLINEFORM1 ",
+      "or equivalently INLINEFORM0 ",
+      " Similarly, setting INLINEFORM0 , we obtain INLINEFORM1 ",
+      "Finally we update the matching between the clusters. To do so, we need to find a bipartite matching match on the cluster indices so as to maximize INLINEFORM0 . We use Hungarian algorithm BIBREF13 to perform the same i.e. we define a bipartite graph with vertices consisting of cluster indices in the two domains. There is an edge from vertex representing cluster indices j (in domain 1) and j' in domain 2, with weight N(j,j'). We find a maximum weight bipartite matching in this graph.",
+      "Similar to Lloyd's algorithm, each step of the above algorithm decreases the cost function. This ensures that the algorithm achieves a local minima of the cost function if it converges. See Algorithm SECREF11 for a formal description of the approach. The centroid update step of the above algorithm also has an intuitive explanation i.e. we are slightly moving away the centroid towards the matched induced centroid. This is consistent with our goal of aligning the clusters together in the two domains."
+    ],
+    [
+      "The algorithm above maintains a mapping between the clusters in each speaker's domain. This mapping serves to give us the alignment between the clusters required to provide a corresponding response for a given user intent."
+    ],
+    [
+      "We performed experiments on synthetically generated dataset since it gives us a better control over the distribution of the data. Specifically we compared the gains obtained using our approach versus the variance of the distribution. We created dataset from the following generative process. [H] Generative Process [1] Generate data",
+      "Pick k points INLINEFORM0 as domain -1 means and a corresponding set of k points INLINEFORM1 as domain-2 means, and covariance matrices INLINEFORM2 ",
+      "iter INLINEFORM0 upto num INLINEFORM1 samples Sample class INLINEFORM2 Sample INLINEFORM3 Sample INLINEFORM4 Add q and a so sampled to the list of q,a pairs We generated the dataset from the above sampling process with means selected on a 2 dimensional grid of size INLINEFORM5 with variance set as INLINEFORM6 in each dimension.10000 sample points were generated. The parameter INLINEFORM7 of the above algorithm was set to 0.5 and k was set to 9 (since the points could be generated from one of the 9 gaussians with centroids on a INLINEFORM8 grid).",
+      "We compared the results with simple K-means clustering with k set to 9. For each of these, the initialization of means was done using INLINEFORM0 sampling approach BIBREF14 ."
+    ],
+    [
+      "To evaluate the clusters we computed the following metrics",
+      "ARI (Adjusted Rand Index): Standard Rand Index is a metric used to check the clustering quality against a given standard set of clusters by comparing the pairwise clustering decisions. It is defined as INLINEFORM0 , where a is the number of true positive pairs, b is the number of true negative pairs, c is the number of false positive pairs and d is the number of false negative pairs. Adjusted rand index corrects the standard rand index for chance and is defined as INLINEFORM1 BIBREF15 .",
+      "We compute ARI score for both the source clusters as well as the target clusters.",
+      "F1 scores: We also report F1 scores for the pairwise clustering decisions. In the above notation we considered the pair-precision as INLINEFORM0 and recall as INLINEFORM1 . The F1 measure is the Harmonic mean given as INLINEFORM2 .",
+      "We used the gaussian index from which an utterance pair was generated as the ground truth label, which served to provide ground truth clusters for computation of the above evaluation metrics. Table TABREF15 shows a comparison of the results on SimCluster versus K-means algorithm. Here our SimCluster algorithm improves the F1-scores from 0.412 and 0.417 in the two domains to 0.442 and 0.441. The ARI scores also improve from 0.176 and 0.180 to 0.203 and 0.204.",
+      "We also performed experiments to see how the performance of SimCluster is affected by the variance in the cluster (controlled by the generative process in Algorithm SECREF11 ). Intuitively we expect SimCluster to obtain an advantage over simple K-means when variance is larger. This is because at larger variance, the data points are more likely to be generated away from the centroid due to which they might be clustered incorrectly with the points from neighbouring cluster. However if the corresponding point from the other domain is generated closer to the centroid, it might help in clustering the given data point correctly. We performed these experiments with points generated from Algorithm SECREF11 at differet values of variance. We generated the points with centroids located on a grid of size INLINEFORM0 in each domain. The value of k was set to 9. The experiment was repeated for each value of variance between 0.1 to 1.0 in the intervals of 0.1. Figures FIGREF22 and FIGREF23 show the percentage improvement on ARI score and F1 score respectively achieved by SimCluster (over K-means) versus variance."
+    ],
+    [
+      "We have experimented on a dataset containing Twitter conversations between customers and Amazon help. The dataset consisted of 92130 conversations between customers and amazon help. We considered the conversations with exactly two speakers Amazon Help and a customer. Consecutive utterances by the same speaker were concatenated and considered as a single utterance. From these we extracted adjacency pairs of the form of a customer utterance followed by an agent (Amazon Help) utterance. We then selected the utterance pairs from 8 different categories, like late delivery of item, refund, unable to sign into the account, replacement of item, claim of warranty, tracking delivery information etc. A total of 1944 utterance pairs were selected.",
+      "To create the vector representation we had used two distinct approaches:-",
+      "Paragraph to vector approach (Doc2Vec) by Le and Mikolov LM14. Here we trained the vectors using distributed memory algorithm and trained for 40 iterations. A window size of 4 was used.",
+      "We also trained the vectors using sequence to sequence approach BIBREF8 , on the Twitter dataset where we considered the task of predicting the reply of Amazon Help for customer's query and vice versa.",
+      "The encoded vector from the input sequence forms the corresponding vector representation. For the task of generating the agent's response for customer utterance the encoding from the input sequence (in the trained model) forms the vector representation for the customer utterance. Similarly for the task of generating the previous customer utterance from the agent's response, the intermediate encoding forms the vector representation for the agent utterance. We used an LSTM based 3-layered sequence to sequence model with attention for this task.",
+      "We ran the K-means clustering algorithm for 5 iterations followed by our SimCluster algorithm for 30 iterations to form clusters in both the (customer and agent) domains. The hyper parameter( INLINEFORM0 ) is chosen based on a validation set. We varied the value of INLINEFORM1 from 0.5 to 1.0 at intervals of 0.025. The initialization of centroids was performed using INLINEFORM2 sampling approach BIBREF14 ."
+    ],
+    [
+      "For the clusters so obtained we have computed F1 and ARI measures as before and compared with the K-means approach. We used the partitioning formed by the 8 categories (from which the utterance pairs were selected) as the ground truth clustering.",
+      "Table TABREF20 summarizes the results. We observe that for K-means algorithm, the vectors generated from sequence to sequence model perform better than the vectors generated using paragraph to vector for both the domains. This is expected as the vectors generated from sequence to sequence model encode some adjacency information as well. We further observe that the SimCluster approach performs better than the K-means approach for both the vector representations. It improves the F1-scores for Doc2Vec representation from 0.787 and 0.783 to 0.88 and 0.887 in the two domains. Also the F1-scores on Seq2Seq based representation improve from 0.83 and 0.9 to 0.86 and 0.916 using SimCluster. However the gains are much more in case of Doc2Vec representations than Seq2Seq representations since Doc2Vec did not have any information from the other domain where as some amount of this information is already captured by Seq2Seq representation. Moreover it is the clustering of customer utterances which is likely to see an improvement. This is because agent utterances tends to follow a generic pattern while customer utterances tend to be more varied. Considering agent utterances while generating clusters in the user domain thus tends to be more helpful than the other way round.",
+      "Table TABREF25 shows qualitative results on the same dataset. Column 1 and 2 consists of clusters of utterances in customer domain and agent domain respectively. The utterances with usual font are representative utterances from clusters obtained through K-means clustering. The utterances in bold face indicate the similar utterances which were incorrectly classified in different clusters using K-means but were correctly classified together with the utterances by SimCluster algorithm."
+    ],
+    [
+      "One of the first steps to automate the construction of conversational systems could be to identify the frequent user utterances and their corresponding system responses. In this paper we proposed an approach to compute these groups of utterances by clustering the utterances in both the domains using our novel SimCluster algorithm which seeks to simultaneously cluster the utterances and align the utterances in two domains. Through our experiments on synthetically generated datset we have shown that SimCluster has more advantage over K-means on datasets with larger variance. Our technique improves upon the ARI and F1 scores on a real dataset containing Twitter conversations."
+    ],
+    [
+      "We thank Dr. David Nahamoo (CTO, Speech Technology and Fellow IBM Research ) for his valuable guidance and feedback. We also acknowledge the anonymous reviewers of IJCNLP 2017 for their comments."
+    ]
+  ]
+}
+```
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diff --git a/qasper-1228/instruction.md b/qasper-1228/instruction.md
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@@ -0,0 +1,3 @@
+Name of Paper: AvgOut: A Simple Output-Probability Measure to Eliminate Dull Responses
+
+Question: To what other competitive baselines is this approach compared?
\ No newline at end of file
diff --git a/qasper-1230/instruction.md b/qasper-1230/instruction.md
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+++ b/qasper-1230/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: AvgOut: A Simple Output-Probability Measure to Eliminate Dull Responses
+
+Question: How much better were results of the proposed models than base LSTM-RNN model?
\ No newline at end of file
diff --git a/qasper-1231/instruction.md b/qasper-1231/instruction.md
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+++ b/qasper-1231/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: AvgOut: A Simple Output-Probability Measure to Eliminate Dull Responses
+
+Question: Which one of the four proposed models performed best?
\ No newline at end of file
diff --git a/qasper-1236/instruction.md b/qasper-1236/instruction.md
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--- /dev/null
+++ b/qasper-1236/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Bidirectional Context-Aware Hierarchical Attention Network for Document Understanding
+
+Question: What languages are the model evaluated on?
\ No newline at end of file
diff --git a/qasper-1237/instruction.md b/qasper-1237/instruction.md
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+++ b/qasper-1237/instruction.md
@@ -0,0 +1,177 @@
+Name of Paper: Bidirectional Context-Aware Hierarchical Attention Network for Document Understanding
+
+Question: Do they compare to other models appart from HAN?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Introduction ::: Observed problem",
+    "Introduction ::: Context-aware HAN",
+    "HAN",
+    "HAN ::: Notation",
+    "HAN ::: Sentence encoder",
+    "HAN ::: Document encoder",
+    "Proposed architecture: CAHAN",
+    "Proposed architecture: CAHAN ::: Summed context (CAHAN-SUM)",
+    "Proposed architecture: CAHAN ::: Summed context (CAHAN-SUM) ::: Left-to-right (LR)",
+    "Proposed architecture: CAHAN ::: Summed context (CAHAN-SUM) ::: Bidirectional (BI)",
+    "Proposed architecture: CAHAN ::: Summed context (CAHAN-SUM) ::: Centroid version (@!START@$\\mu $@!END@)",
+    "Proposed architecture: CAHAN ::: Recurrent Context (CAHAN-RNN)",
+    "Proposed architecture: CAHAN ::: Gated context",
+    "Proposed architecture: CAHAN ::: Complexity and sequentiality",
+    "Experimental setup ::: Datasets",
+    "Experimental setup ::: Model configuration",
+    "Experimental setup ::: Model configuration ::: Preprocessing and word embeddings",
+    "Experimental setup ::: Model configuration ::: Hyperparameters",
+    "Experimental setup ::: Training details",
+    "Experimental setup ::: Training details ::: SGD with cyclical learning rate",
+    "Results",
+    "Results ::: Summing vs. averaging",
+    "Results ::: Gating",
+    "Results ::: CAHAN-RNN-BI",
+    "Results ::: Runtimes",
+    "Related work",
+    "Discussion and next steps",
+    "Conclusion",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "Recently, hierarchical architectures have become ubiquitous in NLP. They have been applied to a wide variety of tasks such as language modeling and generation BIBREF0, BIBREF1, neural machine translation (NMT) BIBREF2, summarization BIBREF3, sentiment and topic classification BIBREF4, BIBREF5, and spoken language understanding BIBREF6, BIBREF7, to cite only a few examples. All hierarchical architectures capitalize on the same intuitive idea that the representation of the input text should be learned in a bottom-up fashion by using a different encoder at each granularity level (e.g., words, sentences, paragraphs), where the encoder at level $l+1$ takes as input the output of the encoder at level $l$.",
+      "One of the earliest and most influential examples is the Hierarchical Attention Network (HAN) of BIBREF5 (see Fig. FIGREF6 and section SECREF2). It is a two-level architecture, where at level 1, each sentence in the document is separately encoded by the same sentence encoder, resulting in a sequence of sentence vectors. That sequence is then processed at level 2 by the document encoder which returns a single vector representing the entire document. The sentence and document encoders are both self-attentional bidirectional Recurrent Neural Networks (RNNs), with different parameters."
+    ],
+    [
+      "HAN was highly successful and established new state of the art on six large-scale sentiment and topic classification datasets. However, it has a major weakness: at level 1, each sentence is encoded in isolation. That is, while producing the representation of a given sentence in the document, HAN completely ignores the other sentences. This lack of communication is obviously suboptimal. For example, in Fig. FIGREF2, the same highly negative feature (\u201cterrible value\u201d) has been repeated at the beginning of each sentence in the document. Because it encodes each sentence independently, HAN has no choice but to spend most of its attentional budget on the most salient feature every time. As a result, HAN neglects the other aspects of the document. On the other hand, CAHAN is informed about the context, and thus quickly stops spending attention weight on the same highly negative pattern, knowing that is has already been covered. CAHAN is then able to cover the other topics in the document (\u201cseafood\u201d,\u201cscallops\u201d and \u201cmussels\u201d; \u201centree\u201d and \u201cappetizer\u201d; triple negation in the fourth sentence).",
+      "As another example, consider the edge case of a document containing the same sentence repeated several times, as shown in Fig. FIGREF3. With HAN, the exact same embedding is produced for each instantiation of the sentence, as a result of the context-blind self-attention mechanism always making the same alignment decisions. However, the context-aware sentence encoder of CAHAN allows it to extract complementary, rather than redundant information, from each instantiation of the sentence. This results in better coverage (\u201creasonably priced\u201d, \u201carrived late\u201d), in a richer document representation, and ultimately in a more accurate prediction (positive instead of very positive).",
+      "One may argue that in basic HAN, the document encoder at level 2 already does capture some notion of context, by assigning importance scores to sentences. However, at level 2, the sentence vectors have already been formed, and it is too late to modify them. Since the document encoder can only rank the sentence representations, it cannot address issues like high redundancy. In that case, important subtopics or details in the document will not be covered, no matter sentence scores."
+    ],
+    [
+      "In this work, we propose and evaluate several modifications of the HAN architecture that allow the sentence encoder at level 1 to make its attentional decisions based on contextual information, allowing it to learn richer document representations. Another significant contribution is the introduction of a bidirectional version of the document encoder, where one RNN processes the document forwards, using the preceding sentences as context, and another one processes it backwards, using the following sentences as context.",
+      "The remainder of this paper is structured as follows. We start by formally introducing basic HAN (section SECREF2), we then explain our contributions (section SECREF3), and detail our experimental setup (section SECREF4). Finally, we interpret our results and list areas of future development (sections SECREF5 and SECREF7). Related work is reviewed in section SECREF6."
+    ],
+    [
+      "The baseline HAN model as introduced by BIBREF5 is shown in Fig. FIGREF6 along with our modifications (disregard the bold lines for the baseline). The sentence and document encoders, used respectively at level 1 and level 2, have different parameters but share the exact same architecture. Thus, in what follows, we only describe the sentence encoder in detail."
+    ],
+    [
+      "Next, we use boldface upper case for tensors, upper case for matrices, boldface lower case for vectors, and lower case for scalars. We define a document $\\mathbf {X} \\in \\mathbb {R}^{N \\times T_i \\times d}$ as a sequence of $N$ sentences $(S_1, \\dots , S_N)$. Each sentence $S_i$ is a sequence of $T_i$ $d$-dimensional word vectors $(\\mathbf {x}_{i1}, \\dots , \\mathbf {x}_{iT_i}) \\in \\mathbb {R}^{T_i \\times d}$."
+    ],
+    [
+      "First, the sentence-level bidirectional RNN $f_s$ processes the input sentence $S_i$ and returns a sequence of $T_i$ $2d_s$-dimensional hidden states $(\\mathbf {h}_{i1},\\dots , \\mathbf {h}_{iT_i}) \\in \\mathbb {R}^{T_i \\times 2d_s}$. $f_s$ is composed of two non-stacking RNNs $\\overrightarrow{f_s}$ and $\\overleftarrow{f_s}$ with Gated Recurrent Units BIBREF8, respectively parsing $S_i$ from left to right and right to left:",
+      "",
+      "$\\overrightarrow{f_s}$ and $\\overleftarrow{f_s}$ have the same hidden layer dimensionality $d_s$, but different parameters. At each time step $t$, the word annotations they return are concatenated, producing $2d_s$-dimensional annotations that summarize the immediate context surrounding each word:",
+      "",
+      "Then, a self-attention mechanism computes the representation $\\mathbf {s}_i$ of sentence $S_i$ as a weighted sum of its word annotations:",
+      "",
+      "Where the vector of attentional coefficients $\\mathbf {\\alpha }$ is a softmax-normalized version of the alignment vector $\\mathbf {e}$, which itself is obtained by passing the word annotations through a dense layer (parameterized by $W_s \\in \\mathbb {R}^{2d_s\\times 2d_s}$) and comparing the output with a trainable vector $\\mathbf {u}_s \\in \\mathbb {R}^{2d_s}$:",
+      "",
+      "$\\mathbf {u}_s$ is initialized randomly. It can be interpreted as a \u201csuper-word\u201d whose vector contains the ideal combination of latent topics, on average. The closest the annotation of a word is to this ideal representation, the more attention that word will be given.",
+      "The sentence encoder is applied to all sentences in document $\\mathbf {X}$, producing a sequence of $N$ sentence vectors $(\\mathbf {s_1},\\dots ,\\mathbf {s_N}) \\in \\mathbb {R}^{N\\times 2d_s}$."
+    ],
+    [
+      "The document encoder is a self-attentional bidirectional GRU-RNN, like the sentence encoder, but it has different parameters. The dimensionality of its hidden states is $2d_d$. The document encoder is applied only once, to the sequence of sentence vectors, to produce the sequence of sentence annotations $(\\mathbf {h}_{1}, \\dots , \\mathbf {h}_{N})$. Then, a self-attention layer outputs the final document vector."
+    ],
+    [
+      "As was previously explained, each sentence is encoded independently by HAN, without considering any kind of contextual information. To solve this issue, we inject a context vector $\\mathbf {c_i}$ into the self-attention mechanism, to guide the model during the computation of the word alignment coefficients. In effect, Eq. DISPLAY_FORM12 becomes:",
+      "",
+      "We propose two approaches for computing $\\mathbf {c_i}$, namely CAHAN-SUM and CAHAN-RNN, shown as the two bolded connections in Fig. FIGREF6."
+    ],
+    [
+      "We introduce two settings, (1) left-to-right and bidirectional. Whenever there is no preceding/following sentence, i.e., at the beginning/end of a document, the context vector is initialized with zeroes."
+    ],
+    [
+      "In the LR case, the context vector is computed as the sum of the preceding sentence representations:",
+      ""
+    ],
+    [
+      "In the BI case, we compute two context vectors, respectively by summing the representations of the sentences preceding and following the current sentence $S_i$. These two vectors are passed to two identical context-aware self-attention mechanisms (Eq. DISPLAY_FORM14) with different parameters. The resulting forward and backward sentence representations are then processed respectively by the forward and backward RNNs of the document encoder at level 2, and the resulting annotations are concatenated to produce the final sentence annotations.",
+      "CAHAN-SUM was inspired by the coverage vectors of seq2seq architectures, which have been shown very effective in addressing under(over)-translation in NMT BIBREF9, and repetition in summarization BIBREF10. Such coverage vectors are typically computed as the sum, over all previous decoder steps, of the attention distribution over the source tokens. However, in our case, we cannot keep track of the attention distribution history, since sentences are unique and cannot be aligned. This is why we work with sentence representations instead."
+    ],
+    [
+      "$\\overrightarrow{\\mathbf {c}_i}$, as defined by Eq. DISPLAY_FORM17, grows larger in magnitude as $i$ increases (the sum has more and more terms), which can blur the alignment decisions for the sentences at the end of a document (LR case), or both at the end and beginning of a document, when reading forwards and backwards (BI case). Therefore, we also experiment with a centroid, rather than sum, context vector:",
+      ""
+    ],
+    [
+      "Here, we capitalize on the capability of RNNs, especially when equipped with LSTM or GRU units, to keep track of information over long time periods. We simply use as context vector the document encoder annotation at the preceding/following time step. That is, we have, in the LR case:",
+      "",
+      "By design, $\\mathbf {h}_{i-1}$ summarizes the entire history $(\\mathbf {s_1},\\dots ,\\mathbf {s_{i-1}})$ of sentence vectors, with a preference for the most recent time steps. If the sequence is very long though, even a GRU-RNN will eventually forget about the first elements. However, for the relatively short documents we experiment with (see Table TABREF29), we can assume the annotations of the document encoder to faithfully represent the entire sequence."
+    ],
+    [
+      "In NMT, BIBREF11 introduced a gating mechanism to allow the decoder to balance the contribution of the source and target information in generating the next word. The same idea can be found in numerous other NMT studies, e.g., BIBREF2, BIBREF12, BIBREF13. Inspired by this line of research, we propose a modification of Eq. DISPLAY_FORM14 to let our model explicitly decide how much contextual information it should take into account in making its alignment decisions:",
+      "",
+      "$\\mathbf {\\lambda }$ is produced by a trainable mechanism taking as input the word annotations and the context vector:",
+      "",
+      "The sigmoid activation ensures that $\\mathbf {\\lambda }$ plays a filtering role, by squashing all its entries to $[0,1]$.",
+      "The gate gives more expressiveness to the attention mechanism. Indeed, contextual information should not always be given the same importance, depending on the situation. E.g., when most of the document has been processed, context is likely to be very important, in order to limit redundancy and increase coverage. However, at the beginning of a document, or in the case of a very short or focused sentence, context might not be useful as only one single topic might be extractable from the sentence anyways.",
+      "From an optimization perspective, $\\mathbf {\\lambda }$ also has the desirable effect of regulating the magnitude of the context vector, preventing it from pushing the tanh to regions of very small gradient. This is especially useful with CAHAN-SUM, as in that case, $\\mathbf {c}_i$ gets large towards the end/beginning of documents (forwards/backwards reading)."
+    ],
+    [
+      "Assuming that $d \\sim 2d_s$ and that $d_s \\sim d_d$, which holds in practice under reasonable settings, all matrix multiplications in the network have similar complexity, of order of magnitude $\\mathcal {O}(d^2)$. Moreover, since we use GRU-RNNs, there are 6 matrix multiplication per encoder. This number is doubled, as we use bidirectional RNNs. Finally, the two self-attention mechanisms, one at each level, add two multiplications. Therefore, in the HAN baseline architecture, there are a total of 26 matrix multiplications (13 at each level).",
+      "To that, CAHAN-SUM and CAHAN-RNN simply add one matrix multiplication ($W_c\\mathbf {c}_i$ in Eq. DISPLAY_FORM14) in the LR case and two in the BI case. This corresponds to negligible 4% and 8% increases in total computational cost. On top of that, gating adds two multiplications in the LR case ($W_{\\lambda _1}\\mathbf {h}_{it}$ and $W_{\\lambda _2}\\mathbf {c}_i$ in Eq. DISPLAY_FORM25) and four in the BI case. All in all, this represents three and six extra multiplications compared to basic HAN, resp. in the LR and BI cases. Again, this corresponds to small increases in computational cost, of 11.5% and 23%, respectively.",
+      "However, with CAHAN-SUM, the representations of the preceding/following sentences are now required before computing the current sentence representation. With CAHAN-RNN, one even has to wait until the level 2 RNN has processed the preceding/following sentence vectors before being able to encode the current sentence. Therefore, the sentence encoding process, which was parallelizable with basic HAN due to independence, has now become a sequential process. This is why in practice, we observe slightly greater runtime increases, in the range 5-22% (see Table TABREF43)."
+    ],
+    [
+      "We evaluate the quality of the document embeddings learned by the different variants of CAHAN and the HAN baseline on three of the large-scale document classification datasets introduced by BIBREF14 and used in the original HAN paper BIBREF5. They fall into two categories: topic classification (Yahoo) and fine-grained sentiment analysis (Amazon, Yelp). Dataset statistics are shown in Table TABREF29. Classes are perfectly balanced, for all datasets."
+    ],
+    [
+      "This subsection describes the preprocessing and hyperparameter setting we used."
+    ],
+    [
+      "For preprocessing (and the HAN baseline), we used the publicly available implementation of BIBREF15, which closely follows the description and details given in the original HAN paper BIBREF5. More precisely, on each dataset, we randomly split the training set into training (90%) and validation (10%). Documents are then tokenized into sentences and sentences are tokenized into tokens. The tokens appearing less than 5 times in the corpus are replaced with a special UNK token. Finally, we pre-train our own word vectors with word2vec BIBREF16 on the training and validation splits."
+    ],
+    [
+      "We do not tune any hyperparameter except the learning rate (see subsection SECREF35). We set the hidden layer dimensionality of the two RNN encoders to $d_s=50$ and $d_d=50$. Thus, the word annotations, sentence vectors, sentence annotations and document vector all have size 100.",
+      "With regularization in mind, we set the dimensionality of the word embeddings to $d=200$ on the very large datasets (Amazon and Yahoo!) and to $d=100$ on Yelp, as shown in Table TABREF29. We also use a greater batch size of 128 on the large datasets, versus 64 on Yelp."
+    ],
+    [
+      "We zero-pad sentences and documents. Like in BIBREF5, to make the most out of each batch, we ensure they are as dense as possible by using a bucketing strategy. More precisely, we build each batch so that it contains documents of approximately the same size, in number of sentences. For regularization, we use dropout BIBREF17 with a rate of 0.5 at each layer. For classification, the document vectors are passed to a dense layer with softmax activation, whose dimensionality is equal to the number of categories to be predicted.",
+      "Initialization has a significant impact on performance. To make sure the differences we measure are due to differences in the models and not in initial condition, we use the same initialization weights for each model."
+    ],
+    [
+      "To minimize the categorical cross-entropy loss, we use the stochastic gradient descent optimizer with a triangular cyclical learning rate schedule and opposite triangular momentum schedule BIBREF18, BIBREF19. Following the authors' recommendations, we use a fixed $[0.85,0.95]$ momentum range, while for the learning rate, we perform a range test on the validation set, for each model, searching the $[0.001,3]$ range. With a triangular schedule, the learning rate linearly increases for a certain number of iterations (half-cycle), and then linearly decreases back to its initial value during the second half of the cycle. Cycles are repeated until training ends. High learning rate values make training faster, by allowing large updates and the use of greater batch sizes while keeping the amount of regularization constant. Also, the cyclical schedule injects beneficial stochastic noise to the gradient updates, which improves generalization BIBREF20.",
+      "We use cycles of 12 epochs, and an early stopping strategy, monitoring the test loss, with a patience of slightly more than one cycle. We set the maximum number of epochs for all models to 50."
+    ],
+    [
+      "As can be seen in Table TABREF37, the best version of CAHAN (SUM-BI-$\\Sigma $) consistently outperforms the HAN baseline, which shows that taking contextual information into account helps producing better document representations.",
+      "Also, the two unidirectional variants (LR) slightly underperform the baseline and are clearly inferior to BI, which illustrates the value added by processing the document forwards and backwards, using preceding and following sentences as context."
+    ],
+    [
+      "In the unidirectional case, it is surprising to note that CAHAN-SUM-LR-$\\mu $ is slightly better than CAHAN-SUM-LR-$\\Sigma $, i.e., the centroid-based context vector (Eq. DISPLAY_FORM20) is better than the sum-based one (Eq. DISPLAY_FORM17). Indeed, from an information theory standpoint, it should be the opposite, as summing keeps track of all information whereas averaging is lossy. We hypothesize that towards the end of a document, the sum-based context vector grows large in magnitude, which perturbs the alignment decisions and deteriorates the quality of the sentence vectors. On the other hand, the centroid-based vector, which has constant magnitude, does not suffer from this issue. We further hypothesize that this issue is attenuated in the bidirectional case (CAHAN-SUM-BI-$\\mu $ and CAHAN-SUM-BI-$\\Sigma $ are on par) due to a counterbalancing phenomenon. Indeed, the last sentences processed by the left-to-right encoder are the first ones processed by the right-to-left encoder. Therefore, through concatenation, the overall quality of the sentence embeddings stays constant."
+    ],
+    [
+      "As expected, gating improves performance, especially for the $\\Sigma $ variants of CAHAN-SUM (and especially the LR ones). To be noted are significant boosts of 0.45 and 0.24 in accuracy respectively for CAHAN-SUM-LR-$\\Sigma $ and CAHAN-SUM-BI-$\\Sigma $ on Yelp. On Amazon, gating also offers CAHAN-SUM-LR-$\\Sigma $ a nice 0.27 improvement. These positive results give a clue that regulating the magnitude of the context vector $\\mathbf {c}_i$ is indeed beneficial.",
+      "Nevertheless, gating also improves the performance of the $\\mu $ variants of CAHAN, which do not suffer from the context vector magnitude issue. This shows that gating is also helpful via giving more expressiveness to the model. For instance, on Amazon, gating boosts the performance of CAHAN-SUM-BI-$\\mu $ by 0.12.",
+      "It is interesting to note that overall, gating is mostly effective on Yelp and Amazon. We attribute this to the difference in task. Sentiment analysis may rely more on contextual information than topic classification."
+    ],
+    [
+      "The consistently bad performance of CAHAN-RNN-BI is to be noted. This was unexpected, as an equivalent approach was used by BIBREF6 for dialogue act classification, with significant improvements. We hypothesize that in our case, CAHAN-RNN-BI is not effective because, unlike utterances in a speech transcription, sentences in a document are not ordered in a temporal fashion. In other words, sentences far away from the current sentence are not necessarily less relevant than closer sentences. Thus, considering each sentence equally is better than imposing an implicit time-decay via a RNN."
+    ],
+    [
+      "We compare the average runtime per iteration of some variants of CAHAN to that of basic HAN in Table TABREF43. For CAHAN-SUM-$\\Sigma $, we observe that the unidirectional variant (LR) is 5.7% slower than basic HAN (37 vs. 35ms per iteration), whereas the bidirectional variant (BI) is 23% slower (43 vs. 35 ms). When gating, these number increase to 14.3% and 37% (40 and 48ms vs. 35ms). These differences are not far from our theoretical expectations (see subsection SECREF26), especially for LR. Indeed, recall that based on matrix multiplication counts, we had forecasted increases of 4% and 8% (11.5% and 23% when using gating), respectively for LR and BI. The gap for BI can be explained by a probable bottleneck in the implementation.",
+      "CAHAN-RNN adds the same number of matrix multiplications as CAHAN-SUM, so we should in principle observe the same increases. However, as was explained in subsection SECREF26, with CAHAN-RNN we have to wait until the level 2 RNN has processed the preceding or preceding/following sentence vectors (LR or BI case) before being able to encode the current sentence. This explains the extra-time needed (40 vs. 37ms and 49 vs. 43ms)."
+    ],
+    [
+      "In what follows, we provide a review of the relevant literature. One should note that by context, in this paper, we do not refer to the intra-sentence or internal context vector of seq2seq encoders BIBREF21, BIBREF11, BIBREF13. Rather, we refer to the cross-sentence, external, or document-level context. A few studies only have focused on developing models that take that type of context into account. Most of these studies originate from NMT. We briefly describe them next.",
+      "BIBREF2 obtain a global context vector by feeding a fixed number of the previous source sentences to HAN. They then compare two ways of injecting it into the encoder-decoder model. First, they propose a warm-start approach, in which the encoder and/or decoder hidden states are initialized with the context vector. Second, they experiment with an auxiliary strategy in which the intra-sentence context vector of the encoder is concatenated with the global context vector and passed either (i) directly to the decoder, or (ii) after going through a filtering gate. However, unlike our mechanism and that of BIBREF11, BIBREF12, BIBREF13, which all feature two coupled gates, the mechanism of BIBREF2 has only one gate. All strategies proposed by BIBREF2 significantly improve performance, but first place is reached by a combination of the warm-start and gated techniques.",
+      "BIBREF22 use an approach similar to the auxiliary approach of BIBREF2, but they compute the context vector only from the sentence immediately preceding the current source sentence. They then pass it to a dedicated encoder featuring a customized attention mechanism.",
+      "BIBREF12 and BIBREF23 both extend the Transformer architecture BIBREF24 with a context encoder featuring self-attentional and feed-forward layers. Then, BIBREF12 combine the context representation with the source representation produced by the basic Transformer encoder via a gating mechanism. They do not modify the decoder part of the Transformer.",
+      "BIBREF23 go one step further by passing the contextual information both to the encoder and the decoder. In both cases, they add a self-attention mechanism over the context representation. For the decoder though, they also replace the residual connection after the context self-attention with a gating mechanism, to limit the influence of the context information on the source information.",
+      "One piece of work closely related to our study is BIBREF3. The authors also use a hierarchical attention architecture, where at level 1, each paragraph of a document is encoded by a dedicated encoder. All encoders share the same stacking bi-RNN architecture. Moreover, they communicate at each layer to produce context-aware annotations of the words in their paragraphs. More precisely, at a given layer of the stacking RNN, a given encoder is passed the average of the representations learned by the other encoders at the corresponding layer (like with CAHAN-SUM-$\\mu $). This context vector is then combined with the hidden states and passed as input to the upper RNN layer. At level 2, the top RNN layer annotations are passed to a word attention mechanism followed by a paragraph attention mechanism. A major difference with our work is that the authors combine the encoder with a decoder, to perform abstractive summarization of long documents, whereas we only focus on the encoding part. The word and paragraph attentional decisions at level 2 are thus made by the decoder. Another significant difference is that the authors use reinforcement learning for training, instead of SGD.",
+      "Context-aware models have also been proposed in other NLP domains. E.g., for spoken language understanding, BIBREF7 prepend and append the current utterance with two special word vectors respectively summarizing the $C$ preceding and following utterances (respectively), where $C$ is a hyperparameter. This indirectly initializes the hidden states of the left-to-right and right-to-left components of a bidirectional RNN, like with the warm-start approach of BIBREF2. On the other hand, BIBREF6 rely on a mechanism equivalent to LR-CAHAN-RNN. They find that it significantly boosts dialogue act classification accuracy. As discussed in section SECREF5, we hypothesize that CAHAN-RNN is not effective in our application because sentences in a document are not ordered in a temporal manner."
+    ],
+    [
+      "While bidirectional CAHAN-SUM systematically outperforms HAN, margins are modest. We attribute this to the fact that the datasets used in our experiments contain short documents (see Table TABREF29) featuring simple sentences. Thus, the superior expressiveness of CAHAN is not able to show. To address this issue, we plan in future work to experiment on datasets featuring long documents containing complex sentences.",
+      "Moreover, the tasks of sentiment and topic classification do not require a deep understanding of the input documents. Even if a given document contains some complex sentences with multiple clauses and subtopics, capturing the polarity of only one simple, unambiguous sentence or pattern may be enough to accurately predict the category of the entire document (e.g., \u201chated\u201d, \u201cloved\u201d, \u201cdefinitely recommends\u201d, \u201cwill never come back\u201d, etc.). Thus, we hypothesize that when trained to solve such tasks, CAHAN does not learn to use its context-aware capabilities to the fullest extent.",
+      "One solution, and promising area of future work, would consist in explicitly giving CAHAN knowledge about coverage, diversity, and redundancy. This could be done by modifying the sentence attention mechanism and/or by adding a term to the loss. Another natural next step is to experiment on tasks requiring a deeper understanding of text, such as end-to-end abstractive summarization. Some other ideas for improvement include combining CAHAN-SUM with CAHAN-RNN, and/or the mean and centroid vectors; for CAHAN-SUM, obtaining the centroid vector through a trainable mechanism rather than via pooling; and experimenting with a trainable matrix (instead of vector) in the self-attention at both level 1 and level 2, like in BIBREF25. Finally, the context vector could be seen as an external, general summary of the document, and be pre-computed offline by a dedicated encoder."
+    ],
+    [
+      "In this paper, we proposed several modifications of the HAN architecture that make the sentence encoder context-aware (CAHAN). Results show that taking context into account is beneficial. Specifically, the bidirectional version of the document encoder, that processes the documents forwards and backwards, using the preceding and following sentences as context, outperforms the HAN baseline on all datasets and is superior to the undirectional variant. Moreover, the computational overhead is small. Experiments on tasks requiring a deeper understanding of the input documents should better highlight the superiority of CAHAN."
+    ],
+    [
+      "We thank Xiang Zhang for sharing the datasets. We are grateful to the NVidia corporation for the donation of a GPU as part of their GPU grant program. This research was supported in part by the open-source project LinTo."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: Bidirectional Context-Aware Hierarchical Attention Network for Document Understanding
+
+Question: What are the datasets used
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Introduction ::: Observed problem",
+    "Introduction ::: Context-aware HAN",
+    "HAN",
+    "HAN ::: Notation",
+    "HAN ::: Sentence encoder",
+    "HAN ::: Document encoder",
+    "Proposed architecture: CAHAN",
+    "Proposed architecture: CAHAN ::: Summed context (CAHAN-SUM)",
+    "Proposed architecture: CAHAN ::: Summed context (CAHAN-SUM) ::: Left-to-right (LR)",
+    "Proposed architecture: CAHAN ::: Summed context (CAHAN-SUM) ::: Bidirectional (BI)",
+    "Proposed architecture: CAHAN ::: Summed context (CAHAN-SUM) ::: Centroid version (@!START@$\\mu $@!END@)",
+    "Proposed architecture: CAHAN ::: Recurrent Context (CAHAN-RNN)",
+    "Proposed architecture: CAHAN ::: Gated context",
+    "Proposed architecture: CAHAN ::: Complexity and sequentiality",
+    "Experimental setup ::: Datasets",
+    "Experimental setup ::: Model configuration",
+    "Experimental setup ::: Model configuration ::: Preprocessing and word embeddings",
+    "Experimental setup ::: Model configuration ::: Hyperparameters",
+    "Experimental setup ::: Training details",
+    "Experimental setup ::: Training details ::: SGD with cyclical learning rate",
+    "Results",
+    "Results ::: Summing vs. averaging",
+    "Results ::: Gating",
+    "Results ::: CAHAN-RNN-BI",
+    "Results ::: Runtimes",
+    "Related work",
+    "Discussion and next steps",
+    "Conclusion",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "Recently, hierarchical architectures have become ubiquitous in NLP. They have been applied to a wide variety of tasks such as language modeling and generation BIBREF0, BIBREF1, neural machine translation (NMT) BIBREF2, summarization BIBREF3, sentiment and topic classification BIBREF4, BIBREF5, and spoken language understanding BIBREF6, BIBREF7, to cite only a few examples. All hierarchical architectures capitalize on the same intuitive idea that the representation of the input text should be learned in a bottom-up fashion by using a different encoder at each granularity level (e.g., words, sentences, paragraphs), where the encoder at level $l+1$ takes as input the output of the encoder at level $l$.",
+      "One of the earliest and most influential examples is the Hierarchical Attention Network (HAN) of BIBREF5 (see Fig. FIGREF6 and section SECREF2). It is a two-level architecture, where at level 1, each sentence in the document is separately encoded by the same sentence encoder, resulting in a sequence of sentence vectors. That sequence is then processed at level 2 by the document encoder which returns a single vector representing the entire document. The sentence and document encoders are both self-attentional bidirectional Recurrent Neural Networks (RNNs), with different parameters."
+    ],
+    [
+      "HAN was highly successful and established new state of the art on six large-scale sentiment and topic classification datasets. However, it has a major weakness: at level 1, each sentence is encoded in isolation. That is, while producing the representation of a given sentence in the document, HAN completely ignores the other sentences. This lack of communication is obviously suboptimal. For example, in Fig. FIGREF2, the same highly negative feature (\u201cterrible value\u201d) has been repeated at the beginning of each sentence in the document. Because it encodes each sentence independently, HAN has no choice but to spend most of its attentional budget on the most salient feature every time. As a result, HAN neglects the other aspects of the document. On the other hand, CAHAN is informed about the context, and thus quickly stops spending attention weight on the same highly negative pattern, knowing that is has already been covered. CAHAN is then able to cover the other topics in the document (\u201cseafood\u201d,\u201cscallops\u201d and \u201cmussels\u201d; \u201centree\u201d and \u201cappetizer\u201d; triple negation in the fourth sentence).",
+      "As another example, consider the edge case of a document containing the same sentence repeated several times, as shown in Fig. FIGREF3. With HAN, the exact same embedding is produced for each instantiation of the sentence, as a result of the context-blind self-attention mechanism always making the same alignment decisions. However, the context-aware sentence encoder of CAHAN allows it to extract complementary, rather than redundant information, from each instantiation of the sentence. This results in better coverage (\u201creasonably priced\u201d, \u201carrived late\u201d), in a richer document representation, and ultimately in a more accurate prediction (positive instead of very positive).",
+      "One may argue that in basic HAN, the document encoder at level 2 already does capture some notion of context, by assigning importance scores to sentences. However, at level 2, the sentence vectors have already been formed, and it is too late to modify them. Since the document encoder can only rank the sentence representations, it cannot address issues like high redundancy. In that case, important subtopics or details in the document will not be covered, no matter sentence scores."
+    ],
+    [
+      "In this work, we propose and evaluate several modifications of the HAN architecture that allow the sentence encoder at level 1 to make its attentional decisions based on contextual information, allowing it to learn richer document representations. Another significant contribution is the introduction of a bidirectional version of the document encoder, where one RNN processes the document forwards, using the preceding sentences as context, and another one processes it backwards, using the following sentences as context.",
+      "The remainder of this paper is structured as follows. We start by formally introducing basic HAN (section SECREF2), we then explain our contributions (section SECREF3), and detail our experimental setup (section SECREF4). Finally, we interpret our results and list areas of future development (sections SECREF5 and SECREF7). Related work is reviewed in section SECREF6."
+    ],
+    [
+      "The baseline HAN model as introduced by BIBREF5 is shown in Fig. FIGREF6 along with our modifications (disregard the bold lines for the baseline). The sentence and document encoders, used respectively at level 1 and level 2, have different parameters but share the exact same architecture. Thus, in what follows, we only describe the sentence encoder in detail."
+    ],
+    [
+      "Next, we use boldface upper case for tensors, upper case for matrices, boldface lower case for vectors, and lower case for scalars. We define a document $\\mathbf {X} \\in \\mathbb {R}^{N \\times T_i \\times d}$ as a sequence of $N$ sentences $(S_1, \\dots , S_N)$. Each sentence $S_i$ is a sequence of $T_i$ $d$-dimensional word vectors $(\\mathbf {x}_{i1}, \\dots , \\mathbf {x}_{iT_i}) \\in \\mathbb {R}^{T_i \\times d}$."
+    ],
+    [
+      "First, the sentence-level bidirectional RNN $f_s$ processes the input sentence $S_i$ and returns a sequence of $T_i$ $2d_s$-dimensional hidden states $(\\mathbf {h}_{i1},\\dots , \\mathbf {h}_{iT_i}) \\in \\mathbb {R}^{T_i \\times 2d_s}$. $f_s$ is composed of two non-stacking RNNs $\\overrightarrow{f_s}$ and $\\overleftarrow{f_s}$ with Gated Recurrent Units BIBREF8, respectively parsing $S_i$ from left to right and right to left:",
+      "",
+      "$\\overrightarrow{f_s}$ and $\\overleftarrow{f_s}$ have the same hidden layer dimensionality $d_s$, but different parameters. At each time step $t$, the word annotations they return are concatenated, producing $2d_s$-dimensional annotations that summarize the immediate context surrounding each word:",
+      "",
+      "Then, a self-attention mechanism computes the representation $\\mathbf {s}_i$ of sentence $S_i$ as a weighted sum of its word annotations:",
+      "",
+      "Where the vector of attentional coefficients $\\mathbf {\\alpha }$ is a softmax-normalized version of the alignment vector $\\mathbf {e}$, which itself is obtained by passing the word annotations through a dense layer (parameterized by $W_s \\in \\mathbb {R}^{2d_s\\times 2d_s}$) and comparing the output with a trainable vector $\\mathbf {u}_s \\in \\mathbb {R}^{2d_s}$:",
+      "",
+      "$\\mathbf {u}_s$ is initialized randomly. It can be interpreted as a \u201csuper-word\u201d whose vector contains the ideal combination of latent topics, on average. The closest the annotation of a word is to this ideal representation, the more attention that word will be given.",
+      "The sentence encoder is applied to all sentences in document $\\mathbf {X}$, producing a sequence of $N$ sentence vectors $(\\mathbf {s_1},\\dots ,\\mathbf {s_N}) \\in \\mathbb {R}^{N\\times 2d_s}$."
+    ],
+    [
+      "The document encoder is a self-attentional bidirectional GRU-RNN, like the sentence encoder, but it has different parameters. The dimensionality of its hidden states is $2d_d$. The document encoder is applied only once, to the sequence of sentence vectors, to produce the sequence of sentence annotations $(\\mathbf {h}_{1}, \\dots , \\mathbf {h}_{N})$. Then, a self-attention layer outputs the final document vector."
+    ],
+    [
+      "As was previously explained, each sentence is encoded independently by HAN, without considering any kind of contextual information. To solve this issue, we inject a context vector $\\mathbf {c_i}$ into the self-attention mechanism, to guide the model during the computation of the word alignment coefficients. In effect, Eq. DISPLAY_FORM12 becomes:",
+      "",
+      "We propose two approaches for computing $\\mathbf {c_i}$, namely CAHAN-SUM and CAHAN-RNN, shown as the two bolded connections in Fig. FIGREF6."
+    ],
+    [
+      "We introduce two settings, (1) left-to-right and bidirectional. Whenever there is no preceding/following sentence, i.e., at the beginning/end of a document, the context vector is initialized with zeroes."
+    ],
+    [
+      "In the LR case, the context vector is computed as the sum of the preceding sentence representations:",
+      ""
+    ],
+    [
+      "In the BI case, we compute two context vectors, respectively by summing the representations of the sentences preceding and following the current sentence $S_i$. These two vectors are passed to two identical context-aware self-attention mechanisms (Eq. DISPLAY_FORM14) with different parameters. The resulting forward and backward sentence representations are then processed respectively by the forward and backward RNNs of the document encoder at level 2, and the resulting annotations are concatenated to produce the final sentence annotations.",
+      "CAHAN-SUM was inspired by the coverage vectors of seq2seq architectures, which have been shown very effective in addressing under(over)-translation in NMT BIBREF9, and repetition in summarization BIBREF10. Such coverage vectors are typically computed as the sum, over all previous decoder steps, of the attention distribution over the source tokens. However, in our case, we cannot keep track of the attention distribution history, since sentences are unique and cannot be aligned. This is why we work with sentence representations instead."
+    ],
+    [
+      "$\\overrightarrow{\\mathbf {c}_i}$, as defined by Eq. DISPLAY_FORM17, grows larger in magnitude as $i$ increases (the sum has more and more terms), which can blur the alignment decisions for the sentences at the end of a document (LR case), or both at the end and beginning of a document, when reading forwards and backwards (BI case). Therefore, we also experiment with a centroid, rather than sum, context vector:",
+      ""
+    ],
+    [
+      "Here, we capitalize on the capability of RNNs, especially when equipped with LSTM or GRU units, to keep track of information over long time periods. We simply use as context vector the document encoder annotation at the preceding/following time step. That is, we have, in the LR case:",
+      "",
+      "By design, $\\mathbf {h}_{i-1}$ summarizes the entire history $(\\mathbf {s_1},\\dots ,\\mathbf {s_{i-1}})$ of sentence vectors, with a preference for the most recent time steps. If the sequence is very long though, even a GRU-RNN will eventually forget about the first elements. However, for the relatively short documents we experiment with (see Table TABREF29), we can assume the annotations of the document encoder to faithfully represent the entire sequence."
+    ],
+    [
+      "In NMT, BIBREF11 introduced a gating mechanism to allow the decoder to balance the contribution of the source and target information in generating the next word. The same idea can be found in numerous other NMT studies, e.g., BIBREF2, BIBREF12, BIBREF13. Inspired by this line of research, we propose a modification of Eq. DISPLAY_FORM14 to let our model explicitly decide how much contextual information it should take into account in making its alignment decisions:",
+      "",
+      "$\\mathbf {\\lambda }$ is produced by a trainable mechanism taking as input the word annotations and the context vector:",
+      "",
+      "The sigmoid activation ensures that $\\mathbf {\\lambda }$ plays a filtering role, by squashing all its entries to $[0,1]$.",
+      "The gate gives more expressiveness to the attention mechanism. Indeed, contextual information should not always be given the same importance, depending on the situation. E.g., when most of the document has been processed, context is likely to be very important, in order to limit redundancy and increase coverage. However, at the beginning of a document, or in the case of a very short or focused sentence, context might not be useful as only one single topic might be extractable from the sentence anyways.",
+      "From an optimization perspective, $\\mathbf {\\lambda }$ also has the desirable effect of regulating the magnitude of the context vector, preventing it from pushing the tanh to regions of very small gradient. This is especially useful with CAHAN-SUM, as in that case, $\\mathbf {c}_i$ gets large towards the end/beginning of documents (forwards/backwards reading)."
+    ],
+    [
+      "Assuming that $d \\sim 2d_s$ and that $d_s \\sim d_d$, which holds in practice under reasonable settings, all matrix multiplications in the network have similar complexity, of order of magnitude $\\mathcal {O}(d^2)$. Moreover, since we use GRU-RNNs, there are 6 matrix multiplication per encoder. This number is doubled, as we use bidirectional RNNs. Finally, the two self-attention mechanisms, one at each level, add two multiplications. Therefore, in the HAN baseline architecture, there are a total of 26 matrix multiplications (13 at each level).",
+      "To that, CAHAN-SUM and CAHAN-RNN simply add one matrix multiplication ($W_c\\mathbf {c}_i$ in Eq. DISPLAY_FORM14) in the LR case and two in the BI case. This corresponds to negligible 4% and 8% increases in total computational cost. On top of that, gating adds two multiplications in the LR case ($W_{\\lambda _1}\\mathbf {h}_{it}$ and $W_{\\lambda _2}\\mathbf {c}_i$ in Eq. DISPLAY_FORM25) and four in the BI case. All in all, this represents three and six extra multiplications compared to basic HAN, resp. in the LR and BI cases. Again, this corresponds to small increases in computational cost, of 11.5% and 23%, respectively.",
+      "However, with CAHAN-SUM, the representations of the preceding/following sentences are now required before computing the current sentence representation. With CAHAN-RNN, one even has to wait until the level 2 RNN has processed the preceding/following sentence vectors before being able to encode the current sentence. Therefore, the sentence encoding process, which was parallelizable with basic HAN due to independence, has now become a sequential process. This is why in practice, we observe slightly greater runtime increases, in the range 5-22% (see Table TABREF43)."
+    ],
+    [
+      "We evaluate the quality of the document embeddings learned by the different variants of CAHAN and the HAN baseline on three of the large-scale document classification datasets introduced by BIBREF14 and used in the original HAN paper BIBREF5. They fall into two categories: topic classification (Yahoo) and fine-grained sentiment analysis (Amazon, Yelp). Dataset statistics are shown in Table TABREF29. Classes are perfectly balanced, for all datasets."
+    ],
+    [
+      "This subsection describes the preprocessing and hyperparameter setting we used."
+    ],
+    [
+      "For preprocessing (and the HAN baseline), we used the publicly available implementation of BIBREF15, which closely follows the description and details given in the original HAN paper BIBREF5. More precisely, on each dataset, we randomly split the training set into training (90%) and validation (10%). Documents are then tokenized into sentences and sentences are tokenized into tokens. The tokens appearing less than 5 times in the corpus are replaced with a special UNK token. Finally, we pre-train our own word vectors with word2vec BIBREF16 on the training and validation splits."
+    ],
+    [
+      "We do not tune any hyperparameter except the learning rate (see subsection SECREF35). We set the hidden layer dimensionality of the two RNN encoders to $d_s=50$ and $d_d=50$. Thus, the word annotations, sentence vectors, sentence annotations and document vector all have size 100.",
+      "With regularization in mind, we set the dimensionality of the word embeddings to $d=200$ on the very large datasets (Amazon and Yahoo!) and to $d=100$ on Yelp, as shown in Table TABREF29. We also use a greater batch size of 128 on the large datasets, versus 64 on Yelp."
+    ],
+    [
+      "We zero-pad sentences and documents. Like in BIBREF5, to make the most out of each batch, we ensure they are as dense as possible by using a bucketing strategy. More precisely, we build each batch so that it contains documents of approximately the same size, in number of sentences. For regularization, we use dropout BIBREF17 with a rate of 0.5 at each layer. For classification, the document vectors are passed to a dense layer with softmax activation, whose dimensionality is equal to the number of categories to be predicted.",
+      "Initialization has a significant impact on performance. To make sure the differences we measure are due to differences in the models and not in initial condition, we use the same initialization weights for each model."
+    ],
+    [
+      "To minimize the categorical cross-entropy loss, we use the stochastic gradient descent optimizer with a triangular cyclical learning rate schedule and opposite triangular momentum schedule BIBREF18, BIBREF19. Following the authors' recommendations, we use a fixed $[0.85,0.95]$ momentum range, while for the learning rate, we perform a range test on the validation set, for each model, searching the $[0.001,3]$ range. With a triangular schedule, the learning rate linearly increases for a certain number of iterations (half-cycle), and then linearly decreases back to its initial value during the second half of the cycle. Cycles are repeated until training ends. High learning rate values make training faster, by allowing large updates and the use of greater batch sizes while keeping the amount of regularization constant. Also, the cyclical schedule injects beneficial stochastic noise to the gradient updates, which improves generalization BIBREF20.",
+      "We use cycles of 12 epochs, and an early stopping strategy, monitoring the test loss, with a patience of slightly more than one cycle. We set the maximum number of epochs for all models to 50."
+    ],
+    [
+      "As can be seen in Table TABREF37, the best version of CAHAN (SUM-BI-$\\Sigma $) consistently outperforms the HAN baseline, which shows that taking contextual information into account helps producing better document representations.",
+      "Also, the two unidirectional variants (LR) slightly underperform the baseline and are clearly inferior to BI, which illustrates the value added by processing the document forwards and backwards, using preceding and following sentences as context."
+    ],
+    [
+      "In the unidirectional case, it is surprising to note that CAHAN-SUM-LR-$\\mu $ is slightly better than CAHAN-SUM-LR-$\\Sigma $, i.e., the centroid-based context vector (Eq. DISPLAY_FORM20) is better than the sum-based one (Eq. DISPLAY_FORM17). Indeed, from an information theory standpoint, it should be the opposite, as summing keeps track of all information whereas averaging is lossy. We hypothesize that towards the end of a document, the sum-based context vector grows large in magnitude, which perturbs the alignment decisions and deteriorates the quality of the sentence vectors. On the other hand, the centroid-based vector, which has constant magnitude, does not suffer from this issue. We further hypothesize that this issue is attenuated in the bidirectional case (CAHAN-SUM-BI-$\\mu $ and CAHAN-SUM-BI-$\\Sigma $ are on par) due to a counterbalancing phenomenon. Indeed, the last sentences processed by the left-to-right encoder are the first ones processed by the right-to-left encoder. Therefore, through concatenation, the overall quality of the sentence embeddings stays constant."
+    ],
+    [
+      "As expected, gating improves performance, especially for the $\\Sigma $ variants of CAHAN-SUM (and especially the LR ones). To be noted are significant boosts of 0.45 and 0.24 in accuracy respectively for CAHAN-SUM-LR-$\\Sigma $ and CAHAN-SUM-BI-$\\Sigma $ on Yelp. On Amazon, gating also offers CAHAN-SUM-LR-$\\Sigma $ a nice 0.27 improvement. These positive results give a clue that regulating the magnitude of the context vector $\\mathbf {c}_i$ is indeed beneficial.",
+      "Nevertheless, gating also improves the performance of the $\\mu $ variants of CAHAN, which do not suffer from the context vector magnitude issue. This shows that gating is also helpful via giving more expressiveness to the model. For instance, on Amazon, gating boosts the performance of CAHAN-SUM-BI-$\\mu $ by 0.12.",
+      "It is interesting to note that overall, gating is mostly effective on Yelp and Amazon. We attribute this to the difference in task. Sentiment analysis may rely more on contextual information than topic classification."
+    ],
+    [
+      "The consistently bad performance of CAHAN-RNN-BI is to be noted. This was unexpected, as an equivalent approach was used by BIBREF6 for dialogue act classification, with significant improvements. We hypothesize that in our case, CAHAN-RNN-BI is not effective because, unlike utterances in a speech transcription, sentences in a document are not ordered in a temporal fashion. In other words, sentences far away from the current sentence are not necessarily less relevant than closer sentences. Thus, considering each sentence equally is better than imposing an implicit time-decay via a RNN."
+    ],
+    [
+      "We compare the average runtime per iteration of some variants of CAHAN to that of basic HAN in Table TABREF43. For CAHAN-SUM-$\\Sigma $, we observe that the unidirectional variant (LR) is 5.7% slower than basic HAN (37 vs. 35ms per iteration), whereas the bidirectional variant (BI) is 23% slower (43 vs. 35 ms). When gating, these number increase to 14.3% and 37% (40 and 48ms vs. 35ms). These differences are not far from our theoretical expectations (see subsection SECREF26), especially for LR. Indeed, recall that based on matrix multiplication counts, we had forecasted increases of 4% and 8% (11.5% and 23% when using gating), respectively for LR and BI. The gap for BI can be explained by a probable bottleneck in the implementation.",
+      "CAHAN-RNN adds the same number of matrix multiplications as CAHAN-SUM, so we should in principle observe the same increases. However, as was explained in subsection SECREF26, with CAHAN-RNN we have to wait until the level 2 RNN has processed the preceding or preceding/following sentence vectors (LR or BI case) before being able to encode the current sentence. This explains the extra-time needed (40 vs. 37ms and 49 vs. 43ms)."
+    ],
+    [
+      "In what follows, we provide a review of the relevant literature. One should note that by context, in this paper, we do not refer to the intra-sentence or internal context vector of seq2seq encoders BIBREF21, BIBREF11, BIBREF13. Rather, we refer to the cross-sentence, external, or document-level context. A few studies only have focused on developing models that take that type of context into account. Most of these studies originate from NMT. We briefly describe them next.",
+      "BIBREF2 obtain a global context vector by feeding a fixed number of the previous source sentences to HAN. They then compare two ways of injecting it into the encoder-decoder model. First, they propose a warm-start approach, in which the encoder and/or decoder hidden states are initialized with the context vector. Second, they experiment with an auxiliary strategy in which the intra-sentence context vector of the encoder is concatenated with the global context vector and passed either (i) directly to the decoder, or (ii) after going through a filtering gate. However, unlike our mechanism and that of BIBREF11, BIBREF12, BIBREF13, which all feature two coupled gates, the mechanism of BIBREF2 has only one gate. All strategies proposed by BIBREF2 significantly improve performance, but first place is reached by a combination of the warm-start and gated techniques.",
+      "BIBREF22 use an approach similar to the auxiliary approach of BIBREF2, but they compute the context vector only from the sentence immediately preceding the current source sentence. They then pass it to a dedicated encoder featuring a customized attention mechanism.",
+      "BIBREF12 and BIBREF23 both extend the Transformer architecture BIBREF24 with a context encoder featuring self-attentional and feed-forward layers. Then, BIBREF12 combine the context representation with the source representation produced by the basic Transformer encoder via a gating mechanism. They do not modify the decoder part of the Transformer.",
+      "BIBREF23 go one step further by passing the contextual information both to the encoder and the decoder. In both cases, they add a self-attention mechanism over the context representation. For the decoder though, they also replace the residual connection after the context self-attention with a gating mechanism, to limit the influence of the context information on the source information.",
+      "One piece of work closely related to our study is BIBREF3. The authors also use a hierarchical attention architecture, where at level 1, each paragraph of a document is encoded by a dedicated encoder. All encoders share the same stacking bi-RNN architecture. Moreover, they communicate at each layer to produce context-aware annotations of the words in their paragraphs. More precisely, at a given layer of the stacking RNN, a given encoder is passed the average of the representations learned by the other encoders at the corresponding layer (like with CAHAN-SUM-$\\mu $). This context vector is then combined with the hidden states and passed as input to the upper RNN layer. At level 2, the top RNN layer annotations are passed to a word attention mechanism followed by a paragraph attention mechanism. A major difference with our work is that the authors combine the encoder with a decoder, to perform abstractive summarization of long documents, whereas we only focus on the encoding part. The word and paragraph attentional decisions at level 2 are thus made by the decoder. Another significant difference is that the authors use reinforcement learning for training, instead of SGD.",
+      "Context-aware models have also been proposed in other NLP domains. E.g., for spoken language understanding, BIBREF7 prepend and append the current utterance with two special word vectors respectively summarizing the $C$ preceding and following utterances (respectively), where $C$ is a hyperparameter. This indirectly initializes the hidden states of the left-to-right and right-to-left components of a bidirectional RNN, like with the warm-start approach of BIBREF2. On the other hand, BIBREF6 rely on a mechanism equivalent to LR-CAHAN-RNN. They find that it significantly boosts dialogue act classification accuracy. As discussed in section SECREF5, we hypothesize that CAHAN-RNN is not effective in our application because sentences in a document are not ordered in a temporal manner."
+    ],
+    [
+      "While bidirectional CAHAN-SUM systematically outperforms HAN, margins are modest. We attribute this to the fact that the datasets used in our experiments contain short documents (see Table TABREF29) featuring simple sentences. Thus, the superior expressiveness of CAHAN is not able to show. To address this issue, we plan in future work to experiment on datasets featuring long documents containing complex sentences.",
+      "Moreover, the tasks of sentiment and topic classification do not require a deep understanding of the input documents. Even if a given document contains some complex sentences with multiple clauses and subtopics, capturing the polarity of only one simple, unambiguous sentence or pattern may be enough to accurately predict the category of the entire document (e.g., \u201chated\u201d, \u201cloved\u201d, \u201cdefinitely recommends\u201d, \u201cwill never come back\u201d, etc.). Thus, we hypothesize that when trained to solve such tasks, CAHAN does not learn to use its context-aware capabilities to the fullest extent.",
+      "One solution, and promising area of future work, would consist in explicitly giving CAHAN knowledge about coverage, diversity, and redundancy. This could be done by modifying the sentence attention mechanism and/or by adding a term to the loss. Another natural next step is to experiment on tasks requiring a deeper understanding of text, such as end-to-end abstractive summarization. Some other ideas for improvement include combining CAHAN-SUM with CAHAN-RNN, and/or the mean and centroid vectors; for CAHAN-SUM, obtaining the centroid vector through a trainable mechanism rather than via pooling; and experimenting with a trainable matrix (instead of vector) in the self-attention at both level 1 and level 2, like in BIBREF25. Finally, the context vector could be seen as an external, general summary of the document, and be pre-computed offline by a dedicated encoder."
+    ],
+    [
+      "In this paper, we proposed several modifications of the HAN architecture that make the sentence encoder context-aware (CAHAN). Results show that taking context into account is beneficial. Specifically, the bidirectional version of the document encoder, that processes the documents forwards and backwards, using the preceding and following sentences as context, outperforms the HAN baseline on all datasets and is superior to the undirectional variant. Moreover, the computational overhead is small. Experiments on tasks requiring a deeper understanding of the input documents should better highlight the superiority of CAHAN."
+    ],
+    [
+      "We thank Xiang Zhang for sharing the datasets. We are grateful to the NVidia corporation for the donation of a GPU as part of their GPU grant program. This research was supported in part by the open-source project LinTo."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM
+
+Question: Do they evaluate only on English datasets?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Sentiment Analysis",
+    "Emojis and Sentiment Analysis",
+    "Methodology",
+    "Bi-sense Embedding",
+    "Word-guide Attention-based LSTM",
+    "Multi-level Attention-based LSTM",
+    "Data Collection and Annotation",
+    "Qualitative Analysis",
+    "Bi-sense Emoji Embedding Visualization",
+    "Conclusions",
+    "Acknowledgement"
+  ],
+  "paragraphs": [
+    [
+      "The rapid growth of social media platforms such as Twitter provides rich multimedia data in large scales for various research opportunities, such as sentiment analysis which focuses on automatically sentiment (positive and negative) prediction on given contents. Sentiment analysis has been widely used in real world applications by analyzing the online user-generated data, such as election prediction, opinion mining and business-related activity analysis. Emojis, which consist of various symbols ranging from cartoon facial expressions to figures such as flags and sports, are widely used in daily communications to express people's feelings . Since their first release in 2010, emojis have taken the place of emoticons (such as \u201c:- INLINEFORM0 \u201d and \u201c:-P\u201d) BIBREF0 to create a new form of language for social media users BIBREF1 . According to recent science reports, there are 2,823 emojis in unicode standard in Emoji 11.0 , with over 50% of the Instagram posts containing one or more emojis BIBREF2 and 92% of the online population using emojis BIBREF3 .",
+      "The extensive use of emojis has drawn a growing attention from researchers BIBREF4 , BIBREF5 because the emojis convey fruitful semantical and sentimental information to visually complement the textual information which is significantly useful in understanding the embedded emotional signals in texts BIBREF6 . For example, emoji embeddings have been proposed to understand the semantics behind the emojis BIBREF7 , BIBREF8 , and the embedding vectors can be used to visualize and predict emoji usages given their corresponding contexts. Previous work also shows that, it is useful to pre-train a deep neural network on an emoji prediction task with pre-trained emoji embeddings to learn the emotional signals of emojis for other tasks including sentiment, emotion and sarcasm prediction BIBREF9 . However, the previous literatures lack in considerations of the linguistic complexities and diversity of emoji. Therefore, previous emoji embedding methods fail to handle the situation when the semantics or sentiments of the learned emoji embeddings contradict the information from the corresponding contexts BIBREF5 , or when the emojis convey multiple senses of semantics and sentiments such as ( and ). In practice, emojis can either summarize and emphasis the original tune of their contexts, or express more complex semantics such as irony and sarcasm by being combined with contexts of contradictory semantics or sentiments. For the examples shown in Table TABREF3 , the emoji () is of consistent sentiment with text to emphasis the sentiment, but is of the opposite sentiment (positive) to the text sentiment (negative) example 3 and 4 to deliver a sense of sarcasm. Conventional emoji analysis can only extract single embedding of each emoji, and such embeddings will confuse the following sentiment analysis model by inconsistent sentiment signals from the input texts and emojis. Moreover, we consider the emoji effect modeling different from the conventional multimodal sentiment analysis which usually includes images and texts in that, image sentiment and text sentiment are usually assumed to be consistent BIBREF10 while it carries no such assumption for texts and emojis.",
+      "To tackle such limitations, we propose a novel scheme that consists of an attention-based recurrent neural network (RNN) with robust bi-sense emoji embeddings. Inspired by the word sense embedding task in natural language processing (NLP) BIBREF11 , BIBREF12 , BIBREF13 where each sense of an ambiguous word responds to one unique embedding vector, the proposed bi-sense embedding is a more robust and fine-grained representation of the complicated semantics for emojis where each emoji is embedded into two distinct vectors, namely positive-sense and negative-sense vector, respectively. For our specific task which is Twitter sentiment analysis BIBREF14 , BIBREF15 , we initialize the bi-sense embedding vectors together with word embedding vectors using word embedding algorithm fasttext BIBREF16 by extracting two distinct embeddings for each emoji according to the sentiment of its corresponding textual contexts, namely bi-sense embedding. A long short-term memory (LSTM) based recurrent neural network is then used for predicting sentiments which is integrated with the pre-trained emoji embedding features by a context-guide and self-selected attention mechanism. Because most of the previous Twitter sentiment datasets exclude emojis and there exists little resource that contains sufficient emoji-tweets with sentiment labels, we construct our own emoji-tweets dataset by automatically generating weak labels using a rule-based sentiment analysis algorithm Vader BIBREF17 for pre-traning the networks, and manually labeling a subset of tweets for fine tuning and testing purposes. The experimental results demonstrate that the bi-sense emoji embedding is capable of extracting more distinguished information from emojis and outperforms the state-of-the-art sentiment analysis models with the proposed attention-based LSTM networks. We further visualize the bi-sense emoji embedding to obtain the sentiments and semantics learned by the proposed approach.",
+      "The main contributions of this paper are summarized as follows."
+    ],
+    [
+      "Sentiment analysis is to extract and quantify subjective information including the status of attitudes, emotions and opinions from a variety of contents such as texts, images and audios BIBREF18 . Sentiment analysis has been drawing great attentions because of its wide applications in business and government intelligence, political science, sociology and psychology BIBREF19 , BIBREF20 , BIBREF21 , BIBREF22 . From a technical perspective, textual sentiment analysis is first explored by researchers as an NLP task. Methods range from lexical-based approaches using features including keywords BIBREF23 , BIBREF24 where each word corresponds to a sentiment vector with entries representing the possibility of the word and each sentiment and phase-level features (n-grams and unigrams) BIBREF25 , BIBREF26 , to deep neural network based embedding approaches including skip-grams, continuous bag-of-words (CBoW) and skip-thoughts BIBREF27 , BIBREF28 , BIBREF16 , BIBREF29 . It was until recent years when researchers start focusing on image and multimodal sentiments BIBREF30 , BIBREF31 and analyzing how to take advantage of the cross-modality resources BIBREF10 , BIBREF32 . For multimodal sentiment analysis, an underlying assumption is that both modalities express similar sentiment and such similarity is enforced in order to train a robust sentiment inference model BIBREF10 . However, the same assumption does not stand in modeling textual tweets and emojis because the complexities of natural language exist extensively, such as the use of irony, jokes, sarcasm, etc. BIBREF9 .",
+      "Models",
+      "We set up the baselines and proposed models as follows:",
+      "LSTM with text embedding: CNNs and LSTMs are widely used to encode textual contents for sentiment analysis in BIBREF45 , BIBREF46 and many online tutorials. Here we select the standard LSTM with pre-trained word embedding as input, and add one fully-connected layer with sigmoid activation top of the LSTM encoder (same as all other models), denoted as T-LSTM.",
+      "LSTM with emoji embedding: We consider the emoji as one special word and input both pre-trained text and emoji embeddings into the same LSTM network, namely E-LSTM. Similarly, we concatenate the pre-trained bi-sense emoji embedding as one special word to feed into the LSTM network. This model is called BiE-LSTM.",
+      "Attention-based LSTM with emojis:We also use the word-emoji embedding to calculate the emoji-word attention following Equation EQREF20 and EQREF21 , and the only difference is that we replace the attention-derived senti-emoji embedding with the pre-trained word-emoji embedding by fasttext, denoted as ATT-E-LSTM.",
+      "LSTM with bi-sense emoji embedding (proposed): As we have introduced in Section SECREF13 , we propose two attention-based LSTM networks based on bi-sense emoji embedding, denoted as MATT-BiE-LSTM and WATT-BiE-LSTM.",
+      "",
+      "Evaluation",
+      "We evaluate the baseline and proposed models on sentiment analysis by F1 scores and accuracies based on the auto-annotated testing set (AA-Sentiment) and human-annotated testing set (HA-Sentiment), as shown in Table TABREF25 . We only test the models after fine-tuning with a subset of the samples with human annotations because training exclusively on the samples with auto-generated weak labels results in relatively poor performances when tested with human annotated data indicating the models after fine-tuning are more robust. The F1 scores and accuracies are overall higher with the AA-Sentiment than the results with HA-sentiment, indicating that the HA-Sentiment is a more challenging task and the sentiments involved are more difficult to identify supported by their relatively lower sentiment scores returned from Vader. We still, however, observe competitive results from HA-Sentiment showing that the models are well-trained and robust to noisy labels with the help of fine-tuning with human annotated data. The T-LSTM baseline achieves decent performance in both experiments with accuracies of 86.6% and 70.7% showing that LSTM is an effective encoder for sentiment analysis as suggested by the references. The models with proposed bi-sense emoji embedding obtain accuracies over 82.4% and we observe improvements on the performance with the attention-based LSTM from our proposed model MATT-BiE-LSTM and WATT-BiE-LSTM, which is consistent with that ATT-E-LSTM (F1@84.6%, accuracy@82.0% on HA-Sentiment) outperforms significantly T-LSTM and E-LSTM.",
+      "Emoji information is useful in sentiment analysis. Most models outperforms the baseline T-LSTM in both dataset suggesting that the emoji information is useful for sentiment analysis as a complement to the textual contents, even with the naive use of emoji embeddings (E-LSTM) when tested with HA-Sentiment. We observe that E-LSTM obtains similar performance to T-LSTM with AA-Sentiment but a significant gain over the T-LSTM when tested with HA-Sentiment indicating that sentiment information is helpful and necessary when the hidden sentiment is relatively subtle and the task is more challenging.",
+      "Bi-sense emoji embedding helps. All the models using bi-sense emoji embedding perform significantly better than the baseline models without emoji feature or with word-emoji embedding. BiE-LSTM outperforms T-LSTM and E-LSTM significantly with the same utilization of emoji embedding indicates that the proposed bi-sense emoji embedding is capable of extracting more informative and distinguishable vectors over the use of conventional word embedding algorithms, which is consistent based on the comparisons between the proposed models (MATT-BiE-LSTM and WATT-BiE-LSTM) with bi-sense emoji embedding and the baseline model ATT-E-LSTM with word-emoji embedding and attention.",
+      "Attention mechanism aligns and performs well with bi-sense embedding. MATT-BiE-LSTM and WATT-BiE-LSTM obtain similar performances when tested on both Vader and human annotated samples, though their ways of computing the attention (weights and vectors) are different that WATT computes attention weights and the senti-emoji embeddings guided by each word, and MATT obtains the senti-emoji embedding based on the LSTM encoder on the whole contexts and computes the attention weights of the senti-emoji embedding across all words. Both models outperforms the state-of-the-art baseline models including ATT-E-LSTM. The proposed attention-based LSTM can be further extended to handle tasks involving multi-sense embedding as inputs, such as the word-sense embedding in NLP, by using context-guide attention to self-select how much to attend on each sense of the embeddings each of which correspond to a distinct sense of semantics or sentiments. In this way we are able to take advantage of the more robust and fine-grained embeddings."
+    ],
+    [
+      "With the overwhelming development of Internet of Things (IOT), the growing accessibility and popularity of subjective contents have provided new opportunities and challenges for sentiment analysis BIBREF33 . For example, social medias such as Twitter and Instagram have been explored because the massive user-generated contents with rich user sentiments BIBREF25 , BIBREF34 , BIBREF35 where emojis (and emoticons) are extensively used. Non-verbal cues of sentiment, such as emoticon which is considered as the previous generation of emoji, has been studied for their sentiment effect before emojis take over BIBREF36 , BIBREF37 , BIBREF38 . For instance, BIBREF36 , BIBREF38 pre-define sentiment labels to emoticons and construct a emoticon-sentiment dictionary. BIBREF37 applies emoticons for smoothing noisy sentiment labels. Similar work from BIBREF39 first considers emoji as a component in extracting the lexical feature for further sentiment analysis. BIBREF40 constructs an emoji sentiment ranking based on the occurrences of emojis and the human-annotated sentiments of the corresponding tweets where each emoji is assigned with a sentiment score from negative to positive , similar to the SentiWordNet BIBREF41 . However, the relatively intuitive use of emojis by lexical- and dictionary-based approaches lacks insightful understanding of the complexed semantics of emojis. Therefore, inspired by the success of word semantic embedding algorithms such as BIBREF28 , BIBREF16 , BIBREF7 obtains semantic embeddings of each emoji by averaging the words from its descriptions and shows it is effective to take advantage of the emoji embedding for the task of Twitter sentiment analysis. BIBREF8 proposes a convoluntional neural network to predict the emoji occurrence and jointly learns the emoji embedding via a matching layer based on cosine similarities. Despite the growing popularity of Twitter sentiment analysis, there is a limited number of emoji datasets with sentiment labels available because previous studies usually filter out urls, emojis and sometimes emoticons. However, BIBREF9 shows that it is effective to extract sentiment information from emojis for emotion classification and sarcasm detection tasks in the absence of learning vector-based emoji representations by pre-training a deep neural network to predict the emoji occurrence."
+    ],
+    [
+      "We propose two mechanisms, namely Word-guide Attention-based LSTM and Multi-level Attention-based LSTM, to take advantage of bi-sense emoji embedding for the sentiment analysis task. The frameworks of these two methods are shown in Figure FIGREF10 and Figure FIGREF19 , respectively. Our workflow includes the following steps: initialization of bi-sense emoji embedding, generating senti-emoji embedding based on self-selected attention, and sentiment classification via the proposed attention-based LSTM networks."
+    ],
+    [
+      "Recent research shows great success in word embedding task such as word2vec and fasttext BIBREF27 , BIBREF16 . We use fasttext to initialize emoji embeddings by considering each emoji as a special word, together with word embeddings. The catch is, different from conventional approaches where each emoji responds to one embedding vector (as we call word-emoji embedding), we embed each emoji into two distinct vectors (bi-sense emoji embedding): we first assign two distinct tokens to each emoji, of which one is for the particular emoji used in positive sentimental contexts and the other one is for this emoji used in negative sentimental contexts (text sentiment initialized by Vader BIBREF17 , details will be discussed in Section SECREF23 ), respectively; the same fasttext training process is used to embed each token into a distinct vector, and we thus obtain the positive-sense and negative-sense embeddings for each emoji.",
+      "The word2vec is based on the skip-gram model whose objective is to maximize the log likelihood calculated by summing the probabilities of current word occurrences given a set of the surrounding words. The fasttext model is different by formatting the problem as a binary classification task to predict the occurrence of each context word, with negative samples being randomly selected from the absent context words. Given an input word sequence INLINEFORM0 , and the context word set INLINEFORM1 and the set of negative word samples INLINEFORM2 of the current word INLINEFORM3 , the objective function is obtained based on binary logistic loss as in Equation EQREF12 : DISPLAYFORM0 ",
+      "where INLINEFORM0 denotes the logistic loss of the score function INLINEFORM1 which is computed by summing up the scalar products between the n-gram embeddings of the current word and the context word embedding which is different from word2vec where the score is the scalar product between the current word and the context word embedding. We select fasttext over word2vec mainly because its computational efficiency. In general, the two models yield competitive performances and the comparison between word embeddings is beyond our discussion. Therefore we only show the results derived by the fasttext initialization within the scope of this work."
+    ],
+    [
+      "Long short-term memory (LSTM) units have been extensively used to encode textual contents. The basic encoder model consists of a text embedding layer, LSTMs layer, and fully-connected layers for further tasks such as text classifications based on the encoded feature. The operations in an LSTM unit for time step INLINEFORM0 is formulated in Equation EQREF14 : DISPLAYFORM0 ",
+      "where INLINEFORM0 and INLINEFORM1 represent the current and previous hidden states, INLINEFORM2 denotes the current LSTM input and here we use the embedding INLINEFORM3 of the current word INLINEFORM4 , and INLINEFORM5 and INLINEFORM6 denote the weight matrices BIBREF42 . In order to take advantage of the bi-sense emoji embedding, we modify the input layer into the LSTM units. We first obtain the senti-emoji embedding as an weighted average of the bi-sense emoji embedding based on the self-selected attention mechanism. Let INLINEFORM7 represent the INLINEFORM8 -th sense embedding of emoji INLINEFORM9 ( INLINEFORM10 in our bi-sense embedding), and INLINEFORM11 denote the attention function conditioned on the current word embedding, the attention weight INLINEFORM12 and senti-emoji embedding vector INLINEFORM13 is formulated as follows: DISPLAYFORM0 ",
+      "We choose a fully-connected layer with ReLU activation as the attention function, and the attention vector INLINEFORM0 is concatenated with the word embedding as the new input of the LSTM. Thus the input vector INLINEFORM1 in Equation EQREF14 becomes INLINEFORM2 . The output of the final LSTM unit is then fed into a fully-connected layer with INLINEFORM3 activation to output the tweet sentiment and binary cross-entropy loss is used as the objection function (Equation EQREF16 ) where INLINEFORM4 is the total number of samples. The motivation behind this model is that each context word guides the attention weights in order to enforce the model to self-select which embedding sense it should attend on. Therefore we denote this model as the Word-guide Attention-based LSTM with Bi-sense emoji embedding (WATT-BiE-LSTM). DISPLAYFORM0 "
+    ],
+    [
+      "There is another way of formulating the attention mechanism where the attention weights indicate how the image information (which is emoji in our case) is distributed through the context words as proposed in BIBREF43 , BIBREF44 . The modified senti-emoji embedding vector INLINEFORM0 is thus at the tweet-level instead of the word-level in Equation EQREF15 by replacing the INLINEFORM1 with the final state vector INLINEFORM2 outputted from the last LSTM unit, as shown in Equation EQREF18 : DISPLAYFORM0 ",
+      "The derived senti-emoji embedding INLINEFORM0 is then used to calculate an additional layer of attention following BIBREF43 , BIBREF44 . Given the input tweet sequence INLINEFORM1 , the attention weight INLINEFORM2 conditioned on the senti-emoji embedding is formulated as follows: DISPLAYFORM0 ",
+      "Therefore, we construct the new input INLINEFORM0 to each LSTM unit by concatenating the original word embedding and the attention vector in Equation EQREF21 to distribute the senti-emoji information to each step. This model is called Multi-level Attention-based LSTM with Bi-sense Emoji Embedding (MATT-BiE-LSTM). We choose the same binary cross-entropy as the loss function with the same network configuration with WATT-BiE-LSTM. DISPLAYFORM0 "
+    ],
+    [
+      "Data Collection",
+      "We construct our own Twitter sentiment dataset by crawling tweets through the REST API which consists of 350,000 users and is magnitude larger comparing to previous work. We collect up to 3,200 tweets from each user and follow the standard tweet preprocessing procedures to remove the tweets without emojis and tweets containing less than ten words, and contents including the urls, mentions, and emails.",
+      "Data Annotation",
+      "For acquiring the sentiment annotations, we first use Vader which is a rule-based sentiment analysis algorithm BIBREF17 for text tweets only to generate weak sentiment labels. The algorithm outputs sentiment scores ranging from -1 (negative) to 1 (positive) with neutral in the middle. We consider the sentiment analysis as a binary classification problem (positive sentiment and negative sentiment), we filter out samples with weak prediction scores within INLINEFORM0 and keep the tweets with strong sentiment signals. Emoji occurrences are calculated separately for positive tweets and negative tweets, and threshold is set to 2,000 to further filter out emojis which are less frequently used in at least one type of sentimental text. In the end, we have constructed a dataset with 1,492,065 tweets and 55 frequently used emojis in total. For the tweets with an absolute sentiment score over 0.70, we keep the auto-generated sentiment label as ground truth because the automatic annotation is reliable with high sentiment scores. On the other hand, we select a subset of the tweets with absolute sentiment scores between INLINEFORM1 for manual labeling by randomly sampling, following the distribution of emoji occurrences where each tweet is labeled by two graduate students. Tweets are discarded if the two annotations disagree with each other or they are labeled as neutral. In the end, we have obtained 4,183 manually labeled tweets among which 60% are used for fine-tuning and 40% are used for testing purposes. The remainder of the tweets with automatic annotations are divided into three sets: 60% are used for pre-training the bi-sense and conventional emoji embedding, 10% for validation and 30% are for testing. We do not include a \u201cneutral\u201d class because it is difficult to obtain valid neutral samples. For auto-generated labels, the neutrals are the samples with low absolute confidence scores and their sentiments are more likely to be model failures other than \u201ctrue neutrals\u201d. Moreover, based on the human annotations, most of the tweets with emojis convey non-neutral sentiment and only few neutral samples are observed during the manual labeling which are excluded from the manually labeled subset.",
+      "In order to valid our motivation that emojis are also extensively used in tweets that contain contradictory information to the emoji sentiments, we calculate the emoji usage in Table TABREF22 according to the sentiment labels where Pos-Ratio means the percentage of each emoji occurs in the positive tweets over its total number of occurrences, AA and HA indicate automatic-annotation and human-annotation, respectively. We present the top-10 most frequently used emojis in our dataset and observe a slight difference in the Pos-Ratios between AA and HA dataset because of the randomness involved in the sampling process. Results from both of the datasets show a fair amount of emoji use in both positive and negative tweets. For example, it is interesting to notice that emoji () occurs more in the positive tweets in with the automatic annotations, while emojis with strong positive sentiment have also been used in negative tweets with about 5% occurrences, such as (, , and ). Given the averaged positive ratio among all emojis in the whole dataset is about 74% and that most emojis have been extensively used in tweets containing both positive and negative sentiments, it suggests that distinguishing the emoji occurrences in both sentiments via bi-sense embedding is worth investigating. Additionally, we observe the Pos-Ratios of the AA-sentiment and HA-sentiment have little differences which are due to two main reasons: 1) Some tweets we sampled to construct the HA-sentiment are discarded because the annotators have disagreements and we only keep the samples that we are confident about; 2) Tweets with absolute sentiment scores between (0.60,0.70) are selected for manual labeling as discussed in Section SECREF23 , which are lower than the tweets used to construct the AA-sentiment (0.7 and above). The lower sentiment scores indicate that Vader is less reliable on the samples of HA-sentiment dataset and the sentiments of these tweets are more likely to be affected by emojis."
+    ],
+    [
+      "In order to obtain insights about why the more fine-grained bi-sense emoji embedding helps in understanding the complexed sentiments behind tweets, we visualize the attention weights for ATT-E-LSTM and MATT-BiE-LSTM for comparison. The example tweets with corresponding attention weights calculated by word-emoji embedding and senti-emoji embedding are shown in Figure FIGREF27 , where the contexts are presented in the captions. The emojis used are , , and , respectively.",
+      "In Figure FIGREF27 (a), the ATT-E-LSTM model (baseline) assigns relatively more weights on the word \u201cno\u201d and \u201cpressure\u201d, while MATT-BiE-LSTM attends mostly on the word \u201chappy\u201d and \u201clovely\u201d. The different attention distributions suggest that the proposed senti-emoji embedding is capable of recognizing words with strong sentiments that are closely related to the true sentiment even with the presence of words with conflicting sentiments, such as \u201cpressure\u201d and \u201chappy\u201d. while ATT-E-LSTM tends to pick up all sentimental words which could raise confusions. The senti-emoji embedding is capable of extracting representations of complexed semantics and sentiments which help guide the attentions even in cases when the word sentiment and emoji sentiment are somewhat contradictory to each other. From Figure FIGREF27 (b) and (c) we can observe that the ATT-E-LSTM assigns more weights on the sentiment-irrelevant words than the MATT-BiE-LSTM such as \u201choodies\u201d, \u201cwait\u201d and \u201cafter\u201d, indicating that the proposed model is more robust to irrelevant words and concentrates better on important words. Because of the senti-emoji embedding obtained through bi-sense emoji embedding and the sentence-level LSTM encoding on the text input (described in Section SECREF13 ), we are able to construct a more robust embedding based on the semantic and sentiment information from the whole context compared to the word-emoji embedding used in ATT-E-LSTM which takes only word-level information into account."
+    ],
+    [
+      "To gain further insights on the bi-sense emoji embedding, we use t-SNE BIBREF47 to project high-dimensional bi-sense embedding vectors into a two-dimensional space and preserving the relative distances between the embedding vectors at the same time. In Figure FIGREF28 we visualize the bi-sense emoji embedding, positive-sense embedding, negative-sense embedding and the subtraction between positive and negative sense embeddings of each emoji, respectively. The subtraction of an emoji between its two sense embeddings indicates the semantic differences between emoji usages in positive and negative sentimental contexts, similarly to the objective of word embeddings BIBREF28 . The positive-sense of emoji ( and ), and the negative-sense of emoji (, and ) are embedded far from the two main clusters as observed in Figure FIGREF28 (a), suggesting that the semantics of these emojis are different from the other popular emojis. The positive-sense embedding and negative-sense embeddings are clustered well with no intersection with each other. Such observation supports our objective of applying bi-sense emoji embedding because there exist such significant differences in the semantics of each emoji when appears in positive and negative sentimental contexts, and it is well-motivated to consider the emoji usages individually according to the sentiment of the contexts to extract the more fine-grained bi-sense embedding. Additionally, we observe consistent patterns in the Figure FIGREF28 (b), (c) and (d) where the sentiments conveyed in the emojis become an important factor. For example, emojis with positive sentiments such as (, and ), and emojis with negative sentiment such as (, and ) are embedded into one clusters in both positive-sense and negative-sense space. The embedding subtractions of emojis in Figure FIGREF28 (d) shows the different usages of emojis across sentiments are similar between emojis and preserve the cluster patterns observed in Figure FIGREF28 (b) and (c)."
+    ],
+    [
+      "In this paper, we present a novel approach to the task of sentiment analysis and achieve the state-of-the-art performance. Different from the previous work, our method combines a more robust and fine-grained bi-sense emoji embedding that effectively represents complex semantic and sentiment information, with attention-based LSTM networks that selectively attend on the correlated sense of the emoji embeddings, and seamlessly fuse the obtained senti-emoji embeddings with the word embeddings for a better understanding of the rich semantics and sentiments involved. In the future, we plan to further extend our attention-based LSTM with bi-embedding work frame to tackle tasks involving multi-sense embedding such as the learning and applications of word-sense embedding."
+    ],
+    [
+      "We would like to thank the support of New York State through the Goergen Institute for Data Science, and NSF Award #1704309."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1242/instruction.md b/qasper-1242/instruction.md
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+Name of Paper: Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM
+
+Question: What is the baseline for experiments?
\ No newline at end of file
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@@ -0,0 +1,3 @@
+Name of Paper: Dreaddit: A Reddit Dataset for Stress Analysis in Social Media
+
+Question: What labels are in the dataset?
\ No newline at end of file
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+Name of Paper: Dreaddit: A Reddit Dataset for Stress Analysis in Social Media
+
+Question: What categories does the dataset come from?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Dataset ::: Reddit Data",
+    "Dataset ::: Data Annotation",
+    "Data Analysis",
+    "Methods",
+    "Methods ::: Supervised Models",
+    "Results and Discussion",
+    "Conclusion and Future Work",
+    "Acknowledgements",
+    "Data Samples",
+    "Full Annotation Guidelines",
+    "Parameter Settings",
+    "Error Analysis Examples"
+  ],
+  "paragraphs": [
+    [
+      "In our online world, social media users tweet, post, and message an incredible number of times each day, and the interconnected, information-heavy nature of our lives makes stress more prominent and easily observable than ever before. With many platforms such as Twitter, Reddit, and Facebook, the scientific community has access to a massive amount of data to study the daily worries and stresses of people across the world.",
+      "Stress is a nearly universal phenomenon, and we have some evidence of its prevalence and recent increase. For example, the American Psychological Association (APA) has performed annual studies assessing stress in the United States since 2007 which demonstrate widespread experiences of chronic stress. Stress is a subjective experience whose effects and even definition can vary from person to person; as a baseline, the APA defines stress as a reaction to extant and future demands and pressures, which can be positive in moderation. Health and psychology researchers have extensively studied the connection between too much stress and physical and mental health BIBREF0, BIBREF1.",
+      "In this work, we present a corpus of social media text for detecting the presence of stress. We hope this corpus will facilitate the development of models for this problem, which has diverse applications in areas such as diagnosing physical and mental illness, gauging public mood and worries in politics and economics, and tracking the effects of disasters. Our contributions are as follows:",
+      "Dreaddit, a dataset of lengthy social media posts in five categories, each including stressful and non-stressful text and different ways of expressing stress, with a subset of the data annotated by human annotators;",
+      "Supervised models, both discrete and neural, for predicting stress, providing benchmarks to stimulate further work in the area; and",
+      "Analysis of the content of our dataset and the performance of our models, which provides insight into the problem of stress detection.",
+      "In the remainder of this paper, we will review relevant work, describe our dataset and its annotation, provide some analysis of the data and stress detection problem, present and discuss results of some supervised models on our dataset, and finally conclude with our summary and future work."
+    ],
+    [
+      "Because of the subjective nature of stress, relevant research tends to focus on physical signals, such as cortisol levels in saliva BIBREF2, electroencephalogram (EEG) readings BIBREF3, or speech data BIBREF4. This work captures important aspects of the human reaction to stress, but has the disadvantage that hardware or physical presence is required. However, because of the aforementioned proliferation of stress on social media, we believe that stress can be observed and studied purely from text.",
+      "Other threads of research have also made this observation and generally use microblog data (e.g., Twitter). The most similar work to ours includes BIBREF5, who use Long Short-Term Memory Networks (LSTMs) to detect stress in speech and Twitter data; BIBREF6, who examine the Facebook and Twitter posts of users who score highly on a diagnostic stress questionnaire; and BIBREF7, who detect stress on microblogging websites using a Convolutional Neural Network (CNN) and factor graph model with a suite of discrete features. Our work is unique in that it uses data from Reddit, which is both typically longer and not typically as conducive to distant labeling as microblogs (which are labeled in the above work with hashtags or pattern matching, such as \u201cI feel stressed\u201d). The length of our posts will ultimately enable research into the causes of stress and will allow us to identify more implicit indicators. We also limit ourselves to text data and metadata (e.g., posting time, number of replies), whereas BIBREF5 also train on speech data and BIBREF7 include information from photos, neither of which is always available. Finally, we label individual parts of longer posts for acute stress using human annotators, while BIBREF6 label users themselves for chronic stress with the users' voluntary answers to a psychological questionnaire.",
+      "Researchers have used Reddit data to examine a variety of mental health conditions such as depression BIBREF8 and other clinical diagnoses such as general anxiety BIBREF9, but to our knowledge, our corpus is the first to focus on stress as a general experience, not only a clinical concept."
+    ],
+    [
+      "Reddit is a social media website where users post in topic-specific communities called subreddits, and other users comment and vote on these posts. The lengthy nature of these posts makes Reddit an ideal source of data for studying the nuances of phenomena like stress. To collect expressions of stress, we select categories of subreddits where members are likely to discuss stressful topics:",
+      "Interpersonal conflict: abuse and social domains. Posters in the abuse subreddits are largely survivors of an abusive relationship or situation sharing stories and support, while posters in the social subreddit post about any difficulty in a relationship (often but not exclusively romantic) and seek advice for how to handle the situation.",
+      "Mental illness: anxiety and Post-Traumatic Stress Disorder (PTSD) domains. Posters in these subreddits seek advice about coping with mental illness and its symptoms, share support and successes, seek diagnoses, and so on.",
+      "Financial need: financial domain. Posters in the financial subreddits generally seek financial or material help from other posters.",
+      "We include ten subreddits in the five domains of abuse, social, anxiety, PTSD, and financial, as detailed in tab:data-spread, and our analysis focuses on the domain level. Using the PRAW API, we scrape all available posts on these subreddits between January 1, 2017 and November 19, 2018; in total, 187,444 posts. As we will describe in sec:annotation, we assign binary stress labels to 3,553 segments of these posts to form a supervised and semi-supervised training set. An example segment is shown in fig:stress-example. Highlighted phrases are indicators that the writer is stressed: the writer mentions common physical symptoms (nausea), explicitly names fear and dread, and uses language indicating helplessness and help-seeking behavior.",
+      "The average length of a post in our dataset is 420 tokens, much longer than most microblog data (e.g., Twitter's character limit as of this writing is 280 characters). While we label segments that are about 100 tokens long, we still have much additional data from the author on which to draw. We feel this is important because, while our goal in this paper is to predict stress, having longer posts will ultimately allow more detailed study of the causes and effects of stress.",
+      "In tab:data-examples, we provide examples of labeled segments from the various domains in our dataset. The samples are fairly typical; the dataset contains mostly first-person narrative accounts of personal experiences and requests for assistance or advice. Our data displays a range of topics, language, and agreement levels among annotators, and we provide only a few examples. Lengthier examples are available in the appendix."
+    ],
+    [
+      "We annotate a subset of the data using Amazon Mechanical Turk in order to begin exploring the characteristics of stress. We partition the posts into contiguous five-sentence chunks for labeling; we wish to annotate segments of the posts because we are ultimately interested in what parts of the post depict stress, but we find through manual inspection that some amount of context is important. Our posts, however, are quite long, and it would be difficult for annotators to read and annotate entire posts. This type of data will allow us in the future not only to classify the presence of stress, but also to locate its expressions in the text, even if they are diffused throughout the post.",
+      "We set up an annotation task in which English-speaking Mechanical Turk Workers are asked to label five randomly selected text segments (of five sentences each) after taking a qualification test; Workers are allowed to select \u201cStress\u201d, \u201cNot Stress\u201d, or \u201cCan't Tell\u201d for each segment. In our instructions, we define stress as follows: \u201cThe Oxford English Dictionary defines stress as `a state of mental or emotional strain or tension resulting from adverse or demanding circumstances'. This means that stress results from someone being uncertain that they can handle some threatening situation. We are interested in cases where that someone also feels negatively about it (sometimes we can find an event stressful, but also find it exciting and positive, like a first date or an interview).\u201d. We specifically ask Workers to decide whether the author is expressing both stress and a negative attitude about it, not whether the situation itself seems stressful. Our full instructions are available in the appendix.",
+      "We submit 4,000 segments, sampled equally from each domain and uniformly within domains, to Mechanical Turk to be annotated by at least five Workers each and include in each batch one of 50 \u201ccheck questions\u201d which have been previously verified by two in-house annotators. After removing annotations which failed the check questions, and data points for which at least half of the annotators selected \u201cCan't Tell\u201d, we are left with 3,553 labeled data points from 2,929 different posts. We take the annotators' majority vote as the label for each segment and record the percentage of annotators who agreed. The resulting dataset is nearly balanced, with 52.3% of the data (1,857 instances) labeled stressful.",
+      "Our agreement on all labeled data is $\\kappa =0.47$, using Fleiss's Kappa BIBREF10, considered \u201cmoderate agreement\u201d by BIBREF11. We observe that annotators achieved perfect agreement on 39% of the data, and for another 32% the majority was 3/5 or less. This suggests that our data displays significant variation in how stress is expressed, which we explore in the next section."
+    ],
+    [
+      "While all our data has the same genre and personal narrative style, we find distinctions among domains with which classification systems must contend in order to perform well, and distinctions between stressful and non-stressful data which may be useful when developing such systems. Posters in each subreddit express stress, but we expect that their different functions and stressors lead to differences in how they do so in each subreddit, domain, and broad category.",
+      "By domain. We examine the vocabulary patterns of each domain on our training data only, not including unlabeled data so that we may extend our analysis to the label level. First, we use the word categories from the Linguistic Inquiry and Word Count (LIWC) BIBREF12, a lexicon-based tool that gives scores for psychologically relevant categories such as sadness or cognitive processes, as a proxy for topic prevalence and expression variety. We calculate both the percentage of tokens per domain which are included in a specific LIWC word list, and the percentage of words in a specific LIWC word list that appear in each domain (\u201ccoverage\u201d of the domain).",
+      "Results of the analysis are highlighted in tab:domain-liwc. We first note that variety of expression depends on domain and topic; for example, the variety in the expression of negative emotions is particularly low in the financial domain (with 1.54% of words being negative emotion (\u201cnegemo\u201d) words and only 31% of \u201cnegemo\u201d words used). We also see clear topic shifts among domains: the interpersonal domains contain roughly 1.5 times as many social words, proportionally, as the others; and domains are stratified by their coverage of the anxiety word list (with the most in the mental illness domains and the least in the financial domain).",
+      "We also examine the overall lexical diversity of each domain by calculating Yule's I measure BIBREF13. fig:domain-yule shows the lexical diversity of our data, both for all words in the vocabulary and for only words in LIWC's \u201cnegemo\u201d word list. Yule's I measure reflects the repetitiveness of the data (as opposed to the broader coverage measured by our LIWC analysis). We notice exceptionally low lexical diversity for the mental illness domains, which we believe is due to the structured, clinical language surrounding mental illnesses. For example, posters in these domains discuss topics such as symptoms, medical care, and diagnoses (fig:stress-example, tab:data-examples). When we restrict our analysis to negative emotion words, this pattern persists only for anxiety; the PTSD domain has comparatively little lexical variety, but what it does have contributes to its variety of expression for negative emotions.",
+      "By label. We perform similar analyses on data labeled stressful or non-stressful by a majority of annotators. We confirm some common results in the mental health literature, including that stressful data uses more first-person pronouns (perhaps reflecting increased self-focus) and that non-stressful data uses more social words (perhaps reflecting a better social support network).",
+      "Additionally, we calculate measures of syntactic complexity, including the percentage of words that are conjunctions, average number of tokens per labeled segment, average number of clauses per sentence, Flesch-Kincaid Grade Level BIBREF14, and Automated Readability Index BIBREF15. These scores are comparable for all splits of our data; however, as shown in tab:label-complexity, we do see non-significant but persistent differences between stressful and non-stressful data, with stressful data being generally longer and more complex but also rated simpler by readability indices. These findings are intriguing and can be explored in future work.",
+      "By agreement. Finally, we examine the differences among annotator agreement levels. We find an inverse relationship between the lexical variety and the proportion of annotators who agree, as shown in fig:agreement-diversity. While the amount of data and lexical variety seem to be related, Yule's I measure controls for length, so we believe that this trend reflects a difference in the type of data that encourages high or low agreement."
+    ],
+    [
+      "In order to train supervised models, we group the labeled segments by post and randomly select 10% of the posts ($\\approx $ 10% of the labeled segments) to form a test set. This ensures that while there is a reasonable distribution of labels and domains in the train and test set, the two do not explicitly share any of the same content. This results in a total of 2,838 train data points (51.6% labeled stressful) and 715 test data points (52.4% labeled stressful). Because our data is relatively small, we train our traditional supervised models with 10-fold cross-validation; for our neural models, we break off a further random 10% of the training data for validation and average the predictions of 10 randomly-initialized trained models.",
+      "In addition to the words of the posts (both as bag-of-n-grams and distributed word embeddings), we include features in three categories:",
+      "Lexical features. Average, maximum, and minimum scores for pleasantness, activation, and imagery from the Dictionary of Affect in Language (DAL) BIBREF16; the full suite of 93 LIWC features; and sentiment calculated using the Pattern sentiment library BIBREF17.",
+      "Syntactic features. Part-of-speech unigrams and bigrams, the Flesch-Kincaid Grade Level, and the Automated Readability Index.",
+      "Social media features. The UTC timestamp of the post; the ratio of upvotes to downvotes on the post, where an upvote roughly corresponds to a reaction of \u201clike\u201d and a downvote to \u201cdislike\u201d (upvote ratio); the net score of the post (karma) (calculated by Reddit, $n_\\text{upvotes} - n_\\text{downvotes}$); and the total number of comments in the entire thread under the post."
+    ],
+    [
+      "We first experiment with a suite of non-neural models, including Support Vector Machines (SVMs), logistic regression, Na\u00efve Bayes, Perceptron, and decision trees. We tune the parameters for these models using grid search and 10-fold cross-validation, and obtain results for different combinations of input and features.",
+      "For input representation, we experiment with bag-of-n-grams (for $n \\in \\lbrace 1..3\\rbrace $), Google News pre-trained Word2Vec embeddings (300-dimensional) BIBREF18, Word2Vec embeddings trained on our large unlabeled corpus (300-dimensional, to match), and BERT embeddings trained on our unlabeled corpus (768-dimensional, the top-level [CLS] embedding) BIBREF19. We experiment with subsets of the above features, including separating the features by category (lexical, syntactic, social) and by magnitude of the Pearson correlation coefficient ($r$) with the training labels. Finally, we stratify the training data by annotator agreement, including separate experiments on only data for which all annotators agreed, data for which at least 4/5 annotators agreed, and so on.",
+      "We finally experiment with neural models, although our dataset is relatively small. We train both a two-layer bidirectional Gated Recurrent Neural Network (GRNN) BIBREF20 and Convolutional Neural Network (CNN) (as designed in BIBREF21) with parallel filters of size 2 and 3, as these have been shown to be effective in the literature on emotion detection in text (e.g., BIBREF22, BIBREF23). Because neural models require large amounts of data, we do not cull the data by annotator agreement for these experiments and use all the labeled data we have. We experiment with training embeddings with random initialization as well as initializing with our domain-specific Word2Vec embeddings, and we also concatenate the best feature set from our non-neural experiments onto the representations after the recurrent and convolutional/pooling layers respectively.",
+      "Finally, we apply BERT directly to our task, fine-tuning the pretrained BERT-base on our classification task for three epochs (as performed in BIBREF19 when applying BERT to any task). Our parameter settings for our various models are available in the appendix."
+    ],
+    [
+      "We present our results in tab:supervised-results. Our best model is a logistic regression classifier with Word2Vec embeddings trained on our unlabeled corpus, high-correlation features ($\\ge $ 0.4 absolute Pearson's $r$), and high-agreement data (at least 4/5 annotators agreed); this model achieves an F-score of 79.8 on our test set, a significant improvement over the majority baseline, the n-gram baseline, and the pre-trained embedding model, (all by the approximate randomization test, $p < 0.01$). The high-correlation features used by this model are LIWC's clout, tone, and \u201cI\u201d pronoun features, and we investigate the use of these features in the other model types. Particularly, we apply different architectures (GRNN and CNN) and different input representations (pretrained Word2Vec, domain-specific BERT).",
+      "We find that our logistic regression classifier described above achieves comparable performance to BERT-base (approximate randomization test, $p > 0.5$) with the added benefits of increased interpretability and less intensive training. Additionally, domain-specific word embeddings trained on our unlabeled corpus (Word2Vec, BERT) significantly outperform n-grams or pretrained embeddings, as expected, signaling the importance of domain knowledge in this problem.",
+      "We note that our basic deep learning models do not perform as well as our traditional supervised models or BERT, although they consistently, significantly outperform the majority baseline. We believe this is due to a serious lack of data; our labeled dataset is orders of magnitude smaller than neural models typically require to perform well. We expect that neural models can make good use of our large unlabeled dataset, which we plan to explore in future work. We believe that the superior performance of the pretrained BERT-base model (which uses no additional features) on our dataset supports this hypothesis as well.",
+      "In tab:data-and-feat-comparison, we examine the impact of different feature sets and levels of annotator agreement on our logistic regressor with domain-specific Word2Vec embeddings and find consistent patterns supporting this model. First, we see a tradeoff between data size and data quality, where lower-agreement data (which can be seen as lower-quality) results in worse performance, but the larger 80% agreement data consistently outperforms the smaller perfect agreement data. Additionally, LIWC features consistently perform well while syntactic features consistently do not, and we see a trend towards the quality of features over their quantity; those with the highest Pearson correlation with the train set (which all happen to be LIWC features) outperform sets with lower correlations, which in turn outperform the set of all features. This suggests that stress detection is a highly lexical problem, and in particular, resources developed with psychological applications in mind, like LIWC, are very helpful.",
+      "Finally, we perform an error analysis of the two best-performing models. Although the dataset is nearly balanced, both BERT-base and our best logistic regression model greatly overclassify stress, as shown in tab:confusion-matrices, and they broadly overlap but do differ in their predictions (disagreeing with one another on approximately 100 instances).",
+      "We note that the examples misclassified by both models are often, though not always, ones with low annotator agreement (with the average percent agreement for misclassified examples being 0.55 for BERT and 0.61 for logistic regression). Both models seem to have trouble with less explicit expressions of stress, framing negative experiences in a positive or retrospective way, and stories where another person aside from the poster is the focus; these types of errors are difficult to capture with the features we used (primarily lexical), and further work should be aware of them. We include some examples of these errors in tab:error-analysis-paper, and further illustrative examples are available in the appendix."
+    ],
+    [
+      "In this paper, we present a new dataset, Dreaddit, for stress classification in social media, and find the current baseline at 80% F-score on the binary stress classification problem. We believe this dataset has the potential to spur development of sophisticated, interpretable models of psychological stress. Analysis of our data and our models shows that stress detection is a highly lexical problem benefitting from domain knowledge, but we note there is still room for improvement, especially in incorporating the framing and intentions of the writer. We intend for our future work to use this dataset to contextualize stress and offer explanations using the content features of the text. Additional interesting problems applicable to this dataset include the development of effective distant labeling schemes, which is a significant first step to developing a quantitative model of stress."
+    ],
+    [
+      "We would like to thank Fei-Tzin Lee, Christopher Hidey, Diana Abagyan, and our anonymous reviewers for their insightful comments during the writing of this paper. This research was funded in part by a Presidential Fellowship from the Fu Foundation School of Engineering and Applied Science at Columbia University."
+    ],
+    [
+      "We include several full posts (with identifying information removed and whitespace collapsed) in fig:data-appendix-1,fig:data-appendix-2,fig:data-appendix-3,fig:data-appendix-4. Posts are otherwise reproduced exactly as obtained (with spelling errors, etc.). The selected examples are deliberately of a reasonable but fairly typical length for readability and space concerns; recall that our average post length is 420 tokens, longer for interpersonal subreddits and shorter for other subreddits."
+    ],
+    [
+      "We provide our annotation instructions in full in fig:annotation. Mechanical Turk Workers were given these instructions and examples followed by five text segments (one of which was one of our 50 check questions) and allowed to select \u201cStress\u201d, \u201cNot Stress', or \u201cCan't Tell\u201d for each. Workers were given one hour to complete the HIT and paid $0.12 for each HIT where they correctly answered the check question, with a limit of 30 total submissions per Worker."
+    ],
+    [
+      "We tune our traditional supervised models' parameters using grid search, all as implemented in Python's scikit-learn library BIBREF25. Our best model uses unbalanced class weights, L2 penalty, and a constant term C=10, with other parameters at their default values. All cross-validation runs were initialized with the same random seed for comparability and reproducibility.",
+      "We train each of our neural models with the Adam optimizer BIBREF24 for up to ten epochs with early stopping measured on the validation set. We apply a dropout rate of 0.5 during training in the recurrent layers and after the convolutional layers. We set our hidden sizes (i.e., the output of the recurrent and pooling layers) as well as our batch size to 128, and tune our learning rate to $5\\cdot 10^{-4}$; we set these parameters relatively small to try to work with our small data. We also experiment with scheduling the learning rate on plateau of the validation loss, and with pre-training the models on a much larger sentiment dataset, the Stanford Sentiment Treebank BIBREF26, to help combat the problem of small data, but this does not improve the performance of our neural networks."
+    ],
+    [
+      "As a supplement to our error analysis discussion in sec:results, we provide additional examples of test data points which one or both of our best models (BERT-base or our best logistic regressor with embeddings trained on our unlabeled corpus and high-correlation discrete features) failed to classify correctly in tab:error-analysis-appendix."
+    ]
+  ]
+}
+```
\ No newline at end of file
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new file mode 100644
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+Name of Paper: Corporate IT-Support Help-Desk Process Hybrid-Automation Solution with Machine Learning Approach
+
+Question: What are all machine learning approaches compared in this work?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Method",
+    "Method ::: Manual Process",
+    "Method ::: Proposed Automation System",
+    "Method ::: Proposed Automation System ::: Quickfixes",
+    "Method ::: Proposed Automation System ::: First mail",
+    "Method ::: Proposed Automation System ::: Forwarded mail",
+    "Email classifier using machine learning ::: Preprocessing",
+    "Email classifier using machine learning ::: Feature selection",
+    "Email classifier using machine learning ::: Machine learning approach",
+    "Email classifier using machine learning ::: Machine learning approach ::: Feature selection",
+    "Email classifier using machine learning ::: Machine learning approach ::: Random forest",
+    "Email classifier using machine learning ::: Machine learning approach ::: XGBoost",
+    "Email classifier using machine learning ::: Machine learning approach ::: Hierarchical Model",
+    "Email classifier using machine learning ::: Deep learning approach ::: LSTM",
+    "Email classifier using machine learning ::: Deep learning approach ::: BERT",
+    "Email classifier using machine learning ::: Threshold Selection",
+    "Results and Analysis",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "In an organization, the Information Technology (IT) support help desk operation is an important unit which handles the IT services of a business. Many large scale organizations would have a comprehensive IT support team to handle engagement and requests with employees on a 24$\\times $7 basis. As any routinized tasks, most processes of the support help desk unit are considered repetitive in nature BIBREF0. Some may occur on a daily basis and others may occur more frequently. Many support engineers and agent would spend time on these repetitive task such as entering information to an application, resetting passwords, unlocking applications, creating credentials, activating services, preparing documentation, etc.",
+      "The industry has now come realize that many repetitive business processes and tasks can be automated by using Robotic Process Automation (RPA) bots or robotic processes automotive software bots BIBREF1. The idea is to take the repetitive workload and hand it over to the RPA bots so that the employees could focus on more value adding tasks and decision making to the organization. The RPA bot would also help to reduce the human errors and make processes more efficient, which would finally intent results in cost saving and productivity increase.",
+      "Our proposed automated approach is not only focused on automating repetitive tasks but also looking at historical data, enabling IT support desk process to identify unforeseen insights and patterns. Analyzing the data from various sources such as email communications, service request information generated from support ticketing applications and even conversational data from chats has helped us to identify the type of Service Requests (SR) raised and their respective solutions, as well as fixes done by the support agents. This approach has helped us create a classification model to identify the issue types and provide quick fixes and resolutions from the collected data."
+    ],
+    [
+      "Wr\u00c3blewska has conducted a project on the topic of RPA of unstructured data which was focused on building an Artificial Intelligence (AI) system dedicated to tasks regarding the processing of formal documents used in different kinds of business procedures BIBREF2. His approach was introduced to automate the debt collecting process. Possible applications of Machine Learning (ML) methods to improve the efficacy of these processes were described. In the case study done by Aguirre, it was concluded that companies should consider RPA to be more suitable for high volume standardized tasks that are rule-driven, with no requirement for subjective judgement, creativity or interpretation skills BIBREF3. Back office business processes such as accounts payable, accounts receivable, billing, travel and expenses, fixed assets and human resource administration are good candidates for RPA.",
+      "Extreme multi-class and multi-label text classification problems are solved by the methodology named Hierarchical Label Set Expansion (HLSE) BIBREF4. This paper presents the deep Learning architecture devoted to text classification, in which the data labels are regularized, the hierarchical label set is defined and different word embeddings are used BIBREF3, BIBREF5, BIBREF6.",
+      "The traditional model performed better than the the deep learning models for 8,841 emails collected over 3 years, because this particular classification task carried out by Haoran may not require the ordered sequence representation of tokens that deep learning models provide BIBREF7. This paper claims that a bagged voting model surpasses the performance of any individual models.",
+      "In their survey, Kamran and other researchers analyzed text feature extractions BIBREF8, BIBREF9, dimentionality reduction methods, existing algorithms and techniques, evaluation methods and limitations BIBREF6 and advantages based on applications. Paramesh et al and Seongwook et al compare the different classification algorithms such as multinomial naive bayes logistic regression, K-Nearest neighbour and Support Vector Machines (SVM) on real-world IT infrastructure ticket classifier system data, using different evaluation metrics in their research BIBREF10, BIBREF11. They claimed that SVM to have performed well on all the data samples. Random forest (RF) or naive bayes (NB) performed best in terms of correctly uncovering human intuitions. Hartmann et al and his team present in their study that RF exhibits high performance in sentiment classification research done on 41 social media data sets covering major social media platforms, where the SVM never outperforms the RF BIBREF12. Cognitive RPA is efficiently undertaken as a low cost solution with Microsoft Azure Language Understanding Intelligent Service (LUIS) BIBREF8 and Azure machine learning studio.",
+      "Section III of this paper elaborates the process of automation. The section IV explains about the email classification approach, and the section V illustrates the results and their respective analysis. Finally, section VI contains the conclusion of the results."
+    ],
+    [
+      "We are proposing a hybrid-process automation, in which we are introducing the automation architecture while adopting the manual process methodology. Incoming emails, that cannot be classified or understood by the knowledge base of the automation system will be sent for manual classification solution."
+    ],
+    [
+      "Providing technical support for large firms around the world has many challenges such as coordinating a vast amounts of mails and matching experts with employees who are in need of that expertise. When a technical issue is raised from a base level employee who works with applications, it is sent to the middle level and then to the higher level management of the respective regional branches throughout the hierarchical business architecture. Once it is approved by the branch manager, the issue email is forwarded to the technical coordinator to categorize the issue based on the priority level and technical requirements. Technical coordinator is responsible for the issues raised from the regional branches all over the world.",
+      "Each regional branch is given a unique name such as New York, Sydney, London, Beijing and Toronto mentioned as Category1 (cat1). Category1 is identified by looking at the email address of the sender. Each regional branch has different plant applications that need different experts' consultation. Plant applications such as SAP, Darwin and infrastructure are mentioned as Category2 (cat2). The possible plot of the issue emails such as computer, manufacturing, userID, userunlock, financial, planning, purchasing issue generated by employees working in various plant applications across various regions are mentioned as Category3.",
+      "Mapping table is created with the plants placed in the regional offices and the issues created by the plants. Category1, Category2, Category3 contains 84, 8 and 77 unique categories to be classified. Table I shows some examples for each categories. Once all three categories are finalized by the technical coordinator, email tickets will be created and assigned to the admin-groups. Respective technical people in the admin-groups will provide consultancy and solve the issues. Not only one technician can handle issues assigned to many different admin groups allocated to him, but also particular admin category can be handled by many technicians as a group as well."
+    ],
+    [
+      "In addition to replacing the technical coordinator role with AI bot to classify the raised email-issue tickets for respective admin groups, we propose instant quick fixes for some emails in an automated manner. High level workflow is described in Fig. 1. The AI bot has three main stages",
+      "Quick fixes",
+      "Static rules",
+      "Email classifier",
+      "All the incoming mails are preprocessed for better quality of inputs. Signatures, greetings, Uniform Resource Locators (URL) are removed. Key body is extracted from the forwarded mails by digging deep into the mail contents. If an email contains attachments, Optical Character Recognition (OCR) is used to extract the text contents from the attachments."
+    ],
+    [
+      "Microsoft LUIS is used for instant quick fixes to provide solution based on prioritized emails. Fig. 2 shows the bot framework LUIS architecture that handles the quick fixes. Quick fixes are trained with most occurring samples that need quick solutions. LUIS is a model that artificial intelligence applications use to predict the intention of phrases spoke. There are 3 main key phases categorized as defining phase, training phase and publishing phase. Natural language is extremely flexible with LUIS. Intents are the type of defined words that are supported by utterances. An action the user wants to perform can be defined by an intent. Fig. 3 elaborates the intent matching breakdown mechanism. Entities are identified form the sentences. Suitable entity will be selected for generating tickets.",
+      "If an incoming email is identified with the matched intent, cat1, cat2, cat3 will be allocated. Tickets will be created for admin-groups. The issue will be solved using automated messages through a chat bot solution. If the issue is solved, then the ticket will be closed by the quick fixes. If it is too complicated for the knowledge of the BOT then it creates ticket for adminGroup for the assistance of consultants.",
+      "The emails identified by static rules and keywords are classified with the highest accuracy. The knowledge base of static rules and keywords are gathered using feature engineering and the insights from the technical coordinator. Remaining emails are sent to a complex ensemble machine learning model to be classified.",
+      "Different types of emails are treated in a different way for efficient execution and to reduce the error."
+    ],
+    [
+      "Fig. 4 shows the flow of email categorization response for new incoming emails. If an incoming mail is a fresh new mail, it is initially subjected to cleaning. OCR will extract the texts from the attachment depending on the attachments' availability. Cat1 is assigned according to the knowledge of the database and sender details. According to the priority, emails are passed through LUIS. Thereafter if LUIS fails to solve the issue ML model will assign the cat2, cat3, Admin group for ticket creation."
+    ],
+    [
+      "If incoming mail is a continuation of previous email, it is directly handled by LUIS question and answer self automated support. Then it follows the normal procedure of categorization. Fig. 5 clearly illustrates the flow.",
+      "Fig. 6 explains the overall architecture. Static rules are mentioned as T-codes. Every categorized mails has to be assigned to respective consultant denoted as assignTo."
+    ],
+    [
+      "Preprocessing is necessary to increase the accuracy of a text classification model, because it avoids the classification model focusing attention on unwanted sentences and intents. Emails are fed into Microsoft-Bot services. It handles the headers and outputs the processed channel-data in JavaScript Object Notation (JSON) format. The channel data summarizes the information such like sender, receiver, body, subject and important metadata. Regular expression (regex) can be used for searching strings by defining a search pattern. Regex findings are created to remove unwanted words from the channel data queries for further processing of the emails.",
+      "OCR has to be accurate in detecting text in an image. Microsoft-OCR is used for text recognition of this automation process. It extracts the recognized characters into a machine-usable character stream. Accuracy of the text recognition depends on the image quality such as blurry images, small text size, complex background, shadows and handwritten text. Since most of the image attachments are computer generated images and screen shots of error messages, Microsoft-OCR capabilities fits for the use case.",
+      "260000 emails are taken from past history. Extracted preprocessed data from Microsoft-Bot and OCR services are saved as Comma-separated Values (CSV) files. It is further processed before feeding to machine learning model. Unwanted words are removed from the context using nltk library stopwords and manually collected stopwords. URLs, punctuation marks are removed. Every word is tokenized, lemmatized and normalized, i.e. title, body, OCR, from, to, CC, Cat1, Cat2, and Cat3."
+    ],
+    [
+      "Since the sender and receiver varies with time because of new employees' arrivals and old employees' resignations. In order to handle this fluctuating situation, To, CC, From columns are dropped from the input data. Cat1 is known from the email address. Cat2, Cat3 for specific cat1 is described in the table1. Cat2 and Cat3 are merged and defined as target category for classification. Nearly 180 custom features are created based on the plant's availability and region mapping. It is embedded to understand the availability of plant and the issue for the given region denoted as Unique-Category. Based on mapping table (extension of table1), custom features ensures that whether the plant application (cat2) and the technical issue (cat3) belongs to the regional plant (cat1).",
+      "By the analysis made from the existing samples and from the human semantic knowledge of the technical coordinator, it is sensed that not only the title of the email is enough to predict the category, but also the attachment and body play a major role."
+    ],
+    [
+      "Even though labelled data set was provided, initially unsupervised learning algorithm K-Nearest Neighbor (KNN) clustering was applied to the data set to observe the possibility of clusters BIBREF13. Since number of unique categories of the target field (Unique-Cat) is 77, there are many common words between categories. It is too confusing and not showing promising categories and accuracies. Multi class multi label classification supervised algorithms such as random forest, XGBoost are used as benchmarks."
+    ],
+    [
+      "Ngrams are a continuous sequence of n items from a given sample of text. From title, body and OCR text words are selected. Ngrams of 3 nearby words are extracted with Term Frequency-Inverse Document Frequency (TF-IDF) vectorizing, then features are filtered using chi squared the feature scoring method.",
+      "Feature hashing is a method to extract the features from the text. It allows to use variable size of feature vectors with standard learning algorithms. 12000 features are hashed from the text, OCR and title. Then using chi-squared statistical analysis 200 best features that fits with target unique-category are selected."
+    ],
+    [
+      "Random Forest is a bagging Algorithm, an ensemble learning method for classification that operates by constructing a multitude of decision trees at training time and outputting the class that has highest mean majority vote of the classesBIBREF14."
+    ],
+    [
+      "XGBoost is a decision-tree-based ensemble Machine Learning algorithm that uses a gradient boosting framework. It is used commonly in the classification problems involving unstructured dataBIBREF5."
+    ],
+    [
+      "Since the number of target labels are high, achieving the higher accuracy is difficult, while keeping all the categories under same feature selection method. Some categories performs well with lower TF-IDF vectorizing range and higher n grams features even though they showed lower accuracy in the overall single model. Therefore, hierarchical machine learning models are built to classify 31 categories in the first classification model and remaining categories are named as low-accu and predicted as one category. In the next model, predicted low-accu categories are again classified into 47 categories. Comparatively this hierarchical model works well since various feature selection methods are used for various categoriesBIBREF5."
+    ],
+    [
+      "Long short term memory is an artificial neural network architecture which outperforms most of the machine learning algorithms. In the deep learning approach, feature selection is done in neurons weight matrix by itself. Bidirectional long short term memory (LSTM) is used with glove word embedding to predict the categoriesBIBREF15."
+    ],
+    [
+      "Even though Bert is the state of the art model, for the considered data set it hasn't shown-up with the maximum breach of accuracy for the expected automationBIBREF16. When we consider the commercial model for the inference, having a dedicated Kubernetes cluster with high performance computer is costly. So complex models with high computation power are not considered as abetter solution."
+    ],
+    [
+      "In order to classify only higher confident emails, the thresholds for each and every 73 categories are defined. For an incoming email, the probability of assigning each category will be calculated. Best category will be selected based on the maximum probability out of those 73 probabilities. By looking at overall F-score, thresholding decisions are made. For the low accuracy categories (accuracy less than 75 percentage) higher threshold level is set. For middle accuracy categories (accuracy less than 90 percentage) min probability of correctly classified samples are taken. Higher accuracy categories (accuracy greater than 90 percentage) are left free with 0 threshold to classify all the incoming emails. The threshold techniques as a bottle neck decreases the number of samples classified by the autonomous process, but it increases the accuracy of the classified samples. The proposed thresholds satisfy the expected manual workload reduction as well as the accuracy percentage.",
+      "In this paper Randomforest, XGBoost, LSTM, Bidirectional LSTM with embeddings are analyzed with different input features. Complex deep-learning models such like transformers are not used in order to go for low cost inference solution. Train set and test set are divided as 80:20 percentage. Precision, recall, F-score are taken as evaluation metrics."
+    ],
+    [
+      "Automation of quick email replies for technical queries increase the overall efficiency of day to day processes by 3 percentage. Even though replacing the manual Human email-assigner entirely with AI bot is not possible, yet the automation ML model handles 61 percentage of incoming emails correctly. It is reducing massive human effort per day. For generalization purpose email's title, body, attachments are considered in increasing accuracy, while ignoring sender, receiver, carbon copy information. Table II shows the accuracy percentages for different models with different feature selection methods. An accuracy of 77.3 percentage was obtained without any thresholding techniques for 73 classes multiclasss multi label classification problem. With threshold adjustments for each categories, it was increased to 85.6 percentage. Increasing threshold values results in reducing the number of mails classified by ML-model. It is necessary to handle limited number of high confident emails by the ML-model due to ensure the promising accuracy levels. Feature Engineering for custom feature selection and, Hierarchical cascade modelling increases the accuracy of the XGBoost machine learning model to reach accuracy of the LSTM models. By cascading model1 (mod1) with 83.2 accuracy for 31 classes and model2 (mod2) with 71.1 accuracy for 47 low-accuracy classes, overall hierarchical model exhibited 76.5 accuracy. All the accuracy terms refers F-score. Selected keywords were used as static rules accurate classification. Since accuracy is considerably satisfactory for the automation process, the system was deployed. The incorrectly classified mails are handled manually after the proper notification by the technical consultant.",
+      "Fig. 7 Shows emails classified by the ML, static rules and manual process represented in daily basis. Incoming emails per day varies between 30 to 120. It clearly illustrates the effect of retraining. After 10-April, the percentages of emails classified per day was increased as well as accuracy.",
+      "Fig. 8 shows average monthly analysis of incoming mails after each retraining. Average Monthly incoming mails are calculated as 1467 per month by considering a 4 months period. Initial training was done on august 2018 with 170,000 samples, model was able to classify nearly 50 percentage of incoming emails. After the second retraining on january 2019 with 200,000 sample, model classified 58 percentage of incoming mails per month. Third retraining was done on April 2019 with 260000 samples. Results stated that nearly 61 percentage of incoming mails were handled by ML model. Nearly 20 percentage of incoming emails were handled by static rules. Automation bot was proved to handle 81 percentage of the total incoming mails per month including ML and static rules, leading to efficient human-machine interaction, Instant problem solving and fast process."
+    ],
+    [
+      "Quick fixes from Microsoft LUIS Bot framework provides instant solutions for the raised email queries. Input text features of emails such as title, body, attachment OCR text and the feature engineered custom features all together outperform for the considered real word email data set. Sure-shot Static rules and hierarchical machine learning model with statistically calculated threshold enhances the accuracy of the overall system to an acceptable percentage. Bidirectional LSTM with word embedding techniques are implemented finally with thresholding techniques. Less complex Machine learning models lead to low cost virtual machine solutions for serving. Robotic Process Automation Architecture reduces human effort of email support desk by 81 percentage while having a reasonable accuracy of 85.6 percentage."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1255/instruction.md b/qasper-1255/instruction.md
new file mode 100644
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+Name of Paper: "How May I Help You?": Modeling Twitter Customer Service Conversations Using Fine-Grained Dialogue Acts
+
+Question: Do they evaluate only on English datasets?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Methodology",
+    "Taxonomy Definition",
+    "Data Collection",
+    "Annotation Results",
+    "Motivation for Multi-Label Classification",
+    "Conversation Modeling",
+    "Features",
+    "Classes",
+    "Experiments",
+    "Conversation Outcome Analysis",
+    "Classifying Problem Outcomes",
+    "Actionable Rules for Automated Customer Support",
+    "Conclusions"
+  ],
+  "paragraphs": [
+    [
+      "The need for real-time, efficient, and reliable customer service has grown in recent years. Twitter has emerged as a popular medium for customer service dialogue, allowing customers to make inquiries and receive instant live support in the public domain. In order to provide useful information to customers, agents must first understand the requirements of the conversation, and offer customers the appropriate feedback. While this may be feasible at the level of a single conversation for a human agent, automatic analysis of conversations is essential for data-driven approaches towards the design of automated customer support agents and systems.",
+      "Analyzing the dialogic structure of a conversation in terms of the \"dialogue acts\" used, such as statements or questions, can give important meta-information about conversation flow and content, and can be used as a first step to developing automated agents. Traditional dialogue act taxonomies used to label turns in a conversation are very generic, in order to allow for broad coverage of the majority of dialogue acts possible in a conversation BIBREF0 , BIBREF1 , BIBREF2 . However, for the purpose of understanding and analyzing customer service conversations, generic taxonomies fall short. Table TABREF1 shows a sample customer service conversation between a human agent and customer on Twitter, where the customer and agent take alternating \"turns\" to discuss the problem. As shown from the dialogue acts used at each turn, simply knowing that a turn is a Statement or Request, as is possible with generic taxonomies, is not enough information to allow for automated handling or response to a problem. We need more fine-grained dialogue acts, such as Informative Statement, Complaint, or Request for Information to capture the speaker's intent, and act accordingly. Likewise, turns often include multiple overlapping dialogue acts, such that a multi-label approach to classification is often more informative than a single-label approach.",
+      "Dialogue act prediction can be used to guide automatic response generation, and to develop diagnostic tools for the fine-tuning of automatic agents. For example, in Table TABREF1 , the customer's first turn (Turn 1) is categorized as a Complaint, Negative Expressive Statement, and Sarcasm, and the agent's response (Turn 2) is tagged as a Request for Information, Yes-No Question, and Apology. Prediction of these dialogue acts in a real-time setting can be leveraged to generate appropriate automated agent responses to similar situations.",
+      "Additionally, important patterns can emerge from analysis of the fine-grained acts in a dialogue in a post-prediction setting. For example, if an agent does not follow-up with certain actions in response to a customer's question dialogue act, this could be found to be a violation of a best practice pattern. By analyzing large numbers of dialogue act sequences correlated with specific outcomes, various rules can be derived, i.e. \"Continuing to request information late in a conversation often leads to customer dissatisfaction.\" This can then be codified into a best practice pattern rules for automated systems, such as \"A request for information act should be issued early in a conversation, followed by an answer, informative statement, or apology towards the end of the conversation.\"",
+      "In this work, we are motivated to predict the dialogue acts in conversations with the intent of identifying problem spots that can be addressed in real-time, and to allow for post-conversation analysis to derive rules about conversation outcomes indicating successful/unsuccessful interactions, namely, customer satisfaction, customer frustration, and problem resolution. We focus on analysis of the dialogue acts used in customer service conversations as a first step to fully automating the interaction. We address various different challenges: dialogue act annotated data is not available for customer service on Twitter, the task of dialogue act annotation is subjective, existing taxonomies do not capture the fine-grained information we believe is valuable to our task, and tweets, although concise in nature, often consist of overlapping dialogue acts to characterize their full intent. The novelty of our work comes from the development of our fine-grained dialogue act taxonomy and multi-label approach for act prediction, as well as our analysis of the customer service domain on Twitter. Our goal is to offer useful analytics to improve outcome-oriented conversational systems.",
+      "We first expand upon previous work and generic dialogue act taxonomies, developing a fine-grained set of dialogue acts for customer service, and conducting a systematic user study to identify these acts in a dataset of 800 conversations from four Twitter customer service accounts (i.e. four different companies in the telecommunication, electronics, and insurance industries). We then aim to understand the conversation flow between customers and agents using our taxonomy, so we develop a real-time sequential SVM-HMM model to predict our fine-grained dialogue acts while a conversation is in progress, using a novel multi-label scheme to classify each turn. Finally, using our dialogue act predictions, we classify conversations based on the outcomes of customer satisfaction, frustration, and overall problem resolution, then provide actionable guidelines for the development of automated customer service systems and intelligent agents aimed at desired customer outcomes BIBREF3 , BIBREF4 .",
+      "We begin with a discussion of related work, followed by an overview of our methodology. Next, we describe our conversation modeling framework, and explain our outcome analysis experiments, to show how we derive useful patterns for designing automated customer service agents. Finally, we present conclusions and directions for future work."
+    ],
+    [
+      "Developing computational speech and dialogue act models has long been a topic of interest BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , with researchers from many different backgrounds studying human conversations and developing theories around conversational analysis and interpretation on intent. Modern intelligent conversational BIBREF3 , BIBREF4 and dialogue systems draw principles from many disciplines, including philosophy, linguistics, computer science, and sociology. In this section, we describe relevant previous work on speech and dialogue act modeling, general conversation modeling on Twitter, and speech and dialogue act modeling of customer service in other data sources.",
+      "Previous work has explored speech act modeling in different domains (as a predecessor to dialogue act modeling). Zhang et al. present work on recognition of speech acts on Twitter, following up with a study on scalable speech act recognition given the difficulty of obtaining labeled training data BIBREF9 . They use a simple taxonomy of four main speech acts (Statement, Question, Suggestion, Comment, and a Miscellaneous category). More recently, Vosoughi et al. develop BIBREF10 a speech act classifier for Twitter, using a modification of the taxonomy defined by Searle in 1975, including six acts they observe to commonly occur on Twitter: Assertion, Recommendation Expression, Question, Request, again plus a Miscellaneous category. They describe good features for speech act classification and the application of such a system to detect stories on social media BIBREF11 . In this work, we are interested in the dialogic characteristics of Twitter conversations, rather than speech acts in stand-alone tweets.",
+      "Different dialogue act taxonomies have been developed to characterize conversational acts. Core and Allen present the Dialogue Act Marking in Several Layers (DAMSL), a standard for discourse annotation that was developed in 1997 BIBREF0 . The taxonomy contains a total of 220 tags, divided into four main categories: communicative status, information level, forward-looking function, and backward-looking function. Jurafsky, Shriberg, and Biasca develop a less fine-grained taxonomy of 42 tags based on DAMSL BIBREF1 . Stolcke et al. employ a similar set for general conversation BIBREF2 , citing that \"content- and task-related distinctions will always play an important role in effective DA [Dialogue Act] labeling.\" Many researchers have tackled the task of developing different speech and dialogue act taxonomies and coding schemes BIBREF12 , BIBREF13 , BIBREF14 , BIBREF15 . For the purposes of our own research, we require a set of dialogue acts that is more closely representative of customer service domain interactions - thus we expand upon previously defined taxonomies and develop a more fine-grained set.",
+      "Modeling general conversation on Twitter has also been a topic of interest in previous work. Honeycutt and Herring study conversation and collaboration on Twitter using individual tweets containing \"@\" mentions BIBREF16 . Ritter et al. explore unsupervised modeling of Twitter conversations, using clustering methods on a corpus of 1.3 million Twitter conversations to define a model of transitional flow between in a general Twitter dialogue BIBREF17 . While these approaches are relevant to understanding the nature of interactions on Twitter, we find that the customer service domain presents its own interesting characteristics that are worth exploring further.",
+      "The most related previous work has explored speech and dialogue act modeling in customer service, however, no previous work has focused on Twitter as a data source. In 2005, Ivanovic uses an abridged set of 12 course-grained dialogue acts (detailed in the Taxonomy section) to describe interactions between customers and agents in instant messaging chats BIBREF18 , BIBREF19 , leading to a proposal on response suggestion using the proposed dialogue acts BIBREF20 . Follow-up work using the taxonomy selected by Ivanovic comes from Kim et al., where they focus on classifying dialogue acts in both one-on-one and multi-party live instant messaging chats BIBREF21 , BIBREF22 . These works are similar to ours in the nature of the problem addressed, but we use a much more fine-grained taxonomy to define the interactions possible in the customer service domain, and focus on Twitter conversations, which are unique in their brevity and the nature of the public interactions.",
+      "The most similar work to our own is that of Herzig et al. on classifying emotions in customer support dialogues on Twitter BIBREF23 . They explore how agent responses should be tailored to the detected emotional response in customers, in order to improve the quality of service agents can provide. Rather than focusing on emotional response, we seek to model the dialogic structure and intents of the speakers using dialogue acts, with emotion included as features in our model, to characterize the emotional intent within each act."
+    ],
+    [
+      "The underlying goal of this work is to show how a well-defined taxonomy of dialogue acts can be used to summarize semantic information in real-time about the flow of a conversation to derive meaningful insights into the success/failure of the interaction, and then to develop actionable rules to be used in automating customer service interactions. We focus on the customer service domain on Twitter, which has not previously been explored in the context of dialogue act classification. In this new domain, we can provide meaningful recommendations about good communicative practices, based on real data. Our methodology pipeline is shown in Figure FIGREF2 ."
+    ],
+    [
+      "As described in the related work, the taxonomy of 12 acts to classify dialogue acts in an instant-messaging scenario, developed by Ivanovic in 2005, has been used by previous work when approaching the task of dialogue act classification for customer service BIBREF18 , BIBREF20 , BIBREF19 , BIBREF21 , BIBREF22 . The dataset used consisted of eight conversations from chat logs in the MSN Shopping Service (around 550 turns spanning around 4,500 words) BIBREF19 . The conversations were gathered by asking five volunteers to use the platform to inquire for help regarding various hypothetical situations (i.e. buying an item for someone) BIBREF19 . The process of selection of tags to develop the taxonomy, beginning with the 42 tags from the DAMSL set BIBREF0 , involved removing tags inappropriate for written text, and collapsing sets of tags into a more coarse-grained label BIBREF18 . The final taxonomy consists of the following 12 dialogue acts (sorted by frequency in the dataset): Statement (36%), Thanking (14.7%), Yes-No Question (13.9%), Response-Acknowledgement (7.2%), Request (5.9%), Open-Question (5.3%), Yes-Answer (5.1%), Conventional-Closing (2.9%), No-Answer (2.5%), Conventional-Opening (2.3%), Expressive (2.3%) and Downplayer (1.9%).",
+      "For the purposes of our own research, focused on customer service on Twitter, we found that the course-grained nature of the taxonomy presented a natural shortcoming in terms of what information could be learned by performing classification at this level. We observe that while having a smaller set of dialogue acts may be helpful for achieving good agreement between annotators (Ivanovic cites kappas of 0.87 between the three expert annotators using this tag set on his data BIBREF18 ), it is unable to offer deeper semantic insight into the specific intent behind each act for many of the categories. For example, the Statement act, which comprises the largest percentage (36% of turns), is an extremely broad category that fails to provide useful information from an analytical perspective. Likewise, the Request category also does not specify any intent behind the act, and leaves much room for improvement.",
+      "For this reason, and motivated by previous work seeking to develop dialogue act taxonomies appropriate for different domains BIBREF19 , BIBREF21 , we convert the list of dialogue acts presented by the literature into a hierarchical taxonomy, shown in Figure FIGREF6 .",
+      "We first organize the taxonomy into six high-level dialogue acts: Greeting, Statement, Request, Question, Answer, and Social Act. Then, we update the taxonomy using two main steps: restructuring and adding additional fine-grained acts.",
+      "We base our changes upon the taxonomy used by Ivanovic and Kim et al. in their work on instant messaging chat dialogues BIBREF19 , BIBREF21 , but also on general dialogue acts observed in the customer service domain, including complaints and suggestions. Our taxonomy does not make any specific restrictions on which party in the dialogue may perform each act, but we do observe that some acts are far more frequent (and sometimes non-existent) in usage, depending on whether the customer or agent is the speaker (for example, the Statement Complaint category never shows up in Agent turns).",
+      "In order to account for gaps in available act selections for annotators, we include an Other act in the broadest categories. While our taxonomy fills in many gaps from previous work in our domain, we do not claim to have handled coverage of all possible acts in this domain. Our taxonomy allows us to more closely specify the intent and motivation behind each turn, and ultimately how to address different situations."
+    ],
+    [
+      "Given our taxonomy of fine-grained dialogue acts that expands upon previous work, we set out to gather annotations for Twitter customer service conversations.",
+      "For our data collection phase, we begin with conversations from the Twitter customer service pages of four different companies, from the electronics, telecommunications, and insurance industries. We perform several forms of pre-processing to the conversations. We filter out conversations if they contain more than one customer or agent speaker, do not have alternating customer/agent speaking turns (single turn per speaker), have less than 5 or more than 10 turns, have less than 70 words in total, and if any turn in the conversation ends in an ellipses followed by a link (indicating that the turn has been cut off due to length, and spans another tweet). Additionally, we remove any references to the company names (substituting with \"Agent\"), any references to customer usernames (substituting with \"Customer\"), and replacing and links or image references with INLINEFORM0 link INLINEFORM1 and INLINEFORM2 img INLINEFORM3 tokens.",
+      "Using these filters as pre-processing methods, we end up with a set of 800 conversations, spanning 5,327 turns. We conduct our annotation study on Amazon Mechanical Turk, presenting Turkers with Human Intelligence Tasks (henceforth, HITs) consisting of a single conversation between a customer and an agent. In each HIT, we present Turkers with a definition of each dialogue act, as well as a sample annotated dialogue for reference. For each turn in the conversation, we allow Turkers to select as many labels from our taxonomy as required to fully characterize the intent of the turn. Additionally, annotators are asked three questions at the end of each conversation HIT, to which they could respond that they agreed, disagreed, or could not tell:",
+      "We ask 5 Turkers to annotate each conversation HIT, and pay $0.20 per HIT. We find the list of \"majority dialogue acts\" for each tweet by finding any acts that have received majority-vote labels (at least 3 out of 5 judgements).",
+      "It is important to note at this point that we make an important choice as to how we will handle dialogue act tagging for each turn. We note that each turn may contain more than one dialogue act vital to carry its full meaning. Thus, we choose not to carry out a specific segmentation task on our tweets, contrary to previous work BIBREF24 , BIBREF25 , opting to characterize each tweet as a single unit composed of different, often overlapping, dialogue acts. Table TABREF16 shows examples of tweets that receive majority vote on more than one label, where the act boundaries are overlapping and not necessarily distinguishable.",
+      "It is clear that the lines differentiating these acts are not very well defined, and that segmentation would not necessarily aid in clearly separating out each intent. For these reasons, and due to the overall brevity of tweets in general, we choose to avoid the overhead of requiring annotators to provide segment boundaries, and instead ask for all appropriate dialogue acts."
+    ],
+    [
+      "Figure FIGREF17 shows the distribution of the number of times each dialogue act in our taxonomy is selected a majority act by the annotators (recall that each turn is annotated by 5 annotators). From the distribution, we see that the largest class is Statement Info which is part of the majority vote list for 2,152 of the 5,327 total turns, followed by Request Info, which appears in 1,088 of the total turns. Although Statement Informative comprises the largest set of majority labels in the data (as did Statement in Ivanovic's distribution), we do observe that other fine-grained categories of Statement occur in the most frequent labels as well, including Statement Complaint, Statement Expressive Negative, and Statement Suggestion \u2013 giving more useful information as to what form of statement is most frequently occurring. We find that 147 tweets receive no majority label (i.e. no single act received 3 or more votes out of 5). At the tail of the distribution, we see less frequent acts, such as Statement Sarcasm, Social Act Downplayer, Statement Promise, Greeting Closing, and Request Other. It is also interesting to note that both opening and closing greetings occur infrequently in the data \u2013 which is understandable given the nature of Twitter conversation, where formal greeting is not generally required.",
+      "Table TABREF19 shows a more detailed summary of the distribution of our top 12 dialogue acts according to the annotation experiments, as presented by Ivanovic BIBREF18 . Since each turn has an overlapping set of labels, the column % of Turns (5,327) represents what fraction of the total 5,327 turns contain that dialogue act label (these values do not sum to 1, since there is overlap). To give a better sense of the percentage appearance of each dialogue act class in terms of the total number of annotated labels given, we also present column % of Annotations (10,343) (these values are percentages). We measure agreement in our annotations using a few different techniques. Since each item in our annotation experiments allows for multiple labels, we first design an agreement measure that accounts for how frequently each annotator selects the acts that agree with the majority-selected labels for the turns they annotated. To calculate this for each annotator, we find the number of majority-selected acts for each conversation they annotated (call this MAJ), and the number of subset those acts that they selected (call this SUBS), and find the ratio (SUBS/MAJ). We use this ratio to systematically fine-tune our set of annotators by running our annotation in four batches, restricting our pool of annotators to those that have above a 0.60 ratio of agreement with the majority from the previous batch, as a sort of quality assurance test. We also measure Fleiss' Kappa BIBREF26 agreement between annotators in two ways: first by normalizing our annotation results into binary-valued items indicating annotators' votes for each label contain within each turn. We find an average Fleiss- INLINEFORM0 for the full dataset, including all turn-and-label items, representing moderate agreement on the 24-label problem.",
+      "We also calculate the Fleiss- INLINEFORM0 values for each label, and use the categories defined by Landis and Koch to bin our speech acts based on agreement BIBREF27 . As shown in Table TABREF18 , we find that the per-label agreement varies from \"almost perfect\" agreement of INLINEFORM1 for lexically defined categories such as Apology and Thanks, with only slight agreement of INLINEFORM2 for less clearly-defined categories, such as Statement (Other), Answer Response Acknowledgement and Request (Other). For the conversation-level questions, we calculate the agreement across the \"Agree\" label for all annotators, finding an average Fleiss- INLINEFORM3 , with question-level results of INLINEFORM4 for customer satisfaction, INLINEFORM5 for problem resolution, and INLINEFORM6 for customer frustration. These results suggest room for improvement for further development of the taxonomy, to address problem areas for annotators and remedy areas of lower agreement."
+    ],
+    [
+      "We test our hypothesis that tweet turns are often characterized by more than one distinct dialogue act label by measuring the percentage overlap between frequent pairs of labels. Of the 5,327 turns annotated, across the 800 conversations, we find that 3,593 of those turns (67.4%) contained more than one majority-act label. Table TABREF22 shows the distribution percentage of the most frequent pairs.",
+      "For example, we observe that answering with informative statements is the most frequent pair, followed by complaints coupled with negative sentiment or informative statements. We also observe that requests are usually formed as questions, but also co-occur frequently with apologies. This experiment validates our intuition that the majority of turns do contain more than a single label, and motivates our use of a multi-label classification method for characterizing each turn in the conversation modeling experiments we present in the next section."
+    ],
+    [
+      "In this section, we describe the setup and results of our conversational modeling experiments on the data we collected using our fine-grained taxonomy of customer service dialogue acts. We begin with an overview of the features and classes used, followed by our experimental setup and results for each experiment performed."
+    ],
+    [
+      "The following list describes the set of features used for our dialogue act classification tasks:",
+      "Word/Punctuation: binary bag-of-word unigrams, binary existence of a question mark, binary existence of an exclamation mark in a turn",
+      "Temporal: response time of a turn (time in seconds elapsed between the posting time of the previous turn and that of the current turn)",
+      "Second-Person Reference: existence of an explicit second-person reference in the turn (you, your, you're)",
+      "Emotion: count of words in each of the 8 emotion classes from the NRC emotion lexicon BIBREF28 (anger, anticipation, disgust, fear, joy, negative, positive, sadness, surprise, and trust)",
+      "Dialogue: lexical indicators in the turn: opening greetings (hi, hello, greetings, etc), closing greetings (bye, goodbye), yes-no questions (turns with questions starting with do, did, can, could, etc), wh- questions (turns with questions starting with who, what, where, etc), thanking (thank*), apology (sorry, apolog*), yes-answer, and no-answer"
+    ],
+    [
+      "Table TABREF30 shows the division of classes we use for each of our experiments. We select our classes using the distribution of annotations we observe in our data collection phase (see Table TABREF19 ), selecting the top 12 classes as candidates.",
+      "While iteratively selecting the most frequently-occurring classes helps to ensure that classes with the most data are represented in our experiments, it also introduces the problem of including classes that are very well-defined lexically, and may not require learning for classification, such as Social Act Apology and Social Act Thanking in the first 10-Class set. For this reason, we call this set 10-Class (Easy), and also experiment using a 10-Class (Hard) set, where we add in the next two less-defined and more semantically rich labels, such as Statement Offer and Question Open. When using each set of classes, a turn is either classified as one of the classes in the set, or it is classified as \"other\" (i.e. any of the other classes). We discuss our experiments in more detail and comment on performance differences in the experiment section."
+    ],
+    [
+      "Following previous work on conversation modeling BIBREF23 , we use a sequential SVM-HMM (using the INLINEFORM0 toolkit BIBREF29 ) for our conversation modeling experiments. We hypothesize that a sequential model is most suited to our dialogic data, and that we will be able to concisely capture conversational attributes such as the order in which dialogue acts often occur (i.e. some Answer act after Question a question act, or Apology acts after Complaints).",
+      "We note that with default settings for a sequence of length INLINEFORM0 , an SVM-HMM model will be able to refine its answers for any turn INLINEFORM1 as information becomes available for turns INLINEFORM2 . However, we opt to design our classifier under a real-time setting, where turn-by-turn classification is required without future knowledge or adaptation of prediction at any given stage. In our setup, turns are predicted in a real-time setting to fairly model conversation available to an intelligent agent in a conversational system. At any point, a turn INLINEFORM3 is predicted using information from turns INLINEFORM4 , and where a prediction is not changed when new information is available.",
+      "We test our hypothesis by comparing our real-time sequential SVM-HMM model to non-sequential baselines from the NLTK BIBREF30 and Scikit-Learn BIBREF31 toolkits. We use our selected feature set (described above) to be generic enough to apply to both our sequential and non-sequential models, in order to allow us to fairly compare performance. We shuffle and divide our data into 70% for training and development (560 conversations, using 10-fold cross-validation for parameter tuning), and hold out 30% of the data (240 conversations) for test.",
+      "Motivated by the prevalent overlap of dialogue acts, we conduct our learning experiments using a multi-label setup. For each of the sets of classes, we conduct binary classification task for each label: for each INLINEFORM0 -class classification task, a turn is labeled as either belonging to the current label, or not (i.e. \"other\"). In this setup, each turn is assigned a binary value for each label (i.e. for the 6-class experiment, each turn receives a value of 0/1 for each indicating whether the classifier predicts it to be relevant to the each of the 6 labels). Thus, for each INLINEFORM1 -class experiment, we end up with INLINEFORM2 binary labels, for example, whether the turn is a Statement Informative or Other, Request Information or Other, etc. We aggregate the INLINEFORM3 binary predictions for each turn, then compare the resultant prediction matrix for all turns to our majority-vote ground-truth labels, where at least 3 out of 5 annotators have selected a label to be true for a given turn. The difficulty of the task increases as the number of classes INLINEFORM4 increases, as there are more classifications done for each turn (i.e., for the 6-class problem, there are 6 classification tasks per turn, while for the 8-class problem, there are 8, etc). Due to the inherent imbalance of label-distribution in the data (shown in Figure FIGREF17 ), we use weighted F-macro to calculate our final scores for each feature set (which finds the average of the metrics for each label, weighted by the number of true instances for that label) BIBREF31 .",
+      "Our first experiment sets out to compare the use of a non-sequential classification algorithm versus a sequential model for dialogue act classification on our dataset. We experiment with the default Naive Bayes (NB) and Linear SVC algorithms from Scikit-Learn BIBREF31 , comparing with our sequential SVM-HMM model. We test each classifier on each of our four class sets, reporting weighted F-macro for each experiment. Figure FIGREF33 shows the results of the experiments.",
+      "From this experiment, we observe that our sequential SVM-HMM outperforms each non-sequential baseline, for each of the four class sets. We select the sequential SVM-HMM model for our preferred model for subsequent experiments. We observe that while performance may be expected to drop as the number of classes increases, we instead get a spike in performance for the 10-Class (Easy) setting. This increase occurs due to the addition of the lexically well-defined classes of Statement Apology and Statement Thanks, which are much simpler for our model to predict. Their addition results in a performance boost, comparable to that of the simpler 6-Class problem. When we remove the two well-defined classes and add in the next two broader dialogue act classes of Statement Offer and Question Open (as defined by the 10-Class (Hard) set), we observe a drop in performance, and an overall result comparable to our 8-Class problem. This result is still strong, since the number of classes has increased, but the overall performance does not drop.",
+      "We also observe that while NB and LinearSVC have the same performance trend for the smaller number of classes, Linear SVC rapidly improves in performance as the number of classes increases, following the same trend as SVM-HMM. The smallest margin of difference between SVM-HMM and Linear SVC also occurs at the 10-Class (Easy) setting, where the addition of highly-lexical classes makes for a more differentiable set of turns.",
+      "Our next experiment tests the differences in performance when training and testing our real-time sequential SVM-HMM model using only a single type of speaker's turns (i.e. only Customer or only Agent turns). Figure FIGREF35 shows the relative performance of using only speaker-specific turns, versus our standard results using all turns.",
+      "We observe that using Customer-only turns gives us lower prediction performance than using both speakers' turns, but that Agent-only turns actually gives us higher performance. Since agents are put through training on how to interact with customers (often using templates), agent behavior is significantly more predictable than customer behavior, and it is easier to predict agent turns even without utilizing any customer turn information (which is more varied, and thus more difficult to predict).",
+      "We again observe a boost in performance at out 10-Class (Easy) set, due to the inclusion of lexically well-defined classes. Notably, we achieve best performance for the 10-Class (Easy) set using only agent turns, where the use of the Apology and Thanks classes are both prevalent and predictable.",
+      "In our final experiment, we explore the changes in performance we get by splitting the training and test data based on company domain. We compare this performance with our standard setup for SVM-HMM from our baseline experiments (Figure FIGREF33 ), where our train-test data splitting is company-independent (i.e. all conversations are randomized, and no information is used to differentiate different companies or domains). To recap, our data consists of conversations from four companies from three different industrial domains (one from the telecommunication domain, two from the electronics domain, and one from the insurance domain). We create four different versions of our 6-class real-time sequential SVM-HMM, where we train on the data from three of the companies, and test on the remaining company. We present our findings in Table TABREF37 .",
+      "From the table, we see that our real-time model achieves best prediction results when we use one of the electronics companies in the test fold, even though the number of training samples is smallest in these cases. On the other hand, when we assign insurance company in the test fold, our model's prediction performance is comparatively low. Upon further investigation, we find that customer-agent conversations in the telecommunication and electronics domains are more similar than those in the insurance domain. Our findings show that our model is robust to different domains as our test set size increases, and that our more generic, company-independent experiment gives us better performance than any domain-specific experiments."
+    ],
+    [
+      "Given our observation that Agent turns are more predictable, and that we achieve best performance in a company-independent setting, we question whether the training that agents receive is actually reliable in terms of resulting in overall \"satisfied customers\", regardless of company domain. Ultimately, our goal is to discover whether we can use the insight we derive from our predicted dialogue acts to better inform conversational systems aimed at offering customer support. Our next set of experiments aims to show the utility of our real-time dialogue act classification as a method for summarizing semantic intent in a conversation into rules that can be used to guide automated systems."
+    ],
+    [
+      "We conduct three supervised classification experiments to better understand full conversation outcome, using the default Linear SVC classifier in Scikit-Learn BIBREF31 (which gave us our best baseline for the dialogue classification task). Each classification experiments centers around one of three problem outcomes: customer satisfaction, problem resolution, and customer frustration. For each outcome, we remove any conversation that did not receive majority consensus for a label, or received majority vote of \"can't tell\". Our final conversation sets consist of 216 satisfied and 500 unsatisfied customer conversations, 271 resolved and 425 unresolved problem conversations, and 534 frustrated and 229 not frustrated customer conversations. We retain the inherent imbalance in the data to match the natural distribution observed. The clear excess of consensus of responses that indicate negative outcomes further motivates us to understand what sorts of dialogic patterns results in such outcomes.",
+      "We run the experiment for each conversation outcome using 10-fold cross-validation, under each of our four class settings: 6-Class, 8-Class, 10-Class (Easy), and 10-Class (Hard). The first feature set we use is Best_Features (from the original dialogue act classification experiments), which we run as a baseline.",
+      "Our second feature set is our Dialogue_Acts predictions for each turn \u2013 we choose the most probable dialogue act prediction for each turn using our dialogue act classification framework to avoid sparsity. In this way, for each class size INLINEFORM0 , each conversation is converted into a vector of INLINEFORM1 (up to 10) features that describe the most strongly associated dialogue act from the dialogue act classification experiments for each turn, and the corresponding turn number. For example, a conversation feature vector may look as follows: INLINEFORM2 ",
+      "Thus, our classifier can then learn patterns based on these features (for example, that specific acts appearing at the end of a conversation are strong indicators of customer satisfaction) that allow us to derive rules about successful/unsuccessful interactions.",
+      "Figure FIGREF38 shows the results of our binary classification experiments for each outcome. For each experiment, the Best_Features set is constant over each class size, while the Dialogue_Act features are affected by class size (since the predicted act for each turn will change based on the set of acts available for that class size). Our first observation is that we achieve high performance on the binary classification task, reaching F-measures of 0.70, 0.65, and 0.83 for the satisfaction, resolution, and frustration outcomes, respectively. Also, we observe that the performance of our predicted dialogue act features is comparable to that of the much larger set of best features for each label (almost identical in the case of frustration).",
+      "In more detail, we note interesting differences comparing the performance of the small set of dialogue act features that \"summarize\" the large, sparse set of best features for each label, as a form of data-driven feature selection. For satisfaction, we see that the best feature set outperforms the dialogue acts for each class set except for 10-Class (Easy), where the dialogue acts are more effective. The existence of the very lexically well-defined Social Act Thanking and Social Act Apology classes makes the dialogue acts ideal for summarization. In the case of problem resolution, we see that the performance of the dialogue acts approaches that of the best feature set as the number of classes increases, showing that the dialogue features are able to express the full intent of the turns well, even at more difficult class settings. Finally, for the frustration experiment, we observe negligible different between the best features and dialogue act features, and very high classification results overall."
+    ],
+    [
+      "While these experiments highlight how we can use dialogue act predictions as a means to greatly reduce feature sparsity and predict conversation outcome, our main aim is to gain good insight from the use of the dialogue acts to inform and automate customer service interactions. We conduct deeper analysis by taking a closer look at the most informative dialogue act features in each experiment.",
+      "Table TABREF44 shows the most informative features and weights for each of our three conversation outcomes. To help guide our analysis, we divide the features into positions based on where they occur in the conversation: start (turns 1-3), middle (turns 4-6), and end (turns 7-10). Desirable outcomes (customers that are satisfied/not frustrated and resolved problems) are shown at the top rows of the table, and undesirable outcomes (unsatisfied/frustrated customers and unresolved problems) are shown at the bottom rows.",
+      "Our analysis helps zone in on how the use of certain dialogue acts may be likely to result in different outcomes. The weights we observe vary in the amount of insight provided: for example, offering extra help at the end of a conversation, or thanking the customer yields more satisfied customers, and more resolved problems (with ratios of above 6:1). However, some outcomes are much more subtle: for example, asking yes-no questions early-on in a conversation is highly associated with problem resolution (ratio 3:1), but asking them at the end of a conversation has as similarly strong association with unsatisfied customers. Giving elaborate answers that are not a simple affirmative, negative, or response acknowledgement (i.e. Answer (Other)) towards the middle of a conversation leads to satisfied customers that are not frustrated. Likewise, requesting information towards the end of a conversation (implying that more information is still necessary at the termination of the dialogue) leads to unsatisfied and unresolved customers, with ratios of at least 4:1.",
+      "By using the feature weights we derive from using our predicted dialogue acts in our outcome classification experiments, we can thus derive data-driven patterns that offer useful insight into good/bad practices. Our goal is to then use these rules as guidelines, serving as a basis for automated response planning in the customer service domain. For example, these rules can be used to recommend certain dialogue act responses given the position in a conversation, and based previous turns. This information, derived from correlation with conversation outcomes, gives a valuable addition to conversational flow for intelligent agents, and is more useful than canned responses."
+    ],
+    [
+      "In this paper, we explore how we can analyze dialogic trends in customer service conversations on Twitter to offer insight into good/bad practices with respect to conversation outcomes. We design a novel taxonomy of fine-grained dialogue acts, tailored for the customer service domain, and gather annotations for 800 Twitter conversations. We show that dialogue acts are often semantically overlapping, and conduct multi-label supervised learning experiments to predict multiple appropriate dialogue act labels for each turn in real-time, under varying class sizes. We show that our sequential SVM-HMM model outperforms all non-sequential baselines, and plan to continue our exploration of other sequential models including Conditional Random Fields (CRF) BIBREF32 and Long Short-Term Memory (LSTM) BIBREF33 , as well as of dialogue modeling using different Markov Decision Process (MDP) BIBREF34 models such as the Partially-Observed MDP (POMDP) BIBREF35 .",
+      "We establish that agents are more predictable than customers in terms of the dialogue acts they utilize, and set out to understand whether the conversation strategies agents employ are well-correlated with desirable conversation outcomes. We conduct binary classification experiments to analyze how our predicted dialogue acts can be used to classify conversations as ending in customer satisfaction, customer frustration, and problem resolution. We observe interesting correlations between the dialogue acts agents use and the outcomes, offering insights into good/bad practices that are more useful for creating context-aware automated customer service systems than generating canned response templates.",
+      "Future directions for this work revolve around the integration of the insights derived in the design of automated customer service systems. To this end, we aim to improve the taxonomy and annotation design by consulting domain-experts and using annotator feedback and agreement information, derive more powerful features for dialogue act prediction, and automate ranking and selection of best-practice rules based on domain requirements for automated customer service system design."
+    ]
+  ]
+}
+```
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diff --git a/qasper-1262/instruction.md b/qasper-1262/instruction.md
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+Name of Paper: Building a Neural Machine Translation System Using Only Synthetic Parallel Data
+
+Question: Where do they collect the synthetic data?
\ No newline at end of file
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+Name of Paper: Hateful People or Hateful Bots? Detection and Characterization of Bots Spreading Religious Hatred in Arabic Social Media
+
+Question: Do they analyze what type of content Arabic bots spread in comparison to English?
\ No newline at end of file
diff --git a/qasper-1264/instruction.md b/qasper-1264/instruction.md
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+Name of Paper: Hateful People or Hateful Bots? Detection and Characterization of Bots Spreading Religious Hatred in Arabic Social Media
+
+Question: Do they propose a new model to better detect Arabic bots specifically?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Background and Related Work",
+    "Challenges of Arabic Language and User-generated Content",
+    "Online Hate Speech",
+    "Malicious Use of Bots in Social Media",
+    "Bot Detection",
+    "Data Collection",
+    "Ground Truth",
+    "Quantifying Hate Speech Sent by Bots",
+    "Methods",
+    "Results",
+    "Features",
+    "Feature Importance & Contribution",
+    "Topic, Source, and Network Analysis",
+    "The Role of Bots in Spreading Hate Speech",
+    "DISCUSSION",
+    "CONCLUSION"
+  ],
+  "paragraphs": [
+    [
+      "The analysis of social media content to understand online human behavior has gained significant importance in recent years BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 . However, a major limitation of the design of such analysis is that it often fails to account for content created by bots, which can significantly influence the messaging in social media. A social bot is an autonomous entity on social media that is typically engineered to pass as a human, often with the intent to manipulate online discourse BIBREF4 . Recent studies have shown that a significant majority of the social media content is generated by bots. For example, a six-week study by the Pew Research Center found that around two-thirds of all tweets with URL links were posted by likely bots BIBREF5 . As a result, the presence of bots can negatively impact the results of social media analysis and misinform our understanding of how humans interact within the online social space. In particular, any social media analysis that doesn't take into account the impact of bots is incomplete. While some bots can be beneficial (e.g., customer service chatbots), the focus in this work is on content-polluter bots that mimic human behavior online to spread falsified information BIBREF6 , create a false sense of public support BIBREF7 , and proliferate dangerous ideologies BIBREF8 , BIBREF9 .",
+      "Bots have vigorously invaded online social communities. A recent study estimated that bots constitute about 15% of all Twitter accounts BIBREF10 . With 321 million Twitter accounts BIBREF11 , the implication is that there are more than 48 million bot accounts on Twitter. Twitter reported that the number of bots and trolls suspended each week is on the rise reaching 9.9 million as of May 2018 BIBREF12 . While this number may seem promising, Twitter's fight against bots is far from over BIBREF13 .",
+      "Detecting bots in social media is a first step to account for the impact of bots in social media analysis. Our interest is in analysis of abuse in Arabic Twitter space, specifically the spread of religious hate, and thus to account for the impact of bots in our research, this paper focuses on detecting Arabic Twitter bots that are active in spreading hateful messages against various religious groups. Detecting bots in social media is challenging as bot designers are using sophisticated techniques to make a social bot look and behave as close to a human as possible BIBREF4 . Several researchers have looked at the problem of detecting bots in Twitter (See Section SECREF10 ), and several bot detection tools are freely available BIBREF14 , BIBREF10 , BIBREF15 providing fairly high detection accuracy. However, we show in this paper that these tools fail to perform as well on Arabic Twitter bots as they do on English Twitter bots. In fact, Arabic Twitter bot detection and analysis is a considerably under-researched area. A study by Abokhodair et al. BIBREF16 analyzed a Twitter botnet that was active during the Syrian civil war to understand how it might have influenced related discussions. El-Mawass et al. BIBREF17 estimated that around 75% of Saudi trending hashtags on Twitter contained spam content, some of which was created by automated spammers.",
+      "In our recent work on hate speech in Arabic social media BIBREF18 , BIBREF19 , we showed that Arabic Twitter is awash with religious hatred which we defined as \u201ca speech that is insulting, offensive, or hurtful and is intended to incite hate, discrimination, or violence against an individual or a group of people on the basis of religious beliefs or lack thereof\". Having such a large volume of hate speech and knowing that ISIS and other radical organizations have been using bots to push their extreme ideologies BIBREF8 , BIBREF9 , we hypothesize that bots may be to blame for a significant amount of this widespread hatred.",
+      "In this work, we build a novel regression model, based on linguistic, content, behavioral and topic features to detect Arabic Twitter bots to understand the impact of bots in spreading religious hatred in Arabic Twitter space. In particular, we quantitatively code and analyze a representative sample of 450 accounts disseminating hate speech from the dataset constructed in our previous work BIBREF18 , BIBREF19 for bot-like behavior. We compare our assigned bot-likelihood scores to those of Botometer BIBREF14 , a well-known machine-learning-based bot detection tool, and we show that Botometer performs a little above average in detecting Arabic bots. Based on our analysis, we build a predictive regression model and train it on various sets of features and show that our regression model outperforms Botometer's by a significant margin (31 points in Spearman's rho). Finally, we provide a large-scale analysis of predictive features that distinguish bots from humans in terms of characteristics and behaviors within the context of social media.",
+      "To facilitate Arabic bot detection research and Twitter automation policy enforcement, this paper provides the following findings and contributions."
+    ],
+    [
+      "In this section, we first discuss the main challenges encountered in analyzing Arabic language and social media content in general. We then survey prior research on online hate speech and bot detection and analysis."
+    ],
+    [
+      "The Arabic language poses unique challenges to the process of analyzing and studying online content BIBREF20 , BIBREF21 . Arabic is a morphologically rich language with a substantial amount of syntactic and relation information encoded at the word level. Arabic is also a pluricentric language with varying dialects corresponding to various regions in the Arab world. Words can have entirely different meanings across dialects. Social media users tend to use Arabic dialects rather than Modern Standard Arabic (MSA). Unlike MSA, Arabic dialects are not standardized, and often fail to follow well-defined language rules and structures. Besides, Arabic is a greatly under-resourced language with few Natural Language Processing (NLP) tools supporting MSA, let alone Arabic dialects BIBREF22 .",
+      "Other challenges that are encountered while studying user-generated content include multilingual text, slangs, misspellings, abbreviations, and lengthening of words. Furthermore, microblogging platforms that impose a maximum length on posts such as Twitter can lead to text that lacks context, which in turn may lead to a flawed analysis. Moreover, some online users tend to mask abusive and hateful content by presenting it as a harmless joke or hiding it inside a comical image. Such behavior can lead to abusive and toxic content going undetected. We describe later in this paper how these aforementioned challenges have been addressed."
+    ],
+    [
+      "Our previous work BIBREF18 , BIBREF19 appears to be the only one focusing on hate speech detection and analysis in Arabic social media. Our study revealed that religious hate speech is widespread on Arabic Twitter. We found that almost half of the tweets discussing religion preached hatred and violence against religious minorities, mostly targeting Jews, Atheists, and Shia (the second largest Islamic sect). In particular, we found that there was a 60% chance that a tweet would be hateful if it contained the Arabic equivalent of the word Jews.",
+      "To provide a sense of comparison between the volume of hate speech on Arabic Twitter and English Twitter, we report the results of a study conducted by Magdy et al. BIBREF1 , in which they analyzed a large volume of English tweets mentioning Islam while reacting to the 2015 Paris attacks. Their analysis suggested that only 17% of such tweets were directing hate toward Muslims, 61% were spreading positive messages about Islam, while 22% were natural.",
+      "A growing body of hate speech research has been conducted on English social media content. Distinguishable among this work are studies related to the detection of online hateful content targeting race and gender using character INLINEFORM0 -grams BIBREF2 , word embeddings BIBREF23 , and document embeddings BIBREF24 . A measurement study conducted by Silva et al. BIBREF0 exploring the main targets of hate speech on Twitter and Whisper, an anonymous social media platform, showed that black people were the most targeted group on both networks, followed by white people on Twitter and fake people on Whisper. While race was the main targeted category on Twitter, behavior (e.g., sensitive people) was the main targeted category on Whisper."
+    ],
+    [
+      "While previous research has studied harmless bots on several collaborative and social platforms such as Wikidata BIBREF25 , Twitch BIBREF26 , and Reddit BIBREF27 , our focus is on malicious bots. Previous studies have thoroughly investigated such nefarious roles that can be played by bots, particularly in English online social space. One of such roles is political astroturfing wherein a flood of bot accounts (usually created by a single entity) creates the illusion of public support for a particular political candidate for the purpose of influencing public opinion. Bessi and Ferrara BIBREF7 suggested that social bots have generated about one-fifth of the 2016 U.S. Presidential election discourse on Twitter. Twitter confirmed this in an official blog post BIBREF28 reporting that approximately 1.4 million accounts were notified about having some form of interactions with suspicious Russian-linked accounts (trolls and bots) who were spreading misinformation during the 2016 U.S. election. This nefarious use of bots is not new to social media; Ratkiewicz et al. BIBREF6 indicated that bots have been used to amplify fake news and misinformation during the 2010 U.S. midterm elections through a coordinated generation and liking of misguiding tweets. It has also been shown that bots are used by ISIS propagandists to inflate their influence on Twitter and popularize their extreme ideologies BIBREF8 , BIBREF9 .",
+      "Limited research has been conducted to study bot behavior on Arabic social media. The only relevant research we are aware of is the work by Abokhodair et al. BIBREF16 , in which they analyzed a Syrian botnet consisting of 130 bots that were active for 35 weeks before being suspended by Twitter. Their analysis suggested that the main task of such bots was to report news from a highly biased news source. A different but related research problem is the detection of spam content which sometimes involves bots. In BIBREF17 , El-Mawass et al. reported that about 74% of tweets in Saudi trending hashtags are spam. They suggested that bots are sometimes used to increase the reach of spam content by coordinated liking and retweeting of spam tweets."
+    ],
+    [
+      "There are two main approaches to detecting social media bots in literature: supervised learning and unsupervised learning. An example of a supervised-based bot detection model is Botometer BIBREF14 , BIBREF10 , which is a freely available tool that employs supervised machine learning algorithms to predict a bot score, from 0 to 5, for public Twitter accounts. This score indicates the likelihood of an account being a bot based on 1,150 features distributed across six feature categories. Botometer also computes an individual bot score for each of the six feature categories, comprised of friend features (e.g., local time and popularity of retweeting and retweeted accounts), network features (e.g., network metrics that describe distribution and density of retweet and mention networks), user features (e.g., number of followers, number of friends, profile language, account age), temporal features (e.g., average time between two consecutive tweets, tweeting rate), content features (e.g., length of tweet, frequency of part-of-speech tags), and sentiment features (e.g., arousal, valence, and dominance scores). Figure FIGREF12 provides an example of Botometer's bot score interface. It is worth noting that although content and sentiment features are computed for non-English tweeting bots, they are only meaningful for English tweeting bots. Botometer conveniently provides a language-independent bot score, which we considered in our study.",
+      "DeBot BIBREF29 , on the other hand, utilizes unsupervised techniques to detect Twitter bots based on synchronicity and activity correlation between accounts. The system has several services that can answer the following questions. Is a given account a bot? How long has it been active? Which bots are currently tweeting about a given topic? Which bots are participating in a given hashtag? They compared their system to Botometer and found that 59% of bots detected using their system had a Botometer bot score exceeding 50% (Botometer's previous scoring scheme ranged from 0% to 100%). Their analysis suggested that bots in a given botnet share the same tweets 87% of the time.",
+      "To our knowledge, no existing work has attempted to specifically detect Arabic bots. In BIBREF30 , Morstatter et al. created a dataset of 3,602 Arabic tweeting bots using a honeypot trap mechanism and a human dataset consisting of 3,107 users\u2014a high bot ratio we argue that doesn't represent an actual bot percentage on Twitter, which is estimated to be between 9% and 15% BIBREF10 . Our work is different from BIBREF30 in several important aspects. First, the main goal in BIBREF30 is to improve recall in detecting bots while our goal is to detect Arabic bots with high precision in the context of religious hate. Second, Morstatter et al. created a binary classifier to classify whether an account is a bot or not; as bots nowadays are very sophisticated with many of them exhibiting both human and bot behaviors at the same time BIBREF10 , we argue that the problem can't be simplified into a binary classification problem. To address this issue of mix behaviors, we adopt two techniques: instead of using any automated mechanism such as setting up a honeypot trap, we rely on manual labeling of accounts by assigning each a score ranging from 0 to 5 which indicates the degree of bot-like behavior an account is showing to get the ground truth; and we create a regression predictive model trained on our manually-labeled accounts to predict bot scores for new Twitter accounts. Finally, our work specifically focuses on the unique characteristics of Arabic bots, and thus provides deep insights into the predictive features that distinguish bots from humans and broadens the understanding of bots' behavior in the context of Arabic social space."
+    ],
+    [
+      "To identify accounts disseminating hate speech, we started working from the hate speech dataset constructed in our previous work BIBREF18 , BIBREF19 , which consists of 6,000 Arabic tweets collected in November 2017 and annotated for religious hate speech by crowdsourced workers (see Table TABREF15 for general statistics of the dataset). The tweets were collected by querying Twitter's Standard search API using impartial terms that refer to one of the six most common religious groups across the Middle East and North Africa. Although we didn't use offensive terms or religious slurs in our data collection process, the number of returned hateful tweets was surprisingly large. More details on the construction and analysis of this dataset can be found in BIBREF18 , BIBREF19 .",
+      "In this dataset, we identified 4,410 unique Twitter accounts. Of these, 543 accounts were suspended, and thus we excluded them from our study. We then looked at the remaining 3,867 active accounts and classified them into accounts with hateful tweets or accounts with non-hateful tweets based on the number of hateful and non-hateful tweets they had authored. If they had authored more hateful tweets than non-hateful tweets, we classified them as accounts with hateful tweets. This resulted in having 1,750 accounts with hateful tweets and 2,117 accounts with non-hateful tweets. Since this study is focused on identifying the role of bots in spreading religious hatred, only accounts with hateful tweets were considered.",
+      "For each account with hateful tweets, we collected up to 3,200 of their recent tweets using the GET statuses/user_timeline method from Twitter's API. The total number of collected tweets was more than 4.2 million tweets. We also collected each account profile information (e.g., location, time zone, language) using the GET users/show API method."
+    ],
+    [
+      "To evaluate the accuracy of Botometer scores in Arabic Twitter, we need to get a ground truth of which accounts are bots. Getting the ground truth for such an inherently difficult task is not straightforward. Some of the approaches proposed in literature are fully automatic approaches without any manual inspection, e.g. setting a honeypot trap BIBREF31 , BIBREF30 , identifying synchronicity and correlation in accounts' behavior BIBREF29 , and observing accounts getting suspended by Twitter BIBREF30 . Others have relied on manual labeling of accounts into bot-like or human-like BIBREF10 . The snowball mechanism has also been used in which researchers identify a seed list of highly suspicious accounts and then snowball their connections to collect a larger set of suspicious accounts BIBREF32 , BIBREF16 .",
+      "The common aspect among all earlier efforts in labeling of bots is that they assign binary labels to accounts, bot or human. Given that there is no simple list of rules that can decisively identify bots, we argue that it is more effective to assign labels on a scale to reflect the inherent uncertainty in recognizing bots. Additionally, since modern bots that attempt to hide themselves are becoming more sophisticated, we argue that any fully automatic approach without any manual inspection to get ground truth about bots is bound to suffer from high inaccuracies.",
+      "Thus, we turn to manual labeling approaches to get the ground truth. Although crowdsourced workers can be helpful in many labeling and classification tasks, we argue that our task of fine-grained scoring of accounts on the level of bot-like behavior they are exhibiting requires a high-level of domain knowledge as well as extensive training that is hard to control in a crowdsource setting. We argue that in order get a reasonable set of ground truth data for identifying bots, manual labeling must be done by experts. Therefore, in order to insure high-quality labeling, the labeling of the accounts was done by two members of the research team who are native Arabic speakers and have gone through the following training steps to gain the required expertise to make a sound and informed judgment.",
+      "First, as a data exploration step, we applied Botometer on the 1,750 accounts with hateful tweets to discern the distribution of their bot scores (illustrated in Figure FIGREF19 ). Recall that bot scores from Botometer (we refer to this as Botometer scores) are on a scale from zero to five, with zero being \u201cmost likely human\u201d and five being \u201cmost likely bot\u201d. A score in the middle of the scale is an indication that Botometer is unsure about the classification. As shown in this figure, the distribution is skewed to the right with the majority of accounts being assigned a Botometer score from 0 to 1.",
+      "Second, in order to gain the required domain knowledge with respect to bot behaviors and characteristics, we carefully examined the top 50 accounts receiving the highest Botometer scores as well as highly suspicious propaganda bots flagged by Botcheck.me BIBREF15 , a free online tool that is trained to identify English propaganda bots. We noted every suspicious behavior exhibited by these highly-suspected bot accounts with respect to account profile information, friends, followers, interaction with other accounts, tweet content, and posting behavior. We also familiarized ourselves with bot characteristics and behaviors reported in previous studies BIBREF10 , BIBREF16 , BIBREF29 , BIBREF4 , BIBREF33 , BIBREF34 . Following this, we have created a list of bot characteristics described in Table TABREF20 .",
+      "Based on this list of bot criteria (Table TABREF20 ), we manually examined each account and assigned a bot-likelihood score (we refer to this as the true score) ranging from 0 to 5, with 0 being \u201cvery unlikely\" and 5 being \u201cvery likely\" based on the extent by which an account exhibited a suspicious bot-like behavior from the list. We also added to the list other suspicious behaviors that we encountered while studying and labeling accounts in our dataset. It is important to note that even human accounts do exhibit one or more of these characteristics at different times (e.g., having a large number of followers). Furthermore, a bot may exhibit only a subset of these characteristics in addition to some human-like characteristics. Therefore, in our manual labeling of bot-like scores, the more characteristics an account exhibited, the higher the bot score it got assigned.",
+      "Since manual labeling is time and effort consuming, we considered a sample subset of accounts with hateful tweets. Using a 95% confidence level and a 4% margin of error, a representative sample of these accounts would be of size 450. To eliminate sampling bias and to ensure that the sample preserves the statistical proportions of the original dataset (see Figure FIGREF19 ), we applied proportionate stratified random sampling, wherein simple random sampling technique is employed to select training examples proportionally from each stratum (i.e., subgroup). This sampling method ensures that accounts with unusually high Botometer scores are still present in our sample and in a proportion similar to that in our original dataset. The final sample consisted of 239 accounts from the 0-1 stratum, 95 accounts from the 1-2 stratum, 47 accounts from the 2-3 stratum, 32 accounts form the 3-4 stratum, and 37 accounts from the 4-5 stratum.",
+      "Finally, to validate the robustness of our labeling process, we calculated the inter-rater agreement score between the two labelers on a subset of 30 independently-labeled accounts. A weighted kappa BIBREF35 score of 0.86 was reported, which indicates an almost perfect agreement BIBREF36 . Given such a high inter-rater agreement score, a well-defined bot criteria (Table TABREF20 ), and a highly time-expensive task (each account required on average a 15-min examination before a score was given), we decided to split the 450 accounts equally between the two labelers."
+    ],
+    [
+      "The results of our manual labeling of the 450 accounts can provide a preliminary indication of how many hateful tweets were sent by bots vs. humans. Assuming that accounts with a true score of 3 or higher were bots, we found that there were 77 (17%) bots and 373 (83%) humans. Bots authored 109 hateful tweets (a per-bot average rate of 1.4 tweets), and human accounts authored 446 hateful tweets (a per-human average rate of 1.2 tweets). The ratio of tweets sent by bots to those sent by humans is 1:4. In other words, bots were responsible for 22.6% of hateful tweets, while humans were responsible for 77.4% of hateful tweets.",
+      "The relatively low per-bot average rate of tweets could be attributed to the fact that we are only considering their tweets in the hate speech dataset. Considering their whole timeline (tweets) and finding how many of those contain an instance of religious hatred is worth investigating in the future. We will extend this analysis in Section SECREF50 to include all 1750 accounts with hateful tweets."
+    ],
+    [
+      "Our manual scoring of accounts as well as Botometer scoring is done on a scale of 0-5 with a higher score implying a higher likelihood of the account being a bot. However, the absolute scores assigned by the two scoring methods may differ. In order to evaluate the accuracy of Botometer, we need to investigate if there is a monotonic relationship between how we score accounts (true scores) and how Botometer scores accounts (Botometer scores). To do this, we applied two rank correlation tests that measure the strength and direction of the association between the two scorings.",
+      "The first rank correlation test is Spearman's rho BIBREF37 , which is a well-known nonparametric statistical test that measures the strength of correlation between two independent variables. As Spearman's rho calculation is based on squaring differences between rankings, it penalizes more for large discrepancies between rankings. In case of tied ranks, the mean of the ranks is assigned. The second evaluation metric is Kendall's tau BIBREF38 , which is also a non-parametric test that is used to measure the strength and direction of correlation between two independent sets of scores. The Tau-b version of Kendall's tau was used to handle tied ranks. While both Spearman's rho and Kendall's tau are based on the ranks of data and not the actual scores, Kendall's tau appears to be less sensitive to wide discrepancies between rankings. The value of both Spearman's rho and Kendall's tau ranges from -1 (perfect negative correlation) to +1 (perfect positive correlation). A value of zero indicates the absence of correlation between the two sets of scores. Finally, we applied mean absolute error (MAE) to measure the average absolute differences (errors) between the true scores and Botometer scores.",
+      "We now present a regression predictive modeling task in which we train a random forest regression model to predict the bot score, from zero to 5, of an account based on various hand-crafted features. Random forest is a tree-based ensemble method that has been successfully used in other bot detection tasks BIBREF14 , BIBREF33 . A great property of random forest is that it inherently computes feature importance, and thus it can give insights into which feature is most informative. Random forest can also control over-fitting by training each tree on randomly selected training examples and features BIBREF39 . Note that we also experimented with other regression algorithms such as logistic regression and gradient boosting regression trees. However, their performance was poorer compared to random forest, and thus we only report the results achieved by random forest.",
+      "We implemented random forest with scikit-learn BIBREF40 , a Python machine learning library. To understand the impact of each individual feature in detecting bots, we trained our regression model on successive combinations of content, tweet, topic, and account features. We tuned the regression model by performing a hyperparameter grid search with 10-fold cross-validation and optimized for Spearman's rho. The regression model was trained on 70% of the accounts and tested on the remaining 30%. Three evaluation metrics were used to compare the performance of our regression model to that of Botometer: Spearman's rho, Kendall's tau, and MAE, as discussed in Section SECREF23 ."
+    ],
+    [
+      "The results of Spearman's rho and Kendall's tau were 0.43 and 0.33, respectively. The results suggest that there is a moderate positive monotonic relationship between the two sets of scores. MAE was 1.14, which indicates that Botometer scores on average are off by 1.14 points. These results indicate that while Botometer performs better than average in detecting Arabic bots, there is a need for developing social bot detection models that can work more effectively on Arabic Twitter accounts.",
+      "To better understand the limitations of Botometer in detecting Arabic bots, we further analyze the results in Figures FIGREF25 and FIGREF26 . Figure FIGREF25 presents a scatter graph plotting the true score for an account against its Botometer score, along with a regression line that best fits the data. The regression line indicates that Botometer tends to assign a higher score to accounts with true scores of 1.5 or less. On the other hand, Botometer tends to assign lower scores to accounts with a true score higher than 1.5. The margin increases as the true score rises. In other words, Botometer appears to be compressing the range of scores by assigning obvious human accounts a higher score than zero and highly suspected bot accounts a lower score than 5. Figure FIGREF26 shows a joint histogram for the two sets of scores using hexagonal binning. The figure represents a heatmap where darker colored hexagons represent higher density. As shown in the graph, the highest agreement is when both the true score and Botometer score are between 0 and 1. We can also see from the two histograms that Botometer scores have higher frequencies in the middle bins than the true scores.",
+      "Finally, in order to gain some insights into the reasons for Botometer's weakness in identifying Arabic bots, we manually inspected accounts with wide discrepancies in their true scores and Botometer scores and have identified the following possible reasons for this. Note that while some of these reasons may be applicable to English bots as well, we verified them to some extent only for Arabic bots. It is also important to note that a larger, more structured investigation would be required in order to fully validate those reasons. We leave that as part of our future work.",
+      "Botometer appears to be assigning a high-bot score to human accounts who have used their Twitter account for a short period of time, and thus have fewer tweets in their timeline. This could be due to the restriction Botometer enforced on their data collection process, which is considering only accounts with at least 200 tweets BIBREF10 . We had 17 accounts in our dataset with at most 100 tweets (i.e., inactive), and 71% of them were given Botometer scores larger than 2.5 while their true scores were less than 2.5. Given that Botometer generally assigned 13% of all accounts in our dataset scores in the upper range, while their true scores were in the lower range, we found this 71% misclassification rate to be significant ( INLINEFORM1 = 46.9, df = 1, INLINEFORM2 -value < 0.001).",
+      "Having unusually small number of followers or friends (followings) appears to be triggering Botometer to assign a high-bot score without taking into considerations other human-like behavior an account is exhibiting. We had 29 accounts in our dataset with followers or friends less than 5, 35% of them were misclassified by Botometer. This was significantly different from what is expected ( INLINEFORM0 = 11.4, df = 1, INLINEFORM1 -value < 0.001).",
+      "As Botometer doesn't support Arabic language, it may miss linguistic and content cues that can giveaway bots, e.g., posting unrelated tweets to a hashtag. We show in Section SECREF35 that linguistic features such as the use of numerics and emojis can be significant distinguishing features. This could be a reason for Botometer assigning a lower score to bot-like accounts with higher true scores.",
+      "Sometimes Arabic Twitter accounts use third-party Islamic applications that post Islamic supplications and/or Quranic verses on their behalf. There were 18 unique Islamic applications that were used by accounts in our dataset. Such behavior may result in Botometer assuming that these accounts are bots although some of them are in fact humans. This could be a reason for Botometer assigning a higher score to obvious human accounts with true scores of 1.5 or less.",
+      "We also considered other reasons that we believed to be causing wide discrepancies between true scores and Botometer scores. For example, including a hashtag in every tweet appeared to be triggering Botometer to assign a high bot score even when the account exhibited more human-like behavior. We had 41 accounts in our dataset with an average of one or more hashtags per tweet, and Botometer assigned higher scores to 15% of them. However, we found this proportion to be statistically insignificant. Another case where we noticed higher scores given to accounts by Botometer is when human accounts appeared to be followed by fake (probably purchased) followers. However, we couldn't verify this claim as such feature (followers being fake or not) was not part of our collected metadata.",
+      "We trained regression models on successive sets of features and assessed their generalization ability on a holdout testing dataset. Although we collected up to 3,200 tweets for each account, training the regression model using up to 200 tweets from each account provided faster training with similar results. Therefore, the results reported here are the ones using features extracted from up to 200 tweets per account, resulting in a total of 86,346 tweets.",
+      "Table TABREF36 compares the performances of these regression models in terms of Spearman's rho, Kendall's tau, and MAE. Highest scores are shown in bold. We have included the performance of Botometer as well in this table as a baseline. As shown in the table, our regression model trained on only simple content features outperformed Botometer which uses user, friend, network, and temporal features. The most informative content features reported by this regression model were the average numbers of account mentions, URL links, numerics, and punctuation marks, respectively. This shows that linguistic cues conveyed in tweets are highly effective in detecting Arabic bots. We will further discuss the importance and direction of contribution of these features in Section SECREF37 .",
+      "By including the tweet features in addition to the content features in training the regression model, the Spearman's coefficient improved by five points. Among the content and tweet features, the most distinguishing features were reply tweet proportion, original tweet proportion, and average number of account mentions, respectively. Adding topic and sentiment features in training further improved the performance of our regression model. These topic features were extracted using bow as opposed to tf-idf, as bow delivered better performance. We found that topic features extracted from lemmatized text achieved superior results to those extracted from stemmed text. However, we also found that not using stemming or lemmatization led to the best performance.",
+      "The best Spearman's rho and Kendall's tau were achieved after adding account features. The 0.74 in Spearman's rho indicates a strong positive correlation between scores predicted by our regression model and the true scores. The most informative features for this regression model were still reply tweet proportion, average number of mentions, and original tweet proportion, respectively.",
+      "The least informative features were mostly from the account feature category such as whether the account has an empty profile description, location, and URL link. Also, whether or not the account has the default profile image or their geotagging feature enabled didn't contribute much to the predicted bot score. This suggests that there wasn't a significant difference between the distribution of humans and bots across those features."
+    ],
+    [
+      "Based on our analysis in Section SECREF24 , we identify four sets of features that we anticipate to be informative for Arabic bot detection. These are content, tweet, topic & sentiment, and account features. Table TABREF34 provides a description of each of these features. For content features, we used average lengths of words and tweets, and average numbers of emojis, punctuation marks, numerics, hashtags, account mentions, URL links and elongated words per tweet. For tweet features, we used the proportions of original tweets, reply tweets and retweet tweets, as well as the number of times an original/reply tweet was retweeted or favorited. For account features, we considered features such as the total number of tweets posted and favorited, numbers of followers and friends as well as features related to the account profile (e.g., account age).",
+      "Sentiment features were obtained by using TextBlob NLP Python library BIBREF41 which offers support for the Arabic language. Topic modeling was implemented using Latent Dirichlet Allocation (LDA) model provided by Gensim BIBREF42 , an unsupervised topic modeling python library. Before extracting topic features, tweets were preprocessed by removing diacritics (i.e., accents), tatweel (i.e., kashida), elongation, two-character words, non-Arabic characters, URL links, punctuation marks and numbers. We also removed Arabic stop words and normalized Arabic letters and hashtags as described in BIBREF18 , and then filtered out very rare words that appeared in less than 10 accounts' timelines and too common words that appeared in more than 50% of accounts' timelines. From the remaining list, we considered the 10K most frequent words. We experimented with both bag-of-words (bow) and term frequency-inverse document frequency (tf-idf) text representation techniques. We also experimented with stemming words using ARLSTem Arabic stemmer from NLTK Python library BIBREF43 and lemmatization using StanfordNLP Python library BIBREF44 . Results of these experiments are provided in the next subsection."
+    ],
+    [
+      "Random forest computes a feature importance score for each feature using the mean decrease impurity feature selection method which reflects how much each feature reduces variance BIBREF45 . The top most-important features, along with their importance scores for the best performing regression model are shown in Table TABREF38 .",
+      "Random forest feature importance score doesn't offer insights into feature contribution (i.e., the direction of feature importance). To understand how much positively or negatively each feature contributed to the final predicted bot score, we used TreeInterpreter Python package BIBREF46 , which breaks down each prediction made by the regression model into the sum of bias (i.e., the average bot score in the training data) and each features' contribution. We selected some of the top informative features and plotted their contribution against their corresponding feature value (see Figure FIGREF39 ). Figure FIGREF39 shows feature contribution for the reply tweet proportion which is the most bot-distinguishing feature. It shows that the more reply tweets an account has, the less likely that account is a bot. This suggests that these bots are not yet smart enough to engage in conversations and interact with other accounts as humans would normally do. The same feature contribution pattern was found for the average number of mentions as illustrated in Figure FIGREF39 . Mentioning other accounts usually implies interacting and communicating with them, and thus the more social an account is, the less likely that account is a bot.",
+      "Figure FIGREF39 shows how the proportion of original tweets (not retweet or reply tweets) contributes to the predicted bot score. If original tweets constitute more than 60% of an account's overall tweets, then the predicted bot-likelihood score would increase as the original tweet percentage increases. Such accounts that don't reply nor retweet might be using third-party applications to post tweets on their behalf, or that their \u201cmasters\u201d programmed them so that they only disseminate prespecified text. This also suggests that human accounts on Twitter usually exhibit a variety of behaviors such as replying, retweeting, and tweeting an original text. As for retweeting, we can see from Figure FIGREF39 that there are two retweeting behaviors that would result in an increase in the predicted bot score. These are never retweeting any tweet (x INLINEFORM0 0) and retweeting extensively (x INLINEFORM1 1). Again, such black and white behavior is more of a bot-like behavior rather than a human-like behavior.",
+      "We also found a clear distinction between English bots and Arabic bots in terms of retweet, reply, and original tweet proportions. It has been claimed that English bots tend to retweet more than posting original tweets BIBREF7 . This was not found to be true in our dataset, i.e., Arabic bots in our dataset were posting original tweets more often than retweeting tweets. In particular, the average retweet, original, and reply proportions for bots were 17%, 76%, and 7%, respectively.",
+      "The average number of emojis per tweet was also one of the highly informative features. This feature was not considered by Botometer because it wasn't trained for Arabic bots. Figure FIGREF39 illustrates that bots tend to not use emojis in their tweets. We believe that this could be due to the fact that people use emojis instinctively to convey different kinds of emotions. Having a URL link in more than 50% of an account's tweets would contribute positively to the predicted bot score as shown in Figure FIGREF39 . This makes sense as many automatically generated tweets contain links to books, news articles, posts from a linked Facebook account, etc.",
+      "Another feature that might not be considered by Botometer as it doesn't extract Arabic-specific features is the average number of numerics. At first, we were surprised to find that the more numbers accounts use in their tweets, the more likely they are bots (see Figure FIGREF39 ). Upon closer look at accounts with a high use of numbers in their tweets and a positive average number of numerics feature contribution, we found that some accounts had random English letters and numbers in their tweets, which suggests that such tweets were generated automatically by computers. Such behavior was previously shown in Figures FIGREF21 and FIGREF21 ."
+    ],
+    [
+      "Here we further investigate the topics that are most discussed by bots (true scores INLINEFORM0 2.5) and humans (true scores INLINEFORM1 2.5). Although LDA doesn't assign names to topics, we inferred those names from the list of terms that are most associated with each topic. To give more insight into what these topics represent, we list in Table TABREF42 the most relevant terms for each topic.",
+      "We then considered the most dominant topic for each account, i.e., the topic with the highest probability distribution. Figure FIGREF43 shows the percentages of dominant topics of humans and bots tweets. We found that the distributions of humans and bots differ significantly among the seven topics ( INLINEFORM0 = 25.9, df = 6, INLINEFORM1 -value < 0.001). The topic distributions for bots is lopsided, i.e., the majority of the posts from bots were concentrated on a small number of topics, while humans' tweets covered a wider range of topics. While the top three discussed topics for both humans and bots were identical, the percentages were different. About 44% of suspected bots were mainly posting Islamic supplications and prayers, while 21% of humans were tweeting about the same topic. Suspected bots were least interested in sports (3.9%); however, they showed somewhat similar interest to that of humans in political topics related to Jerusalem, Jews, and Houthi.",
+      "Twitter provides source label along with tweet metadata which indicates which source (i.e., client) was used to post the tweet to its service. The accounts in our dataset were posting tweets using various official and/or third-party sources. We considered the dominant (i.e., main) used source for each account and grouped these sources into three categories: official Twitter sources, Islamic supplications, and other third-party sources. Official Twitter sources include Twitter Web Client, Twitter Lite, Twitter for IPhone, Twitter for Android, and Twitter for Windows. Islamic supplications include third-party applications mainly for automatically posting Islamic supplications on accounts' behalf. Other third-party sources include Facebook, Instagram, Google, If This Then That (IFTTT), Tweetbot, and Alameednews.com. In total, bots were mainly tweeting using 17 unique sources, while humans were tweeting using 14 unique sources.",
+      "Figure FIGREF46 illustrates the percentages of dominant sources for bots and humans accounts. We found that the distributions of humans and bots differ significantly among the three categories of sources ( INLINEFORM0 = 78.6, df = 2, INLINEFORM1 -value < 0.001). About 92% of humans were mainly posting tweets using official Twitter sources, whereas 53% of bots were mostly using official Twitter sources to post tweets. Posting mainly using third-party sources (including Islamic ones) was a more common behavior of bots (47%) rather than humans (8%).",
+      "It has been shown that bot network characteristics differ significantly from those of humans BIBREF33 . Here we investigate if this holds for Arabic bots as well. Since in our dataset we have more human accounts (373) than bot accounts (77) and to ensure a fair comparison, we randomly selected 77 human accounts to match the set of the 77 bot accounts. We constructed two types of networks: retweet network (see Figure FIGREF48 ) and mention network (see Figure FIGREF49 ). Nodes in these graphs represent accounts in our dataset as well as all other accounts that got retweeted/mentioned by the accounts in our dataset. Edges represent a retweeting/mentioning activity.",
+      "In the retweet network, humans have 2,561 nodes and 2,831 edges, while bots have 1,018 nodes and 1,054 edges, i.e., the human retweet network was more than twice as large as the bot retweet network. This gap was even larger for the mention network; the human mention network (4,978 nodes and 6,514 edges) was more than three times as large as the bot mention network (1,585 nodes 1,666 edges). We can see that bot networks are loosely connected with many singleton nodes, while human networks are highly connected with a very few singleton nodes. These network results are in line with what has been found for English bot networks BIBREF33 ."
+    ],
+    [
+      "To answer the question on how many hateful tweets were sent by bots rather than humans, we extend our analysis presented in Section SECREF22 to include all 1750 accounts with hateful tweets. Three accounts were suspended before we were able to collect their data, and thus they were excluded from this analysis. As we already have true scores for 450 accounts, we applied our regression model to the remaining 1,297 accounts with hateful tweets. Of the 1747 accounts with hateful tweets, we found that 185 (10.6%) accounts were more likely to be bots (predicted/true scores INLINEFORM0 2.5), and 1,562 (89.4%) accounts were more likely to be humans (predicted/true scores INLINEFORM1 2.5). Bots authored 238 hateful tweets (1.29 per-bot average rate), whereas humans authored 1,974 tweets (1.26 per-human average rate). The ratio of hateful tweets sent by bots to those sent by humans is 1:8. In particular, humans were responsible for 89.24% of all hateful tweets, while bots were responsible for 10.76% of all hateful tweets.",
+      "At the time of this writing (March 2019), we checked to see if the bots identified in our study were still active or not. We found that only 11% of them were suspended by Twitter. This indicates that the remaining 89% of the bots have lived for at least 1.4 years. In a recent study by Chavoshi et al. BIBREF29 , Twitter suspended 45% of the bots detected in their study within a three-month period. This shows that Arabic bots can go undetected for a long period of time."
+    ],
+    [
+      "Our analysis suggests that Arabic Twitter bots do have a role in the spread of religious hate on Arabic Twitter. In particular, bots were responsible for about 11% of all hateful tweets in the hate speech dataset. Our topic analysis showed that bots participate in highly controversial political discussions related to Israel/Palestine and Yemen. This builds on prior work that showed participation of Arabic bots, especially through the dissemination of highly polarizing tweets, during the Syrian civil war BIBREF16 .",
+      "Such political use of bots (i.e., disseminating hate speech and highly biased news) has been shown to be true for English bots as well. Bots on English Twitter have been used to promote jihadist propaganda BIBREF8 , BIBREF9 , spread fake news BIBREF6 , and infiltrate political discussions BIBREF7 . Bots have also been used for spamming in both Arabic BIBREF17 and English BIBREF47 Twitter networks. Other nefarious roles of bots that have been explored on English Twitter include manipulating the stock market and stealing personal data BIBREF4 . Unfortunately, there is a significant lack of Arabic-focused research to investigate other roles that can be played by bots in Arabic social networks. Our study serves as a starting point for understanding and detecting Arabic bots, demanding additional research to explore this understudied area.",
+      "While the social roles played by Arabic and English bots can be to some extent similar, our analysis showed that some Arabic bot characteristics are unique and different from English bots. As discussed in Section SECREF37 , Arabic bots in our dataset were posting original tweets more often than retweeting tweets. This found to be in contrast to English bots that tend to retweet more than posting original tweets BIBREF7 . We also showed that Arabic bots can live longer than English bots. Further, it has been shown that English bots tend to have fewer followers than humans BIBREF34 , BIBREF7 . This was not the case for Arabic bots. In our data set, bots on average have 81K followers (std = 588K), while humans on average have 7.5K (std = 25.5K). While manually studying accounts, we noticed that suspected bots tend to have a large number of fake followers to amplify their influence and reach. This use of bots (i.e., inflating popularity) has been found to be used by pro-ISIS Twitter accounts BIBREF8 , BIBREF9 . Another special consideration that must be taken into account when analyzing Arabic bots is that some Arabic users use third-party Islamic applications to post Quranic versus automatically on their behalf. This implies that even if some form of automation exists in an account, it doesn't necessarily mean that such an account is a bot.",
+      "The result of our regression model shows that Arabic bots can be identified with a high level of accuracy. Our feature analysis showed that bots in our dataset exhibit distinct behaviors and features. Unlike humans, bots tend to not communicate and engage in conversations with other accounts. This characteristic has been found to be true for English bots as well BIBREF33 . Significant differences appeared in the distribution of sources used by bots and humans, where we found that bots tend to use third-party applications more often than humans to keep their accounts flowing and active. We also found a significant difference in the distribution of topics discussed by bots and humans. Unlike bots, humans tend to discuss a wider range of topics.",
+      "We found linguistic features to be highly discriminatory in detecting Arabic bots. We showed that training the regression model on simple content and linguistic features outperformed Botometer by 20 points in Spearman's rho. This result emphasizes the importance of considering language-specific features in bot detection tasks. Important informative linguistic features include the use of numerics and emojis. We found that bots tend to include in their tweets less emojis and more numbers than humans. Other informative linguistic features include the average length of words and the average number of punctuations marks. Linguistic features especially deceptive language cues have been found to be highly discriminatory for distinguishing English bots as well BIBREF48 .",
+      "The topic of understanding online human behavior has been of a great interest to CSCW/HCI researchers in various contexts such as mental health BIBREF49 , BIBREF50 , political polarization BIBREF51 , BIBREF1 , and abusive social behaviors BIBREF52 , BIBREF53 . Our findings challenge the assumption often made by such studies that online social media content is always created by humans. We showed that the presence of bots can bias analysis results and disrupt people's online social experience. Platform designers should increase their efforts in combating malicious bots that compromise online democracy. Data scientists should also account for bots in their studies. In particular, Arabic social media studies that are focused on understanding the differences in behaviors and language use between humans and bots can benefit greatly from our bot detection model. For example, a recent study on English Twitter showed how trolls/bots, unlike humans, had been relying on the use of a deceptive/persuasive language in an effort to manipulate the 2016 U.S. elections BIBREF48 . Having a bot detection tool fitted for Arabic such as the one presented in this paper would make such studies possible in Arabic online social spaces.",
+      "While our results mark an important step toward detecting and understanding Arabic bots, our work has potential limitations. First, despite that our model provides a promising performance on detecting current bots, it needs to be updated regularly with new bot examples in order to capture the continuous and inevitable changes in bot behaviors and characteristics. Second, bots in our study were limited to bots that had a role in spreading religious hatred. It will be worth studying Arabic Twitter bots with a wider range of malicious activities and investigate common features among them. Additionally, it may be useful in future works to investigate a larger set of features (e.g., temporal features and features extracted from followers and friends). It will also be important to investigate the efficacy of combining supervised and unsupervised methods to reduce the high cost of manual labeling without sacrificing much of the accuracy.",
+      "Another important future direction is to investigate the impact of bots on human behavior. In particular, it would be valuable to investigate whether bot-disseminated hateful tweets influence/encourage humans to participate in such discourse either through liking, retweeting, or even authoring new hateful tweets. In a political context, this kind of influence has been shown to exist; Twitter reported that nearly 1.4 million human accounts have made some sort of interaction with content created by bots/trolls during the 2016 U.S. election BIBREF28 . If this bot impact on humans can be shown to be effective in the context of hate speech, a more important question would be, can bots be used to decrease online hate speech? In other words, would creating \u201cgood\" bots that promote tolerance, acceptance, and diversity values in Arabic social media make an impact on humans? The effect of social norms on prejudice is strongly supported in social psychological literature BIBREF54 , BIBREF55 . Studies have also shown that people conform to perceived cultural norm of prejudice and that norms can be influenced BIBREF56 . Thus, a more focused question would be, can we leverage bots in online social space to positively influence perceived social norms, which would then make people less prejudiced toward other religious groups? A body of CSCW/HCI research has explored the impact of perceived norms on shaping behavior BIBREF57 , BIBREF58 , BIBREF59 , and thus the potential of bots for positive behavior change is certainly worth investigating in future studies."
+    ],
+    [
+      "In this paper, we have investigated the role of bots in spreading hateful messages on Arabic Twitter. We found that bots were responsible for 11% of hateful tweets in the hate speech dataset. We further showed that English-trained bot detection models deliver a moderate performance in detecting Arabic bots. Therefore, we developed a more accurate bot detection model trained on various sets of features extracted from 86,346 tweets disseminated by 450 manually-labeled accounts. Finally, we presented a thorough analysis of characteristics and behaviors that distinguish Arabic bots from English Bots and from humans in general. Our results facilitate future Arabic bot detection research in contexts beyond spread of religious hate."
+    ]
+  ]
+}
+```
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+Name of Paper: SIM: A Slot-Independent Neural Model for Dialogue State Tracking
+
+Question: How do they prevent the model complexity increasing with the increased number of slots?
\ No newline at end of file
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+++ b/qasper-1273/instruction.md
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+Name of Paper: Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning
+
+Question: How do they evaluate their sentence representations?
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+Name of Paper: Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning
+
+Question: Which model architecture do they for sentence encoding?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Sequence-to-Sequence Learning",
+    "Multi-task Sequence-to-sequence Learning",
+    "Training Objectives & Evaluation",
+    "Multi-task training setup",
+    "Evaluation Strategies, Experimental Results & Discussion",
+    "Evaluation Strategy",
+    "Experimental Results & Discussion",
+    "Conclusion & Future Work",
+    "Acknowledgements",
+    "Model Training",
+    "Vocabulary Expansion & Representation Pooling",
+    "Multi-task model details",
+    "Description of evaluation tasks",
+    "Text Classification",
+    "Paraphrase Identification",
+    "Entailment and Semantic Relatedness",
+    "Semantic Textual Similarity",
+    "Image-caption retrieval",
+    "Quora Duplicate Question Classification",
+    "Sentence Characteristics & Syntax"
+  ],
+  "paragraphs": [
+    [
+      "Transfer learning has driven a number of recent successes in computer vision and NLP. Computer vision tasks like image captioning BIBREF0 and visual question answering typically use CNNs pretrained on ImageNet BIBREF1 , BIBREF2 to extract representations of the image, while several natural language tasks such as reading comprehension and sequence labeling BIBREF3 have benefited from pretrained word embeddings BIBREF4 , BIBREF5 that are either fine-tuned for a specific task or held fixed.",
+      "Many neural NLP systems are initialized with pretrained word embeddings but learn their representations of words in context from scratch, in a task-specific manner from supervised learning signals. However, learning these representations reliably from scratch is not always feasible, especially in low-resource settings, where we believe that using general purpose sentence representations will be beneficial.",
+      "Some recent work has addressed this by learning general-purpose sentence representations BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 . However, there exists no clear consensus yet on what training objective or methodology is best suited to this goal.",
+      "Understanding the inductive biases of distinct neural models is important for guiding progress in representation learning. BIBREF14 and BIBREF15 demonstrate that neural machine translation (NMT) systems appear to capture morphology and some syntactic properties. BIBREF14 also present evidence that sequence-to-sequence parsers BIBREF16 more strongly encode source language syntax. Similarly, BIBREF17 probe representations extracted by sequence autoencoders, word embedding averages, and skip-thought vectors with a multi-layer perceptron (MLP) classifier to study whether sentence characteristics such as length, word content and word order are encoded.",
+      "To generalize across a diverse set of tasks, it is important to build representations that encode several aspects of a sentence. Neural approaches to tasks such as skip-thoughts, machine translation, natural language inference, and constituency parsing likely have different inductive biases. Our work exploits this in the context of a simple one-to-many multi-task learning (MTL) framework, wherein a single recurrent sentence encoder is shared across multiple tasks. We hypothesize that sentence representations learned by training on a reasonably large number of weakly related tasks will generalize better to novel tasks unseen during training, since this process encodes the inductive biases of multiple models. This hypothesis is based on the theoretical work of BIBREF18 . While our work aims at learning fixed-length distributed sentence representations, it is not always practical to assume that the entire \u201cmeaning\u201d of a sentence can be encoded into a fixed-length vector. We merely hope to capture some of its characteristics that could be of use in a variety of tasks.",
+      "The primary contribution of our work is to combine the benefits of diverse sentence-representation learning objectives into a single multi-task framework. To the best of our knowledge, this is the first large-scale reusable sentence representation model obtained by combining a set of training objectives with the level of diversity explored here, i.e. multi-lingual NMT, natural language inference, constituency parsing and skip-thought vectors. We demonstrate through extensive experimentation that representations learned in this way lead to improved performance across a diverse set of novel tasks not used in the learning of our representations. Such representations facilitate low-resource learning as exhibited by significant improvements to model performance for new tasks in the low labelled data regime - achieving comparable performance to a few models trained from scratch using only 6% of the available training set on the Quora duplicate question dataset."
+    ],
+    [
+      "The problem of learning distributed representations of phrases and sentences dates back over a decade. For example, BIBREF19 present an additive and multiplicative linear composition function of the distributed representations of individual words. BIBREF20 combine symbolic and distributed representations of words using tensor products. Advances in learning better distributed representations of words BIBREF4 , BIBREF5 combined with deep learning have made it possible to learn complex non-linear composition functions of an arbitrary number of word embeddings using convolutional or recurrent neural networks (RNNs). A network's representation of the last element in a sequence, which is a non-linear composition of all inputs, is typically assumed to contain a squashed \u201csummary\u201d of the sentence. Most work in supervised learning for NLP builds task-specific representations of sentences rather than general-purpose ones.",
+      "Notably, skip-thought vectors BIBREF6 , an extension of the skip-gram model for word embeddings BIBREF4 to sentences, learn re-usable sentence representations from weakly labeled data. Unfortunately, these models take weeks or often months to train. BIBREF8 address this by considering faster alternatives such as sequential denoising autoencoders and shallow log-linear models. BIBREF21 , however, demonstrate that simple word embedding averages are comparable to more complicated models like skip-thoughts. More recently, BIBREF9 show that a completely supervised approach to learning sentence representations from natural language inference data outperforms all previous approaches on transfer learning benchmarks. Here we use the terms \u201ctransfer learning performance\" on \u201ctransfer tasks\u201d to mean the performance of sentence representations evaluated on tasks unseen during training. BIBREF10 demonstrated that representations learned by state-of-the-art large-scale NMT systems also generalize well to other tasks. However, their use of an attention mechanism prevents the learning of a single fixed-length vector representation of a sentence. As a result, they present a bi-attentive classification network that composes information present in all of the model's hidden states to achieve improvements over a corresponding model trained from scratch. BIBREF11 and BIBREF12 demonstrate that discourse-based objectives can also be leveraged to learn good sentence representations.",
+      "Our work is most similar to that of BIBREF22 , who train a many-to-many sequence-to-sequence model on a diverse set of weakly related tasks that includes machine translation, constituency parsing, image captioning, sequence autoencoding, and intra-sentence skip-thoughts. There are two key differences between that work and our own. First, like BIBREF10 , their use of an attention mechanism prevents learning a fixed-length vector representation for a sentence. Second, their work aims for improvements on the same tasks on which the model is trained, as opposed to learning re-usable sentence representations that transfer elsewhere.",
+      "We further present a fine-grained analysis of how different tasks contribute to the encoding of different information signals in our representations following work by BIBREF14 and BIBREF17 .",
+      " BIBREF23 similarly present a multi-task framework for textual entailment with task supervision at different levels of learning. \u201cUniversal\" multi-task models have also been successfully explored in the context of computer vision problems BIBREF24 , BIBREF25 ."
+    ],
+    [
+      "Five out of the six tasks that we consider for multi-task learning are formulated as sequence-to-sequence problems BIBREF26 , BIBREF27 . Briefly, sequence-to-sequence models are a specific case of encoder-decoder models where the inputs and outputs are sequential. They directly model the conditional distribution of outputs given inputs INLINEFORM0 . The input INLINEFORM1 and output INLINEFORM2 are sequences INLINEFORM3 and INLINEFORM4 . The encoder produces a fixed length vector representation INLINEFORM5 of the input, which the decoder then conditions on to generate an output. The decoder is auto-regressive and breaks down the joint probability of outputs into a product of conditional probabilities via the chain rule: INLINEFORM6 ",
+      " BIBREF26 and BIBREF27 use encoders and decoders parameterized as RNN variants such as Long Short-term Memory (LSTMs) BIBREF28 or Gated Recurrent Units (GRUs) BIBREF29 . The hidden representation INLINEFORM0 is typically the last hidden state of the encoder RNN.",
+      " BIBREF30 alleviate the gradient bottleneck between the encoder and the decoder by introducing an attention mechanism that allows the decoder to condition on every hidden state of the encoder RNN instead of only the last one. In this work, as in BIBREF6 , BIBREF8 , we do not employ an attention mechanism. This enables us to obtain a single, fixed-length, distributed sentence representation. To diminish the effects of vanishing gradient, we condition every decoding step on the encoder hidden representation INLINEFORM0 . We use a GRU for the encoder and decoder in the interest of computational speed. The encoder is a bidirectional GRU while the decoder is a unidirectional conditional GRU whose parameterization is as follows: DISPLAYFORM0 ",
+      "The encoder representation INLINEFORM0 is provided as conditioning information to the reset gate, update gate and hidden state computation in the GRU via the parameters INLINEFORM1 , INLINEFORM2 and INLINEFORM3 to avoid attenuation of information from the encoder."
+    ],
+    [
+      " BIBREF31 present a simple one-to-many multi-task sequence-to-sequence learning model for NMT that uses a shared encoder for English and task-specific decoders for multiple target languages. BIBREF22 extend this by also considering many-to-one (many encoders, one decoder) and many-to-many architectures. In this work, we consider a one-to-many model since it lends itself naturally to the idea of combining inductive biases from different training objectives. The same bidirectional GRU encodes the input sentences from different tasks into a compressed summary INLINEFORM0 which is then used to condition a task-specific GRU to produce the output sentence."
+    ],
+    [
+      "Our motivation for multi-task training stems from theoretical insights presented in BIBREF18 . We refer readers to that work for a detailed discussion of results, but the conclusions most relevant to this discussion are (i) that learning multiple related tasks jointly results in good generalization as measured by the number of training examples required per task; and (ii) that inductive biases learned on sufficiently many training tasks are likely to be good for learning novel tasks drawn from the same environment.",
+      "We select the following training objectives to learn general-purpose sentence embeddings. Our desiderata for the task collection were: sufficient diversity, existence of fairly large datasets for training, and success as standalone training objectives for sentence representations."
+    ],
+    [
+      "Multi-task training with different data sources for each task stills poses open questions. For example: When does one switch to training on a different task? Should the switching be periodic? Do we weight each task equally? If not, what training ratios do we use?",
+      " BIBREF31 use periodic task alternations with equal training ratios for every task. In contrast, BIBREF22 alter the training ratios for each task based on the size of their respective training sets. Specifically, the training ratio for a particular task, INLINEFORM0 , is the fraction of the number of training examples in that task to the total number of training samples across all tasks. The authors then perform INLINEFORM1 parameter updates on task INLINEFORM2 before selecting a new task at random proportional to the training ratios, where N is a predetermined constant.",
+      "We take a simpler approach and pick a new sequence-to-sequence task to train on after every parameter update sampled uniformly. An NLI minibatch is interspersed after every ten parameter updates on sequence-to-sequence tasks (this was chosen so as to complete roughly 6 epochs of the dataset after 7 days of training). Our approach is described formally in the Algorithm below.",
+      "Model details can be found in section SECREF7 in the Appendix.",
+      "0ptcenterline ",
+      "",
+      "A set of INLINEFORM0 tasks with a common source language, a shared encoder INLINEFORM1 across all tasks and a set of INLINEFORM2 task specific decoders INLINEFORM3 . Let INLINEFORM4 denote each model's parameters, INLINEFORM5 a probability vector ( INLINEFORM6 ) denoting the probability of sampling a task such that INLINEFORM7 , datasets for each task INLINEFORM8 and a loss function INLINEFORM9 .",
+      "",
+      " INLINEFORM0 has not converged [1] Sample task INLINEFORM1 . Sample input, output pairs INLINEFORM2 . Input representation INLINEFORM3 . Prediction INLINEFORM4 INLINEFORM5 Adam INLINEFORM6 .",
+      "",
+      " "
+    ],
+    [
+      "In this section, we describe our approach to evaluate the quality of our learned representations, present the results of our evaluation and discuss our findings."
+    ],
+    [
+      "We follow a similar evaluation protocol to those presented in BIBREF6 , BIBREF8 , BIBREF9 which is to use our learned representations as features for a low complexity classifier (typically linear) on a novel supervised task/domain unseen during training without updating the parameters of our sentence representation model. We also consider such a transfer learning evaluation in an artificially constructed low-resource setting. In addition, we also evaluate the quality of our learned individual word representations using standard benchmarks BIBREF36 , BIBREF37 .",
+      "The choice of transfer tasks and evaluation framework are borrowed largely from BIBREF9 . We provide a condensed summary of the tasks in section SECREF10 in the Appendix but refer readers to their paper for a more detailed description.",
+      "https://github.com/kudkudak/word-embeddings-benchmarks/wiki"
+    ],
+    [
+      "Table 2 presents the results of training logistic regression on 10 different supervised transfer tasks using different fixed-length sentence representation. Supervised approaches trained from scratch on some of these tasks are also presented for comparison. We present performance ablations when adding more tasks and increasing the number of hidden units in our GRU (+L). Ablation specifics are presented in section SECREF9 of the Appendix.",
+      "It is evident from Table 2 that adding more tasks improves the transfer performance of our model. Increasing the capacity our sentence encoder with more hidden units (+L) as well as an additional layer (+2L) also lead to improved transfer performance. We observe gains of 1.1-2.0% on the sentiment classification tasks (MR, CR, SUBJ & MPQA) over Infersent. We demonstrate substantial gains on TREC (6% over Infersent and roughly 2% over the CNN-LSTM), outperforming even a competitive supervised baseline. We see similar gains (2.3%) on paraphrase identification (MPRC), closing the gap on supervised approaches trained from scratch. The addition of constituency parsing improves performance on sentence relatedness (SICK-R) and entailment (SICK-E) consistent with observations made by BIBREF48 .",
+      "In Table TABREF19 , we show that simply training an MLP on top of our fixed sentence representations outperforms several strong & complex supervised approaches that use attention mechanisms, even on this fairly large dataset. For example, we observe a 0.2-0.5% improvement over the decomposable attention model BIBREF49 . When using only a small fraction of the training data, indicated by the columns 1k-25k, we are able to outperform the Siamese and Multi-Perspective CNN using roughly 6% of the available training set. We also outperform the Deconv LVM model proposed by BIBREF47 in this low-resource setting.",
+      "Unlike BIBREF9 , who use pretrained GloVe word embeddings, we learn our word embeddings from scratch. Somewhat surprisingly, in Table TABREF18 we observe that the learned word embeddings are competitive with popular methods such as GloVe, word2vec, and fasttext BIBREF50 on the benchmarks presented by BIBREF36 and BIBREF37 .",
+      "In Table TABREF20 , we probe our sentence representations to determine if certain sentence characteristics and syntactic properties can be inferred following work by BIBREF17 and BIBREF14 . We observe that syntactic properties are better encoded with the addition of multi-lingual NMT and parsing. Representations learned solely from NLI do appear to encode syntax but incorporation into our multi-task framework does not amplify this signal. Similarly, we observe that sentence characteristics such as length and word order are better encoded with the addition of parsing.",
+      "In Appendix Table TABREF30 , we note that our sentence representations outperform skip-thoughts and are on par with Infersent for image-caption retrieval. We also observe that comparing sentences using cosine similarities correlates reasonably well with their relatedness on semantic textual similarity benchmarks (Appendix Table TABREF31 ).",
+      "We also present qualitative analysis of our learned representations by visualizations using dimensionality reduction techniques (Figure FIGREF11 ) and nearest neighbor exploration (Appendix Table TABREF32 ). Figure FIGREF11 shows t-sne plots of our sentence representations on three different datasets - SUBJ, TREC and DBpedia. DBpedia is a large corpus of sentences from Wikipedia labeled by category and used by BIBREF51 . Sentences appear to cluster reasonably well according to their labels. The clustering also appears better than that demonstrated in Figure 2 of BIBREF6 on TREC and SUBJ. Appendix Table TABREF32 contains sentences from the BookCorpus and their nearest neighbors. Sentences with some lexical overlap and similar discourse structure appear to be clustered together."
+    ],
+    [
+      "We present a multi-task framework for learning general-purpose fixed-length sentence representations. Our primary motivation is to encapsulate the inductive biases of several diverse training signals used to learn sentence representations into a single model. Our multi-task framework includes a combination of sequence-to-sequence tasks such as multi-lingual NMT, constituency parsing and skip-thought vectors as well as a classification task - natural language inference. We demonstrate that the learned representations yield competitive or superior results to previous general-purpose sentence representation methods. We also observe that this approach produces good word embeddings.",
+      "In future work, we would like understand and interpret the inductive biases that our model learns and observe how it changes with the addition of different tasks beyond just our simple analysis of sentence characteristics and syntax. Having a rich, continuous sentence representation space could allow the application of state-of-the-art generative models of images such as that of BIBREF52 to language. One could also consider controllable text generation by directly manipulating the sentence representations and realizing it by decoding with a conditional language model."
+    ],
+    [
+      "The authors would like to thank Chinnadhurai Sankar, Sebastian Ruder, Eric Yuan, Tong Wang, Alessandro Sordoni, Guillaume Lample and Varsha Embar for useful discussions. We are also grateful to the PyTorch development team BIBREF53 . We thank NVIDIA for donating a DGX-1 computer used in this work and Fonds de recherche du Qu\u00e9bec - Nature et technologies for funding."
+    ],
+    [
+      "We present some architectural specifics and training details of our multi-task framework. Our shared encoder uses a common word embedding lookup table and GRU. We experiment with unidirectional, bidirectional and 2 layer bidirectional GRUs (details in Appendix section SECREF9 ). For each task, every decoder has its separate word embedding lookups, conditional GRUs and fully connected layers that project the GRU hidden states to the target vocabularies. The last hidden state of the encoder is used as the initial hidden state of the decoder and is also presented as input to all the gates of the GRU at every time step. For natural language inference, the same encoder is used to encode both the premise and hypothesis and a concatenation of their representations along with the absolute difference and hadamard product (as described in BIBREF9 ) are given to a single layer MLP with a dropout BIBREF55 rate of 0.3. All models use word embeddings of 512 dimensions and GRUs with either 1500 or 2048 hidden units. We used minibatches of 48 examples and the Adam BIBREF54 optimizer with a learning rate of 0.002. Models were trained for 7 days on an Nvidia Tesla P100-SXM2-16GB GPU. While BIBREF6 report close to a month of training, we only train for 7 days, made possible by advancements in GPU hardware and software (cuDNN RNNs).",
+      "We did not tune any of the architectural details and hyperparameters owing to the fact that we were unable to identify any clear criterion on which to tune them. Gains in performance on a specific task do not often translate to better transfer performance."
+    ],
+    [
+      "In addition to performing 10-fold cross-validation to determine the L2 regularization penalty on the logistic regression models, we also tune the way in which our sentence representations are generated from the hidden states corresponding to words in a sentence. For example, BIBREF6 use the last hidden state while BIBREF9 perform max-pooling across all of the hidden states. We consider both of these approaches and pick the one with better performance on the validation set. We note that max-pooling works best on sentiment tasks such as MR, CR, SUBJ and MPQA, while the last hidden state works better on all other tasks.",
+      "We also employ vocabulary expansion on all tasks as in BIBREF6 by training a linear regression to map from the space of pre-trained word embeddings (GloVe) to our model's word embeddings."
+    ],
+    [
+      "This section describes the specifics our multi-task ablations in the experiments section. These definitions hold for all tables except for TABREF18 and TABREF20 . We refer to skip-thought next as STN, French and German NMT as Fr and De, natural language inference as NLI, skip-thought previous as STP and parsing as Par.",
+      "+STN +Fr +De : The sentence representation INLINEFORM0 is the concatenation of the final hidden vectors from a forward GRU with 1500-dimensional hidden vectors and a bidirectional GRU, also with 1500-dimensional hidden vectors.",
+      "+STN +Fr +De +NLI : The sentence representation INLINEFORM0 is the concatenation of the final hidden vectors from a bidirectional GRU with 1500-dimensional hidden vectors and another bidirectional GRU with 1500-dimensional hidden vectors trained without NLI.",
+      "+STN +Fr +De +NLI +L : The sentence representation INLINEFORM0 is the concatenation of the final hidden vectors from a bidirectional GRU with 2048-dimensional hidden vectors and another bidirectional GRU with 2048-dimensional hidden vectors trained without NLI.",
+      "+STN +Fr +De +NLI +L +STP : The sentence representation INLINEFORM0 is the concatenation of the final hidden vectors from a bidirectional GRU with 2048-dimensional hidden vectors and another bidirectional GRU with 2048-dimensional hidden vectors trained without STP.",
+      "+STN +Fr +De +NLI +2L +STP : The sentence representation INLINEFORM0 is the concatenation of the final hidden vectors from a 2-layer bidirectional GRU with 2048-dimensional hidden vectors and a 1-layer bidirectional GRU with 2048-dimensional hidden vectors trained without STP.",
+      "+STN +Fr +De +NLI +L +STP +Par : The sentence representation INLINEFORM0 is the concatenation of the final hidden vectors from a bidirectional GRU with 2048-dimensional hidden vectors and another bidirectional GRU with 2048-dimensional hidden vectors trained without Par.",
+      "In tables TABREF18 and TABREF20 we do not concatenate the representations of multiple models."
+    ],
+    [
+      " BIBREF6 and BIBREF9 provide a detailed description of tasks that are typically used to evaluate sentence representations. We provide a condensed summary and refer readers to their work for a more thorough description."
+    ],
+    [
+      "We evaluate on text classification benchmarks - sentiment classification on movie reviews (MR), product reviews (CR) and Stanford sentiment (SST), question type classification (TREC), subjectivity/objectivity classification (SUBJ) and opinion polarity (MPQA). Representations are used to train a logistic regression classifier with 10-fold cross validation to tune the L2 weight penalty. The evaluation metric for all these tasks is classification accuracy."
+    ],
+    [
+      "We also evaluate on pairwise text classification tasks such as paraphrase identification on the Microsoft Research Paraphrase Corpus (MRPC) corpus. This is a binary classification problem to identify if two sentences are paraphrases of each other. The evaluation metric is classification accuracy and F1."
+    ],
+    [
+      "To test if similar sentences share similar representations, we evaluate on the SICK relatedness (SICK-R) task where a linear model is trained to output a score from 1 to 5 indicating the relatedness of two sentences. We also evaluate using the entailment labels in the same dataset (SICK-E) which is a binary classification problem. The evaluation metric for SICK-R is Pearson correlation and classification accuracy for SICK-E."
+    ],
+    [
+      "In this evaluation, we measure the relatedness of two sentences using only the cosine similarity between their representations. We use the similarity textual similarity (STS) benchmark tasks from 2012-2016 (STS12, STS13, STS14, STS15, STS16, STSB). The STS dataset contains sentences from a diverse set of data sources. The evaluation criteria is Pearson correlation."
+    ],
+    [
+      "Image-caption retrieval is typically formulated as a ranking task wherein images are retrieved and ranked based on a textual description and vice-versa. We use 113k training images from MSCOCO with 5k images for validation and 5k for testing. Image features are extracted using a pre-trained 110 layer ResNet. The evaluation criterion is Recall@K and the median K across 5 different splits of the data."
+    ],
+    [
+      "In addition to the above tasks which were considered by BIBREF9 , we also evaluate on the recently published Quora duplicate question dataset since it is an order of magnitude larger than the others (approximately 400,000 question pairs). The task is to correctly identify question pairs that are duplicates of one another, which we formulate as a binary classification problem. We use the same data splits as in BIBREF45 . Given the size of this data, we consider a more expressive classifier on top of the representations of both questions. Specifically, we train a 4 layer MLP with 1024 hidden units, with a dropout rate of 0.5 after every hidden layer. The evaluation criterion is classification accuracy. We also artificially create a low-resource setting by reducing the number of training examples between 1,000 and 25,000 using the same splits as BIBREF47 ."
+    ],
+    [
+      "In an attempt to understand what information is encoded in by sentence representations, we consider six different classification tasks where the objective is to predict sentence characteristics such as length, word content and word order BIBREF17 or syntactic properties such as active/passive, tense and the top syntactic sequence (TSS) from the parse tree of a sentence BIBREF14 .",
+      "The sentence characteristic tasks are setup in the same way as described in BIBREF17 . The length task is an 8-way classification problem where sentence lengths are binned into 8 ranges. The content task is formulated as a binary classification problem that takes a concatenation of a sentence representation INLINEFORM0 and a word representation INLINEFORM1 to determine if the word is contained in the sentence. The order task is an extension of the content task where a concatenation of the sentence representation and word representations of two words in sentence is used to determine if the first word occurs before or after the second. We use a random subset of the 1-billion-word dataset for these experiments that were not used to train our multi-task representations.",
+      "The syntactic properties tasks are setup in the same way as described in BIBREF14 .The passive and tense tasks are characterized as binary classification problems given a sentence's representation. The former's objective is to determine if a sentence is written in active/passive voice while the latter's objective is to determine if the sentence is in the past tense or not. The top syntactic sequence (TSS) is a 20-way classification problem with 19 most frequent top syntactic sequences and 1 miscellaneous class. We use the same dataset as the authors but different training, validation and test splits."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: How to Do Simultaneous Translation Better with Consecutive Neural Machine Translation?
+
+Question: What corpora is used?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Background",
+    "Simultaneous NMT",
+    "Simultaneous NMT ::: Prefix Translation",
+    "Simultaneous NMT ::: Stopping Criterion",
+    "Simultaneous NMT ::: Stopping Criterion ::: Length and EOS Control",
+    "Simultaneous NMT ::: Stopping Criterion ::: Learning When to Stop",
+    "Simultaneous NMT ::: Stopping Criterion ::: Learning When to Stop ::: Reward",
+    "Simultaneous NMT ::: Stopping Criterion ::: Learning When to Stop ::: Policy Gradient",
+    "Experiments ::: Settings ::: Dataset",
+    "Experiments ::: Settings ::: Pretrained NMT Model",
+    "Experiments ::: Settings ::: TN Controller",
+    "Experiments ::: Settings ::: Baseline",
+    "Experiments ::: Results",
+    "Experiments ::: Analyze",
+    "Experiments ::: Translation Examples",
+    "Related Work",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Simultaneous translation BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5, the task of producing a partial translation of a sentence before the whole input sentence ends, is useful in many scenarios including outbound tourism, international summit and multilateral negotiations. Different from the consecutive translation in which translation quality alone matters, simultaneous translation trades off between translation quality and latency. The syntactic structure difference between the source and target language makes simultaneous translation more challenging. For example, when translating from a verb-final (SOV) language (e.g., Japanese) to a verb-media (SVO) language (e.g., English), the verb appears much later in the source sequence than in the target language. Some premature translations can lead to significant loss in quality BIBREF5.",
+      "Recently, a number of researchers have endeavored to explore methods for simultaneous translation in the context of NMT BIBREF6, BIBREF7, BIBREF8, BIBREF9. Some of them propose sophisticated training frameworks explicitly designed for simultaneous translation BIBREF5, BIBREF10. These approaches are either memory inefficient during training BIBREF5 or hard to implement BIBREF10. Others utilize a full-sentence base model to perform simultaneous translation by modifications to the encoder and the decoding process. To match the incremental source context, they replace the bidirectional encoder with a left-to-right encoder BIBREF3, BIBREF11, BIBREF4, BIBREF12 or recompute the encoder hidden states BIBREF13. On top of that, heuristic algorithms BIBREF3, BIBREF14 or a READ/WRITE model trained with reinforcement learning BIBREF11, BIBREF4, BIBREF12 or supervised learning BIBREF13 are used to decide, at every step, whether to wait for the next source token or output a target token. However, these models either cannot directly use a pretrained vanilla CNMT model with bidirectional encoder as the base model or work in a sub-optimal way in the decoding stage.",
+      "In this paper, we study the problem of how to do simultaneous translation better with a pretrained vanilla CNMT model. We formulate simultaneous translation as two nested loops: an outer loop that updates input buffer with newly observed source tokens and an inner loop that translates source tokens in the buffer updated at each outer step. For the outer loop, the input buffer can be updated by an ASR system with an arbitrary update schedule. For the inner loop, we perform prefix translation using the pretrained CNMT model with dynamically built encoder and decoder hidden states. We also design two novel stopping criteria for the inner loop: Length and EOS (LE) controller that stops with heuristics, and Trainable (TN) controller that learns to stop with a better quality and latency balance. We evaluate our method on IWSLT16 German-English (DE-EN) translation in both directions, WMT15 English-German (EN-DE) translation in both directions, and NIST Chinese-to-English (ZH$\\rightarrow $EN) translation. The result shows our method consistently improves over the de-facto baselines, and achieves low latency and reasonable BLEU scores."
+    ],
+    [
+      "Given a set of source\u2013target sentence pairs $\\left\\langle \\mathbf {x}_m,\\mathbf {y}^*_m\\right\\rangle _{m=1}^M$, a consecutive NMT model can be trained by maximizing the log-likelihood of the target sentence from its entire source side context:",
+      "where $\\phi $ is a set of model parameters. At inference time, the NMT model first encodes a source language sentence $\\mathbf {x}=\\lbrace x_1,...,x_{T_\\eta }\\rbrace $ with its encoder and passes the encoded representations $\\mathbf {h}=\\lbrace h_1,...,h_{T_\\eta }\\rbrace $ to a greedy decoder. Then the greedy decoder generates a translated sentence in the target language by sequentially choosing the most likely token at each step $t$:",
+      "The distribution of next target word is defined as:",
+      "where $z_{t}$ is the decoder hidden state at position $t$. In consecutive NMT, once obtained, the encoder hidden states $\\mathbf {h}$ and the decoder hidden state $z_t$ are not updated anymore and will be reused during the entire decoding process."
+    ],
+    [
+      "In SNMT, we receive streaming input tokens, and learn to translate them in real-time. We formulate simultaneous translation as two nested loops: the outer loop that updates an input buffer with newly observed source tokens and the inner loop that translates source tokens in the buffer updated at each outer step.",
+      "More precisely, suppose at the end of an outer step $s-1$, the input buffer is $\\mathbf {x}^{s-1} = \\lbrace x_1, ..., x_{\\eta \\left[ s-1\\right]}\\rbrace $, and the output buffer is $\\mathbf {y}^{s-1} = \\lbrace y_1, ..., y_{\\tau \\left[ s-1\\right]}\\rbrace $. Then at outer step $s$, the system translates with the following steps:",
+      "The system observes $c_s > 0$ new source tokens and updates the input buffer to be $\\mathbf {x}^{s} = \\lbrace x_1, ..., x_{\\eta \\left[ s\\right]}\\rbrace $ where $\\eta \\left[ s\\right]=\\eta \\left[ s-1\\right]+c_s$.",
+      "Then, the system starts inner loop translation and writes $w_s>=0$ target tokens to the output buffer. The output buffer is updated to be $\\mathbf {y}^{s} = \\lbrace y_1, ..., y_{\\tau \\left[ s\\right]}\\rbrace $ where $\\tau \\left[ s\\right]=\\tau \\left[ s-1\\right]+w_s$.",
+      "The simultaneous decoding process continues until no more source tokens are added in the outer loop. We define the last outer step as the terminal outer step $S$, and other outer steps as non-terminal outer steps.",
+      "For the outer loop, we make no assumption about the value of $c_s$, while all previous work assumes $c_s=1$. This setting is more realistic because a) increasing $c_s$ can reduce the number of outer steps, thus reducing computation cost; b) in a real speech translation application, an ASR system may generate multiple tokens at a time.",
+      "For the inner loop, we adapt a pretrained vanilla CNMT model to perform partial translation with two important concerns:",
+      "Prefix translation: given a source prefix $\\mathbf {x}^s = \\lbrace x_1, ..., x_{\\eta \\left[ s\\right]}\\rbrace $ and a target prefix $\\mathbf {y}^s_{\\tau \\left[ s-1\\right]} = \\lbrace y_1, ..., y_{\\tau \\left[ s-1\\right]}\\rbrace $, how to predict the remaining target tokens?",
+      "Stopping criterion: since the NMT model is trained with full sentences, how to design the stopping criterion for it when translating partial source sentcnes?"
+    ],
+    [
+      "At an outer step $s$, given encoder hidden states $\\mathbf {h}^s$ for source prefix $\\mathbf {x}^s= \\lbrace x_1, ..., x_{\\eta \\left[ s\\right]}\\rbrace $ and decoder hidden states $\\mathbf {z}_{\\tau \\left[ s\\right]-1}^s$ for target prefix $\\mathbf {y}_{\\tau \\left[ s-1\\right]}^s= \\lbrace y_1, ..., y_{\\tau \\left[ s-1\\right]}\\rbrace $, we perform prefix translation sequentially with a greedy decoder:",
+      "where $t$ starts from $t=\\tau \\left[ s-1\\right]+1$. The prefix translation terminates when a stopping criterion meets, yielding a translation $\\mathbf {y}^s = \\lbrace y_1, ..., y_{\\tau \\left[ s\\right]}\\rbrace $.",
+      "However, a major problem comes from the above translation method: how can we obtain the encoder hidden states $\\mathbf {h}^s$ and decoder hidden states $\\mathbf {z}_{\\tau \\left[ s\\right]-1}^s$ at the beginning of prefix translation? In CNMT, the encoder hidden states and previous decoder hidden states are reused at each decoding time step. Different from CNMT, SNMT is fed with an incremental source side context. On the encoder side, we can address this by either reusing previous encoder hidden states BIBREF3, BIBREF4, BIBREF14, BIBREF12:",
+      "or dynamically re-building all encoder hidden states BIBREF5:",
+      "On the decoder side, since the encoder hidden states have been updated from $\\mathbf {h}^{s-1}$ to $\\mathbf {h}^s$, we can choose to reuse previous decoder hidden states BIBREF3, BIBREF4, BIBREF14, BIBREF5:",
+      "or rebuild all previous decoder hidden states from current encoder hidden states $\\mathbf {h}^s$ with force decoding:",
+      "To better predict the remaining target tokens, we rebuild all encoder and decoder hidden states following Eq. DISPLAY_FORM11 and DISPLAY_FORM13 at the beginning of prefix translation. This strategy ensures that all encoder and decoder hidden states are obtained by attending to the same source tokens, which is consistent with how encoder and decoder hidden states are computed at training time. Besides, these attainable source tokens are all available source context at current time. Compared with using Eq. DISPLAY_FORM10 or DISPLAY_FORM12, our method can potentially better utilize the available source context."
+    ],
+    [
+      "In consecutive NMT, the decoding algorithm such as greedy decoding or beam search terminates when the translator predicts an EOS token or the length of the translation meets a predefined threshold\uff1a",
+      "where $\\text{maxlen}$, $u$ and $v$ are all hyper-parameters. In fairseq-py, they set $\\text{maxlen}=+\\infty $, $u=0$ and $v=200$ at inference time by default. The decoding for most source sentences terminates when the translator predicts the EOS token. In simultaneous decoding, since we use a NMT model pretrained on full sentences to translate partial source sentences, it tends to predict EOS when the source context has been fully translated. However, such strategy could be too aggressive for simultaneous translation. Fig. FIGREF18 shows such an example. At outer step 2, the translator predicts \u201cyou EOS\", emiting target token \u201cyou\". However, \u201cyou\" is not the expected translation for \u201c\u4f60\" in the context of \u201c\u4f60\u597d\u3002\". The right decision is that prefix translation at outer step 2 should stop without emitting any words.",
+      "To alleviate such problems and do better simultaneous translation with pretrained CNMT model, we propose two novel stopping criteria for prefix translation."
+    ],
+    [
+      "In consecutive translation, the decoding process stops mainly when predicting EOS. In contrast, for prefix translation at non-terminal outer step, we use both length and EOS to stop the prefix translation process. We achieve this by setting the hyper-parameters in Eq. DISPLAY_FORM15 as $\\text{maxlen}=+\\infty $, $u=1$ and $v=-d$, where $d$ is a non-negative integer. The hyper-parameter $d$ determines the translation latency of the system.",
+      "More specifically, before prefix translation at outer step $s$, we have source prefix $\\mathbf {x}^s = \\lbrace x_1, ..., x_{\\eta \\left[ s\\right]}\\rbrace $ and target prefix $\\mathbf {y}_{\\tau \\left[ s-1\\right]}^s = \\lbrace y_1, ..., y_{\\tau \\left[ s-1\\right]}\\rbrace $. Prefix translation terminates at inner step $w_s$ when predicting an EOS token or satisfying:",
+      "We call this stopping criterion as Length and EOS (LE) stopping controller."
+    ],
+    [
+      "Although simple and easy to implement, LE controller lacks the capability to learn the optimal timing with which to stop prefix translation. Therefore, we design a small trainable network called Trainable (TN) stopping controller to learn when to stop prefix translation for non-terminal outer step. Fig. FIGREF22 shows the illustration.",
+      "At each inner decoding step $k$ for non-terminal outer step $s$, the TN controller utilizes a stochastic policy $\\pi _\\theta $ parameterized by a neural network to make the binary decision on whether to stop translation at current stage:",
+      "where $z_{\\tau \\left[ s-1\\right]+k}^s$ is the current decoder hidden state. The prefix translation stops if the TN controller predicts $a_{\\tau \\left[ s-1\\right]+k}=1$. The controller function $f_\\theta $ can take on a variety of forms, and for simplicity we implement with a feedforward network with two hidden layers, followed by a softmax layer.",
+      "To train the TN controller, we freeze the NMT model with pretrained parameters, and optimize the TN network with policy gradient for reward maximization $\\mathcal {J}= \\mathbb {E}_{\\pi _{\\theta }}(\\sum _{t=1}^{T_\\tau } r_t )$. With a trained TN controller, prefix translation stops at inner decoding step $w_s$ when predicting an EOS token or satisfying:",
+      "In the following, we talk about the details of the reward function and the training detail with policy gradient."
+    ],
+    [
+      "To trade-off between translation quality and latency, we define the reward function at inner decoding step $k$ of outer step $s$ as:",
+      "where $t=\\tau \\left[ s-1\\right]+k$, and $r_t^Q$ and $r_t^D$ are rewards related to quality and delay, respectively. $\\alpha \\ge 0$ is a hyper-parameter that we adjust to balance the trade-off between translation quality and delay. Similar to BIBREF4, we utilize sentence-level BLEU BIBREF15, BIBREF16 with reward shaping BIBREF17 as the reward for quality:",
+      "where",
+      "is the intermediate reward. Note that the higher the values of BLEU are, the more rewards the TN controller receives.",
+      "Following BIBREF4, BIBREF5, we use average lagging (AL) as the reward for latency:",
+      "where",
+      "$l(t)$ is the number of observed source tokens when generating the $t$-th target token, $t_e= \\mathop {\\rm argmin}_{t}{(l(t)=|\\mathbf {x}|)}$ denotes the earliest point when the system observes the full source sentence, $\\lambda =\\frac{|\\mathbf {y}|}{|\\mathbf {x}|}$ represents the target-to-source length ratio and $d^* \\ge 0$ is a hyper-parameter called target delay that indicates the desired system latency. Note that the lower the values of AL are, the more rewards the TN controller receives."
+    ],
+    [
+      "We train the TN controller with policy gradientBIBREF18, and the gradients are:",
+      "where $R_t=\\sum _{i=t}^{T_\\tau } r_i$ is the cumulative future rewards for the current decision. We can adopt any sampling approach to estimate the expected gradient. In our experiments, we randomly sample multiple action trajectories from the current policy $\\pi _{\\theta }$ and estimate the gradient with the collected accumulated reward. We try the variance reduction techniques by subtracting a baseline average reward estimated by a linear regression model from $R_t$ and find that it does not help to improve the performance. Therefore, we just normalize the reward in each mini batch without using baseline reward for simplicity."
+    ],
+    [
+      "We compare our approach with the baselines on WMT15 German-English (DE-EN) translation in both directions. This is also the most widely used dataset to evaluate SNMT's performance BIBREF3, BIBREF4, BIBREF5, BIBREF10, BIBREF13. To further evaluate our approach's efficacy in trading off translation quality and latency on other language pair and spoken language, we also conduct experiments with the proposed LE and TN method on NIST Chinese-to-English (ZH$\\rightarrow $EN) translation and IWSLT16 German-English (DE-EN) translation in both directions. For WMT15, we use newstest2014 for validation and newstest2015 for test. For NIST, we use MT02 for validation, and MT05, MT06, MT08 for test. For IWSLT16, we use tst13 for validation and tst14 for test. Table TABREF32 shows the details. All the data is tokenized and segmented into subword symbols using byte-pair encoding BIBREF19 to restrict the size of the vocabulary. We use 40,000 joint merge operations on WMT15, and 24,000 on IWSLT16. For NIST, we use 30,000 merge operations for source and target side separately. Without explicitly mention, we simulate simultaneous translation scenario at inference time with these datasets by assuming that the system observes one new source token at each outer step, i.e., $c_s=1$."
+    ],
+    [
+      "We use Transformer BIBREF8 trained with maximum likelihood estimation as the pretrained CNMT model and implement our method based on fairseq-py. We follow the setting in transformer_iwslt_de_en for IWSLT16 dataset, and transformer_wmt_en_de for WMT15 and NIST dataset. Fairseq-py adds an EOS token for all source sentences during training and inference. Therefore, to be consistent with the CNMT model implemented with fairseq-py, we also add an EOS token at the end of the source prefix for prefix translation."
+    ],
+    [
+      "To train the TN controller, we use a mini-batch size of 8,16,16 and sample 5,10,10 trajectories for each sentence pair in a batch for IWSLT16, WMT15 and NIST, respectively. We set the number of newly observed source tokens at each outer step to be 1 during the training for simplicity. We set $\\alpha $ to be $0.04$, and $d^*$ to be $2,5,8$. All our TN controllers are trained with policy gradient using Adam optimizer BIBREF20 with 30,000 updates. We select the last model as our final TN controller."
+    ],
+    [
+      "We compare our model against three baselines that utilize a pretrained CNMT model to perform simultaneous translation:",
+      "test_time_waitk: the test-time waitk simultaneous decoding algorithm proposed in BIBREF5, i.e., using a full-sentence model but decoding it with a waitk policy. We report the results when $k=1,3,5,7,9$.",
+      "SL: the SL model proposed in BIBREF13, which learns an adaptive READ/WRITE policy from oracle READ/WRITE sequences generated with heuristics. We report the results $\\rho =0.65,0.6,0.55,0.5,0.45,0.4$.",
+      "BIBREF4: the adaptation of BIBREF4's two-staged full-sentence model + reinforcement learning on Transformer by BIBREF5. We report the results when using $CW=2,5,8$ as the target delay.",
+      "We report the result with $d=0,2,4,6,8$ for our proposed LE method and $d^*=2,5,8$ for our proposed TN method. For all baselines, we cite the results reported in BIBREF13. Since they did not mention the details of data preprocessing, we cannot compare the BLEU and AL scores directly with theirs. Therefore, we normalize the BLEU and AL scores with its corresponding upper bound, i.e. the BLEU and AL scores obtained when the pretrained Transformer performs standard greedy decoding (Greedy)."
+    ],
+    [
+      "We compare our method with the baselines on the test set of WMT15 EN$\\rightarrow $DE and DE$\\rightarrow $EN translation tasks. Fig. FIGREF40 shows the result. The points closer to the upper left corner indicate better overall performance, namely low latency and high quality. In all these figures, we observe that, as latency increases, all methods improve in quality. The TN stopping controller significantly outperforms all the baseline systems in both translation tasks, demonstrating that it indeed learns the appropriate timing to stop prefix translation. The LE controller outperforms the baselines on WMT15 EN$\\rightarrow $DE translation at high latency region and performs similarly or worse on other cases.",
+      "We show the model's efficacy in trading off quality and latency on other language pair and spoken language in Fig. FIGREF41. The TN controller obtains better performance on all translation tasks, especially at the low latency region. For example, on IWSLT16 EN$\\rightarrow $ DE translation, it is +$2.5$ to +$3.3$ BLEU ahead of the LE method. TN also obtains promising translation quality with acceptable latency: with a lag of $<7$ tokens, TN obtains 96.95%, 97.20% and 94.03% BLEU with respect to consecutive greedy decoding for IWSLT16 EN$\\rightarrow $DE, IWSLT16 DE$\\rightarrow $EN and NIST ZH$\\rightarrow $EN translation, respectively."
+    ],
+    [
+      "We analyze the effect of different ways (Eq. DISPLAY_FORM10-DISPLAY_FORM13) to obtain the encoder and decoder hidden states at the beginning of prefix translation with the LE controller. Fig. FIGREF42 shows the result. We try three variants: a) dynamically rebuild all encoder/decoder hidden states (none); b) reuse decoder hidden states and rebuild all encoder hidden states (decoder); c) reuse previous encoder hidden states and rebuild all decoder hidden states (encoder). The left Y axis and X axis show BLEU-vs-AL curve. We observe that if reusing previous encoder hidden states (encoder), the translation fails. We ascribe this to the discrepancy between training and decoding for the encoder. We also observe that when $d=0,2$, reusing decoder hidden states (decoder) obtain negative AL. To analyze this, we plot the translation to reference length ratio versus AL curve with the right Y axis and X axis. It shows that with decoder, the decoding process stops too early and generates too short translations. Therefore, to avoid such problem and to be consistent with the training process of the CNMT model, it is important to dynamically rebuild all encoder/decoder hidden states for prefix translation.",
+      "Since we make no assumption about the $c_s$, i.e., the number of newly observed source tokens at each outer step, we test the effect of different $c_s$ at this section. Fig. FIGREF43 shows the result with the LE and TN controllers on the test set of WMT15 EN$\\rightarrow $DE translation. We observe that as $c_s$ increases, both LE and TN trend to improve in quality and worsen in latency. When $c_s=1$, LE controller obtains the best balance between quality and latency. In contrast, TN controller obtains similar quality and latency balance with different $c_s$, demonstrating that TN controller successfully learns the right timing to stop regardless of the input update schedule.",
+      "We also analyze the TN controller's adaptability by monitoring the initial delay, i.e., the number of observed source tokens before emitting the first target token, on the test set of WMT15 EN$\\rightarrow $DE translation, as shown in Fig. FIGREF52. $d^*$ is the target delay measured with AL (used in Eq. DISPLAY_FORM29). It demonstrates that the TN controller has a lot of variance in it's initial delay. The distribution of initial delay changes with different target delay: with higher target delay, the average initial delay is larger. For most sentences, the initial delay is within $1-7$.",
+      "In speech translation, listeners are also concerned with long silences during which no translation occurs. Following BIBREF4, BIBREF5, we use Consecutive Wait (CW) to measure this:",
+      "Fig. FIGREF54 shows the BLEU-vs-CW plots for our proposed two algorithms. The TN controller has higher CW than the LE controller. This is because TN controller prefers consecutive updating output buffer (e.g., it often produces $w_s$ as $0\\ 0\\ 0\\ 0\\ 3\\ 0\\ 0\\ 0\\ 0\\ 0\\ 5\\ 0\\ 0\\ 0\\ 0\\ 4\\ ...$) while the LE controller often updates its output buffer following the input buffer (e.g., it often produces $w_s$ as $0\\ 0\\ 0\\ 0\\ 1\\ 1\\ 1\\ 1\\ 1\\ 1\\ ...$ when $d=4$). Although larger than LE, the CW for TN ($< 6$) is acceptable for most speech translation scenarios."
+    ],
+    [
+      "Fig. FIGREF55 shows three translation examples with the LE and TN controllers on the test set of NIST ZH$\\rightarrow $EN and WMT15 EN$\\rightarrow $DE translation. In manual inspection of these examples and others, we find that the TN controller learns a conservative timing for stopping prefix translation. For example, in example 2, our method outputs translation \u201cwu bangguo attended the signing ceremony\u201d when observing \u201c\u5434\u90a6\u56fd \u51fa\u5e2d \u7b7e\u5b57 \u4eea\u5f0f \u5e76\u201d, instead of a more radical translation \u201cwu bangguo attended the signing ceremony and\u201d. Such strategy helps to alleviate the problem of premature translation, i.e., translating before observing enough future context."
+    ],
+    [
+      "A number of works in simultaneous translation divide the translation process into two stages. A segmentation component first divides the incoming text into segments, and then each segment is translated by a translator independently or with previous context. The segmentation boundaries can be predicted by prosodic pauses detected in speech BIBREF0, BIBREF21, linguistic cues BIBREF22, BIBREF23, or a classifier based on alignment information BIBREF24, BIBREF25 and translation accuracy BIBREF1, BIBREF2, BIBREF26.",
+      "Some authors have recently endeavored to perform simultaneous translation in the context of NMT. BIBREF3, BIBREF14, BIBREF5 introduce a manually designed criterion to control when to translate. BIBREF11, BIBREF4, BIBREF12 extend the criterion into a trainable agent in a reinforcement learning framework. However, these work either develop sophisticated training frameworks explicitly designed for simultaneous translation BIBREF5 or fail to use a pretrained consecutive NMT model in an optimal way BIBREF3, BIBREF14, BIBREF11, BIBREF4, BIBREF12, BIBREF13. In contrast, our work is significantly different from theirs in the way of using pretrained consecutive NMT model to perform simultaneous translation and the design of the two stopping criteria."
+    ],
+    [
+      "We have presented a novel framework for improving simultaneous translation with a pretrained consecutive NMT model. The basic idea is to translate partial source sentence with the pretrained consecutive NMT model and stops the translation with two novel stopping criteria. Extensive experiments demonstrate that our method outperforms the state-of-the-art baselines in balancing between translation quality and latency."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1287/instruction.md b/qasper-1287/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..83cb926830150ad0846b3b2421aa6929086e3a65
--- /dev/null
+++ b/qasper-1287/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Empirical Gaussian priors for cross-lingual transfer learning
+
+Question: What languages did they experiment with?
\ No newline at end of file
diff --git a/qasper-1289/instruction.md b/qasper-1289/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..060ed93e5b05729fa864653326f36b2c41d2c7be
--- /dev/null
+++ b/qasper-1289/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Variational Transformers for Diverse Response Generation
+
+Question: What baselines other than standard transformers are used in experiments?
\ No newline at end of file
diff --git a/qasper-1290/instruction.md b/qasper-1290/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..a0f0592df194f305e0516c63955c60e13e049136
--- /dev/null
+++ b/qasper-1290/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Variational Transformers for Diverse Response Generation
+
+Question: What three conversational datasets are used for evaluation?
\ No newline at end of file
diff --git a/qasper-1291/instruction.md b/qasper-1291/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..f1374cf60feea894f76eebc65fe5cc53eff5adff
--- /dev/null
+++ b/qasper-1291/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Czech Text Processing with Contextual Embeddings: POS Tagging, Lemmatization, Parsing and NER
+
+Question: What previous approaches did this method outperform?
\ No newline at end of file
diff --git a/qasper-1296/instruction.md b/qasper-1296/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..0d3b9b76fcec94caf2572cf443bf4899a55b35b2
--- /dev/null
+++ b/qasper-1296/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Language Transfer for Early Warning of Epidemics from Social Media
+
+Question: Is there any explanation why some choice of language pair is better than the other?
\ No newline at end of file
diff --git a/qasper-1297/instruction.md b/qasper-1297/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..af5ddf80e4c98d6c3c79a42b1e0fabaae82ffeb3
--- /dev/null
+++ b/qasper-1297/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data Tasks
+
+Question: Is the new model evaluated on the tasks that BERT and ELMo are evaluated on?
\ No newline at end of file
diff --git a/qasper-1298/instruction.md b/qasper-1298/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..6eecc564e64bfd9f68480c2bc5a3436e36b6cb2f
--- /dev/null
+++ b/qasper-1298/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data Tasks
+
+Question: Does the additional training on supervised tasks hurt performance in some tasks?
\ No newline at end of file
diff --git a/qasper-1299/instruction.md b/qasper-1299/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..e8296b50c881fa113fda85f65daf9547fcbeccd3
--- /dev/null
+++ b/qasper-1299/instruction.md
@@ -0,0 +1,79 @@
+Name of Paper: Back Attention Knowledge Transfer for Low-resource Named Entity Recognition
+
+Question: Which translation system do they use to translate to English?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Model",
+    "Pre-trained Translation and NER Model",
+    "Back Attention Knowledge Transfer",
+    "Named Entity Recognition Architecture",
+    "Experiments",
+    "Experimental Setup",
+    "Settings",
+    "German and Spanish NER",
+    "Chinese NER",
+    "Task-Specific Information from Back Attention Network",
+    "Analysis",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Named entity recognition (NER) is a sequence tagging task that extracts the continuous tokens into specified classes, such as person names, organizations and locations. Current state-of-the-art approaches for NER usually base themselves on long short-term memory recurrent neural networks (LSTM RNNs) and a subsequent conditional random field (CRF) to predict the sequence labels BIBREF0 . Performances of neural NER methods are compromised if the training data are not enough BIBREF1 . This problem is severe for many languages due to a lack of labeled datasets, e.g., German and Spanish. In comparison, NER on English is well developed and there exist abundant labeled data for training purpose. Therefore, in this work, we regard English as a high-resource language, while other languages, even Chinese, as low-resource languages.",
+      "There is an intractable problem when leveraging English NER system for other languages. The sentences with the same meaning in different languages may have different lengths and the positions of words in these sentences usually do not correspond. Previous work such as BIBREF2 used each single word translation information to enrich the monolingual word embedding. To our knowledge, there is no approach that employs the whole translation information to improve the performance of the monolingual NER system.",
+      "To address above problem, we introduce an extension to the BiLSTM-CRF model, which could obtain transferred knowledge from a pre-trained English NER system. First, we translate other languages into English. Since the proposed models of BIBREF3 and BIBREF4 , the performance of attention-based machine translation systems is close to the human level. The attention mechanism can make the translation results more accurate. Furthermore, this mechanism has another useful property: the attention weights can represent the alignment information. After translating the low-resource language into English, we utilize the pre-trained English NER model to predict the sentences and record the output states of BiLSTM in this model. The states contain the semantic and task-specific information of the sentences. By using soft alignment attention weights as a transformation matrix, we manage to transfer the knowledge of high resource language \u2014 English to other languages. Finally, using both word vectors and the transfer knowledge, we obtain new state-of-the-art results on four datasets."
+    ],
+    [
+      "In this section, we will introduce the BAN in three parts. Our model is based on the mainstream NER model BIBREF5 , using BiLSTM-CRF as the basic network structure. Given a sentence INLINEFORM0 and corresponding labels INLINEFORM1 , where INLINEFORM2 denotes the INLINEFORM3 th token and INLINEFORM4 denotes the INLINEFORM5 th label. The NER task is to estimate the probability INLINEFORM6 . Figure FIGREF1 shows the main architecture of our model."
+    ],
+    [
+      "Attention-base translation model We use the system of BIBREF6 , a convolutional sequence to sequence model. It divides translation process into two steps. First, in the encoder step, given an input sentence INLINEFORM0 of length INLINEFORM1 , INLINEFORM2 represents each word as word embedding INLINEFORM3 . After that, we obtain the absolute position of input elements INLINEFORM4 . Both vectors are concatenated to get input sentence representations INLINEFORM5 . Similarly, output elements INLINEFORM6 generated from decoder network have the same structure. A convolutional neural network (CNN) is used to get the hidden state of the sentence representation from left to right. Second, in the decoder step, attention mechanism is used in each CNN layer. In order to acquire the attention value, we combine the current decoder state INLINEFORM7 with the embedding of previous decoder output value INLINEFORM8 : DISPLAYFORM0 ",
+      "For INLINEFORM0 th layer, the attention INLINEFORM1 of the INLINEFORM2 th source element and INLINEFORM3 th state is computed as a dot-product between the decoder state summary INLINEFORM4 and each output INLINEFORM5 of the last encoder layer: DISPLAYFORM0 ",
+      "Then we follow the normal decoder implementation and get target sentence INLINEFORM0 by beam search algorithm.",
+      "Pre-trained English NER model We construct the English NER system following BIBREF7 . This system uses a bidirectional LSTM as a character-level language model to take context information for word embedding generation. The hidden states of the character language model (CharLM) are used to create contextualized word embeddings. The final embedding INLINEFORM0 is concatenated by the CharLM embedding INLINEFORM1 and GLOVE embedding INLINEFORM2 BIBREF8 . A standard BiLSTM-CRF named entity recognition model BIBREF0 takes INLINEFORM3 to address the NER task."
+    ],
+    [
+      "The sentences in low-resource languages are used as input to the model. Given a input sentence INLINEFORM0 in low-resource language, we use pre-trained translation model to translate INLINEFORM1 into English and the output is INLINEFORM2 . Simultaneously, we record the average of values for all INLINEFORM3 attention layers: DISPLAYFORM0 ",
+      "After that, we use the pre-trained English NER model to predict the translated sentence INLINEFORM0 . Then, we have the BiLSTM output states: DISPLAYFORM0 ",
+      "where INLINEFORM0 and INLINEFORM1 denote the INLINEFORM2 th forward and backward outputs, respectively. INLINEFORM3 contains the semantic and task-specific information of the translated sentence. And the INLINEFORM4 th row of attention weights matrix INLINEFORM5 represents the correlation between source word INLINEFORM6 with all words in target sentence INLINEFORM7 . Thereafter, to obtain the transfer information INLINEFORM8 of source word, we reversely use the attention weights: DISPLAYFORM0 ",
+      "where INLINEFORM0 represent the whole outputs of BiLSTM, and INLINEFORM1 , INLINEFORM2 . INLINEFORM3 denotes the transfer information of INLINEFORM4 th word in low-resource language and has the same dimensions with INLINEFORM5 ."
+    ],
+    [
+      "The low-resource language named entity recognition architecture is based on BIBREF5 . The word embeddings of low-resource language are passed into a BiLSTM-CRF sequence labeling network. The embeddings INLINEFORM0 are used as inputs to the BiLSTM. Then we have: DISPLAYFORM0 ",
+      "Before passing the forward and backward output states INLINEFORM0 into CRF, we concatenate INLINEFORM1 and INLINEFORM2 as a new representation: DISPLAYFORM0 ",
+      "CRF model uses INLINEFORM0 to give the final sequence probability on the possible sequence label INLINEFORM1 : DISPLAYFORM0 ",
+      "At last, the named entity labels are predicted by: DISPLAYFORM0 "
+    ],
+    [
+      "We use experiments to evaluate the effectiveness of our proposed method on NER task. On three different low-resource languages, we conducted an experimental evaluation to prove the effectiveness of our back attention mechanism on the NER task. Four datasets are used in our work, including CoNLL 2003 German BIBREF9 , CoNLL 2002 Spanish BIBREF10 , OntoNotes 4 BIBREF11 and Weibo NER BIBREF12 . All the annotations are mapped to the BIOES format. Table TABREF14 shows the detailed statistics of the datasets."
+    ],
+    [
+      "We implement the basic BiLSTM-CRF model using PyTorch framework. FASTTEXT embeddings are used for generating word embeddings. Translation models are trained on United Nation Parallel Corpus. For pre-trained English NER system, we use the default NER model of Flair."
+    ],
+    [
+      "We train our NER model using vanilla SGD with no momentum for 150 epochs, with an initial learning rate of 0.1 and a learning rate annealing method in which the train loss does not fall in 3 consecutive epochs. The hidden size of BiLSTM model is set to 256 and mini-batch size is set to 16. Dropout is applied to word embeddings with a rate of 0.1 and to BiLSTM with a rate of 0.5. We repeat each experiment 5 times under different random seeds and report the average of test set as final performance."
+    ],
+    [
+      "Experimental results of German and Spanish are shown in table TABREF20 . Evaluation metric is F1-score. We can find that our method CharLM+BiLSTM-CRF+BAN yields the best performance on two languages. And after adding our network to each of the basic models, the performance of each model has been improved. This suggests that the transfer information, obtained from BAN, is helpful for low-resource NER."
+    ],
+    [
+      "Chinese is distinct from Latin-based languages. Thence, there are some tricks when processing Chinese corpus. But we only suppose to verify the validity of our method, so we just use the character-level embeddings.",
+      "Table TABREF22 shows the results on Chinese OntoNotes 4.0. Adding BAN to baseline model leads to an increase from 63.25% to 72.15% F1-score. In order to further improve the performance, we use the BERT model BIBREF20 to produce word embeddings. With no segmentation, we surpass the previous state-of-the-art approach by 6.33% F1-score. For Weibo dataset, the experiment results are shown in Table TABREF23 , where NE, NM and Overall denote named entities, nominal entities and both. The baseline model gives a 33.18% F1-score. Using the transfer knowledge by BAN, the baseline model achieves an immense improvement in F1-score, rising by 10.39%. We find that BAN still gets consistent improvement on a strong model. With BAN, the F1-score of BERT+BiLSTM+CRF increases to 70.76%."
+    ],
+    [
+      " BIBREF21 indicates that the representations from higher-level layers of NLP models are more task-specific. Although we do the same task among different languages, the target domains of different datasets are slightly different. So, to prove that back attention knowledge generated by BAN could capture valuable task-specific information between different languages, we use the back attention knowledge alone as word embedding to predict Weibo dataset. We compare three different word embeddings on the baseline model. Experimental results are shown in Table TABREF25 and illustrate that back attention knowledge from BAN has inherent semantic information."
+    ],
+    [
+      "Our proposed approach is the first to leverage hidden states of NER model from another language to improve monolingual NER performance. The training time with or without BAN is almost the same due to the translation module and the English NER module are pre-trained.",
+      "On large datasets, our model makes a small improvement because some of transfer knowledge obtained from our method is duplicated with the information learned by the monolingual models. On small datasets, e.g., Weibo dataset, a great improvement has been achieved after adding transfer knowledge to the baseline model. The reason maybe is that these datasets are too small to be fully trained and the test datasets have many non-existent characters of the training dataset, even some unrecognized characters. Therefore, some tags labeled incorrectly by monolingual models could be labeled correctly with the additional transfer knowledge which contains task-specific information obtained from BAN. So, the transfer information plays an important role in this dataset."
+    ],
+    [
+      "In this paper, we seek to improve the performance of NER on low-resource languages by leveraging the well-trained English NER system. This is achieved by way of BAN, which is a simple but extensible approach. It can transfer information between different languages. Empirical experiments show that, on small datasets, our approach can lead to significant improvement on the performance. This property is of great practical importance for low-resource languages. In future work, we plan to extend our method on other NLP tasks, e.g., relation extraction, coreference resolution."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1318/instruction.md b/qasper-1318/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..ac472381048905b16203a5bef69537b20d711519
--- /dev/null
+++ b/qasper-1318/instruction.md
@@ -0,0 +1,119 @@
+Name of Paper: Learning from Easy to Complex: Adaptive Multi-curricula Learning for Neural Dialogue Generation
+
+Question: What automatic evaluation metrics are used?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Curriculum Plausibility",
+    "Curriculum Plausibility ::: Conversational Attributes ::: Specificity",
+    "Curriculum Plausibility ::: Conversational Attributes ::: Repetitiveness",
+    "Curriculum Plausibility ::: Conversational Attributes ::: Query-relatedness",
+    "Curriculum Plausibility ::: Conversational Attributes ::: Continuity",
+    "Curriculum Plausibility ::: Conversational Attributes ::: Model Confidence",
+    "Curriculum Plausibility ::: Dialogue Analysis ::: Distributions among Attributes",
+    "Curriculum Plausibility ::: Dialogue Analysis ::: Attributes Independence",
+    "Curriculum Dialogue Learning",
+    "Curriculum Dialogue Learning ::: Single Curriculum Dialogue Learning",
+    "Curriculum Dialogue Learning ::: Adaptive Multi-curricula Learning",
+    "Experiments ::: Experiment Settings",
+    "Experiments ::: Implementation and Reproducibility",
+    "Experiments ::: Overall Performance and Human Evaluation",
+    "Experiments ::: Model Analysis ::: Single vs Multi-curricula",
+    "Experiments ::: Model Analysis ::: Effects of Adaptive Multi-curricula Learning",
+    "Experiments ::: Model Analysis ::: Learning Efficiency",
+    "Experiments ::: Model Analysis ::: Multi-curricula Learning Route",
+    "Experiments ::: Model Analysis ::: Examples with Different Learning Frequencies",
+    "Related Work",
+    "Conclusion",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "Teaching machines to converse with humans naturally and engagingly is a fundamentally interesting and challenging problem in AI research. Many contemporary state-of-the-art approaches BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6 for dialogue generation follow the data-driven paradigm: trained on a plethora of query-response pairs, the model attempts to mimic human conversations. As a data-driven approach, the quality of generated responses in neural dialogue generation heavily depends on the training data. As such, in order to train a robust and well-behaved model, most works obtain large-scale query-response pairs by crawling human-generated conversations from publicly available sources such as OpenSubtitles BIBREF7.",
+      "However, due to the subjectivity and open-ended nature of human conversations, the complexity of training dialogues varies greatly BIBREF8. Table TABREF1 shows samples drawn from OpenSubtitles BIBREF7, which contains millions of human-human conversations converted from movie transcripts. The response of the third sample \u201cYurakutei kikuhiko.\u201d looks quite strange in terms of the given query, while the first sample is clearly easier to learn. The noise and uneven complexity of query-response pairs impede the learning efficiency and effects of the neural dialogue generation models.",
+      "Babies learn to speak by first imitating easy and exact utterances repeatedly taught by their patient parents. As children grow up, they learn grade by grade, from simple conversations to more complex ones. Inspired by such human behaviors of learning to converse, in this paper, we introduce curriculum learning to bring the neural dialogue model with easy-to-complex learning curriculum, where the model first learns from easy conversations and then gradually manages more complicated dialogues. Nevertheless, organizing a curriculum with increasing difficulty faces insurmountable obstacles: 1) automatic evaluation of dialogue complexity is a non-trivial task. BIBREF9 defined the difficulty for the training examples with respect to the sentence length and word rarity in neural machine translation. BIBREF10 expressed the difficulty regarding the value of the objective function. So far, there is no unified approach in measuring dialogue complexity. 2) Unlike the single metric of complexity in other tasks, dialogue complexity embodies multiple aspects of attributes BIBREF11\u2014the specificity and repetitiveness of the response, the relevance between the query and the response, etc. As such, in this paper, we study the dialogue distributions along five aspects of attributes to gather multiple perspectives on dialogue complexity, resulting with five curricula accordingly.",
+      "Conventional curriculum learning organizes the training samples into one curriculum, whereas we employ multiple curricula for dialogue learning. Enlightened by the phenomenon that children usually adjust the learning focus of multiple curricula dynamically in order to acquire a good mark, we further propose an adaptive multi-curricula learning framework, established upon the reinforcement learning paradigm, to automatically choose different curricula at different learning stages according to the learning status of the neural dialogue generation model.",
+      "Detailed analysis and experiments demonstrate that the proposed framework effectively increases the learning efficiency and gains better performances on five state-of-the-art dialogue generation models regarding three publicly available conversational corpora. Code for this work is available on https://github.com/hengyicai/Adaptive_Multi-curricula_Learning_for_Dialog."
+    ],
+    [
+      "Intuitively, a well-organized curriculum should provide the model learning with easy dialogues first, and then gradually increase the curriculum difficulty. However, currently, there is no unified approach for dialogue complexity evaluation, where the complexity involves multiple aspects of attributes. In this paper, we prepare the syllabus for dialogue learning with respect to five dialogue attributes. To ensure the universality and general applicability of the curriculum, we perform an in-depth investigation on three publicly available conversation corpora, PersonaChat BIBREF12, DailyDialog BIBREF13 and OpenSubtitles BIBREF7, consisting of 140 248, 66 594 and 358 668 real-life conversation samples, respectively."
+    ],
+    [
+      "A notorious problem for neural dialogue generation model is that the model is prone to generate generic responses. The most unspecific responses are easy to learn, but are short and meaningless, while the most specific responses, consisting of too many rare words, are too difficult to learn, especially at the initial learning stage. Following BIBREF11, we measure the specificity of the response in terms of each word $w$ using Normalized Inverse Document Frequency (NIDF, ranging from 0 to 1):",
+      "where $\\text{IDF}(w)=\\log {\\frac{N_r}{N_w}}$. $N_r$ is the number of responses in the training set and $N_w$ is the number of those responses that contain $w$. $\\text{idf}_{min}$ and $\\text{idf}_{max}$ are the minimum and maximum IDFs, taken over all words in the vocabulary. The specificity of a response $r$ is measured as the mean NIDF of the words in $r$."
+    ],
+    [
+      "Repetitive responses are easy to generate in current auto-regressive response decoding, where response generation loops frequently, whereas diverse and informative responses are much more complicated for neural dialogue generation. We measure the repetitiveness of a response $r$ as:",
+      "where $I(\\cdot )$ is an indicator function that takes the value 1 when $w_i \\in \\lbrace w_0, \\cdots , w_{i-1}\\rbrace $ is true and 0 otherwise."
+    ],
+    [
+      "A conversation is considered to be coherent if the response correlates well with the given query. For example, given a query \u201cI like to paint\u201d, the response \u201cWhat kind of things do you paint?\u201d is more relevant and easier to learn than another loosely-coupled response \u201cDo you have any pets?\u201d. Following previous work BIBREF14, we measure the query-relatedness using the cosine similarities between the query and its corresponding response in the embedding space: $\\textit {cos\\_sim}(\\textit {sent\\_emb}(c), \\textit {sent\\_emb}(r))$, where $c$ is the query and $r$ is the response. The sentence embedding is computed by taking the average word embedding weighted by the smooth inverse frequency $\\textit {sent\\_emb}(e)=\\frac{1}{|e|}\\sum _{w\\in {}e}\\frac{0.001}{0.001 + p(w)}emb(w)$ of words BIBREF15, where $emb(w)$ and $p(w)$ are the embedding and the probability of word $w$ respectively."
+    ],
+    [
+      "A coherent response not only responds to the given query, but also triggers the next utterance. An interactive conversation is carried out for multiple rounds and a response in the current turn also acts as the query in the next turn. As such, we introduce the continuity metric, which is similar to the query-relatedness metric, to assess the continuity of a response $r$ with respect to the subsequent utterance $u$, by measuring the cosine similarities between them."
+    ],
+    [
+      "Despite the heuristic dialogue attributes, we further introduce the model confidence as an attribute, which distinguishes the easy-learnt samples from the under-learnt samples in terms of the model learning ability. A pretrained neural dialogue generation model assigns a relatively higher confidence probability for the easy-learnt samples than the under-learnt samples. Inspired by BIBREF16, BIBREF17, we employ the negative loss value of a dialogue sample under the pretrained model as the model confidence measure, indicating whether a sampled response is easy to be generated. Here we choose the attention-based sequence-to-sequence architecture with a cross-entropy objective as the underlying dialogue model."
+    ],
+    [
+      "The distributions of the data samples regarding the aforementioned five attributes are shown in Figure FIGREF11. Although the attribute score distributions on three corpora are similar, they also have disparities: 1) Outliers frequently appear among all the distributions, which exhibits the uneven dialogue complexity. 2) In terms of query-relatedness and continuity, to our surprise, the medians of the two distributions on PersonaChat are obviously smaller than the corresponding distributions on DailyDialog and OpenSubtitles. PersonaChat is manually created by crowd-sourcing, while DailyDialog and OpenSubtitles are collected from almost real-life conversations. 3) With respect to the model confidence (the negative loss value), the median of PersonaChat is relatively smaller, which illustrates that it is more difficult for the neural dialogue generation model to learn from PersonaChat."
+    ],
+    [
+      "So far, we have analyzed five dialogue attributes. A question might be raised that how well the proposed attributes correlate with each other. To validate the correlations of these conversation attributes, we summarize the statistics of the Kendall $\\tau $ correlations for each dataset in Table TABREF12. We find that these attributes, in general, show little correlations with each other. This partially validates that dialogue complexity involves multiple perspectives."
+    ],
+    [
+      "We propose an adaptive multi-curricula learning framework to accelerate dialogue learning and improve the performance of the neural dialogue generation model."
+    ],
+    [
+      "We first illustrate how a dialogue generation model exploits the curriculum by taking single curriculum dialogue learning as an example, where the curriculum is arranged by sorting each sample in the dialogue training set $\\mathcal {D}_{train}$ according to one attribute. Then, at training time step $t$, a batch of training examples is sampled from the top $f(t)$ portions of the total sorted training samples, where the progressing function $f(t)$ determines the learning rate of the curriculum. Following BIBREF9, we define the progressing function $f(t)$ as $f(t)\\triangleq min(1, \\sqrt{t\\frac{1-c_0^2}{T} + c_0^2})$, where $c_0 > 0$ is set to 0.01 and $T$ is the duration of curriculum learning. At the early stage of the training process, the neural dialogue generation model learns from the samples drawing from the front part of the curriculum. As the advance of the curriculum, the difficulty gradually increases, as more complex training examples appear. After training $T$ batches, each batch of training instances is drawn from the whole training set, which is same as the conventional training procedure without a curriculum."
+    ],
+    [
+      "Dialogue complexity consists of multi-perspectives of attributes. We extend the naive single curriculum learning into the multi-curricula setting, where we provide the neural dialogue generation model with five different learning curricula, and each curriculum is prepared by ordering the training set in terms of the corresponding attribute metric accordingly. Scheduling multiple curricula in the same learning pace is obviously inappropriate. Enlightened by the phenomenon that children usually adjust the learning progress of multiple curricula dynamically in order to acquire a good mark, we further introduce an adaptive multi-curricula learning framework, to automatically choose different curricula at different learning stages according to the learning status of the neural dialogue generation model.",
+      "The adaptive multi-curricula learning framework is established upon the reinforcement learning (RL) paradigm. Figure FIGREF18 illustrates the overall learning process. The multi-curricula learning scheme is scheduled according to the model's performance on the validation set, where the scheduling mechanism acts as the policy $\\pi $ interacting with the dialogue model to acquire the learning status $s$. The reward of the multi-curricula learning mechanism $m_t$ indicates how well the current dialogue model performs. A positive reward is expected if a multi-curricula scheduling action $a_t$ brings improvements on the model's performance, and the current mini-batch of training samples is drawn consulting with the scheduling action $a_t$. The neural dialogue generation model learns from those mini-batches, resulting with a new learning status $s_{t+1}$. The adaptive multi-curricula learning framework is optimized to maximize the reward. Such learning process loops continuously until the performance of the neural dialogue generation model converges.",
+      "More specifically, the learning status of the dialogue model is represented as the state. Similar to other curriculum learning framework BIBREF18, BIBREF19, the learning status consists of several features, including the passed mini-batch number, the average historical training loss, the loss value on the training data, the margin value of predicted probabilities and the last validation metric values. To enable the proposed framework to be aware of the learning progress $\\varrho _i$ regarding each attribute $i$, we also exploit $\\varrho =\\lbrace \\varrho _0, \\varrho _1, \\cdots , \\varrho _{k-1}\\rbrace $ for state representations, where $k$ stands for the number of curricula, here $k=5$, and $\\varrho _i$ can be simply measured as the learning steps on the attribute $i$. The multi-curricula learning framework samples a scheduling action $a_t$ per step by its policy $\\Phi _\\theta (a|s)$ with parameters $\\theta $ to be learnt, and the scheduling action $a_t \\in \\lbrace 0, 1, \\cdots , k-1\\rbrace $ chooses one of the curricula. Then, a mini-batch of dialogue instances is sampled from the top $f(\\varrho _i)$ portions of the chosen curriculum. The dialogue model is validated every $\\Gamma $ training steps and the curriculum policy is updated at $\\Gamma $-round intervals according to a reward $m_\\Gamma $. To accelerate the neural dialogue learning, $m_\\Gamma $ is defined as the ratio of two consecutive performance deviations on a held-out validation set: $m_\\Gamma =\\frac{\\delta _{\\Gamma }}{\\delta _{\\Gamma _{\\text{prev}}}} - 1$. The performance deviation $\\delta _{\\Gamma }$ is calculated in terms of 13 automatic evaluation metrics $\\lbrace \\xi _1, \\xi _2, \\cdots , \\xi _{13}\\rbrace $ used in the experiments:",
+      "where $\\xi _i^{\\Gamma }$ is the evaluation score of metric $i$ computed at the current validation turn and $\\xi _i^{\\Gamma _{\\text{prev}}}$ is computed at the previous validation turn. Each score is normalized into $[0,1]$.",
+      "The curriculum policy is trained by maximizing the expected reward: $J(\\theta )=\\mathbb {E}_{\\Phi _\\theta (a|s)}[M(s,a)]$, where $M(s,a)$ is the state-action value function. Since $M(s,a)$ is non-differentiable w.r.t. $\\theta $, in this work, we use REINFORCE BIBREF20, a likelihood ratio policy gradient algorithm to optimize $J(\\theta )$ based on the gradient:",
+      "where $v_t$ is the sampled estimation of reward $M(s_t, a_t)$ from one episode execution of the policy $\\Phi _\\theta (a|s)$. In our implementation, $v_t$ is computed as the terminal reward $m_\\Gamma $."
+    ],
+    [
+      "We perform experiments using the following state-of-the-art models: (1) SEQ2SEQ: a sequence-to-sequence model with attention mechanisms BIBREF21, (2) CVAE: a conditional variational auto-encoder model with KL-annealing and a BOW loss BIBREF2, (3) Transformer: an encoder-decoder architecture relying solely on attention mechanisms BIBREF22, (4) HRED: a generalized sequence-to-sequence model with the hierarchical RNN encoder BIBREF23, (5) DialogWAE: a conditional Wasserstein auto-encoder, which models the distribution of data by training a GAN within the latent variable space BIBREF6. We adopt several standard metrics widely used in existing works to measure the performance of dialogue generation models, including BLEU BIBREF24, embedding-based metrics (Average, Extrema, Greedy and Coherence) BIBREF25, BIBREF26, entropy-based metrics (Ent-{1,2}) BIBREF0 and distinct metrics (Dist-{1,2,3} and Intra-{1,2,3}) BIBREF1, BIBREF6."
+    ],
+    [
+      "Our experiments are performed using ParlAI BIBREF27. Regarding model implementations, we employ a 2-layer bidirectional LSTM as the encoder and a unidirectional one as the decoder for the SEQ2SEQ and CVAE. The hidden size is set to 512, and the latent size is set to 64 for CVAE. For the Transformer, the hidden size, attention heads and number of hidden layers are set to 512, 8 and 6, respectively. In terms of HRED and DialogWAE, the utterance encoder is a bidirectional GRU with 512 hidden units in each direction. The context encoder and decoder are both GRUs with 512 hidden units. Regarding the curriculum length $T$, we set its value in the following manner: we train the baseline model using the vanilla training procedure and compute the number of training steps it takes to reach approximately 110% of its final loss value. We then set $T$ to this value. Each model is trained using two protocols: the vanilla training procedure without using any curriculum and our proposed adaptive multi-curricula learning procedure, keeping other configurations the same."
+    ],
+    [
+      "The automatic evaluation results of our proposed multi-curricula learning framework and the comparison models are listed in Table TABREF21. Compared with the vanilla training procedure, our curriculum learning framework 1) brings solid improvements for all the five dialogue models regarding almost all the evaluation metrics, 2) achieves competitive performance across three datasets, affirming the superiority and general applicability of our proposed framework. We also notice that the relative improvements of Distinct on OpenSubtitles are much larger (up to 122.46%) than the other two experiment datasets. We conjecture that the OpenSubtitles, with extremely uneven-complexity dialogue samples, benefits more from the multi-curricula learning paradigm.",
+      "We conduct a human evaluation to validate the effectiveness of the proposed multi-curricula learning framework. We employ the DailyDialog as the evaluation corpus since it is closer to our daily conversations and easier for humans to make the judgment. We randomly sampled 100 cases from the test set and compared the generated responses of the models trained with the vanilla learning procedure and the multi-curricula learning framework. Three annotators, who have no knowledge about which system the response is from, are then required to evaluate among win (response$_1$ is better), loss (response$_2$ is better) and tie (they are equally good or bad) independently, considering four aspects: coherence, logical consistency, fluency and diversity. Cases with different rating results are counted as \u201ctie\u201d. Table TABREF25 reveals the results of the subjective evaluation. We observe that our multi-curricula learning framework outperforms the vanilla training method on all the five dialogue models and the kappa scores indicate that the annotators came to a fair agreement in the judgment. We checked the cases on which the vanilla training method loses to our multi-curricula learning method and found that the vanilla training method usually leads to irrelevant, generic and repetitive responses, while our method effectively alleviates such defects."
+    ],
+    [
+      "To further glean the insights regarding the effects of the five conversational attributes on the proposed learning framework, we conduct the ablation test using the SEQ2SEQ model by only exploiting a single attribute during the curriculum learning. Table TABREF26 reports the ablation test results on the DailyDialog. We observe that the curriculum learning leads to consistent performance improvements, even with one single conversational attribute. When applying the multi-curricula learning method to the model, we observe the nearly best performance."
+    ],
+    [
+      "Adaptive multi-curricula learning enables the model to choose different curricula at different learning stages according to the learning status of the underlying model. As shown in Table TABREF27, we notice the performance drops when replacing the RL-based curriculum policy with the random policy, indicating that choosing different curricula according to the learning status of the model benefits the model training. When training the model with anti-curriculum learning, i.e., feeding examples to the model in the complex-to-easy manner, we also observe consistent performance decreases, affirming the effectiveness of the easy-to-complex learning manner."
+    ],
+    [
+      "Figure FIGREF28 shows comparative results when training the SEQ2SEQ model on DailyDialog with different training protocols. As shown in Figure FIGREF28, our training method accelerates the learning effectively and consistently outperforms the baseline by a large margin in most cases."
+    ],
+    [
+      "To glean insights on how the proposed adaptive multi-curricula learning framework performs, we present the choosing curriculum distributions $\\pi (a_t|s_t)$ during the model learning in Figure FIGREF29. We notice that the model focuses more on the curriculum of \u201cquery-relatedness\u201d at the initial learning stage. As the learning proceeds, the model gradually turns its attention to other curricula. At the final stage, the model pays more attention to the \u201cmodel confidence\u201d curriculum. Such dynamic learning route is quite similar to the human learning behavior."
+    ],
+    [
+      "As shown in Table TABREF30, the most frequently learnt examples are comprehensively far better than those seldom learnt examples, which exhibits the effectiveness of the adaptive multi-curricula learning framework."
+    ],
+    [
+      "Neural dialogue generation. Neural generation models for dialogue, despite their ubiquity in current research, are still far from the real-world applications. Previous approaches enhancing neural dialogue generation models mainly focus on the learning systems by incorporating extra information to the dialogue models such as relevant dialogue history BIBREF5, topics BIBREF28, emotions BIBREF3, out-sourcing knowledge BIBREF4 or exemplars BIBREF29. Latent variables BIBREF0, BIBREF2 also benefit the model with more diverse response generations. In contrast with the previous researches, which pay most attention to the underlying dialogue models, in this work, we concentrate on the dialogue learning process and investigate how the performance of existing dialogue models can be improved on the conversation corpora with varying levels of complexity, by simply adapting the training protocols. BIBREF30 attributed the generic/uninteresting responses to the high-entropy utterances in the training set and proposed to improve dataset quality through data filtering. Though straightforward, the filtering threshold need be carefully chosen to prevent the data size decreasing too much. BIBREF8, BIBREF31 proposed to investigate instance weighting into dialogue systems. However, it is difficult to accurately define the \u201cweight\u201d of an example in conversation systems, since the dialogue data is of high diversity and complexity. Our proposed adaptive multi-curricula learning framework, concentrating on different curricula at evolving learning process according to the learning status of the underlying model, enables dialogue systems gradually proceed from easy to more complex samples in training and thus efficiently improves the response quality.",
+      "Curriculum learning in NLP. BIBREF18 examined curriculum learning and demonstrated empirically that such curriculum approaches indeed help decrease training times and sometimes even improve generalization. BIBREF32 managed curriculum learning as an optimization problem. Curriculum learning has also been applied to many NLP tasks. To name a few, BIBREF10 applied self-paced learning for neural question answering. BIBREF33 proposed a curriculum learning based natural answer generation framework, dealing with low-quality QA-pairs first and then gradually learn more complete answers. BIBREF34 proposed curriculum pointer-generator networks for reading comprehension over long narratives. BIBREF9 applied curriculum learning for neural machine translation (NMT), aiming to reduce the need for specialized training heuristics and boost the performance of existing NMT systems. In our work, instead of organizing the curriculum only from a single aspect, we provide an adaptive multi-curricula dialogue learning framework, grounding our analysis on five conversation attributes regarding the dialogue complexity."
+    ],
+    [
+      "In this paper, we propose an adaptive multi-curricula dialogue learning framework, to enable the dialogue models to gradually proceed from easy samples to more complex ones in training. We first define and analyze five conversational attributes regarding the complexity and easiness of dialogue samples, and then present an adaptive multi-curricula learning framework, which chooses different curricula at different training stages according to the learning status of the model. Extensive experiments conducted on three large-scale datasets and five state-of-the-art conversation models show that our proposed learning framework is able to boost the performance of existing dialogue systems."
+    ],
+    [
+      "This work is supported by the National Natural Science Foundation of China-Joint Fund for Basic Research of General Technology under Grant U1836111 and U1736106. Hongshen Chen and Yonghao Song are the corresponding authors."
+    ]
+  ]
+}
+```
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+Name of Paper: Measuring Conversational Fluidity in Automated Dialogue Agents
+
+Question: was bert used?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Introduction ::: Datasets",
+    "Method",
+    "Method ::: BERT Next Sentence Prediction",
+    "Method ::: Repetition Control",
+    "Method ::: Balance of Dialogue",
+    "Method ::: Short-Safe Answers",
+    "Results",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Conversational interactions between humans and Artificial Intelligence (AI) agents could amount to as much as thousands of interactions a day given recent developments BIBREF0. This surge in human-AI interactions has led to an interest in developing more fluid interactions between agent and human. The term `fluidity', when we refer to dialogue systems, tries to measure the concept of how humanlike communication is between a human and an AI entity. Conversational fluidity has historically been measured using metrics such as perplexity, recall, and F1-scores. However, one finds various drawbacks using these metrics. During the automatic evaluation stage of the second Conversational Intelligence Challenge (ConvAI2) BIBREF1 competition, it was noted that consistently replying with \u201cI am you to do and your is like\u201d would outperform the F1-score of all the models in the competition. This nonsensical phrase was constructed simply by picking several frequent words from the training set. Also, Precision at K, or the more specific Hits@1 metric has been used historically in assessing retrieval based aspects of the agent. This is defined as the accuracy of the next dialogue utterance when choosing between the gold response and N\u20131 distractor responses. Since these metrics are somewhat flawed, human evaluations were used in conjunction. Multiple attempts have been made historically to try to develop automatic metrics to assess dialogue fluidity. One of the earliest Eckert et al. (1997), used a stochastic system which regulated user-generated dialogues to debug and evaluate chatbots BIBREF2. In the same year, Marilyn et al. (1997) proposed the PARADISE BIBREF3 model. This framework was developed to evaluate dialogue agents in spoken conversations. A few years later the BLEU BIBREF4 metric was proposed. Subsequently, for almost two decades, this metric has been one of the few to be widely adopted by the research community. The method, which compares the matches in n-grams from the translated outputted text and the input text proved to be quick, inexpensive and has therefore been widely used. Therefore, we use the BLEU metric as a baseline to compare the quality of our proposed model."
+    ],
+    [
+      "For this study, we use two types of data namely single-turn and multi-turn. The first type, single-turn, is defined such that each instance is made up of one statement and one response. This pair is usually a fragment of a larger dialogue. When given to humans for evaluation of fluidity, we ask to give a score on characteristics such as \u201cHow related is the response to the statement?\u201d or \u201cDoes the response contain repeated text from the user's statement?\u201d. These are all things that should not be affected by the fact that no history or context is provided and therefore, can still be classified reasonably. Contrary to the single turn datasets, the second type is the multi-turn dataset. This contains multiple instances of statements and responses, building on each other to create a fuller conversation. With these kinds of datasets, one can also evaluate and classify the data on various other attributes. An example of such evaluations would be something like \u201cDoes this response continue on the flow of the conversation?\u201d or \u201cIs the chatbot using repetitive text from previous responses?\u201d. The details of how we collected each dataset are detailed below.",
+      "Single-Turn: This dataset consists of single-turn instances of statements and responses from the MiM chatbot developed at Constellation AI BIBREF5. The responses provided were then evaluated using Amazon Mechanical Turk (AMT) workers. A total of five AMT workers evaluated each of these pairs. The mean of the five evaluations is then used as the target variable. A sample can be seen in Table TABREF3. This dataset was used during experiments with results published in the Results section.",
+      "Multi-Turn: This dataset is taken from the ConvAI2 challenge and consists of various types of dialogue that have been generated by human-computer conversations. At the end of each dialogue, an evaluation score has been given, for each dialogue, between 1\u20134."
+    ],
+    [
+      "This section discusses the methods and used to develop our attributes and the technical details of how they are combined to create a final classification layer."
+    ],
+    [
+      "BERT BIBREF6 is a state-of-the-art model, which has been pre-trained on a large corpus and is suitable to be fine-tuned for various downstream NLP tasks. The main innovation between this model and existing language models is in how the model is trained. For BERT, the text conditioning happens on both the left and right context of every word and is therefore bidirectional. In previous models BIBREF7, a unidirectional language model was usually used in the pre-training. With BERT, two fully unsupervised tasks are performed. The Masked Language Model and the Next Sentence Prediction (NSP).",
+      "For this study, the NSP is used as a proxy for the relevance of response. Furthermore, in order to improve performance, we fine-tune on a customized dataset which achieved an accuracy of 82.4%. For the main analysis, we used the single-turn dataset, which gave us a correlation of 0.28 between the mean of the AMT evaluation and the BERT NSP. Next, we put each score into a category. For example, if the average score is 2.3, this would be placed in category 2. We then displayed the percentage of positive and negative predictions in a histogram for each of the categories. As seen in Figure FIGREF5, a clear pattern is seen between the higher scores and the positive prediction, and the lower scores and the negative predictions. details of how they are combined to create a final classification layer."
+    ],
+    [
+      "This attribute is calculated by checking each statement and response for various types of repetition by using n-gram overlap. The motivation for including this as an attribute in dialogue fluidity is that repetitive words or n-grams can be bothersome to the end-user.",
+      "Repetitions are measured according to whether they are internal, external or partner. We calculate a percentage based on the single-turn utterance or the entire multi-turn conversation. We use unigram, bigram, and trigram for each repetition type based off BIBREF8.",
+      "We calculate a correlation of each repetition module with respect to human evaluations in order to understand the impact. For the single-turn dataset, the correlation is -0.09 and 0.07 for the internal and partner repetition attribute respectively. For the multi-turn dataset the correlation was -0.05 and -0.02 for the internal and partner repetition attribute respectively. This low correlation is reasonable and was expected. Measuring repetition in this way is not expected to provide huge classification power. However, we will attempt to exploit differences in correlation between these attributes and ones described below, which will provide some classification power."
+    ],
+    [
+      "For this attribute, we calculated the number of questions asked. For this particular case, we are not able to measure a correlation with respect to human evaluations."
+    ],
+    [
+      "Here, we checked for the length of the utterance and the presence of a Named Entity. We checked the correlation of this attribute with the human evaluation scores. The correlation score attained on the single-turn dataset was -0.09, while for the multi-turn dataset the correlation was 0. The full pipeline can be seen diagrammatically in Figure FIGREF9."
+    ],
+    [
+      "To create a final metric, we combine the individual components from Section 2 as features into a Support Vector Machine. The final results for our F1-score from this classification technique are 0.52 and 0.31 for the single and multi-turn data respectively.",
+      "We compare our results for both the single-turn and multi-turn experiments to the accuracies on the test data based off the BLEU score. We see an increase of 6% for our method with respect to the BLEU score in the single turn data, and a no change when using the multi-turn test set."
+    ],
+    [
+      "This study aimed to implement an automatic metric to assess the fluidity of dialogue systems. We wanted to test if any set of attributes could show a high correlation to manual evaluations, thus replacing it entirely. As highlighted in the ConvAI2 challenge, automatic metrics are not reliable for a standalone evaluation of low-level dialogue outputs. For this study, three attributes were investigated. Tests were carried out based on these proposed attributes by making use of single and multi-turn datasets. These attributes, combined with the BERT model, showed that our classifier performed better than the BLEU model for the single-turn dataset. However, no improvement was seen on the multi-turn dataset.",
+      "Concerning feature importance, we observed that internal repetition and NSP are the most important attributes when used to classify fluidity. We believe that further work can be carried out in finding a more discriminating set of attributes."
+    ]
+  ]
+}
+```
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diff --git a/qasper-1342/instruction.md b/qasper-1342/instruction.md
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+Name of Paper: BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs
+
+Question: What were the five English subtasks?
\ No newline at end of file
diff --git a/qasper-1343/instruction.md b/qasper-1343/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..6dbce2f833cd4a89f3e7b5472d5c01c44b164e73
--- /dev/null
+++ b/qasper-1343/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs
+
+Question: How many CNNs and LSTMs were ensembled?
\ No newline at end of file
diff --git a/qasper-1344/instruction.md b/qasper-1344/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..b5324693bb82929e6b5cae141194a00d2ff41eb0
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@@ -0,0 +1,147 @@
+Name of Paper: Classifying movie genres by analyzing text reviews
+
+Question: what was the baseline?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Theory",
+    "Preprocessing",
+    "Models",
+    "Evaluation",
+    "Data",
+    "Method",
+    "Data collection",
+    "Data preprocessing",
+    "Model",
+    "Result",
+    "Discussion",
+    "Conclusion",
+    "All genres"
+  ],
+  "paragraphs": [
+    [
+      "By only reading a single text review of a movie it can be difficult to say what the genre of that movie is, but by using text mining techniques on thousands of movie reviews is it possible to predict the genre?",
+      "This paper explores the possibility of classifying genres of a movie based only on a text review of that movie. This is an interesting problem because to the naked eye it may seem difficult to predict the genre by only looking at a text review. One example of a review can be seen in the following example:",
+      "I liked the film. Some of the action scenes were very interesting, tense and well done. I especially liked the opening scene which had a semi truck in it. A very tense action scene that seemed well done. Some of the transitional scenes were filmed in interesting ways such as time lapse photography, unusual colors, or interesting angles. Also the film is funny is several parts. I also liked how the evil guy was portrayed too. I'd give the film an 8 out of 10.",
+      "http://www.imdb.com/title/tt0211938/reviews",
+      "From the quoted review, one could probably predict the movie falls in the action genre; however, it would be difficult to predict all three of the genres (action, comedy, crime) that International Movie Database (IMDB) lists. With the use of text mining techniques it is feasible to predict multiple genres based on a review.",
+      "There are numerous previous works on classifying the sentiment of reviews, e.g., maas-EtAl:2011:ACL-HLT2011 by BIBREF0 . There are fewer scientific papers available on specifically classifying movie genres based on reviews; therefore, inspiration for this paper comes from papers describing classification of text for other or general contexts. One of those papers is DBLP:journals/corr/cmp-lg-9707002 where BIBREF1 describe how to use a multilayer perceptron (MLP) for genre classification.",
+      "All data, in the form of reviews and genres, used in this paper originates from IMDb."
+    ],
+    [
+      "In this section all relevant theory and methodology is described. Table TABREF1 lists basic terminology and a short description of their meaning."
+    ],
+    [
+      "Data preprocessing is important when working with text data because it can reduce the number of features and it formats the data into the desired form BIBREF2 .",
+      "Removing stop words is a common type of filtering in text mining. Stop words are words that usually contain little or no information by itself and therefore it is better to remove them. Generally words that occur often can be considered stop words such as the, a and it. BIBREF2 ",
+      "Lemmatization is the process of converting verbs into their infinitive tense form and nouns into their singular form. The reason for doing this is to reduce words into their basic forms and thus simplify the data. For example am, are and is are converted to be. BIBREF2 ",
+      "A way of representing a large corpus is to calculate the Term Frequency Inverse Document Frequency (tf-idf) of the corpus and then feed the models the tf-idf. As described in ramos2003using by BIBREF3 tf-idf is both efficient and simple for matching a query of words with a document in a corpus. Tf-idf is calculated by multiplying the Term Frequency (tf) with the Inverse Document Frequency (idf) , which is formulated as DISPLAYFORM0 ",
+      "where INLINEFORM0 is a document in corpus INLINEFORM1 and INLINEFORM2 is a term. INLINEFORM3 is defined as DISPLAYFORM0 ",
+      "and INLINEFORM0 is defined as DISPLAYFORM0 ",
+      "where INLINEFORM0 is the number of times INLINEFORM1 occurs in INLINEFORM2 and INLINEFORM3 total number of documents in the corpus."
+    ],
+    [
+      "MLP is a class of feedforward neural network built up by a layered acyclic graph. An MLP consists of at least three layers and non-linear activations. The first layer is called input layer, the second layer is called hidden layer and the third layer is called output layer. The three layers are fully connected which means that every node in the hidden layer is connected to every node in the other layers. MLP is trained using backpropagation, where the weights are updated by calculating the gradient descent with respect to an error function. BIBREF4 ",
+      "K-nearest Neighbors (KNN) works by evaluating similarities between entities, where INLINEFORM0 stands for how many neighbors are taken into account during the classification. KNN is different from MLP in the sense that it does not require a computationally heavy training step; instead, all of the computation is done at the classification step. There are multiple ways of calculating the similarity, one way is to calculate the Minkowski distance. The Minkowski distance between two points DISPLAYFORM0 ",
+      "and DISPLAYFORM0 ",
+      "is defined by DISPLAYFORM0 ",
+      "where INLINEFORM0 which is equal to the Euclidean distance. BIBREF2 "
+    ],
+    [
+      "When evaluating classifiers it is common to use accuracy, precision and recall as well as Hamming loss. Accuracy, precision and recall are defined by the the four terms true positive ( INLINEFORM0 ), true negative ( INLINEFORM1 ), false positive ( INLINEFORM2 ) and false negative ( INLINEFORM3 ) which can be seen in table TABREF16 .",
+      "Accuracy is a measurement of how correct a model's predictions are and is defined as DISPLAYFORM0 ",
+      ".",
+      "Precision is a ratio of how often positive predictions actually are positve and is defined as DISPLAYFORM0 ",
+      ".",
+      "Recall is a measurement of how good the model is to find all true positives and is defined as DISPLAYFORM0 ",
+      ". BIBREF5 ",
+      "It has been shown that when calculating precision and recall on multi-label classifiers, it can be advantageous to use micro averaged precision and recall BIBREF6 . The formulas for micro averaged precision are expressed as DISPLAYFORM0 DISPLAYFORM1 ",
+      "where INLINEFORM0 is label index and INLINEFORM1 is number of labels.",
+      "Hamming loss is different in the sense that it is a loss and it is defined as the fraction of wrong labels to the total number of labels. Hamming loss can be a good measurement when it comes to evaluating multi-label classifiers. the hamming loss is expressed as DISPLAYFORM0 ",
+      "where INLINEFORM0 is number of documents, INLINEFORM1 number of labels, INLINEFORM2 is the target value and INLINEFORM3 is predicted value. BIBREF7 ",
+      "For evaluation the INLINEFORM0 and INLINEFORM1 was calculated as defined in section SECREF15 for both the MLP model and the KNN model. For precision and recall formulas EQREF20 and EQREF21 were used because of their advantage in multi-label classification. The distribution of predicted genres was also shown in a histogram and compared to the target distribution of genres.",
+      "Furthermore the ratio of reviews that got zero genres predicted was also calculated and can be expressed as DISPLAYFORM0 ",
+      "where INLINEFORM0 is the number of reviews without any predicted genre and INLINEFORM1 is the total amount of predicted reviews."
+    ],
+    [
+      "Data used in this paper comes from two separate sources. The first source was Large Movie Review Dataset v1.0 BIBREF0 which is a dataset for binary sentiment analysis of moview reviews. The dataset contains a total of 50000 reviews in raw text together with information on whether the review is positive or negative and a URL to the movie on IMDb. The sentiment information was not used in this paper. Out of the 50000, reviews only 7000 were used because of limitations on computational power, resulting in a corpus of 7000 documents.",
+      "The second source of data was the genres for all reviews which were scraped from the IMDb site. A total of 27 different genres were scraped. A list of all genres can be find in Appendix SECREF8 . A review can have one genre or multiple genres. For example a review can be for a movie that is both Action, Drama and Thriller at the same time while another move only falls into Drama."
+    ],
+    [
+      "This section presents all steps needed to reproduce the results presented in this paper."
+    ],
+    [
+      "In this paper the data comes from two sources where the first is a collection of text reviews. Those reviews were downloaded from Large Movie Review Datasets website . Because only 7000 reviews was used in this paper all of them were from the `train` folder and split evenly between positive reviews and negative reviews.",
+      "The genres for the reviews where obtained by iterating through all reviews and doing the following steps:",
+      "Save the text of the review.",
+      "Retrieve IMDb URL to the movie from the Large Movie Review Datasets data.",
+      "Scrape that movie website for all genres and download the genres.",
+      "The distribution of genres was plotted in a histogram to check that the scraped data looked reasonable and can be seen in figure FIGREF27 . All genres with less than 50 reviews corresponding to that genre were removed.",
+      "The number of genres per review can be seen in figure FIGREF28 and it shows that it is most common for a review to have three different genres; furthermore, it shows that no review has more than three genres.",
+      "http://ai.stanford.edu/ amaas/data/sentiment"
+    ],
+    [
+      "All reviews were preprocessed according to the following steps:",
+      "Remove all non-alphanumeric characters.",
+      "Lower case all tokens.",
+      "Remove all stopwords.",
+      "Lemmatize all tokens.",
+      "Both the removal of stopwords and lemmatization were done with Python's Natural Language Toolkit (NLTK). Next the reviews and corresponding genres were split into a training set and a test set with INLINEFORM0 devided into the train set and INLINEFORM1 into the test set.",
+      "The preprocessed corpus was then used to calculate a tf-idf representing all reviews. The calculation of the tf-idf was done using scikit-learn'smodule TfidfVectorizer. Both transform and fit were run on the training set and only the transform was run on the test set. The decision to use tf-idf as a data representation is supported by BIBREF3 in ramos2003using which concludes that tf-idf is both simple and effective at categorizing relevant words.",
+      "https://www.python.org http://www.nltk.org http://scikit-learn.org"
+    ],
+    [
+      "This paper experimented with two different models and compared them against each other. The inspiration for the first model comes from BIBREF1 in their paper DBLP:journals/corr/cmp-lg-9707002 where they used an MLP for text genre detection. The model used in this paper comes from scikit-learn's neural_network module and is called MLPClassifier. Table TABREF35 shows all parameters that were changed from the default values.",
+      "The second model was a KNN which was chosen because of it is simple and does not require the pre-training that the MLP needs. The implementation of this model comes from scikit-learn's neighbors module and is called KNeighborsClassifier. The only parameter that was changed after some trial and error was the k-parameter which was set to 3.",
+      "Both models were fitted using the train set and then predictions were done for the test set."
+    ],
+    [
+      "Table TABREF38 shows the INLINEFORM0 , INLINEFORM1 and INLINEFORM2 for the models. The KNN model had a higher accuracy of INLINEFORM3 compared to MPL's accuracy of INLINEFORM4 and the KNN model had a higher recall but slightly lower precision than the MLP model.",
+      "Table TABREF39 shows the INLINEFORM0 and INLINEFORM1 for the models, it shows that the KNN model had lower values for both the INLINEFORM2 and INLINEFORM3 compared to the MLP model.",
+      "Figure FIGREF40 shows the distribution of the genres for the predicted values when using MLP and the test set. The same comparison between KNN and the test set can be seen in figure FIGREF41 ."
+    ],
+    [
+      "When looking at the results it is apparent that KNN is better than MLP in these experiments. In particular, the INLINEFORM0 stands out between KNN and MLP where KNN got INLINEFORM1 and MLP got INLINEFORM2 which is considered a significant difference. Given that the INLINEFORM3 was relatively high for both models, this result hints that the models only predicted genres when the confidence was high, which resulted in fewer genres being predicted than the target. This can also be confirmed by looking at the figures FIGREF40 and FIGREF41 where the absolute number of reviews predicted for most genres was lower than the target. This unsatisfyingly low INLINEFORM4 can be explained by the multi-label nature of the problem in this paper. Even if the model correctly predicted 2 out of three genres it is considered a misclassification. A reason for the low accuracy could be that the models appeared to be on the conservative side when predicting genres.",
+      "Another factor that affected the performance of the models was the INLINEFORM0 which confirmed that over INLINEFORM1 of the reviews for the KNN model and over INLINEFORM2 of the reviews for the MLP model did not receive any predicted genre. Because no review had zero genres all predictions with zero genres are misclassified and this could be a good place to start when improving the models.",
+      "Furthermore, when looking at the INLINEFORM0 it shows that when looking at the individual genres for all reviews the number of wrong predictions are very low which is promising when trying to answer this paper's main question: whether it is possible to predict the genre of the movie associated with a text review. It should be taken into account that this paper only investigated about 7000 movie reviews and the results could change significantly, for better or for worse, if a much larger data set was used. In this paper, some of the genres had very low amounts of training data, which could be why those genres were not predicted in the same frequency as the target. An example of that can be seen by looking at genre Sci-Fi in figure FIGREF40 ."
+    ],
+    [
+      "This paper demonstrates that by only looking at text reviews of a movie, there is enough information to predict its genre with an INLINEFORM0 of INLINEFORM1 . This result implies that movie reviews carry latent information about genres. This paper also shows the complexity of doing prediction on multi-label problems, both in implementation and data processing but also when it comes to evaluation. Regular metrics typically work, but they mask the entire picture and the depth of how good a model is.",
+      "Finally this paper provides an explanation of the whole process needed to conduct an experiment like this. The process includes downloading a data set, web scraping for extra information, data preprocessing, model tuning and evaluation of the results."
+    ],
+    [
+      "Action",
+      "Adult",
+      "Adventure",
+      "Animation",
+      "Biography",
+      "Comedy",
+      "Crime",
+      "Documentary",
+      "Drama",
+      "Family",
+      "Fantasy",
+      "Film-Noir",
+      "Game-Show",
+      "History",
+      "Horror",
+      "Music",
+      "Musical",
+      "Mystery",
+      "Reality-TV",
+      "Romance",
+      "Sci-Fi",
+      "Short",
+      "Sport",
+      "Talk-Show",
+      "Thriller",
+      "War",
+      "Western"
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1372/instruction.md b/qasper-1372/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..b1e14aee7c82ec8132606291fcce5f3e4a8dd238
--- /dev/null
+++ b/qasper-1372/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Zero-Shot Paraphrase Generation with Multilingual Language Models
+
+Question: How much better are results of proposed model compared to pivoting method?
\ No newline at end of file
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new file mode 100644
index 0000000000000000000000000000000000000000..7b359c8ebd4399bbccff8b41ed907ea73aeaa0b8
--- /dev/null
+++ b/qasper-1373/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: WiSeBE: Window-based Sentence Boundary Evaluation
+
+Question: What kind of Youtube video transcripts did they use?
\ No newline at end of file
diff --git a/qasper-1374/instruction.md b/qasper-1374/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..d3ca6bf32ab63f489b1092695a2a38b49e3d16dc
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+++ b/qasper-1374/instruction.md
@@ -0,0 +1,114 @@
+Name of Paper: WiSeBE: Window-based Sentence Boundary Evaluation
+
+Question: Which SBD systems did they compare?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Sentence Boundary Detection",
+    "Sentence Boundary Evaluation",
+    "Window-Based Sentence Boundary Evaluation",
+    "General Reference and Agreement Ratio",
+    "Window-Boundaries Reference",
+    "WiSeBEWiSeBE",
+    "Evaluating with WiSeBEWiSeBE",
+    "Dataset",
+    "Evaluation",
+    "R G AR  R_{G_{AR}} and Fleiss' Kappa correlation",
+    "F1 mean F1_{mean} vs. WiSeBEWiSeBE",
+    "Conclusions",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "The goal of Automatic Speech Recognition (ASR) is to transform spoken data into a written representation, thus enabling natural human-machine interaction BIBREF0 with further Natural Language Processing (NLP) tasks. Machine translation, question answering, semantic parsing, POS tagging, sentiment analysis and automatic text summarization; originally developed to work with formal written texts, can be applied over the transcripts made by ASR systems BIBREF1 , BIBREF2 , BIBREF3 . However, before applying any of these NLP tasks a segmentation process called Sentence Boundary Detection (SBD) should be performed over ASR transcripts to reach a minimal syntactic information in the text.",
+      "To measure the performance of a SBD system, the automatically segmented transcript is evaluated against a single reference normally done by a human. But given a transcript, does it exist a unique reference? Or, is it possible that the same transcript could be segmented in five different ways by five different people in the same conditions? If so, which one is correct; and more important, how to fairly evaluate the automatically segmented transcript? These questions are the foundations of Window-based Sentence Boundary Evaluation (WiSeBE), a new semi-supervised metric for evaluating SBD systems based on multi-reference (dis)agreement.",
+      "The rest of this article is organized as follows. In Section SECREF2 we set the frame of SBD and how it is normally evaluated. WiSeBE is formally described in Section SECREF3 , followed by a multi-reference evaluation in Section SECREF4 . Further analysis of WiSeBE and discussion over the method and alternative multi-reference evaluation is presented in Section SECREF5 . Finally, Section SECREF6 concludes the paper."
+    ],
+    [
+      "Sentence Boundary Detection (SBD) has been a major research topic science ASR moved to more general domains as conversational speech BIBREF4 , BIBREF5 , BIBREF6 . Performance of ASR systems has improved over the years with the inclusion and combination of new Deep Neural Networks methods BIBREF7 , BIBREF8 , BIBREF0 . As a general rule, the output of ASR systems lacks of any syntactic information such as capitalization and sentence boundaries, showing the interst of ASR systems to obtain the correct sequence of words with almost no concern of the overall structure of the document BIBREF9 .",
+      "Similar to SBD is the Punctuation Marks Disambiguation (PMD) or Sentence Boundary Disambiguation. This task aims to segment a formal written text into well formed sentences based on the existent punctuation marks BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 . In this context a sentence is defined (for English) by the Cambridge Dictionary as:",
+      "\u201ca group of words, usually containing a verb, that expresses a thought in the form of a statement, question, instruction, or exclamation and starts with a capital letter when written\u201d.",
+      "PMD carries certain complications, some given the ambiguity of punctuation marks within a sentence. A period can denote an acronym, an abbreviation, the end of the sentence or a combination of them as in the following example:",
+      "The U.S. president, Mr. Donald Trump, is meeting with the F.B.I. director Christopher A. Wray next Thursday at 8p.m.",
+      "However its difficulties, DPM profits of morphological and lexical information to achieve a correct sentence segmentation. By contrast, segmenting an ASR transcript should be done without any (or almost any) lexical information and a flurry definition of sentence.",
+      "The obvious division in spoken language may be considered speaker utterances. However, in a normal conversation or even in a monologue, the way ideas are organized differs largely from written text. This differences, added to disfluencies like revisions, repetitions, restarts, interruptions and hesitations make the definition of a sentence unclear thus complicating the segmentation task BIBREF14 . Table TABREF2 exemplifies some of the difficulties that are present when working with spoken language.",
+      "Stolcke & Shriberg BIBREF6 considered a set of linguistic structures as segments including the following list:",
+      "In BIBREF4 , Meteer & Iyer divided speaker utterances into segments, consisting each of a single independent clause. A segment was considered to begin either at the beginning of an utterance, or after the end of the preceding segment. Any dysfluency between the end of the previous segments and the begging of current one was considered part of the current segments.",
+      "Rott & \u010cerva BIBREF15 aimed to summarize news delivered orally segmenting the transcripts into \u201csomething that is similar to sentences\u201d. They used a syntatic analyzer to identify the phrases within the text.",
+      "A wide study focused in unbalanced data for the SBD task was performed by Liu et al. BIBREF16 . During this study they followed the segmentation scheme proposed by the Linguistic Data Consortium on the Simple Metadata Annotation Specification V5.0 guideline (SimpleMDE_V5.0) BIBREF14 , dividing the transcripts in Semantic Units.",
+      "A Semantic Unit (SU) is considered to be an atomic element of the transcript that manages to express a complete thought or idea on the part of the speaker BIBREF14 . Sometimes a SU corresponds to the equivalent of a sentence in written text, but other times (the most part of them) a SU corresponds to a phrase or a single word.",
+      "SUs seem to be an inclusive conception of a segment, they embrace different previous segment definitions and are flexible enough to deal with the majority of spoken language troubles. For these reasons we will adopt SUs as our segment definition."
+    ],
+    [
+      "SBD research has been focused on two different aspects; features and methods. Regarding the features, some work focused on acoustic elements like pauses duration, fundamental frequencies, energy, rate of speech, volume change and speaker turn BIBREF17 , BIBREF18 , BIBREF19 .",
+      "The other kind of features used in SBD are textual or lexical features. They rely on the transcript content to extract features like bag-of-word, POS tags or word embeddings BIBREF20 , BIBREF18 , BIBREF21 , BIBREF22 , BIBREF15 , BIBREF6 , BIBREF23 . Mixture of acoustic and lexical features have also been explored BIBREF24 , BIBREF25 , BIBREF19 , BIBREF26 , which is advantageous when both audio signal and transcript are available.",
+      "With respect to the methods used for SBD, they mostly rely on statistical/neural machine translation BIBREF18 , BIBREF27 , language models BIBREF9 , BIBREF16 , BIBREF22 , BIBREF6 , conditional random fields BIBREF21 , BIBREF28 , BIBREF23 and deep neural networks BIBREF29 , BIBREF20 , BIBREF13 .",
+      "Despite their differences in features and/or methodology, almost all previous cited research share a common element; the evaluation methodology. Metrics as Precision, Recall, F1-score, Classification Error Rate and Slot Error Rate (SER) are used to evaluate the proposed system against one reference. As discussed in Section SECREF1 , further NLP tasks rely on the result of SBD, meaning that is crucial to have a good segmentation. But comparing the output of a system against a unique reference will provide a reliable score to decide if the system is good or bad?",
+      "Bohac et al. BIBREF24 compared the human ability to punctuate recognized spontaneous speech. They asked 10 people (correctors) to punctuate about 30 minutes of ASR transcripts in Czech. For an average of 3,962 words, the punctuation marks placed by correctors varied between 557 and 801; this means a difference of 244 segments for the same transcript. Over all correctors, the absolute consensus for period (.) was only 4.6% caused by the replacement of other punctuation marks as semicolons (;) and exclamation marks (!). These results are understandable if we consider the difficulties presented previously in this section.",
+      "To our knowledge, the amount of studies that have tried to target the sentence boundary evaluation with a multi-reference approach is very small. In BIBREF24 , Bohac et al. evaluated the overall punctuation accuracy for Czech in a straightforward multi-reference framework. They considered a period (.) valid if at least five of their 10 correctors agreed on its position.",
+      "Kol\u00e1\u0159 & Lamel BIBREF25 considered two independent references to evaluate their system and proposed two approaches. The fist one was to calculate the SER for each of one the two available references and then compute their mean. They found this approach to be very strict because for those boundaries where no agreement between references existed, the system was going to be partially wrong even the fact that it has correctly predicted the boundary. Their second approach tried to moderate the number of unjust penalizations. For this case, a classification was considered incorrect only if it didn't match either of the two references.",
+      "These two examples exemplify the real need and some straightforward solutions for multi-reference evaluation metrics. However, we think that it is possible to consider in a more inclusive approach the similarities and differences that multiple references could provide into a sentence boundary evaluation protocol."
+    ],
+    [
+      "Window-Based Sentence Boundary Evaluation (WiSeBE) is a semi-automatic multi-reference sentence boundary evaluation protocol which considers the performance of a candidate segmentation over a set of segmentation references and the agreement between those references.",
+      "Let INLINEFORM0 be the set of all available references given a transcript INLINEFORM1 , where INLINEFORM2 is the INLINEFORM3 word in the transcript; a reference INLINEFORM4 is defined as a binary vector in terms of the existent SU boundaries in INLINEFORM5 . DISPLAYFORM0 ",
+      "where INLINEFORM0 ",
+      "Given a transcript INLINEFORM0 , the candidate segmentation INLINEFORM1 is defined similar to INLINEFORM2 . DISPLAYFORM0 ",
+      "where INLINEFORM0 "
+    ],
+    [
+      "A General Reference ( INLINEFORM0 ) is then constructed to calculate the agreement ratio between all references in. It is defined by the boundary frequencies of each reference INLINEFORM1 . DISPLAYFORM0 ",
+      "where DISPLAYFORM0 ",
+      "The Agreement Ratio ( INLINEFORM0 ) is needed to get a numerical value of the distribution of SU boundaries over INLINEFORM1 . A value of INLINEFORM2 close to 0 means a low agreement between references in INLINEFORM3 , while INLINEFORM4 means a perfect agreement ( INLINEFORM5 ) in INLINEFORM6 . DISPLAYFORM0 ",
+      "In the equation above, INLINEFORM0 corresponds to the ponderated common boundaries of INLINEFORM1 and INLINEFORM2 to its hypothetical maximum agreement. DISPLAYFORM0 DISPLAYFORM1 "
+    ],
+    [
+      "In Section SECREF2 we discussed about how disfluencies complicate SU segmentation. In a multi-reference environment this causes disagreement between references around a same SU boundary. The way WiSeBE handle disagreements produced by disfluencies is with a Window-boundaries Reference ( INLINEFORM0 ) defined as: DISPLAYFORM0 ",
+      "where each window INLINEFORM0 considers one or more boundaries INLINEFORM1 from INLINEFORM2 with a window separation limit equal to INLINEFORM3 . DISPLAYFORM0 "
+    ],
+    [
+      "WiSeBE is a normalized score dependent of 1) the performance of INLINEFORM0 over INLINEFORM1 and 2) the agreement between all references in INLINEFORM2 . It is defined as: DISPLAYFORM0 ",
+      "where INLINEFORM0 corresponds to the harmonic mean of precision and recall of INLINEFORM1 with respect to INLINEFORM2 (equation EQREF23 ), while INLINEFORM3 is the agreement ratio defined in ( EQREF15 ). INLINEFORM4 can be interpreted as a scaling factor; a low value will penalize the overall WiSeBE score given the low agreement between references. By contrast, for a high agreement in INLINEFORM5 ( INLINEFORM6 ), INLINEFORM7 . DISPLAYFORM0 DISPLAYFORM1 ",
+      "Equations EQREF24 and EQREF25 describe precision and recall of INLINEFORM0 with respect to INLINEFORM1 . Precision is the number of boundaries INLINEFORM2 inside any window INLINEFORM3 from INLINEFORM4 divided by the total number of boundaries INLINEFORM5 in INLINEFORM6 . Recall corresponds to the number of windows INLINEFORM7 with at least one boundary INLINEFORM8 divided by the number of windows INLINEFORM9 in INLINEFORM10 ."
+    ],
+    [
+      "To exemplify the INLINEFORM0 score we evaluated and compared the performance of two different SBD systems over a set of YouTube videos in a multi-reference enviroment. The first system (S1) employs a Convolutional Neural Network to determine if the middle word of a sliding window corresponds to a SU boundary or not BIBREF30 . The second approach (S2) by contrast, introduces a bidirectional Recurrent Neural Network model with attention mechanism for boundary detection BIBREF31 .",
+      "In a first glance we performed the evaluation of the systems against each one of the references independently. Then, we implemented a multi-reference evaluation with INLINEFORM0 ."
+    ],
+    [
+      "We focused evaluation over a small but diversified dataset composed by 10 YouTube videos in the English language in the news context. The selected videos cover different topics like technology, human rights, terrorism and politics with a length variation between 2 and 10 minutes. To encourage the diversity of content format we included newscasts, interviews, reports and round tables.",
+      "During the transcription phase we opted for a manual transcription process because we observed that using transcripts from an ASR system will difficult in a large degree the manual segmentation process. The number of words per transcript oscilate between 271 and 1,602 with a total number of 8,080.",
+      "We gave clear instructions to three evaluators ( INLINEFORM0 ) of how segmentation was needed to be perform, including the SU concept and how punctuation marks were going to be taken into account. Periods (.), question marks (?), exclamation marks (!) and semicolons (;) were considered SU delimiters (boundaries) while colons (:) and commas (,) were considered as internal SU marks. The number of segments per transcript and reference can be seen in Table TABREF27 . An interesting remark is that INLINEFORM1 assigns about INLINEFORM2 less boundaries than the mean of the other two references."
+    ],
+    [
+      "We ran both systems (S1 & S2) over the manually transcribed videos obtaining the number of boundaries shown in Table TABREF29 . In general, it can be seen that S1 predicts INLINEFORM0 more segments than S2. This difference can affect the performance of S1, increasing its probabilities of false positives.",
+      "Table TABREF30 condenses the performance of both systems evaluated against each one of the references independently. If we focus on F1 scores, performance of both systems varies depending of the reference. For INLINEFORM0 , S1 was better in 5 occasions with respect of S2; S1 was better in 2 occasions only for INLINEFORM1 ; S1 overperformed S2 in 3 occasions concerning INLINEFORM2 and in 4 occasions for INLINEFORM3 (bold).",
+      "Also from Table TABREF30 we can observe that INLINEFORM0 has a bigger similarity to S1 in 5 occasions compared to other two references, while INLINEFORM1 is more similar to S2 in 7 transcripts (underline).",
+      "After computing the mean F1 scores over the transcripts, it can be concluded that in average S2 had a better performance segmenting the dataset compared to S1, obtaining a F1 score equal to 0.510. But... What about the complexity of the dataset? Regardless all references have been considered, nor agreement or disagreement between them has been taken into account.",
+      "",
+      "All values related to the INLINEFORM0 score are displayed in Table TABREF31 . The Agreement Ratio ( INLINEFORM1 ) between references oscillates between 0.525 for INLINEFORM2 and 0.767 for INLINEFORM3 . The lower the INLINEFORM4 , the bigger the penalization INLINEFORM5 will give to the final score. A good example is S2 for transcript INLINEFORM6 where INLINEFORM7 reaches a value of 0.800, but after considering INLINEFORM8 the INLINEFORM9 score falls to 0.462.",
+      "It is feasible to think that if all references are taken into account at the same time during evaluation ( INLINEFORM0 ), the score will be bigger compared to an average of independent evaluations ( INLINEFORM1 ); however this is not always true. That is the case of S1 in INLINEFORM2 , which present a slight decrease for INLINEFORM3 compared to INLINEFORM4 .",
+      "An important remark is the behavior of S1 and S2 concerning INLINEFORM0 . If evaluated without considering any (dis)agreement between references ( INLINEFORM1 ), S2 overperforms S1; this is inverted once the systems are evaluated with INLINEFORM2 ."
+    ],
+    [
+      "In Section SECREF3 we described the INLINEFORM0 score and how it relies on the INLINEFORM1 value to scale the performance of INLINEFORM2 over INLINEFORM3 . INLINEFORM4 can intuitively be consider an agreement value over all elements of INLINEFORM5 . To test this hypothesis, we computed the Pearson correlation coefficient ( INLINEFORM6 ) BIBREF32 between INLINEFORM7 and the Fleiss' Kappa BIBREF33 of each video in the dataset ( INLINEFORM8 ).",
+      "A linear correlation between INLINEFORM0 and INLINEFORM1 can be observed in Table TABREF33 . This is confirmed by a INLINEFORM2 value equal to INLINEFORM3 , which means a very strong positive linear correlation between them."
+    ],
+    [
+      "Results form Table TABREF31 may give an idea that INLINEFORM0 is just an scaled INLINEFORM1 . While it is true that they show a linear correlation, INLINEFORM2 may produce a different system ranking than INLINEFORM3 given the integral multi-reference principle it follows. However, what we consider the most profitable about INLINEFORM4 is the twofold inclusion of all available references it performs. First, the construction of INLINEFORM5 to provide a more inclusive reference against to whom be evaluated and then, the computation of INLINEFORM6 , which scales the result depending of the agreement between references."
+    ],
+    [
+      "In this paper we presented WiSeBE, a semi-automatic multi-reference sentence boundary evaluation protocol based on the necessity of having a more reliable way for evaluating the SBD task. We showed how INLINEFORM0 is an inclusive metric which not only evaluates the performance of a system against all references, but also takes into account the agreement between them. According to your point of view, this inclusivity is very important given the difficulties that are present when working with spoken language and the possible disagreements that a task like SBD could provoke.",
+      " INLINEFORM0 shows to be correlated with standard SBD metrics, however we want to measure its correlation with extrinsic evaluations techniques like automatic summarization and machine translation."
+    ],
+    [
+      "We would like to acknowledge the support of CHIST-ERA for funding this work through the Access Multilingual Information opinionS (AMIS), (France - Europe) project.",
+      "We also like to acknowledge the support given by the Prof. Hanifa Boucheneb from VERIFORM Laboratory (\u00c9cole Polytechnique de Montr\u00e9al)."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1375/instruction.md b/qasper-1375/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..66b490059eb6a2eda3d25c6fa4cb618856dcccfc
--- /dev/null
+++ b/qasper-1375/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: WiSeBE: Window-based Sentence Boundary Evaluation
+
+Question: What makes it a more reliable metric?
\ No newline at end of file
diff --git a/qasper-1380/instruction.md b/qasper-1380/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..7ce7a7e981ba6b5e6cbb230eaf407407c7dc4fe3
--- /dev/null
+++ b/qasper-1380/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Gender Representation in Open Source Speech Resources
+
+Question: What representations are presented by this paper?
\ No newline at end of file
diff --git a/qasper-1381/instruction.md b/qasper-1381/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..dd0d7b6327d788dbce528a5774941b1be17483b4
--- /dev/null
+++ b/qasper-1381/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Gender Representation in Open Source Speech Resources
+
+Question: What corpus characteristics correlate with more equitable gender balance?
\ No newline at end of file
diff --git a/qasper-1386/instruction.md b/qasper-1386/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..40a03140f5ee5015c2caafdf75c2e1194b7d98fe
--- /dev/null
+++ b/qasper-1386/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: A Neural Approach to Discourse Relation Signal Detection
+
+Question: Where does proposed metric overlap with juman judgement?
\ No newline at end of file
diff --git a/qasper-1387/instruction.md b/qasper-1387/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..4e962125f4ac44d36cb24863e35dc9c58ecb64e1
--- /dev/null
+++ b/qasper-1387/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Citation Text Generation
+
+Question: Which baseline performs best?
\ No newline at end of file
diff --git a/qasper-1388/instruction.md b/qasper-1388/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..21d94bc03a93314d1a4cd5888f04a5e5e3e623db
--- /dev/null
+++ b/qasper-1388/instruction.md
@@ -0,0 +1,105 @@
+Name of Paper: Citation Text Generation
+
+Question: Which baselines are explored?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Task",
+    "Models",
+    "Models ::: Neural Text Generation",
+    "Models ::: Neural Text Generation ::: Context",
+    "Models ::: Retrieval with Approximate Nearest Neighbors",
+    "Models ::: Language Model Pretraining",
+    "Evaluation",
+    "Analysis",
+    "Analysis ::: Errors",
+    "Analysis ::: Examples",
+    "Analysis ::: Future Work",
+    "Related Work",
+    "Conclusion",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "The output of the world's scientists doubles roughly every nine years BIBREF0, and their pace is quickening. As a result, scientists and other experts must devote significant time to the difficult task of literature review, or coming to understand the context in which they work. Might artificial intelligence help to reduce that time?",
+      "Several lines of research seek to do so. Citation recommendations systems BIBREF1, BIBREF2, BIBREF3 suggest references to relevant published work for a given document such as a current draft. Summarization systems BIBREF4, BIBREF5 condense the information in one or more documents, allowing researchers to more quickly understand the basic ideas in a piece of research.",
+      "We introduce a complementary\u2014but so far unaddressed\u2014problem, citation text generation, where the relationship between a document and one or several others is expressed in natural language text. This differs from traditional summarization in that the primary focus is explaining the relationship between the two documents rather than their content. Automatically describing inter-document relationships could dramatically decrease the time researchers devote to literature review. For instance, a new paper could be explained in terms of its relationships to relevant works that a particular reader is most familiar with, rather than just those which the authors elected to cite (personalization). Further, such technology could be incorporated into writing assistance systems to help less experienced or non-native writers better articulate the connection between their work and prior art. Additionally, users of citation recommendation systems can benefit from natural language explanations of recommendation system choices.",
+      "Beyond the immediate utility of citation text generation systems, the task offers significant challenges for language understanding and generation research. A major challenge is how to represent the information in one or more scientific texts. These documents are longer than those in most other domains typically studied in NLP, and make use of a long-tailed, open-domain technical vocabulary. Often an important phrase in the citing sentence output occurs only in a specific cited document and not elsewhere in the corpus. This requires a model that can learn phrase meanings from very few exposures, an important but unsolved problem for text generation systems. Possibly more challenging is understanding and expressing the various and nuanced relationships between related scientific works.",
+      "In this work, we introduce the task of citation text generation. Leveraging the full texts of English-language scientific articles, we construct a dataset of citation sentences in the computer science domain for training and evaluating citation text generation models. We investigate strong retrieval and neural baseline models against which future work can compare. For use cases where large models can be trained, we extend the successful GPT2 architecture BIBREF6 to the scientific domain with additional pre-training and subsequent fine-tuning on the citation generation task. We experiment with different kinds of document context in the fine-tuning and inference stages. We also explore retrieval-based techniques which may more easily generalize to lower-resource settings. These models retrieve citation sentences from training documents which are most similar to test inputs. Our evaluations show that these techniques often produce plausible citation sentences, but indicate clear directions for improvement. Code and artifacts are provided for future research."
+    ],
+    [
+      "Given the important research challenges posed by the citation text generation task, along with the potential social benefits of its solutions, let us continue with a formalization of the problem. Citation text generation is the task of generating a natural language citing sentence which explains the relationship between two documents. Examples of such citing sentences can be found in scientific documents as in-text citations to a previous work. Thus, we will formally distinguish one document as the source document, from which we will draw citing sentences which reference the cited document.",
+      "If we want to leverage powerful modern neural text generation systems, we are faced with the problem of how to represent the documents in a way that these models can consume. In particular, language models like GPT2 are trained to predict next token probabilities given long stretches of contiguous text from a single document. It is not clear how to mix information from more than one document when providing context to these models.",
+      "An additional difficulty of the citation text generation task is the vocabulary. In this domain, low-frequency, highly meaningful terms regularly appear in output texts. These terms may be completely novel to a single or small collection of papers (consider the phrase \u201ccitation text generation\u201d, for instance), yet they are necessary for explaining the paper.",
+      "This framing suggests a supervised learning setup. Let $t$ denote a citing sentence drawn from $S$, and $S^{\\prime }$ denote $S$ without $t$. Then let",
+      "be the probability of $t$ given $S^{\\prime }$, cited document $C$, and model parameters $\\theta $. The goal of learning a citation text generation model would be to maximize this probability across a large number of $t,S,C$ triples, so long as the parameters also generalize to unseen instances. At inference time, the goal is to generate a sentence $t^\\ast $ which accurately describes the relationship between $S$ and $C$.",
+      "The most appropriate evaluation metric for most text generation tasks is human judgment by potential users of the system. Evaluating citation text requires human judges with scientific expertise. For exploratory purposes, we use the standard automatic metrics for text generation tasks described in Section SECREF4, and we an expert error analysis in Section SECREF14.",
+      "For source and cited documents, we use English-language computer science articles and annotation from the S2-GORC dataset BIBREF7. S2-GORC is a large citation graph dataset which includes full texts of 8.1 million scientific documents. We select a subset of 154K computer science articles as our corpus. From these, we extract 622K citing sentences that link back to other documents in our corpus. We hold 2500 examples for each of the validation and test sets. Detailed statistics can be found in Table TABREF4."
+    ],
+    [
+      "We explore two basic styles of model for citation text generation. Following current work in neural text generation, we fine-tune the predictions of a large pre-trained language model to the citation text generation task. Additionally, we investigate approximate nearest neighbor methods to retrieve plausible human-authored citation sentences from the training data."
+    ],
+    [
+      "Recent work has shown that adapting large pre-trained language models to text generation tasks yields strong results BIBREF8. Due to its widespread use in text generation, we investigate the GPT model of BIBREF6 for the citation text generation task. GPT2 is a transformer model trained on 40 gigabytes of internet text with a language modeling objective BIBREF9. The adaptation process, called fine-tuning, involves continued training of the model on the target objective, in our case citation text generation.",
+      "To fine-tune GPT2 for text generation, it is typical to concatenate the conditioning context $X = x_1 \\ldots x_n$ and citing sentence $Y = y_1 \\ldots y_m$ with a special separator token $\\mho $. The model learns to approximate next token probabilities for each index after $\\mho $:",
+      "for $0<i<m$ and model parameters $\\theta $. Cross-entropy loss is calculated for each $y_i$ and backpropagation is used find parameters $\\theta $ which maximize $p(y_{i+1} \\mid X,\\mho ,y_1,\\ldots ,y_i)$.",
+      "To adapt Equation DISPLAY_FORM6 to the citation text generation task, we construct the conditioning context $X$ from the source and cited documents. We take $j$ tokens from source document $s_1,\\ldots ,s_j$ along with $k$ tokens from the cited document $c_1,\\ldots ,c_k$. (Which tokens are drawn from the two documents is an independent variable that we explore experimentally.) We then condition the generation of citing sentence $Y$ on $X = s_1,\\ldots ,s_j,\\mho ,c_1,\\ldots ,c_k$. This model is trained to predict each token of $Y$ as described above."
+    ],
+    [
+      "The primary question we investigate with this model is what kind of input is best for generating accurate and informative citation sentences. Prior works in citation recommendation have made use of abstracts, which perhaps act as sufficient summaries of document content for this task. Additionally, we explore variants of extended context, such as the introduction or first section after the abstract. Since scientific texts are too long to fit into the context window of our generation model, we also investigate a \u201csampling\u201d approach which samples sentences from throughout the document until the context window is full. In this work, we combine either the abstract or introduction of the source document with each of the abstract, introduction, or sampled sentences from the cited document."
+    ],
+    [
+      "While neural text generation techniques have advanced significantly in recent years, they are still inferior to human authored texts. For some tasks, it is better to retrieve a relevant human-authored text rather than generating novel text automatically BIBREF10. Is this also the case for citation text generation?",
+      "To answer this question, we adapt an approximate nearest neighbor search algorithm to find similar pairs of documents. The basic search procedure is as follows: Given a test instance input $(S,C)$ for source $S$ and cited document $C$, we find the set $\\bf {N}_C$, the nearest neighbors to $C$ in the training data. For each document $N_C$ from $\\bf {N}_C$, let $\\bf {N}_S$ be the set of documents that cite $N_C$. This means that each $N_S \\in {\\bf N}_S$ contains at least one citing sentence $t^{\\prime }$ which cites $N_C$. We return the $t^{\\prime }$ associated with the $(N_S,N_C)$ pair from the training which is closest to $(S,C)$.",
+      "We measure the closeness of two pairs of documents by measuring cosine distances between vector representations of their content. The abstract of each document is embedded into a single dense vector by averaging the contextualized embeddings provided by the SciBERT model of BIBREF11 and normalizing. The distance between $(S,C)$ and candidate $(N_S,N_C)$ is computed as:",
+      "where $\\alpha $ and $\\beta $ control the relative contribution of the two document similarities. We explore setting both $\\alpha $ and $\\beta $ to 1, or tuning them to optimize either BLEU or BERTScore on the validation set."
+    ],
+    [
+      "GPT2-based models have demonstrated an ability to capture long distance dependencies over hundreds of tokens, which we hypothesize will allow them to synthesize information in both the source and cited documents. But citation text generation models must also handle the challenging technical vocabulary of the scientific domain.",
+      "Prior work has shown that pretraining on in-domain data improves the performance of large language models on domain-specific tasks BIBREF11. Inspired by this, we experiment with additional pretraining of GPT2 in the science domain. This model, SciGPT2, is trained for an additional 3 epochs over the full text of the documents in our corpus using a language modeling objective. We note that both SciGPT2 and the SciBERT language models used here have been exposed to citing sentences from the test and validation sets as in-line citations during their pre-training phases, which may improve their performance versus models without this exposure. Such exposure is typical when using pretrained language models, as text from test data cannot be guaranteed to be absent from the large task-independent corpora upon which these models are trained."
+    ],
+    [
+      "We compare the different baseline systems using BLEU BIBREF12, ROUGE (specifically ROUGE 1, 2, and L; BIBREF13), and the recently introduced BertScore BIBREF14, a similarity metric based on BERT embeddings which has been shown to correlate well with human judgements on other tasks. To adapt the BertScore metric to the scientific text domain, we use SciBERT embeddings.",
+      "Table TABREF7 (above the double line) shows the performance of the SciGPT2 model on the test set when provided with the different input context combinations outlined in Section SECREF5. We find that context does make a difference for this category of model, and that models which have access to the intro of the documents outperform those which use abstracts or sampling.",
+      "Automatic evaluation of the retrieval-based methods on the test data are shown below the double line in Table TABREF7. This table shows that the retrieval methods perform well on this task. However we will show the limitations of these automatic metrics in Section SECREF14. We also observe that tuning the $\\alpha $ and $\\beta $ parameters on the validation set results in overfitting for this method. Outputs are largely unchanged by this tuning; fewer than 400 test datapoints differ from the untuned outputs. A larger validation split may alleviate this problem.",
+      "Statistical significance is assessed for select results using bootstrapping with 1000 samples in each of 100 iterations. This test shows that conditioning on the introduction of the source document improves performance compared to conditioning on the abstract when using the SciGPT2 model. However, we see that IR methods perform better than the best neural models. We do not find enough evidence to reject the null hypothesis regarding what context from the cited document should be used."
+    ],
+    [
+      "In this section we take a closer look at the details of the SciGPT2 and IR system outputs on a collection of validation datapoints. We provide a quantitative error analysis as well as qualitative analysis and examples."
+    ],
+    [
+      "In order to better understand the performance of the models, we undertake a quantitative analysis of its output. One author randomly selected 200 datapoints from the validation set and their associated model outputs. Source and cited papers in the topic of NLP were used so as to facilitate expert judgement. For tractability, we limited the context presented to the annotator to the document abstracts and analyze the outputs of the abs $\\times $ abs and IR systems.",
+      "In this analysis, we ask whether the models are producing believable citing sentences given their input. In particular, we are interested in the relative believability of the SciGPT2 and IR systems, as well as how believability of a citing sentence changes when a reader can see the abstract of one document or both.",
+      "We use 100 datapoints with outputs from the SciGPT2 system and 100 with outputs from the IR system. For 50 datapoints from each system, the cited document's abstract is initially masked such that only the source context is visible (Source, One Visible). Based only on the source context, the annotator judged whether the model output (1) could have convincingly been a citation in the source document based solely on the abstract (believable), (2) could have been a citation in the source document, but unclear from the abstract alone and depends on the rest of the paper's content (content-dependent), or (3) is unlikely to appear in this document (not believable). After making this judgment, the annotator was then shown the abstract of the cited document and asked to make the 3-way believability judgment based on both source and cited abstracts (Source, Both Visible). This process is repeated with the remaining 50 datapoints, but with the cited context masked initially (Cited, One Visible and Cited, Both Visible).",
+      "The results of our analysis presented in Table TABREF13. We find that believability in the Cited, One Visible condition correlates well with the Cited, Both Visible condition. In the Source conditions, we see a greater difference in believability between One Visible and Both Visible. These findings makes sense: in-line citations often summarize a prior study rather than highlight the paper's own contributions. Together, these results indicate that the believability of citing sentences is more related to the cited document than to the source.",
+      "Another interesting feature of this analysis is the difference between SciGPT2 and IR in terms of context-dependent citing sentences. We observe fewer such judgements in the IR outputs. This is probably due to the fact that neural text generation systems such as SciGPT2 will sometimes produce generic, uninformative outputs while the IR system outputs are usually specific enough that a stronger believability judgement can be made.",
+      "We also observe an overall higher instance of not believable judgements of the IR model outputs. This implies that automatic metrics such as BLEU, where the IR system scored higher than SciGPT2, do not correlate with factual accuracy in citation text generation.",
+      "Example citations and annotations are shown in Table TABREF15. We find that in the cases where the model generated outputs are unconvincing they are still on topic. All 10 cases in the Source, One Visible and 4 of the cases in Cited, One Visible that were no longer believable in the Both Visible conditions exhibit this quality. A common example (4 cases) of this phenomenon occurs when the model output references a dataset. While the dataset would be potentially relevant to both papers, the cited papers focus on modeling contributions and do not introduce a novel corpus."
+    ],
+    [
+      "Example system outputs for randomly selected validation instances are shown in Table TABREF18. We see that both the SciGPT2 and IR model outputs regularly hit on the correct broad topic of the cited text (such \u201cliterary analysis\u201d or \u201cimage captioning evaluation metrics\u201d). It is notable that the SciGPT2 model outputs syntactically correct and coherent citation sentences, even given the difficulty of the vocabulary in this domain. This is a testament to the power of the domain-specific language model training.",
+      "We also observe that the outputs of the SciGPT2 model are often shorter than the desired citing sentence. Brevity is a known issue for neural text generation and may be alleviated by penalizing brevity in the inference procedure. More problematic are the factual errors in the generated text. In the last example, for instance, we see that SciGPT2 fails to cite the specific image captioning dataset described in the cited paper (Pascal1K) and instead focuses on the more general evaluation metric for the image captioning task (CIDEr). This is typical of neural text generation systems, which often assign high probability to generic or frequent phrases and revert to these in the face of uncertainty."
+    ],
+    [
+      "The fluency and topical relevance of the baseline models show the plausibility of the citation text generation task as well as the utility of including pretrained scientific language models in future models. But based on the kinds of errors we have seen, future work should focus on two complementary goals: ensuring the factual accuracy of the generated text and improved modeling of the cited document. Factual accuracy is difficult to enforce in statistical text generation systems, especially where inference includes sampling procedures. Grounding to knowledge bases could help. For this task, knowledge extracted from candidate generations could be compared with knowledge from the full source and cited documents to prune false or irrelevant statements. Further, modeling input documents as knowledge graphs of their contents may help these algorithms better understand the cited document, resulting in better outputs. However, such a model will have to address the open problem of combining pretrained language models with graph encoding techniques."
+    ],
+    [
+      "The current work builds on recent research in scientific document understanding, including citation recommendation and categorization, as well as scientific document summarization.",
+      "Citation recommendation, or the task of selecting works related to a source document which would be suitable for citing, is a longstanding goal of AI research BIBREF15, BIBREF2, BIBREF16. Recently, researchers have sought to categorize citations using various ontologies of citation intents. BIBREF1 sought to discern \u201chighly influential\u201d citations from others. BIBREF17 uses six categories including \u201cmotivation\u201d, \u201cuses\u201d, and \u201cfuture work\u201d among others. BIBREF3 condense this ontology to just three: \u201cbackground\u201d,\u201cmethod\u201d, and \u201cresult comparison\u201d.",
+      "We view the citation text generation task as an extension of these classification approaches with distinct advantages. While classification requires an extant citation link to exist, our generation task can describe possible relationships between works which do not cite each other, such as contemporaneous works. Additionally, because gold citation texts are readily available in scientific documents, the citation text generation task requires no task-specific annotated training data. In practice, citation classification is used to assist in suggesting relevant works to researchers; citation text generation complements this goal by providing rationales for the recommendation and furthering progress toward explainable AI.",
+      "Generating a citation is also connected to summarizing scientific documents. There is a long history research on summarizing scientific documents BIBREF18, BIBREF19. More recently, researchers have included citing sentences as part of the input for summarization, hoping to capture the contribution of a work along with its content BIBREF20, BIBREF21, BIBREF5. Ours is the first to focus on the specific relationship between two documents when generating such sentences. Because of the emphasis on relational document understanding in our task, citation generation models can be used to assist with drafting papers as well, reducing researcher workload and providing non-native writers with a helpful first draft.",
+      "Our work builds on recent advances in transfer learning in NLP. In particular, large pretrained models such as BERT BIBREF22 and GPT2 BIBREF6 have made strong advances on a number of tasks BIBREF23. It has also been shown that pretraining these models on domain-specific data further improves results on domain-speicific tasks BIBREF11, BIBREF24. In this work, we apply that methodology by adding an additional pretraining phase on in-domain data before finetuning a GPT2 model on the citation text generation task."
+    ],
+    [
+      "We have introduced the challenging but useful task of citation text generation. This task requires reasoning about the relationships between documents and expressing these relationships in natural language text. We have established a dataset for this task and studied the performance of contemporary neural text generation and information retrieval models. Our analysis shows that while these models produce fluent and topical outputs, more research is needed to ensure factual accuracy and specificity in the generated text."
+    ],
+    [
+      "This research was supported by the Office of Naval Research under the MURI grant N00014-18-1-2670."
+    ]
+  ]
+}
+```
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+Name of Paper: Citation Text Generation
+
+Question: What is the size of the corpus?
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+Name of Paper: Mixed Membership Word Embeddings for Computational Social Science
+
+Question: Why is big data not appropriate for this task?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Background",
+    "The Mixed Membership Skip-Gram",
+    "Experimental Results",
+    "Quantitative Experiments",
+    "Computational Social Science Case Studies: State of the Union and NIPS",
+    "Conclusion",
+    "Supplementary Material",
+    "Related Work",
+    "Topic Modeling and Word Embeddings",
+    "Multi-Prototype Embedding Models",
+    "Mixed Membership Modeling",
+    "Case Study on NIPS",
+    "Derivation of the Collapsed Gibbs Update"
+  ],
+  "paragraphs": [
+    [
+      "Word embedding models, which learn to encode dictionary words with vector space representations, have been shown to be valuable for a variety of natural language processing (NLP) tasks such as statistical machine translation BIBREF2 , part-of-speech tagging, chunking, and named entity recogition BIBREF3 , as they provide a more nuanced representation of words than a simple indicator vector into a dictionary. These models follow a long line of research in data-driven semantic representations of text, including latent semantic analysis BIBREF4 and its probabilistic extensions BIBREF5 , BIBREF6 . In particular, topic models BIBREF7 have found broad applications in computational social science BIBREF8 , BIBREF9 and the digital humanities BIBREF10 , where interpretable representations reveal meaningful insights. Despite widespread success at NLP tasks, word embeddings have not yet supplanted topic models as the method of choice in computational social science applications. I speculate that this is due to two primary factors: 1) a perceived reliance on big data, and 2) a lack of interpretability. In this work, I develop new models to address both of these limitations.",
+      "Word embeddings have risen in popularity for NLP applications due to the success of models designed specifically for the big data setting. In particular, BIBREF0 , BIBREF1 showed that very simple word embedding models with high-dimensional representations can scale up to massive datasets, allowing them to outperform more sophisticated neural network language models which can process fewer documents. In this work, I offer a somewhat contrarian perspective to the currently prevailing trend of big data optimism, as exemplified by the work of BIBREF0 , BIBREF1 , BIBREF3 , and others, who argue that massive datasets are sufficient to allow language models to automatically resolve many challenging NLP tasks. Note that \u201cbig\u201d datasets are not always available, particularly in computational social science NLP applications, where the data of interest are often not obtained from large scale sources such as the internet and social media, but from sources such as press releases BIBREF11 , academic journals BIBREF10 , books BIBREF12 , and transcripts of recorded speech BIBREF13 , BIBREF14 , BIBREF15 . A standard practice in the literature is to train word embedding models on a generic large corpus such as Wikipedia, and use the embeddings for NLP tasks on the target dataset, cf. BIBREF3 , BIBREF0 , BIBREF16 , BIBREF17 . However, as we shall see here, this standard practice might not always be effective, as the size of a dataset does not correspond to its degree of relevance for a particular analysis. Even very large corpora have idiosyncrasies that can make their embeddings invalid for other domains. For instance, suppose we would like to use word embeddings to analyze scientific articles on machine learning. In Table TABREF1 , I report the most similar words to the word \u201clearning\u201d based on word embedding models trained on two corpora. For embeddings trained on articles from the NIPS conference, the most similar words are related to machine learning, as desired, while for embeddings trained on the massive, generic Google News corpus, the most similar words relate to learning and teaching in the classroom. Evidently, domain-specific data can be important.",
+      "Even more concerningly, BIBREF18 show that word embeddings can encode implicit sexist assumptions. This suggests that when trained on large generic corpora they could also encode the hegemonic worldview, which is inappropriate for studying, e.g., black female hip-hop artists' lyrics, or poetry by Syrian refugees, and could potentially lead to systematic bias against minorities, women, and people of color in NLP applications with real-world consequences, such as automatic essay grading and college admissions. In order to proactively combat these kinds of biases in large generic datasets, and to address computational social science tasks, there is a need for effective word embeddings for small datasets, so that the most relevant datasets can be used for training, even when they are small. To make word embeddings a viable alternative to topic models for applications in the social sciences, we further desire that the embeddings are semantically meaningful to human analysts.",
+      "In this paper, I introduce an interpretable word embedding model, and an associated topic model, which are designed to work well when trained on a small to medium-sized corpus of interest. The primary insight is to use a data-efficient parameter sharing scheme via mixed membership modeling, with inspiration from topic models. Mixed membership models provide a flexible yet efficient latent representation, in which entities are associated with shared, global representations, but to uniquely varying degrees. I identify the skip-gram word2vec model of BIBREF0 , BIBREF1 as corresponding to a certain naive Bayes topic model, which leads to mixed membership extensions, allowing the use of fewer vectors than words. I show that this leads to better modeling performance without big data, as measured by predictive performance (when the context is leveraged for prediction), as well as to interpretable latent representations that are highly valuable for computational social science applications. The interpretability of the representations arises from defining embeddings for words (and hence, documents) in terms of embeddings for topics. My experiments also shed light on the relative merits of training embeddings on generic big data corpora versus domain-specific data."
+    ],
+    [
+      "In this section, I provide the necessary background on word embeddings, as well as on topic models and mixed membership models. Traditional language models aim to predict words given the contexts that they are found in, thereby forming a joint probabilistic model for sequences of words in a language. BIBREF19 developed improved language models by using distributed representations BIBREF20 , in which words are represented by neural network synapse weights, or equivalently, vector space embeddings.",
+      "Later authors have noted that these word embeddings are useful for semantic representations of words, independently of whether a full joint probabilistic language model is learned, and that alternative training schemes can be beneficial for learning the embeddings. In particular, BIBREF0 , BIBREF1 proposed the skip-gram model, which inverts the language model prediction task and aims to predict the context given an input word. The skip-gram model is a log-bilinear discriminative probabilistic classifier parameterized by \u201cinput\u201d word embedding vectors INLINEFORM0 for the input words INLINEFORM1 , and \u201coutput\u201d word embedding vectors INLINEFORM2 for context words INLINEFORM3 , as shown in Table TABREF2 , top-left.",
+      "Topic models such as latent Dirichlet allocation (LDA) BIBREF7 are another class of probabilistic language models that have been used for semantic representation BIBREF6 . A straightforward way to model text corpora is via unsupervised multinomial naive Bayes, in which a latent cluster assignment for each document selects a multinomial distribution over words, referred to as a topic, with which the documents' words are assumed to be generated. LDA topic models improve over naive Bayes by using a mixed membership model, in which the assumption that all words in a document INLINEFORM0 belong to the same topic is relaxed, and replaced with a distribution over topics INLINEFORM1 . In the model's assumed generative process, for each word INLINEFORM2 in document INLINEFORM3 , a topic assignment INLINEFORM4 is drawn via INLINEFORM5 , then the word is drawn from the chosen topic INLINEFORM6 . The mixed membership formalism provides a useful compromise between model flexibility and statistical efficiency: the INLINEFORM7 topics INLINEFORM8 are shared across all documents, thereby sharing statistical strength, but each document is free to use the topics to its own unique degree. Bayesian inference further aids data efficiency, as uncertainty over INLINEFORM9 can be managed for shorter documents. Some recent papers have aimed to combine topic models and word embeddings BIBREF21 , BIBREF22 , but they do not aim to address the small data problem for computational social science, which I focus on here. I provide a more detailed discussion of related work in the supplementary."
+    ],
+    [
+      "To design an interpretable word embedding model for small corpora, we identify novel connections between word embeddings and topic models, and adapt advances from topic modeling. Following the distributional hypothesis BIBREF23 , the skip-gram's word embeddings parameterize discrete probability distributions over words INLINEFORM0 which tend to co-occur, and tend to be semantically coherent \u2013 a property leveraged by the Gaussian LDA model of BIBREF21 . This suggests that these discrete distributions can be reinterpreted as topics INLINEFORM1 . We thus reinterpret the skip-gram as a parameterization of a certain supervised naive Bayes topic model (Table TABREF2 , top-right). In this topic model, input words INLINEFORM2 are fully observed \u201ccluster assignments,\u201d and the words in INLINEFORM3 's contexts are a \u201cdocument.\u201d The skip-gram differs from this supervised topic model only in the parameterization of the \u201ctopics\u201d via word vectors which encode the distributions with a log-bilinear model. Note that although the skip-gram is discriminative, in the sense that it does not jointly model the input words INLINEFORM4 , we are here equivalently interpreting it as encoding a \u201cconditionally generative\u201d process for the context given the words, in order to develop probabilistic models that extend the skip-gram.",
+      "As in LDA, this model can be improved by replacing the naive Bayes assumption with a mixed membership assumption. By applying the mixed membership representation to this topic model version of the skip-gram, we obtain the model in the bottom-right of Table TABREF2 . After once again parameterizing this model with word embeddings, we obtain our final model, the mixed membership skip-gram (MMSG) (Table TABREF2 , bottom-left). In the model, each input word has a distribution over topics INLINEFORM0 . Each topic has a vector-space embedding INLINEFORM1 and each output word has a vector INLINEFORM2 (a parameter, not an embedding for INLINEFORM3 ). A topic INLINEFORM4 is drawn for each context, and the words in the context are drawn from the log-bilinear model using INLINEFORM5 : DISPLAYFORM0 ",
+      " We can expect that the resulting mixed membership word embeddings are beneficial in the small-to-medium data regime for the following reasons:",
+      "Of course, the model also requires some new parameters to be learned, namely the mixed membership proportions INLINEFORM0 . Based on topic modeling, I hypothesized that with care, these added parameters need not adversely affect performance in the small-medium data regime, for two reasons: 1) we can use a Bayesian approach to effectively manage uncertainty in them, and to marginalize them out, which prevents them being a bottleneck during training; and 2) at test time, using the posterior for INLINEFORM1 given the context, instead of the \u201cprior\u201d INLINEFORM2 , mitigates the impact of uncertainty in INLINEFORM3 due to limited training data: DISPLAYFORM0 ",
+      " To obtain a vector for a word type INLINEFORM0 , we can use the prior mean, INLINEFORM1 . For a word token INLINEFORM2 , we can leverage its context via the posterior mean, INLINEFORM3 . These embeddings are convex combinations of topic vectors (see Figure FIGREF23 for an example). With fewer vectors than words, some model capacity is lost, but the flexibility of the mixed membership representation allows the model to compensate. When the number of shared vectors equals the number of words, the mixed membership skip-gram is strictly more representationally powerful than the skip-gram. With more vectors than words, we can expect that the increased representational power would be beneficial in the big data regime. As this is not my goal, I leave this for future work."
+    ],
+    [
+      "The goals of our experiments were to study the relative merits of big data and domain-specific small data, to validate the proposed methods, and to study their applicability for computational social science research."
+    ],
+    [
+      "I first measured the effectiveness of the embeddings at the skip-gram's training task, predicting context words INLINEFORM0 given input words INLINEFORM1 . This task measures the methods' performance for predictive language modeling. I used four datasets of sociopolitical, scientific, and literary interest: the corpus of NIPS articles from 1987 \u2013 1999 ( INLINEFORM2 million), the U.S. presidential state of the Union addresses from 1790 \u2013 2015 ( INLINEFORM3 ), the complete works of Shakespeare ( INLINEFORM4 ; this version did not contain the Sonnets), and the writings of black scholar and activist W.E.B. Du Bois, as digitized by Project Gutenberg ( INLINEFORM5 ). For each dataset, I held out 10,000 INLINEFORM6 pairs uniformly at random, where INLINEFORM7 , and aimed to predict INLINEFORM8 given INLINEFORM9 (and optionally, INLINEFORM10 ). Since there are a large number of classes, I treat this as a ranking problem, and report the mean reciprocal rank. The experiments were repeated and averaged over 5 train/test splits.",
+      "The results are shown in Table TABREF25 . I compared to a word frequency baseline, the skip-gram (SG), and Tomas Mikolov/Google's vectors trained on Google News, INLINEFORM0 billion, via CBOW. Simulated annealing was performed for 1,000 iterations, NCE was performed for 1 million minibatches of size 128, and 128-dimensional embeddings were used (300 for Google). I used INLINEFORM1 for NIPS, INLINEFORM2 for state of the Union, and INLINEFORM3 for the two smaller datasets. Methods were able to leverage the remainder of the context, either by adding the context's vectors, or via the posterior (Equation EQREF22 ), which helped for all methods except the naive skip-gram. We can identify several noteworthy findings. First, the generic big data vectors (Google+context) were outperformed by the skip-gram on 3 out of 4 datasets (and by the skip-gram topic model on the other), by a large margin, indicating that domain-specific embeddings are often important. Second, the mixed membership models, using posterior inference, beat or matched their naive Bayes counterparts, for both the word embedding models and the topic models. As hypothesized, posterior inference on INLINEFORM4 at test time was important for good performance. Finally, the topic models beat their corresponding word embedding models at prediction. I therefore recommend the use of our MMSG topic model variant for predictive language modeling in the small data regime.",
+      "I tested the performance of the representations as features for document categorization and regression tasks. The results are given in Table TABREF26 . For document categorization, I used three standard benchmark datasets: 20 Newsgroups (19,997 newsgroup posts), Reuters-150 newswire articles (15,500 articles and 150 classes), and Ohsumed medical abstracts on 23 cardiovascular diseases (20,000 articles). I held out 4,000 test documents for 20 Newsgroups, and used the standard train/test splits from the literature in the other corpora (e.g. for Ohsumed, 50% of documents were assigned to training and to test sets). I obtained document embeddings for the MMSG, in the same latent space as the topic embeddings, by summing the posterior mean vectors INLINEFORM0 for each token. Vector addition was similarly used to construct document vectors for the other embedding models. All vectors were normalized to unit length. I also considered a tf-idf baseline. Logistic regression models were trained on the features extracted on the training set for each method.",
+      "Across the three datasets, several clear trends emerged (Table TABREF26 ). First, the generic Google vectors were consistently and substantially outperformed in classification performance by the skipgram (SG) and MMSG vectors, highlighting the importance of corpus-specific embeddings. Second, despite the MMSG's superior performance at language modeling on small datasets, the SG features outperformed the MMSG's at the document categorization task. By encoding vectors at the topic level instead of the word level, the MMSG loses word level resolution in the embeddings, which turned out to be valuable for these particular classification tasks. We are not, however, restricted to use only one type of embedding to construct features for classification. Interestingly, when the SG and MMSG features were concatenated (SG+MMSG), this improved classification performance over these vectors individually. This suggests that the topic-level MMSG vectors and word-level SG vectors encode complementary information, and both are beneficial for performance. Finally, further concatenating the generic Google vectors' features (SG+MMSG+Google) improved performance again, despite the fact that these vectors performed poorly on their own. It should be noted that tf-idf, which is notoriously effective for document categorization, outperformed the embedding methods on these datasets.",
+      "I also analyzed the regression task of predicting the year of a state of the Union address based on its text information. I used lasso-regularized linear regression models, evaluated via a leave-one-out cross-validation experimental setup. Root-mean-square error (RMSE) results are reported in Table TABREF26 (bottom). Unlike for the other tasks, the Google big data vectors were the best individual features in this case, outperforming the domain-specific SG and MMSG embeddings individually. On the other hand, SG+MMSG+Google performed the best overall, showing that domain-specific embeddings can improve performance even when big data embeddings are successful. The tf-idf baseline was beaten by all of the embedding models on this task."
+    ],
+    [
+      "I also performed several case studies. I obtained document embeddings, in the same latent space as the topic embeddings, by summing the posterior mean vectors INLINEFORM0 for each token, and visualized them in two dimensions using INLINEFORM1 -SNE BIBREF24 (all vectors were normalized to unit length). The state of the Union addresses (Figure FIGREF27 ) are embedded almost linearly by year, with a major jump around the New Deal (1930s), and are well separated by party at any given time period. The embedded topics (gray) allow us to interpret the space. The George W. Bush addresses are embedded near a \u201cwar on terror\u201d topic (\u201cweapons, war...\u201d), and the Barack Obama addresses are embedded near a \u201cstimulus\u201d topic (\u201cpeople, work...\u201d).",
+      "On the NIPS corpus, for the input word \u201cBayesian\u201d (Table ), the naive Bayes and skip-gram models learned a topic with words that refer to Bayesian networks, probabilistic models, and neural networks. The mixed membership models are able to separate this into more coherent and specific topics including Bayesian modeling, Bayesian training of neural networks (for which Sir David MacKay was a strong proponent, and Andreas Weigend wrote an influential early paper), and Monte Carlo methods. By performing the additive composition of word vectors, which we obtain by finding the prior mean vector for each word type INLINEFORM0 , INLINEFORM1 (and then normalizing), we obtain relevant topics INLINEFORM2 as nearest neighbors (Figure FIGREF28 ). Similarly, we find that the additive composition of topic and word vectors works correctly: INLINEFORM3 , and INLINEFORM4 .",
+      "The INLINEFORM0 -SNE visualization of NIPS documents (Figure FIGREF28 ) shows some temporal clustering patterns (blue documents are more recent, red documents are older, and gray points are topics). I provide a more detailed case study on NIPS in the supplementary material."
+    ],
+    [
+      "I have proposed a model-based method for training interpretable corpus-specific word embeddings for computational social science, using mixed membership representations, Metropolis-Hastings-Walker sampling, and NCE. Experimental results for prediction, supervised learning, and case studies on state of the Union addresses and NIPS articles, indicate that high-quality embeddings and topics can be obtained using the method. The results highlight the fact that big data is not always best, as domain-specific data can be very valuable, even when it is small. I plan to use this approach for substantive social science applications, and to address algorithmic bias and fairness issues.",
+      "Acknowledgements",
+      "I thank Eric Nalisnick and Padhraic Smyth for many helpful discussions."
+    ],
+    [
+      "]"
+    ],
+    [
+      "In this supplementary document, we discuss related work in the literature and its relation to our proposed methods, provide a case study on NIPS articles, and derive the collapsed Gibbs sampling update for the MMSGTM, which we leverage when training the MMSG."
+    ],
+    [
+      "The Gaussian LDA model of BIBREF21 improves the performance of topic modeling by leveraging the semantic information encoded in word embeddings. Gaussian LDA modifies the generative process of LDA such that each topic is assumed to generate the vectors via its own Gaussian distribution. Similarly to our MMSG model, in Gaussian LDA each topic is encoded with a vector, in this case the mean of the Gaussian. It takes pre-trained word embeddings as input, rather than learning the embeddings from data within the same model, and does not aim to perform word embedding.",
+      "The topical word embedding (TWE) models of BIBREF22 reverse this, as they take LDA topic assignments of words as input, and aim to use them to improve the resultant word embeddings. The authors propose three variants, each of which modifies the skip-gram training objective to use LDA topic assignments together with words. In the best performing variant, called TWE-1, a standard skip-gram word embedding model is trained independently with another skip-gram variant, which tries to predict context words given the input word's topic assignment. The skip-gram embedding and the topic embeddings are concatenated to form the final embedding.",
+      "At test time, a distribution over topics for the word given the context, INLINEFORM0 is estimated according to the topic counts over the other context words. Using this as a prior, a posterior over topics given both the input word and the context is calculated, and similarities between pairs of words (with their contexts) are averaged over this posterior, in a procedure inspired by those used by BIBREF43 , BIBREF36 . The primary similarity to our MMSG approach is the use of a training algorithm involving the prediction of context words, given a topic. Our method does this as part of an overall model-based inference procedure, and we learn mixed membership proportions INLINEFORM1 rather than using empirical counts as the prior over topics for a word token. In accordance with the skip-gram's prediction model, we are thus able to model the context words in the data likelihood term when computing the posterior probability of the topic assignment. TWE-1 requires that topic assignments are available at test time. It provides a mechanism to predict contextual similarity, but not to predict held-out context words, so we are unable to compare to it in our experiments.",
+      "Other neurally-inspired topic models include replicated softmax BIBREF34 , and its successor, DocNADE BIBREF37 . Replicated softmax extends the restricted Boltzmann machine to handle multinomial counts for document modeling. DocNADE builds on the ideas of replicated softmax, but uses the NADE architecture, where observations (i.e. words) are modeled sequentially given the previous observations."
+    ],
+    [
+      "Multi-prototype embeddings models are another relevant line of work. These models address lexical ambiguity by assigning multiple vectors to each word type, each corresponding to a different meaning of that word. BIBREF43 propose to cluster the occurrences of each word type, based on features extracted from its context. Embeddings are then learned for each cluster. BIBREF36 apply a similar approach, but they use initial single-prototype word embeddings to provide the features used for clustering. These clustering methods have some resemblance to our topic model pre-clustering step, although their clustering is applied within instances of a given word type, rather than globally across all word types, as in our methods. This results in models with more vectors than words, while we aim to find fewer vectors than words, to reduce the model's complexity for small datasets. Rather than employing an off-the-shelf clustering algorithm and then applying an unrelated embedding model to its output, our approach aims to perform model-based clustering within an overall joint model of topic/cluster assignments and word vectors.",
+      "Perhaps the most similar model to ours in the literature is the probabilistic multi-prototype embedding model of BIBREF45 , who treat the prototype assignment of a word as a latent variable, assumed drawn from a mixture over prototypes for each word. The embeddings are then trained using EM. Our MMSG model can be understood as the mixed membership version of this model, in which the prototypes (vectors) are shared across all word types, and each word type has its own mixed membership proportions across the shared prototypes. While a similar EM algorithm can be applied to the MMSG, the E-step is much more expensive, as we typically desire many more shared vectors (often in the thousands) than we would prototypes per a single word type (Tian et al. use ten in their experiments). We use the Metropolis-Hastings-Walker algorithm with the topic model reparameterization of our model in order to address this by efficiently pre-solving the E-step."
+    ],
+    [
+      "Mixed membership modeling is a flexible alternative to traditional clustering, in which each data point is assigned to a single cluster. Instead, mixed membership models posit that individual entities are associated with multiple underlying clusters, to differing degrees, as encoded by a mixed membership vector that sums to one across the clusters BIBREF28 , BIBREF26 . These mixed membership proportions are generally used to model lower-level grouped data, such as the words inside a document. Each lower-level data point inside a group is assumed to be assigned to one of the shared, global clusters according to the group-level membership proportions. Thus, a mixed membership model consists of a mixture model for each group, which share common mixture component parameters, but with differing mixture proportions.",
+      "This formalism has lead to probabilistic models for a variety of applications, including medical diagnosis BIBREF39 , population genetics BIBREF42 , survey analysis BIBREF29 , computer vision BIBREF27 , BIBREF30 , text documents BIBREF35 , BIBREF7 , and social network analysis BIBREF25 . Nonparametric Bayesian extensions, in which the number of underlying clusters is learned from data via Bayesian inference, have also been proposed BIBREF44 . In this work, dictionary words are assigned a mixed membership distribution over a set of shared latent vector space embeddings. Each instantiation of a dictionary word (an \u201cinput\u201d word) is assigned to one of the shared embeddings based on its dictionary word's membership vector. The words in its context (\u201coutput\u201d words) are assumed to be drawn based on the chosen embedding."
+    ],
+    [
+      "In Figure FIGREF33 , we show a zoomed in INLINEFORM0 -SNE visualization of NIPS document embeddings. We can see regions of the space corresponding to learning algorithms (bottom), data space and latent space (center), training neural networks (top), and nearest neighbors (bottom-left). We also visualized the authors' embeddings via INLINEFORM1 -SNE (Figure FIGREF34 ). We find regions of latent space for reinforcement learning authors (left: \u201cstate, action,...,\u201d Singh, Barto,Sutton), probabilistic methods (right: \u201cmixture, model,\u201d \u201cmonte, carlo,\u201d Bishop, Williams, Barber, Opper, Jordan, Ghahramani, Tresp, Smyth), and evaluation (top-right: \u201cresults, performance, experiments,...\u201d)."
+    ],
+    [
+      "Let INLINEFORM0 be the number of output words in the INLINEFORM1 th context, let INLINEFORM2 be those output words, and let INLINEFORM3 be the input words other that INLINEFORM4 (similarly, topic assignments INLINEFORM5 and output words INLINEFORM6 ). Then the collapsed Gibbs update samples from the conditional distribution INLINEFORM7 ",
+      " We recognize the first integral as the mean of a Dirichlet distribution which we obtain via conjugacy: INLINEFORM0 ",
+      " The above can also be understood as the probability of the next ball drawn from a multivariate Polya urn model, also known as the Dirichlet-compound multinomial distribution, arising from the posterior predictive distribution of a discrete likelihood with a Dirichlet prior. We will need the full form of such a distribution to analyze the second integral. Once again leveraging conjugacy, we have: INLINEFORM0 ",
+      " INLINEFORM0 ",
+      " where INLINEFORM0 is the number of times that output word INLINEFORM1 occurs in the INLINEFORM2 th context, since the final integral is over the full support of a Dirichlet distribution, which integrates to one. Eliminating terms that aren't affected by the INLINEFORM3 assignment, the above is INLINEFORM4 ",
+      " where we have used the fact that INLINEFORM0 for any INLINEFORM1 , and integer INLINEFORM2 . We can interpret this as the probability of drawing the context words under the multivariate Polya urn model, in which the number of \u201ccolored balls\u201d (word counts plus prior counts) is increased by one each time a certain color (word) is selected. In other words, in each step, corresponding to the selection of each context word, we draw a ball from the urn, then put it back, along with another ball of the same color. The INLINEFORM3 and INLINEFORM4 terms reflect that the counts have been changed by adding these extra balls into the urn in each step. The second to last equation shows that this process is exchangeable: it does not matter which order the balls were drawn in when determining the probability of the sequence. Multiplying this with the term from the first integral, calculated earlier, gives us the final form of the update equation, INLINEFORM5 ",
+      ""
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: Mixed Membership Word Embeddings for Computational Social Science
+
+Question: What is an example of a computational social science NLP task?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Background",
+    "The Mixed Membership Skip-Gram",
+    "Experimental Results",
+    "Quantitative Experiments",
+    "Computational Social Science Case Studies: State of the Union and NIPS",
+    "Conclusion",
+    "Supplementary Material",
+    "Related Work",
+    "Topic Modeling and Word Embeddings",
+    "Multi-Prototype Embedding Models",
+    "Mixed Membership Modeling",
+    "Case Study on NIPS",
+    "Derivation of the Collapsed Gibbs Update"
+  ],
+  "paragraphs": [
+    [
+      "Word embedding models, which learn to encode dictionary words with vector space representations, have been shown to be valuable for a variety of natural language processing (NLP) tasks such as statistical machine translation BIBREF2 , part-of-speech tagging, chunking, and named entity recogition BIBREF3 , as they provide a more nuanced representation of words than a simple indicator vector into a dictionary. These models follow a long line of research in data-driven semantic representations of text, including latent semantic analysis BIBREF4 and its probabilistic extensions BIBREF5 , BIBREF6 . In particular, topic models BIBREF7 have found broad applications in computational social science BIBREF8 , BIBREF9 and the digital humanities BIBREF10 , where interpretable representations reveal meaningful insights. Despite widespread success at NLP tasks, word embeddings have not yet supplanted topic models as the method of choice in computational social science applications. I speculate that this is due to two primary factors: 1) a perceived reliance on big data, and 2) a lack of interpretability. In this work, I develop new models to address both of these limitations.",
+      "Word embeddings have risen in popularity for NLP applications due to the success of models designed specifically for the big data setting. In particular, BIBREF0 , BIBREF1 showed that very simple word embedding models with high-dimensional representations can scale up to massive datasets, allowing them to outperform more sophisticated neural network language models which can process fewer documents. In this work, I offer a somewhat contrarian perspective to the currently prevailing trend of big data optimism, as exemplified by the work of BIBREF0 , BIBREF1 , BIBREF3 , and others, who argue that massive datasets are sufficient to allow language models to automatically resolve many challenging NLP tasks. Note that \u201cbig\u201d datasets are not always available, particularly in computational social science NLP applications, where the data of interest are often not obtained from large scale sources such as the internet and social media, but from sources such as press releases BIBREF11 , academic journals BIBREF10 , books BIBREF12 , and transcripts of recorded speech BIBREF13 , BIBREF14 , BIBREF15 . A standard practice in the literature is to train word embedding models on a generic large corpus such as Wikipedia, and use the embeddings for NLP tasks on the target dataset, cf. BIBREF3 , BIBREF0 , BIBREF16 , BIBREF17 . However, as we shall see here, this standard practice might not always be effective, as the size of a dataset does not correspond to its degree of relevance for a particular analysis. Even very large corpora have idiosyncrasies that can make their embeddings invalid for other domains. For instance, suppose we would like to use word embeddings to analyze scientific articles on machine learning. In Table TABREF1 , I report the most similar words to the word \u201clearning\u201d based on word embedding models trained on two corpora. For embeddings trained on articles from the NIPS conference, the most similar words are related to machine learning, as desired, while for embeddings trained on the massive, generic Google News corpus, the most similar words relate to learning and teaching in the classroom. Evidently, domain-specific data can be important.",
+      "Even more concerningly, BIBREF18 show that word embeddings can encode implicit sexist assumptions. This suggests that when trained on large generic corpora they could also encode the hegemonic worldview, which is inappropriate for studying, e.g., black female hip-hop artists' lyrics, or poetry by Syrian refugees, and could potentially lead to systematic bias against minorities, women, and people of color in NLP applications with real-world consequences, such as automatic essay grading and college admissions. In order to proactively combat these kinds of biases in large generic datasets, and to address computational social science tasks, there is a need for effective word embeddings for small datasets, so that the most relevant datasets can be used for training, even when they are small. To make word embeddings a viable alternative to topic models for applications in the social sciences, we further desire that the embeddings are semantically meaningful to human analysts.",
+      "In this paper, I introduce an interpretable word embedding model, and an associated topic model, which are designed to work well when trained on a small to medium-sized corpus of interest. The primary insight is to use a data-efficient parameter sharing scheme via mixed membership modeling, with inspiration from topic models. Mixed membership models provide a flexible yet efficient latent representation, in which entities are associated with shared, global representations, but to uniquely varying degrees. I identify the skip-gram word2vec model of BIBREF0 , BIBREF1 as corresponding to a certain naive Bayes topic model, which leads to mixed membership extensions, allowing the use of fewer vectors than words. I show that this leads to better modeling performance without big data, as measured by predictive performance (when the context is leveraged for prediction), as well as to interpretable latent representations that are highly valuable for computational social science applications. The interpretability of the representations arises from defining embeddings for words (and hence, documents) in terms of embeddings for topics. My experiments also shed light on the relative merits of training embeddings on generic big data corpora versus domain-specific data."
+    ],
+    [
+      "In this section, I provide the necessary background on word embeddings, as well as on topic models and mixed membership models. Traditional language models aim to predict words given the contexts that they are found in, thereby forming a joint probabilistic model for sequences of words in a language. BIBREF19 developed improved language models by using distributed representations BIBREF20 , in which words are represented by neural network synapse weights, or equivalently, vector space embeddings.",
+      "Later authors have noted that these word embeddings are useful for semantic representations of words, independently of whether a full joint probabilistic language model is learned, and that alternative training schemes can be beneficial for learning the embeddings. In particular, BIBREF0 , BIBREF1 proposed the skip-gram model, which inverts the language model prediction task and aims to predict the context given an input word. The skip-gram model is a log-bilinear discriminative probabilistic classifier parameterized by \u201cinput\u201d word embedding vectors INLINEFORM0 for the input words INLINEFORM1 , and \u201coutput\u201d word embedding vectors INLINEFORM2 for context words INLINEFORM3 , as shown in Table TABREF2 , top-left.",
+      "Topic models such as latent Dirichlet allocation (LDA) BIBREF7 are another class of probabilistic language models that have been used for semantic representation BIBREF6 . A straightforward way to model text corpora is via unsupervised multinomial naive Bayes, in which a latent cluster assignment for each document selects a multinomial distribution over words, referred to as a topic, with which the documents' words are assumed to be generated. LDA topic models improve over naive Bayes by using a mixed membership model, in which the assumption that all words in a document INLINEFORM0 belong to the same topic is relaxed, and replaced with a distribution over topics INLINEFORM1 . In the model's assumed generative process, for each word INLINEFORM2 in document INLINEFORM3 , a topic assignment INLINEFORM4 is drawn via INLINEFORM5 , then the word is drawn from the chosen topic INLINEFORM6 . The mixed membership formalism provides a useful compromise between model flexibility and statistical efficiency: the INLINEFORM7 topics INLINEFORM8 are shared across all documents, thereby sharing statistical strength, but each document is free to use the topics to its own unique degree. Bayesian inference further aids data efficiency, as uncertainty over INLINEFORM9 can be managed for shorter documents. Some recent papers have aimed to combine topic models and word embeddings BIBREF21 , BIBREF22 , but they do not aim to address the small data problem for computational social science, which I focus on here. I provide a more detailed discussion of related work in the supplementary."
+    ],
+    [
+      "To design an interpretable word embedding model for small corpora, we identify novel connections between word embeddings and topic models, and adapt advances from topic modeling. Following the distributional hypothesis BIBREF23 , the skip-gram's word embeddings parameterize discrete probability distributions over words INLINEFORM0 which tend to co-occur, and tend to be semantically coherent \u2013 a property leveraged by the Gaussian LDA model of BIBREF21 . This suggests that these discrete distributions can be reinterpreted as topics INLINEFORM1 . We thus reinterpret the skip-gram as a parameterization of a certain supervised naive Bayes topic model (Table TABREF2 , top-right). In this topic model, input words INLINEFORM2 are fully observed \u201ccluster assignments,\u201d and the words in INLINEFORM3 's contexts are a \u201cdocument.\u201d The skip-gram differs from this supervised topic model only in the parameterization of the \u201ctopics\u201d via word vectors which encode the distributions with a log-bilinear model. Note that although the skip-gram is discriminative, in the sense that it does not jointly model the input words INLINEFORM4 , we are here equivalently interpreting it as encoding a \u201cconditionally generative\u201d process for the context given the words, in order to develop probabilistic models that extend the skip-gram.",
+      "As in LDA, this model can be improved by replacing the naive Bayes assumption with a mixed membership assumption. By applying the mixed membership representation to this topic model version of the skip-gram, we obtain the model in the bottom-right of Table TABREF2 . After once again parameterizing this model with word embeddings, we obtain our final model, the mixed membership skip-gram (MMSG) (Table TABREF2 , bottom-left). In the model, each input word has a distribution over topics INLINEFORM0 . Each topic has a vector-space embedding INLINEFORM1 and each output word has a vector INLINEFORM2 (a parameter, not an embedding for INLINEFORM3 ). A topic INLINEFORM4 is drawn for each context, and the words in the context are drawn from the log-bilinear model using INLINEFORM5 : DISPLAYFORM0 ",
+      " We can expect that the resulting mixed membership word embeddings are beneficial in the small-to-medium data regime for the following reasons:",
+      "Of course, the model also requires some new parameters to be learned, namely the mixed membership proportions INLINEFORM0 . Based on topic modeling, I hypothesized that with care, these added parameters need not adversely affect performance in the small-medium data regime, for two reasons: 1) we can use a Bayesian approach to effectively manage uncertainty in them, and to marginalize them out, which prevents them being a bottleneck during training; and 2) at test time, using the posterior for INLINEFORM1 given the context, instead of the \u201cprior\u201d INLINEFORM2 , mitigates the impact of uncertainty in INLINEFORM3 due to limited training data: DISPLAYFORM0 ",
+      " To obtain a vector for a word type INLINEFORM0 , we can use the prior mean, INLINEFORM1 . For a word token INLINEFORM2 , we can leverage its context via the posterior mean, INLINEFORM3 . These embeddings are convex combinations of topic vectors (see Figure FIGREF23 for an example). With fewer vectors than words, some model capacity is lost, but the flexibility of the mixed membership representation allows the model to compensate. When the number of shared vectors equals the number of words, the mixed membership skip-gram is strictly more representationally powerful than the skip-gram. With more vectors than words, we can expect that the increased representational power would be beneficial in the big data regime. As this is not my goal, I leave this for future work."
+    ],
+    [
+      "The goals of our experiments were to study the relative merits of big data and domain-specific small data, to validate the proposed methods, and to study their applicability for computational social science research."
+    ],
+    [
+      "I first measured the effectiveness of the embeddings at the skip-gram's training task, predicting context words INLINEFORM0 given input words INLINEFORM1 . This task measures the methods' performance for predictive language modeling. I used four datasets of sociopolitical, scientific, and literary interest: the corpus of NIPS articles from 1987 \u2013 1999 ( INLINEFORM2 million), the U.S. presidential state of the Union addresses from 1790 \u2013 2015 ( INLINEFORM3 ), the complete works of Shakespeare ( INLINEFORM4 ; this version did not contain the Sonnets), and the writings of black scholar and activist W.E.B. Du Bois, as digitized by Project Gutenberg ( INLINEFORM5 ). For each dataset, I held out 10,000 INLINEFORM6 pairs uniformly at random, where INLINEFORM7 , and aimed to predict INLINEFORM8 given INLINEFORM9 (and optionally, INLINEFORM10 ). Since there are a large number of classes, I treat this as a ranking problem, and report the mean reciprocal rank. The experiments were repeated and averaged over 5 train/test splits.",
+      "The results are shown in Table TABREF25 . I compared to a word frequency baseline, the skip-gram (SG), and Tomas Mikolov/Google's vectors trained on Google News, INLINEFORM0 billion, via CBOW. Simulated annealing was performed for 1,000 iterations, NCE was performed for 1 million minibatches of size 128, and 128-dimensional embeddings were used (300 for Google). I used INLINEFORM1 for NIPS, INLINEFORM2 for state of the Union, and INLINEFORM3 for the two smaller datasets. Methods were able to leverage the remainder of the context, either by adding the context's vectors, or via the posterior (Equation EQREF22 ), which helped for all methods except the naive skip-gram. We can identify several noteworthy findings. First, the generic big data vectors (Google+context) were outperformed by the skip-gram on 3 out of 4 datasets (and by the skip-gram topic model on the other), by a large margin, indicating that domain-specific embeddings are often important. Second, the mixed membership models, using posterior inference, beat or matched their naive Bayes counterparts, for both the word embedding models and the topic models. As hypothesized, posterior inference on INLINEFORM4 at test time was important for good performance. Finally, the topic models beat their corresponding word embedding models at prediction. I therefore recommend the use of our MMSG topic model variant for predictive language modeling in the small data regime.",
+      "I tested the performance of the representations as features for document categorization and regression tasks. The results are given in Table TABREF26 . For document categorization, I used three standard benchmark datasets: 20 Newsgroups (19,997 newsgroup posts), Reuters-150 newswire articles (15,500 articles and 150 classes), and Ohsumed medical abstracts on 23 cardiovascular diseases (20,000 articles). I held out 4,000 test documents for 20 Newsgroups, and used the standard train/test splits from the literature in the other corpora (e.g. for Ohsumed, 50% of documents were assigned to training and to test sets). I obtained document embeddings for the MMSG, in the same latent space as the topic embeddings, by summing the posterior mean vectors INLINEFORM0 for each token. Vector addition was similarly used to construct document vectors for the other embedding models. All vectors were normalized to unit length. I also considered a tf-idf baseline. Logistic regression models were trained on the features extracted on the training set for each method.",
+      "Across the three datasets, several clear trends emerged (Table TABREF26 ). First, the generic Google vectors were consistently and substantially outperformed in classification performance by the skipgram (SG) and MMSG vectors, highlighting the importance of corpus-specific embeddings. Second, despite the MMSG's superior performance at language modeling on small datasets, the SG features outperformed the MMSG's at the document categorization task. By encoding vectors at the topic level instead of the word level, the MMSG loses word level resolution in the embeddings, which turned out to be valuable for these particular classification tasks. We are not, however, restricted to use only one type of embedding to construct features for classification. Interestingly, when the SG and MMSG features were concatenated (SG+MMSG), this improved classification performance over these vectors individually. This suggests that the topic-level MMSG vectors and word-level SG vectors encode complementary information, and both are beneficial for performance. Finally, further concatenating the generic Google vectors' features (SG+MMSG+Google) improved performance again, despite the fact that these vectors performed poorly on their own. It should be noted that tf-idf, which is notoriously effective for document categorization, outperformed the embedding methods on these datasets.",
+      "I also analyzed the regression task of predicting the year of a state of the Union address based on its text information. I used lasso-regularized linear regression models, evaluated via a leave-one-out cross-validation experimental setup. Root-mean-square error (RMSE) results are reported in Table TABREF26 (bottom). Unlike for the other tasks, the Google big data vectors were the best individual features in this case, outperforming the domain-specific SG and MMSG embeddings individually. On the other hand, SG+MMSG+Google performed the best overall, showing that domain-specific embeddings can improve performance even when big data embeddings are successful. The tf-idf baseline was beaten by all of the embedding models on this task."
+    ],
+    [
+      "I also performed several case studies. I obtained document embeddings, in the same latent space as the topic embeddings, by summing the posterior mean vectors INLINEFORM0 for each token, and visualized them in two dimensions using INLINEFORM1 -SNE BIBREF24 (all vectors were normalized to unit length). The state of the Union addresses (Figure FIGREF27 ) are embedded almost linearly by year, with a major jump around the New Deal (1930s), and are well separated by party at any given time period. The embedded topics (gray) allow us to interpret the space. The George W. Bush addresses are embedded near a \u201cwar on terror\u201d topic (\u201cweapons, war...\u201d), and the Barack Obama addresses are embedded near a \u201cstimulus\u201d topic (\u201cpeople, work...\u201d).",
+      "On the NIPS corpus, for the input word \u201cBayesian\u201d (Table ), the naive Bayes and skip-gram models learned a topic with words that refer to Bayesian networks, probabilistic models, and neural networks. The mixed membership models are able to separate this into more coherent and specific topics including Bayesian modeling, Bayesian training of neural networks (for which Sir David MacKay was a strong proponent, and Andreas Weigend wrote an influential early paper), and Monte Carlo methods. By performing the additive composition of word vectors, which we obtain by finding the prior mean vector for each word type INLINEFORM0 , INLINEFORM1 (and then normalizing), we obtain relevant topics INLINEFORM2 as nearest neighbors (Figure FIGREF28 ). Similarly, we find that the additive composition of topic and word vectors works correctly: INLINEFORM3 , and INLINEFORM4 .",
+      "The INLINEFORM0 -SNE visualization of NIPS documents (Figure FIGREF28 ) shows some temporal clustering patterns (blue documents are more recent, red documents are older, and gray points are topics). I provide a more detailed case study on NIPS in the supplementary material."
+    ],
+    [
+      "I have proposed a model-based method for training interpretable corpus-specific word embeddings for computational social science, using mixed membership representations, Metropolis-Hastings-Walker sampling, and NCE. Experimental results for prediction, supervised learning, and case studies on state of the Union addresses and NIPS articles, indicate that high-quality embeddings and topics can be obtained using the method. The results highlight the fact that big data is not always best, as domain-specific data can be very valuable, even when it is small. I plan to use this approach for substantive social science applications, and to address algorithmic bias and fairness issues.",
+      "Acknowledgements",
+      "I thank Eric Nalisnick and Padhraic Smyth for many helpful discussions."
+    ],
+    [
+      "]"
+    ],
+    [
+      "In this supplementary document, we discuss related work in the literature and its relation to our proposed methods, provide a case study on NIPS articles, and derive the collapsed Gibbs sampling update for the MMSGTM, which we leverage when training the MMSG."
+    ],
+    [
+      "The Gaussian LDA model of BIBREF21 improves the performance of topic modeling by leveraging the semantic information encoded in word embeddings. Gaussian LDA modifies the generative process of LDA such that each topic is assumed to generate the vectors via its own Gaussian distribution. Similarly to our MMSG model, in Gaussian LDA each topic is encoded with a vector, in this case the mean of the Gaussian. It takes pre-trained word embeddings as input, rather than learning the embeddings from data within the same model, and does not aim to perform word embedding.",
+      "The topical word embedding (TWE) models of BIBREF22 reverse this, as they take LDA topic assignments of words as input, and aim to use them to improve the resultant word embeddings. The authors propose three variants, each of which modifies the skip-gram training objective to use LDA topic assignments together with words. In the best performing variant, called TWE-1, a standard skip-gram word embedding model is trained independently with another skip-gram variant, which tries to predict context words given the input word's topic assignment. The skip-gram embedding and the topic embeddings are concatenated to form the final embedding.",
+      "At test time, a distribution over topics for the word given the context, INLINEFORM0 is estimated according to the topic counts over the other context words. Using this as a prior, a posterior over topics given both the input word and the context is calculated, and similarities between pairs of words (with their contexts) are averaged over this posterior, in a procedure inspired by those used by BIBREF43 , BIBREF36 . The primary similarity to our MMSG approach is the use of a training algorithm involving the prediction of context words, given a topic. Our method does this as part of an overall model-based inference procedure, and we learn mixed membership proportions INLINEFORM1 rather than using empirical counts as the prior over topics for a word token. In accordance with the skip-gram's prediction model, we are thus able to model the context words in the data likelihood term when computing the posterior probability of the topic assignment. TWE-1 requires that topic assignments are available at test time. It provides a mechanism to predict contextual similarity, but not to predict held-out context words, so we are unable to compare to it in our experiments.",
+      "Other neurally-inspired topic models include replicated softmax BIBREF34 , and its successor, DocNADE BIBREF37 . Replicated softmax extends the restricted Boltzmann machine to handle multinomial counts for document modeling. DocNADE builds on the ideas of replicated softmax, but uses the NADE architecture, where observations (i.e. words) are modeled sequentially given the previous observations."
+    ],
+    [
+      "Multi-prototype embeddings models are another relevant line of work. These models address lexical ambiguity by assigning multiple vectors to each word type, each corresponding to a different meaning of that word. BIBREF43 propose to cluster the occurrences of each word type, based on features extracted from its context. Embeddings are then learned for each cluster. BIBREF36 apply a similar approach, but they use initial single-prototype word embeddings to provide the features used for clustering. These clustering methods have some resemblance to our topic model pre-clustering step, although their clustering is applied within instances of a given word type, rather than globally across all word types, as in our methods. This results in models with more vectors than words, while we aim to find fewer vectors than words, to reduce the model's complexity for small datasets. Rather than employing an off-the-shelf clustering algorithm and then applying an unrelated embedding model to its output, our approach aims to perform model-based clustering within an overall joint model of topic/cluster assignments and word vectors.",
+      "Perhaps the most similar model to ours in the literature is the probabilistic multi-prototype embedding model of BIBREF45 , who treat the prototype assignment of a word as a latent variable, assumed drawn from a mixture over prototypes for each word. The embeddings are then trained using EM. Our MMSG model can be understood as the mixed membership version of this model, in which the prototypes (vectors) are shared across all word types, and each word type has its own mixed membership proportions across the shared prototypes. While a similar EM algorithm can be applied to the MMSG, the E-step is much more expensive, as we typically desire many more shared vectors (often in the thousands) than we would prototypes per a single word type (Tian et al. use ten in their experiments). We use the Metropolis-Hastings-Walker algorithm with the topic model reparameterization of our model in order to address this by efficiently pre-solving the E-step."
+    ],
+    [
+      "Mixed membership modeling is a flexible alternative to traditional clustering, in which each data point is assigned to a single cluster. Instead, mixed membership models posit that individual entities are associated with multiple underlying clusters, to differing degrees, as encoded by a mixed membership vector that sums to one across the clusters BIBREF28 , BIBREF26 . These mixed membership proportions are generally used to model lower-level grouped data, such as the words inside a document. Each lower-level data point inside a group is assumed to be assigned to one of the shared, global clusters according to the group-level membership proportions. Thus, a mixed membership model consists of a mixture model for each group, which share common mixture component parameters, but with differing mixture proportions.",
+      "This formalism has lead to probabilistic models for a variety of applications, including medical diagnosis BIBREF39 , population genetics BIBREF42 , survey analysis BIBREF29 , computer vision BIBREF27 , BIBREF30 , text documents BIBREF35 , BIBREF7 , and social network analysis BIBREF25 . Nonparametric Bayesian extensions, in which the number of underlying clusters is learned from data via Bayesian inference, have also been proposed BIBREF44 . In this work, dictionary words are assigned a mixed membership distribution over a set of shared latent vector space embeddings. Each instantiation of a dictionary word (an \u201cinput\u201d word) is assigned to one of the shared embeddings based on its dictionary word's membership vector. The words in its context (\u201coutput\u201d words) are assumed to be drawn based on the chosen embedding."
+    ],
+    [
+      "In Figure FIGREF33 , we show a zoomed in INLINEFORM0 -SNE visualization of NIPS document embeddings. We can see regions of the space corresponding to learning algorithms (bottom), data space and latent space (center), training neural networks (top), and nearest neighbors (bottom-left). We also visualized the authors' embeddings via INLINEFORM1 -SNE (Figure FIGREF34 ). We find regions of latent space for reinforcement learning authors (left: \u201cstate, action,...,\u201d Singh, Barto,Sutton), probabilistic methods (right: \u201cmixture, model,\u201d \u201cmonte, carlo,\u201d Bishop, Williams, Barber, Opper, Jordan, Ghahramani, Tresp, Smyth), and evaluation (top-right: \u201cresults, performance, experiments,...\u201d)."
+    ],
+    [
+      "Let INLINEFORM0 be the number of output words in the INLINEFORM1 th context, let INLINEFORM2 be those output words, and let INLINEFORM3 be the input words other that INLINEFORM4 (similarly, topic assignments INLINEFORM5 and output words INLINEFORM6 ). Then the collapsed Gibbs update samples from the conditional distribution INLINEFORM7 ",
+      " We recognize the first integral as the mean of a Dirichlet distribution which we obtain via conjugacy: INLINEFORM0 ",
+      " The above can also be understood as the probability of the next ball drawn from a multivariate Polya urn model, also known as the Dirichlet-compound multinomial distribution, arising from the posterior predictive distribution of a discrete likelihood with a Dirichlet prior. We will need the full form of such a distribution to analyze the second integral. Once again leveraging conjugacy, we have: INLINEFORM0 ",
+      " INLINEFORM0 ",
+      " where INLINEFORM0 is the number of times that output word INLINEFORM1 occurs in the INLINEFORM2 th context, since the final integral is over the full support of a Dirichlet distribution, which integrates to one. Eliminating terms that aren't affected by the INLINEFORM3 assignment, the above is INLINEFORM4 ",
+      " where we have used the fact that INLINEFORM0 for any INLINEFORM1 , and integer INLINEFORM2 . We can interpret this as the probability of drawing the context words under the multivariate Polya urn model, in which the number of \u201ccolored balls\u201d (word counts plus prior counts) is increased by one each time a certain color (word) is selected. In other words, in each step, corresponding to the selection of each context word, we draw a ball from the urn, then put it back, along with another ball of the same color. The INLINEFORM3 and INLINEFORM4 terms reflect that the counts have been changed by adding these extra balls into the urn in each step. The second to last equation shows that this process is exchangeable: it does not matter which order the balls were drawn in when determining the probability of the sequence. Multiplying this with the term from the first integral, calculated earlier, gives us the final form of the update equation, INLINEFORM5 ",
+      ""
+    ]
+  ]
+}
+```
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+Name of Paper: Multimodal Differential Network for Visual Question Generation
+
+Question: Do they report results only on English datasets?
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+Name of Paper: Multimodal Differential Network for Visual Question Generation
+
+Question: What were the previous state of the art benchmarks?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Approach",
+    "Method",
+    "Multimodal Differential Network",
+    "Decoder: Question Generator",
+    "Cost function",
+    "Variations of Proposed Method",
+    "Dataset",
+    "Inference",
+    "Experiments",
+    "Ablation Analysis",
+    "Baseline and State-of-the-Art",
+    "Statistical Significance Analysis",
+    "Perceptual Realism",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "To understand the progress towards multimedia vision and language understanding, a visual Turing test was proposed by BIBREF0 that was aimed at visual question answering BIBREF1 . Visual Dialog BIBREF2 is a natural extension for VQA. Current dialog systems as evaluated in BIBREF3 show that when trained between bots, AI-AI dialog systems show improvement, but that does not translate to actual improvement for Human-AI dialog. This is because, the questions generated by bots are not natural (human-like) and therefore does not translate to improved human dialog. Therefore it is imperative that improvement in the quality of questions will enable dialog agents to perform well in human interactions. Further, BIBREF4 show that unanswered questions can be used for improving VQA, Image captioning and Object Classification.",
+      "An interesting line of work in this respect is the work of BIBREF5 . Here the authors have proposed the challenging task of generating natural questions for an image. One aspect that is central to a question is the context that is relevant to generate it. However, this context changes for every image. As can be seen in Figure FIGREF1 , an image with a person on a skateboard would result in questions related to the event. Whereas for a little girl, the questions could be related to age rather than the action. How can one have widely varying context provided for generating questions? To solve this problem, we use the context obtained by considering exemplars, specifically we use the difference between relevant and irrelevant exemplars. We consider different contexts in the form of Location, Caption, and Part of Speech tags.",
+      "The human annotated questions are (b) for the first image and (a) for the second image.",
+      "Our method implicitly uses a differential context obtained through supporting and contrasting exemplars to obtain a differentiable embedding. This embedding is used by a question decoder to decode the appropriate question. As discussed further, we observe this implicit differential context to perform better than an explicit keyword based context. The difference between the two approaches is illustrated in Figure FIGREF2 . This also allows for better optimization as we can backpropagate through the whole network. We provide detailed empirical evidence to support our hypothesis. As seen in Figure FIGREF1 our method generates natural questions and improves over the state-of-the-art techniques for this problem.",
+      "To summarize, we propose a multimodal differential network to solve the task of visual question generation. Our contributions are: (1) A method to incorporate exemplars to learn differential embeddings that captures the subtle differences between supporting and contrasting examples and aid in generating natural questions. (2) We provide Multimodal differential embeddings, as image or text alone does not capture the whole context and we show that these embeddings outperform the ablations which incorporate cues such as only image, or tags or place information. (3) We provide a thorough comparison of the proposed network against state-of-the-art benchmarks along with a user study and statistical significance test."
+    ],
+    [
+      "Generating a natural and engaging question is an interesting and challenging task for a smart robot (like chat-bot). It is a step towards having a natural visual dialog instead of the widely prevalent visual question answering bots. Further, having the ability to ask natural questions based on different contexts is also useful for artificial agents that can interact with visually impaired people. While the task of generating question automatically is well studied in NLP community, it has been relatively less studied for image-related natural questions. This is still a difficult task BIBREF5 that has gained recent interest in the community.",
+      "Recently there have been many deep learning based approaches as well for solving the text-based question generation task such as BIBREF6 . Further, BIBREF7 have proposed a method to generate a factoid based question based on triplet set {subject, relation and object} to capture the structural representation of text and the corresponding generated question.",
+      "These methods, however, were limited to text-based question generation. There has been extensive work done in the Vision and Language domain for solving image captioning, paragraph generation, Visual Question Answering (VQA) and Visual Dialog. BIBREF8 , BIBREF9 , BIBREF10 proposed conventional machine learning methods for image description. BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 , BIBREF15 , BIBREF16 , BIBREF17 , BIBREF18 have generated descriptive sentences from images with the help of Deep Networks. There have been many works for solving Visual Dialog BIBREF19 , BIBREF20 , BIBREF2 , BIBREF21 , BIBREF22 . A variety of methods have been proposed by BIBREF23 , BIBREF24 , BIBREF1 , BIBREF25 , BIBREF26 , BIBREF27 for solving VQA task including attention-based methods BIBREF28 , BIBREF29 , BIBREF30 , BIBREF31 , BIBREF32 , BIBREF33 , BIBREF34 . However, Visual Question Generation (VQG) is a separate task which is of interest in its own right and has not been so well explored BIBREF5 . This is a vision based novel task aimed at generating natural and engaging question for an image. BIBREF35 proposed a method for continuously generating questions from an image and subsequently answering those questions. The works closely related to ours are that of BIBREF5 and BIBREF36 . In the former work, the authors used an encoder-decoder based framework whereas in the latter work, the authors extend it by using a variational autoencoder based sequential routine to obtain natural questions by performing sampling of the latent variable."
+    ],
+    [
+      "In this section, we clarify the basis for our approach of using exemplars for question generation. To use exemplars for our method, we need to ensure that our exemplars can provide context and that our method generates valid exemplars.",
+      "We first analyze whether the exemplars are valid or not. We illustrate this in figure FIGREF3 . We used a pre-trained RESNET-101 BIBREF37 object classification network on the target, supporting and contrasting images. We observed that the supporting image and target image have quite similar probability scores. The contrasting exemplar image, on the other hand, has completely different probability scores.",
+      "Exemplars aim to provide appropriate context. To better understand the context, we experimented by analysing the questions generated through an exemplar. We observed that indeed a supporting exemplar could identify relevant tags (cows in Figure FIGREF3 ) for generating questions.",
+      "We improve use of exemplars by using a triplet network. This network ensures that the joint image-caption embedding for the supporting exemplar are closer to that of the target image-caption and vice-versa. We empirically evaluated whether an explicit approach that uses the differential set of tags as a one-hot encoding improves the question generation, or the implicit embedding obtained based on the triplet network. We observed that the implicit multimodal differential network empirically provided better context for generating questions. Our understanding of this phenomenon is that both target and supporting exemplars generate similar questions whereas contrasting exemplars generate very different questions from the target question. The triplet network that enhances the joint embedding thus aids to improve the generation of target question. These are observed to be better than the explicitly obtained context tags as can be seen in Figure FIGREF2 . We now explain our method in detail."
+    ],
+    [
+      "The task in visual question generation (VQG) is to generate a natural language question INLINEFORM0 , for an image INLINEFORM1 . We consider a set of pre-generated context INLINEFORM2 from image INLINEFORM3 . We maximize the conditional probability of generated question given image and context as follows: DISPLAYFORM0 ",
+      "where INLINEFORM0 is a vector for all possible parameters of our model. INLINEFORM1 is the ground truth question. The log probability for the question is calculated by using joint probability over INLINEFORM2 with the help of chain rule. For a particular question, the above term is obtained as: INLINEFORM3 ",
+      "where INLINEFORM0 is length of the sequence, and INLINEFORM1 is the INLINEFORM2 word of the question. We have removed INLINEFORM3 for simplicity.",
+      "Our method is based on a sequence to sequence network BIBREF38 , BIBREF12 , BIBREF39 . The sequence to sequence network has a text sequence as input and output. In our method, we take an image as input and generate a natural question as output. The architecture for our model is shown in Figure FIGREF4 . Our model contains three main modules, (a) Representation Module that extracts multimodal features (b) Mixture Module that fuses the multimodal representation and (c) Decoder that generates question using an LSTM-based language model.",
+      "During inference, we sample a question word INLINEFORM0 from the softmax distribution and continue sampling until the end token or maximum length for the question is reached. We experimented with both sampling and argmax and found out that argmax works better. This result is provided in the supplementary material."
+    ],
+    [
+      "The proposed Multimodal Differential Network (MDN) consists of a representation module and a joint mixture module.",
+      "We used an efficient KNN-based approach (k-d tree) with Euclidean metric to obtain the exemplars. This is obtained through a coarse quantization of nearest neighbors of the training examples into 50 clusters, and selecting the nearest as supporting and farthest as the contrasting exemplars. We experimented with ITML based metric learning BIBREF40 for image features. Surprisingly, the KNN-based approach outperforms the latter one. We also tried random exemplars and different number of exemplars and found that INLINEFORM0 works best. We provide these results in the supplementary material.",
+      "We use a triplet network BIBREF41 , BIBREF42 in our representation module. We refereed a similar kind of work done in BIBREF34 for building our triplet network. The triplet network consists of three sub-parts: target, supporting, and contrasting networks. All three networks share the same parameters. Given an image INLINEFORM0 we obtain an embedding INLINEFORM1 using a CNN parameterized by a function INLINEFORM2 where INLINEFORM3 are the weights for the CNN. The caption INLINEFORM4 results in a caption embedding INLINEFORM5 through an LSTM parameterized by a function INLINEFORM6 where INLINEFORM7 are the weights for the LSTM. This is shown in part 1 of Figure FIGREF4 . Similarly we obtain image embeddings INLINEFORM8 & INLINEFORM9 and caption embeddings INLINEFORM10 & INLINEFORM11 . DISPLAYFORM0 ",
+      "The Mixture module brings the image and caption embeddings to a joint feature embedding space. The input to the module is the embeddings obtained from the representation module. We have evaluated four different approaches for fusion viz., joint, element-wise addition, hadamard and attention method. Each of these variants receives image features INLINEFORM0 & the caption embedding INLINEFORM1 , and outputs a fixed dimensional feature vector INLINEFORM2 . The Joint method concatenates INLINEFORM3 & INLINEFORM4 and maps them to a fixed length feature vector INLINEFORM5 as follows: DISPLAYFORM0 ",
+      "where INLINEFORM0 is the 4096-dimensional convolutional feature from the FC7 layer of pretrained VGG-19 Net BIBREF43 . INLINEFORM1 are the weights and INLINEFORM2 is the bias for different layers. INLINEFORM3 is the concatenation operator.",
+      "Similarly, We obtain context vectors INLINEFORM0 & INLINEFORM1 for the supporting and contrasting exemplars. Details for other fusion methods are present in supplementary.The aim of the triplet network BIBREF44 is to obtain context vectors that bring the supporting exemplar embeddings closer to the target embedding and vice-versa. This is obtained as follows: DISPLAYFORM0 ",
+      "where INLINEFORM0 is the euclidean distance between two embeddings INLINEFORM1 and INLINEFORM2 . M is the training dataset that contains all set of possible triplets. INLINEFORM3 is the triplet loss function. This is decomposed into two terms, one that brings the supporting sample closer and one that pushes the contrasting sample further. This is given by DISPLAYFORM0 ",
+      "Here INLINEFORM0 represent the euclidean distance between the target and supporting sample, and target and opposing sample respectively. The parameter INLINEFORM1 controls the separation margin between these and is obtained through validation data."
+    ],
+    [
+      "The role of decoder is to predict the probability for a question, given INLINEFORM0 . RNN provides a nice way to perform conditioning on previous state value using a fixed length hidden vector. The conditional probability of a question token at particular time step INLINEFORM1 is modeled using an LSTM as used in machine translation BIBREF38 . At time step INLINEFORM2 , the conditional probability is denoted by INLINEFORM3 , where INLINEFORM4 is the hidden state of the LSTM cell at time step INLINEFORM5 , which is conditioned on all the previously generated words INLINEFORM6 . The word with maximum probability in the probability distribution of the LSTM cell at step INLINEFORM7 is fed as an input to the LSTM cell at step INLINEFORM8 as shown in part 3 of Figure FIGREF4 . At INLINEFORM9 , we are feeding the output of the mixture module to LSTM. INLINEFORM10 are the predicted question tokens for the input image INLINEFORM11 . Here, we are using INLINEFORM12 and INLINEFORM13 as the special token START and STOP respectively. The softmax probability for the predicted question token at different time steps is given by the following equations where LSTM refers to the standard LSTM cell equations: INLINEFORM14 ",
+      "Where INLINEFORM0 is the probability distribution over all question tokens. INLINEFORM1 is cross entropy loss."
+    ],
+    [
+      "Our objective is to minimize the total loss, that is the sum of cross entropy loss and triplet loss over all training examples. The total loss is: DISPLAYFORM0 ",
+      "where INLINEFORM0 is the total number of samples, INLINEFORM1 is a constant, which controls both the loss. INLINEFORM2 is the triplet loss function EQREF13 . INLINEFORM3 is the cross entropy loss between the predicted and ground truth questions and is given by: INLINEFORM4 ",
+      "where, INLINEFORM0 is the total number of question tokens, INLINEFORM1 is the ground truth label. The code for MDN-VQG model is provided ."
+    ],
+    [
+      "While, we advocate the use of multimodal differential network for generating embeddings that can be used by the decoder for generating questions, we also evaluate several variants of this architecture. These are as follows:",
+      "Tag Net: In this variant, we consider extracting the part-of-speech (POS) tags for the words present in the caption and obtaining a Tag embedding by considering different methods of combining the one-hot vectors. Further details and experimental results are present in the supplementary. This Tag embedding is then combined with the image embedding and provided to the decoder network.",
+      "Place Net: In this variant we explore obtaining embeddings based on the visual scene understanding. This is obtained using a pre-trained PlaceCNN BIBREF45 that is trained to classify 365 different types of scene categories. We then combine the activation map for the input image and the VGG-19 based place embedding to obtain the joint embedding used by the decoder.",
+      "Differential Image Network: Instead of using multimodal differential network for generating embeddings, we also evaluate differential image network for the same. In this case, the embedding does not include the caption but is based only on the image feature. We also experimented with using multiple exemplars and random exemplars.",
+      "Further details, pseudocode and results regarding these are present in the supplementary material."
+    ],
+    [
+      "We conduct our experiments on Visual Question Generation (VQG) dataset BIBREF5 , which contains human annotated questions based on images of MS-COCO dataset. This dataset was developed for generating natural and engaging questions based on common sense reasoning. We use VQG-COCO dataset for our experiments which contains a total of 2500 training images, 1250 validation images, and 1250 testing images. Each image in the dataset contains five natural questions and five ground truth captions. It is worth noting that the work of BIBREF36 also used the questions from VQA dataset BIBREF1 for training purpose, whereas the work by BIBREF5 uses only the VQG-COCO dataset. VQA-1.0 dataset is also built on images from MS-COCO dataset. It contains a total of 82783 images for training, 40504 for validation and 81434 for testing. Each image is associated with 3 questions. We used pretrained caption generation model BIBREF13 to extract captions for VQA dataset as the human annotated captions are not there in the dataset. We also get good results on the VQA dataset (as shown in Table TABREF26 ) which shows that our method doesn't necessitate the presence of ground truth captions. We train our model separately for VQG-COCO and VQA dataset."
+    ],
+    [
+      "We made use of the 1250 validation images to tune the hyperparameters and are providing the results on test set of VQG-COCO dataset. During inference, We use the Representation module to find the embeddings for the image and ground truth caption without using the supporting and contrasting exemplars. The mixture module provides the joint representation of the target image and ground truth caption. Finally, the decoder takes in the joint features and generates the question. We also experimented with the captions generated by an Image-Captioning network BIBREF13 for VQG-COCO dataset and the result for that and training details are present in the supplementary material."
+    ],
+    [
+      "We evaluate our proposed MDN method in the following ways: First, we evaluate it against other variants described in section SECREF19 and SECREF10 . Second, we further compare our network with state-of-the-art methods for VQA 1.0 and VQG-COCO dataset. We perform a user study to gauge human opinion on naturalness of the generated question and analyze the word statistics in Figure FIGREF22 . This is an important test as humans are the best deciders of naturalness. We further consider the statistical significance for the various ablations as well as the state-of-the-art models. The quantitative evaluation is conducted using standard metrics like BLEU BIBREF46 , METEOR BIBREF47 , ROUGE BIBREF48 , CIDEr BIBREF49 . Although these metrics have not been shown to correlate with `naturalness' of the question these still provide a reasonable quantitative measure for comparison. Here we only provide the BLEU1 scores, but the remaining BLEU-n metric scores are present in the supplementary. We observe that the proposed MDN provides improved embeddings to the decoder. We believe that these embeddings capture instance specific differential information that helps in guiding the question generation. Details regarding the metrics are given in the supplementary material."
+    ],
+    [
+      "We considered different variations of our method mentioned in section SECREF19 and the various ways to obtain the joint multimodal embedding as described in section SECREF10 . The results for the VQG-COCO test set are given in table TABREF24 . In this table, every block provides the results for one of the variations of obtaining the embeddings and different ways of combining them. We observe that the Joint Method (JM) of combining the embeddings works the best in all cases except the Tag Embeddings. Among the ablations, the proposed MDN method works way better than the other variants in terms of BLEU, METEOR and ROUGE metrics by achieving an improvement of 6%, 12% and 18% in the scores respectively over the best other variant."
+    ],
+    [
+      "The comparison of our method with various baselines and state-of-the-art methods is provided in table TABREF26 for VQA 1.0 and table TABREF27 for VQG-COCO dataset. The comparable baselines for our method are the image based and caption based models in which we use either only the image or the caption embedding and generate the question. In both the tables, the first block consists of the current state-of-the-art methods on that dataset and the second contains the baselines. We observe that for the VQA dataset we achieve an improvement of 8% in BLEU and 7% in METEOR metric scores over the baselines, whereas for VQG-COCO dataset this is 15% for both the metrics. We improve over the previous state-of-the-art BIBREF35 for VQA dataset by around 6% in BLEU score and 10% in METEOR score. In the VQG-COCO dataset, we improve over BIBREF5 by 3.7% and BIBREF36 by 3.5% in terms of METEOR scores."
+    ],
+    [
+      "We have analysed Statistical Significance BIBREF50 of our MDN model for VQG for different variations of the mixture module mentioned in section SECREF10 and also against the state-of-the-art methods. The Critical Difference (CD) for Nemenyi BIBREF51 test depends upon the given INLINEFORM0 (confidence level, which is 0.05 in our case) for average ranks and N (number of tested datasets). If the difference in the rank of the two methods lies within CD, then they are not significantly different and vice-versa. Figure FIGREF29 visualizes the post-hoc analysis using the CD diagram. From the figure, it is clear that MDN-Joint works best and is statistically significantly different from the state-of-the-art methods."
+    ],
+    [
+      "A human is the best judge of naturalness of any question, We evaluated our proposed MDN method using a `Naturalness' Turing test BIBREF52 on 175 people. People were shown an image with 2 questions just as in figure FIGREF1 and were asked to rate the naturalness of both the questions on a scale of 1 to 5 where 1 means `Least Natural' and 5 is the `Most Natural'. We provided 175 people with 100 such images from the VQG-COCO validation dataset which has 1250 images. Figure FIGREF30 indicates the number of people who were fooled (rated the generated question more or equal to the ground truth question). For the 100 images, on an average 59.7% people were fooled in this experiment and this shows that our model is able to generate natural questions."
+    ],
+    [
+      "In this paper we have proposed a novel method for generating natural questions for an image. The approach relies on obtaining multimodal differential embeddings from image and its caption. We also provide ablation analysis and a detailed comparison with state-of-the-art methods, perform a user study to evaluate the naturalness of our generated questions and also ensure that the results are statistically significant. In future, we would like to analyse means of obtaining composite embeddings. We also aim to consider the generalisation of this approach to other vision and language tasks. Supplementary Material Section SECREF8 will provide details about training configuration for MDN, Section SECREF9 will explain the various Proposed Methods and we also provide a discussion in section regarding some important questions related to our method. We report BLEU1, BLEU2, BLEU3, BLEU4, METEOR, ROUGE and CIDER metric scores for VQG-COCO dataset. We present different experiments with Tag Net in which we explore the performance of various tags (Noun, Verb, and Question tags) and different ways of combining them to get the context vectors. Multimodal Differential Network [1] MDN INLINEFORM0 Finding Exemplars: INLINEFORM1 INLINEFORM2 Compute Triplet Embedding: INLINEFORM3 INLINEFORM4 Compute Triplet Fusion Embedding : INLINEFORM5 INLINEFORM6 INLINEFORM7 Compute Triplet Loss: INLINEFORM8 Compute Decode Question Sentence: INLINEFORM9 INLINEFORM10 \u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2013 Triplet Fusion INLINEFORM11 , INLINEFORM12 INLINEFORM13 :Image feature,14x14x512 INLINEFORM14 : Caption feature,1x512 Match Dimension: INLINEFORM15 ,196x512 INLINEFORM16 196x512 If flag==Joint Fusion: INLINEFORM17 INLINEFORM18 , [ INLINEFORM19 (MDN-Mul), INLINEFORM20 (MDN-Add)] If flag==Attention Fusion : INLINEFORM21 Semb INLINEFORM22 Dataset and Training Details Dataset We conduct our experiments on two types of dataset: VQA dataset BIBREF1 , which contains human annotated questions based on images on MS-COCO dataset. Second one is VQG-COCO dataset based on natural question BIBREF55 . VQA dataset VQA dataset BIBREF1 is built on complex images from MS-COCO dataset. It contains a total of 204721 images, out of which 82783 are for training, 40504 for validation and 81434 for testing. Each image in the MS-COCO dataset is associated with 3 questions and each question has 10 possible answers. So there are 248349 QA pair for training, 121512 QA pairs for validating and 244302 QA pairs for testing. We used pre-trained caption generation model BIBREF53 to extract captions for VQA dataset. VQG dataset The VQG-COCO dataset BIBREF55 , is developed for generating natural and engaging questions that are based on common sense reasoning. This dataset contains a total of 2500 training images, 1250 validation images and 1250 testing images. Each image in the dataset contains 5 natural questions. Training Configuration We have used RMSPROP optimizer to update the model parameter and configured hyper-parameter values to be as follows: INLINEFORM23 to train the classification network . In order to train a triplet model, we have used RMSPROP to optimize the triplet model model parameter and configure hyper-parameter values to be: INLINEFORM24 . We also used learning rate decay to decrease the learning rate on every epoch by a factor given by: INLINEFORM25 where values of a=1500 and b=1250 are set empirically. Ablation Analysis of Model While, we advocate the use of multimodal differential network (MDN) for generating embeddings that can be used by the decoder for generating questions, we also evaluate several variants of this architecture namely (a) Differential Image Network, (b) Tag net and (c) Place net. These are described in detail as follows: Differential Image Network For obtaining the exemplar image based context embedding, we propose a triplet network consist of three network, one is target net, supporting net and opposing net. All these three networks designed with convolution neural network and shared the same parameters. The weights of this network are learnt through end-to-end learning using a triplet loss. The aim is to obtain latent weight vectors that bring the supporting exemplar close to the target image and enhances the difference between opposing examples. More formally, given an image INLINEFORM26 we obtain an embedding INLINEFORM27 using a CNN that we parameterize through a function INLINEFORM28 where INLINEFORM29 are the weights of the CNN. This is illustrated in figure FIGREF43 . Tag net The tag net consists of two parts Context Extractor & Tag Embedding Net. This is illustrated in figure FIGREF45 . Extract Context: The first step is to extract the caption of the image using NeuralTalk2 BIBREF53 model. We find the part-of-speech(POS) tag present in the caption. POS taggers have been developed for two well known corpuses, the Brown Corpus and the Penn Treebanks. For our work, we are using the Brown Corpus tags. The tags are clustered into three category namely Noun tag, Verb tag and Question tags (What, Where, ...). Noun tag consists of all the noun & pronouns present in the caption sentence and similarly, verb tag consists of verb & adverbs present in the caption sentence. The question tags consists of the 7-well know question words i.e., why, how, what, when, where, who and which. Each tag token is represented as a one-hot vector of the dimension of vocabulary size. For generalization, we have considered 5 tokens from each category of the Tags. Tag Embedding Net: The embedding network consists of word embedding followed by temporal convolutions neural network followed by max-pooling network. In the first step, sparse high dimensional one-hot vector is transformed to dense low dimension vector using word embedding. After this, we apply temporal convolution on the word embedding vector. The uni-gram, bi-gram and tri-gram feature are computed by applying convolution filter of size 1, 2 and 3 respectability. Finally, we applied max-pooling on this to get a vector representation of the tags as shown figure FIGREF45 . We concatenated all the tag words followed by fully connected layer to get feature dimension of 512. We also explored joint networks based on concatenation of all the tags, on element-wise addition and element-wise multiplication of the tag vectors. However, we observed that convolution over max pooling and joint concatenation gives better performance based on CIDer score. INLINEFORM30 Where, T_CNN is Temporally Convolution Neural Network applied on word embedding vector with kernel size three. Place net Visual object and scene recognition plays a crucial role in the image. Here, places in the image are labeled with scene semantic categories BIBREF45 , comprise of large and diverse type of environment in the world, such as (amusement park, tower, swimming pool, shoe shop, cafeteria, rain-forest, conference center, fish pond, etc.). So we have used different type of scene semantic categories present in the image as a place based context to generate natural question. A place365 is a convolution neural network is modeled to classify 365 types of scene categories, which is trained on the place2 dataset consist of 1.8 million of scene images. We have used a pre-trained VGG16-places365 network to obtain place based context embedding feature for various type scene categories present in the image. The context feature INLINEFORM31 is obtained by: INLINEFORM32 Where, INLINEFORM33 is Place365_CNN. We have extracted INLINEFORM34 features of dimension 14x14x512 for attention model and FC8 features of dimension 365 for joint, addition and hadamard model of places365. Finally, we use a linear transformation to obtain a 512 dimensional vector. We explored using the CONV5 having feature dimension 14x14 512, FC7 having 4096 and FC8 having feature dimension of 365 of places365. Ablation Analysis Sampling Exemplar: KNN vs ITML Our method is aimed at using efficient exemplar-based retrieval techniques. We have experimented with various exemplar methods, such as ITML BIBREF40 based metric learning for image features and KNN based approaches. We observed KNN based approach (K-D tree) with Euclidean metric is a efficient method for finding exemplars. Also we observed that ITML is computationally expensive and also depends on the training procedure. The table provides the experimental result for Differential Image Network variant with k (number of exemplars) = 2 and Hadamard method: Question Generation approaches: Sampling vs Argmax We obtained the decoding using standard practice followed in the literature BIBREF38 . This method selects the argmax sentence. Also, we evaluated our method by sampling from the probability distributions and provide the results for our proposed MDN-Joint method for VQG dataset as follows: How are exemplars improving Embedding In Multimodel differential network, we use exemplars and train them using a triplet loss. It is known that using a triplet network, we can learn a representation that accentuates how the image is closer to a supporting exemplar as against the opposing exemplar BIBREF42 , BIBREF41 . The Joint embedding is obtained between the image and language representations. Therefore the improved representation helps in obtaining an improved context vector. Further we show that this also results in improving VQG. Are exemplars required? We had similar concerns and validated this point by using random exemplars for the nearest neighbor for MDN. (k=R in table TABREF35 ) In this case the method is similar to the baseline. This suggests that with random exemplar, the model learns to ignore the cue. Are captions necessary for our method? This is not actually necessary. In our method, we have used an existing image captioning method BIBREF13 to generate captions for images that did not have them. For VQG dataset, captions were available and we have used that, but, for VQA dataset captions were not available and we have generated captions while training. We provide detailed evidence with respect to caption-question pairs to ensure that we are generating novel questions. While the caption generates scene description, our proposed method generates semantically meaningful and novel questions. Examples for Figure 1 of main paper: First Image:- Caption- A young man skateboarding around little cones. Our Question- Is this a skateboard competition? Second Image:- Caption- A small child is standing on a pair of skis. Our Question:- How old is that little girl? Intuition behind Triplet Network: The intuition behind use of triplet networks is clear through this paper BIBREF41 that first advocated its use. The main idea is that when we learn distance functions that are \u201cclose\u201d for similar and \u201cfar\u201d from dissimilar representations, it is not clear that close and far are with respect to what measure. By incorporating a triplet we learn distance functions that learn that \u201cA is more similar to B as compared to C\u201d. Learning such measures allows us to bring target image-caption joint embeddings that are closer to supporting exemplars as compared to contrasting exemplars. Analysis of Network Analysis of Tag Context Tag is language based context. These tags are extracted from caption, except question-tags which is fixed as the 7 'Wh words' (What, Why, Where, Who, When, Which and How). We have experimented with Noun tag, Verb tag and 'Wh-word' tag as shown in tables. Also, we have experimented in each tag by varying the number of tags from 1 to 7. We combined different tags using 1D-convolution, concatenation, and addition of all the tags and observed that the concatenation mechanism gives better results. As we can see in the table TABREF33 that taking Nouns, Verbs and Wh-Words as context, we achieve significant improvement in the BLEU, METEOR and CIDEr scores from the basic models which only takes the image and the caption respectively. Taking Nouns generated from the captions and questions of the corresponding training example as context, we achieve an increase of 1.6% in Bleu Score and 2% in METEOR and 34.4% in CIDEr Score from the basic Image model. Similarly taking Verbs as context gives us an increase of 1.3% in Bleu Score and 2.1% in METEOR and 33.5% in CIDEr Score from the basic Image model. And the best result comes when we take 3 Wh-Words as context and apply the Hadamard Model with concatenating the 3 WH-words. Also in Table TABREF34 we have shown the results when we take more than one words as context. Here we show that for 3 words i.e 3 nouns, 3 verbs and 3 Wh-words, the Concatenation model performs the best. In this table the conv model is using 1D convolution to combine the tags and the joint model combine all the tags. Analysis of Context: Exemplars In Multimodel Differential Network and Differential Image Network, we use exemplar images(target, supporting and opposing image) to obtain the differential context. We have performed the experiment based on the single exemplar(K=1), which is one supporting and one opposing image along with target image, based on two exemplar(K=2), i.e. two supporting and two opposing image along with single target image. similarly we have performed experiment for K=3 and K=4 as shown in table- TABREF35 . Mixture Module: Other Variations Hadamard method uses element-wise multiplication whereas Addition method uses element-wise addition in place of the concatenation operator of the Joint method. The Hadamard method finds a correlation between image feature and caption feature vector while the Addition method learns a resultant vector. In the attention method, the output INLINEFORM35 is the weighted average of attention probability vector INLINEFORM36 and convolutional features INLINEFORM37 . The attention probability vector weights the contribution of each convolutional feature based on the caption vector. This attention method is similar to work stack attention method BIBREF54 . The attention mechanism is given by: DISPLAYFORM0 where INLINEFORM38 is the 14x14x512-dimensional convolution feature map from the fifth convolution layer of VGG-19 Net of image INLINEFORM39 and INLINEFORM40 is the caption context vector. The attention probability vector INLINEFORM41 is a 196-dimensional vector. INLINEFORM42 are the weights and INLINEFORM43 is the bias for different layers. We evaluate the different approaches and provide results for the same. Here INLINEFORM44 represents element-wise addition. Evaluation Metrics Our task is similar to encoder -decoder framework of machine translation. we have used same evaluation metric is used in machine translation. BLEU BIBREF46 is the first metric to find the correlation between generated question with ground truth question. BLEU score is used to measure the precision value, i.e That is how much words in the predicted question is appeared in reference question. BLEU-n score measures the n-gram precision for counting co-occurrence on reference sentences. we have evaluated BLEU score from n is 1 to 4. The mechanism of ROUGE-n BIBREF48 score is similar to BLEU-n,where as, it measures recall value instead of precision value in BLEU. That is how much words in the reference question is appeared in predicted question.Another version ROUGE metric is ROUGE-L, which measures longest common sub-sequence present in the generated question. METEOR BIBREF47 score is another useful evaluation metric to calculate the similarity between generated question with reference one by considering synonyms, stemming and paraphrases. the output of the METEOR score measure the word matches between predicted question and reference question. In VQG, it compute the word match score between predicted question with five reference question. CIDer BIBREF49 score is a consensus based evaluation metric. It measure human-likeness, that is the sentence is written by human or not. The consensus is measured, how often n-grams in the predicted question are appeared in the reference question. If the n-grams in the predicted question sentence is appeared more frequently in reference question then question is less informative and have low CIDer score. We provide our results using all these metrics and compare it with existing baselines. "
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: #MeToo on Campus: Studying College Sexual Assault at Scale Using Data Reported on Social Media
+
+Question: How are the topics embedded in the #MeToo tweets extracted?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Dataset ::: Data Collection",
+    "Dataset ::: Text Preprocessing",
+    "Dataset ::: College Metadata",
+    "Methodology ::: Regression Analysis",
+    "Methodology ::: Labeling Sexual Harassment",
+    "Methodology ::: Topic Modeling on #MeToo Tweets",
+    "Methodology ::: Semantic Parsing with TRIPS",
+    "Methodology ::: Connotation Frames and Sentiment Analysis",
+    "Experimental Results ::: Topical Themes of #MeToo Tweets",
+    "Experimental Results ::: Regression Result",
+    "Experimental Results ::: Event-Entity Sentiment Analysis",
+    "Experimental Results ::: Limitations and Ethical Implications",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Sexual harassment is defined as \"bullying or coercion of a sexual nature, or the unwelcome or inappropriate promise of rewards in exchange for sexual favors.\" In fact, it is an ongoing problem in the U.S., especially within the higher education community. According to the National Sexual Violence Resource Center (NSRVC), one in five women and one in sixteen men are sexually assaulted while they are attending college. In addition to the prevalence of campus sexual harassment, it has been shown to have detrimental effects on student's well-being, including health-related disorders and psychological distress BIBREF0, BIBREF1. However, these studies on college sexual misconduct usually collect data based on questionnaires from a small sample of the college population, which might not be sufficiently substantial to capture the big picture of sexual harassment risk of the entire student body.",
+      "Alternatively, social media opens up new opportunities to gather a larger and more comprehensive amount of data and mitigate the risk of false or inaccurate narratives from the studied subjects. On October 15 of 2017, prominent Hollywood actress Alyssa Milano, by accusing Oscar-winning film producer, Harvey Weinstein, for multiple sexual impropriety attempts on herself and many other women in the film industry, ignited the \"MeToo\" trend on social media that called for women and men to share their own sexual harassment experience. According to CNN, over 1.7 million users had used the hash-tag in 85 countries. Benefiting from the tremendous amount of data supplied by this trend and the existing state-of-the-art semantic parser and generative statistical models, we propose a new approach to characterizing sexual harassment by mining the tweets from college users with the hash-tag #metoo on Twitter.",
+      "Our main contributions are several folds. We investigate campus sexual harassment using a big-data approach by collecting data from Twitter. We employ traditional topic modeling and linear regression methods on a new dataset to highlight patterns of the ongoing troubling social behaviors at both institutional and individual levels. We propose a novel approach to combining domain-general deep semantic parsing and sentiment analysis to dissect personal narratives."
+    ],
+    [
+      "Previous works for sexual misconduct in academia and workplace dated back to last few decades, when researchers studied the existence, as well as psychometric and demographic insights regarding this social issue, based on survey and official data BIBREF2, BIBREF3, BIBREF4. However, these methods of gathering data are limited in scale and might be influenced by the psychological and cognitive tendencies of respondents not to provide faithful answers BIBREF5.",
+      "The ubiquity of social media has motivated various research on widely-debated social topics such as gang violence, hate code, or presidential election using Twitter data BIBREF6, BIBREF7, BIBREF8, BIBREF9. Recently, researchers have taken the earliest steps to understand sexual harassment using textual data on Twitter. Using machine learning techniques, Modrek and Chakalov (2019) built predictive models for the identification and categorization of lexical items pertaining to sexual abuse, while analysis on semantic contents remains untouched BIBREF10. Despite the absence of Twitter data, Field et al. (2019) did a study more related to ours as they approach to the subject geared more towards linguistics tasks such as event, entity and sentiment analysis BIBREF11. Their work on event-entity extraction and contextual sentiment analysis has provided many useful insights, which enable us to tap into the potential of our Twitter dataset.",
+      "There are several novelties in our approach to the #MeToo problem. Our target population is restricted to college followers on Twitter, with the goal to explore people's sentiment towards the sexual harassment they experienced and its implication on the society's awareness and perception of the issue. Moreover, the focus on the sexual harassment reality in colleges calls for an analysis on the metadata of this demographics to reveal meaningful knowledge of their distinctive characteristics BIBREF12."
+    ],
+    [
+      "In this study, we limit the sample size to the followers identified as English speakers in the U.S. News Top 200 National Universities. We utilize the Jefferson-Henrique script, a web scraper designed for Twitter to retrieve a total of over 300,000 #MeToo tweets from October 15th, when Alyssa Milano posted the inceptive #MeToo tweet, to November 15th of 2017 to cover a period of a month when the trend was on the rise and attracting mass concerns. Since the lists of the followers of the studied colleges might overlap and many Twitter users tend to reiterate other's tweets, simply putting all the data collected together could create a major redundancy problem. We extract unique users and tweets from the combined result set to generate a dataset of about 60,000 unique tweets, pertaining to 51,104 unique users."
+    ],
+    [
+      "We pre-process the Twitter textual data to ensure that its lexical items are to a high degree lexically comparable to those of natural language. This is done by performing sentiment-aware tokenization, spell correction, word normalization, segmentation (for splitting hashtags) and annotation. The implemented tokenizer with SentiWordnet corpus BIBREF13 is able to avoid splitting expressions or words that should be kept intact (as one token), and identify most emoticons, emojis, expressions such as dates, currencies, acronyms, censored words (e.g. s**t), etc. In addition, we perform modifications on the extracted tokens. For spelling correction, we compose a dictionary for the most commonly seen abbreviations, censored words and elongated words (for emphasis, e.g. \"reallyyy\"). The Viterbi algorithm is used for word segmentation, with word statistics (unigrams and bigrams) computed from the NLTK English Corpus to obtain the most probable segmentation posteriors from the unigrams and bigrams probabilities. Moreover, all texts are lower-cased, and URLs, emails and mentioned usernames are replaced with common designated tags so that they would not need to be annotated by the semantic parser."
+    ],
+    [
+      "The meta-statistics on the college demographics regarding enrollment, geographical location, private/public categorization and male-to-female ratio are obtained. Furthermore, we acquire the Campus Safety and Security Survey dataset from the official U.S. Department of Education website and use rape-related cases statistic as an attribute to complete the data for our linear regression model. The number of such reported cases by these 200 colleges in 2015 amounts to 2,939."
+    ],
+    [
+      "We examine other features regarding the characteristics of the studied colleges, which might be significant factors of sexual harassment. Four factual attributes pertaining to the 200 colleges are extracted from the U.S. News Statistics, which consists of Undergraduate Enrollment, Male/Female Ratio, Private/Public, and Region (Northeast, South, West, and Midwest). We also use the normalized rape-related cases count (number of cases reported per student enrolled) from the stated government resource as another attribute to examine the proximity of our dataset to the official one. This feature vector is then fitted in a linear regression to predict the normalized #metoo users count (number of unique users who posted #MeToo tweets per student enrolled) for each individual college."
+    ],
+    [
+      "Per our topic modeling results, we decide to look deeper into the narratives of #MeToo users who reveal their personal stories. We examine 6,760 tweets from the most relevant topic of our LDA model, and categorize them based on the following metrics: harassment types (verbal, physical, and visual abuse) and context (peer-to-peer, school employee or work employer, and third-parties). These labels are based on definitions by the U.S. Dept. of Education BIBREF14."
+    ],
+    [
+      "In order to understand the latent topics of those #MeToo tweets for college followers, we first utilize Latent Dirichlet Allocation (LDA) to label universal topics demonstrated by the users. We determine the optimal topic number by selecting the one with the highest coherence score. Since certain words frequently appear in those #MeToo tweets (e.g., sexual harassment, men, women, story, etc.), we transform our corpus using TF-IDF, a term-weighting scheme that discounts the influence of common terms."
+    ],
+    [
+      "Learning deep meaning representations, which enables the preservation of rich semantic content of entities, meaning ambiguity resolution and partial relational understanding of texts, is one of the challenges that the TRIPS parser BIBREF15 is tasked to tackle. This kind of meaning is represented by TRIPS Logical Form (LF), which is a graph-based representation that serves as the interface between structural analysis of text (i.e., parse) and the subsequent use of the information to produce knowledge. The LF graphs are obtained by using the semantic types, roles and rule-based relations defined by the TRIPS Ontology BIBREF15 at its core in combination with various linguistic techniques such as Dialogue Act Identification, Dependency Parsing, Named Entity Recognition, and Crowd-sourced Lexicon (Wordnet).",
+      "Figure 1 illustrates an example of the TRIPS LF graph depicting the meaning of the sentence \"He harassed me,\" where the event described though the speech act TELL (i.e. telling a story) is the verb predicate HARASS, which is caused by the agent HE and influences the affected (also called \"theme\" in traditional literature) ME. As seen from the previously discussed example, the action-agent-affected relational structure is applicable to even the simplest sentences used for storytelling, and it is in fact very common for humans to encounter in both spoken and written languages. This makes it well suited for event extraction from short texts, useful for analyzing tweets with Twitter's 280 character limit. Therefore, our implementation of TRIPS parser is particularly tailored for identifying the verb predicates in tweets and their corresponding agent-affected arguments (with $82.4\\%$ F1 score), so that we can have a solid ground for further analysis."
+    ],
+    [
+      "In order to develop an interpretable analysis that focuses on sentiment scores pertaining to the entities and events mentioned in the narratives, as well as the perceptions of readers on such events, we draw from existing literature on connotation frames: a set of verbs annotated according to what they imply about semantically dependent entities. Connotation frames, first introduced by Rashkin, Singh, and Choi (2016), provides a framework for analyzing nuanced dimensions in text by combining polarity annotations with frame semantics (Fillmore 1982). More specifically, verbs are annotated across various dimensions and perspectives so that a verb might elicit a positive sentiment for its subject (i.e. sympathy) but imply a negative effect for its object. We target the sentiments towards the entities and verb predicates through a pre-collected set of 950 verbs that have been annotated for these traits, which can be more clearly demonstrated through the example \"He harassed me.\":",
+      "${Sentiment(\\textrm {verb}) -}$: something negative happened to the writer.",
+      "$Sentiment(\\textrm {affected}) -$: the writer (affected) most likely feels negative about the event.",
+      "$Perspective(\\textrm {affected} \\rightarrow \\textrm {agent})-$: the writer most likely has negative feelings towards the agent as a result of the event.",
+      "$Perspective(\\textrm {reader} \\rightarrow \\textrm {affected})-$: the reader most likely view the agent as the antagonist.",
+      "$Perspective(\\textrm {affected} \\rightarrow \\textrm {affected})+$: the reader most likely feels sympathetic towards the writer.",
+      "In addition to extracting sentiment scores from the pre-annotated corpus, we also need to predict sentiment scores of unknown verbs. To achieve this task, we rely on the 200-dimensional GloVe word embeddings BIBREF16, pretrained on their Twitter dataset, to compute the scores of the nearest neighboring synonyms contained in the annotated verb set and normalize their weighted sum to get the resulting sentiment (Equation 1).",
+      "where $\\mathcal {I}=\\mathbf {1_{w \\in \\mathcal {A}}}$ is the indicator function for whether verb predicate $w$ is in the annotation set $\\mathcal {A}$, $\\gamma (w)$ is the set of nearest neighbors $e$'s of verb $w$. Because our predictive model computes event-entity sentiment scores and generates verb predicate knowledge simultaneously, it is sensitive to data initialization. Therefore, we train the model iteratively on a number of random initialization to achieve the best results."
+    ],
+    [
+      "The results of LDA on #MeToo tweets of college users (Table 1) fall into the same pattern as the research of Modrek and Chakalov (2019), which suggests that a large portion of #MeToo tweets on Twitter focuses on sharing personal traumatic stories about sexual harassment BIBREF10. In fact, in our top 5 topics, Topics 1 and 5 mainly depict gruesome stories and childhood or college time experience. This finding seems to support the validity of the Twitter sample of Modrek and Chakalov (2019), where 11% discloses personal sexual harassment memories and 5.8% of them was in formative years BIBREF10. These users also shows multiple emotions toward this movement, such as compassion (topic 2), determination (topic 3), and hope (topic 4). We will further examine the emotion features in the latter results."
+    ],
+    [
+      "Observing the results of the linear regression in Table 2, we find the normalized governmental reported cases count and regional feature to be statistically significant on the sexual harassment rate in the Twitter data ($p-value<0.05$). Specifically, the change in the number of reported cases constitutes a considerable change in the number of #MeToo users on Twitter as p-value is extremely small at $5.7e-13$. This corresponds to the research by Napolitano (2014) regarding the \"Yes means yes\" movement in higher education institutes in recent years, as even with some limitations and inconsistency, the sexual assault reporting system is gradually becoming more rigorous BIBREF17. Meanwhile, attending colleges in the Northeast, West and South regions increases the possibility of posting about sexual harassment (positive coefficients), over the Midwest region. This finding is interesting and warrants further scrutiny."
+    ],
+    [
+      "We discover that approximately half of users who detailed their sexual harassment experiences with the #MeToo hashtag suffered from physical aggression. Also, more than half of them claimed to encounter the perpetrators outside the college and work environment. The sentimental score for the affected entities and the verb of cases pertaining to faculty are strictly negative, suggesting that academic personnel's actions might be described as more damaging to the students' mental health. This finding resonates a recent research by Cantapulo et al. regarding the potential hazard of sexual harassment conducts by university faculties using data from federal investigation and relevant social science literature BIBREF18. Furthermore, many in this group tend to mention their respective age, typically between 5 and 20 (24% of the studied subset). This observation reveals an alarming number of child and teenager sexual abuse, indicating that although college students are not as prone to sexual harassment from their peers and teachers, they might still be traumatized by their childhood experiences.",
+      "In addition, although verbal abuse experiences accounts for a large proportion of the tweets, it is challenging to gain sentiment insights into them, as the majority of them contains insinuations and sarcasms regarding sexual harassment. This explains why the sentiment scores of the events and entities are very close to neutral."
+    ],
+    [
+      "Our dataset is taken from only a sample of a specific set of colleges, and different samples might yield different results. Our method of identifying college students is simple, and might not reflect the whole student population. Furthermore, the majority of posts on Twitter are short texts (under 50 words). This factor, according to previous research, might hamper the performance of the LDA results, despite the use of the TF-IDF scheme BIBREF19.",
+      "Furthermore, while the main goal of this paper is to shed lights to the ongoing problems in the academia and contribute to the future sociological study using big data analysis, our dataset might be misused for detrimental purposes. Also, data regarding sexual harassment is sensitive in nature, and might have unanticipated effects on those addressed users."
+    ],
+    [
+      "In this study, we discover a novel correlation between the number of college users who participate in the #MeToo movement and the number of official reported cases from the government data. This is a positive sign suggesting that the higher education system is moving into a right direction to effectively utilize Title IV, a portion of the Education Amendments Act of 1972, which requests colleges to submit their sexual misconduct reports to the officials and protect the victims. In addition, we capture several geographic and behavioral characteristics of the #MeToo users related to sexual assault such as region, reaction and narrative content following the trend, as well as sentiment and social interactions, some of which are supported by various literature on sexual harassment. Importantly, our semantic analysis reveals interesting patterns of the assaulting cases. We believe our methodologies on defining these #MeToo users and their features will be applicable to further studies on this and other alarming social issues.",
+      "Furthermore, we find that the social media-driven approach is highly useful in facilitating crime-related sociology research on a large scale and spectrum. Moreover, since social networks appeal to a broad audience, especially those outside academia, studies using these resources are highly useful for raising awareness in the community on concurrent social problems.",
+      "Last but not least, many other aspects of the text data from social media, which could provide many interesting insights on sexual harassment, remain largely untouched. In the future, we intend to explore more sophisticated language features and implement more supervised models with advanced neural network parsing and classification. We believe that with our current dataset, an extension to take advantage of cutting-edge linguistic techniques will be the next step to address the previously unanswered questions and uncover deeper meanings of the tweets on sexual harassment."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1422/instruction.md b/qasper-1422/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..bdb3094775fa26caa1ea6f87f244d84b10a58eb3
--- /dev/null
+++ b/qasper-1422/instruction.md
@@ -0,0 +1,65 @@
+Name of Paper: IIIDYT at SemEval-2018 Task 3: Irony detection in English tweets
+
+Question: What were their results on the test set?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Data and pre-processing",
+    "Proposed Approach",
+    "Word representation",
+    "Model architecture",
+    "Experimental setup",
+    "Results",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Sentiment analysis and emotion recognition, as two closely related subfields of affective computing, play a key role in the advancement of artificial intelligence BIBREF0 . However, the complexity and ambiguity of natural language constitutes a wide range of challenges for computational systems.",
+      "In the past years irony and sarcasm detection have received great traction within the machine learning and NLP community BIBREF1 , mainly due to the high frequency of sarcastic and ironic expressions in social media. Their linguistic collocation inclines to flip polarity in the context of sentiment analysis, which makes machine-based irony detection critical for sentiment analysis BIBREF2 , BIBREF3 . Irony is a profoundly pragmatic and versatile linguistic phenomenon. As its foundations usually lay beyond explicit linguistic patterns in re-constructing contextual dependencies and latent meaning, such as shared knowledge or common knowledge BIBREF1 , automatically detecting it remains a challenging task in natural language processing.",
+      "In this paper, we introduce our system for the shared task of Irony detection in English tweets, a part of the 2018 SemEval BIBREF4 . We note that computational approaches to automatically detecting irony often deploy expensive feature-engineered systems which rely on a rich body of linguistic and contextual cues BIBREF5 , BIBREF6 . The advent of Deep Learning applied to NLP has introduced models that have succeeded in large part because they learn and use their own continuous numeric representations BIBREF7 of words BIBREF8 , offering us the dream of forgetting manually-designed features. To this extent, in this paper we propose a representation learning approach for irony detection, which relies on a bidirectional LSTM and pre-trained word embeddings."
+    ],
+    [
+      "For the shared task, a balanced dataset of 2,396 ironic and 2,396 non-ironic tweets is provided. The ironic corpus was constructed by collecting self-annotated tweets with the hashtags #irony, #sarcasm and #not. The tweets were then cleaned and manually checked and labeled, using a fine-grained annotation scheme BIBREF3 . The corpus comprises different types of irony:",
+      "Verbal irony is often referred to as an utterance that conveys the opposite meaning of what of literally expressed BIBREF9 , BIBREF10 , e.g. I love annoying people. Situational irony appears in settings, that diverge from the expected BIBREF11 , e.g. an old man who won the lottery and died the next day. The latter does not necessarily exhibit polarity contrast or other typical linguistic features, which makes it particularly difficult to classify correctly.",
+      "For the pre-processing we used the Natural Language Toolkit BIBREF12 . As a first step, we removed the following words and hashtagged words: not, sarc, sarcasm, irony, ironic, sarcastic and sarcast, in order to ascertain a clean corpus without topic-related triggers. To ease the tokenizing process with the NLTK TweetTokenizer, we replaced two spaces with one space and removed usernames and urls, as they do not generally provide any useful information for detecting irony.",
+      "We do not stem or lowercase the tokens, since some patterns within that scope might serve as an indicator for ironic tweets, for instance a word or a sequence of words, in which all letters are capitalized BIBREF13 ."
+    ],
+    [
+      "The goal of the subtask A was to build a binary classification system that predicts if a tweet is ironic or non-ironic. In the following sections, we first describe the dataset provided for the task and our pre-processing pipeline. Later, we lay out the proposed model architecture, our experiments and results."
+    ],
+    [
+      "Representation learning approaches usually require extensive amounts of data to derive proper results. Moreover, previous studies have shown that initializing representations using random values generally causes the performance to drop. For these reasons, we rely on pre-trained word embeddings as a means of providing the model the adequate setting. We experiment with GloVe BIBREF14 for small sizes, namely 25, 50 and 100. This is based on previous work showing that representation learning models based on convolutional neural networks perform well compared to traditional machine learning methods with a significantly smaller feature vector size, while at the same time preventing over-fitting and accelerates computation (e.g BIBREF2 .",
+      "GloVe embeddings are trained on a dataset of 2B tweets, with a total vocabulary of 1.2 M tokens. However, we observed a significant overlap with the vocabulary extracted from the shared task dataset. To deal with out-of-vocabulary terms that have a frequency above a given threshold, we create a new vector which is initialized based on the space described by the infrequent words in GloVe. Concretely, we uniformly sample a vector from a sphere centered in the centroid of the 10% less frequent words in the GloVe vocabulary, whose radius is the mean distance between the centroid and all the words in the low frequency set. For the other case, we use the special UNK token.",
+      "To maximize the knowledge that may be recovered from the pre-trained embeddings, specially for out-of-vocabulary terms, we add several token-level and sentence-level binary features derived from simple linguistic patterns, which are concatenated to the corresponding vectors.",
+      "If the token is fully lowercased.",
+      "If the Token is fully uppercased.",
+      "If only the first letter is capitalized.",
+      "If the token contains digits.",
+      "If any token is fully lowercased.",
+      "If any token is fully uppercased.",
+      "If any token appears more than once."
+    ],
+    [
+      "Recurrent neural networks are powerful sequence learning models that have achieved excellent results for a variety of difficult NLP tasks BIBREF15 . In particular, we use the last hidden state of a bidirectional LSTM architecture BIBREF16 to obtain our tweet representations. This setting is currently regarded as the state-of-the-art BIBREF17 for the task on other datasets. To avoid over-fitting we use Dropout BIBREF18 and for training we set binary cross-entropy as a loss function. For evaluation we use our own wrappers of the the official evaluation scripts provided for the shared tasks, which are based on accuracy, precision, recall and F1-score."
+    ],
+    [
+      "Our model is implemented in PyTorch BIBREF19 , which allowed us to easily deal with the variable tweet length due to the dynamic nature of the platform. We experimented with different values for the LSTM hidden state size, as well as for the dropout probability, obtaining best results for a dropout probability of INLINEFORM0 and 150 units for the the hidden vector. We trained our models using 80% of the provided data, while the remaining 20% was used for model development. We used Adam BIBREF20 , with a learning rate of INLINEFORM1 and early stopping when performance did not improve on the development set. Using embeddings of size 100 provided better results in practice. Our final best model is an ensemble of four models with the same architecture but different random initialization.",
+      "To compare our results, we use the provided baseline, which is a non-parameter optimized linear-kernel SVM that uses TF-IDF bag-of-word vectors as inputs. For pre-processing, in this case we do not preserve casing and delete English stopwords."
+    ],
+    [
+      "To understand how our strategies to recover more information from the pre-trained word embeddings affected the results, we ran ablation studies to compare how the token-level and sentence-level features contributed to the performance. Table TABREF16 summarizes the impact of these features in terms of F1-score on the validation set.",
+      "We see that sentence-level features had a positive yet small impact, while token-level features seemed to actually hurt the performance. We think that since the task is performed at the sentence-level, probably features that capture linguistic phenomena at the same level provide useful information to the model, while the contributions of other finer granularity features seem to be too specific for the model to leverage on.",
+      "Table TABREF17 summarizes our best single-model results on the validation set (20% of the provided data) compared to the baseline, as well as the official results of our model ensemble on the test data.",
+      "Out of 43 teams our system ranked 421st with an official F1-score of 0.2905 on the test set. Although our model outperforms the baseline in the validation set in terms of F1-score, we observe important drops for all metrics compared to the test set, showing that the architecture seems to be unable to generalize well. We think these results highlight the necessity of an ad-hoc architecture for the task as well as the relevance of additional information. The work of BIBREF21 offers interesting contributions in these two aspects, achieving good results for a range of tasks that include sarcasm detection, using an additional attention layer over a BiLSTM like ours, while also pre-training their model on an emoji-based dataset of 1246 million tweets.",
+      "Moreover, we think that due to the complexity of the problem and the size of the training data in the context of deep learning better results could be obtained with additional resources for pre-training. Concretely, we see transfer learning as one option to add knowledge from a larger, related dataset could significantly improve the results BIBREF22 . Manually labeling and checking data is a vastly time-consuming effort. Even if noisy, collecting a considerably larger self-annotated dataset such as in BIBREF23 could potentially boost model performance."
+    ],
+    [
+      "In this paper we presented our system to SemEval-2018 shared task on irony detection in English tweets (subtask A), which leverages on a BiLSTM and pre-trained word embeddings for representation learning, without using human-engineered features. Our results showed that although the generalization capabilities of the model are limited, there are clear future directions to improve. In particular, access to more training data and the deployment of methods like transfer learning seem to be promising directions for future research in representation learning-based sarcasm detection."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1425/instruction.md b/qasper-1425/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..854ff3fb1f61abf90bae65ef5dd06dd6ee5951cd
--- /dev/null
+++ b/qasper-1425/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Asking and Answering Questions to Evaluate the Factual Consistency of Summaries
+
+Question: What models are evaluated with QAGS?
\ No newline at end of file
diff --git a/qasper-1432/instruction.md b/qasper-1432/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..45d45a1162cb76076befb57f74dc429ebeabe3e4
--- /dev/null
+++ b/qasper-1432/instruction.md
@@ -0,0 +1,166 @@
+Name of Paper: Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach
+
+Question: What are their baseline models?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Introduction ::: First problem.",
+    "Introduction ::: Second problem.",
+    "Introduction ::: Third problem.",
+    "Introduction ::: Dataset.",
+    "Introduction ::: Evaluation.",
+    "Introduction ::: Entailment approach.",
+    "Related Work",
+    "Benchmark the dataset",
+    "Benchmark the dataset ::: Topic detection ::: Yahoo.",
+    "Benchmark the dataset ::: Emotion detection ::: UnifyEmotion.",
+    "Benchmark the dataset ::: Situation detection",
+    "Benchmark the dataset ::: Situation detection ::: Summary of @!START@$\\textsc {0shot-tc}$@!END@ datasets.",
+    "Benchmark the evaluation",
+    "Benchmark the evaluation ::: Label-partially-unseen.",
+    "Benchmark the evaluation ::: Label-fully-unseen.",
+    "An entailment model for @!START@$\\textsc {0shot-tc}$@!END@",
+    "An entailment model for @!START@$\\textsc {0shot-tc}$@!END@ ::: Convert labels into hypotheses.",
+    "An entailment model for @!START@$\\textsc {0shot-tc}$@!END@ ::: Convert classification data into entailment data.",
+    "An entailment model for @!START@$\\textsc {0shot-tc}$@!END@ ::: Entailment model learning.",
+    "An entailment model for @!START@$\\textsc {0shot-tc}$@!END@ ::: Harsh policy in testing.",
+    "Experiments ::: Label-partially-unseen evaluation",
+    "Experiments ::: Label-partially-unseen evaluation ::: Baselines.",
+    "Experiments ::: Label-partially-unseen evaluation ::: Discussion.",
+    "Experiments ::: Label-fully-unseen evaluation",
+    "Experiments ::: How do the generated hypotheses influence",
+    "Summary",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "Supervised text classification has achieved great success in the past decades due to the availability of rich training data and deep learning techniques. However, zero-shot text classification ($\\textsc {0shot-tc}$) has attracted little attention despite its great potential in real world applications, e.g., the intent recognition of bank consumers. $\\textsc {0shot-tc}$ is challenging because we often have to deal with classes that are compound, ultra-fine-grained, changing over time, and from different aspects such as topic, emotion, etc.",
+      "Existing $\\textsc {0shot-tc}$ studies have mainly the following three problems."
+    ],
+    [
+      "The $\\textsc {0shot-tc}$ problem was modeled in a too restrictive vision. Firstly, most work only explored a single task, which was mainly topic categorization, e.g., BIBREF1, BIBREF2, BIBREF3. We argue that this is only the tiny tip of the iceberg for $\\textsc {0shot-tc}$. Secondly, there is often a precondition that a part of classes are seen and their labeled instances are available to train a model, as we define here as Definition-Restrictive:",
+      "Definition-Restrictive ($\\textsc {0shot-tc}$). Given labeled instances belonging to a set of seen classes $S$, $\\textsc {0shot-tc}$ aims at learning a classifier $f(\\cdot ): X \\rightarrow Y$, where $Y=S\\cup U$; $U$ is a set of unseen classes and belongs to the same aspect as $S$.",
+      "In this work, we formulate the $\\textsc {0shot-tc}$ in a broader vision. As Figure FIGREF2 demonstrates, a piece of text can be assigned labels which interpret the text in different aspects, such as the \u201ctopic\u201d aspect, the \u201cemotion\u201d aspect, or the \u201csituation\u201d aspect described in the text. Different aspects, therefore, differ in interpreting the text. For instance, by \u201ctopic\u201d, it means \u201cthis text is about {health, finance $\\cdots $}\u201d; by \u201cemotion\u201d, it means \u201cthis text expresses a sense of {joy, anger, $\\cdots $}\u201d; by \u201csituation\u201d, it means \u201cthe people there need {shelter, medical assistance, $\\cdots $}\u201d. Figure FIGREF2 also shows another essential property of $\\textsc {0shot-tc}$ \u2013 the applicable label space for a piece of text has no boundary, e.g., \u201cthis text is news\u201d, \u201cthe situation described in this text is serious\u201d, etc. Therefore, we argue that we have to emphasize a more challenging scenario to satisfy the real-world problems: seeing no labels, no label-specific training data. Here is our new $\\textsc {0shot-tc}$ definition:",
+      "Definition-Wild ($\\textsc {0shot-tc}$). $\\textsc {0shot-tc}$ aims at learning a classifier $f(\\cdot ): X \\rightarrow Y$, where classifier $f(\\cdot )$ never sees $Y$-specific labeled data in its model development."
+    ],
+    [
+      "Usually, conventional text classification denotes labels as indices {0,1,2, $\\cdots $, $n$} without understanding neither the aspect's specific interpretation nor the meaning of the labels. This does not apply to $\\textsc {0shot-tc}$ as we can not pre-define the size of the label space anymore, and we can not presume the availability of labeled data. Humans can easily decide the truth value of any upcoming labels because humans can interpret those aspects correctly and understand the meaning of those labels. The ultimate goal of $\\textsc {0shot-tc}$ should be to develop machines to catch up with humans in this capability. To this end, making sure the system can understand the described aspect and the label meanings plays a key role."
+    ],
+    [
+      "Prior work is mostly evaluated on different datasets and adopted different evaluation setups, which makes it hard to compare them fairly. For example, DBLPRiosK18 work on medical data while reporting R@K as metric; DBLPXiaZYCY18 work on SNIPS-NLU intent detection data while only unseen intents are in the label-searching space in evaluation.",
+      "In this work, we benchmark the datasets and evaluation setups of $\\textsc {0shot-tc}$. Furthermore, we propose a textual entailment approach to handle the $\\textsc {0shot-tc}$ problem of diverse aspects in a unified paradigm. To be specific, we contribute in the following three aspects:"
+    ],
+    [
+      "We provide datasets for studying three aspects of $\\textsc {0shot-tc}$: topic categorization, emotion detection, and situation frame detection \u2013 an event level recognition problem. For each dataset, we have standard split for train, dev, and test, and standard separation of seen and unseen classes."
+    ],
+    [
+      "Our standardized evaluations correspond to the Definition-Restrictive and Definition-Wild. i) Label-partially-unseen evaluation. This corresponds to the commonly studied $\\textsc {0shot-tc}$ defined in Definition-Restrictive: for the set of labels of a specific aspect, given training data for a part of labels, predicting in the full label set. This is the most basic setup in $\\textsc {0shot-tc}$. It checks whether the system can generalize to some labels in the same aspect. To satisfy Definition-Wild, we define a new evaluation: ii) Label-fully-unseen evaluation. In this setup, we assume the system is unaware of the upcoming aspects and can not access any labeled data for task-specific training."
+    ],
+    [
+      "Our Definition-Wild challenges the system design \u2013 how to develop a $\\textsc {0shot-tc}$ system, without accessing any task-specific labeled data, to deal with labels from diverse aspects? In this work, we propose to treat $\\textsc {0shot-tc}$ as a textual entailment problem. This is to imitate how humans decide the truth value of labels from any aspects. Usually, humans understand the problem described by the aspect and the meaning of the label candidates. Then humans mentally construct a hypothesis by filling a label candidate, e.g., \u201csports\u201d, into the aspect-defined problem \u201cthe text is about $\\underline{?}$\u201d, and ask ourselves if this hypothesis is true, given the text. We treat $\\textsc {0shot-tc}$ as a textual entailment problem so that our model can gain knowledge from entailment datasets, and we show that it applies to both Definition-Restrictive and Definition-Wild.",
+      "Overall, this work aims at benchmarking the research of $\\textsc {0shot-tc}$ by providing standardized datasets, evaluations, and a state-of-the-art entailment system. All datasets and codes are released."
+    ],
+    [
+      "$\\textsc {Zero-stc}$ was first explored by the paradigm \u201cDataless Classification\u201d BIBREF0. Dataless classification first maps the text and labels into a common space by Explicit Semantic Analysis (ESA) BIBREF4, then picks the label with the highest matching score. Dataless classification emphasizes that the representation of labels takes the equally crucial role as the representation learning of text. Then this idea was further developed in BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9.",
+      "With the prevalence of word embeddings, more and more work adopts pretrained word embeddings to represent the meaning of words, so as to provide the models with the knowledge of labels BIBREF10, BIBREF2, BIBREF11, BIBREF12. DBLPYogatamaDLB17 build generative LSTM to generate text given the embedded labels. DBLPRiosK18 use label embedding to attend the text representation in the developing of a multi-label classifier. But they report R@K, so it is unclear whether the system can really predict unseen labels. DBLPXiaZYCY18 study the zero-shot intent detection problem. The learned representations of intents are still the sum of word embeddings. But during testing, the intent space includes only new intents; seen intents are not covered. All of these studies can only meet the definition in Definition-Restrictive, so they do not really generalize to open aspects of $\\textsc {0shot-tc}$.",
+      "JiangqngGuo enrich the embedding representations by incorporating class descriptions, class hierarchy, and the word-to-label paths in ConceptNet. DBLPMitchellSL18 assume that some natural language explanations about new labels are available. Then those explanations are parsed into formal constraints which are further combined with unlabeled data to yield new label oriented classifiers through posterior regularization. However, those explanatory statements about new labels are collected from crowd-sourcing. This limits its application in real world $\\textsc {0shot-tc}$ scenarios.",
+      "There are a few works that study a specific zero-shot problem by indirect supervision from other problems. DBLPLevySCZ17 and obamuyide2018zero study zero-shot relation extraction by converting it into a machine comprehension and textual entailment problem respectively. Then, a supervised system pretrained on an existing machine comprehension dataset or textual entailment dataset is used to do inference. Our work studies the $\\textsc {0shot-tc}$ by formulating a broader vision: datasets of multiple apsects and evaluations.",
+      "Other zero-shot problems studied in NLP involve entity typing BIBREF13, sequence labeling BIBREF14, etc."
+    ],
+    [
+      "In this work, we standardize the datasets for $\\textsc {0shot-tc}$ for three aspects: topic detection, emotion detection, and situation detection.",
+      "For each dataset, we insist on two principles: i) Label-partially-unseen: A part of labels are unseen. This corresponds to Definition-Restrictive, enabling us to check the performance of unseen labels as well as seen labels. ii) Label-fully-unseen: All labels are unseen. This corresponds to Definition-Wild, enabling us to check the system performance in test-agnostic setups."
+    ],
+    [
+      "We use the large-scale Yahoo dataset released by DBLPZhangZL15. Yahoo has 10 classes: {\u201cSociety & Culture\u201d, \u201cScience & Mathematics\u201d, \u201cHealth\u201d, \u201cEducation & Reference\u201d, \u201cComputers & Internet\u201d, \u201cSports\u201d, \u201cBusiness & Finance\u201d, \u201cEntertainment & Music\u201d, \u201cFamily & Relationships\u201d, \u201cPolitics & Government\u201d}, with original split: 1.4M/60k in train/test (all labels are balanced distributed).",
+      "We reorganize the dataset by first fixing the dev and test sets as follows: for dev, all 10 labels are included, with 6k labeled instances for each; For test, all 10 labels are included, with 10k instances for each. Then training sets are created on remaining instances as follows.",
+      "For label-partially-unseen, we create two versions of Yahoo train for $\\textsc {0shot-tc}$:",
+      "Train-v0: 5 classes: {\u201cSociety & Culture\u201d, \u201cHealth\u201d, \u201cComputers & Internet\u201d, \u201cBusiness & Finance\u201d, \u201cFamily & Relationships\u201d} are included; each is equipped with 130k labeled instances.",
+      "Train-v1: 5 classes: { \u201cScience & Mathematics\u201d, \u201cEducation & Reference\u201d, \u201cSports\u201d, \u201cEntertainment & Music\u201d, \u201cPolitics & Government\u201d} are included; each is equipped with 130k labeled instances.",
+      "We always create two versions of train with non-overlapping labels so as to get rid of the model's over-fitting on one of them.",
+      "Label-fully-unseen share the same test and dev with the label-partially-unseen except that it has no training set. It is worth mentioning that our setup of label-partially-unseen and label-fully-unseen enables us to compare the performance mutually; it can show the system's capabilities while seeing different sizes of classes."
+    ],
+    [
+      "This emotion dataset was released by DBLPBostanK18. It was constructed by unifying the emotion labels of multiple public emotion datasets. This dataset consists of text from multiple domains: tweet, emotional events, fairy tale and artificial sentences, and it contains 9 emotion types {\u201csadness\u201d, \u201cjoy\u201d, \u201canger\u201d, \u201cdisgust\u201d, \u201cfear\u201d, \u201csurprise\u201d, \u201cshame\u201d, \u201cguilt\u201d, \u201clove\u201d} and \u201cnone\u201d (if no emotion applies). We remove the multi-label instances (appro. 4k) so that the remaining instances always have a single positive label. The official evaluation metric is label-weighted F1.",
+      "Since the labels in this dataset has unbalanced distribution. We first directly list the fixed $\\emph {test}$ and $\\emph {dev}$ in Table TABREF9 and Table TABREF10, respectively. They are shared by following label-partial-unseen and label-fully-unseen setups of train.",
+      "Label-partial-unseen has the following two versions of train:",
+      "Train-v0: 5 classes: {\u201csadness\u201d, \u201canger\u201d, \u201cfear\u201d, \u201cshame\u201d, \u201clove\u201d} are included.",
+      "Train-v1: 4 classes: { \u201cjoy\u201d, \u201cdisgust\u201d, \u201csurprise\u201d, \u201cguilt\u201d} are included.",
+      "For label-fully-unseen, no training set is provided."
+    ],
+    [
+      "The situation frame typing is one example of an event-type classification task. A situation frame studied here is a need situation such as the need for water or medical aid, or an issue situation such as crime violence BIBREF16, BIBREF17. It was originally designed for low-resource situation detection, where annotated data is unavailable. This is why it is particularly suitable for $\\textsc {0shot-tc}$.",
+      "We use the Situation Typing dataset released by mayhewuniversity. It has 5,956 labeled instances. Totally 11 situation types: \u201cfood supply\u201d, \u201cinfrastructure\u201d, \u201cmedical assistance\u201d, \u201csearch/rescue\u201d, \u201cshelter\u201d, \u201cutilities, energy, or sanitation\u201d, \u201cwater supply\u201d, \u201cevacuation\u201d, \u201cregime change\u201d, \u201cterrisms\u201d, \u201ccrime violence\u201d and an extra type \u201cnone\u201d \u2013 if none of the 11 types applies. This dataset is a multi-label classification, and label-wise weighted F1 is the official evaluation.",
+      "The train, test and dev are listed in Table TABREF22."
+    ],
+    [
+      "Our three datasets covers single-label classification (i.e., \u201ctopic\u201d and \u201cemotion\u201d) and multi-label classification (i.e., \u201csituation\u201d). In addition, a \u201cnone\u201d type is adopted in \u201cemotion\u201d and \u201csituation\u201d tasks if no predefined types apply \u2013 this makes the problem more realistic."
+    ],
+    [
+      "How to evaluate a $\\textsc {0shot-tc}$ system? This needs to review the original motivation of doing $\\textsc {0shot-tc}$ research. As we discussed in Introduction section, ideally, we aim to build a system that works like humans \u2013 figuring out if a piece of text can be assigned with an open-defined label, without any constrains on the domains and the aspects described by the labels. Therefore, we challenge the system in two setups: label-partially-unseen and label-fully-unseen."
+    ],
+    [
+      "This is the most common setup in existing $\\textsc {0shot-tc}$ literature: for a given dataset of a specific problem such as topic categorization, emotion detection, etc, train a system on a part of the labels, then test on the whole label space. Usually all labels describe the same aspect of the text."
+    ],
+    [
+      "In this setup, we push \u201czero-shot\u201d to the extreme \u2013 no annotated data for any labels. So, we imagine that learning a system through whatever approaches, then testing it on $\\textsc {0shot-tc}$ datasets of open aspects.",
+      "This label-fully-unseen setup is more like the dataless learning principle BIBREF0, in which no task-specific annotated data is provided for training a model (since usually this kind of model fails to generalize in other domains and other tasks), therefore, we are encouraged to learn models with open-data or test-agnostic data. In this way, the learned models behave more like humans."
+    ],
+    [
+      "As one contribution of this work, we propose to deal with $\\textsc {0shot-tc}$ as a textual entailment problem. It is inspired by: i) text classification is essentially a textual entailment problem. Let us think about how humans do classification: we mentally think \u201cwhether this text is about sport?\u201d, or \u201cwhether this text expresses a specific feeling?\u201d, or \u201cwhether the people there need water supply?\u201d and so on. The reason that conventional text classification did not employ entailment approach is it always has pre-defined, fixed-size of classes equipped with annotated data. However, in $\\textsc {0shot-tc}$, we can neither estimate how many and what classes will be handled nor have annotated data to train class-specific parameters. Textual entailment, instead, does not preordain the boundary of the hypothesis space. ii) To pursue the ideal generalization of classifiers, we definitely need to make sure that the classifiers understand the problem encoded in the aspects and understand the meaning of labels. Conventional supervised classifiers fail in this aspect since label names are converted into indices \u2013 this means the classifiers do not really understand the labels, let alone the problem. Therefore, exploring $\\textsc {0shot-tc}$ as a textual entailment paradigm is a reasonable way to achieve generalization."
+    ],
+    [
+      "The first step of dealing with $\\textsc {0shot-tc}$ as an entailment problem is to convert labels into hypotheses. To this end, we first convert each aspect into an interpretation (we discussed before that generally one aspect defines one interpretation). E.g., \u201ctopic\u201d aspect to interpretation \u201cthe text is about the topic\u201d. Table TABREF24 lists some examples for the three aspects: \u201ctopic\u201d, \u201cemotion\u201d and \u201csituation\u201d.",
+      "In this work, we just explored two simple methods to generate the hypotheses. As Table TABREF24 shows, one is to use the label name to complete the interpretation, the other is to use the label's definition in WordNet to complete the interpretation. In testing, once one of them results in an \u201centailment\u201d decision, then we decide the corresponding label is positive. We can definitely create more natural hypotheses through crowd-sourcing, such as \u201cfood\u201d into \u201cthe people there are starving\u201d. Here we just set the baseline examples by automatic approaches, more explorations are left as future work, and we welcome the community to contribute."
+    ],
+    [
+      "For a data split (train, dev and test), each input text, acting as the premise, has a positive hypothesis corresponding to the positive label, and all negative labels in the data split provide negative hypotheses. Note that unseen labels do not provide negative hypotheses for instances in train."
+    ],
+    [
+      "In this work, we make use of the widely-recognized state of the art entailment technique \u2013 BERT BIBREF18, and train it on three mainstream entailment datasets: MNLI BIBREF19, GLUE RTE BIBREF20, BIBREF21 and FEVER BIBREF22, respectively. We convert all datasets into binary case: \u201centailment\u201d vs. \u201cnon-entailment\u201d, by changing the label \u201cneutral\u201d (if exist in some datasets) into \u201cnon-entailment\u201d.",
+      "For our label-fully-unseen setup, we directly apply this pretrained entailment model on the test sets of all $\\textsc {0shot-tc}$ aspects. For label-partially-unseen setup in which we intentionally provide annotated data, we first pretrain BERT on the MNLI/FEVER/RTE, then fine-tune on the provided training data."
+    ],
+    [
+      "Since seen labels have annotated data for training, we adopt different policies to pick up seen and unseen labels. To be specific, we pick a seen label with a harsher rule: i) In single-label classification, if both seen and unseen labels are predicted as positive, we pick the seen label only if its probability of being positive is higher than that of the unseen label by a hyperparameter $\\alpha $. If only seen or unseen labels are predicted as positive, we pick the one with the highest probability; ii) In multi-label classification, if both seen and unseen labels are predicted as positive, we change the seen labels into \u201cnegative\u201d if their probability of being positive is higher than that of the unseen label by less than $\\alpha $. Finally, all labels labeled positive will be selected. If no positive labels, we choose \u201cnone\u201d type.",
+      "$\\alpha $ = 0.05 in our systems, tuned on dev."
+    ],
+    [
+      "In this setup, there is annotated data for partial labels as train. So, we report performance for unseen classes as well as seen classes. We compare our entailment approaches, trained separately on MNLI, FEVER and RTE, with the following baselines."
+    ],
+    [
+      "Majority: the text picks the label of the largest size.",
+      "ESA: A dataless classifier proposed in BIBREF0. It maps the words (in text and label names) into the title space of Wikipedia articles, then compares the text with label names. This method does not rely on train.",
+      "We implemented ESA based on 08/01/2019 Wikipedia dump. There are about 6.1M words and 5.9M articles.",
+      "Word2Vec BIBREF23: Both the representations of the text and the labels are the addition of word embeddings element-wisely. Then cosine similarity determines the labels. This method does not rely on train either.",
+      "Binary-BERT: We fine-tune BERT on train, which will yield a binary classifier for entailment or not; then we test it on test \u2013 picking the label with the maximal probability in single-label scenarios while choosing all the labels with \u201centailment\u201d decision in multi-label cases."
+    ],
+    [
+      "The results of label-partially-unseen are listed in Table TABREF30. \u201cESA\u201d performs slightly worse than \u201cWord2Vec\u201d in topic detection, mainly because the label names, i.e., topics such as \u201csports\u201d, are closer than some keywords such as \u201cbasketball\u201d in Word2Vec space. However, \u201cESA\u201d is clearly better than \u201cWord2Vec\u201d in situation detection; this should be mainly due to the fact that the label names (e.g., \u201cshelter\u201d, \u201cevaculation\u201d, etc.) can hardly find close words in the text by Word2Vec embeddings. Quite the contrary, \u201cESA\u201d is easier to make a class such as \u201cshelter\u201d closer to some keywords like \u201cearthquake\u201d. Unfortunately, both Word2Vec and ESA work poorly for emotion detection problem. We suspect that emotion detection requires more entailment capability. For example, the text snippet \u201cwhen my brother was very late in arriving home from work\u201d, its gold emotion \u201cfear\u201d requires some common-knowledge inference, rather than just word semantic matching through Word2Vec and ESA.",
+      "The supervised method \u201cBinary-BERT\u201d is indeed strong in learning the seen-label-specific models \u2013 this is why it predicts very well for seen classes while performing much worse for unseen classes.",
+      "Our entailment models, especially the one pretrained on MNLI, generally get competitive performance with the \u201cBinary-BERT\u201d for seen (slightly worse on \u201ctopic\u201d and \u201cemotion\u201d while clearly better on \u201csituation\u201d) and improve the performance regarding unseen by large margins. At this stage, fine-tuning on an MNLI-based pretrained entailment model seems more powerful."
+    ],
+    [
+      "Regarding this label-fully-unseen evaluation, apart from our entailment models and three unsupervised baselines \u201cMajority\u201d, \u201cWord2Vec\u201d and \u201cESA\u201d, we also report the following baseline:",
+      "Wikipedia-based: We train a binary classifier based on BERT on a dataset collected from Wikipedia. Wikipedia is a corpus of general purpose, without targeting any specific $\\textsc {0shot-tc}$ task. Collecting categorized articles from Wikipedia is popular way of creating training data for text categorization, such as BIBREF13. More specifically, we collected 100K articles along with their categories in the bottom of each article. For each article, apart from its attached positive categories, we randomly sample three negative categories. Then each article and its positive/negative categories act as training pairs for the binary classifier.",
+      "We notice \u201cWikipedia-based\u201d training indeed contributes a lot for the topic detection task; however, its performances on emotion and situation detection problems are far from satisfactory. We believe this is mainly because the Yahoo-based topic categorization task is much closer to the Wikipedia-based topic categorization task; emotion and situation categorizations, however, are relatively further.",
+      "Our entailment models, pretrained on MNLI/FEVER/RTE respectively, perform more robust on the three $\\textsc {0shot-tc}$ aspects (except for the RTE on emotion). Recall that they are not trained on any text classification data, and never know the domain and the aspects in the test. This clearly shows the great promise of developing textual entailment models for $\\textsc {0shot-tc}$. Our ensemble approach further boosts the performances on all three tasks.",
+      "An interesting phenomenon, comparing the label-partially-unseen results in Table TABREF30 and the label-fully-unseen results in Table TABREF32, is that the pretrained entailment models work in this order for label-fully-unseen case: RTE $>$ FEVER $>$MNLI; on the contrary, if we fine-tune them on the label-partially-unseen case, the MNLI-based model performs best. This could be due to a possibility that, on one hand, the constructed situation entailment dataset is closer to the RTE dataset than to the MNLI dataset, so an RTE-based model can generalize well to situation data, but, on the other hand, it could also be more likely to over-fit the training set of \u201csituation\u201d during fine-tuning. A deeper exploration of this is left as future work."
+    ],
+    [
+      "In Table TABREF24, we listed examples for converting class names into hypotheses. In this work, we only tried to make use of the class names and their definitions in WordNet. Table TABREF33 lists the fine-grained performance of three ways of generating hypotheses: \u201cword\u201d, \u201cdefinition\u201d, and \u201ccombination\u201d (i.e., word&definition).",
+      "This table indicates that: i) Definition alone usually does not work well in any of the three tasks, no matter which pretrained entailment model is used; ii) Whether \u201cword\u201d alone or \u201cword&definition\u201d works better depends on the specific task and the pretrained entailment model. For example, the pretrained MNLI model prefers \u201cword&definition\u201d in both \u201cemotion\u201d and \u201csituation\u201d detection tasks. However, the other two entailment models (RTE and FEVER) mostly prefer \u201cword\u201d. iii) Since it is unrealistic to adopt only one entailment model, such as from {RTE, FEVER, MNLI}, for any open $\\textsc {0shot-tc}$ problem, an ensemble system should be preferred. However, the concrete implementation of the ensemble system also influences the strengths of different hypothesis generation approaches. In this work, our ensemble method reaches the top performance when combining the \u201cword\u201d and \u201cdefinition\u201d. More ensemble systems and hypothesis generation paradigms need to be studied in the future.",
+      "To better understand the impact of generated hypotheses, we dive into the performance of each labels, taking \u201csituation detection\u201d as an example. Figure FIGREF47 illustrates the separate F1 scores for each situation class, predicted by the ensemble model for label-fully-unseen setup. This enables us to check in detail how easily the constructed hypotheses can be understood by the entailment model. Unfortunately, some classes are still challenging, such as \u201cevacuation\u201d, \u201cinfrastructure\u201d, and \u201cregime change\u201d. This should be attributed to their over-abstract meaning. Some classes were well recognized, such as \u201cwater\u201d, \u201cshelter\u201d, and \u201cfood\u201d. One reason is that these labels mostly are common words \u2013 systems can more easily match them to the text; the other reason is that they are situation classes with higher frequencies (refer to Table TABREF22) \u2013 this is reasonable based on our common knowledge about disasters."
+    ],
+    [
+      "In this work, we analyzed the problems of existing research on zero-shot text classification ($\\textsc {0shot-tc}$): restrictive problem definition, the weakness in understanding the problem and the labels' meaning, and the chaos of datasets and evaluation setups. Therefore, we are benchmarking $\\textsc {0shot-tc}$ by standardizing the datasets and evaluations. More importantly, to tackle the broader-defined $\\textsc {0shot-tc}$, we proposed a textual entailment framework which can work with or without the annotated data of seen labels."
+    ],
+    [
+      "The authors would like to thank Jennifer Sheffield and the anonymous reviewers for insightful comments and suggestions. This work was supported by Contracts HR0011-15-C-0113 and HR0011-18-2-0052 with the US Defense Advanced Research Projects Agency (DARPA). Approved for Public Release, Distribution Unlimited. The views expressed are those of the authors and do not reflect the official policy or position of the Department of Defense or the U.S. Government."
+    ]
+  ]
+}
+```
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+Name of Paper: Build Fast and Accurate Lemmatization for Arabic
+
+Question: How was speed measured?
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+Name of Paper: Build Fast and Accurate Lemmatization for Arabic
+
+Question: What were their accuracy results on the task?
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+Name of Paper: Build Fast and Accurate Lemmatization for Arabic
+
+Question: What is the state of the art?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Background",
+    "Data Description",
+    "system Description",
+    "Evaluation",
+    "Error Analysis",
+    "Discussion"
+  ],
+  "paragraphs": [
+    [
+      "Lemmatization is the process of finding the base form (or lemma) of a word by considering its inflected forms. Lemma is also called dictionary form, or citation form, and it refers to all words having the same meaning.",
+      "Lemmatization is an important preprocessing step for many applications of text mining and question-answering systems, and researches in Arabic Information Retrieval (IR) systems show the need for representing Arabic words at lemma level for many applications, including keyphrase extraction BIBREF0 and machine translation BIBREF1 . In addition, lemmatization provides a productive way to generate generic keywords for search engines (SE) or labels for concept maps BIBREF2 .",
+      "Word stem is that core part of the word that never changes even with morphological inflections; the part that remains after prefix and suffix removal. Sometimes the stem of the word is different than its lemma, for example the words: believe, believed, believing, and unbelievable share the stem (believ-), and have the normalized word form (believe) standing for the infinitive of the verb (believe).",
+      "While stemming tries to remove prefixes and suffixes from words that appear with inflections in free text, lemmatization tries to replace word suffixes with (typically) different suffix to get its lemma.",
+      "This extended abstract is organized as follows: Section SECREF2 shows some complexities in building Arabic lemmatization, and surveys prior work on Arabic stemming and lemmatization; Section SECREF3 introduces the dataset that we created to test lemmatization accuracy; Section SECREF4 describes the algorithm of the system that we built and report results and error analysis in section SECREF5 ; and Section SECREF6 discusses the results and concludes the abstract."
+    ],
+    [
+      "Arabic is the largest Semitic language spoken by more than 400 million people. It's one of the six official languages in the United Nations, and the fifth most widely spoken language after Chinese, Spanish, English, and Hindi. Arabic has a very rich morphology, both derivational and inflectional. Generally, Arabic words are derived from a root that uses three or more consonants to define a broad meaning or concept, and they follow some templatic morphological patterns. By adding vowels, prefixes and suffixes to the root, word inflections are generated. For instance, the word \u00d9\u0088\u00d8\u00b3\u00d9\u008a\u00d9\u0081\u00d8\u00aa\u00d8\u00ad\u00d9\u0088\u00d9\u0086> (wsyftHwn) \u201cand they will open\u201d has the triliteral root \u00d9\u0081\u00d8\u00aa\u00d8\u00ad> (ftH), which has the basic meaning of opening, has prefixes \u00d9\u0088\u00d8\u00b3> (ws) \u201cand will\u201d, suffixes \u00d9\u0088\u00d9\u0086> (wn) \u201cthey\u201d, stem \u00d9\u008a\u00d9\u0081\u00d8\u00aa\u00d8\u00ad> (yftH) \u201copen\u201d, and lemma \u00d9\u0081\u00d8\u00aa\u00d8\u00ad> (ftH) \u201cthe concept of opening\u201d.",
+      "IR systems typically cluster words together into groups according to three main levels: root, stem, or lemma. The root level is considered by many researchers in the IR field which leads to high recall but low precision due to language complexity, for example words \u00d9\u0083\u00d8\u00aa\u00d8\u00a8\u00d8\u008c \u00d9 \u00d9\u0083\u00d8\u00aa\u00d8\u00a8\u00d8\u00a9\u00d8\u008c \u00d9\u0083\u00d8\u00aa\u00d8\u00a7\u00d8\u00a8> (ktb, mktbp, ktAb) \u201cwrote, library, book\u201d have the same root \u00d9\u0083\u00d8\u00aa\u00d8\u00a8> (ktb) with the basic meaning of writing, so searching for any of these words by root, yields getting the other words which may not be desirable for many users.",
+      "Other researchers show the importance of using stem level for improving retrieval precision and recall as they capture semantic similarity between inflected words. However, in Arabic, stem patterns may not capture similar words having the same semantic meaning. For example, stem patterns for broken plurals are different from their singular patterns, e.g. the plural \u00d8\u00a3\u00d9\u0082\u00d9\u0084\u00d8\u00a7\u00d9 > (AqlAm) \u201cpens\u201d will not match the stem of its singular form \u00d9\u0082\u00d9\u0084\u00d9 > (qlm) \u201cpen\u201d. The same applies to many imperfect verbs that have different stem patterns than their perfect verbs, e.g. the verbs \u00d8\u00a7\u00d8\u00b3\u00d8\u00aa\u00d8\u00b7\u00d8\u00a7\u00d8\u00b9\u00d8\u008c \u00d9\u008a\u00d8\u00b3\u00d8\u00aa\u00d8\u00b7\u00d9\u008a\u00d8\u00b9> (AstTAE, ystTyE) \u201che could, he can\u201d will not match because they have different stems. Indexing using lemmatization can enhance the performance of Arabic IR systems.",
+      "A lot of work has been done in word stemming and lemmatization in different languages, for example the famous Porter stemmer for English, but for Arabic, there are few work has been done especially in lemmatization, and there is no open-source code and new testing data that can be used by other researchers for word lemmatization. Xerox Arabic Morphological Analysis and Generation BIBREF3 is one of the early Arabic stemmers, and it uses morphological rules to obtain stems for nouns and verbs by looking into a table of thousands of roots.",
+      "Khoja's stemmer BIBREF4 and Buckwalter morphological analyzer BIBREF5 are other root-based analyzers and stemmers which use tables of valid combinations between prefixes and suffixes, prefixes and stems, and stems and suffixes. Recently, MADAMIRA BIBREF6 system has been evaluated using a blind testset (25K words for Modern Standard Arabic (MSA) selected from Penn Arabic Tree bank (PATB)), and the reported accuracy was 96.2% as the percentage of words where the chosen analysis (provided by SAMA morphological analyzer BIBREF7 ) has the correct lemma.",
+      "In this paper, we present an open-source Java code to extract Arabic word lemmas, and a new publicly available testset for lemmatization allowing researches to evaluate using the same dataset that we used, and reproduce same experiments."
+    ],
+    [
+      "To make the annotated data publicly available, we selected 70 news articles from Arabic WikiNews site https://ar.wikinews.org/wiki. These articles cover recent news from year 2013 to year 2015 in multiple genres (politics, economics, health, science and technology, sports, arts, and culture.) Articles contain 18,300 words, and they are evenly distributed among these 7 genres with 10 articles per each.",
+      "Word are white-space and punctuation separated, and some spelling errors are corrected (1.33% of the total words) to have very clean test cases. Lemmatization is done by an expert Arabic linguist where spelling corrections are marked, and lemmas are provided with full diacritization as shown in Figure FIGREF2 .",
+      "As MSA is usually written without diacritics and IR systems normally remove all diacritics from search queries and indexed data as a basic preprocessing step, so another column for undiacritized lemma is added and it's used for evaluating our lemmatizer and comparing with state-of-the-art system for lemmatization; MADAMIRA."
+    ],
+    [
+      "We were inspired by the work done by BIBREF8 for segmenting Arabic words out of context. They achieved an accuracy of almost 99%; slightly better than state-of-the-art system for segmentation (MADAMIRA) which considers surrounding context and many linguistic features. This system shows enhancements in both Machine Translation, and Information Retrieval tasks BIBREF9 . This work can be considered as an extension to word segmentation.",
+      "From a large diacritized corpus, we constructed a dictionary of words and their possible diacritizations ordered by number of occurrences of each diacritized form. This diacritized corpus was created by a commercial vendor and contains 9.7 million words with almost 200K unique surface words. About 73% of the corpus is in MSA and covers variety of genres like politics, economy, sports, society, etc. and the remaining part is mostly religious texts written in classical Arabic (CA). The effectiveness of using this corpus in building state-of-the-art diacritizer was proven in BIBREF10 .For example, the word \u00d9\u0088\u00d8\u00a8\u00d9\u0086\u00d9\u0088\u00d8\u00af> (wbnwd) \u201cand items\u201d is found 4 times in this corpus with two full diacritization forms \u00d9\u0088\u00d9\u008e\u00d8\u00a8\u00d9\u008f\u00d9\u0086\u00d9\u008f\u00d9\u0088\u00d8\u00af\u00d9\u0090\u00d8\u008c \u00d9\u0088\u00d9\u008e\u00d8\u00a8\u00d9\u008f\u00d9\u0086\u00d9\u008f\u00d9\u0088\u00d8\u00af\u00d9\u008d> (wabunudi, wabunudK) \u201citems, with different grammatical case endings\u201d which appeared 3 times and once respectively. All unique undiacritized words in this corpus were analyzed using Buckwalter morphological analyzer which gives all possible word diacritizations, and their segmentation, POS tag and lemma as shown in Figure FIGREF3 .",
+      "The idea is to take the most frequent diacritized form for words appear in this corpus, and find the morphological analysis with highest matching score between its diacritized form and the corpus word. This means that we search for the most common diacritization of the word regardless of its surrounding context. In the above example, the first solution is preferred and hence its lemma \u00d8\u00a8\u00d9\u0086\u00d8\u00af> (banod, bnd after diacritics removal) \u201citem\u201d.",
+      "While comparing two diacritized forms from the corpus and Buckwalter analysis, special cases were applied to solve inconsistencies between the two diacritization schemas, for example while words are fully diacritized in the corpus, Buckwalter analysis gives diacritics without case ending (i.e. without context), and removes short vowels in some cases, for example before long vowels, and after the definite article \u00d8\u00a7\u00d9\u0084> (Al) \u201cthe\u201d, etc.",
+      "It worths mentioning that there are many cases in Buckwalter analysis where for the input word, there are two or more identical diacritizations with different lemmas, and the analyses of such words are provided without any meaningful order. For example the word \u00d8\u00b3\u00d9\u008a\u00d8\u00a7\u00d8\u00b1\u00d8\u00a9> (syArp) \u201ccar\u201d has two morphological analyses with different lemmas, namely \u00d8\u00b3\u00d9\u008a\u00d8\u00a7\u00d8\u00b1> (syAr) \u201cwalker\u201d, and \u00d8\u00b3\u00d9\u008a\u00d8\u00a7\u00d8\u00b1\u00d8\u00a9> (syArp) \u201ccar\u201d in this order while the second lemma is the most common one. To solve tis problem, all these words are reported and the top frequent words are revised and order of lemmas were changed according to actual usage in modern language.",
+      "The lemmatization algorithm can be summarized in Figure FIGREF4 , and the online system can be tested through the site http://alt.qcri.org/farasa/segmenter.html"
+    ],
+    [
+      "Data was formatted in a plain text format where sentences are written in separate lines and words are separated by spaces, and the outputs of MADAMIRA and our system are compared against the undiacritized lemma for each word. For accurate results, all differences were revised manually to accept cases that should not be counted as errors (different writings of foreign names entities for example as in \u00d9\u0087\u00d9\u0088\u00d9\u0086\u00d8\u00ba \u00d9\u0083\u00d9\u0088\u00d9\u0086\u00d8\u00ba\u00d8\u008c \u00d9\u0087\u00d9\u0088\u00d9\u0086\u00d8\u00ac \u00d9\u0083\u00d9\u0088\u00d9\u0086\u00d8\u00ac> (hwng kwng, hwnj kwnj) \u201cHong Kong\u201d, or more than one accepted lemma for some function words, e.g the lemmas \u00d9\u0081\u00d9\u008a\u00d8\u008c \u00d9\u0081\u00d9\u008a\u00d9 \u00d8\u00a7> (fy, fymA) are both valid for the function word \u00d9\u0081\u00d9\u008a\u00d9 \u00d8\u00a7> (fymA) \u201cwhile\u201d).",
+      "Table TABREF5 shows results of testing our system and MADAMIRA on the WikiNews testset (for undiacritized lemmas). Our approach gives +7% relative gain above MADAMIRA in lemmatization task.",
+      "In terms of speed, our system was able to lemmatize 7.4 million words on a personal laptop in almost 2 minutes compared to 2.5 hours for MADAMIRA, i.e. 75 times faster. The code is written entirely in Java without any external dependency which makes its integration in other systems quite simple."
+    ],
+    [
+      "Most of the lemmatization errors in our system are due to fact that we use the most common diacritization of words without considering their contexts which cannot solve the ambiguity in cases like nouns and adjectives that share the same diacritization forms, for example the word \u00d8\u00a3\u00d9\u0083\u00d8\u00a7\u00d8\u00af\u00d9\u008a\u00d9 \u00d9\u008a\u00d8\u00a9> (AkAdymyp) can be either a noun and its lemma is \u00d8\u00a3\u00d9\u0083\u00d8\u00a7\u00d8\u00af\u00d9\u008a\u00d9 \u00d9\u008a\u00d8\u00a9> (AkAdymyp) \u201cacademy\u201d, or an adjective and its lemma is \u00d8\u00a3\u00d9\u0083\u00d8\u00a7\u00d8\u00af\u00d9\u008a\u00d9 \u00d9\u008a> (AkAdymy) \u201cacademic\u201d. Also for MADAMIRA, errors in selecting the correct Part-of-Speech (POS) for ambiguous words, and foreign named entities.",
+      "In the full paper, we will quantify error cases in our lemmatizer and MADAMIRA and give examples for each case which can help in enhancing both systems."
+    ],
+    [
+      "In this paper, we introduce a new dataset for Arabic lemmatization and a very fast and accurate lemmatization algorithm that performs better than state-of-the art system; MADAMIRA. Both the dataset and the code will be publicly available. We show that to build an effective IR system for complex derivational languages like Arabic, there is a a big need for very fast and accurate lemmatization algorithms, and we show that this can be achieved by considering only the most frequent diacritized form for words and matching this form with the morphological analysis with highest similarity score. We plan to study the performance if the algorithm was modified to provide diacritized lemmas which can be useful for other applications."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: Story Ending Generation with Incremental Encoding and Commonsense Knowledge
+
+Question: Which baselines are they using?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Overview",
+    "Background: Encoder-Decoder Framework",
+    "Incremental Encoding Scheme",
+    "Multi-Source Attention (MSA)",
+    "Knowledge Graph Representation",
+    "Loss Function",
+    "Dataset",
+    "Baselines",
+    "Experiment Settings",
+    "Automatic Evaluation",
+    "Manual Evaluation",
+    "Examples and Attention Visualization",
+    "Conclusion and Future Work",
+    "Acknowledgements",
+    "Appendix A: Annotation Statistics",
+    "Appendix B: Error Analysis",
+    "Appendix C: Attention Visualization"
+  ],
+  "paragraphs": [
+    [
+      "Story generation is an important but challenging task because it requires to deal with logic and implicit knowledge BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 . Story ending generation aims at concluding a story and completing the plot given a story context. We argue that solving this task involves addressing the following issues: 1) Representing the context clues which contain key information for planning a reasonable ending; and 2) Using implicit knowledge (e.g., commonsense knowledge) to facilitate understanding of the story and better predict what will happen next.",
+      "Comparing to textual entailment or reading comprehension BIBREF6 , BIBREF7 story ending generation requires more to deal with the logic and causality information that may span multiple sentences in a story context. The logic information in story can be captured by the appropriate sequence of events or entities occurring in a sequence of sentences, and the chronological order or causal relationship between events or entities. The ending should be generated from the whole context clue rather than merely inferred from a single entity or the last sentence. It is thus important for story ending generation to represent the context clues for predicting what will happen in an ending.",
+      "However, deciding a reasonable ending not only depends on representing the context clues properly, but also on the ability of language understanding with implicit knowledge that is beyond the text surface. Humans use their own experiences and implicit knowledge to help understand a story. As shown in Figure 1 , the ending talks about candy which can be viewed as commonsense knowledge about Halloween. Such knowledge can be crucial for story ending generation.",
+      "Figure 1 shows an example of a typical story in the ROCStories corpus BIBREF8 . In this example, the events or entities in the story context constitute the context clues which reveal the logical or causal relationships between events or entities. These concepts, including Halloween, trick or treat, and monster, are connected as a graph structure. A reasonable ending should consider all the connected concepts rather than just some individual one. Furthermore, with the help of commonsense knowledge retrieved from ConceptNet BIBREF9 , it is easier to infer a reasonable ending with the knowledge that candy is highly related to Halloween.",
+      "To address the two issues in story ending generation, we devise a model that is equipped with an incremental encoding scheme to encode context clues effectively, and a multi-source attention mechanism to use commonsense knowledge. The representation of the context clues is built through incremental reading (or encoding) of the sentences in the story context one by one. When encoding a current sentence in a story context, the model can attend not only to the words in the preceding sentence, but also the knowledge graphs which are retrieved from ConceptNet for each word. In this manner, commonsense knowledge can be encoded in the model through graph representation techniques, and therefore, be used to facilitate understanding story context and inferring coherent endings. Integrating the context clues and commonsense knowledge, the model can generate more reasonable endings than state-of-the-art baselines.",
+      "Our contributions are as follows:"
+    ],
+    [
+      "The corpus we used in this paper was first designed for Story Cloze Test (SCT) BIBREF10 , which requires to select a correct ending from two candidates given a story context. Feature-based BIBREF11 , BIBREF12 or neural BIBREF8 , BIBREF13 classification models are proposed to measure the coherence between a candidate ending and a story context from various aspects such as event, sentiment, and topic. However, story ending generation BIBREF14 , BIBREF15 , BIBREF16 is more challenging in that the task requires to modeling context clues and implicit knowledge to produce reasonable endings.",
+      "Story generation, moving forward to complete story comprehension, is approached as selecting a sequence of events to form a story by satisfying a set of criteria BIBREF0 . Previous studies can be roughly categorized into two lines: rule-based methods and neural models. Most of the traditional rule-based methods for story generation BIBREF0 , BIBREF1 retrieve events from a knowledge base with some pre-specified semantic relations. Neural models for story generation has been widely studied with sequence-to-sequence (seq2seq) learning BIBREF17 . And various contents such as photos and independent descriptions are largely used to inspire the story BIBREF3 .To capture the deep meaning of key entities and events, BIBREF2 ( BIBREF2 ) and BIBREF5 ( BIBREF5 ) explicitly modeled the entities mentioned in story with dynamic representation, and BIBREF4 ( BIBREF4 ) decomposed the problem into planning successive events and generating sentences from some given events. BIBREF18 ( BIBREF18 ) adopted a hierarchical architecture to generate the whole story from some given keywords.",
+      "Commonsense knowledge is beneficial for many natural language tasks such as semantic reasoning and text entailment, which is particularly important for story generation. BIBREF19 ( BIBREF19 ) characterized the types of commonsense knowledge mostly involved in recognizing textual entailment. Afterwards, commonsense knowledge was used in natural language inference BIBREF20 , BIBREF21 and language generation BIBREF22 . BIBREF23 ( BIBREF23 ) incorporated external commonsense knowledge into a neural cloze-style reading comprehension model. BIBREF24 ( BIBREF24 ) performed commonsense inference on people's intents and reactions of the event's participants given a short text. Similarly, BIBREF25 ( BIBREF25 ) introduced a new annotation framework to explain psychology of story characters with commonsense knowledge. And commonsense knowledge has also been shown useful to choose a correct story ending from two candidate endings BIBREF12 , BIBREF26 ."
+    ],
+    [
+      "The task of story ending generation can be stated as follows: given a story context consisting of a sentence sequence $X=\\lbrace X_1, X_2, \\cdots , X_K\\rbrace $ , where $X_i=x_1^{(i)}x_2^{(i)}\\cdots x_{l_i}^{(i)}$ represents the $i$ -th sentence containing $l_i$ words, the model should generate a one-sentence ending $Y=y_1y_2...y_l$ which is reasonable in logic, formally as ",
+      "$${Y^*} = \\mathop {argmax}\\limits _{Y} \\mathcal {P}(Y|X).$$   (Eq. 9) ",
+      "As aforementioned, context clue and commonsense knowledge is important for modeling the logic and casual information in story ending generation. To this end, we devise an incremental encoding scheme based on the general encoder-decoder framework BIBREF27 . As shown in Figure 2 , the scheme encodes the sentences in a story context incrementally with a multi-source attention mechanism: when encoding sentence $X_{i}$ , the encoder obtains a context vector which is an attentive read of the hidden states, and the graph vectors of the preceding sentence $X_{i-1}$ . In this manner, the relationship between words (some are entities or events) in sentence $X_{i-1}$ and those in $X_{i}$ is built incrementally, and therefore, the chronological order or causal relationship between entities (or events) in adjacent sentences can be captured implicitly. To leverage commonsense knowledge which is important for generating a reasonable ending, a one-hop knowledge graph for each word in a sentence is retrieved from ConceptNet, and each graph can be represented by a vector in two ways. The incremental encoder not only attends to the hidden states of $X_{i-1}$ , but also to the graph vectors at each position of $X_{i-1}$ . By this means, our model can generate more reasonable endings by representing context clues and encoding commonsense knowledge."
+    ],
+    [
+      "The encoder-decoder framework is a general framework widely used in text generation. Formally, the model encodes the input sequence $X=x_1x_2\\cdots x_m$ into a sequence of hidden states, as follows, ",
+      "$$\\textbf {h}_{t} &= \\mathbf {LSTM}(\\textbf {h}_{t-1}, \\mathbf {e}(x_t)), $$   (Eq. 11) ",
+      " where $\\textbf {h}_{t}$ denotes the hidden state at step $t$ and $\\mathbf {e}(x)$ is the word vector of $x$ .",
+      "At each decoding position, the framework will generate a word by sampling from the word distribution $\\mathcal {P}(y_t|y_{<t},X)$ ( $y_{<t}=y_1y_2\\cdots y_{t-1}$ denotes the sequences that has been generated before step $t$ ), which is computed as follows: ",
+      "$$&\\mathcal {P}(y_t|y_{<t}, X) = \\mathbf {softmax}(\\textbf {W}_{0}\\mathbf {s}_{t}+\\textbf {b}_0), \\\\\n&\\textbf {s}_{ t} = \\mathbf {LSTM}(\\textbf {s}_{ t-1}, \\mathbf {e}(y_{t-1}), \\textbf {c}_{t-1}), $$   (Eq. 12) ",
+      " where $\\textbf {s}_t$ denotes the decoder state at step $t$ . When an attention mechanism is applied, $\\textbf {c}_{t-1}$ is an attentive read of the context, which is a weighted sum of the encoder's hidden states as $\\textbf {c}_{t-1}=\\sum _{i=1}^m\\alpha _{(t-1)i}\\textbf {h}_i$ , and $\\alpha _{(t-1)i}$ measures the association between the decoder state $\\textbf {s}_{t-1}$ and the encoder state $\\textbf {h}_i$ . Refer to BIBREF28 for more details."
+    ],
+    [
+      "Straightforward solutions for encoding the story context can be: 1) Concatenating the $K$ sentences to a long sentence and encoding it with an LSTM ; or 2) Using a hierarchical LSTM with hierarchical attention BIBREF29 , which firstly attends to the hidden states of a sentence-level LSTM, and then to the states of a word-level LSTM. However, these solutions are not effective to represent the context clues which may capture the key logic information. Such information revealed by the chronological order or causal relationship between events or entities in adjacent sentences.",
+      "To better represent the context clues, we propose an incremental encoding scheme: when encoding the current sentence $X_i$ , it obtains a context vector which is an attentive read of the preceding sentence $X_{i-1}$ . In this manner, the order/relationship between the words in adjacent sentences can be captured implicitly.",
+      "This process can be stated formally as follows: ",
+      "$$\\textbf {h}_{j}^{(i)} = \\mathbf {LSTM}(\\textbf {h}_{j-1}^{(i)}, \\mathbf {e}(x_j^{(i)}), \\textbf {c}_{\\textbf {l}j}^{(i)}), ~i\\ge 2. $$   (Eq. 14) ",
+      " where $\\textbf {h}^{(i)}_{j}$ denotes the hidden state at the $j$ -th position of the $i$ -th sentence, $\\mathbf {e}(x_j^{(i)})$ denotes the word vector of the $j$ -th word $x_j^{(i)}$ . $\\textbf {c}_{\\textbf {l},j}^{(i)}$ is the context vector which is an attentive read of the preceding sentence $X_{i-1}$ , conditioned on $\\textbf {h}^{(i)}_{j-1}$ . We will describe the context vector in the next section.",
+      "During the decoding process, the decoder obtains a context vector from the last sentence $X_{K}$ in the context to utilize the context clues. The hidden state is obtained as below: ",
+      "$$&\\textbf {s}_{t} = \\mathbf {LSTM}(\\textbf {s}_{t-1}, \\mathbf {e}(y_{t-1}), \\textbf {c}_{\\textbf {l}t}), $$   (Eq. 15) ",
+      " where $\\textbf {c}_{\\textbf {l}t}$ is the context vector which is the attentive read of the last sentence $X_K$ , conditioned on $\\textbf {s}_{t-1}$ . More details of the context vector will be presented in the next section."
+    ],
+    [
+      "The context vector ( $\\textbf {c}_{\\textbf {l}}$ ) plays a key role in representing the context clues because it captures the relationship between words (or states) in the current sentence and those in the preceding sentence. As aforementioned, story comprehension sometime requires the access of implicit knowledge that is beyond the text. Therefore, the context vector consists of two parts, computed with multi-source attention. The first one $\\textbf {c}_{\\textbf {h}j}^{(i)}$ is derived by attending to the hidden states of the preceding sentence, and the second one $\\textbf {c}_{\\textbf {x}j}^{(i)}$ by attending to the knowledge graph vectors which represent the one-hop graphs in the preceding sentence. The MSA context vector is computed as follows: ",
+      "$$\\textbf {c}_{\\textbf {l}j}^{(i)} = \\textbf {W}_\\textbf {l}([\\textbf {c}_{\\textbf {h}j}^{(i)}; \\textbf {c}_{\\textbf {x}j}^{(i)}])+\\textbf {b}_\\textbf {l},$$   (Eq. 17) ",
+      " where $\\oplus $ indicates vector concatenation. Hereafter, $\\textbf {c}_{\\textbf {h}j}^{(i)}$ is called state context vector, and $\\textbf {c}_{\\textbf {x}j}^{(i)}$ is called knowledge context vector.",
+      "The state context vector is a weighted sum of the hidden states of the preceding sentence $X_{i-1}$ and can be computed as follows: ",
+      "$$\\textbf {c}_{\\textbf {h}j}^{(i)} &= \\sum _{k = 1}^{l_{i-1}}\\alpha _{h_k,j}^{(i)}\\textbf {h}_{k}^{(i-1)}, \\\\\n\\alpha _{h_k,j}^{(i)} &= \\frac{e^{\\beta _{h_k,j}^{(i)}}}{\\;\\sum \\limits _{m=1}^{l_{i-1}}e^{\\beta _{h_m,j}^{(i)}}\\;},\\\\\n\\beta _{h_k,j}^{(i)} &= \\textbf {h}_{j-1}^{(i)\\rm T}\\textbf {W}_\\textbf {s} \\textbf {h}_k^{(i-1)},$$   (Eq. 18) ",
+      " where $\\beta _{h_k,j}^{(i)}$ can be viewed as a weight between hidden state $\\textbf {h}_{j-1}^{(i)}$ in sentence $X_i$ and hidden state $\\textbf {h}_k^{(i-1)}$ in the preceding sentence $X_{i-1}$ .",
+      "Similarly, the knowledge context vector is a weighted sum of the graph vectors for the preceding sentence. Each word in a sentence will be used as a query to retrieve a one-hop commonsense knowledge graph from ConceptNet, and then, each graph will be represented by a graph vector. After obtaining the graph vectors, the knowledge context vector can be computed by: ",
+      "$$\\textbf {c}_{\\textbf {x}j}^{(i)} &= \\sum _{k = 1}^{l_{i-1}}\\alpha _{x_k,j}^{(i)}\\textbf {g}(x_{k}^{(i-1)}), \\\\\n\\alpha _{x_k,j}^{(i)} &= \\frac{e^{\\beta _{x_k,j}^{(i)}}}{\\;\\sum \\limits _{m=1}^{l_{i-1}}e^{\\beta _{x_m,j}^{(i)}}\\;},\\\\\n\\beta _{x_k,j}^{(i)} &= \\textbf {h}_{j-1}^{(i)\\rm T}\\textbf {W} _\\textbf {k}\\textbf {g}(x_k^{(i-1)}),$$   (Eq. 19) ",
+      " where $\\textbf {g}(x_k^{(i-1)})$ is the graph vector for the graph which is retrieved for word $x_k^{(i-1)}$ . Different from $\\mathbf {e}(x_k^{(i-1)})$ which is the word vector, $\\textbf {g}(x_k^{(i-1)})$ encodes commonsense knowledge and extends the semantic representation of a word through neighboring entities and relations.",
+      "During the decoding process, the knowledge context vectors are similarly computed by attending to the last input sentence $X_K$ . There is no need to attend to all the context sentences because the context clues have been propagated within the incremental encoding scheme."
+    ],
+    [
+      "Commonsense knowledge can facilitate language understanding and generation. To retrieve commonsense knowledge for story comprehension, we resort to ConceptNet BIBREF9 . ConceptNet is a semantic network which consists of triples $R=(h, r, t)$ meaning that head concept $h$ has the relation $r$ with tail concept $t$ . Each word in a sentence is used as a query to retrieve a one-hop graph from ConceptNet. The knowledge graph for a word extends (encodes) its meaning by representing the graph from neighboring concepts and relations.",
+      "There have been a few approaches to represent commonsense knowledge. Since our focus in this paper is on using knowledge to benefit story ending generation, instead of devising new methods for representing knowledge, we adopt two existing methods: 1) graph attention BIBREF30 , BIBREF22 , and 2) contextual attention BIBREF23 . We compared the two means of knowledge representation in the experiment.",
+      "Formally, the knowledge graph of word (or concept) $x$ is represented by a set of triples, $\\mathbf {G}(x)=\\lbrace R_1, R_2, \\cdots , R_{N_x}\\rbrace $ (where each triple $R_i$ has the same head concept $x$ ), and the graph vector $\\mathbf {g}(x)$ for word $x$ can be computed via graph attention, as below: ",
+      "$$\\textbf {g}(x) &= \\sum _{i = 1}^{N_x}\\alpha _{R_i}[\\textbf {h}_i ; \\textbf {t}_i],\\\\\n\\alpha _{R_i} &= \\frac{e^{\\beta _{R_i}}}{\\;\\sum \\limits _{j=1}^{N_x}e^{\\beta _{R_j}}\\;},\\\\\n\\beta _{R_i} =\n(\\textbf {W}_{\\textbf {r}}&\\textbf {r}_i)^{\\rm T}\\mathop {tanh}(\\textbf {W}_{\\textbf {h}}\\textbf {h}_i+\\textbf {W}_{\\textbf {t}}\\textbf {t}_i),$$   (Eq. 23) ",
+      " where $(h_i, r_i, t_i) = R_i \\in \\mathbf {G}(x)$ is the $i$ -th triple in the graph. We use word vectors to represent concepts, i.e. $\\textbf {h}_i = \\mathbf {e}(h_i), \\textbf {t}_i = \\mathbf {e}(t_i)$ , and learn trainable vector $\\textbf {r}_i$ for relation $r_i$ , which is randomly initialized.",
+      "Intuitively, the above formulation assumes that the knowledge meaning of a word can be represented by its neighboring concepts (and corresponding relations) in the knowledge base. Note that entities in ConceptNet are common words (such as tree, leaf, animal), we thus use word vectors to represent h/r/t directly, instead of using geometric embedding methods (e.g., TransE) to learn entity and relation embeddings. In this way, there is no need to bridge the representation gap between geometric embeddings and text-contextual embeddings (i.e., word vectors).",
+      "When using contextual attention, the graph vector $\\textbf {g}(x)$ can be computed as follows: ",
+      "$$\\textbf {g}(x)&=\\sum _{i=1}^{N_x}\\alpha _{R_i}\\textbf {M}_{R_i},\\\\\n\\textbf {M}_{R_i}&=BiGRU(\\textbf {h}_i,\\textbf {r}_i,\\textbf {t}_i),\\\\\n\\alpha _{R_i} &= \\frac{e^{\\beta _{R_i}}}{\\;\\sum \\limits _{j=1}^{N_x}e^{\\beta _{R_j}}\\;},\\\\\n\\beta _{R_i}&= \\textbf {h}_{(x)}^{\\rm T}\\textbf {W}_\\textbf {c}\\textbf {M}_{R_i},$$   (Eq. 25) ",
+      " where $\\textbf {M}_{R_i}$ is the final state of a BiGRU connecting the elements of triple $R_i$ , which can be seen as the knowledge memory of the $i$ -th triple, while $\\textbf {h}_{(x)}$ denotes the hidden state at the encoding position of word $x$ ."
+    ],
+    [
+      "As aforementioned, the incremental encoding scheme is central for story ending generation. To better model the chronological order and causal relationship between adjacent sentences, we impose supervision on the encoding network. At each encoding step, we also generate a distribution over the vocabulary, very similar to the decoding process: ",
+      "$$\\mathcal {P}(y_t|y_{<t}, X) =\\mathbf {softmax}(\\textbf {W}_{0}\\textbf {h}_{j}^{(i)}+\\textbf {b}_0),$$   (Eq. 27) ",
+      " Then, we calculate the negative data likelihood as loss function: ",
+      "$$\\Phi &= \\Phi _{en} + \\Phi _{de}\\\\\n\\Phi _{en} &= \\sum _{i=2}^K\\sum _{j=1}^{l_i} - \\log \\mathcal {P}(x_j^{(i)}=\\widetilde{x}_j^{(i)}|x_{<j}^{(i)}, X_{<i}),\\\\\n\\Phi _{de} &= \\sum _t - \\log \\mathcal {P}(y_t=\\tilde{y}_t|y_{<t}, X),$$   (Eq. 28) ",
+      " where $\\widetilde{x}_j^{(i)}$ means the reference word used for encoding at the $j$ -th position in sentence $i$ , and $\\tilde{y}_t$ represents the $j$ -th word in the reference ending. Such an approach does not mean that at each step there is only one correct next sentence, exactly as many other generation tasks. Experiments show that it is better in logic than merely imposing supervision on the decoding network."
+    ],
+    [
+      "We evaluated our model on the ROCStories corpus BIBREF10 . The corpus contains 98,162 five-sentence stories for evaluating story understanding and script learning. The original task is designed to select a correct story ending from two candidates, while our task is to generate a reasonable ending given a four-sentence story context. We randomly selected 90,000 stories for training and the left 8,162 for evaluation. The average number of words in $X_1/X_2/X_3/X_4/Y$ is 8.9/9.9/10.1/10.0/10.5 respectively. The training data contains 43,095 unique words, and 11,192 words appear more than 10 times. For each word, we retrieved a set of triples from ConceptNet and stored those whose head entity and tail entity are noun or verb, meanwhile both occurring in SCT. Moreover, we retained at most 10 triples if there are too many. The average number of triples for each query word is 3.4."
+    ],
+    [
+      "We compared our models with the following state-of-the-art baselines:",
+      "Sequence to Sequence (Seq2Seq): A simple encoder-decoder model which concatenates four sentences to a long sentence with an attention mechanism BIBREF31 .",
+      "Hierarchical LSTM (HLSTM): The story context is represented by a hierarchical LSTM: a word-level LSTM for each sentence and a sentence-level LSTM connecting the four sentences BIBREF29 . A hierarchical attention mechanism is applied, which attends to the states of the two LSTMs respectively.",
+      "HLSTM+Copy: The copy mechanism BIBREF32 is applied to hierarchical states to copy the words in the story context for generation.",
+      "HLSTM+Graph Attention(GA): We applied multi-source attention HLSTM where commonsense knowledge is encoded by graph attention.",
+      "HLSTM+Contextual Attention(CA): Contextual attention is applied to represent commonsense knowledge."
+    ],
+    [
+      "The parameters are set as follows: GloVe.6B BIBREF33 is used as word vectors, and the vocabulary size is set to 10,000 and the word vector dimension to 200. We applied 2-layer LSTM units with 512-dimension hidden states. These settings were applied to all the baselines.",
+      "The parameters of the LSTMs (Eq. 14 and 15 ) are shared by the encoder and the decoder."
+    ],
+    [
+      "We conducted the automatic evaluation on the 8,162 stories (the entire test set). We generated endings from all the models for each story context.",
+      "We adopted perplexity(PPL) and BLEU BIBREF34 to evaluate the generation performance. Smaller perplexity scores indicate better performance. BLEU evaluates $n$ -gram overlap between a generated ending and a reference ending. However, since there is only one reference ending for each story context, BLEU scores will become extremely low for larger $n$ . We thus experimented with $n=1,2$ . Note also that there may exist multiple reasonable endings for the same story context.",
+      "The results of the automatic evaluation are shown in Table 1 , where IE means a simple incremental encoding framework that ablated the knowledge context vector from $\\textbf {c}_{\\textbf {l}}$ in Eq. ( 17 ). Our models have lower perplexity and higher BLEU scores than the baselines. IE and IE+MSA have remarkably lower perplexity than other models. As for BLEU, IE+MSA(CA) obtained the highest BLEU-1 and BLEU-2 scores. This indicates that multi-source attention leads to generate story endings that have more overlaps with the reference endings."
+    ],
+    [
+      "Manual evaluations are indispensable to evaluate the coherence and logic of generated endings. For manual evaluation, we randomly sampled 200 stories from the test set and obtained 1,600 endings from the eight models. Then, we resorted to Amazon Mechanical Turk (MTurk) for annotation. Each ending will be scored by three annotators and majority voting is used to select the final label.",
+      "We defined two metrics - grammar and logicality for manual evaluation. Score 0/1/2 is applied to each metric during annotation.",
+      "Whether an ending is natural and fluent. Score 2 is for endings without any grammar errors, 1 for endings with a few errors but still understandable and 0 for endings with severe errors and incomprehensible.",
+      "Whether an ending is reasonable and coherent with the story context in logic. Score 2 is for reasonable endings that are coherent in logic, 1 for relevant endings but with some discrepancy between an ending and a given context, and 0 for totally incompatible endings.",
+      "Note that the two metrics are scored independently. To produce high-quality annotation, we prepared guidelines and typical examples for each metric score.",
+      "The results of the manual evaluation are also shown in Table 1 . Note that the difference between IE and IE+MSA exists in that IE does not attend to knowledge graph vectors in a preceding sentence, and thus it does use any commonsense knowledge. The incremental encoding scheme without MSA obtained the best grammar score and our full mode IE+MSA(GA) has the best logicality score. All the models have fairly good grammar scores (maximum is 2.0), while the logicality scores differ remarkably, much lower than the maximum score, indicating the challenges of this task.",
+      "More specifically, incremental encoding is effective due to the facts: 1) IE is significantly better than Seq2Seq and HLSTM in grammar (Sign Test, 1.84 vs. $1.74/1.57$ , p-value= $0.046/0.037$ , respectively), and in logicality (1.10 vs. 0.70/0.84, p-value $<0.001/0.001$ ). 2) IE+MSA is significantly better than HLSTM+MSA in logicality (1.26 vs. 1.06, p-value= $0.014$ for GA; 1.24 vs. 1.02, p-value= $0.022$ for CA). This indicates that incremental encoding is more powerful than traditional (Seq2Seq) and hierarchical (HLSTM) encoding/attention in utilizing context clues. Furthermore, using commonsense knowledge leads to significant improvements in logicality. The comparison in logicality between IE+MSA and IE (1.26/1.24 vs. 1.10, p-value= $0.028/0.042$ for GA/CA, respectively), HLSTM+MSA and HLSTM (1.06/1.02 vs. 0.84, p-value $<0.001/0.001$ for GA/CA, respectively), and HLSTM+MSA and HLSTM+Copy (1.06/1.02 vs. 0.90, p-value= $0.044/0.048$ , respectively) all approve this claim. In addition, similar results between GA and CA show that commonsense knowledge is useful but multi-source attention is not sensitive to the knowledge representation scheme.",
+      "More detailed results are listed in Table 2 . Comparing to other models, IE+MSA has a much larger proportion of endings that are good both in grammar and logicality (2-2). The proportion of good logicality (score=2.0) from IE+MSA is much larger than that from IE (45.0%+5.0%/41.0%+4.0% vs. 36.0%+2.0% for GA/CA, respectively), and also remarkable larger than those from other baselines. Further, HLSTM equipped with MSA is better than those without MSA, indicating that commonsense knowledge is helpful. And the kappa measuring inter-rater agreement is 0.29 for three annotators, which implies a fair agreement."
+    ],
+    [
+      "We presented an example of generated story endings in Table 3 . Our model generates more natural and reasonable endings than the baselines.",
+      "In this example, the baselines predicted wrong events in the ending. Baselines (Seq2Seq, HLSTM, and HLSTM+Copy) have predicted improper entities (cake), generated repetitive contents (her family), or copied wrong words (eat). The models equipped with incremental encoding or knowledge through MSA(GA/CA) perform better in this example. The ending by IE+MSA is more coherent in logic, and fluent in grammar. We can see that there may exist multiple reasonable endings for the same story context.",
+      "In order to verify the ability of our model to utilize the context clues and implicit knowledge when planning the story plot, we visualized the attention weights of this example, as shown in Figure 3 . Note that this example is produced from graph attention.",
+      "In Figure 3 , phrases in the box are key events of the sentences that are manually highlighted. Words in blue or purple are entities that can be retrieved from ConceptNet, respectively in story context or in ending. An arrow indicates that the words in the current box (e.g., they eat in $X_2$ ) all have largest attention weights to some words in the box of the preceding sentence (e.g., cooking a special meal in $X_1$ ). Black arrows are for state context vector (see Eq. 18 ) and blue for knowledge context vector (see Eq. 19 ). For instance, eat has the largest knowledge attention to meal through the knowledge graph ( $<$ meal, AtLocation, dinner $>$ , $<$ meal, RelatedTo, eat $>$ ). Similarly, eat also has the second largest attention weight to cooking through the knowledge graph. For attention weights of state context vector, both words in perfects everything has the largest weight to some of everything to be just right (everything $\\rightarrow $ everything, perfect $\\rightarrow $ right).",
+      "The example illustrates how the connection between context clues are built through incremental encoding and use of commonsense knowledge. The chain of context clues, such as ${be\\_cooking}\\rightarrow {want\\_everything\\_be\\_right}\\rightarrow {perfect\\_everything}\\rightarrow {lay\\_down}\\rightarrow {get\\_back}$ , and the commonsense knowledge, such as $<$ cook, AtLocation, kitchen $>$ and $<$ oven, UsedFor, burn $>$ , are useful for generating reasonable story endings."
+    ],
+    [
+      "We present a story ending generation model that builds context clues via incremental encoding and leverages commonsense knowledge with multi-source attention. It encodes a story context incrementally with a multi-source attention mechanism to utilize not only context clues but also commonsense knowledge: when encoding a sentence, the model obtains a multi-source context vector which is an attentive read of the words and the corresponding knowledge graphs of the preceding sentence in the story context. Experiments show that our models can generate more coherent and reasonable story endings.",
+      "As future work, our incremental encoding and multi-source attention for using commonsense knowledge may be applicable to other language generation tasks.",
+      "Refer to the Appendix for more details."
+    ],
+    [
+      "This work was jointly supported by the National Science Foundation of China (Grant No.61876096/61332007), and the National Key R&D Program of China (Grant No. 2018YFC0830200). We would like to thank Prof. Xiaoyan Zhu for her generous support."
+    ],
+    [
+      "We presented the statistics of annotation agreement in Table 4 . The proportion of the annotations in which at least two annotators (3/3+2/3) assigned the same score to an ending is 96% for grammar and 94% for logicality. We can also see that the 3/3 agreement for logicality is much lower than that for grammar, indicating that logicality is more complicated for annotation than grammar."
+    ],
+    [
+      "We analyzed error types by manually checking all 46 bad endings generated by our model, where bad means the average score in terms of at least one metric is not greater than 1.",
+      "There are 3 typical error types: bad grammar (BG), bad logicality (BL), and other errors. The distribution of types is shown in Table 5 .",
+      "We also presented some typical cases for each error type in Table 6 . Note that we only took graph attention as example. The first case shows an instance of bad grammar for repetitive generation. The second case shows that our model predicted a wrong entity at the last position where car is obviously more appropriate than daughter. It happens when the attention focuses on the wrong position, but in more cases it happens due to the noise of the commonsense knowledge base. The ending of the third case contains a relevant event work on his own but the event is not consistent to the previous word relieved. Other cases show that our model is not good at dealing with rare words. However, this can be further improved by applying copy mechanism, as our future work. These errors also indicate that story ending generation is challenging, and logic and implicit knowledge plays a central role in this task."
+    ],
+    [
+      "The multi-source attention mechanism computes the state context vectors and knowledge context vectors respectively as follows: ",
+      "$$\\textbf {c}_{\\textbf {h}j}^{(i)} &= \\sum _{k = 1}^{l_{i-1}}\\alpha _{h_k,j}^{(i)}\\textbf {h}_{k}^{(i-1)}, \\\\\n\\textbf {c}_{\\textbf {x}j}^{(i)} &= \\sum _{k = 1}^{l_{i-1}}\\alpha _{x_k,j}^{(i)}\\textbf {g}(x_{k}^{(i-1)}), $$   (Eq. 53) ",
+      "The visualization analysis in Section 4.6 \u201cGenerated Ending Examples and Attention Visualization\" is based on the attention weights ( $\\alpha _{h_{k,j}}^{(i)}$ and $\\alpha _{x_{k,j}}^{(i)}$ ), as presented in Figure 4 . Similarly we take as example the graph attention method to represent commonsense knowledge.",
+      "The figure illustrates how the incremental encoding scheme with the multi-source attention utilizes context clues and implicit knowledge.",
+      "1) The left column: for utilizing context clues, when the model encodes $X_2$ , cooking in $X_1$ obtains the largest state attention weight ( $\\alpha _{h_{k,j}}^{(i)}$ ), which illustrates cooking is an important word (or event) for the context clue. Similarly, the key events in each sentence have largest attention weights to some entities or events in the preceding sentence, which forms the context clue (e.g., perfects in $X_3$ to right in $X_2$ , lay/down in $X_4$ to perfect/everything in $X_3$ , get/back in $Y$ to lay/down in $X_4$ , etc.).",
+      "2) The right column: for the use of commonsense knowledge, each sentence has attention weights ( $\\alpha _{x_{k,j}}^{(i)}$ ) to the knowledge graphs of the preceding sentence (e.g. eat in $X_2$ to meal in $X_1$ , dinner in $X_3$ to eat in $X_2$ , etc.). In this manner, the knowledge information is added into the encoding process of each sentence, which helps story comprehension for better ending generation (e.g., kitchen in $Y$ to oven in $X_2$ , etc.)."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: Joint learning of ontology and semantic parser from text
+
+Question: How big is their dataset?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Grammar induction",
+    "Textual data representation",
+    "Grammar Definition",
+    "Parser",
+    "Rule induction",
+    "Seed rules",
+    "Ontology induction",
+    "Ontology induction from grammar",
+    "Relation extraction from semantic trees",
+    "Experiments",
+    "Datasets",
+    "Grammar Induction Experiments",
+    "Instance extraction",
+    "Relation extraction",
+    "Related Work",
+    "Semantic parsing",
+    "Ontology Learning",
+    "Other Approaches",
+    "Discussion"
+  ],
+  "paragraphs": [
+    [
+      "One of the ultimate goals of Natural Language Processing (NLP) is machine reading BIBREF0 , the automatic, unsupervised understanding of text. One way of pursuing machine reading is by semantic parsing, which transforms text into its meaning representation. However, capturing the meaning is not the final goal, the meaning representation needs to be predefined and structured in a way that supports reasoning. Ontologies provide a common vocabulary for meaning representations and support reasoning, which is vital for understanding the text. To enable flexibility when encountering new concepts and relations in text, in machine reading we want to be able to learn and extend the ontology while reading. Traditional methods for ontology learning BIBREF1 , BIBREF2 are only concerned with discovering the salient concepts from text. Thus, they work in a macro-reading fashion BIBREF3 , where the goal is to extract facts from a large collection of texts, but not necessarily all of them, as opposed to a micro-reading fashion, where the goal is to extract every fact from the input text. Semantic parsers operate in a micro-reading fashion. Consequently, the ontologies with only the salient concepts are not enough for semantic parsing. Furthermore, the traditional methods learn an ontology for a particular domain, where the text is used just as a tool. On the other hand, ontologies are used just as tool to represent meaning in the semantic parsing setting. When developing a semantic parser it is not trivial to get the best meaning representation for the observed text, especially if the content is not known yet. Semantic parsing datasets have been created by either selecting texts that can be expressed with a given meaning representation, like Free917 dataset BIBREF4 , or by manually deriving the meaning representation given the text, like Atis dataset BIBREF5 . In both datasets, each unit of text has its corresponding meaning representation. While Free917 uses Freebase BIBREF6 , which is a very big multi-domain ontology, it is not possible to represent an arbitrary sentence with Freebase or any other existing ontology.",
+      "In this paper, we propose a novel approach to joint learning of ontology and semantic parsing, which is designed for homogeneous collections of text, where each fact is usually stated only once, therefore we cannot rely on data redundancy. Our approach is text-driven, semi-automatic and based on grammar induction. It is presented in Figure 1 .The input is a seed ontology together with text annotated with concepts from the seed ontology. The result of the process is an ontology with extended instances, classes, taxonomic and non-taxonomic relations, and a semantic parser, which transform basic units of text, i.e sentences, into semantic trees. Compared to trees that structure sentences based on syntactic information, nodes of semantic trees contain semantic classes, like location, profession, color, etc. Our approach does not rely on any syntactic analysis of text, like part-of-speech tagging or dependency parsing. The grammar induction method works on the premise of curriculum learning BIBREF7 , where the parser first learns to parse simple sentences, then proceeds to learn more complex ones. The induction method is iterative, semi-automatic and based on frequent patterns. A context-free grammar (CFG) is induced from the text, which is represented by several layers of semantic annotations. The motivation to use CFG is that it is very suitable for the proposed alternating usage of top-down and bottom-up parsing, where new rules are induced from previously unparsable parts. Furthermore, it has been shown by BIBREF8 that CFGs are expressive enough to model almost every language phenomena. The induction is based on a greedy iterative procedure that involves minor human involvement, which is needed for seed rule definition and rule categorization. Our experiments show that although the grammar is ambiguous, it is scalable enough to parse a large dataset of sentences.",
+      "The grammar and semantic trees serve as an input for the new ontology. Classes, instances and taxonomic relations are constructed from the grammar. We also propose a method for discovering less frequent instances and their classes, and a supervised method to learn relations between instances. Both methods work on semantic trees.",
+      "For experimentation, first sentences of Wikipedia pages describing people are taken as a dataset. These sentences are already annotated with links to other pages, which are also instances of DBpedia knowledge base BIBREF9 . Using relations from DBpedia as a training set, several models to predict relations have been trained and evaluated.",
+      "The rest of the paper is organized in the following way. The grammar induction approach is presented in Section \"Grammar induction\" . The ontology induction approach follows in Section \"Ontology induction\" . In Section \"Experiments\" we present the conducted experiments with grammar induction, and instance and relation extraction. We examine the related work in Section \"Related Work\" , and conclude with the discussion in Section \"Discussion\" ."
+    ],
+    [
+      "In this section, we propose a semi-automatic bootstrapping procedure for grammar induction, which searches for the most frequent patterns and constructs new production rules from them. One of the main challenges is to make the induction in a way that minimizes human involvement and maximizes the quality of semantic trees.",
+      "The input to the process, which is illustrated in Figure 2 , is a set of predefined seed grammar rules (see Section \"Seed rules\" ) and a sample of sentences in a layered representation (see Section \"Experiments\" ) from the dataset. The output of the process is a larger set of rules forming the induced grammar. One rule is added to the grammar on each iteration. At the beginning of each iteration all the sentences are parsed with a top-down parser. The output of parsing a single sentence is a semantic tree \u2013 a set of semantic nodes connected into a tree. Here we distinguish two possible outcomes of the parsing: 1) the sentence was completely parsed, which is the final goal and 2) there is at least one part of the sentence that cannot be parsed. From the perspective of a parser the second scenario happens when there is a node that cannot be parsed by any of the rules. We name these nodes \u2013 null nodes \u2013 and they serve as the input for the next step, the rule induction. In this step several rules are constructed by generalization of null nodes. The generalization (see Section \"Rule induction\" ) is based on utilization of semantic annotations and bottom-up composition of the existing rules. Out of the induced rules, a rule with the highest frequency (the one that was generalized from the highest number of null nodes) is added to the grammar. To improve quality of the grammar, the rules are marked by so called property, which instructs the parser how to use the rule (eg., us it in parsing but not in induction). The property vitally affects result of the parsing in the following iterations potentially causing a huge semantic drift for the rest of process. Consequently, a human user needs to mark the property of each rule. The iterative process runs until a predefined stopping criteria is met. The criteria is either connected to the quality of the grammar or time limitation.",
+      "For the sake of transparency of the experiments, the human is involved in the beginning, when the seed rules are created and later when the rule properties are specified. However, in another setting the user could also define new rules in the middle of the bootstrapping procedure.",
+      "In the following sections, we describe each component of the process in more details.",
+      "Our goal was to develop a semi-automatic method that induces a grammar suitable for our scenario, in which an ontology is extracted, and text is parsed into semantic trees. A survey by BIBREF27 compares several papers on grammar induction. According to their classification, our method falls into unsupervised, text-based (no negative examples of sentences) methods. Many such methods induce context-free grammars. However, their focus is more on learning syntactic structures rather than semantic. This is evident in evaluation strategies, where their parse trees are compared against golden parse trees in treebanks, like Penn treebank BIBREF28 , which are annotated according to syntactic policies. Furthermore, our grammar should not limited to a specific form, like for instance Chomsky normal form or Greibach normal form, instead it may contain arbitrary context-free rules. Several algorithms, like ours, employ the greedy strategy of grammar induction, where the grammar is updated with the best decision at each step. Whereas our method adds a rule after all sentences are parsed, The Incremental Parsing algorithm BIBREF29 updates the grammar after each sentence. This is also done in ADIOS method BIBREF30 , where it has been shown that order of sentences affects the grammar. Our method employs frequency analysis and human supervision to control the grammar construction, while others use Minimum Description Length principle BIBREF31 , clustering of sequences BIBREF32 , or significance of word co-occurrences BIBREF33 ."
+    ],
+    [
+      "The input textual data needs to be properly structured in order to work best with the proposed algorithms. Shallow NLP tools, like sentence splitting, word tokenization, named entity recognition, might help obtaining this structure. The basic unit is a sentence, represented by several layers. An example is presented in Table 1 . Each layer consists of several tokens, which span over one or more words. The basic layer is the lexical layer, where each token represents a single word. All other layers are created from the annotations. Some annotations, like named-entities, may span over several words; some of the words may not have an annotation, thus they are given a null token. It is crucial that all algorithms are aware how to deal with a particular layer. For instance, the parser must not break apart a multi-word annotation. Some layers may be derived from others using the seed ontology. For example, instance layer contains annotations to instances of the ontology and the derived class layer represents the classes of these annotations, which are also from the ontology. Annotation layers are valuable if they provide good means for generalization or connection with the ontology. A term is a subpart of the sentence, defined by the starting and ending position in the sentence. It has different interpretation in each layer. If the interpretation breaks any of the tokens, it is not valid. For instance, term representing Madeira is not valid in named-entity layer in Table 1 because it breaks Person."
+    ],
+    [
+      "Our context-free grammar $G$ is defined by the 5-tuple: $G = (V, \\sigma , P, S, R)$ , where",
+      " $V$ is a set of non-terminals. Each non-terminal represents a semantic class, e.g. $\\langle \\text{Person} \\rangle $ , $\\langle \\text{Color} \\rangle $ , $\\langle \\text{Organization} \\rangle $ . There is also a universal non-terminal $\\langle * \\rangle $ , which can be replaced by any other non-terminal. The same non-terminal replaces all occurrences in a rule. It is used to represent several rules, with a notation. The grammar is still context-free. See seed rule examples in Section \"Seed rules\" .",
+      " $\\sigma $ is a set of terminals. Terminal is any existing non-null token from any sentence layer. We denote a terminal by value{layer}.",
+      "For instance, [location]{named-entity}, Phil_Madeira{instance}. If the terminal is from the lexical layer, the layer is skipped in the denotation.",
+      " $P$ is a set of production rules that represents a relation from $V \\rightarrow (V \\cup E)^*$ . For example,",
+      " $S$ is the starting non-terminal symbol. Since non-terminals represent semantic classes, the starting symbol is chosen based on the semantic class of the input examples. If the input examples are sentences, then the appropriate category may be $\\langle \\text{Relation} \\rangle $ . While if the input examples are noun phrases, the starting symbol may be a more specific category, like $\\langle \\text{Job Title} \\rangle $ .",
+      " $R$ is a set of properties: positive, neutral, negative, non-inducible. The property controls the usage of the rule in the parsing and in the rule induction phase. More details are given in the following subsections."
+    ],
+    [
+      "For parsing, a recursive descent parser with backtracking was developed. This is a top-down parser, which first looks at the higher level sentence structure and then proceeds down the parse tree to identify low level details of the sentence. The advantage of top-down parsing is the ability to partially parse sentences and to detect unparsable parts of sentences.",
+      "The parser takes a layered sentence as an input and returns a semantic tree as an output (see Figure 3 ). The recursive structure of the program closely follows the structure of the parse tree. The recursive function Parse (see Algorithm \"Parser\" ) takes a term and a non-terminal as input and returns a parse node as an output. The parse node contains the class of node (non-terminal), the rule that parsed the node, the term, and the list of children nodes. In order for the rule to parse the node, the left-hand side must match the input non-terminal and the right-hand side must match the layered input. In the pattern matching function Match (line \"Parser\" ), the right hand side of a rule is treated like a regular expression; non-terminals present the ( $+$ ) wildcard characters, which match at least one word. The terminals are treated as literal characters, which are matched against the layer that defines them. The result of successfully matched pattern is a list of terms, where each term represents a non-terminal of the pattern. Due to ambiguity of pattern matching there might be several matches. For each of the term \u2013 non-terminal pair in every list the parse function is recursively called (line \"Parser\" ).",
+      "GetEligibleRulesGetEligibleRules CreateNodeNode MergeSelectBestNode MatchMatch ParseParse",
+      "Grammargrammar Patternpattern NonTerminalsnon terminals",
+      "Rulesrules ChildTreechild node",
+      "Unexpandedinduction nodes",
+      "AListambiguous lists PListterm list Childchild nodes Finalfinal node Ambiguousnodes Inputinput Outputoutput",
+      "FnFunction",
+      "Phrase $p$ , Non-terminal $n$ parse node",
+      " $\\leftarrow $ $\\lbrace \\rbrace $ rule $r$ of n = left side of $r$ $\\leftarrow $ right hand side of $r$ $\\leftarrow $ , $p$ ",
+      "of $\\leftarrow $ $\\lbrace \\rbrace $ $i\\leftarrow 0$ size of $\\leftarrow $ $_i$ , $._i$ add to add $type$ , $p$ , $r$ , to",
+      "is empty $\\leftarrow $ $type$ , $p$ , null, $\\lbrace \\rbrace $ $\\leftarrow $ $\\operatornamewithlimits{arg\\,max}_{n \\in nodes} r(n)$ is not fully parsed add to Pseudocode of the main function parse of the top-down parser.",
+      "Since the grammar is ambiguous, a term can be parsed in multiple ways. There are two types of ambiguity. Two or more rules can expand the same term and one rule can expand the term in more than one way. For each ambiguity one node is created, and the best node according to reliability measure is selected to be the result (line \"Parser\" ). The reliability measure $r(n)$ is ",
+      "$$r(n)=\n{\\left\\lbrace \\begin{array}{ll}\n1, & \\text{if node is fully parsed} \\\\\n\\beta \\cdot (1 -tp(n)) + (1 - \\beta )\\frac{\\displaystyle \\sum \\limits _{c \\in C(n)} |c|\\cdot r(c)}{\\displaystyle \\sum \\limits _{c \\in C(n)} |c|} ,& \\text{if node is partially parsed} \\\\\n0, & \\text{if node is null} \\\\\n\\end{array}\\right.}$$   (Eq. 14) ",
+      "where $tp(n)$ is the trigger probability of the rule that parsed the node $n$ , $\\beta $ is a predefined weight, $C(n)$ is the set of children of $n$ , and $|c|$ is the length of the term of node $c$ . The trigger probability of the rule is the probability that a the right-hand side of the rule pattern matches a random term in the dataset and it is estimated after the rule is induced. The range of the measure is between 0 and 1. The measure was defined in such a way that the more text the node parses, the higher is the reliability (the second summand in the middle row of Eq. 14 ). On the other hand, nodes with rules that are more frequently matched have lower reliability; this penalizes rules that are very loosely defined (the first summand in the middle row of Eq. 14 ). The $\\beta $ parameter was set to 0.05, using grid search, with average F1 score from relation extraction experiment from Section \"Relation extraction\" as a performance measure.",
+      "If none of the rules match the term, a null node is created and added to the list of nodes, which will be later used for grammar induction (line \"Parser\" ). Note that even if a null node is discarded, because it is not the most reliable, it will still be used in the grammar induction step. A node is fully parsed if the node itself and all of its descendants are parsed. If a node is parsed and if at least one of its descendants is not parsed, then the node is partially parsed. All nodes that are not fully parsed are added to the list for induction.",
+      "Since the ambiguity of the grammar may make parsing computationally infeasible, several optimization techniques are used. Memoization BIBREF10 is used to reduce the complexity from exponential time to $\\mathcal {O}(n^3)$ BIBREF11 , where $n$ is the length of the sentence. The parser does not support $\\epsilon $ productions mainly because the grammar induction will not produce them. The patterns that do not contain terminals are the most ambiguous. At most two non-terminals are allowed, and the maximal length of the term that corresponds to the first non-terminal is three tokens. We argue that this is not a huge limitation, since the way human languages are structured, usually two longer terms are connected with a word, like comma or a verb. Furthermore, the way how our induction works, these connectors do not get generalized and become a terminal in the rule. There was an attempt to introduce rules with negative property. Whenever such rule fully parses a node, that indicates that the current parsing path is incorrect. This allows the parser to backtrack sooner and also prevents adding null sister nodes (null sister nodes are in this case usually wrong) to the rule induction. However, it turned out that negative rules actually slow down the parsing, since the grammar gets bigger. It is better to mark these rules as neutral, therefore they are not added to the grammar."
+    ],
+    [
+      "The goal of the rule induction step is to convert the null nodes from the parsing step into rules. Out of these rules, the most frequent one is promoted. The term from the null node is generalized to form the right side of the rule. The class non-terminal of the null node will present the left side of the rule. Recently induced rule will parse all the nodes, from which it was induced, in the following iterations. Additionally, some rules may parse the children of those nodes.",
+      "Generalization is done in two steps. First, terms are generalized on the layer level. The output of this process is a sequence of tokens, which might be from different layers. For each position in the term a single layer is selected, according to predefined layer order. In the beginning, term is generalized with the first layer. All the non-null tokens from this layer are taken to be part of the generalized term. All the positions of the term that have not been generalized are attempted to be generalized with the next layer, etc. The last layer is without null-tokens, therefore each position of the term is assigned a layer. Usually, this is the lexical layer. For example, top part of Table 2 shows generalization of term from Table 1 . The layer list is constructed manually. Good layers for generalization are typically those that express semantic classes of individual terms. Preferably, these types are not too general (loss of information) and not too specific (larger grammar).",
+      "In the next step of generalization, tokens are further generalized using a greedy bottom-up parser using the rules from the grammar. The right sides of all the rules are matched against the input token term. If there is a match, the matched sub-term is replaced with the left side of the rule. Actually, in each iteration all the disjunct matches are replaced. To get only the disjunct matches, overlapping matches are discarded greedily, where longer matches have the priority. This process is repeated until no more rules match the term. An example is presented in the lower part of Table 2 .",
+      "The bottom-up parsing algorithm needs to be fast because the number of unexpanded nodes can be very high due to ambiguities in the top-down parsing. Consequently, the algorithm is greedy, instead of exhaustive, and yields only one result. Aho-Corasick string matching algorithm BIBREF12 is selected for matching for its ability to match all the rules simultaneously. Like the top-down parser, this parser generates partial parses because the bottom-up parser will never fully parse \u2013 the output is the same as the non-terminal type in the unexpanded node. This would generate a cyclical rule, i.e. $<$ Class $>$ :== $<$ Class $>$ . However, this never happens because the top-down parser would already expand the null node.",
+      "The last step of the iteration is assigning the property to the newly induced rule. Property controls the role of the rule in the parsing and induction. The default property is positive, which defines the default behavior of the rule in all procedures. Rules with neutral property are not used in any procedure. They also cannot be re-induced. Some rules are good for parsing, but may introduce errors in the induction. These rules should be given non-inducible property. For instance, rule $<$ Date $>$ :== $<$ Number $>$ is a candidate for the non-inducible property, since years are represented by a single number. On the contrary, not every number is a date.",
+      "In our experiments, the assignment was done manually. The human user sees the induced rule and few examples of the null nodes, from which it was induced. This should provide enough information for the user to decide in a few seconds, which property to assign. After the stopping criteria is met, the iterative procedure can continue automatically by assigning positive property to each rule. Initial experimenting showed that just a single mistake in the assignment can cause a huge drift, making all further rules wrong."
+    ],
+    [
+      "Before the start, a list of seed rules may be needed in order for grammar induction to be successful. Since this step is done manually, it is reasonable to have a list of seed rules short and efficient. Seed rules can be divided in three groups: domain independent linguistic rules, class rules, top-level domain rules. Domain independent linguistic rules, such as",
+      "parse the top and mid-level nodes. They can be applied on many different datasets. Class rules connect class tokens, like named-entity tokens with non-terminals. For example,",
+      "They parse the leaf nodes of the trees. On the other hand, top-level domain rules, define the basic structure of the sentence. For example,",
+      "As the name suggests, they parse nodes close to the root. Altogether, these rule groups parse on all levels of the tree, and may already be enough to parse the most basic sentences, but more importantly, they provide the basis for learning to parse more complex sentences.",
+      "The decision on which and how many seed rules should be defined relies on human judgment whether the current set of seed rules is powerful enough to ignite the bootstrapping procedure. This judgment may be supported by running one iteration and inspecting the top induced rules."
+    ],
+    [
+      "This section describes how to utilize the grammar and manipulate semantic trees to discover ontology components in the textual data."
+    ],
+    [
+      "We propose a procedure for mapping grammar components to ontology components. In particular, classes, instances and taxonomic relations are extracted.",
+      "First, we distinguish between instances and classes in the grammar. Classes are represented by all non-terminals and terminals that come from a layer populated with classes, for example, named-entity layer and class layer from Table 1 . Instances might already exist in the instance layer, or they are created from rules, whose right hand side contains only tokens from the lexical layer. These tokens represent the label of the new instance. For instance rule $<$ Profession $>$ ::= software engineer is a candidate for instance extraction.",
+      "Furthermore, we distinguish between class and instance rules. Class rules have a single symbol representing a class on the right-hand side. Class rules map to subClassOf relations in the ontology. If the rule is positive, then the class on the right side is the subclass of the class on the left side. For instance, rule $<$ Organization $>$ ::= $<$ Company $>$ yields relation (subClassOf Company Organization).",
+      "On the other hand, instance rules have one or more symbols representing an instance on the right side, and define the isa relation. If the rule is positive, then the instance on the right side is a member of a class on the left side. For instance, rule $<$ Profession $>$ ::= software engineer yields relation (isa SoftwareEngineer Profession). If class or instance rule is neutral then the relation can be treated as false. Note that many other relations may be inferred by combing newly induced relations and relations from the seed ontology. For instance, induced relation (subClassOf new-class seed-class) and seed relation (isa seed-class seed-instance) are used to infer a new relation (isa new-class seed-instance).",
+      "In this section, we described how to discover relations on the taxonomic level. In the next section, we describe how to discover relations between instances."
+    ],
+    [
+      "We propose a method for learning relations from semantic trees, which tries to solve the same problem as the classical relation extraction methods. Given a dataset of positive relation examples that represent one relation type, e.g. birthPlace, the goal is to discover new unseen relations.",
+      "The method is based on the assumption that a relation between entities is expressed in the shortest path between them in the semantic tree BIBREF13 . The input for training are sentences in layered representation, corresponding parse trees, and relation examples. Given a relation from the training set, we first try to identify the sentence containing each entity of the relation. The relation can have one, two, or even more entities. Each entity is matched to the layer that corresponds to the entity type. For example, strings are matched to the lexical layer; ontology entities are matched to the layer containing such entities. The result of a successfully matched entity is a sub-term of the sentence. In the next step, the corresponding semantic tree is searched for a node that contains the sub-term.",
+      "At this point, each entity has a corresponding entity node. Otherwise, the relation is discarded from the learning process. Given the entity nodes, a minimum spanning tree containing all off them is extracted. If there is only one entity node, then the resulting subtree is the path between this node and the root node. The extracted sub-tree is converted to a variable tree, so that different semantic trees can have the same variable sub-trees, for example see Figure 4 . The semantic nodes of the sub-tree are converted into variable nodes, by retaining the class and the rule of the node, as well as the places of the children in the original tree. For entity nodes also the position in the relation is memorized. Variable tree extracted from a relation is a positive example in the training process. For negative examples all other sub-trees that do not present any relations are converted to variable trees. Each variable node represents one feature. Therefore, a classification algorithm, such as logistic regression can be used for training.",
+      "When predicting, all possible sub-trees of the semantic tree are predicted. If a sub-tree is predicted as positive, then the terms in the leaf nodes represent the arguments of the relation."
+    ],
+    [
+      "In this section, we present experiments evaluating the proposed approach. We have conducted experimentation on Wikipedia\u2013DBpedia dataset (Section \"Datasets\" ). First, we have induced a grammar on the Wikipedia dataset (Section \"Grammar Induction Experiments\" ) to present its characteristics, and the scalability of the approach. In the next experiment, we present a method for discovering less prominent instances (Section \"Instance extraction\" ). The last experiment demonstrates one application of semantic parsing \u2013 the supervised learning of DBpedia relations(Section \"Relation extraction\" )."
+    ],
+    [
+      "The datasets for experiments were constructed from English Wikipedia and knowledge bases DBpedia BIBREF9 and Freebase BIBREF6 . DBpedia provides structured information about Wikipedia articles that was scraped out of their infoboxes. First sentences of Wikipedia pages describing people were taken as the textual dataset, while DBpedia relations expressing facts about the same people were taken as the dataset for supervised relation learning. Note that each DBpedia instance has a Wikipedia page. A set of person instances was identified by querying DBpedia for instances that have a person class. For the textual dataset, Wikipedia pages representing these entities were parsed by the in-house Wikipedia markup parser to convert the markup into plain text. Furthermore, the links to other Wikipedia pages were retained. Here is an example of a sentence in plain text:",
+      "Victor Francis Hess (24 June 1883 \u2013 17 December 1964) was an Austrian-American physicist, and Nobel laureate in physics, who discovered cosmic rays.",
+      "Using the Standford OpenNLP BIBREF14 on plain texts we obtained sentence and token splits, and named-entity annotation. Notice, that only the first sentence of each page was retained and converted to the proposed layered representation (see Section \"Experiments\" ). The layered representation contains five layers: lexical (plain text), named-entity (named entity recognizer), wiki-link (Wikipedia page in link \u2013 DBpedia instance), dbpedia-class (class of Wikipedia page in Dbpedia) and freebase-class (class of Wikipedia page in Freebase). Freebase also contains its own classes of Wikipedia pages. For the last two layers, there might be several classes per Wikipedia page. Only one was selected using a short priority list of classes. If none of the categories is on the list, then the category is chosen at random. After comparing the dbpedia-class and freebase-class layers, only freebase-class was utilized in the experiments because more wiki-link tokens has a class in freebase-class layer than in dbpedia-class layer.",
+      "There are almost 1.1 million sentences in the collection. The average length of a sentence is 18.3 words, while the median length is 13.8 words. There are 2.3 links per sentence.",
+      "The dataset for supervised relation learning contains all relations where a person instance appears as a subject in DBpedia relation. For example,",
+      "dbpedia:Victor_Francis_Hess dbpedia-owl:birthDate 1883-06-24",
+      "There are 119 different relation types (unique predicates), having from just a few relations to a few million relations. Since DBpedia and Freebase are available in RDF format, we used the RDF store for querying and for storage of existing and new relations."
+    ],
+    [
+      "The grammar was induced on 10.000 random sentences taken from the dataset described in Section \"Datasets\" . First, a list of 45 seed nodes was constructed. There were 22 domain independent linguistic rules, 17 category rules and 6 top-level rules. The property assignment was done by the authors. In every iteration, the best rule is shown together with the number of nodes it was induced from, and ten of those nodes together with the sentences they appear in. The goal was set to stop the iterative process after two hours. We believe this is the right amount of time to still expect quality feedback from a human user.",
+      "There were 689 new rules created. A sample of them is presented in Table 3 . Table 4 presents the distributions of properties. Around $36 \\%$ of rules were used for parsing (non neutral rules). Together with the seed rules there are 297 rules used for parsing. Different properties are very evenly dispersed across the iterations. Using the procedure for conversion of grammar rules into taxonomy presented in Section \"Ontology induction\" , 33 classes and subClassOf relations, and 95 instances and isa relations were generated.",
+      "The grammar was also tested by parsing a sample of 100.000 test sentences. A few statistic are presented in Table 4 . More than a quarter of sentences were fully parsed, meaning that they do not have any null leaf nodes. Coverage represents the fraction of words in a sentence that were parsed (words that are not in null-nodes). The number of operations shows how many times was the Parse function called during the parsing of a sentences. It is highly correlated with the time spend for parsing a sentence, which is on average 0.16ms. This measurement was done on a single CPU core. Consequently, it is feasible to parse a collection of a million sentences, like our dataset. The same statistics were also calculated on the training set, the numbers are very similar to the test set. The fully parsed % and coverage are even slightly lower than on the test set. Some of the statistics were calculated after each iteration, but only when a non neutral rule was created. The graphs in Figure 5 show how have the statistics changed over the course of the grammar induction. Graph 5 shows that coverage and the fraction of fully parsed sentences are correlated and they grow very rapidly at the beginning, then the growth starts to slow down, which indicates that there is a long tail of unparsed nodes/sentences. In the following section, we present a concept learning method, which deals with the long tail. Furthermore, the number of operations per sentence also slows down (see Graph 5 ) with the number of rules, which gives a positive sign of retaining computational feasibility with the growth of the grammar. Graph 5 somewhat elaborates the dynamics of the grammar induction. In the earlier phase of induction many rules that define the upper structure of the tree are induced. These rules can rapidly increase the depth and number of null nodes, like rule 1 and rule 2 . They also explain the spikes on Graph 5 . Their addition to the grammar causes some rules to emerge on the top of the list with a significantly higher frequency. After these rules are induced the frequency gets back to the previous values and slowly decreases over the long run."
+    ],
+    [
+      "In this section, we present an experiment with a method for discovering new instances, which appear in the long tail of null nodes. Note that the majority of the instances were already placed in the ontology by the method in Section \"Ontology induction from grammar\" . Here, less prominent instances are extracted to increase the coverage of semantic parsing. The term and the class of the null node will form an isa relation. The class of the node represents the class of the relation. The terms are converted to instances. They are first generalized on the layer level (see Section \"Experiments\" ). The goal is to exclude non-atomic terms, which do not represent instances. Therefore, only terms consisting of one wiki-link token or exclusively of lexical tokens are retained. The relations were sorted according to their frequency. We observe that accuracy of the relations drops with the frequency. Therefore, relations that occurred less than three times were excluded. The number and accuracy for six classes is reported in Table 5 . Other classes were less accurate. For each class, the accuracy was manually evaluated on a random sample of 100 instance relations. Taking into account the estimated accuracy, there were more than 13.000 correct isa relations."
+    ],
+    [
+      "In this section, we present an experiment of the relation extraction methods presented in Section \"Relation extraction from semantic trees\" . The input for the supervision is the DBpedia relation dataset from Section \"Datasets\" . The subject (first argument) of every relation is a person DBpedia instance \u2013 person Wikipedia page. In the beginning, the first sentence of that Wikipedia page has been identified in the textual dataset. If the object (last argument) of this relation matches a sub-term of this sentence, then the relation is eligible for experiments. We distinguish three types of values in objects. DBpedia resources are matched with wiki-link layer. Dates get converted to the format that is used in English Wikipedia. They are matched against the lexical layer, and so are the string objects.",
+      "Only relation types that have 200 or more eligible relations have been retained. This is 74 out of 119 relations. The macro average number of eligible relations per relation type is 17.7%. While the micro average is 23.8%, meaning that roughly a quarter of all DBpedia person relations are expressed in the first sentence of their Wikipedia page. For the rest of this section, all stated averages are micro-averages.",
+      "The prediction problem is designed in the following way. Given the predicate (relation type) and the first argument of the relation (person), the model predicts the second argument of the relation (object). Because not all relations are functional, like for instance child relation, there can be several values per predicate\u2013person pair; on average there are 1.1. Since only one argument of the relation is predicted, the variable trees presented in Section \"Relation extraction from semantic trees\" , will be paths from the root to a single node. Analysis of variable tree extraction shows that on average 60.8% of eligible relations were successfully converted to variable trees (the object term exactly matches the term in the node). Others were not converted because 8.2% of the terms were split between nodes and 30.9% terms are sub-terms in nodes instead of complete terms. Measuring the diversity of variable trees shows that a distinct variable tree appeared 2.7 times on average.",
+      "Several models based on variable trees were trained for solving this classification problem:",
+      "Basic (Basic model) \u2013 The model contains positive trained variable trees. In the prediction, if the test variable tree matches one of the trees in the model, then the example is predicted positive.",
+      "Net (Automaton model) \u2013 All positive variable trees are paths with start and end points. In this model they are merged into a net, which acts as a deterministic automaton. If the automaton accepts the test variable tree, than it is predicted positive. An example of automaton model is presented in Figure 6 .",
+      "LR (Logistic regression) \u2013 A logistic regression model is trained with positive and negative examples, where nodes in variable trees represents features.",
+      "LRC (Logistic regression + Context nodes) \u2013 All leaf nodes that are siblings of any of the nodes in the variable tree are added to the LR model.",
+      "LRCL (Logistic regression + Context nodes + Lexical Tokens) \u2013 Tokens from the lexical layer of the entity nodes are added to the LRC as features.",
+      "For training all or a maximum of 10.000 eligible relations was taken for each of 74 relation types. A 10-fold cross validation was performed for evaluation. The results are presented in Table 6 . The converted recall and converted F1 score presents recall and F1 on converted examples, which are the one, where relations were successfully converted into variable trees. The performance increases with each model, however the interpretability decreases. We also compared our method to the conditional random fields(CRF). In the CRF method, tokens from all layers with window size 7 were taken as features for sequence prediction. On the converted examples CRF achieved F1 score of 80.8, which is comparable to our best model's (LRCL) F1 score of 80.0."
+    ],
+    [
+      "There are many known approaches to ontology learning and semantic parsing, however, to the best of our knowledge, this is the first work to jointly learn an ontology and semantic parser. In the following sections, we make comparisons to other work on semantic parsing, ontology learning, grammar induction and others."
+    ],
+    [
+      "The goal of semantic parsing is to map text to meaning representations. Several approaches have used Combinatory categorial grammar (CCG) and lambda calculus as a meaning representation BIBREF15 , BIBREF16 . CCG grammar closely connects syntax and semantics with a lexicon, where each entry consist of a term, a syntactical category and a lambda statement. Similarly, our context-free grammar contains production rules. Some of these rules do not contain lexical tokens (the grammar is not lexicalized), which gives ability to express some relations with a single rule. For instance, to parse jazz drummer, rule $<$ Musician_Type $>$ ::= $<$ Musical_Genre $>$ $<$ Musician_Type $>$ is used to directly express the relation, which determines the genre of the musician. Lambda calculus may provide a more formal meaning representation than semantic trees, but the lexicon of CCG requires mappings to lambda statements. Other approaches use dependency-based compositional semantics BIBREF17 , ungrounded graphs BIBREF18 , etc. as meaning representations.",
+      "Early semantic parsers were trained on datasets, such as Geoquery BIBREF19 and Atis BIBREF5 , that map sentences to domain-specific databases. Later on datasets for question answering based on Freebase were created \u2013 Free917 BIBREF4 and WebQuestions BIBREF20 These datasets contain short questions from multiple domains, and since the meaning representations are formed of Freebase concepts, they allow reasoning over Freebase's ontology, which is much richer than databases in GeoQuery and Atis. All those datasets were constructed by either forming sentences given the meaning representation or vice-versa. Consequently, systems that were trained and evaluated on these datasets, might not work on sentences that cannot be represented by the underlying ontology. To overcome this limitation BIBREF16 developed a open vocabulary semantic parser. Their approach uses a CCG parser on questions to from labmda statements, which besides Freebase vocabulary contain underspecified predicates. These lambda statements are together with answers \u2013 Freebase entities \u2013 used to learn a low-dimensional probabilistic database, which is then used to answer fill-in-the-blank natural language questions. In a very similar fashion, BIBREF21 defines underspecified entities, types and relations, when the corresponding concept does not exist in Freebase. In contrast, the purpose of our method is to identify new concepts and ground them in the ontology."
+    ],
+    [
+      "Many ontology learning approaches address the same ontology components as our approach. However, their goal is to learn only the salient concepts for a particular domain, while our goal is to learn all the concepts (including instances, like particular organizations), so that they can be used in the meaning representation. As survey by BIBREF22 summarizes, the learning mechanisms are based either on statistics, linguistics, or logic. Our approach is unique because part of our ontology is constructed from the grammar. Many approaches use lexico-syntactic patterns for ontology learning. These are often based on dependency parses, like in BIBREF2 , BIBREF23 . Our approach does not rely on linguistic preprocessing, which makes it suitable for non-standard texts and poorly resourced languages. Our approach also build patterns, however in form of grammar rules. Instead of lexico-syntactic patterns, which contain linguistic classes, our approach models semantic patterns, which contain semantic classes, like Person and Color. These patterns are constructed in advance, which is sometimes difficult because the constructor is not always aware of all the phenomena that is expressed in the input text. Our approach allows to create a small number of seed patterns in advance, then explore other patterns through process of grammar learning. A similar bootstrapping semi-automatic approach to ontology learning was developed in BIBREF24 , where the user validates lexicalizations of a particular relation to learn new instances, and in BIBREF25 , where the user validates newly identified terms, while in our approach the user validates grammar rules to learn the composition of whole sentences. A similar approach with combining DBpedia with Wikipedia for superised learning has been taken in BIBREF26 , however their focus is more on lexicalization of relations and classes."
+    ],
+    [
+      "Related work linking short terms to ontology concepts BIBREF34 is designed similarly as our approach in terms of bootstrapping procedure to induce patterns. But instead of inducing context-free grammar production rules, suggestions for rewrite rules that transform text directly to ontology language are provided. Another bootstrapping semi-automatic approach was developed for knowledge base population BIBREF35 . The task of knowledge base population is concerned only with extracting instances and relations given the ontology. In our work we also extract the backbone of the ontology \u2013 classes and taxonomic relations. Also, many other approaches focus only on one aspect of knowledge extraction, like taxonomy extraction BIBREF36 , BIBREF37 or relation extraction BIBREF13 , BIBREF38 . Combining these approaches can lead to cumbersome concept matching problems. This problem was also observed by BIBREF39 . Their system OntoUSP tries to overcome this by unsupervised inducing and populating a probabilistic grammar to solve question answering problem. However, the result are logical-form clusters connected in an isa hierarchy, not grounded concepts, which are connected with an existing ontology."
+    ],
+    [
+      "We have presented an approach for joint ontology learning and semantic parsing. The approach was evaluated by building an ontology representing biographies of people. The first sentences of person Wikipedia pages and the combination of DBpedia and Freebase were used as a dataset. This dataset was suitable for our approach, because the text is equipped with human tagged annotations, which are already linked to the ontology. In other cases a named entity disambiguation would be needed to obtain the annotations. The next trait of the dataset, that is suitable for our approach, is the homogeneous style of writing. Otherwise, if the style was more heterogeneous, the users would have to participate in more iterations to achieve the same level of coverage. The participation of the users may be seen a cost, but on the other hand it allows them to learn about the dataset without reading it all. The users does not learn so much about specific facts as they learn about the second order information, like what types of relations are expressed and their distribution.",
+      "Semantic trees offer a compact tree-structured meaning representation, which could be exploited for scenarios not covered by this paper, like relation type discovery and question answering. Furthermore, they can be used for more interpretable representation of meaning, like the automaton representation in Figure 6 , compared to some other methods, like the one based on neural networks BIBREF40 . Our approach may not be superior on one specific part of the ontology learning, but it rather provides an integrated approach for learning on several levels of the ontology. Also, our approach does not use syntactic analysis, like part of speech tags or dependency parsing, which makes our approach more language independent and useful for non-standard texts, where such analysis is not available. On the other hand, we are looking into integrating syntactic analysis for future work. One scenario is to automatically detect the property of the rule. Another idea for future work is to integrate some ideas from other grammar induction methods to detect meaningful patterns without relying on the annotation of text.",
+      "This work was supported by Slovenian Research Agency and the ICT Programme of the EC under XLike (FP7-ICT-288342-STREP) and XLime (FP7-ICT-611346)."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: A Continuous Relaxation of Beam Search for End-to-end Training of Neural Sequence Models
+
+Question: By how much do they outperform basic greedy and cross-entropy beam decoding?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Model",
+    "Discontinuity in Beam Search",
+    "Continuous Approximation to top-k-argmax ",
+    "Training with Continuous Relaxation of Beam Search ",
+    "Decoding ",
+    "Comparison with Max-Margin Objectives ",
+    "Experimental Setup",
+    "Named Entity Recognition",
+    "CCG Supertagging",
+    "Hyperparameter tuning",
+    "Comparison",
+    "Results",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "[t] Standard Beam Search [1] INLINEFORM0 , INLINEFORM1 , INLINEFORM2 , INLINEFORM3 t = 0 to T i = 1 to k INLINEFORM4 INLINEFORM5 INLINEFORM6 is the local output scoring function INLINEFORM7 top-k-max INLINEFORM8 Top k values of the input matrix INLINEFORM9 top-k-argmax INLINEFORM10 Top INLINEFORM11 argmax index pairs of the input matrix i = 1 to k INLINEFORM12 embedding( INLINEFORM13 ) INLINEFORM14 INLINEFORM15 is a nonlinear recurrent function that returns state at next step INLINEFORM16 INLINEFORM17 follow-backpointer( INLINEFORM18 ) INLINEFORM19 Sequence-to-sequence (seq2seq) models have been successfully used for many sequential decision tasks such as machine translation BIBREF0 , BIBREF1 , parsing BIBREF2 , BIBREF3 , summarization BIBREF4 , dialog generation BIBREF5 , and image captioning BIBREF6 . Beam search is a desirable choice of test-time decoding algorithm for such models because it potentially avoids search errors made by simpler greedy methods. However, the typical approach to training neural sequence models is to use a locally normalized maximum likelihood objective (cross-entropy training) BIBREF0 . This objective does not directly reason about the behaviour of the final decoding method. As a result, for cross-entropy trained models, beam decoding can sometimes yield reduced test performance when compared with greedy decoding BIBREF7 , BIBREF8 , BIBREF9 . These negative results are not unexpected. The training procedure was not search-aware: it was not able to consider the effect that changing the model's scores might have on the ease of search while using a beam decoding, greedy decoding, or otherwise.",
+      "We hypothesize that the under-performance of beam search in certain scenarios can be resolved by using a better designed training objective. Because beam search potentially offers more accurate search when compared to greedy decoding, we hope that appropriately trained models should be able to leverage beam search to improve performance. In order to train models that can more effectively make use of beam search, we propose a new training procedure that focuses on the final loss metric (e.g. Hamming loss) evaluated on the output of beam search. While well-defined and a valid training criterion, this \u201cdirect loss\u201d objective is discontinuous and thus difficult to optimize. Hence, in our approach, we form a sub-differentiable surrogate objective by introducing a novel continuous approximation of the beam search decoding procedure. In experiments, we show that optimizing this new training objective yields substantially better results on two sequence tasks (Named Entity Recognition and CCG Supertagging) when compared with both cross-entropy trained greedy decoding and cross-entropy trained beam decoding baselines.",
+      "Several related methods, including reinforcement learning BIBREF10 , BIBREF11 , imitation learning BIBREF12 , BIBREF13 , BIBREF14 , and discrete search based methods BIBREF15 , BIBREF16 , BIBREF17 , BIBREF18 , have also been proposed to make training search-aware. These methods include approaches that forgo direct optimization of a global training objective, instead incorporating credit assignment for search errors by using methods like early updates BIBREF19 that explicitly track the reachability of the gold target sequence during the search procedure. While addressing a related problem \u2013 credit assignment for search errors during training \u2013 in this paper, we propose an approach with a novel property: we directly optimize a continuous and global training objective using backpropagation. As a result, in our approach, credit assignment is handled directly via gradient optimization in an end-to-end computation graph. The most closely related work to our own approach was proposed by Goyal et al. BIBREF20 . They do not consider beam search, but develop a continuous approximation of greedy decoding for scheduled sampling objectives. Other related work involves training a generator with a Gumbel reparamterized sampling module to more reliably find the MAP sequences at decode-time BIBREF21 , and constructing surrogate loss functions BIBREF22 that are close to task losses."
+    ],
+    [
+      "We denote the seq2seq model parameterized by INLINEFORM0 as INLINEFORM1 . We denote the input sequence as INLINEFORM2 , the gold output sequence as INLINEFORM3 and the result of beam search over INLINEFORM4 as INLINEFORM5 . Ideally, we would like to directly minimize a final evaluation loss, INLINEFORM6 , evaluated on the result of running beam search with input INLINEFORM7 and model INLINEFORM8 . Throughout this paper we assume that the evaluation loss decomposes over time steps INLINEFORM9 as: INLINEFORM10 . We refer to this idealized training objective that directly evaluates prediction loss as the \u201cdirect loss\u201d objective and define it as: DISPLAYFORM0 ",
+      " Unfortunately, optimizing this objective using gradient methods is difficult because the objective is discontinuous. The two sources of discontinuity are:",
+      "We introduce a surrogate training objective that avoids these problems and as a result is fully continuous. In order to accomplish this, we propose a continuous relaxation to the composition of our final loss metric, INLINEFORM0 , and our decoder function, INLINEFORM1 : INLINEFORM2 ",
+      "Specifically, we form a continuous function softLB that seeks to approximate the result of running our decoder on input INLINEFORM0 and then evaluating the result against INLINEFORM1 using INLINEFORM2 . By introducing this new module, we are now able to construct our surrogate training objective: DISPLAYFORM0 ",
+      "Specified in more detail in Section SECREF9 , our surrogate objective in Equation 2 will additionally take a hyperparameter INLINEFORM0 that trades approximation quality for smoothness of the objective. Under certain conditions, Equation 2 converges to the objective in Equation 1 as INLINEFORM1 is increased. We first describe the standard discontinuous beam search procedure and then our training approach (Equation 2) involving a continuous relaxation of beam search."
+    ],
+    [
+      "[t] continuous-top-k-argmax [1] INLINEFORM0 INLINEFORM1 , s.t. INLINEFORM2 INLINEFORM3 INLINEFORM4 = 1 to k peaked-softmax will be dominated by scores closer to INLINEFORM5 INLINEFORM6 The square operation is element-wise Formally, beam search is a procedure with hyperparameter INLINEFORM7 that maintains a beam of INLINEFORM8 elements at each time step and expands each of the INLINEFORM9 elements to find the INLINEFORM10 -best candidates for the next time step. The procedure finds an approximate argmax of a scoring function defined on output sequences.",
+      "We describe beam search in the context of seq2seq models in Algorithm SECREF1 \u2013 more specifically, for an encoder-decoder BIBREF0 model with a nonlinear auto-regressive decoder (e.g. an LSTM BIBREF23 ). We define the global model score of a sequence INLINEFORM0 with length INLINEFORM1 to be the sum of local output scores at each time step of the seq2seq model: INLINEFORM2 . In neural models, the function INLINEFORM3 is implemented as a differentiable mapping, INLINEFORM4 , which yields scores for vocabulary elements using the recurrent hidden states at corresponding time steps. In our notation, INLINEFORM5 is the hidden state of the decoder at time step INLINEFORM6 for beam element INLINEFORM7 , INLINEFORM8 is the embedding of the output symbol at time-step INLINEFORM9 for beam element INLINEFORM10 , and INLINEFORM11 is the cumulative model score at step INLINEFORM12 for beam element INLINEFORM13 . In Algorithm SECREF1 , we denote by INLINEFORM14 the cumulative candidate score matrix which represents the model score of each successor candidate in the vocabulary for each beam element. This score is obtained by adding the local output score (computed as INLINEFORM15 ) to the running total of the score for the candidate. The function INLINEFORM16 in Algorithms SECREF1 and SECREF7 yields successive hidden states in recurrent neural models like RNNs, LSTMs etc. The INLINEFORM17 operation maps a word in the vocabulary INLINEFORM18 , to a continuous embedding vector. Finally, backpointers at each time step to the beam elements at the previous time step are also stored for identifying the best sequence INLINEFORM19 , at the conclusion of the search procedure. A backpointer at time step INLINEFORM20 for a beam element INLINEFORM21 is denoted by INLINEFORM22 which points to one of the INLINEFORM23 elements at the previous beam. We denote a vector of backpointers for all the beam elements by INLINEFORM24 . The INLINEFORM25 operation takes as input backpointers ( INLINEFORM26 ) and candidates ( INLINEFORM27 ) for all the beam elements at each time step and traverses the sequence in reverse (from time-step INLINEFORM28 through 1) following backpointers at each time step and identifying candidate words associated with each backpointer that results in a sequence INLINEFORM29 , of length INLINEFORM30 .",
+      "The procedure described in Algorithm SECREF1 is discontinuous because of the top-k-argmax procedure that returns a pair of vectors corresponding to the INLINEFORM0 highest-scoring indices for backpointers and vocabulary items from the score matrix INLINEFORM1 . This index selection results in hard backpointers at each time step which restrict the gradient flow during backpropagation. In the next section, we describe a continuous relaxation to the top-k-argmax procedure which forms the crux of our approach."
+    ],
+    [
+      "[t] Continuous relaxation to beam search [1] INLINEFORM0 , INLINEFORM1 , INLINEFORM2 , INLINEFORM3 , INLINEFORM4 t = 0 to T INLINEFORM5 i=1 to k INLINEFORM6 INLINEFORM7 is a local output scoring function INLINEFORM8 INLINEFORM9 is used to compute INLINEFORM10 INLINEFORM11 Call Algorithm 2 i = 1 to k INLINEFORM12 Soft back pointer computation INLINEFORM13 Contribution from vocabulary items INLINEFORM14 Peaked distribution over the candidates to compute INLINEFORM15 INLINEFORM16 INLINEFORM17 INLINEFORM18 j = 1 to k Get contributions from soft backpointers for each beam element INLINEFORM19 INLINEFORM20 INLINEFORM21 INLINEFORM22 is a nonlinear recurrent function that returns state at next step INLINEFORM23 Pick the loss for the sequence with highest model score on the beam in a soft manner.",
+      "The key property that we use in our approximation is that for a real valued vector INLINEFORM0 , the argmax with respect to a vector of scores, INLINEFORM1 , can be approximated by a temperature controlled softmax operation. The argmax operation can be represented as: INLINEFORM2 ",
+      " which can be relaxed by replacing the indicator function with a peaked-softmax operation with hyperparameter INLINEFORM0 : INLINEFORM1 ",
+      " As INLINEFORM0 , INLINEFORM1 so long as there is only one maximum value in the vector INLINEFORM2 . This peaked-softmax operation has been shown to be effective in recent work BIBREF24 , BIBREF25 , BIBREF20 involving continuous relaxation to the argmax operation, although to our knowledge, this is the first work to apply it to approximate the beam search procedure.",
+      "Using this peaked-softmax operation, we propose an iterative algorithm for computing a continuous relaxation to the top-k-argmax procedure in Algorithm SECREF6 which takes as input a score matrix of size INLINEFORM0 and returns INLINEFORM1 peaked matrices INLINEFORM2 of size INLINEFORM3 . Each matrix INLINEFORM4 represents the index of INLINEFORM5 -th max. For example, INLINEFORM6 will have most of its mass concentrated on the index in the matrix that corresponds to the argmax, while INLINEFORM7 will have most of its mass concentrated on the index of the 2nd-highest scoring element. Specifically, we obtain matrix INLINEFORM8 by computing the squared difference between the INLINEFORM9 -highest score and all the scores in the matrix and then using the peaked-softmax operation over the negative squared differences. This results in scores closer to the INLINEFORM10 -highest score to have a higher mass than scores far away from the INLINEFORM11 -highest score.",
+      "Hence, the continuous relaxation to top-k-argmax operation can be simply implemented by iteratively using the max operation which is continuous and allows for gradient flow during backpropagation. As INLINEFORM0 , each INLINEFORM1 vector converges to hard index pairs representing hard backpointers and successor candidates described in Algorithm SECREF1 . For finite INLINEFORM2 , we introduce a notion of a soft backpointer, represented as a vector INLINEFORM3 in the INLINEFORM4 -probability simplex, which represents the contribution of each beam element from the previous time step to a beam element at current time step. This is obtained by a row-wise sum over INLINEFORM5 to get INLINEFORM6 values representing soft backpointers."
+    ],
+    [
+      "We describe our approach in detail in Algorithm 3 and illustrate the soft beam recurrence step in Figure 1. For composing the loss function and the beam search function for our optimization as proposed in Equation 2, we make use of decomposability of the loss function across time-steps. Thus for a sequence y, the total loss is: INLINEFORM0 . In our experiments, INLINEFORM1 is the Hamming loss which can be easily computed at each time-step by simply comparing gold INLINEFORM2 with INLINEFORM3 . While exact computation of INLINEFORM4 will vary according to the loss, our proposed procedure will be applicable as long as the total loss is decomposable across time-steps. While decomposability of loss is a strong assumption, existing literature on structured prediction BIBREF26 , BIBREF27 has made due with this assumption, often using decomposable losses as surrogates for non-decomposable ones. We detail the continuous relaxation to beam search in Algorithm SECREF7 with INLINEFORM5 being the cumulative loss of beam element INLINEFORM6 at time step INLINEFORM7 and INLINEFORM8 being the embedding matrix of the target vocabulary which is of size INLINEFORM9 where INLINEFORM10 is the size of the embedding vector.",
+      "In Algorithm SECREF7 , all the discrete selection functions have been replaced by their soft, continuous counterparts which can be backpropagated through. This results in all the operations being matrix and vector operations which is ideal for a GPU implementation. An important aspect of this algorithm is that we no longer rely on exactly identifying a discrete search prediction INLINEFORM0 since we are only interested in a continuous approximation to the direct loss INLINEFORM1 (line 18 of Algorithm SECREF7 ), and all the computation is expressed via the soft beam search formulation which eliminates all the sources of discontinuities associated with the training objective in Equation 1. The computational complexity of our approach for training scales linearly with the beam size and hence is roughly INLINEFORM2 times slower than standard CE training for beam size INLINEFORM3 . Since we have established the pointwise convergence of peaked-softmax to argmax as INLINEFORM4 for all vectors that have a unique maximum value, we can establish pointwise convergence of objective in Equation 2 to objective in Equation 1 as INLINEFORM5 , as long as there are no ties among the top-k scores of the beam expansion candidates at any time step. We posit that absolute ties are unlikely due to random initialization of weights and the domain of the scores being INLINEFORM6 . Empirically, we did not observe any noticeable impact of potential ties on the training procedure and our approach performed well on the tasks as discussed in Section SECREF4 . DISPLAYFORM0 ",
+      " We experimented with different annealing schedules for INLINEFORM0 starting with non-peaked softmax moving toward peaked-softmax across epochs so that learning is stable with informative gradients. This is important because cost functions like Hamming distance with very high INLINEFORM1 tend to be non-smooth and are generally flat in regions far away from changepoints and have a very large gradient near the changepoints which makes optimization difficult."
+    ],
+    [
+      "The motivation behind our approach is to make the optimization aware of beam search decoding while maintaining the continuity of the objective. However, since our approach doesn't introduce any new model parameters and optimization is agnostic to the architecture of the seq2seq model, we were able to experiment with various decoding schemes like locally normalized greedy decoding, and hard beam search, once the model has been trained.",
+      "However, to reduce the gap between the training procedure and test procedure, we also experimented with soft beam search decoding. This decoding approach closely follows Algorithm SECREF7 , but along with soft back pointers, we also compute hard back pointers at each time step. After computing all the relevant quantities like model score, loss etc., we follow the hard backpointers to obtain the best sequence INLINEFORM0 . This is very different from hard beam decoding because at each time step, the selection decisions are made via our soft continuous relaxation which influences the scores, LSTM hidden states and input embeddings at subsequent time-steps. The hard backpointers are essentially the MAP estimate of the soft backpointers at each step. With small, finite INLINEFORM1 , we observe differences between soft beam search and hard beam search decoding in our experiments."
+    ],
+    [
+      "Max-margin based objectives are typically motivated as another kind of surrogate training objective which avoid the discontinuities associated with direct loss optimization. Hinge loss for structured prediction typically takes the form: INLINEFORM0 ",
+      " where INLINEFORM0 is the input sequence, INLINEFORM1 is the gold target sequence, INLINEFORM2 is the output search space and INLINEFORM3 is the discontinuous cost function which we assume is decomposable across the time-steps of a sequence. Finding the cost augmented maximum score is generally difficult in large structured models and often involves searching over the output space and computing the approximate cost augmented maximal output sequence and the score associated with it via beam search. This procedure introduces discontinuities in the training procedure of structured max-margin objectives and renders it non amenable to training via backpropagation. Related work BIBREF15 on incorporating beam search into the training of neural sequence models does involve cost-augmented max-margin loss but it relies on discontinuous beam search forward passes and an explicit mechanism to ensure that the gold sequence stays in the beam during training, and hence does not involve back propagation through the beam search procedure itself.",
+      "Our continuous approximation to beam search can very easily be modified to compute an approximation to the structured hinge loss so that it can be trained via backpropagation if the cost function is decomposable across time-steps. In Algorithm SECREF7 , we only need to modify line 5 as: INLINEFORM0 ",
+      " and instead of computing INLINEFORM0 in Algorithm SECREF7 , we first compute the cost augmented maximum score as: INLINEFORM1 ",
+      " and also compute the target score INLINEFORM0 by simply running the forward pass of the LSTM decoder over the gold target sequence. The continuous approximation to the hinge loss to be optimized is then: INLINEFORM1 . We empirically compare this approach with the proposed approach to optimize direct loss in experiments."
+    ],
+    [
+      "Since our goal is to investigate the efficacy of our approach for training generic seq2seq models, we perform experiments on two NLP tagging tasks with very different characteristics and output search spaces: Named Entity Recognition (NER) and CCG supertagging. While seq2seq models are appropriate for CCG supertagging task because of the long-range correlations between the sequential output elements and a large search space, they are not ideal for NER which has a considerably smaller search space and weaker correlations between predictions at subsequent time steps. In our experiments, we observe improvements from our approach on both of the tasks. We use a seq2seq model with a bi-directional LSTM encoder (1 layer with tanh activation function) for the input sequence INLINEFORM0 , and an LSTM decoder (1 layer with tanh activation function) with a fixed attention mechanism that deterministically attends to the INLINEFORM1 -th input token when decoding the INLINEFORM2 -th output, and hence does not involve learning of any attention parameters. Since, computational complexity of our approach for optimization scales linearly with beam size for each instance, it is impractical to use very large beam sizes for training. Hence, beam size for all the beam search based experiments was set to 3 which resulted in improvements on both the tasks as discussed in the results. For both tasks, the direct loss function was the Hamming distance cost which aims to maximize word level accuracy."
+    ],
+    [
+      "For named entity recognition, we use the CONLL 2003 shared task data BIBREF28 for German language and use the provided data splits. We perform no preprocessing on the data. The output vocabulary length (label space) is 10. A peculiar characteristic of this problem is that the training data is naturally skewed toward one default label (`O') because sentences typically do not contain many named entities and the evaluation focuses on the performance recognizing entities. Therefore, we modify the Hamming cost such that incorrect prediction of `O' is doubly penalized compared to other incorrect predictions. We use the hidden layers of size 64 and label embeddings of size 8. As mentioned earlier, seq2seq models are not an ideal choice for NER (tag-level correlations are short-ranged in NER \u2013 the unnecessary expressivity of full seq2seq models over simple encoder-classifier neural models makes training harder). However, we wanted to evaluate the effectiveness of our approach on different instantiations of seq2seq models."
+    ],
+    [
+      "We used the standard splits of CCG bank BIBREF29 for training, development, and testing. The label space of supertags is 1,284 which is much larger than NER. The distribution of supertags in the training data exhibits a long tail because these supertags encode specific syntactic information about the words' usage. The supertag labels are correlated with each other and many tags encode similar information about the syntax. Moreover, this task is sensitive to the long range sequential decisions and search effects because of how it holistically encodes the syntax of the entire sentence. We perform minor preprocessing on the data similar to the preprocessing in BIBREF30 . For this task, we used hidden layers of size 512 and the supertag label embeddings were also of size 512. The standard evaluation metric for this task is the word level label accuracy which directly corresponds to Hamming loss."
+    ],
+    [
+      "For tuning all the hyperparameters related to optimization we trained our models for 50 epochs and picked the models with the best performance on the development set. We also ran multiple random restarts for all the systems evaluated to account for performance variance across randomly started runs. We pretrained all our models with standard cross entropy training which was important for stable optimization of the non convex neural objective with a large parameter search space. This warm starting is a common practice in prior work on complex neural models BIBREF10 , BIBREF4 , BIBREF14 ."
+    ],
+    [
+      "We report performance on validation and test sets for both the tasks in Tables 1 and 2. The baseline model is a cross entropy trained seq2seq model (Baseline CE) which is also used to warm start the the proposed optimization procedures in this paper. This baseline has been compared against the approximate direct loss training objective (Section SECREF9 ), referred to as INLINEFORM0 in the tables, and the approximate max-margin training objective (Section SECREF12 ), referred to as INLINEFORM1 in the tables. Results are reported for models when trained with annealing INLINEFORM2 , and also with a constant setting of INLINEFORM3 which is a very smooth but inaccurate approximation of the original direct loss that we aim to optimize. Comparisons have been made on the basis of performance of the models under different decoding paradigms (represented as different column in the tables): locally normalized decoding (CE greedy), hard beam search decoding and soft beam search decoding described in Section SECREF11 ."
+    ],
+    [
+      "As shown in Tables 1 and 2, our approach INLINEFORM0 shows significant improvements over the locally normalized CE baseline with greedy decoding for both the tasks (+5.5 accuracy points gain for supertagging and +1.5 F1 points for NER). The improvement is more pronounced on the supertagging task, which is not surprising because: (i) the evaluation metric is tag-level accuracy which is congruent with the Hamming loss that INLINEFORM1 directly optimizes and (ii) the supertagging task itself is very sensitive to the search procedure because tags across time-steps tend to exhibit long range dependencies as they encode specialized syntactic information about word usage in the sentence.",
+      "Another common trend to observe is that annealing INLINEFORM0 always results in better performance than training with a constant INLINEFORM1 for both INLINEFORM2 (Section SECREF9 ) and INLINEFORM3 (Section SECREF12 ). This shows that a stable training scheme that smoothly approaches minimizing the actual direct loss is important for our proposed approach. Additionally, we did not observe a large difference when our soft approximation is used for decoding (Section SECREF11 ) compared to hard beam search decoding, which suggests that our approximation to the hard beam search is as effective as its discrete counterpart.",
+      "For supertagging, we observe that the baseline cross entropy trained model improves its predictions with beam search decoding compared to greedy decoding by 2 accuracy points, which suggests that beam search is already helpful for this task, even without search-aware training. Both the optimization schemes proposed in this paper improve upon the baseline with soft direct loss optimization ( INLINEFORM0 ), performing better than the approximate max-margin approach. ",
+      "For NER, we observe that optimizing INLINEFORM0 outperforms all the other approaches but we also observe interesting behaviour of beam search decoding and the approximate max-margin objective for this task. The pretrained CE baseline model yields worse performance when beam search is done instead of greedy locally normalized decoding. This is because the training data is heavily skewed toward the `O' label and hence the absolute score resolution between different tags at each time-step during decoding isn't enough to avoid leading beam search toward a wrong hypothesis path. We observed in our experiments that hard beam search resulted in predicting more `O's which also hurt the prediction of tags at future time steps and hurt precision as well as recall. Encouragingly, INLINEFORM1 optimization, even though warm started with a CE trained model that performs worse with beam search, led to the NER model becoming more search aware, which resulted in superior performance. However, we also observe that the approximate max-margin approach ( INLINEFORM2 ) performs poorly here. We attribute this to a deficiency in the max-margin objective when coupled with approximate search methods like beam search that do not provide guarantees on finding the supremum: one way to drive this objective down is to learn model scores such that the search for the best hypothesis is difficult, so that the value of the loss augmented decode is low, while the gold sequence maintains higher model score. Because we also warm started with a pre-trained model that results in a worse performance with beam search decode than with greedy decode, we observe the adverse effect of this deficiency. The result is a model that scores the gold hypothesis highly, but yields poor decoding outputs. This observation indicates that using max-margin based objectives with beam search during training actually may achieve the opposite of our original intent: the objective can be driven down by introducing search errors.",
+      "The observation that our optimization method led to improvements on both the tasks\u2013even on NER for which hard beam search during decoding on a CE trained model hurt the performance\u2013by making the optimization more search aware, indicates the effectiveness of our approach for training seq2seq models."
+    ],
+    [
+      "While beam search is a method of choice for performing search in neural sequence models, as our experiments confirm, it is not necessarily guaranteed to improve accuracy when applied to cross-entropy-trained models. In this paper, we propose a novel method for optimizing model parameters that directly takes into account the process of beam search itself through a continuous, end-to-end sub-differentiable relaxation of beam search composed with the final evaluation loss. Experiments demonstrate that our method is able to improve overall test-time results for models using beam search as a test-time inference method, leading to substantial improvements in accuracy."
+    ]
+  ]
+}
+```
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+Name of Paper: Weighed Domain-Invariant Representation Learning for Cross-domain Sentiment Analysis
+
+Question: How is DIRL evaluated?
\ No newline at end of file
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+Name of Paper: Weighed Domain-Invariant Representation Learning for Cross-domain Sentiment Analysis
+
+Question: Which sentiment analysis tasks are addressed?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Preliminary and Related Work ::: Domain Adaptation",
+    "Preliminary and Related Work ::: Domain Invariant Representation Learning",
+    "Problem of Domain-Invariant Representation Learning",
+    "Problem of Domain-Invariant Representation Learning ::: Remark.",
+    "Weighted Domain Invariant Representation Learning",
+    "Weighted Domain Invariant Representation Learning ::: Align @!START@$\\rm {P}(\\rm {X}|\\rm {Y})$@!END@ with Class Weight",
+    "Weighted Domain Invariant Representation Learning ::: Align @!START@$\\rm {P}(\\rm {Y}|\\rm {X})$@!END@ with Class Weight",
+    "Experiment ::: Experiment Design",
+    "Experiment ::: Dataset and Task Design",
+    "Experiment ::: Dataset and Task Design ::: Binary-Class.",
+    "Experiment ::: Dataset and Task Design ::: Multi-Class.",
+    "Experiment ::: Implementation Detail",
+    "Experiment ::: Main Result",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Sentiment analysis aims to predict sentiment polarity of user-generated data with emotional orientation like movie reviews. The exponentially increase of online reviews makes it an interesting topic in research and industrial areas. However, reviews can span so many different domains and the collection and preprocessing of large amounts of data for new domains is often time-consuming and expensive. Therefore, cross-domain sentiment analysis is currently a hot topic, which aims to transfer knowledge from a label-rich source domain (S) to the label-few target domain (T).",
+      "In recent years, one of the most popular frameworks for cross-domain sentiment analysis is the domain invariant representation learning (DIRL) framework BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4. Methods of this framework follow the idea of extracting a domain-invariant feature representation, in which the data distributions of the source and target domains are similar. Based on the resultant representations, they learn the supervised classifier using source rich labeled data. The main difference among these methods is the applied technique to force the feature representations to be domain-invariant.",
+      "However, in this work, we discover that applying DIRL may harm domain adaptation in the situation that the label distribution $\\rm {P}(\\rm {Y})$ shifts across domains. Specifically, let $\\rm {X}$ and $\\rm {Y}$ denote the input and label random variable, respectively, and $G(\\rm {X})$ denote the feature representation of $\\rm {X}$. We found out that when $\\rm {P}(\\rm {Y})$ changes across domains while $\\rm {P}(\\rm {X}|\\rm {Y})$ stays the same, forcing $G(\\rm {X})$ to be domain-invariant will make $G(\\rm {X})$ uninformative to $\\rm {Y}$. This will, in turn, harm the generation of the supervised classifier to the target domain. In addition, for the more general condition that both $\\rm {P}(\\rm {Y})$ and $\\rm {P}(\\rm {X}|\\rm {Y})$ shift across domains, we deduced a conflict between the object of making the classification error small and that of making $G(\\rm {X})$ domain-invariant. We argue that the problem is worthy of studying since the shift of $\\rm {P}(\\rm {Y})$ exists in many real-world cross-domain sentiment analysis tasks BIBREF0. For example, the marginal distribution of the sentiment of a product can be affected by the overall social environment and change in different time periods; and for different products, their marginal distributions of the sentiment are naturally considered different. Moreover, there are many factors, such as the original data distribution, data collection time, and data clearing method, that can affect $\\rm {P}(\\rm {Y})$ of the collected target domain unlabeled dataset. Note that in the real-world cross-domain tasks, we do not know the labels of the collected target domain data. Thus, we cannot previously align its label distribution $\\rm {P}_T(\\mathbf {Y})$ with that of source domain labeled data $\\rm {P}_S(\\mathbf {Y})$, as done in many previous works BIBREF0, BIBREF2, BIBREF5, BIBREF4, BIBREF6, BIBREF7.",
+      "To address the problem of DIRL resulted from the shift of $\\rm {P}(\\rm {Y})$, we propose a modification to DIRL, obtaining a weighted domain-invariant representation learning (WDIRL) framework. This framework additionally introduces a class weight $\\mathbf {w}$ to weigh source domain examples by class, hoping to make $\\rm {P}(\\rm {Y})$ of the weighted source domain close to that of the target domain. Based on $\\mathbf {w}$, it resolves domain shift in two steps. In the first step, it forces the marginal distribution $\\rm {P}(\\rm {X})$ to be domain-invariant between the target domain and the weighted source domain instead of the original source, obtaining a supervised classifier $\\rm {P}_S(\\rm {Y}|\\rm {X}; \\mathbf {\\Phi })$ and a class weight $\\mathbf {w}$. In the second step, it resolves the shift of $\\rm {P}(\\rm {Y}|\\rm {X})$ by adjusting $\\rm {P}_S(\\rm {Y}|\\rm {X}; \\mathbf {\\Phi })$ using $\\mathbf {w}$ for label prediction in the target domain. We detail these two steps in \u00a7SECREF4. Moreover, we will illustrate how to transfer existing DIRL models to their WDIRL counterparts, taking the representative metric-based CMD model BIBREF3 and the adversarial-learning-based DANN model BIBREF2 as an example, respectively.",
+      "In summary, the contributions of this paper include: ($\\mathbf {i}$) We theoretically and empirically analyse the problem of DIRL for domain adaptation when the marginal distribution $\\rm {P}(\\rm {Y})$ shifts across domains. ($\\mathbf {ii}$) We proposed a novel method to address the problem and show how to incorporate it with existent DIRL models. ($\\mathbf {iii}$) Experimental studies on extensive cross-domain sentiment analysis tasks show that models of our WDIRL framework can greatly outperform their DIRL counterparts."
+    ],
+    [
+      "For expression consistency, in this work, we consider domain adaptation in the unsupervised setting (however, we argue that our analysis and solution also applies to the supervised and semi-supervised domain adaptation settings). In the unsupervised domain adaptation setting, there are two different distributions over $\\rm {X} \\times \\rm {Y}$: the source domain $\\rm {P}_S(\\rm {X},\\rm {Y})$ and the target domain $\\rm {P}_T(\\rm {X},\\rm {Y})$. And there is a labeled data set $\\mathcal {D}_S$ drawn $i.i.d$ from $\\rm {P}_S(\\rm {X},\\rm {Y})$ and an unlabeled data set $\\mathcal {D}_T$ drawn $i.i.d.$ from the marginal distribution $\\rm {P}_T(\\rm {X})$:",
+      "The goal of domain adaptation is to build a classier $f:\\rm {X} \\rightarrow \\rm {Y}$ that has good performance in the target domain using $\\mathcal {D}_S$ and $\\mathcal {D}_T$.",
+      "For this purpose, many approaches have been proposed from different views, such as instance reweighting BIBREF8, pivot-based information passing BIBREF9, spectral feature alignment BIBREF10 subsampling BIBREF11, and of course the domain-invariant representation learning BIBREF12, BIBREF13, BIBREF14, BIBREF15, BIBREF16, BIBREF17, BIBREF18, BIBREF19, BIBREF20, BIBREF21, BIBREF22."
+    ],
+    [
+      "Domain invariant representation learning (DIRL) is a very popular framework for performing domain adaptation in the cross-domain sentiment analysis field BIBREF23, BIBREF4, BIBREF24, BIBREF7. It is heavily motivated by the following theorem BIBREF25.",
+      "Theorem 1 For a hypothesis $h$,",
+      "Here, $\\mathcal {L}_S(h)$ denotes the expected loss with hypothesis $h$ in the source domain, $\\mathcal {L}_T(h)$ denotes the counterpart in the target domain, $d_1$ is a measure of divergence between two distributions.",
+      "Based on Theorem UNKREF3 and assuming that performing feature transform on $\\rm {X}$ will not increase the values of the first and third terms of the right side of Ineq. (DISPLAY_FORM4), methods of the DIRL framework apply a feature map $G$ onto $\\rm {X}$, hoping to obtain a feature representation $G(\\rm {X})$ that has a lower value of ${d}_{1}(\\rm {P}_S(G(\\rm {X})), \\rm {P}_T(G(\\rm {X})))$. To this end, different methods have been proposed. These methods can be roughly divided into two directions. The first direction is to design a differentiable metric to explicitly evaluate the discrepancy between two distributions. We call methods of this direction as the metric-based DIRL methods. A representative work of this direction is the center-momentum-based model proposed by BIBREF3. In that work, they proposed a central moment discrepancy metric (CMD) to evaluate the discrepancy between two distributions. Specifically, let denote $\\rm {X}_S$ and $\\rm {X}_T$ an $M$ dimensional random vector on the compact interval $[a; b]^M$ over distribution $\\rm {P}_S$ and $\\rm {P}_T$, respectively. The CMD loss between $\\rm {P}_S$ and $\\rm {P}_T$ is defined by:",
+      "Here, $\\mathbb {E}(\\rm {X})$ denotes the expectation of $\\rm {X}$ over distribution $\\rm {P}_S(\\rm {X})$, and",
+      "is the $k$-th momentum, where $\\rm {X}_i$ denotes the $i^{th}$ dimensional variable of $\\rm {X}$.",
+      "The second direction is to perform adversarial training between the feature generator $G$ and a domain discriminator $D$. We call methods of this direction as the adversarial-learning-based methods. As a representative, BIBREF2 trained $D$ to distinguish the domain of a given example $x$ based on its representation $G(x)$. At the same time, they encouraged $G$ to deceive $D$, i.e., to make $D$ unable to distinguish the domain of $x$. More specifically, $D$ was trained to minimize the loss:",
+      "over its trainable parameters, while in contrast $G$ was trained to maximize $\\mathcal {L}_d$. According to the work of BIBREF26, this is equivalent to minimize the Jensen-shannon divergence BIBREF27, BIBREF28 $\\text{JSD}(\\rm {P}_S, \\rm {P}_T)$ between $\\rm {P}_S(G(\\rm {X}))$ and $\\rm {P}_T(G(\\rm {X}))$ over $G$. Here, for a concise expression, we write $\\rm {P}$ as the shorthand for $\\rm {P}(G(\\rm {X}))$.",
+      "The task loss is the combination of the supervised learning loss $\\mathcal {L}_{sup}$ and the domain-invariant learning loss $\\mathcal {L}_{inv}$, which are defined on $\\mathcal {D}_S$ only and on the combination of $\\mathcal {D}_S$ and $\\mathcal {D}_T$, respectively:",
+      "Here, $\\alpha $ is a hyper-parameter for loss balance, and the aforementioned domain adversarial loss $\\text{JSD}(\\rm {P}_S, \\rm {P}_T)$ and $\\text{CMD}_K$ are two concrete forms of $\\mathcal {L}_{inv}$."
+    ],
+    [
+      "In this work, we found out that applying DIRL may harm domain adaptation in the situation that $\\rm {P}(\\rm {Y})$ shifts across domains. Specifically, when $\\rm {P}_S(\\rm {Y})$ differs from $\\rm {P}_T(\\rm {Y})$, forcing the feature representations $G(\\rm {X})$ to be domain-invariant may increase the value of $\\mathcal {L}_S(h)$ in Ineq. (DISPLAY_FORM4) and consequently increase the value of $\\mathcal {L}_T(h)$, which means the decrease of target domain performance. In the following, we start our analysis under the condition that $\\rm {P}_S(\\rm {X}|\\rm {Y})=\\rm {P}_T(\\rm {X}|\\rm {Y})$. Then, we consider the more general condition that $\\rm {P}_S(\\rm {X}|\\rm {Y})$ also differs from $\\rm {P}_T(\\rm {X}|\\rm {Y})$.",
+      "When $\\rm {P}_S(\\rm {X}|\\rm {Y})=\\rm {P}_T(\\rm {X}|\\rm {Y})$, we have the following theorem.",
+      "Theorem 2 Given $\\rm {P}_S(\\rm {X}|\\rm {Y})=\\rm {P}_T(\\rm {X}|\\rm {Y})$, if $\\rm {P}_S(\\rm {Y}=i) \\ne \\rm {P}_T(\\rm {Y}=i)$ and a feature map $G$ makes $\\rm {P}_S \\left( \\mathcal {M}(\\rm {X}))=\\rm {P}_T(\\mathcal {M}(\\rm {X}) \\right)$, then $\\rm {P}_S(\\rm {Y}=i|\\mathcal {M}(\\rm {X}))=\\rm {P}_S(\\rm {Y}=i)$.",
+      "Proofs appear in Appendix A."
+    ],
+    [
+      "According to Theorem UNKREF8, we know that when $\\rm {P}_S(\\rm {X}|\\rm {Y})=\\rm {P}_T(\\rm {X}|\\rm {Y})$ and $\\rm {P}_S(\\rm {Y}=i) \\ne \\rm {P}_T(\\rm {Y}=i)$, forcing $G(\\rm {X})$ to be domain-invariant inclines to make data of class $i$ mix with data of other classes in the space of $G(\\rm {X})$. This will make it difficult for the supervised classifier to distinguish inputs of class $i$ from inputs of the other classes. Think about such an extreme case that every instance $x$ is mapped to a consistent point $g_0$ in $G(\\rm {X})$. In this case, $\\rm {P}_S(G(\\rm {X})=g_0)= \\rm {P}_T(G(\\rm {X})=g_0) = 1$. Therefore, $G(\\rm {X})$ is domain-invariant. As a result, the supervised classifier will assign the label $y^* = \\operatornamewithlimits{arg\\,max}_y \\rm {P}_S(\\rm {Y}=y)$ to all input examples. This is definitely unacceptable. To give a more intuitive illustration of the above analysis, we offer several empirical studies on Theorem UNKREF8 in Appendix B.",
+      "When $\\rm {P}_S(\\rm {Y})\\ne \\rm {P}_T(\\rm {Y})$ and $\\rm {P}_S(\\rm {X}|\\rm {Y}) \\ne \\rm {P}_T(\\rm {X}|\\rm {Y})$, we did not obtain such a strong conclusion as Theorem UNKREF8. Instead, we deduced a conflict between the object of achieving superior classification performance and that of making features domain-invariant.",
+      "Suppose that $\\rm {P}_S(\\rm {Y}=i) \\ne \\rm {P}_T(\\rm {Y}=i)$ and instances of class $i$ are completely distinguishable from instances of the rest classes in $G(\\rm {X})$, i.e.,:",
+      "In DIRL, we hope that:",
+      "Consider the region $x \\in \\mathcal {X}_i$, where $\\rm {P}(G(\\rm {X}=x)|\\rm {Y}=i)>0$. According to the above assumption, we know that $\\rm {P}(G(\\rm {X}=x \\in \\mathcal {X}_i)|\\rm {Y} \\ne i) = 0$. Therefore, applying DIRL will force",
+      "in region $x \\in \\mathcal {X}_i$. Taking the integral of $x$ over $\\mathcal {X}_i$ for both sides of the equation, we have $\\rm {P}_S(\\rm {Y}=i) = \\rm {P}_T(\\rm {Y}=i)$. This deduction contradicts with the setting that $\\rm {P}_S(\\rm {Y}=i) \\ne \\rm {P}_T(\\rm {Y}=i)$. Therefore, $G(\\rm {X})$ is impossible fully class-separable when it is domain-invariant. Note that the object of the supervised learning is exactly to make $G(\\rm {X})$ class-separable. Thus, this actually indicates a conflict between the supervised learning and the domain-invariant representation learning.",
+      "Based on the above analysis, we can conclude that it is impossible to obtain a feature representation $G(X)$ that is class-separable and at the same time, domain-invariant using the DIRL framework, when $\\rm {P}(\\rm {Y})$ shifts across domains. However, the shift of $\\rm {P}(\\rm {Y})$ can exist in many cross-domain sentiment analysis tasks. Therefore, it is worthy of studying in order to deal with the problem of DIRL."
+    ],
+    [
+      "According to the above analysis, we proposed a weighted version of DIRL to address the problem caused by the shift of $\\rm {P}(\\rm {Y})$ to DIRL. The key idea of this framework is to first align $\\rm {P}(\\rm {Y})$ across domains before performing domain-invariant learning, and then take account the shift of $\\rm {P}(\\rm {Y})$ in the label prediction procedure. Specifically, it introduces a class weight $\\mathbf {w}$ to weigh source domain examples by class. Based on the weighted source domain, the domain shift problem is resolved in two steps. In the first step, it applies DIRL on the target domain and the weighted source domain, aiming to alleviate the influence of the shift of $\\rm {P}(\\rm {Y})$ during the alignment of $\\rm {P}(\\rm {X}|\\rm {Y})$. In the second step, it uses $\\mathbf {w}$ to reweigh the supervised classifier $\\rm {P}_S(\\rm {Y}|\\rm {X})$ obtained in the first step for target domain label prediction. We detail these two steps in \u00a7SECREF10 and \u00a7SECREF14, respectively."
+    ],
+    [
+      "The motivation behind this practice is to adjust data distribution of the source domain or the target domain to alleviate the shift of $\\rm {P}(\\rm {Y})$ across domains before applying DIRL. Consider that we only have labels of source domain data, we choose to adjust data distribution of the source domain. To achieve this purpose, we introduce a trainable class weight $\\mathbf {w}$ to reweigh source domain examples by class when performing DIRL, with $\\mathbf {w}_i > 0$. Specifically, we hope that:",
+      "and we denote $\\mathbf {w}^*$ the value of $\\mathbf {w}$ that makes this equation hold. We shall see that when $\\mathbf {w}=\\mathbf {w}^*$, DIRL is to align $\\rm {P}_S(G(\\rm {X})|\\rm {Y})$ with $\\rm {P}_T(G(\\rm {X})|\\rm {Y})$ without the shift of $\\rm {P}(\\rm {Y})$. According to our analysis, we know that due to the shift of $\\rm {P}(\\rm {Y})$, there is a conflict between the training objects of the supervised learning $\\mathcal {L}_{sup}$ and the domain-invariant learning $\\mathcal {L}_{inv}$. And the conflict degree will decrease as $\\rm {P}_S(\\rm {Y})$ getting close to $\\rm {P}_T(\\rm {Y})$. Therefore, during model training, $\\mathbf {w}$ is expected to be optimized toward $\\mathbf {w}^*$ since it will make $\\rm {P}(\\rm {Y})$ of the weighted source domain close to $\\rm {P}_T(\\rm {Y})$, so as to solve the conflict.",
+      "We now show how to transfer existing DIRL models to their WDIRL counterparts with the above idea. Let $\\mathbb {S}:\\rm {P} \\rightarrow {R}$ denote a statistic function defined over a distribution $\\rm {P}$. For example, the expectation function $\\mathbb {E}(\\rm {X})$ in $\\mathbb {E}(\\rm {X}_S) \\equiv \\mathbb {E}(\\rm {X})(\\rm {P}_S(\\rm {X}))$ is a concrete instaintiation of $\\mathbb {S}$. In general, to transfer models from DIRL to WDIRL, we should replace $\\mathbb {S}(\\rm {P}_S(\\rm {X}))$ defined in $\\mathcal {L}_{inv}$ with",
+      "Take the CMD metric as an example. In WDIRL, the revised form of ${\\text{CMD}}_K$ is defined by:",
+      "Here, $\\mathbb {E}(\\rm {X}_S|\\rm {Y}_S=i) \\equiv \\mathbb {E}(\\rm {X})(\\rm {P}_S(\\rm {X}|\\rm {Y}=i))$ denotes the expectation of $\\rm {X}$ over distribution $\\rm {P}_S(\\rm {X}|\\rm {Y}=i)$. Note that both $\\rm {P}_S(\\rm {Y}=i)$ and $\\mathbb {E}(\\rm {X}_S|\\rm {Y}_S=i)$ can be estimated using source labeled data, and $\\mathbb {E}(\\rm {X}_T)$ can be estimated using target unlabeled data.",
+      "As for those adversarial-learning-based DIRL methods, e.g., DANN BIBREF2, the revised domain-invariant loss can be precisely defined by:",
+      "During model training, $D$ is optimized in the direction to minimize $\\hat{\\mathcal {L}}_d$, while $G$ and $\\mathbf {w}$ are optimized to maximize $\\hat{\\mathcal {L}}_d$. In the following, we denote $\\widehat{\\text{JSD}}(\\rm {P}_S, \\rm {P}_T)$ the equivalent loss defined over $G$ for the revised version of domain adversarial learning.",
+      "The general task loss in WDIRL is defined by:",
+      "where $\\hat{\\mathcal {L}}_{inv}$ is a unified representation of the domain-invariant loss in WDIRL, such as $\\widehat{\\text{CMD}}_K$ and $\\widehat{\\text{JSD}}(\\rm {P}_S, \\rm {P}_T)$."
+    ],
+    [
+      "In the above step, we align $\\rm {P}(\\rm {X}|\\rm {Y})$ across domains by performing domain-invariant learning on the class-weighted source domain and the original target domain. In this step, we deal with the shift of $\\rm {P}(\\rm {Y})$. Suppose that we have successfully resolved the shift of $\\rm {P}(\\rm {X}|\\rm {Y})$ with $G$, i.e., $\\rm {P}_S(G(\\rm {X})|\\rm {Y})=\\rm {P}_T(G(\\rm {X})|\\rm {Y})$. Then, according to the work of BIBREF29, we have:",
+      "where $\\gamma (\\rm {Y}=i)={\\rm {P}_T(\\rm {Y}=i)}/{\\rm {P}_S(\\rm {Y}=i)}$. Of course, in most of the real-world tasks, we do not know the value of $\\gamma (\\rm {Y}=i)$. However, note that $\\gamma (\\rm {Y}=i)$ is exactly the expected class weight $\\mathbf {w}^*_i$. Therefore, a natural practice of this step is to estimate $\\gamma (\\rm {Y}=i)$ with the obtained $\\mathbf {w}_i$ in the first step and estimate $\\rm {P}_T(\\rm {Y}|G(\\rm {X}))$ with:",
+      "In summary, to transfer methods of the DIRL paradigm to WDIRL, we should: first revise the definition of $\\mathcal {L}_{inv}$, obtaining its corresponding WDIRL form $\\hat{\\mathcal {L}}_{inv}$; then perform supervised learning and domain-invariant representation learning on $\\mathcal {D}_S$ and $\\mathcal {D}_T$ according to Eq. (DISPLAY_FORM13), obtaining a supervised classifier $\\rm {P}_S(\\rm {Y}|\\rm {X}; \\mathbf {\\Phi })$ and a class weight vector $\\mathbf {w}$; and finally, adjust $\\rm {P}_S(\\rm {Y}|\\rm {X}; \\mathbf {\\Phi })$ using $\\mathbf {w}$ according to Eq. (DISPLAY_FORM16) and obtain the target domain classifier $\\rm {P}_T(\\rm {Y}|\\rm {X}; \\mathbf {\\Phi })$."
+    ],
+    [
+      "Through the experiments, we empirically studied our analysis on DIRL and the effectiveness of our proposed solution in dealing with the problem it suffered from. In addition, we studied the impact of each step described in \u00a7SECREF10 and \u00a7SECREF14 to our proposed solution, respectively. To performe the study, we carried out performance comparison between the following models:",
+      "SO: the source-only model trained using source domain labeled data without any domain adaptation.",
+      "CMD: the centre-momentum-based domain adaptation model BIBREF3 of the original DIRL framework that implements $\\mathcal {L}_{inv}$ with $\\text{CMD}_K$.",
+      "DANN: the adversarial-learning-based domain adaptation model BIBREF2 of the original DIRL framework that implements $\\mathcal {L}_{inv}$ with $\\text{JSD}(\\rm {P}_S, \\rm {P}_T)$.",
+      "$\\text{CMD}^\\dagger $: the weighted version of the CMD model that only applies the first step (described in \u00a7SECREF10) of our proposed method.",
+      "$\\text{DANN}^\\dagger $: the weighted version of the DANN model that only applies the first step of our proposed method.",
+      "$\\text{CMD}^{\\dagger \\dagger }$: the weighted version of the CMD model that applies both the first and second (described in \u00a7SECREF14) steps of our proposed method.",
+      "$\\text{DANN}^{\\dagger \\dagger }$: the weighted version of the DANN model that applies both the first and second steps of our proposed method.",
+      "$\\text{CMD}^{*}$: a variant of $\\text{CMD}^{\\dagger \\dagger }$ that assigns $\\mathbf {w}^*$ (estimate from target labeled data) to $\\mathbf {w}$ and fixes this value during model training.",
+      "$\\text{DANN}^{*}$: a variant of $\\text{DANN}^{\\dagger \\dagger }$ that assigns $\\mathbf {w}^*$ to $\\mathbf {w}$ and fixes this value during model training.",
+      "Intrinsically, SO can provide an empirical lowerbound for those domain adaptation methods. $\\text{CMD}^{*}$ and $\\text{DANN}^{*}$ can provide the empirical upbound of $\\text{CMD}^{\\dagger \\dagger }$ and $\\text{DANN}^{\\dagger \\dagger }$, respectively. In addition, by comparing performance of $\\text{CMD}^{*}$ and $\\text{DANN}^{*}$ with that of $\\text{SO}$, we can know the effectiveness of the DIRL framework when $\\rm {P}(\\rm {Y})$ dose not shift across domains. By comparing $\\text{CMD}^\\dagger $ with $\\text{CMD}$, or comparing $\\text{DANN}^\\dagger $ with $\\text{DANN}$, we can know the effectiveness of the first step of our proposed method. By comparing $\\text{CMD}^{\\dagger \\dagger }$ with $\\text{CMD}^{\\dagger }$, or comparing $\\text{DANN}^{\\dagger \\dagger }$ with $\\text{DANN}^{\\dagger }$, we can know the impact of the second step of our proposed method. And finally, by comparing $\\text{CMD}^{\\dagger \\dagger }$ with $\\text{CMD}$, or comparing $\\text{DANN}^{\\dagger \\dagger }$ with $\\text{DANN}$, we can know the general effectiveness of our proposed solution."
+    ],
+    [
+      "We conducted experiments on the Amazon reviews dataset BIBREF9, which is a benchmark dataset in the cross-domain sentiment analysis field. This dataset contains Amazon product reviews of four different product domains: Books (B), DVD (D), Electronics (E), and Kitchen (K) appliances. Each review is originally associated with a rating of 1-5 stars and is encoded in 5,000 dimensional feature vectors of bag-of-words unigrams and bigrams."
+    ],
+    [
+      "From this dataset, we constructed 12 binary-class cross-domain sentiment analysis tasks: B$\\rightarrow $D, B$\\rightarrow $E, B$\\rightarrow $K, D$\\rightarrow $B, D$\\rightarrow $E, D$\\rightarrow $K, E$\\rightarrow $B, E$\\rightarrow $D, E$\\rightarrow $K, K$\\rightarrow $B, K$\\rightarrow $D, K$\\rightarrow $E. Following the setting of previous works, we treated a reviews as class `1' if it was ranked up to 3 stars, and as class `2' if it was ranked 4 or 5 stars. For each task, $\\mathcal {D}_S$ consisted of 1,000 examples of each class, and $\\mathcal {D}_T$ consists of 1500 examples of class `1' and 500 examples of class `2'. In addition, since it is reasonable to assume that $\\mathcal {D}_T$ can reveal the distribution of target domain data, we controlled the target domain testing dataset to have the same class ratio as $\\mathcal {D}_T$. Using the same label assigning mechanism, we also studied model performance over different degrees of $\\rm {P}(\\rm {Y})$ shift, which was evaluated by the max value of $\\rm {P}_S(\\rm {Y}=i)/\\rm {P}_T(\\rm {Y}=i), \\forall i=1, \\cdots , L$. Please refer to Appendix C for more detail about the task design for this study."
+    ],
+    [
+      "We additionally constructed 12 multi-class cross-domain sentiment classification tasks. Tasks were designed to distinguish reviews of 1 or 2 stars (class 1) from those of 4 stars (class 2) and those of 5 stars (class 3). For each task, $\\mathcal {D}_S$ contained 1000 examples of each class, and $\\mathcal {D}_T$ consisted of 500 examples of class 1, 1500 examples of class 2, and 1000 examples of class 3. Similarly, we also controlled the target domain testing dataset to have the same class ratio as $\\mathcal {D}_T$."
+    ],
+    [
+      "For all studied models, we implemented $G$ and $f$ using the same architectures as those in BIBREF3. For those DANN-based methods (i.e., DANN, $\\text{DANN}^{\\dagger }$, $\\text{DANN}^{\\dagger \\dagger }$, and $\\text{DANN}^{*}$), we implemented the discriminator $D$ using a 50 dimensional hidden layer with relu activation functions and a linear classification layer. Hyper-parameter $K$ of $\\text{CMD}_K$ and $\\widehat{\\text{CMD}}_K$ was set to 5 as suggested by BIBREF3. Model optimization was performed using RmsProp BIBREF30. Initial learning rate of $\\mathbf {w}$ was set to 0.01, while that of other parameters was set to 0.005 for all tasks.",
+      "Hyper-parameter $\\alpha $ was set to 1 for all of the tested models. We searched for this value in range $\\alpha =[1, \\cdots , 10]$ on task B $\\rightarrow $ K. Within the search, label distribution was set to be uniform, i.e., $\\rm {P}(\\rm {Y}=i)=1/L$, for both domain B and K. We chose the value that maximize the performance of CMD on testing data of domain K. You may notice that this practice conflicts with the setting of unsupervised domain adaptation that we do not have labeled data of the target domain for training or developing. However, we argue that this practice would not make it unfair for model comparison since all of the tested models shared the same value of $\\alpha $ and $\\alpha $ was not directly fine-tuned on any tested task. With the same consideration, for every tested model, we reported its best performance achieved on testing data of the target domain during its training.",
+      "To initialize $\\mathbf {w}$, we used label prediction of the source-only model. Specifically, let $\\rm {P}_{SO}(\\rm {Y}|\\rm {X}; \\mathbf {\\theta }_{SO})$ denote the trained source-only model. We initialized $\\mathbf {w}_i$ by:",
+      "Here, $\\mathbb {I}$ denotes the indication function. To offer an intuitive understanding to this strategy, we report performance of WCMD$^{\\dagger \\dagger }$ over different initializations of $\\mathbf {w}$ on 2 within-group (B$\\rightarrow $D, E$\\rightarrow $K) and 2 cross-group (B$\\rightarrow $K, D$\\rightarrow $E) binary-class domain adaptation tasks in Figure FIGREF33. Here, we say that domain B and D are of a group, and domain E and K are of another group since B and D are similar, as are E and K, but the two groups are different from one another BIBREF9. Note that $\\rm {P}_{S}(\\rm {Y}=1)=0.5$ is a constant, which is estimated using source labeled data. From the figure, we can obtain three main observations. First, WCMD$^{\\dagger \\dagger }$ generally outperformed its CMD counterparts with different initialization of $\\mathbf {w}$. Second, it was better to initialize $\\mathbf {w}$ with a relatively balanced value, i.e., $\\mathbf {w}_i \\rm {P}_S(\\rm {Y}=i) \\rightarrow \\frac{1}{L}$ (in this experiment, $L=2$). Finally, $\\mathbf {w}^0$ was often a good initialization of $\\mathbf {w}$, indicating the effectiveness of the above strategy."
+    ],
+    [
+      "Table TABREF27 shows model performance on the 12 binary-class cross-domain tasks. From this table, we can obtain the following observations. First, CMD and DANN underperform the source-only model (SO) on all of the 12 tested tasks, indicating that DIRL in the studied situation will degrade the domain adaptation performance rather than improve it. This observation confirms our analysis. Second, $\\text{CMD}^{\\dagger \\dagger }$ consistently outperformed CMD and SO. This observation shows the effectiveness of our proposed method for addressing the problem of the DIRL framework in the studied situation. Similar conclusion can also be obtained by comparing performance of $\\text{DANN}^{\\dagger \\dagger }$ with that of DANN and SO. Third, $\\text{CMD}^{\\dagger }$ and $\\text{DANN}^{\\dagger }$ consistently outperformed $\\text{CMD}$ and DANN, respectively, which shows the effectiveness of the first step of our proposed method. Finally, on most of the tested tasks, $\\text{CMD}^{\\dagger \\dagger }$ and $\\text{DANN}^{\\dagger \\dagger }$ outperforms $\\text{CMD}^{\\dagger }$ and $\\text{DANN}^{\\dagger }$, respectively. Figure FIGREF35 depicts the relative improvement, e.g., $(\\text{Acc}(\\text{CMD})-\\text{Acc}(\\text{SO}))/\\text{Acc}(\\text{SO})$, of the domain adaptation methods over the SO baseline under different degrees of $\\rm {P}(\\rm {Y})$ shift, on two binary-class domain adaptation tasks (You can refer to Appendix C for results of the other models on other tasks). From the figure, we can see that the performance of CMD generally got worse as the increase of $\\rm {P}(\\rm {Y})$ shift. In contrast, our proposed model $\\text{CMD}^{\\dagger \\dagger }$ performed robustly to the varying of $\\rm {P}(\\rm {Y})$ shift degree. Moreover, it can achieve the near upbound performance characterized by $\\text{CMD}^{*}$. This again verified the effectiveness of our solution.",
+      "Table TABREF34 reports model performance on the 2 within-group (B$\\rightarrow $D, E$\\rightarrow $K) and the 2 cross-group (B$\\rightarrow $K, D$\\rightarrow $E) multi-class domain adaptation tasks (You can refer to Appendix D for results on the other tasks). From this table, we observe that on some tested tasks, $\\text{CMD}^{\\dagger \\dagger }$ and $\\text{DANN}^{\\dagger \\dagger }$ did not greatly outperform or even slightly underperformed $\\text{CMD}^{\\dagger }$ and $\\text{DANN}^{\\dagger }$, respectively. A possible explanation of this phenomenon is that the distribution of $\\mathcal {D}_T$ also differs from that of the target domain testing dataset. Therefore, the estimated or learned value of $\\mathbf {w}$ using $\\mathcal {D}_T$ is not fully suitable for application to the testing dataset. This explanation is verified by the observation that $\\text{CMD}^{\\dagger }$ and $\\text{DANN}^{\\dagger }$ also slightly outperforms $\\text{CMD}^{*}$ and $\\text{DANN}^{*}$ on these tasks, respectively."
+    ],
+    [
+      "In this paper, we studied the problem of the popular domain-invariant representation learning (DIRL) framework for domain adaptation, when $\\rm {P}(\\rm {Y})$ changes across domains. To address the problem, we proposed a weighted version of DIRL (WDIRL). We showed that existing methods of the DIRL framework can be easily transferred to our WDIRL framework. Extensive experimental studies on benchmark cross-domain sentiment analysis datasets verified our analysis and showed the effectiveness of our proposed solution."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: The State of NLP Literature: A Diachronic Analysis of the ACL Anthology
+
+Question: Which NLP area have the highest average citation for woman author?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Size",
+    "Demographics (focus of analysis: gender, age, and geographic diversity)",
+    "Demographics (focus of analysis: gender, age, and geographic diversity) ::: Gender",
+    "Demographics (focus of analysis: gender, age, and geographic diversity) ::: Academic Age",
+    "Demographics (focus of analysis: gender, age, and geographic diversity) ::: Location (Languages)",
+    "Areas of Research",
+    "Impact",
+    "Impact ::: #Citations and Most Cited Papers",
+    "Impact ::: Average Citations by Time Span",
+    "Impact ::: Aggregate Citation Statistics, by Paper Type and Venue",
+    "Impact ::: Citations to Papers by Areas of Research",
+    "Correlation of Age and Gender with Citations",
+    "Correlation of Age and Gender with Citations ::: Correlation of Academic Age with Citations",
+    "Conclusions"
+  ],
+  "paragraphs": [
+    [
+      "The ACL Anthology (AA) is a digital repository of tens of thousands of articles on Natural Language Processing (NLP) / Computational Linguistics (CL). It includes papers published in the family of ACL conferences as well as in other NLP conferences such as LREC and RANLP. AA is the largest single source of scientific literature on NLP.",
+      "This project, which we call NLP Scholar, examines the literature as a whole to identify broad trends in productivity, focus, and impact. We will present the analyses in a sequence of questions and answers. The questions range from fairly mundane to oh-that-will-be-good-to-know. Our broader goal here is simply to record the state of the AA literature: who and how many of us are publishing? what are we publishing on? where and in what form are we publishing? and what is the impact of our publications? The answers are usually in the form of numbers, graphs, and inter-connected visualizations.",
+      "We focus on the following aspects of NLP research: size, demographics, areas of research, impact, and correlation of citations with demographic attributes (age and gender).",
+      "",
+      "Target Audience: The analyses presented here are likely to be of interest to any NLP researcher. This might be particularly the case for those that are new to the field and wish to get a broad overview of the NLP publishing landscape. On the other hand, even seasoned NLP'ers have likely wondered about the questions raised here and might be interested in the empirical evidence.",
+      "",
+      "Data: The analyses presented below are based on information about the papers taken directly from AA (as of June 2019) and citation information extracted from Google Scholar (as of June 2019). Thus, all subsequent papers and citations are not included in the analysis. A fresh data collection is planned for January 2020.",
+      "",
+      "Interactive Visualizations: The visualizations we are developing for this work (using Tableau) are interactive\u2014so one can hover, click to select and filter, move sliders, etc. Since this work is high in the number of visualizations, the main visualizations are presented as figures in the paper and some sets of visualizations are pointed to online. The interactive visualizations and data will be made available through the first author's website after peer review.",
+      "",
+      "Related Work: This work builds on past research, including that on Google Scholar BIBREF0, BIBREF1, BIBREF2, BIBREF3, on the analysis of NLP papers BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9, on citation intent BIBREF10, BIBREF11, BIBREF12, BIBREF13, BIBREF14, BIBREF15, and on measuring scholarly impact BIBREF16, BIBREF17, BIBREF18, BIBREF19, BIBREF20, BIBREF21.",
+      "",
+      "Caveats and Ethical Considerations: We list several caveats and limitations throughout the paper. A compilation of these is also available online in the About NLP Scholar page.",
+      "The analyses presented here are also available as a series of blog posts."
+    ],
+    [
+      "Q. How big is the ACL Anthology (AA)? How is it changing with time?",
+      "A. As of June 2019, AA had $\\sim $50K entries, however, this includes some number of entries that are not truly research publications (for example, forewords, prefaces, table of contents, programs, schedules, indexes, calls for papers/participation, lists of reviewers, lists of tutorial abstracts, invited talks, appendices, session information, obituaries, book reviews, newsletters, lists of proceedings, lifetime achievement awards, erratum, and notes). We discard them for the analyses here. (Note: CL journal includes position papers like squibs, letter to editor, opinion, etc. We do not discard them.) We are then left with 44,896 articles. Figure FIGREF6 shows a graph of the number of papers published in each of the years from 1965 to 2018.",
+      "Discussion: Observe that there was a spurt in the 1990s, but things really took off since the year 2000, and the growth continues. Also, note that the number of publications is considerably higher in alternate years. This is due to biennial conferences. Since 1998 the largest of such conferences has been LREC (In 2018 alone LREC had over 700 main conferences papers and additional papers from its 29 workshops). COLING, another biennial conference (also occurring in the even years) has about 45% of the number of main conference papers as LREC.",
+      "Q. How many people publish in the ACL Anthology (NLP conferences)?",
+      "A. Figure FIGREF7 shows a graph of the number of authors (of AA papers) over the years:",
+      "Discussion: It is a good sign for the field to have a growing number of people join its ranks as researchers. A further interesting question would be:",
+      "Q. How many people are actively publishing in NLP?",
+      "A. It is hard to know the exact number, but we can determine the number of people who have published in AA in the last N years.",
+      "#people who published at least one paper in 2017 and 2018 (2 years): $\\sim $12k (11,957 to be precise)",
+      "#people who published at least one paper 2015 through 2018 (4 years):$\\sim $17.5k (17,457 to be precise)",
+      "Of course, some number of researchers published NLP papers in non-AA venues, and some number are active NLP researchers who may not have published papers in the last few years.",
+      "Q. How many journal papers exist in the AA? How many main conference papers? How many workshop papers?",
+      "A. See Figure FIGREF8.",
+      "Discussion: The number of journal papers is dwarfed by the number of conference and workshop papers. (This is common in computer science. Even though NLP is a broad interdisciplinary field, the influence of computer science practices on NLP is particularly strong.) Shared task and system demo papers are relatively new (introduced in the 2000s), but their numbers are already significant and growing.",
+      "Creating a separate class for \u201cTop-tier Conference\u201d is somewhat arbitrary, but it helps make certain comparisons more meaningful (for example, when comparing the average number of citations, etc.). For this work, we consider ACL, EMNLP, NAACL, COLING, and EACL as top-tier conferences, but certainly other groupings are also reasonable.",
+      "Q. How many papers have been published at ACL (main conference papers)? What are the other NLP venues and what is the distribution of the number of papers across various CL/NLP venues?",
+      "A. # ACL (main conference papers) as of June 2018: 4,839",
+      "The same workshop can co-occur with different conferences in different years, so we grouped all workshop papers in their own class. We did the same for tutorials, system demonstration papers (demos), and student research papers. Figure FIGREF9 shows the number of main conference papers for various venues and paper types (workshop papers, demos, etc.).",
+      "Discussion: Even though LREC is a relatively new conference that occurs only once in two years, it tends to have a high acceptance rate ($\\sim $60%), and enjoys substantial participation. Thus, LREC is already the largest single source of NLP conference papers. SemEval, which started as SenseEval in 1998 and occurred once in two or three years, has now morphed into an annual two-day workshop\u2014SemEval. It is the largest single source of NLP shared task papers."
+    ],
+    [
+      "NLP, like most other areas of research, suffers from poor demographic diversity. There is very little to low representation from certain nationalities, race, gender, language, income, age, physical abilities, etc. This impacts the breadth of technologies we create, how useful they are, and whether they reach those that need it most. In this section, we analyze three specific attributes among many that deserve attention: gender (specifically, the number of women researchers in NLP), age (more precisely, the number of years of NLP paper publishing experience), and the amount of research in various languages (which loosely correlates with geographic diversity)."
+    ],
+    [
+      "The ACL Anthology does not record demographic information about the paper authors. (Until recently, ACL and other NLP conferences did not record demographic information of the authors.) However, many first names have strong associations with a male or female gender. We will use these names to estimate the percentage of female first authors in NLP.",
+      "The US Social Security Administration publishes a database of names and genders of newborns. We use the dataset to identify 55,133 first names that are strongly associated with females (probability $\\ge $99%) and 29,873 first names that are strongly associated with males (probability $\\ge $99%). (As a side, it is interesting to note that there is markedly greater diversity in female names than in male names.) We identified 26,637 of the 44,896 AA papers ($\\sim $60%) where the first authors have one of these names and determine the percentage of female first author papers across the years. We will refer to this subset of AA papers as AA*.",
+      "",
+      "Note the following caveats associated with this analysis:",
+      "",
+      "The names dataset used has a lower representation of names from nationalities other than the US. However, there is a large expatriate population living in the US.",
+      "",
+      "Chinese names (especially in the romanized form) are not good indicators of gender. Thus the method presented here disregards most Chinese names, and the results of the analysis apply to the group of researchers excluding those with Chinese names.",
+      "",
+      "The dataset only records names associated with two genders.",
+      "",
+      "The approach presented here is meant to be an approximation in the absence of true gender information.",
+      "Q. What percent of the AA* papers have female first authors (FFA)? How has this percentage changed with time?",
+      "A. Overall FFA%: 30.3%. Figure FIGREF16 shows how FFA% has changed with time. Common paper title words and FFA% of papers that have those words are shown in the bottom half of the image. Note that the slider at the bottom has been set to 400, i.e., only those title words that occur in 400 or more papers are shown. The legend on the bottom right shows that low FFA scores are shown in shades of blue, whereas relatively higher FFA scores are shown in shades of green.",
+      "",
+      "Discussion: Observe that as a community, we are far from obtaining male-female parity in terms of first authors. A further striking (and concerning) observation is that the female first author percentage has not improved since the years 1999 and 2000 when the FFA percentages were highest (32.9% and 32.8%, respectively). In fact there seems to even be a slight downward trend in recent years. The calculations shown above are for the percentage of papers that have female first authors. The percentage of female first authors is about the same ($\\sim $31%). On average male authors had a slightly higher average number of publications than female authors.",
+      "To put these numbers in context, the percentage of female scientists world wide (considering all areas of research) has been estimated to be around 30%. The reported percentages for many computer science sub-fields are much lower. (See Women in Science (2015).) The percentages are much higher for certain other fields such as psychology and linguistics. (See this study for psychology and this study for linguistics.) If we can identify ways to move the needle on the FFA percentage and get it closer to 50% (or more), NLP can be a beacon to many other fields, especially in the sciences.",
+      "FFA percentages are particularly low for papers that have parsing, neural, and unsupervised in the title. There are some areas within NLP that enjoy a healthier female-male parity in terms of first authors of papers. Figure FIGREF20 shows FFA percentages for papers that have the word discourse in the title. There is burgeoning research on neural NLP in the last few years. Figure FIGREF21 shows FFA percentages for papers that have the word neural in the title.",
+      "Figure FIGREF22 shows lists of terms with the highest and lowest FFA percentages, respectively, when considering terms that occur in at least 50 paper titles (instead of 400 in the analysis above). Observe that FFA percentages are relatively higher in non-English European language research such as papers on Russian, Portuguese, French, and Italian. FFA percentages are also relatively higher for certain areas of NLP such as work on prosody, readability, discourse, dialogue, paraphrasing, and individual parts of speech such as adjectives and verbs. FFA percentages are particularly low for papers on theoretical aspects of statistical modelling, and areas such as machine translation, parsing, and logic. The full lists of terms and FFA percentages will be made available with the rest of the data."
+    ],
+    [
+      "While the actual age of NLP researchers might be an interesting aspect to explore, we do not have that information. Thus, instead, we can explore a slightly different (and perhaps more useful) attribute: NLP academic age. We can define NLP academic age as the number of years one has been publishing in AA. So if this is the first year one has published in AA, then their NLP academic age is 1. If one published their first AA paper in 2001 and their latest AA paper in 2018, then their academic age is 18.",
+      "Q. How old are we? That is, what is the average NLP academic age of those who published papers in 2018? How has the average changed over the years? That is, have we been getting older or younger? What percentage of authors that published in 2018 were publishing their first AA paper?",
+      "A. Average NLP Academic Age of people that published in 2018: 5.41 years",
+      "Median NLP Academic Age of people that published in 2018: 2 years",
+      "Percentage of 2018 authors that published their first AA paper in 2018: 44.9%",
+      "Figure FIGREF24 shows how these numbers have changed over the years.",
+      "Discussion: Observe that the Average academic age has been steadily increasing over the years until 2016 and 2017, when the trend has shifted and the average academic age has started to decrease. The median age was 1 year for most of the 1965 to 1990 period, 2 years for most of the 1991 to 2006 period, 3 years for most of the 2007 to 2015 period, and back to 2 years since then. The first-time AA author percentage decreased until about 1988, after which it sort of stayed steady at around 48% until 2004 with occasional bursts to $\\sim $56%. Since 2005, the first-time author percentage has gone up and down every other year. It seems that the even years (which are also LREC years) have a higher first-time author percentage. Perhaps, this oscillation in first-time authors percentage is related to LREC\u2019s high acceptance rate.",
+      "Q. What is the distribution of authors in various academic age bins? For example, what percentage of authors that published in 2018 had an academic age of 2, 3, or 4? What percentage had an age between 5 and 9? And so on?",
+      "A. See Figure FIGREF25.",
+      "Discussion: Observe that about 65% of the authors that published in 2018 had an academic age of less than 5. This number has steadily reduced since 1965, was in the 60 to 70% range in 1990s, rose to the 70 to 72% range in early 2000s, then declined again until it reached the lowest value ($\\sim $60%) in 2010, and has again steadily risen until 2018 (65%). Thus, even though it may sometimes seem at recent conferences that there is a large influx of new people into NLP (and that is true), proportionally speaking, the average NLP academic age is higher (more experienced) than what it has been in much of its history."
+    ],
+    [
+      "Automatic systems with natural language abilities are growing to be increasingly pervasive in our lives. Not only are they sources of mere convenience, but are crucial in making sure large sections of society and the world are not left behind by the information divide. Thus, the limits of what automatic systems can do in a language, limit the world for the speakers of that language.",
+      "We know that much of the research in NLP is on English or uses English datasets. Many reasons have been proffered, and we will not go into that here. Instead, we will focus on estimating how much research pertains to non-English languages.",
+      "We will make use of the idea that often when work is done focusing on a non-English language, then the language is mentioned in the title. We collected a list of 122 languages indexed by Wiktionary and looked for the presence of these words in the titles of AA papers. (Of course there are hundreds of other lesser known languages as well, but here we wanted to see the representation of these more prominent languages in NLP literature.)",
+      "Figure FIGREF27 is a treemap of the 122 languages arranged alphabetically and shaded such that languages that appear more often in AA paper titles have a darker shade of green.",
+      "Discussion: Even though the amount of work done on English is much larger than that on any other language, often the word English does not appear in the title, and this explains why English is not the first (but the second-most) common language name to appear in the titles. This is likely due to the fact that many papers fail to mention the language of study or the language of the datasets used if it is English. There is growing realization in the community that this is not quite right. However, the language of study can be named in other less prominent places than the title, for example the abstract, introduction, or when the datasets are introduced, depending on how central it is to the paper.",
+      "We can see from the treemap that the most widely spoken Asian and Western European languages enjoy good representation in AA. These include: Chinese, Arabic, Korean, Japanese, and Hindi (Asian) as well as French, German, Swedish, Spanish, Portuguese, and Italian (European). This is followed by the relatively less widely spoken European languages (such as Russian, Polish, Norwegian, Romanian, Dutch, and Czech) and Asian languages (such as Turkish, Thai, and Urdu). Most of the well-represented languages are from the Indo-European language family. Yet, even in the limited landscape of the most common 122 languages, vast swathes are barren with inattention. Notable among these is the extremely low representation of languages from Africa, languages from non-Indo-European language families, and Indigenous languages from around the world."
+    ],
+    [
+      "Natural Language Processing addresses a wide range of research questions and tasks pertaining to language and computing. It encompasses many areas of research that have seen an ebb and flow of interest over the years. In this section, we examine the terms that have been used in the titles of ACL Anthology (AA) papers. The terms in a title are particularly informative because they are used to clearly and precisely convey what the paper is about. Some journals ask authors to separately include keywords in the paper or in the meta-information, but AA papers are largely devoid of this information. Thus titles are an especially useful source of keywords for papers\u2014keywords that are often indicative of the area of research.",
+      "Keywords could also be extracted from abstracts and papers; we leave that for future work. Further work is also planned on inferring areas of research using word embeddings, techniques from topic modelling, and clustering. There are clear benefits to performing analyses using that information. However, those approaches can be sensitive to the parameters used. Here, we keep things simple and explore counts of terms in paper titles. Thus the results are easily reproducible and verifiable.",
+      "Caveat: Even though there is an association between title terms and areas of research, the association can be less strong for some terms and areas. We use the association as one (imperfect) source of information about areas of research. This information may be combined with other sources of information to draw more robust conclusions.",
+      "Title Terms: The title has a privileged position in a paper. It serves many functions, and here are three key ones (from an article by Sneha Kulkarni): \"A good research paper title: 1. Condenses the paper's content in a few words 2. Captures the readers' attention 3. Differentiates the paper from other papers of the same subject area\".",
+      "If we examine the titles of papers in the ACL Anthology, we would expect that because of Function 1 many of the most common terms will be associated with the dominant areas of research. Function 2 (or attempting to have a catchy title) on the other hand, arguably leads to more unique and less frequent title terms. Function 3 seems crucial to the effectiveness of a title; and while at first glance it may seem like this will lead to unique title terms, often one needs to establish a connection with something familiar in order to convey how the work being presented is new or different.",
+      "It is also worth noting that a catchy term today, will likely not be catchy tomorrow. Similarly, a distinctive term today, may not be distinctive tomorrow. For example, early papers used neural in the title to distinguish themselves from non-nerual approaches, but these days neural is not particularly discriminative as far as NLP papers go.",
+      "Thus, competing and complex interactions are involved in the making of titles. Nonetheless, an arguable hypothesis is that: broad trends in interest towards an area of research will be reflected, to some degree, in the frequencies of title terms associated with that area over time. However, even if one does not believe in that hypothesis, it is worth examining the terms in the titles of tens of thousands of papers in the ACL Anthology\u2014spread across many decades.",
+      "Q. What terms are used most commonly in the titles of the AA papers? How has that changed with time?",
+      "A. Figure FIGREF28 shows the most common unigrams (single word) and bigrams (two-word sequences) in the titles of papers published from 1980 to 2019. (Ignoring function words.) The timeline graph at the bottom shows the percentage of occurrences of the unigrams over the years (the colors of the unigrams in the Timeline match those in the Title Unigram list). Note: For a given year, the timeline graph includes a point for a unigram if the sum of the frequency of the unigram in that year and the two years before it is at least ten. The period before 1980 is not included because of the small number of papers.",
+      "Discussion: Appropriately enough, the most common term in the titles of NLP papers is language. Presence of high-ranking terms pertaining to machine translation suggest that it is the area of research that has received considerable attention. Other areas associated with the high-frequency title terms include lexical semantics, named entity recognition, question answering, word sense disambiguation, and sentiment analysis. In fact, the common bigrams in the titles often correspond to names of NLP research areas. Some of the bigrams like shared task and large scale are not areas of research, but rather mechanisms or trends of research that apply broadly to many areas of research. The unigrams, also provide additional insights, such as the interest of the community in Chinese language, and in areas such as speech and parsing.",
+      "The Timeline graph is crowded in this view, but clicking on a term from the unigram list will filter out all other lines from the timeline. This is especially useful for determining whether the popularity of a term is growing or declining. (One can already see from above that neural has broken away from the pack in recent years.) Since there are many lines in the Timeline graph, Tableau labels only some (you can see neural and machine). However, hovering over a line, in the eventual interactive visualization, will display the corresponding term\u2014as shown in the figure.",
+      "Despite being busy, the graph sheds light on the relative dominance of the most frequent terms and how that has changed with time. The vocabulary of title words is smaller when considering papers from the 1980's than in recent years. (As would be expected since the number of papers then was also relatively fewer.) Further, dominant terms such as language and translation accounted for a higher percentage than in recent years where there is a much larger diversity of topics and the dominant research areas are not as dominant as they once were.",
+      "Q. What are the most frequent unigrams and bigrams in the titles of recent papers?",
+      "A. Figure FIGREF29 shows the most frequent unigrams and bigrams in the titles of papers published 2016 Jan to 2019 June (time of data collection).",
+      "Discussion: Some of the terms that have made notable gains in the top 20 unigrams and bigrams lists in recent years include: neural machine (presumably largely due to the phrase neural machine translation), neural network(s), word embeddings, recurrent neural, deep learning and the corresponding unigrams (neural, networks, etc.). We also see gains for terms related to shared tasks such as SemEval and task.",
+      "The sets of most frequent unigrams and bigrams in the titles of AA papers from various time spans are available online. Apart from clicking on terms, one can also enter the query (say parsing) in the search box at the bottom. Apart from filtering the timeline graph (bottom), this action also filters the unigram list (top left) to provide information only about the search term. This is useful because the query term may not be one of the visible top unigrams.",
+      "FigureFIGREF31 shows the timeline graph for parsing.",
+      "Discussion: Parsing seems to have enjoyed considerable attention in the 1980s, began a period of steep decline in the early 1990s, and a period of gradual decline ever since. One can enter multiple terms in the search box or shift/command click multiple terms to show graphs for more than one term.",
+      "FigureFIGREF32 shows the timelines for three bigrams statistical machine, neural machine, and machine translation:",
+      "Discussion: The graph indicates that there was a spike in machine translation papers in 1996, but the number of papers dropped substantially after that. Yet, its numbers have been comparatively much higher than other terms. One can also see the rise of statistical machine translation in the early 2000s followed by its decline with the rise of neural machine translation."
+    ],
+    [
+      "Research articles can have impact in a number of ways\u2014pushing the state of the art, answering crucial questions, finding practical solutions that directly help people, making a new generation of potential-scientists excited about a field of study, and more. As scientists, it seems attractive to quantitatively measure scientific impact, and this is particularly appealing to governments and funding agencies; however, it should be noted that individual measures of research impact are limited in scope\u2014they measure only some kinds of contributions. Citations",
+      "The most commonly used metrics of research impact are derived from citations. A citation of a scholarly article is the explicit reference to that article. Citations serve many functions. However, a simplifying assumption is that regardless of the reason for citation, every citation counts as credit to the influence or impact of the cited work. Thus several citation-based metrics have emerged over the years including: number of citations, average citations, h-index, relative citation ratio, and impact factor.",
+      "It is not always clear why some papers get lots of citations and others do not. One can argue that highly cited papers have captured the imagination of the field: perhaps because they were particularly creative, opened up a new area of research, pushed the state of the art by a substantial degree, tested compelling hypotheses, or produced useful datasets, among other things.",
+      "Note however, that the number of citations is not always a reflection of the quality or importance of a piece of work. Note also that there are systematic biases that prevent certain kinds of papers from accruing citations, especially when the contributions of a piece of work are atypical, not easily quantified, or in an area where the number of scientific publications is low. Further, the citations process can be abused, for example, by egregious self-citations.",
+      "Nonetheless, given the immense volume of scientific literature, the relative ease with which one can track citations using services such as Google Scholar and Semantic Scholar, and given the lack of other easily applicable and effective metrics, citation analysis is an imperfect but useful window into research impact.",
+      "In this section, we examine citations of AA papers. We focus on two aspects:",
+      "Most cited papers: We begin by looking at the most cited papers overall and in various time spans. We will then look at most cited papers by paper-type (long, short, demo, etc) and venue (ACL, LREC, etc.). Perhaps these make interesting reading lists. Perhaps they also lead to a qualitative understanding of the kinds of AA papers that have received lots of citations.",
+      "Aggregate citation metrics by time span, paper type, and venue: Access to citation information allows us to calculate aggregate citation metrics such as average and median citations of papers published in different time periods, published in different venues, etc. These can help answer questions such as: on average, how well cited are papers published in the 1990s? on average, how many citations does a short paper get? how many citations does a long paper get? how many citations for a workshop paper? etc.",
+      "Data: The analyses presented below are based on information about the papers taken directly from AA (as of June 2019) and citation information extracted from Google Scholar (as of June 2019). We extracted citation information from Google Scholar profiles of authors who had a Google Scholar Profile page and had published at least three papers in the ACL Anthology. This yielded citation information for about 75% of the papers (33,051 out of the 44,896 papers). We will refer to this subset of the ACL Anthology papers as AA\u2019. All citation analysis below is on AA\u2019."
+    ],
+    [
+      "Q. How many citations have the AA\u2019 papers received? How is that distributed among the papers published in various decades?",
+      "A. $\\sim $1.2 million citations (as of June 2019). Figure FIGREF36 shows a timeline graph where each year has a bar with height corresponding to the number of citations received by papers published in that year. Further, the bar has colored fragments corresponding to each of the papers and the height of a fragment (paper) is proportional to the number of citations it has received. Thus it is easy to spot the papers that received a large number of citations, and the years when the published papers received a large number of citations. Hovering over individual papers reveals an information box showing the paper title, authors, year of publication, publication venue, and #citations.",
+      "Discussion: With time, not only have the number of papers grown, but also the number of high-citation papers. We see a marked jump in the 1990s over the previous decades, but the 2000s are the most notable in terms of the high number of citations. The 2010s papers will likely surpass the 2000s papers in the years to come.",
+      "Q. What are the most cited papers in AA'?",
+      "A. Figure FIGREF37 shoes the most cited papers in the AA'.",
+      "Discussion: We see that the top-tier conference papers (green) are some of the most cited papers in AA\u2019. There are a notable number of journal papers (dark green) in the most cited list as well, but very few demo (purple) and workshop (orange) papers.",
+      "In the interactive visualizations (to be released later), one can click on the url to be to taken directly to the paper\u2019s landing page in the ACL Anthology website. That page includes links to meta information, the pdf, and associated files such as videos and appendices. There will also be functionality to download the lists. Alas, copying the lists from the screenshots shown here is not easy.",
+      "Q. What are the most cited AA' journal papers ? What are the most cited AA' workshop papers? What are the most cited AA' shared task papers? What are the most cited AA' demo papers? What are the most cited tutorials?",
+      "A. The most cited AA\u2019 journal papers, conference papers, workshop papers, system demo papers, shared task papers, and tutorials can be viewed online. The most cited papers from individual venues (ACL, CL journal, TACL, EMNLP, LREC, etc.) can also be viewed there.",
+      "Discussion: Machine translation papers are well-represented in many of these lists, but especially in the system demo papers list. Toolkits such as MT evaluation ones, NLTK, Stanford Core NLP, WordNet Similarity, and OpenNMT have highly cited demo or workshop papers.",
+      "The shared task papers list is dominated by task description papers (papers by task organizers describing the data and task), especially for sentiment analysis tasks. However, the list also includes papers by top-performing systems in these shared tasks, such as the NRC-Canada, HidelTime, and UKP papers.",
+      "Q. What are the most cited AA' papers in the last decade?",
+      "A. Figure FIGREF39 shows the most cited AA' papers in the 2010s. The most cited AA' papers from the earlier periods are available online.",
+      "Discussion: The early period (1965\u20131989) list includes papers focused on grammar and linguistic structure. The 1990s list has papers addressing many different NLP problems with statistical approaches. Papers on MT and sentiment analysis are frequent in the 2000s list. The 2010s are dominated by papers on word embeddings and neural representations."
+    ],
+    [
+      "Q. How many citations did the papers published between 1990 and 1994 receive? What is the average number of citations that a paper published between 1990 and 1994 has received? What are the numbers for other time spans?",
+      "A. Total citations for papers published between 1990 and 1994: $\\sim $92k",
+      "Average citations for papers published between 1990 and 1994: 94.3",
+      "Figure FIGREF41 shows the numbers for various time spans.",
+      "Discussion: The early 1990s were an interesting period for NLP with the use of data from the World Wide Web and technologies from speech processing. This was the period with the highest average citations per paper, closely followed by the 1965\u20131969 and 1995\u20131999 periods. The 2000\u20132004 period is notable for: (1) a markedly larger number of citations than the previous decades; (2) third highest average number of citations. The drop off in the average citations for recent 5-year spans is largely because they have not had as much time to collect citations."
+    ],
+    [
+      "Q. What are the average number of citations received by different types of papers: main conference papers, workshop papers, student research papers, shared task papers, and system demonstration papers?",
+      "A. In this analysis, we include only those AA\u2019 papers that were published in 2016 or earlier (to allow for at least 2.5 years to collect citations). There are 26,949 such papers. Figures FIGREF42 and FIGREF43 show the average citations by paper type when considering papers published 1965\u20132016 and 2010\u20132016, respectively. Figures FIGREF45 and FIGREF46 show the medians.",
+      "Discussion: Journal papers have much higher average and median citations than other papers, but the gap between them and top-tier conferences is markedly reduced when considering papers published since 2010.",
+      "System demo papers have the third highest average citations; however, shared task papers have the third highest median citations. The popularity of shared tasks and the general importance given to beating the state of the art (SOTA) seems to have grown in recent years\u2014something that has come under criticism.",
+      "It is interesting to note that in terms of citations, workshop papers are doing somewhat better than the conferences that are not top tier. Finally, the citation numbers for tutorials show that even though a small number of tutorials are well cited, a majority receive 1 or no citations. This is in contrast to system demo papers that have average and median citations that are higher or comparable to workshop papers.",
+      "Throughout the analyses in this article, we see that median citation numbers are markedly lower than average citation numbers. This is particularly telling. It shows that while there are some very highly cited papers, a majority of the papers obtain much lower number of citations\u2014and when considering papers other than journals and top-tier conferences, the number of citations is frequently lower than ten.",
+      "Q. What are the average number of citations received by the long and short ACL main conference papers, respectively?",
+      "A. Short papers were introduced at ACL in 2003. Since then ACL is by far the venue with the most number of short papers (compared to other venues). So we compare long and short papers published at ACL since 2003 to determine their average citations. Once again, we limit the papers to those published until 2016 to allow for the papers to have time to collect citations. Figure FIGREF47 shows the average and median citations for long and short papers.",
+      "Discussion: On average, long papers get almost three times as many citations as short papers. However, the median for long papers is two-and-half times that of short papers. This difference might be because some very heavily cited long papers push the average up for long papers.",
+      "Q. Which venue has publications with the highest average number of citations? What is the average number of citations for ACL and EMNLP papers? What is this average for other venues? What are the average citations for workshop papers, system demonstration papers, and shared task papers?",
+      "A. CL journal has the highest average citations per paper. Figure FIGREF49 shows the average citations for AA\u2019 papers published 1965\u20132016 and 2010\u20132016, respectively, grouped by venue and paper type. (Figure with median citations is available online.)",
+      "Discussion: In terms of citations, TACL papers have not been as successful as EMNLP and ACL; however, CL journal (the more traditional journal paper venue) has the highest average and median paper citations (by a large margin). This gap has reduced in papers published since 2010.",
+      "When considering papers published between 2010 and 2016, the system demonstration papers, the SemEval shared task papers, and non-SemEval shared task papers have notably high average (surpassing those of EACL and COLING); however their median citations are lower. This is likely because some heavily cited papers have pushed the average up. Nonetheless, it is interesting to note how, in terms of citations, demo and shared task papers have surpassed many conferences and even become competitive with some top-tier conferences such as EACL and COLING.",
+      "Q. What percent of the AA\u2019 papers that were published in 2016 or earlier are cited more than 1000 times? How many more than 10 times? How many papers are cited 0 times?",
+      "A. Google Scholar invented the i-10 index as another measure of author research impact. It stands for the number of papers by an author that received ten or more citations. (Ten here is somewhat arbitrary, but reasonable.) Similar to that, one can look at the impact of AA\u2019 as a whole and the impact of various subsets of AA\u2019 through the number of papers in various citation bins. Figure FIGREF50 shows the percentage of AA\u2019 papers in various citation bins. (The percentages of papers when considering papers from specific time spans are available online.)",
+      "Discussion: About 56% of the papers are cited ten or more times. 6.4% of the papers are never cited. Note also that some portion of the 1\u20139 bin likely includes papers that only received self-citations. It is interesting that the percentage of papers with 0 citations is rather steady (between 7.4% and 8.7%) for the 1965\u20131989, 1990\u20131999, and 2010\u20132016 periods. The majority of the papers lie in the 10 to 99 citations bin, for all except the recent periods (2010\u20132016 and 2016Jan\u20132016Dec). With time, the recent period should also have the majority of the papers in the 10 to 99 citations bin.",
+      "The numbers for the 2016Jan\u20132016Dec papers show that after 2.5 years, about 89% of the papers have at least one citation and about 33% of the papers have ten or more citations.",
+      "Q. What are the citation bin percentages for individual venues and paper types?",
+      "A. See Figure FIGREF51.",
+      "Discussion: Observe that 70 to 80% of the papers in journals and top-tier conferences have ten or more citations. The percentages are markedly lower (between 30 and 70%) for the other conferences shown above, and even lower for some other conferences (not shown above).",
+      "CL Journal is particularly notable for the largest percentage of papers with 100 or more citations. The somewhat high percentage of papers that are never cited (4.3%) are likely because some of the book reviews from earlier years are not explicitly marked in CL journal, and thus they were not removed from analysis. Also, letters to editors, which are more common in CL journal, tend to often obtain 0 citations.",
+      "CL, EMNLP, and ACL have the best track record for accepting papers that have gone on to receive 1000 or more citations. *Sem, the semantics conference, seems to have notably lower percentage of high-citation papers, even though it has fairly competitive acceptance rates.",
+      "Instead of percentage, if one considers raw numbers of papers that have at least ten citations (i-10 index), then LREC is particularly notable in terms of the large number of papers it accepts that have gone on to obtain ten or more citations ($\\sim $1600). Thus, by producing a large number of moderate-to-high citation papers, and introducing many first-time authors, LREC is one of the notable (yet perhaps undervalued) engines of impact on NLP.",
+      "About 50% of the SemEval shared task papers received 10 or more citations, and about 46% of the non-SemEval Shared Task Papers received 10 or more citations. About 47% of the workshop papers received ten or more citations. About 43% of the demo papers received 10 or more citations."
+    ],
+    [
+      "Q. What is the average number of citations of AA' papers that have machine translation in the title? What about papers that have the term sentiment analysis or word representations?",
+      "A. Different areas of research within NLP enjoy varying amounts of attention. In Part II, we looked at the relative popularity of various areas over time\u2014estimated through the number of paper titles that had corresponding terms. (You may also want to see the discussion on the use of paper title terms to sample papers from various, possibly overlapping, areas.) Figure FIGREF53 shows the top 50 title bigrams ordered by decreasing number of total citations. Only those bigrams that occur in at least 30 AA' papers (published between 1965 and 2016) are considered. (The papers from 2017 and later are not included, to allow for at least 2.5 years for the papers to accumulate citations.)",
+      "Discussion: The graph shows that the bigram machine translation occurred in 1,659 papers that together accrued more than 93k citations. These papers have on average 68.8 citations and the median citations is 14. Not all machine translation (MT) papers have machine translation in the title. However, arguably, this set of 1,659 papers is a representative enough sample of machine translation papers; and thus, the average and median are estimates of MT in general. Second in the list are papers with statistical machine in the title\u2014most commonly from the phrase statistical machine translation. One expects considerable overlap in the papers across the sets of papers with machine translation and statistical machine, but machine translation likely covers a broader range of research including work before statistical MT was introduced, neural MT, and MT evaluation.",
+      "There are fewer papers with sentiment analysis in the title (356), but these have acquired citations at a higher average (104) than both machine translation and statistical machine. The bigram automatic evaluation jumps out because of its high average citations (337). Some of the neural-related bigrams have high median citations, for example, neural machine (49) and convolutional neural (40.5).",
+      "Figure FIGREF54 shows the lists of top 25 bigrams ordered by average citations.",
+      "Discussion: Observe the wide variety of topics covered by this list. In some ways that is reassuring for the health of the field as a whole; however, this list does not show which areas are not receiving sufficient attention. It is less clear to me how to highlight those, as simply showing the bottom 50 bigrams by average citations is not meaningful. Also note that this is not in any way an endorsement to write papers with these high-citation bigrams in the title. Doing so is of course no guarantee of receiving a large number of citations."
+    ],
+    [
+      "In this section, we examine citations across two demographic dimensions: Academic age (number of years one has been publishing) and Gender. There are good reasons to study citations across each of these dimensions including, but not limited to, the following:",
+      "Areas of research: To better understand research contributions in the context of the area where the contribution is made.",
+      "Academic age: To better understand how the challenges faced by researchers at various stages of their career may impact the citations of their papers. For example, how well-cited are first-time NLP authors? On average, at what academic age do citations peak? etc.",
+      "Gender: To better understand the extent to which systematic biases (explicit and implicit) pervasive in society and scientific publishing impact author citations.",
+      "Some of these aspects of study may seem controversial. So it is worth addressing that first. The goal here is not to perpetuate stereotypes about age, gender, or even areas of research. The history of scientific discovery is awash with plenty of examples of bad science that has tried to erroneously show that one group of people is \u201cbetter\u201d than another, with devastating consequences.",
+      "People are far more alike than different. However, different demographic groups have faced (and continue to face) various socio-cultural inequities and biases. Gender and race studies look at how demographic differences shape our experiences. They examine the roles of social institutions in maintaining the inequities and biases.",
+      "This work is in support of those studies. Unless we measure differences in outcomes such as scientific productivity and impact across demographic groups, we will not fully know the extent to which these inequities and biases impact our scientific community; and we cannot track the effectiveness of measures to make our universities, research labs, and conferences more inclusive, equitable, and fair."
+    ],
+    [
+      "We introduced NLP academic age earlier in the paper, where we defined NLP academic age as the number of years one has been publishing in AA. Here we examine whether NLP academic age impacts citations. The analyses are done in terms of the academic age of the first author; however, similar analyses can be done for the last author and all authors. (There are limitations to each of these analyses though as discussed further below.)",
+      "First author is a privileged position in the author list as it is usually reserved for the researcher that has done the most work and writing. The first author is also usually the main driver of the project; although, their mentor or advisor may also be a significant driver of the project. Sometimes multiple authors may be marked as first authors in the paper, but the current analysis simply takes the first author from the author list. In many academic communities, the last author position is reserved for the most senior or mentoring researcher. However, in non-university research labs and in large collaboration projects, the meaning of the last author position is less clear. (Personally, I prefer author names ordered by the amount of work done.)",
+      "Examining all authors is slightly more tricky as one has to decide how to credit the citations to the possibly multiple authors. It might also not be a clear indicator of differences across gender as a large number of the papers in AA have both male and female authors.",
+      "Q. How does the NLP academic age of the first author correlate with the amount of citations? Are first-year authors less cited than those with more experience?",
+      "A. Figure FIGREF59 shows various aggregate citation statistics corresponding to academic age. To produce the graph we put each paper in a bin corresponding to the academic age of the first author when the paper was published. For example, if the first author of a paper had an academic age of 3 when that paper was published, then the paper goes in bin 3. We then calculate #papers, #citations, median citations, and average citations for each bin. For the figure below, We further group the bins 10 to 14, 15 to 19, 20 to 34, and 35 to 50. These groupings are done to avoid clutter, and also because many of the higher age bins have a low number of papers.",
+      "Discussion: Observe that the number of papers where the first author has academic age 1 is much larger than the number of papers in any other bin. This is largely because a large number of authors in AA have written exactly one paper as first author. Also, about 60% of the authors in AA (17,874 out of the 29,941 authors) have written exactly one paper (regardless of author position).",
+      "The curves for the average and median citations have a slight upside down U shape. The relatively lower average and median citations in year 1 (37.26 and 10, respectively) indicate that being new to the field has some negative impact on citations. The average increases steadily from year 1 to year 4, but the median is already at the highest point by year 2. One might say, that year 2 to year 14 are the period of steady and high citations. Year 15 onwards, there is a steady decline in the citations. It is probably wise to not draw too many conclusions from the averages of the 35 to 50 bin, because of the small number of papers. There seems to be a peak in average citations at age 7. However, there is not a corresponding peak in the median. Thus the peak in average might be due to an increase in the number of very highly cited papers. Citations to Papers by First Author Gender",
+      "As noted in Part I, neither ACL nor the ACL Anthology have recorded demographic information for the vast majority of the authors. Thus we use the same setup discussed earlier in the section on demographics, to determine gender using the United States Social Security Administration database of names and genders of newborns to identify 55,133 first names that are strongly associated with females (probability $\\ge $99%) and 29,873 first names that are strongly associated with males (probability $\\ge $99%).",
+      "Q. On average, are women cited less than men?",
+      "A. Yes, on average, female first author papers have received markedly fewer citations than male first author papers (36.4 compared to 52.4). The difference in median is smaller (11 compared to 13). See Figure FIGREF60.",
+      "Discussion: The large difference in averages and smaller difference in medians suggests that there are markedly more very heavily cited male first-author papers than female first-author papers. The gender-unknown category, which here largely consist of authors with Chinese origin names and names that are less strongly associated with one gender have a slightly higher average, but the same median citations, as authors with female-associated first names.",
+      "The differences in citations, or citation gap, across genders may: (1) vary by period of time; (2) vary due to confounding factors such as academic age and areas of research. We explore these next.",
+      "Q. How has the citation gap across genders changed over the years?",
+      "A. Figure FIGREF61 (left side) shows the citation statistics across four time periods.",
+      "Discussion: Observe that female first authors have always been a minority in the history of ACL; however, on average, their papers from the early years (1965 to 1989) received a markedly higher number of citations than those of male first authors from the same period. We can see from the graph that this changed in the 1990s where male first-author papers obtained markedly more citations on average. The citation gap reduced considerably in the 2000s, and the 2010\u20132016 period saw a further slight reduction in the citation gap.",
+      "It is also interesting to note that the gender-unknown category has almost bridged the gap with the males in this most recent time period. Further, the proportion of the gender-unknown authors has increased over the years\u2014arguably, an indication of better representations of authors from around the world in recent years. (Nonetheless, as indicated in Part I, there is still plenty to be done to promote greater inclusion of authors from Africa and South America.)",
+      "Q. How have citations varied by gender and academic age? Are women less cited because of a greater proportion of new-to-NLP female first authors than new-to-NLP male first authors?",
+      "A. Figure FIGREF61 (right side) shows citation statistics broken down by gender and academic age. (This figure is similar to the academic age graph seen earlier, except that it shows separate average and median lines for female, male, and unknown gender first authors.)",
+      "Discussion: The graphs show that female first authors consistently receive fewer citations than male authors for the first fifteen years. The trend is inverted with a small citation gap in the 15th to 34th years period.",
+      "Q. Is the citation gap common across the vast majority of areas of research within NLP? Is the gap simply because more women work in areas that receive low numbers of citations (regardless of gender)?",
+      "A. Figure FIGREF64 shows the most cited areas of research along with citation statistics split by gender of the first authors of corresponding papers. (This figure is similar to the areas of research graph seen earlier, except that it shows separate citation statistics for the genders.) Note that the figure includes rows for only those bigram and gender pairs with at least 30 AA\u2019 papers (published between 1965 and 2016). Thus for some of the bigrams certain gender entries are not shown.",
+      "Discussion: Numbers for an additional 32 areas are available online. Observe that in only about 12% (7 of the top 59) of the most cited areas of research, women received higher average citations than men. These include: sentiment analysis, information extraction, document summarization, spoken dialogue, cross lingual (research), dialogue, systems, language generation. (Of course, note that some of the 59 areas, as estimated using title term bigrams, are overlapping. Also, we did not include large scale in the list above because the difference in averages is very small and it is not really an area of research.) Thus, the citation gap is common across a majority of the high-citations areas within NLP."
+    ],
+    [
+      "This work examined the ACL Anthology to identify broad trends in productivity, focus, and impact. We examined several questions such as: who and how many of us are publishing? what are we publishing on? where and in what form are we publishing? and what is the impact of our publications? Particular attention was paid to the demographics and inclusiveness of the NLP community. Notably, we showed that only about 30% of first authors are female, and that this percentage has not improved since the year 2000. We also showed that, on average, female first authors are cited less than male first authors, even when controlling for academic age. We hope that recording citation and participation gaps across demographic groups will encourage our university, industry, and government research labs to be more inclusive and fair. Several additional aspects of the AA will be explored in future work (see the bottom of the blog posts).",
+      "Acknowledgments",
+      "This work was possible due to the helpful discussion and encouragement from a number of awesome people, including: Dan Jurafsky, Tara Small, Michael Strube, Cyril Goutte, Eric Joanis, Matt Post, Patrick Littell, Torsten Zesch, Ellen Riloff, Norm Vinson, Iryna Gurevych, Rebecca Knowles, Isar Nejadgholi, and Peter Turney. Also, a big thanks to the ACL Anthology team for creating and maintaining a wonderful resource."
+    ]
+  ]
+}
+```
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+Name of Paper: The State of NLP Literature: A Diachronic Analysis of the ACL Anthology
+
+Question: Which 3 NLP areas are cited the most?
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+Name of Paper: The State of NLP Literature: A Diachronic Analysis of the ACL Anthology
+
+Question: Which journal and conference are cited the most in recent years?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Size",
+    "Demographics (focus of analysis: gender, age, and geographic diversity)",
+    "Demographics (focus of analysis: gender, age, and geographic diversity) ::: Gender",
+    "Demographics (focus of analysis: gender, age, and geographic diversity) ::: Academic Age",
+    "Demographics (focus of analysis: gender, age, and geographic diversity) ::: Location (Languages)",
+    "Areas of Research",
+    "Impact",
+    "Impact ::: #Citations and Most Cited Papers",
+    "Impact ::: Average Citations by Time Span",
+    "Impact ::: Aggregate Citation Statistics, by Paper Type and Venue",
+    "Impact ::: Citations to Papers by Areas of Research",
+    "Correlation of Age and Gender with Citations",
+    "Correlation of Age and Gender with Citations ::: Correlation of Academic Age with Citations",
+    "Conclusions"
+  ],
+  "paragraphs": [
+    [
+      "The ACL Anthology (AA) is a digital repository of tens of thousands of articles on Natural Language Processing (NLP) / Computational Linguistics (CL). It includes papers published in the family of ACL conferences as well as in other NLP conferences such as LREC and RANLP. AA is the largest single source of scientific literature on NLP.",
+      "This project, which we call NLP Scholar, examines the literature as a whole to identify broad trends in productivity, focus, and impact. We will present the analyses in a sequence of questions and answers. The questions range from fairly mundane to oh-that-will-be-good-to-know. Our broader goal here is simply to record the state of the AA literature: who and how many of us are publishing? what are we publishing on? where and in what form are we publishing? and what is the impact of our publications? The answers are usually in the form of numbers, graphs, and inter-connected visualizations.",
+      "We focus on the following aspects of NLP research: size, demographics, areas of research, impact, and correlation of citations with demographic attributes (age and gender).",
+      "",
+      "Target Audience: The analyses presented here are likely to be of interest to any NLP researcher. This might be particularly the case for those that are new to the field and wish to get a broad overview of the NLP publishing landscape. On the other hand, even seasoned NLP'ers have likely wondered about the questions raised here and might be interested in the empirical evidence.",
+      "",
+      "Data: The analyses presented below are based on information about the papers taken directly from AA (as of June 2019) and citation information extracted from Google Scholar (as of June 2019). Thus, all subsequent papers and citations are not included in the analysis. A fresh data collection is planned for January 2020.",
+      "",
+      "Interactive Visualizations: The visualizations we are developing for this work (using Tableau) are interactive\u2014so one can hover, click to select and filter, move sliders, etc. Since this work is high in the number of visualizations, the main visualizations are presented as figures in the paper and some sets of visualizations are pointed to online. The interactive visualizations and data will be made available through the first author's website after peer review.",
+      "",
+      "Related Work: This work builds on past research, including that on Google Scholar BIBREF0, BIBREF1, BIBREF2, BIBREF3, on the analysis of NLP papers BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9, on citation intent BIBREF10, BIBREF11, BIBREF12, BIBREF13, BIBREF14, BIBREF15, and on measuring scholarly impact BIBREF16, BIBREF17, BIBREF18, BIBREF19, BIBREF20, BIBREF21.",
+      "",
+      "Caveats and Ethical Considerations: We list several caveats and limitations throughout the paper. A compilation of these is also available online in the About NLP Scholar page.",
+      "The analyses presented here are also available as a series of blog posts."
+    ],
+    [
+      "Q. How big is the ACL Anthology (AA)? How is it changing with time?",
+      "A. As of June 2019, AA had $\\sim $50K entries, however, this includes some number of entries that are not truly research publications (for example, forewords, prefaces, table of contents, programs, schedules, indexes, calls for papers/participation, lists of reviewers, lists of tutorial abstracts, invited talks, appendices, session information, obituaries, book reviews, newsletters, lists of proceedings, lifetime achievement awards, erratum, and notes). We discard them for the analyses here. (Note: CL journal includes position papers like squibs, letter to editor, opinion, etc. We do not discard them.) We are then left with 44,896 articles. Figure FIGREF6 shows a graph of the number of papers published in each of the years from 1965 to 2018.",
+      "Discussion: Observe that there was a spurt in the 1990s, but things really took off since the year 2000, and the growth continues. Also, note that the number of publications is considerably higher in alternate years. This is due to biennial conferences. Since 1998 the largest of such conferences has been LREC (In 2018 alone LREC had over 700 main conferences papers and additional papers from its 29 workshops). COLING, another biennial conference (also occurring in the even years) has about 45% of the number of main conference papers as LREC.",
+      "Q. How many people publish in the ACL Anthology (NLP conferences)?",
+      "A. Figure FIGREF7 shows a graph of the number of authors (of AA papers) over the years:",
+      "Discussion: It is a good sign for the field to have a growing number of people join its ranks as researchers. A further interesting question would be:",
+      "Q. How many people are actively publishing in NLP?",
+      "A. It is hard to know the exact number, but we can determine the number of people who have published in AA in the last N years.",
+      "#people who published at least one paper in 2017 and 2018 (2 years): $\\sim $12k (11,957 to be precise)",
+      "#people who published at least one paper 2015 through 2018 (4 years):$\\sim $17.5k (17,457 to be precise)",
+      "Of course, some number of researchers published NLP papers in non-AA venues, and some number are active NLP researchers who may not have published papers in the last few years.",
+      "Q. How many journal papers exist in the AA? How many main conference papers? How many workshop papers?",
+      "A. See Figure FIGREF8.",
+      "Discussion: The number of journal papers is dwarfed by the number of conference and workshop papers. (This is common in computer science. Even though NLP is a broad interdisciplinary field, the influence of computer science practices on NLP is particularly strong.) Shared task and system demo papers are relatively new (introduced in the 2000s), but their numbers are already significant and growing.",
+      "Creating a separate class for \u201cTop-tier Conference\u201d is somewhat arbitrary, but it helps make certain comparisons more meaningful (for example, when comparing the average number of citations, etc.). For this work, we consider ACL, EMNLP, NAACL, COLING, and EACL as top-tier conferences, but certainly other groupings are also reasonable.",
+      "Q. How many papers have been published at ACL (main conference papers)? What are the other NLP venues and what is the distribution of the number of papers across various CL/NLP venues?",
+      "A. # ACL (main conference papers) as of June 2018: 4,839",
+      "The same workshop can co-occur with different conferences in different years, so we grouped all workshop papers in their own class. We did the same for tutorials, system demonstration papers (demos), and student research papers. Figure FIGREF9 shows the number of main conference papers for various venues and paper types (workshop papers, demos, etc.).",
+      "Discussion: Even though LREC is a relatively new conference that occurs only once in two years, it tends to have a high acceptance rate ($\\sim $60%), and enjoys substantial participation. Thus, LREC is already the largest single source of NLP conference papers. SemEval, which started as SenseEval in 1998 and occurred once in two or three years, has now morphed into an annual two-day workshop\u2014SemEval. It is the largest single source of NLP shared task papers."
+    ],
+    [
+      "NLP, like most other areas of research, suffers from poor demographic diversity. There is very little to low representation from certain nationalities, race, gender, language, income, age, physical abilities, etc. This impacts the breadth of technologies we create, how useful they are, and whether they reach those that need it most. In this section, we analyze three specific attributes among many that deserve attention: gender (specifically, the number of women researchers in NLP), age (more precisely, the number of years of NLP paper publishing experience), and the amount of research in various languages (which loosely correlates with geographic diversity)."
+    ],
+    [
+      "The ACL Anthology does not record demographic information about the paper authors. (Until recently, ACL and other NLP conferences did not record demographic information of the authors.) However, many first names have strong associations with a male or female gender. We will use these names to estimate the percentage of female first authors in NLP.",
+      "The US Social Security Administration publishes a database of names and genders of newborns. We use the dataset to identify 55,133 first names that are strongly associated with females (probability $\\ge $99%) and 29,873 first names that are strongly associated with males (probability $\\ge $99%). (As a side, it is interesting to note that there is markedly greater diversity in female names than in male names.) We identified 26,637 of the 44,896 AA papers ($\\sim $60%) where the first authors have one of these names and determine the percentage of female first author papers across the years. We will refer to this subset of AA papers as AA*.",
+      "",
+      "Note the following caveats associated with this analysis:",
+      "",
+      "The names dataset used has a lower representation of names from nationalities other than the US. However, there is a large expatriate population living in the US.",
+      "",
+      "Chinese names (especially in the romanized form) are not good indicators of gender. Thus the method presented here disregards most Chinese names, and the results of the analysis apply to the group of researchers excluding those with Chinese names.",
+      "",
+      "The dataset only records names associated with two genders.",
+      "",
+      "The approach presented here is meant to be an approximation in the absence of true gender information.",
+      "Q. What percent of the AA* papers have female first authors (FFA)? How has this percentage changed with time?",
+      "A. Overall FFA%: 30.3%. Figure FIGREF16 shows how FFA% has changed with time. Common paper title words and FFA% of papers that have those words are shown in the bottom half of the image. Note that the slider at the bottom has been set to 400, i.e., only those title words that occur in 400 or more papers are shown. The legend on the bottom right shows that low FFA scores are shown in shades of blue, whereas relatively higher FFA scores are shown in shades of green.",
+      "",
+      "Discussion: Observe that as a community, we are far from obtaining male-female parity in terms of first authors. A further striking (and concerning) observation is that the female first author percentage has not improved since the years 1999 and 2000 when the FFA percentages were highest (32.9% and 32.8%, respectively). In fact there seems to even be a slight downward trend in recent years. The calculations shown above are for the percentage of papers that have female first authors. The percentage of female first authors is about the same ($\\sim $31%). On average male authors had a slightly higher average number of publications than female authors.",
+      "To put these numbers in context, the percentage of female scientists world wide (considering all areas of research) has been estimated to be around 30%. The reported percentages for many computer science sub-fields are much lower. (See Women in Science (2015).) The percentages are much higher for certain other fields such as psychology and linguistics. (See this study for psychology and this study for linguistics.) If we can identify ways to move the needle on the FFA percentage and get it closer to 50% (or more), NLP can be a beacon to many other fields, especially in the sciences.",
+      "FFA percentages are particularly low for papers that have parsing, neural, and unsupervised in the title. There are some areas within NLP that enjoy a healthier female-male parity in terms of first authors of papers. Figure FIGREF20 shows FFA percentages for papers that have the word discourse in the title. There is burgeoning research on neural NLP in the last few years. Figure FIGREF21 shows FFA percentages for papers that have the word neural in the title.",
+      "Figure FIGREF22 shows lists of terms with the highest and lowest FFA percentages, respectively, when considering terms that occur in at least 50 paper titles (instead of 400 in the analysis above). Observe that FFA percentages are relatively higher in non-English European language research such as papers on Russian, Portuguese, French, and Italian. FFA percentages are also relatively higher for certain areas of NLP such as work on prosody, readability, discourse, dialogue, paraphrasing, and individual parts of speech such as adjectives and verbs. FFA percentages are particularly low for papers on theoretical aspects of statistical modelling, and areas such as machine translation, parsing, and logic. The full lists of terms and FFA percentages will be made available with the rest of the data."
+    ],
+    [
+      "While the actual age of NLP researchers might be an interesting aspect to explore, we do not have that information. Thus, instead, we can explore a slightly different (and perhaps more useful) attribute: NLP academic age. We can define NLP academic age as the number of years one has been publishing in AA. So if this is the first year one has published in AA, then their NLP academic age is 1. If one published their first AA paper in 2001 and their latest AA paper in 2018, then their academic age is 18.",
+      "Q. How old are we? That is, what is the average NLP academic age of those who published papers in 2018? How has the average changed over the years? That is, have we been getting older or younger? What percentage of authors that published in 2018 were publishing their first AA paper?",
+      "A. Average NLP Academic Age of people that published in 2018: 5.41 years",
+      "Median NLP Academic Age of people that published in 2018: 2 years",
+      "Percentage of 2018 authors that published their first AA paper in 2018: 44.9%",
+      "Figure FIGREF24 shows how these numbers have changed over the years.",
+      "Discussion: Observe that the Average academic age has been steadily increasing over the years until 2016 and 2017, when the trend has shifted and the average academic age has started to decrease. The median age was 1 year for most of the 1965 to 1990 period, 2 years for most of the 1991 to 2006 period, 3 years for most of the 2007 to 2015 period, and back to 2 years since then. The first-time AA author percentage decreased until about 1988, after which it sort of stayed steady at around 48% until 2004 with occasional bursts to $\\sim $56%. Since 2005, the first-time author percentage has gone up and down every other year. It seems that the even years (which are also LREC years) have a higher first-time author percentage. Perhaps, this oscillation in first-time authors percentage is related to LREC\u2019s high acceptance rate.",
+      "Q. What is the distribution of authors in various academic age bins? For example, what percentage of authors that published in 2018 had an academic age of 2, 3, or 4? What percentage had an age between 5 and 9? And so on?",
+      "A. See Figure FIGREF25.",
+      "Discussion: Observe that about 65% of the authors that published in 2018 had an academic age of less than 5. This number has steadily reduced since 1965, was in the 60 to 70% range in 1990s, rose to the 70 to 72% range in early 2000s, then declined again until it reached the lowest value ($\\sim $60%) in 2010, and has again steadily risen until 2018 (65%). Thus, even though it may sometimes seem at recent conferences that there is a large influx of new people into NLP (and that is true), proportionally speaking, the average NLP academic age is higher (more experienced) than what it has been in much of its history."
+    ],
+    [
+      "Automatic systems with natural language abilities are growing to be increasingly pervasive in our lives. Not only are they sources of mere convenience, but are crucial in making sure large sections of society and the world are not left behind by the information divide. Thus, the limits of what automatic systems can do in a language, limit the world for the speakers of that language.",
+      "We know that much of the research in NLP is on English or uses English datasets. Many reasons have been proffered, and we will not go into that here. Instead, we will focus on estimating how much research pertains to non-English languages.",
+      "We will make use of the idea that often when work is done focusing on a non-English language, then the language is mentioned in the title. We collected a list of 122 languages indexed by Wiktionary and looked for the presence of these words in the titles of AA papers. (Of course there are hundreds of other lesser known languages as well, but here we wanted to see the representation of these more prominent languages in NLP literature.)",
+      "Figure FIGREF27 is a treemap of the 122 languages arranged alphabetically and shaded such that languages that appear more often in AA paper titles have a darker shade of green.",
+      "Discussion: Even though the amount of work done on English is much larger than that on any other language, often the word English does not appear in the title, and this explains why English is not the first (but the second-most) common language name to appear in the titles. This is likely due to the fact that many papers fail to mention the language of study or the language of the datasets used if it is English. There is growing realization in the community that this is not quite right. However, the language of study can be named in other less prominent places than the title, for example the abstract, introduction, or when the datasets are introduced, depending on how central it is to the paper.",
+      "We can see from the treemap that the most widely spoken Asian and Western European languages enjoy good representation in AA. These include: Chinese, Arabic, Korean, Japanese, and Hindi (Asian) as well as French, German, Swedish, Spanish, Portuguese, and Italian (European). This is followed by the relatively less widely spoken European languages (such as Russian, Polish, Norwegian, Romanian, Dutch, and Czech) and Asian languages (such as Turkish, Thai, and Urdu). Most of the well-represented languages are from the Indo-European language family. Yet, even in the limited landscape of the most common 122 languages, vast swathes are barren with inattention. Notable among these is the extremely low representation of languages from Africa, languages from non-Indo-European language families, and Indigenous languages from around the world."
+    ],
+    [
+      "Natural Language Processing addresses a wide range of research questions and tasks pertaining to language and computing. It encompasses many areas of research that have seen an ebb and flow of interest over the years. In this section, we examine the terms that have been used in the titles of ACL Anthology (AA) papers. The terms in a title are particularly informative because they are used to clearly and precisely convey what the paper is about. Some journals ask authors to separately include keywords in the paper or in the meta-information, but AA papers are largely devoid of this information. Thus titles are an especially useful source of keywords for papers\u2014keywords that are often indicative of the area of research.",
+      "Keywords could also be extracted from abstracts and papers; we leave that for future work. Further work is also planned on inferring areas of research using word embeddings, techniques from topic modelling, and clustering. There are clear benefits to performing analyses using that information. However, those approaches can be sensitive to the parameters used. Here, we keep things simple and explore counts of terms in paper titles. Thus the results are easily reproducible and verifiable.",
+      "Caveat: Even though there is an association between title terms and areas of research, the association can be less strong for some terms and areas. We use the association as one (imperfect) source of information about areas of research. This information may be combined with other sources of information to draw more robust conclusions.",
+      "Title Terms: The title has a privileged position in a paper. It serves many functions, and here are three key ones (from an article by Sneha Kulkarni): \"A good research paper title: 1. Condenses the paper's content in a few words 2. Captures the readers' attention 3. Differentiates the paper from other papers of the same subject area\".",
+      "If we examine the titles of papers in the ACL Anthology, we would expect that because of Function 1 many of the most common terms will be associated with the dominant areas of research. Function 2 (or attempting to have a catchy title) on the other hand, arguably leads to more unique and less frequent title terms. Function 3 seems crucial to the effectiveness of a title; and while at first glance it may seem like this will lead to unique title terms, often one needs to establish a connection with something familiar in order to convey how the work being presented is new or different.",
+      "It is also worth noting that a catchy term today, will likely not be catchy tomorrow. Similarly, a distinctive term today, may not be distinctive tomorrow. For example, early papers used neural in the title to distinguish themselves from non-nerual approaches, but these days neural is not particularly discriminative as far as NLP papers go.",
+      "Thus, competing and complex interactions are involved in the making of titles. Nonetheless, an arguable hypothesis is that: broad trends in interest towards an area of research will be reflected, to some degree, in the frequencies of title terms associated with that area over time. However, even if one does not believe in that hypothesis, it is worth examining the terms in the titles of tens of thousands of papers in the ACL Anthology\u2014spread across many decades.",
+      "Q. What terms are used most commonly in the titles of the AA papers? How has that changed with time?",
+      "A. Figure FIGREF28 shows the most common unigrams (single word) and bigrams (two-word sequences) in the titles of papers published from 1980 to 2019. (Ignoring function words.) The timeline graph at the bottom shows the percentage of occurrences of the unigrams over the years (the colors of the unigrams in the Timeline match those in the Title Unigram list). Note: For a given year, the timeline graph includes a point for a unigram if the sum of the frequency of the unigram in that year and the two years before it is at least ten. The period before 1980 is not included because of the small number of papers.",
+      "Discussion: Appropriately enough, the most common term in the titles of NLP papers is language. Presence of high-ranking terms pertaining to machine translation suggest that it is the area of research that has received considerable attention. Other areas associated with the high-frequency title terms include lexical semantics, named entity recognition, question answering, word sense disambiguation, and sentiment analysis. In fact, the common bigrams in the titles often correspond to names of NLP research areas. Some of the bigrams like shared task and large scale are not areas of research, but rather mechanisms or trends of research that apply broadly to many areas of research. The unigrams, also provide additional insights, such as the interest of the community in Chinese language, and in areas such as speech and parsing.",
+      "The Timeline graph is crowded in this view, but clicking on a term from the unigram list will filter out all other lines from the timeline. This is especially useful for determining whether the popularity of a term is growing or declining. (One can already see from above that neural has broken away from the pack in recent years.) Since there are many lines in the Timeline graph, Tableau labels only some (you can see neural and machine). However, hovering over a line, in the eventual interactive visualization, will display the corresponding term\u2014as shown in the figure.",
+      "Despite being busy, the graph sheds light on the relative dominance of the most frequent terms and how that has changed with time. The vocabulary of title words is smaller when considering papers from the 1980's than in recent years. (As would be expected since the number of papers then was also relatively fewer.) Further, dominant terms such as language and translation accounted for a higher percentage than in recent years where there is a much larger diversity of topics and the dominant research areas are not as dominant as they once were.",
+      "Q. What are the most frequent unigrams and bigrams in the titles of recent papers?",
+      "A. Figure FIGREF29 shows the most frequent unigrams and bigrams in the titles of papers published 2016 Jan to 2019 June (time of data collection).",
+      "Discussion: Some of the terms that have made notable gains in the top 20 unigrams and bigrams lists in recent years include: neural machine (presumably largely due to the phrase neural machine translation), neural network(s), word embeddings, recurrent neural, deep learning and the corresponding unigrams (neural, networks, etc.). We also see gains for terms related to shared tasks such as SemEval and task.",
+      "The sets of most frequent unigrams and bigrams in the titles of AA papers from various time spans are available online. Apart from clicking on terms, one can also enter the query (say parsing) in the search box at the bottom. Apart from filtering the timeline graph (bottom), this action also filters the unigram list (top left) to provide information only about the search term. This is useful because the query term may not be one of the visible top unigrams.",
+      "FigureFIGREF31 shows the timeline graph for parsing.",
+      "Discussion: Parsing seems to have enjoyed considerable attention in the 1980s, began a period of steep decline in the early 1990s, and a period of gradual decline ever since. One can enter multiple terms in the search box or shift/command click multiple terms to show graphs for more than one term.",
+      "FigureFIGREF32 shows the timelines for three bigrams statistical machine, neural machine, and machine translation:",
+      "Discussion: The graph indicates that there was a spike in machine translation papers in 1996, but the number of papers dropped substantially after that. Yet, its numbers have been comparatively much higher than other terms. One can also see the rise of statistical machine translation in the early 2000s followed by its decline with the rise of neural machine translation."
+    ],
+    [
+      "Research articles can have impact in a number of ways\u2014pushing the state of the art, answering crucial questions, finding practical solutions that directly help people, making a new generation of potential-scientists excited about a field of study, and more. As scientists, it seems attractive to quantitatively measure scientific impact, and this is particularly appealing to governments and funding agencies; however, it should be noted that individual measures of research impact are limited in scope\u2014they measure only some kinds of contributions. Citations",
+      "The most commonly used metrics of research impact are derived from citations. A citation of a scholarly article is the explicit reference to that article. Citations serve many functions. However, a simplifying assumption is that regardless of the reason for citation, every citation counts as credit to the influence or impact of the cited work. Thus several citation-based metrics have emerged over the years including: number of citations, average citations, h-index, relative citation ratio, and impact factor.",
+      "It is not always clear why some papers get lots of citations and others do not. One can argue that highly cited papers have captured the imagination of the field: perhaps because they were particularly creative, opened up a new area of research, pushed the state of the art by a substantial degree, tested compelling hypotheses, or produced useful datasets, among other things.",
+      "Note however, that the number of citations is not always a reflection of the quality or importance of a piece of work. Note also that there are systematic biases that prevent certain kinds of papers from accruing citations, especially when the contributions of a piece of work are atypical, not easily quantified, or in an area where the number of scientific publications is low. Further, the citations process can be abused, for example, by egregious self-citations.",
+      "Nonetheless, given the immense volume of scientific literature, the relative ease with which one can track citations using services such as Google Scholar and Semantic Scholar, and given the lack of other easily applicable and effective metrics, citation analysis is an imperfect but useful window into research impact.",
+      "In this section, we examine citations of AA papers. We focus on two aspects:",
+      "Most cited papers: We begin by looking at the most cited papers overall and in various time spans. We will then look at most cited papers by paper-type (long, short, demo, etc) and venue (ACL, LREC, etc.). Perhaps these make interesting reading lists. Perhaps they also lead to a qualitative understanding of the kinds of AA papers that have received lots of citations.",
+      "Aggregate citation metrics by time span, paper type, and venue: Access to citation information allows us to calculate aggregate citation metrics such as average and median citations of papers published in different time periods, published in different venues, etc. These can help answer questions such as: on average, how well cited are papers published in the 1990s? on average, how many citations does a short paper get? how many citations does a long paper get? how many citations for a workshop paper? etc.",
+      "Data: The analyses presented below are based on information about the papers taken directly from AA (as of June 2019) and citation information extracted from Google Scholar (as of June 2019). We extracted citation information from Google Scholar profiles of authors who had a Google Scholar Profile page and had published at least three papers in the ACL Anthology. This yielded citation information for about 75% of the papers (33,051 out of the 44,896 papers). We will refer to this subset of the ACL Anthology papers as AA\u2019. All citation analysis below is on AA\u2019."
+    ],
+    [
+      "Q. How many citations have the AA\u2019 papers received? How is that distributed among the papers published in various decades?",
+      "A. $\\sim $1.2 million citations (as of June 2019). Figure FIGREF36 shows a timeline graph where each year has a bar with height corresponding to the number of citations received by papers published in that year. Further, the bar has colored fragments corresponding to each of the papers and the height of a fragment (paper) is proportional to the number of citations it has received. Thus it is easy to spot the papers that received a large number of citations, and the years when the published papers received a large number of citations. Hovering over individual papers reveals an information box showing the paper title, authors, year of publication, publication venue, and #citations.",
+      "Discussion: With time, not only have the number of papers grown, but also the number of high-citation papers. We see a marked jump in the 1990s over the previous decades, but the 2000s are the most notable in terms of the high number of citations. The 2010s papers will likely surpass the 2000s papers in the years to come.",
+      "Q. What are the most cited papers in AA'?",
+      "A. Figure FIGREF37 shoes the most cited papers in the AA'.",
+      "Discussion: We see that the top-tier conference papers (green) are some of the most cited papers in AA\u2019. There are a notable number of journal papers (dark green) in the most cited list as well, but very few demo (purple) and workshop (orange) papers.",
+      "In the interactive visualizations (to be released later), one can click on the url to be to taken directly to the paper\u2019s landing page in the ACL Anthology website. That page includes links to meta information, the pdf, and associated files such as videos and appendices. There will also be functionality to download the lists. Alas, copying the lists from the screenshots shown here is not easy.",
+      "Q. What are the most cited AA' journal papers ? What are the most cited AA' workshop papers? What are the most cited AA' shared task papers? What are the most cited AA' demo papers? What are the most cited tutorials?",
+      "A. The most cited AA\u2019 journal papers, conference papers, workshop papers, system demo papers, shared task papers, and tutorials can be viewed online. The most cited papers from individual venues (ACL, CL journal, TACL, EMNLP, LREC, etc.) can also be viewed there.",
+      "Discussion: Machine translation papers are well-represented in many of these lists, but especially in the system demo papers list. Toolkits such as MT evaluation ones, NLTK, Stanford Core NLP, WordNet Similarity, and OpenNMT have highly cited demo or workshop papers.",
+      "The shared task papers list is dominated by task description papers (papers by task organizers describing the data and task), especially for sentiment analysis tasks. However, the list also includes papers by top-performing systems in these shared tasks, such as the NRC-Canada, HidelTime, and UKP papers.",
+      "Q. What are the most cited AA' papers in the last decade?",
+      "A. Figure FIGREF39 shows the most cited AA' papers in the 2010s. The most cited AA' papers from the earlier periods are available online.",
+      "Discussion: The early period (1965\u20131989) list includes papers focused on grammar and linguistic structure. The 1990s list has papers addressing many different NLP problems with statistical approaches. Papers on MT and sentiment analysis are frequent in the 2000s list. The 2010s are dominated by papers on word embeddings and neural representations."
+    ],
+    [
+      "Q. How many citations did the papers published between 1990 and 1994 receive? What is the average number of citations that a paper published between 1990 and 1994 has received? What are the numbers for other time spans?",
+      "A. Total citations for papers published between 1990 and 1994: $\\sim $92k",
+      "Average citations for papers published between 1990 and 1994: 94.3",
+      "Figure FIGREF41 shows the numbers for various time spans.",
+      "Discussion: The early 1990s were an interesting period for NLP with the use of data from the World Wide Web and technologies from speech processing. This was the period with the highest average citations per paper, closely followed by the 1965\u20131969 and 1995\u20131999 periods. The 2000\u20132004 period is notable for: (1) a markedly larger number of citations than the previous decades; (2) third highest average number of citations. The drop off in the average citations for recent 5-year spans is largely because they have not had as much time to collect citations."
+    ],
+    [
+      "Q. What are the average number of citations received by different types of papers: main conference papers, workshop papers, student research papers, shared task papers, and system demonstration papers?",
+      "A. In this analysis, we include only those AA\u2019 papers that were published in 2016 or earlier (to allow for at least 2.5 years to collect citations). There are 26,949 such papers. Figures FIGREF42 and FIGREF43 show the average citations by paper type when considering papers published 1965\u20132016 and 2010\u20132016, respectively. Figures FIGREF45 and FIGREF46 show the medians.",
+      "Discussion: Journal papers have much higher average and median citations than other papers, but the gap between them and top-tier conferences is markedly reduced when considering papers published since 2010.",
+      "System demo papers have the third highest average citations; however, shared task papers have the third highest median citations. The popularity of shared tasks and the general importance given to beating the state of the art (SOTA) seems to have grown in recent years\u2014something that has come under criticism.",
+      "It is interesting to note that in terms of citations, workshop papers are doing somewhat better than the conferences that are not top tier. Finally, the citation numbers for tutorials show that even though a small number of tutorials are well cited, a majority receive 1 or no citations. This is in contrast to system demo papers that have average and median citations that are higher or comparable to workshop papers.",
+      "Throughout the analyses in this article, we see that median citation numbers are markedly lower than average citation numbers. This is particularly telling. It shows that while there are some very highly cited papers, a majority of the papers obtain much lower number of citations\u2014and when considering papers other than journals and top-tier conferences, the number of citations is frequently lower than ten.",
+      "Q. What are the average number of citations received by the long and short ACL main conference papers, respectively?",
+      "A. Short papers were introduced at ACL in 2003. Since then ACL is by far the venue with the most number of short papers (compared to other venues). So we compare long and short papers published at ACL since 2003 to determine their average citations. Once again, we limit the papers to those published until 2016 to allow for the papers to have time to collect citations. Figure FIGREF47 shows the average and median citations for long and short papers.",
+      "Discussion: On average, long papers get almost three times as many citations as short papers. However, the median for long papers is two-and-half times that of short papers. This difference might be because some very heavily cited long papers push the average up for long papers.",
+      "Q. Which venue has publications with the highest average number of citations? What is the average number of citations for ACL and EMNLP papers? What is this average for other venues? What are the average citations for workshop papers, system demonstration papers, and shared task papers?",
+      "A. CL journal has the highest average citations per paper. Figure FIGREF49 shows the average citations for AA\u2019 papers published 1965\u20132016 and 2010\u20132016, respectively, grouped by venue and paper type. (Figure with median citations is available online.)",
+      "Discussion: In terms of citations, TACL papers have not been as successful as EMNLP and ACL; however, CL journal (the more traditional journal paper venue) has the highest average and median paper citations (by a large margin). This gap has reduced in papers published since 2010.",
+      "When considering papers published between 2010 and 2016, the system demonstration papers, the SemEval shared task papers, and non-SemEval shared task papers have notably high average (surpassing those of EACL and COLING); however their median citations are lower. This is likely because some heavily cited papers have pushed the average up. Nonetheless, it is interesting to note how, in terms of citations, demo and shared task papers have surpassed many conferences and even become competitive with some top-tier conferences such as EACL and COLING.",
+      "Q. What percent of the AA\u2019 papers that were published in 2016 or earlier are cited more than 1000 times? How many more than 10 times? How many papers are cited 0 times?",
+      "A. Google Scholar invented the i-10 index as another measure of author research impact. It stands for the number of papers by an author that received ten or more citations. (Ten here is somewhat arbitrary, but reasonable.) Similar to that, one can look at the impact of AA\u2019 as a whole and the impact of various subsets of AA\u2019 through the number of papers in various citation bins. Figure FIGREF50 shows the percentage of AA\u2019 papers in various citation bins. (The percentages of papers when considering papers from specific time spans are available online.)",
+      "Discussion: About 56% of the papers are cited ten or more times. 6.4% of the papers are never cited. Note also that some portion of the 1\u20139 bin likely includes papers that only received self-citations. It is interesting that the percentage of papers with 0 citations is rather steady (between 7.4% and 8.7%) for the 1965\u20131989, 1990\u20131999, and 2010\u20132016 periods. The majority of the papers lie in the 10 to 99 citations bin, for all except the recent periods (2010\u20132016 and 2016Jan\u20132016Dec). With time, the recent period should also have the majority of the papers in the 10 to 99 citations bin.",
+      "The numbers for the 2016Jan\u20132016Dec papers show that after 2.5 years, about 89% of the papers have at least one citation and about 33% of the papers have ten or more citations.",
+      "Q. What are the citation bin percentages for individual venues and paper types?",
+      "A. See Figure FIGREF51.",
+      "Discussion: Observe that 70 to 80% of the papers in journals and top-tier conferences have ten or more citations. The percentages are markedly lower (between 30 and 70%) for the other conferences shown above, and even lower for some other conferences (not shown above).",
+      "CL Journal is particularly notable for the largest percentage of papers with 100 or more citations. The somewhat high percentage of papers that are never cited (4.3%) are likely because some of the book reviews from earlier years are not explicitly marked in CL journal, and thus they were not removed from analysis. Also, letters to editors, which are more common in CL journal, tend to often obtain 0 citations.",
+      "CL, EMNLP, and ACL have the best track record for accepting papers that have gone on to receive 1000 or more citations. *Sem, the semantics conference, seems to have notably lower percentage of high-citation papers, even though it has fairly competitive acceptance rates.",
+      "Instead of percentage, if one considers raw numbers of papers that have at least ten citations (i-10 index), then LREC is particularly notable in terms of the large number of papers it accepts that have gone on to obtain ten or more citations ($\\sim $1600). Thus, by producing a large number of moderate-to-high citation papers, and introducing many first-time authors, LREC is one of the notable (yet perhaps undervalued) engines of impact on NLP.",
+      "About 50% of the SemEval shared task papers received 10 or more citations, and about 46% of the non-SemEval Shared Task Papers received 10 or more citations. About 47% of the workshop papers received ten or more citations. About 43% of the demo papers received 10 or more citations."
+    ],
+    [
+      "Q. What is the average number of citations of AA' papers that have machine translation in the title? What about papers that have the term sentiment analysis or word representations?",
+      "A. Different areas of research within NLP enjoy varying amounts of attention. In Part II, we looked at the relative popularity of various areas over time\u2014estimated through the number of paper titles that had corresponding terms. (You may also want to see the discussion on the use of paper title terms to sample papers from various, possibly overlapping, areas.) Figure FIGREF53 shows the top 50 title bigrams ordered by decreasing number of total citations. Only those bigrams that occur in at least 30 AA' papers (published between 1965 and 2016) are considered. (The papers from 2017 and later are not included, to allow for at least 2.5 years for the papers to accumulate citations.)",
+      "Discussion: The graph shows that the bigram machine translation occurred in 1,659 papers that together accrued more than 93k citations. These papers have on average 68.8 citations and the median citations is 14. Not all machine translation (MT) papers have machine translation in the title. However, arguably, this set of 1,659 papers is a representative enough sample of machine translation papers; and thus, the average and median are estimates of MT in general. Second in the list are papers with statistical machine in the title\u2014most commonly from the phrase statistical machine translation. One expects considerable overlap in the papers across the sets of papers with machine translation and statistical machine, but machine translation likely covers a broader range of research including work before statistical MT was introduced, neural MT, and MT evaluation.",
+      "There are fewer papers with sentiment analysis in the title (356), but these have acquired citations at a higher average (104) than both machine translation and statistical machine. The bigram automatic evaluation jumps out because of its high average citations (337). Some of the neural-related bigrams have high median citations, for example, neural machine (49) and convolutional neural (40.5).",
+      "Figure FIGREF54 shows the lists of top 25 bigrams ordered by average citations.",
+      "Discussion: Observe the wide variety of topics covered by this list. In some ways that is reassuring for the health of the field as a whole; however, this list does not show which areas are not receiving sufficient attention. It is less clear to me how to highlight those, as simply showing the bottom 50 bigrams by average citations is not meaningful. Also note that this is not in any way an endorsement to write papers with these high-citation bigrams in the title. Doing so is of course no guarantee of receiving a large number of citations."
+    ],
+    [
+      "In this section, we examine citations across two demographic dimensions: Academic age (number of years one has been publishing) and Gender. There are good reasons to study citations across each of these dimensions including, but not limited to, the following:",
+      "Areas of research: To better understand research contributions in the context of the area where the contribution is made.",
+      "Academic age: To better understand how the challenges faced by researchers at various stages of their career may impact the citations of their papers. For example, how well-cited are first-time NLP authors? On average, at what academic age do citations peak? etc.",
+      "Gender: To better understand the extent to which systematic biases (explicit and implicit) pervasive in society and scientific publishing impact author citations.",
+      "Some of these aspects of study may seem controversial. So it is worth addressing that first. The goal here is not to perpetuate stereotypes about age, gender, or even areas of research. The history of scientific discovery is awash with plenty of examples of bad science that has tried to erroneously show that one group of people is \u201cbetter\u201d than another, with devastating consequences.",
+      "People are far more alike than different. However, different demographic groups have faced (and continue to face) various socio-cultural inequities and biases. Gender and race studies look at how demographic differences shape our experiences. They examine the roles of social institutions in maintaining the inequities and biases.",
+      "This work is in support of those studies. Unless we measure differences in outcomes such as scientific productivity and impact across demographic groups, we will not fully know the extent to which these inequities and biases impact our scientific community; and we cannot track the effectiveness of measures to make our universities, research labs, and conferences more inclusive, equitable, and fair."
+    ],
+    [
+      "We introduced NLP academic age earlier in the paper, where we defined NLP academic age as the number of years one has been publishing in AA. Here we examine whether NLP academic age impacts citations. The analyses are done in terms of the academic age of the first author; however, similar analyses can be done for the last author and all authors. (There are limitations to each of these analyses though as discussed further below.)",
+      "First author is a privileged position in the author list as it is usually reserved for the researcher that has done the most work and writing. The first author is also usually the main driver of the project; although, their mentor or advisor may also be a significant driver of the project. Sometimes multiple authors may be marked as first authors in the paper, but the current analysis simply takes the first author from the author list. In many academic communities, the last author position is reserved for the most senior or mentoring researcher. However, in non-university research labs and in large collaboration projects, the meaning of the last author position is less clear. (Personally, I prefer author names ordered by the amount of work done.)",
+      "Examining all authors is slightly more tricky as one has to decide how to credit the citations to the possibly multiple authors. It might also not be a clear indicator of differences across gender as a large number of the papers in AA have both male and female authors.",
+      "Q. How does the NLP academic age of the first author correlate with the amount of citations? Are first-year authors less cited than those with more experience?",
+      "A. Figure FIGREF59 shows various aggregate citation statistics corresponding to academic age. To produce the graph we put each paper in a bin corresponding to the academic age of the first author when the paper was published. For example, if the first author of a paper had an academic age of 3 when that paper was published, then the paper goes in bin 3. We then calculate #papers, #citations, median citations, and average citations for each bin. For the figure below, We further group the bins 10 to 14, 15 to 19, 20 to 34, and 35 to 50. These groupings are done to avoid clutter, and also because many of the higher age bins have a low number of papers.",
+      "Discussion: Observe that the number of papers where the first author has academic age 1 is much larger than the number of papers in any other bin. This is largely because a large number of authors in AA have written exactly one paper as first author. Also, about 60% of the authors in AA (17,874 out of the 29,941 authors) have written exactly one paper (regardless of author position).",
+      "The curves for the average and median citations have a slight upside down U shape. The relatively lower average and median citations in year 1 (37.26 and 10, respectively) indicate that being new to the field has some negative impact on citations. The average increases steadily from year 1 to year 4, but the median is already at the highest point by year 2. One might say, that year 2 to year 14 are the period of steady and high citations. Year 15 onwards, there is a steady decline in the citations. It is probably wise to not draw too many conclusions from the averages of the 35 to 50 bin, because of the small number of papers. There seems to be a peak in average citations at age 7. However, there is not a corresponding peak in the median. Thus the peak in average might be due to an increase in the number of very highly cited papers. Citations to Papers by First Author Gender",
+      "As noted in Part I, neither ACL nor the ACL Anthology have recorded demographic information for the vast majority of the authors. Thus we use the same setup discussed earlier in the section on demographics, to determine gender using the United States Social Security Administration database of names and genders of newborns to identify 55,133 first names that are strongly associated with females (probability $\\ge $99%) and 29,873 first names that are strongly associated with males (probability $\\ge $99%).",
+      "Q. On average, are women cited less than men?",
+      "A. Yes, on average, female first author papers have received markedly fewer citations than male first author papers (36.4 compared to 52.4). The difference in median is smaller (11 compared to 13). See Figure FIGREF60.",
+      "Discussion: The large difference in averages and smaller difference in medians suggests that there are markedly more very heavily cited male first-author papers than female first-author papers. The gender-unknown category, which here largely consist of authors with Chinese origin names and names that are less strongly associated with one gender have a slightly higher average, but the same median citations, as authors with female-associated first names.",
+      "The differences in citations, or citation gap, across genders may: (1) vary by period of time; (2) vary due to confounding factors such as academic age and areas of research. We explore these next.",
+      "Q. How has the citation gap across genders changed over the years?",
+      "A. Figure FIGREF61 (left side) shows the citation statistics across four time periods.",
+      "Discussion: Observe that female first authors have always been a minority in the history of ACL; however, on average, their papers from the early years (1965 to 1989) received a markedly higher number of citations than those of male first authors from the same period. We can see from the graph that this changed in the 1990s where male first-author papers obtained markedly more citations on average. The citation gap reduced considerably in the 2000s, and the 2010\u20132016 period saw a further slight reduction in the citation gap.",
+      "It is also interesting to note that the gender-unknown category has almost bridged the gap with the males in this most recent time period. Further, the proportion of the gender-unknown authors has increased over the years\u2014arguably, an indication of better representations of authors from around the world in recent years. (Nonetheless, as indicated in Part I, there is still plenty to be done to promote greater inclusion of authors from Africa and South America.)",
+      "Q. How have citations varied by gender and academic age? Are women less cited because of a greater proportion of new-to-NLP female first authors than new-to-NLP male first authors?",
+      "A. Figure FIGREF61 (right side) shows citation statistics broken down by gender and academic age. (This figure is similar to the academic age graph seen earlier, except that it shows separate average and median lines for female, male, and unknown gender first authors.)",
+      "Discussion: The graphs show that female first authors consistently receive fewer citations than male authors for the first fifteen years. The trend is inverted with a small citation gap in the 15th to 34th years period.",
+      "Q. Is the citation gap common across the vast majority of areas of research within NLP? Is the gap simply because more women work in areas that receive low numbers of citations (regardless of gender)?",
+      "A. Figure FIGREF64 shows the most cited areas of research along with citation statistics split by gender of the first authors of corresponding papers. (This figure is similar to the areas of research graph seen earlier, except that it shows separate citation statistics for the genders.) Note that the figure includes rows for only those bigram and gender pairs with at least 30 AA\u2019 papers (published between 1965 and 2016). Thus for some of the bigrams certain gender entries are not shown.",
+      "Discussion: Numbers for an additional 32 areas are available online. Observe that in only about 12% (7 of the top 59) of the most cited areas of research, women received higher average citations than men. These include: sentiment analysis, information extraction, document summarization, spoken dialogue, cross lingual (research), dialogue, systems, language generation. (Of course, note that some of the 59 areas, as estimated using title term bigrams, are overlapping. Also, we did not include large scale in the list above because the difference in averages is very small and it is not really an area of research.) Thus, the citation gap is common across a majority of the high-citations areas within NLP."
+    ],
+    [
+      "This work examined the ACL Anthology to identify broad trends in productivity, focus, and impact. We examined several questions such as: who and how many of us are publishing? what are we publishing on? where and in what form are we publishing? and what is the impact of our publications? Particular attention was paid to the demographics and inclusiveness of the NLP community. Notably, we showed that only about 30% of first authors are female, and that this percentage has not improved since the year 2000. We also showed that, on average, female first authors are cited less than male first authors, even when controlling for academic age. We hope that recording citation and participation gaps across demographic groups will encourage our university, industry, and government research labs to be more inclusive and fair. Several additional aspects of the AA will be explored in future work (see the bottom of the blog posts).",
+      "Acknowledgments",
+      "This work was possible due to the helpful discussion and encouragement from a number of awesome people, including: Dan Jurafsky, Tara Small, Michael Strube, Cyril Goutte, Eric Joanis, Matt Post, Patrick Littell, Torsten Zesch, Ellen Riloff, Norm Vinson, Iryna Gurevych, Rebecca Knowles, Isar Nejadgholi, and Peter Turney. Also, a big thanks to the ACL Anthology team for creating and maintaining a wonderful resource."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: The State of NLP Literature: A Diachronic Analysis of the ACL Anthology
+
+Question: Which 5 languages appear most frequently in AA paper titles?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Size",
+    "Demographics (focus of analysis: gender, age, and geographic diversity)",
+    "Demographics (focus of analysis: gender, age, and geographic diversity) ::: Gender",
+    "Demographics (focus of analysis: gender, age, and geographic diversity) ::: Academic Age",
+    "Demographics (focus of analysis: gender, age, and geographic diversity) ::: Location (Languages)",
+    "Areas of Research",
+    "Impact",
+    "Impact ::: #Citations and Most Cited Papers",
+    "Impact ::: Average Citations by Time Span",
+    "Impact ::: Aggregate Citation Statistics, by Paper Type and Venue",
+    "Impact ::: Citations to Papers by Areas of Research",
+    "Correlation of Age and Gender with Citations",
+    "Correlation of Age and Gender with Citations ::: Correlation of Academic Age with Citations",
+    "Conclusions"
+  ],
+  "paragraphs": [
+    [
+      "The ACL Anthology (AA) is a digital repository of tens of thousands of articles on Natural Language Processing (NLP) / Computational Linguistics (CL). It includes papers published in the family of ACL conferences as well as in other NLP conferences such as LREC and RANLP. AA is the largest single source of scientific literature on NLP.",
+      "This project, which we call NLP Scholar, examines the literature as a whole to identify broad trends in productivity, focus, and impact. We will present the analyses in a sequence of questions and answers. The questions range from fairly mundane to oh-that-will-be-good-to-know. Our broader goal here is simply to record the state of the AA literature: who and how many of us are publishing? what are we publishing on? where and in what form are we publishing? and what is the impact of our publications? The answers are usually in the form of numbers, graphs, and inter-connected visualizations.",
+      "We focus on the following aspects of NLP research: size, demographics, areas of research, impact, and correlation of citations with demographic attributes (age and gender).",
+      "",
+      "Target Audience: The analyses presented here are likely to be of interest to any NLP researcher. This might be particularly the case for those that are new to the field and wish to get a broad overview of the NLP publishing landscape. On the other hand, even seasoned NLP'ers have likely wondered about the questions raised here and might be interested in the empirical evidence.",
+      "",
+      "Data: The analyses presented below are based on information about the papers taken directly from AA (as of June 2019) and citation information extracted from Google Scholar (as of June 2019). Thus, all subsequent papers and citations are not included in the analysis. A fresh data collection is planned for January 2020.",
+      "",
+      "Interactive Visualizations: The visualizations we are developing for this work (using Tableau) are interactive\u2014so one can hover, click to select and filter, move sliders, etc. Since this work is high in the number of visualizations, the main visualizations are presented as figures in the paper and some sets of visualizations are pointed to online. The interactive visualizations and data will be made available through the first author's website after peer review.",
+      "",
+      "Related Work: This work builds on past research, including that on Google Scholar BIBREF0, BIBREF1, BIBREF2, BIBREF3, on the analysis of NLP papers BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9, on citation intent BIBREF10, BIBREF11, BIBREF12, BIBREF13, BIBREF14, BIBREF15, and on measuring scholarly impact BIBREF16, BIBREF17, BIBREF18, BIBREF19, BIBREF20, BIBREF21.",
+      "",
+      "Caveats and Ethical Considerations: We list several caveats and limitations throughout the paper. A compilation of these is also available online in the About NLP Scholar page.",
+      "The analyses presented here are also available as a series of blog posts."
+    ],
+    [
+      "Q. How big is the ACL Anthology (AA)? How is it changing with time?",
+      "A. As of June 2019, AA had $\\sim $50K entries, however, this includes some number of entries that are not truly research publications (for example, forewords, prefaces, table of contents, programs, schedules, indexes, calls for papers/participation, lists of reviewers, lists of tutorial abstracts, invited talks, appendices, session information, obituaries, book reviews, newsletters, lists of proceedings, lifetime achievement awards, erratum, and notes). We discard them for the analyses here. (Note: CL journal includes position papers like squibs, letter to editor, opinion, etc. We do not discard them.) We are then left with 44,896 articles. Figure FIGREF6 shows a graph of the number of papers published in each of the years from 1965 to 2018.",
+      "Discussion: Observe that there was a spurt in the 1990s, but things really took off since the year 2000, and the growth continues. Also, note that the number of publications is considerably higher in alternate years. This is due to biennial conferences. Since 1998 the largest of such conferences has been LREC (In 2018 alone LREC had over 700 main conferences papers and additional papers from its 29 workshops). COLING, another biennial conference (also occurring in the even years) has about 45% of the number of main conference papers as LREC.",
+      "Q. How many people publish in the ACL Anthology (NLP conferences)?",
+      "A. Figure FIGREF7 shows a graph of the number of authors (of AA papers) over the years:",
+      "Discussion: It is a good sign for the field to have a growing number of people join its ranks as researchers. A further interesting question would be:",
+      "Q. How many people are actively publishing in NLP?",
+      "A. It is hard to know the exact number, but we can determine the number of people who have published in AA in the last N years.",
+      "#people who published at least one paper in 2017 and 2018 (2 years): $\\sim $12k (11,957 to be precise)",
+      "#people who published at least one paper 2015 through 2018 (4 years):$\\sim $17.5k (17,457 to be precise)",
+      "Of course, some number of researchers published NLP papers in non-AA venues, and some number are active NLP researchers who may not have published papers in the last few years.",
+      "Q. How many journal papers exist in the AA? How many main conference papers? How many workshop papers?",
+      "A. See Figure FIGREF8.",
+      "Discussion: The number of journal papers is dwarfed by the number of conference and workshop papers. (This is common in computer science. Even though NLP is a broad interdisciplinary field, the influence of computer science practices on NLP is particularly strong.) Shared task and system demo papers are relatively new (introduced in the 2000s), but their numbers are already significant and growing.",
+      "Creating a separate class for \u201cTop-tier Conference\u201d is somewhat arbitrary, but it helps make certain comparisons more meaningful (for example, when comparing the average number of citations, etc.). For this work, we consider ACL, EMNLP, NAACL, COLING, and EACL as top-tier conferences, but certainly other groupings are also reasonable.",
+      "Q. How many papers have been published at ACL (main conference papers)? What are the other NLP venues and what is the distribution of the number of papers across various CL/NLP venues?",
+      "A. # ACL (main conference papers) as of June 2018: 4,839",
+      "The same workshop can co-occur with different conferences in different years, so we grouped all workshop papers in their own class. We did the same for tutorials, system demonstration papers (demos), and student research papers. Figure FIGREF9 shows the number of main conference papers for various venues and paper types (workshop papers, demos, etc.).",
+      "Discussion: Even though LREC is a relatively new conference that occurs only once in two years, it tends to have a high acceptance rate ($\\sim $60%), and enjoys substantial participation. Thus, LREC is already the largest single source of NLP conference papers. SemEval, which started as SenseEval in 1998 and occurred once in two or three years, has now morphed into an annual two-day workshop\u2014SemEval. It is the largest single source of NLP shared task papers."
+    ],
+    [
+      "NLP, like most other areas of research, suffers from poor demographic diversity. There is very little to low representation from certain nationalities, race, gender, language, income, age, physical abilities, etc. This impacts the breadth of technologies we create, how useful they are, and whether they reach those that need it most. In this section, we analyze three specific attributes among many that deserve attention: gender (specifically, the number of women researchers in NLP), age (more precisely, the number of years of NLP paper publishing experience), and the amount of research in various languages (which loosely correlates with geographic diversity)."
+    ],
+    [
+      "The ACL Anthology does not record demographic information about the paper authors. (Until recently, ACL and other NLP conferences did not record demographic information of the authors.) However, many first names have strong associations with a male or female gender. We will use these names to estimate the percentage of female first authors in NLP.",
+      "The US Social Security Administration publishes a database of names and genders of newborns. We use the dataset to identify 55,133 first names that are strongly associated with females (probability $\\ge $99%) and 29,873 first names that are strongly associated with males (probability $\\ge $99%). (As a side, it is interesting to note that there is markedly greater diversity in female names than in male names.) We identified 26,637 of the 44,896 AA papers ($\\sim $60%) where the first authors have one of these names and determine the percentage of female first author papers across the years. We will refer to this subset of AA papers as AA*.",
+      "",
+      "Note the following caveats associated with this analysis:",
+      "",
+      "The names dataset used has a lower representation of names from nationalities other than the US. However, there is a large expatriate population living in the US.",
+      "",
+      "Chinese names (especially in the romanized form) are not good indicators of gender. Thus the method presented here disregards most Chinese names, and the results of the analysis apply to the group of researchers excluding those with Chinese names.",
+      "",
+      "The dataset only records names associated with two genders.",
+      "",
+      "The approach presented here is meant to be an approximation in the absence of true gender information.",
+      "Q. What percent of the AA* papers have female first authors (FFA)? How has this percentage changed with time?",
+      "A. Overall FFA%: 30.3%. Figure FIGREF16 shows how FFA% has changed with time. Common paper title words and FFA% of papers that have those words are shown in the bottom half of the image. Note that the slider at the bottom has been set to 400, i.e., only those title words that occur in 400 or more papers are shown. The legend on the bottom right shows that low FFA scores are shown in shades of blue, whereas relatively higher FFA scores are shown in shades of green.",
+      "",
+      "Discussion: Observe that as a community, we are far from obtaining male-female parity in terms of first authors. A further striking (and concerning) observation is that the female first author percentage has not improved since the years 1999 and 2000 when the FFA percentages were highest (32.9% and 32.8%, respectively). In fact there seems to even be a slight downward trend in recent years. The calculations shown above are for the percentage of papers that have female first authors. The percentage of female first authors is about the same ($\\sim $31%). On average male authors had a slightly higher average number of publications than female authors.",
+      "To put these numbers in context, the percentage of female scientists world wide (considering all areas of research) has been estimated to be around 30%. The reported percentages for many computer science sub-fields are much lower. (See Women in Science (2015).) The percentages are much higher for certain other fields such as psychology and linguistics. (See this study for psychology and this study for linguistics.) If we can identify ways to move the needle on the FFA percentage and get it closer to 50% (or more), NLP can be a beacon to many other fields, especially in the sciences.",
+      "FFA percentages are particularly low for papers that have parsing, neural, and unsupervised in the title. There are some areas within NLP that enjoy a healthier female-male parity in terms of first authors of papers. Figure FIGREF20 shows FFA percentages for papers that have the word discourse in the title. There is burgeoning research on neural NLP in the last few years. Figure FIGREF21 shows FFA percentages for papers that have the word neural in the title.",
+      "Figure FIGREF22 shows lists of terms with the highest and lowest FFA percentages, respectively, when considering terms that occur in at least 50 paper titles (instead of 400 in the analysis above). Observe that FFA percentages are relatively higher in non-English European language research such as papers on Russian, Portuguese, French, and Italian. FFA percentages are also relatively higher for certain areas of NLP such as work on prosody, readability, discourse, dialogue, paraphrasing, and individual parts of speech such as adjectives and verbs. FFA percentages are particularly low for papers on theoretical aspects of statistical modelling, and areas such as machine translation, parsing, and logic. The full lists of terms and FFA percentages will be made available with the rest of the data."
+    ],
+    [
+      "While the actual age of NLP researchers might be an interesting aspect to explore, we do not have that information. Thus, instead, we can explore a slightly different (and perhaps more useful) attribute: NLP academic age. We can define NLP academic age as the number of years one has been publishing in AA. So if this is the first year one has published in AA, then their NLP academic age is 1. If one published their first AA paper in 2001 and their latest AA paper in 2018, then their academic age is 18.",
+      "Q. How old are we? That is, what is the average NLP academic age of those who published papers in 2018? How has the average changed over the years? That is, have we been getting older or younger? What percentage of authors that published in 2018 were publishing their first AA paper?",
+      "A. Average NLP Academic Age of people that published in 2018: 5.41 years",
+      "Median NLP Academic Age of people that published in 2018: 2 years",
+      "Percentage of 2018 authors that published their first AA paper in 2018: 44.9%",
+      "Figure FIGREF24 shows how these numbers have changed over the years.",
+      "Discussion: Observe that the Average academic age has been steadily increasing over the years until 2016 and 2017, when the trend has shifted and the average academic age has started to decrease. The median age was 1 year for most of the 1965 to 1990 period, 2 years for most of the 1991 to 2006 period, 3 years for most of the 2007 to 2015 period, and back to 2 years since then. The first-time AA author percentage decreased until about 1988, after which it sort of stayed steady at around 48% until 2004 with occasional bursts to $\\sim $56%. Since 2005, the first-time author percentage has gone up and down every other year. It seems that the even years (which are also LREC years) have a higher first-time author percentage. Perhaps, this oscillation in first-time authors percentage is related to LREC\u2019s high acceptance rate.",
+      "Q. What is the distribution of authors in various academic age bins? For example, what percentage of authors that published in 2018 had an academic age of 2, 3, or 4? What percentage had an age between 5 and 9? And so on?",
+      "A. See Figure FIGREF25.",
+      "Discussion: Observe that about 65% of the authors that published in 2018 had an academic age of less than 5. This number has steadily reduced since 1965, was in the 60 to 70% range in 1990s, rose to the 70 to 72% range in early 2000s, then declined again until it reached the lowest value ($\\sim $60%) in 2010, and has again steadily risen until 2018 (65%). Thus, even though it may sometimes seem at recent conferences that there is a large influx of new people into NLP (and that is true), proportionally speaking, the average NLP academic age is higher (more experienced) than what it has been in much of its history."
+    ],
+    [
+      "Automatic systems with natural language abilities are growing to be increasingly pervasive in our lives. Not only are they sources of mere convenience, but are crucial in making sure large sections of society and the world are not left behind by the information divide. Thus, the limits of what automatic systems can do in a language, limit the world for the speakers of that language.",
+      "We know that much of the research in NLP is on English or uses English datasets. Many reasons have been proffered, and we will not go into that here. Instead, we will focus on estimating how much research pertains to non-English languages.",
+      "We will make use of the idea that often when work is done focusing on a non-English language, then the language is mentioned in the title. We collected a list of 122 languages indexed by Wiktionary and looked for the presence of these words in the titles of AA papers. (Of course there are hundreds of other lesser known languages as well, but here we wanted to see the representation of these more prominent languages in NLP literature.)",
+      "Figure FIGREF27 is a treemap of the 122 languages arranged alphabetically and shaded such that languages that appear more often in AA paper titles have a darker shade of green.",
+      "Discussion: Even though the amount of work done on English is much larger than that on any other language, often the word English does not appear in the title, and this explains why English is not the first (but the second-most) common language name to appear in the titles. This is likely due to the fact that many papers fail to mention the language of study or the language of the datasets used if it is English. There is growing realization in the community that this is not quite right. However, the language of study can be named in other less prominent places than the title, for example the abstract, introduction, or when the datasets are introduced, depending on how central it is to the paper.",
+      "We can see from the treemap that the most widely spoken Asian and Western European languages enjoy good representation in AA. These include: Chinese, Arabic, Korean, Japanese, and Hindi (Asian) as well as French, German, Swedish, Spanish, Portuguese, and Italian (European). This is followed by the relatively less widely spoken European languages (such as Russian, Polish, Norwegian, Romanian, Dutch, and Czech) and Asian languages (such as Turkish, Thai, and Urdu). Most of the well-represented languages are from the Indo-European language family. Yet, even in the limited landscape of the most common 122 languages, vast swathes are barren with inattention. Notable among these is the extremely low representation of languages from Africa, languages from non-Indo-European language families, and Indigenous languages from around the world."
+    ],
+    [
+      "Natural Language Processing addresses a wide range of research questions and tasks pertaining to language and computing. It encompasses many areas of research that have seen an ebb and flow of interest over the years. In this section, we examine the terms that have been used in the titles of ACL Anthology (AA) papers. The terms in a title are particularly informative because they are used to clearly and precisely convey what the paper is about. Some journals ask authors to separately include keywords in the paper or in the meta-information, but AA papers are largely devoid of this information. Thus titles are an especially useful source of keywords for papers\u2014keywords that are often indicative of the area of research.",
+      "Keywords could also be extracted from abstracts and papers; we leave that for future work. Further work is also planned on inferring areas of research using word embeddings, techniques from topic modelling, and clustering. There are clear benefits to performing analyses using that information. However, those approaches can be sensitive to the parameters used. Here, we keep things simple and explore counts of terms in paper titles. Thus the results are easily reproducible and verifiable.",
+      "Caveat: Even though there is an association between title terms and areas of research, the association can be less strong for some terms and areas. We use the association as one (imperfect) source of information about areas of research. This information may be combined with other sources of information to draw more robust conclusions.",
+      "Title Terms: The title has a privileged position in a paper. It serves many functions, and here are three key ones (from an article by Sneha Kulkarni): \"A good research paper title: 1. Condenses the paper's content in a few words 2. Captures the readers' attention 3. Differentiates the paper from other papers of the same subject area\".",
+      "If we examine the titles of papers in the ACL Anthology, we would expect that because of Function 1 many of the most common terms will be associated with the dominant areas of research. Function 2 (or attempting to have a catchy title) on the other hand, arguably leads to more unique and less frequent title terms. Function 3 seems crucial to the effectiveness of a title; and while at first glance it may seem like this will lead to unique title terms, often one needs to establish a connection with something familiar in order to convey how the work being presented is new or different.",
+      "It is also worth noting that a catchy term today, will likely not be catchy tomorrow. Similarly, a distinctive term today, may not be distinctive tomorrow. For example, early papers used neural in the title to distinguish themselves from non-nerual approaches, but these days neural is not particularly discriminative as far as NLP papers go.",
+      "Thus, competing and complex interactions are involved in the making of titles. Nonetheless, an arguable hypothesis is that: broad trends in interest towards an area of research will be reflected, to some degree, in the frequencies of title terms associated with that area over time. However, even if one does not believe in that hypothesis, it is worth examining the terms in the titles of tens of thousands of papers in the ACL Anthology\u2014spread across many decades.",
+      "Q. What terms are used most commonly in the titles of the AA papers? How has that changed with time?",
+      "A. Figure FIGREF28 shows the most common unigrams (single word) and bigrams (two-word sequences) in the titles of papers published from 1980 to 2019. (Ignoring function words.) The timeline graph at the bottom shows the percentage of occurrences of the unigrams over the years (the colors of the unigrams in the Timeline match those in the Title Unigram list). Note: For a given year, the timeline graph includes a point for a unigram if the sum of the frequency of the unigram in that year and the two years before it is at least ten. The period before 1980 is not included because of the small number of papers.",
+      "Discussion: Appropriately enough, the most common term in the titles of NLP papers is language. Presence of high-ranking terms pertaining to machine translation suggest that it is the area of research that has received considerable attention. Other areas associated with the high-frequency title terms include lexical semantics, named entity recognition, question answering, word sense disambiguation, and sentiment analysis. In fact, the common bigrams in the titles often correspond to names of NLP research areas. Some of the bigrams like shared task and large scale are not areas of research, but rather mechanisms or trends of research that apply broadly to many areas of research. The unigrams, also provide additional insights, such as the interest of the community in Chinese language, and in areas such as speech and parsing.",
+      "The Timeline graph is crowded in this view, but clicking on a term from the unigram list will filter out all other lines from the timeline. This is especially useful for determining whether the popularity of a term is growing or declining. (One can already see from above that neural has broken away from the pack in recent years.) Since there are many lines in the Timeline graph, Tableau labels only some (you can see neural and machine). However, hovering over a line, in the eventual interactive visualization, will display the corresponding term\u2014as shown in the figure.",
+      "Despite being busy, the graph sheds light on the relative dominance of the most frequent terms and how that has changed with time. The vocabulary of title words is smaller when considering papers from the 1980's than in recent years. (As would be expected since the number of papers then was also relatively fewer.) Further, dominant terms such as language and translation accounted for a higher percentage than in recent years where there is a much larger diversity of topics and the dominant research areas are not as dominant as they once were.",
+      "Q. What are the most frequent unigrams and bigrams in the titles of recent papers?",
+      "A. Figure FIGREF29 shows the most frequent unigrams and bigrams in the titles of papers published 2016 Jan to 2019 June (time of data collection).",
+      "Discussion: Some of the terms that have made notable gains in the top 20 unigrams and bigrams lists in recent years include: neural machine (presumably largely due to the phrase neural machine translation), neural network(s), word embeddings, recurrent neural, deep learning and the corresponding unigrams (neural, networks, etc.). We also see gains for terms related to shared tasks such as SemEval and task.",
+      "The sets of most frequent unigrams and bigrams in the titles of AA papers from various time spans are available online. Apart from clicking on terms, one can also enter the query (say parsing) in the search box at the bottom. Apart from filtering the timeline graph (bottom), this action also filters the unigram list (top left) to provide information only about the search term. This is useful because the query term may not be one of the visible top unigrams.",
+      "FigureFIGREF31 shows the timeline graph for parsing.",
+      "Discussion: Parsing seems to have enjoyed considerable attention in the 1980s, began a period of steep decline in the early 1990s, and a period of gradual decline ever since. One can enter multiple terms in the search box or shift/command click multiple terms to show graphs for more than one term.",
+      "FigureFIGREF32 shows the timelines for three bigrams statistical machine, neural machine, and machine translation:",
+      "Discussion: The graph indicates that there was a spike in machine translation papers in 1996, but the number of papers dropped substantially after that. Yet, its numbers have been comparatively much higher than other terms. One can also see the rise of statistical machine translation in the early 2000s followed by its decline with the rise of neural machine translation."
+    ],
+    [
+      "Research articles can have impact in a number of ways\u2014pushing the state of the art, answering crucial questions, finding practical solutions that directly help people, making a new generation of potential-scientists excited about a field of study, and more. As scientists, it seems attractive to quantitatively measure scientific impact, and this is particularly appealing to governments and funding agencies; however, it should be noted that individual measures of research impact are limited in scope\u2014they measure only some kinds of contributions. Citations",
+      "The most commonly used metrics of research impact are derived from citations. A citation of a scholarly article is the explicit reference to that article. Citations serve many functions. However, a simplifying assumption is that regardless of the reason for citation, every citation counts as credit to the influence or impact of the cited work. Thus several citation-based metrics have emerged over the years including: number of citations, average citations, h-index, relative citation ratio, and impact factor.",
+      "It is not always clear why some papers get lots of citations and others do not. One can argue that highly cited papers have captured the imagination of the field: perhaps because they were particularly creative, opened up a new area of research, pushed the state of the art by a substantial degree, tested compelling hypotheses, or produced useful datasets, among other things.",
+      "Note however, that the number of citations is not always a reflection of the quality or importance of a piece of work. Note also that there are systematic biases that prevent certain kinds of papers from accruing citations, especially when the contributions of a piece of work are atypical, not easily quantified, or in an area where the number of scientific publications is low. Further, the citations process can be abused, for example, by egregious self-citations.",
+      "Nonetheless, given the immense volume of scientific literature, the relative ease with which one can track citations using services such as Google Scholar and Semantic Scholar, and given the lack of other easily applicable and effective metrics, citation analysis is an imperfect but useful window into research impact.",
+      "In this section, we examine citations of AA papers. We focus on two aspects:",
+      "Most cited papers: We begin by looking at the most cited papers overall and in various time spans. We will then look at most cited papers by paper-type (long, short, demo, etc) and venue (ACL, LREC, etc.). Perhaps these make interesting reading lists. Perhaps they also lead to a qualitative understanding of the kinds of AA papers that have received lots of citations.",
+      "Aggregate citation metrics by time span, paper type, and venue: Access to citation information allows us to calculate aggregate citation metrics such as average and median citations of papers published in different time periods, published in different venues, etc. These can help answer questions such as: on average, how well cited are papers published in the 1990s? on average, how many citations does a short paper get? how many citations does a long paper get? how many citations for a workshop paper? etc.",
+      "Data: The analyses presented below are based on information about the papers taken directly from AA (as of June 2019) and citation information extracted from Google Scholar (as of June 2019). We extracted citation information from Google Scholar profiles of authors who had a Google Scholar Profile page and had published at least three papers in the ACL Anthology. This yielded citation information for about 75% of the papers (33,051 out of the 44,896 papers). We will refer to this subset of the ACL Anthology papers as AA\u2019. All citation analysis below is on AA\u2019."
+    ],
+    [
+      "Q. How many citations have the AA\u2019 papers received? How is that distributed among the papers published in various decades?",
+      "A. $\\sim $1.2 million citations (as of June 2019). Figure FIGREF36 shows a timeline graph where each year has a bar with height corresponding to the number of citations received by papers published in that year. Further, the bar has colored fragments corresponding to each of the papers and the height of a fragment (paper) is proportional to the number of citations it has received. Thus it is easy to spot the papers that received a large number of citations, and the years when the published papers received a large number of citations. Hovering over individual papers reveals an information box showing the paper title, authors, year of publication, publication venue, and #citations.",
+      "Discussion: With time, not only have the number of papers grown, but also the number of high-citation papers. We see a marked jump in the 1990s over the previous decades, but the 2000s are the most notable in terms of the high number of citations. The 2010s papers will likely surpass the 2000s papers in the years to come.",
+      "Q. What are the most cited papers in AA'?",
+      "A. Figure FIGREF37 shoes the most cited papers in the AA'.",
+      "Discussion: We see that the top-tier conference papers (green) are some of the most cited papers in AA\u2019. There are a notable number of journal papers (dark green) in the most cited list as well, but very few demo (purple) and workshop (orange) papers.",
+      "In the interactive visualizations (to be released later), one can click on the url to be to taken directly to the paper\u2019s landing page in the ACL Anthology website. That page includes links to meta information, the pdf, and associated files such as videos and appendices. There will also be functionality to download the lists. Alas, copying the lists from the screenshots shown here is not easy.",
+      "Q. What are the most cited AA' journal papers ? What are the most cited AA' workshop papers? What are the most cited AA' shared task papers? What are the most cited AA' demo papers? What are the most cited tutorials?",
+      "A. The most cited AA\u2019 journal papers, conference papers, workshop papers, system demo papers, shared task papers, and tutorials can be viewed online. The most cited papers from individual venues (ACL, CL journal, TACL, EMNLP, LREC, etc.) can also be viewed there.",
+      "Discussion: Machine translation papers are well-represented in many of these lists, but especially in the system demo papers list. Toolkits such as MT evaluation ones, NLTK, Stanford Core NLP, WordNet Similarity, and OpenNMT have highly cited demo or workshop papers.",
+      "The shared task papers list is dominated by task description papers (papers by task organizers describing the data and task), especially for sentiment analysis tasks. However, the list also includes papers by top-performing systems in these shared tasks, such as the NRC-Canada, HidelTime, and UKP papers.",
+      "Q. What are the most cited AA' papers in the last decade?",
+      "A. Figure FIGREF39 shows the most cited AA' papers in the 2010s. The most cited AA' papers from the earlier periods are available online.",
+      "Discussion: The early period (1965\u20131989) list includes papers focused on grammar and linguistic structure. The 1990s list has papers addressing many different NLP problems with statistical approaches. Papers on MT and sentiment analysis are frequent in the 2000s list. The 2010s are dominated by papers on word embeddings and neural representations."
+    ],
+    [
+      "Q. How many citations did the papers published between 1990 and 1994 receive? What is the average number of citations that a paper published between 1990 and 1994 has received? What are the numbers for other time spans?",
+      "A. Total citations for papers published between 1990 and 1994: $\\sim $92k",
+      "Average citations for papers published between 1990 and 1994: 94.3",
+      "Figure FIGREF41 shows the numbers for various time spans.",
+      "Discussion: The early 1990s were an interesting period for NLP with the use of data from the World Wide Web and technologies from speech processing. This was the period with the highest average citations per paper, closely followed by the 1965\u20131969 and 1995\u20131999 periods. The 2000\u20132004 period is notable for: (1) a markedly larger number of citations than the previous decades; (2) third highest average number of citations. The drop off in the average citations for recent 5-year spans is largely because they have not had as much time to collect citations."
+    ],
+    [
+      "Q. What are the average number of citations received by different types of papers: main conference papers, workshop papers, student research papers, shared task papers, and system demonstration papers?",
+      "A. In this analysis, we include only those AA\u2019 papers that were published in 2016 or earlier (to allow for at least 2.5 years to collect citations). There are 26,949 such papers. Figures FIGREF42 and FIGREF43 show the average citations by paper type when considering papers published 1965\u20132016 and 2010\u20132016, respectively. Figures FIGREF45 and FIGREF46 show the medians.",
+      "Discussion: Journal papers have much higher average and median citations than other papers, but the gap between them and top-tier conferences is markedly reduced when considering papers published since 2010.",
+      "System demo papers have the third highest average citations; however, shared task papers have the third highest median citations. The popularity of shared tasks and the general importance given to beating the state of the art (SOTA) seems to have grown in recent years\u2014something that has come under criticism.",
+      "It is interesting to note that in terms of citations, workshop papers are doing somewhat better than the conferences that are not top tier. Finally, the citation numbers for tutorials show that even though a small number of tutorials are well cited, a majority receive 1 or no citations. This is in contrast to system demo papers that have average and median citations that are higher or comparable to workshop papers.",
+      "Throughout the analyses in this article, we see that median citation numbers are markedly lower than average citation numbers. This is particularly telling. It shows that while there are some very highly cited papers, a majority of the papers obtain much lower number of citations\u2014and when considering papers other than journals and top-tier conferences, the number of citations is frequently lower than ten.",
+      "Q. What are the average number of citations received by the long and short ACL main conference papers, respectively?",
+      "A. Short papers were introduced at ACL in 2003. Since then ACL is by far the venue with the most number of short papers (compared to other venues). So we compare long and short papers published at ACL since 2003 to determine their average citations. Once again, we limit the papers to those published until 2016 to allow for the papers to have time to collect citations. Figure FIGREF47 shows the average and median citations for long and short papers.",
+      "Discussion: On average, long papers get almost three times as many citations as short papers. However, the median for long papers is two-and-half times that of short papers. This difference might be because some very heavily cited long papers push the average up for long papers.",
+      "Q. Which venue has publications with the highest average number of citations? What is the average number of citations for ACL and EMNLP papers? What is this average for other venues? What are the average citations for workshop papers, system demonstration papers, and shared task papers?",
+      "A. CL journal has the highest average citations per paper. Figure FIGREF49 shows the average citations for AA\u2019 papers published 1965\u20132016 and 2010\u20132016, respectively, grouped by venue and paper type. (Figure with median citations is available online.)",
+      "Discussion: In terms of citations, TACL papers have not been as successful as EMNLP and ACL; however, CL journal (the more traditional journal paper venue) has the highest average and median paper citations (by a large margin). This gap has reduced in papers published since 2010.",
+      "When considering papers published between 2010 and 2016, the system demonstration papers, the SemEval shared task papers, and non-SemEval shared task papers have notably high average (surpassing those of EACL and COLING); however their median citations are lower. This is likely because some heavily cited papers have pushed the average up. Nonetheless, it is interesting to note how, in terms of citations, demo and shared task papers have surpassed many conferences and even become competitive with some top-tier conferences such as EACL and COLING.",
+      "Q. What percent of the AA\u2019 papers that were published in 2016 or earlier are cited more than 1000 times? How many more than 10 times? How many papers are cited 0 times?",
+      "A. Google Scholar invented the i-10 index as another measure of author research impact. It stands for the number of papers by an author that received ten or more citations. (Ten here is somewhat arbitrary, but reasonable.) Similar to that, one can look at the impact of AA\u2019 as a whole and the impact of various subsets of AA\u2019 through the number of papers in various citation bins. Figure FIGREF50 shows the percentage of AA\u2019 papers in various citation bins. (The percentages of papers when considering papers from specific time spans are available online.)",
+      "Discussion: About 56% of the papers are cited ten or more times. 6.4% of the papers are never cited. Note also that some portion of the 1\u20139 bin likely includes papers that only received self-citations. It is interesting that the percentage of papers with 0 citations is rather steady (between 7.4% and 8.7%) for the 1965\u20131989, 1990\u20131999, and 2010\u20132016 periods. The majority of the papers lie in the 10 to 99 citations bin, for all except the recent periods (2010\u20132016 and 2016Jan\u20132016Dec). With time, the recent period should also have the majority of the papers in the 10 to 99 citations bin.",
+      "The numbers for the 2016Jan\u20132016Dec papers show that after 2.5 years, about 89% of the papers have at least one citation and about 33% of the papers have ten or more citations.",
+      "Q. What are the citation bin percentages for individual venues and paper types?",
+      "A. See Figure FIGREF51.",
+      "Discussion: Observe that 70 to 80% of the papers in journals and top-tier conferences have ten or more citations. The percentages are markedly lower (between 30 and 70%) for the other conferences shown above, and even lower for some other conferences (not shown above).",
+      "CL Journal is particularly notable for the largest percentage of papers with 100 or more citations. The somewhat high percentage of papers that are never cited (4.3%) are likely because some of the book reviews from earlier years are not explicitly marked in CL journal, and thus they were not removed from analysis. Also, letters to editors, which are more common in CL journal, tend to often obtain 0 citations.",
+      "CL, EMNLP, and ACL have the best track record for accepting papers that have gone on to receive 1000 or more citations. *Sem, the semantics conference, seems to have notably lower percentage of high-citation papers, even though it has fairly competitive acceptance rates.",
+      "Instead of percentage, if one considers raw numbers of papers that have at least ten citations (i-10 index), then LREC is particularly notable in terms of the large number of papers it accepts that have gone on to obtain ten or more citations ($\\sim $1600). Thus, by producing a large number of moderate-to-high citation papers, and introducing many first-time authors, LREC is one of the notable (yet perhaps undervalued) engines of impact on NLP.",
+      "About 50% of the SemEval shared task papers received 10 or more citations, and about 46% of the non-SemEval Shared Task Papers received 10 or more citations. About 47% of the workshop papers received ten or more citations. About 43% of the demo papers received 10 or more citations."
+    ],
+    [
+      "Q. What is the average number of citations of AA' papers that have machine translation in the title? What about papers that have the term sentiment analysis or word representations?",
+      "A. Different areas of research within NLP enjoy varying amounts of attention. In Part II, we looked at the relative popularity of various areas over time\u2014estimated through the number of paper titles that had corresponding terms. (You may also want to see the discussion on the use of paper title terms to sample papers from various, possibly overlapping, areas.) Figure FIGREF53 shows the top 50 title bigrams ordered by decreasing number of total citations. Only those bigrams that occur in at least 30 AA' papers (published between 1965 and 2016) are considered. (The papers from 2017 and later are not included, to allow for at least 2.5 years for the papers to accumulate citations.)",
+      "Discussion: The graph shows that the bigram machine translation occurred in 1,659 papers that together accrued more than 93k citations. These papers have on average 68.8 citations and the median citations is 14. Not all machine translation (MT) papers have machine translation in the title. However, arguably, this set of 1,659 papers is a representative enough sample of machine translation papers; and thus, the average and median are estimates of MT in general. Second in the list are papers with statistical machine in the title\u2014most commonly from the phrase statistical machine translation. One expects considerable overlap in the papers across the sets of papers with machine translation and statistical machine, but machine translation likely covers a broader range of research including work before statistical MT was introduced, neural MT, and MT evaluation.",
+      "There are fewer papers with sentiment analysis in the title (356), but these have acquired citations at a higher average (104) than both machine translation and statistical machine. The bigram automatic evaluation jumps out because of its high average citations (337). Some of the neural-related bigrams have high median citations, for example, neural machine (49) and convolutional neural (40.5).",
+      "Figure FIGREF54 shows the lists of top 25 bigrams ordered by average citations.",
+      "Discussion: Observe the wide variety of topics covered by this list. In some ways that is reassuring for the health of the field as a whole; however, this list does not show which areas are not receiving sufficient attention. It is less clear to me how to highlight those, as simply showing the bottom 50 bigrams by average citations is not meaningful. Also note that this is not in any way an endorsement to write papers with these high-citation bigrams in the title. Doing so is of course no guarantee of receiving a large number of citations."
+    ],
+    [
+      "In this section, we examine citations across two demographic dimensions: Academic age (number of years one has been publishing) and Gender. There are good reasons to study citations across each of these dimensions including, but not limited to, the following:",
+      "Areas of research: To better understand research contributions in the context of the area where the contribution is made.",
+      "Academic age: To better understand how the challenges faced by researchers at various stages of their career may impact the citations of their papers. For example, how well-cited are first-time NLP authors? On average, at what academic age do citations peak? etc.",
+      "Gender: To better understand the extent to which systematic biases (explicit and implicit) pervasive in society and scientific publishing impact author citations.",
+      "Some of these aspects of study may seem controversial. So it is worth addressing that first. The goal here is not to perpetuate stereotypes about age, gender, or even areas of research. The history of scientific discovery is awash with plenty of examples of bad science that has tried to erroneously show that one group of people is \u201cbetter\u201d than another, with devastating consequences.",
+      "People are far more alike than different. However, different demographic groups have faced (and continue to face) various socio-cultural inequities and biases. Gender and race studies look at how demographic differences shape our experiences. They examine the roles of social institutions in maintaining the inequities and biases.",
+      "This work is in support of those studies. Unless we measure differences in outcomes such as scientific productivity and impact across demographic groups, we will not fully know the extent to which these inequities and biases impact our scientific community; and we cannot track the effectiveness of measures to make our universities, research labs, and conferences more inclusive, equitable, and fair."
+    ],
+    [
+      "We introduced NLP academic age earlier in the paper, where we defined NLP academic age as the number of years one has been publishing in AA. Here we examine whether NLP academic age impacts citations. The analyses are done in terms of the academic age of the first author; however, similar analyses can be done for the last author and all authors. (There are limitations to each of these analyses though as discussed further below.)",
+      "First author is a privileged position in the author list as it is usually reserved for the researcher that has done the most work and writing. The first author is also usually the main driver of the project; although, their mentor or advisor may also be a significant driver of the project. Sometimes multiple authors may be marked as first authors in the paper, but the current analysis simply takes the first author from the author list. In many academic communities, the last author position is reserved for the most senior or mentoring researcher. However, in non-university research labs and in large collaboration projects, the meaning of the last author position is less clear. (Personally, I prefer author names ordered by the amount of work done.)",
+      "Examining all authors is slightly more tricky as one has to decide how to credit the citations to the possibly multiple authors. It might also not be a clear indicator of differences across gender as a large number of the papers in AA have both male and female authors.",
+      "Q. How does the NLP academic age of the first author correlate with the amount of citations? Are first-year authors less cited than those with more experience?",
+      "A. Figure FIGREF59 shows various aggregate citation statistics corresponding to academic age. To produce the graph we put each paper in a bin corresponding to the academic age of the first author when the paper was published. For example, if the first author of a paper had an academic age of 3 when that paper was published, then the paper goes in bin 3. We then calculate #papers, #citations, median citations, and average citations for each bin. For the figure below, We further group the bins 10 to 14, 15 to 19, 20 to 34, and 35 to 50. These groupings are done to avoid clutter, and also because many of the higher age bins have a low number of papers.",
+      "Discussion: Observe that the number of papers where the first author has academic age 1 is much larger than the number of papers in any other bin. This is largely because a large number of authors in AA have written exactly one paper as first author. Also, about 60% of the authors in AA (17,874 out of the 29,941 authors) have written exactly one paper (regardless of author position).",
+      "The curves for the average and median citations have a slight upside down U shape. The relatively lower average and median citations in year 1 (37.26 and 10, respectively) indicate that being new to the field has some negative impact on citations. The average increases steadily from year 1 to year 4, but the median is already at the highest point by year 2. One might say, that year 2 to year 14 are the period of steady and high citations. Year 15 onwards, there is a steady decline in the citations. It is probably wise to not draw too many conclusions from the averages of the 35 to 50 bin, because of the small number of papers. There seems to be a peak in average citations at age 7. However, there is not a corresponding peak in the median. Thus the peak in average might be due to an increase in the number of very highly cited papers. Citations to Papers by First Author Gender",
+      "As noted in Part I, neither ACL nor the ACL Anthology have recorded demographic information for the vast majority of the authors. Thus we use the same setup discussed earlier in the section on demographics, to determine gender using the United States Social Security Administration database of names and genders of newborns to identify 55,133 first names that are strongly associated with females (probability $\\ge $99%) and 29,873 first names that are strongly associated with males (probability $\\ge $99%).",
+      "Q. On average, are women cited less than men?",
+      "A. Yes, on average, female first author papers have received markedly fewer citations than male first author papers (36.4 compared to 52.4). The difference in median is smaller (11 compared to 13). See Figure FIGREF60.",
+      "Discussion: The large difference in averages and smaller difference in medians suggests that there are markedly more very heavily cited male first-author papers than female first-author papers. The gender-unknown category, which here largely consist of authors with Chinese origin names and names that are less strongly associated with one gender have a slightly higher average, but the same median citations, as authors with female-associated first names.",
+      "The differences in citations, or citation gap, across genders may: (1) vary by period of time; (2) vary due to confounding factors such as academic age and areas of research. We explore these next.",
+      "Q. How has the citation gap across genders changed over the years?",
+      "A. Figure FIGREF61 (left side) shows the citation statistics across four time periods.",
+      "Discussion: Observe that female first authors have always been a minority in the history of ACL; however, on average, their papers from the early years (1965 to 1989) received a markedly higher number of citations than those of male first authors from the same period. We can see from the graph that this changed in the 1990s where male first-author papers obtained markedly more citations on average. The citation gap reduced considerably in the 2000s, and the 2010\u20132016 period saw a further slight reduction in the citation gap.",
+      "It is also interesting to note that the gender-unknown category has almost bridged the gap with the males in this most recent time period. Further, the proportion of the gender-unknown authors has increased over the years\u2014arguably, an indication of better representations of authors from around the world in recent years. (Nonetheless, as indicated in Part I, there is still plenty to be done to promote greater inclusion of authors from Africa and South America.)",
+      "Q. How have citations varied by gender and academic age? Are women less cited because of a greater proportion of new-to-NLP female first authors than new-to-NLP male first authors?",
+      "A. Figure FIGREF61 (right side) shows citation statistics broken down by gender and academic age. (This figure is similar to the academic age graph seen earlier, except that it shows separate average and median lines for female, male, and unknown gender first authors.)",
+      "Discussion: The graphs show that female first authors consistently receive fewer citations than male authors for the first fifteen years. The trend is inverted with a small citation gap in the 15th to 34th years period.",
+      "Q. Is the citation gap common across the vast majority of areas of research within NLP? Is the gap simply because more women work in areas that receive low numbers of citations (regardless of gender)?",
+      "A. Figure FIGREF64 shows the most cited areas of research along with citation statistics split by gender of the first authors of corresponding papers. (This figure is similar to the areas of research graph seen earlier, except that it shows separate citation statistics for the genders.) Note that the figure includes rows for only those bigram and gender pairs with at least 30 AA\u2019 papers (published between 1965 and 2016). Thus for some of the bigrams certain gender entries are not shown.",
+      "Discussion: Numbers for an additional 32 areas are available online. Observe that in only about 12% (7 of the top 59) of the most cited areas of research, women received higher average citations than men. These include: sentiment analysis, information extraction, document summarization, spoken dialogue, cross lingual (research), dialogue, systems, language generation. (Of course, note that some of the 59 areas, as estimated using title term bigrams, are overlapping. Also, we did not include large scale in the list above because the difference in averages is very small and it is not really an area of research.) Thus, the citation gap is common across a majority of the high-citations areas within NLP."
+    ],
+    [
+      "This work examined the ACL Anthology to identify broad trends in productivity, focus, and impact. We examined several questions such as: who and how many of us are publishing? what are we publishing on? where and in what form are we publishing? and what is the impact of our publications? Particular attention was paid to the demographics and inclusiveness of the NLP community. Notably, we showed that only about 30% of first authors are female, and that this percentage has not improved since the year 2000. We also showed that, on average, female first authors are cited less than male first authors, even when controlling for academic age. We hope that recording citation and participation gaps across demographic groups will encourage our university, industry, and government research labs to be more inclusive and fair. Several additional aspects of the AA will be explored in future work (see the bottom of the blog posts).",
+      "Acknowledgments",
+      "This work was possible due to the helpful discussion and encouragement from a number of awesome people, including: Dan Jurafsky, Tara Small, Michael Strube, Cyril Goutte, Eric Joanis, Matt Post, Patrick Littell, Torsten Zesch, Ellen Riloff, Norm Vinson, Iryna Gurevych, Rebecca Knowles, Isar Nejadgholi, and Peter Turney. Also, a big thanks to the ACL Anthology team for creating and maintaining a wonderful resource."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: BERTQA -- Attention on Steroids
+
+Question: What hyperparameters have been tuned?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Methods",
+    "Methods ::: BERTQA - Directed Coattention",
+    "Methods ::: Localized Feature Extraction",
+    "Methods ::: Skip Connections",
+    "Methods ::: Data Augmentation - SQuAD 2.Q",
+    "Methods ::: Hyperparameter Tuning",
+    "Methods ::: BERT Large and Ensembling",
+    "Results and Analysis",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Through this CS224N Pre-trained Contextual Embeddings (PCE) project, we tackle the question answering problem which is one of the most popular in NLP and has been brought to the forefront by datasets such as SQUAD 2.0. This problem's success stems from both the challenge it presents and the recent successes in approaching human level function. As most, if not all, of the problems humans solve every day can be posed as a question, creating an deep learning based solution that has access to the entire internet is a critical milestone for NLP. Through our project, our group had tested the limits of applying attention in BERT BIBREF0 to improving the network's performance on the SQUAD2.0 dataset BIBREF1. BERT applies attention to the concatenation of the query and context vectors and thus attends these vectors in a global fashion. We propose BERTQA BIBREF2 which adds Context-to-Query (C2Q) and Query-to-Context (Q2C) attention in addition to localized feature extraction via 1D convolutions. We implemented the additions ourselves, while the Pytorch baseline BERT code was obtained from BIBREF3. The SQUAD2.0 answers span from a length of zero to multiple words and this additional attention provides hierarchical information that will allow the network to better learn to detect answer spans of varying sizes. We applied the empirical findings from this part of our project to the large BERT model, which has twice as many layers as the base BERT model. We also augmented the SQUAD2.0 dataset with additional backtranslated examples. This augmented dataset will be publicly available on our github BIBREF4 on the completion of this course. After performing hyperparameter tuning, we ensembled our two best networks to get F1 and EM scores of 82.317 and 79.442 respectively. The experiments took around 300 GPU hours to train."
+    ],
+    [
+      "The SQUAD2.0 creators proposed this dataset as a means for networks to actually understand the text they were being interrogated about rather than simply being extractive parsers. Many networks stepped up to the challenge including BERT, BIDAF, and QANET. BERT is a fully feed forward network that is based on the transformer architecture BIBREF5. The base BERT model has 12 transformer encoder layers that terminate in an interchangeable final layer which can be finetuned to the specific task. We chose this network as our baseline because of its use of contextual embeddings and global attention and because of the speed advantage derived from an RNN free architecture. We derived inspiration for our modifications from the BIDAF and QANET models. BIDAF is an LSTM based network that uses character, word, and contextual embeddings which are fed through Context-to-Query (C2Q) and Query-to-Context (Q2C) layers. The final logits are derived from separate Start and End output layers, as opposed to BERT which produces these logits together. Our C2Q/Q2C addition to BERT and the Dense Layer/LSTM based separate final Start and End logit prediction layer were inspired by this paper. We also refered to the QANET model, which is also a fully feed forward network that emphasizes the use of convolutions to capture the local structure of text. Based on this paper, we created a convolutional layer within the C2Q/Q2C architecture to add localized information to BERT's global attention and the C2Q/Q2C coattention.",
+      "In addition to referencing these papers that helped us build a successful model, we also explored many other papers which either didn't work with our transformer based model or simply didn't work in combination with our additions to BERT. The three main papers from which we tried to gain ideas are U-Net: Machine Reading Comprehension with Unanswerable Questions BIBREF6, Attention-over-Attention Neural Networks for Reading Comprehension BIBREF7, and FlowQA: Grasping Flow in History for Conversational Machine Comprehension BIBREF8. We tried implementing the multitask learning methodology presented in U-Net by passing the [CLS] token through a series of convolutional layers to create a probability of whether the question has an answer. We combined this prediction with the prediction of Start and End logits by combining the logits' crossentropy loss and the [CLS] binary crossentropy loss. Unfortunately, this additional loss seemed to be hindering the network's learning ability. We conjecture that this type of multitask learning would benefit from full training instead of the finetuning we were restricted to doing because of resources and time. We looked to Attention-over-Attention as a source of additional ways of injecting attention into our network. Attention-over-Attention has a dot-product based attention mechanism that attends to attention vectors instead of embedding vectors. We believe this method did not help in our case because BERT works with the Context and Query as part of the same vector while the Attention-over-Attention model requires completely uncoupled Context and Query vectors. As a side note, we do separate the Context and Query vector derived from BERT before the coattention layers of our model, but these layers are not negatively affected by the fact that these separated vectors contain 'mixed' information between the Context and Query. Finally, we explored the FlowQA paper which proposed combining embeddings from multiple layers as an input to the final prediction layer. We implemented this idea by combining embeddings from multiple BERT layers as an input to our final prediction layer. This final layer was simply an additional transformer encoder and we think that the encoder does not have the LSTM's ability of being able to aggregate information from multiple sources."
+    ],
+    [
+      "We first focused on directed coattention via context to query and query to context attention as discussed in BIDAF BIBREF9. We then implemented localized feature extraction by 1D convolutions to add local information to coattention based on the QANET architecture BIBREF10. Subsequently, we experimented with different types of skip connections to inject BERT embedding information back into our modified network. We then applied what we learned using the base BERT model to the large BERT model. Finally, we performed hyperparameter tuning by adjusting the number of coattention blocks, the batch size, and the number of epochs trained and ensembled our three best networks. Each part of the project is discussed further in the subsections below."
+    ],
+    [
+      "The base BERT network, the baseline for this project, is built with 12 Transformer encoder blocks. These encoder blocks contain multi-head attention and a feed forward network. Each head of the multi-head attention attends to the concatenation of the context and query input and thus forms a global attention output. The output of each Transformer encoder is fed in to the next layer, creating an attention hierarchy. The benefit of this construction is that the model has access to the entire query and context at each level allowing both embeddings to learn from each other and removing the long term memory bottleneck faced by RNN based models. BERTQA uses directed coattention between the query and context, as opposed to attending to their concatenation (Figure FIGREF2). Our architecture consists of a set of 7 directed coattention blocks that are inserted between the BERT embeddings and the final linear layer before loss calculation.",
+      "The BERT embeddings are masked to produce seperate query and context embedding vectors (Equations DISPLAY_FORM3 , DISPLAY_FORM4).",
+      "Where E is the contextualized embeddings derived from BERT, m is the mask, and c and q are the context and query respectively.",
+      "$E_q$ and $E_c$ are then projected through linear layers to obtain key, value, and query vectors (Equation DISPLAY_FORM5).",
+      "Where Q, K, and V are the query, key and value vectors.",
+      "The Q, K, and V vectors are used in scaled dot-product attention (Equation DISPLAY_FORM6) to create the separate Context-to-Query (C2Q) and Query-to-Context (Q2C) attention vectors.",
+      "Where y is q and z is c for Q2C and y is c and z is q for C2Q.",
+      "The C2Q attention vector is summed with the query input and the Q2C attention vector is summed with the context input via a skip connection. Each sum vector is then pushed through a fully connected block and then is added back to the output of the fully connected block via another skip connection. Each sum is followed by a layer-wise normalization. The two resulting 3D C2Q and Q2C vectors are concatenated along the third (embedding) dimension which are combined by two 1D convolutions to create the final 3D vector representing the combination of the C2Q and Q2C attention. We use two convolution layers here so that the concatenated dimension is reduced more gradually so that too much information is not lost. This vector then goes into a final attention head to perform separate self attention pre-processing for the Start logit and End logit prediction layers. The Start logit is generated by a linear layer and the End logit is generated by the output of an LSTM which takes the concatenation of the start span and end span embeddings as an input. We used the BERT architecture code written in Pytorch from the HuggingFace github BIBREF3. We wrote our own code for all of the subsequent architecture."
+    ],
+    [
+      "To refine the focus of the attention further, we experimented with convolutional feature extraction to add localized information to the coattention output. We added four convolutional layers within the coattention architecture (Figure FIGREF8). The input to these layers were the BERT embeddings and the outputs were added to the outputs of the multi-head attention layers in the coattention architecture and then layer-wise normalized. This combination of coattention and local information provides a hierarchical understanding of the question and context. By itself, BERT provides information about the question and context as a unit, while the coattention extracts information from both the question and context relative to each other. The convolutions extract local features within the question and context to add localized information to the attention and embedding meanings. After adding the separate start and end logic, we found that the localized feature extraction did not allow an improvement in the network's learning via an ablation study where we ran the network without the convolutional layers. We speculate that the convolutions prevented improvement beyond a certain F1 score because they are lossy compressors and the information lost by the convolutions might be essential to downstream learning."
+    ],
+    [
+      "As shown in Figure FIGREF2, we have a skip connection from the BERT embedding layer combined with the convolved directed co-attention output (C2Q and Q2C). We experimented with 3 skip connection configurations: Simple ResNet inspired Skip, Self-Attention Transformer Skip, and a Highway Network. Of these, the Self-Attention Transformer based skip worked best initially. However, when we combined this skip connection with our logit prediction logic, the network was no longer able learn as well. The Simple ResNet inspired skip BIBREF11 connection solved this issue. It seems that the transformer skip connection followed by the additional transformer encoder blocks that form the beginning of the logit prediction logic processed the BERT embeddings too much and thus lost the benefit of the skip connection. Therefore, we decided to use a Simple ResNet inspired skip alongside the self attention heads for logit prediction. This allows the directed co-attention layers to learn distinct information coming from BERT embeddings via the skip and allows for efficient backpropagation to the BERT layers."
+    ],
+    [
+      "Inspired by the work presented in BIBREF12 where the authors present a way of generating new questions out of context and after observing the patterns in SQuAD 2.0 we realized there is a lot of syntatic and gramatical variance in the questions written by cloud workers. To help our network generalize better to these variations we decided to augment the dataset by paraphrasing the questions in the SQuAD training set. We applied backtranslation using Google Cloud Translation (NMT) API BIBREF13 to translate the sentence from English to French and then do a back translation to English, essentially 2 translations per question (Figure FIGREF11).",
+      "We call our augmented dataset SQUAD 2.Q and make 3 different versions (35%, 50%, and 100% augmentation) alongside code to generate them publicly available on our github BIBREF4."
+    ],
+    [
+      "Hyperparameter tuning has been an on-going process for our experiments. Here are the following hyperparameters we have tweaked and tuned for on Bert Base:",
+      "Number of Directed co-Attention layers - We tried various numbers of layers and we found out that N=7 for the co-attention layers gave us optimal performance while being able to fit the model on 2 GPUs (3 F1 score improvement by itself).",
+      "Max Sequence length - After initial experiments with default sequence length (context + query token) 384, we switched to a sequence length of 512. This gave us a 0.6 F1 improvement on our model.",
+      "Batch Size - Default: 12, We had to use a batch size of 6 for all our experiments due to resource constraints and out of memory issues on the GPU for any larger batch size.",
+      "Number of epochs - Default: 2 On increasing the number of epochs we saw a significant degradation in performance (-3 F1 score), we attribute this to the fact that the model starts to overfit to the training data with high variance and since the batch size is smaller the gradient updates could be noisy not allowing it to optimally converge.",
+      "Learning Rate - Default: 3e-5 We wrote a script to help us find the optimal learning rate using grid search and found the optimal learning rates for SQuAD 2.0 and SQuAD 2.Q respectively for batch size of 6."
+    ],
+    [
+      "We applied what we learned from the previous five subsections to the large BERT model, which has twice as many layers as the base model. In order to fit this model on our GPU and still use 7 of our coattention layers, we were limited to two examples on the GPU at a time. However, we also found that BERT large requires a larger batch size to get a good performance. As such, we left the batch size 6 as with the base model and used a gradient accumulation of 3 so that only two examples were on the GPU at a time. Additionally, the large model is very sensitive to the learning rate, and the rate of 3e-5 which we used with the smaller model no longer worked. We ran the model on a subset of the data with various learning rates and found that 1.1e-5 to 1.5e-5 works the best for the large model depending on the dataset used (SQuAD 2.0 or SQUAD 2.Q).",
+      "After experimenting with multiple combinations of the ideas we described above, we ensembled our three best networks to create our final predictions. The configurations of our three best networks are described in Table TABREF19.",
+      "We constructed the ensembled predictions by choosing the answer from the network that had the highest probability and choosing no answer if any of the networks predicted no answer."
+    ],
+    [
+      "Table TABREF20 reports the F1 and EM scores obtained for the experiments on the base model. The first column reports the base BERT baseline scores, while the second reports the results for the C2Q/Q2C attention addition. The two skip columns report scores for the skip connection connecting the BERT embedding layer to the coattention output (Simple Skip) and the scores for the same skip connection containing a Transformer block (Transformer Skip). The final column presents the result of the localized feature extraction added inside the C2Q/Q2C architecture (Inside Conv - Figure FIGREF8).",
+      "The results presented above verify our hypothesis that adding layers of directed attention to BERT improves its performance. The C2Q/Q2C network produced a significant improvement in the No Answer F1 score while causing a symmetric drop in the Has Answer F1 score. The C2Q/Q2C network attends the context relative to the query and vice versa instead of as a concatenated whole. This method of attention provides more information regarding whether there is an answer to the question in the context than the original BERT attention. The skip connections improved the scores further by adding the BERT embeddings back in to the coattention vectors and providing information that may have been lost by the C2Q/Q2C network in addition to providing a convenient path for backpropagation to the BERT embedding layers. The skip connection containing the transformer provides minimal gains while adding a significant overhead to runtime. Therefore, we built the final convolutional experiments on the Simple Skip architecture. The localized feature extraction within the coattention network produced the best results in the base model, but prevented an improvement in our modified BERT large model.",
+      "Table TABREF21 shows the F1 and EM scores obtained for the experiments on the large model. The models labeled 1, 2, and 3 are described in detail in Section 3.6.",
+      "Each of the models built on BERT large used our augmented dataset in addition to the coattention architecture, simple skip connection, and separate start and end logit logic. The Model 1 results show that a moderately augmented (35%) data set helps the training since both unaugmented and highly augmented (50%) models did not perform as well. It seems that adding too much augmented data reduces the F1 because the augmented data is noisy relative to the original data. The performance difference between Model 1 and 2 support the use of the LSTM in creating the End logit predictions. The LSTM is successfully combining the information from the Start logit and the End embeddings to provide a good input to the End logit linear layer. The ensemble model performed the best by far due to a significant increase in the no answer F1 which can be attributed to the ensembling method which is biased towards models that predict no answer.",
+      "We investigated the attention distributions produced by our proposed model by modifying the open source code from BertViz BIBREF14 . For the case where the question has an answer in the context (Figure FIGREF22), the attention heads produce activation around the answer phrase \"in the 10th and 11th centuries\". In the case where there is no answer in the context, the attention heads produce considerable activation on the [SEP] word-piece which is outside the context span.",
+      "As seen in Figure FIGREF25, we conducted an error analysis over different question types. Note that questions that did not fit into the 7 bins were classified as \"Other\". An example of a question in the \"Other\" category would be an \"Is it?\" question which is a minority set in SQUAD 2.0. Over the baseline, our model pretty much presents an overall improvement across the board in the different type of questions in the SQuAD 2.0 dev set. In the case of \"Which\" questions, our model goes wrong 69 times where as the baseline model goes wrong 64 times, a very small numeric difference. However, for the \"What\" questions the baseline model produces incorrect outputs for 776 examples while our model produces 30 fewer incorrect outputs. The reason for this lapse appears to be related to data augmentation where we observed that many a times \"Which\" was backtranslated as \"What\" and vice versa. Thus, the questions in these two classes are mixed and a completely accurate analysis of improvements in these classes is not possible.",
+      "Figure FIGREF26 shows an example cropped context and question that our ensemble model answers correctly while the BERT large model answers incorrectly. It seems that the BERT large model combined the words spirit and Christian to answer this question even thought the word spirit belongs to martial and the word Christian belongs to piety. Our model was able to keep the paired words together and realize that the question has no answer. We believe that our model was able to get the correct answer because of the coattention which is able to keep the words paired together correctly.",
+      "Overall, our model has shown marked qualitative and quantitative improvement over the base and large BERT models. Our SQUAD 2.Q dataset helps improve performance by mimicking the natural variance in questions present in the SQUAD 2.0 dataset. BertQA produces a significant improvement in the No Answer F1 by being able to maintain associations between words via coattention, as seen in Figure FIGREF26, and by ensembling our three best models."
+    ],
+    [
+      "We present a novel architectural scheme to use transformers to help the network learn directed co-attention which has improved performance over BERT baseline. We experimented with several architectural modifications and presented an ablation study. We present SQuAD 2.Q, an augmented dataset, developed using NMT backtranslation which helps our model generalize better over syntatic and grammatical variance of human writing. Our ensemble model gives a 3.5 point improvement over the Bert Large dev F1. We learned a lot about neural architectural techniques through experimenting with various model configurations. We also learned about how different model components do or don't work together and that some architectural choices like convolutional layers that work so well in computer vision do not necessarily work as well in NLP.",
+      "We would like to improve on the quality of data augmentation to limit noise in the dataset and further extend this work to context augmentation as well. Apart from that, we would also like to try recent architectures like Transformer-XL BIBREF15 which has potential to offer additional improvement on HasAns F1 by remembering long term dependencies and evaluate how it scales with our model as a next step. Given sufficient compute resources we would also like to pre-train our C2Q and Q2C layers similar to BERT pre-training to learn deeper language semantics and then fine-tune it on the SQuAD dataset for the task of Question Answering.",
+      "We would like to thank the CS224n Team for all the support throughout the course and also thank the folks at Azure for providing us with Cloud credits."
+    ]
+  ]
+}
+```
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+Name of Paper: BERTQA -- Attention on Steroids
+
+Question: How much F1 was improved after adding skip connections?
\ No newline at end of file
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+Name of Paper: Non-Parametric Adaptation for Neural Machine Translation
+
+Question: Where do they retrieve neighbor n-grams from in their approach?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Semi-parametric NMT",
+    "Retrieval Approaches",
+    "NMT with Context Retrieval",
+    "Data and Evaluation",
+    "Hyper-parameters and Optimization",
+    "Results",
+    "Comparison of retrieval strategies",
+    "Memory Ablation Experiments",
+    "Non-Parametric Adaptation",
+    "Related Work",
+    "Conclusions and Future Work",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "Over the last few years, neural sequence to sequence models BIBREF0 , BIBREF1 , BIBREF2 have revolutionized the field of machine translation by significantly improving translation quality over their phrase based counterparts BIBREF3 , BIBREF4 , BIBREF5 . With more gains arising from continued research on new neural network architectures and accompanying training techniques BIBREF6 , BIBREF7 , BIBREF8 , NMT researchers, both in industry and academia, have doubled down on their ability to train high capacity models on large corpora with gradient based optimization.",
+      "However, despite huge improvements in overall translation quality NMT has shown some glaring weaknesses, including idiom processing, and rare word or phrase translation BIBREF9 , BIBREF10 , BIBREF11 - tasks that should be easy if the model could retain learned information from individual training examples. NMT has also been shown to perform poorly when dealing with multi-domain data BIBREF12 . This `catastrophic forgetting' problem has been well-studied in traditional neural network literature, caused by parameter shift during the training process BIBREF13 , BIBREF14 . Non-parametric methods, on the other hand, are resistant to forgetting but are prone to over-fitting due to their reliance on individual training examples. We focus on a non-parametric extension to NMT, hoping to combine the generalization ability of neural networks with the eidetic memory of non-parametric methods. Given a translation query, we rely on an external retrieval mechanism to find similar source-target instances in the training corpus, which are then utilized by the model.",
+      "There has been some work on semi-parametric NMT BIBREF15 , BIBREF16 , BIBREF17 , but its effectiveness has been confined to narrow domain datasets. Existing approaches have relied on sentence level similarity metrics for retrieval, which works well for domains with high train-test overlap, but fails to retrieve useful candidates for broad domains. Even if we could find training instances with overlapping phrases it's likely that the information in most retrieved source-target pairs is noise for the purpose of translating the current query.",
+      "To retrieve useful candidates when sentence similarity is low, we use n-gram retrieval instead of sentence retrieval. This results in neighbors which have high local overlap with the source sentence, even if they are significantly different in terms of overall sentence similarity. This is intuitively similar to utilizing information from a phrase table BIBREF18 within NMT BIBREF19 , without losing the global context lost when constructing the phrase table. We also propose another simple extension using dense vectors for n-gram retrieval which allows us to exploit similarities beyond lexical overlap.",
+      "To effectively extract the signal from the noisy retrieved neighbors, we develop an extension of the approach proposed in BIBREF17 . While BIBREF17 encode the retrieved targets without any context, we incorporate information from the current and retrieved sources while encoding the retrieved target, in order to distinguish useful information from noise.",
+      "We evaluate our semi-parametric NMT approach on two tasks."
+    ],
+    [
+      "Standard approaches for Neural Machine Translation rely on seq2seq architectures BIBREF0 , BIBREF1 , where given a source sequence INLINEFORM0 and a target sequence INLINEFORM1 , the goal is to model the probability distribution, INLINEFORM2 .",
+      "Semi-parametric NMT BIBREF19 , BIBREF15 approaches this learning problem with a different formulation, by modeling INLINEFORM0 instead, where INLINEFORM1 is the set of sentence pairs where the source sentence is a neighbor of INLINEFORM2 , retrieved from the training corpus using some similarity metric. This relies on a two step approach - the retrieval stage finds training instances, INLINEFORM3 , similar to the source sentence INLINEFORM4 , and the translation stage generates the target sequence INLINEFORM5 given INLINEFORM6 and INLINEFORM7 . We follow this setup, proposing improvements to both stages in order to enhance the applicability of semi-parametric NMT to more general translation tasks."
+    ],
+    [
+      "Existing approaches have proposed using off the shelf search engines for the retrieval stage. However, our objective differs from traditional information retrieval, since the goal of retrieval in semi-parametric NMT is to find neighbors which might improve translation performance, which might not correlate with maximizing sentence similarity.",
+      "Our baseline strategy relies on a sentence level similarity score, similar to those used for standard information retrieval tasks BIBREF24 . We compare this against finer-grained n-gram retrieval using the same similarity metric. We also propose a dense vector based n-gram retrieval strategy, using representations extracted from a pre-trained NMT model.",
+      "Our baseline approach relies on a simple inverse document frequency (IDF) based similarity score. We define the IDF score of any token, INLINEFORM0 , as INLINEFORM1 , where INLINEFORM2 is the number of sentence pairs in training corpus and INLINEFORM3 is the number of sentences INLINEFORM4 occurs in. Let any two sentence pairs in the corpus be INLINEFORM5 and INLINEFORM6 . Then we define the similarity between INLINEFORM7 and INLINEFORM8 by, DISPLAYFORM0 ",
+      "For every sentence in the training, dev and test corpora, we find the INLINEFORM0 most similar training sentence pairs and provide them as context to NMT.",
+      "Motivated by phrase based SMT, we retrieve neighbors which have high local, sub-sentence level overlap with the source sentence. We adapt our approach to retrieve n-grams instead of sentences. We note that the similarity metric defined above for sentences is equally applicable for n-gram retrieval.",
+      "Let INLINEFORM0 be a sentence. Then the set of all possible n-grams of X, for a given INLINEFORM1 , can be defined as INLINEFORM2 (also including padding at the end). To reduce the number of n-grams used to represent every sentence, we define the reduced set of n-grams for X to be INLINEFORM3 .",
+      "We represent every sentence by their reduced n-gram set. For every n-gram in INLINEFORM0 , we find the closest n-gram in the training set using the IDF similarity defined above. For each retrieved n-gram we find the corresponding sentence (In case an n-gram is present in multiple sentences, we choose one randomly). The set of neighbors of INLINEFORM1 is then the set of all sentences in the training corpus that contain an n-gram that maximizes the n-gram similarity with any n-gram in INLINEFORM2 .",
+      "To capture phrases of different lengths we use multiple n-gram widths, INLINEFORM0 . In case a sentence has already been added to the retrieved set, we find the next most similar sentence to avoid having duplicates. The number of neighbors retrieved for each source sentence is proportional to its length.",
+      "We also extend our n-gram retrieval strategy with dense vector based n-gram representations. The objective behind using a dense vector based approach is to incorporate information relevant to the translation task in the retrieval stage. We use a pre-trained Transformer Base BIBREF6 encoder trained on WMT to generate sub-word level dense representations for the sentence. The representation for each n-gram is now defined to be the mean of the representations of all its constituent sub-words. We use the INLINEFORM0 distance of n-gram representations as the retrieval criterion. Note that we use a sub-word level decomposition of sentences for dense retrieval, as compared to word-level for IDF based retrieval (i.e., n-grams are composed of sub-words instead of words).",
+      "Following the approach described for IDF based n-gram retrieval, we use multiple values of INLINEFORM0 , and remove duplicate neighbors while creating the retrieved set."
+    ],
+    [
+      "To incorporate the retrieved neighbors, INLINEFORM0 , within the NMT model, we first encode them using Transformer layers, as described in subsection UID12 . This encoded memory is then used within the decoder via an attention mechanism, as described in subsection UID15 .",
+      "We now describe how each retrieved translation pair, INLINEFORM0 , is encoded. This architecture is illustrated in Figure FIGREF9 .",
+      "We first encode the retrieved source, INLINEFORM0 , in a Transformer layer. Apart from self-attention, we incorporate information from the encoder representation of the current source, INLINEFORM1 , using decoder style cross-attention.",
+      "The retrieved target, INLINEFORM0 , is encoded in a similar manner, attending the encoded representation of INLINEFORM1 generated in the previous step.",
+      "The encoded representations for all targets, INLINEFORM0 , are then concatenated along the time axis to form the Conditional Source Target Memory (CSTM).",
+      "We use gated multi-source attention to combine the context from the source encoder representations and the CSTM. This is similar to the gated attention employed by BIBREF17 . We use a Transformer based decoder that attends to both, the encoder outputs and the CSTM, in every cross-attention layer. The rest of the decoder architecture remains unchanged.",
+      "Let the context vectors obtained by applying multi-head attention to the source and memory, with query INLINEFORM0 be INLINEFORM1 and INLINEFORM2 respectively. Then the gated context vector, INLINEFORM3 , is given by, DISPLAYFORM0 DISPLAYFORM1 ",
+      "where INLINEFORM0 is the scalar gating variable at time-step t, and INLINEFORM1 and INLINEFORM2 are learned parameters. These steps are illustrated in Figure FIGREF10 ."
+    ],
+    [
+      "We compare the performance of a standard Transformer Base model and our semi-parametric NMT approach on an English-French translation task. We create a new heterogeneous dataset, constructed from a combination of the WMT training set (36M pairs), the IWSLT bilingual corpus (237k pairs), JRC-Acquis (797k pairs) and OpenSubtitles (33M pairs). For WMT, we use newstest 13 for validation and newstest 14 for test. For IWSLT, we use a combination of the test corpora from 2012-14 for validation and test 2015 for eval. For OpenSubtitles and JRC-Acquis, we create our own splits for validation and test, since no benchmark split is publicly available. After deduping, the JRC-Acquis test and validation set contain 6574 and 5121 sentence pairs respectively. The OpenSubtitles test and validation sets contain 3975 and 3488 pairs. For multi-domain training, the validation set is a concatenation of the four individual validation sets.",
+      "All datasets are tokenized with the Moses tokenizer BIBREF25 and mixed without any sampling. We use a shared vocabulary Sentence-Piece Model BIBREF26 for sub-word tokenization, with a vocabulary size of 32000 tokens. We train each model for 1M steps, and choose the best checkpoint from the last 5 checkpoints based on validation performance. BLEU scores are computed with tokenized true-cased output and references with multi-bleu.perl from Moses.",
+      "For IDF based sentence retrieval, for each sentence in the training, dev and test corpus, we use INLINEFORM0 neighbors per example during both, training and evaluation. For the N-Gram level retrieval strategies, we used INLINEFORM1 neighbors during training, and neighbors corresponding to all n-grams during decoding. This was meant to limit memory requirements and enable the model to fit on P100s during training. We used n-gram width, INLINEFORM2 , for both IDF and dense vector based n-gram retrieval approaches. For scalability reasons, we restricted the retrieval set to the in-domain training corpus, i.e. neighbors for all train, dev and test sentences in the JRC-Acquis corpus were retrieved from the JRC-Acquis training split, and similarly for the other datasets."
+    ],
+    [
+      "For our baseline model we use the standard Transformer Base model BIBREF6 . For the semi-parametric model, all our hyper-parameters for attention (8 attention heads), model dimensions (512) and hidden dimensions (2048), including those used in the CSTM memory are equivalent to Transformer Base.",
+      "The Transformer baselines are trained on 16 GPUs, with the learning rate, warm-up schedule and batching scheme described in BIBREF6 . The semi-parametric models were trained on 32 GPUs with each replica split over 2 GPUs, one to train the translation model and the other for computing the CSTM. We used a conservative learning rate schedule (3, 40K) BIBREF8 to train the semi-parametric models.",
+      "We apply a dropout rate BIBREF27 of 0.1 to all inputs, residuals, attentions and ReLU connections in both models. We use Adam BIBREF28 to train all models, and apply label smoothing with an uncertainty of 0.1 BIBREF29 . In addition to the transformer layers, layer normalization BIBREF30 was applied to the output of the CSTM. All models are implemented in Tensorflow-Lingvo BIBREF31 ."
+    ],
+    [
+      "We compare the test performance of a multi-domain Transformer Base and our semi-parametric model using dense vector based n-gram retrieval and CSTM in Table TABREF21 . Apart from significantly improving performance by more than 10 BLEU points on JRC-Acquis, 2-3 BLEU on OpenSubtitles and IWSLT, we notice a moderate gain of 0.5 BLEU points on WMT 14."
+    ],
+    [
+      "We compare the performance of all 3 retrieval strategies in Table TABREF21 . The semi-parametric model with sentence level retrieval out-performs the seq2seq model by a huge margin on JRC-Acquis and OpenSubtitles. A sample from the JRC-Acquis dataset where the semi-parametric approach improves significantly over the neural approach is included in Table TABREF22 . We notice that there is a lot of overlap between the source sentence and the retrieved source, resulting in the semi-parametric model copying large chunks from the retrieved target. However, its performance is noticeably worse on WMT and IWSLT. Based on a manual inspection of the retrieved candidates, we attribute these losses to retrieval failures. For broad domain datasets like WMT and IWSLT sentence retrieval fails to find good candidates.",
+      "Switching to n-gram level retrieval brings the WMT performance close to the seq2seq approach, and IWSLT performance to 2 BLEU points above the baseline model. Representative examples from IWSLT and WMT where n-gram retrieval improves over sentence level retrieval can be seen in Tables TABREF24 and TABREF25 . Despite the majority of the retrieved neighbor having nothing in common with the source sentence, n-gram retrieval is able to find neighbors that contain local overlaps.",
+      "Using dense n-gram retrieval allows us to move beyond lexical overlap and retrieve semantically similar n-grams even when the actual tokens are different. As a result, dense n-gram retrieval improves performance over all our models on all 4 datasets. An illustrative example from WMT is included in Table TABREF26 ."
+    ],
+    [
+      "We report the performance of the various memory ablations in Table TABREF27 . We first remove the retrieved sources, INLINEFORM0 , from the CSTM, resulting in an architecture where the encoding of a retrieved target, INLINEFORM1 , only incorporates information from the source INLINEFORM2 , represented by the row CTM in the table. This results in a clear drop in performance on all datasets. We ablate further by removing the attention to the original source INLINEFORM3 , resulting in a slightly smaller drop in performance (represented by TM). These experiments indicate that incorporating context from the sources significantly contributes to performance, by allowing the model to distinguish between relevant context and noise."
+    ],
+    [
+      "Using a semi-parametric formulation for MT opens up the possibility of non-parametric adaptation. The biggest advantage of this approach is the possibility of training a single massively customizable model which can be adapted to any new dataset or document at inference time, by just updating the retrieval dataset.",
+      "We evaluate our model's performance on non-parametric adaptation and compare it against a fully fine-tuned model. In this setting, we train a baseline model and a dense n-gram based semi-parametric model on the WMT training corpus. We only retrieve and train on examples from the WMT corpus during training. We use the same hyper-parameters and training approaches used for the multi-domain experiments, as in Section SECREF3 .",
+      "The baseline model is then fine-tuned independently on JRC-Acquis, OpenSubtitles and IWSLT. The semi-parametric model is adapted non-parametrically to these three datasets, without any parameter updates. Adaptation is achieved via the retrieval mechanism - while evaluating, we retrieve similar examples from their respective training datasets. To quantify headroom, we also fine-tune our semi-parametric model on each of these datasets.",
+      "The results for non-parametric adaptation experiments are documented in Table TABREF30 . We notice that the non-parametric adaptation strategy significantly out-performs the base model on all 4 datasets. More importantly, the we find that our approach is capable of adapting to both, JRC-Acquis and OpenSubtitles, via just the retrieval apparatus, and out-performs the fully fine-tuned model indicating that non-parametric adaptation might be a reasonable approach when adapting to a lot of narrow domains or documents.",
+      "In-domain fine-tuning on top of non-parametric adaptation further improves by 2 BLEU points on all datasets, increasing the gap even further with the seq2seq adapted models."
+    ],
+    [
+      "Tools incorporating information from individual translation pairs, or translation memories BIBREF32 , BIBREF33 , have been widely utilized by human translators in the industry. There have been a few efforts attempting to combine non-parametric methods with NMT BIBREF15 , BIBREF16 , BIBREF17 , but the key difference of our approach is the introduction of local, sub-sentence level similarity in the retrieval process, via n-gram level retrieval. Combined with our architectural improvements, motivated by the target encoder and gated attention from BIBREF17 and the extended transformer model from BIBREF34 , our semi-parametric NMT model is able to out-perform purely neural models in broad multi-domain settings.",
+      "Some works have proposed using phrase tables or the outputs of Phrase based MT within NMT BIBREF19 , BIBREF35 , BIBREF36 . While this reduces the noise present within the retrieved translation pairs, it requires training and maintaining a separate SMT system which might introduce errors of its own.",
+      "Another class of methods requires fine-tuning the entire NMT model to every instance at inference time, using retrieved examples BIBREF37 , BIBREF38 , but these approaches require running expensive gradient descent steps before every translation.",
+      "Beyond NMT, there have been a few other attempts to incorporate non-parametric approaches into neural generative models BIBREF39 , BIBREF40 , BIBREF41 . This strong trend towards combining neural generative models with non-parametric methods is an attempt to counter the weaknesses of neural networks, especially their failure to remember information from individual training instances and the diversity problem of seq2seq models BIBREF42 , BIBREF43 .",
+      "While our approach relies purely on retrieval from the training corpus, there has been quite a lot of work, especially on Question Answering, that attempts to find additional signals to perform the supervised task in the presence of external knowledge sources BIBREF44 , BIBREF45 . Retrieving information from unsupervised corpora by utilizing multilingual representations BIBREF46 might be another interesting extension of this work."
+    ],
+    [
+      "We make two major technical contributions in this work which enable us to improve the quality of semi-parametric NMT on broad domain datasets. First, we propose using n-gram retrieval, with standard Inverse Document Frequency similarity and with dense vector representations, that takes into account local sentence similarities that are critical to translation. As a result we are able to retrieve useful candidates even for broad domain tasks with little train-test overlap. Second, we propose a novel architecture to encode retrieved source-target pairs, allowing the model to distinguish useful information from noise by encoding the retrieved targets in context of the current translation task.",
+      "We demonstrate, for the first time, that semi-parametric methods can beat neural models by significant margins on multi-domain Machine Translation. By successfully training semi-parametric neural models on a broad domain dataset (WMT), we also open the door for non-parametric adaptation, showing huge improvements on new domains without any parameter updates.",
+      "While we constrain this work to retrieved context, our architecture can be utilized to incorporate information from other sources of context, including documents, bilingual dictionaries etc. Using dense representations for retrieval also allows extending semi-parametric neural methods to other input modalities, including images and speech.",
+      "With this work, we hope to motivate further investigation into semi-parametric neural models for and beyond Neural Machine Translation."
+    ],
+    [
+      "We would like to thank Naveen Arivazhagan, Macduff Hughes, Dmitry Lepikhin, Mia Chen, Yuan Cao, Ciprian Chelba, Zhifeng Chen, Melvin Johnson and other members of the Google Brain and Google Translate teams for their useful inputs and discussions. We would also like to thank the entire Lingvo development team for their foundational contributions to this project."
+    ]
+  ]
+}
+```
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+Name of Paper: Non-Parametric Adaptation for Neural Machine Translation
+
+Question: To which systems do they compare their results against?
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+Name of Paper: BERT-Based Arabic Social Media Author Profiling
+
+Question: Does the paper report F1-scores for the age and language variety tasks?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Data",
+    "Experiments",
+    "Experiments ::: Tweet-Level Models ::: Baseline GRU.",
+    "Experiments ::: Tweet-Level Models ::: BERT.",
+    "Experiments ::: Tweet-Level Models ::: Data Augmentation.",
+    "Experiments ::: User-Level Models",
+    "Experiments ::: APDA@FIRE2019 submission",
+    "Conclusion",
+    "Acknowledgement"
+  ],
+  "paragraphs": [
+    [
+      "The proliferation of social media has made it possible to collect user data in unprecedented ways. These data can come in the form of usage and behavior (e.g., who likes what on Facebook), network (e.g., who follows a given user on Instagram), and content (e.g., what people post to Twitter). Availability of such data have made it possible to make discoveries about individuals and communities, mobilizing social and psychological research and employing natural language processing methods. In this work, we focus on predicting social media user age, dialect, and gender based on posted language. More specifically, we use the total of 100 tweets from each manually-labeled user to predict each of these attributes. Our dataset comes from the Arabic author profiling and deception detection shared task (APDA) BIBREF0. We focus on building simple models using pre-trained bidirectional encoders from transformers(BERT) BIBREF1 under various data conditions. Our results show (1) the utility of augmenting training data, and (2) the benefit of using majority votes from our simple classifiers.",
+      "In the rest of the paper, we introduce the dataset, followed by our experimental conditions and results. We then provide a literature review and conclude."
+    ],
+    [
+      "For the purpose of our experiments, we use data released by the APDA shared task organizers. The dataset is divided into train and test by organizers. The training set is distributed with labels for the three tasks of age, dialect, and gender. Following the standard shared tasks set up, the test set is distributed without labels and participants were expected to submit their predictions on test. The shared task predictions are expected by organizers at the level of users. The distribution has 100 tweets for each user, and so each tweet is distributed with a corresponding user id. As such, in total, the distributed training data has 2,250 users, contributing a total of 225,000 tweets. The official task test set contains 720,00 tweets posted by 720 users. For our experiments, we split the training data released by organizers into 90% TRAIN set (202,500 tweets from 2,025 users) and 10% DEV set (22,500 tweets from 225 users). The age task labels come from the tagset {under-25, between-25 and 34, above-35}. For dialects, the data are labeled with 15 classes, from the set {Algeria, Egypt, Iraq, Kuwait, Lebanon-Syria, Lybia, Morocco, Oman, Palestine-Jordan, Qatar, Saudi Arabia, Sudan, Tunisia, UAE, Yemen}. The gender task involves binary labels from the set {male, female}."
+    ],
+    [
+      "As explained earlier, the shared task is set up at the user level where the age, dialect, and gender of each user are the required predictions. In our experiments, we first model the task at the tweet level and then port these predictions at the user level. For our core modelling, we fine-tune BERT on the shared task data. We also introduce an additional in-house dataset labeled with dialect and gender tags to the task as we will explain below. As a baseline, we use a small gated recurrent units (GRU) model. We now introduce our tweet-level models."
+    ],
+    [
+      "Our baseline is a GRU network for each of the three tasks. We use the same network architecture across the 3 tasks. For each network, the network contains a layer unidirectional GRU, with 500 units and an output linear layer. The network is trained end-to-end. Our input embedding layer is initialized with a standard normal distribution, with $\\mu =0$, and $\\sigma =1$, i.e., $W \\sim N(0,1)$. We use a maximum sequence length of 50 tokens, and choose an arbitrary vocabulary size of 100,000 types, where we use the 100,000 most frequent words in TRAIN. To avoid over-fitting, we use dropout BIBREF2 with a rate of 0.5 on the hidden layer. For the training, we use the Adam BIBREF3 optimizer with a fixed learning rate of $1e-3$. We employ batch training with a batch size of 32 for this model. We train the network for 15 epochs and save the model at the end of each epoch, choosing the model that performs highest accuracy on DEV as our best model. We present our best result on DEV in Table TABREF7. We report all our results using accuracy. Our best model obtains 42.48% for age, 37.50% for dialect, and 57.81% for gender. All models obtains best results with 2 epochs."
+    ],
+    [
+      "For each task, we fine-tune on the BERT-Base Muultilingual Cased model relesed by the authors BIBREF1 . The model was pre-trained on Wikipedia of 104 languages (including Arabic) with 12 layer, 768 hidden units each, 12 attention heads, and has 110M parameters in entire model. The vocabulary of the model is 119,547 shared WordPices. We fine-tune the model with maximum sequence length of 50 tokens and a batch size of 32. We set the learning rate to $2e-5$ and train for 15 epochs. We use the same network architecture and parameters across the 3 tasks. As Table TABREF7 shows, comparing with GRU, BERT is 3.16% better for age, 4.85% better for dialect, and 2.45% higher for gender."
+    ],
+    [
+      "To further improve the performance of our models, we introduce in-house labeled data that we use to fine-tune BERT. For the gender classification task, we manually label an in-house dataset of 1,100 users with gender tags, including 550 female users, 550 male users. We obtain 162,829 tweets by crawling the 1,100 users' timelines. We combine this new gender dataset with the gender TRAIN data (from shared task) to obtain an extended dataset, to which we refer as EXTENDED_Gender. For the dialect identification task, we randomly sample 20,000 tweets for each class from an in-house dataset gold labeled with the same 15 classes as the shared task. In this way, we obtain 298,929 tweets (Sudan only has 18,929 tweets). We combine this new dialect data with the shared task dialect TRAIN data to form EXTENDED_Dialect. For both the dialect and gender tasks, we fine-tune BERT on EXTENDED_Dialect and EXTENDED_Gender independently and report performance on DEV. We refer to this iteration of experiments as BERT_EXT. As Table TABREF7 shows, BERT_EXT is 2.18% better than BERT for dialect and 0.75% better than BERT for gender."
+    ],
+    [
+      "Our afore-mentioned models identify user's profiling on the tweet-level, rather than directly detecting the labels of a user. Hence, we follow the work of Zhang & Abdul-Mageed BIBREF4 to identify user-level labels. For each of the three tasks, we use tweet-level predicted labels (and associated softmax values) as a proxy for user-level labels. For each predicted label, we use the softmax value as a threshold for including only highest confidently predicted tweets. Since in some cases softmax values can be low, we try all values between 0.00 and 0.99 to take a softmax-based majority class as the user-level predicted label, fine-tuning on our DEV set. Using this method, we acquire the following results at the user level: BERT models obtain an accuracy of 55.56% for age, 96.00% for dialect, and 80.00% for gender. BERT_EXT models achieve 95.56% accuracy for dialect and 84.00% accuracy for gender."
+    ],
+    [
+      "First submission. For the shared task submission, we use the predictions of BERT_EXT as out first submission for gender and dialect, but only BERT for age (since we have no BERT_EXT models for age, as explained earlier). In each case, we acquire results at tweet-level first, then port the labels at the user-level as explained in the previous section. For our second and third submitted models, we also follow this method of going from tweet to user level. Second submission. We combine our DEV data with our EXTENDED_Dialect and EXTENDED_Gender data, for dialect and gender respectively, and train our second submssions for the two tasks. For age second submsision, we concatenate DEV data to TRAIN and fine-tune the BERT model. We refer to the settings for our second submission models collectively as BERT_EXT+DEV.",
+      "Third submission. Finally, for our third submission, we use a majority vote of (1) first submission, (2) second submission, and (3) predictions from our user-level BERT model. These majority class models (i.e., our third submission) achieve best results on the official test data. We acquire 54.72% accuracy for age, 81.67% accuracy for gender, 93.75% accuracy for dialect, and 40.97% joint accuracy."
+    ],
+    [
+      "In this work, we described our submitted models to the Arabic author profiling and deception detection shared task (APDA) BIBREF0. We focused on detecting age, dialect, and gender using BERT models under various data conditions, showing the utility of additional, in-house data on the task. We also showed that a majority vote of our models trained under different conditions outperforms single models on the official evaluation. In the future, we will investigate automatically extending training data for these tasks as well as better representation learning methods."
+    ],
+    [
+      "We acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC), the Social Sciences Research Council of Canada (SSHRC), and Compute Canada (www.computecanada.ca)."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: Grammatical Analysis of Pretrained Sentence Encoders with Acceptability Judgments
+
+Question: Do the authors have a hypothesis as to why morphological agreement is hardly learned by any model?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Analysis Set",
+    "Annotation",
+    "Feature Descriptions",
+    "Correlations",
+    "Models Evaluated",
+    "Overall CoLA Results",
+    "Analysis Set Results",
+    "Length Analysis",
+    "Conclusion",
+    "Acknowledgments",
+    "Simple",
+    "Pred (Predicates)",
+    "Adjunct",
+    "Argument Types",
+    "Arg Altern (Argument Alternations)",
+    "Imperative",
+    "Binding",
+    "Question",
+    "Comp Clause (Complement Clauses)",
+    "Aux (Auxiliaries)",
+    "to-VP (Infinitival VPs)",
+    "N, Adj (Nouns and Adjectives)",
+    "S-Syntax (Sentence-Level Syntax)",
+    "Determiner",
+    "Violations"
+  ],
+  "paragraphs": [
+    [
+      "The effectiveness and ubiquity of pretrained sentence embeddings for natural language understanding has grown dramatically in recent years. Recent sentence encoders like OpenAI's Generative Pretrained Transformer BIBREF3 and BERT BIBREF2 achieve the state of the art on the GLUE benchmark BIBREF4 . Among the GLUE tasks, these state-of-the-art systems make their greatest gains on the acceptability task with the Corpus of Linguistic Acceptability BIBREF0 . CoLA contains example sentences from linguistics publications labeled by experts for grammatical acceptability, and written to show subtle grammatical features. Because minimal syntactic differences can separate acceptable sentences from unacceptable ones (What did Bo write a book about? / *What was a book about written by Bo?), and acceptability classifiers are more reliable when trained on GPT and BERT than on recurrent models, it stands to reason that GPT and BERT have better implicit knowledge of syntactic features relevant to acceptability.",
+      "Our goal in this paper is to develop an evaluation dataset that can locate which syntactic features that a model successfully learns by identifying the syntactic domains of CoLA in which it performs the best. Using this evaluation set, we compare the syntactic knowledge of GPT and BERT in detail, and investigate the strengths of these models over the baseline BiLSTM model published by warstadt2018neural. The analysis set includes expert annotations labeling the entire CoLA development set for the presence of 63 fine-grained syntactic features.",
+      "We identify many specific syntactic features that make sentences harder to classify, and many that have little effect. For instance, sentences involving unusual or marked argument structures are no harder than the average sentence, while sentences with long distance dependencies are hard to learn. We also find features of sentences that accentuate or minimize the differences between models. Specifically, the transformer models seem to learn long-distance dependencies much better than the recurrent model, yet have no advantage on sentences with morphological violations."
+    ],
+    [
+      "We introduce a grammatically annotated version of the entire CoLA development set to facilitate detailed error analysis of acceptability classifiers. These 1043 sentences are expert-labeled for the presence of 63 minor grammatical features organized into 15 major features. Each minor feature belongs to a single major feature. A sentence belongs to a major feature if it belongs to one or more of the relevant minor features. The Appendix includes descriptions of each feature along with examples and the criteria used for annotation.",
+      "The 63 minor features and 15 major features are illustrated in Table TABREF5 . Considering minor features, an average of 4.31 features is present per sentence (SD=2.59). The average feature is present in 71.3 sentences (SD=54.7). Turning to major features, the average sentence belongs to 3.22 major features (SD=1.66), and the average major feature is present in 224 sentences (SD=112). Every sentence is labeled with at least one feature."
+    ],
+    [
+      "The sentences were annotated manually by one of the authors, who is a PhD student with extensive training in formal linguistics. The features were developed in a trial stage, in which the annotator performed a similar annotation with different annotation schema for several hundred sentences from CoLA not belonging to the development set."
+    ],
+    [
+      "Here we briefly summarize the feature set in order of the major features. Many of these constructions are well-studied in syntax, and further background can be found in textbooks such as adger2003core and sportiche2013introduction.",
+      "This major feature contains only one minor feature, simple, including sentences with a syntactically simplex subject and predicate.",
+      "These three features correspond to predicative phrases, including copular constructions, small clauses (I saw Bo jump), and resultatives/depictives (Bo wiped the table clean).",
+      "These six features mark various kinds of optional modifiers. This includes modifiers of NPs (The boy with blue eyes gasped) or VPs (The cat meowed all morning), and temporal (Bo swam yesterday) or locative (Bo jumped on the bed).",
+      "These five features identify syntactically selected arguments, differentiating, for example, obliques (I gave a book to Bo), PP arguments of NPs and VPs (Bo voted for Jones), and expletives (It seems that Bo left).",
+      "These four features mark VPs with unusual argument structures, including added arguments (I baked Bo a cake) or dropped arguments (Bo knows), and the passive (I was applauded).",
+      "This contains only one feature for imperative clauses (Stop it!).",
+      "These are two minor features, one for bound reflexives (Bo loves himself), and one for other bound pronouns (Bo thinks he won).",
+      "These five features apply to sentences with question-like properties. They mark whether the interrogative is an embedded clause (I know who you are), a matrix clause (Who are you?), or a relative clause (Bo saw the guy who left); whether it contains an island out of which extraction is unacceptable (*What was a picture of hanging on the wall?); or whether there is pied-piping or a multi-word wh-expressions (With whom did you eat?).",
+      "These six features apply to various complement clauses (CPs), including subject CPs (That Bo won is odd); CP arguments of VPs or NPs/APs (The fact that Bo won); CPs missing a complementizer (I think Bo's crazy); or non-finite CPs (This is ready for you to eat).",
+      "These four minor features mark the presence of auxiliary or modal verbs (I can win), negation, or \u201cpseudo-auxiliaries\u201d (I have to win).",
+      "These five features mark various infinitival embedded VPs, including control VPs (Bo wants to win); raising VPs (Bo seemed to fly); VP arguments of NPs or APs (Bo is eager to eat); and VPs with extraction (e.g. This is easy to read ts ).",
+      "These seven features mark complex NPs and APs, including ones with PP arguments (Bo is fond of Mo), or CP/VP arguments; noun-noun compounds (Bo ate mud pie); modified NPs, and NPs derived from verbs (Baking is fun).",
+      "These seven features mark various unrelated syntactic constructions, including dislocated phrases (The boy left who was here earlier); movement related to focus or information structure (This I've gotta see this); coordination, subordinate clauses, and ellipsis (I can't); or sentence-level adjuncts (Apparently, it's raining).",
+      "These four features mark various determiners, including quantifiers, partitives (two of the boys), negative polarity items (I *do/don't have any pie), and comparative constructions.",
+      "These three features apply only to unacceptable sentences, and only ones which are ungrammatical due to a semantic or morphological violation, or the presence or absence of a single salient word."
+    ],
+    [
+      "We wish to emphasize that these features are overlapping and in many cases are correlated, thus not all results from using this analysis set will be independent. We analyzed the pairwise Matthews Correlation Coefficient BIBREF17 of the 63 minor features (giving 1953 pairs), and of the 15 major features (giving 105 pairs). MCC is a special case of Pearson's INLINEFORM0 for Boolean variables. These results are summarized in Table TABREF25 . Regarding the minor features, 60 pairs had a correlation of 0.2 or greater, 17 had a correlation of 0.4 or greater, and 6 had a correlation of 0.6 or greater. None had an anti-correlation of greater magnitude than -0.17. Turning to the major features, 6 pairs had a correlation of 0.2 or greater, and 2 had an anti-correlation of greater magnitude than -0.2.",
+      "We can see at least three reasons for these observed correlations. First, some correlations can be attributed to overlapping feature definitions. For instance, expletive arguments (e.g. There are birds singing) are, by definition, non-canonical arguments, and thus are a subset of add arg. However, some added arguments, such as benefactives (Bo baked Mo a cake), are not expletives. Second, some correlations can be attributed to grammatical properties of the relevant constructions. For instance, question and aux are correlated because main-clause questions in English require subject-aux inversion and in many cases the insertion of auxiliary do (Do lions meow?). Third, some correlations may be a consequence of the sources sampled in CoLA and the phenomena they focus on. For instance, the unusually high correlation of Emb-Q and ellipsis/anaphor can be attributed to BIBREF18 , which is an article about the sluicing construction involving ellipsis of an embedded interrogative (e.g. I saw someone, but I don't know who).",
+      "Finally, two strongest anti-correlations between major features are between simple and the two features related to argument structure, argument types and arg altern. This follows from the definition of simple, which excludes any sentence containing a large number or unusual configuration of arguments."
+    ],
+    [
+      "We train MLP acceptability classifiers for CoLA on top of three sentence encoders: (1) the CoLA baseline encoder with ELMo-style embeddings, (2) OpenAI GPT, and (3) BERT. We use publicly available sentence encoders with pretrained weights."
+    ],
+    [
+      "The overall performance of the three sentence encoders is shown in Table TABREF33 . Performance on CoLA is measured using MCC BIBREF14 . We present the best single restart for each encoder, the mean over restarts for an encoder, and the result of ensembling the restarts for a given encoder, i.e. taking the majority classification for a given sentence, or the majority label of acceptable if tied. For BERT results, we exclude 5 out of the 20 restarts because they were degenerate (MCC=0).",
+      "Across the board, BERT outperforms GPT, which outperforms the CoLA baseline. However, BERT and GPT are much closer in performance than they are to CoLA baseline. While ensemble performance exceeded the average for BERT and GPT, it did not outperform the best single model."
+    ],
+    [
+      "The results for the major features and minor features are shown in Figures FIGREF26 and FIGREF35 , respectively. For each feature, we measure the MCC of the sentences including that feature. We plot the mean of these results across the different restarts for each model, and error bars mark the mean INLINEFORM0 standard deviation. For the Violations features, MCC is technically undefined because these features only contain unacceptable sentences. We report MCC in these cases by including for each feature a single acceptable example that is correctly classified by all models.",
+      "Comparison across features reveals that the presence of certain features has a large effect on performance, and we comment on some overall patterns below. Within a given feature, the effect of model type is overwhelmingly stable, and resembles the overall difference in performance. However, we observe several interactions, i.e. specific features where the relative performance of models does not track their overall relative performance.",
+      "Among the major features (Figure FIGREF26 ), performance is universally highest on the simple sentences, and is higher than each model's overall performance. Though these sentences are simple, we notice that the proportion of ungrammatical ones is on par with the entire dataset. Otherwise we find that a model's performance on sentences of a given feature is on par with or lower than its overall performance, reflecting the fact that features mark the presence of unusual or complex syntactic structure.",
+      "Performance is also high (and close to overall performance) on sentences with marked argument structures (Argument Types and Arg(ument) Alt(ernation)). While these models are still worse than human (overall) performance on these sentences, this result indicates that argument structure is relatively easy to learn.",
+      "Comparing different kinds of embedded content, we observe higher performance on sentences with embedded clauses (major feature=Comp Clause) embedded VPs (major feature=to-VP) than on sentences with embedded interrogatives (minor features=Emb-Q, Rel Clause). An exception to this trend is the minor feature No C-izer, which labels complement clauses without a complementizer (e.g. I think that you're crazy). Low performance on these sentences compared to most other features in Comp Clause might indicate that complementizers are an important syntactic cue for these models.",
+      "As the major feature Question shows, the difficulty of sentences with question-like syntax applies beyond just embedded questions. Excluding polar questions, sentences with question-like syntax almost always involve extraction of a wh-word, creating a long-distance dependency between the wh-word and its extraction site, which may be difficult for models to recognize.",
+      "The most challenging features are all related to Violations. Low performance on Infl/Agr Violations, which marks morphological violations (He washed yourself, This is happy), is especially striking because a relatively high proportion (29%) of these sentences are Simple. These models are likely to be deficient in encoding morphological features is that they are word level models, and do not have direct access sub-word information like inflectional endings, which indicates that these features are difficult to learn effectively purely from lexical distributions.",
+      "Finally, unusual performance on some features is due to small samples, and have a high standard deviation, suggesting the result is unreliable. This includes CP Subj, Frag/Paren, imperative, NPI/FCI, and Comparative.",
+      "Comparing within-feature performance of the three encoders to their overall performance, we find they have differing strengths and weaknesses. BERT stands out over other models in Deep Embed, which includes challenging sentences with doubly-embedded, as well as in several features involving extraction (i.e. long-distance dependencies) such as VP+Extract and Info-Struc. The transformer models show evidence of learning long-distance dependencies better than the CoLA baseline. They outperform the CoLA baseline by an especially wide margin on Bind:Refl, which all involves establishing a dependency between a reflexive and its antecedent (Bo tries to love himself). They also have a large advantage in dislocation, in which expressions are separated from their dependents (Bo practiced on the train an important presentation). The advantage of BERT and GPT may be due in part to their use of the transformer architecture. Unlike the BiLSTM used by the CoLA baseline, the transformer uses a self-attention mechanism that associates all pairs of words regardless of distance.",
+      "In some cases models showed surprisingly good or bad performance, revealing possible idiosyncrasies of the sentence embeddings they output. For instance, the CoLA baseline performs on par with the others on the major feature adjunct, especially considering the minor feature Particle (Bo looked the word up).",
+      "Furthermore, all models struggle equally with sentences in Violation, indicating that the advantages of the transformer models over the CoLA baseline does not extend to the detection of morphological violations (Infl/Agr Violation) or single word anomalies (Extra/Missing Expr)."
+    ],
+    [
+      "For comparison, we analyze the effect of sentence length on acceptability classifier performance. The results are shown in Figure FIGREF39 . The results for the CoLA baseline are inconsistent, but do drop off as sentence length increases. For BERT and GPT, performance decreases very steadily with length. Exceptions are extremely short sentences (length 1-3), which may be challenging due to insufficient information; and extremely long sentences, where we see a small (but somewhat unreliable) boost in BERT's performance. BERT and GPT are generally quite close in performance, except on the longest sentences, where BERT's performance is considerably better."
+    ],
+    [
+      "Using a new grammatically annotated analysis set, we identify several syntactic phenomena that are predictive of good or bad performance of current state of the art sentence encoders on CoLA. We also use these results to develop hypotheses about why BERT is successful, and why transformer models outperform sequence models.",
+      "Our findings can guide future work on sentence embeddings. A current weakness of all sentence encoders we investigate, including BERT, is the identification of morphological violations. Future engineering work should investigate whether switching to a character-level model can mitigate this problem. Additionally, transformer models appear to have an advantage over sequence models with long-distance dependencies, but still struggle with these constructions relative to more local phenomena. It stands to reason that this performance gap might be widened by training larger or deeper transformer models, or training on longer or more complex sentences. This analysis set can be used by engineers interested in evaluating the syntactic knowledge of their encoders.",
+      "Finally, these findings suggest possible controlled experiments that could confirm whether there is a causal relation between the presence of the syntactic features we single out as interesting and model performance. Our results are purely correlational, and do not mark whether a particular construction is crucial for the acceptability of the sentence. Future experiments following ettinger2018assessing and kann2019verb can semi-automatically generate datasets manipulating, for example, length of long-distance dependencies, inflectional violations, or the presence of interrogatives, while controlling for factors like sentence length and word choice, in order determine the extent to which these features impact the quality of sentence embeddings."
+    ],
+    [
+      "We would like to thank Jason Phang and Thibault F\u00e9vry for sharing GPT and BERT model predictions on CoLA, and Alex Wang for feedback."
+    ],
+    [
+      "These are sentences with transitive or intransitive verbs appearing with their default syntax and argument structure. All arguments are noun phrases (DPs), and there are no modifiers or adjuncts on DPs or the VP.",
+      ". Included J\u0307ohn owns the book. (37) Park Square has a festive air. (131) *Herself likes Mary's mother. (456)",
+      ". Excluded \u1e02ill has eaten cake. I gave Joe a book."
+    ],
+    [
+      "These are sentences including the verb be used predicatively. Also, sentences where the object of the verb is itself a predicate, which applies to the subject. Not included are auxiliary uses of be or other predicate phrases that are not linked to a subject by a verb.",
+      ". Included J\u0307ohn is eager. (27) He turned into a frog. (150) To please John is easy. (315)",
+      ". Excluded \u1e6ahere is a bench to sit on. (309) John broke the geode open. The cake was eaten.",
+      "These sentences involve predication of a non-subject argument by another non-subject argument, without the presence of a copula. Some of these cases may be analyzed as small clauses. BIBREF35 ",
+      ". Included J\u0307ohn called the president a fool. (234) John considers himself proud of Mary. (464) They want them arrested. (856) the election of John president surprised me. (1001)",
+      "Modifiers that act as predicates of an argument. Resultatives express a resulting state of that argument, and depictives describe that argument during the matrix event. See BIBREF24 .",
+      ". Included \u1e58esultative T\u0323he table was wiped by John clean. (625) The horse kicked me black and blue. (898) . Depictive J\u0307ohn left singing. (971) In which car was the man seen? (398)",
+      ". Excluded \u1e22e turned into a frog. (150)"
+    ],
+    [
+      "Particles are lone prepositions associated with verbs. When they appear with transitive verbs they may immediately follow the verb or the object. Verb-particle pairs may have a non-compositional (idiomatic) meaning. See [pp. 69-70]carnie2013syntax and [pp. 16-17]kim2008syntax.",
+      ". Included T\u0323he argument was summed by the coach up. (615) Some sentences go on and on and on. (785) *He let the cats which were whining out. (71)",
+      "Adjuncts modifying verb phrases. Adjuncts are (usually) optional, and they do not change the category of the expression they modify. See BIBREF33 .",
+      ". Included \u1e56P-adjuncts, e.g. locative, temporal, instrumental, beneficiary \u1e44obody who hates to eat anything should work in a delicatessen. (121) Felicia kicked the ball off the bench. (127) . Adverbs \u1e40ary beautifully plays the violin. (40) John often meets Mary. (65) . Purpose VPs \u1e86e need another run to win. (769) .",
+      "0.5em. Excluded \u1e56P arguments S\u0323ue gave to Bill a book. (42) Everything you like is on the table. (736) . S-adjuncts J\u0307ohn lost the race, unfortunately.",
+      "These are adjuncts modifying noun phrases. Adjuncts are (usually) optional, and they do not change the category of the expression they modify. Single-word prenominal adjectives are excluded, as are relative clauses (this has another category). . Included \u1e56P-adjuncts T\u0323om's dog with one eye attacked Frank's with three legs. (676) They were going to meet sometime on Sunday, but the faculty didn't know when. (565) . Phrasal adjectives \u0226s a statesman, scarcely could he do anything worth mentioning. (292) . Verbal modifiers \u1e6ahe horse raced past the barn fell. (900)",
+      ". Excluded \u1e56renominal Adjectives \u0130t was the policeman met that several young students in the park last night. (227) . Relative Clauses NP arguments",
+      "These are adjuncts of VPs and NPs that specify a time or modify tense or aspect or frequency of an event. Adjuncts are (usually) optional, and they do not change the category of the expression they modify. . Included \u1e60hort adverbials (never, today, now, always) W\u0323hich hat did Mike quip that she never wore? (95) . PPs \u1e1eiona might be here by 5 o'clock. (426) . When \u0130 inquired when could we leave. (520)",
+      "These are adjuncts of VPs and NPs that specify a location of an event or a part of an event, or of an individual. Adjuncts are (usually) optional, and they do not change the category of the expression they modify. . Included \u1e60hort adverbials PPs \u1e6ahe bed was slept in. (298) *Anson demonized up the Khyber (479) Some people consider dogs in my neighborhood dangerous. (802) Mary saw the boy walking toward the railroad station. (73) . Where \u0130 found the place where we can relax. (307)",
+      ". Excluded \u013focative arguments S\u0323am gave the ball out of the basket. (129) Jessica loaded boxes on the wagon. (164) I went to Rome.",
+      "These are adjuncts of VPs and NPs not described by some other category (with the exception of (6-7)), i.e. not temporal, locative, or relative clauses. Adjuncts are (usually) optional, and they do not change the category of the expression they modify.",
+      ". Included \u1e02eneficiary I\u0323 know which book Jos\u00e9 didn't read for class, and which book Lilly did it for him. (58) . Instrument \u013fee saw the student with a telescope. (770) . Comitative J\u0307oan ate dinner with someone but I don't know who. (544) . VP adjuncts \u1e86hich article did Terry file papers without reading? (431) . Purpose \u1e86e need another run to win. (769)"
+    ],
+    [
+      "Oblique arguments of verbs are individual-denoting arguments (DPs or PPs) which act as the third argument of verb, i.e. not a subject or (direct) object. They may or may not be marked by a preposition. Obliques are only found in VPs that have three or more individual arguments. Arguments are selected for by the verb, and they are (generally) not optional, though in some cases they may be omitted where they are understood or implicitly existentially quantified over. See [p.40]kim2008syntax.",
+      ". Included \u1e56repositional S\u0323ue gave to Bill a book. (42) Mary has always preferred lemons to limes. (70) *Janet broke Bill on the finger. (141) . Benefactives \u1e40artha carved the baby a toy out of wood. (139) . Double object \u1e60usan told her a story. (875) Locative arguments \u0226nn may spend her vacation in Italy. (289) . High-arity Passives M\u0323ary was given by John the book. (626)",
+      ". Excluded \u1e44on-DP arguments \u1e86e want John to win (28) . 3rd argments where not all three arguments are DPs \u1e86e want John to win (28)",
+      "Prepositional Phrase arguments of VPs are individual-denoting arguments of a verb which are marked by a proposition. They may or may not be obliques. Arguments are selected for by the verb, and they are (generally) not optional, though in some cases they may be omitted where they are understood or implicitly existentially quantified over.",
+      ". Included \u1e0aative S\u0323ue gave to Bill a book. (42) . Conative (at) C\u0323arla slid at the book. (179) . Idiosyncratic prepositional verbs \u0130 wonder who to place my trust in. (711) She voted for herself. (743) . Locative J\u0307ohn was found in the office. (283) . PP predicates \u0116verything you like is on the table. (736)",
+      ". Excluded \u1e56P adjuncts Particles Arguments of deverbal expressions t\u0323he putter of books left. (892) . By-phrase \u1e6aed was bitten by the spider. (613)",
+      "Prepositional Phrase arguments of NPs or APs are individual-denoting arguments of a noun or adjective which are marked by a proposition. Arguments are selected for by the head, and they are (generally) not optional, though in some cases they may be omitted where they are understood or implicitly existentially quantified over.",
+      ". Included \u1e58elational adjectives \u1e40any people were fond of Pat. (936) *I was already aware of fact. (824) . Relational nouns \u1e86e admired the pictures of us in the album. (759) They found the book on the atom. (780) . Arguments of deverbal nouns t\u0323he putter of books left. (892)",
+      "Prepositional arguments introduced with by. Usually, this is the (semantic) subject of a passive verb, but in rare cases it may be the subject of a nominalized verb. Arguments are usually selected for by the head, and they are generally not optional. In this case, the argument introduced with by is semantically selected for by the verb, but it is syntactically optional. See [p.190]adger2003core and []collins2005smuggling.",
+      ". Included \u1e56assives \u1e6aed was bitten by the spider. (613) . Subjects of deverbal nouns \u1e6bhe attempt by John to leave surprised me. (1003)",
+      "Expletives, or \u201cdummy\u201d arguments, are semantically inert arguments. The most common expletives in English are it and there, although not all occurrences of these items are expletives. Arguments are usually selected for by the head, and they are generally not optional. In this case, the expletive occupies a syntactic argument slot, but it is not semantically selected by the verb, and there is often a syntactic variation without the expletive. See [p.170-172]adger2003core and [p.82-83]kim2008syntax.",
+      ". Included \u1e6ahere\u2014inserted, existential T\u0323here loved Sandy. (939) There is a nurse available. (466) . It\u2014cleft, inserted \u0130t was a brand new car that he bought. (347) It bothers me that John coughs. (314) It is nice to go abroad. (47) . Environmental it K\u0307erry remarked it was late. (821) Poor Bill, it had started to rain and he had no umbrella. (116) You've really lived it up. (160)",
+      ". Excluded J\u0307ohn counted on Bill to get there on time. (996) I bought it to read. (1026)"
+    ],
+    [
+      "These are verbs with 3 or more arguments of any kind. Arity refers to the number of arguments that a head (or function) selects for. Arguments are usually selected for by the head, and they are generally not optional. They may be DPs, PPs, CPs, VPs, APs or other categories.",
+      ". Included \u1e0aitransitive [\u0323Sue] gave [to Bill] [a book]. (42) [Martha] carved [the baby] [a toy] out of wood. (139) . VP arguments [\u0323We] believed [John] [to be a fountain in the park]. (274) [We] made [them] [be rude]. (260) . Particles He] let [the cats which were whining] [out]. (71) . Passives with by-phrase [\u0323A good friend] is remained [to me] [by him]. (237) . Expletives [\u0323We] expect [there] [to will rain]. (282) [There] is [a seat] [available]. (934) [It] bothers [me] [that he is here]. (1009) . Small clause John] considers [Bill] [silly]. (1039)",
+      ". Excluded \u1e58esults, depictives John] broke [the geode] [open].",
+      "These are VPs where a canonical argument of the verb is missing. This can be difficult to determine, but in many cases the missing argument is understood with existential quantification or generically, or contextually salient. See [p.106-109]sportiche2013introduction.",
+      ". Included \u1e40iddle voice/causative inchoative T\u0323he problem perceives easily. (66) . Passive \u1e6ahe car was driven. (296) . Null complement anaphora J\u0307ean persuaded Robert. (380) Nobody told Susan. (883) . Dropped argument K\u0323im put in the box. (253) The guests dined. (835) I wrote to Bill. (1030) . Transitive adjective J\u0307ohn is eager. (27) We pulled free. (144) . Transitive noun \u0130 sensed his eagerness. (155) . Expletive insertion I\u0323t loved Sandy. (949)",
+      ". Excluded \u1e6aed was bitten by the spider. (613)",
+      "These are VPs in which a non-canonical argument of the verb has been added. These cases are clearer to identify where the additional argument is a DP. In general, PPs which mark locations, times, beneficiaries, or purposes should be analyzed as adjuncts, while PPs marking causes can be considered arguments. See []pylkkanen2008introducing.",
+      ". Included \u0116xtra argument L\u0323inda winked her lip. (202) Sharon fainted from hunger. (204) I shaved myself. (526) . Causative I\u0323 squeaked the door. (207) . Expletive insertion \u1e6ahere is a monster in Loch Ness. (928) It annoys people that dogs bark. (943) . Benefactive \u1e40artha carved the baby a toy out of wood. (139)",
+      "The passive voice is marked by the demotion of the subject (either complete omission or to a by-phrase) and the verb appearing as a past participle. In the stereotypical construction there is an auxiliary be verb, though this may be absent. See [p.175-190]kim2008syntax, collins2005smuggling, and [p.311-333]sag2003syntactic.",
+      ". Included V\u0307erbs \u1e6ahe earth was believed to be round. (157) . Psuedopassive \u1e6ahe bed was slept in. (298) . Past participle adjuncts \u1e6ahe horse raced past the barn fell. (900)"
+    ],
+    [
+      "The imperative mood is marked by the absence of the a subject and the bare form of the verb, and expresses a command, request, or other directive speech act.",
+      ". Included W\u0323ash you! (224) Somebody just left - guess who. (528)"
+    ],
+    [
+      "These are cases in which a reflexive (non-possessive) pronoun, usually bound by an antecedent. See [p.163-186]sportiche2013introduction and [p.203-226]sag2003syntactic.",
+      ". Included O\u0323urselves like ourselves. (742) Which pictures of himself does John like? (386)",
+      "These are cases in which a non-reflexive pronoun appears along with its antecedent. This includes donkey anaphora, quantificational binding, and bound possessives, among other bound pronouns. See [p.163-186]sportiche2013introduction and [p.203-226]sag2003syntactic.",
+      ". Included \u1e02ound possessor \u1e6ahe children admire their mother. (382) . Quantificational binding \u0116verybody gets on well with a certain relative, but often only his therapist knows which one. (562) . Bound pronoun W\u0323e gave us to the cause. (747)"
+    ],
+    [
+      "These are sentences in which the matrix clause is interrogative (either a wh- or polar question). See [pp.282-213]adger2003core, [pp.193-222]kim2008syntax, and [p.315-350]carnie2013syntax.",
+      ". Included \u1e86h-question \u1e86ho always drinks milk? (684) . Polar question \u1e0aid Athena help us? (486)",
+      "These are embedded interrogative clauses appearing as arguments of verbs, nouns, and adjectives. Not including relative clauses and free relatives. See [p.297]adger2003core.",
+      ". Included U\u0307nder VP \u0130 forgot how good beer tastes. (235) *What did you ask who saw? (508) . Under NP \u1e6ahat is the reason why he resigned. (313) . Under AP \u1e6ahey claimed they had settled on something, but it wasn't clear what they had settled on. (529) . Free relative \u1e86hat the water did to the bottle was fill it. (33)",
+      ". Excluded Relative clauses, free relatives",
+      "These are phrasal Wh-phrases, in which the wh-word moves along with other expressions, including prepositions (pied-piping) or nouns in the case of determiner wh-words such as how many and which.",
+      ". Included \u1e56ied-piping T\u0323he ship sank, but I don't know with what. (541) . Other phrasal wh-phrases \u0130 know which book Mag read, and which book Bob read my report that you hadn't. (61) How sane is Peter? (88)",
+      "Relative clauses are noun modifiers appearing with a relativizer (either that or a wh-word) and an associated gap. See [p.223-244]kim2008syntax.",
+      ". Included \u1e6ahough he may hate those that criticize Carter, it doesn't matter. (332) *The book what inspired them was very long. (686) Everything you like is on the table. (736)",
+      ". Excluded T\u0323he more you would want, the less you would eat. (6)",
+      "This is wh-movement out of an extraction island, or near-island. Islands include, for example, complex NPs, adjuncts, embedded questions, coordination. A near-island is an extraction that closely resembles an island violation, such as extraction out of an embedded clause, or across-the-board extraction. See [pp.323-333]adger2003core and [pp.332-334]carnie2013syntax.",
+      ". Included \u0116mbedded question *What did you ask who Medea gave? (493) . Adjunct W\u0323hat did you leave before they did? (598) . Parasitic gaps \u1e86hich topic did you choose without getting his approval? (311) . Complex NP \u1e86ho did you get an accurate description of? (483)"
+    ],
+    [
+      "These are complement clauses acting as the (syntactic) subject of verbs. See [pp.90-91]kim2008syntax.",
+      ". Included \u1e6ahat dogs bark annoys people. (942) The socks are ready for for you to put on to be planned. (112)",
+      ". Excluded \u0116xpletive insertion \u0130t bothers me that John coughs. (314)",
+      "These are complement clauses acting as (non-subject) arguments of verbs. See [pp.84-90]kim2008syntax.",
+      ". Included \u0130 can't believe Fred won't, either. (50) I saw that gas can explode. (222) It bothers me that John coughs. (314) Clefts \u0130t was a brand new car that he bought. (347)",
+      "These are complement clauses acting as an argument of a noun or adjective. See [pp.91-94]kim2008syntax.",
+      ". Included U\u0307nder NP \u1e0ao you believe the claim that somebody was looking for something? (99) . Under AP T\u0323he children are fond that they have ice cream. (842)",
+      "These are complement clauses with a non-finite matrix verb. Often, the complementizer is for, or there is no complementizer. See [pp.252-253,256-260]adger2003core.",
+      ". Included \u1e1eor complementizer \u0130 would prefer for John to leave. (990) . No Complementizer \u1e40ary intended John to go abroad. (48) . Ungrammatical \u1e22eidi thinks that Andy to eat salmon flavored candy bars. (363) . V-ing \u022enly Churchill remembered Churchill giving the Blood, Sweat and Tears speech. (469)",
+      "These are complement clauses with no overt complementizer.",
+      ". Included \u010aomplement clause \u0130'm sure we even got these tickets! (325) He announced he would marry the woman he loved most, but none of his relatives could figure out who. (572) . Relative clause \u1e6ahe Peter we all like was at the party (484)",
+      "These are sentences with three or nested verbs, where VP is not an aux or modal, i.e. with the following syntax: [S ...[ VP ...[ VP ...[ VP ...] ...] ...] ...]",
+      ". Included \u0116mbedded VPs \u1e40ax seemed to be trying to force Ted to leave the room, and Walt, Ira. (657) . Embedded clauses \u0130 threw away a book that Sandy thought we had read. (713)"
+    ],
+    [
+      "Any occurrence of negation in a sentence, including sentential negation, negative quantifiers, and negative adverbs.",
+      ". Included \u1e60entential \u0130 can't remember the name of somebody who had misgivings. (123) . Quantifier \u1e44o writer, and no playwright, meets in Vienna. (124) . Adverb \u1e6ahey realised that never had Sir Thomas been so offended. (409)",
+      "Modal verbs (may, might, can, could, will, would, shall, should, must). See [pp.152-155]kim2008syntax.",
+      ". Included J\u0307ohn can kick the ball. (280) As a statesman, scarcely could he do anything worth mentioning. (292)",
+      ". Excluded \u1e56seudo-modals \u1e60andy was trying to work out which students would be able to solve a certain problem. (600)",
+      "Auxiliary verbs (e.g. be, have, do). See [pp.149-174]kim2008syntax.",
+      ". Included \u1e6ahey love to play golf, but I do not. (290) The car was driven. (296) he had spent five thousand dollars. (301)",
+      ". Excluded \u1e56seudo-auxiliaries S\u0323ally asked if somebody was going to fail math class, but I can't remember who. (589) The cat got bitten. (926)",
+      "These are predicates acting as near-auxiliary (e.g. get-passive) or near-modals (e.g. willing)",
+      ". Included \u1e44ear-auxiliaries M\u0323ary came to be introduced by the bartender and I also came to be. (55) *Sally asked if somebody was going to fail math class, but I can't remember who. (589) The cat got bitten. (926) . Near-modals \u010alinton is anxious to find out which budget dilemmas Panetta would be willing to tackle in a certain way, but he won't say in which. (593) Sandy was trying to work out which students would be able to solve a certain problem. (600)"
+    ],
+    [
+      "These are VPs with control verbs, where one argument is a non-finite to-VP without a covert subject co-indexed with an argument of the matrix verb. See [pp.252,266-291]adger2003core, [pp.203-222]sportiche2013introduction, and [pp.125-148]kim2008syntax.",
+      ". Included \u0130ntransitive subject control I\u0323t tries to leave the country. (275) . Transitive subject control J\u0307ohn promised Bill to leave. (977) . Transitive object control \u0130 want her to dance. (379) John considers Bill to be silly. (1040)",
+      ". Excluded V\u0307P args of NP/AP \u1e6ahis violin is difficult to play sonatas on. (114) . Purpose \u1e6ahere is a bench to sit on. (309) . Subject VPs \u1e6ao please John is easy. (315) . Argument present participles \u1e40edea denied poisoning the phoenix. (490) . Raising \u0226nson believed himself to be handsome. (499)",
+      "These are VPs with raising predicates, where one argument is a non-finite to-VP without a covert subject co-indexed with an argument of the matrix verb. Unlike control verbs, the coindexed argument is not a semantic argument of the raising predicate. See [pp.260-266]adger2003core, [pp.203-222]sportiche2013introduction, and [pp.125-148]kim2008syntax.",
+      ". Included \u1e60ubject raising U\u0307nder the bed seems to be a fun place to hide. (277) . Object raising \u0226nson believed himself to be handsome. (499) . Raising adjective J\u0307ohn is likely to leave. (370)",
+      "These are embedded infinitival VPs containing a (non-subject) gap that is filled by an argument in the upper clause. Examples are purpose-VPs and tough-movement. See [pp.246-252]kim2008syntax.",
+      ". Included \u1e6aough-movement D\u0323rowning cats, which is against the law, are hard to rescue. (79) . Infinitival relatives F\u0323ed knows which politician her to vote for. (302) . Purpose \u1e6bhe one with a red cover takes a very long time to read. (352) . Other non-finite VPs with extraction \u0226s a statesman, scarcely could he do anything worth mentioning. (292)",
+      "These are non-finite VP arguments of nouns and adjectives.",
+      ". Included \u1e58aising adjectives J\u0307ohn is likely to leave. (370) . Control adjectives \u1e6ahe administration has issued a statement that it is willing to meet a student group, but I'm not sure which one. (604) . Control nouns \u0226s a teacher, you have to deal simultaneously with the administration's pressure on you to succeed, and the children's to be a nice guy. (673) . Purpose VPs \u1e6bhere is nothing to do. (983)",
+      "These are miscellaneous non-finite VPs.",
+      ". Included \u0130 saw that gas can explode. (222) Gerunds/Present participles S\u0323tudents studying English reads Conrad's Heart of Darkness while at university. (262) Knowing the country well, he took a short cut. (411) John became deadly afraid of flying. (440) . Subject VPs \u1e6ao please John is easy. (315) . Nominalized VPs W\u0323hat Mary did Bill was give a book. (473)",
+      ". Excluded \u1e6bo-VPs acting as complements or modifiers of verbs, nouns, or adjectives"
+    ],
+    [
+      "These are nouns and adjectives derived from verbs.",
+      ". Included \u1e0aeverbal nouns t\u0323he election of John president surprised me. (1001) . \u201cLight\u201d verbs \u1e6ahe birds give the worm a tug. (815) . Gerunds \u0130f only Superman would stop flying planes! (773) . Event-wh \u1e86hat the water did to the bottle was fill it. (33) . Deverbal adjectives \u1e22is or her least known work. (95)",
+      "Relational nouns are NPs with an obligatory (or existentially closed) argument. A particular relation holds between the members of the extension of NP and the argument. The argument must be a DP possessor or a PP. See [pp.82-83]kim2008syntax.",
+      ". Included \u1e44ouns with of-arguments J\u0307ohn has a fear of dogs. (353) . Nouns with other PP-arguments \u1e22enri wants to buy which books about cooking? (442) . Measure nouns \u0130 bought three quarts of wine and two of Clorox. (667) . Possessed relational nouns J\u0323ohn's mother likes himself. (484)",
+      ". Excluded \u1e44ouns with PP modifiers \u1e60ome people consider dogs in my neighborhood dangerous. (802)",
+      "Transitive (non-relational) nouns take a VP or CP argument. See [pp.82-83]kim2008syntax.",
+      ". Included V\u0307P argument \u1e6bhe attempt by John to leave surprised me. (1003) . CP argument W\u0323hich report that John was incompetent did he submit? (69) . QP argument \u1e6ahat is the reason why he resigned. (313)",
+      "These are complex NPs, including coordinated nouns and nouns with modifiers (excluding prenominal adjectives).",
+      ". Included \u1e40odified NPs T\u0323he madrigals which Henry plays the lute and sings sound lousy. (84) John bought a book on the table. (233) . NPs with coordination T\u0323he soundly and furry cat slept. (871) The love of my life and mother of my children would never do such a thing. (806)",
+      "Noun-noun compounds are NPs consisting of two constituent nouns.",
+      ". Included \u0130t was the peasant girl who got it. (320) A felon was elected to the city council. (938)",
+      "These are adjectives that take an obligatory (or existentially closed) argument. A particular relation holds between the members of the extension of the modified NP and the argument. The argument must be a DP or PP. See [pp.80-82]kim2008syntax.",
+      ". Included \u022ef-arguments \u1e6ahe chickens seem fond of the farmer. (254) . Other PP arguments \u1e6ahis week will be a difficult one for us. (241) John made Bill mad at himself. (1035)",
+      "A transitive (non-relational) adjective. I.e. an adjectives that takes a VP or CP argument. See [pp.80-82]kim2008syntax.",
+      ". Included V\u0307P argument J\u0307ohn is likely to leave. (370) . CP argument J\u0307ohn is aware of it that Bill is here. (1013) . QP argument \u1e6ahe administration has issued a statement that it is willing to meet a student group, but I'm not sure which one. (604)"
+    ],
+    [
+      "These are expressions with non-canonical word order. See, for example, [p.76]sportiche2013introduction.",
+      ". Includes \u1e56article shift M\u0323ickey looked up it. (24) . Preposed modifiers \u022eut of the box jumped a little white rabbit. (215) *Because she's so pleasant, as for Mary I really like her. (331) . Quantifier float \u1e6ahe men will all leave. (43) . Preposed argument \u1e86ith no job would John be happy. (333) . Relative clause extraposition \u1e86hich book's, author did you meet who you liked? (731) . Misplaced phrases \u1e40ary was given by John the book. (626)",
+      "This includes topicalization and focus constructions. See [pp.258-269]kim2008syntax and [pp.68-75]sportiche2013introduction.",
+      ". Included \u1e6aopicalization \u1e40ost elections are quickly forgotten, but the election of 2000, everyone will remember for a long time. (807) . Clefts \u0130t was a brand new car that he bought. (347) . Pseudo-clefts \u1e86hat John promised is to be gentle. (441)",
+      ". Excluded \u1e6ahere-insertion Passive",
+      "These are parentheticals or fragmentary expressions. . Included \u1e56arenthetical \u1e40ary asked me if, in St. Louis, John could rent a house cheap. (704) . Fragments \u1e6ahe soup cooks, thickens. (448) . Tag question \u0120eorge has spent a lot of money, hasn't he? (291)",
+      "Coordinations and disjunctions are expressions joined with and, but, or, etc. See [pp.61-68]sportiche2013introduction.",
+      ". Included \u1e0aP coordination \u1e0aave, Dan, Erin, Jaime, and Alina left. (341) . Right Node Raising K\u0307im gave a dollar to Bobbie and a dime to Jean. (435) . Clausal coordination \u1e60he talked to Harry, but I don't know who else. (575) . Or, nor N\u0323o writer, nor any playwright, meets in Vienna. (125) . Pseudo-coordination \u0130 want to try and buy some whiskey. (432) . Juxtaposed clauses \u013fights go out at ten. There will be no talking afterwards. (779)",
+      "This includes subordinate clauses, especially with subordinating conjunctions, and conditionals.",
+      ". Included \u010aonditional \u0130f I can, I will work on it. (56) . Subordinate clause W\u0323hat did you leave before they did? (598) *Because Steve's of a spider's eye had been stolen, I borrowed Fred's diagram of a snake's fang. (677) . Correlative A\u0323s you eat the most, you want the least. (5)",
+      "This includes VP or NP ellipsis, or anaphora standing for VPs or NPs (not DPs). See [pp.55-61]sportiche2013introduction.",
+      ". Included V\u0307P Ellipsis \u0130f I can, I will work on it. (56) Mary likes to tour art galleries, but Bill hates to. (287) . VP Anaphor \u0130 saw Bill while you did so Mary. (472) . NP Ellipsis \u1e6aom's dog with one eye attacked Fred's. (679) . NP anaphor \u1e6bhe one with a red cover takes a very long time to read. (352) . Sluicing \u1e40ost columnists claim that a senior White House official has been briefing them, and the newspaper today reveals which one. (557) . Gapping \u1e02ill ate the peaches, but Harry the grapes. (646)",
+      "These are adjuncts modifying sentences, sentence-level adverbs, subordinate clauses.",
+      ". Included \u1e60entence-level adverbs \u1e60uddenly, there arrived two inspectors from the INS. (447) . Subordinate clauses \u1e6ahe storm arrived while we ate lunch. (852)"
+    ],
+    [
+      "These are quantificational DPs, i.e. the determiner is a quantifier.",
+      ". Included Q\u0307uantifiers E\u0323very student, and he wears socks, is a swinger. (118) We need another run to win. (769) . Partitive N\u0323either of students failed. (265)",
+      "These are quantifiers that take PP arguments, and measure nouns. See [pp.109-118]kim2008syntax.",
+      ". Included Q\u0307uantifiers with PP arguments N\u0323either of students failed. (265) . Numerals \u022ene of Korea's most famous poets wrote these lines. (294) . Measure nouns \u0130 bought three quarts of wine and two of Clorox. (667)",
+      "These are negative polarity items (any, ever, etc.) and free choice items (any). See kadmon1993any.",
+      ". Included \u1e44PI \u0116verybody around here who ever buys anything on credit talks in his sleep. (122) I didn't have a red cent. (350) . FCI \u0226ny owl hunts mice. (387)",
+      "These are comparative constructions. See BIBREF22 .",
+      ". Included \u010aorrelative \u1e6ahe angrier Mary got, the more she looked at pictures. (9) They may grow as high as bamboo. (337) I know you like the back of my hand. (775)"
+    ],
+    [
+      "These are sentences that include a semantic violation, including type mismatches, violations of selectional restrictions, polarity violations, definiteness violations.",
+      ". Included V\u0307olation of selectional restrictions m\u0323any information was provided. (218) *It tries to leave the country. (275) . Aspectual violations J\u0323ohn is tall on several occasions. (540) . Definiteness violations I\u0323t is the problem that he is here. (1018) . Polarity violations \u0226ny man didn't eat dinner. (388)",
+      "These are sentences that include a violation in inflectional morphology, including tense-aspect marking, or agreement.",
+      ". Included \u010aase U\u0323s love they. (46) . Agreement S\u0323tudents studying English reads Conrad's Heart of Darkness while at university. (262) . Gender S\u0323ally kissed himself. (339) . Tense/Aspect K\u0323im alienated cats and beating his dog. (429)",
+      "These are sentences with a violation that can be identified with the presence or absence of a single word.",
+      ". Included \u1e40issing word J\u0323ohn put under the bathtub. (247) *I noticed the. (788) . Extra word E\u0323veryone hopes everyone to sleep. (467) *He can will go (510)"
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1493/instruction.md b/qasper-1493/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..e4fa0b7e80302a5407ed9cfacc75e5c494f89b23
--- /dev/null
+++ b/qasper-1493/instruction.md
@@ -0,0 +1,278 @@
+Name of Paper: Grammatical Analysis of Pretrained Sentence Encoders with Acceptability Judgments
+
+Question: Which models are best for learning long-distance movement?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Analysis Set",
+    "Annotation",
+    "Feature Descriptions",
+    "Correlations",
+    "Models Evaluated",
+    "Overall CoLA Results",
+    "Analysis Set Results",
+    "Length Analysis",
+    "Conclusion",
+    "Acknowledgments",
+    "Simple",
+    "Pred (Predicates)",
+    "Adjunct",
+    "Argument Types",
+    "Arg Altern (Argument Alternations)",
+    "Imperative",
+    "Binding",
+    "Question",
+    "Comp Clause (Complement Clauses)",
+    "Aux (Auxiliaries)",
+    "to-VP (Infinitival VPs)",
+    "N, Adj (Nouns and Adjectives)",
+    "S-Syntax (Sentence-Level Syntax)",
+    "Determiner",
+    "Violations"
+  ],
+  "paragraphs": [
+    [
+      "The effectiveness and ubiquity of pretrained sentence embeddings for natural language understanding has grown dramatically in recent years. Recent sentence encoders like OpenAI's Generative Pretrained Transformer BIBREF3 and BERT BIBREF2 achieve the state of the art on the GLUE benchmark BIBREF4 . Among the GLUE tasks, these state-of-the-art systems make their greatest gains on the acceptability task with the Corpus of Linguistic Acceptability BIBREF0 . CoLA contains example sentences from linguistics publications labeled by experts for grammatical acceptability, and written to show subtle grammatical features. Because minimal syntactic differences can separate acceptable sentences from unacceptable ones (What did Bo write a book about? / *What was a book about written by Bo?), and acceptability classifiers are more reliable when trained on GPT and BERT than on recurrent models, it stands to reason that GPT and BERT have better implicit knowledge of syntactic features relevant to acceptability.",
+      "Our goal in this paper is to develop an evaluation dataset that can locate which syntactic features that a model successfully learns by identifying the syntactic domains of CoLA in which it performs the best. Using this evaluation set, we compare the syntactic knowledge of GPT and BERT in detail, and investigate the strengths of these models over the baseline BiLSTM model published by warstadt2018neural. The analysis set includes expert annotations labeling the entire CoLA development set for the presence of 63 fine-grained syntactic features.",
+      "We identify many specific syntactic features that make sentences harder to classify, and many that have little effect. For instance, sentences involving unusual or marked argument structures are no harder than the average sentence, while sentences with long distance dependencies are hard to learn. We also find features of sentences that accentuate or minimize the differences between models. Specifically, the transformer models seem to learn long-distance dependencies much better than the recurrent model, yet have no advantage on sentences with morphological violations."
+    ],
+    [
+      "We introduce a grammatically annotated version of the entire CoLA development set to facilitate detailed error analysis of acceptability classifiers. These 1043 sentences are expert-labeled for the presence of 63 minor grammatical features organized into 15 major features. Each minor feature belongs to a single major feature. A sentence belongs to a major feature if it belongs to one or more of the relevant minor features. The Appendix includes descriptions of each feature along with examples and the criteria used for annotation.",
+      "The 63 minor features and 15 major features are illustrated in Table TABREF5 . Considering minor features, an average of 4.31 features is present per sentence (SD=2.59). The average feature is present in 71.3 sentences (SD=54.7). Turning to major features, the average sentence belongs to 3.22 major features (SD=1.66), and the average major feature is present in 224 sentences (SD=112). Every sentence is labeled with at least one feature."
+    ],
+    [
+      "The sentences were annotated manually by one of the authors, who is a PhD student with extensive training in formal linguistics. The features were developed in a trial stage, in which the annotator performed a similar annotation with different annotation schema for several hundred sentences from CoLA not belonging to the development set."
+    ],
+    [
+      "Here we briefly summarize the feature set in order of the major features. Many of these constructions are well-studied in syntax, and further background can be found in textbooks such as adger2003core and sportiche2013introduction.",
+      "This major feature contains only one minor feature, simple, including sentences with a syntactically simplex subject and predicate.",
+      "These three features correspond to predicative phrases, including copular constructions, small clauses (I saw Bo jump), and resultatives/depictives (Bo wiped the table clean).",
+      "These six features mark various kinds of optional modifiers. This includes modifiers of NPs (The boy with blue eyes gasped) or VPs (The cat meowed all morning), and temporal (Bo swam yesterday) or locative (Bo jumped on the bed).",
+      "These five features identify syntactically selected arguments, differentiating, for example, obliques (I gave a book to Bo), PP arguments of NPs and VPs (Bo voted for Jones), and expletives (It seems that Bo left).",
+      "These four features mark VPs with unusual argument structures, including added arguments (I baked Bo a cake) or dropped arguments (Bo knows), and the passive (I was applauded).",
+      "This contains only one feature for imperative clauses (Stop it!).",
+      "These are two minor features, one for bound reflexives (Bo loves himself), and one for other bound pronouns (Bo thinks he won).",
+      "These five features apply to sentences with question-like properties. They mark whether the interrogative is an embedded clause (I know who you are), a matrix clause (Who are you?), or a relative clause (Bo saw the guy who left); whether it contains an island out of which extraction is unacceptable (*What was a picture of hanging on the wall?); or whether there is pied-piping or a multi-word wh-expressions (With whom did you eat?).",
+      "These six features apply to various complement clauses (CPs), including subject CPs (That Bo won is odd); CP arguments of VPs or NPs/APs (The fact that Bo won); CPs missing a complementizer (I think Bo's crazy); or non-finite CPs (This is ready for you to eat).",
+      "These four minor features mark the presence of auxiliary or modal verbs (I can win), negation, or \u201cpseudo-auxiliaries\u201d (I have to win).",
+      "These five features mark various infinitival embedded VPs, including control VPs (Bo wants to win); raising VPs (Bo seemed to fly); VP arguments of NPs or APs (Bo is eager to eat); and VPs with extraction (e.g. This is easy to read ts ).",
+      "These seven features mark complex NPs and APs, including ones with PP arguments (Bo is fond of Mo), or CP/VP arguments; noun-noun compounds (Bo ate mud pie); modified NPs, and NPs derived from verbs (Baking is fun).",
+      "These seven features mark various unrelated syntactic constructions, including dislocated phrases (The boy left who was here earlier); movement related to focus or information structure (This I've gotta see this); coordination, subordinate clauses, and ellipsis (I can't); or sentence-level adjuncts (Apparently, it's raining).",
+      "These four features mark various determiners, including quantifiers, partitives (two of the boys), negative polarity items (I *do/don't have any pie), and comparative constructions.",
+      "These three features apply only to unacceptable sentences, and only ones which are ungrammatical due to a semantic or morphological violation, or the presence or absence of a single salient word."
+    ],
+    [
+      "We wish to emphasize that these features are overlapping and in many cases are correlated, thus not all results from using this analysis set will be independent. We analyzed the pairwise Matthews Correlation Coefficient BIBREF17 of the 63 minor features (giving 1953 pairs), and of the 15 major features (giving 105 pairs). MCC is a special case of Pearson's INLINEFORM0 for Boolean variables. These results are summarized in Table TABREF25 . Regarding the minor features, 60 pairs had a correlation of 0.2 or greater, 17 had a correlation of 0.4 or greater, and 6 had a correlation of 0.6 or greater. None had an anti-correlation of greater magnitude than -0.17. Turning to the major features, 6 pairs had a correlation of 0.2 or greater, and 2 had an anti-correlation of greater magnitude than -0.2.",
+      "We can see at least three reasons for these observed correlations. First, some correlations can be attributed to overlapping feature definitions. For instance, expletive arguments (e.g. There are birds singing) are, by definition, non-canonical arguments, and thus are a subset of add arg. However, some added arguments, such as benefactives (Bo baked Mo a cake), are not expletives. Second, some correlations can be attributed to grammatical properties of the relevant constructions. For instance, question and aux are correlated because main-clause questions in English require subject-aux inversion and in many cases the insertion of auxiliary do (Do lions meow?). Third, some correlations may be a consequence of the sources sampled in CoLA and the phenomena they focus on. For instance, the unusually high correlation of Emb-Q and ellipsis/anaphor can be attributed to BIBREF18 , which is an article about the sluicing construction involving ellipsis of an embedded interrogative (e.g. I saw someone, but I don't know who).",
+      "Finally, two strongest anti-correlations between major features are between simple and the two features related to argument structure, argument types and arg altern. This follows from the definition of simple, which excludes any sentence containing a large number or unusual configuration of arguments."
+    ],
+    [
+      "We train MLP acceptability classifiers for CoLA on top of three sentence encoders: (1) the CoLA baseline encoder with ELMo-style embeddings, (2) OpenAI GPT, and (3) BERT. We use publicly available sentence encoders with pretrained weights."
+    ],
+    [
+      "The overall performance of the three sentence encoders is shown in Table TABREF33 . Performance on CoLA is measured using MCC BIBREF14 . We present the best single restart for each encoder, the mean over restarts for an encoder, and the result of ensembling the restarts for a given encoder, i.e. taking the majority classification for a given sentence, or the majority label of acceptable if tied. For BERT results, we exclude 5 out of the 20 restarts because they were degenerate (MCC=0).",
+      "Across the board, BERT outperforms GPT, which outperforms the CoLA baseline. However, BERT and GPT are much closer in performance than they are to CoLA baseline. While ensemble performance exceeded the average for BERT and GPT, it did not outperform the best single model."
+    ],
+    [
+      "The results for the major features and minor features are shown in Figures FIGREF26 and FIGREF35 , respectively. For each feature, we measure the MCC of the sentences including that feature. We plot the mean of these results across the different restarts for each model, and error bars mark the mean INLINEFORM0 standard deviation. For the Violations features, MCC is technically undefined because these features only contain unacceptable sentences. We report MCC in these cases by including for each feature a single acceptable example that is correctly classified by all models.",
+      "Comparison across features reveals that the presence of certain features has a large effect on performance, and we comment on some overall patterns below. Within a given feature, the effect of model type is overwhelmingly stable, and resembles the overall difference in performance. However, we observe several interactions, i.e. specific features where the relative performance of models does not track their overall relative performance.",
+      "Among the major features (Figure FIGREF26 ), performance is universally highest on the simple sentences, and is higher than each model's overall performance. Though these sentences are simple, we notice that the proportion of ungrammatical ones is on par with the entire dataset. Otherwise we find that a model's performance on sentences of a given feature is on par with or lower than its overall performance, reflecting the fact that features mark the presence of unusual or complex syntactic structure.",
+      "Performance is also high (and close to overall performance) on sentences with marked argument structures (Argument Types and Arg(ument) Alt(ernation)). While these models are still worse than human (overall) performance on these sentences, this result indicates that argument structure is relatively easy to learn.",
+      "Comparing different kinds of embedded content, we observe higher performance on sentences with embedded clauses (major feature=Comp Clause) embedded VPs (major feature=to-VP) than on sentences with embedded interrogatives (minor features=Emb-Q, Rel Clause). An exception to this trend is the minor feature No C-izer, which labels complement clauses without a complementizer (e.g. I think that you're crazy). Low performance on these sentences compared to most other features in Comp Clause might indicate that complementizers are an important syntactic cue for these models.",
+      "As the major feature Question shows, the difficulty of sentences with question-like syntax applies beyond just embedded questions. Excluding polar questions, sentences with question-like syntax almost always involve extraction of a wh-word, creating a long-distance dependency between the wh-word and its extraction site, which may be difficult for models to recognize.",
+      "The most challenging features are all related to Violations. Low performance on Infl/Agr Violations, which marks morphological violations (He washed yourself, This is happy), is especially striking because a relatively high proportion (29%) of these sentences are Simple. These models are likely to be deficient in encoding morphological features is that they are word level models, and do not have direct access sub-word information like inflectional endings, which indicates that these features are difficult to learn effectively purely from lexical distributions.",
+      "Finally, unusual performance on some features is due to small samples, and have a high standard deviation, suggesting the result is unreliable. This includes CP Subj, Frag/Paren, imperative, NPI/FCI, and Comparative.",
+      "Comparing within-feature performance of the three encoders to their overall performance, we find they have differing strengths and weaknesses. BERT stands out over other models in Deep Embed, which includes challenging sentences with doubly-embedded, as well as in several features involving extraction (i.e. long-distance dependencies) such as VP+Extract and Info-Struc. The transformer models show evidence of learning long-distance dependencies better than the CoLA baseline. They outperform the CoLA baseline by an especially wide margin on Bind:Refl, which all involves establishing a dependency between a reflexive and its antecedent (Bo tries to love himself). They also have a large advantage in dislocation, in which expressions are separated from their dependents (Bo practiced on the train an important presentation). The advantage of BERT and GPT may be due in part to their use of the transformer architecture. Unlike the BiLSTM used by the CoLA baseline, the transformer uses a self-attention mechanism that associates all pairs of words regardless of distance.",
+      "In some cases models showed surprisingly good or bad performance, revealing possible idiosyncrasies of the sentence embeddings they output. For instance, the CoLA baseline performs on par with the others on the major feature adjunct, especially considering the minor feature Particle (Bo looked the word up).",
+      "Furthermore, all models struggle equally with sentences in Violation, indicating that the advantages of the transformer models over the CoLA baseline does not extend to the detection of morphological violations (Infl/Agr Violation) or single word anomalies (Extra/Missing Expr)."
+    ],
+    [
+      "For comparison, we analyze the effect of sentence length on acceptability classifier performance. The results are shown in Figure FIGREF39 . The results for the CoLA baseline are inconsistent, but do drop off as sentence length increases. For BERT and GPT, performance decreases very steadily with length. Exceptions are extremely short sentences (length 1-3), which may be challenging due to insufficient information; and extremely long sentences, where we see a small (but somewhat unreliable) boost in BERT's performance. BERT and GPT are generally quite close in performance, except on the longest sentences, where BERT's performance is considerably better."
+    ],
+    [
+      "Using a new grammatically annotated analysis set, we identify several syntactic phenomena that are predictive of good or bad performance of current state of the art sentence encoders on CoLA. We also use these results to develop hypotheses about why BERT is successful, and why transformer models outperform sequence models.",
+      "Our findings can guide future work on sentence embeddings. A current weakness of all sentence encoders we investigate, including BERT, is the identification of morphological violations. Future engineering work should investigate whether switching to a character-level model can mitigate this problem. Additionally, transformer models appear to have an advantage over sequence models with long-distance dependencies, but still struggle with these constructions relative to more local phenomena. It stands to reason that this performance gap might be widened by training larger or deeper transformer models, or training on longer or more complex sentences. This analysis set can be used by engineers interested in evaluating the syntactic knowledge of their encoders.",
+      "Finally, these findings suggest possible controlled experiments that could confirm whether there is a causal relation between the presence of the syntactic features we single out as interesting and model performance. Our results are purely correlational, and do not mark whether a particular construction is crucial for the acceptability of the sentence. Future experiments following ettinger2018assessing and kann2019verb can semi-automatically generate datasets manipulating, for example, length of long-distance dependencies, inflectional violations, or the presence of interrogatives, while controlling for factors like sentence length and word choice, in order determine the extent to which these features impact the quality of sentence embeddings."
+    ],
+    [
+      "We would like to thank Jason Phang and Thibault F\u00e9vry for sharing GPT and BERT model predictions on CoLA, and Alex Wang for feedback."
+    ],
+    [
+      "These are sentences with transitive or intransitive verbs appearing with their default syntax and argument structure. All arguments are noun phrases (DPs), and there are no modifiers or adjuncts on DPs or the VP.",
+      ". Included J\u0307ohn owns the book. (37) Park Square has a festive air. (131) *Herself likes Mary's mother. (456)",
+      ". Excluded \u1e02ill has eaten cake. I gave Joe a book."
+    ],
+    [
+      "These are sentences including the verb be used predicatively. Also, sentences where the object of the verb is itself a predicate, which applies to the subject. Not included are auxiliary uses of be or other predicate phrases that are not linked to a subject by a verb.",
+      ". Included J\u0307ohn is eager. (27) He turned into a frog. (150) To please John is easy. (315)",
+      ". Excluded \u1e6ahere is a bench to sit on. (309) John broke the geode open. The cake was eaten.",
+      "These sentences involve predication of a non-subject argument by another non-subject argument, without the presence of a copula. Some of these cases may be analyzed as small clauses. BIBREF35 ",
+      ". Included J\u0307ohn called the president a fool. (234) John considers himself proud of Mary. (464) They want them arrested. (856) the election of John president surprised me. (1001)",
+      "Modifiers that act as predicates of an argument. Resultatives express a resulting state of that argument, and depictives describe that argument during the matrix event. See BIBREF24 .",
+      ". Included \u1e58esultative T\u0323he table was wiped by John clean. (625) The horse kicked me black and blue. (898) . Depictive J\u0307ohn left singing. (971) In which car was the man seen? (398)",
+      ". Excluded \u1e22e turned into a frog. (150)"
+    ],
+    [
+      "Particles are lone prepositions associated with verbs. When they appear with transitive verbs they may immediately follow the verb or the object. Verb-particle pairs may have a non-compositional (idiomatic) meaning. See [pp. 69-70]carnie2013syntax and [pp. 16-17]kim2008syntax.",
+      ". Included T\u0323he argument was summed by the coach up. (615) Some sentences go on and on and on. (785) *He let the cats which were whining out. (71)",
+      "Adjuncts modifying verb phrases. Adjuncts are (usually) optional, and they do not change the category of the expression they modify. See BIBREF33 .",
+      ". Included \u1e56P-adjuncts, e.g. locative, temporal, instrumental, beneficiary \u1e44obody who hates to eat anything should work in a delicatessen. (121) Felicia kicked the ball off the bench. (127) . Adverbs \u1e40ary beautifully plays the violin. (40) John often meets Mary. (65) . Purpose VPs \u1e86e need another run to win. (769) .",
+      "0.5em. Excluded \u1e56P arguments S\u0323ue gave to Bill a book. (42) Everything you like is on the table. (736) . S-adjuncts J\u0307ohn lost the race, unfortunately.",
+      "These are adjuncts modifying noun phrases. Adjuncts are (usually) optional, and they do not change the category of the expression they modify. Single-word prenominal adjectives are excluded, as are relative clauses (this has another category). . Included \u1e56P-adjuncts T\u0323om's dog with one eye attacked Frank's with three legs. (676) They were going to meet sometime on Sunday, but the faculty didn't know when. (565) . Phrasal adjectives \u0226s a statesman, scarcely could he do anything worth mentioning. (292) . Verbal modifiers \u1e6ahe horse raced past the barn fell. (900)",
+      ". Excluded \u1e56renominal Adjectives \u0130t was the policeman met that several young students in the park last night. (227) . Relative Clauses NP arguments",
+      "These are adjuncts of VPs and NPs that specify a time or modify tense or aspect or frequency of an event. Adjuncts are (usually) optional, and they do not change the category of the expression they modify. . Included \u1e60hort adverbials (never, today, now, always) W\u0323hich hat did Mike quip that she never wore? (95) . PPs \u1e1eiona might be here by 5 o'clock. (426) . When \u0130 inquired when could we leave. (520)",
+      "These are adjuncts of VPs and NPs that specify a location of an event or a part of an event, or of an individual. Adjuncts are (usually) optional, and they do not change the category of the expression they modify. . Included \u1e60hort adverbials PPs \u1e6ahe bed was slept in. (298) *Anson demonized up the Khyber (479) Some people consider dogs in my neighborhood dangerous. (802) Mary saw the boy walking toward the railroad station. (73) . Where \u0130 found the place where we can relax. (307)",
+      ". Excluded \u013focative arguments S\u0323am gave the ball out of the basket. (129) Jessica loaded boxes on the wagon. (164) I went to Rome.",
+      "These are adjuncts of VPs and NPs not described by some other category (with the exception of (6-7)), i.e. not temporal, locative, or relative clauses. Adjuncts are (usually) optional, and they do not change the category of the expression they modify.",
+      ". Included \u1e02eneficiary I\u0323 know which book Jos\u00e9 didn't read for class, and which book Lilly did it for him. (58) . Instrument \u013fee saw the student with a telescope. (770) . Comitative J\u0307oan ate dinner with someone but I don't know who. (544) . VP adjuncts \u1e86hich article did Terry file papers without reading? (431) . Purpose \u1e86e need another run to win. (769)"
+    ],
+    [
+      "Oblique arguments of verbs are individual-denoting arguments (DPs or PPs) which act as the third argument of verb, i.e. not a subject or (direct) object. They may or may not be marked by a preposition. Obliques are only found in VPs that have three or more individual arguments. Arguments are selected for by the verb, and they are (generally) not optional, though in some cases they may be omitted where they are understood or implicitly existentially quantified over. See [p.40]kim2008syntax.",
+      ". Included \u1e56repositional S\u0323ue gave to Bill a book. (42) Mary has always preferred lemons to limes. (70) *Janet broke Bill on the finger. (141) . Benefactives \u1e40artha carved the baby a toy out of wood. (139) . Double object \u1e60usan told her a story. (875) Locative arguments \u0226nn may spend her vacation in Italy. (289) . High-arity Passives M\u0323ary was given by John the book. (626)",
+      ". Excluded \u1e44on-DP arguments \u1e86e want John to win (28) . 3rd argments where not all three arguments are DPs \u1e86e want John to win (28)",
+      "Prepositional Phrase arguments of VPs are individual-denoting arguments of a verb which are marked by a proposition. They may or may not be obliques. Arguments are selected for by the verb, and they are (generally) not optional, though in some cases they may be omitted where they are understood or implicitly existentially quantified over.",
+      ". Included \u1e0aative S\u0323ue gave to Bill a book. (42) . Conative (at) C\u0323arla slid at the book. (179) . Idiosyncratic prepositional verbs \u0130 wonder who to place my trust in. (711) She voted for herself. (743) . Locative J\u0307ohn was found in the office. (283) . PP predicates \u0116verything you like is on the table. (736)",
+      ". Excluded \u1e56P adjuncts Particles Arguments of deverbal expressions t\u0323he putter of books left. (892) . By-phrase \u1e6aed was bitten by the spider. (613)",
+      "Prepositional Phrase arguments of NPs or APs are individual-denoting arguments of a noun or adjective which are marked by a proposition. Arguments are selected for by the head, and they are (generally) not optional, though in some cases they may be omitted where they are understood or implicitly existentially quantified over.",
+      ". Included \u1e58elational adjectives \u1e40any people were fond of Pat. (936) *I was already aware of fact. (824) . Relational nouns \u1e86e admired the pictures of us in the album. (759) They found the book on the atom. (780) . Arguments of deverbal nouns t\u0323he putter of books left. (892)",
+      "Prepositional arguments introduced with by. Usually, this is the (semantic) subject of a passive verb, but in rare cases it may be the subject of a nominalized verb. Arguments are usually selected for by the head, and they are generally not optional. In this case, the argument introduced with by is semantically selected for by the verb, but it is syntactically optional. See [p.190]adger2003core and []collins2005smuggling.",
+      ". Included \u1e56assives \u1e6aed was bitten by the spider. (613) . Subjects of deverbal nouns \u1e6bhe attempt by John to leave surprised me. (1003)",
+      "Expletives, or \u201cdummy\u201d arguments, are semantically inert arguments. The most common expletives in English are it and there, although not all occurrences of these items are expletives. Arguments are usually selected for by the head, and they are generally not optional. In this case, the expletive occupies a syntactic argument slot, but it is not semantically selected by the verb, and there is often a syntactic variation without the expletive. See [p.170-172]adger2003core and [p.82-83]kim2008syntax.",
+      ". Included \u1e6ahere\u2014inserted, existential T\u0323here loved Sandy. (939) There is a nurse available. (466) . It\u2014cleft, inserted \u0130t was a brand new car that he bought. (347) It bothers me that John coughs. (314) It is nice to go abroad. (47) . Environmental it K\u0307erry remarked it was late. (821) Poor Bill, it had started to rain and he had no umbrella. (116) You've really lived it up. (160)",
+      ". Excluded J\u0307ohn counted on Bill to get there on time. (996) I bought it to read. (1026)"
+    ],
+    [
+      "These are verbs with 3 or more arguments of any kind. Arity refers to the number of arguments that a head (or function) selects for. Arguments are usually selected for by the head, and they are generally not optional. They may be DPs, PPs, CPs, VPs, APs or other categories.",
+      ". Included \u1e0aitransitive [\u0323Sue] gave [to Bill] [a book]. (42) [Martha] carved [the baby] [a toy] out of wood. (139) . VP arguments [\u0323We] believed [John] [to be a fountain in the park]. (274) [We] made [them] [be rude]. (260) . Particles He] let [the cats which were whining] [out]. (71) . Passives with by-phrase [\u0323A good friend] is remained [to me] [by him]. (237) . Expletives [\u0323We] expect [there] [to will rain]. (282) [There] is [a seat] [available]. (934) [It] bothers [me] [that he is here]. (1009) . Small clause John] considers [Bill] [silly]. (1039)",
+      ". Excluded \u1e58esults, depictives John] broke [the geode] [open].",
+      "These are VPs where a canonical argument of the verb is missing. This can be difficult to determine, but in many cases the missing argument is understood with existential quantification or generically, or contextually salient. See [p.106-109]sportiche2013introduction.",
+      ". Included \u1e40iddle voice/causative inchoative T\u0323he problem perceives easily. (66) . Passive \u1e6ahe car was driven. (296) . Null complement anaphora J\u0307ean persuaded Robert. (380) Nobody told Susan. (883) . Dropped argument K\u0323im put in the box. (253) The guests dined. (835) I wrote to Bill. (1030) . Transitive adjective J\u0307ohn is eager. (27) We pulled free. (144) . Transitive noun \u0130 sensed his eagerness. (155) . Expletive insertion I\u0323t loved Sandy. (949)",
+      ". Excluded \u1e6aed was bitten by the spider. (613)",
+      "These are VPs in which a non-canonical argument of the verb has been added. These cases are clearer to identify where the additional argument is a DP. In general, PPs which mark locations, times, beneficiaries, or purposes should be analyzed as adjuncts, while PPs marking causes can be considered arguments. See []pylkkanen2008introducing.",
+      ". Included \u0116xtra argument L\u0323inda winked her lip. (202) Sharon fainted from hunger. (204) I shaved myself. (526) . Causative I\u0323 squeaked the door. (207) . Expletive insertion \u1e6ahere is a monster in Loch Ness. (928) It annoys people that dogs bark. (943) . Benefactive \u1e40artha carved the baby a toy out of wood. (139)",
+      "The passive voice is marked by the demotion of the subject (either complete omission or to a by-phrase) and the verb appearing as a past participle. In the stereotypical construction there is an auxiliary be verb, though this may be absent. See [p.175-190]kim2008syntax, collins2005smuggling, and [p.311-333]sag2003syntactic.",
+      ". Included V\u0307erbs \u1e6ahe earth was believed to be round. (157) . Psuedopassive \u1e6ahe bed was slept in. (298) . Past participle adjuncts \u1e6ahe horse raced past the barn fell. (900)"
+    ],
+    [
+      "The imperative mood is marked by the absence of the a subject and the bare form of the verb, and expresses a command, request, or other directive speech act.",
+      ". Included W\u0323ash you! (224) Somebody just left - guess who. (528)"
+    ],
+    [
+      "These are cases in which a reflexive (non-possessive) pronoun, usually bound by an antecedent. See [p.163-186]sportiche2013introduction and [p.203-226]sag2003syntactic.",
+      ". Included O\u0323urselves like ourselves. (742) Which pictures of himself does John like? (386)",
+      "These are cases in which a non-reflexive pronoun appears along with its antecedent. This includes donkey anaphora, quantificational binding, and bound possessives, among other bound pronouns. See [p.163-186]sportiche2013introduction and [p.203-226]sag2003syntactic.",
+      ". Included \u1e02ound possessor \u1e6ahe children admire their mother. (382) . Quantificational binding \u0116verybody gets on well with a certain relative, but often only his therapist knows which one. (562) . Bound pronoun W\u0323e gave us to the cause. (747)"
+    ],
+    [
+      "These are sentences in which the matrix clause is interrogative (either a wh- or polar question). See [pp.282-213]adger2003core, [pp.193-222]kim2008syntax, and [p.315-350]carnie2013syntax.",
+      ". Included \u1e86h-question \u1e86ho always drinks milk? (684) . Polar question \u1e0aid Athena help us? (486)",
+      "These are embedded interrogative clauses appearing as arguments of verbs, nouns, and adjectives. Not including relative clauses and free relatives. See [p.297]adger2003core.",
+      ". Included U\u0307nder VP \u0130 forgot how good beer tastes. (235) *What did you ask who saw? (508) . Under NP \u1e6ahat is the reason why he resigned. (313) . Under AP \u1e6ahey claimed they had settled on something, but it wasn't clear what they had settled on. (529) . Free relative \u1e86hat the water did to the bottle was fill it. (33)",
+      ". Excluded Relative clauses, free relatives",
+      "These are phrasal Wh-phrases, in which the wh-word moves along with other expressions, including prepositions (pied-piping) or nouns in the case of determiner wh-words such as how many and which.",
+      ". Included \u1e56ied-piping T\u0323he ship sank, but I don't know with what. (541) . Other phrasal wh-phrases \u0130 know which book Mag read, and which book Bob read my report that you hadn't. (61) How sane is Peter? (88)",
+      "Relative clauses are noun modifiers appearing with a relativizer (either that or a wh-word) and an associated gap. See [p.223-244]kim2008syntax.",
+      ". Included \u1e6ahough he may hate those that criticize Carter, it doesn't matter. (332) *The book what inspired them was very long. (686) Everything you like is on the table. (736)",
+      ". Excluded T\u0323he more you would want, the less you would eat. (6)",
+      "This is wh-movement out of an extraction island, or near-island. Islands include, for example, complex NPs, adjuncts, embedded questions, coordination. A near-island is an extraction that closely resembles an island violation, such as extraction out of an embedded clause, or across-the-board extraction. See [pp.323-333]adger2003core and [pp.332-334]carnie2013syntax.",
+      ". Included \u0116mbedded question *What did you ask who Medea gave? (493) . Adjunct W\u0323hat did you leave before they did? (598) . Parasitic gaps \u1e86hich topic did you choose without getting his approval? (311) . Complex NP \u1e86ho did you get an accurate description of? (483)"
+    ],
+    [
+      "These are complement clauses acting as the (syntactic) subject of verbs. See [pp.90-91]kim2008syntax.",
+      ". Included \u1e6ahat dogs bark annoys people. (942) The socks are ready for for you to put on to be planned. (112)",
+      ". Excluded \u0116xpletive insertion \u0130t bothers me that John coughs. (314)",
+      "These are complement clauses acting as (non-subject) arguments of verbs. See [pp.84-90]kim2008syntax.",
+      ". Included \u0130 can't believe Fred won't, either. (50) I saw that gas can explode. (222) It bothers me that John coughs. (314) Clefts \u0130t was a brand new car that he bought. (347)",
+      "These are complement clauses acting as an argument of a noun or adjective. See [pp.91-94]kim2008syntax.",
+      ". Included U\u0307nder NP \u1e0ao you believe the claim that somebody was looking for something? (99) . Under AP T\u0323he children are fond that they have ice cream. (842)",
+      "These are complement clauses with a non-finite matrix verb. Often, the complementizer is for, or there is no complementizer. See [pp.252-253,256-260]adger2003core.",
+      ". Included \u1e1eor complementizer \u0130 would prefer for John to leave. (990) . No Complementizer \u1e40ary intended John to go abroad. (48) . Ungrammatical \u1e22eidi thinks that Andy to eat salmon flavored candy bars. (363) . V-ing \u022enly Churchill remembered Churchill giving the Blood, Sweat and Tears speech. (469)",
+      "These are complement clauses with no overt complementizer.",
+      ". Included \u010aomplement clause \u0130'm sure we even got these tickets! (325) He announced he would marry the woman he loved most, but none of his relatives could figure out who. (572) . Relative clause \u1e6ahe Peter we all like was at the party (484)",
+      "These are sentences with three or nested verbs, where VP is not an aux or modal, i.e. with the following syntax: [S ...[ VP ...[ VP ...[ VP ...] ...] ...] ...]",
+      ". Included \u0116mbedded VPs \u1e40ax seemed to be trying to force Ted to leave the room, and Walt, Ira. (657) . Embedded clauses \u0130 threw away a book that Sandy thought we had read. (713)"
+    ],
+    [
+      "Any occurrence of negation in a sentence, including sentential negation, negative quantifiers, and negative adverbs.",
+      ". Included \u1e60entential \u0130 can't remember the name of somebody who had misgivings. (123) . Quantifier \u1e44o writer, and no playwright, meets in Vienna. (124) . Adverb \u1e6ahey realised that never had Sir Thomas been so offended. (409)",
+      "Modal verbs (may, might, can, could, will, would, shall, should, must). See [pp.152-155]kim2008syntax.",
+      ". Included J\u0307ohn can kick the ball. (280) As a statesman, scarcely could he do anything worth mentioning. (292)",
+      ". Excluded \u1e56seudo-modals \u1e60andy was trying to work out which students would be able to solve a certain problem. (600)",
+      "Auxiliary verbs (e.g. be, have, do). See [pp.149-174]kim2008syntax.",
+      ". Included \u1e6ahey love to play golf, but I do not. (290) The car was driven. (296) he had spent five thousand dollars. (301)",
+      ". Excluded \u1e56seudo-auxiliaries S\u0323ally asked if somebody was going to fail math class, but I can't remember who. (589) The cat got bitten. (926)",
+      "These are predicates acting as near-auxiliary (e.g. get-passive) or near-modals (e.g. willing)",
+      ". Included \u1e44ear-auxiliaries M\u0323ary came to be introduced by the bartender and I also came to be. (55) *Sally asked if somebody was going to fail math class, but I can't remember who. (589) The cat got bitten. (926) . Near-modals \u010alinton is anxious to find out which budget dilemmas Panetta would be willing to tackle in a certain way, but he won't say in which. (593) Sandy was trying to work out which students would be able to solve a certain problem. (600)"
+    ],
+    [
+      "These are VPs with control verbs, where one argument is a non-finite to-VP without a covert subject co-indexed with an argument of the matrix verb. See [pp.252,266-291]adger2003core, [pp.203-222]sportiche2013introduction, and [pp.125-148]kim2008syntax.",
+      ". Included \u0130ntransitive subject control I\u0323t tries to leave the country. (275) . Transitive subject control J\u0307ohn promised Bill to leave. (977) . Transitive object control \u0130 want her to dance. (379) John considers Bill to be silly. (1040)",
+      ". Excluded V\u0307P args of NP/AP \u1e6ahis violin is difficult to play sonatas on. (114) . Purpose \u1e6ahere is a bench to sit on. (309) . Subject VPs \u1e6ao please John is easy. (315) . Argument present participles \u1e40edea denied poisoning the phoenix. (490) . Raising \u0226nson believed himself to be handsome. (499)",
+      "These are VPs with raising predicates, where one argument is a non-finite to-VP without a covert subject co-indexed with an argument of the matrix verb. Unlike control verbs, the coindexed argument is not a semantic argument of the raising predicate. See [pp.260-266]adger2003core, [pp.203-222]sportiche2013introduction, and [pp.125-148]kim2008syntax.",
+      ". Included \u1e60ubject raising U\u0307nder the bed seems to be a fun place to hide. (277) . Object raising \u0226nson believed himself to be handsome. (499) . Raising adjective J\u0307ohn is likely to leave. (370)",
+      "These are embedded infinitival VPs containing a (non-subject) gap that is filled by an argument in the upper clause. Examples are purpose-VPs and tough-movement. See [pp.246-252]kim2008syntax.",
+      ". Included \u1e6aough-movement D\u0323rowning cats, which is against the law, are hard to rescue. (79) . Infinitival relatives F\u0323ed knows which politician her to vote for. (302) . Purpose \u1e6bhe one with a red cover takes a very long time to read. (352) . Other non-finite VPs with extraction \u0226s a statesman, scarcely could he do anything worth mentioning. (292)",
+      "These are non-finite VP arguments of nouns and adjectives.",
+      ". Included \u1e58aising adjectives J\u0307ohn is likely to leave. (370) . Control adjectives \u1e6ahe administration has issued a statement that it is willing to meet a student group, but I'm not sure which one. (604) . Control nouns \u0226s a teacher, you have to deal simultaneously with the administration's pressure on you to succeed, and the children's to be a nice guy. (673) . Purpose VPs \u1e6bhere is nothing to do. (983)",
+      "These are miscellaneous non-finite VPs.",
+      ". Included \u0130 saw that gas can explode. (222) Gerunds/Present participles S\u0323tudents studying English reads Conrad's Heart of Darkness while at university. (262) Knowing the country well, he took a short cut. (411) John became deadly afraid of flying. (440) . Subject VPs \u1e6ao please John is easy. (315) . Nominalized VPs W\u0323hat Mary did Bill was give a book. (473)",
+      ". Excluded \u1e6bo-VPs acting as complements or modifiers of verbs, nouns, or adjectives"
+    ],
+    [
+      "These are nouns and adjectives derived from verbs.",
+      ". Included \u1e0aeverbal nouns t\u0323he election of John president surprised me. (1001) . \u201cLight\u201d verbs \u1e6ahe birds give the worm a tug. (815) . Gerunds \u0130f only Superman would stop flying planes! (773) . Event-wh \u1e86hat the water did to the bottle was fill it. (33) . Deverbal adjectives \u1e22is or her least known work. (95)",
+      "Relational nouns are NPs with an obligatory (or existentially closed) argument. A particular relation holds between the members of the extension of NP and the argument. The argument must be a DP possessor or a PP. See [pp.82-83]kim2008syntax.",
+      ". Included \u1e44ouns with of-arguments J\u0307ohn has a fear of dogs. (353) . Nouns with other PP-arguments \u1e22enri wants to buy which books about cooking? (442) . Measure nouns \u0130 bought three quarts of wine and two of Clorox. (667) . Possessed relational nouns J\u0323ohn's mother likes himself. (484)",
+      ". Excluded \u1e44ouns with PP modifiers \u1e60ome people consider dogs in my neighborhood dangerous. (802)",
+      "Transitive (non-relational) nouns take a VP or CP argument. See [pp.82-83]kim2008syntax.",
+      ". Included V\u0307P argument \u1e6bhe attempt by John to leave surprised me. (1003) . CP argument W\u0323hich report that John was incompetent did he submit? (69) . QP argument \u1e6ahat is the reason why he resigned. (313)",
+      "These are complex NPs, including coordinated nouns and nouns with modifiers (excluding prenominal adjectives).",
+      ". Included \u1e40odified NPs T\u0323he madrigals which Henry plays the lute and sings sound lousy. (84) John bought a book on the table. (233) . NPs with coordination T\u0323he soundly and furry cat slept. (871) The love of my life and mother of my children would never do such a thing. (806)",
+      "Noun-noun compounds are NPs consisting of two constituent nouns.",
+      ". Included \u0130t was the peasant girl who got it. (320) A felon was elected to the city council. (938)",
+      "These are adjectives that take an obligatory (or existentially closed) argument. A particular relation holds between the members of the extension of the modified NP and the argument. The argument must be a DP or PP. See [pp.80-82]kim2008syntax.",
+      ". Included \u022ef-arguments \u1e6ahe chickens seem fond of the farmer. (254) . Other PP arguments \u1e6ahis week will be a difficult one for us. (241) John made Bill mad at himself. (1035)",
+      "A transitive (non-relational) adjective. I.e. an adjectives that takes a VP or CP argument. See [pp.80-82]kim2008syntax.",
+      ". Included V\u0307P argument J\u0307ohn is likely to leave. (370) . CP argument J\u0307ohn is aware of it that Bill is here. (1013) . QP argument \u1e6ahe administration has issued a statement that it is willing to meet a student group, but I'm not sure which one. (604)"
+    ],
+    [
+      "These are expressions with non-canonical word order. See, for example, [p.76]sportiche2013introduction.",
+      ". Includes \u1e56article shift M\u0323ickey looked up it. (24) . Preposed modifiers \u022eut of the box jumped a little white rabbit. (215) *Because she's so pleasant, as for Mary I really like her. (331) . Quantifier float \u1e6ahe men will all leave. (43) . Preposed argument \u1e86ith no job would John be happy. (333) . Relative clause extraposition \u1e86hich book's, author did you meet who you liked? (731) . Misplaced phrases \u1e40ary was given by John the book. (626)",
+      "This includes topicalization and focus constructions. See [pp.258-269]kim2008syntax and [pp.68-75]sportiche2013introduction.",
+      ". Included \u1e6aopicalization \u1e40ost elections are quickly forgotten, but the election of 2000, everyone will remember for a long time. (807) . Clefts \u0130t was a brand new car that he bought. (347) . Pseudo-clefts \u1e86hat John promised is to be gentle. (441)",
+      ". Excluded \u1e6ahere-insertion Passive",
+      "These are parentheticals or fragmentary expressions. . Included \u1e56arenthetical \u1e40ary asked me if, in St. Louis, John could rent a house cheap. (704) . Fragments \u1e6ahe soup cooks, thickens. (448) . Tag question \u0120eorge has spent a lot of money, hasn't he? (291)",
+      "Coordinations and disjunctions are expressions joined with and, but, or, etc. See [pp.61-68]sportiche2013introduction.",
+      ". Included \u1e0aP coordination \u1e0aave, Dan, Erin, Jaime, and Alina left. (341) . Right Node Raising K\u0307im gave a dollar to Bobbie and a dime to Jean. (435) . Clausal coordination \u1e60he talked to Harry, but I don't know who else. (575) . Or, nor N\u0323o writer, nor any playwright, meets in Vienna. (125) . Pseudo-coordination \u0130 want to try and buy some whiskey. (432) . Juxtaposed clauses \u013fights go out at ten. There will be no talking afterwards. (779)",
+      "This includes subordinate clauses, especially with subordinating conjunctions, and conditionals.",
+      ". Included \u010aonditional \u0130f I can, I will work on it. (56) . Subordinate clause W\u0323hat did you leave before they did? (598) *Because Steve's of a spider's eye had been stolen, I borrowed Fred's diagram of a snake's fang. (677) . Correlative A\u0323s you eat the most, you want the least. (5)",
+      "This includes VP or NP ellipsis, or anaphora standing for VPs or NPs (not DPs). See [pp.55-61]sportiche2013introduction.",
+      ". Included V\u0307P Ellipsis \u0130f I can, I will work on it. (56) Mary likes to tour art galleries, but Bill hates to. (287) . VP Anaphor \u0130 saw Bill while you did so Mary. (472) . NP Ellipsis \u1e6aom's dog with one eye attacked Fred's. (679) . NP anaphor \u1e6bhe one with a red cover takes a very long time to read. (352) . Sluicing \u1e40ost columnists claim that a senior White House official has been briefing them, and the newspaper today reveals which one. (557) . Gapping \u1e02ill ate the peaches, but Harry the grapes. (646)",
+      "These are adjuncts modifying sentences, sentence-level adverbs, subordinate clauses.",
+      ". Included \u1e60entence-level adverbs \u1e60uddenly, there arrived two inspectors from the INS. (447) . Subordinate clauses \u1e6ahe storm arrived while we ate lunch. (852)"
+    ],
+    [
+      "These are quantificational DPs, i.e. the determiner is a quantifier.",
+      ". Included Q\u0307uantifiers E\u0323very student, and he wears socks, is a swinger. (118) We need another run to win. (769) . Partitive N\u0323either of students failed. (265)",
+      "These are quantifiers that take PP arguments, and measure nouns. See [pp.109-118]kim2008syntax.",
+      ". Included Q\u0307uantifiers with PP arguments N\u0323either of students failed. (265) . Numerals \u022ene of Korea's most famous poets wrote these lines. (294) . Measure nouns \u0130 bought three quarts of wine and two of Clorox. (667)",
+      "These are negative polarity items (any, ever, etc.) and free choice items (any). See kadmon1993any.",
+      ". Included \u1e44PI \u0116verybody around here who ever buys anything on credit talks in his sleep. (122) I didn't have a red cent. (350) . FCI \u0226ny owl hunts mice. (387)",
+      "These are comparative constructions. See BIBREF22 .",
+      ". Included \u010aorrelative \u1e6ahe angrier Mary got, the more she looked at pictures. (9) They may grow as high as bamboo. (337) I know you like the back of my hand. (775)"
+    ],
+    [
+      "These are sentences that include a semantic violation, including type mismatches, violations of selectional restrictions, polarity violations, definiteness violations.",
+      ". Included V\u0307olation of selectional restrictions m\u0323any information was provided. (218) *It tries to leave the country. (275) . Aspectual violations J\u0323ohn is tall on several occasions. (540) . Definiteness violations I\u0323t is the problem that he is here. (1018) . Polarity violations \u0226ny man didn't eat dinner. (388)",
+      "These are sentences that include a violation in inflectional morphology, including tense-aspect marking, or agreement.",
+      ". Included \u010aase U\u0323s love they. (46) . Agreement S\u0323tudents studying English reads Conrad's Heart of Darkness while at university. (262) . Gender S\u0323ally kissed himself. (339) . Tense/Aspect K\u0323im alienated cats and beating his dog. (429)",
+      "These are sentences with a violation that can be identified with the presence or absence of a single word.",
+      ". Included \u1e40issing word J\u0323ohn put under the bathtub. (247) *I noticed the. (788) . Extra word E\u0323veryone hopes everyone to sleep. (467) *He can will go (510)"
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1494/instruction.md b/qasper-1494/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..47841d54b77e0b00af2550190bcfba6acdfa5c09
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@@ -0,0 +1,278 @@
+Name of Paper: Grammatical Analysis of Pretrained Sentence Encoders with Acceptability Judgments
+
+Question: Where does the data in CoLA come from?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Analysis Set",
+    "Annotation",
+    "Feature Descriptions",
+    "Correlations",
+    "Models Evaluated",
+    "Overall CoLA Results",
+    "Analysis Set Results",
+    "Length Analysis",
+    "Conclusion",
+    "Acknowledgments",
+    "Simple",
+    "Pred (Predicates)",
+    "Adjunct",
+    "Argument Types",
+    "Arg Altern (Argument Alternations)",
+    "Imperative",
+    "Binding",
+    "Question",
+    "Comp Clause (Complement Clauses)",
+    "Aux (Auxiliaries)",
+    "to-VP (Infinitival VPs)",
+    "N, Adj (Nouns and Adjectives)",
+    "S-Syntax (Sentence-Level Syntax)",
+    "Determiner",
+    "Violations"
+  ],
+  "paragraphs": [
+    [
+      "The effectiveness and ubiquity of pretrained sentence embeddings for natural language understanding has grown dramatically in recent years. Recent sentence encoders like OpenAI's Generative Pretrained Transformer BIBREF3 and BERT BIBREF2 achieve the state of the art on the GLUE benchmark BIBREF4 . Among the GLUE tasks, these state-of-the-art systems make their greatest gains on the acceptability task with the Corpus of Linguistic Acceptability BIBREF0 . CoLA contains example sentences from linguistics publications labeled by experts for grammatical acceptability, and written to show subtle grammatical features. Because minimal syntactic differences can separate acceptable sentences from unacceptable ones (What did Bo write a book about? / *What was a book about written by Bo?), and acceptability classifiers are more reliable when trained on GPT and BERT than on recurrent models, it stands to reason that GPT and BERT have better implicit knowledge of syntactic features relevant to acceptability.",
+      "Our goal in this paper is to develop an evaluation dataset that can locate which syntactic features that a model successfully learns by identifying the syntactic domains of CoLA in which it performs the best. Using this evaluation set, we compare the syntactic knowledge of GPT and BERT in detail, and investigate the strengths of these models over the baseline BiLSTM model published by warstadt2018neural. The analysis set includes expert annotations labeling the entire CoLA development set for the presence of 63 fine-grained syntactic features.",
+      "We identify many specific syntactic features that make sentences harder to classify, and many that have little effect. For instance, sentences involving unusual or marked argument structures are no harder than the average sentence, while sentences with long distance dependencies are hard to learn. We also find features of sentences that accentuate or minimize the differences between models. Specifically, the transformer models seem to learn long-distance dependencies much better than the recurrent model, yet have no advantage on sentences with morphological violations."
+    ],
+    [
+      "We introduce a grammatically annotated version of the entire CoLA development set to facilitate detailed error analysis of acceptability classifiers. These 1043 sentences are expert-labeled for the presence of 63 minor grammatical features organized into 15 major features. Each minor feature belongs to a single major feature. A sentence belongs to a major feature if it belongs to one or more of the relevant minor features. The Appendix includes descriptions of each feature along with examples and the criteria used for annotation.",
+      "The 63 minor features and 15 major features are illustrated in Table TABREF5 . Considering minor features, an average of 4.31 features is present per sentence (SD=2.59). The average feature is present in 71.3 sentences (SD=54.7). Turning to major features, the average sentence belongs to 3.22 major features (SD=1.66), and the average major feature is present in 224 sentences (SD=112). Every sentence is labeled with at least one feature."
+    ],
+    [
+      "The sentences were annotated manually by one of the authors, who is a PhD student with extensive training in formal linguistics. The features were developed in a trial stage, in which the annotator performed a similar annotation with different annotation schema for several hundred sentences from CoLA not belonging to the development set."
+    ],
+    [
+      "Here we briefly summarize the feature set in order of the major features. Many of these constructions are well-studied in syntax, and further background can be found in textbooks such as adger2003core and sportiche2013introduction.",
+      "This major feature contains only one minor feature, simple, including sentences with a syntactically simplex subject and predicate.",
+      "These three features correspond to predicative phrases, including copular constructions, small clauses (I saw Bo jump), and resultatives/depictives (Bo wiped the table clean).",
+      "These six features mark various kinds of optional modifiers. This includes modifiers of NPs (The boy with blue eyes gasped) or VPs (The cat meowed all morning), and temporal (Bo swam yesterday) or locative (Bo jumped on the bed).",
+      "These five features identify syntactically selected arguments, differentiating, for example, obliques (I gave a book to Bo), PP arguments of NPs and VPs (Bo voted for Jones), and expletives (It seems that Bo left).",
+      "These four features mark VPs with unusual argument structures, including added arguments (I baked Bo a cake) or dropped arguments (Bo knows), and the passive (I was applauded).",
+      "This contains only one feature for imperative clauses (Stop it!).",
+      "These are two minor features, one for bound reflexives (Bo loves himself), and one for other bound pronouns (Bo thinks he won).",
+      "These five features apply to sentences with question-like properties. They mark whether the interrogative is an embedded clause (I know who you are), a matrix clause (Who are you?), or a relative clause (Bo saw the guy who left); whether it contains an island out of which extraction is unacceptable (*What was a picture of hanging on the wall?); or whether there is pied-piping or a multi-word wh-expressions (With whom did you eat?).",
+      "These six features apply to various complement clauses (CPs), including subject CPs (That Bo won is odd); CP arguments of VPs or NPs/APs (The fact that Bo won); CPs missing a complementizer (I think Bo's crazy); or non-finite CPs (This is ready for you to eat).",
+      "These four minor features mark the presence of auxiliary or modal verbs (I can win), negation, or \u201cpseudo-auxiliaries\u201d (I have to win).",
+      "These five features mark various infinitival embedded VPs, including control VPs (Bo wants to win); raising VPs (Bo seemed to fly); VP arguments of NPs or APs (Bo is eager to eat); and VPs with extraction (e.g. This is easy to read ts ).",
+      "These seven features mark complex NPs and APs, including ones with PP arguments (Bo is fond of Mo), or CP/VP arguments; noun-noun compounds (Bo ate mud pie); modified NPs, and NPs derived from verbs (Baking is fun).",
+      "These seven features mark various unrelated syntactic constructions, including dislocated phrases (The boy left who was here earlier); movement related to focus or information structure (This I've gotta see this); coordination, subordinate clauses, and ellipsis (I can't); or sentence-level adjuncts (Apparently, it's raining).",
+      "These four features mark various determiners, including quantifiers, partitives (two of the boys), negative polarity items (I *do/don't have any pie), and comparative constructions.",
+      "These three features apply only to unacceptable sentences, and only ones which are ungrammatical due to a semantic or morphological violation, or the presence or absence of a single salient word."
+    ],
+    [
+      "We wish to emphasize that these features are overlapping and in many cases are correlated, thus not all results from using this analysis set will be independent. We analyzed the pairwise Matthews Correlation Coefficient BIBREF17 of the 63 minor features (giving 1953 pairs), and of the 15 major features (giving 105 pairs). MCC is a special case of Pearson's INLINEFORM0 for Boolean variables. These results are summarized in Table TABREF25 . Regarding the minor features, 60 pairs had a correlation of 0.2 or greater, 17 had a correlation of 0.4 or greater, and 6 had a correlation of 0.6 or greater. None had an anti-correlation of greater magnitude than -0.17. Turning to the major features, 6 pairs had a correlation of 0.2 or greater, and 2 had an anti-correlation of greater magnitude than -0.2.",
+      "We can see at least three reasons for these observed correlations. First, some correlations can be attributed to overlapping feature definitions. For instance, expletive arguments (e.g. There are birds singing) are, by definition, non-canonical arguments, and thus are a subset of add arg. However, some added arguments, such as benefactives (Bo baked Mo a cake), are not expletives. Second, some correlations can be attributed to grammatical properties of the relevant constructions. For instance, question and aux are correlated because main-clause questions in English require subject-aux inversion and in many cases the insertion of auxiliary do (Do lions meow?). Third, some correlations may be a consequence of the sources sampled in CoLA and the phenomena they focus on. For instance, the unusually high correlation of Emb-Q and ellipsis/anaphor can be attributed to BIBREF18 , which is an article about the sluicing construction involving ellipsis of an embedded interrogative (e.g. I saw someone, but I don't know who).",
+      "Finally, two strongest anti-correlations between major features are between simple and the two features related to argument structure, argument types and arg altern. This follows from the definition of simple, which excludes any sentence containing a large number or unusual configuration of arguments."
+    ],
+    [
+      "We train MLP acceptability classifiers for CoLA on top of three sentence encoders: (1) the CoLA baseline encoder with ELMo-style embeddings, (2) OpenAI GPT, and (3) BERT. We use publicly available sentence encoders with pretrained weights."
+    ],
+    [
+      "The overall performance of the three sentence encoders is shown in Table TABREF33 . Performance on CoLA is measured using MCC BIBREF14 . We present the best single restart for each encoder, the mean over restarts for an encoder, and the result of ensembling the restarts for a given encoder, i.e. taking the majority classification for a given sentence, or the majority label of acceptable if tied. For BERT results, we exclude 5 out of the 20 restarts because they were degenerate (MCC=0).",
+      "Across the board, BERT outperforms GPT, which outperforms the CoLA baseline. However, BERT and GPT are much closer in performance than they are to CoLA baseline. While ensemble performance exceeded the average for BERT and GPT, it did not outperform the best single model."
+    ],
+    [
+      "The results for the major features and minor features are shown in Figures FIGREF26 and FIGREF35 , respectively. For each feature, we measure the MCC of the sentences including that feature. We plot the mean of these results across the different restarts for each model, and error bars mark the mean INLINEFORM0 standard deviation. For the Violations features, MCC is technically undefined because these features only contain unacceptable sentences. We report MCC in these cases by including for each feature a single acceptable example that is correctly classified by all models.",
+      "Comparison across features reveals that the presence of certain features has a large effect on performance, and we comment on some overall patterns below. Within a given feature, the effect of model type is overwhelmingly stable, and resembles the overall difference in performance. However, we observe several interactions, i.e. specific features where the relative performance of models does not track their overall relative performance.",
+      "Among the major features (Figure FIGREF26 ), performance is universally highest on the simple sentences, and is higher than each model's overall performance. Though these sentences are simple, we notice that the proportion of ungrammatical ones is on par with the entire dataset. Otherwise we find that a model's performance on sentences of a given feature is on par with or lower than its overall performance, reflecting the fact that features mark the presence of unusual or complex syntactic structure.",
+      "Performance is also high (and close to overall performance) on sentences with marked argument structures (Argument Types and Arg(ument) Alt(ernation)). While these models are still worse than human (overall) performance on these sentences, this result indicates that argument structure is relatively easy to learn.",
+      "Comparing different kinds of embedded content, we observe higher performance on sentences with embedded clauses (major feature=Comp Clause) embedded VPs (major feature=to-VP) than on sentences with embedded interrogatives (minor features=Emb-Q, Rel Clause). An exception to this trend is the minor feature No C-izer, which labels complement clauses without a complementizer (e.g. I think that you're crazy). Low performance on these sentences compared to most other features in Comp Clause might indicate that complementizers are an important syntactic cue for these models.",
+      "As the major feature Question shows, the difficulty of sentences with question-like syntax applies beyond just embedded questions. Excluding polar questions, sentences with question-like syntax almost always involve extraction of a wh-word, creating a long-distance dependency between the wh-word and its extraction site, which may be difficult for models to recognize.",
+      "The most challenging features are all related to Violations. Low performance on Infl/Agr Violations, which marks morphological violations (He washed yourself, This is happy), is especially striking because a relatively high proportion (29%) of these sentences are Simple. These models are likely to be deficient in encoding morphological features is that they are word level models, and do not have direct access sub-word information like inflectional endings, which indicates that these features are difficult to learn effectively purely from lexical distributions.",
+      "Finally, unusual performance on some features is due to small samples, and have a high standard deviation, suggesting the result is unreliable. This includes CP Subj, Frag/Paren, imperative, NPI/FCI, and Comparative.",
+      "Comparing within-feature performance of the three encoders to their overall performance, we find they have differing strengths and weaknesses. BERT stands out over other models in Deep Embed, which includes challenging sentences with doubly-embedded, as well as in several features involving extraction (i.e. long-distance dependencies) such as VP+Extract and Info-Struc. The transformer models show evidence of learning long-distance dependencies better than the CoLA baseline. They outperform the CoLA baseline by an especially wide margin on Bind:Refl, which all involves establishing a dependency between a reflexive and its antecedent (Bo tries to love himself). They also have a large advantage in dislocation, in which expressions are separated from their dependents (Bo practiced on the train an important presentation). The advantage of BERT and GPT may be due in part to their use of the transformer architecture. Unlike the BiLSTM used by the CoLA baseline, the transformer uses a self-attention mechanism that associates all pairs of words regardless of distance.",
+      "In some cases models showed surprisingly good or bad performance, revealing possible idiosyncrasies of the sentence embeddings they output. For instance, the CoLA baseline performs on par with the others on the major feature adjunct, especially considering the minor feature Particle (Bo looked the word up).",
+      "Furthermore, all models struggle equally with sentences in Violation, indicating that the advantages of the transformer models over the CoLA baseline does not extend to the detection of morphological violations (Infl/Agr Violation) or single word anomalies (Extra/Missing Expr)."
+    ],
+    [
+      "For comparison, we analyze the effect of sentence length on acceptability classifier performance. The results are shown in Figure FIGREF39 . The results for the CoLA baseline are inconsistent, but do drop off as sentence length increases. For BERT and GPT, performance decreases very steadily with length. Exceptions are extremely short sentences (length 1-3), which may be challenging due to insufficient information; and extremely long sentences, where we see a small (but somewhat unreliable) boost in BERT's performance. BERT and GPT are generally quite close in performance, except on the longest sentences, where BERT's performance is considerably better."
+    ],
+    [
+      "Using a new grammatically annotated analysis set, we identify several syntactic phenomena that are predictive of good or bad performance of current state of the art sentence encoders on CoLA. We also use these results to develop hypotheses about why BERT is successful, and why transformer models outperform sequence models.",
+      "Our findings can guide future work on sentence embeddings. A current weakness of all sentence encoders we investigate, including BERT, is the identification of morphological violations. Future engineering work should investigate whether switching to a character-level model can mitigate this problem. Additionally, transformer models appear to have an advantage over sequence models with long-distance dependencies, but still struggle with these constructions relative to more local phenomena. It stands to reason that this performance gap might be widened by training larger or deeper transformer models, or training on longer or more complex sentences. This analysis set can be used by engineers interested in evaluating the syntactic knowledge of their encoders.",
+      "Finally, these findings suggest possible controlled experiments that could confirm whether there is a causal relation between the presence of the syntactic features we single out as interesting and model performance. Our results are purely correlational, and do not mark whether a particular construction is crucial for the acceptability of the sentence. Future experiments following ettinger2018assessing and kann2019verb can semi-automatically generate datasets manipulating, for example, length of long-distance dependencies, inflectional violations, or the presence of interrogatives, while controlling for factors like sentence length and word choice, in order determine the extent to which these features impact the quality of sentence embeddings."
+    ],
+    [
+      "We would like to thank Jason Phang and Thibault F\u00e9vry for sharing GPT and BERT model predictions on CoLA, and Alex Wang for feedback."
+    ],
+    [
+      "These are sentences with transitive or intransitive verbs appearing with their default syntax and argument structure. All arguments are noun phrases (DPs), and there are no modifiers or adjuncts on DPs or the VP.",
+      ". Included J\u0307ohn owns the book. (37) Park Square has a festive air. (131) *Herself likes Mary's mother. (456)",
+      ". Excluded \u1e02ill has eaten cake. I gave Joe a book."
+    ],
+    [
+      "These are sentences including the verb be used predicatively. Also, sentences where the object of the verb is itself a predicate, which applies to the subject. Not included are auxiliary uses of be or other predicate phrases that are not linked to a subject by a verb.",
+      ". Included J\u0307ohn is eager. (27) He turned into a frog. (150) To please John is easy. (315)",
+      ". Excluded \u1e6ahere is a bench to sit on. (309) John broke the geode open. The cake was eaten.",
+      "These sentences involve predication of a non-subject argument by another non-subject argument, without the presence of a copula. Some of these cases may be analyzed as small clauses. BIBREF35 ",
+      ". Included J\u0307ohn called the president a fool. (234) John considers himself proud of Mary. (464) They want them arrested. (856) the election of John president surprised me. (1001)",
+      "Modifiers that act as predicates of an argument. Resultatives express a resulting state of that argument, and depictives describe that argument during the matrix event. See BIBREF24 .",
+      ". Included \u1e58esultative T\u0323he table was wiped by John clean. (625) The horse kicked me black and blue. (898) . Depictive J\u0307ohn left singing. (971) In which car was the man seen? (398)",
+      ". Excluded \u1e22e turned into a frog. (150)"
+    ],
+    [
+      "Particles are lone prepositions associated with verbs. When they appear with transitive verbs they may immediately follow the verb or the object. Verb-particle pairs may have a non-compositional (idiomatic) meaning. See [pp. 69-70]carnie2013syntax and [pp. 16-17]kim2008syntax.",
+      ". Included T\u0323he argument was summed by the coach up. (615) Some sentences go on and on and on. (785) *He let the cats which were whining out. (71)",
+      "Adjuncts modifying verb phrases. Adjuncts are (usually) optional, and they do not change the category of the expression they modify. See BIBREF33 .",
+      ". Included \u1e56P-adjuncts, e.g. locative, temporal, instrumental, beneficiary \u1e44obody who hates to eat anything should work in a delicatessen. (121) Felicia kicked the ball off the bench. (127) . Adverbs \u1e40ary beautifully plays the violin. (40) John often meets Mary. (65) . Purpose VPs \u1e86e need another run to win. (769) .",
+      "0.5em. Excluded \u1e56P arguments S\u0323ue gave to Bill a book. (42) Everything you like is on the table. (736) . S-adjuncts J\u0307ohn lost the race, unfortunately.",
+      "These are adjuncts modifying noun phrases. Adjuncts are (usually) optional, and they do not change the category of the expression they modify. Single-word prenominal adjectives are excluded, as are relative clauses (this has another category). . Included \u1e56P-adjuncts T\u0323om's dog with one eye attacked Frank's with three legs. (676) They were going to meet sometime on Sunday, but the faculty didn't know when. (565) . Phrasal adjectives \u0226s a statesman, scarcely could he do anything worth mentioning. (292) . Verbal modifiers \u1e6ahe horse raced past the barn fell. (900)",
+      ". Excluded \u1e56renominal Adjectives \u0130t was the policeman met that several young students in the park last night. (227) . Relative Clauses NP arguments",
+      "These are adjuncts of VPs and NPs that specify a time or modify tense or aspect or frequency of an event. Adjuncts are (usually) optional, and they do not change the category of the expression they modify. . Included \u1e60hort adverbials (never, today, now, always) W\u0323hich hat did Mike quip that she never wore? (95) . PPs \u1e1eiona might be here by 5 o'clock. (426) . When \u0130 inquired when could we leave. (520)",
+      "These are adjuncts of VPs and NPs that specify a location of an event or a part of an event, or of an individual. Adjuncts are (usually) optional, and they do not change the category of the expression they modify. . Included \u1e60hort adverbials PPs \u1e6ahe bed was slept in. (298) *Anson demonized up the Khyber (479) Some people consider dogs in my neighborhood dangerous. (802) Mary saw the boy walking toward the railroad station. (73) . Where \u0130 found the place where we can relax. (307)",
+      ". Excluded \u013focative arguments S\u0323am gave the ball out of the basket. (129) Jessica loaded boxes on the wagon. (164) I went to Rome.",
+      "These are adjuncts of VPs and NPs not described by some other category (with the exception of (6-7)), i.e. not temporal, locative, or relative clauses. Adjuncts are (usually) optional, and they do not change the category of the expression they modify.",
+      ". Included \u1e02eneficiary I\u0323 know which book Jos\u00e9 didn't read for class, and which book Lilly did it for him. (58) . Instrument \u013fee saw the student with a telescope. (770) . Comitative J\u0307oan ate dinner with someone but I don't know who. (544) . VP adjuncts \u1e86hich article did Terry file papers without reading? (431) . Purpose \u1e86e need another run to win. (769)"
+    ],
+    [
+      "Oblique arguments of verbs are individual-denoting arguments (DPs or PPs) which act as the third argument of verb, i.e. not a subject or (direct) object. They may or may not be marked by a preposition. Obliques are only found in VPs that have three or more individual arguments. Arguments are selected for by the verb, and they are (generally) not optional, though in some cases they may be omitted where they are understood or implicitly existentially quantified over. See [p.40]kim2008syntax.",
+      ". Included \u1e56repositional S\u0323ue gave to Bill a book. (42) Mary has always preferred lemons to limes. (70) *Janet broke Bill on the finger. (141) . Benefactives \u1e40artha carved the baby a toy out of wood. (139) . Double object \u1e60usan told her a story. (875) Locative arguments \u0226nn may spend her vacation in Italy. (289) . High-arity Passives M\u0323ary was given by John the book. (626)",
+      ". Excluded \u1e44on-DP arguments \u1e86e want John to win (28) . 3rd argments where not all three arguments are DPs \u1e86e want John to win (28)",
+      "Prepositional Phrase arguments of VPs are individual-denoting arguments of a verb which are marked by a proposition. They may or may not be obliques. Arguments are selected for by the verb, and they are (generally) not optional, though in some cases they may be omitted where they are understood or implicitly existentially quantified over.",
+      ". Included \u1e0aative S\u0323ue gave to Bill a book. (42) . Conative (at) C\u0323arla slid at the book. (179) . Idiosyncratic prepositional verbs \u0130 wonder who to place my trust in. (711) She voted for herself. (743) . Locative J\u0307ohn was found in the office. (283) . PP predicates \u0116verything you like is on the table. (736)",
+      ". Excluded \u1e56P adjuncts Particles Arguments of deverbal expressions t\u0323he putter of books left. (892) . By-phrase \u1e6aed was bitten by the spider. (613)",
+      "Prepositional Phrase arguments of NPs or APs are individual-denoting arguments of a noun or adjective which are marked by a proposition. Arguments are selected for by the head, and they are (generally) not optional, though in some cases they may be omitted where they are understood or implicitly existentially quantified over.",
+      ". Included \u1e58elational adjectives \u1e40any people were fond of Pat. (936) *I was already aware of fact. (824) . Relational nouns \u1e86e admired the pictures of us in the album. (759) They found the book on the atom. (780) . Arguments of deverbal nouns t\u0323he putter of books left. (892)",
+      "Prepositional arguments introduced with by. Usually, this is the (semantic) subject of a passive verb, but in rare cases it may be the subject of a nominalized verb. Arguments are usually selected for by the head, and they are generally not optional. In this case, the argument introduced with by is semantically selected for by the verb, but it is syntactically optional. See [p.190]adger2003core and []collins2005smuggling.",
+      ". Included \u1e56assives \u1e6aed was bitten by the spider. (613) . Subjects of deverbal nouns \u1e6bhe attempt by John to leave surprised me. (1003)",
+      "Expletives, or \u201cdummy\u201d arguments, are semantically inert arguments. The most common expletives in English are it and there, although not all occurrences of these items are expletives. Arguments are usually selected for by the head, and they are generally not optional. In this case, the expletive occupies a syntactic argument slot, but it is not semantically selected by the verb, and there is often a syntactic variation without the expletive. See [p.170-172]adger2003core and [p.82-83]kim2008syntax.",
+      ". Included \u1e6ahere\u2014inserted, existential T\u0323here loved Sandy. (939) There is a nurse available. (466) . It\u2014cleft, inserted \u0130t was a brand new car that he bought. (347) It bothers me that John coughs. (314) It is nice to go abroad. (47) . Environmental it K\u0307erry remarked it was late. (821) Poor Bill, it had started to rain and he had no umbrella. (116) You've really lived it up. (160)",
+      ". Excluded J\u0307ohn counted on Bill to get there on time. (996) I bought it to read. (1026)"
+    ],
+    [
+      "These are verbs with 3 or more arguments of any kind. Arity refers to the number of arguments that a head (or function) selects for. Arguments are usually selected for by the head, and they are generally not optional. They may be DPs, PPs, CPs, VPs, APs or other categories.",
+      ". Included \u1e0aitransitive [\u0323Sue] gave [to Bill] [a book]. (42) [Martha] carved [the baby] [a toy] out of wood. (139) . VP arguments [\u0323We] believed [John] [to be a fountain in the park]. (274) [We] made [them] [be rude]. (260) . Particles He] let [the cats which were whining] [out]. (71) . Passives with by-phrase [\u0323A good friend] is remained [to me] [by him]. (237) . Expletives [\u0323We] expect [there] [to will rain]. (282) [There] is [a seat] [available]. (934) [It] bothers [me] [that he is here]. (1009) . Small clause John] considers [Bill] [silly]. (1039)",
+      ". Excluded \u1e58esults, depictives John] broke [the geode] [open].",
+      "These are VPs where a canonical argument of the verb is missing. This can be difficult to determine, but in many cases the missing argument is understood with existential quantification or generically, or contextually salient. See [p.106-109]sportiche2013introduction.",
+      ". Included \u1e40iddle voice/causative inchoative T\u0323he problem perceives easily. (66) . Passive \u1e6ahe car was driven. (296) . Null complement anaphora J\u0307ean persuaded Robert. (380) Nobody told Susan. (883) . Dropped argument K\u0323im put in the box. (253) The guests dined. (835) I wrote to Bill. (1030) . Transitive adjective J\u0307ohn is eager. (27) We pulled free. (144) . Transitive noun \u0130 sensed his eagerness. (155) . Expletive insertion I\u0323t loved Sandy. (949)",
+      ". Excluded \u1e6aed was bitten by the spider. (613)",
+      "These are VPs in which a non-canonical argument of the verb has been added. These cases are clearer to identify where the additional argument is a DP. In general, PPs which mark locations, times, beneficiaries, or purposes should be analyzed as adjuncts, while PPs marking causes can be considered arguments. See []pylkkanen2008introducing.",
+      ". Included \u0116xtra argument L\u0323inda winked her lip. (202) Sharon fainted from hunger. (204) I shaved myself. (526) . Causative I\u0323 squeaked the door. (207) . Expletive insertion \u1e6ahere is a monster in Loch Ness. (928) It annoys people that dogs bark. (943) . Benefactive \u1e40artha carved the baby a toy out of wood. (139)",
+      "The passive voice is marked by the demotion of the subject (either complete omission or to a by-phrase) and the verb appearing as a past participle. In the stereotypical construction there is an auxiliary be verb, though this may be absent. See [p.175-190]kim2008syntax, collins2005smuggling, and [p.311-333]sag2003syntactic.",
+      ". Included V\u0307erbs \u1e6ahe earth was believed to be round. (157) . Psuedopassive \u1e6ahe bed was slept in. (298) . Past participle adjuncts \u1e6ahe horse raced past the barn fell. (900)"
+    ],
+    [
+      "The imperative mood is marked by the absence of the a subject and the bare form of the verb, and expresses a command, request, or other directive speech act.",
+      ". Included W\u0323ash you! (224) Somebody just left - guess who. (528)"
+    ],
+    [
+      "These are cases in which a reflexive (non-possessive) pronoun, usually bound by an antecedent. See [p.163-186]sportiche2013introduction and [p.203-226]sag2003syntactic.",
+      ". Included O\u0323urselves like ourselves. (742) Which pictures of himself does John like? (386)",
+      "These are cases in which a non-reflexive pronoun appears along with its antecedent. This includes donkey anaphora, quantificational binding, and bound possessives, among other bound pronouns. See [p.163-186]sportiche2013introduction and [p.203-226]sag2003syntactic.",
+      ". Included \u1e02ound possessor \u1e6ahe children admire their mother. (382) . Quantificational binding \u0116verybody gets on well with a certain relative, but often only his therapist knows which one. (562) . Bound pronoun W\u0323e gave us to the cause. (747)"
+    ],
+    [
+      "These are sentences in which the matrix clause is interrogative (either a wh- or polar question). See [pp.282-213]adger2003core, [pp.193-222]kim2008syntax, and [p.315-350]carnie2013syntax.",
+      ". Included \u1e86h-question \u1e86ho always drinks milk? (684) . Polar question \u1e0aid Athena help us? (486)",
+      "These are embedded interrogative clauses appearing as arguments of verbs, nouns, and adjectives. Not including relative clauses and free relatives. See [p.297]adger2003core.",
+      ". Included U\u0307nder VP \u0130 forgot how good beer tastes. (235) *What did you ask who saw? (508) . Under NP \u1e6ahat is the reason why he resigned. (313) . Under AP \u1e6ahey claimed they had settled on something, but it wasn't clear what they had settled on. (529) . Free relative \u1e86hat the water did to the bottle was fill it. (33)",
+      ". Excluded Relative clauses, free relatives",
+      "These are phrasal Wh-phrases, in which the wh-word moves along with other expressions, including prepositions (pied-piping) or nouns in the case of determiner wh-words such as how many and which.",
+      ". Included \u1e56ied-piping T\u0323he ship sank, but I don't know with what. (541) . Other phrasal wh-phrases \u0130 know which book Mag read, and which book Bob read my report that you hadn't. (61) How sane is Peter? (88)",
+      "Relative clauses are noun modifiers appearing with a relativizer (either that or a wh-word) and an associated gap. See [p.223-244]kim2008syntax.",
+      ". Included \u1e6ahough he may hate those that criticize Carter, it doesn't matter. (332) *The book what inspired them was very long. (686) Everything you like is on the table. (736)",
+      ". Excluded T\u0323he more you would want, the less you would eat. (6)",
+      "This is wh-movement out of an extraction island, or near-island. Islands include, for example, complex NPs, adjuncts, embedded questions, coordination. A near-island is an extraction that closely resembles an island violation, such as extraction out of an embedded clause, or across-the-board extraction. See [pp.323-333]adger2003core and [pp.332-334]carnie2013syntax.",
+      ". Included \u0116mbedded question *What did you ask who Medea gave? (493) . Adjunct W\u0323hat did you leave before they did? (598) . Parasitic gaps \u1e86hich topic did you choose without getting his approval? (311) . Complex NP \u1e86ho did you get an accurate description of? (483)"
+    ],
+    [
+      "These are complement clauses acting as the (syntactic) subject of verbs. See [pp.90-91]kim2008syntax.",
+      ". Included \u1e6ahat dogs bark annoys people. (942) The socks are ready for for you to put on to be planned. (112)",
+      ". Excluded \u0116xpletive insertion \u0130t bothers me that John coughs. (314)",
+      "These are complement clauses acting as (non-subject) arguments of verbs. See [pp.84-90]kim2008syntax.",
+      ". Included \u0130 can't believe Fred won't, either. (50) I saw that gas can explode. (222) It bothers me that John coughs. (314) Clefts \u0130t was a brand new car that he bought. (347)",
+      "These are complement clauses acting as an argument of a noun or adjective. See [pp.91-94]kim2008syntax.",
+      ". Included U\u0307nder NP \u1e0ao you believe the claim that somebody was looking for something? (99) . Under AP T\u0323he children are fond that they have ice cream. (842)",
+      "These are complement clauses with a non-finite matrix verb. Often, the complementizer is for, or there is no complementizer. See [pp.252-253,256-260]adger2003core.",
+      ". Included \u1e1eor complementizer \u0130 would prefer for John to leave. (990) . No Complementizer \u1e40ary intended John to go abroad. (48) . Ungrammatical \u1e22eidi thinks that Andy to eat salmon flavored candy bars. (363) . V-ing \u022enly Churchill remembered Churchill giving the Blood, Sweat and Tears speech. (469)",
+      "These are complement clauses with no overt complementizer.",
+      ". Included \u010aomplement clause \u0130'm sure we even got these tickets! (325) He announced he would marry the woman he loved most, but none of his relatives could figure out who. (572) . Relative clause \u1e6ahe Peter we all like was at the party (484)",
+      "These are sentences with three or nested verbs, where VP is not an aux or modal, i.e. with the following syntax: [S ...[ VP ...[ VP ...[ VP ...] ...] ...] ...]",
+      ". Included \u0116mbedded VPs \u1e40ax seemed to be trying to force Ted to leave the room, and Walt, Ira. (657) . Embedded clauses \u0130 threw away a book that Sandy thought we had read. (713)"
+    ],
+    [
+      "Any occurrence of negation in a sentence, including sentential negation, negative quantifiers, and negative adverbs.",
+      ". Included \u1e60entential \u0130 can't remember the name of somebody who had misgivings. (123) . Quantifier \u1e44o writer, and no playwright, meets in Vienna. (124) . Adverb \u1e6ahey realised that never had Sir Thomas been so offended. (409)",
+      "Modal verbs (may, might, can, could, will, would, shall, should, must). See [pp.152-155]kim2008syntax.",
+      ". Included J\u0307ohn can kick the ball. (280) As a statesman, scarcely could he do anything worth mentioning. (292)",
+      ". Excluded \u1e56seudo-modals \u1e60andy was trying to work out which students would be able to solve a certain problem. (600)",
+      "Auxiliary verbs (e.g. be, have, do). See [pp.149-174]kim2008syntax.",
+      ". Included \u1e6ahey love to play golf, but I do not. (290) The car was driven. (296) he had spent five thousand dollars. (301)",
+      ". Excluded \u1e56seudo-auxiliaries S\u0323ally asked if somebody was going to fail math class, but I can't remember who. (589) The cat got bitten. (926)",
+      "These are predicates acting as near-auxiliary (e.g. get-passive) or near-modals (e.g. willing)",
+      ". Included \u1e44ear-auxiliaries M\u0323ary came to be introduced by the bartender and I also came to be. (55) *Sally asked if somebody was going to fail math class, but I can't remember who. (589) The cat got bitten. (926) . Near-modals \u010alinton is anxious to find out which budget dilemmas Panetta would be willing to tackle in a certain way, but he won't say in which. (593) Sandy was trying to work out which students would be able to solve a certain problem. (600)"
+    ],
+    [
+      "These are VPs with control verbs, where one argument is a non-finite to-VP without a covert subject co-indexed with an argument of the matrix verb. See [pp.252,266-291]adger2003core, [pp.203-222]sportiche2013introduction, and [pp.125-148]kim2008syntax.",
+      ". Included \u0130ntransitive subject control I\u0323t tries to leave the country. (275) . Transitive subject control J\u0307ohn promised Bill to leave. (977) . Transitive object control \u0130 want her to dance. (379) John considers Bill to be silly. (1040)",
+      ". Excluded V\u0307P args of NP/AP \u1e6ahis violin is difficult to play sonatas on. (114) . Purpose \u1e6ahere is a bench to sit on. (309) . Subject VPs \u1e6ao please John is easy. (315) . Argument present participles \u1e40edea denied poisoning the phoenix. (490) . Raising \u0226nson believed himself to be handsome. (499)",
+      "These are VPs with raising predicates, where one argument is a non-finite to-VP without a covert subject co-indexed with an argument of the matrix verb. Unlike control verbs, the coindexed argument is not a semantic argument of the raising predicate. See [pp.260-266]adger2003core, [pp.203-222]sportiche2013introduction, and [pp.125-148]kim2008syntax.",
+      ". Included \u1e60ubject raising U\u0307nder the bed seems to be a fun place to hide. (277) . Object raising \u0226nson believed himself to be handsome. (499) . Raising adjective J\u0307ohn is likely to leave. (370)",
+      "These are embedded infinitival VPs containing a (non-subject) gap that is filled by an argument in the upper clause. Examples are purpose-VPs and tough-movement. See [pp.246-252]kim2008syntax.",
+      ". Included \u1e6aough-movement D\u0323rowning cats, which is against the law, are hard to rescue. (79) . Infinitival relatives F\u0323ed knows which politician her to vote for. (302) . Purpose \u1e6bhe one with a red cover takes a very long time to read. (352) . Other non-finite VPs with extraction \u0226s a statesman, scarcely could he do anything worth mentioning. (292)",
+      "These are non-finite VP arguments of nouns and adjectives.",
+      ". Included \u1e58aising adjectives J\u0307ohn is likely to leave. (370) . Control adjectives \u1e6ahe administration has issued a statement that it is willing to meet a student group, but I'm not sure which one. (604) . Control nouns \u0226s a teacher, you have to deal simultaneously with the administration's pressure on you to succeed, and the children's to be a nice guy. (673) . Purpose VPs \u1e6bhere is nothing to do. (983)",
+      "These are miscellaneous non-finite VPs.",
+      ". Included \u0130 saw that gas can explode. (222) Gerunds/Present participles S\u0323tudents studying English reads Conrad's Heart of Darkness while at university. (262) Knowing the country well, he took a short cut. (411) John became deadly afraid of flying. (440) . Subject VPs \u1e6ao please John is easy. (315) . Nominalized VPs W\u0323hat Mary did Bill was give a book. (473)",
+      ". Excluded \u1e6bo-VPs acting as complements or modifiers of verbs, nouns, or adjectives"
+    ],
+    [
+      "These are nouns and adjectives derived from verbs.",
+      ". Included \u1e0aeverbal nouns t\u0323he election of John president surprised me. (1001) . \u201cLight\u201d verbs \u1e6ahe birds give the worm a tug. (815) . Gerunds \u0130f only Superman would stop flying planes! (773) . Event-wh \u1e86hat the water did to the bottle was fill it. (33) . Deverbal adjectives \u1e22is or her least known work. (95)",
+      "Relational nouns are NPs with an obligatory (or existentially closed) argument. A particular relation holds between the members of the extension of NP and the argument. The argument must be a DP possessor or a PP. See [pp.82-83]kim2008syntax.",
+      ". Included \u1e44ouns with of-arguments J\u0307ohn has a fear of dogs. (353) . Nouns with other PP-arguments \u1e22enri wants to buy which books about cooking? (442) . Measure nouns \u0130 bought three quarts of wine and two of Clorox. (667) . Possessed relational nouns J\u0323ohn's mother likes himself. (484)",
+      ". Excluded \u1e44ouns with PP modifiers \u1e60ome people consider dogs in my neighborhood dangerous. (802)",
+      "Transitive (non-relational) nouns take a VP or CP argument. See [pp.82-83]kim2008syntax.",
+      ". Included V\u0307P argument \u1e6bhe attempt by John to leave surprised me. (1003) . CP argument W\u0323hich report that John was incompetent did he submit? (69) . QP argument \u1e6ahat is the reason why he resigned. (313)",
+      "These are complex NPs, including coordinated nouns and nouns with modifiers (excluding prenominal adjectives).",
+      ". Included \u1e40odified NPs T\u0323he madrigals which Henry plays the lute and sings sound lousy. (84) John bought a book on the table. (233) . NPs with coordination T\u0323he soundly and furry cat slept. (871) The love of my life and mother of my children would never do such a thing. (806)",
+      "Noun-noun compounds are NPs consisting of two constituent nouns.",
+      ". Included \u0130t was the peasant girl who got it. (320) A felon was elected to the city council. (938)",
+      "These are adjectives that take an obligatory (or existentially closed) argument. A particular relation holds between the members of the extension of the modified NP and the argument. The argument must be a DP or PP. See [pp.80-82]kim2008syntax.",
+      ". Included \u022ef-arguments \u1e6ahe chickens seem fond of the farmer. (254) . Other PP arguments \u1e6ahis week will be a difficult one for us. (241) John made Bill mad at himself. (1035)",
+      "A transitive (non-relational) adjective. I.e. an adjectives that takes a VP or CP argument. See [pp.80-82]kim2008syntax.",
+      ". Included V\u0307P argument J\u0307ohn is likely to leave. (370) . CP argument J\u0307ohn is aware of it that Bill is here. (1013) . QP argument \u1e6ahe administration has issued a statement that it is willing to meet a student group, but I'm not sure which one. (604)"
+    ],
+    [
+      "These are expressions with non-canonical word order. See, for example, [p.76]sportiche2013introduction.",
+      ". Includes \u1e56article shift M\u0323ickey looked up it. (24) . Preposed modifiers \u022eut of the box jumped a little white rabbit. (215) *Because she's so pleasant, as for Mary I really like her. (331) . Quantifier float \u1e6ahe men will all leave. (43) . Preposed argument \u1e86ith no job would John be happy. (333) . Relative clause extraposition \u1e86hich book's, author did you meet who you liked? (731) . Misplaced phrases \u1e40ary was given by John the book. (626)",
+      "This includes topicalization and focus constructions. See [pp.258-269]kim2008syntax and [pp.68-75]sportiche2013introduction.",
+      ". Included \u1e6aopicalization \u1e40ost elections are quickly forgotten, but the election of 2000, everyone will remember for a long time. (807) . Clefts \u0130t was a brand new car that he bought. (347) . Pseudo-clefts \u1e86hat John promised is to be gentle. (441)",
+      ". Excluded \u1e6ahere-insertion Passive",
+      "These are parentheticals or fragmentary expressions. . Included \u1e56arenthetical \u1e40ary asked me if, in St. Louis, John could rent a house cheap. (704) . Fragments \u1e6ahe soup cooks, thickens. (448) . Tag question \u0120eorge has spent a lot of money, hasn't he? (291)",
+      "Coordinations and disjunctions are expressions joined with and, but, or, etc. See [pp.61-68]sportiche2013introduction.",
+      ". Included \u1e0aP coordination \u1e0aave, Dan, Erin, Jaime, and Alina left. (341) . Right Node Raising K\u0307im gave a dollar to Bobbie and a dime to Jean. (435) . Clausal coordination \u1e60he talked to Harry, but I don't know who else. (575) . Or, nor N\u0323o writer, nor any playwright, meets in Vienna. (125) . Pseudo-coordination \u0130 want to try and buy some whiskey. (432) . Juxtaposed clauses \u013fights go out at ten. There will be no talking afterwards. (779)",
+      "This includes subordinate clauses, especially with subordinating conjunctions, and conditionals.",
+      ". Included \u010aonditional \u0130f I can, I will work on it. (56) . Subordinate clause W\u0323hat did you leave before they did? (598) *Because Steve's of a spider's eye had been stolen, I borrowed Fred's diagram of a snake's fang. (677) . Correlative A\u0323s you eat the most, you want the least. (5)",
+      "This includes VP or NP ellipsis, or anaphora standing for VPs or NPs (not DPs). See [pp.55-61]sportiche2013introduction.",
+      ". Included V\u0307P Ellipsis \u0130f I can, I will work on it. (56) Mary likes to tour art galleries, but Bill hates to. (287) . VP Anaphor \u0130 saw Bill while you did so Mary. (472) . NP Ellipsis \u1e6aom's dog with one eye attacked Fred's. (679) . NP anaphor \u1e6bhe one with a red cover takes a very long time to read. (352) . Sluicing \u1e40ost columnists claim that a senior White House official has been briefing them, and the newspaper today reveals which one. (557) . Gapping \u1e02ill ate the peaches, but Harry the grapes. (646)",
+      "These are adjuncts modifying sentences, sentence-level adverbs, subordinate clauses.",
+      ". Included \u1e60entence-level adverbs \u1e60uddenly, there arrived two inspectors from the INS. (447) . Subordinate clauses \u1e6ahe storm arrived while we ate lunch. (852)"
+    ],
+    [
+      "These are quantificational DPs, i.e. the determiner is a quantifier.",
+      ". Included Q\u0307uantifiers E\u0323very student, and he wears socks, is a swinger. (118) We need another run to win. (769) . Partitive N\u0323either of students failed. (265)",
+      "These are quantifiers that take PP arguments, and measure nouns. See [pp.109-118]kim2008syntax.",
+      ". Included Q\u0307uantifiers with PP arguments N\u0323either of students failed. (265) . Numerals \u022ene of Korea's most famous poets wrote these lines. (294) . Measure nouns \u0130 bought three quarts of wine and two of Clorox. (667)",
+      "These are negative polarity items (any, ever, etc.) and free choice items (any). See kadmon1993any.",
+      ". Included \u1e44PI \u0116verybody around here who ever buys anything on credit talks in his sleep. (122) I didn't have a red cent. (350) . FCI \u0226ny owl hunts mice. (387)",
+      "These are comparative constructions. See BIBREF22 .",
+      ". Included \u010aorrelative \u1e6ahe angrier Mary got, the more she looked at pictures. (9) They may grow as high as bamboo. (337) I know you like the back of my hand. (775)"
+    ],
+    [
+      "These are sentences that include a semantic violation, including type mismatches, violations of selectional restrictions, polarity violations, definiteness violations.",
+      ". Included V\u0307olation of selectional restrictions m\u0323any information was provided. (218) *It tries to leave the country. (275) . Aspectual violations J\u0323ohn is tall on several occasions. (540) . Definiteness violations I\u0323t is the problem that he is here. (1018) . Polarity violations \u0226ny man didn't eat dinner. (388)",
+      "These are sentences that include a violation in inflectional morphology, including tense-aspect marking, or agreement.",
+      ". Included \u010aase U\u0323s love they. (46) . Agreement S\u0323tudents studying English reads Conrad's Heart of Darkness while at university. (262) . Gender S\u0323ally kissed himself. (339) . Tense/Aspect K\u0323im alienated cats and beating his dog. (429)",
+      "These are sentences with a violation that can be identified with the presence or absence of a single word.",
+      ". Included \u1e40issing word J\u0323ohn put under the bathtub. (247) *I noticed the. (788) . Extra word E\u0323veryone hopes everyone to sleep. (467) *He can will go (510)"
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: Grammatical Analysis of Pretrained Sentence Encoders with Acceptability Judgments
+
+Question: How is the CoLA grammatically annotated?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Analysis Set",
+    "Annotation",
+    "Feature Descriptions",
+    "Correlations",
+    "Models Evaluated",
+    "Overall CoLA Results",
+    "Analysis Set Results",
+    "Length Analysis",
+    "Conclusion",
+    "Acknowledgments",
+    "Simple",
+    "Pred (Predicates)",
+    "Adjunct",
+    "Argument Types",
+    "Arg Altern (Argument Alternations)",
+    "Imperative",
+    "Binding",
+    "Question",
+    "Comp Clause (Complement Clauses)",
+    "Aux (Auxiliaries)",
+    "to-VP (Infinitival VPs)",
+    "N, Adj (Nouns and Adjectives)",
+    "S-Syntax (Sentence-Level Syntax)",
+    "Determiner",
+    "Violations"
+  ],
+  "paragraphs": [
+    [
+      "The effectiveness and ubiquity of pretrained sentence embeddings for natural language understanding has grown dramatically in recent years. Recent sentence encoders like OpenAI's Generative Pretrained Transformer BIBREF3 and BERT BIBREF2 achieve the state of the art on the GLUE benchmark BIBREF4 . Among the GLUE tasks, these state-of-the-art systems make their greatest gains on the acceptability task with the Corpus of Linguistic Acceptability BIBREF0 . CoLA contains example sentences from linguistics publications labeled by experts for grammatical acceptability, and written to show subtle grammatical features. Because minimal syntactic differences can separate acceptable sentences from unacceptable ones (What did Bo write a book about? / *What was a book about written by Bo?), and acceptability classifiers are more reliable when trained on GPT and BERT than on recurrent models, it stands to reason that GPT and BERT have better implicit knowledge of syntactic features relevant to acceptability.",
+      "Our goal in this paper is to develop an evaluation dataset that can locate which syntactic features that a model successfully learns by identifying the syntactic domains of CoLA in which it performs the best. Using this evaluation set, we compare the syntactic knowledge of GPT and BERT in detail, and investigate the strengths of these models over the baseline BiLSTM model published by warstadt2018neural. The analysis set includes expert annotations labeling the entire CoLA development set for the presence of 63 fine-grained syntactic features.",
+      "We identify many specific syntactic features that make sentences harder to classify, and many that have little effect. For instance, sentences involving unusual or marked argument structures are no harder than the average sentence, while sentences with long distance dependencies are hard to learn. We also find features of sentences that accentuate or minimize the differences between models. Specifically, the transformer models seem to learn long-distance dependencies much better than the recurrent model, yet have no advantage on sentences with morphological violations."
+    ],
+    [
+      "We introduce a grammatically annotated version of the entire CoLA development set to facilitate detailed error analysis of acceptability classifiers. These 1043 sentences are expert-labeled for the presence of 63 minor grammatical features organized into 15 major features. Each minor feature belongs to a single major feature. A sentence belongs to a major feature if it belongs to one or more of the relevant minor features. The Appendix includes descriptions of each feature along with examples and the criteria used for annotation.",
+      "The 63 minor features and 15 major features are illustrated in Table TABREF5 . Considering minor features, an average of 4.31 features is present per sentence (SD=2.59). The average feature is present in 71.3 sentences (SD=54.7). Turning to major features, the average sentence belongs to 3.22 major features (SD=1.66), and the average major feature is present in 224 sentences (SD=112). Every sentence is labeled with at least one feature."
+    ],
+    [
+      "The sentences were annotated manually by one of the authors, who is a PhD student with extensive training in formal linguistics. The features were developed in a trial stage, in which the annotator performed a similar annotation with different annotation schema for several hundred sentences from CoLA not belonging to the development set."
+    ],
+    [
+      "Here we briefly summarize the feature set in order of the major features. Many of these constructions are well-studied in syntax, and further background can be found in textbooks such as adger2003core and sportiche2013introduction.",
+      "This major feature contains only one minor feature, simple, including sentences with a syntactically simplex subject and predicate.",
+      "These three features correspond to predicative phrases, including copular constructions, small clauses (I saw Bo jump), and resultatives/depictives (Bo wiped the table clean).",
+      "These six features mark various kinds of optional modifiers. This includes modifiers of NPs (The boy with blue eyes gasped) or VPs (The cat meowed all morning), and temporal (Bo swam yesterday) or locative (Bo jumped on the bed).",
+      "These five features identify syntactically selected arguments, differentiating, for example, obliques (I gave a book to Bo), PP arguments of NPs and VPs (Bo voted for Jones), and expletives (It seems that Bo left).",
+      "These four features mark VPs with unusual argument structures, including added arguments (I baked Bo a cake) or dropped arguments (Bo knows), and the passive (I was applauded).",
+      "This contains only one feature for imperative clauses (Stop it!).",
+      "These are two minor features, one for bound reflexives (Bo loves himself), and one for other bound pronouns (Bo thinks he won).",
+      "These five features apply to sentences with question-like properties. They mark whether the interrogative is an embedded clause (I know who you are), a matrix clause (Who are you?), or a relative clause (Bo saw the guy who left); whether it contains an island out of which extraction is unacceptable (*What was a picture of hanging on the wall?); or whether there is pied-piping or a multi-word wh-expressions (With whom did you eat?).",
+      "These six features apply to various complement clauses (CPs), including subject CPs (That Bo won is odd); CP arguments of VPs or NPs/APs (The fact that Bo won); CPs missing a complementizer (I think Bo's crazy); or non-finite CPs (This is ready for you to eat).",
+      "These four minor features mark the presence of auxiliary or modal verbs (I can win), negation, or \u201cpseudo-auxiliaries\u201d (I have to win).",
+      "These five features mark various infinitival embedded VPs, including control VPs (Bo wants to win); raising VPs (Bo seemed to fly); VP arguments of NPs or APs (Bo is eager to eat); and VPs with extraction (e.g. This is easy to read ts ).",
+      "These seven features mark complex NPs and APs, including ones with PP arguments (Bo is fond of Mo), or CP/VP arguments; noun-noun compounds (Bo ate mud pie); modified NPs, and NPs derived from verbs (Baking is fun).",
+      "These seven features mark various unrelated syntactic constructions, including dislocated phrases (The boy left who was here earlier); movement related to focus or information structure (This I've gotta see this); coordination, subordinate clauses, and ellipsis (I can't); or sentence-level adjuncts (Apparently, it's raining).",
+      "These four features mark various determiners, including quantifiers, partitives (two of the boys), negative polarity items (I *do/don't have any pie), and comparative constructions.",
+      "These three features apply only to unacceptable sentences, and only ones which are ungrammatical due to a semantic or morphological violation, or the presence or absence of a single salient word."
+    ],
+    [
+      "We wish to emphasize that these features are overlapping and in many cases are correlated, thus not all results from using this analysis set will be independent. We analyzed the pairwise Matthews Correlation Coefficient BIBREF17 of the 63 minor features (giving 1953 pairs), and of the 15 major features (giving 105 pairs). MCC is a special case of Pearson's INLINEFORM0 for Boolean variables. These results are summarized in Table TABREF25 . Regarding the minor features, 60 pairs had a correlation of 0.2 or greater, 17 had a correlation of 0.4 or greater, and 6 had a correlation of 0.6 or greater. None had an anti-correlation of greater magnitude than -0.17. Turning to the major features, 6 pairs had a correlation of 0.2 or greater, and 2 had an anti-correlation of greater magnitude than -0.2.",
+      "We can see at least three reasons for these observed correlations. First, some correlations can be attributed to overlapping feature definitions. For instance, expletive arguments (e.g. There are birds singing) are, by definition, non-canonical arguments, and thus are a subset of add arg. However, some added arguments, such as benefactives (Bo baked Mo a cake), are not expletives. Second, some correlations can be attributed to grammatical properties of the relevant constructions. For instance, question and aux are correlated because main-clause questions in English require subject-aux inversion and in many cases the insertion of auxiliary do (Do lions meow?). Third, some correlations may be a consequence of the sources sampled in CoLA and the phenomena they focus on. For instance, the unusually high correlation of Emb-Q and ellipsis/anaphor can be attributed to BIBREF18 , which is an article about the sluicing construction involving ellipsis of an embedded interrogative (e.g. I saw someone, but I don't know who).",
+      "Finally, two strongest anti-correlations between major features are between simple and the two features related to argument structure, argument types and arg altern. This follows from the definition of simple, which excludes any sentence containing a large number or unusual configuration of arguments."
+    ],
+    [
+      "We train MLP acceptability classifiers for CoLA on top of three sentence encoders: (1) the CoLA baseline encoder with ELMo-style embeddings, (2) OpenAI GPT, and (3) BERT. We use publicly available sentence encoders with pretrained weights."
+    ],
+    [
+      "The overall performance of the three sentence encoders is shown in Table TABREF33 . Performance on CoLA is measured using MCC BIBREF14 . We present the best single restart for each encoder, the mean over restarts for an encoder, and the result of ensembling the restarts for a given encoder, i.e. taking the majority classification for a given sentence, or the majority label of acceptable if tied. For BERT results, we exclude 5 out of the 20 restarts because they were degenerate (MCC=0).",
+      "Across the board, BERT outperforms GPT, which outperforms the CoLA baseline. However, BERT and GPT are much closer in performance than they are to CoLA baseline. While ensemble performance exceeded the average for BERT and GPT, it did not outperform the best single model."
+    ],
+    [
+      "The results for the major features and minor features are shown in Figures FIGREF26 and FIGREF35 , respectively. For each feature, we measure the MCC of the sentences including that feature. We plot the mean of these results across the different restarts for each model, and error bars mark the mean INLINEFORM0 standard deviation. For the Violations features, MCC is technically undefined because these features only contain unacceptable sentences. We report MCC in these cases by including for each feature a single acceptable example that is correctly classified by all models.",
+      "Comparison across features reveals that the presence of certain features has a large effect on performance, and we comment on some overall patterns below. Within a given feature, the effect of model type is overwhelmingly stable, and resembles the overall difference in performance. However, we observe several interactions, i.e. specific features where the relative performance of models does not track their overall relative performance.",
+      "Among the major features (Figure FIGREF26 ), performance is universally highest on the simple sentences, and is higher than each model's overall performance. Though these sentences are simple, we notice that the proportion of ungrammatical ones is on par with the entire dataset. Otherwise we find that a model's performance on sentences of a given feature is on par with or lower than its overall performance, reflecting the fact that features mark the presence of unusual or complex syntactic structure.",
+      "Performance is also high (and close to overall performance) on sentences with marked argument structures (Argument Types and Arg(ument) Alt(ernation)). While these models are still worse than human (overall) performance on these sentences, this result indicates that argument structure is relatively easy to learn.",
+      "Comparing different kinds of embedded content, we observe higher performance on sentences with embedded clauses (major feature=Comp Clause) embedded VPs (major feature=to-VP) than on sentences with embedded interrogatives (minor features=Emb-Q, Rel Clause). An exception to this trend is the minor feature No C-izer, which labels complement clauses without a complementizer (e.g. I think that you're crazy). Low performance on these sentences compared to most other features in Comp Clause might indicate that complementizers are an important syntactic cue for these models.",
+      "As the major feature Question shows, the difficulty of sentences with question-like syntax applies beyond just embedded questions. Excluding polar questions, sentences with question-like syntax almost always involve extraction of a wh-word, creating a long-distance dependency between the wh-word and its extraction site, which may be difficult for models to recognize.",
+      "The most challenging features are all related to Violations. Low performance on Infl/Agr Violations, which marks morphological violations (He washed yourself, This is happy), is especially striking because a relatively high proportion (29%) of these sentences are Simple. These models are likely to be deficient in encoding morphological features is that they are word level models, and do not have direct access sub-word information like inflectional endings, which indicates that these features are difficult to learn effectively purely from lexical distributions.",
+      "Finally, unusual performance on some features is due to small samples, and have a high standard deviation, suggesting the result is unreliable. This includes CP Subj, Frag/Paren, imperative, NPI/FCI, and Comparative.",
+      "Comparing within-feature performance of the three encoders to their overall performance, we find they have differing strengths and weaknesses. BERT stands out over other models in Deep Embed, which includes challenging sentences with doubly-embedded, as well as in several features involving extraction (i.e. long-distance dependencies) such as VP+Extract and Info-Struc. The transformer models show evidence of learning long-distance dependencies better than the CoLA baseline. They outperform the CoLA baseline by an especially wide margin on Bind:Refl, which all involves establishing a dependency between a reflexive and its antecedent (Bo tries to love himself). They also have a large advantage in dislocation, in which expressions are separated from their dependents (Bo practiced on the train an important presentation). The advantage of BERT and GPT may be due in part to their use of the transformer architecture. Unlike the BiLSTM used by the CoLA baseline, the transformer uses a self-attention mechanism that associates all pairs of words regardless of distance.",
+      "In some cases models showed surprisingly good or bad performance, revealing possible idiosyncrasies of the sentence embeddings they output. For instance, the CoLA baseline performs on par with the others on the major feature adjunct, especially considering the minor feature Particle (Bo looked the word up).",
+      "Furthermore, all models struggle equally with sentences in Violation, indicating that the advantages of the transformer models over the CoLA baseline does not extend to the detection of morphological violations (Infl/Agr Violation) or single word anomalies (Extra/Missing Expr)."
+    ],
+    [
+      "For comparison, we analyze the effect of sentence length on acceptability classifier performance. The results are shown in Figure FIGREF39 . The results for the CoLA baseline are inconsistent, but do drop off as sentence length increases. For BERT and GPT, performance decreases very steadily with length. Exceptions are extremely short sentences (length 1-3), which may be challenging due to insufficient information; and extremely long sentences, where we see a small (but somewhat unreliable) boost in BERT's performance. BERT and GPT are generally quite close in performance, except on the longest sentences, where BERT's performance is considerably better."
+    ],
+    [
+      "Using a new grammatically annotated analysis set, we identify several syntactic phenomena that are predictive of good or bad performance of current state of the art sentence encoders on CoLA. We also use these results to develop hypotheses about why BERT is successful, and why transformer models outperform sequence models.",
+      "Our findings can guide future work on sentence embeddings. A current weakness of all sentence encoders we investigate, including BERT, is the identification of morphological violations. Future engineering work should investigate whether switching to a character-level model can mitigate this problem. Additionally, transformer models appear to have an advantage over sequence models with long-distance dependencies, but still struggle with these constructions relative to more local phenomena. It stands to reason that this performance gap might be widened by training larger or deeper transformer models, or training on longer or more complex sentences. This analysis set can be used by engineers interested in evaluating the syntactic knowledge of their encoders.",
+      "Finally, these findings suggest possible controlled experiments that could confirm whether there is a causal relation between the presence of the syntactic features we single out as interesting and model performance. Our results are purely correlational, and do not mark whether a particular construction is crucial for the acceptability of the sentence. Future experiments following ettinger2018assessing and kann2019verb can semi-automatically generate datasets manipulating, for example, length of long-distance dependencies, inflectional violations, or the presence of interrogatives, while controlling for factors like sentence length and word choice, in order determine the extent to which these features impact the quality of sentence embeddings."
+    ],
+    [
+      "We would like to thank Jason Phang and Thibault F\u00e9vry for sharing GPT and BERT model predictions on CoLA, and Alex Wang for feedback."
+    ],
+    [
+      "These are sentences with transitive or intransitive verbs appearing with their default syntax and argument structure. All arguments are noun phrases (DPs), and there are no modifiers or adjuncts on DPs or the VP.",
+      ". Included J\u0307ohn owns the book. (37) Park Square has a festive air. (131) *Herself likes Mary's mother. (456)",
+      ". Excluded \u1e02ill has eaten cake. I gave Joe a book."
+    ],
+    [
+      "These are sentences including the verb be used predicatively. Also, sentences where the object of the verb is itself a predicate, which applies to the subject. Not included are auxiliary uses of be or other predicate phrases that are not linked to a subject by a verb.",
+      ". Included J\u0307ohn is eager. (27) He turned into a frog. (150) To please John is easy. (315)",
+      ". Excluded \u1e6ahere is a bench to sit on. (309) John broke the geode open. The cake was eaten.",
+      "These sentences involve predication of a non-subject argument by another non-subject argument, without the presence of a copula. Some of these cases may be analyzed as small clauses. BIBREF35 ",
+      ". Included J\u0307ohn called the president a fool. (234) John considers himself proud of Mary. (464) They want them arrested. (856) the election of John president surprised me. (1001)",
+      "Modifiers that act as predicates of an argument. Resultatives express a resulting state of that argument, and depictives describe that argument during the matrix event. See BIBREF24 .",
+      ". Included \u1e58esultative T\u0323he table was wiped by John clean. (625) The horse kicked me black and blue. (898) . Depictive J\u0307ohn left singing. (971) In which car was the man seen? (398)",
+      ". Excluded \u1e22e turned into a frog. (150)"
+    ],
+    [
+      "Particles are lone prepositions associated with verbs. When they appear with transitive verbs they may immediately follow the verb or the object. Verb-particle pairs may have a non-compositional (idiomatic) meaning. See [pp. 69-70]carnie2013syntax and [pp. 16-17]kim2008syntax.",
+      ". Included T\u0323he argument was summed by the coach up. (615) Some sentences go on and on and on. (785) *He let the cats which were whining out. (71)",
+      "Adjuncts modifying verb phrases. Adjuncts are (usually) optional, and they do not change the category of the expression they modify. See BIBREF33 .",
+      ". Included \u1e56P-adjuncts, e.g. locative, temporal, instrumental, beneficiary \u1e44obody who hates to eat anything should work in a delicatessen. (121) Felicia kicked the ball off the bench. (127) . Adverbs \u1e40ary beautifully plays the violin. (40) John often meets Mary. (65) . Purpose VPs \u1e86e need another run to win. (769) .",
+      "0.5em. Excluded \u1e56P arguments S\u0323ue gave to Bill a book. (42) Everything you like is on the table. (736) . S-adjuncts J\u0307ohn lost the race, unfortunately.",
+      "These are adjuncts modifying noun phrases. Adjuncts are (usually) optional, and they do not change the category of the expression they modify. Single-word prenominal adjectives are excluded, as are relative clauses (this has another category). . Included \u1e56P-adjuncts T\u0323om's dog with one eye attacked Frank's with three legs. (676) They were going to meet sometime on Sunday, but the faculty didn't know when. (565) . Phrasal adjectives \u0226s a statesman, scarcely could he do anything worth mentioning. (292) . Verbal modifiers \u1e6ahe horse raced past the barn fell. (900)",
+      ". Excluded \u1e56renominal Adjectives \u0130t was the policeman met that several young students in the park last night. (227) . Relative Clauses NP arguments",
+      "These are adjuncts of VPs and NPs that specify a time or modify tense or aspect or frequency of an event. Adjuncts are (usually) optional, and they do not change the category of the expression they modify. . Included \u1e60hort adverbials (never, today, now, always) W\u0323hich hat did Mike quip that she never wore? (95) . PPs \u1e1eiona might be here by 5 o'clock. (426) . When \u0130 inquired when could we leave. (520)",
+      "These are adjuncts of VPs and NPs that specify a location of an event or a part of an event, or of an individual. Adjuncts are (usually) optional, and they do not change the category of the expression they modify. . Included \u1e60hort adverbials PPs \u1e6ahe bed was slept in. (298) *Anson demonized up the Khyber (479) Some people consider dogs in my neighborhood dangerous. (802) Mary saw the boy walking toward the railroad station. (73) . Where \u0130 found the place where we can relax. (307)",
+      ". Excluded \u013focative arguments S\u0323am gave the ball out of the basket. (129) Jessica loaded boxes on the wagon. (164) I went to Rome.",
+      "These are adjuncts of VPs and NPs not described by some other category (with the exception of (6-7)), i.e. not temporal, locative, or relative clauses. Adjuncts are (usually) optional, and they do not change the category of the expression they modify.",
+      ". Included \u1e02eneficiary I\u0323 know which book Jos\u00e9 didn't read for class, and which book Lilly did it for him. (58) . Instrument \u013fee saw the student with a telescope. (770) . Comitative J\u0307oan ate dinner with someone but I don't know who. (544) . VP adjuncts \u1e86hich article did Terry file papers without reading? (431) . Purpose \u1e86e need another run to win. (769)"
+    ],
+    [
+      "Oblique arguments of verbs are individual-denoting arguments (DPs or PPs) which act as the third argument of verb, i.e. not a subject or (direct) object. They may or may not be marked by a preposition. Obliques are only found in VPs that have three or more individual arguments. Arguments are selected for by the verb, and they are (generally) not optional, though in some cases they may be omitted where they are understood or implicitly existentially quantified over. See [p.40]kim2008syntax.",
+      ". Included \u1e56repositional S\u0323ue gave to Bill a book. (42) Mary has always preferred lemons to limes. (70) *Janet broke Bill on the finger. (141) . Benefactives \u1e40artha carved the baby a toy out of wood. (139) . Double object \u1e60usan told her a story. (875) Locative arguments \u0226nn may spend her vacation in Italy. (289) . High-arity Passives M\u0323ary was given by John the book. (626)",
+      ". Excluded \u1e44on-DP arguments \u1e86e want John to win (28) . 3rd argments where not all three arguments are DPs \u1e86e want John to win (28)",
+      "Prepositional Phrase arguments of VPs are individual-denoting arguments of a verb which are marked by a proposition. They may or may not be obliques. Arguments are selected for by the verb, and they are (generally) not optional, though in some cases they may be omitted where they are understood or implicitly existentially quantified over.",
+      ". Included \u1e0aative S\u0323ue gave to Bill a book. (42) . Conative (at) C\u0323arla slid at the book. (179) . Idiosyncratic prepositional verbs \u0130 wonder who to place my trust in. (711) She voted for herself. (743) . Locative J\u0307ohn was found in the office. (283) . PP predicates \u0116verything you like is on the table. (736)",
+      ". Excluded \u1e56P adjuncts Particles Arguments of deverbal expressions t\u0323he putter of books left. (892) . By-phrase \u1e6aed was bitten by the spider. (613)",
+      "Prepositional Phrase arguments of NPs or APs are individual-denoting arguments of a noun or adjective which are marked by a proposition. Arguments are selected for by the head, and they are (generally) not optional, though in some cases they may be omitted where they are understood or implicitly existentially quantified over.",
+      ". Included \u1e58elational adjectives \u1e40any people were fond of Pat. (936) *I was already aware of fact. (824) . Relational nouns \u1e86e admired the pictures of us in the album. (759) They found the book on the atom. (780) . Arguments of deverbal nouns t\u0323he putter of books left. (892)",
+      "Prepositional arguments introduced with by. Usually, this is the (semantic) subject of a passive verb, but in rare cases it may be the subject of a nominalized verb. Arguments are usually selected for by the head, and they are generally not optional. In this case, the argument introduced with by is semantically selected for by the verb, but it is syntactically optional. See [p.190]adger2003core and []collins2005smuggling.",
+      ". Included \u1e56assives \u1e6aed was bitten by the spider. (613) . Subjects of deverbal nouns \u1e6bhe attempt by John to leave surprised me. (1003)",
+      "Expletives, or \u201cdummy\u201d arguments, are semantically inert arguments. The most common expletives in English are it and there, although not all occurrences of these items are expletives. Arguments are usually selected for by the head, and they are generally not optional. In this case, the expletive occupies a syntactic argument slot, but it is not semantically selected by the verb, and there is often a syntactic variation without the expletive. See [p.170-172]adger2003core and [p.82-83]kim2008syntax.",
+      ". Included \u1e6ahere\u2014inserted, existential T\u0323here loved Sandy. (939) There is a nurse available. (466) . It\u2014cleft, inserted \u0130t was a brand new car that he bought. (347) It bothers me that John coughs. (314) It is nice to go abroad. (47) . Environmental it K\u0307erry remarked it was late. (821) Poor Bill, it had started to rain and he had no umbrella. (116) You've really lived it up. (160)",
+      ". Excluded J\u0307ohn counted on Bill to get there on time. (996) I bought it to read. (1026)"
+    ],
+    [
+      "These are verbs with 3 or more arguments of any kind. Arity refers to the number of arguments that a head (or function) selects for. Arguments are usually selected for by the head, and they are generally not optional. They may be DPs, PPs, CPs, VPs, APs or other categories.",
+      ". Included \u1e0aitransitive [\u0323Sue] gave [to Bill] [a book]. (42) [Martha] carved [the baby] [a toy] out of wood. (139) . VP arguments [\u0323We] believed [John] [to be a fountain in the park]. (274) [We] made [them] [be rude]. (260) . Particles He] let [the cats which were whining] [out]. (71) . Passives with by-phrase [\u0323A good friend] is remained [to me] [by him]. (237) . Expletives [\u0323We] expect [there] [to will rain]. (282) [There] is [a seat] [available]. (934) [It] bothers [me] [that he is here]. (1009) . Small clause John] considers [Bill] [silly]. (1039)",
+      ". Excluded \u1e58esults, depictives John] broke [the geode] [open].",
+      "These are VPs where a canonical argument of the verb is missing. This can be difficult to determine, but in many cases the missing argument is understood with existential quantification or generically, or contextually salient. See [p.106-109]sportiche2013introduction.",
+      ". Included \u1e40iddle voice/causative inchoative T\u0323he problem perceives easily. (66) . Passive \u1e6ahe car was driven. (296) . Null complement anaphora J\u0307ean persuaded Robert. (380) Nobody told Susan. (883) . Dropped argument K\u0323im put in the box. (253) The guests dined. (835) I wrote to Bill. (1030) . Transitive adjective J\u0307ohn is eager. (27) We pulled free. (144) . Transitive noun \u0130 sensed his eagerness. (155) . Expletive insertion I\u0323t loved Sandy. (949)",
+      ". Excluded \u1e6aed was bitten by the spider. (613)",
+      "These are VPs in which a non-canonical argument of the verb has been added. These cases are clearer to identify where the additional argument is a DP. In general, PPs which mark locations, times, beneficiaries, or purposes should be analyzed as adjuncts, while PPs marking causes can be considered arguments. See []pylkkanen2008introducing.",
+      ". Included \u0116xtra argument L\u0323inda winked her lip. (202) Sharon fainted from hunger. (204) I shaved myself. (526) . Causative I\u0323 squeaked the door. (207) . Expletive insertion \u1e6ahere is a monster in Loch Ness. (928) It annoys people that dogs bark. (943) . Benefactive \u1e40artha carved the baby a toy out of wood. (139)",
+      "The passive voice is marked by the demotion of the subject (either complete omission or to a by-phrase) and the verb appearing as a past participle. In the stereotypical construction there is an auxiliary be verb, though this may be absent. See [p.175-190]kim2008syntax, collins2005smuggling, and [p.311-333]sag2003syntactic.",
+      ". Included V\u0307erbs \u1e6ahe earth was believed to be round. (157) . Psuedopassive \u1e6ahe bed was slept in. (298) . Past participle adjuncts \u1e6ahe horse raced past the barn fell. (900)"
+    ],
+    [
+      "The imperative mood is marked by the absence of the a subject and the bare form of the verb, and expresses a command, request, or other directive speech act.",
+      ". Included W\u0323ash you! (224) Somebody just left - guess who. (528)"
+    ],
+    [
+      "These are cases in which a reflexive (non-possessive) pronoun, usually bound by an antecedent. See [p.163-186]sportiche2013introduction and [p.203-226]sag2003syntactic.",
+      ". Included O\u0323urselves like ourselves. (742) Which pictures of himself does John like? (386)",
+      "These are cases in which a non-reflexive pronoun appears along with its antecedent. This includes donkey anaphora, quantificational binding, and bound possessives, among other bound pronouns. See [p.163-186]sportiche2013introduction and [p.203-226]sag2003syntactic.",
+      ". Included \u1e02ound possessor \u1e6ahe children admire their mother. (382) . Quantificational binding \u0116verybody gets on well with a certain relative, but often only his therapist knows which one. (562) . Bound pronoun W\u0323e gave us to the cause. (747)"
+    ],
+    [
+      "These are sentences in which the matrix clause is interrogative (either a wh- or polar question). See [pp.282-213]adger2003core, [pp.193-222]kim2008syntax, and [p.315-350]carnie2013syntax.",
+      ". Included \u1e86h-question \u1e86ho always drinks milk? (684) . Polar question \u1e0aid Athena help us? (486)",
+      "These are embedded interrogative clauses appearing as arguments of verbs, nouns, and adjectives. Not including relative clauses and free relatives. See [p.297]adger2003core.",
+      ". Included U\u0307nder VP \u0130 forgot how good beer tastes. (235) *What did you ask who saw? (508) . Under NP \u1e6ahat is the reason why he resigned. (313) . Under AP \u1e6ahey claimed they had settled on something, but it wasn't clear what they had settled on. (529) . Free relative \u1e86hat the water did to the bottle was fill it. (33)",
+      ". Excluded Relative clauses, free relatives",
+      "These are phrasal Wh-phrases, in which the wh-word moves along with other expressions, including prepositions (pied-piping) or nouns in the case of determiner wh-words such as how many and which.",
+      ". Included \u1e56ied-piping T\u0323he ship sank, but I don't know with what. (541) . Other phrasal wh-phrases \u0130 know which book Mag read, and which book Bob read my report that you hadn't. (61) How sane is Peter? (88)",
+      "Relative clauses are noun modifiers appearing with a relativizer (either that or a wh-word) and an associated gap. See [p.223-244]kim2008syntax.",
+      ". Included \u1e6ahough he may hate those that criticize Carter, it doesn't matter. (332) *The book what inspired them was very long. (686) Everything you like is on the table. (736)",
+      ". Excluded T\u0323he more you would want, the less you would eat. (6)",
+      "This is wh-movement out of an extraction island, or near-island. Islands include, for example, complex NPs, adjuncts, embedded questions, coordination. A near-island is an extraction that closely resembles an island violation, such as extraction out of an embedded clause, or across-the-board extraction. See [pp.323-333]adger2003core and [pp.332-334]carnie2013syntax.",
+      ". Included \u0116mbedded question *What did you ask who Medea gave? (493) . Adjunct W\u0323hat did you leave before they did? (598) . Parasitic gaps \u1e86hich topic did you choose without getting his approval? (311) . Complex NP \u1e86ho did you get an accurate description of? (483)"
+    ],
+    [
+      "These are complement clauses acting as the (syntactic) subject of verbs. See [pp.90-91]kim2008syntax.",
+      ". Included \u1e6ahat dogs bark annoys people. (942) The socks are ready for for you to put on to be planned. (112)",
+      ". Excluded \u0116xpletive insertion \u0130t bothers me that John coughs. (314)",
+      "These are complement clauses acting as (non-subject) arguments of verbs. See [pp.84-90]kim2008syntax.",
+      ". Included \u0130 can't believe Fred won't, either. (50) I saw that gas can explode. (222) It bothers me that John coughs. (314) Clefts \u0130t was a brand new car that he bought. (347)",
+      "These are complement clauses acting as an argument of a noun or adjective. See [pp.91-94]kim2008syntax.",
+      ". Included U\u0307nder NP \u1e0ao you believe the claim that somebody was looking for something? (99) . Under AP T\u0323he children are fond that they have ice cream. (842)",
+      "These are complement clauses with a non-finite matrix verb. Often, the complementizer is for, or there is no complementizer. See [pp.252-253,256-260]adger2003core.",
+      ". Included \u1e1eor complementizer \u0130 would prefer for John to leave. (990) . No Complementizer \u1e40ary intended John to go abroad. (48) . Ungrammatical \u1e22eidi thinks that Andy to eat salmon flavored candy bars. (363) . V-ing \u022enly Churchill remembered Churchill giving the Blood, Sweat and Tears speech. (469)",
+      "These are complement clauses with no overt complementizer.",
+      ". Included \u010aomplement clause \u0130'm sure we even got these tickets! (325) He announced he would marry the woman he loved most, but none of his relatives could figure out who. (572) . Relative clause \u1e6ahe Peter we all like was at the party (484)",
+      "These are sentences with three or nested verbs, where VP is not an aux or modal, i.e. with the following syntax: [S ...[ VP ...[ VP ...[ VP ...] ...] ...] ...]",
+      ". Included \u0116mbedded VPs \u1e40ax seemed to be trying to force Ted to leave the room, and Walt, Ira. (657) . Embedded clauses \u0130 threw away a book that Sandy thought we had read. (713)"
+    ],
+    [
+      "Any occurrence of negation in a sentence, including sentential negation, negative quantifiers, and negative adverbs.",
+      ". Included \u1e60entential \u0130 can't remember the name of somebody who had misgivings. (123) . Quantifier \u1e44o writer, and no playwright, meets in Vienna. (124) . Adverb \u1e6ahey realised that never had Sir Thomas been so offended. (409)",
+      "Modal verbs (may, might, can, could, will, would, shall, should, must). See [pp.152-155]kim2008syntax.",
+      ". Included J\u0307ohn can kick the ball. (280) As a statesman, scarcely could he do anything worth mentioning. (292)",
+      ". Excluded \u1e56seudo-modals \u1e60andy was trying to work out which students would be able to solve a certain problem. (600)",
+      "Auxiliary verbs (e.g. be, have, do). See [pp.149-174]kim2008syntax.",
+      ". Included \u1e6ahey love to play golf, but I do not. (290) The car was driven. (296) he had spent five thousand dollars. (301)",
+      ". Excluded \u1e56seudo-auxiliaries S\u0323ally asked if somebody was going to fail math class, but I can't remember who. (589) The cat got bitten. (926)",
+      "These are predicates acting as near-auxiliary (e.g. get-passive) or near-modals (e.g. willing)",
+      ". Included \u1e44ear-auxiliaries M\u0323ary came to be introduced by the bartender and I also came to be. (55) *Sally asked if somebody was going to fail math class, but I can't remember who. (589) The cat got bitten. (926) . Near-modals \u010alinton is anxious to find out which budget dilemmas Panetta would be willing to tackle in a certain way, but he won't say in which. (593) Sandy was trying to work out which students would be able to solve a certain problem. (600)"
+    ],
+    [
+      "These are VPs with control verbs, where one argument is a non-finite to-VP without a covert subject co-indexed with an argument of the matrix verb. See [pp.252,266-291]adger2003core, [pp.203-222]sportiche2013introduction, and [pp.125-148]kim2008syntax.",
+      ". Included \u0130ntransitive subject control I\u0323t tries to leave the country. (275) . Transitive subject control J\u0307ohn promised Bill to leave. (977) . Transitive object control \u0130 want her to dance. (379) John considers Bill to be silly. (1040)",
+      ". Excluded V\u0307P args of NP/AP \u1e6ahis violin is difficult to play sonatas on. (114) . Purpose \u1e6ahere is a bench to sit on. (309) . Subject VPs \u1e6ao please John is easy. (315) . Argument present participles \u1e40edea denied poisoning the phoenix. (490) . Raising \u0226nson believed himself to be handsome. (499)",
+      "These are VPs with raising predicates, where one argument is a non-finite to-VP without a covert subject co-indexed with an argument of the matrix verb. Unlike control verbs, the coindexed argument is not a semantic argument of the raising predicate. See [pp.260-266]adger2003core, [pp.203-222]sportiche2013introduction, and [pp.125-148]kim2008syntax.",
+      ". Included \u1e60ubject raising U\u0307nder the bed seems to be a fun place to hide. (277) . Object raising \u0226nson believed himself to be handsome. (499) . Raising adjective J\u0307ohn is likely to leave. (370)",
+      "These are embedded infinitival VPs containing a (non-subject) gap that is filled by an argument in the upper clause. Examples are purpose-VPs and tough-movement. See [pp.246-252]kim2008syntax.",
+      ". Included \u1e6aough-movement D\u0323rowning cats, which is against the law, are hard to rescue. (79) . Infinitival relatives F\u0323ed knows which politician her to vote for. (302) . Purpose \u1e6bhe one with a red cover takes a very long time to read. (352) . Other non-finite VPs with extraction \u0226s a statesman, scarcely could he do anything worth mentioning. (292)",
+      "These are non-finite VP arguments of nouns and adjectives.",
+      ". Included \u1e58aising adjectives J\u0307ohn is likely to leave. (370) . Control adjectives \u1e6ahe administration has issued a statement that it is willing to meet a student group, but I'm not sure which one. (604) . Control nouns \u0226s a teacher, you have to deal simultaneously with the administration's pressure on you to succeed, and the children's to be a nice guy. (673) . Purpose VPs \u1e6bhere is nothing to do. (983)",
+      "These are miscellaneous non-finite VPs.",
+      ". Included \u0130 saw that gas can explode. (222) Gerunds/Present participles S\u0323tudents studying English reads Conrad's Heart of Darkness while at university. (262) Knowing the country well, he took a short cut. (411) John became deadly afraid of flying. (440) . Subject VPs \u1e6ao please John is easy. (315) . Nominalized VPs W\u0323hat Mary did Bill was give a book. (473)",
+      ". Excluded \u1e6bo-VPs acting as complements or modifiers of verbs, nouns, or adjectives"
+    ],
+    [
+      "These are nouns and adjectives derived from verbs.",
+      ". Included \u1e0aeverbal nouns t\u0323he election of John president surprised me. (1001) . \u201cLight\u201d verbs \u1e6ahe birds give the worm a tug. (815) . Gerunds \u0130f only Superman would stop flying planes! (773) . Event-wh \u1e86hat the water did to the bottle was fill it. (33) . Deverbal adjectives \u1e22is or her least known work. (95)",
+      "Relational nouns are NPs with an obligatory (or existentially closed) argument. A particular relation holds between the members of the extension of NP and the argument. The argument must be a DP possessor or a PP. See [pp.82-83]kim2008syntax.",
+      ". Included \u1e44ouns with of-arguments J\u0307ohn has a fear of dogs. (353) . Nouns with other PP-arguments \u1e22enri wants to buy which books about cooking? (442) . Measure nouns \u0130 bought three quarts of wine and two of Clorox. (667) . Possessed relational nouns J\u0323ohn's mother likes himself. (484)",
+      ". Excluded \u1e44ouns with PP modifiers \u1e60ome people consider dogs in my neighborhood dangerous. (802)",
+      "Transitive (non-relational) nouns take a VP or CP argument. See [pp.82-83]kim2008syntax.",
+      ". Included V\u0307P argument \u1e6bhe attempt by John to leave surprised me. (1003) . CP argument W\u0323hich report that John was incompetent did he submit? (69) . QP argument \u1e6ahat is the reason why he resigned. (313)",
+      "These are complex NPs, including coordinated nouns and nouns with modifiers (excluding prenominal adjectives).",
+      ". Included \u1e40odified NPs T\u0323he madrigals which Henry plays the lute and sings sound lousy. (84) John bought a book on the table. (233) . NPs with coordination T\u0323he soundly and furry cat slept. (871) The love of my life and mother of my children would never do such a thing. (806)",
+      "Noun-noun compounds are NPs consisting of two constituent nouns.",
+      ". Included \u0130t was the peasant girl who got it. (320) A felon was elected to the city council. (938)",
+      "These are adjectives that take an obligatory (or existentially closed) argument. A particular relation holds between the members of the extension of the modified NP and the argument. The argument must be a DP or PP. See [pp.80-82]kim2008syntax.",
+      ". Included \u022ef-arguments \u1e6ahe chickens seem fond of the farmer. (254) . Other PP arguments \u1e6ahis week will be a difficult one for us. (241) John made Bill mad at himself. (1035)",
+      "A transitive (non-relational) adjective. I.e. an adjectives that takes a VP or CP argument. See [pp.80-82]kim2008syntax.",
+      ". Included V\u0307P argument J\u0307ohn is likely to leave. (370) . CP argument J\u0307ohn is aware of it that Bill is here. (1013) . QP argument \u1e6ahe administration has issued a statement that it is willing to meet a student group, but I'm not sure which one. (604)"
+    ],
+    [
+      "These are expressions with non-canonical word order. See, for example, [p.76]sportiche2013introduction.",
+      ". Includes \u1e56article shift M\u0323ickey looked up it. (24) . Preposed modifiers \u022eut of the box jumped a little white rabbit. (215) *Because she's so pleasant, as for Mary I really like her. (331) . Quantifier float \u1e6ahe men will all leave. (43) . Preposed argument \u1e86ith no job would John be happy. (333) . Relative clause extraposition \u1e86hich book's, author did you meet who you liked? (731) . Misplaced phrases \u1e40ary was given by John the book. (626)",
+      "This includes topicalization and focus constructions. See [pp.258-269]kim2008syntax and [pp.68-75]sportiche2013introduction.",
+      ". Included \u1e6aopicalization \u1e40ost elections are quickly forgotten, but the election of 2000, everyone will remember for a long time. (807) . Clefts \u0130t was a brand new car that he bought. (347) . Pseudo-clefts \u1e86hat John promised is to be gentle. (441)",
+      ". Excluded \u1e6ahere-insertion Passive",
+      "These are parentheticals or fragmentary expressions. . Included \u1e56arenthetical \u1e40ary asked me if, in St. Louis, John could rent a house cheap. (704) . Fragments \u1e6ahe soup cooks, thickens. (448) . Tag question \u0120eorge has spent a lot of money, hasn't he? (291)",
+      "Coordinations and disjunctions are expressions joined with and, but, or, etc. See [pp.61-68]sportiche2013introduction.",
+      ". Included \u1e0aP coordination \u1e0aave, Dan, Erin, Jaime, and Alina left. (341) . Right Node Raising K\u0307im gave a dollar to Bobbie and a dime to Jean. (435) . Clausal coordination \u1e60he talked to Harry, but I don't know who else. (575) . Or, nor N\u0323o writer, nor any playwright, meets in Vienna. (125) . Pseudo-coordination \u0130 want to try and buy some whiskey. (432) . Juxtaposed clauses \u013fights go out at ten. There will be no talking afterwards. (779)",
+      "This includes subordinate clauses, especially with subordinating conjunctions, and conditionals.",
+      ". Included \u010aonditional \u0130f I can, I will work on it. (56) . Subordinate clause W\u0323hat did you leave before they did? (598) *Because Steve's of a spider's eye had been stolen, I borrowed Fred's diagram of a snake's fang. (677) . Correlative A\u0323s you eat the most, you want the least. (5)",
+      "This includes VP or NP ellipsis, or anaphora standing for VPs or NPs (not DPs). See [pp.55-61]sportiche2013introduction.",
+      ". Included V\u0307P Ellipsis \u0130f I can, I will work on it. (56) Mary likes to tour art galleries, but Bill hates to. (287) . VP Anaphor \u0130 saw Bill while you did so Mary. (472) . NP Ellipsis \u1e6aom's dog with one eye attacked Fred's. (679) . NP anaphor \u1e6bhe one with a red cover takes a very long time to read. (352) . Sluicing \u1e40ost columnists claim that a senior White House official has been briefing them, and the newspaper today reveals which one. (557) . Gapping \u1e02ill ate the peaches, but Harry the grapes. (646)",
+      "These are adjuncts modifying sentences, sentence-level adverbs, subordinate clauses.",
+      ". Included \u1e60entence-level adverbs \u1e60uddenly, there arrived two inspectors from the INS. (447) . Subordinate clauses \u1e6ahe storm arrived while we ate lunch. (852)"
+    ],
+    [
+      "These are quantificational DPs, i.e. the determiner is a quantifier.",
+      ". Included Q\u0307uantifiers E\u0323very student, and he wears socks, is a swinger. (118) We need another run to win. (769) . Partitive N\u0323either of students failed. (265)",
+      "These are quantifiers that take PP arguments, and measure nouns. See [pp.109-118]kim2008syntax.",
+      ". Included Q\u0307uantifiers with PP arguments N\u0323either of students failed. (265) . Numerals \u022ene of Korea's most famous poets wrote these lines. (294) . Measure nouns \u0130 bought three quarts of wine and two of Clorox. (667)",
+      "These are negative polarity items (any, ever, etc.) and free choice items (any). See kadmon1993any.",
+      ". Included \u1e44PI \u0116verybody around here who ever buys anything on credit talks in his sleep. (122) I didn't have a red cent. (350) . FCI \u0226ny owl hunts mice. (387)",
+      "These are comparative constructions. See BIBREF22 .",
+      ". Included \u010aorrelative \u1e6ahe angrier Mary got, the more she looked at pictures. (9) They may grow as high as bamboo. (337) I know you like the back of my hand. (775)"
+    ],
+    [
+      "These are sentences that include a semantic violation, including type mismatches, violations of selectional restrictions, polarity violations, definiteness violations.",
+      ". Included V\u0307olation of selectional restrictions m\u0323any information was provided. (218) *It tries to leave the country. (275) . Aspectual violations J\u0323ohn is tall on several occasions. (540) . Definiteness violations I\u0323t is the problem that he is here. (1018) . Polarity violations \u0226ny man didn't eat dinner. (388)",
+      "These are sentences that include a violation in inflectional morphology, including tense-aspect marking, or agreement.",
+      ". Included \u010aase U\u0323s love they. (46) . Agreement S\u0323tudents studying English reads Conrad's Heart of Darkness while at university. (262) . Gender S\u0323ally kissed himself. (339) . Tense/Aspect K\u0323im alienated cats and beating his dog. (429)",
+      "These are sentences with a violation that can be identified with the presence or absence of a single word.",
+      ". Included \u1e40issing word J\u0323ohn put under the bathtub. (247) *I noticed the. (788) . Extra word E\u0323veryone hopes everyone to sleep. (467) *He can will go (510)"
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1512/instruction.md b/qasper-1512/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..944132ce1b2271b03c3c607ec3cbc89aa8539a94
--- /dev/null
+++ b/qasper-1512/instruction.md
@@ -0,0 +1,92 @@
+Name of Paper: The Effect of Context on Metaphor Paraphrase Aptness Judgments
+
+Question: What document context was added?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Annotating Metaphor-Paraphrase Pairs in Contexts",
+    "Annotation Results",
+    "Modelling Paraphrase Judgments in Context",
+    "MPAT Modelling Results",
+    "Related Cognitive Work on Metaphor Aptness",
+    "Conclusions and Future Work"
+  ],
+  "paragraphs": [
+    [
+      "A metaphor is a way of forcing the normal boundaries of a word's meaning in order to better express an experience, a concept or an idea. To a native speaker's ear some metaphors sound more conventional (like the usage of the words ear and sound in this sentence), others more original. This is not the only dimension along which to judge a metaphor. One of the most important qualities of a metaphor is its appropriateness, its aptness: how good is a metaphor for conveying a given experience or concept. While a metaphor's degree of conventionality can be measured through probabilistic methods, like language models, it is harder to represent its aptness. BIBREF0 define aptness as \u201cthe extent to which a comparison captures important features of the topic\".",
+      "It is possible to express an opinion about some metaphors' and similes' aptness (at least to a degree) without previously knowing what they are trying to convey, or the context in which they appear. For example, we don't need a particular context or frame of reference to construe the simile She was screaming like a turtle as strange, and less apt for expressing the quality of a scream than She was screaming like a banshee. In this case, the reason why the simile in the second sentence works best is intuitive. A salient characteristic of a banshee is a powerful scream. Turtles are not known for screaming, and so it is harder to define the quality of a scream through such a comparison, except as a form of irony. Other cases are more complicated to decide upon. The simile crying like a fire in the sun (It's All Over Now, Baby Blue, Bob Dylan) is powerfully apt for many readers, but simply odd for others. Fire and sun are not known to cry in any way. But at the same time the simile can capture the association we draw between something strong and intense in other senses - vision, touch, etc. - and a loud cry.",
+      "Nonetheless, most metaphors and similes need some kind of context, or external reference point to be interpreted. The sentence The old lady had a heart of stone is apt if the old lady is cruel or indifferent, but it is inappropriate as a description of a situation in which the old lady is kind and caring. We assume that, to an average reader's sensibility, the sentence models the situation in a satisfactory way only in the first case.",
+      "This is the approach to metaphor aptness that we assume in this paper. Following BIBREF3 , we treat a metaphor as apt in relation to a literal expression that it paraphrases. If the metaphor is judged to be a good paraphrase, then it closely expresses the core information of the literal sentence through its metaphorical shift. We refer to the prediction of readers' judgments on the aptness candidates for the literal paraphrase of a metaphor as the metaphor paraphrase aptness task (MPAT). BIBREF3 address the MPAT by using Amazon Mechanical Turk (AMT) to obtain crowd sourced annotations of metaphor-paraphrase candidate pairs. They train a composite Deep Neural Network (DNN) on a portion of their annotated corpus, and test it on the remaining part. Testing involves using the DNN as a binary classifier on paraphrase candidates. They derive predictions of gradient paraphrase aptness for their test set, and assess them by Pearson coefficient correlation to the mean judgments of their crowd sourced annotation of this set. Both training and testing are done independently of any document context for the metaphorical sentence and its literal paraphrase candidates.",
+      "In this paper we study the role of context on readers' judgments concerning the aptness of metaphor paraphrase candidates. We look at the accuracy of BIBREF3 's DNN when trained and tested on contextually embedded metaphor-paraphrase pairs for the MPAT. In Section SECREF2 we describe an AMT experiment in which annotators judge metaphors and paraphrases embodied in small document contexts, and in Section SECREF3 we discuss the results of this experiment. In Section SECREF4 we describe our MPAT modeling experiment, and in Section SECREF5 we discuss the results of this experiment. Section SECREF6 briefly surveys some related work. In Section SECREF7 we draw conclusions from our study, and we indicate directions for future work in this area."
+    ],
+    [
+      " BIBREF3 have recently produced a dataset of paraphrases containing metaphors designed to allow both supervised binary classification and gradient ranking. This dataset contains several pairs of sentences, where in each pair the first sentence contains a metaphor, and the second is a literal paraphrase candidate.",
+      "This corpus was constructed with a view to representing a large variety of syntactic structures and semantic phenomena in metaphorical sentences. Many of these structures and phenomena do not occur as metaphorical expressions, with any frequency, in natural text and were therefore introduced through hand crafted examples.",
+      "Each pair of sentences in the corpus has been rated by AMT annotators for paraphrase aptness on a scale of 1-4, with 4 being the highest degree of aptness. In BIBREF3 's dataset, sentences come in groups of five, where the first element is the \u201creference element\" with a metaphorical expression, and the remaining four sentences are \u201ccandidates\" that stand in a degree of paraphrasehood to the reference. Here is an example of a metaphor-paraphrase candidate pair.",
+      "The average AMT paraphrase score for this pair is 4.0, indicating a high degree of aptness.",
+      "We extracted 200 sentence pairs from BIBREF3 's dataset and provided each pair with a document context consisting of a preceding and a following sentence, as in the following example.",
+      "One of the authors constructed most of these contexts by hand. In some cases, it was possible to locate the original metaphor in an existing document. This was the case for",
+      "For these cases, a variant of the existing context was added to both the metaphorical and the literal sentences. We introduced small modifications to keep the context short and clear, and to avoid copyright issues. We lightly modified the contexts of metaphors extracted from corpora when the original context was too long, ie. when the contextual sentences of the selected metaphor were longer than the maximum length we specified for our corpus. In such cases we reduced the length of the sentence, while sustaining its meaning.",
+      "The context was designed to sound as natural as possible. Since the same context is used for metaphors and their literal candidate paraphrases, we tried to design short contexts that make sense for both the figurative and the literal sentences, even when the pair had been judged as non-paraphrases. We kept the context as neutral as possible in order to avoid a distortion in crowd source ratings.",
+      "For example, in the following pair of sentences, the literal sentence is not a good paraphrase of the figurative one (a simile).",
+      "We opted for a context that is natural for both sentences.",
+      "We sought to avoid, whenever possible, an incongruous context for one of the sentences that could influence our annotators' ratings.",
+      "We collected a sub-corpus of 200 contextually embedded pairs of sentences. We tried to keep our data as balanced as possible, drawing from all four rating classes of paraphrase aptness ratings (between 1 to 4) that BIBREF3 obtained. We selected 44 pairs of 1 ratings, 51 pairs of 2, 43 pairs of 3 and 62 pairs of 4.",
+      "We then used AMT crowd sourcing to rate the contextualized paraphrase pairs, so that we could observe the effect of document context on assessments of metaphor paraphrase aptness.",
+      "To test the reproducibility of BIBREF3 's ratings, we launched a pilot study for 10 original non-contextually embedded pairs, selected from all four classes of aptness. We observed that the annotators provided mean ratings very similar to those reported in BIBREF3 . The Pearson coefficent correlation between the mean judgments of our out-of-context pilot annotations and BIBREF3 's annotations for the same pair was over 0.9. We then conducted an AMT annotation task for the 200 contextualised pairs. On average, 20 different annotators rated each pair. We considered as \u201crogue\" those annotators who rated the large majority of pairs with very high or very low scores, and those who responded inconsistently to two \u201ctrap\" pairs. After filtering out the rogues, we had an average of 14 annotators per pair."
+    ],
+    [
+      "We found a Pearson correlation of 0.81 between the in-context and out-of-context mean human paraphrase ratings for our two corpora. This correlation is virtually identical to the one that BIBREF5 report for mean acceptability ratings of out-of-context to in-context sentences in their crowd source experiment. It is interesting that a relatively high level of ranking correspondence should occur in mean judgments for sentences presented out of and within document contexts, for two entirely distinct tasks.",
+      "Our main result concerns the effect of context on mean paraphrase judgment. We observed that it tends to flatten aptness ratings towards the center of the rating scale. 71.1% of the metaphors that had been considered highly apt (average rounded score of 4) in the context-less pairs received a more moderate judgment (average rounded score of 3), but the reverse movement was rare. Only 5% of pairs rated 3 out of context (2 pairs) were boosted to a mean rating of 4 in context. At the other end of the scale, 68.2% of the metaphors judged at 1 category of aptness out of context were raised to a mean of 2 in context, while only the 3.9% of pairs rated 2 out of context were lowered to 1 in context.",
+      "Ratings at the middle of the scale - 2 (defined as semantically related non-paraphrases) and 3 (imperfect or loose paraphrases) - remained largely stable, with little movement in either direction. 9.8% of pairs rated 2 were re-ranked as 3 when presented in context, and 10% of pairs ranked at 3 changed to 2. The division between 2 and 3 separates paraphrases from non-paraphrases. Our results suggest that this binary rating of paraphrase aptness was not strongly affected by context. Context operates at the extremes of our scale, raising low aptness ratings and lowering high aptness ratings. This effect is clearly indicated in the regression chart in Fig FIGREF15 .",
+      "This effect of context on human ratings is very similar to the one reported in BIBREF5 . They find that sentences rated as ill formed out of context are improved when they are presented in their document contexts. However the mean ratings for sentences judged to be highly acceptable out of context declined when assessed in context. BIBREF5 's linear regression chart for the correlation between out-of-context and in-context acceptability judgments looks remarkably like our Fig FIGREF15 . There is, then, a striking parallel in the compression pattern that context appears to exert on human judgments for two entirely different linguistic properties.",
+      "This pattern requires an explanation. BIBREF5 suggest that adding context causes speakers to focus on broader semantic and pragmatic issues of discourse coherence, rather than simply judging syntactic well formedness (measured as naturalness) when a sentence is considered in isolation. On this view, compression of rating results from a pressure to construct a plausible interpretation for any sentence within its context.",
+      "If this is the case, an analogous process may generate the same compression effect for metaphor aptness assessment of sentence pairs in context. Speakers may attempt to achieve broader discourse coherence when assessing the metaphor-paraphrase aptness relation in a document context. Out of context they focus more narrowly on the semantic relations between a metaphorical sentence and its paraphrase candidate. Therefore, this relation is at the centre of a speaker's concern, and it receives more fine-grained assessment when considered out of context than in context. This issue clearly requires further research."
+    ],
+    [
+      "We use the DNN model described in BIBREF3 to predict aptness judgments for in-context paraphrase pairs. It has three main components:",
+      "The encoder for each pair of sentences taken as input is composed of two parallel \"Atrous\" Convolutional Neural Networks (CNNs) and LSTM RNNs, feeding two sequenced fully connected layers.",
+      "The encoder is preloaded with the lexical embeddings from Word2vec BIBREF6 . The sequences of word embeddings that we use as input provides the model with dense word-level information, while the model tries to generalize over these embedding patterns.",
+      "The combination of a CNN and an LSTM allows us to capture both long-distance syntactic and semantic relations, best identified by a CNN, and the sequential nature of the input, most efficiently identified by an LSTM. Several existing studies, cited in BIBREF4 , demonstrate the advantages of combining CNNs and LSTMs to process texts.",
+      "The model produces a single classifier value between 0 and 1. We transform this score into a binary output of 0 or 1 by applying a threshold of 0.5 for assigning 1.",
+      "The architecture of the model is given in Fig FIGREF19 .",
+      "We use the same general protocol as BIBREF3 for training with supervised learning, and testing the model.",
+      "Using BIBREF3 's out-of- context metaphor dataset and our contextualized extension of this set, we apply four variants of the training and testing protocol.",
+      "When we train or test the model on the out-of-context dataset, we use BIBREF3 's original annotated corpus of 800 metaphor-paraphrase pairs. The in-context dataset contains 200 annotated pairs."
+    ],
+    [
+      "We use the model both to predict binary classification of a metaphor paraphrase candidate, and to generate gradient aptness ratings on the 4 category scale (see BIBREF3 for details). A positive binary classification is accurate if it is INLINEFORM0 a 2.5 mean human rating. The gradient predictions are derived from the softmax distribution of the output layer of the model. The results of our modelling experiments are given in Table TABREF24 .",
+      "The main result that we obtain from these experiments is that the model learns binary classification to a reasonable extent on the in-context dataset, both when trained on the same kind of data (in-context pairs), and when trained on BIBREF3 's original dataset (out-of-context pairs). However, the model does not perform well in predicting gradient in-context judgments when trained on in-context pairs. It improves slightly for this task when trained on out-of-context pairs.",
+      "By contrast, it does well in predicting both binary and gradient ratings when trained and tested on out-of-context data sets.",
+      " BIBREF5 also note a decline in Pearson correlation for their DNN models on the task of predicting human in-context acceptability judgments, but it is less drastic. They attribute this decline to the fact that the compression effect renders the gradient judgments less separable, and so harder to predict. A similar, but more pronounced version of this effect may account for the difficulty that our model encounters in predicting gradient in-context ratings. The binary classifier achieves greater success for these cases because its training tends to polarise the data in one direction or the other.",
+      "We also observe that the best combination seems to consist in training our model on the original out-of-context dataset and testing it on the in-context pairs. In this configuration we reach an F-score (0.72) only slightly lower than the one reported in BIBREF3 (0.74), and we record the highest Pearson correlation, 0.3 (which is still not strong, compared to BIBREF3 's best run, 0.75). This result may partly be an artifact of the the larger amount of training data provided by the out-of-context pairs.",
+      "We can use this variant (out-of-context training and in-context testing) to perform a fine-grained comparison of the model's predicted ratings for the same sentences in and out of context. When we do this, we observe that out of 200 sentence pairs, our model scores the majority (130 pairs) higher when processed in context than out of context. A smaller but significant group (70 pairs) receives a lower score when processed in context. The first group's average score before adding context (0.48) is consistently lower than that of the second group (0.68). Also, as Table TABREF26 indicates, the pairs that our model rated, out of context, with a score lower than 0.5 (on the model's softmax distribution), received on average a higher rating in context, while the opposite is true for the pairs rated with a score higher than 0.5. In general, sentence pairs that were rated highly out of context receive a lower score in context, and vice versa. When we did linear regression on the DNNs in and out of context predicted scores, we observed substantially the same compression pattern exhibited by our AMT mean human judgments. Figure FIGREF27 plots this regression graph."
+    ],
+    [
+      " BIBREF7 present ratings of aptness and comprehensibility for 64 metaphors from two groups of subjects. They note that metaphors were perceived as more apt and more comprehensible to the extent that their terms occupied similar positions within dissimilar domains. Interestingly, BIBREF8 also present experimental results to claim that imagery does not clearly correlate with metaphor aptness. Aptness judgments are also subjected to individual differences.",
+      " BIBREF9 points to such individual differences in metaphor processing. She asked 27 participants to rate 37 metaphors for difficulty, aptness and familiarity, and to write one or more interpretations of the metaphor. Subjects with higher working memory span were able to give more detailed and elaborate interpretations of metaphors. Familiarity and aptness correlated with both high and low span subjects. For high span subjects aptness of metaphor positively correlated with number of interpretations, while for low span subjects the opposite was true.",
+      " BIBREF10 analyses the aptness of metaphors with and without extended context. She finds that domain similarity correlates with aptness judgments in isolated metaphors, but not in contextualized metaphors. She also reports that there is no clear correlation between metaphor aptness ratings in isolated and in contextualized examples. BIBREF0 study the relation between aptness and comprehensibility in metaphors and similes. They provide experimental results indicating that aptness is a better predictor than comprehensibility for the \u201ctransformation\" of a simile into a metaphor. Subjects tended to remember similes as metaphors (i.e. remember the dancer's arms moved like startled rattlesnakes as the dancer's arms were startled rattlesnakes) if they were judged to be particularly apt, rather than particularly comprehensible. They claim that context might play an important role in this process. They suggest that context should ease the transparency and increase the aptness of both metaphors and similes.",
+      " BIBREF11 present a series of experiments indicating that metaphors tend to be interpreted through emergent features that were not rated as particularly relevant, either for the tenor or for the vehicle of the metaphor. The number of emergent features that subjects were able to draw from a metaphor seems to correlate with their aptness judgments.",
+      " BIBREF12 use Event-Related Brain Potentials (ERPs) to study the temporal dynamics of metaphor processing in reading literary texts. They emphasize the influence of context on the ability of a reader to smoothly interpret an unusual metaphor.",
+      " BIBREF13 use electrophysiological experiments to try to disentangle the effect of a metaphor from that of its context. They find that de-contextualized metaphors elicited two different brain responses, INLINEFORM0 and INLINEFORM1 , while contextualized metaphors only produced the INLINEFORM2 effect. They attribute the INLINEFORM3 effect, often observed in neurological studies of metaphors, to expectations about upcoming words in the absence of a predictive context that \u201cprepares\" the reader for the metaphor. They suggest that the INLINEFORM4 effect reflects the actual interpretative processing of the metaphor.",
+      "This view is supported by several neurological studies showing that the INLINEFORM0 effect arises with unexpected elements, like new presuppositions introduced into a text in a way not implied by the context BIBREF14 , or unexpected associations with a noun-verb combination, not indicated by previous context (for example preceded by neutral context, as in BIBREF15 )."
+    ],
+    [
+      "We have observed that embedding metaphorical sentences and their paraphrase candidates in a document context generates a compression effect in human metaphor aptness ratings. Context seems to mitigate the perceived aptness of metaphors in two ways. Those metaphor-paraphrase pairs given very low scores out of context receive increased scores in context, while those with very high scores out of context decline in rating when presented in context. At the same time, the demarcation line between paraphrase and non-paraphrase is not particularly affected by the introduction of extended context.",
+      "As previously observed by BIBREF10 , we found that context has an influence on human aptness ratings for metaphors, although, unlike her results, we did find a correlation between the two sets of ratings. BIBREF0 's expectation that context should facilitate a metaphor's aptness was supported only in one sense. Aptness increases for low-rated pairs. But it decreases for high-rated pairs.",
+      "We applied BIBREF3 's DNN for the MAPT to an in-context test set, experimenting with both out-of-context and in-context training corpora. We obtained reasonable results for binary classification of paraphrase candidates for aptness, but the performance of the model declined sharply for the prediction of human gradient aptness judgments, relative to its performance on a corresponding out-of-context test set. This appears to be the result of the increased difficulty in separating rating categories introduced by the compression effect.",
+      "Strikingly, the linear regression analyses of human aptness judgments for in- and out-of-context paraphrase pairs, and of our DNN's predictions for these pairs reveal similar compression patterns. These patterns produce ratings that cannot be clearly separated along a linear ranking scale.",
+      "To the best of our knowledge ours is the first study of the effect of context on metaphor aptness on a corpus of this dimension, using crowd sourced human judgments as the gold standard for assessing the predictions of a computational model of paraphrase. We also present the first comparative study of both human and model judgments of metaphor paraphrase for in-context and out-of-context variants of metaphorical sentences.",
+      "Finally, the compression effect that context induces on paraphrase judgments corresponds closely to the one observed independently in another task, which is reported in BIBREF5 . We regard this effect as a significant discovery that increases the plausibility and the interest of our results. The fact that it appears clearly with two tasks involving different sorts of DNNs and distinct learning regimes (unsupervised learning with neural network language models for the acceptability prediction task discussed, as opposed to supervised learning with our composite DNN for paraphrase prediction) reduces the likelihood that this effect is an artefact of our experimental design.",
+      "While our dataset is still small, we are presenting an initial investigation of a phenomenon which is, to date, little studied. We are working to enlarge our dataset and in future work we will expand both our in- and out-of-context annotated metaphor-paraphrase corpora.",
+      "While the corpus we used contains a number of hand crafted examples, it would be preferable to find these example types in natural corpora, and we are currently working on this. We will be extracting a dataset of completely natural (corpus-driven) examples. We are seeking to expand the size of the data set to improve the reliability of our modelling experiments.",
+      "We will also experiment with alternative DNN architectures for the MAPT. We will conduct qualitative analyses on the kinds of metaphors and similes that are more prone to a context-induced rating switch.",
+      "One of our main concerns in future research will be to achieve a better understanding of the compression effect of context on human judgments and DNN models."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1514/instruction.md b/qasper-1514/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..116064d38df70d720e13fbb06449b47045834304
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+++ b/qasper-1514/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Drug-drug Interaction Extraction via Recurrent Neural Network with Multiple Attention Layers
+
+Question: How big is the evaluated dataset?
\ No newline at end of file
diff --git a/qasper-1515/instruction.md b/qasper-1515/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..a5d7d015246929a6f2c8839acbdf058896fb73fc
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+++ b/qasper-1515/instruction.md
@@ -0,0 +1,104 @@
+Name of Paper: Drug-drug Interaction Extraction via Recurrent Neural Network with Multiple Attention Layers
+
+Question: By how much does their model outperform existing methods?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Proposed Model",
+    "Preprocessing",
+    "Embedding Layer",
+    "Bidirectional RNN Encoding Layer",
+    "Word Level Attention",
+    "Sentence Level Attention",
+    "Classification and Training",
+    "DDI Prediction",
+    "Datasets and Evaluation Metrics",
+    "Hyperparameter Settings and Training",
+    "Experimental Results",
+    "Conclusion and Future Work",
+    "Acknowledgment"
+  ],
+  "paragraphs": [
+    [
+      "Drug-drug interaction (DDI) is a situation when one drug increases or decreases the effect of another drug BIBREF0 . Adverse drug reactions may cause severe side effect, if two or more medicines were taken and their DDI were not investigated in detail. DDI is a common cause of illness, even a cause of death BIBREF1 . Thus, DDI databases for clinical medication decisions are proposed by some researchers. These databases such as SFINX BIBREF2 , KEGG BIBREF3 , CredibleMeds BIBREF4 help physicians and pharmacists avoid most adverse drug reactions.",
+      "Traditional DDI databases are manually constructed according to clinical records, scientific research and drug specifications. For instance, The sentence \u201cWith combined use, clinicians should be aware, when phenytoin is added, of the potential for reexacerbation of pulmonary symptomatology due to lowered serum theophylline concentrations BIBREF5 \u201d, which is from a pharmacotherapy report, describe the side effect of phenytoin and theophylline's combined use. Then this information on specific medicines will be added to DDI databases. As drug-drug interactions have being increasingly found, manually constructing DDI database would consume a lot of manpower and resources.",
+      "There has been many efforts to automatically extract DDIs from natural language BIBREF0 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , mainly medical literature and clinical records. These works can be divided into the following categories:",
+      "To avoid complex feature engineering and NLP toolkits' usage, we employ deep learning approaches for sentence comprehension as a whole. Our model takes in a sentence from biomedical literature which contains a drug pair and outputs what kind of DDI this drug pair belongs. This assists physicians refrain from improper combined use of drugs. In addition, the word and sentence level attentions are introduced to our model for better DDI predictions.",
+      "We train our language comprehension model with labeled instances. Figure FIGREF5 shows partial records in DDI corpus BIBREF16 . We extract the sentence and drug pairs in the records. There are 3 drug pairs in this example thus we have 3 instances. The DDI corpus annotate each drug pair in the sentence with a DDI type. The DDI type, which is the most concerned information, is described in table TABREF4 . The details about how we train our model and extract the DDI type from text are described in the remaining sections."
+    ],
+    [
+      "In DDI extraction task, NLP methods or machine learning approaches are proposed by most of the work. Chowdhury BIBREF14 and Thomas et al. BIBREF11 proposed methods that use linguistic phenomenons and two-stage SVM to classify DDIs. FBK-irst BIBREF10 is a follow-on work which applies kernel method to the existing model and outperforms it.",
+      "Neural network based approaches have been proposed by several works. Liu et al. BIBREF9 employ CNN for DDI extraction for the first time which outperforms the traditional machine learning based methods. Limited by the convolutional kernel size, the CNN can only extracted features of continuous 3 to 5 words rather than distant words. Liu et al. BIBREF8 proposed dependency-based CNN to handle distant but relevant words. Sahu et al. BIBREF12 proposed LSTM based DDI extraction approach and outperforms CNN based approach, since LSTM handles sentence as a sequence instead of slide windows. To conclude, Neural network based approaches have advantages of 1) less reliance on extra NLP toolkits, 2) simpler preprocessing procedure, 3) better performance than text analysis and machine learning methods.",
+      "Drug-drug interaction extraction is a relation extraction task of natural language processing. Relation extraction aims to determine the relation between two given entities in a sentence. In recent years, attention mechanism and various neural networks are applied to relation extraction BIBREF17 , BIBREF18 , BIBREF19 , BIBREF20 , BIBREF21 . Convolutional deep neural network are utilized for extracting sentence level features in BIBREF19 . Then the sentence level features are concatenated with lexical level features, which are obtained by NLP toolkit WordNet BIBREF22 , followed by a multilayer perceptron (MLP) to classify the entities' relation. A fixed work is proposed by Nguyen et al. BIBREF21 . The convolutional kernel is set various size to capture more n-gram features. In addition, the word and position embedding are trained automatically instead of keeping constant as in BIBREF19 . Wang et al. BIBREF20 introduce multi-level attention mechanism to CNN in order to emphasize the keywords and ignore the non-critical words during relation detection. The attention CNN model outperforms previous state-of-the-art methods.",
+      "Besides CNN, Recurrent neural network (RNN) has been applied to relation extraction as well. Zhang et al. BIBREF18 utilize long short-term memory network (LSTM), a typical RNN model, to represent sentence. The bidirectional LSTM chronologically captures the previous and future information, after which a pooling layer and MLP have been set to extract feature and classify the relation. Attention mechanism is added to bidirectional LSTM in BIBREF17 for relation extraction. An attention layer gives each memory cell a weight so that classifier can catch the principal feature for the relation detection. The Attention based bidirectional LSTM has been proven better than previous work."
+    ],
+    [
+      "In this section, we present our bidirectional recurrent neural network with multiple attention layer model. The overview of our architecture is shown in figure FIGREF15 . For a given instance, which describes the details about two or more drugs, the model represents each word as a vector in embedding layer. Then the bidirectional RNN layer generates a sentence matrix, each column vector in which is the semantic representation of the corresponding word. The word level attention layer transforms the sentence matrix to vector representation. Then sentence level attention layer generates final representation for the instance by combining several relevant sentences in view of the fact that these sentences have the same drug pair. Followed by a softmax classifier, the model classifies the drug pair in the given instance as specific DDI."
+    ],
+    [
+      "The DDI corpus contains thousands of XML files, each of which are constructed by several records. For a sentence containing INLINEFORM0 drugs, there are INLINEFORM1 drug pairs. We replace the interested two drugs with \u201cdrug1\u201d and \u201cdrug2\u201d while the other drugs are replaced by \u201cdurg0\u201d, as in BIBREF9 did. This step is called drug blinding. For example, the sentence in figure FIGREF5 generates 3 instances after drug blinding: \u201cdrug1: an increased risk of hepatitis has been reported to result from combined use of drug2 and drug0\u201d, \u201cdrug1: an increased risk of hepatitis has been reported to result from combined use of drug0 and drug2\u201d, \u201cdrug0: an increased risk of hepatitis has been reported to result from combined use of drug1 and drug2\u201d. The drug blinded sentences are the instances that are fed to our model.",
+      "We put the sentences with the same drug pairs together as a set, since the sentence level attention layer (will be described in Section SECREF21 ) will use the sentences which contain the same drugs."
+    ],
+    [
+      "Given an instance INLINEFORM0 which contains specified two drugs INLINEFORM1 , INLINEFORM2 , each word is embedded in a INLINEFORM3 dimensional space ( INLINEFORM4 , INLINEFORM5 are the dimension of word embedding and position embedding). The look up table function INLINEFORM6 maps a word or a relative position to a column vector. After embedding layer the sentence is represented by INLINEFORM7 , where DISPLAYFORM0 ",
+      "The INLINEFORM0 function is usually implemented with matrix-vector product. Let INLINEFORM1 , INLINEFORM2 denote the one-hot representation (column vector) of word and relative distance. INLINEFORM3 , INLINEFORM4 are word and position embedding query matrix. The look up functions are implemented by DISPLAYFORM0 ",
+      "Then the word sequence INLINEFORM0 is fed to the RNN layer. Note that the sentence will be filled with INLINEFORM1 if its length is less than INLINEFORM2 ."
+    ],
+    [
+      "The words in the sequence are read by RNN's gated recurrent unit (GRU) one by one. The GRU takes the current word INLINEFORM0 and the previous GRU's hidden state INLINEFORM1 as input. The current GRU encodes INLINEFORM2 and INLINEFORM3 into a new hidden state INLINEFORM4 (its dimension is INLINEFORM5 , a hyperparameter), which can be regarded as informations the GRU remembered.",
+      "Figure FIGREF25 shows the details in GRU. The reset gate INLINEFORM0 selectively forgets informations delivered by previous GRU. Then the hidden state becomes INLINEFORM1 . The update gate INLINEFORM2 updates the informations according to INLINEFORM3 and INLINEFORM4 . The equations below describe these procedures. Note that INLINEFORM5 stands for element wise multiplication. DISPLAYFORM0 DISPLAYFORM1 ",
+      "The bidirectional RNN contains forward RNN and backward RNN. Forward RNN reads sentence from INLINEFORM0 to INLINEFORM1 , generating INLINEFORM2 , INLINEFORM3 , ..., INLINEFORM4 . Backward RNN reads sentence from INLINEFORM5 to INLINEFORM6 , generating INLINEFORM7 , INLINEFORM8 , ..., INLINEFORM9 . Then the encode result of this layer is DISPLAYFORM0 ",
+      "We apply dropout technique in RNN layer to avoid overfitting. Each GRU have a probability (denoted by INLINEFORM0 , also a hyperparameter) of being dropped. The dropped GRU has no output and will not affect the subsequent GRUs. With bidirectional RNN and dropout technique, the input INLINEFORM1 is encoded into sentence matrix INLINEFORM2 ."
+    ],
+    [
+      "The purpose of word level attention layer is to extract sentence representation (also known as feature vector) from encoded matrix. We use word level attention instead of max pooling, since attention mechanism can determine the importance of individual encoded word in each row of INLINEFORM0 . Let INLINEFORM1 denotes the attention vector (column vector), INLINEFORM2 denotes the filter that gives each element in the row of INLINEFORM3 a weight. The following equations shows the attention operation, which is also illustrated in figure FIGREF15 . DISPLAYFORM0 DISPLAYFORM1 ",
+      "The softmax function takes a vector INLINEFORM0 as input and outputs a vector, DISPLAYFORM0 ",
+      " INLINEFORM0 denotes the feature vector captured by this layer. Several approaches BIBREF12 , BIBREF17 use this vector and softmax classifier for classification. Inspired by BIBREF23 we propose the sentence level attention to combine the information of other sentences for a improved DDI classification."
+    ],
+    [
+      "The previous layers captures the features only from the given sentence. However, other sentences may contains informations that contribute to the understanding of this sentence. It is reasonable to look over other relevant instances when determine two drugs' interaction from the given sentence. In our implementation, the instances that have the same drug pair are believed to be relevant. The relevant instances set is denoted by INLINEFORM0 , where INLINEFORM1 is the sentence feature vector. INLINEFORM2 stands for how well the instance INLINEFORM3 matches its DDI INLINEFORM4 (Vector representation of a specific DDI). INLINEFORM5 is a diagonal attention matrix, multiplied by which the feature vector INLINEFORM6 can concentrate on those most representative features. DISPLAYFORM0 DISPLAYFORM1 ",
+      " INLINEFORM0 is the softmax result of INLINEFORM1 . The final sentence representation is decided by all of the relevant sentences' feature vector, as Equation EQREF24 shows. DISPLAYFORM0 ",
+      "Note that the set INLINEFORM0 is gradually growing as new sentence with the same drugs pairs is found when training. An instance INLINEFORM1 is represented by INLINEFORM2 before sentence level attention. The sentence level attention layer finds the set INLINEFORM3 , instances in which have the same drug pair as in INLINEFORM4 , and put INLINEFORM5 in INLINEFORM6 . Then the final sentence representation INLINEFORM7 is calculated in this layer."
+    ],
+    [
+      "A given sentence INLINEFORM0 is finally represented by the feature vector INLINEFORM1 . Then we feed it to a softmax classifier. Let INLINEFORM2 denotes the set of all kinds of DDI. The output INLINEFORM3 is the probabilities of each class INLINEFORM4 belongs. DISPLAYFORM0 ",
+      "We use cross entropy cost function and INLINEFORM0 regularization as the optimization objective. For INLINEFORM1 -th instance, INLINEFORM2 denotes the one-hot representation of it's label, where the model outputs INLINEFORM3 . The cross entropy cost is: DISPLAYFORM0 ",
+      "For a mini-batch INLINEFORM0 , the optimization objective is: DISPLAYFORM0 ",
+      "All parameters in this model is: DISPLAYFORM0 ",
+      "We optimize the parameters of objective function INLINEFORM0 with Adam BIBREF24 , which is a variant of mini-batch stochastic gradient descent. During each train step, the gradient of INLINEFORM1 is calculated. Then INLINEFORM2 is adjusted according to the gradient. After the end of training, we have a model that is able to predict two drugs' interactions when a sentence about these drugs is given."
+    ],
+    [
+      "The model is trained for DDI classification. The parameters in list INLINEFORM0 are tuned during the training process. Given a new sentence with two drugs, we can use this model to classify the DDI type.",
+      "The DDI prediction follows the procedure described in Section SECREF6 - SECREF26 . The given sentence is eventually represented by feature vector INLINEFORM0 . Then INLINEFORM1 is classified to a specific DDI type with a softmax classifier. In next section, we will evaluate our model's DDI prediction performance and see the advantages and shortcomings of our model."
+    ],
+    [
+      "We use the DDI corpus of the 2013 DDIExtraction challenge BIBREF16 to train and test our model. The DDIs in this corpus are classified as five types. We give the definitions of these types and their example sentences, as shown in table TABREF4 . This standard dataset is made up of training set and testing set. We use the same metrics as in other drug-drug interaction extraction literature BIBREF11 , BIBREF10 , BIBREF25 , BIBREF9 , BIBREF8 , BIBREF12 : the overall precision, recall, and F1 score on testing set. INLINEFORM0 denotes the set of {False, Mechanism, Effect, Advise, Int}. The precision and recall of each INLINEFORM1 are calculated by DISPLAYFORM0 DISPLAYFORM1 ",
+      "Then the overall precision, recall, and F1 score are calculated by DISPLAYFORM0 ",
+      "Besides, we evaluate the captured feature vectors with t-SNE BIBREF26 , a visualizing and intuitive way to map a high dimensional vector into a 2 or 3-dimensional space. If the points in a low dimensional space are easy to be split, the feature vectors are believed to be more distinguishable."
+    ],
+    [
+      "We use TensorFlow BIBREF27 r0.11 to implement the proposed model. The input of each word is an ordered triple (word, relative distance from drug1, relative distance from drug2). The sentence, which is represented as a matrix, is fed to the model. The output of the model is a INLINEFORM0 -dimensional vector representing the probabilities of being corresponding DDI. It is the network, parameters, and hyperparameters which decides the output vector. The network's parameters are adjusted during training, where the hyperparameters are tuned by hand. The hyperparameters after tuning are as follows. The word embedding's dimension INLINEFORM1 , the position embedding's dimension INLINEFORM2 , the hidden state's dimension INLINEFORM3 , the probability of dropout INLINEFORM4 , other hyperparameters which are not shown here are set to TensorFlow's default values.",
+      "The word embedding is initialized by pre-trained word vectors using GloVe BIBREF28 , while other parameters are initialized randomly. During each training step, a mini-batch (the mini-batch size INLINEFORM0 in our implementation) of sentences is selected from training set. The gradient of objective function is calculated for parameters updating (See Section SECREF26 ).",
+      "Figure FIGREF32 shows the training process. The objective function INLINEFORM0 is declining as the training mini-batches continuously sent to the model. As the testing mini-batches, the INLINEFORM1 function is fluctuating while its overall trend is descending. The instances in testing set are not participated in training so that INLINEFORM2 function is not descending so fast. However, training and testing instances have similar distribution in sample space, causing that testing instances' INLINEFORM3 tends to be smaller along with the training process. INLINEFORM4 has inverse relationship with the performance measurement. The F1 score is getting fluctuating around a specific value after enough training steps. The reason why fluctuating range is considerable is that only a tiny part of the whole training or testing set has been calculated the F1 score. Testing the whole set during every step is time consuming and not necessary. We will evaluate the model on the whole testing set in Section SECREF47 ."
+    ],
+    [
+      "We save our model every 100 step and predict all the DDIs of the instances in the testing set. These predictions' F1 score is shown in figure FIGREF40 . To demonstrate the sentence level attention layer is effective, we drop this layer and then directly use INLINEFORM0 for softmax classification (See figure FIGREF15 ). The result is shown with \u201cRNN + dynamic word embedding + ATT\u201d curve, which illustrates that the sentence level attention layer contributes to a more accurate model.",
+      "Whether a dynamic or static word embedding is better for a DDI extraction task is under consideration. Nguyen et al. BIBREF21 shows that updating word embedding at the time of other parameters being trained makes a better performance in relation extraction task. We let the embedding be static when training, while other conditions are all the same. The \u201cRNN + static word embedding + 2ATT\u201d curve shows this case. We can draw a conclusion that updating the initialized word embedding trains more suitable word vectors for the task, which promotes the performance.",
+      "We compare our best F1 score with other state-of-the-art approaches in table TABREF39 , which shows our model has competitive advantage in dealing with drug-drug interaction extraction. The predictions confusion matrix is shown in table TABREF46 . The DDIs other than false being classified as false makes most of the classification error. It may perform better if a classifier which can tells true and false DDI apart is trained. We leave this two-stage classifier to our future work. Another phenomenon is that the \u201cInt\u201d type is often classified as \u201cEffect\u201d. The \u201cInt\u201d sentence describes there exists interaction between two drugs and this information implies the two drugs' combination will have good or bed effect. That's the reason why \u201cInt\u201d and \u201cEffect\u201d are often obfuscated.",
+      "To evaluate the features our model captured, we employ scikit-learn BIBREF29 's t-SNE class to map high dimensional feature vectors to 2-dimensional vectors, which can be depicted on a plane. We depict all the features of the instances in testing set, as shown in figure FIGREF41 . The RNN model using dynamic word embedding and 2 layers of attention is the most distinguishable one. Unfortunately, the classifier can not classify all the instances into correct classes. Comparing table TABREF46 with figure UID44 , both of which are from the best performed model, we can observe some conclusions. The \u201cInt\u201d DDIs are often misclassified as \u201cEffect\u201d, for the reason that some of the \u201cInt\u201d points are in the \u201cEffect\u201d cluster. The \u201cEffect\u201d points are too scattered so that plenty of \u201cEffect\u201d DDIs are classified to other types. The \u201cMechanism\u201d points are gathered around two clusters, causing that most of the \u201cmechanism\u201d DDIs are classified to two types: \u201cFalse\u201d and \u201cMechanism\u201d. In short, the visualizability of feature mapping gives better explanations for the prediction results and the quality of captured features."
+    ],
+    [
+      "To conclude, we propose a recurrent neural network with multiple attention layers to extract DDIs from biomedical text. The sentence level attention layer, which combines other sentences containing the same drugs, has been added to our model. The experiments shows that our model outperforms the state-of-the-art DDI extraction systems. Task relevant word embedding and two attention layers improved the performance to some extent.",
+      "The imbalance of the classes and the ambiguity of semantics cause most of the misclassifications. We consider that instance generation using generative adversarial networks would cover the instance shortage in specific category. It is also reasonable to use distant supervision learning (which utilize other relevant material) for knowledge supplement and obtain a better performed DDI extraction system."
+    ],
+    [
+      "This work is supported by the NSFC under Grant 61303191, 61303190, 61402504, 61103015."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1522/instruction.md b/qasper-1522/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..f107d969f1e5f3d1cb3161c89343f8747d1e983c
--- /dev/null
+++ b/qasper-1522/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Mind Your Language: Abuse and Offense Detection for Code-Switched Languages
+
+Question: Do all the instances contain code-switching?
\ No newline at end of file
diff --git a/qasper-1523/instruction.md b/qasper-1523/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..624cf035aaf4c724dcef1f33070466807a9d3a21
--- /dev/null
+++ b/qasper-1523/instruction.md
@@ -0,0 +1,43 @@
+Name of Paper: Mind Your Language: Abuse and Offense Detection for Code-Switched Languages
+
+Question: What embeddings do they use?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Methodology",
+    "Pre-Processing",
+    "Training Word Embeddings",
+    "Classifier Model",
+    "Results",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "With the penetration of internet among masses, the content being posted on social media channels has uptaken. Specifically, in the Indian subcontinent, number of Internet users has crossed 500 mi, and is rising rapidly due to inexpensive data. With this rise, comes the problem of hate speech, offensive and abusive posts on social media. Although there are many previous works which deal with Hindi and English hate speech (the top two languages in India), but very few on the code-switched version (Hinglish) of the two BIBREF0 . This is partially due to the following reasons: (i) Hinglish consists of no-fixed grammar and vocabulary. It derives a part of its semantics from Devnagari and another part from the Roman script. (ii) Hinglish speech and written text consists of a concoction of words spoken in Hindi as well as English, but written in the Roman script. This makes the spellings variable and dependent on the writer of the text. Hence code-switched languages present tough challenges in terms of parsing and getting the meaning out of the text. For instance, the sentence, \u201cModiji foreign yatra par hai\u201d, is in the Hinglish language. Somewhat correct translation of this would be, \u201cMr. Modi is on a foriegn tour\u201d. However, even this translation has some flaws due to no direct translation available for the word ji, which is used to show respect. Verbatim translation would lead to \u201cMr. Modi foreign tour on is\u201d. Moreover, the word yatra here, can have phonetic variations, which would result in multiple spellings of the word as yatra, yaatra, yaatraa, etc. Also, the problem of hate speech has been rising in India, and according to the policies of the government and the various social networks, one is not allowed to misuse his right to speech to abuse some other community or religion. Due to the various difficulties associated with the Hinglish language, it is challenging to automatically detect and ban such kind of speech.",
+      "Thus, with this in mind, we build a transfer learning based model for the code-switched language Hinglish, which outperforms the baseline model of BIBREF0 . We also release the embeddings and the model trained."
+    ],
+    [
+      "Our methodology primarily consists of these steps: Pre-processing of the dataset, training of word embeddings, training of the classifier model and then using that on HEOT dataset."
+    ],
+    [
+      "In this work, we use the datasets released by BIBREF1 and HEOT dataset provided by BIBREF0 . The datasets obtained pass through these steps of processing: (i) Removal of punctuatios, stopwords, URLs, numbers, emoticons, etc. This was then followed by transliteration using the Xlit-Crowd conversion dictionary and translation of each word to English using Hindi to English dictionary. To deal with the spelling variations, we manually added some common variations of popular Hinglish words. Final dictionary comprised of 7200 word pairs. Additionally, to deal with profane words, which are not present in Xlit-Crowd, we had to make a profanity dictionary (with 209 profane words) as well. Table TABREF3 gives some examples from the dictionary."
+    ],
+    [
+      "We tried Glove BIBREF2 and Twitter word2vec BIBREF3 code for training embeddings for the processed tweets. The embeddings were trained on both the datasets provided by BIBREF1 and HEOT. These embeddings help to learn distributed representations of tweets. After experimentation, we kept the size of embeddings fixed to 100."
+    ],
+    [
+      "Both the HEOT and BIBREF1 datasets contain tweets which are annotated in three categories: offensive, abusive and none (or benign). Some examples from the dataset are shown in Table TABREF4 . We use a LSTM based classifier model for training our model to classify these tweets into these three categories. An overview of the model is given in the Figure FIGREF12 . The model consists of one layer of LSTM followed by three dense layers. The LSTM layer uses a dropout value of 0.2. Categorical crossentropy loss was used for the last layer due to the presence of multiple classes. We use Adam optimizer along with L2 regularisation to prevent overfitting. As indicated by the Figure FIGREF12 , the model was initially trained on the dataset provided by BIBREF1 , and then re-trained on the HEOT dataset so as to benefit from the transfer of learned features in the last stage. The model hyperparameters were experimentally selected by trying out a large number of combinations through grid search."
+    ],
+    [
+      "Table TABREF9 shows the performance of our model (after getting trained on BIBREF1 ) with two types of embeddings in comparison to the models by BIBREF0 and BIBREF1 on the HEOT dataset averaged over three runs. We also compare results on pre-trained embeddings. As shown in the table, our model when given Glove embeddings performs better than all other models. For comparison purposes, in Table TABREF10 we have also evaluated our results on the dataset by BIBREF1 ."
+    ],
+    [
+      "In this paper, we presented a pipeline which given Hinglish text can classify it into three categories: offensive, abusive and benign. This LSTM based model performs better than the other systems present. We also release the code, the dictionary made and the embeddings trained in the process. We believe this model would be useful in hate speech detection tasks for code-switched languages."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1524/instruction.md b/qasper-1524/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..0fc24d76d31b21fd15e73641f3a2cd1431ca707a
--- /dev/null
+++ b/qasper-1524/instruction.md
@@ -0,0 +1,43 @@
+Name of Paper: Mind Your Language: Abuse and Offense Detection for Code-Switched Languages
+
+Question: Do they perform some annotation?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Methodology",
+    "Pre-Processing",
+    "Training Word Embeddings",
+    "Classifier Model",
+    "Results",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "With the penetration of internet among masses, the content being posted on social media channels has uptaken. Specifically, in the Indian subcontinent, number of Internet users has crossed 500 mi, and is rising rapidly due to inexpensive data. With this rise, comes the problem of hate speech, offensive and abusive posts on social media. Although there are many previous works which deal with Hindi and English hate speech (the top two languages in India), but very few on the code-switched version (Hinglish) of the two BIBREF0 . This is partially due to the following reasons: (i) Hinglish consists of no-fixed grammar and vocabulary. It derives a part of its semantics from Devnagari and another part from the Roman script. (ii) Hinglish speech and written text consists of a concoction of words spoken in Hindi as well as English, but written in the Roman script. This makes the spellings variable and dependent on the writer of the text. Hence code-switched languages present tough challenges in terms of parsing and getting the meaning out of the text. For instance, the sentence, \u201cModiji foreign yatra par hai\u201d, is in the Hinglish language. Somewhat correct translation of this would be, \u201cMr. Modi is on a foriegn tour\u201d. However, even this translation has some flaws due to no direct translation available for the word ji, which is used to show respect. Verbatim translation would lead to \u201cMr. Modi foreign tour on is\u201d. Moreover, the word yatra here, can have phonetic variations, which would result in multiple spellings of the word as yatra, yaatra, yaatraa, etc. Also, the problem of hate speech has been rising in India, and according to the policies of the government and the various social networks, one is not allowed to misuse his right to speech to abuse some other community or religion. Due to the various difficulties associated with the Hinglish language, it is challenging to automatically detect and ban such kind of speech.",
+      "Thus, with this in mind, we build a transfer learning based model for the code-switched language Hinglish, which outperforms the baseline model of BIBREF0 . We also release the embeddings and the model trained."
+    ],
+    [
+      "Our methodology primarily consists of these steps: Pre-processing of the dataset, training of word embeddings, training of the classifier model and then using that on HEOT dataset."
+    ],
+    [
+      "In this work, we use the datasets released by BIBREF1 and HEOT dataset provided by BIBREF0 . The datasets obtained pass through these steps of processing: (i) Removal of punctuatios, stopwords, URLs, numbers, emoticons, etc. This was then followed by transliteration using the Xlit-Crowd conversion dictionary and translation of each word to English using Hindi to English dictionary. To deal with the spelling variations, we manually added some common variations of popular Hinglish words. Final dictionary comprised of 7200 word pairs. Additionally, to deal with profane words, which are not present in Xlit-Crowd, we had to make a profanity dictionary (with 209 profane words) as well. Table TABREF3 gives some examples from the dictionary."
+    ],
+    [
+      "We tried Glove BIBREF2 and Twitter word2vec BIBREF3 code for training embeddings for the processed tweets. The embeddings were trained on both the datasets provided by BIBREF1 and HEOT. These embeddings help to learn distributed representations of tweets. After experimentation, we kept the size of embeddings fixed to 100."
+    ],
+    [
+      "Both the HEOT and BIBREF1 datasets contain tweets which are annotated in three categories: offensive, abusive and none (or benign). Some examples from the dataset are shown in Table TABREF4 . We use a LSTM based classifier model for training our model to classify these tweets into these three categories. An overview of the model is given in the Figure FIGREF12 . The model consists of one layer of LSTM followed by three dense layers. The LSTM layer uses a dropout value of 0.2. Categorical crossentropy loss was used for the last layer due to the presence of multiple classes. We use Adam optimizer along with L2 regularisation to prevent overfitting. As indicated by the Figure FIGREF12 , the model was initially trained on the dataset provided by BIBREF1 , and then re-trained on the HEOT dataset so as to benefit from the transfer of learned features in the last stage. The model hyperparameters were experimentally selected by trying out a large number of combinations through grid search."
+    ],
+    [
+      "Table TABREF9 shows the performance of our model (after getting trained on BIBREF1 ) with two types of embeddings in comparison to the models by BIBREF0 and BIBREF1 on the HEOT dataset averaged over three runs. We also compare results on pre-trained embeddings. As shown in the table, our model when given Glove embeddings performs better than all other models. For comparison purposes, in Table TABREF10 we have also evaluated our results on the dataset by BIBREF1 ."
+    ],
+    [
+      "In this paper, we presented a pipeline which given Hinglish text can classify it into three categories: offensive, abusive and benign. This LSTM based model performs better than the other systems present. We also release the code, the dictionary made and the embeddings trained in the process. We believe this model would be useful in hate speech detection tasks for code-switched languages."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1525/instruction.md b/qasper-1525/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..b880caa160e86734db04f7df6a3ca03feef15191
--- /dev/null
+++ b/qasper-1525/instruction.md
@@ -0,0 +1,43 @@
+Name of Paper: Mind Your Language: Abuse and Offense Detection for Code-Switched Languages
+
+Question: Do they use dropout?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Methodology",
+    "Pre-Processing",
+    "Training Word Embeddings",
+    "Classifier Model",
+    "Results",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "With the penetration of internet among masses, the content being posted on social media channels has uptaken. Specifically, in the Indian subcontinent, number of Internet users has crossed 500 mi, and is rising rapidly due to inexpensive data. With this rise, comes the problem of hate speech, offensive and abusive posts on social media. Although there are many previous works which deal with Hindi and English hate speech (the top two languages in India), but very few on the code-switched version (Hinglish) of the two BIBREF0 . This is partially due to the following reasons: (i) Hinglish consists of no-fixed grammar and vocabulary. It derives a part of its semantics from Devnagari and another part from the Roman script. (ii) Hinglish speech and written text consists of a concoction of words spoken in Hindi as well as English, but written in the Roman script. This makes the spellings variable and dependent on the writer of the text. Hence code-switched languages present tough challenges in terms of parsing and getting the meaning out of the text. For instance, the sentence, \u201cModiji foreign yatra par hai\u201d, is in the Hinglish language. Somewhat correct translation of this would be, \u201cMr. Modi is on a foriegn tour\u201d. However, even this translation has some flaws due to no direct translation available for the word ji, which is used to show respect. Verbatim translation would lead to \u201cMr. Modi foreign tour on is\u201d. Moreover, the word yatra here, can have phonetic variations, which would result in multiple spellings of the word as yatra, yaatra, yaatraa, etc. Also, the problem of hate speech has been rising in India, and according to the policies of the government and the various social networks, one is not allowed to misuse his right to speech to abuse some other community or religion. Due to the various difficulties associated with the Hinglish language, it is challenging to automatically detect and ban such kind of speech.",
+      "Thus, with this in mind, we build a transfer learning based model for the code-switched language Hinglish, which outperforms the baseline model of BIBREF0 . We also release the embeddings and the model trained."
+    ],
+    [
+      "Our methodology primarily consists of these steps: Pre-processing of the dataset, training of word embeddings, training of the classifier model and then using that on HEOT dataset."
+    ],
+    [
+      "In this work, we use the datasets released by BIBREF1 and HEOT dataset provided by BIBREF0 . The datasets obtained pass through these steps of processing: (i) Removal of punctuatios, stopwords, URLs, numbers, emoticons, etc. This was then followed by transliteration using the Xlit-Crowd conversion dictionary and translation of each word to English using Hindi to English dictionary. To deal with the spelling variations, we manually added some common variations of popular Hinglish words. Final dictionary comprised of 7200 word pairs. Additionally, to deal with profane words, which are not present in Xlit-Crowd, we had to make a profanity dictionary (with 209 profane words) as well. Table TABREF3 gives some examples from the dictionary."
+    ],
+    [
+      "We tried Glove BIBREF2 and Twitter word2vec BIBREF3 code for training embeddings for the processed tweets. The embeddings were trained on both the datasets provided by BIBREF1 and HEOT. These embeddings help to learn distributed representations of tweets. After experimentation, we kept the size of embeddings fixed to 100."
+    ],
+    [
+      "Both the HEOT and BIBREF1 datasets contain tweets which are annotated in three categories: offensive, abusive and none (or benign). Some examples from the dataset are shown in Table TABREF4 . We use a LSTM based classifier model for training our model to classify these tweets into these three categories. An overview of the model is given in the Figure FIGREF12 . The model consists of one layer of LSTM followed by three dense layers. The LSTM layer uses a dropout value of 0.2. Categorical crossentropy loss was used for the last layer due to the presence of multiple classes. We use Adam optimizer along with L2 regularisation to prevent overfitting. As indicated by the Figure FIGREF12 , the model was initially trained on the dataset provided by BIBREF1 , and then re-trained on the HEOT dataset so as to benefit from the transfer of learned features in the last stage. The model hyperparameters were experimentally selected by trying out a large number of combinations through grid search."
+    ],
+    [
+      "Table TABREF9 shows the performance of our model (after getting trained on BIBREF1 ) with two types of embeddings in comparison to the models by BIBREF0 and BIBREF1 on the HEOT dataset averaged over three runs. We also compare results on pre-trained embeddings. As shown in the table, our model when given Glove embeddings performs better than all other models. For comparison purposes, in Table TABREF10 we have also evaluated our results on the dataset by BIBREF1 ."
+    ],
+    [
+      "In this paper, we presented a pipeline which given Hinglish text can classify it into three categories: offensive, abusive and benign. This LSTM based model performs better than the other systems present. We also release the code, the dictionary made and the embeddings trained in the process. We believe this model would be useful in hate speech detection tasks for code-switched languages."
+    ]
+  ]
+}
+```
\ No newline at end of file
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@@ -0,0 +1,82 @@
+Name of Paper: Do We Really Need Fully Unsupervised Cross-Lingual Embeddings?
+
+Question: How are seed dictionaries obtained by fully unsupervised methods?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction and Motivation",
+    "Methodology",
+    "Projection-Based CLWE Approaches",
+    "Three Key Components",
+    "Experimental Setup",
+    "Results and Discussion",
+    "Further Discussion and Conclusion",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "The wide use and success of monolingual word embeddings in NLP tasks BIBREF0 , BIBREF1 has inspired further research focus on the induction of cross-lingual word embeddings (CLWEs). CLWE methods learn a shared cross-lingual word vector space where words with similar meanings obtain similar vectors regardless of their actual language. CLWEs benefit cross-lingual NLP, enabling multilingual modeling of meaning and supporting cross-lingual transfer for downstream tasks and resource-lean languages. CLWEs provide invaluable cross-lingual knowledge for, inter alia, bilingual lexicon induction BIBREF2 , BIBREF3 , information retrieval BIBREF4 , BIBREF5 , machine translation BIBREF6 , BIBREF7 , document classification BIBREF8 , cross-lingual plagiarism detection BIBREF9 , domain adaptation BIBREF10 , cross-lingual POS tagging BIBREF11 , BIBREF12 , and cross-lingual dependency parsing BIBREF13 , BIBREF14 .",
+      "The landscape of CLWE methods has recently been dominated by the so-called projection-based methods BIBREF15 , BIBREF16 , BIBREF17 . They align two monolingual embedding spaces by learning a projection/mapping based on a training dictionary of translation pairs. Besides their simple conceptual design and competitive performance, their popularity originates from the fact that they rely on rather weak cross-lingual supervision. Originally, the seed dictionaries typically spanned several thousand word pairs BIBREF15 , BIBREF18 , BIBREF19 , but more recent work has shown that CLWEs can be induced with even weaker supervision from small dictionaries spanning several hundred pairs BIBREF20 , identical strings BIBREF21 , or even only shared numerals BIBREF22 .",
+      "Taking the idea of reducing cross-lingual supervision to the extreme, the latest CLWE developments almost exclusively focus on fully unsupervised approaches BIBREF23 , BIBREF24 , BIBREF25 , BIBREF26 , BIBREF27 , BIBREF28 , BIBREF29 , BIBREF30 : they fully abandon any source of (even weak) supervision and extract the initial seed dictionary by exploiting topological similarities between pre-trained monolingual embedding spaces. Their modus operandi can roughly be described by three main components: C1) unsupervised extraction of a seed dictionary; C2) a self-learning procedure that iteratively refines the dictionary to learn projections of increasingly higher quality; and C3) a set of preprocessing and postprocessing steps (e.g., unit length normalization, mean centering, (de)whitening) BIBREF31 that make the entire learning process more robust.",
+      "The induction of fully unsupervised CLWEs is an inherently interesting research topic per se. Nonetheless, the main practical motivation for developing such approaches in the first place is to facilitate the construction of multilingual NLP tools and widen the access to language technology for resource-poor languages and language pairs. However, the first attempts at fully unsupervised CLWE induction failed exactly for these use cases, as shown by sogaard2018on. Therefore, the follow-up work aimed to improve the robustness of unsupervised CLWE induction by introducing more robust self-learning procedures BIBREF24 , BIBREF32 . Besides increased robustness, recent work claims that fully unsupervised projection-based CLWEs can even match or surpass their supervised counterparts BIBREF23 , BIBREF24 , BIBREF27 , BIBREF33 , BIBREF34 .",
+      "In this paper, we critically examine these claims on robustness and improved performance of unsupervised CLWEs by running a large-scale evaluation in the bilingual lexicon induction (BLI) task on 15 languages (i.e., 210 languages pairs, see Table 2 in \u00a7 \"Experimental Setup\" ). The languages were selected to represent different language families and morphological types, as we argue that fully unsupervised CLWEs have been designed to support exactly these setups. However, we show that even the most robust unsupervised CLWE method BIBREF24 still fails for a large number of language pairs: 87/210 BLI setups are unsuccessful, yielding (near-)zero BLI performance. Further, even when the unsupervised method succeeds, it is because the components C2 (self-learning) and C3 (pre-/post-processing) can mitigate the undesired effects of noisy seed lexicon extraction. We then demonstrate that the combination of C2 and C3 with a small provided seed dictionary (e.g., 500 or 1K pairs) outscores the unsupervised method in all cases, often with a huge margin, and does not fail for any language pair. Furthermore, we show that the most robust unsupervised CLWE approach still fails completely when it relies on monolingual word vectors trained on domain-dissimilar corpora. We also empirically verify that unsupervised approaches cannot outperform weakly supervised approaches also for closely related languages (e.g., Swedish\u2013Danish, Spanish\u2013Catalan).",
+      "While the \u201cno supervision at all\u201d premise behind fully unsupervised CLWE methods is indeed seductive, our study strongly suggests that future research efforts should revisit the main motivation behind these methods and focus on designing even more robust solutions, given their current inability to support a wide spectrum of language pairs. In hope of boosting induction of CLWEs for more diverse and distant language pairs, we make all 210 training and test dictionaries used in this work publicly available at: https://github.com/ivulic/panlex-bli."
+    ],
+    [
+      "We now dissect a general framework for unsupervised CLWE learning, and show that the \u201cbag of tricks of the trade\u201d used to increase their robustness (which often slips under the radar) can be equally applied to (weakly) supervised projection-based approaches, leading to their fair(er) comparison."
+    ],
+    [
+      "In short, projection-based CLWE methods learn to (linearly) align independently trained monolingual spaces $\\mathbf {X}$ and $\\mathbf {Z}$ , using a word translation dictionary $D_0$ to guide the alignment process. Let $\\mathbf {X}_D \\subset \\mathbf {X}$ and $\\mathbf {Z}_D \\subset \\mathbf {Z}$ be the row-aligned subsets of monolingual spaces containing vectors of aligned words from $D_0$ . Alignment matrices $\\mathbf {X}_D$ and $\\mathbf {Z}_D$ are then used to learn orthogonal transformations $\\mathbf {W}_x$ and $\\mathbf {W}_z$ that define the joint bilingual space $\\mathbf {Z}$0 . While supervised projection-based CLWE models learn the mapping using a provided external (clean) dictionary $\\mathbf {Z}$1 , their unsupervised counterparts automatically induce the seed dictionary in an unsupervised way (C1) and then refine it in an iterative fashion (C2).",
+      "Unsupervised CLWEs. These methods first induce a seed dictionary $D^{(1)}$ leveraging only two unaligned monolingual spaces (C1). While the algorithms for unsupervised seed dictionary induction differ, they all strongly rely on the assumption of similar topological structure between the two pretrained monolingual spaces. Once the seed dictionary is obtained, the two-step iterative self-learning procedure (C2) takes place: 1) a dictionary $D^{(k)}$ is first used to learn the joint space $\\mathbf {Y}^{(k)} = \\mathbf {X{W}}^{(k)}_x \\cup \\mathbf {Z{W}}^{(k)}_z$ ; 2) the nearest neighbours in $\\mathbf {Y}^{(k)}$ then form the new dictionary $D^{(k+1)}$ . We illustrate the general structure in Figure 1 .",
+      "A recent empirical survey paper BIBREF17 has compared a variety of latest unsupervised CLWE methods BIBREF23 , BIBREF27 , BIBREF33 , BIBREF24 in several downstream tasks (e.g., BLI, cross-lingual information retrieval, document classification). The results of their study indicate that the vecmap model of artetxe2018robust is by far the most robust and best performing unsupervised CLWE model. For the actual results and analyses, we refer the interested reader to the original paper of glavas2019howto. Another recent evaluation paper BIBREF35 as well as our own preliminary BLI tests (not shown for brevity) have further verified their findings. We thus focus on vecmap in our analyses, and base the following description of the components C1-C3 on that model."
+    ],
+    [
+      "C1. Seed Lexicon Extraction. vecmap induces the initial seed dictionary using the following heuristic: monolingual similarity distributions for words with similar meaning will be similar across languages. The monolingual similarity distributions for the two languages are given as rows (or columns; the matrices are symmetric) of $\\mathbf {M}_x = \\mathbf {X}\\mathbf {X}^T$ and $\\mathbf {M}_z = \\mathbf {Z}\\mathbf {Z}^T$ . For the distributions of similarity scores to be comparable, the values in each row of $\\mathbf {M}_x$ and $\\mathbf {M}_z$ are first sorted. The initial dictionary $D^{(1)}$ is finally obtained by searching for mutual nearest neighbours between the rows of $\\sqrt{\\mathbf {M}_x}$ and of $\\sqrt{\\mathbf {M}_z}$ .",
+      "C2. Self-Learning. Not counting the preprocessing and postprocessing steps (component C3), self-learning then iteratively repeats two steps:",
+      "1) Let $\\mathbf {D}^{(k)}$ be the binary matrix indicating the aligned words in the dictionary $D^{(k)}$ . The orthogonal transformation matrices are then obtained as $\\mathbf {W}^{(k)}_x = \\mathbf {U}$ and $\\mathbf {W}^{(k)}_z = \\mathbf {V}$ , where $\\mathbf {U}\\mathbf {\\Sigma }\\mathbf {V}^T$ is the singular value decomposition of the matrix $\\mathbf {X}^T\\mathbf {D}^{(k)}\\mathbf {Z}$ . The cross-lingual space of the $D^{(k)}$0 -th iteration is then $D^{(k)}$1 .",
+      "2) The new dictionary $D^{(k+1)}$ is then built by identifying nearest neighbours in $\\mathbf {Y}^{(k)}$ . These can be easily extracted from the matrix $\\mathbf {P} = \\mathbf {X}\\mathbf {W}^{(k)}_x( \\mathbf {Z}\\mathbf {W}^{(k)}_z)^T$ . All nearest neighbours can be used, or additional symmetry constraints can be imposed to extract only mutual nearest neighbours: all pairs of indices ( $i, j$ ) for which $\\mathbf {P}_{ij}$ is the largest value both in row $i$ and column $j$ .",
+      "The above procedure, however, often converges to poor local optima. To remedy for this, the second step (i.e., dictionary induction) is extended with techniques that make self-learning more robust. First, the vocabularies of $\\mathbf {X}$ and $\\mathbf {Z}$ are cut to the top $k$ most frequent words. Second, similarity scores in $\\mathbf {P}$ are kept with probability $p$ , and set to zero otherwise. This dropout allows for a wider exploration of possible word pairs in the dictionary and contributes to escaping poor local optima given the noisy seed lexicon in the first iterations.",
+      "C3. Preprocessing and Postprocessing Steps. While iteratively learning orthogonal transformations $\\mathbf {W}_{x}$ and $\\mathbf {W}_{z}$ for $\\mathbf {X}$ and $\\mathbf {Z}$ is the central step of unsupervised projection-based CLWE methods, preprocessing and postprocessing techniques are additionally applied before and after the transformation. While such techniques are often overlooked in model comparisons, they may have a great impact on the model's final performance, as we validate in \u00a7 \"Results and Discussion\" . We briefly summarize two pre-processing (S1 and S2) and post-processing (S3 and S4) steps used in our evaluation, originating from the framework of artetxe2018generalizing.",
+      "S1) Normalization and mean centering. We first apply unit length normalization: all vectors in $\\mathbf {X}$ and $\\mathbf {Z}$ are normalized to have a unit Euclidean norm. Following that, $\\mathbf {X}$ and $\\mathbf {Z}$ are mean centered dimension-wise and then again length-normalized.",
+      "S2) Whitening. ZCA whitening BIBREF36 is applied on (S1-processed) $\\mathbf {X}$ and $\\mathbf {Z}$ : it transforms the matrices such that each dimension has unit variance and that the dimensions are uncorrelated. Intuitively, the vector spaces are easier to align along directions of high variance.",
+      "S3) Dewhitening. A transformation inverse to S2: for improved performance it is important to restore the variance information after the projection, if whitening was applied in S2 BIBREF31 .",
+      "S4) Symmetric re-weighting. This step attempts to further align the embeddings in the cross-lingual embedding space by measuring how well a dimension in the space correlates across languages for the current iteration dictionary $D^{(k)}$ . The best results are obtained when re-weighting is neutral to the projection direction, that is, when it is applied symmetrically in both languages.",
+      " In the actual implementation S1 is applied only once, before self-learning. S2, S3 and S4 are applied in each self-learning iteration.",
+      "Model Configurations. Note that C2 and C3 can be equally used on top of any (provided) seed lexicon (i.e., $D^{(1)}$ := $D_0$ ) to enable weakly supervised learning, as we propose here. In fact, the variations of the three key components, C1) seed lexicon, C2) self-learning, and C3) preprocessing and postprocessing, construct various model configurations which can be analyzed to probe the importance of each component in the CLWE induction process. A selection of representative configurations evaluated later in \u00a7 \"Results and Discussion\" is summarized in Table 1 ."
+    ],
+    [
+      "Evaluation Task. Our task is bilingual lexicon induction (BLI). It has become the de facto standard evaluation for projection-based CLWEs BIBREF16 , BIBREF17 . In short, after a shared CLWE space has been induced, the task is to retrieve target language translations for a test set of source language words. Its lightweight nature allows us to conduct a comprehensive evaluation across a large number of language pairs. Since BLI is cast as a ranking task, following glavas2019howto we use mean average precision (MAP) as the main evaluation metric: in our BLI setup with only one correct translation for each \u201cquery\u201d word, MAP is equal to mean reciprocal rank (MRR).",
+      "(Selection of) Language Pairs. Our selection of test languages is guided by the following goals: a) following recent initiatives in other NLP research (e.g., for language modeling) BIBREF39 , BIBREF40 , we aim to ensure the coverage of different genealogical and typological language properties, and b) we aim to analyze a large set of language pairs and offer new evaluation data which extends and surpasses other work in the CLWE literature. These two properties will facilitate analyses between (dis)similar language pairs and offer a comprehensive set of evaluation setups that test the robustness and portability of fully unsupervised CLWEs. The final list of 15 diverse test languages is provided in Table 2 , and includes samples from different languages types and families. We run BLI evaluations for all language pairs in both directions, for a total of 15 $\\times $ 14=210 BLI setups.",
+      "Monolingual Embeddings. We use the 300-dim vectors of Grave:2018lrec for all 15 languages, pretrained on Common Crawl and Wikipedia with fastText BIBREF41 . We trim all vocabularies to the 200K most frequent words.",
+      "Training and Test Dictionaries. They are derived from PanLex BIBREF43 , BIBREF44 , which was used in prior work on cross-lingual word embeddings BIBREF45 , BIBREF46 . PanLex currently spans around 1,300 language varieties with over 12M expressions: it offers some support and supervision also for low-resource language pairs BIBREF47 . For each source language ( $L_1$ ), we automatically translate their vocabulary words (if they are present in PanLex) to all 14 target ( $L_2$ ) languages. To ensure the reliability of the translation pairs, we retain only unigrams found in the vocabularies of the respective $L_2$ monolingual spaces which scored above a PanLex-predefined threshold.",
+      "As in prior work BIBREF23 , BIBREF17 , we then reserve the 5K pairs created from the more frequent $L_1$ words for training, while the next 2K pairs are used for test. Smaller training dictionaries (1K and 500 pairs) are created by again selecting pairs comprising the most frequent $L_1$ words.",
+      "Training Setup. In all experiments, we set the hyper-parameters to values that were tuned in prior research. When extracting the unsupervised seed lexicon, the 4K most frequent words of each language are used; self-learning operates on the 20K most frequent words of each language; with dropout the keep probability $p$ is 0.1; CSLS with $k=10$ nearest neighbors BIBREF24 .",
+      "Again, Table 1 lists the main model configurations in our comparison. For the fully unsupervised model we always report the best performing configuration after probing different self-learning strategies (i.e., +sl, +sl+nod, and +sl+sym are tested). The results for unsupervised are always reported as averages over 5 restarts: this means that with unsupervised we count BLI setups as unsuccessful only if the results are close to zero in all 5/5 runs. orthg-super is the standard supervised model with orthogonal projections from prior work BIBREF21 , BIBREF17 ."
+    ],
+    [
+      "Main BLI results averaged over each source language ( $L_1$ ) are provided in Table 3 and Table 4 . We now summarize and discuss the main findings across several dimensions of comparison.",
+      "Unsupervised vs. (Weakly) Supervised. First, when exactly the same components C2 and C3 are used, unsupervised is unable to outperform a (weakly) supervised full+sl+sym variant, and the gap in final performance is often substantial. In fact, full+sl+sym and full+sl+nod outperform the best unsupervised for all 210/210 BLI setups: we observe the same phenomenon with varying dictionary sizes, that is, it equally holds when we seed self-learning with 5K, 1K, and 500 translation pairs, see also Figure 2 . This also suggests that the main reason why unsupervised approaches were considered on-par with supervised approaches in prior work BIBREF23 , BIBREF24 is because they were not compared under fair circumstances: while unsupervised relied heavily on the components C2 and C3, these were omitted when running supervised baselines. Our unbiased comparison reveals that there is a huge gap even when supervised projection-based approaches consume only several hundred translation pairs to initiate self-learning.",
+      "Are Unsupervised CLWEs Robust? The results also indicate that, contrary to the beliefs established by very recent work BIBREF24 , BIBREF30 , fully unsupervised approaches are still prone to getting stuck in local optima, and still suffer from robustness issues when dealing with distant language pairs: 87 out of 210 BLI setups ( $=41.4\\%$ ) result in (near-)zero BLI performance, see also Table 4 . At the same time, weakly supervised methods with a seed lexicon of 1k or 500 pairs do not suffer from the robustness problem and always converge to a good solution, as also illustrated by the results reported in Table 5 .",
+      "How Important are Preprocessing and Postprocessing? The comparisons between orthg-super (and orthg+sl+sym) on the one hand, and full-super (and full+sl+sym) on the other hand clearly indicate that the component C3 plays a substantial role in effective CLWE learning. full-super, which employs all steps S1-S4 (see \u00a7 \"Methodology\" ), outperforms orthg-super in 208/210 setups with $|D_0|$ =5k and in 210/210 setups with $|D_0|$ =1k. Similarly, full+sl+sym is better than orthg+sl+sym in 210/210 setups (both for $|D_0|$ =1k,5k). The scores also indicate that dropout with self-learning is useful only when we work with noisy unsupervised seed lexicons: full+sl+nod and full+sl+sym without dropout consistently outperform full+sl across the board.",
+      "How Important is (Robust) Self-Learning? We note that the best self-learning method is often useful even when $|D_0|=5k$ (i.e., full+sl+sym is better than full-super in 164/210 setups). However, the importance of robust self-learning gets more pronounced as we decrease the size of $D_0$ : full+sl+sym is better than full-super in 210/210 setups when $|D_0|=500$ or $|D_0|=1,000$ . The gap between the two models, as shown in Figure 2 , increases dramatically in favor of full+sl+sym as we decrease $|D_0|$ .",
+      "Again, just comparing full-super and unsupervised in Figure 2 might give a false impression that fully unsupervised CLWE methods can match their supervised counterparts, but the comparison to full+sl+sym reveals the true extent of performance drop when we abandon even weak supervision. The scores also reveal that the choice of self-learning (C2) does matter: all best performing BLI runs with $|D_0|=1k$ are obtained by two configs with self-learning, and full+sl+sym is the best configuration for 177/210 setups (see Table 4 ).",
+      "Language Pairs. As suggested before by sogaard2018on and further verified by glavas2019howto and doval2019onthe, the language pair at hand can have a huge impact on CLWE induction: the adversarial method of conneau2018word often gets stuck in poor local optima and yields degenerate solutions for distant language pairs such as English-Finnish. More recent CLWE methods BIBREF24 , BIBREF30 focus on mitigating this robustness issue. However, they still rely on one critical assumption which leads them to degraded performance for distant language pairs: they assume approximate isomorphism BIBREF49 , BIBREF48 between monolingual embedding spaces to learn the initial seed dictionary. In other words, they assume very similar geometric constellations between two monolingual spaces: due to the Zipfian phenomena in language BIBREF50 such near-isomorphism can be satisfied only for similar languages and for similar domains used for training monolingual vectors. This property is reflected in the results reported in Table 3 , the number of unsuccessful setups in Table 4 , as well as later in Figure 4 .",
+      "For instance, the largest number of unsuccessful BLI setups with the unsupervised model is reported for Korean, Thai (a tonal language), and Basque (a language isolate): their morphological and genealogical properties are furthest away from other languages in our comparison. A substantial number of unsuccessful setups is also observed with other two language outliers from our set (see Table 2 again), Georgian and Indonesian, as well as with morphologically-rich languages such as Estonian or Turkish.",
+      "One setting in which fully unsupervised methods did show impressive results in prior work are similar language pairs. However, even in these settings when the comparison to the weakly supervised full-super+sym is completely fair (i.e., the same components C2 and C3 are used for both), unsupervised still falls short of full-super+sym. These results for three source languages are summarized in Figure 3 . What is more, one could argue that we do not need unsupervised CLWEs for similar languages in the first place: we can harvest cheap supervision here, e.g., cognates. The main motivation behind unsupervised approaches is to support dissimilar and resource-poor language pairs for which supervision cannot be guaranteed.",
+      "Domain Differences. Finally, we also verify that unsupervised CLWEs still cannot account for domain differences when training monolingual vectors. We rely on the probing test of sogaard2018on: 300-dim fastText vectors are trained on 1.1M sentences on three corpora: 1) EuroParl.v7 BIBREF51 (parliamentary proceedings); 2) Wikipedia BIBREF52 , and 3) EMEA BIBREF53 (medical), and BLI evaluation for three language pairs is conducted on standard MUSE BLI test sets BIBREF23 . The results, summarized in Figure 4 , reveal that unsupervised methods are able to yield a good solution only when there is no domain mismatch and for the pair with two most similar languages (English-Spanish), again questioning their robustness and portability to truly low-resource and more challenging setups. Weakly supervised methods ( $|D_0|=500$ or $D_0$ seeded with identical strings), in contrast, yield good solutions for all setups."
+    ],
+    [
+      "The superiority of weakly supervised methods (e.g., full+sl+sym) over unsupervised methods is especially pronounced for distant and typologically heterogeneous language pairs. However, our study also indicates that even carefully engineered projection-based methods with some seed supervision yield lower absolute performance for such pairs. While we have witnessed the proliferation of fully unsupervised CLWE models recently, some fundamental questions still remain. For instance, the underlying assumption of all projection-based methods (both supervised and unsupervised) is the topological similarity between monolingual spaces, which is why standard simple linear projections result in lower absolute BLI scores for distant pairs (see Table 4 and results in the supplemental material). Unsupervised approaches even exploit the assumption twice as their seed extraction is fully based on the topological similarity.",
+      "Future work should move beyond the restrictive assumption by exploring new methods that can, e.g., 1) increase the isomorphism between monolingual spaces BIBREF54 by distinguishing between language-specific and language-pair-invariant subspaces; 2) learn effective non-linear or multiple local projections between monolingual spaces similar to the preliminary work of nakashole2018norma; 3) similar to vulic2016on and Lubin:2019naacl \u201cdenoisify\u201d seed lexicons during the self-learning procedure. For instance, keeping only mutual/symmetric nearest neighbour as in full+sl+sym can be seen as a form of rudimentary denoisifying: it is indicative to see that the best overall performance in this work is reported with that model configuration.",
+      "Further, the most important contributions of unsupervised CLWE models are, in fact, the improved and more robust self-learning procedures (component C2) and technical enhancements (component C3). In this work we have demonstrated that these components can be equally applied to weakly supervised approaches: starting from a set of only several hundred pairs, they can guarantee consistently improved performance across the board. As there is still no clear-cut use case scenario for unsupervised CLWEs, instead of \u201cgoing fully unsupervised\u201d, one pragmatic approach to widen the scope of CLWE learning and its application might invest more effort into extracting at least some seed supervision for a variety of language pairs BIBREF22 . This finding aligns well with the ongoing initiatives of the PanLex project BIBREF44 and the ASJP database BIBREF56 , which aim to collate at least some translation pairs in most of the world\u2019s languages.",
+      "Finally, this paper demonstrates that, in order to enable fair comparisons, future work on CLWEs should focus on evaluating the CLWE methods' constituent components (e.g, components C1-C3 from this work) instead of full-blown composite systems directly. One goal of the paper is to acknowledge that the work on fully unsupervised CLWE methods has indeed advanced state-of-the-art in cross-lingual word representation learning by offering new solutions also to weakly supervised CLWE methods. However, the robustness problems are still prominent with fully unsupervised CLWEs, and future work should invest more time and effort into developing more robust and more effective methods, e.g., by reaching beyond projection-based methods towards joint approaches BIBREF16 , BIBREF57 ."
+    ],
+    [
+      "This work is supported by the ERC Consolidator Grant LEXICAL: Lexical Acquisition Across Languages (no 648909). The work of Goran Glava\u0161 is supported by the Baden-W\u00fcrttemberg Stiftung (AGREE grant of the Eliteprogramm). Roi Reichart is partially funded by ISF personal grants No. 1625/18. We thank the three anonymous reviewers for their encouraging comments and suggestions."
+    ]
+  ]
+}
+```
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+++ b/qasper-1571/instruction.md
@@ -0,0 +1,72 @@
+Name of Paper: CA-EHN: Commonsense Word Analogy from E-HowNet
+
+Question: What types of word representations are they evaluating?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "E-HowNet",
+    "Lexicon",
+    "Taxonomy",
+    "Commonsense Analogy",
+    "Analogical Word Pairs",
+    "Accurate Analogy",
+    "Analogy Datasets",
+    "Word Embeddings",
+    "Commonsense Infusing",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Commonsense reasoning is fundamental for natural language agents to generalize inference beyond their training corpora. Although the natural language inference (NLI) task BIBREF0 , BIBREF1 has proved a good pre-training objective for sentence representations BIBREF2 , commonsense coverage is limited and most models are still end-to-end, relying heavily on word representations to provide background world knowledge.",
+      "Therefore, we propose modeling commonsense knowledge down to word-level analogical reasoning. In this sense, existing analogy benchmarks are lackluster. For Chinese analogy (CA), the simplified Chinese dataset CA8 BIBREF3 and the traditional Chinese dataset CA-Google BIBREF4 translated from the English BIBREF5 contain only a few dozen relations, most of which are either morphological, e.g., a shared prefix, or about named entities, e.g., capital-country.",
+      "However, commonsense knowledge bases such as WordNet BIBREF6 and ConceptNet BIBREF7 have long annotated relations in our lexicon. Among them, E-HowNet BIBREF4 , extended from HowNet BIBREF8 , currently annotates 88K traditional Chinese words with their structured definitions and English translations.",
+      "In this paper, we propose an algorithm for the extraction of accurate commonsense analogies from E-HowNet. We present CA-EHN, the first commonsense analogy dataset containing 85,226 analogies covering 5,563 words and 6,490 commonsense relations."
+    ],
+    [
+      "E-HowNet 2.0 consists of two major parts: A lexicon of words and concepts with multi-layered annotations, and a taxonomy of concepts with attached word senses."
+    ],
+    [
+      "The E-HowNet lexicon consists of two types of tokens: 88K words and 4K concepts. Words and concepts are distinguished by whether there is a vertical bar and an English string in the token. For example, UTF8bkai\u4eba (person) and UTF8bkai\u96de (chicken) are words, and human $\\vert $ UTF8bkai\u4eba and UTF8bkai\u96de $\\vert $ chicken are concepts. In this work, the order of English and Chinese within a concept does not matter. In addition, E-HowNet also contains dozens of relations, which comes fully in English, e.g., or, theme, telic.",
+      "Words and concepts in E-HowNet are annotated with one or more structured definitions consisting of concepts and relations. Table 1 provides several examples with gradually increasing complexity: UTF8bkai\u4eba (person) is defined simply as a human $\\vert $ UTF8bkai\u4eba; UTF8bkai\u99ff\u99ac $\\vert $ ExcellentSteed is defined as a UTF8bkai\u99ac $\\vert $ horse which has a qualification relation with HighQuality $\\vert $ UTF8bkai\u512a\u8cea; UTF8bkai\u5be6\u9a57\u5ba4 (laboratory) is defined as a InstitutePlace $\\vert $ UTF8bkai\u5834\u6240 used for conducting experiments or research. Each concept has only one definition, but a word may have multiple senses and hence multiple definitions. In this work, we use E-HowNet word sense definitions to extract commonsense analogies (Section \"Commonsense Analogy\" ). In addition, word senses are annotated with their English translations, which could be used to transfer our extracted analogies to English multi-word expressions (MWE)."
+    ],
+    [
+      "Concepts in E-HowNet are additionally organized into a taxonomy. Figure 1 shows the partially expanded tree. Each word sense in the E-HowNet lexicon is attached to a taxon in the tree. In this work, we show that infusing E-HowNet taxonomy tree into word embeddings boosts performance across benchmarks (Section \"Commonsense Infusing\" )."
+    ],
+    [
+      "We extract commonsense word analogies with a rich coverage of words and relations by comparing word sense definitions. The extraction algorithm is further refined with multiple filters, including putting the linguist in the loop."
+    ],
+    [
+      "Illustrated in Figure 2 , the extraction algorithm before refinement consists of five steps.",
+      "Definition concept expansion. As many words are synonymous with some concepts, many word senses are defined trivially by one concept. For example, the definition of UTF8bkai\u99ff\u99ac (excellent steed) is simply {UTF8bkai\u99ff\u99ac $\\vert $ ExcellentSteed}. The triviality is resolved by expanding such definitions by one layer, e.g., replacing {UTF8bkai\u99ff\u99ac $\\vert $ ExcellentSteed} with {UTF8bkai\u99ac $\\vert $ horse:qualification={HighQuality $\\vert $ UTF8bkai\u512a\u8cea}}, i.e., the definition of UTF8bkai\u99ff\u99ac $\\vert $ ExcellentSteed.",
+      "Definition string parsing. We parse each definition into a directed graph. Each node in the graph is either a word, a concept, or a function relation, e.g., or() at the bottom of Table 1 . Each edge is either an attribute relation edge, e.g., :telic=, or a dummy argument edge connecting a function node with one of its argument nodes.",
+      "Definition graph comparison. For every sense pair of two different words in the E-HowNet lexicon, we determine if their definition graphs differ only in one concept node. If they do, the two (word, concept) pairs are analogical to one another. For example, since the graph of UTF8bkai\u826f\u6750 sense#2 (the good timber) and the expanded graph of UTF8bkai\u99ff\u99ac sense#1 (an excellent steed) differs only in wood $\\vert $ UTF8bkai\u6728 and UTF8bkai\u99ac $\\vert $ horse, we extract the following concept analogy: UTF8bkai\u826f\u6750:wood $\\vert $ UTF8bkai\u6728=UTF8bkai\u99ff\u99ac:UTF8bkai\u99ac $\\vert $ horse.",
+      "Left concept expansion. For each concept analogy, we expand the left concept to those words that have one sense defined trivially by it. For example, there is only one word UTF8bkai\u6728\u982d (wood) defined as {wood $\\vert $ UTF8bkai\u6728}. Thus after expansion, there is still only one analogy: UTF8bkai\u826f\u6750:UTF8bkai\u6728\u982d=UTF8bkai\u99ff\u99ac:UTF8bkai\u99ac $\\vert $ horse. Most of the time, this step yields multiple analogies per concept analogy.",
+      "Right concept expansion. Finally, the remaining concept in each analogy is again expanded to the list of words with a sense trivially defined by it. However, this time we do not use them to form multiple analogies. Instead, the word list is kept as a synset. For example, as UTF8bkai\u5c71\u99ac (orohippus), UTF8bkai\u99ac (horse), UTF8bkai\u99ac\u5339 (horses), UTF8bkai\u99d9 (side horse) all have one sense defined as {UTF8bkai\u99ac $\\vert $ horse}, the final analogy becomes UTF8bkai\u826f\u6750:UTF8bkai\u6728\u982d=UTF8bkai\u99ff\u99ac:{UTF8bkai\u5c71\u99ac,UTF8bkai\u99ac,UTF8bkai\u99ac\u5339,UTF8bkai\u99d9}. When evaluating embeddings on our benchmark, we consider it a correct prediction as long as it belongs to the synset."
+    ],
+    [
+      "As the core procedure yields an excessively large benchmark, added to the fact that E-HowNet word sense definitions are sometimes inaccurate, we made several refinements to the extraction process.",
+      "Concrete concepts. As we found that E-HowNet tends to provide more accurate definitions for more concrete concepts, we require words and concepts at every step of the process to be under physical $\\vert $ UTF8bkai\u7269\u8cea, which is one layer below thing $\\vert $ UTF8bkai\u842c\u7269 in Figure 1 . This restriction shrinks the benchmark by half.",
+      "Common words. At every step of the process, we require words to occur at least five times in ASBC 4.0 BIBREF9 , a segmented traditional Chinese corpus containing 10M words from articles between 1981 and 2007. This eliminates uncommon, ancient words or words with synonymous but uncommon, ancient characters. As shown in Table 3 , the benchmark size is significantly reduced by this restriction.",
+      "Linguist checking. We added two data checks into the extraction process between definition graph comparison and left concept expansion. As shown in Table 3 , each of the 36,100 concept analogies were checked by a linguist, leaving 24,439 accurate ones. Furthermore, each synset needed in the 24,439 concept analogies was checked again to remove words that are not actually synonymous with the defining concept. For example, UTF8bkai\u82b1\u8349, UTF8bkai\u5c71\u8336\u82b1, UTF8bkai\u85b0\u8863\u8349, UTF8bkai\u9cf6\u5c3e\u82b1 are all common words with a sense defined trivially as {FlowerGrass $\\vert $ UTF8bkai\u82b1\u8349}. However, the last three (camellia, lavender, iris) are not actually synonyms but hyponyms to the concept. This step also helps eliminate words in a synset that are using their rare senses, as we do not expect embeddings to encode those senses without word sense disambiguation (WSD). After the second-pass linguist check, we arrived at 85,226 accurate analogies."
+    ],
+    [
+      "Table 3 compares Chinese word analogy datasets. Most analogies in existing datasets are morphological (morph.) or named entity (entity) relations. For example, CA8-Morphological BIBREF3 uses 21 shared prefix characters, e.g., UTF8bkai\u7b2c, to form 2,553 analogies, e.g., UTF8bkai\u4e00 : UTF8bkai\u7b2c\u4e00 = UTF8bkai\u4e8c : UTF8bkai\u7b2c\u4e8c (one : first = two : second). As for named entities, some 20 word pairs of the capital-country relation can be permuted to form 190 analogies, which require a knowledge base but not commonsense to solve. Only the nature part of CA8 and the man-woman part of CA-Google BIBREF10 contains a handful of relations that requires commonsense world knowledge. In constrast, CA-EHN extracts 85K linguist-checked analogies covering 6,490 concept pairs, e.g., (wood $\\vert $ UTF8bkai\u6728, UTF8bkai\u99ac $\\vert $ horse). Table 2 shows a small list of the data, covering such diverse domains as onomatopoeia, disability, kinship, and zoology. Full CA-EHN is available in the supplementary materials."
+    ],
+    [
+      "We trained word embeddings using either GloVe BIBREF11 or SGNS BIBREF12 on a small or a large corpus. The small corpus consists of the traditional Chinese part of Chinese Gigaword BIBREF13 and ASBC 4.0 BIBREF9 . The large corpus additionally includes the Chinese part of Wikipedia.",
+      "Table 4 shows embedding performance across analogy benchmarks. Cov denotes the number of analogies of which the first three words exist in the embedding. Analogies that are not covered are excluded from that evaluation. Still, we observe that the larger corpus yields higher accuracy across all benchmarks. In addition, using SGNS instead of GloVe provides universal boosts in performance.",
+      "While performance on CA-EHN correlates well to that on other benchmarks, commonsense analogies prove to be much more difficult than morphological or named entity analogies for distributed word representations."
+    ],
+    [
+      "E-HowNet comes in two major parts: a lexicon and a taxonomy. For the lexicon, we have used it to extract the CA-EHN commonsense analogies. For the taxonomy, we experiment infusing its hypo-hyper and same-taxon relations to distributed word representations by retrofitting BIBREF14 . For example, in Figure 1 , the word vector of UTF8bkai\u7a7a\u9593 is optimized to be close to both its distributed representation and the word vectors of UTF8bkai\u7a7a\u9699 (same-taxon) and UTF8bkai\u4e8b\u7269 (hypo-hyper). Table 4 shows that retrofitting embeddings with E-HowNet taxonomy improves performance on most benchmarks, and all three embeddings have doubled accuracies on CA-EHN. This shows that CA-EHN is a great indicator of how well word representations embed commonsense knowledge."
+    ],
+    [
+      "We have presented CA-EHN, the first commonsense word analogy dataset, by leveraging word sense definitions in E-HowNet. After linguist checking, we have 85,226 Chinese analogies covering 5,563 words and 6,490 commonsense relations. We anticipate that CA-EHN will become an important benchmark testing how well future embedding methods capture commonsense knowledge, which is crucial for models to generalize inference beyond their training corpora. With translations provided by E-HowNet, Chinese words in CA-EHN can be transferred to English MWEs."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1582/instruction.md b/qasper-1582/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..bc0d60e869cbf4938c66e2276d8a4de71190c5a2
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@@ -0,0 +1,98 @@
+Name of Paper: TutorialVQA: Question Answering Dataset for Tutorial Videos
+
+Question: What baseline algorithms were presented?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "TutorialVQA Dataset",
+    "TutorialVQA Dataset ::: Overview",
+    "TutorialVQA Dataset ::: Basis",
+    "TutorialVQA Dataset ::: Data Collection",
+    "TutorialVQA Dataset ::: Dataset Details",
+    "Baselines",
+    "Baselines ::: Baseline1: Sentence-level prediction",
+    "Baselines ::: Baseline2: Segment retrieval",
+    "Baselines ::: Baseline3: Pipeline Segment retrieval",
+    "Baselines ::: Results",
+    "Discussion and Future Work",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Video is the fastest growing medium to create and deliver information today. Consequentially, videos have been increasingly used as main data sources in many question answering problems BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF2, BIBREF5. These previous studies have mostly focused on factoid questions, each of which can be answered in a few words or phrases generated by understanding multimodal contents in a short video clip.",
+      "However, this problem definition of video question answering causes some practical limitations for the following reasons. First, factoid questions are just a small part of what people actually want to ask on video contents. Especially if a short video is given to users, most fragmentary facts within the scope of previous tasks can be easily perceived by themselves even before asking questions. Thus, video question answering is expected to provide answers to more complicated non-factoid questions beyond the simple facts. For example, those questions could be the ones asking about a how procedure as shown in Fig. FIGREF5, and the answers should contain all necessary steps to complete the task.",
+      "Accordingly, the answer format needs to also be improved towards more flexible ways than multiple choice BIBREF1, BIBREF2 or fill-in-the-blank questions BIBREF3, BIBREF4. Although open-ended video question answering BIBREF0, BIBREF2, BIBREF5 has been explored, it still aims to generate just a short word or phrase-level answer, which is not enough to cover various granularities of non-factoid question answering.",
+      "The other issue is that most videos with sufficient amount of information, which are likely to be asked, have much longer lengths than the video clips in the existing datasets. Therefore, the most relevant part of a whole video needs to be determined prior to each answer generation in practice. However, this localization task has been out of scope for previous studies.",
+      "In this work, we propose a new question answering problem for non-factoid questions on instructional videos. According to the nature of the media created for educational purposes, we assume that many answers already exist within the given video contents. Under this assumption, we formulate the problem as a localization task to specify the span of a video segment as the direct answer to a given video and a question, as illustrated in Figure FIGREF1.",
+      "The remainder of this paper is structured as follows. Section SECREF3 introduces TutorialVQA dataset as a case study of our proposed problem. The dataset includes about 6,000 triples, comprised of videos, questions, and answer spans manually collected from screencast tutorial videos with spoken narratives for a photo-editing software. Section SECREF4 presents the baseline models and their experiment details on the sentence-level prediction and video segment retrieval tasks on our dataset. Then, we discuss the experimental results in Section SECREF5 and conclude the paper in Section SECREF6."
+    ],
+    [
+      "Most relevant to our proposed work is the reading comprehension task, which is a question answering task involving a piece of text such as a paragraph or article. Such datasets for the reading comprehension task, such as SQuAD BIBREF6 based on Wikipedia, TriviaQA BIBREF7 constructed from trivia questions with answer evidence from Wikipedia, or those from Hermann et al. based on CNN and Daily Mail articles BIBREF8 are factoid-based, meaning the answers typically involve a single entity. Differing from video transcripts, the structures of these data sources, namely paragraphs from Wikipedia and news sources, are typically straightforward since they are meant to be read. In contrast, video transcripts originate from spoken dialogue, which can verbose, unstructured, and disconnected. Furthermore, the answers in instructional video transcripts can be longer, spanning multiple sentences if the process is multi-step or even fragmented into multiple segments throughout the video.",
+      "Visual corpora in particular have proven extremely valuable to visual questions-answering tasks BIBREF9, the most similar being MovieQA BIBREF1 and VideoQA BIBREF0. Similar to how our data is generated from video tutorials, the MovieQA and VideoQA corpus is generated from movie scripts and news transcipts, respectively. MovieQA's answers have a shorter span than the answers collected in our corpus, because questions and answer pairs were generated after each paragraph in a movie's plot synopsis BIBREF1. The MovieQA dataset also contains specific annotated answers with incorrect examples for each question. In the VideoQA dataset, questions focus on a single entity, contrary to our instructional video dataset. Although not necessarily a visual question-answering task, the work proposed by BIBREF10 involved answering questions over transcript data. Contrary to our work, Gupta's dataset is not publically available and their examples only showcase factoid-style questions involving single entity answers.",
+      "BIBREF11 focus on aligning a set of instructions to a video of someone carrying out those instructions. In their task, they use the video transcript to represent the video, which they later augment with a visual cue detector on food entities. Their task focuses on procedure-based cooking videos, and contrary to our task is primarily a text alignment task. In our task we aim to answer questions-using the transcripts-on instructional-style videos, in which the answer can involve steps not mentioned in the question."
+    ],
+    [
+      "In this section, we introduce the TutorialVQA dataset and describe the data collection process ."
+    ],
+    [
+      "Our dataset consists of 76 tutorial videos pertaining to an image editing software. All of the videos include spoken instructions which are transcribed and manually segmented into multiple segments. Specifically, we asked the annotators to manually divide each video into multiple segments such that each of the segments can serve as an answer to any question. For example, Fig. FIGREF1 shows example segments marked in red (each which are a complete unit as an answer span). Each sentence is associated with the starting and ending time-stamps, which can be used to access the relevant visual information.",
+      "The dataset contains 6,195 non-factoid QA pairs, where the answers are the segments that were manually annotated. Fig. FIGREF5 shows an example of the annotations. video_id can be used to retrieve the video information such as meta information and the transcripts. answer_start and answer_end denote the starting and ending sentence indexes of the answer span. Table. TABREF4 shows the statistics of our dataset, with each answer segment having on average about 6 sentences, showing that our answers are more verbose than those in previous factoid QA tasks."
+    ],
+    [
+      "We chose videos pertaining to an image editing software because of the complexity and variety of tasks involved. In these videos, a narrator is communicating an overall goal by utilizing an example. For example, in FIGREF1 the video pertains to combining multiple layers into one image. However, throughout the videos multiple subtasks are achieved, such as the opening of multiple images, the masking of images, and the placement of two images side-by-side. These subtasks involve multiple steps and are of interest to us in segmenting the videos. Each segment can be seen as a subtask within a larger video dictating an example. We thus chose these videos because of the amount of procedural information stored in each video for which the user may ask. Though there is only one domain, each video corresponds to a different overall goal."
+    ],
+    [
+      "We downloaded 76 videos from a tutorial website about an image editing program . Each video is pre-processed to provide the transcripts and the time-stamp information for each sentence in the transcript. We then used Amazon Mechanical Turk to collect the question-answer pairs . One naive way of collecting the data is to prepare a question list and then, for each question, ask the workers to find the relevant parts in the video. However, this approach is not feasible and error-prone because the videos are typically long and finding a relevant part from a long video is difficult. Doing so might also cause us to miss questions which were relevant to the video segment. Instead, we took a reversed approach. First, for each video, we manually identified the sentence spans that can serve as answers. These candidates are of various granularity and may overlap. The segments are also complete in that they encompass the beginning and end of a task. In total, we identified 408 segments from the 76 videos. Second we asked AMT workers to provide question annotations for the videos.",
+      "Our AMT experiment consisted of two parts. In the first part, we presented the workers with the video content of a segment. For each segment, we asked workers to generate questions that can be answered by the presented segment. We did not limit the number of questions a worker can input to a corresponding segment and encouraged them to input a diverse set of questions which the span can answer. Along with the questions, the workers were also required to provide a justification as to why they made their questions. We manually checked this justification to filter out the questions with poor quality by removing those questions which were unrelated to the video. One initial challenge worth mentioning is that at first some workers input questions they had about the video and not questions which the video could answer. This was solved by providing them with an unrelated example. The second part of the question collection framework consisted of a paraphrasing task. In this task we presented workers with the questions generated by the first task and asked them to write the questions differently while keeping the semantics the same. In this way, we expanded our question dataset. After filtering out the questions with low quality, we collected a total of 6,195 questions.",
+      "It is important to note the differences between our data collection process and the the query generation process employed in the Search and Hyperlinking Task at MediaEval BIBREF12. In the Search and Hyperlinking Task, 30 users were tasked to first browse the collection of videos, select interesting segments with start and end times, and then asked to conjecture questions that they would use on a search query to find the interesting video segments. This was done in order to emulate their thought-proces mechanism. While the nature of their task involves queries relating to the overall videos themselves, hence coming from a video's interestingness, our task involves users already being given a video and formulating questions where the answers themselves come from within a video. By presenting the same video segment to many users, we maintain a consistent set of video segments and extend the possibility to generate a diverse set of question for the same segment."
+    ],
+    [
+      "Table TABREF12 presents some extracted sample questions from our dataset. The first column corresponds to an AMT generated question, while the second column corresponds to the video ID where the segment can be found. As can be seen in the first two rows, multiple types of questions can be answered within the same video (but different segments). The last two rows display questions which belong to the same segment but correspond to different properties of the same entity, 'crop tool'. Here we observe different types of questions, such as \"why\", \"how\", \"what\", and \"where\", and can see why the answers may involve multiple steps. Some questions that the worked paraphrased were in the \"yes/no\" style, however our answer segments then provide an explanation to these questions.",
+      "Each answer segment was extracted from an image editing tutorial video that involved multiple steps and procedures to produce a final image, which can partially be seen in FIGREF1. The average number of sentences per video was approximately 52, with the maximum number of sentences contained in a video being 187. The sub-tasks in the tutorial include segments (and thus answers) on editing parts of images, instructions on using certain tools, possible actions that can be performed on an image, and identifying the locations of tools and features, with the shortest and longest segment having a span of 1 and 37 sentences respectively, demonstrating the heterogeneity of the answer spans."
+    ],
+    [
+      "Our video question answering task is novel and to our knowledge, no model has been designed specifically for this task. As a first step towards solving this problem, we evaluated the performance of state-of-the-art models developed for other QA tasks, including a sentence-level prediction task and two segment retrieval tasks. In this section, we report their results on the TutorialVQA dataset."
+    ],
+    [
+      "Given a transcript (a sequence of sentences) and a question, Baseline1 predicts (starting sentence index, ending sentence index). The model is based on RaSor BIBREF13, which has been developed for the SQuAD QA task BIBREF6. RaSor concatenates the embedding vectors of the starting and the ending words to represent a span. Following this idea, Baseline1 represents a span of sentences by concatenating the vectors of the starting and ending sentences. The left diagram in Fig. FIGREF15 illustrates the Baseline1 model.",
+      "Model. The model takes two inputs, a transcript, $\\lbrace s_1, s_2, ... s_n\\rbrace $ where $s_i$ are individual sentences and a question, $q$. The output is the span scores, $y$, the scores over all possible spans. GLoVe BIBREF14 is used for the word representations in the transcript and the questions. We use two bi-LSTMs BIBREF15 to encode the transcript.",
+      "where n is the number of sentences . The output of Passage-level Encoding, $p$, is a sequence of vector, $p_i$, which represents the latent meaning of each sentence. Then, the model combines each pair of sentence embeddings ($p_i$, $p_j$) to generate a span embedding.",
+      "where [$\\cdot $,$\\cdot $] indicates the concatenation. Finally, we use a one-layer feed forward network to compute a score between each span and a question.",
+      "In training, we use cross-entropy as an objective function. In testing, the span with the highest score is picked as an answer.",
+      "Metrics. We use tolerance accuracy BIBREF16, which measures how far away the predicted span is from the gold standard span, as a metric. The rationale behind the metric is that, in practice, it suffices to recommend a rough span which contains the answer \u2013 a difference of a few seconds would not matter much to the user.",
+      "Specifically, the predicted span is counted as correct if $|pred_{start} - gt_{start}| + |pred_{end} - gt_{end}| <=$ $k$, where $pred_{start/end}$ and $gt_{start/end}$ indicate the indices of the predicted and ground-truth starting and ending sentences, respectively. We then measure the percentage of correctly predicted questions among the entire test questions."
+    ],
+    [
+      "We also considered a simpler task by casting our problem as a retrieval task. Specifically, in addition to a plain transcript, we also provided the model with the segmentation information which was created during the data collection phrase (See Section. SECREF3). Note that each segments corresponds to a candidate answer. Then, the task is to pick the best segment for given a query. This task is easier than Baseline1's task in that the segmentation information is provided to the model. Unlike Baseline1, however, it is unable to return an answer span at various granularities. Baseline2 is based on the attentive LSTM BIBREF17, which has been developed for the InsuranceQA task. The right diagram in Fig. FIGREF15 illustrates the Baseline2 model.",
+      "Model. The two inputs, $s$ and $q$ represent the segment text and a question. The model first encodes the two inputs.",
+      "$h^s$ is then re-weighted using attention weights.",
+      "where $\\odot $ denotes the element-wise multiplication operation. The final score is computed using a one-layer feed-forward network.",
+      "During training, the model requires negative samples. For each positive example, (question, ground-truth segment), all the other segments in the same transcript are used as negative samples. Cross entropy is used as an objective function.",
+      "Metrics. We used accuracy and MRR (Mean Reciprocal Ranking) as metrics. The accuracy is",
+      "We split the ground-truth dataset to train/dev/test into the ratio of 6/2/2. The resulting size is 3,718 (train), 1,238 (dev) and 1,239 qa pairs (test)."
+    ],
+    [
+      "We construct a pipelined approach through another segment retrieval task, calculating the cosine similarities between the segment and question embeddings. In this task however, we want to test the accuracy of retrieving the segments given that we first retrieve the correct video from our 76 videos. First, we generate the TF-IDF embeddings for the whole video transcripts and questions. The next step involves retrieving the videos which have the lowest cosine distance between the video transcripts and question. We then filter and store the top ten videos, reducing the number of computations required in the next step. Finally, we calculate the cosine distances between the question and the segments which belong to the filtered top 10 videos, marking it as correct if found in these videos. While the task is less computationally expensive than the previous baseline, we do not learn the segment representations, as this task is a simple retrieval task based on TF-IDF embeddings.",
+      "Model. The first two inputs are are the question, q, and video transcript, v, encoded by their TF-IDF vectors: BIBREF18:",
+      "We then filter the top 10 video transcripts(out of 76) with the minimum cosine distance, and further compute the TF-IDF vectors for their segments, Stop10n, where n = 10. We repeat the process for the corresponding segments:",
+      "selecting the segment with the minimal cosine distance distance to the query.",
+      "Metrics. To evaluate our pipeline approach we use overall accuracy after filtering and accuracy given that the segment is in the top 10 videos. While the first metric is similar to SECREF17, the second can indicate if initially searching on the video space can be used to improve our selection:"
+    ],
+    [
+      "Tables TABREF20, TABREF21, TABREF22 show the results. First, the tables show that the two first baselines under-perform for our task. Even with a tolerance window of 6, Baseline1 merely achieves an accuracy of .14. Baseline2, despite being a simpler task, has only an accuracy of .23. Second, while we originally hypothesized that the segment selection task should be easier than the sentence prediction task, Table TABREF21 shows that the task is also challenging. One possible reason is that the segments contained within the same transcript have similar contents, due to the composition of the overall task in each video, and differentiating among them may require a more sophisticated model than just using a sequence model for segment representation. Table TABREF22 shows the accuracy of retrieving the correct segment, for baseline both overall and given that the video selected is within the top 10 videos. While the overall accuracy is only .16, by reducing the search space to 10 relevant videos our accuracy increases to 0.6385. In future iterations, it may then be useful to find better approaches in filtering large paragraphs of text before predicting the correct segment."
+    ],
+    [
+      "We performed an error analysis on Baseline1's results. We first observe that, in 92% of the errors, the predicted span and the ground-truth overlap. Furthermore, in 56% of the errors, the predicted spans are a subset or superset of the ground-truth spans. This indicates that the model finds the rough answer regions but fails to locate the precise boundaries. To address this issue, we plan on exploring the Pointer-network BIBREF19, which finds an answer span by selecting the boundary sentences. Unlike Baseline1 which avoids an explicit segmentation step, the Pointer-network can explicitly model which sentences are likely to be a boundary sentence. Moreover, the search space of the spans in the Pointer-network is $2n$ where $n$ is the number of sentences, because it selects only two boundary sentences. Note that the search space of Baseline1 is $n^2$. A much smaller search space might improve the accuracy by making the model consider fewer candidates.",
+      "In future work, we also plan to use multi-modal information. While our baselines only used the transcript, complementing the narratives with the visual information may improve the performance, similarly to the text alignment task in BIBREF11."
+    ],
+    [
+      "We have described the collection, analysis, and baseline results of TutorialVQA, a new type of dataset used to find answer spans in tutorial videos. Our data collection method for question-answer pairs on instructional video can be further adopted to other domains where the answers involve multiple steps and are part of an overall goal, such as cooking or educational videos. We have shown that current baseline models for finding the answer spans are not sufficient for achieving high accuracy and hope that by releasing this new dataset and task, more appropriate question answering models can be developed for question answering on instructional videos."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: A Multi-task Learning Model for Chinese-oriented Aspect Polarity Classification and Aspect Term Extraction
+
+Question: What was state of the art on SemEval-2014 task4 Restaurant and Laptop dataset?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Works",
+    "Related Works ::: Aspect Term Extraction",
+    "Related Works ::: Aspect Polarity Classification",
+    "Methodology",
+    "Methodology ::: Task Definition ::: Aspect Term Extraction",
+    "Methodology ::: Task Definition ::: Aspect Polarity Classification",
+    "Methodology ::: Model Architecture",
+    "Methodology ::: Model Architecture ::: BERT-Shared Layer",
+    "Methodology ::: Multi-Head Self-Attention",
+    "Methodology ::: Local Context Focus ::: Semantic-Relative Distance",
+    "Methodology ::: Local Context Focus ::: Context-features Dynamic Mask",
+    "Methodology ::: Local Context Focus ::: Context-features Dynamic Weighting",
+    "Methodology ::: Feature Interactive Learning",
+    "Methodology ::: Aspect Polarity Classifier",
+    "Methodology ::: Aspect Term Extractor",
+    "Methodology ::: Training Details",
+    "Experiments ::: Datasets and Hyperparameters Setting",
+    "Experiments ::: Compared Methods",
+    "Experiments ::: Results Analysis",
+    "Experiments ::: Results Analysis ::: Performance on Chinese Review Datasets",
+    "Experiments ::: Results Analysis ::: Performance on SemEval-2014 task4",
+    "Experiments ::: Overall Performance Analysis",
+    "Experiments ::: Overall Performance Analysis ::: Effectiveness of Multi-task Learning",
+    "Experiments ::: Overall Performance Analysis ::: Domain-adaption for LCF-ATEPC",
+    "Experiments ::: Overall Performance Analysis ::: SRD Sensitivity on Different Datasets",
+    "Conclusion",
+    "Acknowledgments and Funding"
+  ],
+  "paragraphs": [
+    [
+      "Aspect-based sentiment analysis BIBREF0, BIBREF1, BIBREF2 (ABSA) is a fine-grained task compared with traditional sentiment analysis, which requires the model to be able to automatic extract the aspects and predict the polarities of all the aspects. For example, given a restaurant review: \"The dessert at this restaurant is delicious but the service is poor,\" the full-designed model for ABSA needs to extract the aspects \"dessert\" and \"service\" and correctly reason about their polarity. In this review, the consumers' opinions on \"dessert\" and \"service\" are not consistent, with positive and negative sentiment polarity respectively.",
+      "Generally, aspects and their polarity need to be manually labeled before running the aspect polarity classification procedure in the supervised deep learning models. However, most of the proposed models for aspect-based sentiment analysis tasks only focus on improving the classification accuracy of aspect polarity and ignore the research of aspect term extraction. Therefore, when conducting transfer learning on aspect-based sentiment analysis, those proposed models often fall into the dilemma of lacking aspect extraction method on targeted tasks because there is not enough research support.",
+      "The APC task is a kind of classification problem. The researches concerning APC tasks is more abundant than the ATE task, and a large number of deep learning-based models have been proposed to solve APC problems, such as the models BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8 based on long short-term memory (LSTM) and the methodologies BIBREF9, BIBREF10 based on transformer BIBREF11. The purpose of the APC task is to predict the exact sentiment polarity of different aspects in their context, rather than to fuzzily analyze the overall sentiment polarity on the sentence-level or document-level. In the APC task, the polarities are most usually classified into three categories: positive, negative, and neutral. It is obvious that the sentiment polarity classified based on aspects can better mine the fine-grained emotional tendency in reviews or tweets, thus providing a more accurate reference for decision-makers.",
+      "Similar to the named entity recognition BIBREF12 (NER) task, the ATE task is a sequence labeling task, which aims to extract aspects from the reviews or tweet. In most researches BIBREF13, BIBREF14, BIBREF15, the ATE task is studied independently, away from the APC task. The ATE task first segments a review into separate tokens and then infers whether the tokens belong to any aspect. The tokens may be labeled in different forms in different studies, but most of the studies have adopted the IOB label to annotate tokens.",
+      "Aiming to automatically extract aspects from the text efficiently and analyze the sentiment polarity of aspects simultaneously, this paper proposes a multi-task learning model for aspect-based sentiment analysis. Multilingual processing is an important research orientation of natural language processing. The LCF-ATEPC model proposed in this paper is a novel multilingual and multi-task-oriented model. Apart from achieving state-of-the-art performance in commonly used SemEval-2014 task4 datasets, the experimental results in four Chinese review datasets also validate that this model has a strong ability to expand and adapt to the needs of multilingual task. The proposed model is based on multi-head self-attention (MHSA) and integrates the pre-trained BERT BIBREF16 and the local context focus mechanism, namely LCF-ATEPC. By training on a small amount of annotated data of aspect and their polarity, the model can be adapted to a large-scale dataset, automatically extracting the aspects and predicting the sentiment polarities. In this way, the model can discover the unknown aspects and avoids the tedious and huge cost of manually annotating all aspects and polarities. It is of great significance for the field-specific aspect-based sentiment analysis.",
+      "The main contributions of this article are as follows:",
+      "For the first time, this paper studies the multi-task model of APC subtask and ATE subtask for multilingual reviews, which provides a new idea for the research of Chinese aspect extraction.",
+      "This paper firstly applies self-attention and local context focus techniques to aspect word extraction task, and fully explore their potential in aspect term extraction task.",
+      "The LCF-ATEPC model proposed in this paper integrates the pre-trained BERT model, significantly improves both the performance of ATE task and APC subtask, and achieves new state-of-the-art performance especially the F1 score of ATE task. Besides, we adopted the domain-adapted BERT model trained on the domain-related corpus to the ABSA joint-task learning model. The experimental results show that the domain-adapted BERT model significantly promotes the performance of APC tasks on the three datasets, especially the Restaurant dataset.",
+      "We designed and applied dual labels for the input sequence applicable for the SemEval-2014 and Chinese review datasets of ABSA joint-task, the aspect term label, and the sentiment polarity label, respectively. The dual label improves the learning efficiency of the proposed model."
+    ],
+    [
+      "Most ABSA-oriented methodologies regard the ATE and the APC as independent tasks and major in one of them. Accordingly, this section will introduce the related works of ATE and APC in two parts."
+    ],
+    [
+      "The approaches to ATE tasks are classified into two categories: the early dictionary-based or rule-based approaches, and methodologies based on machine-learning or deep learning. BIBREF17 proposed a new rule-based approach to extracting aspects from product reviews using common sense and sentence dependency trees to detect explicit and implicit aspects. BIBREF18 adopts an unsupervised and domain-independent aspect extraction method that relies on syntactic dependency rules and can selects rules automatically.",
+      "Compared with manually annotating all aspects in the dataset, the models for ATE can learn the features of aspects and automatically extract aspects in the text, which greatly saves labor and time. BIBREF19 proposed a model that can extract and cluster aspects simultaneously according to the seed words provided by users for several aspect categories. By classification, synonymous aspects can be grouped into the same category. BIBREF20 proposed the first aspect-oriented deep learning model in opinion mining, which deploys a 7-layer deep convolutional neural network to mark each word in the sentences with opinions as an aspect or non-aspect word. BIBREF21 proposed a new method for aspect term extraction, which utilizes word embedding to explore the co-occurrence distribution of words and applies the attention mechanism to weaken the irrelevant words and further improves the coherence of all aspects. BIBREF22 proposed a deep neural network-based model namely coupled multilevel attention, which does not require any parser or other linguistic resources to be pre-processed and provides an end-to-end solution. Besides, the proposed model is a multi-layer attention network, where each layer deploys a pair of attentions. This model allows the aspect terms and opinion terms learned interactively and dual propagate during the training process.",
+      "For the Chinese-oriented ATE task, a multi-aspect bootstrapping (MAB) method BIBREF23 is proposed to extract the aspects of Chinese restaurant reviews. BIBREF24 introduced machine learning methods to explore and extract aspect terms from Chinese hotel reviews. they chose the optimal feature-dimension, feature representation, and maximum entropy (ME) classifier according to the empirical results, and studied the integral effect of aspect extraction.",
+      "Up to now, the MHSA and pre-trained model has not been applied in the ATE task. This paper explores the potential of the new techniques of deep learning and new network architecture in the ATE task."
+    ],
+    [
+      "Aspect polarity classification is another important subtask of ABSA. The approaches designed for the APC task can be categorized into traditional machine learning and recent deep learning methods.The APC task has been comprehensively turned to the the deep neural networks. Therefore, this section mainly introduces approaches based on deep learning techniques.",
+      "The most commonly applied deep neural network architectures for APC task are recurrent neural networks BIBREF5, BIBREF6, BIBREF7, BIBREF25, BIBREF26 (RNNs) and convolutional neural networks (CNNs) BIBREF14, BIBREF15, BIBREF27. TD-LSTM BIBREF5 first divides the context of aspects into the left and right parts and modeling for them independently. Attention mechanism BIBREF28 has been adapted to APC task in the last few years. ATAE-LSTM takes the feature representation of aspects and context words as the input of the model and applies an attention mechanism to dynamically calculate the attention weight according to the relationship between aspects and context words, and finally predicts the polarity of aspects according to the weighted context features. Another LSTM-based model IAN BIBREF7 deployed with attention mechanism equips two independent LSTM networks to capture the features of the context and aspect, with interactively integrating and learning the inner correlation of the features of context and targeted aspects. The RAM BIBREF13 is a bi-directional LSTM-based architecture deploys a multi-layer deep neural network with dedicated memory layers. The multi-layer network utilizes the token features learned based on the attention mechanism and GRUs to finally obtain the global semantic features of the text to predict the sentiment polarities of targeted aspects. In order to retard the loss of context features during the training process, TNet BIBREF25 introduced a conventional transformation architecture based on context-preserving transformation (CPT) units. TNet integrates the bidirectional LSTM network and convolutional neural network and significantly improves the accuracy of sentiment polarity prediction. Multi-grained attention network BIBREF8 (MGAN) is a new deep neural network model, which equips with a variety of fine-grained attention mechanisms, and applies the fine-grained attention mechanisms to interactively learn the token-level features between aspects and context, making great use of the inherent semantic correlation of aspects and context.",
+      "BIBREF29 proposed the methods for the Chinese language APC task, which conducted the APC task at the aspect level via three granularities. Two fusion methods for the granularities in the Chinese APC task are introduced and applied. Empirical results show that the proposed methods achieved promising performance on the most commonly used ABSA datasets and four Chinese review datasets. Meanwhile, a joint framework aimed to aspect sentiment classification subtask and aspect-opinion pair identification subtask is proposedby BIBREF30, in which the external knowledge are considered and put into the network to alleviate the problem of insufficient train data. The gated alternate neural network (GANN) BIBREF31 proposed for APC task aimed to solve the shortcomings of traditional RNNs and CNNs. The GANN applied the gate truncation RNN (GTR) to learn the aspect-dependent sentiment clue representations. BIBREF32 proposed an end-to-end neural network model for the ABSA task based on joint learning, and the experimental results on a Chinese review show that the proposed model works fine while conducting ATE and APC subtask simultaneously.",
+      "BERT-SPC is the BERT text pair classification model, it is a variation model of Bert and is adapted to solve the ABSA task in BIBREF9 and achieve high performance. LCF-Bert BIBREF10 proposed a feature-level local context focus mechanism based on self-attention, which can be applied to aspect level emotion analysis and many other fine-grained natural language processing tasks. BERT-ADA BIBREF33 shows that although the pre-trained model based on a large universal corpus, and is easy to be applied to most tasks and improve performance. Still, it is not task-specific. For specific tasks, if the pre-trained BERT is adapted to specific tasks through the fine-tuning process on a task-related corpus, the task performance can be further improved."
+    ],
+    [
+      "Aspect-based sentiment analysis relies on the targeted aspects, and most existing studies focus on the classification of aspect polarity, leaving the problem of aspect term extraction. To propose an effective aspect-based sentiment analysis model based on multi-task learning, we adopted domain-adapted BERT model from BERT-ADA and integrated the local context focus mechanism into the proposed model. This section introduces the architecture and methodology of LCF-ATEPC.",
+      "This section introduces the methodology of the APC module and the ATE module, respectively. and the contents are organized by order of the network layer hierarchy."
+    ],
+    [
+      "Similar to name entity recognition (NER) task, the ATE task is a kind of sequence labeling task, and prepare the input based on IOB labels. We design the IOB labels as $B_{asp}, I_{asp}, O$, and the labels indicate the beginning, inside and outside of the aspect terms, respectively. For ATE task, the input of the example review \u201cThe price is reasonable although the service is poor.\u201d will be prepared as $S=\\lbrace w_1,w_2 \\cdots w_n\\rbrace $, and $w$ stands for a token after tokenization, $n=10$ is the total number of tokens. The example will be labeled in $Y=\\lbrace O, B_{asp}, O, O, O, O, B_{asp}, O, O, O\\rbrace $."
+    ],
+    [
+      "Aspect polarity classification is a multi-grained sub-task of sentiment analysis, aiming at predicting the aspect polarity for targeted aspects. Suppose that \u201cThe price is reasonable although the service is poor . \u201d is the input for APC task, consistently with ATE task, $S=\\lbrace w_1,w_2 \\cdots w_n\\rbrace $ stands for all the token of the review, and $S^t=\\lbrace w_i,w_{i+1} \\cdots w_{j}\\rbrace (1<=i<j<=n)$ is the aspect sequence within $S$, $i$ and $j$ are the beginning and end positions in $S$ respectively."
+    ],
+    [
+      "Aiming at the problem of insufficient research on aspect term extraction task, a joint deep learning model is designed in this section. This model combines aspect polarity classification task and aspect term extraction task, and two independent Bert layers are adopted to model the global context and the local context respectively. For conducting multi-task training at the same time, the input sequences are tokenized into different tokens and the each token is assigned two kinds of label. The first label indicates whether the token belongs to an aspect; the second label marks the polarity of the tokens belongs to the aspect.",
+      "Fig FIGREF18 is the network architecture of LCF-ATEPC. Local context feature generator (LCFG) unit is on the left and a global context feature generator (GCFG) unit is on the right. Both context feature generator units contain an independent pre-trained BERT layer, $BERT^l$ and $BERT^g$ respectively. The LCFG unit extracts the features of the local context by a local context focus layer and a MHSA encoder. The GCFG unit deploys only one MHSA encoder to learn the global context feature. The feature interactive learning (FIL) layer combines the learning of the interaction between local context features and global context features and predicts the sentiment polarity of aspects. The extraction of aspects based on the features of the global context."
+    ],
+    [
+      "The pre-trained BERT model is designed to improve performance for most NLP tasks, and The LCF-ATEPC model deploys two independent BERT-Shared layers that are aimed to extract local and global context features. For pre-trained BERT, the fine-tuning learning process is indispensable. Both BERT-Shared layers are regarded as embedded layers, and the fine-tuning process is conducted independently according to the joint loss function of multi-task learning. $X^{l}$ and $X^{g}$ are used to represent the tokenized inputs of LCFG and GCFG respectively, and we can obtain the preliminary outputs of local and global context features.",
+      "$O^{l}_{BERT}$ and $O^{g}_{BERT}$ are the output features of the LCFG and the GCFG, respectively. $BERT^{l}$ and $BERT^{g}$ are the corresponding BERT-shared layer embedded in the LCFG and the GCFG respectively."
+    ],
+    [
+      "Multi-head self-attention is based on multiple scale-dot attention (SDA), which can be utilized to extract deep semantic features in the context, and the features are represented in self-attention score. The MHSA can avoids the negative influence caused by the long distance dependence of the context when learning the features. Suppose $X_{SDA}$ is the input features learned by the LCFG. The scale-dot attention is calculate as follows:",
+      "$Q$, $K$ and $V$ are the abstract matrices packed from the input features of SDA by three weight matrices $W_{q} \\in \\mathbb {R}^{d_{h} \\times d_{q}}$, $W_{k} \\in \\mathbb {R}^{d_{h} \\times d_{k}}$, $W_{v} \\in \\mathbb {R}^{d_{h} \\times d_{v}}$. The MHSA performs multiple scaled-dot attention in parallel and concatenate the output features, then transform the features by multiplying a vector $W^{M H}$. $h$ represents the number of the attention heads and equal to 12.",
+      "The \u201c;\u201d means feature concatenation of each head. $W^{M H} \\in \\mathbb {R}^{hd_{v} \\times d_{h}}$ is the parameter matrices for projection . Additionally, we apply a $\\tanh $ activation function for the MHSA learning process, which significantly enhanced feature-capture capability."
+    ],
+    [
+      "The determination of local context depends on semantic-relative distance (SRD), which is proposed to determine whether the context word belongs to the local context of a targeted aspect to help the model capture the local context. Local context is a new concept that can be adapted to most fine-grained NLP tasks. In the ABSA field, existing models generally segment input sequences into aspect sequences and context sequences, treat aspects and context as independent segments and model their characteristics separately. Instead of leaving the aspect alone as part of the input, this paper mines the aspect and its local context, because the empirical result shows the local context of the target aspect contains more important information.",
+      "SRD is a concept based on token-aspect pairs, describing how far a token is from the aspect. It counts the number of tokens between each specific token towards a targeted aspect as the SRD of all token-aspect pairs. The SRD is calculated as:",
+      "where $i$ $(1<i<n)$ is the position of the specific token, $P_{a}$ is the central position of aspect. $m$ is the length of targeted aspect, and $SRD_{i}$ represents for the SRD between the $ i $-th token and the targeted aspect.",
+      "Figure FIGREF30 and Figure FIGREF31 are two implementations of the local context focus mechanism, the context-feature dynamic mask (CDM) layer and context-feature dynamic weighting (CDW) layer, respectively. The bottom and top of the figures represent the feature input and output positions (POS) corresponding to each token. The self-attention mechanism treats all tokens equally, so that each token can generate the self-attention score with other tokens through parallel matrix operation. According to the definition of MHSA, the features of the output position corresponding to each token are more closely related to itself. After calculating the output of all tokens by MHSA encoder, the output features of each output position will be masked or attenuated, except that the local context will be retained intact."
+    ],
+    [
+      "Apart from to the features of the local context, the CDM layer will mask non-local context's features learned by the $BERT^l$ layer. Although it is easy to directly mask the non-local context words in the input sequence, it is inevitable to discard the features of non-local context words. As the CDM layer is deployed, only a relatively small amount of the semantic context itself will be masked at the corresponding output position. The relative representation of context words and aspects with relatively few semantics is preserved in the corresponding output position.",
+      "According to the CDM implementation, the features on all the positions of non-local context words will be set to zero vectors. In order to avoid the unbalanced distribution of features after the CDM operation, an MHSA encoder is utilized to learn and rebalance the masked local context features. Suppose that the $O_{BERT^l}$ is the preliminary output features of $BERT^l$, then we get the local context feature output as follows,",
+      "To mask the features of non-local context, we defines a feature masking matrix $M$, and $ V_{i}^{m} $ is the mask vectors for each token in the input sequence. $\\alpha $ is the SRD threshold and $n$ is the length of input sequence including aspect. Tokens whose SRD regarding to the targeted aspect is less than the threshold $\\alpha $ are the local contexts. The $E \\in \\mathbb {R}^{d_{h}}$ represents the ones vector and $O \\in \\mathbb {R}^{d_{h}}$ is the zeros vectors. \u201c$.$\u201d denotes the dot-product operation of the vectors.",
+      "Finally the local context features learned by the CDM layer are delivered as $O^{l}$."
+    ],
+    [
+      "Although empirical results show that the CDM has achieved excellent performance compared with existing models, we design the CDW to explore the potential of LCF mechanism. The CDW is another implementation of the LCF mechanism, takes a more modest strategy compared to the CDM layer, which simply drops the features of the non-local context completely. While the features of local context retained intact, the features of the non-local context words will be weighted decay according to their SRD concerning a targeted aspect.",
+      "where $W$ is the constructed weight matrix and $V_{i}^{w}$ is the weight vector for each non-local context words. Consistently with CDM, $SRD_{i}$ is the SRD between the i-th context token and a targeted aspect. $n$ is the length of the input sequence. $\\alpha $ is the SRD threshold. \u201c$.$\u201d denotes the vector dot-product operation.",
+      "$O_{C D W}^{l}$ is the output of CDW layer. The CDM and CDW layers are independent, which mean they are alternative. Both the output features of CDM and CDW layers are denoted as $O^{l}$. Besides, we tried to concatenate the learned features of CDM and CDW layers and take linear transformation as the features of local context.",
+      "$W^{f}$, $O^{f}$ and $b^{f}$ are weight matrix and bias vector, respectively. The model can choose one of the three approaches to learn the local context features."
+    ],
+    [
+      "LCF-ATEPC does not only rely on local context features for sentiment polarity classification, but combines and learns the local context features and the global context features to conduct polarity classification.",
+      "$O^{l} $ and $ O^{g}$ are the local context features and global context features, respectively. $ W^{lg} \\in \\mathbb {R}^{d_{h} \\times 2d_{h}}$ and $ b^{lg} \\in \\mathbb {R}^{d_{h}}$ are the weights and bias vectors, respectively. To learn the features of the concatenated vectors, an MHSA encoding process is performed on the $O_{dense}^{l g}$."
+    ],
+    [
+      "Aspect polarity classifier performs a head-pooling on the learned concatenated context features. Head-pooling is to extract the hidden states on the corresponding position of the first token in the input sequence. then a Softmax operation is applied to predict the sentiment polarity.",
+      "where $C$ is the number of sentiment categories, and $Y_{polarity}$ represents the polarity predicted by aspect polarity classifier."
+    ],
+    [
+      "Aspect term extractor first performs the token-level classification for each token, suppose $T_{i}$ is the features on the corresponding position of token $T$,",
+      "where $N$ is the number of token categories, and $Y_{term}$ represents the token category inferred by aspect polarity classifier."
+    ],
+    [
+      "The LCFG and the GCFG are based on the BERT-BASE and BERT-SPC models, respectively. And the BERT-SPC BIBREF9 significantly improved the performance of APC tasks. In LCF-ATEPC, BERT-SPC only refactored the input sequence form compared with BERT-BASE model. The input sequence of BERT-BASE is formed in \u201c[CLS]\u201d + sequence + \u201c[SEP]\u201d, while it is formed in \u201c[CLS]\u201d + sequence + \u201c[SEP]\u201d + aspect + \u201c[SEP]\u201d for BERT-SPC.",
+      "Since LCF-ATEPC is a multi-task learning model, we redesigned the form of data input and adopted dual labels of sentiment polarity and token category. The Figure FIGREF55 are the input samples of BERT-BASE and BERT-SPC model, respectively.",
+      "The cross-entropy loss is adopted for APC and ATE subtask and the $\\mathbf {L}_{2}$ regularization is applied in LCF-ATEPC, here is the loss function for APC task,",
+      "where $C$ is the number of polarity categories, $\\lambda $ is the $L_{2}$ regularization parameter, and $\\Theta $ is the parameter-set of the LCF-ATEPC. The loss function for ATE task is",
+      "where $N$ is the number of token classes and $k$ is the sum of the tokens in each input sequence. Accordingly, the loss function of LCF-ATEPC is as follows:"
+    ],
+    [
+      "To comprehensive evaluate the performance of the proposed model, the experiments were conducted in three most commonly used ABSA datasets, the Laptops and Restaurant datasets of SemEval-2014 Task4 subtask2 BIBREF0 and an ACL Twitter social dataset BIBREF34. To evaluate our model capability with processing the Chinese language, we also tested the performance of LCF-ATEPC on four Chinese comment datasets BIBREF35, BIBREF36, BIBREF29 (Car, Phone, Notebook, Camera). We preprocessed the seven datasets. We reformatted the origin dataset and annotated each sample with the IOB labels for ATE task and polarity labels for APC tasks, respectively. The polarity of each aspect on the Laptops, Restaurants and datasets may be positive, neutral, and negative, and the conflicting labels of polarity are not considered. The reviews in the four Chinese datasets have been purged, with each aspect may be positive or negative binary polarity. To verify the effectiveness and performance of LCF-ATEPC models on multilingual datasets, we built a multilingual dataset by mixing the 7 datasets. We adopt this dataset to conduct multilingual-oriented ATE and APC experiments.",
+      "The table demonstrates the details of these datasets.",
+      "The samples distribution of those datasets is not balanced. For example, most samples in the restaurant dataset are positive, while the neutral samples in the Twitter dataset account for the majority.",
+      "Apart from some hyperparameters setting referred to previous researches, we also conducted the controlled trials and analyzed the experimental results to optimize the hyperparameters setting. The superior hyperparameters are listed in Table TABREF65. The default SRD setting for all experiments is 5, with additional instructions for experiments with different SRD."
+    ],
+    [
+      "We compare the LCF-ATEPC model to current state-of-the-art methods. Experimental results show that the proposed model achieves state-of-the-art performance both in the ATE and APC tasks.",
+      "ATAE-LSTM BIBREF6 is a classical LSTM-based network for the APC task, which applies the attention mechanism to focus on the important words in the context. Besides, ATAE-LSTM appends aspect embedding and the learned features to make full use of the aspect features. The ATAE-LSTM can be adapted to the Chinese review datasets.",
+      "ATSM-S BIBREF29 is a baseline model of the ATSM variations for Chinese language-oriented ABSA task. This model learns the sentence and aspect terms at three perspectives of granularity.",
+      "GANN is novel neural network model for APC task aimed to solve the shortcomings of traditional RNNs and CNNs. The GANN applied the Gate Truncation RNN (GTR) to learn informative aspect-dependent sentiment clue representations. GANN obtained the state-of-the-art APC performance on the Chinese review datasets.",
+      "AEN-BERT BIBREF9 is an attentional encoder network based on the pretrained BERT model, which aims to solve the aspect polarity classification.",
+      "BERT-PT BIBREF37 is a BERT-adapted model for Review Reading Comprehension (RRC) task, a task inspired by machine reading comprehension (MRC), it could be adapted to aspect-level sentiment classification task.",
+      "BERT-BASE BIBREF16 is the basic pretrained BERT model. We adapt it to ABSA multi-task learning, which equips the same ability to automatically extract aspect terms and classify aspects polarity as LCF-ATEPC model.",
+      "BERT-SPC BIBREF9 is a pretrained BERT model designed for the sentence-pair classification task. Consistent with the basic BERT model, we implemented this model for ABSA multitasking.",
+      "BERT-ADA BIBREF33 is a domain-adapted BERT-based model proposed for the APC task, which fine-tuned the BERT-BASE model on task-related corpus. This model obtained state-of-the-art accuracy on the Laptops dataset.",
+      "LCF-ATEPC is the multi-task learning model for the ATE and APC tasks, which is based on the the BERT-SPC model and local context focus mechanism.",
+      "LCF-ATE are the variations of the LCF-ATEPC model which only optimize for the ATE task.",
+      "LCF-APC are the variations of LCF-ATEPC and it only optimize for the APC task during training process."
+    ],
+    [
+      "The experiments are conducted in several segments. First, the baseline performance of LCF-ATEPC on all Chinese and English data sets was tested, and then the effectiveness of multi-task learning was demonstrated. Finally, the assistance of domain-adapted BERT model in improving performance was evaluated and the sensitivity of different datasets to SRD was studied."
+    ],
+    [
+      "Table TABREF70 are the experimental results of LCF-ATEPC models on four Chinese review datasets."
+    ],
+    [
+      "Table TABREF72 lists the main experimental results of LCF-ATEPC models to compare the performance with other ABSA-oriented models.",
+      "The LCF-ATEPC models are multilingual-oriented. To demonstrate its ability to simultaneously input and analyze reviews in multiple languages, we constructed and experimented with a multilingual dataset fore-mentioned. And result on the multilingual mixed dataset illustrates the effectiveness of the LCF-ATEPC models."
+    ],
+    [
+      "Many models for ABSA tasks do not take into account the ATE subtask, but there are still some joint models BIBREF38 based on the traditional neural network architecture to conduct the APC and ATE tasks simultaneously. Benefit from the joint training process, the two ABSA subtasks of APC and ATE can promote each other and improve the performance.",
+      "The CDM layer works better on twitter dataset because there are a lot of non-standard grammar usage and language abbreviations within it, and the local context focus techniques can promote to infer the polarity of terms. Surprisingly, for the Laptop and Restaurant datasets, guests occasionally have a unified \u201cglobal\u201d view in a specific review. That is, if the customer is not satisfied with one aspect, it is likely to criticize the other. Things will be the same if a customer prefers a restaurant he would be tolerant of some small disamenity, so the CDW mechanism performs better because it does not completely mask the local context of the other aspect. In the multi-task learning process, the convergence rate of APC and ATE tasks is different, so the model does not achieve the optimal effect at the same time.",
+      "We build a joint model for the multi-task of ATE and APC based on the BERT-BASE model. After optimizing the model parameters according to the empirical result, the joint model based on BERT-BASE achieved hopeful performance on all three datasets and even surpassed other proposed BERT based improved models on some datasets, such as BERT-PT, AEN-BERT, SDGCN-BERT, and so on. Meanwhile, we implement the joint-task model based on BERT-SPC. Compared with the BERT-BASE model, BERT-SPC significantly improves the accuracy and F1 score of aspect polarity classification. In addition, for the first time, BERT-SPC has increased the F1 score of ATE subtask on three datasets up to 99%.",
+      "ATEPC-Fusion is a supplementary scheme of LCF mechanism, and it adopts a moderate approach to generate local context features. The experimental results show that its performance is also better than the existing BERT-based models."
+    ],
+    [
+      "Keeping the main architecture of the LCF-ATEPC model unchanged, we tried to only optimize parameters for a single task in the multi-task model to explore the difference between the optimal performance of a single task and the multi-task learning model .",
+      "The Figure TABREF76 depicts the performance of the LCF-ATEPC model when performing an single APC or ATE task. Experimental results show that on some datasets the LCF-ATEPC model performs better concerning APC or ATE single task than conducting ABSA multi-task on some datasets. In general, the proposed model LCF-ATEPC proposed in this paper is still superior to other ABSA-oriented multi-task models and even the single-task models aim to APC or ATE. When optimizing the model parameters for through back-propagation of multiple tasks, the multi-task learning model needs to take into account multiple loss functions of the different subtasks. So sometimes the multi-task learning cannot achieve as the best effect as single-task learning does, which is also the compromise of the multi-task learning model when dealing with multiple tasks."
+    ],
+    [
+      "The BERT-BASE model is trained on a large-scale general corpus, so the fine-tuning during process during training process is significant and inevitable for BERT-based models. Meanwhile, the ABSA datasets commonly benchmarked are generally small with the domain-specific characteristic, the effect of BERT-BASE model on the most ABSA datasets can be further improved through domain-adaption. Domain adaption is a effective technique while integrating the pre-trained BERT-BASE model. By further training the BERT-BASE model in a domain-related corpus similar to or homologous to the target ABSA dataset, then domain-related pretrained BERT model can be obtained. We adopted the method proposed in BIBREF33 to obtain the domain-adapted pre-trained BERT model based on the corpus of Yelp Dataset Challenge reviews and the amazon Laptops review datasetBIBREF39. Table TABREF78 shows that the performance of APC task significantly improved by domain-adapted BERT model. The accuracy benchmark in the classical Restaurant achieving more than 90%, which means that the LCF-ATEPC is the first ABSA-oriented model obtained up to 90% accuracy on the Restaurant dataset. In addition, experimental result on the Laptop dataset also prove the effectiveness of domain-adaption in multi-task learning. Besides, the experimental results on the laptop dataset also validate the effectiveness of domain-adapted BERT model for ABSA multi-task learning."
+    ],
+    [
+      "We tested the sensitivity of SRD threshold on the typical Chinese and English ABSA datasets: the Phone dataset and The Restaurant dataset, respectively. Besides, for the evaluation of the restaurant dataset, we adopted the domain-adapted BERT model as the underlying architecture of the LCF-ATEPC model. The experimental result of Figure FIGREF81, FIGREF84 are evaluated in multi-task learning process.",
+      "For the Chinese Phone dataset, the LCF-ATEPC-CDM model can achieve the best APC accuracy and F1 score when the SRD threshold is about 4-5, while the best ATE task performance reaches the highest when the SRD threshold is about 1-3. The LCF-ATEPC-CDW model obtains the best APC performance on the Phone dataset when the SRD threshold is 5, while the best ATE F1 score is approximately obtained when the SRD threshold is 7.",
+      "For the Restaurant dataset, the optimal APC accuracy and F1 score achieved by LCF-ATEPC-CDM while the SRD threshold is approximately between 4 and 6. While the SRD threshold for the LCF-ATEPC-CDW is set to 8, the model achieves the optimal aspect classification accuracy and F1 score. However, the F1 score of the ATE task is less sensitive to the SRD threshold, indicating that aspect polarity classification task has less assistance on it during the joint learning process."
+    ],
+    [
+      "The ATE and APC subtasks were treated as independent tasks in previous studies. Moreover, the multi-task learning model for ATE and APC subtasks has not attracted enough attention from researchers. Besides, the researches concerning the Chinese language-oriented ABSA task are not sufficient and urgent to be proposed and developed. To address the above problems, this paper proposes a multi-task learning model LCF-ATEPC for aspect-based sentiment analysis based on the MHSA and the LCF mechanisms and applies the pre-trained BERT to the ATE sub-tasks for the first time. Not only for the Chinese language, but the models proposed in this paper are multilingual and applicable to the classic English review sentiment analysis task, such as the SemEval-2014 task4. The proposed model can automatically extract aspects from reviews and infer aspects' polarity. Empirical results on 3 commonly English datasets and four Chinese review datasets for ABSA tasks show that, compared with all models based on basic BERT, the LCF-ATEPC model achieves state-of-the-art performance on ATE and APC tasks."
+    ],
+    [
+      "Thanks to the anonymous reviewers and the scholars who helped us. This research is supported by the Innovation Project of Graduate School of South China Normal University and funded by National Natural Science Foundation of China, Multi-modal Brain-Computer Interface and Its Application in Patients with Consciousness Disorder, Project approval number: 61876067."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: Natural- to formal-language generation using Tensor Product Representations
+
+Question: What is the performance proposed model achieved on MathQA?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "INTRODUCTION",
+    "Background: Review of Tensor-Product Representation",
+    "TP-N2F Model",
+    "TP-N2F Model ::: Role-level description of N2F tasks",
+    "TP-N2F Model ::: Role-level description of N2F tasks ::: Role-level description for natural-language input",
+    "TP-N2F Model ::: Role-level description of N2F tasks ::: Role-level description for relational representations",
+    "TP-N2F Model ::: Role-level description of N2F tasks ::: The TP-N2F Scheme for Learning the input-output mapping",
+    "TP-N2F Model ::: The TP-N2F Model for Natural- to Formal-Language Generation",
+    "TP-N2F Model ::: The TP-N2F Model for Natural- to Formal-Language Generation ::: The TP-N2F natural-language Encoder",
+    "TP-N2F Model ::: The TP-N2F Model for Natural- to Formal-Language Generation ::: The TP-N2F Relational-Tuple Decoder",
+    "TP-N2F Model ::: Inference and The Learning Strategy of the TP-N2F Model",
+    "EXPERIMENTS",
+    "EXPERIMENTS ::: Generating operation sequences to solve math problems",
+    "EXPERIMENTS ::: Generating program trees from natural-language descriptions",
+    "EXPERIMENTS ::: Interpretation of learned structure",
+    "Related work",
+    "CONCLUSION AND FUTURE WORK",
+    "Appendix ::: Implementations of TP-N2F for experiments",
+    "Appendix ::: Detailed equations of TP-N2F ::: TP-N2F encoder",
+    "Appendix ::: Detailed equations of TP-N2F ::: Structure Mapping",
+    "Appendix ::: Detailed equations of TP-N2F ::: TP-N2F decoder",
+    "Appendix ::: The tensor that is input to the decoder's Unbinding Module is a TPR",
+    "Appendix ::: Dataset samples ::: Data sample from MathQA dataset",
+    "Appendix ::: Dataset samples ::: Data sample from AlgoLisp dataset",
+    "Appendix ::: Generated programs comparison",
+    "Appendix ::: Unbinding relation vector clustering"
+  ],
+  "paragraphs": [
+    [
+      "When people perform explicit reasoning, they can typically describe the way to the conclusion step by step via relational descriptions. There is ample evidence that relational representations are important for human cognition (e.g., BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4). Although a rapidly growing number of researchers use deep learning to solve complex symbolic reasoning and language tasks (a recent review is BIBREF5), most existing deep learning models, including sequence models such as LSTMs, do not explicitly capture human-like relational structure information.",
+      "In this paper we propose a novel neural architecture, TP-N2F, to solve natural- to formal-language generation tasks (N2F). In the tasks we study, math or programming problems are stated in natural-language, and answers are given as programs, sequences of relational representations, to solve the problem. TP-N2F encodes the natural-language symbolic structure of the problem in an input vector space, maps this to a vector in an intermediate space, and uses that vector to produce a sequence of output vectors that are decoded as relational structures. Both input and output structures are modelled as Tensor Product Representations (TPRs) BIBREF6. During encoding, NL-input symbolic structures are encoded as vector space embeddings using TPR `binding' (following BIBREF7); during decoding, symbolic constituents are extracted from structure-embedding output vectors using TPR `unbinding' (following BIBREF8, BIBREF9).",
+      "Our contributions in this work are as follows. (i) We propose a role-level analysis of N2F tasks. (ii) We present a new TP-N2F model which gives a neural-network-level implementation of a model solving the N2F task under the role-level description proposed in (i). To our knowledge, this is the first model to be proposed which combines both the binding and unbinding operations of TPRs to achieve generation tasks through deep learning. (iii) State-of-the-art performance on two recently developed N2F tasks shows that the TP-N2F model has significant structure learning ability on tasks requiring symbolic reasoning through program synthesis."
+    ],
+    [
+      "The TPR mechanism is a method to create a vector space embedding of complex symbolic structures. The type of a symbol structure is defined by a set of structural positions or roles, such as the left-child-of-root position in a tree, or the second-argument-of-$R$ position of a given relation $R$. In a particular instance of a structural type, each of these roles may be occupied by a particular filler, which can be an atomic symbol or a substructure (e.g., the entire left sub-tree of a binary tree can serve as the filler of the role left-child-of-root). For now, we assume the fillers to be atomic symbols.",
+      "The TPR embedding of a symbol structure is the sum of the embeddings of all its constituents, each constituent comprising a role together with its filler. The embedding of a constituent is constructed from the embedding of a role and the embedding of the filler of that role: these are joined together by the TPR `binding' operation, the tensor (or generalized outer) product $\\otimes $.",
+      "Formally, suppose a symbolic type is defined by the roles $\\lbrace r_i \\rbrace $, and suppose that in a particular instance of that type, ${S}$, role $r_i$ is bound by filler $f_i$. The TPR embedding of ${S}$ is the order-2 tensor = i i i = i i i where $\\lbrace _i \\rbrace $ are vector embeddings of the fillers and $\\lbrace _i \\rbrace $ are vector embeddings of the roles. In Eq. SECREF2, and below, for notational simplicity we conflate order-2 tensors and matrices.",
+      "As a simple example, consider the symbolic type string, and choose roles to be $r_1 = $ first_element, $r_2 = $ second_element, etc. Then in the specific string S = cba, the first role $r_1$ is filled by c, and $r_2$ and $r_3$ by b and a, respectively. The TPR for S is $\\otimes _1 + \\otimes _2 + \\otimes _3$, where $, , $ are the vector embeddings of the symbols a, b, c, and $_i$ is the vector embedding of role $r_i$.",
+      "A TPR scheme for embedding a set of symbol structures is defined by a decomposition of those structures into roles bound to fillers, an embedding of each role as a role vector, and an embedding of each filler as a filler vector. Let the total number of roles and fillers available be $n_{\\mathrm {R}}, n_{\\mathrm {F}}$, respectively. Define the matrix of all possible role vectors to be $\\in ^{d_{\\mathrm {R}}\\times n_{\\mathrm {R}}}$, with column $i$, $[]_{:i} = _i \\in ^{d_{\\mathrm {R}}}$, comprising the embedding of $r_i$. Similarly let $\\in ^{d_{\\mathrm {F}}\\times n_{\\mathrm {F}}}$ be the matrix of all possible filler vectors. The TPR $\\in ^{d_{\\mathrm {F}}\\times d_{\\mathrm {R}}}$. Below, $d_{\\mathrm {R}}, n_{\\mathrm {R}}, d_{\\mathrm {F}}, n_{\\mathrm {F}}$ will be hyper-parameters, while $, $ will be learned parameter matrices.",
+      "Using summation in Eq.SECREF2 to combine the vectors embedding the constituents of a structure risks non-recoverability of those constituents given the embedding $$ of the the structure as a whole. The tensor product is chosen as the binding operation in order to enable recovery of the filler of any role in a structure ${S}$ given its TPR $$. This can be done with perfect precision if the embeddings of the roles are linearly independent. In that case the role matrix $$ has a left inverse $$: $= $. Now define the unbinding (or dual) vector for role $r_j$, $_j$, to be the $j^{{\\mathrm {th}}}$ column of $^\\top $: $U_{:j}^\\top $. Then, since $[]_{ji} = []_{ji} = _{j:} _{:i} = [^\\top _{:j}]^\\top _{:i} =_j^\\top _i = _i^\\top _j$, we have $_i^\\top _j = \\delta _{ji}$. This means that, to recover the filler of $r_j$ in the structure with TPR $$, we can take its tensor inner product (or matrix-vector product) with $_j$: j = [ i i i] j = i i ij = j",
+      "In the architecture proposed here, we will make use of both TPR binding using the tensor product with role vectors $_i$ and TPR unbinding using the tensor inner product with unbinding vectors $_j$. Binding will be used to produce the order-2 tensor $_S$ embedding of the NL problem statement. Unbinding will be used to generate output relational tuples from an order-3 tensor $$. Because they pertain to different representations (of different orders in fact), the binding and unbinding vectors we will use are not related to one another."
+    ],
+    [
+      "We propose a general TP-N2F neural network architecture operating over TPRs to solve N2F tasks under a proposed role-level description of those tasks. In this description, natural-language input is represented as a straightforward order-2 role structure, and formal-language relational representations of outputs are represented with a new order-3 recursive role structure proposed here. Figure FIGREF3 shows an overview diagram of the TP-N2F model. It depicts the following high-level description.",
+      "As shown in Figure FIGREF3, while the natural-language input is a sequence of words, the output is a sequence of multi-argument relational tuples such as $(R \\hspace{2.84526pt}A_1 \\hspace{2.84526pt}A_2)$, a 3-tuple consisting of a binary relation (or operation) $R$ with its two arguments. The \u201cTP-N2F encoder\u201d uses two LSTMs to produce a pair consisting of a filler vector and a role vector, which are bound together with the tensor product. These tensor products, concatenated, comprise the \u201ccontext\u201d over which attention will operate in the decoder. The sum of the word-level TPRs, flattened to a vector, is treated as a representation of the entire problem statement; it is fed to the \u201cReasoning MLP\u201d, which transforms this encoding of the problem into a vector encoding the solution. This is the initial state of the \u201cTP-N2F decoder\u201d attentional LSTM, which outputs at each time step an order-3 tensor representing a relational tuple. To generate a correct tuple from decoder operations, the model must learn to give the order-3 tensor the form of a TPR for a $(R \\hspace{2.84526pt}A_1 \\hspace{2.84526pt}A_2)$ tuple (detailed explanation in Sec. SECREF7). In the following sections, we first introduce the details of our proposed role-level description for N2F tasks, and then present how our proposed TP-N2F model uses TPR binding and unbinding operations to create a neural network implementation of this description of N2F tasks."
+    ],
+    [
+      "In this section, we propose a role-level description of N2F tasks, which specifies the filler/role structures of the input natural-language symbolic expressions and the output relational representations."
+    ],
+    [
+      "Instead of encoding each token of a sentence with a non-compositional embedding vector looked up in a learned dictionary, we use a learned role-filler decomposition to compose a tensor representation for each token. Given a sentence $S$ with $n$ word tokens $\\lbrace w^0,w^1,...,w^{n-1}\\rbrace $, each word token $w^t$ is assigned a learned role vector $^t$, soft-selected from the learned dictionary $$, and a learned filler vector $^t$, soft-selected from the learned dictionary $$ (Sec. SECREF2). The mechanism closely follows that of BIBREF7, and we hypothesize similar results: the role and filler approximately encode the grammatical role of the token and its lexical semantics, respectively. Then each word token $w^t$ is represented by the tensor product of the role vector and the filler vector: $^t=^t \\otimes ^t$. In addition to the set of all its token embeddings $\\lbrace ^0, \\ldots , ^{n-1} \\rbrace $, the sentence $S$ as a whole is assigned a TPR equal to the sum of the TPR embeddings of all its word tokens: $_S = \\sum _{t=0}^{n-1} ^t$.",
+      "Using TPRs to encode natural language has several advantages. First, natural language TPRs can be interpreted by exploring the distribution of tokens grouped by the role and filler vectors they are assigned by a trained model (as in BIBREF7). Second, TPRs avoid the Bag of Word (BoW) confusion BIBREF8: the BoW encoding of Jay saw Kay is the same as the BoW encoding of Kay saw Jay but the encodings are different with TPR embedding, because the role filled by a symbol changes with its context."
+    ],
+    [
+      "In this section, we propose a novel recursive role-level description for representing symbolic relational tuples. Each relational tuple contains a relation token and multiple argument tokens. Given a binary relation $rel$, a relational tuple can be written as $(rel \\hspace{2.84526pt}arg_1 \\hspace{2.84526pt}arg_2)$ where $arg_1,arg_2$ indicate two arguments of relation $rel$. Let us adopt the two positional roles, $p_i^{rel} = $ arg$_i$-of-$rel$ for $i=1,2$. The filler of role $p_i^{rel}$ is $arg_i$. Now let us use role decomposition recursively, noting that the role $p_i^{rel}$ can itself be decomposed into a sub-role $p_i = $ arg$_i$-of-$\\underline{\\hspace{5.69054pt}}$ which has a sub-filler $rel$. Suppose that $arg_i, rel, p_i$ are embedded as vectors $_i, , _i$. Then the TPR encoding of $p_i^{rel}$ is $_{rel} \\otimes _i$, so the TPR encoding of filler $arg_i$ bound to role $p_i^{rel}$ is $_i \\otimes (_{rel} \\otimes _i)$. The tensor product is associative, so we can omit parentheses and write the TPR for the formal-language expression, the relational tuple $(rel \\hspace{2.84526pt}arg_1 \\hspace{2.84526pt}arg_2)$, as: = 1 rel 1 + 2 rel 2. Given the unbinding vectors $^{\\prime }_i$ for positional role vectors $_i$ and the unbinding vector $^{\\prime }_{rel}$ for the vector $_{rel}$ that embeds relation $rel$, each argument can be unbound in two steps as shown in Eqs. SECREF7\u2013SECREF7. i' = [ 1 rel 1 + 2 rel 2 ] i' = i rel",
+      "[ i rel ] 'rel = i Here $\\cdot $ denotes the tensor inner product, which for the order-3 $$ and order-1 $^{\\prime }_i$ in Eq. SECREF7 can be defined as $[\\cdot ^{\\prime }_i]_{jk} = \\sum _l []_{jkl} [^{\\prime }_i]_l$; in Eq. SECREF7, $\\cdot $ is equivalent to the matrix-vector product.",
+      "Our proposed scheme can be contrasted with the TPR scheme in which $(rel \\hspace{2.84526pt}arg_1 \\hspace{2.84526pt}arg_2)$ is embedded as $_{rel} \\otimes _1 \\otimes _2$ (e.g., BIBREF11, BIBREF12). In that scheme, an $n$-ary-relation tuple is embedded as an order-($n+1$) tensor, and unbinding an argument requires knowing all the other arguments (to use their unbinding vectors). In the scheme proposed here, an $n$-ary-relation tuple is still embedded as an order-3 tensor: there are just $n$ terms in the sum in Eq. SECREF7, using $n$ position vectors $_1, \\dots , _n$; unbinding simply requires knowing the unbinding vectors for these fixed position vectors.",
+      "In the model, the order-3 tensor $$ of Eq. SECREF7 has a different status than the order-2 tensor $_S$ of Sec. SECREF5. $_S$ is a TPR by construction, whereas $$ is a TPR as a result of successful learning. To generate the output relational tuples, the decoder assumes each tuple has the form of Eq. SECREF7, and performs the unbinding operations which that structure calls for. In Appendix Sec. SECREF65, it is shown that, if unbinding each of a set of roles from some unknown tensor $$ gives a target set of fillers, then $$ must equal the TPR generated by those role/filler pairs, plus some tensor that is irrelevant because unbinding from it produces the zero vector. In other words, if the decoder succeeds in producing filler vectors that correspond to output relational tuples that match the target, then, as far as what the decoder can see, the tensor that it operates on is the TPR of Eq. SECREF7."
+    ],
+    [
+      "To generate formal relational tuples from natural-language descriptions, a learning strategy for the mapping between the two structures is particularly important. As shown in (SECREF8), we formalize the learning scheme as learning a mapping function $f_{\\mathrm {mapping}}(\\cdot )$, which, given a structural representation of the natural-language input, $_S$, outputs a tensor $_F$ from which the structural representation of the output can be generated. At the role level of description, there's nothing more to be said about this mapping; how it is modeled at the neural network level is discussed in Sec. SECREF10. F = fmapping(S)"
+    ],
+    [
+      "As shown in Figure FIGREF3, the TP-N2F model is implemented with three steps: encoding, mapping, and decoding. The encoding step is implemented by the TP-N2F natural-language encoder (TP-N2F Encoder), which takes the sequence of word tokens as inputs, and encodes them via TPR binding according to the TP-N2F role scheme for natural-language input given in Sec. SECREF5. The mapping step is implemented by an MLP called the Reasoning Module, which takes the encoding produced by the TP-N2F Encoder as input. It learns to map the natural-language-structure encoding of the input to a representation that will be processed under the assumption that it follows the role scheme for output relational-tuples specified in Sec. SECREF7: the model needs to learn to produce TPRs such that this processing generates correct output programs. The decoding step is implemented by the TP-N2F relational tuples decoder (TP-N2F Decoder), which takes the output from the Reasoning Module (Sec. SECREF8) and decodes the target sequence of relational tuples via TPR unbinding. The TP-N2F Decoder utilizes an attention mechanism over the individual-word TPRs $^t$ produced by the TP-N2F Encoder. The detailed implementations are introduced below."
+    ],
+    [
+      "The TP-N2F encoder follows the role scheme in Sec. SECREF5 to encode each word token $w^t$ by soft-selecting one of $n_{\\mathrm {F}}$ fillers and one of $n_{\\mathrm {R}}$ roles. The fillers and roles are embedded as vectors. These embedding vectors, and the functions for selecting fillers and roles, are learned by two LSTMs, the Filler-LSTM and the Role-LSTM. (See Figure FIGREF11.) At each time-step $t$, the Filler-LSTM and the Role-LSTM take a learned word-token embedding $^t$ as input. The hidden state of the Filler-LSTM, $_{\\mathrm {F}}^t$, is used to compute softmax scores $u_k^{\\mathrm {F}}$ over $n_{\\mathrm {F}}$ filler slots, and a filler vector $^{t} = ^{\\mathrm {F}}$ is computed from the softmax scores (recall from Sec. SECREF2 that $$ is the learned matrix of filler vectors). Similarly, a role vector is computed from the hidden state of the Role-LSTM, $_{\\mathrm {R}}^t$. $f_{\\mathrm {F}}$ and $f_{\\mathrm {R}}$ denote the functions that generate $^{t}$ and $^t$ from the hidden states of the two LSTMs. The token $w^t$ is encoded as $^t$, the tensor product of $^{t}$ and $^t$. $^t$ replaces the hidden vector in each LSTM and is passed to the next time step, together with the LSTM cell-state vector $^t$: see (SECREF10)\u2013(SECREF10). After encoding the whole sequence, the TP-N2F encoder outputs the sum of all tensor products $\\sum _t ^t$ to the next module. We use an MLP, called the Reasoning MLP, for TPR mapping; it takes an order-2 TPR from the encoder and maps it to the initial state of the decoder. Detailed equations and implementation are provided in Sec. SECREF22 of the Appendix. Ft = fFiller-LSTM(t,t-1, Ft-1) Rt = fRole-LSTM(t,t-1, Rt-1)",
+      "t = t t = fF(Ft) fR(Rt)"
+    ],
+    [
+      "The TP-N2F Decoder is an RNN that takes the output from the reasoning MLP as its initial hidden state for generating a sequence of relational tuples (Figure FIGREF13). This decoder contains an attentional LSTM called the Tuple-LSTM which feeds an unbinding module: attention operates on the context vector of the encoder, consisting of all individual encoder outputs $\\lbrace ^t \\rbrace $. The hidden-state $$ of the Tuple-LSTM is treated as a TPR of a relational tuple and is unbound to a relation and arguments. During training, the Tuple-LSTM needs to learn a way to make $$ suitably approximate a TPR. At each time step $t$, the hidden state $^t$ of the Tuple-LSTM with attention (The version in BIBREF13) (SECREF12) is fed as input to the unbinding module, which regards $^t$ as if it were the TPR of a relational tuple with $m$ arguments possessing the role structure described in Sec. SECREF7: $^t \\approx \\sum _{i=1}^{m} _{i}^t \\otimes _{rel}^t \\otimes _i$. (In Figure FIGREF13, the assumed hypothetical form of $^t$, as well as that of $_i^t$ below, is shown in a bubble with dashed border.) To decode a binary relational tuple, the unbinding module decodes it from $^t$ using the two steps of TPR unbinding given in (SECREF7)\u2013(SECREF7). The positional unbinding vectors $^{\\prime }_{i}$ are learned during training and shared across all time steps. After the first unbinding step (SECREF7), i.e., the inner product of $^t$ with $^{\\prime }_i$, we get tensors $_{i}^t$ (SECREF12). These are treated as the TPRs of two arguments $_i^t$ bound to a relation $_{rel}^t$. A relational unbinding vector $_{rel}^{\\prime t}$ is computed by a linear function from the sum of the $_{i}^t$ and used to compute the inner product with each $_i^t$ to yield $_i^t$, which are treated as the embedding of argument vectors (SECREF12). Based on the TPR theory, $_{rel}^{\\prime t}$ is passed to a linear function to get $_{rel}^t$ as the embedding of a relation vector. Finally, the softmax probability distribution over symbolic outputs is computed for relations and arguments separately. In generation, the most probable symbol is selected. (Detailed equations are in Appendix Sec. SECREF42) t = Atten(fTuple-LSTM(relt,arg1t,arg2t,t-1,ct-1),[0,...,n-1])",
+      "1t = t 1' 2t = t 2'",
+      "rel't = flinear(1t + 2t) 1t = 1t rel't 2t = 2t rel't"
+    ],
+    [
+      "During inference time, natural language questions are encoded via the encoder and the Reasoning MLP maps the output of the encoder to the input of the decoder. We use greedy decoding (selecting the most likely class) to decode one relation and its arguments. The relation and argument vectors are concatenated to construct a new vector as the input for the Tuple-LSTM in the next step.",
+      "TP-N2F is trained using back-propagation BIBREF14 with the Adam optimizer BIBREF15 and teacher-forcing. At each time step, the ground-truth relational tuple is provided as the input for the next time step. As the TP-N2F decoder decodes a relational tuple at each time step, the relation token is selected only from the relation vocabulary and the argument tokens from the argument vocabulary. For an input ${\\mathcal {I}}$ that generates $N$ output relational tuples, the loss is the sum of the cross entropy loss ${\\mathcal {L}}$ between the true labels $L$ and predicted tokens for relations and arguments as shown in (SECREF14). LI = i=0N-1L(reli, Lreli) + i=0N-1j=12L(argji, Largji)"
+    ],
+    [
+      "The proposed TP-N2F model is evaluated on two N2F tasks, generating operation sequences to solve math problems and generating Lisp programs. In both tasks, TP-N2F achieves state-of-the-art performance. We further analyze the behavior of the unbinding relation vectors in the proposed model. Results of each task and the analysis of the unbinding relation vectors are introduced in turn. Details of experiments and datasets are described in Sec. SECREF20 in the Appendix."
+    ],
+    [
+      "Given a natural-language math problem, we need to generate a sequence of operations (operators and corresponding arguments) from a set of operators and arguments to solve the given problem. Each operation is regarded as a relational tuple by viewing the operator as relation, e.g., $(add, n1, n2)$. We test TP-N2F for this task on the MathQA dataset BIBREF16. The MathQA dataset consists of about 37k math word problems, each with a corresponding list of multi-choice options and the corresponding operation sequence. In this task, TP-N2F is deployed to generate the operation sequence given the question. The generated operations are executed with the execution script from BIBREF16 to select a multi-choice answer. As there are about 30% noisy data (where the execution script returns the wrong answer when given the ground-truth program; see Sec. SECREF20 of the Appendix), we report both execution accuracy (of the final multi-choice answer after running the execution engine) and operation sequence accuracy (where the generated operation sequence must match the ground truth sequence exactly). TP-N2F is compared to a baseline provided by the seq2prog model in BIBREF16, an LSTM-based seq2seq model with attention. Our model outperforms both the original seq2prog, designated SEQ2PROG-orig, and the best reimplemented seq2prog after an extensive hyperparameter search, designated SEQ2PROG-best. Table TABREF16 presents the results. To verify the importance of the TP-N2F encoder and decoder, we conducted experiments to replace either the encoder with a standard LSTM (denoted LSTM2TP) or the decoder with a standard attentional LSTM (denoted TP2LSTM). We observe that both the TPR components of TP-N2F are important for achieving the observed performance gain relative to the baseline."
+    ],
+    [
+      "Generating Lisp programs requires sensitivity to structural information because Lisp code can be regarded as tree-structured. Given a natural-language query, we need to generate code containing function calls with parameters. Each function call is a relational tuple, which has a function as the relation and parameters as arguments. We evaluate our model on the AlgoLisp dataset for this task and achieve state-of-the-art performance. The AlgoLisp dataset BIBREF17 is a program synthesis dataset. Each sample contains a problem description, a corresponding Lisp program tree, and 10 input-output testing pairs. We parse the program tree into a straight-line sequence of tuples (same style as in MathQA). AlgoLisp provides an execution script to run the generated program and has three evaluation metrics: the accuracy of passing all test cases (Acc), the accuracy of passing 50% of test cases (50p-Acc), and the accuracy of generating an exactly matching program (M-Acc). AlgoLisp has about 10% noisy data (details in the Appendix), so we report results both on the full test set and the cleaned test set (in which all noisy testing samples are removed). TP-N2F is compared with an LSTM seq2seq with attention model, the Seq2Tree model in BIBREF17, and a seq2seq model with a pre-trained tree decoder from the Tree2Tree autoencoder (SAPS) reported in BIBREF18. As shown in Table TABREF18, TP-N2F outperforms all existing models on both the full test set and the cleaned test set. Ablation experiments with TP2LSTM and LSTM2TP show that, for this task, the TP-N2F Decoder is more helpful than TP-N2F Encoder. This may be because lisp codes rely more heavily on structure representations."
+    ],
+    [
+      "To interpret the structure learned by the model, we extract the trained unbinding relation vectors from the TP-N2F Decoder and reduce the dimension of vectors via Principal Component Analysis. K-means clustering results on the average vectors are presented in Figure FIGREF71 and Figure FIGREF72 (in Appendix A.6). Results show that unbinding vectors for operators or functions with similar semantics tend to be close to each other. For example, with 5 clusters in the MathQA dataset, arithmetic operators such as add, subtract, multiply, divide are clustered together, and operators related to square or volume of geometry are clustered together. With 4 clusters in the AlgoLisp dataset, partial/lambda functions and sort functions are in one cluster, and string processing functions are clustered together. Note that there is no direct supervision to inform the model about the nature of the operations, and the TP-N2F decoder has induced this role structure using weak supervision signals from question/operation-sequence-answer pairs. More clustering results are presented in the Appendix A.6."
+    ],
+    [
+      "N2F tasks include many different subtasks such as symbolic reasoning or semantic parsing BIBREF19, BIBREF20, BIBREF21, BIBREF16, BIBREF17, BIBREF18. These tasks require models with strong structure-learning ability. TPR is a promising technique for encoding symbolic structural information and modeling symbolic reasoning in vector space. TPR binding has been used for encoding and exploring grammatical structural information of natural language BIBREF7, BIBREF9. TPR unbinding has also been used to generate natural language captions from images BIBREF8. Some researchers use TPRs for modeling deductive reasoning processes both on a rule-based model and deep learning models in vector space BIBREF22, BIBREF11, BIBREF12. However, none of these previous models takes advantage of combining TPR binding and TPR unbinding to learn structure representation mappings explicitly, as done in our model. Although researchers are paying increasing attention to N2F tasks, most of the proposed models either do not encode structural information explicitly or are specialized to particular tasks. Our proposed TP-N2F neural model can be applied to many tasks."
+    ],
+    [
+      "In this paper we propose a new scheme for neural-symbolic relational representations and a new architecture, TP-N2F, for formal-language generation from natural-language descriptions. To our knowledge, TP-N2F is the first model that combines TPR binding and TPR unbinding in the encoder-decoder fashion. TP-N2F achieves the state-of-the-art on two instances of N2F tasks, showing significant structure learning ability. The results show that both the TP-N2F encoder and the TP-N2F decoder are important for improving natural- to formal-language generation. We believe that the interpretation and symbolic structure encoding of TPRs are a promising direction for future work. We also plan to combine large-scale deep learning models such as BERT with TP-N2F to take advantage of structure learning for other generation tasks."
+    ],
+    [
+      "In this section, we present details of the experiments of TP-N2F on the two datasets. We present the implementation of TP-N2F on each dataset.",
+      "The MathQA dataset consists of about 37k math word problems ((80/12/8)% training/dev/testing problems), each with a corresponding list of multi-choice options and an straight-line operation sequence program to solve the problem. An example from the dataset is presented in the Appendix A.4. In this task, TP-N2F is deployed to generate the operation sequence given the question. The generated operations are executed to generate the solution for the given math problem. We use the execution script from BIBREF16 to execute the generated operation sequence and compute the multi-choice accuracy for each problem. During our experiments we observed that there are about 30% noisy examples (on which the execution script fails to get the correct answer on the ground truth program). Therefore, we report both execution accuracy (the final multi-choice answer after running the execution engine) and operation sequence accuracy (where the generated operation sequence must match the ground truth sequence exactly).",
+      "The AlgoLisp dataset BIBREF17 is a program synthesis dataset, which has 79k/9k/10k training/dev/testing samples. Each sample contains a problem description, a corresponding Lisp program tree, and 10 input-output testing pairs. We parse the program tree into a straight-line sequence of commands from leaves to root and (as in MathQA) use the symbol $\\#_i$ to indicate the result of the $i^{\\mathrm {th}}$ command (generated previously by the model). A dataset sample with our parsed command sequence is presented in the Appendix A.4. AlgoLisp provides an execution script to run the generated program and has three evaluation metrics: accuracy of passing all test cases (Acc), accuracy of passing 50% of test cases (50p-Acc), and accuracy of generating an exactly matched program (M-Acc). AlgoLisp has about 10% noise data (where the execution script fails to pass all test cases on the ground truth program), so we report results both on the full test set and the cleaned test set (in which all noisy testing samples are removed).",
+      "We use $d_{\\mathrm {R}}, n_{\\mathrm {R}}, d_{\\mathrm {F}}, n_{\\mathrm {F}}$ to indicate the TP-N2F encoder hyperparameters, the dimension of role vectors, the number of roles, the dimension of filler vectors and the number of fillers. $d_{Rel}, d_{Arg},d_{Pos}$ indicate the TP-N2F decoder hyper-parameters, the dimension of relation vectors, the dimension of argument vectors, and the dimension of position vectors.",
+      "In the experiment on the MathQA dataset, we use $n_{\\mathrm {F}}= 150$, $n_{\\mathrm {R}}= 50$, $d_{\\mathrm {F}}= 30$, $d_{\\mathrm {R}}= 20$, $d_{Rel} = 20$, $d_{Arg} = 10$, $d_{Pos} = 5$ and we train the model for 60 epochs with learning rate 0.00115. The reasoning module only contains one layer. As most of the math operators in this dataset are binary, we replace all operators taking three arguments with a set of binary operators based on hand-encoded rules, and for all operators taking one argument, a padding symbol is appended. For the baseline SEQ2PROG-orig, TP2LSTM and LSTM2TP, we use hidden size 100, single-direction, one-layer LSTM. For the SEQ2PROG-best, we performed a hyperparameter search on the hidden size for both encoder and decoder; the best score is reported.",
+      "In the experiment on the AlgoLisp dataset, we use $n_{\\mathrm {F}}= 150$, $n_{\\mathrm {R}}= 50$, $d_{\\mathrm {F}}= 30$, $d_{\\mathrm {R}}= 30$, $d_{Rel} = 30$, $d_{Arg} = 20$, $d_{Pos} = 5$ and we train the model for 50 epochs with learning rate 0.00115. We also use one-layer in the reasoning module like in MathQA. For this dataset, most function calls take three arguments so we simply add padding symbols for those functions with fewer than three arguments."
+    ],
+    [
+      "Filler-LSTM in TP-N2F encoder",
+      "This is a standard LSTM, governed by the equations:",
+      "$\\varphi , \\tanh $ are the logistic sigmoid and tanh functions applied elementwise. $\\flat $ flattens (reshapes) a matrix in $^{d_{\\mathrm {F}} \\times d_{\\mathrm {R}}}$ into a vector in $^{d_{\\mathrm {T}}}$, where $d_{\\mathrm {T}} = d_{\\mathrm {F}} d_{\\mathrm {R}}$. $\\odot $ is elementwise multiplication. The variables have the following dimensions: ft, ft, ft, ft, ft, ft, ff, fg, fi, fo, \u266d(t-1) RdT",
+      "wt Rd",
+      "ff, fg, fi, fo RdT d",
+      "ff, fg, fi, fo RdT dT",
+      "Filler vector",
+      "The filler vector for input token $w^t$ is $^t$, defined through an attention vector over possible fillers, $_{\\mathrm {f}}^t$:",
+      "($W_{\\mathrm {f}}$ is the same as $$ of Sec. SECREF2.) The variables' dimensions are: fa RnF dT",
+      "ft RnF",
+      "f RdF nF",
+      "t RdF $T$ is the temperature factor, which is fixed at 0.1.",
+      "Role-LSTM in TP-N2F encoder",
+      "Similar to the Filler-LSTM, the Role-LSTM is also a standard LSTM, governed by the equations:",
+      "The variable dimensions are: rt, rt, rt, rt, rt, rt, rf, rg, ri, ro, \u266d(t-1) RdT",
+      "wt Rd",
+      "rf, rg, ri, ro RdT d",
+      "rf, rg, ri, ro RdT dT",
+      "Role vector",
+      "The role vector for input token $w^t$ is determined analogously to its filler vector:",
+      "The dimensions are: ra RnR dT",
+      "rt RnR",
+      "r RdR nR",
+      "t RdR",
+      "Binding",
+      "The TPR for the filler/role binding for token $w^t$ is then:",
+      "where t RdR dF"
+    ],
+    [
+      "$^0 \\in \\mathbb {R}^{d_{\\mathrm {H}}}$, where $d_{\\mathrm {H}} = d_{\\mathrm {A}}, d_{\\mathrm {O}}, d_{\\mathrm {P}}$ are dimension of argument vector, operator vector and position vector. $f_{\\mathrm {mapping}}$ is implemented with a MLP (linear layer followed by a tanh) for mapping the $_t \\in \\mathbb {R}^{d_{\\mathrm {T}}}$ to the initial state of decoder $^0$."
+    ],
+    [
+      "Tuple-LSTM",
+      "The output tuples are also generated via a standard LSTM:",
+      "Here, $\\gamma $ is the concatenation function. $_{Rel}^{t-1}$ is the trained embedding vector for the Relation of the input binary tuple, $_{Arg1}^{t-1}$ is the embedding vector for the first argument and $_{Arg2}^{t-1}$ is the embedding vector for the second argument. Then the input for the Tuple LSTM is the concatenation of the embedding vectors of relation and arguments, with dimension $d_{\\mathrm {dec}}$. t, t, t, t, t, inputt, f, g, i, o, \u266d(t-1) RdH",
+      "dt Rddec",
+      "f, g, i, o RdH ddec",
+      "f, g, i, o RdH dH",
+      "t RdH ${\\mathrm {Atten}}$ is the attention mechanism used in BIBREF13, which computes the dot product between $_{\\mathrm {input}}^t$ and each $_{t^{\\prime }}$. Then a linear function is used on the concatenation of $_{\\mathrm {input}}^t$ and the softmax scores on all dot products to generate $^t$. The following equations show the attention mechanism:",
+      "${\\mathrm {score}}$ is the score function of the attention. In this paper, the score function is dot product. T RdH n",
+      "t Rn",
+      "t RdH",
+      "RdH (dT+n)",
+      "Unbinding",
+      "At each timestep $t$, the 2-step unbinding process described in Sec. SECREF7 operates first on an encoding of the triple as a whole, $$, using two unbinding vectors $_i^{\\prime }$ that are learned but fixed for all tuples. This first unbinding gives an encoding of the two operator-argument bindings, $_i$. The second unbinding operates on the $_i$, using a generated unbinding vector for the operator, $_{rel}^{\\prime }$, giving encodings of the arguments, $_i$. The generated unbinding vector for the operator, $^{\\prime }$, and the generated encodings of the arguments, $_i$, each produce a probability distribution over symbolic operator outputs $Rel$ and symbolic argument outputs $Arg_i$; these probabilities are used in the cross-entropy loss function. For generating a single symbolic output, the most-probable symbols are selected.",
+      "The dimensions are: rel't RdO",
+      "1t, 2t RdA",
+      "'1, '2 RdP",
+      "1t, 2t RdA dO",
+      "dual RdH",
+      "rt RnO dO",
+      "at RnA dA",
+      "rt RnR",
+      "a1t, a2t RnA"
+    ],
+    [
+      "Here we show that, if learning is successful, the order-3 tensor $$ that each iteration of the decoder's Tuple LSTM feeds to the decoder's Unbinding Module (Figure FIGREF13) will be a TPR of the form assumed in Eq. SECREF7, repeated here: = j j rel j. The operations performed by the decoder are given in Eqs. SECREF7\u2013SECREF7, and Eqs. SECREF12\u2013SECREF12, rewritten here: i' = i",
+      "i rel' = i This is the standard TPR unbinding operation, used recursively: first with the unbinding vectors for positions, $_i^{\\prime }$, then with the unbinding vector for the operator, $_{rel}^{\\prime }$. It therefore suffices to analyze a single unbinding; the result can then be used recursively. This in effect reduces the problem to the order-2 case. What we will show is: given a set of unbinding vectors $\\lbrace _i^{\\prime } \\rbrace $ which are dual to a set of role vectors $\\lbrace _i \\rbrace $, with $i$ ranging over some index set $I$, if $$ is an order-2 tensor such that 'i = i, i I then = i I i i + TPR + for some tensor $$ that annihilates all the unbinding vectors: 'i = 0, i I. If learning is successful, the processing in the decoder will generate the target relational tuple $(R, A_1, A_2)$ by obeying Eq. SECREF65 in the first unbinding, where we have $_i^{\\prime } = _i^{\\prime }, _i = _i, I = \\lbrace 1, 2\\rbrace $, and obeying Eq. SECREF65 in the second unbinding, where we have $_i^{\\prime } = _{rel}^{\\prime }, _i^{\\prime } = _i$, with $I =$ the set containing only the null index.",
+      "Treat rank-2 tensors as matrices; then unbinding is simply matrix-vector multiplication. Assume the set of unbinding vectors is linearly independent (otherwise there would in general be no way to satisfy Eq. SECREF65 exactly, contrary to assumption). Then expand the set of unbinding vectors, if necessary, into a basis $\\lbrace ^{\\prime }_k\\rbrace _{k \\in K \\supseteq I}$. Find the dual basis, with $_k$ dual to $^{\\prime }_k$ (so that $_l^\\top _j^{\\prime } = \\delta _{lj}$). Because $\\lbrace ^{\\prime }_k\\rbrace _{k \\in K}$ is a basis, so is $\\lbrace _k\\rbrace _{k \\in K}$, so any matrix $$ can be expanded as $= \\sum _{k \\in K} _k _k^{\\top }$. Since $^{\\prime }_i = _i, \\forall i \\in I$ are the unbinding conditions (Eq. SECREF65), we must have $_i = _i, i \\in I$. Let $_{{\\mathrm {TPR}}} \\equiv \\sum _{i \\in I} _i _i^{\\top }$. This is the desired TPR, with fillers $_i$ bound to the role vectors $_i$ which are the duals of the unbinding vectors $_i^{\\prime }$ ($i \\in I$). Then we have $= _{{\\mathrm {TPR}}} + $ (Eq. SECREF65) where $\\equiv \\sum _{j \\in K, j \\notin I} _j _j^{\\top }$; so $_i^{\\prime } = {\\mathbf {0}}, i \\in I$ (Eq. SECREF65). Thus, if training is successful, the model must have learned how to feed the decoder with order-3 TPRs with the structure posited in Eq. SECREF65.",
+      "The argument so far addresses the case where the unbinding vectors are linearly independent, making it possible to satisfy Eq. SECREF65 exactly. In relatively high-dimensional vector spaces, it will often happen that even when the number of unbinding vectors exceeds the dimension of their space by a factor of 2 or 3 (which applies to the TP-N2F models presented here), there is a set of role vectors $\\lbrace _k \\rbrace _{k \\in K}$ approximately dual to $\\lbrace ^{\\prime }_k \\rbrace _{k \\in K}$, such that $_l^\\top _j^{\\prime } = \\delta _{lj} \\hspace{2.84526pt}\\forall l, j \\in K$ holds to a good approximation. (If the distribution of normalized unbinding vectors is approximately uniform on the unit sphere, then choosing the approximate dual vectors to equal the unbinding vectors themselves will do, since they will be nearly orthonormal BIBREF10. If the $\\lbrace ^{\\prime }_k \\rbrace _{k \\in K}$ are not normalized, we just rescale the role vectors, choosing $_k = _k^{\\prime } / \\Vert _k^{\\prime } \\Vert ^2$.) When the number of such role vectors exceeds the dimension of the embedding space, they will be overcomplete, so while it is still true that any matrix $$ can be expanded as above ($= \\sum _{k \\in K} _k _k^{\\top }$), this expansion will no longer be unique. So while it remains true that $$ a TPR, it is no longer uniquely decomposable into filler/role pairs. The claim above does not claim uniqueness in this sense, and remains true.)"
+    ],
+    [
+      "Problem: The present polulation of a town is 3888. Population increase rate is 20%. Find the population of town after 1 year?",
+      "Options: a) 2500, b) 2100, c) 3500, d) 3600, e) 2700",
+      "Operations: multiply(n0,n1), divide(#0,const-100), add(n0,#1)"
+    ],
+    [
+      "Problem: Consider an array of numbers and a number, decrements each element in the given array by the given number, what is the given array?",
+      "Program Nested List: (map a (partial1 b \u2013))",
+      "Command-Sequence: (partial1 b \u2013), (map a #0)"
+    ],
+    [
+      "In this section, we display some generated samples from the two datasets, where the TP-N2F model generates correct programs but LSTM-Seq2Seq does not.",
+      "Question: A train running at the speed of 50 km per hour crosses a post in 4 seconds. What is the length of the train?",
+      "TP-N2F(correct):",
+      "(multiply,n0,const1000) (divide,#0,const3600) (multiply,n1,#1)",
+      "LSTM(wrong):",
+      "(multiply,n0,const0.2778) (multiply,n1,#0)",
+      "Question: 20 is subtracted from 60 percent of a number, the result is 88. Find the number?",
+      "TP-N2F(correct):",
+      "(add,n0,n2) (divide,n1,const100) (divide,#0,#1)",
+      "LSTM(wrong):",
+      "(add,n0,n2) (divide,n1,const100) (divide,#0,#1) (multiply,#2,n3) (subtract,#3,n0)",
+      "Question: The population of a village is 14300. It increases annually at the rate of 15 percent. What will be its population after 2 years?",
+      "TP-N2F(correct):",
+      "(divide,n1,const100) (add,#0,const1) (power,#1,n2) (multiply,n0,#2)",
+      "LSTM(wrong):",
+      "(multiply,const4,const100) (sqrt,#0)",
+      "Question: There are two groups of students in the sixth grade. There are 45 students in group a, and 55 students in group b. If, on a particular day, 20 percent of the students in group a forget their homework, and 40 percent of the students in group b forget their homework, then what percentage of the sixth graders forgot their homework?",
+      "TP-N2F(correct):",
+      "(add,n0,n1) (multiply,n0,n2) (multiply,n1,n3) (divide,#1,const100) (divide,#2,const100) (add,#3,#4) (divide,#5,#0) (multiply,#6,const100)",
+      "LSTM(wrong):",
+      "(multiply,n0,n1) (subtract,n0,n1) (divide,#0,#1)",
+      "Question: 1 divided by 0.05 is equal to",
+      "TP-N2F(correct):",
+      "(divide,n0,n1)",
+      "LSTM(wrong):",
+      "(divide,n0,n1) (multiply,n2,#0)",
+      "Question: Consider a number a, compute factorial of a",
+      "TP-N2F(correct):",
+      "( <=,arg1,1 ) ( -,arg1,1 ) ( self,#1 ) ( *,#2,arg1 ) ( if,#0,1,#3 ) ( lambda1,#4 ) ( invoke1,#5,a )",
+      "LSTM(wrong):",
+      "( <=,arg1,1 ) ( -,arg1,1 ) ( self,#1 ) ( *,#2,arg1 ) ( if,#0,1,#3 ) ( lambda1,#4 ) ( len,a ) ( invoke1,#5,#6 )",
+      "Question: Given an array of numbers and numbers b and c, add c to elements of the product of elements of the given array and b, what is the product of elements of the given array and b?",
+      "TP-N2F(correct):",
+      "( partial, b,* ) ( partial1,c,+ ) ( map,a,#0 ) ( map,#2,#1 )",
+      "LSTM(wrong):",
+      "( partial1,b,+ ) ( partial1,c,+ ) ( map,a,#0 ) ( map,#2,#1 )",
+      "Question: You are given an array of numbers a and numbers b , c and d , let how many times you can replace the median in a with sum of its digits before it becomes a single digit number and b be the coordinates of one end and c and d be the coordinates of another end of segment e , your task is to find the length of segment e rounded down",
+      "TP-N2F(correct):",
+      "( digits arg1 ) ( len #0 ) ( == #1 1 ) ( digits arg1 ) ( reduce #3 0 + ) ( self #4 ) ( + 1 #5 ) ( if #2 0 #6 ) ( lambda1 #7 ) ( sort a ) ( len a ) ( / #10 2 ) ( deref #9 #11 ) ( invoke1 #8 #12 ) ( - #13 c ) ( digits arg1 ) ( len #15 ) ( == #16 1 ) ( digits arg1 ) ( reduce #18 0 + ) ( self #19 ) ( + 1 #20 ) ( if #17 0 #21 ) ( lambda1 #22 ) ( sort a ) ( len a ) ( / #25 2 ) ( deref #24 #26 ) ( invoke1 #23 #27 ) ( - #28 c ) ( * #14 #29 ) ( - b d ) ( - b d ) ( * #31 #32 ) ( + #30 #33 ) ( sqrt #34 ) ( floor #35 )",
+      "LSTM(wrong): ( digits arg1 ) ( len #0 ) ( == #1 1 ) ( digits arg1 ) ( reduce #3 0 + ) ( self #4 ) ( + 1 #5 ) ( if #2 0 #6 ) ( lambda1 #7 ) ( sort a ) ( len a ) ( / #10 2 ) ( deref #9 #11 ) ( invoke1 #8 #12 c ) ( - #13 ) ( - b d ) ( - b d ) ( * #15 #16 ) ( * #14 #17 ) ( + #18 ) ( sqrt #19 ) ( floor #20 )",
+      "Question: Given numbers a , b , c and e , let d be c , reverse digits in d , let a and the number in the range from 1 to b inclusive that has the maximum value when its digits are reversed be the coordinates of one end and d and e be the coordinates of another end of segment f , find the length of segment f squared",
+      "TP-N2F(correct):",
+      "( digits c ) ( reverse #0 ) ( * arg1 10 ) ( + #2 arg2 ) ( lambda2 #3 ) ( reduce #1 0 #4 ) ( - a #5 ) ( digits c ) ( reverse #7 ) ( * arg1 10 ) ( + #9 arg2 ) ( lambda2 #10 ) ( reduce #8 0 #11 ) ( - a #12 ) ( * #6 #13 ) ( + b 1 ) ( range 0 #15 ) ( digits arg1 ) ( reverse #17 ) ( * arg1 10 ) ( + #19 arg2 ) ( lambda2 #20 ) ( reduce #18 0 #21 ) ( digits arg2 ) ( reverse #23 ) ( * arg1 10 ) ( + #25 arg2 ) ( lambda2 #26 ) ( reduce #24 0 #27 ) ( > #22 #28 ) ( if #29 arg1 arg2 ) ( lambda2 #30 ) ( reduce #16 0 #31 ) ( - #32 e ) ( + b 1 ) ( range 0 #34 ) ( digits arg1 ) ( reverse #36 ) ( * arg1 10 ) ( + #38 arg2 ) ( lambda2 #39 ) ( reduce #37 0 #40 ) ( digits arg2 ) ( reverse #42 ) ( * arg1 10 ) ( + #44 arg2 ) ( lambda2 #45 ) ( reduce #43 0 #46 ) ( > #41 #47 ) ( if #48 arg1 arg2 ) ( lambda2 #49 ) ( reduce #35 0 #50 ) ( - #51 e ) ( * #33 #52 ) ( + #14 #53 )",
+      "LSTM(wrong):",
+      "( - a d ) ( - a d ) ( * #0 #1 ) ( digits c ) ( reverse #3 ) ( * arg1 10 ) ( + #5 arg2 ) ( lambda2 #6 ) ( reduce #4 0 #7 ) ( - #8 e ) ( + b 1 ) ( range 0 #10 ) ( digits arg1 ) ( reverse #12 ) ( * arg1 10 ) ( + #14 arg2 ) ( lambda2 #15 ) ( reduce #13 0 #16 ) ( digits arg2 ) ( reverse #18 ) ( * arg1 10 ) ( + #20 arg2 ) ( lambda2 #21 ) ( reduce #19 0 #22 ) ( > #17 #23 ) ( if #24 arg1 arg2 ) ( lambda2 #25 ) ( reduce #11 0 #26 ) ( - #27 e ) ( * #9 #28 ) ( + #2 #29 )"
+    ],
+    [
+      "We run K-means clustering on both datasets with $k = 3,4,5,6$ clusters and the results are displayed in Figure FIGREF71 and Figure FIGREF72. As described before, unbinding-vectors for operators or functions with similar semantics tend to be closer to each other. For example, in the MathQA dataset, arithmetic operators such as add, subtract, multiply, divide are clustered together at middle, and operators related to geometry such as square or volume are clustered together at bottom left. In AlgoLisp dataset, basic arithmetic functions are clustered at middle, and string processing functions are clustered at right."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1601/instruction.md b/qasper-1601/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..de4f073aa8d3d540a5dbe5e38d8d9a9dd5e14af7
--- /dev/null
+++ b/qasper-1601/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Local Contextual Attention with Hierarchical Structure for Dialogue Act Recognition
+
+Question: How do previous methods perform on the Switchboard Dialogue Act and DailyDialog datasets?
\ No newline at end of file
diff --git a/qasper-1606/instruction.md b/qasper-1606/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..7b0fa4e04761a247cf1f3329fadd15880eeaecac
--- /dev/null
+++ b/qasper-1606/instruction.md
@@ -0,0 +1,129 @@
+Name of Paper: Select and Attend: Towards Controllable Content Selection in Text Generation
+
+Question: How does the model perform in comparison to end-to-end headline generation models?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Background and Notation",
+    "Content Selection",
+    "Bottom-up",
+    "Soft-Select",
+    "Reinforce-Select",
+    "Variational Reinforce-Select",
+    "Degree of Controllability",
+    "Related Work",
+    "Experiments",
+    "Tasks and Setup",
+    "Results and Analysis",
+    "Conclusion",
+    "Acknowledgments",
+    "Performance/Controllability trade-off",
+    "Example from Wikibio",
+    "Posterior inference"
+  ],
+  "paragraphs": [
+    [
+      "Many text generation tasks, e.g., data-to-text, summarization and image captioning, can be naturally divided into two steps: content selection and surface realization. The generations are supposed to have two levels of diversity: (1) content-level diversity reflecting multiple possibilities of content selection (what to say) and (2) surface-level diversity reflecting the linguistic variations of verbalizing the selected contents (how to say) BIBREF0 , BIBREF1 . Recent neural network models handle these tasks with the encoder-decoder (Enc-Dec) framework BIBREF2 , BIBREF3 , which simultaneously performs selecting and verbalizing in a black-box way. Therefore, both levels of diversity are entangled within the generation. This entanglement, however, sacrifices the controllability and interpretability, making it diffifcult to specify the content to be conveyed in the generated text BIBREF4 , BIBREF5 .",
+      "With this in mind, this paper proposes decoupling content selection from the Enc-Dec framework to allow finer-grained control over the generation. Table TABREF2 shows an example. We can easily modify the content selection to generate text with various focuses, or sample multiple paraphrases by fixing the content selection.",
+      "Though there has been much work dealing with content selection for the Enc-Dec, none of them is able to address the above concerns properly. Current methods can be categorized into the following three classes and have different limits:",
+      "In this paper, we treat the content selection as latent variables and train with amortized variational inference BIBREF10 , BIBREF11 . This provides a lower training variance than Reinforce-select. The selector and generator are co-trained within the same objective, the generations are thus more faithful to the selected contents than Bottom-up methods. Our model is task-agnostic, end-to-end trainable and can be seamlessly inserted into any encoder-decoder architecture. On both the data-to-text and headline generation task, we show our model outperforms others regarding content-level diversity and controllability while maintaining comparable performance. The performance/controllability trade-off can be effectively adjusted by adjusting a single hyperparameter in the training stage, which constrains an upper bound of the conditional mutual information (CMI) between the selector and generated text BIBREF12 , BIBREF13 . A higher CMI leads to stronger controllability with a bit more risk of text disfluency.",
+      "In summary, our contributions are (1) systematically studying the problem of controllable content selection for Enc-Dec text generation, (2) proposing a task-agnostic training framework achieving promising results and (3) introducing an effective way to achieve the trade-off between performance and controllability."
+    ],
+    [
+      "Let INLINEFORM0 denote a source-target pair. INLINEFORM1 is a sequence of INLINEFORM2 and can be either some structured data or unstructured text/image depending on the task. INLINEFORM3 corresponds to INLINEFORM4 which is a text description of INLINEFORM5 . The goal of text generation is to learn a distribution INLINEFORM6 to automatically generate proper text.",
+      "The Enc-Dec architecture handles this task with an encode-attend-decode process BIBREF3 , BIBREF14 . The encoder first encodes each INLINEFORM0 into a vector INLINEFORM1 . At each time step, the decoder pays attentions to some source embeddings and outputs the probability of the next token by INLINEFORM2 . INLINEFORM3 is a weighted average of source embeddings: DISPLAYFORM0 ",
+      " INLINEFORM0 is the hidden state of the decoder at time step INLINEFORM1 . INLINEFORM2 is a score function to compute the similarity between INLINEFORM3 and INLINEFORM4 BIBREF15 ."
+    ],
+    [
+      "Our goal is to decouple the content selection from the decoder by introducing an extra content selector. We hope the content-level diversity can be fully captured by the content selector for a more interpretable and controllable generation process. Following BIBREF6 , BIBREF16 , we define content selection as a sequence labeling task. Let INLINEFORM0 denote a sequence of binary selection masks. INLINEFORM1 if INLINEFORM2 is selected and 0 otherwise. INLINEFORM3 is assumed to be independent from each other and is sampled from a bernoulli distribution INLINEFORM4 . INLINEFORM6 is the bernoulli parameter, which we estimate using a two-layer feedforward network on top of the source encoder. Text are generated by first sampling INLINEFORM7 from INLINEFORM8 to decide which content to cover, then decode with the conditional distribution INLINEFORM9 . The text is expected to faithfully convey all selected contents and drop unselected ones. Fig. FIGREF8 depicts this generation process. Note that the selection is based on the token-level context-aware embeddings INLINEFORM10 and will maintain information from the surrounding contexts. It encourages the decoder to stay faithful to the original information instead of simply fabricating random sentences by connecting the selected tokens.",
+      "For each source-target pair, the ground-truth selection mask is unknown, so training is challenging. In the following session, we discuss several training possibilities and introduce the proposed model in detail."
+    ],
+    [
+      "The most intuitive way is training the content selector to target some heuristically extracted contents. For example, we can train the selector to select overlapped words between the source and target BIBREF6 , sentences with higher tf-idf scores BIBREF20 or identified image objects that appear in the caption BIBREF21 . A standard encoder-decoder model is independently trained. In the testing stage, the prediction of the content selector is used to hard-mask the attention vector to guide the text generation in a bottom-up way. Though easy to train, Bottom-up generation has the following two problems: (1) The heuristically extracted contents might be coarse and cannot reflect the variety of human languages and (2) The selector and decoder are independently trained towards different objectives thus might not adapt to each other well.",
+      " INLINEFORM0 as Latent Variable: Another way is to treat INLINEFORM1 as a latent variable and co-train selector and generator by maximizing the marginal data likelihood. By doing so, the selector has the potential to automatically explore optimal selecting strategies best fit for the corresponding generator component.",
+      "With this in mind. We design INLINEFORM0 by changing the original decoder in the following way: (1) We initialize hidden states of the decoder from a mean pooling over selected contents to inform the decoder which contents to cover and (2) Unselected contents will be prohibited from being attended to: DISPLAYFORM0 ",
+      " INLINEFORM0 is the initial decoder hidden state and MLP denotes multi-layer-perceptron.",
+      "Since computing the exact marginal likelihood INLINEFORM0 requires enumerating over all possible combinations of INLINEFORM1 (complexity INLINEFORM2 ), we need some way to efficiently estimate the likelihood."
+    ],
+    [
+      "Soft-select falls back on a deterministic network to output the likelihood function's first-order Taylor series approximation expanded at INLINEFORM0 : INLINEFORM1 ",
+      "By moving the expectation into the decoding function, we can deterministically compute the likelihood by setting INLINEFORM0 , reducing complexity to INLINEFORM1 . Each attention weight will first be \u201csoft-masked\" by INLINEFORM2 before being passed to the decoder. soft-select is fully differentiable and can be easily trained by gradient descent. However, this soft-approximation is normally inaccurate, especially when INLINEFORM3 has a high entropy, which is common in one-to-many text generation tasks. The gap between INLINEFORM4 and INLINEFORM5 will be large BIBREF22 , BIBREF23 . In practice, this would lead to unrealistic generations when sampling INLINEFORM6 from the deterministically trained distribution."
+    ],
+    [
+      "Reinforce-select (RS) BIBREF24 , BIBREF9 utilizes reinforcement learning to approximate the marginal likelihood. Specifically, it is trained to maximize a lower bound of the likelihood by applying the Jensen inequalily: DISPLAYFORM0 ",
+      "The gradient to INLINEFORM0 is approximated with Monte-Carlo sampling by applying the REINFORCE algorithm BIBREF25 , BIBREF26 . To speed up convergence, we pre-train the selector by some distant supervision, which is a common practice in reinforcement learning. REINFORCE is unbiased but has a high variance. Many research have proposed sophisticated techniques for variance reduction BIBREF11 , BIBREF27 , BIBREF28 . In text generation, the high-variance problem is aggravated because there exists multiple valid selections. Accurately estimating the likelihood becomes difficult. Another issue is its tendency to avoid stochasticity BIBREF29 , which we will show in Sec SECREF27 that it results in low content-level diversity."
+    ],
+    [
+      "We propose Variational Reinforce-Select (VRS) which applies variational inference BIBREF10 for variance reduction. Instead of directly integrating over INLINEFORM0 , it imposes a proposal distribution INLINEFORM1 for importance sampling. The marginal likelihood is lower bounded by: DISPLAYFORM0 ",
+      "By choosing a proper INLINEFORM0 , the bound will be improved and the variance can be largely reduced compared with REINFORCE. If INLINEFORM1 equals the posterior distribution INLINEFORM2 , the bound is tight and the variance would be zero BIBREF30 . We define INLINEFORM3 as a mean-field distribution parameterized by a set of global parameters INLINEFORM4 to approach the true posterior distribution. INLINEFORM5 , INLINEFORM6 and INLINEFORM7 are simultaneously trained by minimizing the last tine of Eq. EQREF15 . INLINEFORM8 also allows us to further perform posterior inference: Given an arbitrary text INLINEFORM9 for a source INLINEFORM10 , we can infer which source contents are included in INLINEFORM11 (An example is given in Appendix SECREF9 ).",
+      "In Eq. EQREF15 , the KL divergence term can be computed analytically. As for the independence assumption, it can be summed over each individual INLINEFORM0 . The likelihood term is differentiable to INLINEFORM1 but not to INLINEFORM2 , we estimate the gradient to INLINEFORM3 in Eq EQREF15 by applying the REINFORCE estimator: DISPLAYFORM0 ",
+      " INLINEFORM0 is the control variate BIBREF25 . The optimal INLINEFORM1 would be BIBREF31 : DISPLAYFORM0 ",
+      "which we set as a soft-select approximation: DISPLAYFORM0 ",
+      "We estimate Eq. EQREF16 with a single sample from INLINEFORM0 for efficiency. Though multiple-sample could potentially further tighten the bound and reduce the variance BIBREF32 , BIBREF33 , BIBREF34 , it brings significant computational overhead, especially in text generation tasks where the whole sentence needs to be decoded."
+    ],
+    [
+      "In practice, when treating content selection as latent variables, the model tends to end up with a trivial solution of always selecting all source tokens BIBREF35 , BIBREF36 . This behavior is understandable since Eq. EQREF10 strictly masks unselected tokens. Wrongly unselecting one token will largely deteriorate the likelihood. Under the maximum likelihood (MLE) objective, this high risk pushes the selector to take a conservative strategy of always keeping all tokens, then the whole model degenerates to the standard Enc-Dec and the selection mask loses effects on the generation. Usually people apply a penalty term to the selecting ratio when optimizing the likelihood: DISPLAYFORM0 ",
+      " INLINEFORM0 is the MLE loss function, INLINEFORM1 is the mean of INLINEFORM2 and INLINEFORM3 is the target selecting ratio. This forces the selector to select the most important INLINEFORM4 tokens for each source input instead of keeping all of them.",
+      "In our VRS model, we can easily adjust the degree of controllability by limiting an upper bound of the conditional mutual information (CMI) INLINEFORM0 BIBREF13 . Specifically, we can change our objective into: DISPLAYFORM0 ",
+      " INLINEFORM0 is a fixed lagrangian multiplier. Eq. EQREF21 can be proved equal to maximum likelihood with the constraint INLINEFORM1 given proper INLINEFORM2 BIBREF12 . A higher INLINEFORM3 indicates INLINEFORM4 has more influences to INLINEFORM5 (higher controllability) while always safely selecting all tokens will lead INLINEFORM6 . It is preferred over Eq. EQREF20 because (a) CMI directly considers the dependency between the selection and multiple-possible text while limiting the ratio aims at finding the single most salient parts for each source. (b) Unlike CMI, limiting the ratio is coarse. It considers only the total selected size and ignores its internal distribution.",
+      "[tb] Variational Reinforce-Select (VRS) Parameters: INLINEFORM0 INLINEFORM1 TRUE Sample X,Y from the corpus; Encode X into INLINEFORM2 ;",
+      " INLINEFORM0 Update INLINEFORM1 with distant supervision;",
+      "Update INLINEFORM0 by INLINEFORM1 Eq. EQREF15 ; Update INLINEFORM2 by INLINEFORM3 Eq. EQREF21 ; INLINEFORM4 FALSE if Eq. EQREF15 degrades convergence and INLINEFORM5 is False",
+      "In practice, we can set INLINEFORM0 to adjust the degree of controllability we want. Later we will show it leads to a trade-off with performance. The final algorithm is detailed in Algorithm SECREF19 . To keep fairness, we trian RS and VRS with the same control variate and pre-training strategy."
+    ],
+    [
+      "Most content selection models train the selector with heuristic rules BIBREF39 , BIBREF20 , BIBREF16 , BIBREF6 , BIBREF40 , BIBREF41 , which fail to fully capture the relation between selection and generation. BIBREF7 , BIBREF8 , BIBREF42 , BIBREF20 \u201csoft-select\" word or sentence embeddings based on a gating function. The output score from the gate is a deterministic vector without any probabilistic variations, so controlling the selection to generate diverse text is impossible. Very few works explicitly define a bernoulli distribution for the selector, then train with the REINFORCE algorithm BIBREF24 , BIBREF9 , but the selection targets at a high recall regardless of the low precision, so the controllability over generated text is weak. BIBREF43 control the generation by manually concatenating entity embeddings, while our model is much more flexible by explicitly defining the selection probability over all source tokens. Our work is closely related with learning discrete representations with variational inference BIBREF44 , BIBREF45 , BIBREF46 , BIBREF33 , where we treat content selection as the latent representation. Limiting the KL-term is a common technique to deal with the \u201cposterior collapse\" problem BIBREF47 , BIBREF48 , BIBREF49 . We adopt a similar approach and use it to further control the selecting strategy."
+    ],
+    [
+      "For the experiments, we focus on comparing (1) Bottom-up generation (Bo.Up.), (2) soft-select (SS), (3) Reinforce-select (RS) and (4) Variational-Reinforce-select (VRS) regarding their performance on content selection. SS and RS are trained with the selecting ratio constraint in Eq. EQREF20 . For the SS model, we further add a regularization term to encourage the maximum value of INLINEFORM0 to be close to 1 as in BIBREF7 . We first briefly introduce the tasks and important setup, then present the evaluation results."
+    ],
+    [
+      "We test content-selection models on the headline and data-to-text generation task. Both tasks share the same framework with the only difference of source-side encoders.",
+      "Headline Generation: We use English Gigaword preprocessed by BIBREF50 , which pairs first sentences of news articles with their headlines. We keep most settings same as in BIBREF8 , but use a vocabulary built by byte-pair-encoding BIBREF51 . We find it speeds up training with superior performance.",
+      "Data-to-Text Generation: We use the Wikibio dataset BIBREF52 . The source is a Wikipedia infobox and the target is a one-sentence biography description. Most settings are the same as in BIBREF53 , but we use a bi-LSTM encoder for better performance.",
+      "Heuristically extracted content: This is used to train the selector for bottom up models and pre-train the RS and VRS model. For wikibio, we simply extract overlapped words between the source and target. In Gigaword, as the headline is more abstractive, we select the closest source word for each target word in the embedding space. Stop words and punctuations are prohibited from being selected.",
+      "Choice of INLINEFORM0 : As seen in Sec SECREF19 , we need to set the hyperparameter INLINEFORM1 for RS/SS and INLINEFORM2 for VRS. INLINEFORM3 corresponds to the selecting ratio. We set them as INLINEFORM4 for Wikibio and INLINEFORM5 for Gigaword. The value is decided by running a human evaluation to get the empirical estimation. To keep comparison fairness, we tune INLINEFORM6 to make VRS select similar amount of tokens with RS. The values we get are INLINEFORM7 for Wikibio and INLINEFORM8 for Gigaword. INLINEFORM9 is the number of source tokens. "
+    ],
+    [
+      "Ideally we would expect the learned content selector to (1) have reasonable diversity so that text with various contents can be easily sampled, (2) properly control the contents described in the generated text and (3) not hurt performance. The following section will evaluate these three points in order.",
+      "Diversity: We first look into the diversity of content selection learned by different models. For each test data, 50 selection masks are randomly sampled from the model's learned distribution. Greedy decoding is run to generate the text for each mask. We measure the entropy of the selector, proportion of unique selection masks and generated text in the 50 samples. We further define the \u201ceffect\" of the selector as the ratio of sampled unique text and mask. This indicates how often changing the selection mask will also lead to a change in the generated text. The results are averaged over all test data. Following BIBREF50 and BIBREF52 , we measure the quality of generated text with ROUGE-1, 2, L F-score for Gigaword and ROUGE-4, BLEU-4, NIST for Wikibio. As there is only one reference text for each source, we report an oracle upper bound of these scores by assuming an \u201coracle\" that can choose the best text among all the candidates BIBREF54 , BIBREF21 . Namely, out of each 50 sampled text, we pick the one with the maximum metric score. The final metric score is evaluated on these \u201coracle\" picked samples. The intuition is that if the content selector is properly trained, at least one out of the 50 samples should describe similar contents with the reference text, the metric score between it and the reference text should be high. Table TABREF25 lists the results. We can have the following observations:",
+      "RS model completely fails to capture the content-level diversity. Its selector is largely deterministic, with a lowest entropy value among all models. In contrast, the selector from SS, VRS and Bo.Up. have reasonable diversity, with over INLINEFORM0 and INLINEFORM1 unique selection masks for Gigaword and Wikibio respectively.",
+      "The selector from VRS has the strongest effect to the generator, especially on the Gigaword data where modifying the content selection changes the corresponding text in more than 95% of the cases. RS has the lowest effect value, which indicates that even with the selecting ratio constraint, its generator still ignores the selection mask to a large extent.",
+      "The oracle metric score of VRS is much higher than the other two. This is beneficial when people want to apply the model to generate a few candidate text then hand-pick the suitable one. VRS has more potential than the other three to contain the expected text. SS performs worst. The gap between the soft approximation and the real distribution, as mentioned before, indeed results in a large drop of performance.",
+      "In short, compared with others, the content selector of VRS is (1) diverse, (2) has stronger effect on the text generation and (3) with a larger potential of producing an expected text.",
+      "Controllability: We have shown the content selector of VRS is diverse and has strong effect on the text generation. This section aims at examining whether such effect is desirable, i.e., whether the selector is able to properly control the contents described in the text. We measure it based on the self-bleu metric and a human evaluation.",
+      "The self-bleu metric measures the controllability by evaluating the \u201cintra-selection\" similarity of generated text. Intuitively, by fixing the selection mask, multiple text sampled from the decoder are expected to describe the same contents and thereby should be highly similar to each other. The decoder should only model surface-level diversity without further modifying the selected contents. With this in mind, for each test data, we randomly sample a selection mask from the selector's distribution, then fix the mask and run the decoder to sample 10 different text. The self-BLEU-1 score BIBREF55 on the sampled text is reported, which is the average BLEU score between each text pair. A higher self-BLEU score indicates the sampled text are more similar with each other. The results are shown in Table TABREF31 . We can see generations from VRS have a clearly higher intra-selection similarity. SS performs even worse than RS, despite having a high effect score in Table TABREF25 . The selector from SS affects the generation in an undesirable way, which also explain why SS has a lowest oracle metric score though with a high score on content diversity and effect.",
+      "We further run a human evaluation to measure the text-content consistency among different models. 100 source text are randomly sampled from the human-written DUC 2004 data for task 1&2 BIBREF56 . Bo.Up, SS, RS and VRS are applied to generate the target text by first sampling a selection mask, then run beam search decoding with beam size 10. We are interested in seeing (1) if multiple generations from the same selection mask are paraphrases to each other (intra-consistent) and (2) if generations from different selection masks do differ in the content they described (inter-diverse). The results in Table TABREF32 show that VRS significantly outperforms the other two in both intra-consistency and inter-diversity. RS has the lowest score on both because the selector has very weak effects on the generation as measured in the last section. Bo.Up and SS lay between them. Overall VRS is able to maintain the highest content-text consistency among them.",
+      "Performance INLINEFORM0 Trade-off: To see if the selector affects performance, we also ask human annotators to judge the text fluency. The fluency score is computed as the average number of text being judged as fluent. We include generations from the standard Enc-Dec model. Table TABREF32 shows the best fluency is achieved for Enc-Dec. Imposing a content selector always affects the fluency a bit. The main reason is that when the controllability is strong, the change of selection will directly affect the text realization so that a tiny error of content selection might lead to unrealistic text. If the selector is not perfectly trained, the fluency will inevitably be influenced. When the controllability is weaker, like in RS, the fluency is more stable because it will not be affected much by the selection mask. For SS and Bo.Up, the drop of fluency is significant because of the gap of soft approximation and the independent training procedure. In general, VRS does properly decouple content selection from the enc-dec architecture, with only tiny degrade on the fluency.",
+      "Table TABREF33 / TABREF34 further measure the metric scores on Gigaword/Wikibio by decoding text from the best selection mask based on the selector's distribution (set INLINEFORM0 if INLINEFORM1 and 0 otherwise). We include results from VRS model with INLINEFORM2 , which puts no constraint on the mutual information. We further report the score by generating the best selection mask from the learned posterior distribution INLINEFORM3 for VRS model. Two current SOTA results from BIBREF8 and BIBREF53 and the proportion of selected source words for each model are also included. We have the following observations:",
+      "As the value of INLINEFORM0 decreases, the performance of VRS improves, but the selector loses more controllability because the model tends to over-select contents (over INLINEFORM1 source words selected). The text-content consistency will become low.",
+      "Increasing INLINEFORM0 sacrifices a bit performance, but still comparable with SOTA. Especially on Wikibio where the performance drop is minor. The reason should be that Wikibio is relatively easier to predict the selection but Gigaword has more uncertainty.",
+      "Increasing INLINEFORM0 improves the accuracy of the posterior selection. This would be useful when we want to perform posterior inference for some source-target pair.",
+      "Setting INLINEFORM0 can actually outperform SOTA seq2seq which keeps all tokens, suggesting it is still beneficial to use the VRS model even if we do not care about the controllability.",
+      "Figure FIGREF39 visualizes how changing the value of INLINEFORM0 affects the negative log likelihood (NLL), entropy of the selector and self-bleu score, which roughly correlates with performance, diversity and controllability. NLL is evaluated based on the lower bound in Eq EQREF15 BIBREF57 . We can see as INLINEFORM1 increases, the performance decreases gradually but the content selection gains more diversity and controllability. In practice we can tune the INLINEFORM2 value to achieve a trade-off.",
+      "Generation Example: Figure FIGREF40 shows some examples from Gigaword. As can be seen, decodings from the VRS model are largely consistent with each other, in most cases only replacing one or two words with corresponding synonyms. Samples are able to faithfully convey all selected contents. In contrast, generations from SS. Bo.Up. and RS are unpreditable, differing in both selected contents and also the way of saying. SS and Bo.Up also suffer more from the text disfluency. The generations from them are largely uncertain."
+    ],
+    [
+      "In this paper, we tackle the unaddressed problem of controllable content selection in text generation. We propose a general framework based on variational inference that can be potentiall applied to arbitrary tasks. On both the headline generation and data-to-text tasks, our model outperforms state-of-the-art models regarding the diversity and controllability of content selection. We further introduce an effective way to achieve a performance/controllability trade-off, which can be easily tuned to meet specific requirement."
+    ],
+    [
+      "We thank anonymous reviewers for valuable comments, thank Aditya Mogadala, Shun Kiyono, Thomas Mclachlan and other members of the LIAT team at RIKEN AIP for useful discussions. Xiaoyu Shen is supported by IMPRS-CS fellowship. The work of J. Suzuki was partly supported by JSPS KAKENHI Grant Number JP19104418 and AIRPF Grant Number 30AI036-8. This work is also partially funded by DFG collaborative research center SFB 1102."
+    ],
+    [
+      "The trade-off between performance and interpretability has been a long-standing problem in feature selection BIBREF60 , BIBREF59 . The trade-off exists because it is usually very difficult to accurately find the exact features needed to make the prediction. Safely keeping more features will almost always lead to better performance. Some models do succeed in achieving superior performance by selecting only a subset of the input. However, they mostly still target at the recall of the selection BIBREF39 , BIBREF9 , BIBREF35 , i.e., to select all possible content that might help predict the target. The final selected contents reduce some most useful information from the source, but they still contain many redundant contents (same like our VRS-( INLINEFORM0 ) as in Table TABREF34 and TABREF33 ). This makes them unsuitable for controllable content selection. In text generation, a recent work from BIBREF41 shows they could control the contents by integrating a symbolic selector into the neural network. However, their selector is tailored by some rules only for the RDF triples. Moreover, even based on their fine-tuned selector, the fluency they observe is still slightly worse than a standard seq2seq.",
+      "We assume the content selector is the major bottle if we want a model that can achieve controllability without sacrificing the performance. We can clearly observe in Table TABREF34 that the performance drop in Wikibio is marginal compared with Gigaword. The reason should be that the selection on Wikibio is much easier than Gigaword. The biography of a person almost always follow some simple patterns, like name, birthday and profession, but for news headlines, it can contain information with various focuses. In our two tasks, due to the independence assumption we made on INLINEFORM0 and the model capacity limit, the content selector cannot fully fit the true selecting distribution, so the trade-off is necessary. Improving the selector with SOTA sequence labelling models like Bert BIBREF17 would be worth trying.",
+      "There are also other ways to improve. For example, we could learn a ranker to help us choose the best contents BIBREF63 . Or we could manually define some matching rules to help rank the selection BIBREF58 . In Table TABREF25 , we show the VRS model achieves very high metric scores based on an oracle ranker, so learning a ranker should be able to improve the performance straightforwardly."
+    ],
+    [
+      "To see how we can manually control the content selection, Figure FIGREF42 shows an example from Wikibio, the model is mostly able to form a proper sentence covering all selected information. If the selector assigns very high probability to select some content and we force to remove it, the resulting text could be unnatual (as in summary 4 in Figure FIGREF42 because the model has seen very few text without containing the birthday information in the training corpus). However, thanks to the diversity of the content selector as shown in the previous section, it is able to handle most combinatorial patterns of content selection."
+    ],
+    [
+      "Figure FIGREF41 further provides an example of how we can perform posterior inference given a provided text. Our model is able to infer which source contents are covered in the given summary. With the inferred selection, we can sample multiple paraphrases describing the same contents. As seen in Table TABREF34 and TABREF33 , the metric scores are remarkably high when decoding from the posterior inferred selections (last three rows), suggesting the posterior distribution is well trained. The posterior inference part could be beneficial for other tasks like content transfer among text BIBREF38 , BIBREF62 . The described source contents can be first predicted with the posterior inference, then transferred to a new text."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1607/instruction.md b/qasper-1607/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..e865decd56440e78cf9ef71fd34bd8cfb3b730e9
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@@ -0,0 +1,129 @@
+Name of Paper: Select and Attend: Towards Controllable Content Selection in Text Generation
+
+Question: How is the model trained to do only content selection?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Background and Notation",
+    "Content Selection",
+    "Bottom-up",
+    "Soft-Select",
+    "Reinforce-Select",
+    "Variational Reinforce-Select",
+    "Degree of Controllability",
+    "Related Work",
+    "Experiments",
+    "Tasks and Setup",
+    "Results and Analysis",
+    "Conclusion",
+    "Acknowledgments",
+    "Performance/Controllability trade-off",
+    "Example from Wikibio",
+    "Posterior inference"
+  ],
+  "paragraphs": [
+    [
+      "Many text generation tasks, e.g., data-to-text, summarization and image captioning, can be naturally divided into two steps: content selection and surface realization. The generations are supposed to have two levels of diversity: (1) content-level diversity reflecting multiple possibilities of content selection (what to say) and (2) surface-level diversity reflecting the linguistic variations of verbalizing the selected contents (how to say) BIBREF0 , BIBREF1 . Recent neural network models handle these tasks with the encoder-decoder (Enc-Dec) framework BIBREF2 , BIBREF3 , which simultaneously performs selecting and verbalizing in a black-box way. Therefore, both levels of diversity are entangled within the generation. This entanglement, however, sacrifices the controllability and interpretability, making it diffifcult to specify the content to be conveyed in the generated text BIBREF4 , BIBREF5 .",
+      "With this in mind, this paper proposes decoupling content selection from the Enc-Dec framework to allow finer-grained control over the generation. Table TABREF2 shows an example. We can easily modify the content selection to generate text with various focuses, or sample multiple paraphrases by fixing the content selection.",
+      "Though there has been much work dealing with content selection for the Enc-Dec, none of them is able to address the above concerns properly. Current methods can be categorized into the following three classes and have different limits:",
+      "In this paper, we treat the content selection as latent variables and train with amortized variational inference BIBREF10 , BIBREF11 . This provides a lower training variance than Reinforce-select. The selector and generator are co-trained within the same objective, the generations are thus more faithful to the selected contents than Bottom-up methods. Our model is task-agnostic, end-to-end trainable and can be seamlessly inserted into any encoder-decoder architecture. On both the data-to-text and headline generation task, we show our model outperforms others regarding content-level diversity and controllability while maintaining comparable performance. The performance/controllability trade-off can be effectively adjusted by adjusting a single hyperparameter in the training stage, which constrains an upper bound of the conditional mutual information (CMI) between the selector and generated text BIBREF12 , BIBREF13 . A higher CMI leads to stronger controllability with a bit more risk of text disfluency.",
+      "In summary, our contributions are (1) systematically studying the problem of controllable content selection for Enc-Dec text generation, (2) proposing a task-agnostic training framework achieving promising results and (3) introducing an effective way to achieve the trade-off between performance and controllability."
+    ],
+    [
+      "Let INLINEFORM0 denote a source-target pair. INLINEFORM1 is a sequence of INLINEFORM2 and can be either some structured data or unstructured text/image depending on the task. INLINEFORM3 corresponds to INLINEFORM4 which is a text description of INLINEFORM5 . The goal of text generation is to learn a distribution INLINEFORM6 to automatically generate proper text.",
+      "The Enc-Dec architecture handles this task with an encode-attend-decode process BIBREF3 , BIBREF14 . The encoder first encodes each INLINEFORM0 into a vector INLINEFORM1 . At each time step, the decoder pays attentions to some source embeddings and outputs the probability of the next token by INLINEFORM2 . INLINEFORM3 is a weighted average of source embeddings: DISPLAYFORM0 ",
+      " INLINEFORM0 is the hidden state of the decoder at time step INLINEFORM1 . INLINEFORM2 is a score function to compute the similarity between INLINEFORM3 and INLINEFORM4 BIBREF15 ."
+    ],
+    [
+      "Our goal is to decouple the content selection from the decoder by introducing an extra content selector. We hope the content-level diversity can be fully captured by the content selector for a more interpretable and controllable generation process. Following BIBREF6 , BIBREF16 , we define content selection as a sequence labeling task. Let INLINEFORM0 denote a sequence of binary selection masks. INLINEFORM1 if INLINEFORM2 is selected and 0 otherwise. INLINEFORM3 is assumed to be independent from each other and is sampled from a bernoulli distribution INLINEFORM4 . INLINEFORM6 is the bernoulli parameter, which we estimate using a two-layer feedforward network on top of the source encoder. Text are generated by first sampling INLINEFORM7 from INLINEFORM8 to decide which content to cover, then decode with the conditional distribution INLINEFORM9 . The text is expected to faithfully convey all selected contents and drop unselected ones. Fig. FIGREF8 depicts this generation process. Note that the selection is based on the token-level context-aware embeddings INLINEFORM10 and will maintain information from the surrounding contexts. It encourages the decoder to stay faithful to the original information instead of simply fabricating random sentences by connecting the selected tokens.",
+      "For each source-target pair, the ground-truth selection mask is unknown, so training is challenging. In the following session, we discuss several training possibilities and introduce the proposed model in detail."
+    ],
+    [
+      "The most intuitive way is training the content selector to target some heuristically extracted contents. For example, we can train the selector to select overlapped words between the source and target BIBREF6 , sentences with higher tf-idf scores BIBREF20 or identified image objects that appear in the caption BIBREF21 . A standard encoder-decoder model is independently trained. In the testing stage, the prediction of the content selector is used to hard-mask the attention vector to guide the text generation in a bottom-up way. Though easy to train, Bottom-up generation has the following two problems: (1) The heuristically extracted contents might be coarse and cannot reflect the variety of human languages and (2) The selector and decoder are independently trained towards different objectives thus might not adapt to each other well.",
+      " INLINEFORM0 as Latent Variable: Another way is to treat INLINEFORM1 as a latent variable and co-train selector and generator by maximizing the marginal data likelihood. By doing so, the selector has the potential to automatically explore optimal selecting strategies best fit for the corresponding generator component.",
+      "With this in mind. We design INLINEFORM0 by changing the original decoder in the following way: (1) We initialize hidden states of the decoder from a mean pooling over selected contents to inform the decoder which contents to cover and (2) Unselected contents will be prohibited from being attended to: DISPLAYFORM0 ",
+      " INLINEFORM0 is the initial decoder hidden state and MLP denotes multi-layer-perceptron.",
+      "Since computing the exact marginal likelihood INLINEFORM0 requires enumerating over all possible combinations of INLINEFORM1 (complexity INLINEFORM2 ), we need some way to efficiently estimate the likelihood."
+    ],
+    [
+      "Soft-select falls back on a deterministic network to output the likelihood function's first-order Taylor series approximation expanded at INLINEFORM0 : INLINEFORM1 ",
+      "By moving the expectation into the decoding function, we can deterministically compute the likelihood by setting INLINEFORM0 , reducing complexity to INLINEFORM1 . Each attention weight will first be \u201csoft-masked\" by INLINEFORM2 before being passed to the decoder. soft-select is fully differentiable and can be easily trained by gradient descent. However, this soft-approximation is normally inaccurate, especially when INLINEFORM3 has a high entropy, which is common in one-to-many text generation tasks. The gap between INLINEFORM4 and INLINEFORM5 will be large BIBREF22 , BIBREF23 . In practice, this would lead to unrealistic generations when sampling INLINEFORM6 from the deterministically trained distribution."
+    ],
+    [
+      "Reinforce-select (RS) BIBREF24 , BIBREF9 utilizes reinforcement learning to approximate the marginal likelihood. Specifically, it is trained to maximize a lower bound of the likelihood by applying the Jensen inequalily: DISPLAYFORM0 ",
+      "The gradient to INLINEFORM0 is approximated with Monte-Carlo sampling by applying the REINFORCE algorithm BIBREF25 , BIBREF26 . To speed up convergence, we pre-train the selector by some distant supervision, which is a common practice in reinforcement learning. REINFORCE is unbiased but has a high variance. Many research have proposed sophisticated techniques for variance reduction BIBREF11 , BIBREF27 , BIBREF28 . In text generation, the high-variance problem is aggravated because there exists multiple valid selections. Accurately estimating the likelihood becomes difficult. Another issue is its tendency to avoid stochasticity BIBREF29 , which we will show in Sec SECREF27 that it results in low content-level diversity."
+    ],
+    [
+      "We propose Variational Reinforce-Select (VRS) which applies variational inference BIBREF10 for variance reduction. Instead of directly integrating over INLINEFORM0 , it imposes a proposal distribution INLINEFORM1 for importance sampling. The marginal likelihood is lower bounded by: DISPLAYFORM0 ",
+      "By choosing a proper INLINEFORM0 , the bound will be improved and the variance can be largely reduced compared with REINFORCE. If INLINEFORM1 equals the posterior distribution INLINEFORM2 , the bound is tight and the variance would be zero BIBREF30 . We define INLINEFORM3 as a mean-field distribution parameterized by a set of global parameters INLINEFORM4 to approach the true posterior distribution. INLINEFORM5 , INLINEFORM6 and INLINEFORM7 are simultaneously trained by minimizing the last tine of Eq. EQREF15 . INLINEFORM8 also allows us to further perform posterior inference: Given an arbitrary text INLINEFORM9 for a source INLINEFORM10 , we can infer which source contents are included in INLINEFORM11 (An example is given in Appendix SECREF9 ).",
+      "In Eq. EQREF15 , the KL divergence term can be computed analytically. As for the independence assumption, it can be summed over each individual INLINEFORM0 . The likelihood term is differentiable to INLINEFORM1 but not to INLINEFORM2 , we estimate the gradient to INLINEFORM3 in Eq EQREF15 by applying the REINFORCE estimator: DISPLAYFORM0 ",
+      " INLINEFORM0 is the control variate BIBREF25 . The optimal INLINEFORM1 would be BIBREF31 : DISPLAYFORM0 ",
+      "which we set as a soft-select approximation: DISPLAYFORM0 ",
+      "We estimate Eq. EQREF16 with a single sample from INLINEFORM0 for efficiency. Though multiple-sample could potentially further tighten the bound and reduce the variance BIBREF32 , BIBREF33 , BIBREF34 , it brings significant computational overhead, especially in text generation tasks where the whole sentence needs to be decoded."
+    ],
+    [
+      "In practice, when treating content selection as latent variables, the model tends to end up with a trivial solution of always selecting all source tokens BIBREF35 , BIBREF36 . This behavior is understandable since Eq. EQREF10 strictly masks unselected tokens. Wrongly unselecting one token will largely deteriorate the likelihood. Under the maximum likelihood (MLE) objective, this high risk pushes the selector to take a conservative strategy of always keeping all tokens, then the whole model degenerates to the standard Enc-Dec and the selection mask loses effects on the generation. Usually people apply a penalty term to the selecting ratio when optimizing the likelihood: DISPLAYFORM0 ",
+      " INLINEFORM0 is the MLE loss function, INLINEFORM1 is the mean of INLINEFORM2 and INLINEFORM3 is the target selecting ratio. This forces the selector to select the most important INLINEFORM4 tokens for each source input instead of keeping all of them.",
+      "In our VRS model, we can easily adjust the degree of controllability by limiting an upper bound of the conditional mutual information (CMI) INLINEFORM0 BIBREF13 . Specifically, we can change our objective into: DISPLAYFORM0 ",
+      " INLINEFORM0 is a fixed lagrangian multiplier. Eq. EQREF21 can be proved equal to maximum likelihood with the constraint INLINEFORM1 given proper INLINEFORM2 BIBREF12 . A higher INLINEFORM3 indicates INLINEFORM4 has more influences to INLINEFORM5 (higher controllability) while always safely selecting all tokens will lead INLINEFORM6 . It is preferred over Eq. EQREF20 because (a) CMI directly considers the dependency between the selection and multiple-possible text while limiting the ratio aims at finding the single most salient parts for each source. (b) Unlike CMI, limiting the ratio is coarse. It considers only the total selected size and ignores its internal distribution.",
+      "[tb] Variational Reinforce-Select (VRS) Parameters: INLINEFORM0 INLINEFORM1 TRUE Sample X,Y from the corpus; Encode X into INLINEFORM2 ;",
+      " INLINEFORM0 Update INLINEFORM1 with distant supervision;",
+      "Update INLINEFORM0 by INLINEFORM1 Eq. EQREF15 ; Update INLINEFORM2 by INLINEFORM3 Eq. EQREF21 ; INLINEFORM4 FALSE if Eq. EQREF15 degrades convergence and INLINEFORM5 is False",
+      "In practice, we can set INLINEFORM0 to adjust the degree of controllability we want. Later we will show it leads to a trade-off with performance. The final algorithm is detailed in Algorithm SECREF19 . To keep fairness, we trian RS and VRS with the same control variate and pre-training strategy."
+    ],
+    [
+      "Most content selection models train the selector with heuristic rules BIBREF39 , BIBREF20 , BIBREF16 , BIBREF6 , BIBREF40 , BIBREF41 , which fail to fully capture the relation between selection and generation. BIBREF7 , BIBREF8 , BIBREF42 , BIBREF20 \u201csoft-select\" word or sentence embeddings based on a gating function. The output score from the gate is a deterministic vector without any probabilistic variations, so controlling the selection to generate diverse text is impossible. Very few works explicitly define a bernoulli distribution for the selector, then train with the REINFORCE algorithm BIBREF24 , BIBREF9 , but the selection targets at a high recall regardless of the low precision, so the controllability over generated text is weak. BIBREF43 control the generation by manually concatenating entity embeddings, while our model is much more flexible by explicitly defining the selection probability over all source tokens. Our work is closely related with learning discrete representations with variational inference BIBREF44 , BIBREF45 , BIBREF46 , BIBREF33 , where we treat content selection as the latent representation. Limiting the KL-term is a common technique to deal with the \u201cposterior collapse\" problem BIBREF47 , BIBREF48 , BIBREF49 . We adopt a similar approach and use it to further control the selecting strategy."
+    ],
+    [
+      "For the experiments, we focus on comparing (1) Bottom-up generation (Bo.Up.), (2) soft-select (SS), (3) Reinforce-select (RS) and (4) Variational-Reinforce-select (VRS) regarding their performance on content selection. SS and RS are trained with the selecting ratio constraint in Eq. EQREF20 . For the SS model, we further add a regularization term to encourage the maximum value of INLINEFORM0 to be close to 1 as in BIBREF7 . We first briefly introduce the tasks and important setup, then present the evaluation results."
+    ],
+    [
+      "We test content-selection models on the headline and data-to-text generation task. Both tasks share the same framework with the only difference of source-side encoders.",
+      "Headline Generation: We use English Gigaword preprocessed by BIBREF50 , which pairs first sentences of news articles with their headlines. We keep most settings same as in BIBREF8 , but use a vocabulary built by byte-pair-encoding BIBREF51 . We find it speeds up training with superior performance.",
+      "Data-to-Text Generation: We use the Wikibio dataset BIBREF52 . The source is a Wikipedia infobox and the target is a one-sentence biography description. Most settings are the same as in BIBREF53 , but we use a bi-LSTM encoder for better performance.",
+      "Heuristically extracted content: This is used to train the selector for bottom up models and pre-train the RS and VRS model. For wikibio, we simply extract overlapped words between the source and target. In Gigaword, as the headline is more abstractive, we select the closest source word for each target word in the embedding space. Stop words and punctuations are prohibited from being selected.",
+      "Choice of INLINEFORM0 : As seen in Sec SECREF19 , we need to set the hyperparameter INLINEFORM1 for RS/SS and INLINEFORM2 for VRS. INLINEFORM3 corresponds to the selecting ratio. We set them as INLINEFORM4 for Wikibio and INLINEFORM5 for Gigaword. The value is decided by running a human evaluation to get the empirical estimation. To keep comparison fairness, we tune INLINEFORM6 to make VRS select similar amount of tokens with RS. The values we get are INLINEFORM7 for Wikibio and INLINEFORM8 for Gigaword. INLINEFORM9 is the number of source tokens. "
+    ],
+    [
+      "Ideally we would expect the learned content selector to (1) have reasonable diversity so that text with various contents can be easily sampled, (2) properly control the contents described in the generated text and (3) not hurt performance. The following section will evaluate these three points in order.",
+      "Diversity: We first look into the diversity of content selection learned by different models. For each test data, 50 selection masks are randomly sampled from the model's learned distribution. Greedy decoding is run to generate the text for each mask. We measure the entropy of the selector, proportion of unique selection masks and generated text in the 50 samples. We further define the \u201ceffect\" of the selector as the ratio of sampled unique text and mask. This indicates how often changing the selection mask will also lead to a change in the generated text. The results are averaged over all test data. Following BIBREF50 and BIBREF52 , we measure the quality of generated text with ROUGE-1, 2, L F-score for Gigaword and ROUGE-4, BLEU-4, NIST for Wikibio. As there is only one reference text for each source, we report an oracle upper bound of these scores by assuming an \u201coracle\" that can choose the best text among all the candidates BIBREF54 , BIBREF21 . Namely, out of each 50 sampled text, we pick the one with the maximum metric score. The final metric score is evaluated on these \u201coracle\" picked samples. The intuition is that if the content selector is properly trained, at least one out of the 50 samples should describe similar contents with the reference text, the metric score between it and the reference text should be high. Table TABREF25 lists the results. We can have the following observations:",
+      "RS model completely fails to capture the content-level diversity. Its selector is largely deterministic, with a lowest entropy value among all models. In contrast, the selector from SS, VRS and Bo.Up. have reasonable diversity, with over INLINEFORM0 and INLINEFORM1 unique selection masks for Gigaword and Wikibio respectively.",
+      "The selector from VRS has the strongest effect to the generator, especially on the Gigaword data where modifying the content selection changes the corresponding text in more than 95% of the cases. RS has the lowest effect value, which indicates that even with the selecting ratio constraint, its generator still ignores the selection mask to a large extent.",
+      "The oracle metric score of VRS is much higher than the other two. This is beneficial when people want to apply the model to generate a few candidate text then hand-pick the suitable one. VRS has more potential than the other three to contain the expected text. SS performs worst. The gap between the soft approximation and the real distribution, as mentioned before, indeed results in a large drop of performance.",
+      "In short, compared with others, the content selector of VRS is (1) diverse, (2) has stronger effect on the text generation and (3) with a larger potential of producing an expected text.",
+      "Controllability: We have shown the content selector of VRS is diverse and has strong effect on the text generation. This section aims at examining whether such effect is desirable, i.e., whether the selector is able to properly control the contents described in the text. We measure it based on the self-bleu metric and a human evaluation.",
+      "The self-bleu metric measures the controllability by evaluating the \u201cintra-selection\" similarity of generated text. Intuitively, by fixing the selection mask, multiple text sampled from the decoder are expected to describe the same contents and thereby should be highly similar to each other. The decoder should only model surface-level diversity without further modifying the selected contents. With this in mind, for each test data, we randomly sample a selection mask from the selector's distribution, then fix the mask and run the decoder to sample 10 different text. The self-BLEU-1 score BIBREF55 on the sampled text is reported, which is the average BLEU score between each text pair. A higher self-BLEU score indicates the sampled text are more similar with each other. The results are shown in Table TABREF31 . We can see generations from VRS have a clearly higher intra-selection similarity. SS performs even worse than RS, despite having a high effect score in Table TABREF25 . The selector from SS affects the generation in an undesirable way, which also explain why SS has a lowest oracle metric score though with a high score on content diversity and effect.",
+      "We further run a human evaluation to measure the text-content consistency among different models. 100 source text are randomly sampled from the human-written DUC 2004 data for task 1&2 BIBREF56 . Bo.Up, SS, RS and VRS are applied to generate the target text by first sampling a selection mask, then run beam search decoding with beam size 10. We are interested in seeing (1) if multiple generations from the same selection mask are paraphrases to each other (intra-consistent) and (2) if generations from different selection masks do differ in the content they described (inter-diverse). The results in Table TABREF32 show that VRS significantly outperforms the other two in both intra-consistency and inter-diversity. RS has the lowest score on both because the selector has very weak effects on the generation as measured in the last section. Bo.Up and SS lay between them. Overall VRS is able to maintain the highest content-text consistency among them.",
+      "Performance INLINEFORM0 Trade-off: To see if the selector affects performance, we also ask human annotators to judge the text fluency. The fluency score is computed as the average number of text being judged as fluent. We include generations from the standard Enc-Dec model. Table TABREF32 shows the best fluency is achieved for Enc-Dec. Imposing a content selector always affects the fluency a bit. The main reason is that when the controllability is strong, the change of selection will directly affect the text realization so that a tiny error of content selection might lead to unrealistic text. If the selector is not perfectly trained, the fluency will inevitably be influenced. When the controllability is weaker, like in RS, the fluency is more stable because it will not be affected much by the selection mask. For SS and Bo.Up, the drop of fluency is significant because of the gap of soft approximation and the independent training procedure. In general, VRS does properly decouple content selection from the enc-dec architecture, with only tiny degrade on the fluency.",
+      "Table TABREF33 / TABREF34 further measure the metric scores on Gigaword/Wikibio by decoding text from the best selection mask based on the selector's distribution (set INLINEFORM0 if INLINEFORM1 and 0 otherwise). We include results from VRS model with INLINEFORM2 , which puts no constraint on the mutual information. We further report the score by generating the best selection mask from the learned posterior distribution INLINEFORM3 for VRS model. Two current SOTA results from BIBREF8 and BIBREF53 and the proportion of selected source words for each model are also included. We have the following observations:",
+      "As the value of INLINEFORM0 decreases, the performance of VRS improves, but the selector loses more controllability because the model tends to over-select contents (over INLINEFORM1 source words selected). The text-content consistency will become low.",
+      "Increasing INLINEFORM0 sacrifices a bit performance, but still comparable with SOTA. Especially on Wikibio where the performance drop is minor. The reason should be that Wikibio is relatively easier to predict the selection but Gigaword has more uncertainty.",
+      "Increasing INLINEFORM0 improves the accuracy of the posterior selection. This would be useful when we want to perform posterior inference for some source-target pair.",
+      "Setting INLINEFORM0 can actually outperform SOTA seq2seq which keeps all tokens, suggesting it is still beneficial to use the VRS model even if we do not care about the controllability.",
+      "Figure FIGREF39 visualizes how changing the value of INLINEFORM0 affects the negative log likelihood (NLL), entropy of the selector and self-bleu score, which roughly correlates with performance, diversity and controllability. NLL is evaluated based on the lower bound in Eq EQREF15 BIBREF57 . We can see as INLINEFORM1 increases, the performance decreases gradually but the content selection gains more diversity and controllability. In practice we can tune the INLINEFORM2 value to achieve a trade-off.",
+      "Generation Example: Figure FIGREF40 shows some examples from Gigaword. As can be seen, decodings from the VRS model are largely consistent with each other, in most cases only replacing one or two words with corresponding synonyms. Samples are able to faithfully convey all selected contents. In contrast, generations from SS. Bo.Up. and RS are unpreditable, differing in both selected contents and also the way of saying. SS and Bo.Up also suffer more from the text disfluency. The generations from them are largely uncertain."
+    ],
+    [
+      "In this paper, we tackle the unaddressed problem of controllable content selection in text generation. We propose a general framework based on variational inference that can be potentiall applied to arbitrary tasks. On both the headline generation and data-to-text tasks, our model outperforms state-of-the-art models regarding the diversity and controllability of content selection. We further introduce an effective way to achieve a performance/controllability trade-off, which can be easily tuned to meet specific requirement."
+    ],
+    [
+      "We thank anonymous reviewers for valuable comments, thank Aditya Mogadala, Shun Kiyono, Thomas Mclachlan and other members of the LIAT team at RIKEN AIP for useful discussions. Xiaoyu Shen is supported by IMPRS-CS fellowship. The work of J. Suzuki was partly supported by JSPS KAKENHI Grant Number JP19104418 and AIRPF Grant Number 30AI036-8. This work is also partially funded by DFG collaborative research center SFB 1102."
+    ],
+    [
+      "The trade-off between performance and interpretability has been a long-standing problem in feature selection BIBREF60 , BIBREF59 . The trade-off exists because it is usually very difficult to accurately find the exact features needed to make the prediction. Safely keeping more features will almost always lead to better performance. Some models do succeed in achieving superior performance by selecting only a subset of the input. However, they mostly still target at the recall of the selection BIBREF39 , BIBREF9 , BIBREF35 , i.e., to select all possible content that might help predict the target. The final selected contents reduce some most useful information from the source, but they still contain many redundant contents (same like our VRS-( INLINEFORM0 ) as in Table TABREF34 and TABREF33 ). This makes them unsuitable for controllable content selection. In text generation, a recent work from BIBREF41 shows they could control the contents by integrating a symbolic selector into the neural network. However, their selector is tailored by some rules only for the RDF triples. Moreover, even based on their fine-tuned selector, the fluency they observe is still slightly worse than a standard seq2seq.",
+      "We assume the content selector is the major bottle if we want a model that can achieve controllability without sacrificing the performance. We can clearly observe in Table TABREF34 that the performance drop in Wikibio is marginal compared with Gigaword. The reason should be that the selection on Wikibio is much easier than Gigaword. The biography of a person almost always follow some simple patterns, like name, birthday and profession, but for news headlines, it can contain information with various focuses. In our two tasks, due to the independence assumption we made on INLINEFORM0 and the model capacity limit, the content selector cannot fully fit the true selecting distribution, so the trade-off is necessary. Improving the selector with SOTA sequence labelling models like Bert BIBREF17 would be worth trying.",
+      "There are also other ways to improve. For example, we could learn a ranker to help us choose the best contents BIBREF63 . Or we could manually define some matching rules to help rank the selection BIBREF58 . In Table TABREF25 , we show the VRS model achieves very high metric scores based on an oracle ranker, so learning a ranker should be able to improve the performance straightforwardly."
+    ],
+    [
+      "To see how we can manually control the content selection, Figure FIGREF42 shows an example from Wikibio, the model is mostly able to form a proper sentence covering all selected information. If the selector assigns very high probability to select some content and we force to remove it, the resulting text could be unnatual (as in summary 4 in Figure FIGREF42 because the model has seen very few text without containing the birthday information in the training corpus). However, thanks to the diversity of the content selector as shown in the previous section, it is able to handle most combinatorial patterns of content selection."
+    ],
+    [
+      "Figure FIGREF41 further provides an example of how we can perform posterior inference given a provided text. Our model is able to infer which source contents are covered in the given summary. With the inferred selection, we can sample multiple paraphrases describing the same contents. As seen in Table TABREF34 and TABREF33 , the metric scores are remarkably high when decoding from the posterior inferred selections (last three rows), suggesting the posterior distribution is well trained. The posterior inference part could be beneficial for other tasks like content transfer among text BIBREF38 , BIBREF62 . The described source contents can be first predicted with the posterior inference, then transferred to a new text."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1608/instruction.md b/qasper-1608/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..40017e63750516ffc37c5e1503636de9a9e7513b
--- /dev/null
+++ b/qasper-1608/instruction.md
@@ -0,0 +1,100 @@
+Name of Paper: Review Conversational Reading Comprehension
+
+Question: What is the baseline model used?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Works",
+    "Preliminary",
+    "Task-awareness Pre-tuning",
+    "Textual Format",
+    "Pre-tuning Data Generation",
+    "Auxilary Objective",
+    "Experiments",
+    "Pre-tuning datasets",
+    "(RC) 2 (\\text{RC})_2 Datasets",
+    "Compared Methods",
+    "Hyper-parameters and Evaluation Metrics",
+    "Result Analysis",
+    "Conclusions"
+  ],
+  "paragraphs": [
+    [
+      "Seeking information to assess whether some products or services suit one's needs is a vital activity for consumer decision making. In online businesses, one major hindrance is that customers have limited access to answers to their specific questions or concerns about products and user experiences. Given the ever-changing environment of products and services, it is very hard, if not impossible, to pre-compile an up-to-date knowledge base to answer user questions as in KB-QA BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 . As a compromise, community question-answering (CQA) BIBREF4 is leveraged to enable existing customers or sellers to answer customer questions. However, one obvious drawback of this approach is that many questions are not answered, and even if they are answered, the answers and the following up questions are delayed, which is not suitable for interactive QA. Although existing studies have used information retrieval (IR) techniques BIBREF4 , BIBREF5 to identify a whole review as an answer to a question, it is time-consuming to read a whole review and the approach has difficulty to answer questions in multiple turns.",
+      "Inspired by recent research in Conversational Reading Comprehension (CRC) BIBREF6 , BIBREF7 , we explore the possibility of turning reviews as a source of valuable knowledge of experiences and to provide a natural way of answering customers' multiple-turn questions in a dialogue setting. The conversational setting of machine reading comprehension (MRC) enables more specific questions and allow customers to either omit or co-reference information in context. As an example in a laptop domain shown in Table 1 , a customer may have 5 turns of questions based on the context. The customer first has an opinion question targeting an aspect \u201cretina display\u201d of a to-be-purchased laptop. Then the customer carries (and omit) the question type opinion from the first question to the second and continually asking the second aspect \u201cboot-up speed\u201d. For the third question, the customer carries the aspect of the second question but change the question type to opinion explanation. Later, the customer can co-reference the aspect \u201cSSD\u201d from the previous answer and ask for the capacity (a sub-aspect) of \u201cSSD\u201d. Unfortunately, there is no answer in this review for the fourth question so the review may say \u201cI don't know\u201d. But the customer can still ask other aspects as in the fifth question. We formally define this problem as follows and call it review conversational reading comprehension (RCRC).",
+      "Problem Definition: Given a review that consists of a sequence of $n$ tokens $d=(d_1, \\dots , d_n)$ , a history of past $k-1$ questions and answers as the context $C=(q_1, a_1, q_2, a_2, \\dots , q_{k-1}, a_{k-1})$ and the current question $q_k$ , find a sequence of tokens (a textual span) $a=(d_s, \\dots , d_e)$ in $d$ that answers $q_k$ based on $C$ , where $1 \\le s \\le n$ , $d=(d_1, \\dots , d_n)$0 , and $d=(d_1, \\dots , d_n)$1 , or return NO ANSWER ( $d=(d_1, \\dots , d_n)$2 ) if the review does not contain any answer for $d=(d_1, \\dots , d_n)$3 .",
+      "RCRC is a novel QA task that requires the understanding of both the current question $q_k$ and dialogue context $C$ . Compared to the traditional single-turn MRC, the key challenge is how to understand the context $C$ and the current question $q_k$ given it may have a co-reference resolution or context carryover.",
+      "To the best of our knowledge, there are no existing review datasets for RCRC. We first build a dataset called $(\\text{RC})_2$ based on laptop and restaurant reviews from SemEval 2016 Task 5. We choose this dataset to better align with existing research on review-based tasks in sentiment analysis. Each review is annotated with a few dialogues focusing on some topics. Note that although one dialogue is annotated on a single review, a trained RCRC model can potentially be deployed among an open set of reviews BIBREF8 where the context may potentially contain answers from different reviews. Given the wide spectrum of domains in online business (e.g., thousands of categories on Amazon.com) and the prohibitive cost of annotation, $(\\text{RC})_2$ is designed to have limited supervision as in other tasks of sentiment analysis. We adopt BERT (Bidirectional Encoder Representation from Transformers BIBREF9 ) as our base model since its variants achieve dominant performance on MRC BIBREF10 , BIBREF11 and CRC BIBREF6 tasks. However, BERT is designed to learn features for a wide spectrum of NLP tasks with a large amount of training examples. The task-awareness of BERT can be hindered by the weak supervision of the $(\\text{RC})_2$ dataset. To resolve this challenge, we introduce a novel pre-tuning stage between pre-training and end-task fine-tuning for BERT. The pre-tuning stage is formulated in a similar fashion as the RCRC task but requires no annotated RCRC data and just domain QA pairs (from CQA) and reviews, which are readily available online BIBREF4 . We bring certain characteristics of the RCRC task (inputs/outputs) to pre-tuning to encourage BERT's weight to be prepared for understanding the current question and locate the answer if there exists one. The proposed pre-tuning step is general and can potentially be used in MRC or CRC tasks in other domains.",
+      "The main contributions of this paper are as follows. (1) It proposes a practical new task on reviews that allows multi-turn conversational QA. (2) To address this problem, an annotated dataset is first created. (3) It then proposes a pre-tuning stage to learn task-aware representation. Experimental results show that the proposed approach achieves competitive performance even compared with the supervised approach on a large-scale training data."
+    ],
+    [
+      "MRC (or CRC) has been studied in many domains with formal written texts, e.g., Wikipedia (WikiReading BIBREF12 , SQuAD BIBREF10 , BIBREF11 , WikiHop BIBREF13 , DRCD BIBREF14 , QuAC BIBREF7 , HotpotQA BIBREF15 ), fictional stories (MCTest BIBREF16 , CBT BIBREF17 , NarrativeQA BIBREF18 ), general Web documents (MS MARCO BIBREF19 , TriviaQA BIBREF20 , SearchQA BIBREF21 ) and news articles (NewsQA BIBREF22 , CNN/Daily Mail BIBREF23 , and RACE BIBREF24 ). Recently, CRC BIBREF6 , BIBREF25 , BIBREF26 gains increasing popularity as it allows natural multi-turn questions. Examples are QuAC BIBREF7 and CoQA BIBREF6 . CoQA is built from multiple sources, such as Wikipedia, Reddit, News, Mid/High School Exams, Literature, etc. To the best of our knowledge, CRC has not been used on reviews, which are primarily subjective. Our $(\\text{RC})_2$ dataset is compatible with the format of CoQA datasets so all CoQA-based models can be easily adapted to our dataset. Answers from $(\\text{RC})_2$ are intended to be extractive (similar to SQuAD BIBREF10 , BIBREF11 ) rather than abstractive (generative) (such as in MS MARCO BIBREF19 and CoQA BIBREF6 ) because we believe online businesses are cost-sensitive so relying on human written answers are more reliable than machine generated answers.",
+      "Traditionally, knowledge bases (KBs) (such as Freebase BIBREF27 , BIBREF3 , BIBREF28 or DBpedia BIBREF29 , BIBREF30 ) have been used for question-answering BIBREF5 . However, the ever-changing environment of online businesses launches new products and services appear constantly, making it prohibitive to build a high-quality KB to cover all new products, services and subjective experiences from customers. Community QA (CQA) is widely adopted by online businesses BIBREF4 to help users get answers for their questions. However, since the answers are written by humans, it often takes a long time to get a question answered or even not answered at all as we discussed in the introduction section. There exist researches that align reviews to questions in CQA as an information retrieval task BIBREF4 , BIBREF5 , but a whole review is hard to read and not suitable for follow-up questions. We novelly use CQA data for CRC (or potentially for MRC), which play a significant role in encouraging domain representation learning on questions and contexts, which are largely ignored in existing research on MRC (or CRC)."
+    ],
+    [
+      "In this section, we briefly review BERT (Bidirectional Encoder Representation from Transformers BIBREF9 ), which is one of the key innovations of unsupervised contextualized representation learning BIBREF31 , BIBREF32 , BIBREF33 , BIBREF9 . The idea behind these innovations is that although the word embedding BIBREF34 , BIBREF35 layer is trained from large-scale corpora, relying on the limited supervised data from end-tasks to train the contextualized representation is insufficient. Unlike ELMo BIBREF31 and ULMFiT BIBREF32 that are designed to provide additional features for an end task, BERT adopts a fine-tuning approach that requires almost no specific architecture design for end tasks, but parameter intensive models on BERT itself. As such, BERT requires pre-training on large-scale data (Wikipedia articles) to fill intensive parameters in exchange for human structured architecture designs for specific end-tasks that carry human's understanding of data of those tasks.",
+      "One training example of BERT is formulated as $(\\texttt {[CLS]}, x_{1:j}, \\texttt {[SEP]}, x_{j+1:n}, \\texttt {[SEP]})$ , where [CLS] and [SEP] are special tokens and $x_{1:n}$ is a document splited into two sides of sentences $x_{1:j}$ and $x_{j+1:n}$ . The key performance gain of BERT comes from two novel pre-training objectives: masked language model (MLM) and next text sentence prediction.",
+      "Masked Language Model enables learning bidirectional language models and essentially encourages a BERT model to predict randomly masked words given their contexts. This is crucial for RCRC. For example, an example can be \u201cits amazingly [MASK] when it boots up because of the [MASK] storage\u201d. These two [MASK]'s encourage BERT to guess that the first mark could be \u201cfast\u201dand the second mask could be\u201cSSD\u201d so as to learn some common knowledge on aspects of laptops and their potential opinions.",
+      "Next Sentence Prediction further encourages BERT to learn inter-sentence representations by predicting whether two sides around the first $\\texttt {[SEP]}$ are from the same document or not. We remove this objective in our pre-tuning as the text format is different from BERT pre-training (discussed in the next Section).",
+      "In summary, we can see that the pre-trained BERT severely lacks RCRC task-awareness as there is no formulation for either context $C$ , the current turn question $q_{k}$ or possible answer spans as Wikipedia contains almost no questions or domain knowledge about online businesses. We resolve these issues in the next section."
+    ],
+    [
+      "To address the limitation of BERT on task-awareness, we introduce an intermediate stage of pre-tuning between BERT pre-training and fine-tuning on RCRC. This works in a similar spirit to the invention of BERT (or any other pre-trained language models) because it is also insufficient to learn the end task definition (or setting) solely on the limited supervised data (of that task). The task-awareness is determined by the inputs and outputs of RCRC, which introduce two directions for pre-tuning: (1) understanding the text inputs, including both domains and text formats (e.g., contexts, current questions). (2) understanding the goal of RCRC, including both having a text span or no answer. As such, we first define the textual format that is shared by both the RCRC and BERT pre-tuning in Section \"Conclusions\" . Then we introduce an auxiliary pre-tuning objective in Section \"Auxilary Objective\" ."
+    ],
+    [
+      "Inspired by the recent implementation of DrQA for CoQA BIBREF6 and BERT for SQuAD, we formuate an input example $x$ for pre-tuning (or RCRC) from the context $C$ , the current question $q_k$ , and the review $d$ as follows: ",
+      "$$\\begin{split}\nx=(\\texttt {[CLS]} \\texttt {[Q]} q_1 \\texttt {[A]} a_1 \\dots \\texttt {[Q]} q_{k-1} \\texttt {[A]} a_{k-1} \\\\\n\\texttt {[Q]} q_{k} \\texttt {[SEP]} d_{1:n} \\texttt {[SEP]}),\n\\nonumber \\end{split}$$   (Eq. 7) ",
+      "where past QA pairs $q_1, a_1, \\dots , q_{k-1}, a_{k-1}$ in $C$ are concatenated and separated by two special tokens [Q] and [A] and then concatenate with the current question $q_k$ as the left side of BERT and the right side is the review document. This format will be used for both pre-tuning and RCRC task fine-tuning. Note that the answer for a question with no answer in the context is written as a single word \u201cunknown\u201d. One can observe that although this format is simple and intuitive for humans to read, BERT's pre-trained weights have no idea the semantics behind this format (e.g., where is the current question, how many turns in the context and where is the previous turn), let alone the special tokens [Q] and [A] never appear during BERT pre-training."
+    ],
+    [
+      "Based on the format defined in Section \"Conclusions\" , we can observe that getting BERT to be familiar with domain reviews is as easy as continually training BERT on reviews. However, enabling BERT to understand the context $C$ and the current question $q_k$ is more challenging as the pre-training data of BERT has almost no question. To resolve this issue, we combine QA pairs (from CQA data) and reviews to formulate the pre-tuning examples, as shown in Algorithm \"Pre-tuning Data Generation\" . Note that these two kinds of data are often readily available across a wide range of products in Amazon.com and Yelp.com.",
+      "[t] Data Generation Algorithm InputInput OutputOutput $\\mathcal {T}$ : pre-tuning data.",
+      " $\\mathcal {T} \\leftarrow \\lbrace \\rbrace $ $(q^{\\prime }, a^{\\prime }) \\in \\mathcal {Q}$ $x \\leftarrow \\texttt {[CLS]} $ $h \\leftarrow \\text{RandInteger}([0, h_{\\text{max}}]) $ $1 \\rightarrow h$ $q^{\\prime \\prime }, a^{\\prime \\prime } \\leftarrow \\text{RandSelect}(\\mathcal {Q}\\backslash (q^{\\prime }, a^{\\prime }))$ $ x \\leftarrow x \\oplus \\texttt {[Q]} \\oplus q^{\\prime \\prime } \\oplus \\texttt {[A]} \\oplus a^{\\prime \\prime } $ $ x \\leftarrow x \\oplus \\texttt {[Q]} \\oplus q^{\\prime } \\texttt {[SEP]} $ $ r_{1:s} \\leftarrow \\text{RandSelect}(\\mathcal {R}) $ $\\text{RandFloat}([0.0, 1.0]) > 0.5$ $(q^{\\prime }, a^{\\prime }) \\in \\mathcal {Q}$0 $(q^{\\prime }, a^{\\prime }) \\in \\mathcal {Q}$1 $(q^{\\prime }, a^{\\prime }) \\in \\mathcal {Q}$2 $(q^{\\prime }, a^{\\prime }) \\in \\mathcal {Q}$3 $(q^{\\prime }, a^{\\prime }) \\in \\mathcal {Q}$4 $(q^{\\prime }, a^{\\prime }) \\in \\mathcal {Q}$5 $(q^{\\prime }, a^{\\prime }) \\in \\mathcal {Q}$6 $(q^{\\prime }, a^{\\prime }) \\in \\mathcal {Q}$7 $(q^{\\prime }, a^{\\prime }) \\in \\mathcal {Q}$8 $(q^{\\prime }, a^{\\prime }) \\in \\mathcal {Q}$9 ",
+      "To ensure the topic of a pre-tuning example is consistent between QAs and reviews, we assume QA pairs and reviews are organized under each entity (a laptop or a restaurant in our experiment) that customers focus on. The inputs to Algorithm \"Pre-tuning Data Generation\" are a set of QA pairs and reviews belonging to the same entity and the maximum turns in the context that is the same as the RCRC datasets. The output is the pre-tuning data as initialized in Line 1, where each example is denoted as $(x, (s, e))$ . Here $x$ is the input example and $(s, e)$ is the two pointers for the auxiliary objective (discussed in Section \"Auxilary Objective\" ). Given a QA pair $(q^{\\prime }, a^{\\prime })$ in Line 2, we first build the left side of input example $x$ in Line 3-9. After initializing input $x$ in Line 3, we randomly determine the number of turns as context in Line 4 and concatenate $\\oplus $ these turns of QA pairs in Line 5-8, where $\\mathcal {Q}\\backslash (q^{\\prime }, a^{\\prime })$ ensures the current QA pair $(q^{\\prime }, a^{\\prime })$ is not chosen. In Line 9, we concatenate the current question $q^{\\prime }$ . Lines 10-23 build the right side of input example $x$0 and the outputs pointers In Line 10, we randomly draw a review with $x$1 sentences. To challenge the pre-tuning stage to discover the semantic relatedness between $x$2 and $x$3 (as for the auxiliary objective), we first decide whether to allow the right side of $x$4 contains $x$5 (Line 16) for $x$6 or a fake random answer $x$7 Lines 11-12. We also come up with two pointers $x$8 and $x$9 initialized in Lines 13 and 17. Then, we insert $(s, e)$0 into review $(s, e)$1 by randomly pick one from the $(s, e)$2 locations in Lines 19-20. This gives us $(s, e)$3 , which has $(s, e)$4 tokens. We further update $(s, e)$5 and $(s, e)$6 to allow them to point to the chunk boundaries of $(s, e)$7 . Otherwise, BERT should detect as no $(s, e)$8 on the right side and point to [CLS] ( $(s, e)$9 ). Finally, examples are aggregated in Line 25.",
+      "Algorithm \"Pre-tuning Data Generation\" is run $p=10$ times to allow for enough samplings of data. As we can see, although labeled training examples for RCRC are expensive to obtain, harvest a large amount of pre-tuning data is easy. Following the success of BERT, we still randomly mask some words in each example $x$ to learn contextualized representations on domain texts."
+    ],
+    [
+      "Besides adapting input $x$ to domains and RCRC task, it is also desirable to allow pre-tuning to adapt BERT to the goal of RCRC tasks, which is to predict a token span or NO ANSWER. Besides MLM from BERT, we further introduce an auxiliary objective called answer chunk detection to align BERT to a similar fashion as RCRC, except that we only predict the token spans of an answer chunk from CQA. Further, these tasks challenge BERT to be prepared for predicting NO ANSWER from a review by detecting a negative randomly drawn answer.",
+      "Let $\\text{BERT}(\\cdot )$ be the BERT's transformer model. We first obtain the hidden representation of BERT as $h=\\text{BERT}(x)$ . Then the hidden representation is passed to two separate dense layers followed by softmax functions: $l_1=\\text{softmax}(W_1 \\cdot h + b_1)$ and $l_2=\\text{softmax}(W_2 \\cdot h + b_2)$ , where $W_1$ , $W_2 \\in \\mathbb {R}^{r_h}$ and $b_1, b_2 \\in \\mathbb {R}$ and $r_h$ is the size of the hidden dimension (e.g., 768 for $\\text{BERT}_{\\text{BASE}}$ ). Training involves minimizing the averaged cross entropy on the two pointers $s$ and $h=\\text{BERT}(x)$0 generated in Algorithm \"Pre-tuning Data Generation\" : $h=\\text{BERT}(x)$1 ",
+      "where $\\mathbb {I}(s)$ and $\\mathbb {I}(e)$ are one-hot vectors representing the two starting and ending positions. For a positive example (with true answer $a^{\\prime }$ randomly inserted in the review), $s$ and $e$ are expected to be $\\text{Idx}_{\\texttt {[SEP]}} < s\\le |x|$ and $s\\le e\\le |x|$ , respectively, where $\\text{Idx}_{\\texttt {[SEP]}}$ is the position of the first [SEP]. For a negative example (with a random answer (not $a^{\\prime }$ ) mixed into the review), $s,e=1$ indicates the two pointers must point to [CLS].",
+      "After pre-tuning, we fine-tune on the RCRC task in a similar fashion to the auxiliary objective, except this time there is no need to perform MLM."
+    ],
+    [
+      "We aim to answer the following research questions (RQs) in the experiment:",
+      "RQ1: What is the performance of using BERT compared against CoQA baselines ?",
+      "RQ2: Upon ablation studies of different applications of BERT, what is the performance gain of pre-tuning ?",
+      "RQ3: What is the performance of pre-tuning compared to using (large-scale) supervised data?"
+    ],
+    [
+      "To be consistent with existing research on review-based tasks such as sentiment analysis, we adopt SemEval 2016 Task 5 as the review source for RCRC, which contains two domains laptop and restaurant. Then we collect reviews and QA pairs for these two domains. For the laptop domain, we collect the reviews from BIBREF36 and QA pairs from BIBREF37 both under the laptop category of Amazon.com. We exclude products in the test data of $(\\text{RC})_2$ to make sure the test data is not used for on any model parameters. This gives us 113,728 laptop reviews and 18,589 QA pairs. For the restaurant domain, we collect reviews from Yelp dataset challenges but crawl QA pairs from Yelp.com . We select restaurants with at least 100 reviews as other restaurants seldom have any QA pairs. This ends with 753,096 restaurant reviews and 15,457 QA pairs.",
+      "To compare with a supervised pre-tuning approach, we further leverage the CoQA dataset BIBREF6 . It comes with 7,199 documents (passages) and 108,647 QA pairs of supervised training data with domains in Children\u2019s Story. Literature Mid/High School, News, and Wikipedia."
+    ],
+    [
+      "To the best of our knowledge, there are no existing datasets for RCRC. We keep the split of training and testing of the SemEval 2016 Task 5 datasets and annotate dialogues of QAs on each review. To ensure our questions are real-world questions, annotators are first asked to read CQAs of the pre-tuning data. Each dialogue is annotated to focus on certain topics of a review. The textual spans are kept to be as short as possible but still human-readable. No-answer questions are also annotated, which have certain topical connections with the nearby questions or answers. Annotators are encouraged to label about 2 dialogues from a testing review to get enough testing examples. One training review is encouraged to have 1 dialogue to have good coverage of reviews. Each question is shortened as much as possible to omit existing information in the past turns. The annotated data is in the format of CoQA BIBREF6 to help future research. The statistics of $(\\text{RC})_2$ dataset is shown in Table 2 . We split 20% of the training reviews as the validation set for each domain."
+    ],
+    [
+      "We compare the following methods by training/fine-tuning on $(\\text{RC})_2$ . All the baselines are run using their default hyper-parameters.",
+      "DrQA is a CRC baseline coming with the CoQA dataset. Note that this implementation of DrQA is different from DrQA for SQuAD BIBREF8 in that it is modified to support answering no answer questions by having a special token unknown at the end of the document. So having a span with unknown indicates NO ANSWER. This baseline answers the research question RQ1.",
+      "DrQA+CoQA is the above baseline pre-tuned on CoQA dataset and then fine-tuned on $(\\text{RC})_2$ . We use this baseline to show that even DrQA pre-trained on CoQA is sub-optimal for RCRC. This baseline is used to answer RQ1 and RQ3.",
+      "BERT is the vanilla BERT model directly fine-tuned on $(\\text{RC})_2$ . We use this baseline for ablation study on the effectiveness of pre-tuning. All these BERT's variants are used to answer RQ2.",
+      "BERT+review first tunes BERT on domain reviews using the same objectives as BERT pre-training and then fine-tunes on $(\\text{RC})_2$ . We use this baseline to show that a simple domain-adaptation of BERT is not good.",
+      "BERT+CoQA first fine-tunes BERT on the supervised CoQA data and then fine-tunes on $(\\text{RC})_2$ . We use this baseline to show that pre-tuning is very competitive even compared with models trained from large-scale supervised data. This also answers RQ3.",
+      "BERT+Pre-tuning first pre-tunes BERT as proposed and then fine-tunes on $(\\text{RC})_2$ ."
+    ],
+    [
+      "We choose BERT base model as our pre-tuning and fine-tuning model, which has 12 layers, 768 hidden dimensions and 12 attention heads (in transformer) with total parameters of 110M. We cannot use the BERT large model as we cannot fit it into our GPU memory for training. We set the maximum length to be 256 with a batch size of 16. We perform pre-tuning for 10k steps as further increasing the pre-tuning steps doesn't yield better results. We fine-tune 6 epochs, though most runs converged just within 3 epochs due to the pre-trained/tuned weights of BERT. Results are reported as averages of 3 runs of fine-tuning (3 different random seeds for tuning batch generation).",
+      "To be consistent with existing research, we leverage the same evaluation script from CoQA. Similar to the evaluation of SQuAD 2.0, CoQA script reports turn-level Exact Match (EM) and F1 scores for all turns in all dialogues. EM requires the answers to have exact string match with human annotated answer spans. F1 score is the averaged F1 scores of individual answers, which is typically higher than EM and is the major metric."
+    ],
+    [
+      "As shown in Table 3 , BERT+Pre-tuning has significant performance gains over many baselines. To answer RQ1, we can see that BERT is better than DrQA baseline from CoQA. To answer RQ2, we notice that by leveraging BERT+Pre-tuning, we have about 9% performance gain. Note that directly using review documents to continually pre-training BERT does not yield better results for BERT+review. We suspect the task of RCRC still requires certain degrees of general language understanding and BERT+review has the effect of (catastrophic) forgetting BIBREF38 the strength of BERT. To answer RQ3, we notice that large-scale supervised CoQA data can boost the performance for both DrQA and BERT. However, our pre-tuning stage still has competitive performance and it requires no annotation at all."
+    ],
+    [
+      "In this paper, we propose a novel task called review conversational reading comprehension (RCRC). We investigate the possibility of interactive question answering by using reviews as knowledge of user experiences. We first build a dataset called $(\\text{RC})_2$ , which is derived from popular review datasets for sentiment analysis. To resolve the issues of limited supervision introduced by the prohibitive cost of annotation, we introduce a novel pre-tuning stage to perform task-adaptation from a language model. This pre-tuning stage can potentially be used for any MRC or CRC task given it has no requirement on annotation but large QA and review corpora available online. Experimental results show that the pre-tuning approach is highly effective and outperforms existing baselines or highly competitive with supervised baselines trained from a large-scale dataset."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1609/instruction.md b/qasper-1609/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..b8ea3965a6ed131224bc910faaec2003e9508217
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@@ -0,0 +1,133 @@
+Name of Paper: Human Languages in Source Code: Auto-Translation for Localized Instruction
+
+Question: Is this auto translation tool based on neural networks?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Introduction ::: Related Work",
+    "Human Languages on GitHub",
+    "Human Languages on GitHub ::: Non-English in Java",
+    "Human Languages on GitHub ::: Transliteration on GitHub",
+    "Human Languages on GitHub ::: Right-to-Left Languages on GitHub",
+    "Code International",
+    "Code International ::: Translating Identifiers",
+    "Code International ::: Translating comments",
+    "Code International ::: Translating Right-to-Left languages",
+    "Code International ::: Prior and posterior translations",
+    "Translating Github",
+    "International Karel",
+    "International Karel ::: Line-highlighting in any language",
+    "International Karel ::: Usage in Classrooms",
+    "Discussion",
+    "Conclusion",
+    "Conclusion ::: Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "Reading and writing comments, method names and variable names is a crucial part of software engineering and as such, programs have both a human language, the language of identifiers and comments, in addition to the source-code language (eg Java or Python). This has meant that non-English speakers are often second class citizens when learning to program BIBREF0. In this paper we present a tool for translating a program from one human-language to another to assist in code education, which could reduce the barrier to computer science education for non-English speakers.",
+      "The main contributions presented in this paper are:",
+      "Analysis of 1.1M non-English code projects on GitHub",
+      "CodeInternational: A tool which can translate code between human languages, powered by Google Translate.",
+      "Validation of CodeInternational by evaluating the translation of 1,000 randomly chosen projects from GitHub.",
+      "Use of CodeInternational to automatically translate the popular Karel textbook into 100+ languages. We further extend the textbook to parse and run KarelJava code in any language; we report adoption by classrooms around the world.",
+      "Our human-language code translator was inspired by a desire to make programming more accessible BIBREF1. An accurate and useful translator would enable faster localization of instruction materials and it would allow learners (as well as practitioners) to translate code that they are working with.",
+      "As programming becomes more of a requisite common knowledge skill, we expect coding education to become open-access to everyone. One barrier to this goal is human language. English is currently the modal language of programming instruction perhaps given that the keywords of most of the popular languages, Java, JavaScript etc, are in English (even including Python and Lua, invented in the Netherlands and Brasil respectively). However, a majority of the world, estimated in 2008 at 80%, can't \u201cuse\" English for communication and substantially more don't speak English as their L1 language (the technical term for one's arterial language, aka, mother tongue) BIBREF2. Should the more than 6 billion non-English speakers learn to program in their native language or in English? This question is debated, which we address in the discussion.",
+      "We take the position that whether or not code instruction is in English, if students do not speak English as their L1 language, their code education would benefit from the ability to translate Code between their preferred language and English."
+    ],
+    [
+      "To the best of our knowledge, automatic translation of code between human languages, did not appear in literature, making us hypothesize: it is either difficult, or had remained ignored. Nonetheless, we summarize related work that motivate our contribution.",
+      "Translation of Text automatic translation of natural language has recently achieved high accuracy and is used in highly sensitive contexts BIBREF3, BIBREF4, BIBREF5. At the time of writing this article, Google Translate uses Neural Machine Translation BIBREF6 to translate pairwise between languages and has become incredibly accurate, at least for languages common on the web BIBREF7. Further research has been done on transliterating text BIBREF8, BIBREF9. However, current state-of-the-art methods for text translation fail at translating code. Directly running a translation algorithm on code would fail to distinguish between code syntax and identifiers, would not recognize terms embedded in identifiers e.g. with camel case getElementAt, and could produce code with one identifier name having different translations on separate lines. As such, current automatic text translation, if ran directly on code, would produce malfunctional code. Code Instruction in Non-English In 2017, Dasgupta and Hill published seminal work outlining the importance of learning to code in one's own language. They conclude that \"novice users who code with their programming language keywords and environment localized into their home countries' primary language demonstrate new programming concepts at a faster rate than users from the same countries whose interface is in English\" BIBREF10. Since then, there has been a large set of papers expanding on the barriers for non-native English speakers. Guo et al survey over 800 non-English students learning who report on the many challenges that come with not understanding English while coding. BIBREF11 reinforced by BIBREF12, BIBREF13. This has led to preliminary work into translating compiler errors BIBREF14 and advocation for language-free block free programming BIBREF15. However, while language-free programming is a great step forward for younger students, it doesn't address the needs of CS1 students who program in common programming languages like Python or Java. While all of this work motivates our contribution, none has attempted an automatic solution to the problem, making crowd-translation a viable alternative BIBREF16.",
+      "Mining Github To understand the patterns of code that students and practitioners use, we analyze public repositories on GitHub. Other researchers also analyzed GitHub, sometimes via the dataset and tools provided by BIBREF17, including work on social diversity of teams BIBREF18 and affiliation influence on code popularity BIBREF19. This has led to a set of best practices for navigating the promises and perils of mining GitHub BIBREF20. A growing number of students are using GitHub in software engineering courses BIBREF21 which makes it a valuable resource for understanding code of the general population, including students.",
+      "Code Conversion There is a rich literature of work to translate code between programming languages, such as C or C++ to Java BIBREF22, BIBREF23, or even from English to code BIBREF24. However, the emphasis is often on maintaining efficiency, not on making code readable for students. We focus on translating the human language of code. Byckling et al BIBREF25 analyze naming conventions of identifiers based their function (fixed, iterators, transformers, etc), and correlate the naming consistency with the students' learning experience. This motivates aspects of our translation. See Section SECREF22."
+    ],
+    [
+      "How do non-English speakers program in a language like Java, where the keywords and core libraries are written in English? We employ a data driven approach to tell the story of non-English code and inform the decisions we made in our auto-translator. We analyzed Java repositories on GitHub, the largest host of source code in the world, where 1.1 million unique users host 2.9 million public Java projects. We downloaded and analyzed the human language used for writing comments (in Java code), naming identifiers (method and variable names), and writing git commit messages. We focused on Java code as it is both one of the most popular source-code languages on GitHub and in the classroom. A selection of results from this study are that:",
+      "Non-English code is a large-scale phenomena.",
+      "Transliteration is common in identifiers for all languages.",
+      "Languages clusters into three distinct groups based on how speakers use identifiers/comments/transliteration.",
+      "Non-latin script users write comments in their L1 script but write identifiers in English.",
+      "Right-to-left (RTL) language scripts, such as Arabic, have no observed prevalence on GitHub identifiers, implying that existing coders who speak RTL languages have substantial barriers in using their native script in code.",
+      "This is, to the best of our knowledge, the first analysis of the human languages on GitHub. See Figure FIGREF6 for an overview.",
+      "Users on GitHub do not state their L1 (arterial) language. While a subset of users optionally state their country this is neither common nor reliable. To estimate a user's preferred language we use the language that they use in the git commit message. To find subsets of users who speak a given language, we search for all users who write git commits in that language. We observe that, especially in personal projects, users write commit messages in their L1 language at a higher rate than comments or identifiers. To identify languages we use Google Language Detect which is highly accurate (more so for common internet languages) and can identify languages with non-Roman Alphabet text which has been transliterated, for example it can detect bothUTF8gbsn\u7b97\u6cd5 the Chinese characters for \u201calgorithm\" and \"suanfa\", the Mandarin transliteration, as Chinese.",
+      "Of the 1.1 million GitHub users, 12.7% wrote commit messages in non-English languages. Of the non-English languages Chinese was the most common (28.6% of non-English committers), followed by Spanish, Portuguese, French, and Japanese. More than 100 languages were detected in commit messages on public Java projects. Figure FIGREF6 contains breakdowns and the appendix contains the full list. This does not match the distribution of non-English in web content (55% English) with both major and minor languages underrepresented. For example the prevalence of Spanish on GitHub (2.1%) is about half of webcontent (5.1% BIBREF26) and further trails native speakers (7.8% of the worlds population BIBREF27).",
+      "Github does not present a random sample of programs written in the world, and we consider the relevant confounds this introduces. To that point, we believe the under-representation of certain languages is a form of Survivorship Bias. It suggests that users have found barriers to entry towards joining the GitHub community. Those barriers could derive from the English dominance of programming languages, code instruction, or the github interface."
+    ],
+    [
+      "The use of non-English in identifiers and comments is large for the population of users who we define as non-English \"speakers\" (those who use non-English in their git-commit messages). 90% of users who use a non-English language in the commit messages also use that language in their comments or as identifiers. We note that, in Java, identifiers can be written in any script.",
+      "Surprisingly, the patterns of non-English usage differs substantially when we condition on users \"speaking\" different languages. For example, among the detected Spanish speakers, 87.2% percent of users write identifiers in Spanish. On the other hand, among Chinese users only 23.3% of users write code with Chinese identifiers (either in Chinese script or ASCII). Figure 1b shows coding patterns conditioned on users speaking different languages. For each language we plot the percent of projects with identifiers in the language against the percent of projects with comments in the language. Languages naturally cluster into three categories: (1) Major-Euro-Latin: languages with high use of non-English identifier including Spanish, German and French (2) Non-Latin: languages in non-latin scripts including Russian and Chinese which have low use of non-English identifiers and (3) English-Comment: Programmers write their comments in English (> 70% of projects only have English comments). This group contains many smaller and non-European languages like Dutch and Bahasa Indonesia. 0% of projects in this group still uses their L1 language in identifiers.",
+      "The use of identifiers in local language (as opposed to English) is very clearly split on whether languages use the Latin alphabet. On average 82% of projects from users speak languages with different scripts like Chinese, Korean, or Russian have only English identifiers, compared to 12% of projects from Latin alphabet users ($p < 0.0001$). The percentage of projects with only English comments is roughly correlated to the English Proficiency Index BIBREF28 of the corresponding countries ($\\rho = 0.42$ $p < 0.01$)."
+    ],
+    [
+      "Transliteration is the process of transferring a word from the alphabet of one language to another (eg -> namaste duniya). We observed that most Java code with human languages that have non-ascii scripts like Kanji, Devanagari, or even Spanish accents like \u00f1, will have been \"transliterated\" into ascii.",
+      "The Java Language Specification states that, \"letters and digits (in identifiers) may be drawn from the entire Unicode character set, which supports most writing scripts\". This specification is not widely known, and even if Java supports non-ascii , there can be complexities of file encodings across different operating systems.",
+      "We find that regardless of L1 language most users transliterate identifiers: among L1 Chinese speakers, 93% of projects have identifiers which are only written in ASCII. Similarly in Spanish 88% of projects have only ASCII identifiers. As a concrete example, in GitHub Java code \"numero\" is 3.8x more common than \"n\u00famero\". Among comments languages differ greatly: 99% of Chinese projects have non ASCII comments compared to only 53% of Spanish. As an example a comment above a method specifies in script that it is calculating the Fibonacci sequence however the method name (an identifier) is transliterated \"//UTF8gbsn\u6590\u6ce2\u90a3\u5951\" however the code uses a transliteration of the phonemes in the script \"public int feibonaqie(int n)\". This is a common pattern: Within comments, UTF8gbsn\u8ba1\u6570 chinese for count), is 4.0x more common than jishu, the transliteration. However in identifiers jishu is 4.8x more common. The difference in transliteration patterns between Chinese and Spanish suggests a different intent: in Spanish transliteration is used to avoid file encoding errors, in Chinese it is to prevent a mix of scripts among identifiers."
+    ],
+    [
+      "One question that we did not have a solid pre-conception for was: How do Java users who speak languages with right-to-left (RTL) scripts like Arabic, Urdu or Hebrew, write code?",
+      "18,961 users on GitHub report their country as one where a RTL script (Arabic or Hebrew) is the primary script. Those users have 8,060 public Java repositories of which only 50 repositories (0.6%) have Arabic or Hebrew script (excluding string literals). Of those repositories, only a single Java file had a single identifier written in Arabic and none in Hebrew. It is extremely rare for methods or identifiers to be a mix of RTL and LTR."
+    ],
+    [
+      "The GitHub analysis is coherent with the contemporary narrative: there are perhaps hundreds of millions of learners who will not speak English as their L1 language. For those learners, teachers need a tool to translate code so they can give examples with less congitive load. Similarly students need a tool to understand the non-English code they encounter. Finally, to a growing extent English speakers will begin to interact with code written in other languages.",
+      "To adress this need, we designed a tool to help programmers, regardless of their spoken language, access code in many languages. The tool, which we call CodeInternational, takes in code written in either Java or Python with comments and identifiers written in a human-language and translates the comments and identifiers into another human-language. It supports the growing set of human languages covered by Google Translate and is adaptive to the particular context of source-code. To translate code, it first parses the code and extracts four types of tokens:",
+      "[leftmargin=3mm]",
+      "Comments: inline or multi-line comments. Their purpose is for the programmer to communicate to programmers (including herself) on the purpose of code sections.",
+      "Immutable: consisting of language keywords (while, void, etc), and identifiers imported from libraries that are external to the code being translated (e.g. FileReader of java.io). By default this group is not translated.",
+      "Target identifiers: including variable and function names that are defined in the code base undergoing translation.",
+      "String literals: In some cases a user may want String literals to be translated, other times they should be unchanged.",
+      "Our translation algorithm is as follows. We (1) collect all of the target identifiers defined in the codebase and (2) translate them (enforcing that if two identifiers have the same name, they are given the same translation). Once the identifiers are translated we (3) translate the comments preserving structure and references to identifiers. (4) Finally string literals are optionally translated. See Figure FIGREF20 for a highlevel depiction and Figure FIGREF23 for a concrete example. Each of these steps has surprising challenges. In this section we cover the corresponding solutions we developed. The mapping of identifier translations that the tool decides on is preserved to assist any external source which needs to refer to the newly translated identifiers (such as text in a text-book or code in a related project).",
+      "CodeInternational is implemented in Python. Tokenization is performed using a modified version of \"Javalang\" (for Java) and the \"Parser\" library (for Python). Supporting other programming languages requires a small amount of extra work."
+    ],
+    [
+      "In order to properly translate identifiers, we consider the following:",
+      "",
+      "Identifier segmentation: Translating an identifier using a tool like Google Translate does not work by default as identifiers are often composed of unsegmented words. For example: getFavoriteNumber is readable to a human as \"get favorite number\" but is not parsable by an online translator. We segment identifiers using naming conventions (e.g. camelCaseVariable, PascalCaseClass, UPPERCASE_CONSTANT). We thus segment identifiers into phrases which we feed into an automatic translator. We then recombine the translated phrase using the original casing convention. For example, to translate the method name identifier \"turnAround\" into Spanish: \"turnAround\" is segmented into \"turn around\" which is translated into \"media vuelta\" which is formatted into the original camelCase \"mediaVuelta\". Advances in artificial intelligence for word segmentation enable a future version of this tool to break up words without a given segmentation (eg \"turnaround\").",
+      "",
+      "Verb prior: The correct translation for a phrase can be ambiguous, especially without context. As an example the method \"move\" translated into Spanish could be translated into a noun (\"movimiento\", movement) or a verb (\"moverse\"). For method identifiers there is an implicit context that an action is being performed. We incorporate this context by placing a prior on the first word being a verb. Thus, for example, when we translate \"move()\" into Spanish we chose \"moverse()\" instead of \"movimiento()\", the noun movement, as Google suggests.",
+      "In addition to knowing the translations of methods should start with verbs, we also have a select number of reasonable tenses for the verb: infinitive (eg \"toMove\"), third person present (eg \"moves\" as in \"he moves\") and imperative (eg \"move\"). In most languages, including English, we translate verbs with a prior that they be the imperative tense. In English you would expect a method to be \"getObject()\" the imperative. However some languages, especially Romance languages, use the infinitive of the verb: as an example, \"obtener\" the infinitive of \"obtain\" is 200x more common on GitHub then \"obtenga\" the imperative.",
+      "",
+      "Translating short identifiers: Short variable names that are used for mathematical symbols or as iterators should not be translated. This is especially important to pay attention to for the cannonical for loop identifier \"i\". For example translating the code \"for(int i = 0; i < 10; i++)\" into Spanish should not produce \"for(int yo = 0; yo < 10; yo++)\" even though \"yo\" is the translation of the pronoun \"I\". We only translate identifiers which are at least two characters long. This exception has its own edge-case: CJK (Chinese, Japanese Korean) identifiers can be non-mathematical names even if only a character long."
+    ],
+    [
+      "Once we have finished translating identifiers, we translate the comments in a program. Translating comments has two complexities: (1) we would like to maintain the comment structure, eg if it is a block javadoc comment, we would like to reserve the column of '*'s on the left margin of the comment and (2) we want references to identifiers to be translated exactly as they were in the code.",
+      "To translate a comment we classify the structure (eg JavaDoc, BlockComment PythonDocString). We then strip the text out, translate it, and reformat it back into the same structure. For multi-line comments we are conscious not to increase the maximum length of a line, taking into account the wider width of CJK characters."
+    ],
+    [
+      "Arabic, Hebrew, Farsi, and Urdu are popular right-to-left (RTL) natural languages. When translating code to RTL languages, comment can be translated (mixing RTL within the left-to-right syntax) and optionally transliterated (keeping left-to-right flow). Some of the difficulty in RTL transliteration is in distinguishing between short- and long-vowels. Further, these languages contains consonant that cannot be described using Latin alphabets, which are generally represented with numbers in the transliteration culture \u2013 e.g. 7 for utf8 \u062d > , which is closest to Latin alphabet \u201ch\u201d e.g. in \u201cAhmad\u201d.",
+      "When translating non-Latin scripts which are LTR we give the user the option to transliterate identifiers and separately, to transliterate comments or not. Transliteration is currently supported in Arabic, Chinese, Hebrew, Japanese, Korean, and Russian."
+    ],
+    [
+      "Translations of code need to be coherent with respect to other translations of written text (or other files) that refers to the code. To that end our translator takes in, and uses, a preset identifier translation map and returns the translations it made. This system enables having humans override translations, translating text-books with text that references embedded code and more."
+    ],
+    [
+      "How good is a translation of source-code from one human language to another? Evaluating quality of a translation is hard without a large collection of native speakers and since we are powered by Google, evaluation can devolve into evaluating how accurate Google Translation is. Such an evaluation is a moving target: Google Translation is perennially improving.",
+      "To evaluate out translator we randomly selected 1,000 (1k) single file projects from public GitHub Java and translated them into the languages: Chinese, Spanish and Arabic. We measure (1) how often the translated code still compiles and (2) what percent of identifiers that we attempt to translate are translatable.",
+      "Of the 1k projects 100% maintained their ability to be compiled regardless of whether we translated or transliterated the comments or identifiers. From the 1k projects 91% of the identifiers were able to be translated. The nine percent that were not able to be translated were mainly abbreviations (such as users who named a variable frac instead of fraction or pct instead of percent). This is an opportunity for future work. Overall the results paint the picture of a functioning tool which is ready for use."
+    ],
+    [
+      "Our motivation for developing an automatic human-language code translation tool was to support education for non-English speakers. To that end we used CodeInternational to translate a web-version of the popular Karel the Robot learns Java reader by Eric Robers BIBREF29 a textbook for a Karel the Robot, a grid world robot invented by Richard Pattis BIBREF30 to help CS1 students learn to program. Karel has been the inspiration for assignments on platforms such as Code.org and CodeHS and is a staple of the first weeks of CS1 BIBREF31.",
+      "We translated a Karel reader in Python and Java to 100 languages. The translated web-reader is free to use, and is hosted at [redacted]. At time of publication the reader has been public (without advertising) and has already been used by over 3,000 people from 50 countries. With permission from Eric Roberts, we first made an eBook version of his Karel reader and simplified the English used BIBREF32. The reader merges text and code in a seemless fashion. Then, for each language: we (a) translated code on each chapter using CodeInternational and (b) translated the reader text such that any reference to identifiers in the example code would use the same translations. In order to have text which is consistent with the corresponding code we heavily rely on the \"Posterior identifier translation map\" from CodeInternational's translations."
+    ],
+    [
+      "To make the Karel reader a fantastic learning experience we made it so that each code-snippet is runnable. When run, the program executes the code and highlights the corresponding lines as the program is run, regardless of the complexity of the program's control flow. In order to line-highlight we parse and compile the Python-Karel or Java-Karel programs using an engine written in JavaScript. Our line-highlighter builds upon the compiler described in Informatics Education using Nothing but a Browser BIBREF33.",
+      "Our Karel reader can run and line-highlight in any human-language that we translate into. For example our compiler can execute and line-highlight the command \"moverse()\" if the code is written in Spanish, \"UTF8gbsn\u79fb\u52a8()\" if the program is written in Chinese, \"emshi()\" if the program is written in Arabic, or \"move()\" if the Karel program is written in English. We chose to only transliterate commands for RTL scripts. Figure FIGREF27 shows three screenshots from the international Karel reader, though of course a PDF is unable to capture the ability of the reader to line-highlight code."
+    ],
+    [
+      "We know of four classes where the internationalized Karel eReader has been used. These classes are around the world in: Istanbul, Bogota, Prague and [Redacted]. The eReader has been visited by >1k users in 3 months and both the English and the non-English version of the website have a high average session duration (9.7 min and 10.1 min respectively). Moreover, the tool has been used to translate the CSBridge curriculum website and assignments; HTML that mixes code and description (used by 400 students / year)."
+    ],
+    [
+      "Whether English should be used as the sole language of instruction has been debated. Case for code instruction in English: In order to program professionally, one will have to interact with keywords and libraries that are written for English speakers. English is the language of code, and it is practically required from anyone who wants to interact globally: correspond via email, read stack-overflow, watch educational videos, travel, etc. For classrooms where English is the main form of instruction, but students are not yet fluent, CodeInternational can be used to assist learning English and learning to program. Students could improve their English through coding, e.g. by placing English code against their L1 code, side-by-side. Case for instruction on transl(iter)ated code: On the other hand, people argue that it is beneficial for students to have much of their coding instruction in their L1 language, and doing so benefits access to CS. The primary reason for this intuitive: the cognitive-load of learning to program is already high. Moreover, if students learn coding using their L1 language and enjoy it, they become intrinsically motivated to learn English, knowing that English would broaden their access to learning material (learning earning a language, with no short-term motives, could be dull especially for young students). In this context CodeInternational can help students who are interacting with libraries in English. Perhaps more importantly our tool can help teachers rapidly develop localized content that builds off English content. The alternative: manual-translation of API, code-examples and website text, can be a huge barrier to translating material. Finally, our tool builds off GoogleTranslate, which is high accurate, but charges $1 per 50,000 characters. A free version would have a huge impact on utility.",
+      "We call for future work from tool experts, for extending popular code-editors (e.g. vim, XCode, Visual Studio, Eclipse) to integrate with our APIs for back-and-forth translation and side-by-side display. Optionally, integrating with automatic text-to-speech (e.g. BIBREF34) could allow students learn English pronunciation of code components. Moreover, one remaining feature in automatic human-translation of code is identifier consistency: if two identifiers have specific terms in common, eg getHeight, setHeight, we would like the translation of \"height\" to be consistent. While they are often consistent in our work, it is not enforced. Full consistency is hard, but not impossible, with modern neural machine translation."
+    ],
+    [
+      "We analyze millions of non-English Java programs on GitHub to inform our understanding of patterns of human-language and make some surprising observations. We build CodeInternational, an open-source tool which can translate Java or Python code between human languages. We evaluate our tool and use it to make an internationalized Karel eReader (with runnable code) in 100+ languages. Our tool is already being used in classrooms around the world, a trend we hope to continue supporting."
+    ],
+    [
+      "We would like to graciously thank XXX and YYY for contributing code to this translation project. We would also like to thank ZZZ for her inspiration. We also thank the WWW teachers for educating students around the world in their local language."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1610/instruction.md b/qasper-1610/instruction.md
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@@ -0,0 +1,133 @@
+Name of Paper: Human Languages in Source Code: Auto-Translation for Localized Instruction
+
+Question: What are results of public code repository study?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Introduction ::: Related Work",
+    "Human Languages on GitHub",
+    "Human Languages on GitHub ::: Non-English in Java",
+    "Human Languages on GitHub ::: Transliteration on GitHub",
+    "Human Languages on GitHub ::: Right-to-Left Languages on GitHub",
+    "Code International",
+    "Code International ::: Translating Identifiers",
+    "Code International ::: Translating comments",
+    "Code International ::: Translating Right-to-Left languages",
+    "Code International ::: Prior and posterior translations",
+    "Translating Github",
+    "International Karel",
+    "International Karel ::: Line-highlighting in any language",
+    "International Karel ::: Usage in Classrooms",
+    "Discussion",
+    "Conclusion",
+    "Conclusion ::: Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "Reading and writing comments, method names and variable names is a crucial part of software engineering and as such, programs have both a human language, the language of identifiers and comments, in addition to the source-code language (eg Java or Python). This has meant that non-English speakers are often second class citizens when learning to program BIBREF0. In this paper we present a tool for translating a program from one human-language to another to assist in code education, which could reduce the barrier to computer science education for non-English speakers.",
+      "The main contributions presented in this paper are:",
+      "Analysis of 1.1M non-English code projects on GitHub",
+      "CodeInternational: A tool which can translate code between human languages, powered by Google Translate.",
+      "Validation of CodeInternational by evaluating the translation of 1,000 randomly chosen projects from GitHub.",
+      "Use of CodeInternational to automatically translate the popular Karel textbook into 100+ languages. We further extend the textbook to parse and run KarelJava code in any language; we report adoption by classrooms around the world.",
+      "Our human-language code translator was inspired by a desire to make programming more accessible BIBREF1. An accurate and useful translator would enable faster localization of instruction materials and it would allow learners (as well as practitioners) to translate code that they are working with.",
+      "As programming becomes more of a requisite common knowledge skill, we expect coding education to become open-access to everyone. One barrier to this goal is human language. English is currently the modal language of programming instruction perhaps given that the keywords of most of the popular languages, Java, JavaScript etc, are in English (even including Python and Lua, invented in the Netherlands and Brasil respectively). However, a majority of the world, estimated in 2008 at 80%, can't \u201cuse\" English for communication and substantially more don't speak English as their L1 language (the technical term for one's arterial language, aka, mother tongue) BIBREF2. Should the more than 6 billion non-English speakers learn to program in their native language or in English? This question is debated, which we address in the discussion.",
+      "We take the position that whether or not code instruction is in English, if students do not speak English as their L1 language, their code education would benefit from the ability to translate Code between their preferred language and English."
+    ],
+    [
+      "To the best of our knowledge, automatic translation of code between human languages, did not appear in literature, making us hypothesize: it is either difficult, or had remained ignored. Nonetheless, we summarize related work that motivate our contribution.",
+      "Translation of Text automatic translation of natural language has recently achieved high accuracy and is used in highly sensitive contexts BIBREF3, BIBREF4, BIBREF5. At the time of writing this article, Google Translate uses Neural Machine Translation BIBREF6 to translate pairwise between languages and has become incredibly accurate, at least for languages common on the web BIBREF7. Further research has been done on transliterating text BIBREF8, BIBREF9. However, current state-of-the-art methods for text translation fail at translating code. Directly running a translation algorithm on code would fail to distinguish between code syntax and identifiers, would not recognize terms embedded in identifiers e.g. with camel case getElementAt, and could produce code with one identifier name having different translations on separate lines. As such, current automatic text translation, if ran directly on code, would produce malfunctional code. Code Instruction in Non-English In 2017, Dasgupta and Hill published seminal work outlining the importance of learning to code in one's own language. They conclude that \"novice users who code with their programming language keywords and environment localized into their home countries' primary language demonstrate new programming concepts at a faster rate than users from the same countries whose interface is in English\" BIBREF10. Since then, there has been a large set of papers expanding on the barriers for non-native English speakers. Guo et al survey over 800 non-English students learning who report on the many challenges that come with not understanding English while coding. BIBREF11 reinforced by BIBREF12, BIBREF13. This has led to preliminary work into translating compiler errors BIBREF14 and advocation for language-free block free programming BIBREF15. However, while language-free programming is a great step forward for younger students, it doesn't address the needs of CS1 students who program in common programming languages like Python or Java. While all of this work motivates our contribution, none has attempted an automatic solution to the problem, making crowd-translation a viable alternative BIBREF16.",
+      "Mining Github To understand the patterns of code that students and practitioners use, we analyze public repositories on GitHub. Other researchers also analyzed GitHub, sometimes via the dataset and tools provided by BIBREF17, including work on social diversity of teams BIBREF18 and affiliation influence on code popularity BIBREF19. This has led to a set of best practices for navigating the promises and perils of mining GitHub BIBREF20. A growing number of students are using GitHub in software engineering courses BIBREF21 which makes it a valuable resource for understanding code of the general population, including students.",
+      "Code Conversion There is a rich literature of work to translate code between programming languages, such as C or C++ to Java BIBREF22, BIBREF23, or even from English to code BIBREF24. However, the emphasis is often on maintaining efficiency, not on making code readable for students. We focus on translating the human language of code. Byckling et al BIBREF25 analyze naming conventions of identifiers based their function (fixed, iterators, transformers, etc), and correlate the naming consistency with the students' learning experience. This motivates aspects of our translation. See Section SECREF22."
+    ],
+    [
+      "How do non-English speakers program in a language like Java, where the keywords and core libraries are written in English? We employ a data driven approach to tell the story of non-English code and inform the decisions we made in our auto-translator. We analyzed Java repositories on GitHub, the largest host of source code in the world, where 1.1 million unique users host 2.9 million public Java projects. We downloaded and analyzed the human language used for writing comments (in Java code), naming identifiers (method and variable names), and writing git commit messages. We focused on Java code as it is both one of the most popular source-code languages on GitHub and in the classroom. A selection of results from this study are that:",
+      "Non-English code is a large-scale phenomena.",
+      "Transliteration is common in identifiers for all languages.",
+      "Languages clusters into three distinct groups based on how speakers use identifiers/comments/transliteration.",
+      "Non-latin script users write comments in their L1 script but write identifiers in English.",
+      "Right-to-left (RTL) language scripts, such as Arabic, have no observed prevalence on GitHub identifiers, implying that existing coders who speak RTL languages have substantial barriers in using their native script in code.",
+      "This is, to the best of our knowledge, the first analysis of the human languages on GitHub. See Figure FIGREF6 for an overview.",
+      "Users on GitHub do not state their L1 (arterial) language. While a subset of users optionally state their country this is neither common nor reliable. To estimate a user's preferred language we use the language that they use in the git commit message. To find subsets of users who speak a given language, we search for all users who write git commits in that language. We observe that, especially in personal projects, users write commit messages in their L1 language at a higher rate than comments or identifiers. To identify languages we use Google Language Detect which is highly accurate (more so for common internet languages) and can identify languages with non-Roman Alphabet text which has been transliterated, for example it can detect bothUTF8gbsn\u7b97\u6cd5 the Chinese characters for \u201calgorithm\" and \"suanfa\", the Mandarin transliteration, as Chinese.",
+      "Of the 1.1 million GitHub users, 12.7% wrote commit messages in non-English languages. Of the non-English languages Chinese was the most common (28.6% of non-English committers), followed by Spanish, Portuguese, French, and Japanese. More than 100 languages were detected in commit messages on public Java projects. Figure FIGREF6 contains breakdowns and the appendix contains the full list. This does not match the distribution of non-English in web content (55% English) with both major and minor languages underrepresented. For example the prevalence of Spanish on GitHub (2.1%) is about half of webcontent (5.1% BIBREF26) and further trails native speakers (7.8% of the worlds population BIBREF27).",
+      "Github does not present a random sample of programs written in the world, and we consider the relevant confounds this introduces. To that point, we believe the under-representation of certain languages is a form of Survivorship Bias. It suggests that users have found barriers to entry towards joining the GitHub community. Those barriers could derive from the English dominance of programming languages, code instruction, or the github interface."
+    ],
+    [
+      "The use of non-English in identifiers and comments is large for the population of users who we define as non-English \"speakers\" (those who use non-English in their git-commit messages). 90% of users who use a non-English language in the commit messages also use that language in their comments or as identifiers. We note that, in Java, identifiers can be written in any script.",
+      "Surprisingly, the patterns of non-English usage differs substantially when we condition on users \"speaking\" different languages. For example, among the detected Spanish speakers, 87.2% percent of users write identifiers in Spanish. On the other hand, among Chinese users only 23.3% of users write code with Chinese identifiers (either in Chinese script or ASCII). Figure 1b shows coding patterns conditioned on users speaking different languages. For each language we plot the percent of projects with identifiers in the language against the percent of projects with comments in the language. Languages naturally cluster into three categories: (1) Major-Euro-Latin: languages with high use of non-English identifier including Spanish, German and French (2) Non-Latin: languages in non-latin scripts including Russian and Chinese which have low use of non-English identifiers and (3) English-Comment: Programmers write their comments in English (> 70% of projects only have English comments). This group contains many smaller and non-European languages like Dutch and Bahasa Indonesia. 0% of projects in this group still uses their L1 language in identifiers.",
+      "The use of identifiers in local language (as opposed to English) is very clearly split on whether languages use the Latin alphabet. On average 82% of projects from users speak languages with different scripts like Chinese, Korean, or Russian have only English identifiers, compared to 12% of projects from Latin alphabet users ($p < 0.0001$). The percentage of projects with only English comments is roughly correlated to the English Proficiency Index BIBREF28 of the corresponding countries ($\\rho = 0.42$ $p < 0.01$)."
+    ],
+    [
+      "Transliteration is the process of transferring a word from the alphabet of one language to another (eg -> namaste duniya). We observed that most Java code with human languages that have non-ascii scripts like Kanji, Devanagari, or even Spanish accents like \u00f1, will have been \"transliterated\" into ascii.",
+      "The Java Language Specification states that, \"letters and digits (in identifiers) may be drawn from the entire Unicode character set, which supports most writing scripts\". This specification is not widely known, and even if Java supports non-ascii , there can be complexities of file encodings across different operating systems.",
+      "We find that regardless of L1 language most users transliterate identifiers: among L1 Chinese speakers, 93% of projects have identifiers which are only written in ASCII. Similarly in Spanish 88% of projects have only ASCII identifiers. As a concrete example, in GitHub Java code \"numero\" is 3.8x more common than \"n\u00famero\". Among comments languages differ greatly: 99% of Chinese projects have non ASCII comments compared to only 53% of Spanish. As an example a comment above a method specifies in script that it is calculating the Fibonacci sequence however the method name (an identifier) is transliterated \"//UTF8gbsn\u6590\u6ce2\u90a3\u5951\" however the code uses a transliteration of the phonemes in the script \"public int feibonaqie(int n)\". This is a common pattern: Within comments, UTF8gbsn\u8ba1\u6570 chinese for count), is 4.0x more common than jishu, the transliteration. However in identifiers jishu is 4.8x more common. The difference in transliteration patterns between Chinese and Spanish suggests a different intent: in Spanish transliteration is used to avoid file encoding errors, in Chinese it is to prevent a mix of scripts among identifiers."
+    ],
+    [
+      "One question that we did not have a solid pre-conception for was: How do Java users who speak languages with right-to-left (RTL) scripts like Arabic, Urdu or Hebrew, write code?",
+      "18,961 users on GitHub report their country as one where a RTL script (Arabic or Hebrew) is the primary script. Those users have 8,060 public Java repositories of which only 50 repositories (0.6%) have Arabic or Hebrew script (excluding string literals). Of those repositories, only a single Java file had a single identifier written in Arabic and none in Hebrew. It is extremely rare for methods or identifiers to be a mix of RTL and LTR."
+    ],
+    [
+      "The GitHub analysis is coherent with the contemporary narrative: there are perhaps hundreds of millions of learners who will not speak English as their L1 language. For those learners, teachers need a tool to translate code so they can give examples with less congitive load. Similarly students need a tool to understand the non-English code they encounter. Finally, to a growing extent English speakers will begin to interact with code written in other languages.",
+      "To adress this need, we designed a tool to help programmers, regardless of their spoken language, access code in many languages. The tool, which we call CodeInternational, takes in code written in either Java or Python with comments and identifiers written in a human-language and translates the comments and identifiers into another human-language. It supports the growing set of human languages covered by Google Translate and is adaptive to the particular context of source-code. To translate code, it first parses the code and extracts four types of tokens:",
+      "[leftmargin=3mm]",
+      "Comments: inline or multi-line comments. Their purpose is for the programmer to communicate to programmers (including herself) on the purpose of code sections.",
+      "Immutable: consisting of language keywords (while, void, etc), and identifiers imported from libraries that are external to the code being translated (e.g. FileReader of java.io). By default this group is not translated.",
+      "Target identifiers: including variable and function names that are defined in the code base undergoing translation.",
+      "String literals: In some cases a user may want String literals to be translated, other times they should be unchanged.",
+      "Our translation algorithm is as follows. We (1) collect all of the target identifiers defined in the codebase and (2) translate them (enforcing that if two identifiers have the same name, they are given the same translation). Once the identifiers are translated we (3) translate the comments preserving structure and references to identifiers. (4) Finally string literals are optionally translated. See Figure FIGREF20 for a highlevel depiction and Figure FIGREF23 for a concrete example. Each of these steps has surprising challenges. In this section we cover the corresponding solutions we developed. The mapping of identifier translations that the tool decides on is preserved to assist any external source which needs to refer to the newly translated identifiers (such as text in a text-book or code in a related project).",
+      "CodeInternational is implemented in Python. Tokenization is performed using a modified version of \"Javalang\" (for Java) and the \"Parser\" library (for Python). Supporting other programming languages requires a small amount of extra work."
+    ],
+    [
+      "In order to properly translate identifiers, we consider the following:",
+      "",
+      "Identifier segmentation: Translating an identifier using a tool like Google Translate does not work by default as identifiers are often composed of unsegmented words. For example: getFavoriteNumber is readable to a human as \"get favorite number\" but is not parsable by an online translator. We segment identifiers using naming conventions (e.g. camelCaseVariable, PascalCaseClass, UPPERCASE_CONSTANT). We thus segment identifiers into phrases which we feed into an automatic translator. We then recombine the translated phrase using the original casing convention. For example, to translate the method name identifier \"turnAround\" into Spanish: \"turnAround\" is segmented into \"turn around\" which is translated into \"media vuelta\" which is formatted into the original camelCase \"mediaVuelta\". Advances in artificial intelligence for word segmentation enable a future version of this tool to break up words without a given segmentation (eg \"turnaround\").",
+      "",
+      "Verb prior: The correct translation for a phrase can be ambiguous, especially without context. As an example the method \"move\" translated into Spanish could be translated into a noun (\"movimiento\", movement) or a verb (\"moverse\"). For method identifiers there is an implicit context that an action is being performed. We incorporate this context by placing a prior on the first word being a verb. Thus, for example, when we translate \"move()\" into Spanish we chose \"moverse()\" instead of \"movimiento()\", the noun movement, as Google suggests.",
+      "In addition to knowing the translations of methods should start with verbs, we also have a select number of reasonable tenses for the verb: infinitive (eg \"toMove\"), third person present (eg \"moves\" as in \"he moves\") and imperative (eg \"move\"). In most languages, including English, we translate verbs with a prior that they be the imperative tense. In English you would expect a method to be \"getObject()\" the imperative. However some languages, especially Romance languages, use the infinitive of the verb: as an example, \"obtener\" the infinitive of \"obtain\" is 200x more common on GitHub then \"obtenga\" the imperative.",
+      "",
+      "Translating short identifiers: Short variable names that are used for mathematical symbols or as iterators should not be translated. This is especially important to pay attention to for the cannonical for loop identifier \"i\". For example translating the code \"for(int i = 0; i < 10; i++)\" into Spanish should not produce \"for(int yo = 0; yo < 10; yo++)\" even though \"yo\" is the translation of the pronoun \"I\". We only translate identifiers which are at least two characters long. This exception has its own edge-case: CJK (Chinese, Japanese Korean) identifiers can be non-mathematical names even if only a character long."
+    ],
+    [
+      "Once we have finished translating identifiers, we translate the comments in a program. Translating comments has two complexities: (1) we would like to maintain the comment structure, eg if it is a block javadoc comment, we would like to reserve the column of '*'s on the left margin of the comment and (2) we want references to identifiers to be translated exactly as they were in the code.",
+      "To translate a comment we classify the structure (eg JavaDoc, BlockComment PythonDocString). We then strip the text out, translate it, and reformat it back into the same structure. For multi-line comments we are conscious not to increase the maximum length of a line, taking into account the wider width of CJK characters."
+    ],
+    [
+      "Arabic, Hebrew, Farsi, and Urdu are popular right-to-left (RTL) natural languages. When translating code to RTL languages, comment can be translated (mixing RTL within the left-to-right syntax) and optionally transliterated (keeping left-to-right flow). Some of the difficulty in RTL transliteration is in distinguishing between short- and long-vowels. Further, these languages contains consonant that cannot be described using Latin alphabets, which are generally represented with numbers in the transliteration culture \u2013 e.g. 7 for utf8 \u062d > , which is closest to Latin alphabet \u201ch\u201d e.g. in \u201cAhmad\u201d.",
+      "When translating non-Latin scripts which are LTR we give the user the option to transliterate identifiers and separately, to transliterate comments or not. Transliteration is currently supported in Arabic, Chinese, Hebrew, Japanese, Korean, and Russian."
+    ],
+    [
+      "Translations of code need to be coherent with respect to other translations of written text (or other files) that refers to the code. To that end our translator takes in, and uses, a preset identifier translation map and returns the translations it made. This system enables having humans override translations, translating text-books with text that references embedded code and more."
+    ],
+    [
+      "How good is a translation of source-code from one human language to another? Evaluating quality of a translation is hard without a large collection of native speakers and since we are powered by Google, evaluation can devolve into evaluating how accurate Google Translation is. Such an evaluation is a moving target: Google Translation is perennially improving.",
+      "To evaluate out translator we randomly selected 1,000 (1k) single file projects from public GitHub Java and translated them into the languages: Chinese, Spanish and Arabic. We measure (1) how often the translated code still compiles and (2) what percent of identifiers that we attempt to translate are translatable.",
+      "Of the 1k projects 100% maintained their ability to be compiled regardless of whether we translated or transliterated the comments or identifiers. From the 1k projects 91% of the identifiers were able to be translated. The nine percent that were not able to be translated were mainly abbreviations (such as users who named a variable frac instead of fraction or pct instead of percent). This is an opportunity for future work. Overall the results paint the picture of a functioning tool which is ready for use."
+    ],
+    [
+      "Our motivation for developing an automatic human-language code translation tool was to support education for non-English speakers. To that end we used CodeInternational to translate a web-version of the popular Karel the Robot learns Java reader by Eric Robers BIBREF29 a textbook for a Karel the Robot, a grid world robot invented by Richard Pattis BIBREF30 to help CS1 students learn to program. Karel has been the inspiration for assignments on platforms such as Code.org and CodeHS and is a staple of the first weeks of CS1 BIBREF31.",
+      "We translated a Karel reader in Python and Java to 100 languages. The translated web-reader is free to use, and is hosted at [redacted]. At time of publication the reader has been public (without advertising) and has already been used by over 3,000 people from 50 countries. With permission from Eric Roberts, we first made an eBook version of his Karel reader and simplified the English used BIBREF32. The reader merges text and code in a seemless fashion. Then, for each language: we (a) translated code on each chapter using CodeInternational and (b) translated the reader text such that any reference to identifiers in the example code would use the same translations. In order to have text which is consistent with the corresponding code we heavily rely on the \"Posterior identifier translation map\" from CodeInternational's translations."
+    ],
+    [
+      "To make the Karel reader a fantastic learning experience we made it so that each code-snippet is runnable. When run, the program executes the code and highlights the corresponding lines as the program is run, regardless of the complexity of the program's control flow. In order to line-highlight we parse and compile the Python-Karel or Java-Karel programs using an engine written in JavaScript. Our line-highlighter builds upon the compiler described in Informatics Education using Nothing but a Browser BIBREF33.",
+      "Our Karel reader can run and line-highlight in any human-language that we translate into. For example our compiler can execute and line-highlight the command \"moverse()\" if the code is written in Spanish, \"UTF8gbsn\u79fb\u52a8()\" if the program is written in Chinese, \"emshi()\" if the program is written in Arabic, or \"move()\" if the Karel program is written in English. We chose to only transliterate commands for RTL scripts. Figure FIGREF27 shows three screenshots from the international Karel reader, though of course a PDF is unable to capture the ability of the reader to line-highlight code."
+    ],
+    [
+      "We know of four classes where the internationalized Karel eReader has been used. These classes are around the world in: Istanbul, Bogota, Prague and [Redacted]. The eReader has been visited by >1k users in 3 months and both the English and the non-English version of the website have a high average session duration (9.7 min and 10.1 min respectively). Moreover, the tool has been used to translate the CSBridge curriculum website and assignments; HTML that mixes code and description (used by 400 students / year)."
+    ],
+    [
+      "Whether English should be used as the sole language of instruction has been debated. Case for code instruction in English: In order to program professionally, one will have to interact with keywords and libraries that are written for English speakers. English is the language of code, and it is practically required from anyone who wants to interact globally: correspond via email, read stack-overflow, watch educational videos, travel, etc. For classrooms where English is the main form of instruction, but students are not yet fluent, CodeInternational can be used to assist learning English and learning to program. Students could improve their English through coding, e.g. by placing English code against their L1 code, side-by-side. Case for instruction on transl(iter)ated code: On the other hand, people argue that it is beneficial for students to have much of their coding instruction in their L1 language, and doing so benefits access to CS. The primary reason for this intuitive: the cognitive-load of learning to program is already high. Moreover, if students learn coding using their L1 language and enjoy it, they become intrinsically motivated to learn English, knowing that English would broaden their access to learning material (learning earning a language, with no short-term motives, could be dull especially for young students). In this context CodeInternational can help students who are interacting with libraries in English. Perhaps more importantly our tool can help teachers rapidly develop localized content that builds off English content. The alternative: manual-translation of API, code-examples and website text, can be a huge barrier to translating material. Finally, our tool builds off GoogleTranslate, which is high accurate, but charges $1 per 50,000 characters. A free version would have a huge impact on utility.",
+      "We call for future work from tool experts, for extending popular code-editors (e.g. vim, XCode, Visual Studio, Eclipse) to integrate with our APIs for back-and-forth translation and side-by-side display. Optionally, integrating with automatic text-to-speech (e.g. BIBREF34) could allow students learn English pronunciation of code components. Moreover, one remaining feature in automatic human-translation of code is identifier consistency: if two identifiers have specific terms in common, eg getHeight, setHeight, we would like the translation of \"height\" to be consistent. While they are often consistent in our work, it is not enforced. Full consistency is hard, but not impossible, with modern neural machine translation."
+    ],
+    [
+      "We analyze millions of non-English Java programs on GitHub to inform our understanding of patterns of human-language and make some surprising observations. We build CodeInternational, an open-source tool which can translate Java or Python code between human languages. We evaluate our tool and use it to make an internationalized Karel eReader (with runnable code) in 100+ languages. Our tool is already being used in classrooms around the world, a trend we hope to continue supporting."
+    ],
+    [
+      "We would like to graciously thank XXX and YYY for contributing code to this translation project. We would also like to thank ZZZ for her inspiration. We also thank the WWW teachers for educating students around the world in their local language."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: A Simple Approach to Multilingual Polarity Classification in Twitter
+
+Question: How many texts/datapoints are in the SemEval, TASS and SENTIPOLC datasets?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Our Approach: Multilingual Polarity Classification",
+    "Cross-language Features",
+    "Language Dependent Features",
+    "Text Representation",
+    "Parameter Optimization",
+    "Datasets and contests",
+    "Experimental Results",
+    "Performance on sentiment analysis contests",
+    "Conclusions",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "Sentiment analysis is a crucial task in opinion mining field where the goal is to extract opinions, emotions, or attitudes to different entities (person, objects, news, among others). Clearly, this task is of interest for all languages; however, there exists a significant gap between English state-of-the-art methods and other languages. It is expected that some researchers decided to test the straightforward approach which consists in, first, translating the messages to English, and, then, use a high performing English sentiment classifier (for instance, see BIBREF0 and BIBREF1 ) instead of creating a sentiment classifier optimized for a given language. However, the advantages of a properly tuned sentiment classifier have been studied for different languages (for instance, see BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 ).",
+      "This manuscript focuses on the particular case of multilingual sentiment analysis of short informal texts such as Twitter messages. Our aim is to provide an easy-to-use tool to create sentiment classifiers based on supervised learning (i.e., labeled dataset) where the classifier should be competitive to those sentiment classifiers carefully tuned by some given languages. Furthermore, our second contribution is to create a well-performing baseline to compare new sentiment classifiers in a broad range of languages or to bootstrap new sentiment analysis systems. Our approach is based on selecting the best text-transforming techniques that optimize some performance measures where the chosen techniques are robust to typical writing errors.",
+      "In this context, we propose a robust multilingual sentiment analysis method, tested in eight different languages: Spanish, English, Italian, Arabic, German, Portuguese, Russian and Swedish. We compare our approach ranking in three international contests: TASS'15, SemEval'15-16 and SENTIPOLC'14, for Spanish, English and Italian respectively; the remaining languages are compared directly with the results reported in the literature. The experimental results locate our approach in good positions for all considered competitions; and excellent results in the other five languages tested. Finally, even when our method is almost cross-language, it can be extended to take advantage of language dependencies; we also provide experimental evidence of the advantages of using these language-dependent techniques.",
+      "The rest of the manuscript is organized as follows. Section SECREF2 describes our proposed Sentiment Analysis method. Section SECREF3 describes the datasets and contests used to test our approach; whereas, the experimental results, and, the discussion are presented on Section SECREF4 . Finally, Section SECREF5 concludes."
+    ],
+    [
+      "We propose a method for multilingual polarity classification that can serve as a baseline as well as a framework to build more complex sentiment analysis systems due to its simplicity and availability as an open source software. As we mentioned, this baseline algorithm for multilingual Sentiment Analysis (B4MSA) was designed with the purpose of being multilingual and easy to implement. B4MSA is not a na\u00efve baseline which is experimentally proved by evaluating it on several international competitions.",
+      "In a nutshell, B4MSA starts by applying text-transformations to the messages, then transformed text is represented in a vector space model (see Subsection SECREF13 ), and finally, a Support Vector Machine (with linear kernel) is used as the classifier. B4MSA uses a number of text transformations that are categorized in cross-language features (see Subsection SECREF3 ) and language dependent features (see Subsection SECREF9 ). It is important to note that, all the text-transformations considered are either simple to implement or there is a well-known library (e.g. BIBREF6 , BIBREF7 ) to use them. It is important to note that to maintain the cross-language property, we limit ourselves to not use additional knowledge, this include knowledge from affective lexicons or models based on distributional semantics.",
+      "To obtain the best performance, one needs to select those text-transformations that work best for a particular dataset, therefore, B4MSA uses a simple random search and hill-climbing (see Subsection SECREF14 ) in space of text-transformations to free the user from this delicate and time-consuming task. Before going into the details of each text-transformation, Table TABREF2 gives a summary of the text-transformations used as well as their parameters associated."
+    ],
+    [
+      "We defined cross-language features as a set of features that could be applied in most similar languages, not only related language families such as Germanic languages (English, German, etc.), Romance languages (Spanish, Italian, etc.), among others; but also similar surface features such as punctuation, diacritics, symbol duplication, case sensitivity, etc. Later, the combination of these features will be explored to find the best configuration for a given classifier.",
+      "Generally, Twitter messages are full of slang, misspelling, typographical and grammatical errors among others; in order to tackle these aspects we consider different parameters to study this effect. The following points are the parameters to be considered as spelling features. Punctuation (del-punc) considers the use of symbols such as question mark, period, exclamation point, commas, among other spelling marks. Diacritic symbols (del-diac) are commonly used in languages such as Spanish, Italian, Russian, etc., and its wrong usage is one of the main sources of orthographic errors in informal texts; this parameter considers the use or absence of diacritical marks. Symbol reduction (del-d1), usually, twitter messages use repeated characters to emphasize parts of the word to attract user's attention. This aspect makes the vocabulary explodes. We applied the strategy of replacing the repeated symbols by one occurrence of the symbol. Case sensitivity (lc) considers letters to be normalized in lowercase or to keep the original source; the aim is to cut the words that are the same in uppercase and lowercase.",
+      "We classified around 500 most popular emoticons, included text emoticons, and the whole set of unicode emoticons (around INLINEFORM0 ) defined by BIBREF8 into three classes: positive, negative and neutral, which are grouped under its corresponding polarity word defined by the class name.",
+      "Table TABREF6 shows an excerpt of the dictionary that maps emoticons to their corresponding polarity class.",
+      "N-words (word sequences) are widely used in many NLP tasks, and they have also been used in Sentiment Analysis BIBREF9 and BIBREF10 . To compute the N-words, the text is tokenized and N-words are calculated from tokens. For example, let INLINEFORM0 be the text, so its 1-words (unigrams) are each word alone, and its 2-words (bigrams) set are the sequences of two words, the set ( INLINEFORM1 ), and so on. INLINEFORM2 = {the lights, lights and, and shadows, shadows of, of your, your future}, so, given text of size INLINEFORM3 words, we obtain a set containing at most INLINEFORM4 elements. Generally, N-words are used up to 2 or 3-words because it is uncommon to find, between texts, good matches of word sequences greater than three or four words BIBREF11 .",
+      "In addition to the traditional N-words representation, we represent the resulting text as q-grams. A q-grams is an agnostic language transformation that consists in representing a document by all its substring of length INLINEFORM0 . For example, let INLINEFORM1 be the text, its 3-grams set are INLINEFORM2 ",
+      "so, given text of size INLINEFORM0 characters, we obtain a set with at most INLINEFORM1 elements. Notice that this transformation handles white-spaces as part of the text. Since there will be q-grams connecting words, in some sense, applying q-grams to the entire text can capture part of the syntactic and contextual information in the sentence. The rationale of q-grams is also to tackle misspelled sentences from the approximate pattern matching perspective BIBREF12 ."
+    ],
+    [
+      "The following features are language dependent because they use specific information from the language concerned. Usually, the use of stopwords, stemming and negations are traditionally used in Sentiment Analysis. The users of this approach could add other features such as part of speech, affective lexicons, etc. to improve the performance BIBREF13 .",
+      "In many languages, there is a set of extremely common words such as determiners or conjunctions ( INLINEFORM0 or INLINEFORM1 ) which help to build sentences but do not carry any meaning for themselves. These words are known as Stopwords, and they are removed from text before any attempt to classify them. Generally, a stopword list is built using the most frequent terms from a huge document collection. We used the Spanish, English and Italian stopword lists included in the NLTK Python package BIBREF6 in order to identify them.",
+      "Stemming is a well-known heuristic process in Information Retrieval field that chops off the end of words and often includes the removal of derivational affixes. This technique uses the morphology of the language coded in a set of rules that are applied to find out word stems and reduce the vocabulary collapsing derivationally related words. In our study, we use the Snowball Stemmer for Spanish and Italian, and the Porter Stemmer for English that are implemented in NLTK package BIBREF6 .",
+      "Negation markers might change the polarity of the message. Thus, we attached the negation clue to the nearest word, similar to the approaches used in BIBREF9 . A set of rules was designed for common negation structures that involve negation markers for Spanish, English and Italian. For instance, negation markers used for Spanish are no (not), nunca, jam\u00e1s (never), and sin (without). The rules (regular expressions) are processed in order, and their purpose is to negate the nearest word to the negation marker using only the information on the text, e.g., avoiding mainly pronouns and articles. For example, in the sentence El coche no es bonito (The car is not nice), the negation marker no and not (for English) is attached to its adjective no_bonito (not_nice)."
+    ],
+    [
+      "After text-transformations, it is needed to represent the text in suitable form in order to use a traditional classifier such as SVM. It was decided to select the well known vector representation of a text given its simplicity and powerful representation. Particularly, it is used the Term Frequency-Inverse Document Frequency which is a well-known weighting scheme in NLP. TF-IDF computes a weight that represents the importance of words or terms inside a document in a collection of documents, i.e., how frequently they appear across multiple documents. Therefore, common words such as the and in, which appear in many documents, will have a low score, and words that appear frequently in a single document will have high score. This weighting scheme selects the terms that represent a document."
+    ],
+    [
+      "The model selection, sometimes called hyper-parameter optimization, is essential to ensure the performance of a sentiment classifier. In particular, our approach is highly parametric; in fact, we use such property to adapt to several languages. Table TABREF2 summarizes the parameters and their valid values. The search space contains more than 331 thousand configurations when limited to multilingual and language independent parameters; while the search space reaches close to 4 million configurations when we add our three language-dependent parameters. Depending on the size of the training set, each configuration needs several minutes on a commodity server to be evaluated; thus, an exhaustive exploration of the parameter space can be quite expensive making the approach useless in practice. To tackle the efficiency problems, we perform the model selection using two hyper-parameter optimization algorithms.",
+      "The first corresponds to Random Search, described in depth in BIBREF14 . Random search consists on randomly sampling the parameter space and select the best configuration among the sample. The second algorithm consists on a Hill Climbing BIBREF15 , BIBREF16 implemented with a memory to avoid testing a configuration twice. The main idea behind hill climbing H+M is to take a pivoting configuration, explore the configuration's neighborhood, and greedily moves to the best neighbor. The process is repeated until no improvement is possible. The configuration neighborhood is defined as the set of configurations such that these differ in just one parameter's value. This rule is strengthened for tokenizer (see Table TABREF2 ) to differ in a single internal value not in the whole parameter value. More precisely, let INLINEFORM0 be a valid value for tokenizer and INLINEFORM1 the set of valid values for neighborhoods of INLINEFORM2 , then INLINEFORM3 and INLINEFORM4 for any INLINEFORM5 .",
+      "To guarantee a better or equal performance than random search, the H+M process starts with the best configuration found in the random search. By using H+M, sample size can be set to 32 or 64, as rule of thumb, and even reach improvements in most cases (see \u00a7 SECREF4 ). Nonetheless, this simplification and performance boosting comes along with possible higher optimization times. Finally, the performance of each configuration is obtained using a cross-validation technique on the training data, and the metrics are usually used in classification such as: accuracy, score INLINEFORM0 , and recall, among others."
+    ],
+    [
+      "Nowadays, there are several international competitions related to text mining, which include diverse tasks such as: polarity classification (at different levels), subjectivity classification, entity detection, and iron detection, among others. These competitions are relevant to measure the potential of different proposed techniques. In this case, we focused on polarity classification task, hence, we developed a baseline method with an acceptable performance achieved in three different contests, namely, TASS'15 (Spanish) BIBREF17 , SemEval'15-16 (English) BIBREF18 , BIBREF19 , and SENTIPOLC'14 (Italian) BIBREF20 . In addition, our approach was tested with other languages (Arabic, German, Portuguese, Russian, and Swedish) to show that is feasible to use our framework as basis for building more complex sentiment analysis systems. From these languages, datasets and results can be seen in BIBREF21 , BIBREF3 and BIBREF2 .",
+      "Table TABREF15 presents the details of each of the competitions considered as well as the other languages tested. It can be observed, from the table, the number of examples as well as the number of instances for each polarity level, namely, positive, neutral, negative and none. The training and development (only in SemEval) sets are used to train the sentiment classifier, and the gold set is used to test the classifier. In the case there dataset was not split in training and gold (Arabic, German, Portuguese, Russian, and Swedish) then a cross-validation (10 folds) technique is used to test the classifier. The performance of the classifier is presented using different metrics depending the competition. SemEval uses the average of score INLINEFORM0 of positive and negative labels, TASS uses the accuracy and SENTIPOLC uses a custom metric (see BIBREF17 , BIBREF18 , BIBREF19 , BIBREF20 )."
+    ],
+    [
+      "We tested our framework on two kinds of datasets. On one hand, we compare our performance on three languages having well known sentiment analysis contests; here, we compare our work against competitors of those challenges. On the other hand, we selected five languages without popular opinion mining contests; for these languages, we compare our approach against research works reporting the used corpus."
+    ],
+    [
+      "Figure FIGREF17 shows the performance on four contests, corresponding to three different languages. The performance corresponds to the multilingual set of features, i.e., we do not used language-dependent techniques.",
+      "Figures UID18 - UID21 illustrates the results on each challenge, all competitors are ordered in score's descending order (higher is better). The achieved performance of our approach is marked with a horizontal line on each figure. Figure UID22 briefly describes each challenge and summarizes our performance on each contest; also, we added three standard measures to simplify the insight's creation of the reader.",
+      "The winner method in SENTIPOLC'14 (Italian) is reported in BIBREF22 . This method uses three groups of features: keyword and micro-blogging characteristics, Sentiment Lexicons, SentiWordNet and MultiWordNet, and Distributional Semantic Model (DSM) with a SVM classifier. In contrast with our method, in BIBREF22 three external sentiment lexicons dictionaries were employed; that is, external information.",
+      "In TASS'15 (Spanish) competition, the winner reported method was BIBREF23 , which proposed an adaptation based on a tokenizer of tweets Tweetmotif BIBREF24 , Freeling BIBREF25 as lemmatizer, entity detector, morphosyntactic labeler and a translation of the Afinn dictionary. In contrast with our method, BIBREF23 employs several complex and expensive tools. In this task we reached the fourteenth position with an accuracy of INLINEFORM0 . Figure UID19 shows the B4MSA performance to be over two thirds of the competitors.",
+      "The remaining two contests correspond to the SemEval'15-16. The B4MSA performance in SemEval is depicted in Figures UID20 and UID21 ; here, B4MSA does not perform as well as in other challenges, mainly because, contrary to other challenges, SemEval rules promotes the enrichment of the official training set. To be consistent with the rest of the experiments, B4MSA uses only the official training set. The results can be significantly improved using larger training datasets; for example, joining SemEval'13 and SemEval'16 training sets, we can reach INLINEFORM0 for SemEval'16, which improves the B4MSA's performance (see Table FIGREF17 ).",
+      "In SemEval'15, the winner method is BIBREF26 , which combines three approaches among the participants of SemEval'13, teams: NRC-Canada, GU-MLT-LT and KLUE, and from SemEval'14 the participant TeamX all of them employing external information. In SemEval'16, the winner method was BIBREF27 is composed with an ensemble of two subsystems based on convolutional neural networks, the first subsystem is created using 290 million tweets, and the second one is feeded with 150 million tweets. All these tweets were selected from a very large unlabeled dataset through distant supervision techniques.",
+      "Table TABREF23 shows the multilingual set of techniques and the set with language-dependent techniques; for each, we optimized the set of parameters through Random Search and INLINEFORM0 (see Subsection SECREF14 ). The reached performance is reported using both cross-validation and the official gold-standard. Please notice how INLINEFORM1 consistently reaches better performances, even on small sampling sizes. The sampling size is indicated with subscripts in Table TABREF23 . Note that, in SemEval challenges, the cross-validation performances are higher than those reached by evaluating the gold-standard, mainly because the gold-standard does not follow the distribution of training set. This can be understood because the rules of SemEval promote the use of external knowledge.",
+      "Table TABREF24 compares our performance on five different languages; we do not apply language-dependent techniques. For each comparison, we took a labeled corpus from BIBREF3 (Arabic) and BIBREF21 (the remaining languages). According to author's reports, all tweets were manually labeled by native speakers as pos, neg, or neu. The Arabic dataset contains INLINEFORM0 items; the other datasets contain from 58 thousand tweets to more than 157 thousand tweets. We were able to fetch a fraction of the original datasets; so, we drop the necessary items to hold the original class-population ratio. The ratio of tweets in our training dataset, respect to the original dataset, is indicated beside the name. As before, we evaluate our algorithms through a 10-fold cross validation.",
+      "In BIBREF3 , BIBREF2 , the authors study the effect of translation in sentiment classifiers; they found better to use native Arabic speakers as annotators than fine-tuned translators plus fine-tuned English sentiment classifiers. In BIBREF21 , the idea is to measure the effect of the agreement among annotators on the production of a sentiment-analysis corpus. Both, on the technical side, both papers use fine tuned classifiers plus a variety of pre-processing techniques to prove their claims. Table TABREF24 supports the idea of choosing B4MSA as a bootstrapping sentiment classifier because, in the overall, B4MSA reaches superior performances regardless of the language. Our approach achieves those performance's levels since it optimizes a set of parameters carefully selected to work on a variety of languages and being robust to informal writing. The latter problem is not properly tackled in many cases."
+    ],
+    [
+      "We presented a simple to implement multilingual framework for polarity classification whose main contributions are in two aspects. On one hand, our approach can serve as a baseline to compare other classification systems. It considers techniques for text representation such as spelling features, emoticons, word-based n-grams, character-based q-grams and language dependent features. On the other hand, our approach is a framework for practitioners or researchers looking for a bootstrapping sentiment classifier method in order to build more elaborated systems.",
+      "Besides the text-transformations, the proposed framework uses a SVM classifier (with linear kernel), and, hyper-parameter optimization using random search and H+M over the space of text-transformations. The experimental results show good overall performance in all international contests considered, and the best results in the other five languages tested.",
+      "It is important to note that all the methods that outperformed B4MSA in the sentiment analysis contests use extra knowledge (lexicons included) meanwhile B4MSA uses only the information provided by each contests. In future work, we will extend our methodology to include extra-knowledge in order to improve the performance."
+    ],
+    [
+      "We would like to thank Valerio Basile, Julio Villena-Roman, and Preslav Nakov for kindly give us access to the gold-standards of SENTIPOLC'14, TASS'15 and SemEval 2015 & 2016, respectively. The authors also thank Elio Villase\u00f1or for the helpful discussions in early stages of this research."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1619/instruction.md b/qasper-1619/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..de559e6f4eb5a4d88f915d9a4293e5960546abaf
--- /dev/null
+++ b/qasper-1619/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: A Simple Approach to Multilingual Polarity Classification in Twitter
+
+Question: What eight language are reported on?
\ No newline at end of file
diff --git a/qasper-1626/instruction.md b/qasper-1626/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..30f7b0cf62b9dd6e8533880c484a3bb7a0ffd147
--- /dev/null
+++ b/qasper-1626/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: On Evaluating the Generalization of LSTM Models in Formal Languages
+
+Question: What training settings did they try?
\ No newline at end of file
diff --git a/qasper-1628/instruction.md b/qasper-1628/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..f2a83ab2fc3ec6bfef75fe61976db6aa01dd4f77
--- /dev/null
+++ b/qasper-1628/instruction.md
@@ -0,0 +1,94 @@
+Name of Paper: On Evaluating the Generalization of LSTM Models in Formal Languages
+
+Question: Are the unobserved samples from the same distribution as the training data?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "The Sequence Prediction Task",
+    "Languages",
+    "The LSTM Model",
+    "Training and Testing",
+    "Length Distributions",
+    " Length Windows",
+    "Model Capacity",
+    "Training Length Windows",
+    "Number of Hidden Units",
+    "Discussion",
+    "Conclusion",
+    "Acknowledgment"
+  ],
+  "paragraphs": [
+    [
+      "Recurrent Neural Networks (RNNs) are powerful machine learning models that can capture and exploit sequential data. They have become standard in important natural language processing tasks such as machine translation BIBREF0 , BIBREF1 and speech recognition BIBREF2 . Despite the ubiquity of various RNN architectures in natural language processing, there still lies an unanswered fundamental question: What classes of languages can, empirically or theoretically, be learned by neural networks? This question has drawn much attention in the study of formal languages, with previous results on both the theoretical BIBREF3 , BIBREF4 and empirical capabilities of RNNs, showing that different RNN architectures can learn certain regular BIBREF5 , BIBREF6 , context-free BIBREF7 , BIBREF8 , and context-sensitive languages BIBREF9 .",
+      "In a common experimental setup for investigating whether a neural network can learn a formal language, one formulates a supervised learning problem where the network is presented one character at a time and predicts the next possible character(s). The performance of the network can then be evaluated based on its ability to recognize sequences shown in the training set and \u2013 more importantly \u2013 to generalize to unseen sequences. There are, however, various methods of evaluation in a language learning task. In order to define the generalization of a network, one may consider the length of the shortest sequence in a language whose output was incorrectly produced by the network, or the size of the largest accepted test set, or the accuracy on a fixed test set BIBREF10 , BIBREF11 , BIBREF9 , BIBREF12 . These formulations follow narrow and bounded evaluation schemes though: They often define a length threshold in the test set and report the performance of the model on this fixed set.",
+      "We acknowledge three unsettling issues with these formulations. First, the sequences in the training set are usually assumed to be uniformly or geometrically distributed, with little regard to the nature and complexity of the language. This assumption may undermine any conclusions drawn from empirical investigations, especially given that natural language is not uniformly distributed, an aspect that is known to affect learning in modern RNN architectures BIBREF13 . Second, in a test set where the sequences are enumerated by their lengths, if a network makes an error on a sequence of, say, length 7, but correctly recognizes longer sequences of length up to 1000, would we consider the model's generalization as good or bad? In a setting where we monitor only the shortest sequence that was incorrectly predicted by the network, this scheme clearly misses the potential success of the model after witnessing a failure, thereby misportraying the capabilities of the network. Third, the test sets are often bounded in these formulations, making it challenging to compare and contrast the performance of models if they attain full accuracy on their fixed test sets.",
+      "In the present work, we address these limitations by providing a more nuanced evaluation of the learning capabilities of RNNs. In particular, we investigate the effects of three different aspects of a network's generalization: data distribution, length-window, and network capacity. We define an informative protocol for assessing the performance of RNNs: Instead of training a single network until it has learned its training set and then evaluating it on its test set, as BIBREF9 do in their study, we monitor and test the network's performance at each epoch during the entire course of training. This approach allows us to study the stability of the solutions reached by the network. Furthermore, we do not restrict ourselves to a test set of sequences of fixed lengths during testing. Rather, we exhaustively enumerate all the sequences in a language by their lengths and then go through the sequences in the test set one by one until our network errs $k$ times, thereby providing a more fine-grained evaluation criterion of its generalization capabilities.",
+      "Our experimental evaluation is focused on the Long Short-Term Memory (LSTM) network BIBREF14 , a particularly popular RNN variant. We consider three formal languages, namely $a^n b^n$ , $a^n b^n c^n$ , and $a^n b^n c^n d^n$ , and investigate how LSTM networks learn these languages under different training regimes. Our investigation leads to the following insights: (1) The data distribution has a significant effect on generalization capability, with discrete uniform and U-shaped distributions often leading to the best generalization amongst all the four distributions in consideration. (2) Widening the training length-window, naturally, enables LSTM models to generalize better to longer sequences, and interestingly, the networks seem to learn to generalize to shorter sequences when trained on long sequences. (3) Higher model capacity \u2013 having more hidden units \u2013 leads to better stability, but not necessarily better generalization levels. In other words, over-parameterized models are more stable than models with theoretically sufficient but far fewer parameters. We explain this phenomenon by conjecturing that a collaborative counting mechanism arises in over-parameterized networks."
+    ],
+    [
+      "It has been shown that RNNs with a finite number of states can process regular languages by acting like a finite-state automaton using different units in their hidden layers BIBREF5 , BIBREF6 . RNNs, however, are not limited to recognizing only regular languages. BIBREF3 and BIBREF4 showed that first-order RNNs (with rational state weights and infinite numeric precision) can simulate a pushdown automaton with two-stacks, thereby demonstrating that RNNs are Turing-complete. In theory, RNNs with infinite numeric precision are capable of expressing recursively enumerable languages. Yet, in practice, modern machine architectures do not contain computational structures that support infinite numeric precision. Thus, the computational power of RNNs with finite precision may not necessarily be the same as that of RNNs with infinite precision.",
+      " BIBREF7 investigated the learning capabilities of simple RNNs to process and formalize a context-free grammar containing hierarchical (recursively embedded) dependencies: He observed that distinct parts of the networks were able to learn some complex representations to encode certain grammatical structures and dependencies of the context-free grammar. Later, BIBREF8 introduced an RNN with an external stack memory to learn simple context-free languages, such as $a^n b^m$ , $a^nb^ncb^ma^m$ , and $a^{n+m} b^n c^m$ . Similar studies BIBREF15 , BIBREF16 , BIBREF17 , BIBREF10 , BIBREF11 have explored the existence of stable counting mechanisms in simple RNNs, which would enable them to learn various context-free and context-sensitive languages, but none of the RNN architectures proposed in the early days were able to generalize the training set to longer (or more complex) test samples with substantially high accuracy.",
+      " BIBREF9 , on the other hand, proposed a variant of Long Short-Term Memory (LSTM) networks to learn two context-free languages, $a^n b^n$ , $a^n b^m B^m A^n$ , and one strictly context-sensitive language, $a^n b^n c^n$ . Given only a small fraction of samples in a formal language, with values of $n$ (and $m$ ) ranging from 1 to a certain training threshold $N$ , they trained an LSTM model until its full convergence on the training set and then tested it on a more generalized set. They showed that their LSTM model outperformed the previous approaches in capturing and generalizing the aforementioned formal languages. By analyzing the cell states and the activations of the gates in their LSTM model, they further demonstrated that the network learns how to count up and down at certain places in the sample sequences to encode information about the underlying structure of each of these formal languages.",
+      "Following this approach, BIBREF19 and BIBREF20 studied the stability of the LSTM networks in learning context-free and context-sensitive languages and examined the processing mechanism developed by the hidden states during the training phase. They observed that the weight initialization of the hidden states in the LSTM network had a significant effect on the inductive capabilities of the model and that the solutions were often unstable in the sense that the numbers up to which the LSTM models were able to generalize using the training dataset sporadically oscillated."
+    ],
+    [
+      "Following the traditional approach adopted by BIBREF7 , BIBREF12 , BIBREF9 and many other studies, we train our neural network as follows. At each time step, we present one input character to our model and then ask it to predict the set of next possible characters, based on the current character and the prior hidden states. Given a vocabulary $\\mathcal {V}^{(i)}$ of size $d$ , we use a one-hot representation to encode the input values; therefore, all the input vectors are $d$ -dimensional binary vectors. The output values are $(d+1)$ -dimensional though, since they may further contain the termination symbol $\\dashv $ , in addition to the symbols in $\\mathcal {V}^{(i)}$ . The output values are not always one-hot encoded, because there can be multiple possibilities for the next character in the sequence, therefore we instead use a $k$ -hot representation to encode the output values. Our objective is to minimize the mean-squared error (MSE) of the sequence predictions. During testing, we use an output threshold criterion of $0.5$ for the sigmoid output layer to indicate which characters were predicted by the model. We then turn this prediction task into a classification task by accepting a sample if our model predicts all of its output values correctly and rejecting it otherwise."
+    ],
+    [
+      "We consider the following three formal languages in our predictions tasks: $a^n b^n$ , $a^n b^n c^n$ , and $a^n b^n c^n d^n$ , where $n \\ge 1$ . Of these three languages, the first one is a context-free language and the last two are strictly context-sensitive languages. Table 1 provides example input-output pairs for these languages under the sequence prediction task. In the rest of this section, we formulate the sequence prediction task for each language in more detail.",
+      "The input vocabulary $\\mathcal {V}^{(i)}$ for $a^n b^n$ consists of $a$ and $b$ . The output vocabulary $\\mathcal {V}^{(o)}$ is the union of $\\mathcal {V}^{(i)}$ and $\\lbrace \\dashv \\rbrace $ . Therefore, the input vectors are 2-dimensional, and the output vectors are 3-dimensional. Before the occurrence of the first $b$ in a sequence, the model always predicts $a$ or $b$ (which we notate $a^n b^n$0 ) whenever it sees an $a^n b^n$1 . However, after it encounters the first $a^n b^n$2 , the rest of the sequence becomes entirely deterministic: Assuming that the model observes $a^n b^n$3 $a^n b^n$4 's in a sequence, it outputs $a^n b^n$5 $a^n b^n$6 's for the next $a^n b^n$7 $a^n b^n$8 's and the terminal symbol $a^n b^n$9 for the last $a$0 in the sequence. Summarizing, we define the input-target scheme for $a$1 as follows: ",
+      "$$a^n b^n \\Rightarrow (a/b)^n b^{n-1}\\dashv $$   (Eq. 8) ",
+      "The input vocabulary $\\mathcal {V}^{(i)}$ for $a^n b^n c^n$ consists of three characters: $a$ , $b$ , and $c$ . The output vocabulary $\\mathcal {V}^{(o)}$ is $\\mathcal {V}^{(i)} \\cup \\lbrace \\dashv \\rbrace $ . The input and output vectors are 3- and 4-dimensional, respectively. The input-target scheme for $a^n b^n c^n$ is: ",
+      "$$a^n b^n c^n\\Rightarrow (a/b)^{n}b^{n-1}c^{n}\\dashv $$   (Eq. 10) ",
+      "The vocabulary $\\mathcal {V}^{(i)}$ for the last language $a^n b^n c^n d^n$ consists of $a$ , $b$ , $c$ , and $d$ . The input vectors are 4-dimensional, and the output vectors are 5-dimensional. As in the case of the previous two languages, a sequence becomes entirely deterministic after the observance of the first $b$ , hence the input-target scheme for $a^n b^n c^n d^n$ is: ",
+      "$$a^n b^n c^n d^n\\Rightarrow (a/b)^n b^{n-1} c^n d^{n}\\dashv $$   (Eq. 12) "
+    ],
+    [
+      "We use a single-layer LSTM model to perform the sequence prediction task, followed by a linear layer that maps to the output vocabulary size. The linear layer is followed by a sigmoid unit layer. The loss is the sum of the mean squared error between the prediction and the correct output at each character. See Figure 1 for an illustration. In our implementation, we used the standard LSTM module in PyTorch BIBREF22 and initialized the initial hidden and cell states, $h_0$ and $c_0$ , to zero."
+    ],
+    [
+      "Training and testing are done in alternating steps: In each epoch, for training, we first present to an LSTM network 1000 samples in a given language, which are generated according to a certain discrete probability distribution supported on a closed finite interval. We then freeze all the weights in our model, exhaustively enumerate all the sequences in the language by their lengths, and determine the first $k$ shortest sequences whose outputs the model produces inaccurately. We remark, for the sake of clarity, that our test design is slightly different from the traditional testing approaches used by BIBREF10 , BIBREF9 , BIBREF12 , since we do not consider the shortest sequence in a language whose output was incorrectly predicted by the model, or the largest accepted test set, or the accuracy of the model on a fixed test set.",
+      "Our testing approach, as we will see shortly in the following subsections, gives more information about the inductive capabilities of our LSTM networks than the previous techniques and proves itself to be useful especially in the cases where the distribution of the length of our training dataset is skewed towards one of the boundaries of the distribution's support. For instance, LSTM models sometimes fail to capture some of the short sequences in a language during the testing phase, but they then predict a large number of long sequences correctly. If we were to report only the shortest sequence whose output our model incorrectly predicts, we would then be unable to capture the model's inductive capabilities. Furthermore, we test and report the performance of the model after each full pass of the training set. Finally, in all our investigations, we repeated each experiment ten times. In each trial, we only changed the weights of the hidden states of the model \u2013 all the other parameters were kept the same."
+    ],
+    [
+      "Previous studies have examined various length distribution models to generate appropriate training sets for each formal language: BIBREF16 , BIBREF11 , BIBREF12 , for instance, used length distributions that were skewed towards having more short sequences than long sequences given a training length-window, whereas BIBREF9 used a uniform distribution scheme to generate their training sets. The latter briefly comment that the distribution of lengths of sequences in the training set does influence the generalization ability and convergence speed of neural networks, and mention that training sets containing abundant numbers of both short and long sequences are learned by networks much more quickly than uniformly distributed regimes. Nevertheless, they do not systematically compare or explicitly report their findings. To study the effect of various length distributions on the learning capability and speed of LSTM models, we experimented with four discrete probability distributions supported on bounded intervals (Figure 2 ) to sample the lengths of sequences for the languages. We briefly recall the probability distribution functions for discrete uniform and Beta-Binomial distributions used in our data generation procedure.",
+      "Given $N \\in \\mathbb {N}$ , if a random variable $X \\sim U (1, N)$ , then the probability distribution function of $X$ is given as follows: $\nP(x) =\n{\\left\\lbrace \\begin{array}{ll}\n\\frac{1}{N} & \\text{if } x \\in \\lbrace 1, \\ldots , N\\rbrace  \\\\\n0 & \\text{otherwise.}\n\\end{array}\\right.}\n$ ",
+      "To generate training data with uniformly distributed lengths, we simply draw $n$ from $U (1, N)$ as defined above.",
+      "Similarly, given $N \\in \\mathbb {Z}^{\\ge 0}$ and two parameters $\\alpha $ and $ \\beta \\in \\mathbb {R}^{>0}$ , if a random variable $X \\sim \\text{BetaBin} (N, \\alpha , \\beta )$ , then the probability distribution function of $X$ is given as follows: $\nP(x) =\n{\\left\\lbrace \\begin{array}{ll}\n\\binom{N}{x} \\frac{B(x+\\alpha , N-x+\\beta )}{B(\\alpha , \\beta )} & \\text{if } x \\in \\lbrace 0, \\ldots , N\\rbrace  \\\\\n0 & \\text{otherwise.}\n\\end{array}\\right.}\n$ ",
+      "where $B(\\alpha , \\beta )$ is the Beta function. We set different values of $\\alpha $ and $\\beta $ as such in order to generate the following distributions:",
+      "U-shaped ( $\\alpha = 0.25$ , $\\beta = 0.25$ ): The probabilities of having short and long sequences are equally high, but the probability of having an average-length sequence is low.",
+      "Right-tailed ( $\\alpha = 1$ , $\\beta = 5$ ): Short sequences are more probable than long sequences.",
+      "Left-tailed ( $\\alpha = 5$ , $\\beta = 1$ ): Long sequences are more probable than short sequences.",
+      "Figure 3 exhibits the generalization graphs for the three formal languages trained with LSTM models under different length distribution regimes. Each single-color sequence in a generalization graph shows the average performance of ten LSTMs trained under the same settings but with different weight initializations. In all these experiments, the training sets had the same length-window $[1, 50]$ . On the other hand, we used 2, 3, and 4 hidden units in our LSTM architectures for the languages $a^n b^n$ , $a^n b^n c^n$ , and $a^n b^n c^n d^n$ , respectively. The top three plots show the average lengths of the shortest sequences ( $e_1$ ) whose outputs were incorrectly predicted by the model at test time, whereas the bottom plots show the fifth such shortest lengths ( $e_5$ ). We note that the models trained on uniformly distributed samples seem to perform the best amongst all the four distributions in all the three languages. Furthermore, for the languages $a^n b^n c^n$ and $a^n b^n c^n d^n$ , the U-shaped Beta-Binomial distribution appears to help the LSTM models generalize better than the left- and right-tailed Beta Binomial distributions, in which the lengths of the samples are intentionally skewed towards one end of the training length-window.",
+      "When we look at the plots for the $e_1$ values, we observe that all the distribution regimes seem to facilitate learning at least up to the longest sequences in their respective training datasets, drawn by the light blue horizontal lines on the plots, except for the left-tailed Beta-Binomial distribution for which we see errors at lengths shorter than the training length threshold in the languages $a^n b^n c^n$ and $a^n b^n c^n d^n$ . For instance, if we were to consider only the $e_1$ values in our analysis, it would be tempting to argue that the model trained under the left-tailed Beta-Binomial distribution regime did not learn to recognize the language $a^n b^n c^n d^n$ . By looking at the $e_5$ values, in addition to the $e_1$ values, we however realize that the model was actually learning many of the sequences in the language, but it was just struggling to recognize and correctly predict the outputs of some of the short sequences in the language. This phenomenon can be explained by the under-representation of short sequences in left-tailed Beta-Binomial distributions. Our observation clearly emphasizes the significance of looking beyond $e_1$ , the shortest error length at test time, in order to obtain a more complete picture of the model's generalizing capabilities."
+    ],
+    [
+      "Most of the previous studies trained networks on sequences of lengths $n \\in [1, N]$ , where typical $N$ values were between 10 and 50 BIBREF11 , BIBREF9 , and more recently 100 BIBREF23 . To determine the impact of the choice of training length-window on the stability and inductive capabilities of the LSTM networks, we experimented with three different length-windows for $n$ : $[1, 30]$ , $[1, 50]$ , and $[50, 100]$ . In the third window setting $[50, 100]$ , we further wanted to see whether LSTM are capable of generalizing to short sequences that are contained in the window range $[1, 50]$ , as well as to sequences that are longer than the sequences seen in the training set."
+    ],
+    [
+      "It has been shown by BIBREF9 that LSTMs can learn $a^n b^n$ and $a^n b^n c^n$ with 1 and 2 hidden units, respectively. Similarly, BIBREF24 demonstrated that a simple RNN architecture containing a single hidden unit with carefully tuned parameters can develop a canonical linear counting mechanism to recognize the simple context-free language $a^n b^n$ , for $n \\le 250$ . We wanted to explore whether the stability of the networks would improve with an increase in capacity of the LSTM model. We, therefore, varied the number of hidden units in our LSTM models as follows. We experimented with 1, 2, 3, and 36 hidden units for $a^n b^n$ ; 2, 3, 4, and 36 hidden units for $a^n b^n c^n$ ; and 3, 4, 5, and 36 hidden units for $a^n b^n c^n d^n$ . The 36 hidden unit case represents an over-parameterized network with more than enough theoretical capacity to recognize all these languages."
+    ],
+    [
+      "Figure 4 shows the generalization graphs for the three formal languages trained with LSTM models under different training windows. We note that enlarging the training length-window, naturally, enables an LSTM model to generalize far beyond its training length threshold. Besides, we see that the models with the training length-window of $[50, 100]$ performed slightly better than the other two window ranges in the case of $a^n b^n c^n$ (green line, bottom middle plot). Moreover, we acknowledge the capability of LSTMs to recognize longer sequences, as well as shorter sequences. For instance, when trained on the training length-window $[50, 100]$ , our models learned to recognize not only the longer sequences but also the shorter sequences not presented in the training sets for the languages $a^n b^n$ and $a^n b^n c^n$ .",
+      "Finally, we highlight the importance of the $e_5$ values once again: If we were to consider only the $e_1$ values, for instance, we would not have captured the inductive learning capabilities of the models trained with a length-window of $[50, 100]$ in the case of $a^n b^n c^n$ , since the models always failed at recognizing the shortest sequence $ab$ in the language. Yet, considering $e_5$ values helped us evaluate the performance of the LSTM models more accurately."
+    ],
+    [
+      "There seems to be a positive correlation between the number of hidden units in an LSTM network and its stability while learning a formal language. As Figure 5 demonstrates, increasing the number of hidden units in an LSTM network both increases the network's stability and also leads to faster convergence. However, it does not necessarily result in a better generalization. We conjecture that, with more hidden units, we simply offer more resources to our LSTM models to regulate their hidden states to learn these languages. The next section supports this hypothesis by visualizing the hidden state activations during sequence processing."
+    ],
+    [
+      "In addition to the analysis of our empirical results in the previous section, we would like to touch upon two important characteristics of LSTM models when they learn formal languages, namely the convergence issue and counting behavior of LSTM models."
+    ],
+    [
+      "In this paper, we have addressed the influence of various length distribution regimes and length-window sizes on the generalizing ability of LSTMs to learn simple context-free and context-sensitive languages, namely $a^n b^n$ , $a^n b^n c^n$ , and $a^n b^n c^n d^n$ . Furthermore, we have discussed the effect of the number of hidden units in LSTM models on the stability of a representation learned by the network: We show that increasing the number of hidden units in an LSTM model improves the stability of the network, but not necessarily the inductive power. Finally, we have exhibited the importance of weight initialization to the convergence of the network: Our results indicate that different hidden weight initializations can yield different convergence values, given that all the other parameters are unchanged. Throughout our analysis, we emphasized the importance of a fine-grained evaluation, considering generalization beyond the first error and during training. We therefore concluded that there are an abundant number of parameters that can influence the inductive ability of an LSTM to learn a formal language and that the notion of learning, from a neural network's perspective, should be treated carefully."
+    ],
+    [
+      "The first author gratefully acknowledges the support of the Harvard College Research Program (HCRP) and the Harvard Center for Research on Computation and Society Research Fellowship for Undergraduate Students. The second author was supported by the Harvard Mind, Brain, and Behavior Initiative. The authors also thank Sebastian Gehrmann for his helpful comments and discussion at the beginning of the project. The computations in this paper were run on the Odyssey cluster supported by the FAS Division of Science, Research Computing Group at Harvard University."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1630/instruction.md b/qasper-1630/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..7038d5b041c47af1012c1cf7e76bb8e94ba7ac1c
--- /dev/null
+++ b/qasper-1630/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment Classification
+
+Question: What are the performance results?
\ No newline at end of file
diff --git a/qasper-1631/instruction.md b/qasper-1631/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..9fc27e6b392fd10a21e77e500c50677e581b8c68
--- /dev/null
+++ b/qasper-1631/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Bi-Directional Lattice Recurrent Neural Networks for Confidence Estimation
+
+Question: What is a confusion network or lattice?
\ No newline at end of file
diff --git a/qasper-1636/instruction.md b/qasper-1636/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..0a2fd47a9dc097118474c50397a94c5a60f0dfb1
--- /dev/null
+++ b/qasper-1636/instruction.md
@@ -0,0 +1,188 @@
+Name of Paper: Robust Multilingual Named Entity Recognition with Shallow Semi-Supervised Features
+
+Question: what are the baselines?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Contributions",
+    "Related Work",
+    "Datasets",
+    "Related Approaches",
+    "System Description",
+    "Local Features",
+    "Gazetteers",
+    "Clustering Features",
+    "Experimental Results",
+    "In-domain evaluation",
+    "Reducing training data",
+    "Out-of-domain evaluations",
+    "Discussion",
+    "Conclusion and Future Work",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "A named entity can be mentioned using a great variety of surface forms (Barack Obama, President Obama, Mr. Obama, B. Obama, etc.) and the same surface form can refer to a variety of named entities. For example, according to the English Wikipedia, the form `Europe' can ambiguously be used to refer to 18 different entities, including the continent, the European Union, various Greek mythological entities, a rock band, some music albums, a magazine, a short story, etc. Furthermore, it is possible to refer to a named entity by means of anaphoric pronouns and co-referent expressions such as `he', `her', `their', `I', `the 35 year old', etc. Therefore, in order to provide an adequate and comprehensive account of named entities in text it is necessary to recognize the mention of a named entity and to classify it by a pre-defined type (e.g, person, location, organization). Named Entity Recognition and Classification (NERC) is usually a required step to perform Named Entity Disambiguation (NED), namely to link `Europe' to the right Wikipedia article, and to resolve every form of mentioning or co-referring to the same entity.",
+      "Nowadays NERC systems are widely being used in research for tasks such as Coreference Resolution BIBREF0 , Named Entity Disambiguation BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 for which a lot of interest has been created by the TAC KBP shared tasks BIBREF6 , Machine Translation BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , Aspect Based Sentiment Analysis BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 , Event Extraction BIBREF15 , BIBREF16 , BIBREF17 , BIBREF18 , BIBREF19 and Event Ordering BIBREF20 .",
+      "Moreover, NERC systems are integrated in the processing chain of many industrial software applications, mostly by companies offering specific solutions for a particular industrial sector which require recognizing named entities specific of their domain. There is therefore a clear interest in both academic research and industry to develop robust and efficient NERC systems: For industrial vendors it is particularly important to diversify their services by including NLP technology for a variety of languages whereas in academic research NERC is one of the foundations of many other NLP end-tasks.",
+      "Most NERC taggers are supervised statistical systems that extract patterns and term features which are considered to be indications of Named Entity (NE) types using the manually annotated training data (extracting orthographic, linguistic and other types of evidence) and often external knowledge resources. As in other NLP tasks, supervised statistical NERC systems are more robust and obtain better performance on available evaluation sets, although sometimes the statistical models can also be combined with specific rules for some NE types. For best performance, supervised statistical approaches require manually annotated training data, which is both expensive and time-consuming. This has seriously hindered the development of robust high performing NERC systems for many languages but also for other domains and text genres BIBREF21 , BIBREF22 , in what we will henceforth call `out-of-domain' evaluations.",
+      "Moreover, supervised NERC systems often require fine-tuning for each language and, as some of the features require language-specific knowledge, this poses yet an extra complication for the development of robust multilingual NERC systems. For example, it is well-known that in German every noun is capitalized and that compounds including named entities are pervasive. This also applies to agglutinative languages such as Basque, Korean, Finnish, Japanese, Hungarian or Turkish. For this type of languages, it had usually been assumed that linguistic features (typically Part of Speech (POS) and lemmas, but also semantic features based on WordNet, for example) and perhaps specific hand-crafted rules, were a necessary condition for good NERC performance as they would allow to capture better the most recurrent declensions (cases) of named entities for Basque BIBREF23 or to address problems such as sparsity and capitalization of every noun for German BIBREF24 , BIBREF25 , BIBREF26 . This language dependency was easy to see in the CoNLL 2002 and 2003 tasks, in which systems participating in the two available languages for each edition obtained in general different results for each language. This suggests that without fine-tuning for each corpus and language, the systems did not generalize well across languages BIBREF27 .",
+      "This paper presents a multilingual and robust NERC system based on simple, general and shallow features that heavily relies on word representation features for high performance. Even though we do not use linguistic motivated features, our approach also works well for inflected languages such as Basque and German. We demonstrate the robustness of our approach by reporting best results for five languages (Basque, Dutch, German, English and Spanish) on 12 different datasets, including seven in-domain and eight out-of-domain evaluations."
+    ],
+    [
+      "The main contributions of this paper are the following: First, we show how to easily develop robust NERC systems across datasets and languages with minimal human intervention, even for languages with declension and/or complex morphology. Second, we empirically show how to effectively use various types of simple word representation features thereby providing a clear methodology for choosing and combining them. Third, we demonstrate that our system still obtains very competitive results even when the supervised data is reduced by half (even less in some cases), alleviating the dependency on costly hand annotated data. These three main contributions are based on:",
+      "A simple and shallow robust set of features across languages and datasets, even in out-of-domain evaluations.",
+      "The lack of linguistic motivated features, even for languages with agglutinative (e.g., Basque) and/or complex morphology (e.g., German).",
+      "A clear methodology for using and combining various types of word representation features by leveraging public unlabeled data.",
+      "Our approach consists of shallow local features complemented by three types of word representation (clustering) features: Brown clusters BIBREF28 , Clark clusters BIBREF29 and K-means clusters on top of the word vectors obtained by using the Skip-gram algorithm BIBREF30 . We demonstrate that combining and stacking different clustering features induced from various data sources (Reuters, Wikipedia, Gigaword, etc.) allows to cover different and more varied types of named entities without manual feature tuning. Even though our approach is much simpler than most, we obtain the best results for Dutch, Spanish and English and comparable results in German (on CoNLL 2002 and 2003). We also report best results for German using the GermEval 2014 shared task data and for Basque using the Egunkaria testset BIBREF23 .",
+      "We report out-of-domain evaluations in three languages (Dutch, English and Spanish) using four different datasets to compare our system with the best publicly available systems for those languages: Illinois NER BIBREF31 for English, Stanford NER BIBREF32 for English and Spanish, SONAR-1 NERD for Dutch BIBREF33 and Freeling for Spanish BIBREF34 . We outperform every other system in the eight out-of-domain evaluations reported in Section SECREF79 . Furthermore, the out-of-domain results show that our clustering features provide a simple and easy method to improve the robustness of NERC systems.",
+      "Finally, and inspired by previous work BIBREF35 , BIBREF36 we measure how much supervision is required to obtain state of the art results. In Section SECREF75 we show that we can still obtain very competitive results reducing the supervised data by half (and sometimes even more). This, together with the lack of linguistic features, means that our system considerably saves data annotation costs, which is quite convenient when trying to develop a NERC system for a new language and/or domain.",
+      "Our system learns Perceptron models BIBREF37 using the Machine Learning machinery provided by the Apache OpenNLP project with our own customized (local and clustering) features. Our NERC system is publicly available and distributed under the Apache 2.0 License and part of the IXA pipes tools BIBREF38 . Every result reported in this paper is obtained using the conlleval script from the CoNLL 2002 and CoNLL 2003 shared tasks. To guarantee reproducibility of results we also make publicly available the models and the scripts used to perform the evaluations. The system, models and evaluation scripts can be found in the ixa-pipe-nerc website.",
+      "Next Section reviews related work, focusing on best performing NERC systems for each language evaluated on standard shared evaluation task data. Section SECREF3 presents the design of our system and our overall approach to NERC. In Section SECREF4 we report the evaluation results obtained by our system for 5 languages (Basque, Dutch, German, English and Spanish) on 12 different datasets, distributed in 7 in-domain and 8 out-of-domain evaluations. Section SECREF5 discusses the results and contributions of our approach. In Section SECREF6 we highlight the main aspects of our work providing some concluding remarks and future work to be done using our NERC approach applied to other text genres, domains and sequence labeling tasks."
+    ],
+    [
+      "The Named Entity Recognition and Classification (NERC) task was first defined for the Sixth Message Understanding Conference (MUC 6) BIBREF39 . The MUC 6 tasks focused on Information Extraction (IE) from unstructured text and NERC was deemed to be an important IE sub-task with the aim of recognizing and classifying nominal mentions of persons, organizations and locations, and also numeric expressions of dates, money, percentage and time. In the following years, research on NERC increased as it was considered to be a crucial source of information for other Natural Language Processing tasks such as Question Answering (QA) and Textual Entailment (RTE) BIBREF39 . Furthermore, while MUC 6 was solely devoted to English as target language, the CoNLL shared tasks (2002 and 2003) boosted research on language independent NERC for 3 additional target languages: Dutch, German and Spanish BIBREF40 , BIBREF41 .",
+      "The various MUC, ACE and CoNLL evaluations provided a very convenient framework to test and compare NERC systems, algorithms and approaches. They provided manually annotated data for training and testing the systems as well as an objective evaluation methodology. Using such framework, research rapidly evolved from rule-based approaches (consisting of manually handcrafted rules) to language independent systems focused on learning supervised statistical models. Thus, while in the MUC 6 competition 5 out of 8 systems were rule-based, in CoNLL 2003 16 teams participated in the English task all using statistical-based NERC BIBREF39 ."
+    ],
+    [
+      "Table TABREF10 describes the 12 datasets used in this paper. The first half lists the corpora used for in-domain evaluation whereas the lower half contains the out-of-domain datasets. The CoNLL NER shared tasks focused on language independent machine learning approaches for 4 entity types: person, location, organization and miscellaneous entities. The 2002 edition provided manually annotated data in Dutch and Spanish whereas in 2003 the languages were German and English. In addition to the CoNLL data, for English we also use the formal run of MUC 7 and Wikigold for out-of-domain evaluation. Very detailed descriptions of CoNLL and MUC data can easily be found in the literature, including the shared task descriptions themselves BIBREF42 , BIBREF40 , BIBREF41 , so in the following we will describe the remaining, newer datasets.",
+      "The Wikigold corpus consists of 39K words of English Wikipedia manually annotated following the CoNLL 2003 guidelines BIBREF27 . For Spanish and Dutch, we also use Ancora 2.0 BIBREF43 and SONAR-1 BIBREF33 respectively. SONAR-1 is a one million word Dutch corpus with both coarse-grained and fine-grained named entity annotations. The coarse-grained level includes product and event entity types in addition to the four types defined in CoNLL data. Ancora adds date and number types to the CoNLL four main types. In Basque the only gold standard corpus is Egunkaria BIBREF23 . Although the Basque Egunkaria dataset is annotated with four entity types, the miscellaneous class is extremely sparse, occurring only in a proportion of 1 to 10. Thus, in the training data there are 156 entities annotated as MISC whereas each of the other three classes contain around 1200 entities.",
+      "In the datasets described so far, named entities were assumed to be non-recursive and non-overlapping. During the annotation process, if a named entity was embedded in a longer one, then only the longest mention was annotated. The exceptions are the GermEval 2014 shared task data for German and MEANTIME, where nested entities are also annotated (both inner and outer spans).",
+      "The GermEval 2014 NER shared task BIBREF25 aimed at improving the state of the art of German NERC which was perceived to be comparatively lower than the English NERC. Two main extensions were introduced in GermEval 2014; (i) fine grained named entity sub-types to indicate derivations and compounds; (ii) embedded entities (and not only the longest span) are annotated. In total, there are 12 types for classification: person, location, organization, other plus their sub-types annotated at their inner and outer levels.",
+      "Finally, the MEANTIME corpus BIBREF44 is a multilingual (Dutch, English, Italian and Spanish) publicly available evaluation set annotated within the Newsreader project. It consists of 120 documents, divided into 4 topics: Apple Inc., Airbus and Boeing, General Motors, Chrysler and Ford, and the stock market. The articles are selected in such a way that the corpus contains different articles that deal with the same topic over time (e.g. launch of a new product, discussion of the same financial indexes). Moreover, it contains nested entities so the evaluation results will be provided in terms of the outer and the inner spans of the named entities. MEANTIME includes six named entity types: person, location, organization, product, financial and mixed."
+    ],
+    [
+      "Named entity recognition is a task with a long history in NLP. Therefore, we will summarize those approaches that are most relevant to our work, especially those we will directly compared with in Section SECREF4 . Since CoNLL shared tasks, the most competitive approaches have been supervised systems learning CRF, SVM, Maximum Entropy or Averaged Perceptron models. In any case, while the machine learning method is important, it has also been demonstrated that good performance might largely be due to the feature set used BIBREF45 . Table TABREF13 provides an overview of the features used by previous best scoring approaches for each of the five languages we address in this paper.",
+      "Traditionally, local features have included contextual and orthographic information, affixes, character-based features, prediction history, etc. As argued by the CoNLL 2003 organizers, no feature set was deemed to be ideal for NERC BIBREF41 , although many approaches for English refer to BIBREF46 as a useful general approach.",
+      "Some of the CoNLL participants use linguistic information (POS, lemmas, chunks, but also specific rules or patterns) for Dutch and English BIBREF47 , BIBREF45 , although these type of features was deemed to be most important for German, for which the use of linguistic features is pervasive BIBREF25 . This is caused by the sparsity caused by the declension cases, the tendency to form compounds containing named entities and by the capitalization of every noun BIBREF24 . For example, the best system among the 11 participants in GermEval 2014, ExB, uses morphological features and specific suffix lists aimed at capturing frequent patterns in the endings of named entities BIBREF48 .",
+      "In agglutinative languages such as Basque, which contains declension cases for named entities, linguistic features are considered to be a requirement. For example, the country name `Espainia' (Spain in Basque) can occur in several forms, Espainian, Espainiera, Espainiak, Espainiarentzat, Espainiako, and many more. Linguistic information has been used to treat this phenomenon. The only previous work for Basque developed Eihera, a rule-based NERC system formalized as finite state transducers to take into account declension classes BIBREF23 . The features of Eihera include word, lemma, POS, declension case, capitalized lemma, etc. These features are complemented with gazetteers extracted from the Euskaldunon Egunkaria newspaper and semantic information from the Basque WordNet.",
+      "Dictionaries are widely used to inject world knowledge via gazetteer matches as features in machine learning approaches to NERC. The best performing systems carefully compile their own gazetteers from a variety of sources BIBREF47 . BIBREF31 leverage a collection of 30 gazetteers and matches against each one are weighted as a separate feature. In this way they trust each gazetteer to a different degree. BIBREF49 carefully compiled a large collection of English gazetteers extracted from US Census data and Wikipedia and applied them to the process of inducing word embeddings with very good results.",
+      "While it is possible to automatically extract them from various corpora or resources, they still require careful manual inspection of the target data. Thus, our approach only uses off the shelf gazetteers whenever they are publicly available. Furthermore, our method collapses every gazetteer into one dictionary. This means that we only add a feature per token, instead of a feature per token and gazetteer.",
+      "The intuition behind non-local (or global) features is to treat similarly all occurrences of the same named entity in a text. BIBREF47 proposed a method to produce the set of named entities for the whole sentence, where the optimal set of named entities for the sentence is the coherent set of named entities which maximizes the summation of confidences of the named entities in the set. BIBREF31 developed three types of non-local features, analyzing global dependencies in a window of between 200 and 1000 tokens.",
+      "Semi-supervised approaches leveraging unlabeled text had already been applied to improve results in various NLP tasks. More specifically, it had been previously shown how to apply Brown clusters BIBREF28 for Chinese Word Segmentation BIBREF50 , dependency parsing BIBREF35 , NERC BIBREF51 and POS tagging BIBREF36 .",
+      " BIBREF31 used Brown clusters as features obtaining what was at the time the best published result of an English NERC system on the CoNLL 2003 testset. BIBREF52 made a rather exhaustive comparison of Brown clusters, Collobert and Weston's embeddings BIBREF53 and HLBL embeddings BIBREF54 to improve chunking and NERC. They show that in some cases the combination of word representation features was positive but, although they used Ratinov and Roth's (2009) system as starting point, they did not manage to improve over the state of the art. Furthermore, they reported that Brown clustering features performed better than the word embeddings.",
+      " BIBREF49 extend the Skip-gram algorithm to learn 50-dimensional lexicon infused phrase embeddings from 22 different gazetteers and the Wikipedia. The resulting embeddings are used as features by scaling them by a hyper-parameter which is a real number tuned on the development data. BIBREF49 report best results up to date for English NERC on CoNLL 2003 test data, 90.90 F1.",
+      "The best German CoNLL 2003 system (an ensemble) was outperformed by BIBREF24 . They trained the Stanford NER system BIBREF32 , which uses a linear-chain Conditional Random Field (CRF) with a variety of features, including lemma, POS tag, etc. Crucially, they included \u201cdistributional similarity\u201d features in the form of Clark clusters BIBREF29 induced from large unlabeled corpora: the Huge German Corpus (HGC) of around 175M tokens of newspaper text and the deWac corpus BIBREF55 consisting of 1.71B tokens of web-crawled data. Using the clusters induced from deWac as a form of semi-supervision improved the results over the best CoNLL 2003 system by 4 points in F1.",
+      "The best participant of the English CoNLL 2003 shared task used the results of two externally trained NERC taggers to create an ensemble system BIBREF56 . BIBREF49 develop a stacked linear-chain CRF system: they train two CRFs with roughly the same features; the second CRF can condition on the predictions made by the first CRF. Their \u201cbaseline\u201d system uses a similar local featureset as Ratinov and Roth's (2009) but complemented with gazetteers. Their baseline system combined with their phrase embeddings trained with infused lexicons allow them to report the best CoNLL 2003 result so far.",
+      "The best system of the GermEval 2014 task built an ensemble of classifiers and pattern extractors to find the most likely tag sequence BIBREF48 . They paid special attention to out of vocabulary words which are addressed by semi-supervised word representation features and an ensemble of POS taggers. Furthermore, remaining unknown candidate mentions are tackled by look-up via the Wikipedia API.",
+      "Apart from the feature types, the last two columns of Table TABREF13 refer to whether the systems are publicly available and whether any external resources used for training are made available (e.g., induced word embeddings, gazetteers or corpora). This is desirable to be able to re-train the systems on different datasets. For example, we would have been interested in training the Stanford NER system with the full Ancora corpus for the evaluation presented in Table TABREF85 , but their Spanish cluster lexicon is not available. Alternatively, we would have liked to train our system with the same Ancora partition used to train Stanford NER, but that is not available either."
+    ],
+    [
+      "The design of ixa-pipe-nerc aims at establishing a simple and shallow feature set, avoiding any linguistic motivated features, with the objective of removing any reliance on costly extra gold annotations (POS tags, lemmas, syntax, semantics) and/or cascading errors if automatic language processors are used. The underlying motivation is to obtain robust models to facilitate the development of NERC systems for other languages and datasets/domains while obtaining state of the art results. Our system consists of:",
+      "Table TABREF24 provides an example of the features generated by our system."
+    ],
+    [
+      "The local features constitute our baseline system on top of which the clustering features are added. We implement the following feature set, partially inspired by previous work BIBREF46 :",
+      "Token: Current lowercase token (w), namely, ekuadorko in Table TABREF24 .",
+      "Token Shape: Current lowercase token (w) plus current token shape (wc), where token shape consist of: (i) The token is either lowercase or a 2 digit word or a 4 digit word; (ii) If the token contains digits, then whether it also contains letters, or slashes, or hyphens, or commas, or periods or is numeric; (iii) The token is all uppercase letters or is an acronym or is a one letter uppercase word or starts with capital letter. Thus, in Table TABREF24 1994an is a 4 digit word (4d), Ekuadorko has an initial capital shape (ic) and hiriburuan is lowercase (lc).",
+      "Previous prediction: the previous outcome (pd) for the current token. The previous predictions in our example are null because these words have not been seen previously, except for the comma.",
+      "Sentence: Whether the token is the beginning of the sentence. None of the tokens in our example is at the beginning of the sentence, so this feature is not active in Table TABREF24 .",
+      "Prefix: Two prefixes consisting of the first three and four characters of the current token: Eku and Ekua.",
+      "Suffix: The four suffixes of length one to four from the last four characters of the current token.",
+      "Bigram: Bigrams including the current token and the token shape.",
+      "Trigram: Trigrams including the current token and the token shape.",
+      "Character n-gram: All lowercase character bigrams, trigrams, fourgrams and fivegrams from the current token (ng).",
+      "Token, token shape and previous prediction features are placed in a 5 token window, namely, for these these three features we also consider the previous and the next two words, as shown in Table TABREF24 ."
+    ],
+    [
+      "We add gazetteers to our system only if they are readily available to use, but our approach does not fundamentally depend upon them. We perform a look-up in a gazetteer to check if a named entity occurs in the sentence. The result of the look-up is represented with the same encoding chosen for the training process, namely, the BIO or BILOU scheme. Thus, for the current token we add the following features:",
+      "The current named entity class in the encoding schema. Thus, in the BILOU encoding we would have \u201cunit\u201d, \u201cbeginning\u201d, \u201clast\u201d, \u201cinside\u201d, or if not match is found, \u201coutside\u201d, combined with the specific named entity type (LOC, ORG, PER, MISC, etc.).",
+      "The current named entity class as above and the current token."
+    ],
+    [
+      "The general idea is that by using some type of semantic similarity or word cluster induced over large unlabeled corpora it is possible to improve the predictions for unseen words in the test set. This type of semi-supervised learning may be aimed at improving performance over a fixed amount of training data or, given a fixed target performance level, to establish how much supervised data is actually required to reach such performance BIBREF35 .",
+      "So far the most successful approaches have only used one type of word representation BIBREF49 , BIBREF24 , BIBREF31 . However, our simple baseline combined with one type of word representation features are not able to compete with previous, more complex, systems. Thus, instead of encoding more elaborate features, we have devised a simple method to combine and stack various types of clustering features induced over different data sources or corpora. In principle, our method can be used with any type of word representations. However, for comparison purposes, we decided to use word representations previously used in successful NERC approaches: Brown clusters BIBREF31 , BIBREF52 , Word2vec clusters BIBREF49 and Clark clusters BIBREF32 , BIBREF24 . As can be observed in Table TABREF24 , our clustering features are placed in a 5 token window.",
+      "The Brown clustering algorithm BIBREF28 is a hierarchical algorithm which clusters words to maximize the mutual information of bigrams. Thus, it is a class-based bigram model in which:",
+      "The probability of a document corresponds to the product of the probabilities of its bigrams,",
+      "the probability of each bigram is calculated by multiplying the probability of a bigram model over latent classes by the probability of each class generating the actual word types in the bigram, and",
+      "each word type has non-zero probability only on a single class.",
+      "The Brown algorithm takes a vocabulary of words to be clustered and a corpus of text containing these words. It starts by assigning each word in the vocabulary to its own separate cluster, then iteratively merges the pair of clusters which leads to the smallest decrease in the likelihood of the text corpus. This produces a hierarchical clustering of the words, which is usually represented as a binary tree, as shown in Figure FIGREF44 . In this tree every word is uniquely identified by its path from the root, and the path can be represented by a bit string. It is also possible to choose different levels of word abstraction by choosing different depths along the path from the root to the word. Therefore, by using paths of various lengths, we obtain clustering features of different granularities BIBREF57 .",
+      "We use paths of length 4, 6, 10 and 20 as features BIBREF31 . However, we introduce several novelties in the design of our Brown clustering features:",
+      "For each feature which is token-based, we add a feature containing the paths computed for the current token. Thus, taking into account our baseline system, we will add the following Brown clustering features:",
+      "Brown Token: existing paths of length 4, 6, 10 and 20 for the current token.",
+      "Brown Token Shape: existing paths of length 4, 6, 10, 20 for the current token and current token shape.",
+      "Brown Bigram: existing paths of length 4, 6, 10, 20 for bigrams including the current token.",
+      "Brown clustering features benefit from two additional features:",
+      "Previous prediction plus token: the previous prediction (pd) for the current token and the current token.",
+      "Previous two predictions: the previous prediction for the current and the previous token.",
+      "For space reasons, Table TABREF24 only shows the Brown Token (bt) and Brown Token Shape (c) features for paths of length 4 and 6. We use the publicly available tool implemented by BIBREF50 with default settings. The input consists of a corpus tokenized and segmented one sentence per line, without punctuation. Furthermore, we follow previous work and remove all sentences which consist of less than 90% lowercase characters BIBREF50 , BIBREF52 before inducing the Brown clusters.",
+      " BIBREF29 presents a number of unsupervised algorithms, based on distributional and morphological information, for clustering words into classes from unlabeled text. The focus is on clustering infrequent words on a small numbers of clusters from comparatively small amounts of data. In particular, BIBREF29 presents an algorithm combining distributional information with morphological information of words \u201cby composing the Ney-Essen clustering model with a model for the morphology within a Bayesian framework\u201d. The objective is to bias the distributional information to put words that are morphologically similar in the same cluster. We use the code released by BIBREF29 off the shelf to induce Clark clusters using the Ney-Essen with morphological information method. The input of the algorithm is a sequence of lowercase tokens without punctuation, one token per line with sentence breaks.",
+      "Our Clark clustering features are very simple: we perform a look-up of the current token in the clustering lexicon. If a match is found, we add as a feature the clustering class, or the lack of match if the token is not found (see Clark-a and Clark-b in Table TABREF24 ).",
+      "Another family of language models that produces word representations are the neural language models. These approaches produce representation of words as continuous vectors BIBREF53 , BIBREF54 , also called word embeddings. Nowadays, perhaps the most popular among them is the Skip-gram algorithm BIBREF30 . The Skip-gram algorithm uses shallow log-linear models to compute vector representation of words which are more efficient than previous word representations induced on neural language models. Their objective is to produce word embeddings by computing the probability of each n-gram as the product of the conditional probabilities of each context word in the n-gram conditioned on its central word BIBREF30 .",
+      "Instead of using continuous vectors as real numbers, we induce clusters or word classes from the word vectors by applying K-means clustering. In this way we can use the cluster classes as simple binary features by injecting unigram match features. We use the Word2vec tool released by BIBREF30 with a 5 window context to train 50-dimensional word embeddings and to obtain the word clusters on top of them. The input of the algorithm is a corpus tokenized, lowercased, with punctuation removed and in one line. The Word2vec features are implemented exactly like the Clark features.",
+      "We successfully combine clustering features from different word representations. Furthermore, we also stack or accumulate features of the same type of word representation induced from different data sources, trusting each clustering lexicon to a different degree, as shown by the five encoded clustering features in Table TABREF24 : two Clark and Word2vec features from different source data and one Brown feature. When using word representations as semi-supervised features for a task like NERC, two principal factors need to be taken into account: (i) the source data or corpus used to induce the word representations and (ii) the actual word representation used to encode our features which in turn modify the weight of our model's parameters in the training process.",
+      "For the clustering features to be effective the induced clusters need to contain as many words appearing in the training, development and test sets as possible. This can be achieved by using corpora closely related to the text genre or domain of the data sets or by using very large unlabeled corpora which, although not closely domain-related, be large enough to include many relevant words. For example, with respect to the CoNLL 2003 English dataset an example of the former would be the Reuters corpus while the Wikipedia would be an example of the latter.",
+      "The word representations obtained by different algorithms would capture different distributional properties of words in a given corpus or data source. Therefore, each type of clustering would allow us to capture different types of occurring named entity types. In other words, combining and stacking different types of clustering features induced over a variety of data sources should help to capture more similarities between different words in the training and test sets, increasing the contribution to the weights of the model parameters in the training process."
+    ],
+    [
+      "In this Section we report on the experiments performed with the ixa-pipe-nerc system as described in the previous section. The experiments are performed in 5 languages: Basque, Dutch, English, German and Spanish. For comparison purposes, in-domain results are presented in Section SECREF61 using the most common NERC datasets for each language as summarized in Table TABREF10 . Section SECREF75 analyzes the performance when reducing training data and Section SECREF79 presents eight out-of-domain evaluations for three languages: Dutch, English and Spanish.",
+      "The results for Dutch, English and Spanish do not include trigrams and character n-grams in the local featureset described in Section SECREF25 , except for the models in each in-domain evaluation which are marked with \u201ccharngram 1:6\u201d.",
+      "We also experiment with dictionary features but, in contrast to previous approaches such as BIBREF49 , we only use currently available gazetteers off-the-shelf. For every model marked with \u201cdict\u201d we use the thirty English Illinois NER gazetteers BIBREF31 , irrespective of the target language. Additionally, the English models use six gazetteers about the Global Automotive Industry provided by LexisNexis to the Newsreader project, whereas the German models include, in addition to the Illinois gazetteers, the German dictionaries distributed in the CoNLL 2003 shared task. The gazetteers are collapsed into one large dictionary and deployed as described in Section SECREF35 .",
+      "Finally, the clustering features are obtained by processing the following clusters from publicly available corpora: (i) 1000 Brown clusters; (ii) Clark and Word2vec clusters in the 100-600 range. To choose the best combination of clustering features we test the available permutations of Clark and Word2vec clusters with and without the Brown clusters on the development data. Table TABREF58 provides details of every corpus used to induce the clusters. For example, the first row reads: \u201cReuters RCV1 was used; the original 63 million words were reduced to 35 million after pre-processing for inducing Brown clusters. Clark and Word2vec clusters were trained on the whole corpus\u201d. The pre-processing and tokenization is performed with the IXA pipes tools BIBREF38 .",
+      "Every evaluation is carried out using the CoNLL NER evaluation script. The results are obtained with the BILOU encoding for every experimental setting except for German CoNLL 2003."
+    ],
+    [
+      "In this section the results are presented by language. In two cases, Dutch and German, we use two different datasets, making it a total of seven in-domain evaluations.",
+      "We tested our system in the highly competitive CoNLL 2003 dataset. Table TABREF63 shows that three of our models outperform previous best results reported for English in the CoNLL 2003 dataset BIBREF49 . Note that the best F1 score (91.36) is obtained by adding trigrams and character n-gram features to the best model (91.18). The results also show that these models improve the baseline provided by the local features by around 7 points in F1 score. The most significant gain is in terms of recall, almost 9 points better than the baseline.",
+      "We also report very competitive results, only marginally lower than BIBREF49 , based on the stacking and combination of clustering features as described in Section UID57 . Thus, both best cluster and comp models, based on local plus clustering features only, outperform very competitive and more complex systems such as those of BIBREF31 and BIBREF52 , and obtain only marginally lower results than BIBREF49 . The stacking and combining effect manifests itself very clearly when we compare the single clustering feature models (BR, CW600, W2VG200 and W2VW400) with the light, comp and best cluster models which improve the overall F1 score by 1.30, 1.72 and 1.85 respectively over the best single clustering model (CW600).",
+      "It is worth mentioning that our models do not score best in the development data. As the development data is closer in style and genre to the training data BIBREF31 , this may suggest that our system generalizes better on test data that is not close to the training data; indeed, the results reported in Section SECREF79 seem to confirm this hypothesis.",
+      "We also compared our results with respect to the best two publicly available English NER systems trained on the same data. We downloaded the Stanford NER system distributed in the 2015-01-30 package. We evaluated their CoNLL model and, while the result is substantially better than their reference paper BIBREF32 , our clustering models obtain better results. The Illinois NER tagger is used by BIBREF31 and BIBREF52 , both of which are outperformed by our system.",
+      "We tested our system in the GermEval 2014 dataset. Table TABREF65 compares our results with the best two systems (ExB and UKP) by means of the M3 metric, which separately analyzes the performance in terms of the outer and inner named entity spans. Table TABREF65 makes explicit the significant improvements achieved by the clustering features on top of the baseline system, particularly in terms of recall (almost 11 points in the outer level). The official results of our best configuration (de-cluster-dict) are reported in Table TABREF66 showing that our system marginally improves the best systems' results on that task (ExB and UKP).",
+      "We also compare our system, in the last three rows, with the publicly available GermaNER BIBREF26 , which reports results for the 4 main outer level entity types (person, location, organization and other). For this experiment we trained the de-cluster and de-cluster + dict models on the four main classes, improving GermaNER's results by almost 3 F1 points. The GermaNER method of evaluation is interesting because allows researchers to directly compare their systems with a publicly available system trained on GermEval data.",
+      "Table TABREF67 compares our German CoNLL 2003 results with the best previous work trained on public data. Our best CoNLL 2003 model obtains results similar to the state of the art performance with respect to the best system published up to date BIBREF24 using public data.",
+      " BIBREF24 also report 78.20 F1 with a model trained with Clark clusters induced using the Huge German Corpus (HGC). Unfortunately, the corpus or the induced clusters were not available.",
+      "The best system up to date on the CoNLL 2002 dataset, originally published by BIBREF47 , is distributed as part of the Freeling library BIBREF34 . Table TABREF69 lists four models that improve over their reported results, almost by 3 points in F1 measure in the case of the es-cluster model (with our without trigram and character n-gram features).",
+      "Despite using clusters from one data source only (see Table TABREF58 ), results in Table TABREF71 show that our nl-cluster model outperforms the best result published on CoNLL 2002 BIBREF45 by 3.83 points in F1 score. Adding the English Illinois NER gazetteers BIBREF31 and trigram and character n-gram features increases the score to 85.04 F1, 5.41 points better than previous published work on this dataset.",
+      "We also compared our system with the more recently developed SONAR-1 corpus and the companion NERD system distributed inside its release BIBREF33 . They report 84.91 F1 for the six main named entity types via 10-fold cross validation. For this comparison we chose the local, nl-cluster and nl-cluster-dict configurations from Table TABREF71 and run them on SONAR-1 using the same settings. The results reported in Table TABREF72 shows our system's improvement over previous results on this dataset.",
+      "Table TABREF74 reports on the experiments using the Egunkaria NER dataset provided by BIBREF23 . Due to the sparsity of the MISC class mentioned in Section SECREF9 , we decided to train our models on three classes only (location, organization and person). Thus, the results are obtained training our models in the customary manner and evaluating on 3 classes. However, for direct comparison with previous work BIBREF23 , we also evaluate our best eu-cluster model (trained on 3 classes) on 4 classes.",
+      "The results show that our eu-cluster model clearly improves upon previous work by 4 points in F1 measure (75.40 vs 71.35). These results are particularly interesting as it had been so far assumed that complex linguistic features and language-specific rules were required to perform well for agglutinative languages such as Basque BIBREF23 . Finally, it is worth noting that the eu-cluster model increases the overall F1 score by 11.72 over the baseline, of which 10 points are gained in precision and 13 in terms of recall."
+    ],
+    [
+      "So far, we have seen how, given a fixed amount of supervised training data, leveraging unlabeled data using multiple cluster sources helped to obtain state of the art results in seven different in-domain settings for five languages. In this section we will investigate to what extent our system allows to reduce the dependency on supervised training data.",
+      "We first use the English CoNLL 2003 dataset for this experiment. The training set consists of around 204K words and we use various smaller versions of it to test the performance of our best cluster model reported in Table TABREF63 . Table TABREF76 displays the F1 results of the baseline system consisting of local features and the best cluster model. The INLINEFORM0 column refers to the gains of our best cluster model with respect to the baseline model for every portion of the training set.",
+      "While we have already commented the substantial gains obtained simply by adding our clustering features, it is also interesting to note that the gains are much substantial when less supervised training data is available. Furthermore, it is striking that training our clustering features using only one eight of the training data (30K words) allows to obtain similar performance to the baseline system trained on the full training set. Equally interesting is the fact that cutting by half the training data only marginally harms the overall performance. Finally, training on just a quarter of the training set (60K) results in a very competitive model when compared with other publicly available NER systems for English trained on the full training set: it roughly matches Stanford NER's performance, it outperforms models using external knowledge or non-local features reported by BIBREF31 , and also several models reported by BIBREF52 , which use one type of word representations on top of the baseline system.",
+      "We have also re-trained the Illinois NER system BIBREF31 and our best CoNLL 2003 model (en-91-18) for comparison. First, we can observe that for every portion of the training set, both our best cluster and en-91-18 model outperform the Illinois NER system. The best cluster results are noteworthy because, as opposed to Illinois NER, it does not use gazetteers or global features for extra performance.",
+      "These results are mirrored by those obtained for the rest of the languages and datasets. Thus, Table TABREF77 displays, for each language, the F1 results of the baseline system and of the best cluster models on top of the baseline. Overall, it confirms that our cluster-based models obtain state of the art results using just one half of the data. Furthermore, using just one quarter of the training data we are able to match results of other publicly available systems for every language, outperforming in some cases, such as Basque, much complex systems of classifiers exploiting linguistic specific rules and features (POS tags, lemmas, semantic information from WordNet, etc.). Considering that Basque is a low-resourced language, it is particularly relevant to be able to reduce as much as possible the amount of gold supervised data required to develop a competitive NERC system."
+    ],
+    [
+      "NERC systems are often used in out-of-domain settings, namely, to annotate data that greatly differs from the data from which the NERC models were learned. These differences can be of text genre and/or domain, but also because the assumptions of what constitutes a named entity might differ. It is therefore interesting to develop robust NERC systems across both domains and datasets. In this section we demonstrate that our approach, consisting of basic, general local features and the combination and stacking of clusters, produces robust NERC systems in three out-of-domain evaluation settings:",
+      "Class disagreements: Named entities are assigned to different classes in training and test.",
+      "Different text genre: The text genre of training and test data differs.",
+      "Annotation guidelines: The gold annotation of the test data follows different guidelines from the training data. This is usually reflected in different named entity spans.",
+      "The datasets and languages chosen for these experiments are based on the availability of both previous results and publicly distributed NERC systems to facilitate direct comparison of our system with other approaches. Table TABREF83 specifies the datasets used for each out-of-domain setting and language. Details of each dataset can be found Table TABREF10 .",
+      "MUC 7 annotates seven entity types, including four that are not included in CoNLL data: DATE, MONEY, NUMBER and TIME entities. Furthermore, CoNLL includes the MISC class, which was absent in MUC 7. This means that there are class disagreements in the gold standard annotation between the training and test datasets. In addition to the four CoNLL classes, SONAR-1 includes PRODUCT and EVENT whereas Ancora also annotates DATE and NUMBER. For example, consider the following sentence of the MUC 7 gold standard (example taken from BIBREF31 ):",
+      "\u201c...baloon, called the Virgin Global Challenger.\u201d",
+      "The gold annotation in MUC 7 establishes that there is one named entity:",
+      "\u201c...baloon, called [ORG Virgin] Global Challenger.\u201d",
+      "However, according to CoNLL 2003 guidelines, the entire name should be annotated like MISC:",
+      "\u201c...baloon, called [MISC Virgin Global Challenger].\u201d",
+      "In this setting some adjustments are made to the NERC systems' output. Following previous work BIBREF31 , every named entity that is not LOC, ORG, PER or MISC is labeled as `O'. Additionally for MUC 7 every MISC named entity is changed to `O'. For English we used the models reported in Section UID62 . For Spanish and Dutch we trained our system with the Ancora and SONAR-1 corpora using the configurations described in Sections UID68 and UID70 respectively. Table TABREF85 compares our results with previous approaches: using MUC 7, BIBREF52 provide standard phrase results whereas BIBREF31 score token based F1 results, namely, each token is considered a chunk, instead of considering multi-token spans too. For Spanish we use the Stanford NER Spanish model (2015-01-30 version) trained with Ancora. For Dutch we compare our SONAR-1 system with the companion system distributed with the SONAR-1 corpus BIBREF33 . The results are summarized in Table TABREF85 .",
+      "In this setting the out-of-domain character is given by the differences in text genre between the English CoNLL 2003 set and the Wikigold corpus. We compare our system with English models trained on large amounts of silver-standard text (3.5M tokens) automatically created from the Wikipedia BIBREF27 . They report results on Wikigold showing that they outperformed their own CoNLL 2003 gold-standard model by 10 points in F1 score. We compare their result with our best cluster model in Table TABREF87 . While the results of our baseline model confirms theirs, our clustering model score is slightly higher. This result is interesting because it is arguably more simple to induce the clusters we use to train ixa-pipe-nerc rather than create the silver standard training set from Wikipedia as described in BIBREF27 .",
+      "In this section the objective is studying not so much the differences in textual genre as the influence of substantially different annotation standards. We only use three classes (location, organization and person) to evaluate the best models presented for in-domain evaluations labeling `O' every entity which is not LOC, ORG or PER.",
+      "The text genre of MEANTIME is not that different from CoNLL data. However, differences in the gold standard annotation result in significant disagreements regarding the span of the named entities BIBREF59 . For example, the following issues are markedly different with respect to the training data we use for each language:",
+      "Different criteria to decide when a named entity is annotated: in the expression \u201c40 billion US air tanker contract\u201d the MEANTIME gold standard does not mark `US' as location, whereas in the training data this is systematically annotated.",
+      "Mentions including the definite article within the name entity span: `the United States' versus `United States'.",
+      "Longer extents containing common nouns: in the MEANTIME corpus there are many entities such as \u201cUnited States airframer Boeing\u201d, which in this case is considered an organization, whereas in the training data this span will in general consists of two entities: `United States' as location and `Boeing' as organization.",
+      "Common nouns modifying the proper name: `Spokeswoman Sandy Angers' is annotated as a named entity of type PER whereas in the training data used the span of the named entity would usually be `Sandy Angers'.",
+      "CoNLL NER phrase based evaluation punishes any bracketing error as both false positive and negative. Thus, these span-related disagreements make this setting extremely hard for models trained according to other annotation guidelines, as shown by Table TABREF93 . Our baseline models degrade around 40 F1 points and the cluster-based models around 35. Other systems' results worsen much more, especially for Spanish and Dutch. The token-based scores are in general better but the proportion in performance between systems across languages is similar.",
+      "As an additional experiment, we also tested the English model recommended by Stanford NER which is trained for three classes (LOC, PER, ORG) using a variety of public and (not identified) private corpora (referred to as Stanford NER 3 class (ALL) in Table TABREF94 ). The results with respect to their CoNLL model improved by around 3 points in F1 score across named entity labels and evaluation types (phrase or token based). In view of these results, we experimented with multi-corpora training data added to our best CoNLL 2003 model (en-91-18). Thus, we trained using three public training sets: MUC 7, CoNLL 2003 and Ontonotes 4.0. The local model with the three training sets (Local ALL) improved 12 and 17 points in F1 score across evaluations and entity types, outperforming our best model trained only with CoNLL 2003. Adding the clustering features gained between 2 and 5 points more surpassing the Stanford NER 3 class multi-corpora model in every evaluation. We believe that the main reason to explain these improvements is the variety and quantity of annotations provided by Ontonotes (1M word corpus), and to a lesser extent by MUC 7, which includes some spans containing common nouns and determiners making the model slightly more robust regarding the mention spans."
+    ],
+    [
+      "Despite the simplicity of the ixa-pipe-nerc approach, we report best results for English in 4 different datasets: for CoNLL 2003 and for the three English out-of-domain evaluations. For German we improve the results of the best system in the GermEval 2014 task and obtain comparable results to previous work in the CoNLL 2003 dataset using publicly available data. In Spanish we provide results on CoNLL 2002 and in two out-of-domain evaluations clearly outperforming previous best results. For Dutch we improve over previous results in CoNLL 2002 and SONAR-1 data and two out-of-domain evaluations. Finally, for Basque (Egunkaria) the improvements are considerable."
+    ],
+    [
+      "We have shown how to develop robust NERC systems across languages and datasets with minimal human intervention, even for languages with inflected named entities. This is based on adequately combining word representation features on top of shallow and general local features. Crucially, we have empirically demonstrate how to effectively combine various types of simple word representation features depending on the source data available. This has resulted in a clear methodology for using the three types of clustering features which produces very competitive results in both in-domain and out-of-domain settings.",
+      "Thus, despite the relative simplicity of our approach, we report state of the art results for Dutch, English, German, Spanish and Basque in seven in-domain evaluations.",
+      "We also outperform previous work in eight out-of-domain evaluations, showing that our clustering features improve the robustness of NERC systems across datasets. Finally, we have measured how much our system's performance degrades when the amount of supervised data is drastically cut. The results show our models are still very competitive even when reducing the supervised data by half or more. This, together with the lack of linguistic features, facilitates the easy and fast development of NERC systems for new domains or languages.",
+      "In future work we would like to explore more the various types of domain adaptation required for robust performance across text genres and domains, perhaps including micro-blog and noisy text such as tweets. Furthermore, we are also planning to adapt our techniques to other sequence labeling problems such as Opinion Target Extraction BIBREF13 , BIBREF14 and Super Sense tagging BIBREF60 ."
+    ],
+    [
+      "We would like to thank the anonymous reviewers for their comments to improve this paper. We would also like to thank Sebastian Pad\u00f3 for his help training the Clark clusters. This work has been supported by the European projects NewsReader, EC/FP7/316404 and QTLeap - EC/FP7/610516, and by the Spanish Ministry for Science and Innovation (MICINN) SKATER, Grant No. TIN2012-38584-C06-01 and TUNER, TIN2015-65308-C5-1-R."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: Irony Detection in a Multilingual Context
+
+Question: What multilingual word representations are used?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Motivations",
+    "Data",
+    "Monolingual Irony Detection",
+    "Cross-lingual Irony Detection",
+    "Discussions and Conclusion",
+    "Acknowledgment"
+  ],
+  "paragraphs": [
+    [
+      "Figurative language makes use of figures of speech to convey non-literal meaning BIBREF0, BIBREF1. It encompasses a variety of phenomena, including metaphor, humor, and irony. We focus here on irony and uses it as an umbrella term that covers satire, parody and sarcasm.",
+      "Irony detection (ID) has gained relevance recently, due to its importance to extract information from texts. For example, to go beyond the literal matches of user queries, Veale enriched information retrieval with new operators to enable the non-literal retrieval of creative expressions BIBREF2. Also, the performances of sentiment analysis systems drastically decrease when applied to ironic texts BIBREF3, BIBREF4. Most related work concern English BIBREF5, BIBREF6 with some efforts in French BIBREF7, Portuguese BIBREF8, Italian BIBREF9, Dutch BIBREF10, Hindi BIBREF11, Spanish variants BIBREF12 and Arabic BIBREF13, BIBREF14. Bilingual ID with one model per language has also been explored, like English-Czech BIBREF15 and English-Chinese BIBREF16, but not within a cross-lingual perspective.",
+      "In social media, such as Twitter, specific hashtags (#irony, #sarcasm) are often used as gold labels to detect irony in a supervised learning setting. Although recent studies pointed out the issue of false-alarm hashtags in self-labeled data BIBREF17, ID via hashtag filtering provides researchers positive examples with high precision. On the other hand, systems are not able to detect irony in languages where such filtering is not always possible. Multilingual prediction (either relying on machine translation or multilingual embedding methods) is a common solution to tackle under-resourced languages BIBREF18, BIBREF19. While multilinguality has been widely investigated in information retrieval BIBREF20, BIBREF21 and several NLP tasks (e.g., sentiment analysis BIBREF22, BIBREF23 and named entity recognition BIBREF24), no one explored it for irony.",
+      "We aim here to bridge the gap by tackling ID in tweets from both multilingual (French, English and Arabic) and multicultural perspectives (Indo-European languages whose speakers share quite the same cultural background vs. less culturally close languages). Our approach does not rely either on machine translation or parallel corpora (which are not always available), but rather builds on previous corpus-based studies that show that irony is a universal phenomenon and many languages share similar irony devices. For example, Karoui et. al BIBREF25 concluded that their multi-layer annotated schema, initially used to annotate French tweets, is portable to English and Italian, observing relatively the same tendencies in terms of irony categories and markers. Similarly, Chakhachiro BIBREF26 studies irony in English and Arabic, and shows that both languages share several similarities in the rhetorical (e.g., overstatement), grammatical (e.g., redundancy) and lexical (e.g., synonymy) usage of irony devices. The next step now is to show to what extent these observations are still valid from a computational point of view. Our contributions are:",
+      "A new freely available corpus of Arabic tweets manually annotated for irony detection.",
+      "Monolingual ID: We propose both feature-based models (relying on language-dependent and language-independent features) and neural models to measure to what extent ID is language dependent.",
+      "Cross-lingual ID: We experiment using cross-lingual word representation by training on one language and testing on another one to measure how the proposed models are culture-dependent. Our results are encouraging and open the door to ID in languages that lack of annotated data for irony."
+    ],
+    [
+      "Arabic dataset (Ar=$11,225$ tweets). Our starting point was the corpus built by BIBREF13 that we extended to different political issues and events related to the Middle East and Maghreb that hold during the years 2011 to 2018. Tweets were collected using a set of predefined keywords (which targeted specific political figures or events) and containing or not Arabic ironic hashtags (\u0633\u062e\u0631\u064a\u0629>#, \u0645\u0633\u062e\u0631\u0629>#, \u062a\u0647\u0643\u0645>#, \u0627\u0633\u062a\u0647\u0632\u0627\u0621>#) . The collection process resulted in a set of $6,809$ ironic tweets ($I$) vs. $15,509$ non ironic ($NI$) written using standard (formal) and different Arabic language varieties: Egypt, Gulf, Levantine, and Maghrebi dialects.",
+      "To investigate the validity of using the original tweets labels, a sample of $3,000$ $I$ and $3,000$ $NI$ was manually annotated by two Arabic native speakers which resulted in $2,636$ $I$ vs. $2,876$ $NI$. The inter-annotator agreement using Cohen's Kappa was $0.76$, while the agreement score between the annotators' labels and the original labels was $0.6$. Agreements being relatively good knowing the difficulty of the task, we sampled $5,713$ instances from the original unlabeled dataset to our manually labeled part. The added tweets have been manually checked to remove duplicates, very short tweets and tweets that depend on external links, images or videos to understand their meaning.",
+      "French dataset (Fr=$7,307$ tweets). We rely on the corpus used for the DEFT 2017 French shared task on irony BIBREF3 which consists of tweets relative to a set of topics discussed in the media between 2014 and 2016 and contains topic keywords and/or French irony hashtags (#ironie, #sarcasme). Tweets have been annotated by three annotators (after removing the original labels) with a reported Cohen's Kappa of $0.69$.",
+      "English dataset (En=$11,225$ tweets). We use the corpus built by BIBREF15 which consists of $100,000$ tweets collected using the hashtag #sarcasm. It was used as benchmark in several works BIBREF27, BIBREF28. We sliced a subset of approximately $11,200$ tweets to match the sizes of the other languages' datasets.",
+      "Table TABREF6 shows the tweet distribution in all corpora. Across the three languages, we keep a similar number of instances for train and test sets to have fair cross-lingual experiments as well (see Section SECREF4). Also, for French, we use the original dataset without any modification, keeping the same number of records for train and test to better compare with state-of-the-art results. For the classes distribution (ironic vs. non ironic), we do not choose a specific ratio but we use the resulted distribution from the random shuffling process.",
+      "",
+      ""
+    ],
+    [
+      "It is important to note that our aim is not to outperform state-of-the-art models in monolingual ID but to investigate which of the monolingual architectures (neural or feature-based) can achieve comparable results with existing systems. The result can show which kind of features works better in the monolingual settings and can be employed to detect irony in a multilingual setting. In addition, it can show us to what extend ID is language dependent by comparing their results to multilingual results. Two models have been built, as explained below. Prior to learning, basic preprocessing steps were performed for each language (e.g., removing foreign characters, ironic hashtags, mentions, and URLs).",
+      "Feature-based models. We used state-of-the-art features that have shown to be useful in ID: some of them are language-independent (e.g., punctuation marks, positive and negative emoticons, quotations, personal pronouns, tweet's length, named entities) while others are language-dependent relying on dedicated lexicons (e.g., negation, opinion lexicons, opposition words). Several classical machine learning classifiers were tested with several feature combinations, among them Random Forest (RF) achieved the best result with all features. Neural model with monolingual embeddings. We used Convolutional Neural Network (CNN) network whose structure is similar to the one proposed by BIBREF29. For the embeddings, we relied on $AraVec$ BIBREF30 for Arabic, FastText BIBREF31 for French, and Word2vec Google News BIBREF32 for English . For the three languages, the size of the embeddings is 300 and the embeddings were fine-tuned during the training process. The CNN network was tuned with 20% of the training corpus using the $Hyperopt$ library.",
+      "Results. Table TABREF9 shows the results obtained when using train-test configurations for each language. For English, our results, in terms of macro F-score ($F$), were not comparable to those of BIBREF15, BIBREF33, as we used 11% of the original dataset. For French, our scores are in line with those reported in state of the art (cf. best system in the irony shared task achieved $F=78.3$ BIBREF3). They outperform those obtained for Arabic ($A=71.7$) BIBREF13 and are comparable to those recently reported in the irony detection shared task in Arabic tweets BIBREF14, BIBREF34 ($F=84.4$). Overall, the results show that semantic-based information captured by the embedding space are more productive comparing to standard surface and lexicon-based features.",
+      "",
+      ""
+    ],
+    [
+      "We use the previous CNN architecture with bilingual embedding and the RF model with surface features (e.g., use of personal pronoun, presence of interjections, emoticon or specific punctuation) to verify which pair of the three languages: (a) has similar ironic pragmatic devices, and (b) uses similar text-based pattern in the narrative of the ironic tweets. As continuous word embedding spaces exhibit similar structures across (even distant) languages BIBREF35, we use a multilingual word representation which aims to learn a linear mapping from a source to a target embedding space. Many methods have been proposed to learn this mapping such as parallel data supervision and bilingual dictionaries BIBREF35 or unsupervised methods relying on monolingual corpora BIBREF36, BIBREF37, BIBREF38. For our experiments, we use Conneau et al 's approach as it showed superior results with respect to the literature BIBREF36. We perform several experiments by training on one language ($lang_1$) and testing on another one ($lang_2$) (henceforth $lang_1\\rightarrow lang_2$). We get 6 configurations, plus two others to evaluate how irony devices are expressed cross-culturally, i.e. in European vs. non European languages. In each experiment, we took 20% from the training to validate the model before the testing process. Table TABREF11 presents the results.",
+      "",
+      "",
+      "From a semantic perspective, despite the language and cultural differences between Arabic and French languages, CNN results show a high performance comparing to the other languages pairs when we train on each of these two languages and test on the other one. Similarly, for the French and English pair, but when we train on French they are quite lower. We have a similar case when we train on Arabic and test on English. We can justify that by, the language presentation of the Arabic and French tweets are quite informal and have many dialect words that may not exist in the pretrained embeddings we used comparing to the English ones (lower embeddings coverage ratio), which become harder for the CNN to learn a clear semantic pattern. Another point is the presence of Arabic dialects, where some dialect words may not exist in the multilingual pretrained embedding model that we used. On the other hand, from the text-based perspective, the results show that the text-based features can help in the case when the semantic aspect shows weak detection; this is the case for the $Ar\\longrightarrow En$ configuration. It is worthy to mention that the highest result we get in this experiment is from the En$\\rightarrow $Fr pair, as both languages use Latin characters. Finally, when investigating the relatedness between European vs. non European languages (cf. (En/Fr)$\\rightarrow $Ar), we obtain similar results than those obtained in the monolingual experiment (macro F-score 62.4 vs. 68.0) and best results are achieved by Ar $\\rightarrow $(En/Fr). This shows that there are pragmatic devices in common between both sides and, in a similar way, similar text-based patterns in the narrative way of the ironic tweets."
+    ],
+    [
+      "This paper proposes the first multilingual ID in tweets. We show that simple monolingual architectures (either neural or feature-based) trained separately on each language can be successfully used in a multilingual setting providing a cross-lingual word representation or basic surface features. Our monolingual results are comparable to state of the art for the three languages. The CNN architecture trained on cross-lingual word representation shows that irony has a certain similarity between the languages we targeted despite the cultural differences which confirm that irony is a universal phenomena, as already shown in previous linguistic studies BIBREF39, BIBREF25, BIBREF40. The manual analysis of the common misclassified tweets across the languages in the multilingual setup, shows that classification errors are due to three main factors. (1) First, the absence of context where writers did not provide sufficient information to capture the ironic sense even in the monolingual setting, as in \u0646\u0628\u062f\u0627 \u062a\u0627\u0646\u064a \u064a\u0633\u0642\u0637 \u064a\u0633\u0642\u0637 \u062d\u0633\u0646\u064a \u0645\u0628\u0627\u0631\u0643 !! > (Let's start again, get off get off Mubarak!!) where the writer mocks the Egyptian revolution, as the actual president \"Sisi\" is viewed as Mubarak's fellows. (2) Second, the presence of out of vocabulary (OOV) terms because of the weak coverage of the mutlilingual embeddings which make the system fails to generalize when the OOV set of unseen words is large during the training process. We found tweets in all the three languages written in a very informal way, where some characters of the words were deleted, duplicated or written phonetically (e.g phat instead of fat). (3) Another important issue is the difficulty to deal with the Arabic language. Arabic tweets are often characterized by non-diacritised texts, a large variations of unstandardized dialectal Arabic (recall that our dataset has 4 main varieties, namely Egypt, Gulf, Levantine, and Maghrebi), presence of transliterated words (e.g. the word table becomes \u0637\u0627\u0628\u0644\u0629> (tabla)), and finally linguistic code switching between Modern Standard Arabic and several dialects, and between Arabic and other languages like English and French. We found some tweets contain only words from one of the varieties and most of these words do not exist in the Arabic embeddings model. For example in \u0645\u0628\u0627\u0631\u0643 \u0628\u0642\u0627\u0644\u0647 \u0643\u0627\u0645 \u064a\u0648\u0645 \u0645\u0627\u0645\u062a\u0634 .. \u0647\u0648 \u0639\u064a\u0627\u0646 \u0648\u0644\u0627\u0647 \u0627\u064a\u0647 #\u0645\u0635\u0631 > (Since many days Mubarak didn't die .. is he sick or what? #Egypt), only the words \u064a\u0648\u0645> (day), \u0645\u0628\u0627\u0631\u0643> (Mubarak), and \u0647\u0648> (he) exist in the embeddings. Clearly, considering only these three available words, we are not able to understand the context or the ironic meaning of the tweet. To conclude, our multilingual experiments confirmed that the door is open towards multilingual approaches for ID. Furthermore, our results showed that ID can be applied to languages that lack of annotated data. Our next step is to experiment with other languages such as Hindi and Italian."
+    ],
+    [
+      "The work of Paolo Rosso was partially funded by the Spanish MICINN under the research project MISMIS-FAKEnHATE (PGC2018-096212-B-C31)."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1638/instruction.md b/qasper-1638/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..4634d773f902029e18d2801032fd71b3d1ff9f65
--- /dev/null
+++ b/qasper-1638/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Irony Detection in a Multilingual Context
+
+Question: Do the authors identify any cultural differences in irony use?
\ No newline at end of file
diff --git a/qasper-1639/instruction.md b/qasper-1639/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..eab13bd5ab64991f2e3753b0ed8827e444146fa0
--- /dev/null
+++ b/qasper-1639/instruction.md
@@ -0,0 +1,57 @@
+Name of Paper: Irony Detection in a Multilingual Context
+
+Question: What neural architectures are used?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Motivations",
+    "Data",
+    "Monolingual Irony Detection",
+    "Cross-lingual Irony Detection",
+    "Discussions and Conclusion",
+    "Acknowledgment"
+  ],
+  "paragraphs": [
+    [
+      "Figurative language makes use of figures of speech to convey non-literal meaning BIBREF0, BIBREF1. It encompasses a variety of phenomena, including metaphor, humor, and irony. We focus here on irony and uses it as an umbrella term that covers satire, parody and sarcasm.",
+      "Irony detection (ID) has gained relevance recently, due to its importance to extract information from texts. For example, to go beyond the literal matches of user queries, Veale enriched information retrieval with new operators to enable the non-literal retrieval of creative expressions BIBREF2. Also, the performances of sentiment analysis systems drastically decrease when applied to ironic texts BIBREF3, BIBREF4. Most related work concern English BIBREF5, BIBREF6 with some efforts in French BIBREF7, Portuguese BIBREF8, Italian BIBREF9, Dutch BIBREF10, Hindi BIBREF11, Spanish variants BIBREF12 and Arabic BIBREF13, BIBREF14. Bilingual ID with one model per language has also been explored, like English-Czech BIBREF15 and English-Chinese BIBREF16, but not within a cross-lingual perspective.",
+      "In social media, such as Twitter, specific hashtags (#irony, #sarcasm) are often used as gold labels to detect irony in a supervised learning setting. Although recent studies pointed out the issue of false-alarm hashtags in self-labeled data BIBREF17, ID via hashtag filtering provides researchers positive examples with high precision. On the other hand, systems are not able to detect irony in languages where such filtering is not always possible. Multilingual prediction (either relying on machine translation or multilingual embedding methods) is a common solution to tackle under-resourced languages BIBREF18, BIBREF19. While multilinguality has been widely investigated in information retrieval BIBREF20, BIBREF21 and several NLP tasks (e.g., sentiment analysis BIBREF22, BIBREF23 and named entity recognition BIBREF24), no one explored it for irony.",
+      "We aim here to bridge the gap by tackling ID in tweets from both multilingual (French, English and Arabic) and multicultural perspectives (Indo-European languages whose speakers share quite the same cultural background vs. less culturally close languages). Our approach does not rely either on machine translation or parallel corpora (which are not always available), but rather builds on previous corpus-based studies that show that irony is a universal phenomenon and many languages share similar irony devices. For example, Karoui et. al BIBREF25 concluded that their multi-layer annotated schema, initially used to annotate French tweets, is portable to English and Italian, observing relatively the same tendencies in terms of irony categories and markers. Similarly, Chakhachiro BIBREF26 studies irony in English and Arabic, and shows that both languages share several similarities in the rhetorical (e.g., overstatement), grammatical (e.g., redundancy) and lexical (e.g., synonymy) usage of irony devices. The next step now is to show to what extent these observations are still valid from a computational point of view. Our contributions are:",
+      "A new freely available corpus of Arabic tweets manually annotated for irony detection.",
+      "Monolingual ID: We propose both feature-based models (relying on language-dependent and language-independent features) and neural models to measure to what extent ID is language dependent.",
+      "Cross-lingual ID: We experiment using cross-lingual word representation by training on one language and testing on another one to measure how the proposed models are culture-dependent. Our results are encouraging and open the door to ID in languages that lack of annotated data for irony."
+    ],
+    [
+      "Arabic dataset (Ar=$11,225$ tweets). Our starting point was the corpus built by BIBREF13 that we extended to different political issues and events related to the Middle East and Maghreb that hold during the years 2011 to 2018. Tweets were collected using a set of predefined keywords (which targeted specific political figures or events) and containing or not Arabic ironic hashtags (\u0633\u062e\u0631\u064a\u0629>#, \u0645\u0633\u062e\u0631\u0629>#, \u062a\u0647\u0643\u0645>#, \u0627\u0633\u062a\u0647\u0632\u0627\u0621>#) . The collection process resulted in a set of $6,809$ ironic tweets ($I$) vs. $15,509$ non ironic ($NI$) written using standard (formal) and different Arabic language varieties: Egypt, Gulf, Levantine, and Maghrebi dialects.",
+      "To investigate the validity of using the original tweets labels, a sample of $3,000$ $I$ and $3,000$ $NI$ was manually annotated by two Arabic native speakers which resulted in $2,636$ $I$ vs. $2,876$ $NI$. The inter-annotator agreement using Cohen's Kappa was $0.76$, while the agreement score between the annotators' labels and the original labels was $0.6$. Agreements being relatively good knowing the difficulty of the task, we sampled $5,713$ instances from the original unlabeled dataset to our manually labeled part. The added tweets have been manually checked to remove duplicates, very short tweets and tweets that depend on external links, images or videos to understand their meaning.",
+      "French dataset (Fr=$7,307$ tweets). We rely on the corpus used for the DEFT 2017 French shared task on irony BIBREF3 which consists of tweets relative to a set of topics discussed in the media between 2014 and 2016 and contains topic keywords and/or French irony hashtags (#ironie, #sarcasme). Tweets have been annotated by three annotators (after removing the original labels) with a reported Cohen's Kappa of $0.69$.",
+      "English dataset (En=$11,225$ tweets). We use the corpus built by BIBREF15 which consists of $100,000$ tweets collected using the hashtag #sarcasm. It was used as benchmark in several works BIBREF27, BIBREF28. We sliced a subset of approximately $11,200$ tweets to match the sizes of the other languages' datasets.",
+      "Table TABREF6 shows the tweet distribution in all corpora. Across the three languages, we keep a similar number of instances for train and test sets to have fair cross-lingual experiments as well (see Section SECREF4). Also, for French, we use the original dataset without any modification, keeping the same number of records for train and test to better compare with state-of-the-art results. For the classes distribution (ironic vs. non ironic), we do not choose a specific ratio but we use the resulted distribution from the random shuffling process.",
+      "",
+      ""
+    ],
+    [
+      "It is important to note that our aim is not to outperform state-of-the-art models in monolingual ID but to investigate which of the monolingual architectures (neural or feature-based) can achieve comparable results with existing systems. The result can show which kind of features works better in the monolingual settings and can be employed to detect irony in a multilingual setting. In addition, it can show us to what extend ID is language dependent by comparing their results to multilingual results. Two models have been built, as explained below. Prior to learning, basic preprocessing steps were performed for each language (e.g., removing foreign characters, ironic hashtags, mentions, and URLs).",
+      "Feature-based models. We used state-of-the-art features that have shown to be useful in ID: some of them are language-independent (e.g., punctuation marks, positive and negative emoticons, quotations, personal pronouns, tweet's length, named entities) while others are language-dependent relying on dedicated lexicons (e.g., negation, opinion lexicons, opposition words). Several classical machine learning classifiers were tested with several feature combinations, among them Random Forest (RF) achieved the best result with all features. Neural model with monolingual embeddings. We used Convolutional Neural Network (CNN) network whose structure is similar to the one proposed by BIBREF29. For the embeddings, we relied on $AraVec$ BIBREF30 for Arabic, FastText BIBREF31 for French, and Word2vec Google News BIBREF32 for English . For the three languages, the size of the embeddings is 300 and the embeddings were fine-tuned during the training process. The CNN network was tuned with 20% of the training corpus using the $Hyperopt$ library.",
+      "Results. Table TABREF9 shows the results obtained when using train-test configurations for each language. For English, our results, in terms of macro F-score ($F$), were not comparable to those of BIBREF15, BIBREF33, as we used 11% of the original dataset. For French, our scores are in line with those reported in state of the art (cf. best system in the irony shared task achieved $F=78.3$ BIBREF3). They outperform those obtained for Arabic ($A=71.7$) BIBREF13 and are comparable to those recently reported in the irony detection shared task in Arabic tweets BIBREF14, BIBREF34 ($F=84.4$). Overall, the results show that semantic-based information captured by the embedding space are more productive comparing to standard surface and lexicon-based features.",
+      "",
+      ""
+    ],
+    [
+      "We use the previous CNN architecture with bilingual embedding and the RF model with surface features (e.g., use of personal pronoun, presence of interjections, emoticon or specific punctuation) to verify which pair of the three languages: (a) has similar ironic pragmatic devices, and (b) uses similar text-based pattern in the narrative of the ironic tweets. As continuous word embedding spaces exhibit similar structures across (even distant) languages BIBREF35, we use a multilingual word representation which aims to learn a linear mapping from a source to a target embedding space. Many methods have been proposed to learn this mapping such as parallel data supervision and bilingual dictionaries BIBREF35 or unsupervised methods relying on monolingual corpora BIBREF36, BIBREF37, BIBREF38. For our experiments, we use Conneau et al 's approach as it showed superior results with respect to the literature BIBREF36. We perform several experiments by training on one language ($lang_1$) and testing on another one ($lang_2$) (henceforth $lang_1\\rightarrow lang_2$). We get 6 configurations, plus two others to evaluate how irony devices are expressed cross-culturally, i.e. in European vs. non European languages. In each experiment, we took 20% from the training to validate the model before the testing process. Table TABREF11 presents the results.",
+      "",
+      "",
+      "From a semantic perspective, despite the language and cultural differences between Arabic and French languages, CNN results show a high performance comparing to the other languages pairs when we train on each of these two languages and test on the other one. Similarly, for the French and English pair, but when we train on French they are quite lower. We have a similar case when we train on Arabic and test on English. We can justify that by, the language presentation of the Arabic and French tweets are quite informal and have many dialect words that may not exist in the pretrained embeddings we used comparing to the English ones (lower embeddings coverage ratio), which become harder for the CNN to learn a clear semantic pattern. Another point is the presence of Arabic dialects, where some dialect words may not exist in the multilingual pretrained embedding model that we used. On the other hand, from the text-based perspective, the results show that the text-based features can help in the case when the semantic aspect shows weak detection; this is the case for the $Ar\\longrightarrow En$ configuration. It is worthy to mention that the highest result we get in this experiment is from the En$\\rightarrow $Fr pair, as both languages use Latin characters. Finally, when investigating the relatedness between European vs. non European languages (cf. (En/Fr)$\\rightarrow $Ar), we obtain similar results than those obtained in the monolingual experiment (macro F-score 62.4 vs. 68.0) and best results are achieved by Ar $\\rightarrow $(En/Fr). This shows that there are pragmatic devices in common between both sides and, in a similar way, similar text-based patterns in the narrative way of the ironic tweets."
+    ],
+    [
+      "This paper proposes the first multilingual ID in tweets. We show that simple monolingual architectures (either neural or feature-based) trained separately on each language can be successfully used in a multilingual setting providing a cross-lingual word representation or basic surface features. Our monolingual results are comparable to state of the art for the three languages. The CNN architecture trained on cross-lingual word representation shows that irony has a certain similarity between the languages we targeted despite the cultural differences which confirm that irony is a universal phenomena, as already shown in previous linguistic studies BIBREF39, BIBREF25, BIBREF40. The manual analysis of the common misclassified tweets across the languages in the multilingual setup, shows that classification errors are due to three main factors. (1) First, the absence of context where writers did not provide sufficient information to capture the ironic sense even in the monolingual setting, as in \u0646\u0628\u062f\u0627 \u062a\u0627\u0646\u064a \u064a\u0633\u0642\u0637 \u064a\u0633\u0642\u0637 \u062d\u0633\u0646\u064a \u0645\u0628\u0627\u0631\u0643 !! > (Let's start again, get off get off Mubarak!!) where the writer mocks the Egyptian revolution, as the actual president \"Sisi\" is viewed as Mubarak's fellows. (2) Second, the presence of out of vocabulary (OOV) terms because of the weak coverage of the mutlilingual embeddings which make the system fails to generalize when the OOV set of unseen words is large during the training process. We found tweets in all the three languages written in a very informal way, where some characters of the words were deleted, duplicated or written phonetically (e.g phat instead of fat). (3) Another important issue is the difficulty to deal with the Arabic language. Arabic tweets are often characterized by non-diacritised texts, a large variations of unstandardized dialectal Arabic (recall that our dataset has 4 main varieties, namely Egypt, Gulf, Levantine, and Maghrebi), presence of transliterated words (e.g. the word table becomes \u0637\u0627\u0628\u0644\u0629> (tabla)), and finally linguistic code switching between Modern Standard Arabic and several dialects, and between Arabic and other languages like English and French. We found some tweets contain only words from one of the varieties and most of these words do not exist in the Arabic embeddings model. For example in \u0645\u0628\u0627\u0631\u0643 \u0628\u0642\u0627\u0644\u0647 \u0643\u0627\u0645 \u064a\u0648\u0645 \u0645\u0627\u0645\u062a\u0634 .. \u0647\u0648 \u0639\u064a\u0627\u0646 \u0648\u0644\u0627\u0647 \u0627\u064a\u0647 #\u0645\u0635\u0631 > (Since many days Mubarak didn't die .. is he sick or what? #Egypt), only the words \u064a\u0648\u0645> (day), \u0645\u0628\u0627\u0631\u0643> (Mubarak), and \u0647\u0648> (he) exist in the embeddings. Clearly, considering only these three available words, we are not able to understand the context or the ironic meaning of the tweet. To conclude, our multilingual experiments confirmed that the door is open towards multilingual approaches for ID. Furthermore, our results showed that ID can be applied to languages that lack of annotated data. Our next step is to experiment with other languages such as Hindi and Italian."
+    ],
+    [
+      "The work of Paolo Rosso was partially funded by the Spanish MICINN under the research project MISMIS-FAKEnHATE (PGC2018-096212-B-C31)."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1652/instruction.md b/qasper-1652/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..67e0fde8cc398af3785b457f7cd81cc95072f7e3
--- /dev/null
+++ b/qasper-1652/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: A Byte-sized Approach to Named Entity Recognition
+
+Question: Do they evaluate ablated versions of their CNN+RNN model?
\ No newline at end of file
diff --git a/qasper-1653/instruction.md b/qasper-1653/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..d689072bced7c15941360be9c4cd4c854b177380
--- /dev/null
+++ b/qasper-1653/instruction.md
@@ -0,0 +1,100 @@
+Name of Paper: The Intelligent Voice 2016 Speaker Recognition System
+
+Question: Do they single out a validation set from the fixed SRE training set?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Training Condition",
+    "Front-End Processing",
+    "Speech Activity Detection",
+    "Acoustic Features",
+    "i-Vector Features",
+    "Back-End Processing",
+    "Nearest-neighbor Discriminant Analysis (NDA)",
+    "Short-Duration Variability Compensation",
+    "Language Normalization",
+    "PLDA",
+    "Domain Adaptation",
+    "Score Computation and Normalization",
+    "Quality Measure Function",
+    "Calibration",
+    "Results and Discussion",
+    "Time Analysis",
+    "Conclusions and Perspectives"
+  ],
+  "paragraphs": [
+    [
+      "Compared to previous years, the 2016 NIST speaker recognition evaluation (SRE) marked a major shift from English towards Austronesian and Chinese languages. The task like previous years is to perform speaker detection with the focus on telephone speech data recorded over a variety of handset types. The main challenges introduced in this evaluation are duration and language variability. The potential variation of languages addressed in this evaluation, recording environment, and variability of test segments duration influenced the design of our system. Our goal was to utilize recent advances in language normalization, domain adaptation, speech activity detection and session compensation techniques to mitigate the adverse bias introduced in this year's evaluation.",
+      "Over recent years, the i-vector representation of speech segments has been widely used by state-of-the-art speaker recognition systems BIBREF0 . The speaker recognition technology based on i-vectors currently dominates the research field due to its performance, low computational cost and the compatibility of i-vectors with machine learning techniques. This dominance is reflected by the recent NIST i-vector machine learning challenge BIBREF1 which was designed to find the most promising algorithmic approaches to speaker recognition specifically on the basis of i-vectors BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 . The outstanding ability of DNN for frame alignment which has achieved remarkable performance in text-independent speaker recognition for English data BIBREF6 , BIBREF7 , failed to provide even comparable recognition performance to the traditional GMM. Therefore, we concentrated on the cepstral based GMM/i-vector system.",
+      "We outline in this paper the Intelligent Voice system, techniques and results obtained on the SRE 2016 development set that will mirror the evaluation condition as well as the timing report. Section SECREF2 describes the data used for the system training. The front-end and back-end processing of the system are presented in Sections SECREF3 and SECREF4 respectively. In Section SECREF5 , we describe experimental evaluation of the system on the SRE 2016 development set. Finally, we present a timing analysis of the system in Section SECREF6 ."
+    ],
+    [
+      "The fixed training condition is used to build our speaker recognition system. Only conversational telephone speech data from datasets released through the linguistic data consortium (LDC) have been used, including NIST SRE 2004-2010 and the Switchboard corpora (Switchboard Cellular Parts I and II, Switchboard2 Phase I,II and III) for different steps of system training. A more detailed description of the data used in the system training is presented in Table TABREF1 . We have also included the unlabelled set of 2472 telephone calls from both minor (Cebuano and Mandarin) and major (Tagalog and Cantonese) languages provided by NIST in the system training. We will indicate when and how we used this set in the training in the following sections."
+    ],
+    [
+      "In this section we will provide a description of the main steps in front-end processing of our speaker recognition system including speech activity detection, acoustic and i-vector feature extraction."
+    ],
+    [
+      "The first stage of any speaker recognition system is to detect the speech content in an audio signal. An accurate speech activity detector (SAD) can improve the speaker recognition performance. Several techniques have been proposed for SAD, including unsupervised methods based on a thresholding signal energy, and supervised methods that train a speech/non-speech classifier such as support vector machines (SVM) BIBREF8 and Gaussian mixture models (GMMs) BIBREF9 . Hidden markov models (HMMs) BIBREF10 have also been successful. Recently, it has been shown that DNN systems achieve impressive improvement in performance especially in low signal to noise ratios (SNRs) BIBREF11 . In our work we have utilized a two-class DNN-HMM classifier to perform this task. The DNN-HMM hybrid configuration with cross-entropy as the objective function has been trained with the back-propagation algorithm. The softmax layer produces posterior probabilities for speech and non-speech which were then converted into log-likelihoods. Using 2-state HMMs corresponding to speech and non-speech, frame-wise decisions are made by Viterbi decoding. As input to the network, we fed 40-dimensional filter-bank features along with 7 frames from each side. The network has 6 hidden layers with 512 units each. The architecture of our DNN-HMM SAD is shown in Figure FIGREF3 . Approximately 100 hours of speech data from the Switchboard telephony data with word alignments as ground-truth were used to train our SAD. The DNN training in performed on an NVIDIA TITAN X GPU, using Kaldi software BIBREF12 . Evaluated on 50 hours of telephone speech data from the same database, our DNN-HMM SAD indicated a frame-level miss-classification (speech/non-speech) rate of 5.9% whereas an energy-based SAD did not perform better than 20%."
+    ],
+    [
+      "For acoustic features we have experimented with different configurations of cepstral features. We have used 39-dimensional PLP features and 60-dimensional MFCC features (including their first and second order derivatives) as acoustic features. Moreover, our experiments indicated that the combination of these two feature sets performs particularly well in score fusion. Both PLP and MFCC are extracted at 8kHz sample frequency using Kaldi BIBREF12 with 25 and 20 ms frame lengths, respectively, and a 10 ms overlap (other configurations are the same as Kaldi defaults). For each utterance, the features are centered using a short-term (3s window) cepstral mean and variance normalization (ST-CMVN). Finally, we employed our DNN-HMM speech activity detector (SAD) to drop non-speech frames."
+    ],
+    [
+      "Since the introduction of i-vectors in BIBREF0 , the speaker recognition community has seen a significant increase in recognition performance. i-Vectors are low-dimensional representations of Baum-Welch statistics obtained with respect to a GMM, referred to as universal background model (UBM), in a single subspace which includes all characteristics of speaker and inter-session variability, named total variability matrix BIBREF0 . We trained on each acoustic feature a full covariance, gender-independent UBM model with 2048 Gaussians followed by a 600-dimensional i-vector extractor to establish our MFCC- and PLP-based i-vector systems. The unlabeled set of development data was used in the training of both the UBM and the i-vector extractor. The open-source Kaldi software has been used for all these processing steps BIBREF12 .",
+      "It has been shown that successive acoustic observation vectors tend to be highly correlated. This may be problematic for maximum a posteriori (MAP) estimation of i-vectors. To investigating this issue, scaling the zero and first order Baum-Welch statistics before presenting them to the i-vector extractor has been proposed. It turns out that a scale factor of 0.33 gives a slight edge, resulting in a better decision cost function BIBREF13 . This scaling factor has been performed in training the i-vector extractor as well as in the testing."
+    ],
+    [
+      "This section provides the steps performed in back-end processing of our speaker recognition system."
+    ],
+    [
+      "The nearest-neighbor discriminant analysis is a nonparametric discriminant analysis technique which was proposed in BIBREF14 , and recently used in speaker recognition BIBREF15 . The nonparametric within- and between-class scatter matrices INLINEFORM0 and INLINEFORM1 , respectively, are computed based on INLINEFORM2 nearest neighbor sample information. The NDA transform is then formed using eigenvectors of INLINEFORM3 . It has been shown that as the number of nearest neighbors INLINEFORM4 approaches the number of samples in each class, the NDA essentially becomes the LDA projection. Based on the finding in BIBREF15 , NDA outperformed LDA due to the ability in capturing the local structure and boundary information within and across different speakers. We applied a INLINEFORM5 NDA projection matrix computed using the 10 nearest sample information on centered i-vectors. The resulting dimensionality reduced i-vectors are then whitened using both the training data and the unlabelled development set."
+    ],
+    [
+      "The enrolment condition of the development set is supposed to provide at least 60 seconds of speech data for each target speaker. Nevertheless, our SAD indicates that the speech content is as low as 26 seconds in some cases. The test segments duration which ranges from 9 to 60 seconds of speech material can result in poor performance for lower duration segments. As indicated in Figure FIGREF8 , more than one third of the test segments have speech duration of less than 20 seconds. We have addressed this issue by proposing a short duration variability compensation method. The proposed method works by first extracting from each audio segment in the unlabelled development set, a partial excerpt of 10 seconds of speech material with random selection of the starting point (Figure FIGREF9 ). Each audio file in the unlabelled development set, with the extracted audio segment will result in two 400-dimensional i-vectors, one with at most 10 seconds of speech material. Considering each pair as one class, we computed a INLINEFORM0 LDA projection matrix to remove directions attributed to duration variability. Moreover, the projected i-vectors are also subjected to a within-class covariance normalization (WCCN) using the same class labels."
+    ],
+    [
+      "Language-source normalization is an effective technique for reducing language dependency in the state-of-the-art i-vector/PLDA speaker recognition system BIBREF16 . It can be implemented by extending SN-LDA BIBREF17 in order to mitigate variations that separate languages. This can be accomplished by using the language label to identify different sources during training. Language Normalized-LDA (LN-LDA) utilizes a language-normalized within-speaker scatter matrix INLINEFORM0 which is estimated as the variability not captured by the between-speaker scatter matrix, DISPLAYFORM0 ",
+      "where INLINEFORM0 and INLINEFORM1 are the total scatter and normalized between-speaker scatter matrices respectively, and are formulated as follows: DISPLAYFORM0 ",
+      "where INLINEFORM0 is the total number of i-vectors and DISPLAYFORM0 ",
+      "where INLINEFORM0 is the number of languages in the training set, INLINEFORM1 is the number of speakers in language INLINEFORM2 , INLINEFORM3 is the mean of INLINEFORM4 i-vectors from speaker INLINEFORM5 and language INLINEFORM6 and finally INLINEFORM7 is the mean of all i-vectors in language INLINEFORM8 . We applied a INLINEFORM9 SN-LDA projection matrix to reduce the i-vector dimensions down to 300."
+    ],
+    [
+      "Probabilistic Linear Discriminant Analysis (PLDA) provides a powerful mechanism to distinguish between-speaker variability, separating sources which characterizes speaker information, from all other sources of undesired variability that characterize distortions. Since i-vectors are assumed to be generated by some generative model, we can break it down into statistically independent speaker- and session-components with Gaussian distributions BIBREF18 , BIBREF19 . Although it has been shown that their distribution follow Student\u2019s INLINEFORM0 rather than Gaussian BIBREF19 distributions, length normalizing the entire set of i-vectors as a pre-processing step can approximately Gaussianize their distributions BIBREF18 and as a result improve the performance of Gaussian PLDA to that of heavy-tailed PLDA BIBREF19 . A standard Gaussian PLDA assumes that an i-vector INLINEFORM1 , is modelled according to DISPLAYFORM0 ",
+      "where, INLINEFORM0 is the mean of i-vectors, the columns of matrix INLINEFORM1 contains the basis for the between-speaker subspace, the latent identity variable INLINEFORM2 denotes the speaker factor that represents the identity of the speaker and the residual INLINEFORM3 which is normally distributed with zero mean and full covariance matrix INLINEFORM4 , represents within-speaker variability.",
+      "For each acoustic feature we have trained two PLDA models. The first out-domain PLDA ( INLINEFORM0 , INLINEFORM1 ) is trained using the training set presented in Table TABREF1 , and the second in-domain PLDA ( INLINEFORM2 , INLINEFORM3 ) was trained using the unlabelled development set. Our efforts to cluster the development set (e.g using the out-domain PLDA) was not very successful as it sounds that almost all of them are uttered by different speakers. Therefore, each i-vector was considered to be uttered by one speaker. We also set the number of speaker factors to 200."
+    ],
+    [
+      "Domain adaptation has gained considerable attention with the aim of compensating for cross-speech-source variability of in-domain and out-of-domain data. The framework presented in BIBREF20 for unsupervised adaptation of out-domain PLDA parameters resulted in better performance for in-domain data. Using in-domain and out-domain PLDA trained in Section SECREF14 , we interpolated their parameters as follow: DISPLAYFORM0 ",
+      "We chose INLINEFORM0 for making our submission."
+    ],
+    [
+      "For the one-segment enrolment condition, the speaker model is the length normalized i-vector of that segment, however, for the three-segment enrolment condition, we simply used a length-normalized mean vector of the length-normalizated i-vectors as the speaker model. Each speaker model is tested against each test segment as in the trial list. For each two trial i-vectors INLINEFORM0 and INLINEFORM1 , the PLDA score is computed as DISPLAYFORM0 ",
+      "in which DISPLAYFORM0 DISPLAYFORM1 ",
+      "and INLINEFORM0 and INLINEFORM1 . It has been shown and proved in our experiments that score normalization can have a great impact on the performance of the recognition system. We used the symmetric s-norm proposed in BIBREF19 which normalizes the score INLINEFORM2 of the pair INLINEFORM3 using the formula DISPLAYFORM0 ",
+      "where the means INLINEFORM0 and standard deviations INLINEFORM1 are computed by matching INLINEFORM2 and INLINEFORM3 against the unlabelled set as the impostor speakers, respectively."
+    ],
+    [
+      "It has been shown that there is a dependency between the value of the INLINEFORM0 threshold and the duration of both enrolment and test segments. Applying the quality measure function (QMF) BIBREF3 enabled us to compensate for the shift in the INLINEFORM1 threshold due to the differences in speech duration. We conducted some experiments to estimate the dependency between the INLINEFORM2 threshold shift on the duration of test segment and used the following QMF for PLDA verfication scores: DISPLAYFORM0 ",
+      "where INLINEFORM0 is the duration of the test segment in seconds."
+    ],
+    [
+      "In the literature, the performance of speaker recognition is usually reported in terms of calibrated-insensitive equal error rate (EER) or the minimum decision cost function ( INLINEFORM0 ). However, in real applications of speaker recognition there is a need to present recognition results in terms of calibrated log-likelihood-ratios. We have utilized the BOSARIS Toolkit BIBREF21 for calibration of scores. INLINEFORM1 provides an ideal reference value for judging calibration. If INLINEFORM2 is minimized, then the system can be said to be well calibrated.",
+      "The choice of target probability ( INLINEFORM0 ) had a great impact on the performance of the calibration. However, we set INLINEFORM1 for our primary submission which performed the best on the development set. For our secondary submission INLINEFORM2 was used."
+    ],
+    [
+      "In this section we present the results obtained on the protocol provided by NIST on the development set which is supposed to mirror that of evaluation set. The results are shown in Table TABREF26 . The first part of the table indicates the result obtained by the primary system. As can be seen, the fusion of MFCC and PLP (a simple sum of both MFCC and PLP scores) resulted in a relative improvement of almost 10%, as compared to MFCC alone, in terms of both INLINEFORM0 and INLINEFORM1 . In order to quantify the contribution of the different system components we have defined different scenarios. In scenario A, we have analysed the effect of using LDA instead of NDA. As can be seen from the results, LDA outperforms NDA in the case of PLP, however, in fusion we can see that NDA resulted in better performance in terms of the primary metric. In scenario B, we analysed the effect of using the short-duration compensation technique proposed in Section SECREF7 . Results indicate superior performance using this technique. In scenario C, we investigated the effects of language normalization on the performance of the system. If we replace LN-LDA with simple LDA, we can see performance degradation in MFCC as well as fusion, however, PLP seems not to be adversely affected. The effect of using QMF is also investigated in scenario D. Finally in scenario E, we can see the major improvement obtained through the use of the domain adaptation technique explained in Section SECREF16 . For our secondary submission, we incorporated a disjoint portion of the labelled development set (10 out of 20 speakers) in either LN-LDA and in-domain PLDA training. We evaluated the system on almost 6k out of 24k trials from the other portion to avoid any over-fitting, particularly important for the domain adaptation technique. This resulted in a relative improvement of 11% compared to the primary system in terms of the primary metric. However, the results can be misleading, since the recording condition may be the same for all speakers in the development set."
+    ],
+    [
+      "This section reports on the CPU execution time (single threaded), and the amount of memory used to process a single trial, which includes the time for creating models from the enrolment data and the time needed for processing the test segments. The analysis was performed on an Intel(R) Xeon(R) CPU E5-2670 2.60GHz. The results are shown in Table TABREF27 . We used the time command in Unix to report these results. The user time is the actual CPU time used in executing the process (single thread). The real time is the wall clock time (the elapsed time including time slices used by other processes and the time the process spends blocked). The system time is also the amount of CPU time spent in the kernel within the process. We have also reported the memory allocated for each stage of execution. The most computationally intensive stage is the extraction of i-vectors (both MFCC- and PLP-based i-vectors), which also depends on the duration of the segments. For enrolment, we have reported the time required to extract a model from a segment with a duration of 140 seconds and speech duration of 60 seconds. The time and memory required for front-end processing are negligible compared to the i-vector extraction stage, since they only include matrix operations. The time required for our SAD is also reported which increases linearly with the duration of segment."
+    ],
+    [
+      "We have presented the Intelligent Voice speaker recognition system used for the NIST 2016 speaker recognition evaluation. Our system is based on a score fusion of MFCC- and PLP-based i-vector/PLDA systems. We have described the main components of the system including, acoustic feature extraction, speech activity detection, i-vector extraction as front-end processing, and language normalization, short-duration compensation, channel compensation and domain adaptation as back-end processing. For our future work, we intend to use the ALISP segmentation technique BIBREF22 in order to extract meaningful acoustic units so as to train supervised GMM or DNN models."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1654/instruction.md b/qasper-1654/instruction.md
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index 0000000000000000000000000000000000000000..c0955942132c9e133949f207f0c3c817535e908a
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+++ b/qasper-1654/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: The Intelligent Voice 2016 Speaker Recognition System
+
+Question: How well does their system perform on the development set of SRE?
\ No newline at end of file
diff --git a/qasper-1655/instruction.md b/qasper-1655/instruction.md
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+Name of Paper: The Intelligent Voice 2016 Speaker Recognition System
+
+Question: Which are the novel languages on which SRE placed emphasis on?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Training Condition",
+    "Front-End Processing",
+    "Speech Activity Detection",
+    "Acoustic Features",
+    "i-Vector Features",
+    "Back-End Processing",
+    "Nearest-neighbor Discriminant Analysis (NDA)",
+    "Short-Duration Variability Compensation",
+    "Language Normalization",
+    "PLDA",
+    "Domain Adaptation",
+    "Score Computation and Normalization",
+    "Quality Measure Function",
+    "Calibration",
+    "Results and Discussion",
+    "Time Analysis",
+    "Conclusions and Perspectives"
+  ],
+  "paragraphs": [
+    [
+      "Compared to previous years, the 2016 NIST speaker recognition evaluation (SRE) marked a major shift from English towards Austronesian and Chinese languages. The task like previous years is to perform speaker detection with the focus on telephone speech data recorded over a variety of handset types. The main challenges introduced in this evaluation are duration and language variability. The potential variation of languages addressed in this evaluation, recording environment, and variability of test segments duration influenced the design of our system. Our goal was to utilize recent advances in language normalization, domain adaptation, speech activity detection and session compensation techniques to mitigate the adverse bias introduced in this year's evaluation.",
+      "Over recent years, the i-vector representation of speech segments has been widely used by state-of-the-art speaker recognition systems BIBREF0 . The speaker recognition technology based on i-vectors currently dominates the research field due to its performance, low computational cost and the compatibility of i-vectors with machine learning techniques. This dominance is reflected by the recent NIST i-vector machine learning challenge BIBREF1 which was designed to find the most promising algorithmic approaches to speaker recognition specifically on the basis of i-vectors BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 . The outstanding ability of DNN for frame alignment which has achieved remarkable performance in text-independent speaker recognition for English data BIBREF6 , BIBREF7 , failed to provide even comparable recognition performance to the traditional GMM. Therefore, we concentrated on the cepstral based GMM/i-vector system.",
+      "We outline in this paper the Intelligent Voice system, techniques and results obtained on the SRE 2016 development set that will mirror the evaluation condition as well as the timing report. Section SECREF2 describes the data used for the system training. The front-end and back-end processing of the system are presented in Sections SECREF3 and SECREF4 respectively. In Section SECREF5 , we describe experimental evaluation of the system on the SRE 2016 development set. Finally, we present a timing analysis of the system in Section SECREF6 ."
+    ],
+    [
+      "The fixed training condition is used to build our speaker recognition system. Only conversational telephone speech data from datasets released through the linguistic data consortium (LDC) have been used, including NIST SRE 2004-2010 and the Switchboard corpora (Switchboard Cellular Parts I and II, Switchboard2 Phase I,II and III) for different steps of system training. A more detailed description of the data used in the system training is presented in Table TABREF1 . We have also included the unlabelled set of 2472 telephone calls from both minor (Cebuano and Mandarin) and major (Tagalog and Cantonese) languages provided by NIST in the system training. We will indicate when and how we used this set in the training in the following sections."
+    ],
+    [
+      "In this section we will provide a description of the main steps in front-end processing of our speaker recognition system including speech activity detection, acoustic and i-vector feature extraction."
+    ],
+    [
+      "The first stage of any speaker recognition system is to detect the speech content in an audio signal. An accurate speech activity detector (SAD) can improve the speaker recognition performance. Several techniques have been proposed for SAD, including unsupervised methods based on a thresholding signal energy, and supervised methods that train a speech/non-speech classifier such as support vector machines (SVM) BIBREF8 and Gaussian mixture models (GMMs) BIBREF9 . Hidden markov models (HMMs) BIBREF10 have also been successful. Recently, it has been shown that DNN systems achieve impressive improvement in performance especially in low signal to noise ratios (SNRs) BIBREF11 . In our work we have utilized a two-class DNN-HMM classifier to perform this task. The DNN-HMM hybrid configuration with cross-entropy as the objective function has been trained with the back-propagation algorithm. The softmax layer produces posterior probabilities for speech and non-speech which were then converted into log-likelihoods. Using 2-state HMMs corresponding to speech and non-speech, frame-wise decisions are made by Viterbi decoding. As input to the network, we fed 40-dimensional filter-bank features along with 7 frames from each side. The network has 6 hidden layers with 512 units each. The architecture of our DNN-HMM SAD is shown in Figure FIGREF3 . Approximately 100 hours of speech data from the Switchboard telephony data with word alignments as ground-truth were used to train our SAD. The DNN training in performed on an NVIDIA TITAN X GPU, using Kaldi software BIBREF12 . Evaluated on 50 hours of telephone speech data from the same database, our DNN-HMM SAD indicated a frame-level miss-classification (speech/non-speech) rate of 5.9% whereas an energy-based SAD did not perform better than 20%."
+    ],
+    [
+      "For acoustic features we have experimented with different configurations of cepstral features. We have used 39-dimensional PLP features and 60-dimensional MFCC features (including their first and second order derivatives) as acoustic features. Moreover, our experiments indicated that the combination of these two feature sets performs particularly well in score fusion. Both PLP and MFCC are extracted at 8kHz sample frequency using Kaldi BIBREF12 with 25 and 20 ms frame lengths, respectively, and a 10 ms overlap (other configurations are the same as Kaldi defaults). For each utterance, the features are centered using a short-term (3s window) cepstral mean and variance normalization (ST-CMVN). Finally, we employed our DNN-HMM speech activity detector (SAD) to drop non-speech frames."
+    ],
+    [
+      "Since the introduction of i-vectors in BIBREF0 , the speaker recognition community has seen a significant increase in recognition performance. i-Vectors are low-dimensional representations of Baum-Welch statistics obtained with respect to a GMM, referred to as universal background model (UBM), in a single subspace which includes all characteristics of speaker and inter-session variability, named total variability matrix BIBREF0 . We trained on each acoustic feature a full covariance, gender-independent UBM model with 2048 Gaussians followed by a 600-dimensional i-vector extractor to establish our MFCC- and PLP-based i-vector systems. The unlabeled set of development data was used in the training of both the UBM and the i-vector extractor. The open-source Kaldi software has been used for all these processing steps BIBREF12 .",
+      "It has been shown that successive acoustic observation vectors tend to be highly correlated. This may be problematic for maximum a posteriori (MAP) estimation of i-vectors. To investigating this issue, scaling the zero and first order Baum-Welch statistics before presenting them to the i-vector extractor has been proposed. It turns out that a scale factor of 0.33 gives a slight edge, resulting in a better decision cost function BIBREF13 . This scaling factor has been performed in training the i-vector extractor as well as in the testing."
+    ],
+    [
+      "This section provides the steps performed in back-end processing of our speaker recognition system."
+    ],
+    [
+      "The nearest-neighbor discriminant analysis is a nonparametric discriminant analysis technique which was proposed in BIBREF14 , and recently used in speaker recognition BIBREF15 . The nonparametric within- and between-class scatter matrices INLINEFORM0 and INLINEFORM1 , respectively, are computed based on INLINEFORM2 nearest neighbor sample information. The NDA transform is then formed using eigenvectors of INLINEFORM3 . It has been shown that as the number of nearest neighbors INLINEFORM4 approaches the number of samples in each class, the NDA essentially becomes the LDA projection. Based on the finding in BIBREF15 , NDA outperformed LDA due to the ability in capturing the local structure and boundary information within and across different speakers. We applied a INLINEFORM5 NDA projection matrix computed using the 10 nearest sample information on centered i-vectors. The resulting dimensionality reduced i-vectors are then whitened using both the training data and the unlabelled development set."
+    ],
+    [
+      "The enrolment condition of the development set is supposed to provide at least 60 seconds of speech data for each target speaker. Nevertheless, our SAD indicates that the speech content is as low as 26 seconds in some cases. The test segments duration which ranges from 9 to 60 seconds of speech material can result in poor performance for lower duration segments. As indicated in Figure FIGREF8 , more than one third of the test segments have speech duration of less than 20 seconds. We have addressed this issue by proposing a short duration variability compensation method. The proposed method works by first extracting from each audio segment in the unlabelled development set, a partial excerpt of 10 seconds of speech material with random selection of the starting point (Figure FIGREF9 ). Each audio file in the unlabelled development set, with the extracted audio segment will result in two 400-dimensional i-vectors, one with at most 10 seconds of speech material. Considering each pair as one class, we computed a INLINEFORM0 LDA projection matrix to remove directions attributed to duration variability. Moreover, the projected i-vectors are also subjected to a within-class covariance normalization (WCCN) using the same class labels."
+    ],
+    [
+      "Language-source normalization is an effective technique for reducing language dependency in the state-of-the-art i-vector/PLDA speaker recognition system BIBREF16 . It can be implemented by extending SN-LDA BIBREF17 in order to mitigate variations that separate languages. This can be accomplished by using the language label to identify different sources during training. Language Normalized-LDA (LN-LDA) utilizes a language-normalized within-speaker scatter matrix INLINEFORM0 which is estimated as the variability not captured by the between-speaker scatter matrix, DISPLAYFORM0 ",
+      "where INLINEFORM0 and INLINEFORM1 are the total scatter and normalized between-speaker scatter matrices respectively, and are formulated as follows: DISPLAYFORM0 ",
+      "where INLINEFORM0 is the total number of i-vectors and DISPLAYFORM0 ",
+      "where INLINEFORM0 is the number of languages in the training set, INLINEFORM1 is the number of speakers in language INLINEFORM2 , INLINEFORM3 is the mean of INLINEFORM4 i-vectors from speaker INLINEFORM5 and language INLINEFORM6 and finally INLINEFORM7 is the mean of all i-vectors in language INLINEFORM8 . We applied a INLINEFORM9 SN-LDA projection matrix to reduce the i-vector dimensions down to 300."
+    ],
+    [
+      "Probabilistic Linear Discriminant Analysis (PLDA) provides a powerful mechanism to distinguish between-speaker variability, separating sources which characterizes speaker information, from all other sources of undesired variability that characterize distortions. Since i-vectors are assumed to be generated by some generative model, we can break it down into statistically independent speaker- and session-components with Gaussian distributions BIBREF18 , BIBREF19 . Although it has been shown that their distribution follow Student\u2019s INLINEFORM0 rather than Gaussian BIBREF19 distributions, length normalizing the entire set of i-vectors as a pre-processing step can approximately Gaussianize their distributions BIBREF18 and as a result improve the performance of Gaussian PLDA to that of heavy-tailed PLDA BIBREF19 . A standard Gaussian PLDA assumes that an i-vector INLINEFORM1 , is modelled according to DISPLAYFORM0 ",
+      "where, INLINEFORM0 is the mean of i-vectors, the columns of matrix INLINEFORM1 contains the basis for the between-speaker subspace, the latent identity variable INLINEFORM2 denotes the speaker factor that represents the identity of the speaker and the residual INLINEFORM3 which is normally distributed with zero mean and full covariance matrix INLINEFORM4 , represents within-speaker variability.",
+      "For each acoustic feature we have trained two PLDA models. The first out-domain PLDA ( INLINEFORM0 , INLINEFORM1 ) is trained using the training set presented in Table TABREF1 , and the second in-domain PLDA ( INLINEFORM2 , INLINEFORM3 ) was trained using the unlabelled development set. Our efforts to cluster the development set (e.g using the out-domain PLDA) was not very successful as it sounds that almost all of them are uttered by different speakers. Therefore, each i-vector was considered to be uttered by one speaker. We also set the number of speaker factors to 200."
+    ],
+    [
+      "Domain adaptation has gained considerable attention with the aim of compensating for cross-speech-source variability of in-domain and out-of-domain data. The framework presented in BIBREF20 for unsupervised adaptation of out-domain PLDA parameters resulted in better performance for in-domain data. Using in-domain and out-domain PLDA trained in Section SECREF14 , we interpolated their parameters as follow: DISPLAYFORM0 ",
+      "We chose INLINEFORM0 for making our submission."
+    ],
+    [
+      "For the one-segment enrolment condition, the speaker model is the length normalized i-vector of that segment, however, for the three-segment enrolment condition, we simply used a length-normalized mean vector of the length-normalizated i-vectors as the speaker model. Each speaker model is tested against each test segment as in the trial list. For each two trial i-vectors INLINEFORM0 and INLINEFORM1 , the PLDA score is computed as DISPLAYFORM0 ",
+      "in which DISPLAYFORM0 DISPLAYFORM1 ",
+      "and INLINEFORM0 and INLINEFORM1 . It has been shown and proved in our experiments that score normalization can have a great impact on the performance of the recognition system. We used the symmetric s-norm proposed in BIBREF19 which normalizes the score INLINEFORM2 of the pair INLINEFORM3 using the formula DISPLAYFORM0 ",
+      "where the means INLINEFORM0 and standard deviations INLINEFORM1 are computed by matching INLINEFORM2 and INLINEFORM3 against the unlabelled set as the impostor speakers, respectively."
+    ],
+    [
+      "It has been shown that there is a dependency between the value of the INLINEFORM0 threshold and the duration of both enrolment and test segments. Applying the quality measure function (QMF) BIBREF3 enabled us to compensate for the shift in the INLINEFORM1 threshold due to the differences in speech duration. We conducted some experiments to estimate the dependency between the INLINEFORM2 threshold shift on the duration of test segment and used the following QMF for PLDA verfication scores: DISPLAYFORM0 ",
+      "where INLINEFORM0 is the duration of the test segment in seconds."
+    ],
+    [
+      "In the literature, the performance of speaker recognition is usually reported in terms of calibrated-insensitive equal error rate (EER) or the minimum decision cost function ( INLINEFORM0 ). However, in real applications of speaker recognition there is a need to present recognition results in terms of calibrated log-likelihood-ratios. We have utilized the BOSARIS Toolkit BIBREF21 for calibration of scores. INLINEFORM1 provides an ideal reference value for judging calibration. If INLINEFORM2 is minimized, then the system can be said to be well calibrated.",
+      "The choice of target probability ( INLINEFORM0 ) had a great impact on the performance of the calibration. However, we set INLINEFORM1 for our primary submission which performed the best on the development set. For our secondary submission INLINEFORM2 was used."
+    ],
+    [
+      "In this section we present the results obtained on the protocol provided by NIST on the development set which is supposed to mirror that of evaluation set. The results are shown in Table TABREF26 . The first part of the table indicates the result obtained by the primary system. As can be seen, the fusion of MFCC and PLP (a simple sum of both MFCC and PLP scores) resulted in a relative improvement of almost 10%, as compared to MFCC alone, in terms of both INLINEFORM0 and INLINEFORM1 . In order to quantify the contribution of the different system components we have defined different scenarios. In scenario A, we have analysed the effect of using LDA instead of NDA. As can be seen from the results, LDA outperforms NDA in the case of PLP, however, in fusion we can see that NDA resulted in better performance in terms of the primary metric. In scenario B, we analysed the effect of using the short-duration compensation technique proposed in Section SECREF7 . Results indicate superior performance using this technique. In scenario C, we investigated the effects of language normalization on the performance of the system. If we replace LN-LDA with simple LDA, we can see performance degradation in MFCC as well as fusion, however, PLP seems not to be adversely affected. The effect of using QMF is also investigated in scenario D. Finally in scenario E, we can see the major improvement obtained through the use of the domain adaptation technique explained in Section SECREF16 . For our secondary submission, we incorporated a disjoint portion of the labelled development set (10 out of 20 speakers) in either LN-LDA and in-domain PLDA training. We evaluated the system on almost 6k out of 24k trials from the other portion to avoid any over-fitting, particularly important for the domain adaptation technique. This resulted in a relative improvement of 11% compared to the primary system in terms of the primary metric. However, the results can be misleading, since the recording condition may be the same for all speakers in the development set."
+    ],
+    [
+      "This section reports on the CPU execution time (single threaded), and the amount of memory used to process a single trial, which includes the time for creating models from the enrolment data and the time needed for processing the test segments. The analysis was performed on an Intel(R) Xeon(R) CPU E5-2670 2.60GHz. The results are shown in Table TABREF27 . We used the time command in Unix to report these results. The user time is the actual CPU time used in executing the process (single thread). The real time is the wall clock time (the elapsed time including time slices used by other processes and the time the process spends blocked). The system time is also the amount of CPU time spent in the kernel within the process. We have also reported the memory allocated for each stage of execution. The most computationally intensive stage is the extraction of i-vectors (both MFCC- and PLP-based i-vectors), which also depends on the duration of the segments. For enrolment, we have reported the time required to extract a model from a segment with a duration of 140 seconds and speech duration of 60 seconds. The time and memory required for front-end processing are negligible compared to the i-vector extraction stage, since they only include matrix operations. The time required for our SAD is also reported which increases linearly with the duration of segment."
+    ],
+    [
+      "We have presented the Intelligent Voice speaker recognition system used for the NIST 2016 speaker recognition evaluation. Our system is based on a score fusion of MFCC- and PLP-based i-vector/PLDA systems. We have described the main components of the system including, acoustic feature extraction, speech activity detection, i-vector extraction as front-end processing, and language normalization, short-duration compensation, channel compensation and domain adaptation as back-end processing. For our future work, we intend to use the ALISP segmentation technique BIBREF22 in order to extract meaningful acoustic units so as to train supervised GMM or DNN models."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1662/instruction.md b/qasper-1662/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..a9e9744652488a2cf604011746a406663519a512
--- /dev/null
+++ b/qasper-1662/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Unsupervised Question Decomposition for Question Answering
+
+Question: How large is the improvement over the baseline?
\ No newline at end of file
diff --git a/qasper-1663/instruction.md b/qasper-1663/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..87ab0e2e05b24d6bfc25364553ba352eecebbb2d
--- /dev/null
+++ b/qasper-1663/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Unsupervised Question Decomposition for Question Answering
+
+Question: What is the strong baseline that this work outperforms?
\ No newline at end of file
diff --git a/qasper-1664/instruction.md b/qasper-1664/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..66cd52f918b68c764f81bd594e92f72dfd53e5e2
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+++ b/qasper-1664/instruction.md
@@ -0,0 +1,105 @@
+Name of Paper: Simultaneous Neural Machine Translation using Connectionist Temporal Classification
+
+Question: Which dataset do they use?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Simultaneous machine translation by Wait-k model",
+    "Simultaneous machine translation by Wait-k model ::: Encoder",
+    "Simultaneous machine translation by Wait-k model ::: Decoder with Attention",
+    "Proposed Method",
+    "Proposed Method ::: Softmax Cross-Entropy",
+    "Proposed Method ::: Connectionist Temporal Classification",
+    "Proposed Method ::: Delay Penalty",
+    "Proposed Method ::: Objective Function",
+    "Experiments",
+    "Experiments ::: Settings",
+    "Experiments ::: Experiments with Small-scale Corpus",
+    "Experiments ::: Experiments with Mid-scale Corpus",
+    "Experiments ::: Discussion",
+    "Conclusion",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "Simultaneous translation is a translation task where the translation process starts before the end of an input. It helps real-time spoken language communications such as human conversations and public talks. A usual machine translation system works in the sentence level and starts its translation process after it reads the end of a sentence. It would not be appropriate for spoken languages due to roughly two issues: (1) sentence boundaries are not clear and (2) a large latency occurs for a long input.",
+      "Previous studies tackled this problem by an incremental process, in order to reduce the translation latency for a given input. fujita13interspeech proposed a phrase-based approach to the simultaneous translation based on phrasal reordering probabilities. oda-etal-2015-syntax proposed a syntax-based method to determine when to start translation of observed inputs. Such an approach faces a trade-off between speed and accuracy; reducing the translation latency using very limited context information also causes the loss in the translation accuracy. This becomes more serious especially in a syntactically-distant language pair such as English and Japanese, where we sometimes have to wait a latter part of a source language to determine the corresponding former part in a target language.",
+      "Recent neural machine translation (NMT) studies tried an incremental processing for the simultaneous translation. gu2017learning proposed a reinforcement learning approach to determine when to translate based on two different actions: READ to take one input token and WRITE to generate one output token. While they reported some latency reduction without the loss of translation accuracy, the NMT model itself is trained independently from this incremental manner and is not fully optimized for simultaneous translation. ma2018stacl proposed a very simple incremental method called Wait-k, where the decoder starts to generate output tokens after the encoder reads k tokens and then works token-by-token. Here, some required inputs may not be observed by the encoder; however, the decoder has to predict the next output token even in that case. This approach enables a simple end-to-end simultaneous NMT with implicit anticipation of unobserved inputs. It showed high translation accuracy with small latency on some common English-to-German and Chinese-to-English datasets. The latency hyperparameter k can be used to control the speed-accuracy trade-off, but it has to be large enough for a distant language pair like English-Japanese. We observed a problem in translating a phrase longer than k tokens in our pilot study on English-to-Japanese translation.",
+      "In this work, we propose a novel incremental NMT method that uses a special token <wait> in the target language which is generated when the translation model chooses to read the next input token instead of generating an output token. The proposed method uses Connectionist Temporal Classification (CTC) BIBREF0 to handle ambiguities in possible positions inserting <wait> in the training time. CTC is applied to sequential model training such as automatic speech recognition, where we have a reference word sequence but do not have the corresponding segmentation or alignment in an acoustic signal. We conduct experiments in English-to-Japanese simultaneous translation with the proposed and baseline methods and show the proposed method achieves a good translation performance with relatively small latency. The proposed method can determine when to wait or translate in an adaptive manner and is useful in simultaneous translation tasks."
+    ],
+    [
+      "First, we review a general NMT model following the formulation by BIBREF1 and the \u201cWait-k\" model BIBREF2 that is the baseline model for simultaneous NMT.",
+      "Given a source sentence $X$ and a target sentence $Y$ as follows:",
+      "where $\\textbf {x}_i \\in \\mathbb {R}^{S \\times 1}$ is a one-hot vector of the i-th input word, $I$ is the length of the input sentence $X$, $\\textbf {y}_i \\in \\mathbb {R}^{T \\times 1}$ is a one-hot vector of the i-th output word, and $J$ is the length of the output sentence $Y$.",
+      "The problem of translation from the source to the target language can be solved by finding the best target language sentence $\\hat{Y}$ that maximizes the conditional probability",
+      "In general NMT manner, the conditional probability is decomposed by the product of conditional generation probabilities of $\\textbf {y}_{j}$ given the source sentence $X$ and preceding target words $\\textbf {y}_{<j}$:",
+      "where $\\textbf {y}_{<j}$ represents the target words up to position $j$, and $\\theta $ indicates the model parameters. In contrast, the model for simultaneous translation has to output translated words given only prefix words of the source sentence. Therefore, the conditional probability is decomposed as follows:",
+      "where $\\textbf {x}_{<g(j)}$ are the target words up to position $g(j)$ and $g(j)$ represents the number of encoded source tokens when the model outputs $j$ words. In the \u201cWait-k\" model, $g(j)$ is defined as follows:",
+      "Here, $k$ is the hyperparameter which indicates the target sentence generation is $k$ tokens behind the source sentence input and it takes a constant value in the \u201cWait-k\" model.",
+      "The model is composed of an encoder (\u00a7SECREF5) and a decoder with the attention mechanism (\u00a7SECREF7) that are both implemented using recurrent neural networks (RNNs); the encoder converts source words into a sequence of vectors, and the decoder generates target language words one-by-one with the attention mechanism based on the conditional probability shown in the equation DISPLAY_FORM2 and DISPLAY_FORM3. The details are described below."
+    ],
+    [
+      "The encoder takes a sequence of a source sentence $X$ as inputs and returns forward hidden vectors $\\overrightarrow{\\textbf {h}_i}(1 \\le i \\le I)$ of the forward RNNs:",
+      "In the general NMT model, they also calculate backward hidden vectors of backward RNNs from a reversed source sentence. However, we only use forward hidden vectors because we cannot use the information of the whole sentence on the simultaneous translation task."
+    ],
+    [
+      "The decoder takes source hidden vectors as inputs and returns target language words one-by-one with the attention mechanism. The decoder RNNs recurrently generates target words using its hidden state and an output context. The conditional generation probability of the target word $\\textbf {y}_i$ defined as follows:",
+      "Here, $\\textbf {W}_c, \\textbf {W}_p$ are trainable parameters and $\\textbf {c}_j$ is a context vector to retrieve source language inputs in forms of a weighted sum of the source hidden vectors $\\textbf {h}_j$, defined as follows.",
+      "The score function above can be defined in some different ways as discussed by BIBREF1. In this paper, we use dot attention for this score function."
+    ],
+    [
+      "In this work, we proposed the method to decide the output timing adaptively. The proposed method introduces a special token <wait> which is output instead of delaying translation to target-side vocabulary.",
+      "In this section, we first review a standard objective function, softmax cross-entropy and show the problem that occurs when this function is applied to <wait> (\u00a7SECREF10). After that, we introduce an objective function, called Connectionist Temporal Classification, to handle this problem (\u00a7SECREF12). Finally, we propose a new objective function to adjust a trade-off between translation accuracy and latency (\u00a7SECREF14) and explain how to combine these objective functions (\u00a7SECREF16)."
+    ],
+    [
+      "Softmax Cross-Entropy (SCE) is a commonly used token-level objective function for multi-class classification including word generation in NMT, defined as follows:",
+      "where $\\textbf {y}_{ij}$ is a j-th element of the one-hot vector corresponding to the i-th words of the reference sentence and $p(\\textbf {y}_{jk}|\\cdot )$ is the generation probability of $\\textbf {y}_{jk}$.",
+      "A correct sequence that corresponds to an output sequence one-by-one is necessary to use SCE as an objective function for NMT. However, in the proposed method, we cannot simply use SCE because we don't know when we should cause delay. To avoid this problem, we set the loss for delay tokens to 0 during the time step $t\\ (t \\le g(I))$ which the model can output <wait> , or while a source sentence is inputted."
+    ],
+    [
+      "As we mentioned in the previous section, we set the loss value for <wait> to 0, but this causes the problem that it does not optimize about generating <wait> . Against this problem, we use an objective function called Connectionist Temporal Classification (CTC) BIBREF0 for sequence-level optimization.",
+      "CTC extends output sequence, called Path $\\mathbf {\\pi } = \\Omega (\\textbf {y})$, to the length $T$ by allowing token repetitions and outputting <wait> . Conversely, we can obtain an original output sequence $\\textbf {y} = \\Omega ^{-1}(\\mathbf {\\pi })$ by removing <wait> and all token repetitions. The objective function is defined the sum of the probabilities of all possible paths $\\mathbf {\\pi } \\in \\Omega (\\textbf {y})$ by using the forward-backward algorithm, as follows:",
+      "where $\\pi _t$ is a t-th element of $\\mathbf {\\pi }$."
+    ],
+    [
+      "Furthermore, we introduce a new objective function, called Delay Penalty, to control latency. We use this function only when an output token causes the delay; that is, when the model outputs <wait> or the same token as a previous one. Delay Penalty is defined by a negative log-likelihood of the probabilities for non-delayed tokens, as follows:"
+    ],
+    [
+      "For optimization, we combine three objective functions introduced so far, as follows:",
+      "Here, $\\alpha $ is a hyperparameter to adjust the amount of latency directly."
+    ],
+    [
+      "We conducted simultaneous translation experiments from English to Japanese and discussed accuracy, latency, and issues for translation results."
+    ],
+    [
+      "All models were implemented as described in the previous sections using PyTorch. Both the encoders and the decoders were two-layered uni-direcitional LSTM BIBREF3, and the decoder used input feedingBIBREF1. The number of dimensions in word embeddings and hidden vectors was set to 512, and the minibatch size was 64. We use Adam BIBREF4 for optimization with the default parameters. The learning rate was set to $10^{-1}$, and gradient clipping was set to 5. The dropout probability was set to $0.3$. The learning rate was adjusted by a decay factor of $1/\\sqrt{2}$ when the validation loss was larger than that in the previous epoch. Then, we chose the best parameter/model with the smallest validation loss for evaluation.",
+      "We used two different corpora for the experiments: small_parallel_enja and Asian Scientific Paper Excerpt Corpus (ASPEC) BIBREF5. small_parallel_enja is a small-scale corpus that is consist of sentences filtered sentence length 4 to 16 words, and ASPEC is a mid-scale corpus of the scientific paper domain. Table TABREF21 shows their detailed statistics.",
+      "All datasets were tokenized into subword unit BIBREF6, BIBREF7 by using Sentencepiece . The source and target language vocabularies were independent, and their size was set to 4000 tokens for small_parallel_enja and 8000 tokens for ASPEC, respectively. We filtered out the sentence whose number of tokens was more than 60 tokens, or the length ratio was more than 9 from the training set.",
+      "We used \u201cWait-k\u201d models and general NMT models as baseline models. General NMT models were attention-based encoder-decoder and it translated sentences from full-length source sentences (called Full Sentence). For evaluation metrics, we used BLEU BIBREF8 and RIBES BIBREF9 to measure translation accuracy, and token-level delay to measure latency. We used Kytea BIBREF10 as a tokenize method for evaluations of Japanese translation accuracy."
+    ],
+    [
+      "We conducted small-scale experiments using small_parallel_enja. We compared different hyperparameters: $k = \\lbrace 3, 5\\rbrace $ and $\\alpha = \\lbrace 0, 0.01, 0.03, 0.05\\rbrace $.",
+      "Table TABREF24 shows the results in latency and automatic evaluation scores on small_parallel_enja. The full sentence scores are upper bounds of incremental methods. The proposed method reduced the average latency in more than 50% from the full sentence baseline with some loss in BLEU and RIBES. The BLEU and RIBES results by the proposed method were worse than those by Wait-k. Th would be due to some degradation in smaller latency parts that were determined adaptively by the proposed methods while Wait-k keeps the fixed latency."
+    ],
+    [
+      "We investigated the performance on longer and more complex sentences by the experiments using ASPEC. We compared different hyperparameters: $k = \\lbrace 5, 7\\rbrace $ and $\\alpha = \\lbrace 0.03, 0.05, 0.1\\rbrace $.",
+      "Table TABREF26 shows the results in latency and automatic evaluation scores on ASPEC. We can see the proposed method showed much larger latency than Wait-k. This is probably due to many long and complex phrases used in scientific articles in ASPEC. Wait-k has to translate such a long phrase without sufficient input observations due to its strict fixed latency strategy. On the other hand, the proposed method can wait for more input tokens adaptively by generating <wait> at the cost of large latency."
+    ],
+    [
+      "In the experimental results above, the proposed method determined the translation latency adaptively, short delay for short and simple inputs as in small_parallel_enja and long delay for long and complex inputs as in ASPEC. Here we discuss our results in detail using some examples.",
+      "Table TABREF28 shows translation examples on small_parallel_enja. In the first example, the proposed method gives a correct translation result by adaptive waits. Wait-k generated unrelated words UTF8min\u91ce\u7403 (baseball) and UTF8min\u98f2-\u307f (drink) due to the poor input observations with its small fixed latency. The proposed method waited until a subword swim was observed and successfully generate a word UTF8min\u6cf3-\u3050 (swim).",
+      "However, the proposed method sometimes generated consecutive <wait> symbols until the end of input, as shown in the second example. This is probably due to our training strategy; the latency penalty would not be large enough to choose small latency translation at the cost of some increase in SCE- and CTC-based loss. The translation data in the experiments are not from simultaneous interpretation but standard translation, so the current task does not match with the proposed approach. The use of specialized data for simultaneous translation would be important in practice, such as monotonic translations like simultaneous translation."
+    ],
+    [
+      "In this paper, we proposed an adaptive latency control method for simultaneous neural machine translation in syntactically distant language pairs. We introduced a meta token <wait> to wait until the observation of the next input token. We proposed a CTC-based loss function to perform optimization using bilingual data without appropriate positions of <wait> , which is used along with the latency penalty and a standard word prediction loss. The experimental results suggest the proposed method determines when to translate or when to wait in an adaptive manner. Future work includes further analyses on translation accuracy in different latency conditions and time-based latency evaluation instead of the token-based one."
+    ],
+    [
+      "A part of this work is supported by JSPS Kakenhi JP17H06101."
+    ]
+  ]
+}
+```
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+Name of Paper: Simultaneous Neural Machine Translation using Connectionist Temporal Classification
+
+Question: Do they trim the search space of possible output sequences?
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+Name of Paper: Sentiment Analysis for Twitter : Going Beyond Tweet Text
+
+Question: What linguistic features are used?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Data-analysis",
+    "Data pre-processing",
+    "Normalization",
+    "Hashtag Segmentation",
+    "Processing URLs",
+    "Algorithmic Framework",
+    "Feature Extraction",
+    "Classifier",
+    "Parameter Tuning",
+    "Evaluation and Analysis",
+    "Comparison with Stanford Sentiment Analysis Tool",
+    "Enhancements",
+    "Harvest New Sentiment Terms",
+    "Predicting Strength of Sentiment",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Analysing sentiment from text is a well-known NLP problem. Several state-of-the-art tools exist that can achieve this with reasonable accuracy. However most of the existing tools perform well on well-formatted text. In case of tweets, the user generated content is short, noisy, and in many cases ( INLINEFORM0 ) doesn't follow proper grammatical structure. Additionally, numerous internet slangs, abbreviations, urls, emoticons, and unconventional style of capitalization are found in the tweets. As a result, the accuracy of the state-of-the art NLP tools decreases sharply. In this project, we develop new features to incorporate the styles salient in short, informal user generated contents like tweets. We achieve an F1-accuracy of INLINEFORM1 for predicting the sentiment of tweets in our data-set. We also propose a method to discover new sentiment terms from the tweets.",
+      "In section SECREF2 we present analysis of the data-set. We describe the data-preprocessing that we have done in section SECREF3 . In section SECREF4 we describe how the feature-set was extracted, the classification framework, and also the tuning of the parameters for reasonable accuracy. In section SECREF5 we report the performance of our system. We also report how the different features affect the accuracy of the system. We describe how we harvest new sentiment terms using our framework in section SECREF6 . In this section we also present how we predict strength of sentiment from the tweets. We finally conclude with some possible future directions of work in section SECREF7 ."
+    ],
+    [
+      "Tweets are short messages, restricted to 140 characters in length. Due to the nature of this microblogging service (quick and short messages), people use acronyms, make spelling mistakes, use emoticons and other characters that express special meanings. Following is a brief terminology associated with tweets:",
+      "Our dataset contains tweets about `ObamaCare' in USA collected during march 2010. It is divided into three subsets (train, dev, and test). Some tweets are manually annotated with one of the following classes.",
+      " positive, negative, neutral, unsure, and irrelevant",
+      "We ignore the tweets which are annotated unsure, or irrelevant. We present some preliminary statistics about the training data and test data in Table TABREF5 . We observe that there is an imbalance in the dataset. In training dataset, the ratio of positive tweets to negative ones is almost 1:2. In test set, it is heavily skewed with the ratio being less than 1:3. We handle this data imbalance problem using class prior parameters of the learning algorithm. We discuss this is detail in section SECREF38 ."
+    ],
+    [
+      "Since tweets are informal in nature, some pre-processing is required. Consider the following tweet.",
+      "\u201c#Healthcare #Ins. Cigna denies #MD prescribed #tx 2 customers 20% of the time. - http://bit.ly/5PoQfo #HCR #Passit #ILDems #p2 PLS RT\"",
+      "It is difficult to understand what is the content of the tweet unless it is normalized. We process all the tweets through the following stages."
+    ],
+    [
+      "Normalization is done as follows:",
+      "Removing patterns like 'RT', '@user_name', url.",
+      "Tokenizing tweet text using NLTK BIBREF0 word tokenizer.",
+      "Making use of the stopwords list by NLTK to remove them from the tweet text.",
+      "Rectifying informal/misspelled words using normalization dictionary BIBREF1 . For example, \u201cfoundation\" for \u201cfoudation\", \u201cforgot\" for \u201cforgt\".",
+      "Expanding abbreviations using slang dictionary. For example, \u201cbtw\" is expanded to \u201cby the way\".",
+      "Removing emoticons. However we keep the number of positive and negative emoticons in each tweet as feature. We make use of the emoticon dictionary(Table TABREF14 ) presented in BIBREF2 ."
+    ],
+    [
+      "We segment a hashtag into meaningful English phrases. The `#' character is removed from the tweet text. As for example, #killthebill is transformed into kill the bill.",
+      "In order to achieve this, we use a dictionary of English words. We recursively break the hashtagged phrase into segments and match the segments in the dictionary until we get a complete set of meaningful words. This is important since many users tend to post tweets where the actual message of the tweet is expressed in form of terse hashtagged phrases."
+    ],
+    [
+      "The urls embedded in the tweet are a good source of additional context to the actual short tweet content. Sometimes tweets are too terse to comprehend just from the text content of it alone. However if there is a url embedded in the tweet, that can help us understand the context of it \u2013 perhaps the sentiment expressed as well.",
+      "In order to leverage this additional source of information, we identify all the urls present in the tweets and crawl the web-pages using AlchemyAPI. The API retrieves only the textual body of the article in a web-page. We analyze the article texts later on to get more context for the tweet."
+    ],
+    [
+      "We employ a supervised learning model using the manually labeled data as training set and a collection of handcrafted features. In this section we describe the features and the classification model used in this task."
+    ],
+    [
+      "Table TABREF19 presents the set of features we use in our experiment. We have used some basic features (that are commonly used for text classification task) as well as some advanced ones suitable for this particular domain.",
+      "We use two basic features:",
+      "Parts of Speech (POS) tags: We use the POS tagger of NLTK to tag the tweet texts BIBREF0 . We use counts of noun, adjective, adverb, verb words in a tweet as POS features.",
+      "Prior polarity of the words: We use a polarity dictionary BIBREF3 to get the prior polarity of words. The dictionary contains positive, negative and neutral words along with their polarity strength (weak or strong). The polarity of a word is dependent on its POS tag. For example, the word `excuse' is negative when used as `noun' or `adjective', but it carries a positive sense when used as a `verb'. We use the tags produced by NLTK postagger while selecting the prior polarity of a word from the dictionary. We also employ stemming (Porter Stemmer implementation from NLTK) while performing the dictionary lookup to increase number of matches. We use the counts of weak positive words, weak negative words, strong positive words and strong negative words in a tweet as features.",
+      "We have also explored some advanced features that helps improve detecting sentiment of tweets.",
+      "Emoticons: We use the emoticon dictionary from BIBREF2 , and count the positive and negtive emocicons for each tweet.",
+      "The sentiment of url: Since almost all the articles are written in well-formatted english, we analyze the sentiment of the first paragraph of the article using Standford Sentiment Analysis tool BIBREF4 . It predicts sentiment for each sentence within the article. We calculate the fraction of sentences that are negative, positive, and neutral and use these three values as features.",
+      "Hashtag: We count the number of hashtags in each tweet.",
+      "Capitalization: We assume that capitalization in the tweets has some relationship with the degree of sentiment. We count the number of words with capitalization in the tweets.",
+      "Retweet: This is a boolean feature indicating whether the tweet is a retweet or not.",
+      "User Mention: A boolean feature indicating whether the tweet contains a user mention.",
+      "Negation: Words like `no', `not', `won't' are called negation words since they negate the meaning of the word that is following it. As for example `good' becomes `not good'. We detect all the negation words in the tweets. If a negation word is followed by a polarity word, then we negate the polarity of that word. For example, if `good' is preceeded by a `not', we change the polarity from `weak positive' to `weak negative'.",
+      "Text Feature: We use tf-idf based text features to predict the sentiment of a tweet. We perform tf-idf based scoring of words in a tweet and the hashtags present in the tweets. We use the tf-idf vectors to train a classifier and predict the sentiment. This is then used as a stacked prediction feature in the final classifier.",
+      "Target: We use the target of the tweet as categorical feature for our classifier.",
+      "User: On a particular topic one particular user will generally have a single viewpoint (either positive or negative or neutral). If there are multiple posts within a short period of time from a user, then possibly the posts will contain the same sentiment. We use the user id as a categorical feature. On an average there are INLINEFORM0 tweets per user in the dataset, and over INLINEFORM1 users in the train set have expressed a single viewpoint for all their tweets (either positive or negative). Hence we believe this feature should be able to capture a user's viewpoint on the topic.",
+      "."
+    ],
+    [
+      "We experiment with the following set of machine learning classifiers. We train the model with manually labeled data and used the above described features to predict the sentiment. We consider only positive, negative and neutral classes.",
+      "Multinomial Naive Bayes : Naive Bayes have been one of the most commonly used classifiers for text classification problems over the years. Naive Bayes classifier makes the assumption that the value of a particular feature is independent of the value of any other feature, given the class variable. This independence assumption makes the classifier both simple and scalable. Bayes classifier assigns a class label INLINEFORM0 for some k according to the following equation: DISPLAYFORM0 ",
+      "The assumptions on distributions of features define the event model of the Naive Bayes classifier. We use multinomial Naive Bayes classifer, which is suitable for discrete features (like counts and frequencies).",
+      "Linear SVM : Support Vector Machines are linear non-probabilistic learning algorithms that given training examples, depending on features, build a model to classify new data points to one of the probable classes. We have used support vector machine with stochastic gradient descent learning where gradient of loss is estimated and model is updated at each sample with decreasing strength.",
+      ". For this task we found Multinomial Naive Bayes performs slightly better than Linear SVM, hence in the evaluation we report accuracy with this classifier."
+    ],
+    [
+      "Parameter tuning or hyperparameter optimization is an important step in model selection since it prevents the model from overfitting and optimize the performance of a model on an independent dataset. We perform hyperparameter optimization by using grid search, i.e. an exhaustive searching through a manually specified subset of the hyperparameter space for a learning algorithm. We do grid search and set the `best parameters' by doing cross validation on training set and verified the improvement of accuracy on the validation set. Finally we use the model with best hyperparameters to make predictions on the test set."
+    ],
+    [
+      "Table TABREF39 shows the test results when features are added incrementally. We start with our basic model (with only POS tag features and word polarity features) and subsequently add various sets of features. First we add emoticon features, it has not much effect. This is reasonable since only 8 positive emoticons and 3 negative emoticons are detected(Table TABREF5 ) out of 40049 tokens. So the significance of emoticon can be neglected in this dataset. Then we add hashtag and capitalization features, and obtain an overall gain of 2% over the basic model. By adding the sentiment features from URL articles, we get overall 6% improvement over baseline. Further twitter specific features and user features improve the f1 by 12%. Last, we add TF-IDF feature, and the result improves a lot, and our sentiment classifier reaches the best classification results with an F1-accuracy of INLINEFORM0 as shown in the table.",
+      "Analyzing the results for different classes, we observe that the classifier works best for negative tweets. This can be explained by the number of training tweets for each classes, since proportion of negative tweets were considerably higher in both train and test sets as mentioned in Section SECREF2 ."
+    ],
+    [
+      "In this section we compare the performance of our framework with an openly available state-of-the-art sentiment analysis tool. We choose Stanford coreNLP package as the baseline. It uses recursive deep models to do sentiment analysis and achieves good accuracy ( INLINEFORM0 ) for formal corpora BIBREF4 . However for noisy and informal texts like tweets, their performance decreases sharply. We present the performance of Stanford coreNLP tool over the test dataset.",
+      "Comparing table TABREF41 with table TABREF39 we observe that our framework outperforms stanford coreNLP by a significant margin ( INLINEFORM0 ). This owes to the fact that stanford coreNLP is not able to handle text with lot of noise, lack of formality, and slangs/abbreviations. This proves the effectiveness of our framework."
+    ],
+    [
+      "Apart from sentiment prediction, we also present some extensions to our system."
+    ],
+    [
+      "We have used a static dictionary to get prior polarity of a word, which helps detect the overall sentiment of a sentence. However the usage of words varies depending on conversation medium (e.g. : informal social media, blogs, news media), context and topic. For instance, the word `simple' is generally used in positive sense, but consider its use while describing the storyline of a movie. In this context, a `simple storyline' will probably hint at a negative sentiment. For a dynamic media like Twitter, where the topic mix and word mix change often, having a static dictionary of words with fixed polarity will not suffice. To get temporal and topic-specific sentiment terms, we make use of the tweets classified by our classifier.",
+      "We consider the words that appear in the positive, neutral and negative tweets. A word that very frequently occurs in tweets with positive (negative) sentiment and hardly occurs with tweets with a negative (positive) sentiment, will probably have a positive (negative) orientation for that particular topic. To implement this hypothesis, we first count the word frequency in each tweet collection. Then for each collection, we select top INLINEFORM0 most frequent words and deduct from top INLINEFORM1 words from other two collections. For example, in Algorithm SECREF42 , if we want to get new negative words, we find the words in top INLINEFORM2 from negative collection. And we compare the words that appear in top INLINEFORM3 of the other two, remove words that co-appear. Part of the new negative terms we find are shown in Table TABREF43 . We use same procedure to find new positive and neutral words.",
+      "Harvest New Negative Words Algorithm negativeCol, positiveCol, neutralCol new negative words from data collection",
+      " INLINEFORM0 INLINEFORM1 INLINEFORM2 INLINEFORM3 INLINEFORM4 INLINEFORM5 drop word"
+    ],
+    [
+      "Apart from predicting the sentiment class of tweets we are also interested in predicting the strength or intensity of the sentiment associated. Consider the following tweets.",
+      "t1: `GO TO YOUR US REPS OFFICE ON SATURDAY AND SAY VOTE NO! ON #HCR #Obama #cnn #killthebill #p2 #msnbc #foxnews #congress #tcot'",
+      "t2: `Thankfully the Democrat Party isn't too big to fail. #tcot #hcr'",
+      "Although both the tweets have negative sentiment towards `ObamaCare', the intensity in both are not the same. The first tweet (t1) is quite aggressive whereas the other one (t2) is not that much. Here we propose a technique to predict the strength of sentiment.",
+      "We consider few features from the tweet in order to do this. If our classifier predicts the sentiment to be neutral we say that the strength of sentiment is 0. However if it is not i.e., if it is either positive or negative, we increase strength of sentiment for each of the following features of the tweet.",
+      "Number of capitalized words.",
+      "Number of strong positive words.",
+      "Number of strong negative words.",
+      "Number of weak positive words.",
+      "Number of weak negative words.",
+      "Each of these features contributes to the strength score of a tweet. Once calculated, we normalize the score within [0-5]. Finally we assign sentiment polarity depending on the overall sentiment of the tweet. As for example, if a tweet has score of 3 and the overall predicted sentiment is negative then we give it a score of `-3'. It denotes that the tweet is moderately negative. Having said that, strength of sentiment is highly subjective. A tweet can appear to be very much aggressive to some person whereas the same may appear to not to be that aggressive to some other person."
+    ],
+    [
+      "In this report we have presented a sentiment analysis tool for Twitter posts. We have discussed the characteristics of Twitter that make existing sentiment analyzers perform poorly. The model proposed in this report has addressed the challenges by using normalization methods and features specific to this media. We show that using external knowledge outside the tweet text (from landing pages of URLs) and user features can significantly improve performance. We have presented experimental results and comparison with state-of-the-art tools.",
+      "We have presented two enhanced functionalities, i.e. discovering new sentiment terms and predicting strength of the sentiment. Due to the absence of labelled data we couldn't discuss the accuracies of these two enhancements. In the future, we plan to use these as feedback mechanism to classify new tweets."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1680/instruction.md b/qasper-1680/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..911f923a5f5c83a0402a068e2f25cff8ed588ad1
--- /dev/null
+++ b/qasper-1680/instruction.md
@@ -0,0 +1,39 @@
+Name of Paper: Large Arabic Twitter Dataset on COVID-19
+
+Question: Is the dataset focused on a region?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Dataset Description",
+    "Dataset Access",
+    "Future Work",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "On December 31, 2019, Chinese public health authorities reported several cases of a respiratory syndrome caused by an unknown disease, which subsequently became known as COVID-19 in the city of Wuhan, China. This highly contagious disease continued to spread worldwide, leading the World Health Organization (WHO) to declare a global health emergency on January 30, 2020. On March 11, 2020 the disease has been identified as pandemic by WHO, and many countries around the world including Saudi Arabia, United States, United Kingdom, Italy, Canada, and Germany have continued reporting more cases of the disease BIBREF0. As the time of writing this paper, this pandemic is affecting more than 208 countries around the globe with more than one million and half confirmed cases BIBREF1.",
+      "Since the outbreak of COVID-19, many governments around the world enforced different measures to contain the spread of the virus. The measures include travel restrictions, curfews, ban of mass gatherings, social distancing, and probably cities lock-down. This has impacted the routine of people around the globe, and many of them have turned to social media platforms for both news and communication. Since the emergence of COVID-19, Twitter platform plays a significant role in crisis communications where millions of tweets related to the virus are posted daily. Arabic is the official language of more than 22 countries with nearly 300 million native speakers worldwide. Furthermore, there is a large daily Arabic content in Twitter as millions of Arabic users use the social media network to communicate. For instance, Saudi Arabia alone has nearly 15 million Twitter users as of January, 2020 BIBREF2. Hence, it is important to analyze the Arabic users' behavior and sentiment during this pandemic. Other Twitter COVID-19 datasets have been recently proposed BIBREF3, BIBREF4 but with no significant content for the Arabic language.",
+      "In this work, we provide the first dataset dedicated to Arabic tweets related to COVID-19. The dataset is available at https://github.com/SarahAlqurashi/COVID-19-Arabic-Tweets-Dataset. We have been collecting data in real-time from Twitter API since January 1, 2020, by tracking COVID-19 related keywords which resulted in more than 3,934,610 Arabic tweets so far. The presented dataset is believed to be helpful for both researchers and policy makers in studying the pandemic from social perspective, as well as analyzing the human behaviour and information spreading during pandemics.",
+      "In what follows, we describe the dataset and the collection methods, present the initial data statistics, and provide information about how to use the dataset."
+    ],
+    [
+      "We collected COVID-19 related Arabic tweets from January 1, 2020 until April 15, 2020, using Twitter streaming API and the Tweepy Python library. We have collected more than 3,934,610 million tweets so far. In our dataset, we store the full tweet object including the id of the tweet, username, hashtags, and geolocation of the tweet. We created a list of the most common Arabic keywords associated with COVID-19. Using Twitter\u2019s streaming API, we searched for any tweet containing the keyword(s) in the text of the tweet. Table TABREF1 shows the list of keywords used along with the starting date of tracking each keyword. Furthermore, Table TABREF2 shows the list of hashtags we have been tracking along with the number of tweets collected from each hashtag. Indeed, some tweets were irrelevant, and we kept only those that were relevant to the pandemic.",
+      "A summary over the dataset is given in Table TABREF3. While collecting data, we have observed that the number of retweets increased significantly in late March. This is likely due to the exponential increase in confirmed COVID-19 cases worldwide, including the Arabic speaking countries. A relatively small percentage of tweets were geotagged. Figure FIGREF6 presents the location of tweets observed as of 14 April 2020."
+    ],
+    [
+      "The dataset is accessible on GitHub at this address: https://github.com/SarahAlqurashi/COVID-19-Arabic-Tweets-Dataset",
+      "However, to comply with Twitter\u2019s content redistribution policy, we are distributing only the IDs of the collected tweets. There are several tools (such as Hydrator) that can be used to retrieve the full tweet object. We also plan to provide more details on the pre-processing phase in the GitHub page."
+    ],
+    [
+      "We are continuously updating the dataset to maintain more aspects of COVID-19 Arabic conversations and discussions happening on Twitter. We also plan to study how different groups respond to the pandemic and analyze information sharing behavior among the users."
+    ],
+    [
+      "The authors wish to express their thanks to Batool Mohammed Hmawi for her help in data collection."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1687/instruction.md b/qasper-1687/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..ea72853ad9348cc19a2cdf966e5d073632284e6c
--- /dev/null
+++ b/qasper-1687/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Event detection in Twitter: A keyword volume approach
+
+Question: How are the keywords associated with events such as protests selected?
\ No newline at end of file
diff --git a/qasper-1690/instruction.md b/qasper-1690/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..ab158cd41527f5d551c78b961d3fbbe943281486
--- /dev/null
+++ b/qasper-1690/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Analysis of Speeches in Indian Parliamentary Debates
+
+Question: Do any speeches not fall in these categories?
\ No newline at end of file
diff --git a/qasper-1691/instruction.md b/qasper-1691/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..1cf02f37f0ce3cc248fba6144ae62c3cfbb8ac07
--- /dev/null
+++ b/qasper-1691/instruction.md
@@ -0,0 +1,187 @@
+Name of Paper: BERT Can See Out of the Box: On the Cross-modal Transferability of Text Representations
+
+Question: What is different in BERT-gen from standard BERT?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work ::: Unsupervised Pre-trained Language Models",
+    "Related Work ::: Multi-modal Language Models",
+    "Related Work ::: Visual Question Generation",
+    "Model",
+    "Model ::: Representing an Image as Text",
+    "Model ::: BERT-gen: Text Generation with BERT",
+    "Model ::: BERT-gen: Text Generation with BERT ::: Decoder",
+    "Model ::: BERT-gen: Text Generation with BERT ::: Next Token Prediction",
+    "Model ::: BERT-gen: Text Generation with BERT ::: Attention Trick",
+    "Model ::: BERT-gen: Text Generation with BERT ::: Modality-specific setups",
+    "Experimental Protocol",
+    "Experimental Protocol ::: 1. Caption only",
+    "Experimental Protocol ::: 2. Image only",
+    "Experimental Protocol ::: 3. Image + Caption",
+    "Experimental Protocol ::: Ablations",
+    "Experimental Protocol ::: Datasets",
+    "Experimental Protocol ::: Datasets ::: @!START@$VQG_{COCO}$@!END@",
+    "Experimental Protocol ::: Datasets ::: @!START@$VQA$@!END@",
+    "Experimental Protocol ::: Baselines",
+    "Experimental Protocol ::: Baselines ::: Sample",
+    "Experimental Protocol ::: Baselines ::: Max",
+    "Experimental Protocol ::: Baselines ::: MDN-Joint",
+    "Experimental Protocol ::: Baselines ::: MC-SBN",
+    "Experimental Protocol ::: Metrics",
+    "Experimental Protocol ::: Metrics ::: BLEU",
+    "Experimental Protocol ::: Metrics ::: ROUGE",
+    "Experimental Protocol ::: Metrics ::: METEOR",
+    "Experimental Protocol ::: Metrics ::: CIDEr",
+    "Experimental Protocol ::: Implementation details",
+    "Results",
+    "Results ::: Human Evaluation",
+    "Model Discussion ::: What does the model look at?",
+    "Model Discussion ::: Cross-modal alignment",
+    "Conclusion and Perspectives"
+  ],
+  "paragraphs": [
+    [
+      "The BERT language model BIBREF0 is a Deep Bidirectional Transformer BIBREF1 pre-trained on textual corpora (BookCorpus and Wikipedia) using a Masked Language Model (MLM) objective \u2013 predicting some words that are randomly masked in the sentence, along with a sentence entailment loss. Recent research efforts BIBREF2 have shown how BERT encodes abstractions that generalize across languages, even when trained on monolingual data only. This contradicts the common belief BIBREF3, BIBREF4 that a shared vocabulary and joint training on multiple languages are essential to achieve cross-lingual generalization capabilities. In this work, we further investigate the generalization potentials of large pre-trained LMs, this time moving to a cross-modal setup: does BERT contain abstractions that generalize beyond text?",
+      "In the Artificial Intelligence community, several works have investigated the longstanding research question of whether textual representations encode visual information. On the one hand, a large body of research called language grounding considers that textual representations lack visual commonsense BIBREF5, and intend to ground the meaning of words BIBREF6, BIBREF7 and sentences BIBREF8, BIBREF9 in the perceptual world. In another body of work, textual representations have successfully been used to tackle multi-modal tasks BIBREF10 such as Zero-Shot Learning BIBREF11, Visual Question Answering BIBREF12 or Image Captioning BIBREF13. Following the latter line of research, in this paper we evaluate the potential of pre-trained language models to generalize in the context of Visual Question Generation (VQG) BIBREF14.",
+      "The Visual Question Generation task allows us to investigate the cross-modal capabilities of BERT: unlike Image Captioning (where the input is only visual) or VQA (where the input is visual and textual), VQG is a multi-modal task where input can be textual and/or visual. VQG data usually includes images and the associated captions, along with corresponding questions about the image; thus, different experimental setups can be designed to analyze the impact of each modality. Indeed, the questions can be generated using i) textual (the caption), ii) visual (the image), or iii) multi-modal (both the caption and the image) input.",
+      "From a practical standpoint, the VQG task has several applications: robots or AI assistants could ask questions rooted in multi-modal data (e.g. fusing conversational data with visual information from captors and cameras), in order to refine their interpretation of the situation they are presented with. It could also allow systems relying on knowledge-bases to gain visual common sense and deal with the Human Reporting Bias BIBREF15, which states that the content of images and text are intrinsically different, since visual common sense is rarely explicitly stated in text.",
+      "Recently, BERT-based Multi-Modal Language Models have been proposed BIBREF16, BIBREF17, BIBREF18, BIBREF19 to tackle multi-modal tasks, using different approaches to incorporate visual data within BERT. From these works, it is left to explore whether the cross-modal alignment is fully learned, or it is to some extent already encoded in the BERT abstractions. Therefore, in contrast with those approaches, we explicitly avoid using the following complex mechanisms:",
+      "Multi-modal supervision: all previous works exploit an explicit multi-modal supervision through a pre-training step; the models have access to text/image pairs as input, to align their representations. In contrast, our model can switch from text-only to image-only mode without any explicit alignment.",
+      "Image-specific losses: specific losses such as Masked RoI (Region of Interest) Classification with Linguistic Clues BIBREF19 or sentence-image prediction BIBREF18 have been reported helpful to align visual and text modalities. Instead, we only use the original MLM loss from BERT (and not its entailment loss).",
+      "Non-linearities: we explore a scenario in which the only learnable parameters, for aligning image representations to BERT, are those of simple linear projection layer. This allows us to assess whether the representations encoded in BERT can transfer out-of-the-box to another modality.",
+      "Furthermore, to the best of our knowledge, this paper is the first attempt to investigate multi-modal text generation using pre-trained language models. We introduce BERT-gen, a text generator based on BERT, that can be applied both in mono and multi-modal settings. We treat images similarly to text: while a sentence is seen as a sequence of (sub)word tokens, an image is seen as a sequence of objects associated to their corresponding positions (bounding boxes). We show how a simple linear mapping, projecting visual embeddings into the first layer, is enough to ground BERT in the visual realm: text and image object representations are found to be effectively aligned, and the attention over words transfers to attention over the relevant objects in the image.",
+      "Our contributions can be summarized as follows:",
+      "we introduce BERT-gen, a novel method for generating text using BERT, that can be applied in both mono and multi-modal settings;",
+      "we show that the semantic abstractions encoded in pre-trained BERT can generalize to another modality;",
+      "we report state-of-the art results on the VQG task;",
+      "we provide extensive ablation analyses to interpret the behavior of BERT-gen under different configurations (mono- or multi- modal)."
+    ],
+    [
+      "Learning unsupervised textual representations that can be applied to downstream tasks is a widely investigated topic in the literature. Text representations have been learned at different granularities: words with Word2vec BIBREF20, sentences with SkipThought BIBREF21, paragraphs with ParagraphVector BIBREF22 and contextualized word vectors with ELMo BIBREF23. Other methods leverage a transfer-learning approach by fine-tuning all parameters of a pre-trained model on a target task, a paradigm which has become mainstream since the introduction of BERT BIBREF0. BERT alleviates the problem of the uni-directionality of most language models (i.e. where the training objective aims at predicting the next word) by proposing a new objective called Masked Language Model (MLM). Under MLM, some words, that are randomly selected, are masked; the training objective aims at predicting them."
+    ],
+    [
+      "Following the successful application of BERT BIBREF0, and its derivatives, across a great majority of NLP tasks, several research efforts have focused on the design of multi-modal versions of BERT. VideoBERT BIBREF24, a joint video and text model, is pre-trained on a huge corpus of YouTube videos, and applied to action classification and video captioning tasks on the YouCook II dataset BIBREF25. The video is treated as a \u201cvisual sentence\" (each frame being a \u201cvisual word\") that is processed by the BERT Transformer.",
+      "Concerning models jointly treating information from images and text, visual features extracted from the image are used as \u201cvisual words\", and a [SEP] special token is employed to separate textual and visual tokens. In the literature, visual features are object features extracted with a Faster R-CNN BIBREF26 \u2013 with the notable exception of BIBREF27 who used pooling layers from a CNN. A first body of work exploit single-stream Transformers in which visual features are incorporated in a BERT-like Transformer: this is the case for VisualBERT BIBREF18, VL-BERT BIBREF19, Unicoder-VL BIBREF28 and B2T2 BIBREF29. Other works, such as ViLBERT BIBREF16 and LXMERT BIBREF17 have investigated two-stream approaches: these models employ modality-specific encoders built on standard Transformer blocks, which are then fused into a cross-modal encoder. Interestingly, none of the aforementioned models have been used for generation tasks such as VQG, tackled in this work."
+    ],
+    [
+      "The text-based Question Generation task has been largely studied by the NLP community BIBREF30, BIBREF31, BIBREF32, BIBREF33, BIBREF34, BIBREF35, BIBREF36. However, its visual counterpart, Visual Question Generation (VQG), has been comparatively less explored than standard well-known multi-modal tasks such as Visual Question Answering (VQA) BIBREF37, BIBREF38, BIBREF39, BIBREF40, Visual Dialog BIBREF41, BIBREF42, or Image Captioning BIBREF43, BIBREF44, BIBREF45.",
+      "The VQG task was first introduced by BIBREF46 in their Neural Self Talk model: the goal is to gain knowledge about an image by iteratively generating questions (VQG) and answering them (VQA). The authors tackle the task with a simple RNN conditioned on the image, following Image Captioning works such as BIBREF45.",
+      "Suitable data for the VQG task can come from standard image datasets on which questions have been manually annotated, such as $VQG_{COCO}$, $VQG_{Flickr}$, $VQG_{Bing}$ BIBREF14 , each consisting of 5000 images with 5 questions per image. Alternatively, VQG samples can be derived from Visual Question Answering datasets, such as $VQA1.0$ BIBREF47, by \u201creversing\" them (taking images as inputs and questions as outputs).",
+      "A variety of approaches have been proposed. BIBREF14 use a standard Gated Recurrent Neural Network, i.e. a CNN encoder followed by a GRU decoder to generate questions. BIBREF48 aim at generating, for a given image, multiple visually grounded questions of varying types (what, when, where, etc.); similarly, BIBREF49 generate diverse questions using Variational Autoencoders. In BIBREF50, VQG is jointly tackled along its dual task (VQA), just as BIBREF46. In BIBREF51, BIBREF52, the image (processed by a CNN) and the caption (processed by a LSTM) are combined in a mixture module, followed by a LSTM decoder to generate the question, leading to state-of-the-art results on the VQG task on $VQA1.0$ data. More recently, BIBREF53 incorporate multiple cues \u2013 place information obtained from PlaceCNN BIBREF54, caption, tags \u2013 and combine them within a deep Bayesian framework where the contribution of each cue is weighted to predict a question, obtaining the current state-of-the-art results on $VQG_{COCO}$."
+    ],
+    [
+      "In VQG, the objective is to generate a relevant question from an image and/or its caption. The caption $X_{txt}$ is composed of $M$ tokens $txt_1, ..., txt_M$; these tokens can be words or subwords (smaller than word) units depending on the tokenization strategy used. As BERT uses subword tokenization, throughout this paper we will refer to subwords as our tokenization units.",
+      "The proposed model is illustrated in Figure FIGREF11. In SECREF12, we detail how images are incorporated in the Transformer framework. In SECREF14, we present BERT-gen, a novel approach to use BERT for text generation."
+    ],
+    [
+      "In this work, we treat textual and visual inputs similarly, by considering both as sequences. Since an image is not a priori sequential, we consider the image $X_{img}$ as a sequence of object regions $img_1, ..., img_N$, as described below.",
+      "The images are first processed as in BIBREF17: a Faster-RCNN BIBREF26, pre-trained on Visual Genome BIBREF55, detects the $N=36$ most salient regions (those likely to include an object) per image. The weights of the Faster-RCNN are fixed during training, as we use the precomputed representations made publicly available by BIBREF56. Each image is thus represented by a sequence of $N=36$ semantic embeddings $f_1, ... f_{N}$ (one for each object region) of dimension 2048, along with the corresponding bounding box coordinates $b_1, ... b_{N}$ of dimension 4. With this approach, the BERT attention can be computed at the level of objects or salient image regions; had we represented images with traditional CNN features, the attention would instead correspond to a uniform grid of image regions without particular semantics, as noted in BIBREF56. To build an object embedding $o_j$ encoding both the object region semantics and its location in the image, we concatenate $f_j$ and $b_j$ ($j\\in [1,N]$). Hence, an image is seen as a sequence of $N=36$ visual representations (each corresponding to an object region) $o_1,..., o_N$. Object region representations $o_i$ are ordered by the relevance of the object detected, and the model has access to their relative location in the image through the vectors $b_i$.",
+      "To investigate whether our BERT-based model can transfer knowledge beyond language, we consider image features as simple visual tokens that can be presented to the model analogously to textual token embeddings. In order to make the $o_j$ vectors (of dimension $2048+4=2052$) comparable to BERT embeddings (of dimension 768), we use a simple linear cross-modal projection layer $W$ of dimensions $2052\\hspace{-1.00006pt}\\times \\hspace{-1.00006pt}768$. The $N$ object regions detected in an image, are thus represented as $X_{img} = (W.o_1,...,W.o_N)$. Once mapped into the BERT embedding space with $W$, the image is seen by the rest of the model as a sequence of units with no explicit indication if it is a text or an image embedding."
+    ],
+    [
+      "We cast the VQG task as a classic sequence-to-sequence BIBREF57 modeling framework:",
+      "where the input $X=X_{txt}$ in caption-only mode, $X = X_{img}$ in image-only mode, and $X =X_{img} \\oplus X_{txt}$ in a multi-modal setup; $Y = {y_1,..., y_T}$ is the question composed of $T$ tokens. $\\Theta $ are the parameters of the BERT model; $W$ represents the weights of the linear layer used for projecting visual input to the BERT embedding layer.",
+      "As mentioned earlier, BERT is a Transformer BIBREF1 encoder pre-trained using the Masked Language Model (MLM) objective: tokens within the text are replaced with a [MASK] special token, and the model is trained to predict them. Since BERT was not trained with an unidirectional objective, its usage for text generation is not straightforward.",
+      "To generate text, BIBREF58 propose to stack a Transformer decoder, symmetric to BERT. However, the authors report training difficulties since the stacked decoder is not pre-trained, and propose a specific training regime, with the side-effect of doubling the number of parameters. BIBREF59 opt for an intermediate step of self-supervised training, introducing a unidirectional loss. As detailed below, we propose a relatively simpler, yet effective, method to use BERT out-of-the-box for text generation."
+    ],
+    [
+      "We simply use the original BERT decoder as is, initially trained to generate the tokens masked during its pre-training phase. It consists in a feed-forward layer, followed by normalization, transposition of the embedding layer, and a softmax over the vocabulary."
+    ],
+    [
+      "At inference time, to generate the first token of the question $y_1$, we concatenate [MASK] to the input tokens $X$, then encode $X \\oplus \\texttt {[MASK]}$ with the BERT encoder, and feed the output of the encoder to the decoder; $y_1$ is the output of the decoder for the [MASK] token. Subsequently, given $y_1$, we concatenate it to the input tokens and encode $X \\oplus y_1 \\oplus \\texttt {[MASK]}$ to predict the next token $y_2$. This procedure is repeated until the generation of a special token [EOS] signaling the end of the sentence."
+    ],
+    [
+      "As we iteratively concatenate the generated tokens, the BERT bi-directional self-attention mechanism would impact, at every new token, the representations of the previous tokens. To counter that, we use a left-to-right attention mask, similar to the one employed in the original Transformer decoder BIBREF1. For the input tokens in $X$, we apply such mask to all the target tokens $Y$ that were concatenated to $X$, so that input tokens can only attend to the other input tokens. Conversely, for target tokens $y_t$, we put an attention mask on all tokens $y_{>t}$, allowing target tokens $y_t$ to attend only to the input tokens and the already generated target tokens.",
+      "This novel method allows to use pre-trained encoders for text generation. In this work, we initialize our model with the parameters from BERT-base. Nonetheless, the methodology can be applied to any pre-trained Transformer encoders such as RoBERTa BIBREF60, or Ernie BIBREF61."
+    ],
+    [
+      "The proposed model can be used in either mono- or multi- modal setups. This is accomplished by activating or deactivating specific modules."
+    ],
+    [
+      "Our main objective is to measure whether the textual knowledge encoded in pre-trained BERT can be beneficial in a cross-modal task. Thus, we define the three following experimental setups, which we refer to as Step 1, 2, and 3:"
+    ],
+    [
+      "Deactivating the Visual embedding module (see Figure FIGREF11), the model has only access to textual input, i.e. the caption. The model is initialized with the BERT weights and trained according to Equation DISPLAY_FORM15."
+    ],
+    [
+      "Conversely, deactivating the Textual embedding module (see Figure FIGREF11), the model has only access to the input image, not the caption. To indicate the position $t$ of $img_t$ in the sequence, we sum the BERT positional embedding of $t$ to the visual representation of $img_t$, just as we would do for a text token $txt_t$. The model is initialized with the weights learned during step 1. All BERT-gen $\\Theta $ weights are frozen, and only the linear layer $W$ is learnable. Hence, if the model is able to learn to generate contextualized questions w.r.t. the image, it shows that a simple linear layer is enough to bridge the two modalities."
+    ],
+    [
+      "The full model is given access to both image and caption inputs. In this setup, we separate the two different inputs by a special BERT token [SEP]. Thus, the input sequence for the model takes the form of $\\texttt {[CLS]}, img_1,..., img_N, \\texttt {[SEP]}, txt_1,..., txt_M$. In step 1, only BERT-gen $\\Theta $ parameters are learned, as no image input was given. In step 2, $W$ is trained while keeping $\\Theta $ frozen. Finally then, in step 3, we fine-tune the model using both image and text inputs: the model is initialized with the parameters $\\Theta $ learned during step 1 and the $W$ learned during step 2, and we unfreeze all parameters."
+    ],
+    [
+      "Additionally, we report results obtained with: Image only (unfreeze), where the BERT-gen parameters $\\Theta $ are not frozen; and Image+Caption (from scratch) where the model is learned without the intermediate steps 1 and 2: the BERT-gen parameters $\\Theta $ are initialized with the weights from pre-trained BERT while $W$ is randomly initialized."
+    ],
+    [
+      "We conduct our experiments using two established datasets for Visual Question Generation:"
+    ],
+    [
+      "Introduced by BIBREF14, it contains 2500 training images, 1250 validation images and 1250 test images from MS COCO BIBREF62; each image has 5 corresponding questions and 5 ground-truth captions."
+    ],
+    [
+      "The Visual Question Answering BIBREF47 dataset can be used to derive VQG data BIBREF50. The task is reversed: instead of answering the question based on the image (VQA), models are called to generate a relevant question given the image (VQG). Also based on MS COCO, it contains 82783 training images, 40504 validation images and 81434 testing images. In $VQA1.0$, each image has 3 associated questions. Since the test set of MS COCO does not contain ground-truth captions, we generated artificial captions for it using NeuralTalk2 BIBREF45: for fair comparison, we used exactly the same model as BIBREF52 (MDN-Joint)."
+    ],
+    [
+      "We compare the proposed model to the following:"
+    ],
+    [
+      "BIBREF46 Questions are generated by a RNN conditioned on the image: at each generation step, the distribution over the vocabulary is computed and used to sample the next generated word. This baseline enables to generate diverse questions over the same image, as the word selection process is non-deterministic."
+    ],
+    [
+      "BIBREF46 Using the above model, selecting words with maximum probability from the computed distribution."
+    ],
+    [
+      "BIBREF52 State-of-the-art model on $VQA1.0$, based on joint usage of caption and image information."
+    ],
+    [
+      "BIBREF53 State-of-the-art on $VQG_{COCO}$. The model jointly leverages on multiple cues (the image, place information, caption, tags) to generate questions."
+    ],
+    [
+      "We report the following metrics for all experiments, consistently with previous works:"
+    ],
+    [
+      "BIBREF63 A precision-oriented metric, originally proposed to evaluate machine translation. It is based on the counts of overlapping n-grams between the generated sequences and the human references."
+    ],
+    [
+      "BIBREF64 The recall-oriented counterpart to BLEU metrics, again based on n-gram overlaps."
+    ],
+    [
+      "BIBREF65 The harmonic mean between precision and recall w.r.t. unigrams. As opposed to the other metrics, it also accounts for stemming and synonymy matching."
+    ],
+    [
+      "BIBREF66 Originally designed for Image Captioning, it uses human consensus among the multiple references, favoring rare words and penalizing frequent words. This feature is particularly relevant for our task, as the automatically generated questions often follow similar patterns such as \u201cWhat is the [...] ?\". Indeed, we verify experimentally (cf Table and Table ) that the CIDEr metric is the most discriminant in our quantitative results."
+    ],
+    [
+      "All models are implemented in PyText BIBREF67. For all our experiments we used a single NVIDIA RTX 2080 Ti GPU, a batch size of 128 and 5 epochs. We used the Adam optimizer with the recommended parameters for BERT: learning rate is set at $2e^{-5}$ with a warmup of $0.1$. The most computationally expensive experiment is the step 3 described above: for this model, completion of one epoch demands 30 seconds and 2 minutes for $VQG_{COCO}$ and $VQA$ datasets, respectively. Metrics were computed using the Python package released by BIBREF33."
+    ],
+    [
+      "In Table , we report quantitative results for the VQG task on $VQA1.0$. The Caption only model already shows strong improvements for all metrics over state-of-the-art models. For this text-only model, the impressive performance can mostly be attributed to BERT, demonstrating once again the benefits obtained using pre-trained language models. In our second step (Image only), the BERT $\\Theta $ parameters are frozen and only those of the cross-modal projection matrix $W$ are learned. Despite using a simple linear layer, the model is found to perform well, generating relevant questions given only visual inputs.",
+      "This suggests that the conceptual representations encoded in pre-trained language models such as BERT can effectively be used beyond text. Further, we report an additional Image only experiment, this time unfreezing the BERT parameters $\\Theta $ \u2013 see Step 2 (unfreeze) in Table . As could be expected, since the model is allowed more flexibility, the performance is found to further improve.",
+      "Finally, in our third step (Image + Caption), we obtain the highest scores, for all metrics. This indicates that the model is able to effectively leverage the combination of textual and visual inputs. Indeed, complementary information from both modalities can be exploited by the self-attention mechanism, making visual and textual tokens interact to generate the output sequences. Again, we additionally report the results obtained bypassing the intermediate steps 1 and 2: for the model denoted as Step 3 (from scratch) (last row of Table ), $\\Theta $ parameters are initialized with the original weights from pre-trained BERT, while the $W$ matrix is randomly initialized. Under this experimental condition, we observe lower performances, a finding that consolidates the importance of the multi-step training procedure we adopted.",
+      "In Table , we report quantitative VQG results on $VQG_{COCO}$. These are globally consistent with the ones above for $VQA1.0$. However, we observe two main differences. First, a bigger relative improvement over the state-of-the-art. As the efficacy of pre-trained models is boosted in small-data scenarios BIBREF68, this difference can be explained by the smaller size of $VQG_{COCO}$. Second, we note that the Caption only model globally outperforms all other models, especially on the discriminant CIDEr metric. This can be explained by the fact that, in $VQG_{COCO}$, the captions are human-written (whereas they are automatically generated for $VQA1.0$) and, thus, of higher quality; moreover, the smaller size of the dataset could play a role hindering the ability to adapt to the visual modality. Nonetheless, the strong performances obtained for Step 2 compared to the baselines highlight the effectiveness of our method to learn a cross-modal projection even with a relatively small number of training images."
+    ],
+    [
+      "To get more in-depth understanding of our models, we report human assessment results in Table . We randomly sampled 50 images from the test set of $VQA1.0$. Each image is paired with its caption, the human-written question used as ground-truth, and the output for our three models: Caption only, Image only and Image+Caption. We asked 3 human annotators to assess the quality of each question using a Likert scale ranging from 1 to 5, for the following criteria: readability, measuring how well-written the question is; caption relevance, how relevant the question is w.r.t. to the caption; and, image relevance, how relevant the question is toward the image. For caption and image relevance, the annotators were presented with only the caption and only the image, respectively.",
+      "We observe that all evaluated models produce well-written sentences, as readability does not significantly differ compared to human's questions. Unsurprisingly, the Caption only model shows a higher score for caption relevance, while the relatively lower image relevance score can be explained by the automatically generated and thus imperfect captions in the $VQA1.0$ dataset. Comparatively, the Image only model obtains lower caption relevance and higher image relevance scores; this indicates that the cross modal projection is sufficient to bridge modalities, allowing BERT to generate relevant questions toward the image. Finally, the Image + Caption model obtains the best image relevance among our models, consistently the quantitative results reported in Tables and ."
+    ],
+    [
+      "To interpret the behavior of attention-based models, it is useful to look at which tokens are given higher attention BIBREF69. In Figure FIGREF44, we present two images $A$ and $B$, along with their captions and the three generated questions corresponding to our three experimental setups (Caption only, Image only and Image + Caption). For this analysis, we average the attention vectors of all the heads in the last layer, and highlight the textual and visual tokens most attended by the models.",
+      "For both images, the Caption only model attends to salient words in the caption. The Image only model remains at least as much relevant: on image $A$, it generates a question about a table (with an unclear attention). Interestingly, for image $B$, the Image only model corrects a mistake from step 1: it is a woman holding an umbrella rather than a man, and the attention is indeed focused on the woman in the image. Finally, the Image + Caption model is able to generate fitting questions about the image, with relatively little relevance to the caption: for image $A$, Image + Caption the model generates \u201cWhat time is it?\" while paying attention to the clock; for image $B$, Image + Caption generates \u201cWhat is the color of the umbrella ?\", focusing the attention on the umbrella. The captions of either samples include no mentions of clocks or umbrellas, further indicating effective alignment between visual and textual representations."
+    ],
+    [
+      "We carry out an additional experiment to analyze the text/vision alignment for each model. Figure FIGREF46 shows the cross-modal similarity $X_{sim}$ for different model scenarios, computed at each BERT-base layer from 1 to 12. We define the cross-modal similarity $X_{sim}$ as the cosine similarity between the vector representations of both modalities. These vectors are the two continuous space representations from a model when given as input either i) an image, or ii) its corresponding caption. We represent these captions and images vectors with the special BERT token [CLS], following previous works BIBREF70 where [CLS] is used to represent the entire sequence.",
+      "The reported values correspond to the average cross-modal similarity calculated for all the examples of $VQG_{COCO}$ test set. In addition to the setups described in Section SECREF4 (Caption-only, Image-only and Image + Caption), we also report $X_{sim}$ for Random Transformer, a BERT architecture with random weights. As expected, its $X_{sim}$ is close to zero.",
+      "All the other models are based on BERT. As suggested by BIBREF71, the first layers in BERT tend to encode lower-level language information. This might explain why the models show similar $X_{sim}$ scores up to the 9th layer, and diverge afterwards: the weights for those layers remain very similar between our fine-tuned models.",
+      "For the last layer ($l=12$), we observe that $\\textit {Caption only} < \\textit {Image only} < \\textit {Image + Caption}$. The Caption only model has never seen images during training, and therefore is not able to encode semantic information given only images as input. Still, its reported $X_{sim} > 0$ can be attributed to the fact that, when fine-tuned on VQG during Step 1, BERT-gen encodes task-specific information in the [CLS] token embedding (e.g. a question ends with a \u201c?\" and often begins with \u201cWhat/Where/Who\"). $\\textit {Image only} > \\textit {Caption only}$ can be explained by the learning of the cross-modal projection $W$. However, since BERT is not fine-tuned, the model learns a \u201ccontortion\" allowing it to align text and vision. Finally, Image + Caption $>$ Image only can be attributed to BERT fine-tuning, contributing to an increase in the observed gap, and its emergence in earlier layers."
+    ],
+    [
+      "We investigated whether the abstractions encoded in a pre-trained BERT model can generalize beyond text. We proposed BERT-gen, a novel methodology that allows to directly generate text from out-of-the-box pre-trained encoders, either in mono- or multi- modal setups. Moreover, we applied BERT-gen to Visual Question Generation, obtaining state-of-the-art results on two established datasets. We showed how a simple linear projection is sufficient to effectively align visual and textual representations.",
+      "In future works, we plan to extend BERT-gen to other modalities, such as audio or video, exploring the potential interactions that can emerge in scenarios where more than two modalities are present."
+    ]
+  ]
+}
+```
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+Name of Paper: Leveraging Discourse Information Effectively for Authorship Attribution
+
+Question: How are discourse embeddings analyzed?
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+Name of Paper: Leveraging Discourse Information Effectively for Authorship Attribution
+
+Question: What was the previous state-of-the-art?
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+Name of Paper: Leveraging Discourse Information Effectively for Authorship Attribution
+
+Question: How are discourse features incorporated into the model?
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+Name of Paper: Leveraging Discourse Information Effectively for Authorship Attribution
+
+Question: What discourse features are used?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Background",
+    "Models",
+    "Experiments and Results",
+    "Datasets",
+    "Featurization",
+    "Experiments",
+    "Results",
+    "Analysis",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Authorship attribution (AA) is the task of identifying the author of a text, given a set of author-labeled training texts. This task typically makes use of stylometric cues at the surface lexical and syntactic level BIBREF0 , although BIBREF1 and BIBREF2 go beyond the sentence level, showing that discourse information can help. However, they achieve limited performance gains and lack an in-depth analysis of discourse featurization techniques. More recently, convolutional neural networks (CNNs) have demonstrated considerable success on AA relying only on character-level INLINEFORM0 -grams BIBREF3 , BIBREF4 . The strength of these models is evidenced by findings that traditional stylometric features such as word INLINEFORM1 -grams and POS-tags do not improve, and can sometimes even hurt performance BIBREF3 , BIBREF5 . However, none of these CNN models make use of discourse.",
+      "Our work builds upon these prior studies by exploring an effective method to (i) featurize the discourse information, and (ii) integrate discourse features into the best text classifier (i.e., CNN-based models), in the expectation of achieving state-of-the-art results in AA.",
+      " BIBREF1 (henceforth F&H14) made the first comprehensive attempt at using discourse information for AA. They employ an entity-grid model, an approach introduced by BIBREF6 for the task of ordering sentences. This model tracks how the grammatical relations of salient entities (e.g., subj, obj, etc.) change between pairs of sentences in a document, thus capturing a form of discourse coherence. The grid is summarized into a vector of transition probabilities. However, because the model only records the transition between two consecutive sentences at a time, the coherence is local. BIBREF2 (henceforth F15) further extends the entity-grid model by replacing grammatical relations with discourse relations from Rhetorical Structure Theory BIBREF7 . Their study uses a linear-kernel SVM to perform pairwise author classifications, where a non-discourse model captures lexical and syntactic features. They find that adding the entity-grid with grammatical relations enhances the non-discourse model by almost 1% in accuracy, and using RST relations provides an improvement of 3%. The study, however, works with only one small dataset and their models produce overall unremarkable performance ( INLINEFORM0 85%). BIBREF8 propose an advanced Recursive Neural Network (RecNN) architecture to work with RST in the more general area of text categorization and present impressive results. However, we suspect that the massive number of parameters of RecNNs would likely cause overfitting when working with smaller datasets, as is often the case in AA tasks.",
+      "In our paper, we opt for a state-of-the-art character bigram CNN classifier BIBREF4 , and investigate various ways in which the discourse information can be featurized and integrated into the CNN. Specifically,",
+      "We explore these questions using two approaches to represent salient entities: grammatical relations, and RST discourse relations. We apply these models to datasets of varying sizes and genres, and find that adding any discourse information improves AA consistently on longer documents, but has mixed results on shorter documents. Further, embedding the discourse features in a parallel CNN at the input end yields better performance than concatenating them to the output layer as a feature vector (Section SECREF3 ). The global featurization is more effective than the local one. We also show that SVMs, which can only use discourse probability vectors, neither produce a competitive performance (even with fine-tuning), nor generalize in using the discourse information effectively."
+    ],
+    [
+      "Entity-grid model. Typical lexical features for AA are relatively superficial and restricted to within the same sentence. F&H14 hypothesize that discourse features beyond the sentence level also help authorship attribution. In particular, they propose an author has a particular style for representing entities across a discourse. Their work is based on the entity-grid model of BIBREF6 (henceforth B&L).",
+      "The entity-grid model tracks the grammatical relation (subj, obj, etc.) that salient entities take on throughout a document as a way to capture local coherence . A salient entity is defined as a noun phrase that co-occurs at least twice in a document. Extensive literature has shown that subject and object relations are a strong signal for salience and it follows from the Centering Theory that you want to avoid rough shifts in the center BIBREF9 , BIBREF10 . B&L thus focus on whether a salient entity is a subject (s), object (o), other (x), or is not present (-) in a given sentence, as illustrated in Table TABREF1 . Every sentence in a document is encoded with the grammatical relation of all the salient entities, resulting in a grid similar to Table TABREF6 .",
+      "The local coherence of a document is then defined on the basis of local entity transitions. A local entity transition is the sequence of grammatical relations that an entity can assume across INLINEFORM0 consecutive sentences, resulting in {s,o,x,-} INLINEFORM1 possible transitions. Following B&L, F&H14 consider sequences of length INLINEFORM2 =2, that is, transitions between two consecutive sentences, resulting in INLINEFORM3 =16 possible transitions. The probability for each transition is then calculated as the frequency of the transition divided by the total number of transitions. This step results in a single probability vector for every document, as illustrated in Table TABREF2 .",
+      "B&L apply this model to a sentence ordering task, where the more coherent option, as evidenced by its transition probabilities, was chosen. In authorship attribution, texts are however assumed to already be coherent. F&H14 instead hypothesize that an author unconsciously employs the same methods for describing entities as the discourse unfolds, resulting in discernible transition probability patterns across multiple of their texts. Indeed, F&H14 find that adding the B&L vectors increases the accuracy of AA by almost 1% over a baseline lexico-syntactic model.",
+      "RST discourse relations. F15 extends the notion of tracking salient entities to RST. Instead of using grammatical relations in the grid, RST discourse relations are specified. An RST discourse relation defines the relationship between two or more elementary discourse units (EDUs), which are spans of text that typically correspond to syntactic clauses. In a relation, an EDU can function as a nucleus (e.g., result.N) or as a satellite (e.g., summary.S). All the relations in a document then form a tree as in Figure FIGREF8 .",
+      "F15 finds that RST relations are more effective for AA than grammatical relations. In our paper, we populate the entity-grid in the same way as F15's \u201cShallow RST-style\u201d encoding, but use fine-grained instead of coarse-grained RST relations, and do not distinguish between intra-sentential and multi-sentential RST relations, or salient and non-salient entities. We explore various featurization techniques using the coding scheme.",
+      "CNN model. shrestha2017 propose a convolutional neural network formulation for AA tasks (detailed in Section SECREF3 ). They report state-of-the-art performance on a corpus of Twitter data BIBREF11 , and compare their models with alternative architectures proposed in the literature: (i) SCH: an SVM that also uses character n-grams, among other stylometric features BIBREF11 ; (ii) LSTM-2: an LSTM trained on bigrams BIBREF12 ; (iii) CHAR: a Logistic Regression model that takes character n-grams BIBREF13 ; (iv) CNN-W: a CNN trained on word embeddings BIBREF14 . The authors show that the model CNN2 produces the best performance overall. Ruder:16 apply character INLINEFORM0 -gram CNNs to a wide range of datasets, providing strong empirical evidence that the architecture generalizes well. Further, they find that including word INLINEFORM1 -grams in addition to character INLINEFORM2 -grams reduces performance, which is in agreement with BIBREF5 's findings."
+    ],
+    [
+      "Building on shrestha2017's work, we employ their character-bigram CNN (CNN2), and propose two extensions which utilize discourse information: (i) CNN2 enhanced with relation probability vectors (CNN2-PV), and (ii) CNN2 enhanced with discourse embeddings (CNN2-DE). The CNN2-PV allows us to conduct a comparison with F&H14 and F15, which also use relation probability vectors.",
+      "CNN2. CNN2 is the baseline model with no discourse features. Illustrated in Figure FIGREF10 (center), it consists of (i) an embedding layer, (ii) a convolution layer, (iii) a max-pooling layer, and (iv) a softmax layer. We briefly sketch the processing procedure and refer the reader to BIBREF4 for mathematical details.",
+      "The network takes a sequence of character bigrams INLINEFORM0 as input, and outputs a multinomial INLINEFORM1 over class labels as the prediction. The model first looks up the embedding matrix to produce a sequence of embeddings for INLINEFORM2 (i.e., the matrix INLINEFORM3 ), then pushes the embedding sequence through convolutional filters of three bigram-window sizes INLINEFORM4 , each yielding INLINEFORM5 feature maps. We then apply the max-over-time pooling BIBREF15 to the feature maps from each filter, and concatenate the resulting vectors to obtain a single vector INLINEFORM6 , which then goes through the softmax layer to produce predictions.",
+      "CNN2-PV. This model (Figure FIGREF10 , left+center) featurizes discourse information into a vector of relation probabilities. In order to derive the discourse features, an entity grid is constructed by feeding the document through an NLP pipeline to identify salient entities. Two flavors of discourse features are created by populating the entity grid with either (i) grammatical relations (GR) or (ii) RST discourse relations (RST). The GR features are represented as grammatical relation transitions derived from the entity grid, e.g., INLINEFORM0 . The RST features are represented as RST discourse relations with their nuclearity, e.g., INLINEFORM1 . The probability vectors are then distributions over relation types. For GR, the vector is a distribution over all the entity role transitions, i.e., INLINEFORM2 (see Table TABREF2 ). For RST, the vector is a distribution over all the RST discourse relations, i.e., INLINEFORM3 Denoting a feature as such with INLINEFORM4 , we construct the pooling vector INLINEFORM5 for the char-bigrams, and concatenate INLINEFORM6 to INLINEFORM7 before feeding the resulting vector to the softmax layer.",
+      "CNN2-DE. In this model (Figure FIGREF10 , center+right), we embed discourse features in high-dimensional space (similar to char-bigram embeddings). Let INLINEFORM0 be a sequence of discourse features, we treat it in a similar fashion to the char-bigram sequence INLINEFORM1 , i.e. feeding it through a \u201cparallel\u201d convolutional net (Figure FIGREF10 right). The operation results in a pooling vector INLINEFORM2 . We concatenate INLINEFORM3 to the pooling vector INLINEFORM4 (which is constructed from INLINEFORM5 ) then feed INLINEFORM6 to the softmax layer for the final prediction."
+    ],
+    [
+      "We begin by introducing the datasets (Section SECREF15 ), followed by detailing the featurization methods (Section SECREF17 ), the experiments (Section SECREF22 ), and finally reporting results (Section SECREF26 )."
+    ],
+    [
+      "The statistics for the three datasets used in the experiments are summarized in Table TABREF16 .",
+      "novel-9. This dataset was compiled by F&H14: a collection of 19 novels by 9 nineteenth century British and American authors in the Project Gutenberg. To compare to F&H14, we apply the same resampling method (F&H14, Section 4.2) to correct the imbalance in authors by oversampling the texts of less-represented authors.",
+      "novel-50. This dataset extends novel-9, compiling the works of 50 randomly selected authors of the same period. For each author, we randomly select 5 novels for a total 250 novels.",
+      "IMDB62. IMDB62 consists of 62K movie reviews from 62 users (1,000 each) from the Internet Movie dataset, compiled by Seroussi:11. Unlike the novel datasets, the reviews are considerably shorter, with a mean of 349 words per text."
+    ],
+    [
+      "As described in Section SECREF2 , in both the GR and RST variants, from each input entry we start by obtaining an entity grid.",
+      "CNN2-PV. We collect the probabilities of entity role transitions (in GR) or discourse relations (in RST) for the entries. Each entry corresponds to a probability distribution vector.",
+      "CNN2-DE. We employ two schema for creating discourse feature sequences from an entity grid. While we always read the grid by column (by a salient entity), we vary whether we track the entity across a number of sentences (n rows at a time) or across the entire document (one entire column at a time), denoted as local and global reading respectively.",
+      "For the GR discourse features, in the case of local reading, we process the entity roles one sentence pair at a time (Figure FIGREF18 , left). For example, in processing the pair INLINEFORM0 , we find the first non-empty role INLINEFORM1 for entity INLINEFORM2 in INLINEFORM3 . If INLINEFORM4 also has a non-empty role INLINEFORM5 in the INLINEFORM6 , we collect the entity role transition INLINEFORM7 . We then proceed to the following entity INLINEFORM8 , until we process all the entities in the grid and move to the next sentence pair. For the global reading, we instead read the entity roles by traversing one column of the entire document at a time (Figure FIGREF18 , right). The entity roles in all the sentences are read for one entity: we collect transitions for all the non-empty roles (e.g., INLINEFORM9 , but not INLINEFORM10 ).",
+      "For the RST discourse features, we process non-empty discourse relations also through either local or global reading. In the local reading, we read all the discourse relations in a sentence (a row) then move on to the next sentence. In the global reading, we read in discourse relations for one entity at a time. This results in sequences of discourse relations for the input entries."
+    ],
+    [
+      "Baseline-dataset experiments. All the baseline-dataset experiments are evaluated on novel-9. As a comparison to previous work (F15), we evaluate our models using a pairwise classification task with GR discourse features. In her model, novels are partitioned into 1000-word chunks, and the model is evaluated with accuracy. Surpassing F15's SVM model by a large margin, we then further evaluate the more difficult multi-class task, i.e., all-class prediction simultaneously, with both GR and RST discourse features and the more robust F1 evaluation. In this multi-class task, we implement two SVMs to extend F15's SVM models: (i) SVM2: a linear-kernel SVM which takes char-bigrams as input, as our CNNs, and (ii) SVM2-PV: an updated SVM2 which takes also probability vector features.",
+      "Further, we are interested in finding a performance threshold on the minimally-required input text length for discourse information to \u201ckick in\u201d. To this end, we chunk the novels into different sizes: 200-2000 words, at 200-word intervals, and evaluate our CNNs in the multi-class condition.",
+      "Generalization-dataset experiments. To confirm that our models generalize, we pick the best models from the baseline-dataset experiments and evaluate on the novel-50 and IMDB62 datasets. For novel-50, the chunking size applied is 2000-word as per the baseline-dataset experiment results, and for IMDB62, texts are not chunked (i.e., we feed the models with the original reviews directly). For model comparison, we also run the SVMs (i.e., SVM2 and SVM2-PV) used in the baseline-dataset experiment. All the experiments conducted here are multi-class classification with macro-averaged F1 evaluation.",
+      "Model configurations. Following F15, we perform 5-fold cross-validation. The embedding sizes are tuned on novel-9 (multi-class condition): 50 for char-bigrams; 20 for discourse features. The learning rate is 0.001 using the Adam Optimizer BIBREF18 . For all models, we apply dropout regularization of 0.75 BIBREF19 , and run 50 epochs (batch size 32). The SVMs in the baseline-dataset experiments use default settings, following F15. For the SVMs in the generalization-dataset experiments, we tuned the hyperparameters on novel-9 with a grid search, and found the optimal setting as: stopping condition tol is 1e-5, at a max-iteration of 1,500."
+    ],
+    [
+      "Baseline-dataset experiments. The results of the baseline-dataset experiments are reported in Table TABREF24 , TABREF25 and Figure FIGREF27 . In Table TABREF24 , Baseline denotes the dumb baseline model which always predicts the more-represented author of the pair. Both SVMs are from F15, and we report her results. SVM (LexSyn) takes character and word bi/trigrams and POS tags. SVM (LexSyn-PV) additionally includes probability vectors, similar to our CNN2-PV. In this part of the experiment, while the CNNs clear a large margin over SVMs, adding discourse in CNN2-PV brings only a small performance gain.",
+      "Table TABREF25 reports the results from the multi-class classification task, the more difficult task. Here, probability vector features (i.e., PV) again fail to contribute much. The discourse embedding features, on the other hand, manage to increase the F1 score by a noticeable amount, with the maximal improvement seen in the CNN2-DE (global) model with RST features (by 2.6 points). In contrast, the discourse-enhanced SVM2-PVs increase F1 by about 1 point, with overall much lower scores in comparison to the CNNs. In general, RST features work better than GR features.",
+      "The results of the varying-sizes experiments are plotted in Figure FIGREF27 . Again, we observe the overall pattern that discourse features improve the F1 score, and RST features procure superior performance. Crucially, however, we note there is no performance boost below the chunk size of 1000 for GR features, and below 600 for RST features. Where discourse features do help, the GR-based models achieve, on average, 1 extra point on F1, and the RST-based models around 2.",
+      "Generalization-dataset experiments. Table TABREF28 summarizes the results of the generalization-dataset experiments. On novel-50, most discourse-enhanced models improve the performance of the baseline non-discourse CNN2 to varying degrees. The clear pattern again emerges that RST features work better, with the best F1 score evidenced in the CNN2-DE (global) model (3.5 improvement in F1). On IMDB62, as expected with short text inputs (mean=349 words/review), the discourse features in general do not add further contribution. Even the best model CNN2-DE brings only marginal improvement, confirming our findings from varying the chunk size on novel-9, where discourse features did not help at this input size. Equipped with discourse features, SVM2-PV performs slightly better than SVM2 on novel-50 (by 0.4 with GR, 0.9 with RST features). On IMDB62, the same pattern persists for the SVMs: discourse features do not make noticeable improvements (by 0.0 and 0.5 with GR and RST respectively)."
+    ],
+    [
+      "General analysis. Overall, we have shown that discourse information can improve authorship attribution, but only when properly encoded. This result is critical in demonstrating the particular value of discourse information, because typical stylometric features such as word INLINEFORM0 -grams and POS tags do not add additional performance improvements BIBREF3 , BIBREF5 .",
+      "In addition, the type of discourse information and the way in which it is featurized are tantamount to this performance improvement: RST features provide overall stronger improvement, and the global reading scheme for discourse embedding works better than the local one. The discourse embedding proves to be a superior featurization technique, as evidenced by the generally higher performance of CNN2-DE models over CNN2-PV models. With an SVM, where the option is not available, we are only able to use relation probability vectors to obtain a very modest performance improvement.",
+      "Further, we found an input-length threshold for the discourse features to help (Section SECREF26 ). Not surprisingly, discourse does not contribute on shorter texts. Many of the feature grids are empty for these shorter texts\u2013 either there are no coreference chains or they are not correctly resolved. Currently we only have empirical results on short novel chunks and movie reviews, but believe the finding would generalize to Twitter or blog posts.",
+      "Discourse embeddings. It does not come as a surprise that discourse embedding-based models perform better than their relation probability-based peers. The former (i) leverages the weight learning of the entire computational graph of the CNN rather than only the softmax layer, as the PV models do, and (ii) provides a more fine-grained featurization of the discourse information. Rather than merely taking a probability over grammatical relation transitions (in GR) or discourse relation types (in RST), in DE-based models we learn the dependency between grammatical relation transitions/discourse relations through the INLINEFORM0 -sized filter sweeps.",
+      "To further study the information encoded in the discourse embeddings, we perform t-SNE clustering BIBREF20 on them, using the best performing model CNN2-DE (global). We examine the closest neighbors of each embedding, and observe that similar discourse relations tend to go together (e.g., explanation and interpretation; consequence and result). Some examples are given in Table TABREF29 . However, it is unclear how this pattern helps improve classification performance. We intend to investigate this question in future work.",
+      "Global vs. Local featurization. As described in Section SECREF17 , the global reading processes all the discourse features for one entity at a time, while the local approach reads one sentence (or one sentence pair) at a time. In all the relevant experiments, global featurization showed a clear performance advantage (on average 1 point gain in F1). Recall that the creation of the grids (both GR and RST) depend on coreference chains of entities (Section SECREF2 ), and only the global reading scheme takes advantage of the coreference pattern whereas the local reading breaks the chains. To find out whether coreference pattern is the key to the performance difference, we further ran a probe experiment where we read RST discourse relations in the order in which EDUs are arranged in the RST tree (i.e., left-to-right), and evaluated this model on novel-50 and IMDB62 with the same hyperparameter setting. The F1 scores turned out to be very close to the CNN2-DE (local) model, at 97.5 and 90.9. Based on this finding, we tentatively confirm the importance of the coreference pattern, and intend to further investigate how exactly it matters for the classification performance.",
+      "GR vs. RST. RST features in general effect higher performance gains than GR features (Table TABREF28 ). The RST parser produces a tree of discourse relations for the input text, thus introducing a \u201cglobal view.\u201d The GR features, on the other hand, are more restricted to a \u201clocal view\u201d on entities between consecutive sentences. While a deeper empirical investigation is needed, one can intuitively imagine that identifying authorship by focusing on the local transitions between grammatical relations (as in GR) is more difficult than observing how the entire text is organized (as in RST)."
+    ],
+    [
+      "We have conducted an in-depth investigation of techniques that (i) featurize discourse information, and (ii) effectively integrate discourse features into the state-of-the-art character-bigram CNN classifier for AA. Beyond confirming the overall superiority of RST features over GR features in larger and more difficult datasets, we present a discourse embedding technique that is unavailable for previously proposed discourse-enhanced models. The new technique enabled us to push the envelope of the current performance ceiling by a large margin.",
+      "Admittedly, in using the RST features with entity-grids, we lose the valuable RST tree structure. In future work, we intend to adopt more sophisticated methods such as RecNN, as per Ji:17, to retain more information from the RST trees while reducing the parameter size. Further, we aim to understand how discourse embeddings contribute to AA tasks, and find alternatives to coreference chains for shorter texts."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1710/instruction.md b/qasper-1710/instruction.md
new file mode 100644
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+++ b/qasper-1710/instruction.md
@@ -0,0 +1,100 @@
+Name of Paper: Learning to Ask Unanswerable Questions for Machine Reading Comprehension
+
+Question: How do they ensure the generated questions are unanswerable?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Problem Formulation",
+    "Sequence-to-Sequence Model",
+    "Pair-to-Sequence Model",
+    "Training and Inference",
+    "Experiments",
+    "Unanswerable Question Generation",
+    "Data Augmentation for Machine Reading Comprehension",
+    "Conclusions",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "Extractive reading comprehension BIBREF0 , BIBREF1 obtains great attentions from both research and industry in recent years. End-to-end neural models BIBREF2 , BIBREF3 , BIBREF4 have achieved remarkable performance on the task if answers are assumed to be in the given paragraph. Nonetheless, the current systems are still not good at deciding whether no answer is presented in the context BIBREF5 . For unanswerable questions, the systems are supposed to abstain from answering rather than making unreliable guesses, which is an embodiment of language understanding ability.",
+      "We attack the problem by automatically generating unanswerable questions for data augmentation to improve question answering models. The generated unanswerable questions should not be too easy for the question answering model so that data augmentation can better help the model. For example, a simple baseline method is randomly choosing a question asked for another paragraph, and using it as an unanswerable question. However, it would be trivial to determine whether the retrieved question is answerable by using word-overlap heuristics, because the question is irrelevant to the context BIBREF6 . In this work, we propose to generate unanswerable questions by editing an answerable question and conditioning on the corresponding paragraph that contains the answer. So the generated unanswerable questions are more lexically similar and relevant to the context. Moreover, by using the answerable question as a prototype and its answer span as a plausible answer, the generated examples can provide more discriminative training signal to the question answering model.",
+      "To create training data for unanswerable question generation, we use (plausible) answer spans in paragraphs as pivots to align pairs of answerable questions and unanswerable questions. As shown in Figure 1 , the answerable and unanswerable questions of a paragraph are aligned through the text span \u201cVictoria Department of Education\u201d for being both the answer and plausible answer. These two questions are lexically similar and both asked with the same answer type in mind. In this way, we obtain the data with which the models can learn to ask unanswerable questions by editing answerable ones with word exchanges, negations, etc. Consequently, we can generate a mass of unanswerable questions with existing large-scale machine reading comprehension datasets.",
+      "Inspired by the neural reading comprehension models BIBREF7 , BIBREF8 , we introduce a pair-to-sequence model to better capture the interactions between questions and paragraphs. The proposed model first encodes input question and paragraph separately, and then conducts attention-based matching to make them aware of each other. Finally, the context-aware representations are used to generate outputs. To facilitate the use of context words during the generation process, we also incorporate the copy mechanism BIBREF9 , BIBREF10 .",
+      "Experimental results on the unanswerable question generation task shows that the pair-to-sequence model generates consistently better results over the sequence-to-sequence baseline and performs better with long paragraphs than with short answer sentences. Further experimental results show that the generated unanswerable questions can improve multiple machine reading comprehension models. Even using BERT fine-tuning as a strong reading comprehension model, we can still obtain a $1.9$ % absolute improvement of F1 score with BERT-base model and $1.7$ % absolute F1 improvement with BERT-large model."
+    ],
+    [
+      "Machine Reading Comprehension (MRC) Various large-scale datasets BIBREF0 , BIBREF1 , BIBREF11 , BIBREF12 , BIBREF5 , BIBREF13 have spurred rapid progress on machine reading comprehension in recent years. SQuAD BIBREF1 is an extractive benchmark whose questions and answers spans are annotated by humans. Neural reading comprehension systems BIBREF14 , BIBREF2 , BIBREF3 , BIBREF15 , BIBREF8 , BIBREF16 , BIBREF4 , BIBREF17 have outperformed humans on this task in terms of automatic metrics. The SQuAD 2.0 dataset BIBREF5 extends SQuAD with more than $50,000$ crowdsourced unanswerable questions. So far, neural reading comprehension models still fall behind humans on SQuAD 2.0. Abstaining from answering when no answer can be inferred from the given document does require more understanding than barely extracting an answer.",
+      "Question Generation for MRC In recent years, there has been an increasing interest in generating questions for reading comprehension. BIBREF18 show that neural models based on the encoder-decoder framework can generate significantly better questions than rule-based systems BIBREF19 . To generate answer-focused questions, one can simply indicate the answer positions in the context with extra features BIBREF20 , BIBREF21 , BIBREF22 , BIBREF23 , BIBREF24 . BIBREF25 and BIBREF26 separate answer representations for further matching. BIBREF27 introduce a latent variable for capturing variability and an observed variable for controlling question types. In summary, the above mentioned systems aim to generate answerable questions with certain context. On the contrary, our goal is to generate unanswerable questions.",
+      "Adversarial Examples for MRC To evaluate the language understanding ability of pre-trained systems, BIBREF28 construct adversarial examples by adding distractor sentences that do not contradict question answering for humans to the paragraph. BIBREF29 and BIBREF30 use questions to retrieve paragraphs that do not contain the answer as adversarial examples. BIBREF5 create unanswerable questions through rigid rules, which swap entities, numbers and antonyms of answerable questions. It has been shown that adversarial examples generated by rule-based systems are much easier to detect than ones in the SQuAD 2.0 dataset.",
+      "Data Augmentation for MRC Several attempts have been made to augment training data for machine reading comprehension. We categorize these work according to the type of the augmentation data: external data source, paragraphs or questions. BIBREF31 fine-tune BERT on the SQuAD dataset jointly with another dataset TriviaQA BIBREF12 . BIBREF4 paraphrase paragraphs with backtranslation. Another line of work adheres to generate answerable questions. BIBREF32 propose to generate questions based on the unlabeled text for semi-supervised question answering. BIBREF33 propose a rule-based system to generate multiple-choice questions with candidate options upon the paragraphs. We aim at generating unanswerable questions as a means of data augmentation."
+    ],
+    [
+      "Given an answerable question $q$ and its corresponding paragraph $p$ that contains the answer $a$ , we aim to generate unanswerable questions $\\tilde{q}$ that fulfills certain requirements. First, it cannot be answered by paragraph $p$ . Second, it must be relevant to both answerable question $q$ and paragraph $p$ , which refrains from producing irrelevant questions. Third, it should ask for something of the same type as answer $a$ .",
+      "As shown in Figure 2 , we investigate two simple neural models built upon encoder-decoder architecture BIBREF34 , BIBREF35 to generate unanswerable questions. A sequence-to-sequence model takes the concatenated paragraph and question as input, and encodes the input in a sequential manner. A pair-to-sequence model is further introduced to capture the interactions between inputs. The decoder of two models generates unanswerable questions sequentially. We factorize the probability of generating the unanswerable question $P(\\tilde{q}|q,p,a)$ as: P(q|q,p,a)=t=1|q|P(qt|q<t,q,p,a) where $\\tilde{q}_{<t}=\\tilde{q}_1 \\dots \\tilde{q}_{t-1}$ ."
+    ],
+    [
+      "In the sequence-to-sequence model, paragraph and question pairs are packed into an ordered sequence $x$ with a special separator in between. To indicate answers in paragraphs, we introduce token type embeddings which can also be used to distinguish questions from paragraphs in sequence-to-sequence model. As we can see in Figure 2 , the token type can be answer (A), paragraph (P), or question (Q). For a given token, we construct the input representation $\\mathbf {e}_i$ by summing the corresponding word embeddings, character embeddings and token type embeddings. Here characters are embedded by an embedding matrix followed by a max pooling layer.",
+      "We apply a single-layer bi-directional recurrent neural networks with long short-term memory units (LSTM; BIBREF36 ) to produce encoder hidden states $\\mathbf {h}_i=(\\mathbf {h}_{i-1}, \\mathbf {e}_i)$ . On each decoding step $t$ , the hidden states of decoder (a single-layer unidirectional LSTM network) are computed by $\\mathbf {s}_t=(\\mathbf {s}_{t-1}, [\\mathbf {y}_{t-1}; \\mathbf {c}_{t-1}])$ , where $\\mathbf {y}_{t-1}$ is the word embedding of previously predicted token and $\\mathbf {c}_{t-1}$ is the encoder context vector of previous step. Besides, we use an attention mechanism to summarize the encoder-side information into $\\mathbf {c}_{t}$ for current step. The attention distribution $\\gamma _t$ over source words is computed as in BIBREF37 : score(hi , st)=hiTWst",
+      "i,t=(score(hi,st)) / Zt",
+      "ct=i|x|i,t hi where $Z_t = {\\sum _{k}^{|x|}\\exp (score(\\mathbf {h}_k,\\mathbf {s}_t))}$ , $\\mathbf {W}_\\gamma $ in score function is a learnable parameter.",
+      "Next, $\\mathbf {s}_t$ is concatenated with $\\mathbf {c}_t$ to produce the vocabulary distribution $P_{v}$ : ",
+      "$$P_{v}=(\\mathbf {W}_v[\\mathbf {s}_t;\\mathbf {c}_t] + \\mathbf {b}_{v})$$   (Eq. 4) ",
+      "where $\\mathbf {W}_v$ and $\\mathbf {b}_{v}$ are learnable parameters. Copy mechanism BIBREF10 is incorporated to directly copy words from inputs, because words in paragraphs or source questions are of great value for unanswerable question generation. Specifically, we use $\\mathbf {s}_t$ and $\\mathbf {c}_t$ to produce a gating probability $g_t$ : ",
+      "$$g_t=(\\mathbf {W}_g[\\mathbf {s}_t;\\mathbf {c}_t] + \\mathbf {b}_{g})$$   (Eq. 5) ",
+      "where $\\mathbf {W}_g$ and $\\mathbf {b}_{g}$ are learnable parameters. The gate $g_t$ determines whether generating a word from the vocabulary or copying a word from inputs. Finally, we obtain the probability of generating $\\tilde{q}_t$ by: ",
+      "$$P(\\tilde{q}_t|\\tilde{q}_{<t},q,p,a)=g_t P_{v}(\\tilde{q}_t) + (1-g_t)\\sum _{i \\in \\zeta _{\\tilde{q}_t}}\\hat{\\gamma }_{i,t} \\nonumber $$   (Eq. 6) ",
+      "where $\\zeta _{\\tilde{q}_t}$ denotes all the occurrence of $\\tilde{q}_t$ in inputs, and the copying score $\\hat{\\gamma }_t$ is computed in the same way as attention scores $\\gamma _t$ (see Equation ( \"Sequence-to-Sequence Model\" )) while using different parameters."
+    ],
+    [
+      "Paragraph and question interactions play a vitally important role in machine reading comprehension. The interactions make the paragraph and question aware of each other and help to predict the answer more precisely. Therefore we propose a pair-to-sequence model, conducting attention based interactions in encoder and subsequently decoding with two series of representations.",
+      "In pair-to-sequence model, the paragraph and question are embedded as in sequence-to-sequence model, but encoded separately by weight-shared bi-directional LSTM networks, yielding $\\mathbf {h}_i^p=(\\mathbf {h}_{i-1}^p, \\mathbf {e}_{i-1}^p)$ as paragraph encodings and $\\mathbf {h}_i^q=(\\mathbf {h}_{i-1}^q, \\mathbf {e}_{i-1}^q)$ as question encodings. The same attention mechanism as in sequence-to-sequence model is used in the following interaction layer to produce question-aware paragraph representations ${\\mathbf {h}}_i^p$ : i,j=(score(hip,hjq))/Zi",
+      "hip=j=1|q|i,jhjq",
+      "hip=(Wp[hip;hip] + bp) where $Z_i=\\sum _{k=1}^{|q|}\\exp (score(\\mathbf {h}_i^p,\\mathbf {h}_k^q))$ , $\\mathbf {W}_p$ and $\\mathbf {b}_p$ are learnable parameters. Similarly, the paragraph-aware question representations ${\\mathbf {h}}_i^q$ are produced by: i,j=(score(hip,hjq))/Zj",
+      "hiq=i=1|p|i,jhip",
+      "hjq=(Wq[hjq;hjq] + bq) where $Z_j=\\sum _{k=1}^{|p|}\\exp (score(\\mathbf {h}_k^p,\\mathbf {h}_j^q))$ , $\\mathbf {W}_q$ and $\\mathbf {b}_q$ are learnable parameters.",
+      "Accordingly, the decoder now takes paragraph context $\\mathbf {c}^p_{t-1}$ and question context $\\mathbf {c}^q_{t-1}$ as encoder context, computed as $\\mathbf {c}_t$ (see Equation ( \"Sequence-to-Sequence Model\" )) in sequence-to-sequence model, to update decoder hidden states $\\mathbf {s}_t=(\\mathbf {s}_{t-1},[\\mathbf {y}_{t-1};\\mathbf {c}^p_{t-1};\\mathbf {c}^q_{t-1}])$ and predict tokens. Copy mechanism is also adopted as described before, and copying words from both the paragraph and question is viable."
+    ],
+    [
+      "The training objective is to minimize the negative likelihood of the aligned unanswerable question $\\tilde{q}$ given the answerable question $q$ and its corresponding paragraph $p$ that contains the answer $a$ : L=-(q,q,p,a)DP(q|q,p,a;) where $\\mathcal {D}$ is the training corpus and $\\theta $ denotes all the parameters. Sequence-to-sequence and pair-to-sequence models are trained with the same objective.",
+      "During inference, the unanswerable question for question answering pair $(q,p,a)$ is obtained via $\\textrm {argmax}_{q^{\\prime }}P(q^{\\prime }|q,p,a)$ , where $q^{\\prime }$ represents candidate outputs. Beam search is used to avoid iterating over all possible outputs."
+    ],
+    [
+      "We conduct experiments on the SQuAD 2.0 dataset BIBREF5 . The extractive machine reading benchmark contains about $100,000$ answerable questions and over $50,000$ crowdsourced unanswerable questions towards Wikipedia paragraphs. Crowdworkers are requested to craft unanswerable questions that are relevant to the given paragraph. Moreover, for each unanswerable question, a plausible answer span is annotated, which indicates the incorrect answer obtained by only relying on type-matching heuristics. Both answers and plausible answers are text spans in the paragraphs."
+    ],
+    [
+      "We use (plausible) answer spans in paragraphs as pivots to align pairs of answerable questions and unanswerable questions. An aligned pair is shown in Figure 1 . As to the spans that correspond to multiple answerable and unanswerable questions, we sort the pairs by Levenshtein distance BIBREF38 and keep the pair with the minimum distance, and make sure that each question is only paired once.",
+      "We obtain $20,240$ aligned pairs from the SQuAD 2.0 dataset in total. The Levenshtein distance between the answerable and unanswerable questions in pairs is $3.5$ on average. Specifically, the $17,475$ pairs extracted from the SQuAD 2.0 training set are used to train generation models. Since the SQuAD 2.0 test set is hidden, we randomly sample 46 articles from the SQuAD 2.0 training set with $1,805$ ( $\\sim $ 10%) pairs as holdout set and evaluate generation models with $2,765$ pairs extracted the SQuAD 2.0 development set.",
+      "We implement generation models upon OpenNMT BIBREF39 . We preprocess the corpus with the spaCy toolkit for tokenization and sentence segmentation. We lowercase tokens and build the vocabulary on SQuAD 2.0 training set with word frequency threshold of 9 to remove most noisy tokens introduced in data collection and tokenization. We set word, character and token type embeddings dimension to 300. We use the glove.840B.300d pre-trained embeddings BIBREF40 to initialize word embeddings, and do further updates during training. Both encoder and decoder share the same vocabulary and word embeddings. The hidden state size of LSTM network is 150. Dropout probability is set to $0.2$ . The data are shuffled and split into mini-batches of size 32 for training. The model is optimized with Adagrad BIBREF41 with an initial learning rate of $0.15$ . During inference, the beam size is 5. We prohibit producing unknown words by setting the score of <unk> token to -inf. We filter the beam outputs that make no differences to the input question.",
+      "The generation quality is evaluated using three automatic evaluation metrics: BLEU BIBREF42 , ROUGE BIBREF43 and GLEU BIBREF44 . BLEU is a commonly used metric in machine translation that computes n-gram precisions over references. Recall-oriented ROUGE metric is widely adopted in summarization, and ROUGE-L measures longest common subsequence between system outputs and references. GLEU is a variant of BLEU with the modification that penalizes system output n-grams that present in input but absent from the reference. This makes GLEU a preferable metric for tasks with subtle but critical differences in a monolingual setting as in our unanswerable question generation task.",
+      "We also conduct human evaluation on 100 samples in three criteria: (1) unanswerability, which indicates whether the question is unanswerable or not; (2) relatedness, which measures semantic relatedness between the generated question and input question answering pair; (3) readability, which indicates the grammaticality and fluency. We ask three raters to score the generated questions in terms of relatedness and readability on a 1-3 scale (3 for the best) and determine the answerability in binary (1 for unanswerable). The raters are not aware of the question generation methods in advance.",
+      "Results of the automatic evaluation are shown in Table 1 . We find that the proposed pair-to-sequence model that captures interactions between paragraph and question performs consistently better than sequence-to-sequence model. Moreover, replacing the input paragraph with the answer sentence hurts model performance, which indicates that using the whole paragraph as context provides more helpful information to unanswerable question generation. We also try to generate unanswerable questions by only relying on answerable questions (see \u201c-Paragraph\u201d), or the paragraph (see \u201c-Question\u201d). Unsurprisingly, both ablation models obtain worse performance compared with the full model. These two ablation results also demonstrate that the input answerable question helps more to improve performance compared with the input paragraph. We argue that the original answerable question provides more direct information due to the fact that the average edit distance between the example pairs is $3.5$ . At last, we remove the copy mechanism that restrains prediction tokens to the vocabulary. The results indicate the necessity of copying tokens from answerable questions and paragraphs to outputs, which relieves the out-of-vocabulary problem.",
+      "Table 3 shows the human evaluation results of generated unanswerable questions. We compare with the baseline method TfIdf, which uses the input answerable question to retrieve similar questions towards other articles as outputs. The retrieved questions are mostly unanswerable and readable, but they are not quite relevant to the question answering pair. Notice that being relevant is demonstrated to be important for data augmentation in further experiments on machine reading comprehension. Here pair-to-sequence model still outperforms sequence-to-sequence model in terms of all three metrics. But the differences in human evaluation are not as notable as in the automatic metrics.",
+      "As shown in Table 4 , we further randomly sample 100 system outputs to analyze the types of generated unanswerable questions. We borrow the types defined in BIBREF5 for SQuAD 2.0. We categorize the outputs with grammatical errors that make them hard to understand into Other. Samples that fall into Impossible Condition are mainly produced by non-entity substitution. We can see that models tend to generate unanswerable questions by inserting negation and swapping entities. These two types are also most commonly used when crowdworkers pose unanswerable questions according to answerable ones. We also find that the current models still have difficulties in utilizing antonyms and exclusion conditions, which could be improved by incorporating external resources.",
+      "In Figure 3 , we present a sample paragraph and its corresponding answerable questions and generated unanswerable questions. In the first example, two models generate unanswerable questions by swapping the location entity \u201cVictoria\u201d with \u201ctexas\u201d and inserting negation word \u201cnever\u201d, respectively. In the second example, sequence-to-sequence model omits the condition \u201cin Victoria\u201d and yields an answerable question. Pair-to-sequence model inserts the negation \u201cno longer\u201d properly, which is not mentioned in the paragraph. In the third example, grammatical errors are found in the output of . The last example shows that inserting negation words in different positions (\u201cn't public\u201d versus \u201cnot in victoria\u201d) can express different meanings. Such cases are critical for generated questions' answerability, which is hard to handle in a rule-based system."
+    ],
+    [
+      "We apply our automatically generated unanswerable questions as augmentation data to the following reading comprehension models:",
+      "BiDAF BIBREF2 is a benchmark model on extractive machine reading comprehension. Based on BiDAF, BIBREF45 propose the BiDAF-No-Answer model to predict the distribution of answer candidates and the probability of a question being unanswerable at the same time.",
+      " BIBREF29 propose the DocQA model to address document-level reading comprehension. The no-answer probability is also predicted jointly.",
+      "It is the state-of-the-art model on unanswerable machine reading comprehension. We adopt the uncased version of BERT BIBREF31 for fine-tuning. The batch sizes of BERT-base and BERT-large are set to 12 and 24 respectively. The rest hyperparameters are kept untouched as in the official instructions of fine-tuning BERT-Large on SQuAD 2.0.",
+      "We first generate unanswerable questions using the trained generation model. Specifically, we use the answerable questions in the SQuAD 2.0 training set, besides ones aligned before, to generate unanswerable questions. Then we use the paragraph and answers of answerable questions along with the generated questions to construct training examples. At last, we have an augmentation data containing $69,090$ unanswerable examples.",
+      "We train question answering models with augmentation data in two separate phases. In the first phase, we train the models by combining the augmentation data and all $86,821$ SQuAD 2.0 answerable examples. Subsequently, we use the original SQuAD 2.0 training data alone to further fine-tune model parameters.",
+      "Exact Match (EM) and F1 are two metrics used to evaluate model performance. EM measures the percentage of predictions that match ground truth answers exactly. F1 measures the word overlap between the prediction and ground truth answers. We use pair-to-sequence model with answerable questions and paragraphs for data augmentation by default.",
+      "Table 2 shows the exact match and F1 scores of multiple reading comprehension models with and without data augmentation. We can see that the generated unanswerable questions can improve both specifically designed reading comprehension models and strong BERT fine-tuning models, yielding $1.9$ absolute F1 improvement with BERT-base model and $1.7$ absolute F1 improvement with BERT-large model. Our submitted model obtains an EM score of $80.75$ and an F1 score of $83.85$ on the hidden test set.",
+      "As shown in Table 5 , pair-to-sequence model proves to be a better option for generating augmentation data than other three methods. Besides the sequence-to-sequence model, we use answerable questions to retrieve questions from other articles with TfIdf. The retrieved questions are of little help to improve the model, because they are less relevant to the paragraph as shown in Table 3 . We refer to the rule-based method BIBREF28 that swaps entities and replaces words with antonyms as Rule. In comparison to the above methods, pair-to-sequence model can yield the largest improvement.",
+      "Results in Table 6 show that enlarging the size of augmentation data can further improve model performance, especially with the BERT-base model. We conduct experiments using two and three times the size of the base augmentation data (i.e., $69,090$ unanswerable questions). We generate multiple unanswerable questions for each answerable question by using beam search. Because we only generate unanswerable questions, the data imbalance problem could mitigate the improvement of incorporating more augmentation data."
+    ],
+    [
+      "In this paper, we propose to generate unanswerable questions as a means of data augmentation for machine reading comprehension. We produce relevant unanswerable questions by editing answerable questions and conditioning on the corresponding paragraph. A pair-to-sequence model is introduced in order to capture the interactions between question and paragraph. We also present a way to construct training data for unanswerable question generation models. Both automatic and human evaluations show that the proposed model consistently outperforms the sequence-to-sequence baseline. The results on the SQuAD 2.0 dataset show that our generated unanswerable questions can help to improve multiple reading comprehension models. As for future work, we would like to enhance the ability to utilize antonyms for unanswerable question generation by leveraging external resources."
+    ],
+    [
+      "We thank anonymous reviewers for their helpful comments. Qin and Liu were supported by National Natural Science Foundation of China (NSFC) via grants 61632011 and 61772156."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1711/instruction.md b/qasper-1711/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..2e10911e4226e52c9ebac67c33e0fddd6230f0a2
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+++ b/qasper-1711/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Learning to Ask Unanswerable Questions for Machine Reading Comprehension
+
+Question: Does their approach require a dataset of unanswerable questions mapped to similar answerable questions?
\ No newline at end of file
diff --git a/qasper-1716/instruction.md b/qasper-1716/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..5a78521702d580ecf80cb915de64092fe2630795
--- /dev/null
+++ b/qasper-1716/instruction.md
@@ -0,0 +1,62 @@
+Name of Paper: Evaluating KGR10 Polish word embeddings in the recognition of temporal expressions using BiLSTM-CRF
+
+Question: How was the KGR10 corpus created?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Available word embeddings",
+    "Building a larger corpus",
+    "plWordNet Corpus 10.0 (KGR10)",
+    "KGR10 word embeddings",
+    "Temporal expressions",
+    "Recognition of timexes",
+    "Experiments and Results",
+    "Conclusions",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "Recent studies in information extraction domain (but also in other natural language processing fields) show that deep learning models produce state-of-the-art results BIBREF0 . Deep architectures employ multiple layers to learn hierarchical representations of the input data. In the last few years, neural networks based on dense vector representations provided the best results in various NLP tasks, including named entities recognition BIBREF1 , semantic role labelling BIBREF2 , question answering BIBREF3 and multitask learning BIBREF4 . The core element of most deep learning solutions is the dense distributed semantic representation of words, often called word embeddings. Distributional vectors follow the distributional hypothesis that words with a similar meaning tend to appear in similar contexts. Word embeddings capture the similarity between words and are often used as the first layer in deep learning models. Two of the most common and very efficient methods to produce word embeddings are Continuous Bag-of-Words (CBOW) and Skip-gram (SG), which produce distributed representations of words in a vector space, grouping them by similarity BIBREF5 , BIBREF6 . With the progress of machine learning techniques, it is possible to train such models on much larger data sets, and these often outperform the simple ones. It is possible to use a set of text documents containing even billions of words as training data. Both architectures (CBOW and SG) describe how the neural network learns the vector word representations for each word. In CBOW architecture the task is predicting the word given its context and in SG the task in predicting the context given the word.",
+      "Due to a significant increase of quality using deep learning methods together with word embeddings as the input layer for neural networks, many word vector sets have been created, using different corpora. The widest range of available word embeddings is available for English BIBREF7 and there were not so many options for less popular languages, e.g. Polish. There was a definite need within CLARIN-PL project and Sentimenti to increase the quality of NLP methods for Polish which were utilising available Polish word vectors BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 but only FastText modification of Skip-gram BIBREF9 was able to produce vectors for unknown words, based on character n-grams. The observation was that even using a sophisticated deep neural structure, the result strongly depends on the initial distributional representation. There was a need to build a massive corpus of Polish and create high-quality word vectors from that corpus. This work describes how we extended KGR7 1G corpus to become KGR10 with 4 billion words. Next, we present the different variants of word embeddings produced using this corpus. In the article about the recognition of named entities for Polish from the previous year, these embeddings were used in one of the three voting models to obtain the best results and the final system PolDeepNer BIBREF12 took the second place in PolEval2018 Task 2 BIBREF13 . In this article, we evaluated KGR10 FastText word embeddings in recognition of timexes."
+    ],
+    [
+      "At the time we were testing word embeddings for different applications, there were 2 most popular sources of word vectors. The first one, called IPIPAN, is the result of the project Compositional distributional semantic models for identification, discrimination and disambiguation of senses in Polish texts, the process of creating word embeddings is described in article BIBREF10 and corpora used were National Corpus of Polish (NKJP) BIBREF14 and Wikipedia (Wiki). The second one, called FASTTEXT, is original FastText word embeddings set, created for 157 languages (including Polish). Authors used Wikipedia and Common Crawl as the linguistic data source. Table TABREF6 shows the number of tokens in each corpus and the name of the institution which prepared it. There is also information about the public availability of the resource.",
+      "Table TABREF7 presents the most commonly used word embeddings in CLARIN-PL before the creation of our embeddings."
+    ],
+    [
+      "KGR7 corpus (also called plWordNet Corpus 7.0, PLWNC 7.0) BIBREF15 , BIBREF16 was created at the Wroclaw University of Science and Technology by G4.19 Group. Due to the licences of documents in this corpus, this resource is not publicly available. Table TABREF8 contains KGR7 subcorpora and statistics BIBREF17 . One of the subcorpora in KGR7 is KIPI (the IPI PAN Corpus) BIBREF18 . KGR7 covers texts from a wide range of domains like: blogs, science, stenographic recordings, news, journalism, books and parliamentary transcripts. All texts come from the second half of the 20th century and represent the modern Polish language."
+    ],
+    [
+      "KGR10, also known as plWordNet Corpus 10.0 (PLWNC 10.0), is the result of the work on the toolchain to automatic acquisition and extraction of the website content, called CorpoGrabber BIBREF19 . It is a pipeline of tools to get the most relevant content of the website, including all subsites (up to the user-defined depth). The proposed toolchain can be used to build a big Web corpus of text documents. It requires the list of the root websites as the input. Tools composing CorpoGrabber are adapted to Polish, but most subtasks are language independent. The whole process can be run in parallel on a single machine and includes the following tasks: download of the HTML subpages of each input page URL with HTTrack, extraction of plain text from each subpage by removing boilerplate content (such as navigation links, headers, footers, advertisements from HTML pages) BIBREF20 , deduplication of plain text BIBREF20 , bad quality documents removal utilising Morphological Analysis Converter and Aggregator (MACA) BIBREF21 , documents tagging using Wroc\u0142aw CRF Tagger (WCRFT) BIBREF22 . Last two steps are available only for Polish.",
+      "In order to significantly expand the set of documents in KGR7, we utilised DMOZ (short for directory.mozilla.org) \u2013 a multilingual open content directory of World Wide Web links, also known as Open Directory Project (ODP). The website with directory was closed in 2017, but the database still can be found on the web. Polish part of this directory contains more than 30,000 links to Polish websites. We used these links as root URLs for CorpoGrabber, and we downloaded more than 7TB of HTML web pages. After the extraction of text from HTML pages, deduplication of documents (including texts from KGR7) and removing bad quality documents (containing more than 30% of words outside the Morfeusz BIBREF23 dictionary) the result is KGR10 corpus, which contains 4,015,569,051 tokens and 18,084,712 unique words. Due to component licenses, KGR10 corpus is not publicly available."
+    ],
+    [
+      "We created a new Polish word embeddings models using the KGR10 corpus. We built 16 models of word embeddings using the implementation of CBOW and Skip-gram methods in the FastText tool BIBREF9 . These models are available under an open license in the CLARIN-PL project DSpace repository. The internal encoding solution based on embeddings of n-grams composing each word makes it possible to obtain FastText vector representations, also for words which were not processed during the creation of the model. A vector representation is associated with character n-gram and each word is represented as the sum of its n-gram vector representations. Previous solutions ignored the morphology of words and were assigning a distinct vector to each word. This is a limitation for languages with large vocabularies and many rare words, like Turkish, Finnish or Polish BIBREF9 . Authors observed that using word representations trained with subword information outperformed the plain Skip-gram model and the improvement was most significant for morphologically rich Slavic languages such as Czech (8% reduction of perplexity over SG) and Russian (13% reduction) BIBREF9 . We expected that word embeddings created that way for Polish should also provide such improvements. There were also previous attempts to build KGR10 word vectors with other methods (including FastText), and the results are presented in the article BIBREF8 . We selected the best models from that article \u2013 with embedding ID prefix EP (embeddings, previous) in Table TABREF13 \u2013 to compare with new models, marked as embedding ID prefix EC in Table TABREF13 ).",
+      "The word embeddings models used in PolDeepNer for recognition of timexes and named entities were EE1, . It was built on a plain KGR10. The dimension of word embedding is 300, the method of constructing vectors was Skip-gram BIBREF9 , and the number of negative samples for each positive example was 10."
+    ],
+    [
+      "Temporal expressions (henceforth timexes) tell us when something happens, how long something lasts, or how often something occurs. The correct interpretation of a timex often involves knowing the context. Usually, a person is aware of their location in time, i.e., they know what day, month and year it is, and whether it is the beginning or the end of week or month. Therefore, they refer to specific dates, using incomplete expressions such as 12 November, Thursday, the following week, after three days. The temporal context is often necessary to determine to which specific date and time timexes refer. These examples do not exhaust the complexity of the problem of recognising timexes.",
+      "TimeML BIBREF24 is a markup language for describing timexes that has been adapted to many languages. One of the best-known methods of recognition of timexes called HeidelTime BIBREF25 , which uses the TIMEX3 annotation standard, currently supports 13 languages (with the use of hand-crafted resources). PLIMEX is a specification for the description of Polish timexes. It is based on TIMEX3 used in TimeML. Classes proposed in TimeML are adapted, namely: date, time, duration, set."
+    ],
+    [
+      "There are many methods for recognising timexes that are widely used in natural language engineering. For English (but not exclusively), in approaches based on supervised learning, sequence labelling methods are often used, especially Conditional Random Fields BIBREF26 . A review of the methods in the article BIBREF27 about the recognition of timexes for English and Spanish has shown a certain shift within the most popular solutions. As with the normalisation of timexes, the best results are still achieved with rule-based methods, many new solutions have been introduced in the area of recognition. The best systems listed in BIBREF27 , called TIPSem BIBREF28 and ClearTK BIBREF29 , use CRFs for recognition, so initially, we decided to apply the CRF-based approach for this task. The results were described in BIBREF30 , BIBREF31 .",
+      "In recent years, solutions based on deep neural networks, using word representation in the form of word embeddings, created with the use of large linguistic corpus, have begun to dominate in the field of recognition of word expressions. The most popular solutions include bidirectional long short-term memory neural networks (henceforth Bi-LSTM), often in combination with conditional random fields, as presented in the paper BIBREF32 dedicated to the recognition of proper names. For the Polish language, deep networks have also recently been used to recognise word expressions. In the issue of recognition of timexes, a bidirectional gated recurrent unit network (GRU) has been used BIBREF33 , BIBREF34 . GRU network is described in detail in the article BIBREF35 . In case of recognition of event descriptions using Bi-LSTM and Bi-GRU, where most of the Liner2 features were included in the input feature vector, better results were obtained BIBREF36 than for the Liner2 method (but without taking into account domain dictionaries). In last year's publication on the issue of named entities recognition using BiLSTM+CRF (together with G4.19 Group members), we received a statistically significant improvement in the quality of recognition compared to a solution using CRF only. The solution has been called PolDeepNer BIBREF12 ."
+    ],
+    [
+      "Experiments were carried out by the method proposed in BIBREF27 . The first part is described as Task A, the purpose of which is to identify the boundaries of timexes and assign them to one of the following classes: date, time, duration, set.",
+      "We trained the final models using the train set and we evaluated it using the test set, which was the reproduction of analysis performed in articles BIBREF37 , BIBREF38 . The division is presented in Table TABREF16 . We used BiLSTM+CRF classifier as in previous work BIBREF12 . We used precision, recall and F1 metrics from the classic NER task BIBREF12 , where true positive system answer has the same boundaries and type as annotation in gold data set. We evaluated all 17 word embeddings models using these metrics. The results are presented in Tables TABREF17 , TABREF18 and TABREF19 .",
+      "We chose the best 3 results from each word embeddings group (EE, EP, EC) from Table TABREF19 presenting F1-scores for all models. Then we evaluated these results using more detailed measures for timexes, presented in BIBREF27 . The following measures were used to evaluate the quality of boundaries and class recognition, so-called strict match: strict precision (Str.P), strict recall (Str.R) and strict F1-score (Str.F1). A relaxed match (Rel.P, Rel.R, Rel.F1) evaluation has also been carried out to determine whether there is an overlap between the system entity and gold entity, e.g. [Sunday] and [Sunday morning] BIBREF27 . If there was an overlap, a relaxed type F1-score (Type.F1) was calculated BIBREF27 . The results are presented in Table TABREF20 ."
+    ],
+    [
+      "The analysis of results from Tables TABREF17 , TABREF18 and TABREF19 show that 12 of 15 best results were obtained using new word embeddings. The evaluation results presented in Table TABREF20 (the chosen best embeddings models from Table TABREF19 ) prove that the best group of word embeddings is EC. The highest type F1-score was obtained for EC1 model, built using binary FastText Skip-gram method utilising subword information, with vector dimension equal to 300 and negative sampling equal to 10. The ability of the model to provide vector representation for the unknown words seems to be the most important. Also, previous models built using KGR10 (EP) are probably less accurate due to an incorrect tokenisation of the corpus. We used WCRFT tagger BIBREF22 , which utilises Toki BIBREF21 to tokenise the input text before the creation of the embeddings model. The comparison of EC1 with previous results obtained using only CRF BIBREF38 show the significant improvement across all the tested metrics: 3.6pp increase in strict F1-score, 1.36pp increase in relaxed precision, 5.61pp increase in relaxed recall and 3.51pp increase in relaxed F1-score."
+    ],
+    [
+      "Work co-financed as part of the investment in the CLARIN-PL research infrastructure funded by the Polish Ministry of Science and Higher Education and in part by the National Centre for Research and Development, Poland, under grant no POIR.01.01.01-00-0472/16."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: Speech Corpus of Ainu Folklore and End-to-end Speech Recognition for Ainu Language
+
+Question: How big are improvements with multilingual ASR training vs single language training?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Overview of the Ainu Language",
+    "Overview of the Ainu Language ::: Background",
+    "Overview of the Ainu Language ::: The Ainu Language and its Writing System",
+    "Overview of the Ainu Language ::: Types of Ainu Recordings",
+    "Overview of the Ainu Language ::: Previous Work",
+    "Ainu Speech Corpus",
+    "Ainu Speech Corpus ::: Numbers of Speakers and Episodes",
+    "Ainu Speech Corpus ::: Data Annotation",
+    "End-to-end Speech Recognition",
+    "End-to-end Speech Recognition ::: End-to-end Modeling",
+    "End-to-end Speech Recognition ::: Modeling Units",
+    "End-to-end Speech Recognition ::: Modeling Units ::: Phone",
+    "End-to-end Speech Recognition ::: Modeling Units ::: Syllable",
+    "End-to-end Speech Recognition ::: Modeling Units ::: Word Piece",
+    "End-to-end Speech Recognition ::: Modeling Units ::: Word",
+    "End-to-end Speech Recognition ::: Multilingual Training",
+    "Experimental Evaluation",
+    "Experimental Evaluation ::: Data Setup",
+    "Experimental Evaluation ::: Experimental Setting",
+    "Experimental Evaluation ::: Results",
+    "Summary",
+    "Acknowledgement"
+  ],
+  "paragraphs": [
+    [
+      "Automatic speech recognition (ASR) technology has been made a dramatic progress and is currently brought to a pratical levels of performance assisted by large speech corpora and the introduction of deep learning techniques. However, this is not the case for low-resource languages which do not have large corpora like English and Japanese have. There are about 5,000 languages in the world over half of which are faced with the danger of extinction. Therefore, constructing ASR systems for these endangered languages is an important issue.",
+      "The Ainu are an indigenous people of northern Japan and Sakhakin in Russia, but their language has been fading away ever since the Meiji Restoration and Modernization. On the other hand, active efforts to preserve their culture have been initiated by the Government of Japan, and exceptionally large oral recordings have been made. Nevertheless, a majority of the recordings have not been transcribed and utilized effectively. Since transcribing them requires expertise in the Ainu language, not so many people are able to work on this task. Hence, there is a strong demand for an ASR system for the Ainu language. We started a project of Ainu ASR and this article is the first report of this project.",
+      "We have built an Ainu speech corpus based on data provided by the Ainu Museum and the Nibutani Ainu Culture Museum. The oral recordings in this data consist of folklore and folk songs, and we chose the former to construct the ASR model. The end-to-end method of speech recognition has been proposed recently and has achieved performance comparable to that of the conventional DNN-HMM hybrid modeling BIBREF0, BIBREF1, BIBREF2. End-to-end systems do not have a complex hierarchical structure and do not require expertise in target languages such as their phonology and morphology. In this study we adopt the attention mechanism BIBREF3, BIBREF4 and combine it with Connectionist Temporal Classification (CTC) BIBREF5, BIBREF6. In this work, we investigate the modeling unit and utilization of corpora of other languages."
+    ],
+    [
+      "This section briefly overviews the background of the data collection, the Ainu language, and its writing system. After that, we describe how Ainu recordings are classified and review previous works dealing with the Ainu language."
+    ],
+    [
+      "The Ainu people had total population of about 20,000 in the mid-19th century BIBREF7 and they used to live widely distributed in the area that includes Hokkaido, Sakhalin, and the Kuril Islands. The number of native speakers, however, rapidly decreased through the assimilation policy after late 19th century. At present, there are only less than 10 native speakers, and UNESCO listed their language as critically endangered in 2009 BIBREF8. In response to this situation, Ainu folklore and songs have been actively recorded since the late 20th century in efforts initiated by the Government of Japan. For example, the Ainu Museum started audio recording of Ainu folklore in 1976 with the cooperation of a few Ainu elders which resulted in the collection of speech data with the total duration of roughly 700 hours. This kind of data should be a key to the understanding of Ainu culture, but most of it is not transcribed and fully studied yet."
+    ],
+    [
+      "The Ainu language is an agglutinative language and has some similarities to Japanese. However, its genealogical relationship with other languages has not been clearly understood yet. Among its features such as closed syllables and personal verbal affixes, one important feature is that there are many compound words. For example, a word atuykorkamuy (means \u201ca sea turtle\u201d) can be disassembled into atuy (\u201cthe sea\u201d), kor (\u201cto have\u201d), and kamuy (\u201cgod\u201d).",
+      "Although the Ainu people did not traditionally have a writing system, the Ainu language is currently written following the examples in a reference book \u201cAkor itak\u201d BIBREF9. With this writing system, it is transcribed with sixteen Roman letters {a, c, e, h, i, k, m, n, o, p, r, s, t, u, w, y}. Since each of these letters correspond to a unique pronunciation, we call them \u201cphones\u201d for convenience. In addition, the symbol {=} is used for connecting a verb and a personal affix and { ' } is used to represent the pharyngeal stop. For the purpose of transcribing recordings, consonant symbols {b, d, g, z} are additionally used to transcribe Japanese sounds the speakers utter. The symbols { _ , __ } are used to transcribe drops and liaisons of phones. An example is shown below."
+    ],
+    [
+      "The Ainu oral traditions are classified into three types: \u201cyukar\u201d (heroic epics), \u201ckamuy yukar\u201d (mythic epics), and \u201cuwepeker\u201d (prose tales). Yukar and kamuy yukar are recited in the rhythm while uwepeker is not. In this study we focus on the the prose tales as the first step."
+    ],
+    [
+      "There have so far been a few studies dealing with the Ainu language. ainulrec built a dependency tree bank in the scheme of Universal Dependencies. postag developed tools for part-of-speech (POS) tagging and word segmentation. Ainu speech recognition was tried by ainutrans with 2.5 hours of Ainu folklore data even though the Ainu language was not their main target. Their phone error rare was about 40% which is not an accuracy level for practical use yet.",
+      "It appears that there has not been a substantial Ainu speech recognition study yet that utilizes corpora of a reasonable size. Therefore, our first step was to build a speech corpus for ASR based on the data sets provided by the Ainu Museum and the Nibutani Ainu Culture Museum."
+    ],
+    [
+      "In this section we explain the content of the data sets and how we modified it for our ASR corpus."
+    ],
+    [
+      "The corpus we have prepared for ASR in this study is composed of text and speech. Table 1 shows the number of episodes and the total speech duration for each speaker. Among the total of eight speakers, the data of the speakers KM and UT is from the Ainu Museum, and the rest is from Nibutani Ainu Culture Museum. All speakers are female. The length of the recording for a speaker varies depending on the circumstances at the recording times. A sample text and its English translation are shown in Table 2."
+    ],
+    [
+      "For efficient training of ASR model, we have made some modifications to the provided data. First, from the transcripts explained in Section 2.1, the symbols {_ , __ , '} have been removed as seen in the example below.",
+      "Though the equal symbol (`=') does not represent a sound, we keep it because it is used in almost all of the Ainu documents and provides grammatical information.",
+      "To train an ASR system, the speech data needs to be segmented into a set of manageable chunks. For the ease of automatic processing, we chose to segment speech into inter-pausal units (IPUs) BIBREF10which is a stretch of speech bounded by pauses. The number of IPUs for each speaker is shown in Table 1."
+    ],
+    [
+      "In this section, the two approaches to end-to-end speech recognition that we adopt in this work are summarized. Then, we introduce four modeling units we explained, i.e., phone, syllable, word piece, and word. We also discuss multilingual training that we adopt for tackling the low resource problem."
+    ],
+    [
+      "End-to-end models have an architecture much simpler than that of conventional DNN-HMM hybrid models. Since they predict character or word symbols directly from acoustic features, pronunciation dictionaries and language modeling are not required explicitly. In this paper, we utilize two kinds of end-to-end models, namely, Connectionist Temporal Classification (CTC) and the attention-based encoder-decoder model.",
+      "CTC augments the output symbol set with the \u201cblank\u201d symbol `$\\phi $'. It outputs symbols by contracting frame-wise outputs from recurrent neural networks (RNNs). This is done by first collapsed repeating symbols and then removing all blank symbols as in the following example:",
+      "The probability of an output sequence $\\mathbf {L}$ for an input acoustic feature sequence $\\mathbf {X}$, where $|\\mathbf {L}| < |\\mathbf {X}|$, is defined as follows.",
+      "$\\mathcal {B}$ is a function to contract the outputs of RNNs, so $\\mathcal {B}^{-1}(\\mathbf {L})$ means the set of symbol sequences which is reduced to $\\mathbf {L}$. The model is trained to maximize (1).",
+      "The attention-based encoder-decoder model is another method for mapping between two sequences with different lengths. It has two RNNs called the \u201cencoder\u201d and the \u201cdecoder\u201d. In naive encoder-decoder model, the encoder converts the input sequence into a single context vector which is the last hidden state of the encoder RNN from which the decoder infers output symbols. In an attention-based model, the context vector $\\mathbf {c}_l$ at $l$-th decoding step is the sum of the product of all encoder outputs $h_1, ... , h_\\mathrm {T}$ and the $l$-th attention weight $\\alpha _{1,l}, ... , \\alpha _{\\mathrm {T},l}$ as shown in (2). Here, $\\mathrm {T}$ is the length of the encoder output.",
+      "The attention weights $\\alpha _{1,l}, ... , \\alpha _{\\mathrm {T},l}$ indicates the relative importances of the encoder output frames for the $l$-th decoding step and the model parameters to generate these weights are determined in an end-to-end training.",
+      "In our model, the attention-based model and the CTC share the encoder and are optimized simultaneously as shown in Figure 1.BIBREF11 Long Short-Term Memory (LSTM) BIBREF12 is used for RNNs in the encoder and the decoder."
+    ],
+    [
+      "In the conventional DNN-HMM hybrid modeling, the acoustic model outputs probabilities triphone states from each acoustic feature which is converted into the most likely word sequence. An end-to-end model, on the other hand, has some degree of freedom in the modeling unit other than phones, and there are some studies that use characters or words as a unit BIBREF13, BIBREF14. A word unit based end-to-end model can take long context into consideration at the inference time, but it has the data sparsity problem due to its large vocabulary size. Though phone unit based model does not have such a problem, it cannot grasp so long context. It depends on the size of available corpora to decide which to adopt. In addition to these both models, a word piece unit, which is defined by automatically dividing a word into frequent parts, has been proposed BIBREF15, BIBREF16, and its vocabulary size can be determined almost freely.",
+      "In this paper, we investigate the modeling unit for the end-to-end Ainu speech recognition since the optimal unit for this size of corpus is not obvious. BIBREF17 It is presupposed that all units can be converted into word units automatically. The candidates are phone, syllable, word piece (WP), and word. Examples of them are shown in Table 3 and the details of each unit are described below."
+    ],
+    [
+      "As mentioned in Section 2.1, we regard the Roman letters as phones. `=' and the special symbol `$\\langle $wb$\\rangle $', which means a word boundary, are added to make it possible to convert the output into a sequence of words like the `original' in Table 3."
+    ],
+    [
+      "A syllable of the Ainu language takes the form of either V, CV, VC, or CVC, where `C' and `V' mean consonant and vowel, respectively. The phones {a, e, i, o, u} are vowels and the rest of the Roman letters in Section 2.2 are consonants. In this work, every word is divided into syllables by the following procedure.",
+      "A word with a single letter is unchanged.",
+      "Two consecutive Cs and Vs are given a syllable boundary between them.",
+      "",
+      "R$^*${CC, VV}R$^*$$\\rightarrow $ R$^*${C-C, V-V}R$^*$",
+      "(R $$ {C, V})",
+      "Put a syllable boundary after the segment-initial V if it is following by at least two phones.",
+      "",
+      "VCR$^+$$\\rightarrow $ V-CR$^+$",
+      "Put a syllable boundary after CV repeatedly from left to right until only CV or CVC is left.",
+      "",
+      "(CV)$^*${CV, CVC} $\\rightarrow $ (CV-)$^*${CV, CVC}",
+      "In addition, `=' and `$\\langle $wb$\\rangle $' are added as explained in Section 4.2.1. through the model training process.",
+      "This procedure does not always generate a morphologically relevant syllable segmentation. For example, a word isermakus (meaning \u201c(for a god) to protect from behind\u201d) is divided as i-ser-ma-kus, but the right syllabification is i-ser-mak-us."
+    ],
+    [
+      "The byte pair encoding (BPE) BIBREF18 and the unigram language modeling BIBREF19 are alternative methods for dividing a word into word pieces. The former repeatedly replaces the most common character pair with a new single symbol until the vocabulary becomes the intended size. The latter decides the segmentation to maximize the likelihood of occurrence of the sequence. We adopt the latter and use the open-source software SentencePiece BIBREF20. With this tool, `$\\langle $wb$\\rangle $' and other units are often merged to constitute a single piece as seen in Table 3."
+    ],
+    [
+      "The original text can be segmented into words separated by spaces. To make the vocabulary smaller for the ease of training, `=' is treated as a word and infrequent words are replaced with a special label `$\\langle $unk$\\rangle $'. As seen in Table 3, `a=saha' is dealt with as three words (`a', `=', `saha') and the word `kokopan' is replaced with `$\\langle $unk$\\rangle $'."
+    ],
+    [
+      "When an enough amount of data is not available for the target languages, the ASR model training can be enhanced by taking advantage of data from other languages BIBREF21, BIBREF22. There are some similarities between Ainu and Japanese language BIBREF23. For instance, both have almost the same set of vowels and do not have consonant clusters (like `str' of `strike' in English). Hence, the multilingual training with a Japanese corpus is expected to be effective. In addition, an English corpus is used for the purpose of comparison. The corpora used are the JNAS corpus BIBREF24 (in Japanese) and the WSJ corpus BIBREF25 (in English). JNAS comprises roughly 80 hours from 320 speakers, and WSJ has about 70 hours of speech from 280 speakers.",
+      "In the multilingual training, the encoder and the attention module are shared among the Ainu ASR model and the models for other languages, and they are trained using data for all languages. Figure 2 shows the architecture for the multilingual learning with two corpora. When the input acoustic features are from the Ainu ASR corpus, they go through the shared encoder and attention module and are delivered into the decoder on the left side in Figure 2 as a context vector. In this case, the right-side decoder is not trained."
+    ],
+    [
+      "In this section the setting and results of ASR experiments are described and the results are discussed."
+    ],
+    [
+      "The ASR experiments were performed in speaker-open condition as well as speaker-closed condition.",
+      "In the speaker-closed condition, two episodes were set aside from each speaker as development and test sets. Thereafter, the total sizes of the development and test sets turns out to be 1585 IPUs spanning 2 hours 23 minutes and 1841 IPUs spanning 2 hours and 48 minutes respectively. The ASR model is trained with the rest data. In the speaker-open condition, all the data except for the test speaker's were used for training As it would be difficult to train the model if all of the data of speaker KM or UT were removed, experiments using their speaker-open conditions were not conducted."
+    ],
+    [
+      "The input acoustic features were 120-dimensional vectors made by frame stacking BIBREF26 three 40-dimensional log-mel filter banks features at contiguous time frames. The window length and the frame shift were set to be 25ms and 10ms. The encoder was composed of five BiLSTM layers and the attention-based decoder had a single layer of LSTM. Each LSTM had 320 cells and their weights were randomly initialized using a uniform distribution DBLP:journals/corr/HeZR015 with biases of zero. The fully connected layers were initialized following $\\mathcal {U}{(-0.1, 0.1)}$. The weight decay BIBREF27 whose rate was $10^{-5}$ and the dropout BIBREF28 following $\\mathcal {B}e(0.2)$ were used to alleviate overfitting. The parameters were optimized with Adam BIBREF29. The learning rate was $10^{-3}$ at first and was multiplied by $10^{-1}$ at the beginning of 31st and 36th epoch BIBREF30. The mini-batch size was 30 and the utterances (IPUs) were sorted in an ascending order of length. To stabilize the training, we removed utterances longer than 12 seconds.",
+      "The loss function of the model was a linear sum of the loss from CTC and the attention-based decoder,",
+      "where $\\lambda $ was set to be 0.5. Through all experiments, the phone labels are used to train the auxiliary CTC task because it is reported that the hierarchical architecture, using few and general labels in the auxiliary task, improves the performance BIBREF31.",
+      "Strictly speaking, the number of each modeling units depends on the training set, but there are roughly 25-phone, 500-syllable, and 5,000-word units including special symbols that represent the start and end of a sentence. The words occurring less than twice were replaced with `$\\langle $unk$\\rangle $'. The vocabulary size for word piece modeling was set to be 500. These settings were based on the results of preliminary experiments with the development set.",
+      "For the multilingual training, we made three training scripts by concatenating the script of Ainu and other languages (JNAS, WSJ, JNAS and WSJ). The model was trained by these scripts until 30th epoch. From 31$^{\\rm {st}}$ and 40th epoch, the model was fine-turned by the Ainu script. Phone units are used for JNAS and WSJ throughout the experiments."
+    ],
+    [
+      "Table 4 shows the phone error rates (PERs) and word error rates (WERs) for the speaker-closed and speaker-open settings. The `average' is weighted by the numbers of tokens in the ground truth transcriptions for speaker-wise evaluation sets.",
+      "The word recognition accuracy reached about 80% in the speaker-closed setting. In the speaker-open setting it was 60% on average and varied greatly from speaker to speaker (from 50% to 70%). The best phone accuracies in the speaker-closed and speaker-open settings were about 94% and 86%. Regardless of the settings, the syllable-based modeling yielded the best WER and PER. This suggests that syllables provide reasonable coverage and constraints for the Ainu language in a corpus of this size.",
+      "The PERs of the word unit model were larger than those of other units. This is because the word model often outputs the `$\\langle $unk$\\rangle $' symbols while other unit models are able to output symbols similar in sound as below.",
+      "In this example, the PER of the syllable model is 5% and that of the word model is 30% even though the WERs are the same. (The output of the syllable model is rewritten into words using the `$\\langle $wb$\\rangle $' symbol.)",
+      "WERs are generally much larger than PERs and it is further aggravated with the Ainu language. This is because, as mentioned in Section 2.1, the Ainu language has a lot of compound words and the model may be confused about whether the output is multiple words or a single compound word. The actual outputs frequently contain errors as below. The WER of this example is 57% though the PER is zero.",
+      "The results of multilingual training in which the modeling unit is syllables are presented in Table 5. All error rates are the weighted averages of all evaluated speakers. Here, `+ both' represents the result of training with both JNAS and WSJ corpora. The multilingual training is effective in the speaker-open setting, providing a relative WER improvement of 10%. The JNAS corpus was more helpful than the WSJ corpus because of the similarities between Ainu and Japanese language."
+    ],
+    [
+      "In this study, we first developed a speech corpus for Ainu ASR and then, using the end-to-end model with CTC and the attention mechanism, compared four modeling units: phones, syllables, word pieces, and words. The best performance was obtained with the syllable unit, with which WERs in the speaker-closed and speaker-open settings were respectively about 20% and 40% while PERs were about 6% and 14%. Multilingual training using the JNAS improved the performance in the speaker-open setting. Future tasks include reducing the between-speaker performance differences by using speaker adaptation techniques."
+    ],
+    [
+      "The data sets used in this study are provided by the Ainu Museum and Nibutani Ainu Culture Museum. The authors would like to thank Prof. Osami Okuda of Sapporo Gakuin University for his useful advices on the Ainu language."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1718/instruction.md b/qasper-1718/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..6bff8c2bdc4224c1154aa374219433c0049d209b
--- /dev/null
+++ b/qasper-1718/instruction.md
@@ -0,0 +1,143 @@
+Name of Paper: Speech Corpus of Ainu Folklore and End-to-end Speech Recognition for Ainu Language
+
+Question: How much transcribed data is available for for Ainu language?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Overview of the Ainu Language",
+    "Overview of the Ainu Language ::: Background",
+    "Overview of the Ainu Language ::: The Ainu Language and its Writing System",
+    "Overview of the Ainu Language ::: Types of Ainu Recordings",
+    "Overview of the Ainu Language ::: Previous Work",
+    "Ainu Speech Corpus",
+    "Ainu Speech Corpus ::: Numbers of Speakers and Episodes",
+    "Ainu Speech Corpus ::: Data Annotation",
+    "End-to-end Speech Recognition",
+    "End-to-end Speech Recognition ::: End-to-end Modeling",
+    "End-to-end Speech Recognition ::: Modeling Units",
+    "End-to-end Speech Recognition ::: Modeling Units ::: Phone",
+    "End-to-end Speech Recognition ::: Modeling Units ::: Syllable",
+    "End-to-end Speech Recognition ::: Modeling Units ::: Word Piece",
+    "End-to-end Speech Recognition ::: Modeling Units ::: Word",
+    "End-to-end Speech Recognition ::: Multilingual Training",
+    "Experimental Evaluation",
+    "Experimental Evaluation ::: Data Setup",
+    "Experimental Evaluation ::: Experimental Setting",
+    "Experimental Evaluation ::: Results",
+    "Summary",
+    "Acknowledgement"
+  ],
+  "paragraphs": [
+    [
+      "Automatic speech recognition (ASR) technology has been made a dramatic progress and is currently brought to a pratical levels of performance assisted by large speech corpora and the introduction of deep learning techniques. However, this is not the case for low-resource languages which do not have large corpora like English and Japanese have. There are about 5,000 languages in the world over half of which are faced with the danger of extinction. Therefore, constructing ASR systems for these endangered languages is an important issue.",
+      "The Ainu are an indigenous people of northern Japan and Sakhakin in Russia, but their language has been fading away ever since the Meiji Restoration and Modernization. On the other hand, active efforts to preserve their culture have been initiated by the Government of Japan, and exceptionally large oral recordings have been made. Nevertheless, a majority of the recordings have not been transcribed and utilized effectively. Since transcribing them requires expertise in the Ainu language, not so many people are able to work on this task. Hence, there is a strong demand for an ASR system for the Ainu language. We started a project of Ainu ASR and this article is the first report of this project.",
+      "We have built an Ainu speech corpus based on data provided by the Ainu Museum and the Nibutani Ainu Culture Museum. The oral recordings in this data consist of folklore and folk songs, and we chose the former to construct the ASR model. The end-to-end method of speech recognition has been proposed recently and has achieved performance comparable to that of the conventional DNN-HMM hybrid modeling BIBREF0, BIBREF1, BIBREF2. End-to-end systems do not have a complex hierarchical structure and do not require expertise in target languages such as their phonology and morphology. In this study we adopt the attention mechanism BIBREF3, BIBREF4 and combine it with Connectionist Temporal Classification (CTC) BIBREF5, BIBREF6. In this work, we investigate the modeling unit and utilization of corpora of other languages."
+    ],
+    [
+      "This section briefly overviews the background of the data collection, the Ainu language, and its writing system. After that, we describe how Ainu recordings are classified and review previous works dealing with the Ainu language."
+    ],
+    [
+      "The Ainu people had total population of about 20,000 in the mid-19th century BIBREF7 and they used to live widely distributed in the area that includes Hokkaido, Sakhalin, and the Kuril Islands. The number of native speakers, however, rapidly decreased through the assimilation policy after late 19th century. At present, there are only less than 10 native speakers, and UNESCO listed their language as critically endangered in 2009 BIBREF8. In response to this situation, Ainu folklore and songs have been actively recorded since the late 20th century in efforts initiated by the Government of Japan. For example, the Ainu Museum started audio recording of Ainu folklore in 1976 with the cooperation of a few Ainu elders which resulted in the collection of speech data with the total duration of roughly 700 hours. This kind of data should be a key to the understanding of Ainu culture, but most of it is not transcribed and fully studied yet."
+    ],
+    [
+      "The Ainu language is an agglutinative language and has some similarities to Japanese. However, its genealogical relationship with other languages has not been clearly understood yet. Among its features such as closed syllables and personal verbal affixes, one important feature is that there are many compound words. For example, a word atuykorkamuy (means \u201ca sea turtle\u201d) can be disassembled into atuy (\u201cthe sea\u201d), kor (\u201cto have\u201d), and kamuy (\u201cgod\u201d).",
+      "Although the Ainu people did not traditionally have a writing system, the Ainu language is currently written following the examples in a reference book \u201cAkor itak\u201d BIBREF9. With this writing system, it is transcribed with sixteen Roman letters {a, c, e, h, i, k, m, n, o, p, r, s, t, u, w, y}. Since each of these letters correspond to a unique pronunciation, we call them \u201cphones\u201d for convenience. In addition, the symbol {=} is used for connecting a verb and a personal affix and { ' } is used to represent the pharyngeal stop. For the purpose of transcribing recordings, consonant symbols {b, d, g, z} are additionally used to transcribe Japanese sounds the speakers utter. The symbols { _ , __ } are used to transcribe drops and liaisons of phones. An example is shown below."
+    ],
+    [
+      "The Ainu oral traditions are classified into three types: \u201cyukar\u201d (heroic epics), \u201ckamuy yukar\u201d (mythic epics), and \u201cuwepeker\u201d (prose tales). Yukar and kamuy yukar are recited in the rhythm while uwepeker is not. In this study we focus on the the prose tales as the first step."
+    ],
+    [
+      "There have so far been a few studies dealing with the Ainu language. ainulrec built a dependency tree bank in the scheme of Universal Dependencies. postag developed tools for part-of-speech (POS) tagging and word segmentation. Ainu speech recognition was tried by ainutrans with 2.5 hours of Ainu folklore data even though the Ainu language was not their main target. Their phone error rare was about 40% which is not an accuracy level for practical use yet.",
+      "It appears that there has not been a substantial Ainu speech recognition study yet that utilizes corpora of a reasonable size. Therefore, our first step was to build a speech corpus for ASR based on the data sets provided by the Ainu Museum and the Nibutani Ainu Culture Museum."
+    ],
+    [
+      "In this section we explain the content of the data sets and how we modified it for our ASR corpus."
+    ],
+    [
+      "The corpus we have prepared for ASR in this study is composed of text and speech. Table 1 shows the number of episodes and the total speech duration for each speaker. Among the total of eight speakers, the data of the speakers KM and UT is from the Ainu Museum, and the rest is from Nibutani Ainu Culture Museum. All speakers are female. The length of the recording for a speaker varies depending on the circumstances at the recording times. A sample text and its English translation are shown in Table 2."
+    ],
+    [
+      "For efficient training of ASR model, we have made some modifications to the provided data. First, from the transcripts explained in Section 2.1, the symbols {_ , __ , '} have been removed as seen in the example below.",
+      "Though the equal symbol (`=') does not represent a sound, we keep it because it is used in almost all of the Ainu documents and provides grammatical information.",
+      "To train an ASR system, the speech data needs to be segmented into a set of manageable chunks. For the ease of automatic processing, we chose to segment speech into inter-pausal units (IPUs) BIBREF10which is a stretch of speech bounded by pauses. The number of IPUs for each speaker is shown in Table 1."
+    ],
+    [
+      "In this section, the two approaches to end-to-end speech recognition that we adopt in this work are summarized. Then, we introduce four modeling units we explained, i.e., phone, syllable, word piece, and word. We also discuss multilingual training that we adopt for tackling the low resource problem."
+    ],
+    [
+      "End-to-end models have an architecture much simpler than that of conventional DNN-HMM hybrid models. Since they predict character or word symbols directly from acoustic features, pronunciation dictionaries and language modeling are not required explicitly. In this paper, we utilize two kinds of end-to-end models, namely, Connectionist Temporal Classification (CTC) and the attention-based encoder-decoder model.",
+      "CTC augments the output symbol set with the \u201cblank\u201d symbol `$\\phi $'. It outputs symbols by contracting frame-wise outputs from recurrent neural networks (RNNs). This is done by first collapsed repeating symbols and then removing all blank symbols as in the following example:",
+      "The probability of an output sequence $\\mathbf {L}$ for an input acoustic feature sequence $\\mathbf {X}$, where $|\\mathbf {L}| < |\\mathbf {X}|$, is defined as follows.",
+      "$\\mathcal {B}$ is a function to contract the outputs of RNNs, so $\\mathcal {B}^{-1}(\\mathbf {L})$ means the set of symbol sequences which is reduced to $\\mathbf {L}$. The model is trained to maximize (1).",
+      "The attention-based encoder-decoder model is another method for mapping between two sequences with different lengths. It has two RNNs called the \u201cencoder\u201d and the \u201cdecoder\u201d. In naive encoder-decoder model, the encoder converts the input sequence into a single context vector which is the last hidden state of the encoder RNN from which the decoder infers output symbols. In an attention-based model, the context vector $\\mathbf {c}_l$ at $l$-th decoding step is the sum of the product of all encoder outputs $h_1, ... , h_\\mathrm {T}$ and the $l$-th attention weight $\\alpha _{1,l}, ... , \\alpha _{\\mathrm {T},l}$ as shown in (2). Here, $\\mathrm {T}$ is the length of the encoder output.",
+      "The attention weights $\\alpha _{1,l}, ... , \\alpha _{\\mathrm {T},l}$ indicates the relative importances of the encoder output frames for the $l$-th decoding step and the model parameters to generate these weights are determined in an end-to-end training.",
+      "In our model, the attention-based model and the CTC share the encoder and are optimized simultaneously as shown in Figure 1.BIBREF11 Long Short-Term Memory (LSTM) BIBREF12 is used for RNNs in the encoder and the decoder."
+    ],
+    [
+      "In the conventional DNN-HMM hybrid modeling, the acoustic model outputs probabilities triphone states from each acoustic feature which is converted into the most likely word sequence. An end-to-end model, on the other hand, has some degree of freedom in the modeling unit other than phones, and there are some studies that use characters or words as a unit BIBREF13, BIBREF14. A word unit based end-to-end model can take long context into consideration at the inference time, but it has the data sparsity problem due to its large vocabulary size. Though phone unit based model does not have such a problem, it cannot grasp so long context. It depends on the size of available corpora to decide which to adopt. In addition to these both models, a word piece unit, which is defined by automatically dividing a word into frequent parts, has been proposed BIBREF15, BIBREF16, and its vocabulary size can be determined almost freely.",
+      "In this paper, we investigate the modeling unit for the end-to-end Ainu speech recognition since the optimal unit for this size of corpus is not obvious. BIBREF17 It is presupposed that all units can be converted into word units automatically. The candidates are phone, syllable, word piece (WP), and word. Examples of them are shown in Table 3 and the details of each unit are described below."
+    ],
+    [
+      "As mentioned in Section 2.1, we regard the Roman letters as phones. `=' and the special symbol `$\\langle $wb$\\rangle $', which means a word boundary, are added to make it possible to convert the output into a sequence of words like the `original' in Table 3."
+    ],
+    [
+      "A syllable of the Ainu language takes the form of either V, CV, VC, or CVC, where `C' and `V' mean consonant and vowel, respectively. The phones {a, e, i, o, u} are vowels and the rest of the Roman letters in Section 2.2 are consonants. In this work, every word is divided into syllables by the following procedure.",
+      "A word with a single letter is unchanged.",
+      "Two consecutive Cs and Vs are given a syllable boundary between them.",
+      "",
+      "R$^*${CC, VV}R$^*$$\\rightarrow $ R$^*${C-C, V-V}R$^*$",
+      "(R $$ {C, V})",
+      "Put a syllable boundary after the segment-initial V if it is following by at least two phones.",
+      "",
+      "VCR$^+$$\\rightarrow $ V-CR$^+$",
+      "Put a syllable boundary after CV repeatedly from left to right until only CV or CVC is left.",
+      "",
+      "(CV)$^*${CV, CVC} $\\rightarrow $ (CV-)$^*${CV, CVC}",
+      "In addition, `=' and `$\\langle $wb$\\rangle $' are added as explained in Section 4.2.1. through the model training process.",
+      "This procedure does not always generate a morphologically relevant syllable segmentation. For example, a word isermakus (meaning \u201c(for a god) to protect from behind\u201d) is divided as i-ser-ma-kus, but the right syllabification is i-ser-mak-us."
+    ],
+    [
+      "The byte pair encoding (BPE) BIBREF18 and the unigram language modeling BIBREF19 are alternative methods for dividing a word into word pieces. The former repeatedly replaces the most common character pair with a new single symbol until the vocabulary becomes the intended size. The latter decides the segmentation to maximize the likelihood of occurrence of the sequence. We adopt the latter and use the open-source software SentencePiece BIBREF20. With this tool, `$\\langle $wb$\\rangle $' and other units are often merged to constitute a single piece as seen in Table 3."
+    ],
+    [
+      "The original text can be segmented into words separated by spaces. To make the vocabulary smaller for the ease of training, `=' is treated as a word and infrequent words are replaced with a special label `$\\langle $unk$\\rangle $'. As seen in Table 3, `a=saha' is dealt with as three words (`a', `=', `saha') and the word `kokopan' is replaced with `$\\langle $unk$\\rangle $'."
+    ],
+    [
+      "When an enough amount of data is not available for the target languages, the ASR model training can be enhanced by taking advantage of data from other languages BIBREF21, BIBREF22. There are some similarities between Ainu and Japanese language BIBREF23. For instance, both have almost the same set of vowels and do not have consonant clusters (like `str' of `strike' in English). Hence, the multilingual training with a Japanese corpus is expected to be effective. In addition, an English corpus is used for the purpose of comparison. The corpora used are the JNAS corpus BIBREF24 (in Japanese) and the WSJ corpus BIBREF25 (in English). JNAS comprises roughly 80 hours from 320 speakers, and WSJ has about 70 hours of speech from 280 speakers.",
+      "In the multilingual training, the encoder and the attention module are shared among the Ainu ASR model and the models for other languages, and they are trained using data for all languages. Figure 2 shows the architecture for the multilingual learning with two corpora. When the input acoustic features are from the Ainu ASR corpus, they go through the shared encoder and attention module and are delivered into the decoder on the left side in Figure 2 as a context vector. In this case, the right-side decoder is not trained."
+    ],
+    [
+      "In this section the setting and results of ASR experiments are described and the results are discussed."
+    ],
+    [
+      "The ASR experiments were performed in speaker-open condition as well as speaker-closed condition.",
+      "In the speaker-closed condition, two episodes were set aside from each speaker as development and test sets. Thereafter, the total sizes of the development and test sets turns out to be 1585 IPUs spanning 2 hours 23 minutes and 1841 IPUs spanning 2 hours and 48 minutes respectively. The ASR model is trained with the rest data. In the speaker-open condition, all the data except for the test speaker's were used for training As it would be difficult to train the model if all of the data of speaker KM or UT were removed, experiments using their speaker-open conditions were not conducted."
+    ],
+    [
+      "The input acoustic features were 120-dimensional vectors made by frame stacking BIBREF26 three 40-dimensional log-mel filter banks features at contiguous time frames. The window length and the frame shift were set to be 25ms and 10ms. The encoder was composed of five BiLSTM layers and the attention-based decoder had a single layer of LSTM. Each LSTM had 320 cells and their weights were randomly initialized using a uniform distribution DBLP:journals/corr/HeZR015 with biases of zero. The fully connected layers were initialized following $\\mathcal {U}{(-0.1, 0.1)}$. The weight decay BIBREF27 whose rate was $10^{-5}$ and the dropout BIBREF28 following $\\mathcal {B}e(0.2)$ were used to alleviate overfitting. The parameters were optimized with Adam BIBREF29. The learning rate was $10^{-3}$ at first and was multiplied by $10^{-1}$ at the beginning of 31st and 36th epoch BIBREF30. The mini-batch size was 30 and the utterances (IPUs) were sorted in an ascending order of length. To stabilize the training, we removed utterances longer than 12 seconds.",
+      "The loss function of the model was a linear sum of the loss from CTC and the attention-based decoder,",
+      "where $\\lambda $ was set to be 0.5. Through all experiments, the phone labels are used to train the auxiliary CTC task because it is reported that the hierarchical architecture, using few and general labels in the auxiliary task, improves the performance BIBREF31.",
+      "Strictly speaking, the number of each modeling units depends on the training set, but there are roughly 25-phone, 500-syllable, and 5,000-word units including special symbols that represent the start and end of a sentence. The words occurring less than twice were replaced with `$\\langle $unk$\\rangle $'. The vocabulary size for word piece modeling was set to be 500. These settings were based on the results of preliminary experiments with the development set.",
+      "For the multilingual training, we made three training scripts by concatenating the script of Ainu and other languages (JNAS, WSJ, JNAS and WSJ). The model was trained by these scripts until 30th epoch. From 31$^{\\rm {st}}$ and 40th epoch, the model was fine-turned by the Ainu script. Phone units are used for JNAS and WSJ throughout the experiments."
+    ],
+    [
+      "Table 4 shows the phone error rates (PERs) and word error rates (WERs) for the speaker-closed and speaker-open settings. The `average' is weighted by the numbers of tokens in the ground truth transcriptions for speaker-wise evaluation sets.",
+      "The word recognition accuracy reached about 80% in the speaker-closed setting. In the speaker-open setting it was 60% on average and varied greatly from speaker to speaker (from 50% to 70%). The best phone accuracies in the speaker-closed and speaker-open settings were about 94% and 86%. Regardless of the settings, the syllable-based modeling yielded the best WER and PER. This suggests that syllables provide reasonable coverage and constraints for the Ainu language in a corpus of this size.",
+      "The PERs of the word unit model were larger than those of other units. This is because the word model often outputs the `$\\langle $unk$\\rangle $' symbols while other unit models are able to output symbols similar in sound as below.",
+      "In this example, the PER of the syllable model is 5% and that of the word model is 30% even though the WERs are the same. (The output of the syllable model is rewritten into words using the `$\\langle $wb$\\rangle $' symbol.)",
+      "WERs are generally much larger than PERs and it is further aggravated with the Ainu language. This is because, as mentioned in Section 2.1, the Ainu language has a lot of compound words and the model may be confused about whether the output is multiple words or a single compound word. The actual outputs frequently contain errors as below. The WER of this example is 57% though the PER is zero.",
+      "The results of multilingual training in which the modeling unit is syllables are presented in Table 5. All error rates are the weighted averages of all evaluated speakers. Here, `+ both' represents the result of training with both JNAS and WSJ corpora. The multilingual training is effective in the speaker-open setting, providing a relative WER improvement of 10%. The JNAS corpus was more helpful than the WSJ corpus because of the similarities between Ainu and Japanese language."
+    ],
+    [
+      "In this study, we first developed a speech corpus for Ainu ASR and then, using the end-to-end model with CTC and the attention mechanism, compared four modeling units: phones, syllables, word pieces, and words. The best performance was obtained with the syllable unit, with which WERs in the speaker-closed and speaker-open settings were respectively about 20% and 40% while PERs were about 6% and 14%. Multilingual training using the JNAS improved the performance in the speaker-open setting. Future tasks include reducing the between-speaker performance differences by using speaker adaptation techniques."
+    ],
+    [
+      "The data sets used in this study are provided by the Ainu Museum and Nibutani Ainu Culture Museum. The authors would like to thank Prof. Osami Okuda of Sapporo Gakuin University for his useful advices on the Ainu language."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1719/instruction.md b/qasper-1719/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..05ab70c6a6991b8fb4b4cad10a03d768ad90c0ab
--- /dev/null
+++ b/qasper-1719/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Speech Corpus of Ainu Folklore and End-to-end Speech Recognition for Ainu Language
+
+Question: What is the difference between speaker-open and speaker-closed setting?
\ No newline at end of file
diff --git a/qasper-1720/instruction.md b/qasper-1720/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..82a139d0c6e7aaad8b863f4445f1de8fb68389c6
--- /dev/null
+++ b/qasper-1720/instruction.md
@@ -0,0 +1,138 @@
+Name of Paper: Revealing the Importance of Semantic Retrieval for Machine Reading at Scale
+
+Question: What baseline approaches do they compare against?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Method",
+    "Method ::: Modeling and Training",
+    "Experimental Setup",
+    "Experimental Setup ::: Tasks and Datasets",
+    "Experimental Setup ::: Metrics",
+    "Results on Benchmarks",
+    "Analysis and Ablations",
+    "Analysis and Ablations ::: Ablation Studies ::: Setups:",
+    "Analysis and Ablations ::: Ablation Studies ::: Results:",
+    "Analysis and Ablations ::: Sub-Module Change Analysis",
+    "Analysis and Ablations ::: Sub-Module Change Analysis ::: Effects of Paragraph-level Retrieval",
+    "Analysis and Ablations ::: Sub-Module Change Analysis ::: Effects of Sentence-level Retrieval",
+    "Analysis and Ablations ::: Answer Breakdown",
+    "Analysis and Ablations ::: Examples",
+    "Conclusion",
+    "Acknowledgments",
+    "Training Details",
+    "Term-Based Retrieval Details ::: FEVER",
+    "Term-Based Retrieval Details ::: HotpotQA",
+    "Detailed Results",
+    "Examples and Case Study"
+  ],
+  "paragraphs": [
+    [
+      "Extracting external textual knowledge for machine comprehensive systems has long been an important yet challenging problem. Success requires not only precise retrieval of the relevant information sparsely restored in a large knowledge source but also a deep understanding of both the selected knowledge and the input query to give the corresponding output. Initiated by chen2017drqa, the task was termed as Machine Reading at Scale (MRS), seeking to provide a challenging situation where machines are required to do both semantic retrieval and comprehension at different levels of granularity for the final downstream task.",
+      "Progress on MRS has been made by improving individual IR or comprehension sub-modules with recent advancements on representative learning BIBREF0, BIBREF1, BIBREF2. However, partially due to the lack of annotated data for intermediate retrieval in an MRS setting, the evaluations were done mainly on the final downstream task and with much less consideration on the intermediate retrieval performance. This led to the convention that upstream retrieval modules mostly focus on getting better coverage of the downstream information such that the upper-bound of the downstream score can be improved, rather than finding more exact information. This convention is misaligned with the nature of MRS where equal effort should be put in emphasizing the models' joint performance and optimizing the relationship between the semantic retrieval and the downstream comprehension sub-tasks.",
+      "Hence, to shed light on the importance of semantic retrieval for downstream comprehension tasks, we start by establishing a simple yet effective hierarchical pipeline system for MRS using Wikipedia as the external knowledge source. The system is composed of a term-based retrieval module, two neural modules for both paragraph-level retrieval and sentence-level retrieval, and a neural downstream task module. We evaluated the system on two recent large-scale open domain benchmarks for fact verification and multi-hop QA, namely FEVER BIBREF3 and HotpotQA BIBREF4, in which retrieval performance can also be evaluated accurately since intermediate annotations on evidences are provided. Our system achieves the start-of-the-art results with 45.32% for answer EM and 25.14% joint EM on HotpotQA (8% absolute improvement on answer EM and doubling the joint EM over the previous best results) and with 67.26% on FEVER score (3% absolute improvement over previously published systems).",
+      "We then provide empirical studies to validate design decisions. Specifically, we prove the necessity of both paragraph-level retrieval and sentence-level retrieval for maintaining good performance, and further illustrate that a better semantic retrieval module not only is beneficial to achieving high recall and keeping high upper bound for downstream task, but also plays an important role in shaping the downstream data distribution and providing more relevant and high-quality data for downstream sub-module training and inference. These mechanisms are vital for a good MRS system on both QA and fact verification."
+    ],
+    [
+      "Machine Reading at Scale First proposed and formalized in chen2017drqa, MRS has gained popularity with increasing amount of work on both dataset collection BIBREF5, BIBREF6 and MRS model developments BIBREF7, BIBREF8, BIBREF9. In some previous work BIBREF10, paragraph-level retrieval modules were mainly for improving the recall of required information, while in some other works BIBREF4, sentence-level retrieval modules were merely for solving the auxiliary sentence selection task. In our work, we focus on revealing the relationship between semantic retrieval at different granularity levels and the downstream comprehension task. To the best of our knowledge, we are the first to apply and optimize neural semantic retrieval at both paragraph and sentence levels for MRS.",
+      "Automatic Fact Checking: Recent work BIBREF11 formalized the task of automatic fact checking from the viewpoint of machine learning and NLP. The release of FEVER BIBREF3 stimulates many recent developments BIBREF12, BIBREF13, BIBREF14 on data-driven neural networks for automatic fact checking. We consider the task also as MRS because they share almost the same setup except that the downstream task is verification or natural language inference (NLI) rather than QA.",
+      "Information Retrieval Success in deep neural networks inspires their application to information retrieval (IR) tasks BIBREF15, BIBREF16, BIBREF17, BIBREF18. In typical IR settings, systems are required to retrieve and rank BIBREF19 elements from a collection of documents based on their relevance to the query. This setting might be very different from the retrieval in MRS where systems are asked to select facts needed to answer a question or verify a statement. We refer the retrieval in MRS as Semantic Retrieval since it emphasizes on semantic understanding."
+    ],
+    [
+      "In previous works, an MRS system can be complicated with different sub-components processing different retrieval and comprehension sub-tasks at different levels of granularity, and with some sub-components intertwined. For interpretability considerations, we used a unified pipeline setup. The overview of the system is in Fig. FIGREF2.",
+      "To be specific, we formulate the MRS system as a function that maps an input tuple $(q, \\mathbf {K})$ to an output tuple $(\\hat{y}, \\mathbf {S})$ where $q$ indicates the input query, $\\mathbf {K}$ is the textual KB, $\\hat{y}$ is the output prediction, and $\\mathbf {S}$ is selected supporting sentences from Wikipedia. Let $\\mathbf {E}$ denotes a set of necessary evidences or facts selected from $\\mathbf {K}$ for the prediction. For a QA task, $q$ is the input question and $\\hat{y}$ is the predicted answer. For a verification task, $q$ is the input claim and $\\hat{y}$ is the predicted truthfulness of the input claim. For all tasks, $\\mathbf {K}$ is Wikipedia.",
+      "The system procedure is listed below:",
+      "(1) Term-Based Retrieval: To begin with, we used a combination of the TF-IDF method and a rule-based keyword matching method to narrow the scope from whole Wikipedia down to a set of related paragraphs; this is a standard procedure in MRS BIBREF20, BIBREF10, BIBREF12. The focus of this step is to efficiently select a candidate set $\\mathbf {P_I}$ that can cover the information as much as possible ($\\mathbf {P_I} \\subset \\mathbf {K}$) while keeping the size of the set acceptable enough for downstream processing.",
+      "(2) Paragraph-Level Neural Retrieval: After obtaining the initial set, we compare each paragraph in $\\mathbf {P_I}$ with the input query $q$ using a neural model (which will be explained later in Sec SECREF4). The outputs of the neural model are treated as the relatedness score between the input query and the paragraphs. The scores will be used to sort all the upstream paragraphs. Then, $\\mathbf {P_I}$ will be narrowed to a new set $\\mathbf {P_N}$ ($\\mathbf {P_N} \\subset \\mathbf {P_I}$) by selecting top $k_p$ paragraphs having relatedness score higher than some threshold value $h_p$ (going out from the P-Level grey box in Fig. FIGREF2). $k_p$ and $h_p$ would be chosen by keeping a good balance between the recall and precision of the paragraph retrieval.",
+      "(3) Sentence-Level Neural Retrieval: Next, we select the evidence at the sentence-level by decomposing all the paragraphs in $\\mathbf {P_N}$ into sentences. Similarly, each sentence is compared with the query using a neural model (see details in Sec SECREF4) and obtain a set of sentences $\\mathbf {S} \\subset \\mathbf {P_N}$ for the downstream task by choosing top $k_s$ sentences with output scores higher than some threshold $h_s$ (S-Level grey box in Fig. FIGREF2). During evaluation, $\\mathbf {S}$ is often evaluated against some ground truth sentence set denoted as $\\mathbf {E}$.",
+      "(4) Downstream Modeling: At the final step, we simply applied task-specific neural models (e.g., QA and NLI) on the concatenation of all the sentences in $\\mathbf {S}$ and the query, obtaining the final output $\\hat{y}$.",
+      "In some experiments, we modified the setup for certain analysis or ablation purposes which will be explained individually in Sec SECREF6."
+    ],
+    [
+      "Throughout all our experiments, we used BERT-Base BIBREF2 to provide the state-of-the-art contextualized modeling of the input text.",
+      "Semantic Retrieval: We treated the neural semantic retrieval at both the paragraph and sentence level as binary classification problems with models' parameters updated by minimizing binary cross entropy loss. To be specific, we fed the query and context into BERT as:",
+      "We applied an affine layer and sigmoid activation on the last layer output of the [$\\mathit {CLS}$] token which is a scalar value. The parameters were updated with the objective function:",
+      "where $\\hat{p}_i$ is the output of the model, $\\mathbf {T}^{p/s}_{pos}$ is the positive set and $\\mathbf {T}^{p/s}_{neg}$ is the negative set. As shown in Fig. FIGREF2, at sentence level, ground-truth sentences were served as positive examples while other sentences from upstream retrieved set were served as negative examples. Similarly at the paragraph-level, paragraphs having any ground-truth sentence were used as positive examples and other paragraphs from the upstream term-based retrieval processes were used as negative examples.",
+      "QA: We followed devlin2018bert for QA span prediction modeling. To correctly handle yes-or-no questions in HotpotQA, we fed the two additional \u201c$\\mathit {yes}$\" and \u201c$\\mathit {no}$\" tokens between [$\\mathit {CLS}$] and the $Query$ as:",
+      "where the supervision was given to the second or the third token when the answer is \u201cyes\" or \u201cno\", such that they can compete with all other predicted spans. The parameters of the neural QA model were trained to maximize the log probabilities of the true start and end indexes as:",
+      "where $\\hat{y}^s_i$ and $\\hat{y}^e_i$ are the predicted probability on the ground-truth start and end position for the $i$th example, respectively. It is worth noting that we used ground truth supporting sentences plus some other sentences sampled from upstream retrieved set as the context for training the QA module such that it will adapt to the upstream data distribution during inference.",
+      "Fact Verification: Following Thorne18Fever, we formulate downstream fact verification as the 3-way natural language inference (NLI) classification problem BIBREF21, BIBREF22 and train the model with 3-way cross entropy loss. The input format is the same as that of semantic retrieval and the objective is $\\mathcal {J}_{ver} = -\\sum _{i} \\mathbf {y}_i \\cdot \\log (\\hat{\\mathbf {y}}_i)$, where $\\hat{\\mathbf {y}}_i \\in \\mathbf {R^3}$ denotes the model's output for the three verification labels, and $\\mathbf {y}_i$ is a one-hot embedding for the ground-truth label. For verifiable queries, we used ground truth evidential sentences plus some other sentences sampled from upstream retrieved set as new evidential context for NLI. For non-verifiable queries, we only used sentences sampled from upstream retrieved set as context because those queries are not associated with ground truth evidential sentences. This detail is important for the model to identify non-verifiable queries and will be explained more in Sec SECREF6. Additional training details and hyper-parameter selections are in the Appendix (Sec. SECREF8; Table TABREF27).",
+      "It is worth noting that each sub-module in the system relies on its preceding sub-module to provide data both for training and inference. This means that there will be upstream data distribution misalignment if we trained the sub-module in isolation without considering the properties of its precedent upstream module. The problem is similar to the concept of internal covariate shift BIBREF23, where the distribution of each layer's inputs changes inside a neural network. Therefore, it makes sense to study this issue in a joint MRS setting rather than a typical supervised learning setting where training and test data tend to be fixed and modules being isolated. We release our code and the organized data both for reproducibility and providing an off-the-shelf testbed to facilitate future research on MRS."
+    ],
+    [
+      "MRS requires a system not only to retrieve relevant content from textual KBs but also to poccess enough understanding ability to solve the downstream task. To understand the impact or importance of semantic retrieval on the downstream comprehension, we established a unified experimental setup that involves two different downstream tasks, i.e., multi-hop QA and fact verification."
+    ],
+    [
+      "HotpotQA: This dataset is a recent large-scale QA dataset that brings in new features: (1) the questions require finding and reasoning over multiple documents; (2) the questions are diverse and not limited to pre-existing KBs; (3) it offers a new comparison question type BIBREF4. We experimented our system on HotpotQA in the fullwiki setting, where a system must find the answer to a question in the scope of the entire Wikipedia, an ideal MRS setup. The sizes of the train, dev and test split are 90,564, 7,405, and 7,405. More importantly, HotpotQA also provides human-annotated sentence-level supporting facts that are needed to answer each question. Those intermediate annotations enable evaluation on models' joint ability on both fact retrieval and answer span prediction, facilitating our direct analysis on the explainable predictions and its relations with the upstream retrieval.",
+      "FEVER: The Fact Extraction and VERification dataset BIBREF3 is a recent dataset collected to facilitate the automatic fact checking. The work also proposes a benchmark task in which given an arbitrary input claim, candidate systems are asked to select evidential sentences from Wikipedia and label the claim as either Support, Refute, or Not Enough Info, if the claim can be verified to be true, false, or non-verifiable, respectively, based on the evidence. The sizes of the train, dev and test split are 145,449, 19,998, and 9,998. Similar to HotpotQA, the dataset provides annotated sentence-level facts needed for the verification. These intermediate annotations could provide an accurate evaluation on the results of semantic retrieval and thus suits well for the analysis on the effects of retrieval module on downstream verification.",
+      "As in chen2017drqa, we use Wikipedia as our unique knowledge base because it is a comprehensive and self-evolving information source often used to facilitate intelligent systems. Moreover, as Wikipedia is the source for both HotpotQA and FEVER, it helps standardize any further analysis of the effects of semantic retrieval on the two different downstream tasks."
+    ],
+    [
+      "Following Thorne18Fever, yang2018hotpotqa, we used annotated sentence-level facts to calculate the F1, Precision and Recall scores for evaluating sentence-level retrieval. Similarly, we labeled all the paragraphs that contain any ground truth fact as ground truth paragraphs and used the same three metrics for paragraph-level retrieval evaluation. For HotpotQA, following yang2018hotpotqa, we used exact match (EM) and F1 metrics for QA span prediction evaluation, and used the joint EM and F1 to evaluate models' joint performance on both retrieval and QA. The joint EM and F1 are calculated as: $P_j = P_a \\cdot P_s; R_j = R_a \\cdot R_s; F_j = \\frac{2P_j \\cdot R_j}{P_j + R_j}; \\text{EM}_j = \\text{EM}_a \\cdot \\text{EM}_s$, where $P$, $R$, and $\\text{EM}$ denote precision, recall and EM; the subscript $a$ and $s$ indicate that the scores are for answer span and supporting facts.",
+      "For the FEVER task, following Thorne18Fever, we used the Label Accuracy for evaluating downstream verification and the Fever Score for joint performance. Fever score will award one point for each example with the correct predicted label only if all ground truth facts were contained in the predicted facts set with at most 5 elements. We also used Oracle Score for the two retrieval modules. The scores were proposed in nie2019combining and indicate the upperbound of final FEVER Score at one intermediate layer assuming all downstream modules are perfect. All scores are averaged over examples in the whole evaluation set."
+    ],
+    [
+      "We chose the best system based on the dev set, and used that for submitting private test predictions on both FEVER and HotpotQA .",
+      "As can be seen in Table TABREF8, with the proposed hierarchical system design, the whole pipeline system achieves new start-of-the-art on HotpotQA with large-margin improvements on all the metrics. More specifically, the biggest improvement comes from the EM for the supporting fact which in turn leads to doubling of the joint EM on previous best results. The scores for answer predictions are also higher than all previous best results with $\\sim $8 absolute points increase on EM and $\\sim $9 absolute points on F1. All the improvements are consistent between test and dev set evaluation.",
+      "Similarly for FEVER, we showed F1 for evidence, the Label Accuracy, and the FEVER Score (same as benchmark evaluation) for models in Table TABREF9. Our system obtained substantially higher scores than all previously published results with a $\\sim $4 and $\\sim $3 points absolute improvement on Label Accuracy and FEVER Score. In particular, the system gains 74.62 on the evidence F1, 22 points greater that of the second system, demonstrating its ability on semantic retrieval.",
+      "Previous systems BIBREF24, BIBREF4 on HotpotQA treat supporting fact retrieval (sentence-level retrieval) just as an auxiliary task for providing extra model explainability. In nie2019combining, although they used a similar three-stage system for FEVER, they only applied one neural retrieval module at sentence-level which potentially weaken its retrieval ability. Both of these previous best systems are different from our fully hierarchical pipeline approach. These observations lead to the assumption that the performance gain comes mainly from the hierarchical retrieval and its positive effects on downstream. Therefore, to validate the system design decisions in Sec SECREF3 and reveal the importance of semantic retrieval towards downstream, we conducted a series of ablation and analysis experiments on all the modules. We started by examining the necessity of both paragraph and sentence retrieval and give insights on why both of them matters."
+    ],
+    [
+      "Intuitively, both the paragraph-level and sentence-level retrieval sub-module help speeding up the downstream processing. More importantly, since downstream modules were trained by sampled data from upstream modules, both of neural retrieval sub-modules also play an implicit but important role in controlling the immediate retrieval distribution i.e. the distribution of set $\\mathbf {P_N}$ and set $\\mathbf {S}$ (as shown in Fig. FIGREF2), and providing better inference data and training data for downstream modules."
+    ],
+    [
+      "To reveal the importance of neural retrieval modules at both paragraph and sentence level for maintaining the performance of the overall system, we removed either of them and examine the consequences. Because the removal of a module in the pipeline might change the distribution of the input of the downstream modules, we re-trained all the downstream modules accordingly. To be specific, in the system without the paragraph-level neural retrieval module, we re-trained the sentence-level retrieval module with negative sentences directly sampled from the term-based retrieval set and then also re-trained the downstream QA or verification module. In the system without the sentence-level neural retrieval module, we re-train the downstream QA or verification module by sampling data from both ground truth set and retrieved set directly from the paragraph-level module. We tested the simplified systems on both FEVER and HotpotQA."
+    ],
+    [
+      "Table TABREF13 and TABREF14 shows the ablation results for the two neural retrieval modules at both paragraph and sentence level on HotpotQA and FEVER. To begin with, we can see that removing paragraph-level retrieval module significantly reduces the precision for sentence-level retrieval and the corresponding F1 on both tasks. More importantly, this loss of retrieval precision also led to substantial decreases for all the downstream scores on both QA and verification task in spite of their higher upper-bound and recall scores. This indicates that the negative effects on downstream module induced by the omission of paragraph-level retrieval can not be amended by the sentence-level retrieval module, and focusing semantic retrieval merely on improving the recall or the upper-bound of final score will risk jeopardizing the performance of the overall system.",
+      "Next, the removal of sentence-level retrieval module induces a $\\sim $2 point drop on EM and F1 score in the QA task, and a $\\sim $15 point drop on FEVER Score in the verification task. This suggests that rather than just enhance explainability for QA, the sentence-level retrieval module can also help pinpoint relevant information and reduce the noise in the evidence that might otherwise distract the downstream comprehension module. Another interesting finding is that without sentence-level retrieval module, the QA module suffered much less than the verification module; conversely, the removal of paragraph-level retrieval neural induces a 11 point drop on answer EM comparing to a $\\sim $9 point drop on Label Accuracy in the verification task. This seems to indicate that the downstream QA module relies more on the upstream paragraph-level retrieval whereas the verification module relies more on the upstream sentence-level retrieval. Finally, we also evaluate the F1 score on FEVER for each classification label and we observe a significant drop of F1 on Not Enough Info category without retrieval module, meaning that semantic retrieval is vital for the downstream verification module's discriminative ability on Not Enough Info label."
+    ],
+    [
+      "To further study the effects of upstream semantic retrieval towards downstream tasks, we change training or inference data between intermediate layers and then examine how this modification will affect the downstream performance."
+    ],
+    [
+      "We fixed $h_p=0$ (the value achieving the best performance) and re-trained all the downstream parameters and track their performance as $k_p$ (the number of selected paragraph) being changed from 1 to 12. The increasing of $k_p$ means a potential higher coverage of the answer but more noise in the retrieved facts. Fig. FIGREF17 shows the results. As can be seen that the EM scores for supporting fact retrieval, answer prediction, and joint performance increase sharply when $k_p$ is changed from 1 to 2. This is consistent with the fact that at least two paragraphs are required to ask each question in HotpotQA. Then, after the peak, every score decrease as $k_p$ becomes larger except the recall of supporting fact which peaks when $k_p=4$. This indicates that even though the neural sentence-level retrieval module poccesses a certain level of ability to select correct facts from noisier upstream information, the final QA module is more sensitive to upstream data and fails to maintain the overall system performance. Moreover, the reduction on answer EM and joint EM suggests that it might be risky to give too much information for downstream modules with a unit of a paragraph."
+    ],
+    [
+      "Similarly, to study the effects of neural sentence-level retrieval module towards downstream QA and verification modules, we fixed $k_s$ to be 5 and set $h_s$ ranging from 0.1 to 0.9 with a 0.1 interval. Then, we re-trained the downstream QA and verification modules with different $h_s$ value and experimented on both HotpotQA and FEVER.",
+      "Question Answering: Fig. FIGREF18 shows the trend of performance. Intuitively, the precision increase while the recall decrease as the system becomes more strict about the retrieved sentences. The EM score for supporting fact retrieval and joint performance reaches their highest value when $h_s=0.5$, a natural balancing point between precision and recall. More interestingly, the EM score for answer prediction peaks when $h_s=0.2$ and where the recall is higher than the precision. This misalignment between answer prediction performance and retrieval performance indicates that unlike the observation at paragraph-level, the downstream QA module is able to stand a certain amount of noise at sentence-level and benefit from a higher recall.",
+      "Fact Verification: Fig. FIGREF19 shows the trends for Label Accuracy, FEVER Score, and Evidence F1 by modifying upstream sentence-level threshold $h_s$. We observed that the general trend is similar to that of QA task where both the label accuracy and FEVER score peak at $h_s=0.2$ whereas the retrieval F1 peaks at $h_s=0.5$. Note that, although the downstream verification could take advantage of a higher recall, the module is more sensitive to sentence-level retrieval comparing to the QA module in HotpotQA. More detailed results are in the Appendix."
+    ],
+    [
+      "We further sample 200 examples from HotpotQA and manually tag them according to several common answer types BIBREF4. The proportion of different answer types is shown in Figure FIGREF24. The performance of the system on each answer type is shown in Table TABREF23. The most frequent answer type is 'Person' (24%) and the least frequent answer type is 'Event' (2%). It is also interesting to note that the model performs the best in Yes/No questions as shown in Table TABREF23, reaching an accuracy of 70.6%."
+    ],
+    [
+      "Fig. FIGREF26 shows an example that is correctly handled by the full pipeline system but not by the system without paragraph-level retrieval module. We can see that it is very difficult to filter the distracting sentence after sentence-level either by the sentence retrieval module or the QA module.",
+      "Above findings in both FEVER and HotpotQA bring us some important guidelines for MRS: (1) A paragraph-level retrieval module is imperative; (2) Downstream task module is able to undertake a certain amount of noise from sentence-level retrieval; (3) Cascade effects on downstream task might be caused by modification at paragraph-level retrieval."
+    ],
+    [
+      "We proposed a simple yet effective hierarchical pipeline system that achieves state-of-the-art results on two MRS tasks. Ablation studies demonstrate the importance of semantic retrieval at both paragraph and sentence levels in the MRS system. The work can give general guidelines on MRS modeling and inspire future research on the relationship between semantic retrieval and downstream comprehension in a joint setting."
+    ],
+    [
+      "We thank the reviewers for their helpful comments and Yicheng Wang for useful comments. This work was supported by awards from Verisk, Google, Facebook, Salesforce, and Adobe (plus Amazon and Google GPU cloud credits). The views, opinions, and/or findings contained in this article are those of the authors and should not be interpreted as representing the official views or policies, either expressed or implied, of the funding agency."
+    ],
+    [
+      "The hyper-parameters were chosen based on the performance of the system on the dev set. The hyper-parameters search space is shown in Table TABREF27 and the learning rate was set to $10^{-5}$ in all experiments."
+    ],
+    [
+      "We used the same key-word matching method in nie2019combining to get a candidate set for each query. We also used TF-IDF BIBREF20 method to get top-5 related documents for each query. Then, the two sets were combined to get final term-based retrieval set for FEVER. The mean and standard deviation of the number of the retrieved paragraph in the merged set were 8.06 and 4.88."
+    ],
+    [
+      "We first used the same procedure on FEVER to get an initial candidate set for each query in HotpotQA. Because HotpotQA requires at least 2-hop reasoning for each query, we then extract all the hyperlinked documents from the retrieved documents in the initial candidate set, rank them with TF-IDF BIBREF20 score and then select top-5 most related documents and add them to the candidate set. This gives the final term-based retrieval set for HotpotQA. The mean and standard deviation of the number of the retrieved paragraph for each query in HotpotQA were 39.43 and 16.05."
+    ],
+    [
+      "The results of sentence-level retrieval and downstream QA with different values of $h_s$ on HotpotQA are in Table TABREF28.",
+      "The results of sentence-level retrieval and downstream verification with different values of $h_s$ on FEVER are in Table TABREF34.",
+      "The results of sentence-level retrieval and downstream QA with different values of $k_p$ on HotpotQA are in Table TABREF35."
+    ],
+    [
+      "We further provide examples, case study and error analysis for the full pipeline system. The examples are shown from Tables TABREF37, TABREF38, TABREF39, TABREF40, TABREF41. The examples show high diversity on the semantic level and the error occurs often due to the system's failure of extracting precise (either wrong, surplus or insufficient) information from KB."
+    ]
+  ]
+}
+```
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diff --git a/qasper-1726/instruction.md b/qasper-1726/instruction.md
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+Name of Paper: Katecheo: A Portable and Modular System for Multi-Topic Question Answering
+
+Question: how many domains did they experiment with?
\ No newline at end of file
diff --git a/qasper-1727/instruction.md b/qasper-1727/instruction.md
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+++ b/qasper-1727/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Katecheo: A Portable and Modular System for Multi-Topic Question Answering
+
+Question: what pretrained models were used?
\ No newline at end of file
diff --git a/qasper-1728/instruction.md b/qasper-1728/instruction.md
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+Name of Paper: Transductive Learning with String Kernels for Cross-Domain Text Classification
+
+Question: What domains are contained in the polarity classification dataset?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Cross-Domain Classification",
+    "String Kernels",
+    "Transductive String Kernels",
+    "Transductive Kernel Classifier",
+    "Polarity Classification",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "",
+      "Domain shift is a fundamental problem in machine learning, that has attracted a lot of attention in the natural language processing and vision communities BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 . To understand and address this problem, generated by the lack of labeled data in a target domain, researchers have studied the behavior of machine learning methods in cross-domain settings BIBREF2 , BIBREF11 , BIBREF10 and came up with various domain adaptation techniques BIBREF12 , BIBREF5 , BIBREF6 , BIBREF9 . In cross-domain classification, a classifier is trained on data from a source domain and tested on data from a (different) target domain. The accuracy of machine learning methods is usually lower in the cross-domain setting, due to the distribution gap between different domains. However, researchers proposed several domain adaptation techniques by using the unlabeled test data to obtain better performance BIBREF13 , BIBREF14 , BIBREF15 , BIBREF16 , BIBREF7 . Interestingly, some recent works BIBREF10 , BIBREF17 indicate that string kernels can yield robust results in the cross-domain setting without any domain adaptation. In fact, methods based on string kernels have demonstrated impressive results in various text classification tasks ranging from native language identification BIBREF18 , BIBREF19 , BIBREF20 , BIBREF21 and authorship identification BIBREF22 to dialect identification BIBREF23 , BIBREF17 , BIBREF24 , sentiment analysis BIBREF10 , BIBREF25 and automatic essay scoring BIBREF26 . As long as a labeled training set is available, string kernels can reach state-of-the-art results in various languages including English BIBREF19 , BIBREF10 , BIBREF26 , Arabic BIBREF27 , BIBREF20 , BIBREF17 , BIBREF24 , Chinese BIBREF25 and Norwegian BIBREF20 . Different from all these recent approaches, we use unlabeled data from the test set in a transductive setting in order to significantly increase the performance of string kernels. In our recent work BIBREF28 , we proposed two transductive learning approaches combined into a unified framework that improves the results of string kernels in two different tasks. In this paper, we provide a formal and detailed description of our transductive algorithm and present results in cross-domain English polarity classification.",
+      "The paper is organized as follows. Related work on cross-domain text classification and string kernels is presented in Section SECREF2 . Section SECREF3 presents our approach to obtain domain adapted string kernels. The transductive transfer learning method is described in Section SECREF4 . The polarity classification experiments are presented in Section SECREF5 . Finally, we draw conclusions and discuss future work in Section SECREF6 .",
+      ""
+    ],
+    [
+      ""
+    ],
+    [
+      "Transfer learning (or domain adaptation) aims at building effective classifiers for a target domain when the only available labeled training data belongs to a different (source) domain. Domain adaptation techniques can be roughly divided into graph-based methods BIBREF1 , BIBREF29 , BIBREF9 , BIBREF30 , probabilistic models BIBREF3 , BIBREF4 , knowledge-based models BIBREF14 , BIBREF31 , BIBREF11 and joint optimization frameworks BIBREF12 . The transfer learning methods from the literature show promising results in a variety of real-world applications, such as image classification BIBREF12 , text classification BIBREF13 , BIBREF16 , BIBREF3 , polarity classification BIBREF1 , BIBREF29 , BIBREF4 , BIBREF6 , BIBREF30 and others BIBREF32 .",
+      "General transfer learning approaches. Long et al. BIBREF12 proposed a novel transfer learning framework to model distribution adaptation and label propagation in a unified way, based on the structural risk minimization principle and the regularization theory. Shu et al. BIBREF5 proposed a method that bridges the distribution gap between the source domain and the target domain through affinity learning, by exploiting the existence of a subset of data points in the target domain that are distributed similarly to the data points in the source domain. In BIBREF7 , deep learning is employed to jointly optimize the representation, the cross-domain transformation and the target label inference in an end-to-end fashion. More recently, Sun et al. BIBREF8 proposed an unsupervised domain adaptation method that minimizes the domain shift by aligning the second-order statistics of source and target distributions, without requiring any target labels. Chang et al. BIBREF9 proposed a framework based on using a parallel corpus to calibrate domain-specific kernels into a unified kernel for leveraging graph-based label propagation between domains.",
+      "Cross-domain text classification. Joachims BIBREF13 introduced the Transductive Support Vector Machines (TSVM) framework for text classification, which takes into account a particular test set and tries to minimize the error rate for those particular test samples. Ifrim et al. BIBREF14 presented a transductive learning approach for text classification based on combining latent variable models for decomposing the topic-word space into topic-concept and concept-word spaces, and explicit knowledge models with named concepts for populating latent variables. Guo et al. BIBREF16 proposed a transductive subspace representation learning method to address domain adaptation for cross-lingual text classification. Zhuang et al. BIBREF3 presented a probabilistic model, by which both the shared and distinct concepts in different domains can be learned by the Expectation-Maximization process which optimizes the data likelihood. In BIBREF33 , an algorithm to adapt a classification model by iteratively learning domain-specific features from the unlabeled test data is described.",
+      "Cross-domain polarity classification. In recent years, cross-domain sentiment (polarity) classification has gained popularity due to the advances in domain adaptation on one side, and to the abundance of documents from various domains available on the Web, expressing positive or negative opinion, on the other side. Some of the general domain adaptation frameworks have been applied to polarity classification BIBREF3 , BIBREF33 , BIBREF9 , but there are some approaches that have been specifically designed for the cross-domain sentiment classification task BIBREF0 , BIBREF34 , BIBREF1 , BIBREF29 , BIBREF11 , BIBREF4 , BIBREF6 , BIBREF10 , BIBREF30 . To the best of our knowledge, Blitzer et al. BIBREF0 were the first to report results on cross-domain classification proposing the structural correspondence learning (SCL) method, and its variant based on mutual information (SCL-MI). Pan et al. BIBREF1 proposed a spectral feature alignment (SFA) algorithm to align domain-specific words from different domains into unified clusters, using domain-independent words as a bridge. Bollegala et al. BIBREF31 used a cross-domain lexicon creation to generate a sentiment-sensitive thesaurus (SST) that groups different words expressing the same sentiment, using unigram and bigram features as BIBREF0 , BIBREF1 . Luo et al. BIBREF4 proposed a cross-domain sentiment classification framework based on a probabilistic model of the author's emotion state when writing. An Expectation-Maximization algorithm is then employed to solve the maximum likelihood problem and to obtain a latent emotion distribution of the author. Franco-Salvador et al. BIBREF11 combined various recent and knowledge-based approaches using a meta-learning scheme (KE-Meta). They performed cross-domain polarity classification without employing any domain adaptation technique. More recently, Fern\u00e1ndez et al. BIBREF6 introduced the Distributional Correspondence Indexing (DCI) method for domain adaptation in sentiment classification. The approach builds term representations in a vector space common to both domains where each dimension reflects its distributional correspondence to a highly predictive term that behaves similarly across domains. A graph-based approach for sentiment classification that models the relatedness of different domains based on shared users and keywords is proposed in BIBREF30 .",
+      ""
+    ],
+    [
+      "In recent years, methods based on string kernels have demonstrated remarkable performance in various text classification tasks BIBREF35 , BIBREF36 , BIBREF22 , BIBREF19 , BIBREF10 , BIBREF17 , BIBREF26 . String kernels represent a way of using information at the character level by measuring the similarity of strings through character n-grams. Lodhi et al. BIBREF35 used string kernels for document categorization, obtaining very good results. String kernels were also successfully used in authorship identification BIBREF22 . More recently, various combinations of string kernels reached state-of-the-art accuracy rates in native language identification BIBREF19 and Arabic dialect identification BIBREF17 . Interestingly, string kernels have been used in cross-domain settings without any domain adaptation, obtaining impressive results. For instance, Ionescu et al. BIBREF19 have employed string kernels in a cross-corpus (and implicitly cross-domain) native language identification experiment, improving the state-of-the-art accuracy by a remarkable INLINEFORM0 . Gim\u00e9nez-P\u00e9rez et al. BIBREF10 have used string kernels for single-source and multi-source polarity classification. Remarkably, they obtain state-of-the-art performance without using knowledge from the target domain, which indicates that string kernels provide robust results in the cross-domain setting without any domain adaptation. Ionescu et al. BIBREF17 obtained the best performance in the Arabic Dialect Identification Shared Task of the 2017 VarDial Evaluation Campaign BIBREF37 , with an improvement of INLINEFORM1 over the second-best method. It is important to note that the training and the test speech samples prepared for the shared task were recorded in different setups BIBREF37 , or in other words, the training and the test sets are drawn from different distributions. Different from all these recent approaches BIBREF19 , BIBREF10 , BIBREF17 , we use unlabeled data from the target domain to significantly increase the performance of string kernels in cross-domain text classification, particularly in English polarity classification.",
+      ""
+    ],
+    [
+      "",
+      "String kernels. Kernel functions BIBREF38 capture the intuitive notion of similarity between objects in a specific domain. For example, in text mining, string kernels can be used to measure the pairwise similarity between text samples, simply based on character n-grams. Various string kernel functions have been proposed to date BIBREF35 , BIBREF38 , BIBREF19 . Perhaps one of the most recently introduced string kernels is the histogram intersection string kernel BIBREF19 . For two strings over an alphabet INLINEFORM0 , INLINEFORM1 , the intersection string kernel is formally defined as follows: DISPLAYFORM0 ",
+      "where INLINEFORM0 is the number of occurrences of n-gram INLINEFORM1 as a substring in INLINEFORM2 , and INLINEFORM3 is the length of INLINEFORM4 . The spectrum string kernel or the presence bits string kernel can be defined in a similar fashion BIBREF19 .",
+      "Transductive string kernels. We present a simple and straightforward approach to produce a transductive similarity measure suitable for strings. We take the following steps to derive transductive string kernels. For a given kernel (similarity) function INLINEFORM0 , we first build the full kernel matrix INLINEFORM1 , by including the pairwise similarities of samples from both the train and the test sets. For a training set INLINEFORM2 of INLINEFORM3 samples and a test set INLINEFORM4 of INLINEFORM5 samples, such that INLINEFORM6 , each component in the full kernel matrix is defined as follows: DISPLAYFORM0 ",
+      "where INLINEFORM0 and INLINEFORM1 are samples from the set INLINEFORM2 , for all INLINEFORM3 . We then normalize the kernel matrix by dividing each component by the square root of the product of the two corresponding diagonal components: DISPLAYFORM0 ",
+      "We transform the normalized kernel matrix into a radial basis function (RBF) kernel matrix as follows: DISPLAYFORM0 ",
+      "Each row in the RBF kernel matrix INLINEFORM0 is now interpreted as a feature vector. In other words, each sample INLINEFORM1 is represented by a feature vector that contains the similarity between the respective sample INLINEFORM2 and all the samples in INLINEFORM3 . Since INLINEFORM4 includes the test samples as well, the feature vector is inherently adapted to the test set. Indeed, it is easy to see that the features will be different if we choose to apply the string kernel approach on a set of test samples INLINEFORM5 , such that INLINEFORM6 . It is important to note that through the features, the subsequent classifier will have some information about the test samples at training time. More specifically, the feature vector conveys information about how similar is every test sample to every training sample. We next consider the linear kernel, which is given by the scalar product between the new feature vectors. To obtain the final linear kernel matrix, we simply need to compute the product between the RBF kernel matrix and its transpose: DISPLAYFORM0 ",
+      "In this way, the samples from the test set, which are included in INLINEFORM0 , are used to obtain new (transductive) string kernels that are adapted to the test set at hand.",
+      "[!tpb] Transductive Kernel Algorithm",
+      "Input:",
+      " INLINEFORM0 \u2013 the training set of INLINEFORM1 training samples and associated class labels;",
+      " INLINEFORM0 \u2013 the set of INLINEFORM1 test samples;",
+      " INLINEFORM0 \u2013 a kernel function;",
+      " INLINEFORM0 \u2013 the number of test samples to be added in the second round of training;",
+      " INLINEFORM0 \u2013 a binary kernel classifier.",
+      "Domain-Adapted Kernel Matrix Computation Steps:",
+      " INLINEFORM0 INLINEFORM1 ; INLINEFORM2 ; INLINEFORM3 ; INLINEFORM4 ",
+      " INLINEFORM0 INLINEFORM1 INLINEFORM2 ",
+      " INLINEFORM0 INLINEFORM1 INLINEFORM2 ",
+      " INLINEFORM0 ",
+      " INLINEFORM0 ",
+      "Transductive Kernel Classifier Steps:",
+      " INLINEFORM0 ",
+      " INLINEFORM0 ",
+      " INLINEFORM0 ",
+      " INLINEFORM0 INLINEFORM1 ",
+      " INLINEFORM0 ",
+      " INLINEFORM0 ",
+      " INLINEFORM0 INLINEFORM1 the dual weights of INLINEFORM2 trained on INLINEFORM3 with the labels INLINEFORM4 ",
+      " INLINEFORM0 ",
+      " INLINEFORM0 ; INLINEFORM1 ",
+      " INLINEFORM0 INLINEFORM1 ",
+      " INLINEFORM0 ",
+      " INLINEFORM0 INLINEFORM1 sort INLINEFORM2 in descending order and return the sorted indexes",
+      " INLINEFORM0 ",
+      " INLINEFORM0 ",
+      " INLINEFORM0 ",
+      " INLINEFORM0 ",
+      " INLINEFORM0 ",
+      "Output:",
+      " INLINEFORM0 \u2013 the set of predicted labels for the test samples in INLINEFORM1 . ",
+      ""
+    ],
+    [
+      "",
+      "We next present a simple yet effective approach for adapting a one-versus-all kernel classifier trained on a source domain to a different target domain. Our transductive kernel classifier (TKC) approach is composed of two learning iterations. Our entire framework is formally described in Algorithm SECREF3 .",
+      "Notations. We use the following notations in the algorithm. Sets, arrays and matrices are written in capital letters. All collection types are considered to be indexed starting from position 1. The elements of a set INLINEFORM0 are denoted by INLINEFORM1 , the elements of an array INLINEFORM2 are alternatively denoted by INLINEFORM3 or INLINEFORM4 , and the elements of a matrix INLINEFORM5 are denoted by INLINEFORM6 or INLINEFORM7 when convenient. The sequence INLINEFORM8 is denoted by INLINEFORM9 . We use sequences to index arrays or matrices as well. For example, for an array INLINEFORM10 and two integers INLINEFORM11 and INLINEFORM12 , INLINEFORM13 denotes the sub-array INLINEFORM14 . In a similar manner, INLINEFORM15 denotes a sub-matrix of the matrix INLINEFORM16 , while INLINEFORM17 returns the INLINEFORM18 -th row of M and INLINEFORM19 returns the INLINEFORM20 -th column of M. The zero matrix of INLINEFORM21 components is denoted by INLINEFORM22 , and the square zero matrix is denoted by INLINEFORM23 . The identity matrix is denoted by INLINEFORM24 .",
+      "Algorithm description. In steps 8-17, we compute the domain-adapted string kernel matrix, as described in the previous section. In the first learning iteration (when INLINEFORM0 ), we train several classifiers to distinguish each individual class from the rest, according to the one-versus-all (OVA) scheme. In step 27, the kernel classifier INLINEFORM1 is trained to distinguish a class from the others, assigning a dual weight to each training sample from the source domain. The returned column vector of dual weights is denoted by INLINEFORM2 and the bias value is denoted by INLINEFORM3 . The vector of weights INLINEFORM4 contains INLINEFORM5 values, such that the weight INLINEFORM6 corresponds to the training sample INLINEFORM7 . When the test kernel matrix INLINEFORM8 of INLINEFORM9 components is multiplied with the vector INLINEFORM10 in step 28, the result is a column vector of INLINEFORM11 positive or negative scores. Afterwards (step 34), the test samples are sorted in order to maximize the probability of correctly predicted labels. For each test sample INLINEFORM12 , we consider the score INLINEFORM13 (step 32) produced by the classifier for the chosen class INLINEFORM14 (step 31), which is selected according to the OVA scheme. The sorting is based on the hypothesis that if the classifier associates a higher score to a test sample, it means that the classifier is more confident about the predicted label for the respective test sample. Before the second learning iteration, a number of INLINEFORM15 test samples from the top of the sorted list are added to the training set (steps 35-39) for another round of training. As the classifier is more confident about the predicted labels INLINEFORM16 of the added test samples, the chance of including noisy examples (with wrong labels) is minimized. On the other hand, the classifier has the opportunity to learn some useful domain-specific patterns of the test domain. We believe that, at least in the cross-domain setting, the added test samples bring more useful information than noise. We would like to stress out that the ground-truth test labels are never used in our transductive algorithm. Although the test samples are required beforehand, their labels are not necessary. Hence, our approach is suitable in situations where unlabeled data from the target domain can be collected cheaply, and such situations appear very often in practice, considering the great amount of data available on the Web.",
+      ""
+    ],
+    [
+      "",
+      "Data set. For the cross-domain polarity classification experiments, we use the second version of Multi-Domain Sentiment Dataset BIBREF0 . The data set contains Amazon product reviews of four different domains: Books (B), DVDs (D), Electronics (E) and Kitchen appliances (K). Reviews contain star ratings (from 1 to 5) which are converted into binary labels as follows: reviews rated with more than 3 stars are labeled as positive, and those with less than 3 stars as negative. In each domain, there are 1000 positive and 1000 negative reviews.",
+      "Baselines. We compare our approach with several methods BIBREF1 , BIBREF31 , BIBREF11 , BIBREF8 , BIBREF10 , BIBREF39 in two cross-domain settings. Using string kernels, Gim\u00e9nez-P\u00e9rez et al. BIBREF10 reported better performance than SST BIBREF31 and KE-Meta BIBREF11 in the multi-source domain setting. In addition, we compare our approach with SFA BIBREF1 , CORAL BIBREF8 and TR-TrAdaBoost BIBREF39 in the single-source setting.",
+      "Evaluation procedure and parameters. We follow the same evaluation methodology of Gim\u00e9nez-P\u00e9rez et al. BIBREF10 , to ensure a fair comparison. Furthermore, we use the same kernels, namely the presence bits string kernel ( INLINEFORM0 ) and the intersection string kernel ( INLINEFORM1 ), and the same range of character n-grams (5-8). To compute the string kernels, we used the open-source code provided by Ionescu et al. BIBREF19 , BIBREF40 . For the transductive kernel classifier, we select INLINEFORM2 unlabeled test samples to be included in the training set for the second round of training. We choose Kernel Ridge Regression BIBREF38 as classifier and set its regularization parameter to INLINEFORM3 in all our experiments. Although Gim\u00e9nez-P\u00e9rez et al. BIBREF10 used a different classifier, namely Kernel Discriminant Analysis, we observed that Kernel Ridge Regression produces similar results ( INLINEFORM4 ) when we employ the same string kernels. As Gim\u00e9nez-P\u00e9rez et al. BIBREF10 , we evaluate our approach in two cross-domain settings. In the multi-source setting, we train the models on all domains, except the one used for testing. In the single-source setting, we train the models on one of the four domains and we independently test the models on the remaining three domains.",
+      "Results in multi-source setting. The results for the multi-source cross-domain polarity classification setting are presented in Table TABREF8 . Both the transductive presence bits string kernel ( INLINEFORM0 ) and the transductive intersection kernel ( INLINEFORM1 ) obtain better results than their original counterparts. Moreover, according to the McNemar's test BIBREF41 , the results on the DVDs, the Electronics and the Kitchen target domains are significantly better than the best baseline string kernel, with a confidence level of INLINEFORM2 . When we employ the transductive kernel classifier (TKC), we obtain even better results. On all domains, the accuracy rates yielded by the transductive classifier are more than INLINEFORM3 better than the best baseline. For example, on the Books domain the accuracy of the transductive classifier based on the presence bits kernel ( INLINEFORM4 ) is INLINEFORM5 above the best baseline ( INLINEFORM6 ) represented by the intersection string kernel. Remarkably, the improvements brought by our transductive string kernel approach are statistically significant in all domains.",
+      "Results in single-source setting. The results for the single-source cross-domain polarity classification setting are presented in Table TABREF9 . We considered all possible combinations of source and target domains in this experiment, and we improve the results in each and every case. Without exception, the accuracy rates reached by the transductive string kernels are significantly better than the best baseline string kernel BIBREF10 , according to the McNemar's test performed at a confidence level of INLINEFORM0 . The highest improvements (above INLINEFORM1 ) are obtained when the source domain contains Books reviews and the target domain contains Kitchen reviews. As in the multi-source setting, we obtain much better results when the transductive classifier is employed for the learning task. In all cases, the accuracy rates of the transductive classifier are more than INLINEFORM2 better than the best baseline string kernel. Remarkably, in four cases (E INLINEFORM3 B, E INLINEFORM4 D, B INLINEFORM5 K and D INLINEFORM6 K) our improvements are greater than INLINEFORM7 . The improvements brought by our transductive classifier based on string kernels are statistically significant in each and every case. In comparison with SFA BIBREF1 , we obtain better results in all but one case (K INLINEFORM8 D). Remarkably, we surpass the other state-of-the-art approaches BIBREF8 , BIBREF39 in all cases.",
+      ""
+    ],
+    [
+      "",
+      "In this paper, we presented two domain adaptation approaches that can be used together to improve the results of string kernels in cross-domain settings. We provided empirical evidence indicating that our framework can be successfully applied in cross-domain text classification, particularly in cross-domain English polarity classification. Indeed, the polarity classification experiments demonstrate that our framework achieves better accuracy rates than other state-of-the-art methods BIBREF1 , BIBREF31 , BIBREF11 , BIBREF8 , BIBREF10 , BIBREF39 . By using the same parameters across all the experiments, we showed that our transductive transfer learning framework can bring significant improvements without having to fine-tune the parameters for each individual setting. Although the framework described in this paper can be generally applied to any kernel method, we focused our work only on string kernel approaches used in text classification. In future work, we aim to combine the proposed transductive transfer learning framework with different kinds of kernels and classifiers, and employ it for other cross-domain tasks."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1729/instruction.md b/qasper-1729/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..ecd73146b4aa797353da476d2a82e41d65d057c0
--- /dev/null
+++ b/qasper-1729/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Transductive Learning with String Kernels for Cross-Domain Text Classification
+
+Question: How long is the dataset?
\ No newline at end of file
diff --git a/qasper-1743/instruction.md b/qasper-1743/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..f0042bfc9a6587eaeea7ab8a2a21cd2855f3ef17
--- /dev/null
+++ b/qasper-1743/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: MedDialog: A Large-scale Medical Dialogue Dataset
+
+Question: How did they annotate the dataset?
\ No newline at end of file
diff --git a/qasper-1788/instruction.md b/qasper-1788/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..7a9ee70cef7f7c64c58454126ec33dbf484624cd
--- /dev/null
+++ b/qasper-1788/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Recurrently Controlled Recurrent Networks
+
+Question: Does their model have more parameters than other models?
\ No newline at end of file
diff --git a/qasper-1800/instruction.md b/qasper-1800/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..845a5deff3ee5883a906096ee3630ec7f3a9498e
--- /dev/null
+++ b/qasper-1800/instruction.md
@@ -0,0 +1,12 @@
+Name of Paper: Vietnamese Open Information Extraction
+
+Question: Do they consider relations other than binary relations?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [],
+  "paragraphs": []
+}
+```
\ No newline at end of file
diff --git a/qasper-1801/instruction.md b/qasper-1801/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..ad471de0a90aedf1259fa49188dfa18dcd252de6
--- /dev/null
+++ b/qasper-1801/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Vietnamese Open Information Extraction
+
+Question: Are the grammar clauses manually created?
\ No newline at end of file
diff --git a/qasper-1806/instruction.md b/qasper-1806/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..4ee3a2fcc358a6da761ebde18e933cb893b0cdd9
--- /dev/null
+++ b/qasper-1806/instruction.md
@@ -0,0 +1,115 @@
+Name of Paper: TLT-school: a Corpus of Non Native Children Speech
+
+Question: What proficiency indicators are used to the score the utterances?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Data Acquisition",
+    "Data Acquisition ::: Prompts",
+    "Data Acquisition ::: Written Data",
+    "Data Acquisition ::: Spoken Data",
+    "Manual Transcriptions",
+    "Usage of the Data",
+    "Usage of the Data ::: ASR-related Challenges",
+    "Usage of the Data ::: Data Annotation",
+    "Usage of the Data ::: Proficiency Assessment of L2 Learners",
+    "Usage of the Data ::: Modelling Pronunciation",
+    "Distribution of the Corpus",
+    "Conclusions and Future Works",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "We have acquired large sets of both written and spoken data during the implementation of campaigns aimed at assessing the proficiency, at school, of Italian pupils learning both German and English. Part of the acquired data has been included in a corpus, named \"Trentino Language Testing\" in schools (TLT-school), that will be described in the following.",
+      "All the collected sentences have been annotated by human experts in terms of some predefined \u201cindicators\u201d which, in turn, were used to assign the proficiency level to each student undertaking the assigned test. This level is expressed according to the well-known Common European Framework of Reference for Languages (Council of Europe, 2001) scale. The CEFR defines 6 levels of proficiency: A1 (beginner), A2, B1, B2, C1 and C2. The levels considered in the evaluation campaigns where the data have been collected are: A1, A2 and B1.",
+      "The indicators measure the linguistic competence of test takers both in relation to the content (e.g. grammatical correctness, lexical richness, semantic coherence, etc.) and to the speaking capabilities (e.g. pronunciation, fluency, etc.). Refer to Section SECREF2 for a description of the adopted indicators.",
+      "The learners are Italian students, between 9 and 16 years old. They took proficiency tests by answering question prompts provided in written form. The \u201cTLT-school\u201d corpus, that we are going to make publicly available, contains part of the spoken answers (together with the respective manual transcriptions) recorded during some of the above mentioned evaluation campaigns. We will release the written answers in future. Details and critical issues found during the acquisition of the answers of the test takers will be discussed in Section SECREF2.",
+      "The tasks that can be addressed by using the corpus are very challenging and pose many problems, which have only partially been solved by the interested scientific community.",
+      "From the ASR perspective, major difficulties are represented by: a) recognition of both child and non-native speech, i.e. Italian pupils speaking both English and German, b) presence of a large number of spontaneous speech phenomena (hesitations, false starts, fragments of words, etc.), c) presence of multiple languages (English, Italian and German words are frequently uttered in response to a single question), d) presence of a significant level of background noise due to the fact that the microphone remains open for a fixed time interval (e.g. 20 seconds - depending on the question), and e) presence of non-collaborative speakers (students often joke, laugh, speak softly, etc.). Refer to Section SECREF6 for a detailed description of the collected spoken data set.",
+      "Furthermore, since the sets of data from which \u201cTLT-school\u201d was derived were primarily acquired for measuring proficiency of second language (L2) learners, it is quite obvious to exploit the corpus for automatic speech rating. To this purpose, one can try to develop automatic approaches to reliably estimate the above-mentioned indicators used by the human experts who scored the answers of the pupils (such an approach is described in BIBREF0). However, it has to be noticed that scientific literature proposes to use several features and indicators for automatic speech scoring, partly different from those adopted in \u201cTLT-school\u201d corpus (see below for a brief review of the literature). Hence, we believe that adding new annotations to the corpus, related to particular aspects of language proficiency, can stimulate research and experimentation in this area.",
+      "Finally, it is worth mentioning that also written responses of \u201cTLT-school\u201d corpus are characterised by a high level of noise due to: spelling errors, insertion of word fragments, presence of words belonging to multiple languages, presence of off-topic answers (e.g. containing jokes, comments not related to the questions, etc.). This set of text data will allow scientists to investigate both language and behaviour of pupils learning second languages at school. Written data are described in detail in Section SECREF5",
+      "Relation to prior work. Scientific literature is rich in approaches for automated assessment of spoken language proficiency. Performance is directly dependent on ASR accuracy which, in turn, depends on the type of input, read or spontaneous, and on the speakers' age, adults or children (see BIBREF1 for an overview of spoken language technology for education). A recent publication reporting an overview of state-of-the-art automated speech scoring technology as it is currently used at Educational Testing Service (ETS) can be found in BIBREF2.",
+      "In order to address automatic assessment of complex spoken tasks requiring more general communication capabilities from L2 learners, the AZELLA data set BIBREF3, developed by Pearson, has been collected and used as benchmark for some researches BIBREF4, BIBREF3. The corpus contains $1,500$ spoken tests, each double graded by human professionals, from a variety of tasks.",
+      "A public set of spoken data has been recently distributed in a spoken CALL (Computer Assisted Language Learning) shared task where Swiss students learning English had to answer to both written and spoken prompts. The goal of this challenge is to label students' spoken responses as \u201caccept\u201d or \u201creject\u201d. Refer to BIBREF5 for details of the challenge and of the associated data sets.",
+      "Many non-native speech corpora (mostly in English as target language) have been collected during the years. A list, though not recent, as well as a brief description of most of them can be found in BIBREF6. The same paper also gives information on how the data sets are distributed and can be accessed (many of them are available through both LDC and ELDA agencies). Some of the corpora also provide proficiency ratings to be used in CALL applications. Among them, we mention the ISLE corpus BIBREF7, which also contains transcriptions at the phonetic level and was used in the experiments reported in BIBREF0. Note that all corpora mentioned in BIBREF6 come from adult speech while, to our knowledge, the access to publicly available non-native children's speech corpora, as well as of children's speech corpora in general, is still scarce. Specifically concerning non-native children's speech, we believe worth mentioning the following corpora. The PF-STAR corpus (see BIBREF8) contains English utterances read by both Italian and German children, between 6 and 13 years old. The same corpus also contains utterances read by English children. The ChildIt corpus BIBREF9 contains English utterances (both read and imitated) by Italian children.",
+      "By distributing \u201cTLT-school\u201d corpus, we hope to help researchers to investigate novel approaches and models in the areas of both non-native and children's speech and to build related benchmarks."
+    ],
+    [
+      "In Trentino, an autonomous region in northern Italy, there is a series of evaluation campaigns underway for testing L2 linguistic competence of Italian students taking proficiency tests in both English and German. A set of three evaluation campaigns is underway, two having been completed in 2016 and 2018, and a final one scheduled in 2020. Note that the \u201cTLT-school\u201d corpus refers to only the 2018 campaign, that was split in two parts: 2017 try-out data set (involving about 500 pupils) and the actual 2018 data (about 2500 pupils). Each of the three campaigns (i.e. 2016, 2018 and 2020) involves about 3000 students ranging from 9 to 16 years, belonging to four different school grade levels and three proficiency levels (A1, A2, B1). The schools involved in the evaluations are located in most part of the Trentino region, not only in its main towns; Table highlights some information about the pupils that took part to the campaigns. Several tests, aimed at assessing the language learning skills of the students, were carried out by means of multiple-choice questions, which can be evaluated automatically. However, a detailed linguistic evaluation cannot be performed without allowing the students to express themselves in both written sentences and spoken utterances, which typically require the intervention of human experts to be scored.",
+      "Tables and report some statistics extracted from both the written and spoken data collected so far in all the campaigns. Each written or spoken item received a total score by human experts, computed by summing up the scores related to 6 indicators in 2017/2018 (from 3 to 6 in the 2016 campaign, according to the proficiency levels and the type of test). Each indicator can assume a value 0, 1, 2, corresponding to bad, medium, good, respectively.",
+      "The list of the indicators used by the experts to score written sentences and spoken utterances in the evaluations, grouped by similarity, is reported in Table . Since every utterance was scored by only one expert, it was not possible to evaluate any kind of agreement among experts. For future evaluations, more experts are expected to provide independent scoring on same data sets, so this kind of evaluation will be possible."
+    ],
+    [
+      "The speaking part of the proficiency tests in 2017/2018 consists of 47 question prompts provided in written form: 24 in English and 23 in German, divided according to CEFR levels. Apart from A1 level, which differs in the number of questions (11 for English; 10 for German), both English and German A2 and B1 levels have respectively 6 and 7 questions each. As for A1 level, the first four introductory questions are the same (How old are you?, Where do you live?, What are your hobbies?, Wie alt bist du?, Wo wohnst du?, Was sind deine Hobbys?) or slightly different (What's your favourite pet?, Welche Tiere magst du?) in both languages, whereas the second part of the test puts the test-takers in the role of a customer in a pizzeria (English) or in a bar (German).",
+      "A2 level test is composed of small talk questions which relate to everyday life situations. In this case, questions are more open-ended than the aforementioned ones and allow the test-takers to interact by means of a broader range of answers. Finally, as for B1 level, questions are similar to A2 ones, but they include a role-play activity in the final part, which allows a good amount of freedom and creativity in answering the question."
+    ],
+    [
+      "Table reports some statistics extracted from the written data collected so far. In this table, the number of pupils taking part in the English and German evaluation is reported, along with the number of sentences and tokens, identified as character sequences bounded by spaces.",
+      "It is worth mentioning that the collected texts contain a large quantity of errors of several types: orthographic, syntactic, code-switched words (i.e. words not in the required language), jokes, etc. Hence, the original written sentences have been processed in order to produce \u201ccleaner\u201d versions, in order to make the data usable for some research purposes (e.g. to train language models, to extract features for proficiency assessment, ...).",
+      "To do this, we have applied some text processing, that in sequence:",
+      "$\\bullet $ removes strange characters;",
+      "$\\bullet $ performs some text normalisation (lowercase, umlaut, numbers, ...) and tokenisation (punctuation, etc.)",
+      "$\\bullet $ removes / corrects non words (e.g. hallooooooooooo becomes hallo; aaaaaaaaeeeeeeeeiiiiiiii is removed)",
+      "$\\bullet $ identifies the language of each word, choosing among Italian, English, German;",
+      "$\\bullet $ corrects common typing errors (e.g. ai em becomes i am)",
+      "$\\bullet $ replaces unknown words, with respect to a large lexicon, with the label $<$unk$>$.",
+      "Table reports some samples of written answers."
+    ],
+    [
+      "Table reports some statistics extracted from the acquired spoken data. Speech was recorded in classrooms, whose equipment depended on each school. In general, around 20 students took the test together, at the same time and in the same classrooms, so it is quite common that speech of mates or teachers often overlaps with the speech of the student speaking in her/his microphone. Also, the type of microphone depends on the equipment of the school. On average, the audio signal quality is nearly good, while the main problem is caused by a high percentage of extraneous speech. This is due to the fact that organisers decided to use a fixed duration - which depends on the question - for recording spoken utterances, so that all the recordings for a given question have the same length. However, while it is rare that a speaker has not enough time to answer, it is quite common that, especially after the end of the utterance, some other speech (e.g. comments, jokes with mates, indications from the teachers, etc.) is captured. In addition, background noise is often present due to several sources (doors, steps, keyboard typing, background voices, street noises if the windows are open, etc). Finally, it has to be pointed out that many answers are whispered and difficult to understand."
+    ],
+    [
+      "In order to create both an adaptation and an evaluation set for ASR, we manually transcribed part of the 2017 data sets. We defined an initial set of guidelines for the annotation, which were used by 5 researchers to manually transcribe about 20 minutes of audio data. This experience led to a discussion, from which a second set of guidelines originated, aiming at reaching a reasonable trade-off between transcription accuracy and speed. As a consequence, we decided to apply the following transcription rules:",
+      "only the main speaker has to be transcribed; presence of other voices (schoolmates, teacher) should be reported only with the label \u201c@voices\u201d,",
+      "presence of whispered speech was found to be significant, so it should be explicitly marked with the label \u201c()\u201d,",
+      "badly pronounced words have to be marked by a \u201c#\u201d sign, without trying to phonetically transcribe the pronounced sounds; \u201c#*\u201d marks incomprehensible speech;",
+      "speech in a different language from the target language has to be reported by means of an explicit marker \u201cI am 10 years old @it(io ho gi\u00e0 risposto)\u201d.",
+      "Next, we concatenated utterances to be transcribed into blocks of about 5 minutes each. We noticed that knowing the question and hearing several answers could be of great help for transcribing some poorly pronounced words or phrases. Therefore, each block contains only answers to the same question, explicitly reported at the beginning of the block.",
+      "We engaged about 30 students from two Italian linguistic high schools (namely \u201cC\u201d and \u201cS\u201d) to perform manual transcriptions.",
+      "After a joint training session, we paired students together. Each pair first transcribed, individually, the same block of 5 minutes. Then, they went through a comparison phase, where each pair of students discussed their choices and agreed on a single transcription for the assigned data. Transcriptions made before the comparison phase were retained to evaluate inter-annotator agreement. Apart from this first 5 minute block, each utterance was transcribed by only one transcriber. Inter-annotator agreement for the 5-minute blocks is shown in Table in terms of words (after removing hesitations and other labels related to background voices and noises, etc.). The low level of agreement reflects the difficulty of the task.",
+      "In order to assure quality of the manual transcriptions, every sentence transcribed by the high school students was automatically processed to find out possible formal errors, and manually validated by researchers in our lab.",
+      "Speakers were assigned either to training or evaluation sets, with proportions of $\\frac{2}{3}$ and $\\frac{1}{3}$, respectively; then training and evaluation lists were built, accordingly. Table reports statistics from the spoken data set. The id All identifies the whole data set, while Clean defines the subset in which sentences containing background voices, incomprehensible speech and word fragments were excluded."
+    ],
+    [
+      "From the above description it appears that the corpus can be effectively used in many research directions."
+    ],
+    [
+      "The spoken corpus features non-native speech recordings in real classrooms and, consequently, peculiar phenomena appear and can be investigated. Phonological and cross-language interference requires specific approaches for accurate acoustic modelling. Moreover, for coping with cross-language interference it is important to consider alternative ways to represent specific words (e.g. words of two languages with the same graphemic representation).",
+      "Table , extracted from BIBREF0, reports WERs obtained on evaluation data sets with a strongly adapted ASR, demonstrating the difficulty of the related speech recognition task for both languages. Refer to BIBREF10 for comparisons with a different non-native children speech data set and to scientific literature BIBREF11, BIBREF12, BIBREF13, BIBREF14, BIBREF15, BIBREF16, BIBREF17, BIBREF18, BIBREF19 for detailed descriptions of children speech recognition and related issues. Important, although not exhaustive of the topic, references on non-native speech recognition can be found in BIBREF20, BIBREF21, BIBREF22, BIBREF23, BIBREF24, BIBREF25, BIBREF26, BIBREF27, BIBREF28, BIBREF29.",
+      "As for language models, accurate transcriptions of spoken responses demand for models able to cope with not well-formed expressions (due to students' grammatical errors). Also the presence of code-switched words, words fragments and spontaneous speech phenomena requires specific investigations to reduce their impact on the final performance.",
+      "We believe that the particular domain and set of data pave the way to investigate into various ASR topics, such as: non-native speech, children speech, spontaneous speech, code-switching, multiple pronunciation, etc."
+    ],
+    [
+      "The corpus has been (partly) annotated using the guidelines presented in Section SECREF3 on the basis of a preliminary analysis of the most common acoustic phenomena appearing in the data sets.",
+      "Additional annotations could be included to address topics related to other spurious segments, as for example: understandable words pronounced in other languages or by other students, detection of phonological interference, detection of spontaneous speech phenomena, detection of overlapped speech, etc. In order to measure specific proficiency indicators, e.g. related to pronunciation and fluency, suprasegmental annotations can be also inserted in the corpus."
+    ],
+    [
+      "The corpus is a valuable resource for training and evaluating a scoring classifier based on different approaches. Preliminary results BIBREF0 show that the usage of suitable linguistic features mainly based on statistical language models allow to predict the scores assigned by the human experts.",
+      "The evaluation campaign has been conceived to verify the expected proficiency level according to class grade; as a result, although the proposed test cannot be used to assign a precise score to a given student, it allows to study typical error patterns according to age and level of the students.",
+      "Furthermore, the fine-grained annotation, at sentence level, of the indicators described above is particularly suitable for creating a test bed for approaches based on \u201cword embeddings\u201d BIBREF30, BIBREF31, BIBREF32 to automatically estimate the language learner proficiency. Actually, the experiments reported in BIBREF30 demonstrate superior performance of word-embeddings for speech scoring with respect to the well known (feature-based) SpeechRater system BIBREF33, BIBREF2. In this regard, we believe that additional, specific annotations can be developed and included in the \u201cTLT-school\u201d corpus."
+    ],
+    [
+      "By looking at the manual transcriptions, it is straightforward to detect the most problematic words, i.e. frequently occurring words, which were often marked as mispronounced (preceded by label \u201c#\u201d). This allows to prepare a set of data composed by good pronounced vs. bad pronounced words.",
+      "A list of words, partly mispronounced, is shown in Table , from which one can try to model typical pronunciation errors (note that other occurrences of the selected words could be easily extracted from the non-annotated data). Finally, as mentioned above, further manual checking and annotation could be introduced to improve modelling of pronunciation errors."
+    ],
+    [
+      "The corpus to be released is still under preparation, given the huge amount of spoken and written data; in particular, some checks are in progress in order to:",
+      "remove from the data responses with personal or inadequate content (e.g. bad language);",
+      "normalise the written responses (e.g. upper/lower case, punctuation, evident typos);",
+      "normalise and verify the consistency of the transcription of spoken responses;",
+      "check the available human scores and - if possible - merge or map the scores according to more general performance categories (e.g. delivery, language use, topic development) and an acknowledged scale (e.g. from 0 to 4).",
+      "In particular, the proposal for an international challenge focused on non-native children speech recognition is being submitted where an English subset will be released and the perspective participants are invited to propose and evaluate state-of-art techniques for dealing with the multiple issues related to this challenging ASR scenario (acoustic and language models, non-native lexicon, noisy recordings, etc.)."
+    ],
+    [
+      "We have described \u201cTLT-school\u201d, a corpus of both spoken and written answers collected during language evaluation campaigns carried out in schools of northern Italy. The procedure used for data acquisition and for their annotation in terms of proficiency indicators has been also reported. Part of the data has been manually transcribed according to some guidelines: this set of data is going to be made publicly available. With regard to data acquisition, some limitations of the corpus have been observed that might be easily overcome during next campaigns. Special attention should be paid to enhancing the elicitation techniques, starting from adjusting the questions presented to test-takers. Some of the question prompts show some lacks that can be filled in without major difficulty: on the one hand, in the spoken part, questions do not require test-takers to shift tense and some are too suggestive and close-ended; on the other hand, in the written part, some question prompts are presented both in source and target language, thus causing or encouraging code-mixing and negative transfer phenomena. The elicitation techniques in a broader sense will be object of revision (see BIBREF34 and specifically on children speech BIBREF35) in order to maximise the quality of the corpus. As for proficiency indicators, one first step that could be taken in order to increase accuracy in the evaluation phase both for human and automatic scoring would be to divide the second indicator (pronunciation and fluency) into two different indicators, since fluent students might not necessarily have good pronunciation skills and vice versa, drawing for example on the IELTS Speaking band descriptors. Also, next campaigns might consider an additional indicator specifically addressed to score prosody (in particular intonation and rhythm), especially for A2 and B1 level test-takers. Considering the scope of the evaluation campaign, it is important to be aware of the limitations of the associated data sets: proficiency levels limited to A1, B1 and B2 (CEFR); custom indicators conceived for expert evaluation (not particularly suitable for automated evaluation); limited amount of responses per speaker. Nevertheless, as already discussed, the fact that the TLT campaign was carried out in 2016 and 2018 in the whole Trentino region makes the corpus a valuable linguistic resource for a number of studies associated to second language acquisition and evaluation. In particular, besides the already introduced proposal for an ASR challenge in 2020, other initiatives for the international community can be envisaged: a study of a fully-automated evaluation procedure without the need of experts' supervision; the investigation of end-to-end classifiers that directly use the spoken response as input and produce proficiency scores according to suitable rubrics."
+    ],
+    [
+      "This work has been partially funded by IPRASE (http://www.iprase.tn.it) under the project \u201cTLT - Trentino Language Testing 2018\u201d. We thank ISIT (http://www.isit.tn.it) for having provided the data and the reference scores."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1807/instruction.md b/qasper-1807/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..b839f859daf9ecf9053deac397276780c4f53896
--- /dev/null
+++ b/qasper-1807/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: TLT-school: a Corpus of Non Native Children Speech
+
+Question: What accuracy is achieved by the speech recognition system?
\ No newline at end of file
diff --git a/qasper-1808/instruction.md b/qasper-1808/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..da95f77b8f9d667ebee408f038101db4fe45c08f
--- /dev/null
+++ b/qasper-1808/instruction.md
@@ -0,0 +1,115 @@
+Name of Paper: TLT-school: a Corpus of Non Native Children Speech
+
+Question: How is the speech recognition system evaluated?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Data Acquisition",
+    "Data Acquisition ::: Prompts",
+    "Data Acquisition ::: Written Data",
+    "Data Acquisition ::: Spoken Data",
+    "Manual Transcriptions",
+    "Usage of the Data",
+    "Usage of the Data ::: ASR-related Challenges",
+    "Usage of the Data ::: Data Annotation",
+    "Usage of the Data ::: Proficiency Assessment of L2 Learners",
+    "Usage of the Data ::: Modelling Pronunciation",
+    "Distribution of the Corpus",
+    "Conclusions and Future Works",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "We have acquired large sets of both written and spoken data during the implementation of campaigns aimed at assessing the proficiency, at school, of Italian pupils learning both German and English. Part of the acquired data has been included in a corpus, named \"Trentino Language Testing\" in schools (TLT-school), that will be described in the following.",
+      "All the collected sentences have been annotated by human experts in terms of some predefined \u201cindicators\u201d which, in turn, were used to assign the proficiency level to each student undertaking the assigned test. This level is expressed according to the well-known Common European Framework of Reference for Languages (Council of Europe, 2001) scale. The CEFR defines 6 levels of proficiency: A1 (beginner), A2, B1, B2, C1 and C2. The levels considered in the evaluation campaigns where the data have been collected are: A1, A2 and B1.",
+      "The indicators measure the linguistic competence of test takers both in relation to the content (e.g. grammatical correctness, lexical richness, semantic coherence, etc.) and to the speaking capabilities (e.g. pronunciation, fluency, etc.). Refer to Section SECREF2 for a description of the adopted indicators.",
+      "The learners are Italian students, between 9 and 16 years old. They took proficiency tests by answering question prompts provided in written form. The \u201cTLT-school\u201d corpus, that we are going to make publicly available, contains part of the spoken answers (together with the respective manual transcriptions) recorded during some of the above mentioned evaluation campaigns. We will release the written answers in future. Details and critical issues found during the acquisition of the answers of the test takers will be discussed in Section SECREF2.",
+      "The tasks that can be addressed by using the corpus are very challenging and pose many problems, which have only partially been solved by the interested scientific community.",
+      "From the ASR perspective, major difficulties are represented by: a) recognition of both child and non-native speech, i.e. Italian pupils speaking both English and German, b) presence of a large number of spontaneous speech phenomena (hesitations, false starts, fragments of words, etc.), c) presence of multiple languages (English, Italian and German words are frequently uttered in response to a single question), d) presence of a significant level of background noise due to the fact that the microphone remains open for a fixed time interval (e.g. 20 seconds - depending on the question), and e) presence of non-collaborative speakers (students often joke, laugh, speak softly, etc.). Refer to Section SECREF6 for a detailed description of the collected spoken data set.",
+      "Furthermore, since the sets of data from which \u201cTLT-school\u201d was derived were primarily acquired for measuring proficiency of second language (L2) learners, it is quite obvious to exploit the corpus for automatic speech rating. To this purpose, one can try to develop automatic approaches to reliably estimate the above-mentioned indicators used by the human experts who scored the answers of the pupils (such an approach is described in BIBREF0). However, it has to be noticed that scientific literature proposes to use several features and indicators for automatic speech scoring, partly different from those adopted in \u201cTLT-school\u201d corpus (see below for a brief review of the literature). Hence, we believe that adding new annotations to the corpus, related to particular aspects of language proficiency, can stimulate research and experimentation in this area.",
+      "Finally, it is worth mentioning that also written responses of \u201cTLT-school\u201d corpus are characterised by a high level of noise due to: spelling errors, insertion of word fragments, presence of words belonging to multiple languages, presence of off-topic answers (e.g. containing jokes, comments not related to the questions, etc.). This set of text data will allow scientists to investigate both language and behaviour of pupils learning second languages at school. Written data are described in detail in Section SECREF5",
+      "Relation to prior work. Scientific literature is rich in approaches for automated assessment of spoken language proficiency. Performance is directly dependent on ASR accuracy which, in turn, depends on the type of input, read or spontaneous, and on the speakers' age, adults or children (see BIBREF1 for an overview of spoken language technology for education). A recent publication reporting an overview of state-of-the-art automated speech scoring technology as it is currently used at Educational Testing Service (ETS) can be found in BIBREF2.",
+      "In order to address automatic assessment of complex spoken tasks requiring more general communication capabilities from L2 learners, the AZELLA data set BIBREF3, developed by Pearson, has been collected and used as benchmark for some researches BIBREF4, BIBREF3. The corpus contains $1,500$ spoken tests, each double graded by human professionals, from a variety of tasks.",
+      "A public set of spoken data has been recently distributed in a spoken CALL (Computer Assisted Language Learning) shared task where Swiss students learning English had to answer to both written and spoken prompts. The goal of this challenge is to label students' spoken responses as \u201caccept\u201d or \u201creject\u201d. Refer to BIBREF5 for details of the challenge and of the associated data sets.",
+      "Many non-native speech corpora (mostly in English as target language) have been collected during the years. A list, though not recent, as well as a brief description of most of them can be found in BIBREF6. The same paper also gives information on how the data sets are distributed and can be accessed (many of them are available through both LDC and ELDA agencies). Some of the corpora also provide proficiency ratings to be used in CALL applications. Among them, we mention the ISLE corpus BIBREF7, which also contains transcriptions at the phonetic level and was used in the experiments reported in BIBREF0. Note that all corpora mentioned in BIBREF6 come from adult speech while, to our knowledge, the access to publicly available non-native children's speech corpora, as well as of children's speech corpora in general, is still scarce. Specifically concerning non-native children's speech, we believe worth mentioning the following corpora. The PF-STAR corpus (see BIBREF8) contains English utterances read by both Italian and German children, between 6 and 13 years old. The same corpus also contains utterances read by English children. The ChildIt corpus BIBREF9 contains English utterances (both read and imitated) by Italian children.",
+      "By distributing \u201cTLT-school\u201d corpus, we hope to help researchers to investigate novel approaches and models in the areas of both non-native and children's speech and to build related benchmarks."
+    ],
+    [
+      "In Trentino, an autonomous region in northern Italy, there is a series of evaluation campaigns underway for testing L2 linguistic competence of Italian students taking proficiency tests in both English and German. A set of three evaluation campaigns is underway, two having been completed in 2016 and 2018, and a final one scheduled in 2020. Note that the \u201cTLT-school\u201d corpus refers to only the 2018 campaign, that was split in two parts: 2017 try-out data set (involving about 500 pupils) and the actual 2018 data (about 2500 pupils). Each of the three campaigns (i.e. 2016, 2018 and 2020) involves about 3000 students ranging from 9 to 16 years, belonging to four different school grade levels and three proficiency levels (A1, A2, B1). The schools involved in the evaluations are located in most part of the Trentino region, not only in its main towns; Table highlights some information about the pupils that took part to the campaigns. Several tests, aimed at assessing the language learning skills of the students, were carried out by means of multiple-choice questions, which can be evaluated automatically. However, a detailed linguistic evaluation cannot be performed without allowing the students to express themselves in both written sentences and spoken utterances, which typically require the intervention of human experts to be scored.",
+      "Tables and report some statistics extracted from both the written and spoken data collected so far in all the campaigns. Each written or spoken item received a total score by human experts, computed by summing up the scores related to 6 indicators in 2017/2018 (from 3 to 6 in the 2016 campaign, according to the proficiency levels and the type of test). Each indicator can assume a value 0, 1, 2, corresponding to bad, medium, good, respectively.",
+      "The list of the indicators used by the experts to score written sentences and spoken utterances in the evaluations, grouped by similarity, is reported in Table . Since every utterance was scored by only one expert, it was not possible to evaluate any kind of agreement among experts. For future evaluations, more experts are expected to provide independent scoring on same data sets, so this kind of evaluation will be possible."
+    ],
+    [
+      "The speaking part of the proficiency tests in 2017/2018 consists of 47 question prompts provided in written form: 24 in English and 23 in German, divided according to CEFR levels. Apart from A1 level, which differs in the number of questions (11 for English; 10 for German), both English and German A2 and B1 levels have respectively 6 and 7 questions each. As for A1 level, the first four introductory questions are the same (How old are you?, Where do you live?, What are your hobbies?, Wie alt bist du?, Wo wohnst du?, Was sind deine Hobbys?) or slightly different (What's your favourite pet?, Welche Tiere magst du?) in both languages, whereas the second part of the test puts the test-takers in the role of a customer in a pizzeria (English) or in a bar (German).",
+      "A2 level test is composed of small talk questions which relate to everyday life situations. In this case, questions are more open-ended than the aforementioned ones and allow the test-takers to interact by means of a broader range of answers. Finally, as for B1 level, questions are similar to A2 ones, but they include a role-play activity in the final part, which allows a good amount of freedom and creativity in answering the question."
+    ],
+    [
+      "Table reports some statistics extracted from the written data collected so far. In this table, the number of pupils taking part in the English and German evaluation is reported, along with the number of sentences and tokens, identified as character sequences bounded by spaces.",
+      "It is worth mentioning that the collected texts contain a large quantity of errors of several types: orthographic, syntactic, code-switched words (i.e. words not in the required language), jokes, etc. Hence, the original written sentences have been processed in order to produce \u201ccleaner\u201d versions, in order to make the data usable for some research purposes (e.g. to train language models, to extract features for proficiency assessment, ...).",
+      "To do this, we have applied some text processing, that in sequence:",
+      "$\\bullet $ removes strange characters;",
+      "$\\bullet $ performs some text normalisation (lowercase, umlaut, numbers, ...) and tokenisation (punctuation, etc.)",
+      "$\\bullet $ removes / corrects non words (e.g. hallooooooooooo becomes hallo; aaaaaaaaeeeeeeeeiiiiiiii is removed)",
+      "$\\bullet $ identifies the language of each word, choosing among Italian, English, German;",
+      "$\\bullet $ corrects common typing errors (e.g. ai em becomes i am)",
+      "$\\bullet $ replaces unknown words, with respect to a large lexicon, with the label $<$unk$>$.",
+      "Table reports some samples of written answers."
+    ],
+    [
+      "Table reports some statistics extracted from the acquired spoken data. Speech was recorded in classrooms, whose equipment depended on each school. In general, around 20 students took the test together, at the same time and in the same classrooms, so it is quite common that speech of mates or teachers often overlaps with the speech of the student speaking in her/his microphone. Also, the type of microphone depends on the equipment of the school. On average, the audio signal quality is nearly good, while the main problem is caused by a high percentage of extraneous speech. This is due to the fact that organisers decided to use a fixed duration - which depends on the question - for recording spoken utterances, so that all the recordings for a given question have the same length. However, while it is rare that a speaker has not enough time to answer, it is quite common that, especially after the end of the utterance, some other speech (e.g. comments, jokes with mates, indications from the teachers, etc.) is captured. In addition, background noise is often present due to several sources (doors, steps, keyboard typing, background voices, street noises if the windows are open, etc). Finally, it has to be pointed out that many answers are whispered and difficult to understand."
+    ],
+    [
+      "In order to create both an adaptation and an evaluation set for ASR, we manually transcribed part of the 2017 data sets. We defined an initial set of guidelines for the annotation, which were used by 5 researchers to manually transcribe about 20 minutes of audio data. This experience led to a discussion, from which a second set of guidelines originated, aiming at reaching a reasonable trade-off between transcription accuracy and speed. As a consequence, we decided to apply the following transcription rules:",
+      "only the main speaker has to be transcribed; presence of other voices (schoolmates, teacher) should be reported only with the label \u201c@voices\u201d,",
+      "presence of whispered speech was found to be significant, so it should be explicitly marked with the label \u201c()\u201d,",
+      "badly pronounced words have to be marked by a \u201c#\u201d sign, without trying to phonetically transcribe the pronounced sounds; \u201c#*\u201d marks incomprehensible speech;",
+      "speech in a different language from the target language has to be reported by means of an explicit marker \u201cI am 10 years old @it(io ho gi\u00e0 risposto)\u201d.",
+      "Next, we concatenated utterances to be transcribed into blocks of about 5 minutes each. We noticed that knowing the question and hearing several answers could be of great help for transcribing some poorly pronounced words or phrases. Therefore, each block contains only answers to the same question, explicitly reported at the beginning of the block.",
+      "We engaged about 30 students from two Italian linguistic high schools (namely \u201cC\u201d and \u201cS\u201d) to perform manual transcriptions.",
+      "After a joint training session, we paired students together. Each pair first transcribed, individually, the same block of 5 minutes. Then, they went through a comparison phase, where each pair of students discussed their choices and agreed on a single transcription for the assigned data. Transcriptions made before the comparison phase were retained to evaluate inter-annotator agreement. Apart from this first 5 minute block, each utterance was transcribed by only one transcriber. Inter-annotator agreement for the 5-minute blocks is shown in Table in terms of words (after removing hesitations and other labels related to background voices and noises, etc.). The low level of agreement reflects the difficulty of the task.",
+      "In order to assure quality of the manual transcriptions, every sentence transcribed by the high school students was automatically processed to find out possible formal errors, and manually validated by researchers in our lab.",
+      "Speakers were assigned either to training or evaluation sets, with proportions of $\\frac{2}{3}$ and $\\frac{1}{3}$, respectively; then training and evaluation lists were built, accordingly. Table reports statistics from the spoken data set. The id All identifies the whole data set, while Clean defines the subset in which sentences containing background voices, incomprehensible speech and word fragments were excluded."
+    ],
+    [
+      "From the above description it appears that the corpus can be effectively used in many research directions."
+    ],
+    [
+      "The spoken corpus features non-native speech recordings in real classrooms and, consequently, peculiar phenomena appear and can be investigated. Phonological and cross-language interference requires specific approaches for accurate acoustic modelling. Moreover, for coping with cross-language interference it is important to consider alternative ways to represent specific words (e.g. words of two languages with the same graphemic representation).",
+      "Table , extracted from BIBREF0, reports WERs obtained on evaluation data sets with a strongly adapted ASR, demonstrating the difficulty of the related speech recognition task for both languages. Refer to BIBREF10 for comparisons with a different non-native children speech data set and to scientific literature BIBREF11, BIBREF12, BIBREF13, BIBREF14, BIBREF15, BIBREF16, BIBREF17, BIBREF18, BIBREF19 for detailed descriptions of children speech recognition and related issues. Important, although not exhaustive of the topic, references on non-native speech recognition can be found in BIBREF20, BIBREF21, BIBREF22, BIBREF23, BIBREF24, BIBREF25, BIBREF26, BIBREF27, BIBREF28, BIBREF29.",
+      "As for language models, accurate transcriptions of spoken responses demand for models able to cope with not well-formed expressions (due to students' grammatical errors). Also the presence of code-switched words, words fragments and spontaneous speech phenomena requires specific investigations to reduce their impact on the final performance.",
+      "We believe that the particular domain and set of data pave the way to investigate into various ASR topics, such as: non-native speech, children speech, spontaneous speech, code-switching, multiple pronunciation, etc."
+    ],
+    [
+      "The corpus has been (partly) annotated using the guidelines presented in Section SECREF3 on the basis of a preliminary analysis of the most common acoustic phenomena appearing in the data sets.",
+      "Additional annotations could be included to address topics related to other spurious segments, as for example: understandable words pronounced in other languages or by other students, detection of phonological interference, detection of spontaneous speech phenomena, detection of overlapped speech, etc. In order to measure specific proficiency indicators, e.g. related to pronunciation and fluency, suprasegmental annotations can be also inserted in the corpus."
+    ],
+    [
+      "The corpus is a valuable resource for training and evaluating a scoring classifier based on different approaches. Preliminary results BIBREF0 show that the usage of suitable linguistic features mainly based on statistical language models allow to predict the scores assigned by the human experts.",
+      "The evaluation campaign has been conceived to verify the expected proficiency level according to class grade; as a result, although the proposed test cannot be used to assign a precise score to a given student, it allows to study typical error patterns according to age and level of the students.",
+      "Furthermore, the fine-grained annotation, at sentence level, of the indicators described above is particularly suitable for creating a test bed for approaches based on \u201cword embeddings\u201d BIBREF30, BIBREF31, BIBREF32 to automatically estimate the language learner proficiency. Actually, the experiments reported in BIBREF30 demonstrate superior performance of word-embeddings for speech scoring with respect to the well known (feature-based) SpeechRater system BIBREF33, BIBREF2. In this regard, we believe that additional, specific annotations can be developed and included in the \u201cTLT-school\u201d corpus."
+    ],
+    [
+      "By looking at the manual transcriptions, it is straightforward to detect the most problematic words, i.e. frequently occurring words, which were often marked as mispronounced (preceded by label \u201c#\u201d). This allows to prepare a set of data composed by good pronounced vs. bad pronounced words.",
+      "A list of words, partly mispronounced, is shown in Table , from which one can try to model typical pronunciation errors (note that other occurrences of the selected words could be easily extracted from the non-annotated data). Finally, as mentioned above, further manual checking and annotation could be introduced to improve modelling of pronunciation errors."
+    ],
+    [
+      "The corpus to be released is still under preparation, given the huge amount of spoken and written data; in particular, some checks are in progress in order to:",
+      "remove from the data responses with personal or inadequate content (e.g. bad language);",
+      "normalise the written responses (e.g. upper/lower case, punctuation, evident typos);",
+      "normalise and verify the consistency of the transcription of spoken responses;",
+      "check the available human scores and - if possible - merge or map the scores according to more general performance categories (e.g. delivery, language use, topic development) and an acknowledged scale (e.g. from 0 to 4).",
+      "In particular, the proposal for an international challenge focused on non-native children speech recognition is being submitted where an English subset will be released and the perspective participants are invited to propose and evaluate state-of-art techniques for dealing with the multiple issues related to this challenging ASR scenario (acoustic and language models, non-native lexicon, noisy recordings, etc.)."
+    ],
+    [
+      "We have described \u201cTLT-school\u201d, a corpus of both spoken and written answers collected during language evaluation campaigns carried out in schools of northern Italy. The procedure used for data acquisition and for their annotation in terms of proficiency indicators has been also reported. Part of the data has been manually transcribed according to some guidelines: this set of data is going to be made publicly available. With regard to data acquisition, some limitations of the corpus have been observed that might be easily overcome during next campaigns. Special attention should be paid to enhancing the elicitation techniques, starting from adjusting the questions presented to test-takers. Some of the question prompts show some lacks that can be filled in without major difficulty: on the one hand, in the spoken part, questions do not require test-takers to shift tense and some are too suggestive and close-ended; on the other hand, in the written part, some question prompts are presented both in source and target language, thus causing or encouraging code-mixing and negative transfer phenomena. The elicitation techniques in a broader sense will be object of revision (see BIBREF34 and specifically on children speech BIBREF35) in order to maximise the quality of the corpus. As for proficiency indicators, one first step that could be taken in order to increase accuracy in the evaluation phase both for human and automatic scoring would be to divide the second indicator (pronunciation and fluency) into two different indicators, since fluent students might not necessarily have good pronunciation skills and vice versa, drawing for example on the IELTS Speaking band descriptors. Also, next campaigns might consider an additional indicator specifically addressed to score prosody (in particular intonation and rhythm), especially for A2 and B1 level test-takers. Considering the scope of the evaluation campaign, it is important to be aware of the limitations of the associated data sets: proficiency levels limited to A1, B1 and B2 (CEFR); custom indicators conceived for expert evaluation (not particularly suitable for automated evaluation); limited amount of responses per speaker. Nevertheless, as already discussed, the fact that the TLT campaign was carried out in 2016 and 2018 in the whole Trentino region makes the corpus a valuable linguistic resource for a number of studies associated to second language acquisition and evaluation. In particular, besides the already introduced proposal for an ASR challenge in 2020, other initiatives for the international community can be envisaged: a study of a fully-automated evaluation procedure without the need of experts' supervision; the investigation of end-to-end classifiers that directly use the spoken response as input and produce proficiency scores according to suitable rubrics."
+    ],
+    [
+      "This work has been partially funded by IPRASE (http://www.iprase.tn.it) under the project \u201cTLT - Trentino Language Testing 2018\u201d. We thank ISIT (http://www.isit.tn.it) for having provided the data and the reference scores."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1809/instruction.md b/qasper-1809/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..5dc9e71b1e9db82908394e0896041cfd632bca78
--- /dev/null
+++ b/qasper-1809/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: TLT-school: a Corpus of Non Native Children Speech
+
+Question: How many of the utterances are transcribed?
\ No newline at end of file
diff --git a/qasper-1821/instruction.md b/qasper-1821/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..eb3006f389a19284a6e4a1838f92e18f093574db
--- /dev/null
+++ b/qasper-1821/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: CM-Net: A Novel Collaborative Memory Network for Spoken Language Understanding
+
+Question: What is the source of the CAIS dataset?
\ No newline at end of file
diff --git a/qasper-1826/instruction.md b/qasper-1826/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..9868f48ca42d80641b77370473a6bf1aa25ceea7
--- /dev/null
+++ b/qasper-1826/instruction.md
@@ -0,0 +1,67 @@
+Name of Paper: Fortia-FBK at SemEval-2017 Task 5: Bullish or Bearish? Inferring Sentiment towards Brands from Financial News Headlines
+
+Question: How do they incorporate lexicon into the neural network?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Works",
+    "Data",
+    "Method",
+    "Sentence representation and preprocessing",
+    "Architectural Details",
+    "Results",
+    "Conclusions"
+  ],
+  "paragraphs": [
+    [
+      "Real time information is key for decision making in highly technical domains such as finance. The explosive growth of financial technology industry (Fintech) continued in 2016, partially due to the current interest in the market for Artificial Intelligence-based technologies.",
+      "Opinion-rich texts such as micro-blogging and news can have an important impact in the financial sector (e.g. raise or fall in stock value) or in the overall economy (e.g. the Greek public debt crisis). In such a context, having granular access to the opinions of an important part of the population is of key importance to any public and private actor in the field. In order to take advantage of this raw data, it is thus needed to develop machine learning methods allowing to convert unstructured text into information that can be managed and exploited.",
+      "In this paper, we address the sentiment analysis problem applied to financial headlines, where the goal is, for a given news headline and target company, to infer its polarity score i.e. how positive (or negative) the sentence is with respect to the target company. Previous research BIBREF0 has highlighted the association between news items and market fluctiations; hence, in the financial domain, sentiment analysis can be used as a proxy for bullish (i.e. positive, upwards trend) or bearish (i.e. negative, downwards trend) attitude towards a specific financial actor, allowing to identify and monitor in real-time the sentiment associated with e.g. stocks or brands.",
+      "Our contribution leverages pre-trained word embeddings (GloVe, trained on wikipedia+gigaword corpus), the DepecheMood affective lexicon, and convolutional neural networks."
+    ],
+    [
+      "While image and sound come with a natural high dimensional embedding, the issue of which is the best representation is still an open research problem in the context of natural language and text. It is beyond the scope of this paper to do a thorough overview of word representations, for this we refer the interest reader to the excellent review provided by BIBREF1 . Here, we will just introduce the main representations that are related to the proposed method."
+    ],
+    [
+      "The data consists of a set of financial news headlines, crawled from several online outlets such as Yahoo Finance, where each sentence contains one or more company names/brands.",
+      "Each tuple (headline, company) is annotated with a sentiment score ranging from -1 (very negative, bearish) to 1 (very positive, bullish). The training/test sets provided contain 1142 and 491 annotated sentences, respectively.",
+      "A sample instance is reported below:",
+      "Headline: \u201cMorrisons book second consecutive quarter of sales growth\u201d",
+      "Company name: \u201cMorrisons\u201d",
+      "Sentiment score: 0.43"
+    ],
+    [
+      "In Figure FIGREF5 , we can see the overall architecture of our model."
+    ],
+    [
+      "Minimal preprocessing was adopted in our approach: we replaced the target company's name with a fixed word <company> and numbers with <number>. The sentences were then tokenized using spaces as separator and keeping punctuation symbols as separate tokens.",
+      "The words are represented as fixed length vectors INLINEFORM0 resulting from the concatenation of GloVe pre-trained embeddings and DepecheMood BIBREF19 lexicon representation. Since we cannot directly concatenate token-based embeddings (provided in GloVe) with the lemma#PoS-based representation available in DepecheMood, we proceeded to re-build the latter in token-based form, applying the exact same methodology albeit with two differences: we started from a larger dataset (51.9K news articles instead of 25.3K) and used a frequency cut-off, i.e. keeping only those tokens that appear at least 5 times in the corpus.",
+      "These word-level representation are used as the first layer of our network. During training we allow the weights of the representation to be updated. We further add the VADER score for the sentence under analysis. The complete sentence representation is presented in Algorithm UID8 .",
+      "InputInput OutputOutput The sentence embedding INLINEFORM0 INLINEFORM1 ",
+      " INLINEFORM0 in INLINEFORM1 INLINEFORM2 = [GloVe( INLINEFORM3 , INLINEFORM4 ), DepecheMood( INLINEFORM5 )] INLINEFORM6 Sentence representation"
+    ],
+    [
+      "A 1D convolutional layer with filters of multiple sizes {2, 3, 4} is applied to the sequence of word embeddings. The filters are used to learn useful translation-invariant representations of the sequential input data. A global max-pooling is then applied across the sequence for each filter output.",
+      "We apply the concatenation layer to the output of the global max-pooling and the output of VADER.",
+      "The activation function used between layers is ReLU BIBREF24 except for the out layer where tanh is used to map the output into [-1, 1] range.",
+      "Dropout BIBREF25 was used to avoid over-fitting to the training data: it prevents the co-adaptation of the neurones and it also provides an inexpensive way to average an exponential number of networks. In addition, we averaged the output of multiple networks with the same architecture but trained independently with different random seeds in order to reduce noise.",
+      "The loss function used is the cosine distance between the predicted scores and the gold standard for each batch. Even though stochastic optimization methods like Adam BIBREF26 are usually applied to loss functions that are written as a sum of per-sample loss, which is not the case for the cosine, it converges to an acceptable solution. The loss can be written as : DISPLAYFORM0 ",
+      "where INLINEFORM0 and INLINEFORM1 are the predicted and true sentiment scores for batch INLINEFORM2 , respectively.",
+      "The algorithm for training/testing our model is reported in Algorithm UID15 .",
+      "InputInput OutputOutput ParameterParameters A set of trained models INLINEFORM0 , and the predictions INLINEFORM1 for the test set INLINEFORM2 The number INLINEFORM3 of models to train INLINEFORM4 see sec 3.1 INLINEFORM5 in INLINEFORM6 INLINEFORM7 ) see Alg. UID8 INLINEFORM8 INLINEFORM9 see Eq. EQREF16 INLINEFORM10 INLINEFORM11 INLINEFORM12 Training/Testing algorithm. To build our model, we set N=10."
+    ],
+    [
+      "In this section, we report the results obtained by our model according to challenge official evaluation metric, which is based cosine-similarity and described in BIBREF27 . Results are reported for three diverse configurations: (i) the full system; (ii) the system without using word embeddings (i.e. Glove and DepecheMood); and (iii) the system without using pre-processing. In Table TABREF17 we show model's performances on the challenge training data, in a 5-fold cross-validation setting.",
+      "Further, the final performances obtained with our approach on the challenge test set are reported in Table TABREF18 . Consistently with the cross-validation performances shown earlier, we observe the beneficial impact of word-representations and basic pre-processing."
+    ],
+    [
+      "In this paper, we presented the network architecture used for the Fortia-FBK submission to the Semeval-2017 Task 5, Subtask 2 challenge, with the goal of predicting positive (bullish) or negative (bearish) attitude towards a target brand from financial news headlines. The proposed system ranked 1st in such challenge.",
+      "Our approach is based on 1d convolutions and uses fine-tuning of unsupervised word representations and a rule based sentiment model in its inputs. We showed that the use of pre-computed word representations allows to reduce over-fitting and to achieve significantly better generalization, while some basic pre-processing was needed to further improve the performance."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1830/instruction.md b/qasper-1830/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..ed5fb8c3f7c803b3c4db96ab8ab70809d97545a9
--- /dev/null
+++ b/qasper-1830/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Fortia-FBK at SemEval-2017 Task 5: Bullish or Bearish? Inferring Sentiment towards Brands from Financial News Headlines
+
+Question: What embeddings do they use?
\ No newline at end of file
diff --git a/qasper-1831/instruction.md b/qasper-1831/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..f77c2c63b09100a8c33a9ec16ab095102a2779c0
--- /dev/null
+++ b/qasper-1831/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions
+
+Question: did they use other pretrained language models besides bert?
\ No newline at end of file
diff --git a/qasper-1836/instruction.md b/qasper-1836/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..9a25c03c2a8db1377997443ec242752ab7897d6a
--- /dev/null
+++ b/qasper-1836/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: User Generated Data: Achilles' heel of BERT
+
+Question: What is the performance change of the textual semantic similarity task when no error and maximum errors (noise) are present?
\ No newline at end of file
diff --git a/qasper-1837/instruction.md b/qasper-1837/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..a6dc92047d4632ae12c7c56d3d24e47e3a513abc
--- /dev/null
+++ b/qasper-1837/instruction.md
@@ -0,0 +1,69 @@
+Name of Paper: User Generated Data: Achilles' heel of BERT
+
+Question: Which sentiment analysis data set has a larger performance drop when a 10% error is introduced?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Experiment",
+    "Results",
+    "Results ::: Key Findings",
+    "Conclusion and Future Work"
+  ],
+  "paragraphs": [
+    [
+      "In recent times, pre-trained contextual language models have led to significant improvement in the performance for many NLP tasks. Among the family of these models, the most popular one is BERT BIBREF0, which is also the focus of this work. The strength of the BERT model FIGREF2 stems from its transformerBIBREF1 based encoder architectureFIGREF1. While it is still not very clear as to why BERT along with its embedding works so well for downstream tasks when it is fine tuned, there has been some work in this direction that that gives some important cluesBIBREF2, BIBREF3.",
+      "At a high level, BERT\u2019s pipelines looks as follows: given a input sentence, BERT tokenizes it using wordPiece tokenizerBIBREF4. The tokens are then fed as input to the BERT model and it learns contextualized embeddings for each of those tokens. It does so via pre-training on two tasks - Masked Language Model (MLM)BIBREF0 and Next Sentence Prediction (NSP)BIBREF0.",
+      "The focus of this work is to understand the issues that a practitioner can run into while trying to use BERT for building NLP applications in industrial settings. It is a well known fact that NLP applications in industrial settings often have to deal with the noisy data. There are different kinds of possible noise namely non-canonical text such as spelling mistakes, typographic errors, colloquialisms, abbreviations, slang, internet jargon, emojis, embedded metadata (such as hashtags, URLs, mentions), non standard syntactic constructions and spelling variations, grammatically incorrect text, mixture of two or more languages to name a few. Such noisy data is a hallmark of user generated text content and commonly found on social media, chats, online reviews, web forums to name a few. Owing to this noise a common issue that NLP models have to deal with is Out Of Vocabulary (OOV) words. These are words that are found in test and production data but not part of training data. In this work we highlight how BERT fails to handle Out Of Vocabulary(OOV) words, given its limited vocabulary. We show that this negatively impacts the performance of BERT when working with user generated text data and evaluate the same.",
+      "This evaluation is motivated from the business use case we are solving where we are building a dialogue system to screen candidates for blue collar jobs. Our candidate user base, coming from underprivileged backgrounds, are often high school graduates. This coupled with \u2018fat finger\u2019 problem over a mobile keypad leads to a lot of typos and spelling mistakes in the responses sent to the dialogue system. Hence, for this work we focus on spelling mistakes as the noise in the data. While this work is motivated from our business use case, our findings are applicable across various use cases in industry - be it be sentiment classification on twitter data or topic detection of a web forum.",
+      "To simulate noise in the data, we begin with a clean dataset and introduce spelling errors in a fraction of words present in it. These words are chosen randomly. We will explain this process in detail later. Spelling mistakes introduced mimic the typographical errors in the text introduced by our users. We then use the BERT model for tasks using both clean and noisy datasets and compare the results. We show that the introduction of noise leads to a significant drop in performance of the BERT model for the task at hand as compared to clean dataset. We further show that as we increase the amount of noise in the data, the performance degrades sharply."
+    ],
+    [
+      "In recent years pre-trained language models ((e.g. ELMoBIBREF5, BERTBIBREF0) have made breakthroughs in several natural language tasks. These models are trained over large corpora that are not human annotated and are easily available. Chief among these models is BERTBIBREF0. The popularity of BERT stems from its ability to be fine-tuned for a variety of downstream NLP tasks such as text classification, regression, named-entity recognition, question answeringBIBREF0, machine translationBIBREF6 etc. BERT has been able to establish State-of-the-art (SOTA) results for many of these tasks. People have been able to show how one can leverage BERT to improve searchBIBREF7.",
+      "Owing to its success, researchers have started to focus on uncovering drawbacks in BERT, if any. BIBREF8 introduce TEXTFOOLER, a system to generate adversarial text. They apply it to NLP tasks of text classification and textual entailment to attack the BERT model. BIBREF9 evaluate three models - RoBERTa, XLNet, and BERT in Natural Language Inference (NLI) and Question Answering (QA) tasks for robustness. They show that while RoBERTa, XLNet and BERT are more robust than recurrent neural network models to stress tests for both NLI and QA tasks; these models are still very fragile and show many unexpected behaviors. BIBREF10 discuss length-based and sentence-based misclassification attacks for the Fake News Detection task trained using a context-aware BERT model and they show 78% and 39% attack accuracy respectively.",
+      "Our contribution in this paper is to answer that can we use large language models like BERT directly over user generated data."
+    ],
+    [
+      "For our experiments, we use pre-trained BERT implementation as given by huggingface transformer library. We use the BERTBase uncased model. We work with three datasets namely - IMDB movie reviewsBIBREF11, Stanford Sentiment Treebank (SST-2) BIBREF12 and Semantic Textual Similarity (STS-B) BIBREF13.",
+      "IMDB dataset is a popular dataset for sentiment analysis tasks, which is a binary classification problem with equal number of positive and negative examples. Both STS-B and SST-2 datasets are a part of GLUE benchmark[2] tasks . In STS-B too, we predict positive and negative sentiments. In SST-2 we predict textual semantic similarity between two sentences. It is a regression problem where the similarity score varies between 0 to 5. To evaluate the performance of BERT we use standard metrics of F1-score for imdb and STS-B, and Pearson-Spearman correlation for SST-2.",
+      "In Table TABREF5, we give the statistics for each of the datasets.",
+      "We take the original datasets and add varying degrees of noise (i.e. spelling errors to word utterances) to create datasets for our experiments. From each dataset, we create 4 additional datasets each with varying percentage levels of noise in them. For example from IMDB, we create 4 variants, each having 5%, 10%, 15% and 20% noise in them. Here, the number denotes the percentage of words in the original dataset that have spelling mistakes. Thus, we have one dataset with no noise and 4 variants datasets with increasing levels of noise. Likewise, we do the same for SST-2 and STS-B.",
+      "All the parameters of the BERTBase model remain the same for all 5 experiments on the IMDB dataset and its 4 variants. This also remains the same across other 2 datasets and their variants. For all the experiments, the learning rate is set to 4e-5, for optimization we use Adam optimizer with epsilon value 1e-8. We ran each of the experiments for 10 and 50 epochs."
+    ],
+    [
+      "Let us discuss the results from the above mentioned experiments. We show the plots of accuracy vs noise for each of the tasks. For IMDB, we fine tune the model for the sentiment analysis task. We plot F1 score vs % of error, as shown in Figure FIGREF6. Figure FIGREF6imdba shows the performance after fine tuning for 10 epochs, while Figure FIGREF6imdbb shows the performance after fine tuning for 50 epochs.",
+      "Similarly, Figure FIGREF9ssta and Figure FIGREF9sstb) shows F1 score vs % of error for Sentiment analysis on SST-2 dataset after fine tuning for 10 and 50 epochs respectively.",
+      "Figure FIGREF12stsa and FIGREF12stsb shows Pearson-Spearman correlation vs % of error for textual semantic similarity on STS-B dataset after fine tuning for 10 and 50 epochs respectively."
+    ],
+    [
+      "It is clear from the above plots that as we increase the percentage of error, for each of the three tasks, we see a significant drop in BERT\u2019s performance. Also, from the plots it is evident that the reason for this drop in performance is introduction of noise (spelling mistakes). After all we get very good numbers, for each of the three tasks, when there is no error (0.0 % error). To understand the reason behind the drop in performance, first we need to understand how BERT processes input text data. BERT uses WordPiece tokenizer to tokenize the text. WordPiece tokenizer utterances based on the longest prefix matching algorithm to generate tokens . The tokens thus obtained are fed as input of the BERT model.",
+      "When it comes to tokenizing noisy data, we see a very interesting behaviour from WordPiece tokenizer. Owing to the spelling mistakes, these words are not directly found in BERT\u2019s dictionary. Hence WordPiece tokenizer tokenizes noisy words into subwords. However, it ends up breaking them into subwords whose meaning can be very different from the meaning of the original word. Often, this changes the meaning of the sentence completely, therefore leading to substantial dip in the performance.",
+      "To understand this better, let us look into two examples, one each from the IMDB and STS-B datasets respectively, as shown below. Here, (a) is the sentence as it appears in the dataset ( before adding noise) while (b) is the corresponding sentence after adding noise. The mistakes are highlighted with italics. The sentences are followed by the corresponding output of the WordPiece tokenizer on these sentences: In the output \u2018##\u2019 is WordPiece tokenizer\u2019s way of distinguishing subwords from words. \u2018##\u2019 signifies subwords as opposed to words.",
+      "Example 1 (imdb example):",
+      "\u201cthat loves its characters and communicates something rather beautiful about human nature\u201d (0% error)",
+      "\u201cthat loves 8ts characters abd communicates something rathee beautiful about human natuee\u201d (5% error)",
+      "Output of wordPiece tokenizer:",
+      "['that', 'loves', 'its', 'characters', 'and', 'communicate', '##s', 'something', 'rather', 'beautiful', 'about', 'human','nature'] (0% error IMDB example)",
+      "['that', 'loves', '8', '##ts', 'characters', 'abd', 'communicate','##s', 'something','rat', '##hee', 'beautiful', 'about', 'human','nat', '##ue', '##e'] (5% error IMDB example)",
+      "Example 2(STS example):",
+      "\u201cpoor ben bratt could n't find stardom if mapquest emailed himpoint-to-point driving directions.\u201d (0% error)",
+      "\u201cpoor ben bratt could n't find stardom if mapquest emailed him point-to-point drivibg dirsctioge.\u201d (5% error)",
+      "Output of wordPiece tokenizer:",
+      "['poor', 'ben', 'brat', '##t', 'could', 'n', \"'\", 't', 'find','star', '##dom', 'if', 'map', '##quest', 'email', '##ed', 'him','point', '-', 'to', '-', 'point', 'driving', 'directions', '.'] (0% error STS example)",
+      "['poor', 'ben', 'brat', '##t', 'could', 'n', \"'\", 't', 'find','star', '##dom', 'if', 'map', '##quest', 'email', '##ed', 'him', 'point', '-', 'to', '-', 'point', 'dr', '##iv', '##ib','##g','dir','##sc', '##ti', '##oge', '.'] (5% error STS example)",
+      "In example 1, the tokenizer splits communicates into [\u2018communicate\u2019, \u2018##s\u2019] based on longest prefix matching because there is no exact match for \u201ccommunicates\u201d in BERT vocabulary. The longest prefix in this case is \u201ccommunicate\u201d and left over is \u201cs\u201d both of which are present in the vocabulary of BERT. We have contextual embeddings for both \u201ccommunicate\u201d and \u201c##s\u201d. By using these two embeddings, one can get an approximate embedding for \u201ccommunicates\u201d. However, this approach goes for a complete toss when the word is misspelled. In example 1(b) the word natuee (\u2018nature\u2019 is misspelled) is split into ['nat', '##ue', '##e'] based on the longest prefix match. Combining the three embeddings one cannot approximate the embedding of nature. This is because the word nat has a very different meaning (it means \u2018a person who advocates political independence for a particular country\u2019). This misrepresentation in turn impacts the performance of downstream subcomponents of BERT bringing down the overall performance of BERT model. Hence, as we systematically introduce more errors, the quality of output of the tokenizer degrades further, resulting in the overall performance drop.",
+      "Our results and analysis shows that one cannot apply BERT blindly to solve NLP problems especially in industrial settings. If the application you are developing gets data from channels that are known to introduce noise in the text, then BERT will perform badly. Examples of such scenarios are applications working with twitter data, mobile based chat system, user comments on platforms like youtube, reddit to name a few. The reason for the introduction of noise could vary - while for twitter, reddit it's often deliberate because that is how users prefer to write, while for mobile based chat it often suffers from \u2018fat finger\u2019 typing error problem. Depending on the amount of noise in the data, BERT can perform well below expectations.",
+      "We further conducted experiments with different tokenizers other than WordPiece tokenizer. For this we used stanfordNLP WhiteSpace BIBREF14 and Character N-gram BIBREF15 tokenizers. WhiteSpace tokenizer splits text into tokens based on white space. Character N-gram tokenizer splits words that have more than n characters in them. Thus, each token has at most n characters in them. The resultant tokens from the respective tokenizer are fed to BERT as inputs. For our case, we work with n = 6.",
+      "Results of these experiments are presented in Table TABREF25. Even though wordPiece tokenizer has the issues stated earlier, it is still performing better than whitespace and character n-gram tokenizer. This is primarily because of the vocabulary overlap between STS-B dataset and BERT vocabulary."
+    ],
+    [
+      "In this work we systematically studied the effect of noise (spelling mistakes) in user generated text data on the performance of BERT. We demonstrated that as the noise increases, BERT\u2019s performance drops drastically. We further investigated the BERT system to understand the reason for this drop in performance. We show that the problem lies with how misspelt words are tokenized to create a representation of the original word.",
+      "There are 2 ways to address the problem - either (i) preprocess the data to correct spelling mistakes or (ii) incorporate ways in BERT architecture to make it robust to noise. The problem with (i) is that in most industrial settings this becomes a separate project in itself. We leave (ii) as a future work to fix the issues."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1838/instruction.md b/qasper-1838/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..c05a2577ddaeb2e116fe8bd407bb666886340cec
--- /dev/null
+++ b/qasper-1838/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: User Generated Data: Achilles' heel of BERT
+
+Question: What kind is noise is present in typical industrial data?
\ No newline at end of file
diff --git a/qasper-1839/instruction.md b/qasper-1839/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..91f55794a69be57bfc8c27a06b9f6282afa277fb
--- /dev/null
+++ b/qasper-1839/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: User Generated Data: Achilles' heel of BERT
+
+Question: What is the reason behind the drop in performance using BERT for some popular task?
\ No newline at end of file
diff --git a/qasper-1842/instruction.md b/qasper-1842/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..a9e7cfea35cfa458890513a6c1dd921a0b8a4995
--- /dev/null
+++ b/qasper-1842/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Paying Attention to Attention: Highlighting Influential Samples in Sequential Analysis
+
+Question: How do they gather human reviews?
\ No newline at end of file
diff --git a/qasper-1845/instruction.md b/qasper-1845/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..3b66b6ecca689f6f6d099e18cbbb603eb1fcd521
--- /dev/null
+++ b/qasper-1845/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Video Highlight Prediction Using Audience Chat Reactions
+
+Question: What was the baseline?
\ No newline at end of file
diff --git a/qasper-1852/instruction.md b/qasper-1852/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..d5746d8a6129027963f9edfe532d6bc417583c54
--- /dev/null
+++ b/qasper-1852/instruction.md
@@ -0,0 +1,40 @@
+Name of Paper: Creation of an Annotated Corpus of Spanish Radiology Reports
+
+Question: Did they experiment on this corpus?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Annotation process",
+    "Dataset Analysis",
+    "Discussion and Conclusions"
+  ],
+  "paragraphs": [
+    [
+      "The availability of annotated corpora from the biomedical domain, in particular for non-English texts, is scarce. There are two main reasons for that: the generation of new annotated data is expensive due to the need of expert knowledge and to privacy issues: the patient and the physician should not be identified from the texts. So, although the availability of annotated data is a highly valuable asset for the research community, it is very difficult to access it.",
+      "We are interested in supporting physicians with automatic text processing methods, such as named entity recognition (NER), relation extraction (RE), and negation and uncertainty detection in Spanish radiology reports. The extraction of entities and relations from the reports could suggest possible medical problems, that might lead to surgical interventions, such as seen in Do:2013, Morioka:2016 and Lakhani:2009. To the best of our knowledge, there are no publicly available annotated datasets of Spanish medical reports for these tasks. For this reason, this work focuses on creating an annotated corpus of Spanish radiology reports. There are some datasets available for other languages in the clinical domain, eg. for English BIBREF0 , BIBREF1 , BIBREF2 , Swedish BIBREF3 , French BIBREF4 , Polish BIBREF5 and German BIBREF6 . Oronoz:2015:corpus presented an annotated dataset in Spanish for adverse drug reactions analysis.",
+      "There are different kind of medical reports. In our case, reports are very short, sentences are not always well formed and many of them have a telegraphic style. They contain spelling mistakes and the use of non-standard abbreviations and acronyms is frequent. This, added to the use of specialized language of the medical domain, makes the annotation task difficult.",
+      "This work describes the annotation schema, the main guidelines and a brief analysis of the resulting corpus. We are evaluating the possibility of releasing the dataset publicly."
+    ],
+    [
+      "We developed an annotation guideline, which we improved with three iterations of a process consisting of annotation and revision of doubts in the criteria.",
+      "A set of 513 different kinds of ultrasound reports (e.g. kidney, abdominal, small parts) that were written in a hospital in Argentina were selected for annotation. They contain only one section that includes findings, conclusions and suggestions. The reports were anonymized by removing the date of the study, the report number and the patient identification number. Additionally, information about the physicians that performed the study was removed. Regular expressions have been used for this purpose considering the different ways of writing the title of the physicians (e.g. DR, Dr., doctor, Dra.), the doctor's names, the enrollment numbers and the order among these terms. Also names of the doctors appearing with titles or enrollments were searched to see if they appeared without titles and without enrollments and were removed.",
+      "The main entities and characteristics that were annotated are presented in Table TABREF2 . An example is included for each case. Abbreviations and acronyms were only annotated for anatomical entities and findings. Table TABREF3 shows the main relations annotated. This table exhibits the name of the relation and the entities involved in it. The relation occurs in, for instance, is always constructed between a finding and anatomical entity and explicits in which anatomical entity the finding occurred. A texture can be related by the texture relation to a finding or to an anatomical entity.",
+      "Reports were annotated according to the following main guidelines:",
+      "Finally, a concept, that we called multisegment term, was introduced: constructions like intra and extrahepatic had to lead to the annotation of the entities intrahepatic and extrahepatic. Examples of our annotated corpus are given in Figure FIGREF16 and FIGREF17 .",
+      "In order to decrease the annotation time, entities, negation and uncertainty terms were pre-annotated automatically. Therefore, regular expressions, UMLS and a manually-created dictionary were used. Based on the annotation guideline, two native speakers of Spanish annotated the pre-annotated reports using brat BIBREF7 . Annotations wrongly made by the pre-annotation tool were corrected and missing concepts were included. Relations were introduced by the annotators. Overall, annotators worked for approximately a total of 160 hours on the generation of the corpus. In addition to that, the authors had various discussions to define the final annotation schema based on previous annotations revisions and annotators doubts. We assume that with a stable annotation schema and once the annotators have less doubts, the annotation process would be quicker."
+    ],
+    [
+      "Among other entities, 4398 (405 different) AE, 2637 (745) FI, 1489 (51) NT and 109 (26) UT have been annotated. There are 2161 (750) occurs in and 1478 (164) negates, among other relations. There appear 470 abbreviations or acronyms corresponding to AE and 7 to FI. 7.89% (867 out of a total of 10987) of the relations are across-sentence relations. The inter-annotator agreement (IAA) for the final annotation schema is 0.89. It was calculated for the 61 reports annotated by both annotators on a token level using the Cohen's Kappa coefficient ( INLINEFORM0 ) BIBREF8 ."
+    ],
+    [
+      "We presented a manually annotated corpus of entities and relations in radiology reports written in Spanish. The goal was twofold: to have an annotated dataset available for evaluating NER and RE algorithms results and for training of supervised models and to present an annotation guideline that can be used by other researchers with similar needs. The creation of the corpus was not an easy task. Many annotation-revision iterations had to be performed in order to arrive to a stabilized annotation schema. According to what we expected, INLINEFORM0 improved in each annotation iteration step. Data had to be anonymized. Furthermore, the shortness of the texts, the abundance of abbreviations and acronyms (about 6% of the AE and FI are written as such and there are 105 different abbreviations or acronyms in 513 reports), the specificity of the medical language, the existence of multi-segment terms and the existence of relations between sentences, makes not only the NER and RE tasks, but also the annotation task a difficult one.",
+      "The relation of findings with temporal terms, negation terms and uncertainty terms should be taken into account to determine their factuality. The abundance of negated findings (56%) might lead to the implementation of methods to detect negated findings in reports (see negex:2001, and CotikACLNegExSpanish for Spanish). The difference of criteria among the annotators helps us determine that the evaluation of NER systems is not an easy task.",
+      "Finally, the pre-annotation helped us speed the annotation process, although it might have biased the annotation results."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: DP-LSTM: Differential Privacy-inspired LSTM for Stock Prediction Using Financial News
+
+Question: Is the model compared against a linear regression baseline?
\ No newline at end of file
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+Name of Paper: DP-LSTM: Differential Privacy-inspired LSTM for Stock Prediction Using Financial News
+
+Question: What is the prediction accuracy of the model?
\ No newline at end of file
diff --git a/qasper-1855/instruction.md b/qasper-1855/instruction.md
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@@ -0,0 +1,128 @@
+Name of Paper: DP-LSTM: Differential Privacy-inspired LSTM for Stock Prediction Using Financial News
+
+Question: What is the dataset used in the paper?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Problem Statement",
+    "Problem Statement ::: ARMA Model",
+    "Problem Statement ::: Sentiment Analysis",
+    "Problem Statement ::: Sentiment-ARMA Model and Loss Function",
+    "Problem Statement ::: Overview of LSTM",
+    "Problem Statement ::: Definition of Differential Privacy",
+    "Training DP-LSTM Neural Network",
+    "Training DP-LSTM Neural Network ::: Data Preprocessing and Normalization ::: Data Preprocessing",
+    "Training DP-LSTM Neural Network ::: Data Preprocessing and Normalization ::: Normalization",
+    "Training DP-LSTM Neural Network ::: Adding Noise",
+    "Training DP-LSTM Neural Network ::: Training Setting",
+    "Performance Evaluation",
+    "Conclusion",
+    "References"
+  ],
+  "paragraphs": [
+    [
+      "Stock prediction is crucial for quantitative analysts and investment companies. Stocks' trends, however, are affected by a lot of factors such as interest rates, inflation rates and financial news [12]. To predict stock prices accurately, one must use these variable information. In particular, in the banking industry and financial services, analysts' armies are dedicated to pouring over, analyzing, and attempting to quantify qualitative data from news. A large amount of stock trend information is extracted from the large amount of text and quantitative information that is involved in the analysis.",
+      "Investors may judge on the basis of technical analysis, such as charts of a company, market indices, and on textual information such as news blogs or newspapers. It is however difficult for investors to analyze and predict market trends according to all of these information [22]. A lot of artificial intelligence approaches have been investigated to automatically predict those trends [3]. For instance, investment simulation analysis with artificial markets or stock trend analysis with lexical cohesion based metric of financial news' sentiment polarity. Quantitative analysis today is heavily dependent on data. However, the majority of such data is unstructured text that comes from sources like financial news articles. The challenge is not only the amount of data that are involved, but also the kind of language that is used in them to express sentiments, which means emoticons. Sifting through huge volumes of this text data is difficult as well as time-consuming. It also requires a great deal of resources and expertise to analyze all of that [4].",
+      "To solve the above problem, in this paper we use sentiment analysis to extract information from textual information. Sentiment analysis is the automated process of understanding an opinion about a given subject from news articles [5]. The analyzed data quantifies reactions or sentiments of the general public toward people, ideas or certain products and reveal the information's contextual polarity. Sentiment analysis allows us to understand if newspapers are talking positively or negatively about the financial market, get key insights about the stock's future trend market.",
+      "We use valence aware dictionary and sentiment reasoner (VADER) to extract sentiment scores. VADER is a lexicon and rule-based sentiment analysis tool attuned to sentiments that are expressed in social media specifically [6]. VADER has been found to be quite successful when dealing with NY Times editorials and social media texts. This is because VADER not only tells about the negativity score and positively but also tells us about how positive or negative a sentiment is.",
+      "However, news reports are not all objective. We may increase bias because of some non-objective reports, if we rely on the information that is extracted from the news for prediction fully. Therefore, in order to enhance the prediction model's robustness, we will adopt differential privacy (DP) method. DP is a system for sharing information about a dataset publicly by describing groups' patterns within the dataset while withholding information about individuals in the dataset. DP can be achieved if the we are willing to add random noise to the result. For example, rather than simply reporting the sum, we can inject noise from a Laplace or gaussian distribution, producing a result that\u2019s not quite exact, that masks the contents of any given row.",
+      "In the last several years a promising approach to private data analysis has emerged, based on DP, which ensures that an analysis outcome is \"roughly as likely\" to occur independent of whether any individual opts in to, or to opts out of, the database. In consequence, any one individual's specific data can never greatly affect the results. General techniques for ensuring DP have now been proposed, and a lot of datamining tasks can be carried out in a DP method, frequently with very accurate results [21]. We proposed a DP-LSTM neural network, which increase the accuracy of prediction and robustness of model at the same time.",
+      "The remainder of the paper is organized as follows. In Section 2, we introduce stock price model, the sentiment analysis and differential privacy method. In Section 3, we develop the different privacy-inspired LSTM (DP-LSTM) deep neural network and present the training details. Prediction results are provided in Section 4. Section 5 concludes the paper."
+    ],
+    [
+      "In this section, we first introduce the background of the stock price model, which is based on the autoregressive moving average (ARMA) model. Then, we present the sentiment analysis details of the financial news and introduce how to use them to improve prediction performance. At last, we introduce the differential privacy framework and the loss function."
+    ],
+    [
+      "The ARMA model, which is one of the most widely used linear models in time series prediction [17], where the future value is assumed as a linear combination of the past errors and past values. ARMA is used to set the stock midterm prediction problem up. Let ${X}_t^\\text{A}$ be the variable based on ARMA at time $t$, then we have",
+      "where $X_{t-i}$ denotes the past value at time $t-i$; $\\epsilon _{t}$ denotes the random error at time $t$; $\\phi _i$ and $\\psi _j$ are the coefficients; $\\mu $ is a constant; $p$ and $q$ are integers that are often referred to as autoregressive and moving average polynomials, respectively."
+    ],
+    [
+      "Another variable highly related to stock price is the textual information from news, whose changes may be a precursor to price changes. In our paper, news refers to a news article's title on a given trading day. It has been used to infer whether an event had informational content and whether investors' interpretations of the information were positive, negative or neutral. We hence use sentiment analysis to identify and extract opinions within a given text. Sentiment analysis aims at gauging the attitude, sentiments, evaluations and emotions of a speaker or writer based on subjectivity's computational treatment in a text [19]-[20].",
+      "Figure FIGREF3 shows an example of the sentiment analysis results obtained from financial news titles that were based on VADER. VADER uses a combination of a sentiment lexicon which are generally labelled according to their semantic orientation as either negative or positive. VADER has been found to be quite successful when dealing with news reviews. It is fully open-sourced under the MIT License. The result of VADER represent as sentiment scores, which include the positive, negative and neutral scores represent the proportion of text that falls in these categories. This means all these three scores should add up to 1. Besides, the Compound score is a metric that calculates the sum of all the lexicon ratings which have been normalized between -1(most extreme negative) and +1 (most extreme positive). Figure FIGREF5 shows the positive and negative wordcloud, which is an intuitive analysis of the number of words in the news titles."
+    ],
+    [
+      "To take the sentiment analysis results of the financial news into account, we introduce the sentiment-ARMA model as follows",
+      "where $\\alpha $ and $\\lambda $ are weighting factors; $c$ is a constant; and $f_2(\\cdot )$ is similar to $f_1(\\cdot )$ in the ARMA model (DISPLAY_FORM2) and is used to describe the prediction problem.",
+      "In this paper, the LSTM neural network is used to predict the stock price, the input data is the previous stock price and the sentiment analysis results. Hence, the sentiment based LSTM neural network (named sentiment-LSTM) is aimed to minimize the following loss function:",
+      "where $T$ denotes the number of prediction time slots, i.e., $t = 1,...,p$ are the observations (training input data), $t = p+1,...,p+T$ are the predicts (training output data); and $\\hat{X}_t$ is given in (DISPLAY_FORM7)."
+    ],
+    [
+      "Denote $\\mathcal {X}_t^{\\text{train}} = \\lbrace X_{t-i},S_{t-i}\\rbrace _{i=1}^p$ as the training input data. Figure FIGREF10 shows the LSTM's structure network, which comprises one or more hidden layers, an output layer and an input layer [16]. LSTM networks' main advantage is that the hidden layer comprises memory cells. Each memory cell recurrently has a core self-connected linear unit called \u201c Constant Error Carousel (CEC)\u201d [13], which provides short-term memory storage and has three gates:",
+      "Input gate, which controls the information from a new input to the memory cell, is given by",
+      "where $h_{t-1}$ is the hidden state at the time step $t-1$; $i_t$ is the output of the input gate layer at the time step $t$; $\\hat{c}_t$ is the candidate value to be added to the output at the time step $t$; $b_i$ and $b_c$ are biases of the input gate layer and the candidate value computation, respectively; $W_i$ and $W_c$ are weights of the input gate and the candidate value computation, respectively; and $\\sigma (x) = 1/(1+e^{-x})$ is the pointwise nonlinear activation function.",
+      "Forget gate, which controls the limit up to which a value is saved in the memory, is given by",
+      "where $f_t$ is the forget state at the time step $t$, $W_f$ is the weight of the forget gate; and $b_f$ is the bias of the forget gate.",
+      "Output gate, which controls the information output from the memory cell, is given by",
+      "where new cell states $c_t$ are calculated based on the results of the previous two steps; $o_t$ is the output at the time step $t$; $W_o$ is the weight of the output gate; and $b_o$ is the bias of the output gate [14]."
+    ],
+    [
+      "Differential privacy is one of privacy's most popular definitions today, which is a system for publicly sharing information about a dataset by describing the patterns of groups within the dataset while withholding information about individuals in the dataset. It intuitively requires that the mechanism that outputs information about an underlying dataset is robust to one sample's any change, thus protecting privacy. A mechanism ${f}$ is a random function that takes a dataset $\\mathcal {N}$ as input, and outputs a random variable ${f}(\\mathcal {N})$. For example, suppose $\\mathcal {N}$ is a news articles dataset, then the function that outputs compound score of articles in $\\mathcal {N}$ plus noise from the standard normal distribution is a mechanism [7].",
+      "Although differential privacy was originally developed to facilitate secure analysis over sensitive data, it can also enhance the robustness of the data. Note that finance data, especially news data and stock data, is unstable with a lot of noise, with a more robust data the accuracy of prediction will be improved. Since we predict stock price by fusing news come from different sources, which might include fake news. Involving differential privacy in the training to improve the robustness of the finance news is meaningful."
+    ],
+    [
+      "It is known that it is risky to predict stocks by considering news factors, because news can't guarantee full notarization and objectivity, many times extreme news will have a big impact on prediction models. To solve this problem, we consider entering the idea of the differential privacy when training. In this section, our DP-LSTM deep neural network training strategy is presented. The input data consists of three components: stock price, sentiment analysis compound score and noise."
+    ],
+    [
+      "The data for this project are two parts, the first part is the historical S&P 500 component stocks, which are downloaded from the Yahoo Finance. We use the data over the period of from 12/07/2017 to 06/01/2018. The second part is the news article from financial domain are collected with the same time period as stock data. Since our paper illustrates the relationship between the sentiment of the news articles and stocks' price. Hence, only news article from financial domain are collected. The data is mainly taken from Webhose archived data, which consists of 306242 news articles present in JSON format, dating from December 2017 up to end of June 2018. The former 85% of the dataset is used as the training data and the remainder 15% is used as the testing data. The News publishers for this data are CNBC.com, Reuters.com, WSJ.com, Fortune.com. The Wall Street Journal is one of the largest newspapers in the United States, which coverage of breaking news and current headlines from the US and around the world include top stories, photos, videos, detailed analysis and in-depth thoughts; CNBC primarily carries business day coverage of U.S. and international financial markets, which following the end of the business day and on non-trading days; Fortune is an American multinational business magazine; Reuters is an international news organization. We preprocess the raw article body and use NLTK sentiment package alence Aware Dictionary and Sentiment Reasoner (VADER) to extract sentiment scores.",
+      "The stocks with missing data are deleted, and the dataset we used eventually contains 451 stocks and 4 news resources (CNBC.com, Reuters.com, WSJ.comFortune.com.). Each stock records the adjust close price and news compound scores of 121 trading days.",
+      "A rolling window with size 10 is used to separate data, that is, We predict the stock price of the next trading day based on historical data from the previous 10 days, hence resulting in a point-by-point prediction [15]. In particular, the training window is initialized with all real training data. Then we shift the window and add the next real point to the last point of training window to predict the next point and so forth. Then, according to the length of the window, the training data is divided into 92 sets of training input data (each set length 10) and training output data (each set length 1). The testing data is divided into input and output data of 9 windows (see Figure FIGREF20)."
+    ],
+    [
+      "To detect stock price pattern, it is necessary to normalize the stock price data. Since the LSTM neural network requires the stock patterns during training, we use \u201cmin-max\u201d normalization method to reform dataset, which keeps the pattern of the data [11], as follow:",
+      "where $X_{t}^{n}$ denotes the data after normalization. Accordingly, de-normalization is required at the end of the prediction process to get the original price, which is given by",
+      "where $\\hat{X}_{t}^{n}$ denotes the predicted data and $\\hat{X}_{t}$ denotes the predicted data after de-normalization.",
+      "Note that compound score is not normalized, since the compound score range from -1 to 1, which means all the compound score data has the same scale, so it is not require the normalization processing."
+    ],
+    [
+      "We consider the differential privacy as a method to improve the robustness of the LSTM predictions [8]. We explore the interplay between machine learning and differential privacy, and found that differential privacy has several properties that make it particularly useful in application such as robustness to extract textual information [9]. The robustness of textual information means that accuracy is guaranteed to be unaffected by certain false information [10].",
+      "The input data of the model has 5 dimensions, which are the stock price and four compound scores as $(X^t, S_1^t, S_2^t, S_3^t, S_4^t), t=1,...,T$, where $X^t$ represents the stock price and $S_i^t,~i=1,...,4$ respectively denote the mean compound score calculated from WSJ, CNBC, Fortune and Reuters. According to the process of differential privacy, we add Gaussian noise with different variances to the news according to the variance of the news, i.e., the news compound score after adding noise is given by",
+      "where $\\text{var}(\\cdot )$ is the variance operator, $\\lambda $ is a weighting factor and $\\mathcal {N}(\\cdot )$ denotes the random Gaussian process with zero mean and variance $\\lambda \\text{var}(S_i)$.",
+      "We used python to crawl the news from the four sources of each trading day, perform sentiment analysis on the title of the news, and get the compound score. After splitting the data into training sets and test sets, we separately add noise to each of four news sources of the training set, then, for $n$-th stock, four sets of noise-added data $(X^n_t, {\\widetilde{S}^t_1}, S^t_2, S^t_3, S^t_4)$, $(X^n_t, {S^t_1}, \\widetilde{S}^t_2, S^t_3, S^t_4)$, $(X^n_t, { S^t_1}, S^t_2, \\widetilde{S}^t_3, S^t_4)$, $(X^n_t, { S^t_1}, S^t_2, S^t_3, \\widetilde{S}^t_4)$ are combined into a new training data through a rolling window. The stock price is then combined with the new compound score training data as input data for our DP-LSTM neural network."
+    ],
+    [
+      "The LSTM model in figure FIGREF10 has six layers, followed by an LSTM layer, a dropout layer, an LSTM layer, an LSTM layer, a dropout layer, a dense layer, respectively. The dropout layers (with dropout rate 0.2) prevent the network from overfitting. The dense layer is used to reshape the output. Since a network will be difficult to train if it contains a large number of LSTM layers [16], we use three LSTM layers here.",
+      "In each LSTM layer, the loss function is the mean square error (MSE), which is the sum of the squared distances between our target variable and the predicted value. In addition, the ADAM [17] is used as optimizer, since it is straightforward to implement, computationally efficient and well suited for problems with large data set and parameters.",
+      "There are many methods and algorithms to implement sentiment analysis systems. In this paper, we use rule-based systems that perform sentiment analysis based on a set of manually crafted rules. Usually, rule-based approaches define a set of rules in some kind of scripting language that identify subjectivity, polarity, or the subject of an opinion. We use VADER, a simple rule-based model for general sentiment analysis."
+    ],
+    [
+      "In this section, we validate our DP-LSTM based on the S&P 500 stocks. We calculate the mean prediction accuracy (MPA) to evaluate the proposed methods, which is defined as",
+      "where $X_{t,\\ell }$ is the real stock price of the $\\ell $-th stock on the $t$-th day, $L$ is the number of stocks and $\\hat{X}_{t,\\ell }$ is the corresponding prediction result.",
+      "Figure FIGREF27 plots the average score for all news on the same day over the period. The compound score is fluctuating between -0.3 and 0.15, indicating an overall neutral to slightly negative sentiment. The Positive, Negative and Neutral scores represent the proportion of text that falls in these categories. The Compound score is a metric that calculates the sum of all the lexicon ratings which have been normalized between -1 (most extreme negative) and +1 (most extreme positive).",
+      "Figure FIGREF29 shows the $\\text{MPAs}$ of the proposed DP-LSTM and vanilla LSTM for comparison. In Table TABREF30, we give the mean MPA results for the prediction prices, which shows the accuracy performance of DP-LSTM is 0.32% higer than the LSTM with news. The result means the DP framework can make the prediction result more accuracy and robustness.",
+      "Note that the results are obtained by running many trials, since we train stocks separately and predict each price individually due to the different patterns and scales of stock prices. This in total adds up to 451 runs. The results shown in Table TABREF30 is the average of these 451 runs. Furthermore, we provide results for 9 duration over a period in Figure FIGREF29. The performance of our DP-LSTM is always better than the LSTM with news. Based on the sentiment-ARMA model and adding noise for training, the proposed DP-LSTM is more robust. The investment risk based on this prediction results is reduced.",
+      "In Figure FIGREF31, we can see the prediction results of DP-LSTM with is closer to the real S&P 500 index price line than other methods. The two lines (prediction results of LSTM with news and LSTM without news) almost coincide in Figure FIGREF31. We can tell the subtle differences from the Table TABREF32, that DP-LSTM is far ahead, and LSTM with news is slightly better than LSTM without news."
+    ],
+    [
+      "In this paper, we integrated the deep neural network with the famous NLP models (VADER) to identify and extract opinions within a given text, combining the stock adjust close price and compound score to reduce the investment risk. We first proposed a sentiment-ARMA model to represent the stock price, which incorporates influential variables (price and news) based on the ARMA model. Then, a DP-LSTM deep neural network was proposed to predict stock price according to the sentiment-ARMA model, which combines the LSTM, compound score of news articles and differential privacy method. News are not all objective. If we rely on the information extracted from the news for prediction fully, we may increase bias because of some non-objective reports. Therefore, the DP-LSTM enhance robustness of the prediction model. Experiment results based on the S&P 500 stocks show that the proposed DP-LSTM network can predict the stock price accurately with robust performance, especially for S&P 500 index that reflects the general trend of the market. S&P 500 prediction results show that the differential privacy method can significantly improve the robustness and accuracy."
+    ],
+    [
+      "[1] X. Li, Y. Li, X.-Y. Liu, D. Wang, \u201cRisk management via anomaly circumvent: mnemonic deep learning for midterm stock prediction.\u201d in Proceedings of 2nd KDD Workshop on Anomaly Detection in Finance (Anchorage \u201919), 2019.",
+      "[2] P. Chang, C. Fan, and C. Liu, \u201cIntegrating a piece-wise linear representation method and a neural network model for stock trading points prediction.\u201d IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 39, 1 (2009), 80\u201392.",
+      "[3] Akita, Ryo, et al. \u201cDeep learning for stock prediction using numerical and textual information.\u201d IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS). IEEE, 2016.",
+      "[4] Li, Xiaodong, et al. \u201cDoes summarization help stock prediction? A news impact analysis.\u201d IEEE Intelligent Systems 30.3 (2015): 26-34.",
+      "[5] Ding, Xiao, et al. \u201cDeep learning for event-driven stock prediction.\u201d Twenty-fourth International Joint Conference on Artificial Intelligence. 2015.",
+      "[6] Hutto, Clayton J., and Eric Gilbert. \u201cVader: A parsimonious rule-based model for sentiment analysis of social media text.\u201d Eighth International AAAI Conference on Weblogs and Social Media, 2014.",
+      "[7] Ji, Zhanglong, Zachary C. Lipton, and Charles Elkan. \u201cDifferential privacy and machine learning: a survey and review.\u201d arXiv preprint arXiv:1412.7584 (2014).",
+      "[8] Abadi, Martin, et al. \u201cDeep learning with differential privacy.\u201d Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, ACM, 2016.",
+      "[9] McMahan, H. Brendan, and Galen Andrew. \u201cA general approach to adding differential privacy to iterative training procedures.\u201d arXiv preprint arXiv:1812.06210 (2018).",
+      "[10] Lecuyer, Mathias, et al. \u201cCertified robustness to adversarial examples with differential privacy.\u201d arXiv preprint arXiv:1802.03471 (2018).",
+      "[11] Hafezi, Reza, Jamal Shahrabi, and Esmaeil Hadavandi. \u201cA bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price.\u201d Applied Soft Computing, 29 (2015): 196-210.",
+      "[12] Chang, Pei-Chann, Chin-Yuan Fan, and Chen-Hao Liu. \u201cIntegrating a piecewise linear representation method and a neural network model for stock trading points prediction.\u201d IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 39.1 (2008): 80-92.",
+      "[13] Gers, Felix A., Nicol N. Schraudolph, and J\u00fcrgen Schmidhuber. \u201cLearning precise timing with LSTM recurrent networks.\u201d Journal of Machine Learning Research 3.Aug (2002): 115-143.",
+      "[14] Qin, Yao, et al. \u201cA dual-stage attention-based recurrent neural network for time series prediction.\u201d arXiv preprint arXiv:1704.02971 (2017).",
+      "[15] Malhotra, Pankaj, et al. \u201cLong short term memory networks for anomaly detection in time series.\u201d Proceedings. Presses universitaires de Louvain, 2015.",
+      "[16] Sak, Ha\u015fim, Andrew Senior, and Fran\u00e7oise Beaufays. \u201cLong short-term memory recurrent neural network architectures for large scale acoustic modeling.\u201d Fifteenth annual conference of the international speech communication association, 2014.",
+      "[17] Kingma, Diederik P., and Jimmy Ba. \u201cAdam: A method for stochastic optimization.\u201d arXiv preprint arXiv:1412.6980 (2014).",
+      "[18] Box, George EP, et al. Time series analysis: forecasting and control. John Wiley & Sons, 2015.",
+      "[19] Pang, Bo, and Lillian Lee. \u201cOpinion mining and sentiment analysis.\u201d Foundations and Trends in Information Retrieval 2.1\u20132 (2008): 1-135.",
+      "[20] Cambria, Erik. \u201cAffective computing and sentiment analysis.\u201d IEEE Intelligent Systems 31.2 (2016): 102-107.",
+      "[21] Dwork C, Lei J. Differential privacy and robust statistics//STOC. 2009, 9: 371-380.",
+      "[22] X. Li, Y. Li, Y. Zhan, and X.-Y. Liu. \u201cOptimistic bull or pessimistic bear: adaptive deep reinforcement learning for stock portfolio allocation.\u201d in Proceedings of the 36th International Conference on Machine Learning, 2019."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1862/instruction.md b/qasper-1862/instruction.md
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+Name of Paper: Non-native Speaker Verification for Spoken Language Assessment
+
+Question: What systems are tested?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Speaker Verification Systems",
+    "Speaker Verification Systems ::: Deep neural network embedding extractor",
+    "Speaker Verification Systems ::: PLDA classifier and adaptation",
+    "Non-native Spoken English Corpora",
+    "Experimental Setup",
+    "Experimental results ::: Baseline system performance",
+    "Experimental results ::: Impostor attributes analysis",
+    "Experimental results ::: Overall system performance",
+    "Conclusions"
+  ],
+  "paragraphs": [
+    [
+      "Automatic spoken assessment systems are becoming increasingly popular, especially for English with the high demand around the world for learning of English as a second language BIBREF0, BIBREF1, BIBREF2, BIBREF3. In addition to assessing a candidate's English ability such as fluency and pronunciation and giving feedback to the candidate, these automatic systems also need to ensure the integrity of the candidate's score by detecting malpractice, as shown in Figure FIGREF1. Malpractice is the action by a candidate that breaks the assessment regulation and potentially threatens the reliability of the exam and associated certification. Malpractice can take a range of forms in spoken language assessment scenarios, such as using or trying to use unauthorised materials, impersonation, speaking irrelevant to prompts/questions, speaking in his/her first language (L1) instead of the target language for spoken tests, etc. This work aims to investigate the problem of automatically detecting impersonation, in which a candidate attempts to impersonate another in a speaking test. This is closely related to speaker verification.",
+      "",
+      "Speaker verification is the process to accept or reject an identity claim by comparing the speaker-specific information extracted from the verification speech with that from the enrolment speech of the claimed identity. These approaches can be directly applied to detect impersonation in spoken language tests. The performance of speaker verification systems has advanced considerably in the last decade with the development of i-vector modelling BIBREF4, in which a speech segment or a speaker is represented as a low-dimensional feature vector. Extraction of i-vectors is normally based on a Gaussian mixture model (GMM) based universal background model (UBM). This fixed length representation can then be used with a probabilistic linear discriminant analysis (PLDA) model to produce verification scores by comparing speaker representations, which are then used to make valid or impostor speaker decisions BIBREF5, BIBREF6, BIBREF7, BIBREF8. Recently, with developments in deep learning, performance of speaker verification systems has been improved by replacing the GMM with a deep neural network (DNN) to derive statistics for extracting speaker representations. This DNN is usually trained to take a fixed length window of the acoustics and discriminate between speakers using supplied speaker labels as targets. To handle the variable-length nature of the acoustic signal, a pooling layer is used to yield the final fixed-dimensional speaker representation. In BIBREF9, a DNN was trained at the frame level, and pooling was performed by averaging activation vectors of the last hidden layer over all frames of an input utterance. In BIBREF10, BIBREF11, BIBREF12, segment-level embeddings were extracted, which are referred to as x-vectors BIBREF12 with data augmentation. By leveraging data augmentation based on background noise and acoustic reverberation, these x-vectors based systems can achieve better performance than i-vector and d-vector based systems on standard speaker verification tasks.",
+      "There has been some previous work on tasks related to non-native speech data using speaker verification approaches, such as detection of non-native speech BIBREF13, classification of native/non-native English BIBREF14 and L1 detection BIBREF15. In BIBREF16, meta-data (L1) sensitive bottleneck features were employed within the i-vector framework to improve the performance of speaker verification with non-native speech. In contrast, this paper focuses on making use of the state-of-the-art deep-learning based speaker verification approaches to detect candidate impersonation in an English speaking test. As there is limited amounts of data available for the non-native learner task, it is of interest to investigate adapting a standard speaker verification task to this non-native task. Here a system based on the VoxCeleb dataset BIBREF17, BIBREF18 is adapted to the BULATS task. Two forms of adaptation are examined: modifying the PLDA distance measure; and adapting the process for extracting the speaker representation by \u201cfine-tuning\" the network to the target domain. Furthermore, detailed analysis of performance is also done with respect to speaker attributes. Gender is an important attribute in impostor selection for standard speaker verification tasks, and for non-native speech, there are two additional speaker attributes: the L1 and the language proficiency level, which should also be taken into consideration for speaker verification.",
+      "This paper is organised as follows. Section 2 gives an overview of speaker verification systems, and Section 3 introduces the non-native spoken English corpora used in this work. Experimental setup is described in Section 4, results and analysis are detailed in Section 5, and finally, conclusions are drawn in Section 6."
+    ],
+    [
+      "In this work both i-vector and x-vector representations are used. For the i-vector speaker representation the form described in BIBREF4, BIBREF19 is used. This section will just discuss the x-vector speaker representation as this is the form that is adapted to the non-native verification task."
+    ],
+    [
+      "There are three blocks to form the DNN for extracting the utterance-level speaker representation, or embedding. The first block of the deep embedding extractor is a frame-level feature extractor. The input to this block is a sequence of acoustic feature vectors $\\lbrace \\mathbf {x}_{1},\\mathbf {x}_{2},\\cdots \\mathbf {x}_{T}\\rbrace $ of $T$ frames. This part normally consists of a number of hidden layers such as long short-term memory (LSTM) BIBREF20 or time delay neural network (TDNN) layers BIBREF11, BIBREF12. The activations of the last hidden layer of this block for the input frames, $\\lbrace \\mathbf {h}_{1},\\mathbf {h}_{2},\\cdots \\mathbf {h}_{T}\\rbrace $, form the input to the second block which is a statistics pooling layer. This layer converts variable-length frame-level features into a fixed-dimensional vector by calculating the mean vector, $\\mu $ and standard deviation vector $\\sigma $ of the frame-level feature vectors over the $T$ frames. The third block takes the statistics as the input and produces utterance-level representations using a number of stacked fully-connected hidden layers. The output of the DNN extractor is a softmax layer, and each of the nodes corresponds to one speaker identity. This DNN extractor is trained based on a cross-entropy loss function using the supplied speaker labels to get the targets. Consider there are $N$ training segments and $S$ speakers, the cross-entropy can be written as",
+      "where $\\theta $ represents the parameters of the DNN and $\\delta \\left(\\cdot \\right)$ represents the Kronecker delta function. $s_{k}^{\\left(n\\right)}$ represents that the speaker label for segment $n$ is $s_{k}$. After the DNN is trained, the utterance-level embeddings, $\\mathbf {e}_{d}$, are normally extracted from the output of the affine component that is with or without the nonlinear activation function applied of one hidden layer in the third block of the DNN BIBREF11, BIBREF12."
+    ],
+    [
+      "After the speaker embeddings are extracted, they are used to train a PLDA model that yields the score (distance) between speaker embeddings. The training of the PLDA models aims to maximise the between-speaker difference and minimise the within-speaker variation, typically using expectation maximisation (EM). A number of variants of PLDA models have been introduced into the speaker verification task based on this \u201cstandard\" PLDA BIBREF5: two-covariance PLDA BIBREF21 and heavy-tailed PLDA BIBREF6. The variant implemented in the Kaldi toolkit BIBREF19, and used in this work, follows BIBREF22 and is similar to the two-covariance model. This model can be written as",
+      "",
+      "where $\\mathbf {e}$ is the speaker embedding. The vector $\\mathbf {y}$ represents the underlying speaker vector and $\\mu $ represents its mean. $\\mathbf {z}$ is the Gaussian noise vector. For speaker verification tasks, estimation of this PLDA model can be performed by estimating the between-speaker covariance matrix, $\\Gamma $, and within-speaker covariance matrix, $\\Lambda $, using the EM algorithm.",
+      "PLDA is a powerful approach to classifying speakers given a large amounts of training data with speaker labels BIBREF23, BIBREF24, BIBREF25. However, large amounts of labelled training data may not be available in the domain of interest such as the one considered in this paper, the non-native speaker verification. One approach to alleviate this problem is to do adaptation from a pre-trained out-of-domain model to the target domain. There are a number of methods for adapting the PLDA model in both supervised and unsupervised manners BIBREF26, BIBREF25. The Kaldi toolkit implements an unsupervised adaptation method which does not require knowledge of speaker labels BIBREF19. This method aims at adapting $\\Gamma $ and $\\Lambda $ of the out-of-domain PLDA model to better match the total covariance of the in-domain adaptation data."
+    ],
+    [
+      "The Business Language Testing Service (BULATS) test of Cambridge Assessment English BIBREF27 is a multi-level computer-based English test. It consists of read speech and free-speaking components, with the candidate responding to prompts. The BULATS spoken test has five sections, all with materials appropriate to business scenarios. The first section (A) contains eight questions about the candidate and their work. The second section (B) is a read-aloud section in which the candidates are asked to read eight sentences. The last three sections (C, D and E) have longer utterances of spontaneous speech elicited by prompts. In section C the candidates are asked to talk for one minute about a prompted business related topic. In section D, the candidate has one minute to describe a business situation illustrated in graphs or charts, such as pie or bar charts. The prompt for section E asks the candidate to imagine they are in a specific conversation and to respond to questions they may be asked in that situation (e.g. advice about planning a conference). This section is made up of 5x 20 seconds responses.",
+      "Each section is scored between 0 and 6; the overall score is therefore between 0 and 30. This score is then mapped into Common European Framework of Reference (CEFR) BIBREF28 language proficiency levels, which is an international standard for describing language ability on a six-level scale. Each candidate is finally assigned a \u201cgrade\", ranging from minimal (A1) and basic (A2) command, through limited but effective (B1) and generally effective (B2) command, to good operational (C1) and fully operational (C2) command of the spoken language.",
+      "In this work, non-native speech from the BULATS test is used as both training and test data for the speaker verification systems. To investigate how the systems generalise, data for testing is also taken from the Cambridge Assessment English Linguaskill online test. Like BULATS, this is also a multi-level test and has a similar format composed of the same five sections as described before but assesses general English ability."
+    ],
+    [
+      "A set of 8,480 candidates from BULATS was used for training. The approximately 280 hours of speech covers a wide range of more than 70 different L1s. There are 15 major L1s with more than 100 candidates for each, including Tamil, Gujarati, Hindi, Telugu, Malayalam, Bengali, Spanish, Russian, Kannada, Portuguese, French, etc. Data augmentation was applied to the training set, and each recording was processed with a randomly selected source from \u201cbabble\", \u201cmusic\", \u201cnoise\" and \u201creverb\" BIBREF12, which roughly doubled the size of the original training set. Another set of 8,318 BULATS candidates was used as one test set to evaluate the system performance. There are 7 major L1s in this set, each of which has more than 100 candidates: Spanish, Thai, Tamil, Arabic, Vietnamese, Polish and Dutch. There are no overlapping candidates between the BULATS training and test sets. The other test set of 2,540 candidates came from the Linguaskill test, of which there are 6 major L1s each with more than 100 candidates: Hindi, Portuguese, Japanese, Spanish, Thai and Vietnamese. Each of the training set and two test sets was fairly gender balanced, with approximately one third of candidates graded as B1, one third graded as B2, and the rest graded as A1, A2, C1, or C2, according to CEFR ability levels. For each test set candidate, responses from sections A and B were used for speaker enrolment (approximately 180s), while the more challenging free-speaking sections C, D, and E were used for whole section-level verification (approximately 60s for each section)."
+    ],
+    [
+      "Gender is generally considered an important speaker attribute, and impostor trials were first selected from the same gender group as the reference speaker, as commonly done in standard speaker verification tasks. This resulted in a total of 104.8 million verification trials for the BULATS test set and 9.7 million trials for the Linguaskill test set.",
+      "An i-vector/PLDA system and an x-vector/PLDA system were first trained on the \u201cin-domain\" BULATS training set. For the i-vector system, 13-dimensional perceptual linear predictive (PLP) features were extracted using the HTK toolkit BIBREF29 with a frame-length of 25ms. A UBM of 2,048 mixture components was first trained with full-covariance matrices, and then 600-dimensional i-vectors were extracted for both training and test sets. For the x-vector system, 40-dimensional filterbank features were also extracted using HTK with a frame-length of 25ms. DNN configurations were the same as used in BIBREF12, and 512-dimensional x-vectors were extracted from the affine component of the segment-level layer immediately following the statistics pooling layer.",
+      "Performance of the two baseline systems is shown in Table TABREF9 in terms of equal error rate (EER). The x-vector system yielded lower EERs on both BULATS and Linguaskill test sets.",
+      "In addition to the models trained on the BULATS data, it is also interesting to investigate the application of \u201cout-of-the-box\" models for standard speaker verification tasks to this non-native speaker verification task as there is limited amounts of non-native learner English data that is publicly available. In this paper, the Kaldi-released BIBREF19 VoxCeleb x-vector/PLDA system was used as imported models, which was trained on augmented VoxCeleb 1 BIBREF17 and VoxCeleb 2 BIBREF18. There are more than 7,000 speakers in the VoxCeleb dataset with more than 2,000 hours of audio data, making it the largest publicly available speaker recognition dataset. 30 dimensional mel-frequency cepstral coefficients (MFCCs) were used as input features and system configurations were the same as the BULATS x-vector/PLDA one. It can be seen from Table TABREF10 that these out-of-domain models gave worse performance than baseline systems trained on a far smaller amount of BULATS data due to domain mismatch. Thus, two kinds of in-domain adaptation strategies were explored to make use of the BULATS training set: PLDA adaptation and x-vector extractor fine-tuning. For PLDA adaptation, x-vectors of the BULATS training set were first extracted using the VoxCeleb-trained x-vector extractor, and then employed to adapt the VoxCeleb-trained PLDA model with their mean and variance. For x-vector extractor fine-tuning, with all other layers of the VoxCeleb-trained model kept still, the output layer was re-initialised using the BULATS training set with the number of targets adjusted accordingly, and then all layers were fine-tuned on the BULATS training set. Here the PLDA adaptation system is referred to as X1 and the extractor fine-tuning system is referred to as X2. Both adaptation approaches can yield good performance gains as can be seen from Table TABREF10. PLDA adaptation is a straightforward yet effective way, while the system with x-vector extractor fine-tuning gave slightly lower EERs on both BULATS and Linguaskill test sets by virtue of a relatively \u201cin-domain\" extractor prior to the PLDA back-end.",
+      "Detection Error Tradeoff (DET) curves of the four x-vector/PLDA systems on the BULATS test set were illustrated in Figure FIGREF11. It can be seen that, both adaptation systems outperformed the original VoxCeleb-trained system in any threshold of the false alarm (FA) probability and the miss (MS) probability. The extractor fine-tuning system only gave higher MS probability than the PLDA adapted one with FA probability below 0.4%, while for a large range of FA probabilities above 0.4%, the extractor fine-tuning system outperformed the PLDA adapted one.",
+      "Furthermore, by leveraging the large-scale VoxCeleb dataset, both adaptation systems produced lower EERs than baseline systems solely trained on BULATS data, especially the extractor fine-tuning one, which gave a reduction rate of 26$\\%$ in EER over the baseline x-vector/PLDA system on the BULATS test set. It can also be seen from Figure FIGREF11 that, the extractor fine-tuning system gave consistently better performance than the baseline systems for almost any threshold of FA and MS."
+    ],
+    [
+      "As mentioned in Section SECREF8, gender is an important attribute when selecting impostors. For the non-native English speech data considered in this work, there are two additional attributes that may significantly impact performance, the candidate speaking ability (grade) and L1. In this section, the impact of both attributes on verification performance is analysed on the BULATS test set using the extractor fine-tuning system (X2) detailed in Section SECREF8 with impostors selected from the same gender group as the reference speaker. Taking EER as the operating threshold, both grade and L1 breakdown are investigated with respect to the number of impostor trials resulting in false alarm (FA) errors.",
+      "As there were only a small number of speakers graded as C1 or C2 in the BULATS test set, the two grade groups were merged into one group as C in the following analysis. Also for a fair comparison, 200 speakers were randomly selected (roughly gender balanced) for each grade group from the BULATS test set, and the grade breakdown is shown in Table TABREF13. For lower grades, impostor trials from the grade group of A1 dominated FA errors as A1 speakers tend to speak short utterances, which is more challenging for the systems. For higher grades (B2 and C), impostor trials from the grade group of C constituted a larger portion of FA errors probably due to the fact that C speakers tend to speak long utterances in a more \u201cnative\" way and they are also similar to B2 speakers.",
+      "",
+      "The numbers of speakers from different L1 groups also varied in the BULATS test set. For a fair comparison, 200 speakers were randomly selected (roughly gender balanced) for each of 6 major L1s. The L1 breakdown is shown in Table TABREF14, where impostor trials from the same L1 group as the reference speaker generally dominated FA errors. English learners from the same L1 group tend to have similar accents when speaking English, which makes them more confusable to speaker verification systems compared to learners from a different L1 group. Particularly, impostors of Thai L1 constitute a considerable portion of FA errors for each L1, as A1 and A2 speakers dominate Thai L1 in the BULATS test set, which is different from other L1s where B1 and B2 speakers dominate."
+    ],
+    [
+      "Based on the analysis in the previous section, the impact of speaker attributes beyond gender, the grade and L1, were used as additional restrictions on the imposter set selection. The following forms of impostor selection were examined:",
+      "gender, impostors from the same gender group as the reference speaker, as in Section SECREF8;",
+      "",
+      "grade, impostors from the same grade group as the reference speaker;",
+      "$>$grade, impostors from higher grade groups than the reference speaker if the grade of the reference speaker is lower than C, otherwise from C; this case is of practical interest for impersonation in spoken language tests;",
+      "L1, impostors from the same L1 group as the reference speaker;",
+      "The number of total verification trials decreases with further restriction on impostors, which is shown in Table TABREF20. Table TABREF21 shows the impact on EER of restricting the possible set of impostors according to gender, L1 or grade on both BULATS and Linguaskill test sets. Due to the lack of data for each L1 or grade, X1 and X2 systems that are adapted or fine-tuned on all of the BULATS training set are used for verification. As expected, restricting possible impostors according to speaker attributes yielded higher EERs as the percentage of impostors \u201cclose\" to the reference speaker increased. Take gender as the starting point, which is the configuration used in previous experiments in Section SECREF8. Further restricting the set of impostors to L1 again increased EERs agreeing with the results shown in Table TABREF14, similarly to grade. An interesting result in terms of handling impersonation is that, if the set of impostors is further restricted to $>$grade, EERs decrease compared to simply restricted to gender. The highest EER for both systems was achieved by restricted to gender+L1+grade, which indicates that all these are important speaker attributes of non-native data. The gender+L1+$>$grade case is more related to practical scenarios of impersonation, since it is more likely that a candidate chooses a substitute from the same gender and L1 group but speak the target language better to impersonate him/herself in order to obtain a higher grade in a spoken language test.",
+      "",
+      "",
+      "For the impersonation scenario where the impostor trials are restricted to gender+L1+$>$grade, the DET curves for all systems including the unadapted VoxCeleb and BULATS trained systems are shown in Figure FIGREF22 for the BULATS test set. This allows the overall distribution of FA and MS errors for the aforementioned systems to be evaluated. It can be seen that, compared with the fine-tuned X2 system, the PLDA-adapted X1 system had a lower MS probability when the FA probability was low and had a higher MS probability when the FA probability was high. This implies that the X1 system tends to accept imposters as reference speakers while the X2 system tends to reject reference speakers as impostors. For malpractice candidate impersonation in spoken language tests, the X2 system may have a high cost as it may incorrectly identify malpractice in valid candidates. This would require manual checks to confirm this classification. In contrast, the X1 system may result in a lower level of security because it has a higher chance of misidentifying the candidate who is impersonating another. Based on these complementary trends, a score-level linear combination of the two systems was performed with weights of 0.7 and 0.3 for X1 and X2 systems, respectively. The combination system gave consistently better performance for a wide range of FA and MS probabilities than the aforementioned systems with an EER of 0.58% on the BULATS test set, as demonstrated in Figure FIGREF22. The same trend was also observed at these weightings on the Linguaskill test set with an EER of 0.72% for the combination system, approximately 8% relative reduction in EER from the X1 system. Thus, the combination of the two adapted systems making use of both large-scale VoxCeleb data and in-domain BULATS data, can serve as a sensible configuration for impersonation detection in spoken language tests.",
+      "",
+      ""
+    ],
+    [
+      "This paper has investigated malpractice in the form of candidate impersonation for spoken language assessment. This task has close relationships to standard speaker verification, but applied to the domain of non-native speech. Advanced neural network based speaker verification systems were built on both limited non-native spoken English data from the BULATS test, and a large standard corpus VoxCeleb. For the configuration used all systems yielded relatively low EERs of less than 1%. Though built with only limited data the systems trained on just BULATS systems outperformed the \u201cout-of-the-box\" VoxCeleb based system. However by adapting both the PLDA model and the deep speaker representation, the VoxCeleb-based systems could yield lower EERs. The attributes of the \u201cimpostors\" was then analysed in terms of both the impostor's grade and L1. As expected, L1 was the most important attribute of the impostor selected, though the grade did also influence performance. With the most likely scenario of impersonation by restricting impostors to be from the same gender, same L1, and higher grade group, the combination of the two adapted systems gave consistently better performance for a wide range of FA and MS probabilities, making it a sensible configuration for impersonation detection."
+    ]
+  ]
+}
+```
\ No newline at end of file
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index 0000000000000000000000000000000000000000..2991126820b7634ee98f61a81ff03162bf0c46ff
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+++ b/qasper-1863/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Domain Adaptation for Neural Networks by Parameter Augmentation
+
+Question: How many examples are there in the source domain?
\ No newline at end of file
diff --git a/qasper-1864/instruction.md b/qasper-1864/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..b83d26bcb06fdc4506103ccb65b21650c43dfcd2
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@@ -0,0 +1,112 @@
+Name of Paper: Domain Adaptation for Neural Networks by Parameter Augmentation
+
+Question: How many examples are there in the target domain?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Domain adaptation and language generation",
+    "Domain adaptation for language generation",
+    "Standard and baseline methods",
+    "Revisiting the feature augmentation method",
+    "Proposed method",
+    "Experiments",
+    "Adaptation to food domain captioning",
+    "Adaptation between MS COCO and Flickr30K",
+    "Answer sentence selection",
+    "Conclusion and Future Work"
+  ],
+  "paragraphs": [
+    [
+      "Domain adaptation is a machine learning paradigm that aims at improving the generalization performance of a new (target) domain by using a dataset from the original (source) domain. Suppose that, as the source domain dataset, we have a captioning corpus, consisting of images of daily lives and each image has captions. Suppose also that we would like to generate captions for exotic cuisine, which are rare in the corpus. It is usually very costly to make a new corpus for the target domain, i.e., taking and captioning those images. The research question here is how we can leverage the source domain dataset to improve the performance on the target domain.",
+      "As described by Daum\u00e9 daume:07, there are mainly two settings of domain adaptation: fully supervised and semi-supervised. Our focus is the supervised setting, where both of the source and target domain datasets are labeled. We would like to use the label information of the source domain to improve the performance on the target domain.",
+      "Recently, Recurrent Neural Networks (RNNs) have been successfully applied to various tasks in the field of natural language processing (NLP), including language modeling BIBREF0 , caption generation BIBREF1 and parsing BIBREF2 .",
+      "For neural networks, there are two standard methods for supervised domain adaptation BIBREF3 . The first method is fine tuning: we first train the model with the source dataset and then tune it with the target domain dataset BIBREF4 , BIBREF5 . Since the objective function of neural network training is non-convex, the performance of the trained model can depend on the initialization of the parameters. This is in contrast with the convex methods such as Support Vector Machines (SVMs). We expect that the first training gives a good initialization of the parameters, and therefore the latter training gives a good generalization even if the target domain dataset is small. The downside of this approach is the lack of the optimization objective.",
+      "The other method is to design the neural network so that it has two outputs. The first output is trained with the source dataset and the other output is trained with the target dataset, where the input part is shared among the domains. We call this method dual outputs. This type of network architecture has been successfully applied to multi-task learning in NLP such as part-of-speech tagging and named-entity recognition BIBREF6 , BIBREF7 .",
+      "In the NLP community, there has been a large body of previous work on domain adaptation. One of the state-of-the-art methods for the supervised domain adaptation is feature augmentation BIBREF8 . The central idea of this method is to augment the original features/parameters in order to model the source specific, target specific and general behaviors of the data. However, it is not straight-forward to apply it to neural network models in which the cost function has a form of log probabilities.",
+      "In this paper, we propose a new domain adaptation method for neural networks. We reformulate the method of daume:07 and derive an objective function using convexity of the loss function. From a high-level perspective, this method shares the idea of feature augmentation. We use redundant parameters for the source, target and general domains, where the general parameters are tuned to model the common characteristics of the datasets and the source/target parameters are tuned for domain specific aspects.",
+      "In the latter part of this paper, we apply our domain adaptation method to a neural captioning model and show performance improvement over other standard methods on several datasets and metrics. In the datasets, the source and target have different word distributions, and thus adaptation of output parameters is important. We augment the output parameters to facilitate adaptation. Although we use captioning models in the experiments, our method can be applied to any neural networks trained with a cross-entropy loss."
+    ],
+    [
+      "There are several recent studies applying domain adaptation methods to deep neural networks. However, few studies have focused on improving the fine tuning and dual outputs methods in the supervised setting.",
+      "sun2015return have proposed an unsupervised domain adaptation method and apply it to the features from deep neural networks. Their idea is to minimize the domain shift by aligning the second-order statistics of source and target distributions. In our setting, it is not necessarily true that there is a correspondence between the source and target input distributions, and therefore we cannot expect their method to work well.",
+      "wen2016multi have proposed a procedure to generate natural language for multiple domains of spoken dialogue systems. They improve the fine tuning method by pre-trainig with synthesized data. However, the synthesis protocol is only applicable to the spoken dialogue system. In this paper, we focus on domain adaptation methods which can be applied without dataset-specific tricks.",
+      "yang2016multitask have conducted a series of experiments to investigate the transferability of neural networks for NLP. They compare the performance of two transfer methods called INIT and MULT, which correspond to the fine tuning and dual outputs methods in our terms. They conclude that MULT is slightly better than or comparable to INIT; this is consistent with our experiments shown in section \"Experiments\" . Although they obtain little improvement by transferring the output parameters, we achieve significant improvement by augmenting parameters in the output layers."
+    ],
+    [
+      "We start with the basic notations and formalization for domain adaptation. Let $\\mathcal {X}$ be the set of inputs and $\\mathcal {Y}$ be the outputs. We have a source domain dataset $D^s$ , which is sampled from some distribution $\\mathcal {D}^s$ . Also, we have a target domain dataset $D^t$ , which is sampled from another distribution $\\mathcal {D}^t$ . Since we are considering supervised settings, each element of the datasets has a form of input output pair $(x,y)$ . The goal of domain adaptation is to learn a function $f : \\mathcal {X} \\rightarrow \\mathcal {Y}$ that models the input-output relation of $D^t$ . We implicitly assume that there is a connection between the source and target distributions and thus can leverage the information of the source domain dataset. In the case of image caption generation, the input $x$ is an image (or the feature vector of an image) and $\\mathcal {Y}$0 is the caption (a sequence of words).",
+      "In language generation tasks, a sequence of words is generated from an input $x$ . A state-of-the-art model for language generation is LSTM (Long Short Term Memory) initialized by a context vector computed by the input BIBREF1 . LSTM is a particular form of recurrent neural network, which has three gates and a memory cell. For each time step $t$ , the vectors $c_t$ and $h_t$ are computed from $u_t, c_{t-1}$ and $h_{t-1}$ by the following equations: $\n&i = \\sigma (W_{ix} u_t + W_{ih} h_{t-1}) \\\\\n&f = \\sigma (W_{fx} u_t + W_{fh} h_{t-1}) \\\\\n&o = \\sigma (W_{ox} u_t + W_{oh} h_{t-1}) \\\\\n&g = \\tanh (W_{gx} u_t + W_{gh} h_{t-1}) \\\\\n&c_t = f \\odot c_{t-1} + i \\odot g \\\\\n&h_t = o \\odot \\tanh (c_t),\n$ ",
+      " where $\\sigma $ is the sigmoid function and $\\odot $ is the element-wise product. Note that all the vectors in the equations have the same dimension $n$ , called the cell size. The probability of the output word at the $t$ -th step, $y_t$ , is computed by ",
+      "$$p(y_t|y_1,\\ldots ,y_{t-1},x) = {\\rm Softmax}(W h_t), $$   (Eq. 1) ",
+      "where $W$ is a matrix with a size of vocabulary size times $n$ . We call this matrix as the parameter of the output layer. The input $u_t$ is given by the word embedding of $y_{t-1}$ .",
+      "To generate a caption, we first compute feature vectors of the image, and put it into the beginning of the LSTM as ",
+      "$$u_{0} = W_{0} {\\rm CNN}(x),$$   (Eq. 2) ",
+      "where $W_0$ is a tunable parameter matrix and ${\\rm CNN}$ is a feature extractor usually given by a convolutional neural network. Output words, $y_t$ , are selected in order and each caption ends with special symbol <EOS>. The process is illustrated in Figure 1 .",
+      "Note that the cost function for the generated caption is $\n\\log p(y|x) = \\sum _{t} \\log p(y_t|y_1,\\ldots ,y_{t-1}, x),\n$ ",
+      "where the conditional distributions are given by Eq. ( 1 ). The parameters of the model are optimized to minimize the cost on the training dataset. We also note that there are extensions of the models with attentions BIBREF9 , BIBREF10 , but the forms of the cost functions are the same."
+    ],
+    [
+      "In this section, we review standard domain adaptation techniques which are applicable to the neural language generation. The performance of these methods is compared in the next section."
+    ],
+    [
+      "A trivial method of domain adaptation is simply ignoring the source dataset, and train the model using only the target dataset. This method is hereafter denoted by TgtOnly. This is a baseline and any meaningful method must beat it.",
+      "Another trivial method is SrcOnly, where only the source dataset is used for the training. Typically, the source dataset is bigger than that of the target, and this method sometimes works better than TgtOnly.",
+      "Another method is All, in which the source and target datasets are combined and used for the training. Although this method uses all the data, the training criteria enforce the model to perform well on both of the domains, and therefore the performance on the target domain is not necessarily high. ",
+      "An approach widely used in the neural network community is FineTune. We first train the model with the source dataset and then it is used as the initial parameters for training the model with the target dataset. The training process is stopped in reference to the development set in order to avoid over-fitting. We could extend this method by posing a regularization term (e.g. $l_2$ regularization) in order not to deviate from the pre-trained parameter. In the latter experiments, however, we do not pursue this direction because we found no performance gain. Note that it is hard to control the scales of the regularization for each part of the neural net because there are many parameters having different roles.",
+      "Another common approach for neural domain adaptation is Dual. In this method, the output of the network is \u201cdualized\u201d. In other words, we use different parameters $W$ in Eq. ( 1 ) for the source and target domains. For the source dataset, the model is trained with the first output and the second for the target dataset. The rest of the parameters are shared among the domains. This type of network design is often used for multi-task learning."
+    ],
+    [
+      "Before proceeding to our new method, we describe the feature augmentation method BIBREF8 from our perspective. let us start with the feature augmentation method.",
+      "Here we consider the domain adaptation of a binary classification problem. Suppose that we train SVM models for the source and target domains separately. The objective functions have the form of $\n\\frac{1}{n_s} \\sum _{(x,y) \\in \\mathcal {D}_s}\n\\max (0, 1 - y(w_s^T \\Phi (x))) + \\lambda \\Vert w_s \\Vert ^2 \\\\\n\\frac{1}{n_t} \\sum _{(x,y) \\in \\mathcal {D}_t}\n\\max (0, 1 - y(w_t^T \\Phi (x))) + \\lambda \\Vert w_t \\Vert ^2 ,\n$ ",
+      " where $\\Phi (x)$ is the feature vector and $w_s, w_t$ are the SVM parameters. In the feature augmentation method, the parameters are decomposed to $w_s = \\theta _g + \\theta _s$ and $w_t = \\theta _g + \\theta _t$ . The optimization objective is different from the sum of the above functions: $\n& \\frac{1}{n_s}\n\\sum _{(x,y) \\in \\mathcal {D}_s} \\max (0, 1 - y(w_s^T \\Phi (x))) \\\\\n&+\\lambda (\\Vert \\theta _g \\Vert ^2 + \\Vert \\theta _s \\Vert ^2 ) \\\\\n&+ \\frac{1}{n_t} \\sum _{(x,y) \\in \\mathcal {D}_t} \\max (0, 1 - y(w_t^T \\Phi (x))) \\\\\n&+ \\lambda (\\Vert \\theta _g \\Vert ^2 + \\Vert \\theta _t \\Vert ^2 ),\n$ ",
+      " where the quadratic regularization terms $\\Vert \\theta _g + \\theta _s \\Vert ^2$ and $\\Vert \\theta _g + \\theta _t \\Vert ^2$ are changed to $\\Vert \\theta _g \\Vert ^2 + \\Vert \\theta _s \\Vert ^2$ and $\\Vert \\theta _g \\Vert ^2 + \\Vert \\theta _t \\Vert ^2$ , respectively. Since the parameters $\\theta _g$ are shared, we cannot optimize the problems separately.",
+      "This change of the objective function can be understood as adding additional regularization terms $\n2(\\Vert \\theta _g \\Vert ^2 + \\Vert \\theta _t \\Vert ^2 ) - \\Vert \\theta _g + \\theta _t \\Vert ^2, \\\\\n2(\\Vert \\theta _g \\Vert ^2 + \\Vert \\theta _s \\Vert ^2 ) - \\Vert \\theta _g + \\theta _s \\Vert ^2.\n$ ",
+      " We can easily see that those are equal to $\\Vert \\theta _g - \\theta _t \\Vert ^2$ and $\\Vert \\theta _g - \\theta _s \\Vert ^2$ , respectively and thus this additional regularization enforces $\\theta _g$ and $\\theta _t$ (and also $\\theta _g$ and $\\theta _s$ ) not to be far away. This is how the feature augmentation method shares the domain information between the parameters $w_s$ and $w_t$ ."
+    ],
+    [
+      "Although the above formalization is for an SVM, which has the quadratic cost of parameters, we can apply the idea to the log probability case.",
+      "In the case of RNN language generation, the loss function of each output is a cross entropy applied to the softmax output ",
+      "$$-\\log & p_s(y|y_1, \\ldots , y_{t-1}, x) \\nonumber \\\\\n&= -w_{s,y}^T h + \\log Z(w_s;h), $$   (Eq. 8) ",
+      " where $Z$ is the partition function and $h$ is the hidden state of the LSTM computed by $y_0, \\ldots , y_{t-1}$ and $x$ . Again we decompose the word output parameter as $w_s = \\theta _g + \\theta _s$ . Since $\\log Z$ is convex with respect to $w_s$ , we can easily show that the Eq. ( 8 ) is bounded above by $\n-&\\theta _{g,y}^T h + \\frac{1}{2} \\log Z(2 \\theta _g;x) \\\\\n&-\\theta _{s,y}^T h +\\frac{1}{2} \\log Z(2 \\theta _s;x).\n$ ",
+      " The equality holds if and only if $\\theta _g = \\theta _s$ . Therefore, optimizing this upper-bound effectively enforces the parameters to be close as well as reducing the cost.",
+      "The exact same story can be applied to the target parameter $w_t = \\theta _g + \\theta _t$ . We combine the source and target cost functions and optimize the sum of the above upper-bounds. Then the derived objective function is $\n\\frac{1}{n_s} \\sum _{(x,y) \\in \\mathcal {D}_s}\n[\n-\\theta _{g,y}^T h& + \\frac{1}{2} \\log Z(2 \\theta _g;x) \\\\\n&-\\theta _{s,y}^T h + \\frac{1}{2} \\log Z(2 \\theta _s;x)\n]\n\\\\\n+ \\frac{1}{n_t} \\sum _{(x,y) \\in \\mathcal {D}_t}\n[\n-\\theta _{g,y}^T h &+ \\frac{1}{2} \\log Z(2 \\theta _g;x) \\\\\n& -\\theta _{t,y}^T h + \\frac{1}{2} \\log Z(2 \\theta _t;x)\n].\n$ ",
+      "If we work with the sum of the source and target versions of Eq. ( 8 ), the method is actually the same as Dual because the parameters $\\theta _g$ is completely redundant. The difference between this objective and the proposed upper bound works as a regularization term, which results in a good generalization performance.",
+      "Although our formulation has the unique objective, there are three types of cross entropy loss terms given by $\\theta _g$ , $\\theta _s$ and $\\theta _t$ . We denote them by $\\ell (\\theta _g), \\ell (\\theta _s)$ and $\\ell (\\theta _t)$ , respectively. For the source data, the sum of general and source loss terms is optimized, and for the target dataset the sum of general and target loss terms is optimized.",
+      "The proposed algorithm is summarized in Algorithm \"Proposed method\" . Note that $\\theta _h$ is the parameters of the LSTM except for the output part. In one epoch of the training, we use all data once. We can combine any parameter update methods for neural network training such as Adam BIBREF11 .",
+      "boxruled [t] Proposed Method True Select a minibatch of data from source or target dataset",
+      "source Optimize $\\ell (\\theta _g) + \\ell (\\theta _s)$ with respect to $\\theta _g, \\theta _s, \\theta _h$ for the minibatch Optimize $\\ell (\\theta _g) + \\ell (\\theta _t)$ with respect to $\\theta _g, \\theta _t, \\theta _h$ for the minibatch development error increases break Compute $w_t = \\theta _g + \\theta _t$ and $w_s = \\theta _g + \\theta _s$ . Use these parameters as the output parameters for each domain."
+    ],
+    [
+      "We have conducted domain adaptation experiments on the following three datasets. The first experiment focuses on the situation where the domain adaptation is useful. The second experiment show the benefit of domain adaptation for both directions: from source to target and target to source. The third experiment shows an improvement in another metric. Although our method is applicable to any neural network with a cross entropy loss, all the experiments use caption generation models because it is one of the most successful neural network applications in NLP."
+    ],
+    [
+      "This experiment highlights a typical scenario in which domain adaptation is useful. Suppose that we have a large dataset of captioned images, which are taken from daily lives, but we would like to generate high quality captions for more specialized domain images such as minor sports and exotic food. However, captioned images for those domains are quite limited due to the annotation cost. We use domain adaptation methods to improve the captions of the target domain.",
+      "To simulate the scenario, we split the Microsoft COCO dataset into food and non-food domain datasets. The MS COCO dataset contains approximately 80K images for training and 40K images for validation; each image has 5 captions BIBREF12 . The dataset contains images of diverse categories, including animals, indoor scenes, sports, and foods. We selected the \u201cfood category\u201d data by scoring the captions according to how much those are related to the food category. The score is computed based on wordnet similarities BIBREF13 . The training and validation datasets are split by the score with the same threshold. Consequently, the food dataset has 3,806 images for training and 1,775 for validation. The non-food dataset has 78,976 images for training and 38,749 for validation.",
+      "The selected pictures from the food domain are typically a close-up of foods or people eating some foods. Table 1 shows some captions from the food and non-food domain datasets. Table 2 shows the top twenty frequent words in the two datasets except for the stop words. We observe that the frequent words are largely different, but still there are some words common in both datasets.",
+      "To model the image captaining, we use LSTMs as described in the previous section. The image features are computed by the trained GoogLeNet and all the LSTMs have a single layer with 300 hidden units BIBREF14 . We use a standard optimization method, Adam BIBREF11 with hyper parameters $\\alpha =0.001$ , $\\beta _1=0.9$ and $\\beta _2=0.999$ . We stop the training based on the loss on the development set. After the training we generate captions by beam search, where the size of the beam is 5. These settings are the same in the latter experiments.",
+      "We compare the proposed method with other baseline methods. For all the methods, we use Adam with the same hyper parameters. In FineTune, we did not freeze any parameters during the target training. In Dual, all samples in source and target datasets are weighted equally.",
+      "We evaluated the performance of the domain adaptation methods by the qualities of the generated captions. We used BLEU, METOR and CIDEr scores for the evaluation. The results are summarized in Table 3 . We see that the proposed method improves in most of the metrics. The baseline methods SrcOnly and TgtOnly are worse than other methods, because they use limited data for the training. Note that the CIDEr scores correlate with human evaluations better than BLEU and METOR scores BIBREF15 .",
+      "Generated captions for sample images are shown in Table 4 . In the first example, All fails to identify the chocolate cake because there are birds in the source dataset which somehow look similar to chocolate cake. We argue that Proposed learns birds by the source parameters and chocolate cakes by the target parameters, and thus succeeded in generating appropriate captions."
+    ],
+    [
+      "In this experiment, we explore the benefit of adaptation from both sides of the domains. Flickr30K is another captioning dataset, consisting of 30K images, and each image has five captions BIBREF16 . Although the formats of the datasets are almost the same, the model trained by the MS COCO dataset does not work well for the Flickr 30K dataset and vice versa. The word distributions of the captions are considerably different. If we ignore words with less than 30 counts, MS COCO has 3,655 words and Flicker30K has 2732 words; and only 1,486 words are shared. Also, the average lengths of captions are different. The average length of captions in Flickr30K is 12.3 while that of MS COCO is 10.5.",
+      "The first result is the domain adaptation from MS COCO to Flickr30K, summarized in Table 5 . Again, we observe that the proposed method achieves the best score among the other methods. The difference between All and FineTune is bigger than in the previous setting because two datasets have different captions even for similar images. The scores of FineTune and Dual are at almost the same level.",
+      "The second result is the domain adaptation from Flickr30K to MS COCO shown in Table 6 . This may not be a typical situation because the number of samples in the target domain is larger than that of the source domain. The SrcOnly model is trained only with Flickr30K and tested on the MS COCO dataset. We observe that FineTune gives little benefit over TgtOnly, which implies that the difference of the initial parameters has little effect in this case. Also, Dual gives little benefit over TgtOnly, meaning that the parameter sharing except for the output layer is not important in this case. Note that the CIDEr score of Proposed is slightly improved.",
+      "Figure 2 shows the comparison of FineTune and Proposed, changing the number of the Flickr samples to 1600, 6400 and 30K. We observe that FineTune works relatively well when the target domain dataset is small."
+    ],
+    [
+      "In this experiment, we use the captioning model as an affinity measure of images and sentences. TOEIC part 1 test consists of four-choice questions for English learners. The correct choice is the sentence that best describes the shown image. Questions are not easy because there are confusing keywords in wrong choices. An example of the question is shown in Table 7 . We downloaded 610 questions from http://www.english-test.net/toeic/ listening/.",
+      "Our approach here is to select the most probable choice given the image by captioning models. We train captioning models with the images and correct answers from the training set. Since the TOEIC dataset is small, domain adaptation can give a large benefit. We compared the domain adaptation methods by the percentage of correct answers. The source dataset is 40K samples from MS COCO and the target dataset is the TOEIC dataset. We split the TOEIC dataset to 400 samples for training and 210 samples for testing.",
+      "The percentages of correct answers for each method are summarized in Table 8 . Since the questions have four choices, all methods should perform better than 25%. TgtOnly is close to the baseline because the model is trained with only 400 samples. As in the previous experiments, FineTune and Dual are better than All and Proposed is better than the other methods."
+    ],
+    [
+      "We have proposed a new method for supervised domain adaptation of neural networks. On captioning datasets, we have shown that the method outperforms other standard adaptation methods applicable to neural networks.",
+      "The proposed method only decomposes the output word parameters, where other parameters, such as word embedding, are completely shared across the domains. Augmentation of parameters in the other part of the network would be an interesting direction of future work."
+    ]
+  ]
+}
+```
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+Name of Paper: Domain Adaptation for Neural Networks by Parameter Augmentation
+
+Question: Did they only experiment with captioning task?
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+Name of Paper: MonaLog: a Lightweight System for Natural Language Inference Based on Monotonicity
+
+Question: How do they combine MonaLog with BERT?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Our System: MonaLog",
+    "Our System: MonaLog ::: Polarization (Arrow Tagging)",
+    "Our System: MonaLog ::: Knowledge Base @!START@${K}$@!END@ and Sentence Base @!START@${S}$@!END@",
+    "Our System: MonaLog ::: Generation",
+    "Our System: MonaLog ::: Generation ::: Entailments/inferences",
+    "Our System: MonaLog ::: Generation ::: Contradictory sentences",
+    "Our System: MonaLog ::: Generation ::: Neutral sentences",
+    "Our System: MonaLog ::: Search",
+    "MonaLog and SICK",
+    "MonaLog and SICK ::: The SICK Dataset",
+    "MonaLog and SICK ::: Hand-corrected SICK",
+    "Experiment 1: Using MonaLog Directly ::: Setup and Preprocessing",
+    "Experiment 1: Using MonaLog Directly ::: Results",
+    "Experiment 1: Using MonaLog Directly ::: Error Analysis",
+    "Experiment 2: Data Generation Using MonaLog",
+    "Experiment 2: Data Generation Using MonaLog ::: Setup",
+    "Experiment 2: Data Generation Using MonaLog ::: Data Filtering and Quality Control",
+    "Experiment 2: Data Generation Using MonaLog ::: Results",
+    "Conclusions and Future Work",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "There has been rapid progress on natural language inference (NLI) in the last several years, due in large part to recent advances in neural modeling BIBREF0 and the introduction of several new large-scale inference datasets BIBREF1, BIBREF2, BIBREF3, BIBREF4. Given the high performance of current state-of-the-art models, there has also been interest in understanding the limitations of these models (given their uninterpretability) BIBREF5, BIBREF6, as well as finding systematic biases in benchmark datasets BIBREF7, BIBREF8. In parallel to these efforts, there have also been recent logic-based approaches to NLI BIBREF9, BIBREF10, BIBREF11, BIBREF12, BIBREF13, which take inspiration from linguistics. In contrast to early attempts at using logic BIBREF14, these approaches have proven to be more robust. However they tend to use many rules and their output can be hard to interpret. It is sometimes unclear whether the attendant complexity is justified, especially given that such models are currently far outpaced by data-driven models and are generally hard to hybridize with data-driven techniques.",
+      "In this work, we introduce a new logical inference engine called MonaLog, which is based on natural logic and work on monotonicity stemming from vanBenthemEssays86. In contrast to the logical approaches cited above, our starting point is different in that we begin with the following two questions: 1) what is the simplest logical system that one can come up with to solve empirical NLI problems (i.e., the system with minimal amounts of primitives and background knowledge)?; and 2) what is the lower-bound performance of such a model? Like other approaches to natural logic BIBREF15, BIBREF16, our model works by reasoning over surface forms (as opposed to translating to symbolic representations) using a small inventory of monotonicity facts about quantifiers, lexical items and token-level polarity BIBREF17; proofs in the calculus are hence fully interpretable and expressible in ordinary language. Unlike existing work on natural logic, however, our model avoids the need for having expensive alignment and search sub-procedures BIBREF18, BIBREF19, and relies on a much smaller set of background knowledge and primitive relations than MacCartneyManning.",
+      "To show the effectiveness of our approach, we show results on the SICK dataset BIBREF1, a common benchmark for logic-based NLI, and find MonaLog to be competitive with more complicated logic-based approaches (many of which require full semantic parsing and more complex logical machinery). We also introduce a supplementary version of SICK that corrects several common annotation mistakes (e.g., asymmetrical inference annotations) based on previous work by kalouli2017entail,kalouli2018. Positive results on both these datasets show the ability of lightweight monotonicity models to handle many of the inferences found in current NLI datasets, hence putting a more reliable lower-bound on what results the simplest logical approach is capable of achieving on this benchmark.",
+      "Since our logic operates over surface forms, it is straightforward to hybridize our models. We investigate using MonaLog in combination with the language model BERT BIBREF20, including for compositional data augmentation, i.e, re-generating entailed versions of examples in our training sets. To our knowledge, our approach is the first attempt to use monotonicity for data augmentation, and we show that such augmentation can generate high-quality training data with which models like BERT can improve performance."
+    ],
+    [
+      "The goal of NLI is to determine, given a premise set $P$ and a hypothesis sentence $H$, whether $H$ follows from the meaning of $P$ BIBREF21. In this paper, we look at single-premise problems that involve making a standard 3-way classification decision (i.e., Entailment (H), Contradict (C) and Neutral (N)). Our general monotonicity reasoning system works according to the pipeline in Figure FIGREF1. Given a premise text, we first do Arrow Tagging by assigning polarity annotations (i.e., the arrows $\\uparrow ,\\downarrow $, which are the basic primitives of our logic) to tokens in text. These surface-level annotations, in turn, are associated with a set of natural logic inference rules that provide instructions for how to generate entailments and contradictions by span replacements over these arrows (which relies on a library of span replacement rules). For example, in the sentence All schoolgirls are on the train, the token schoolgirls is associated with a polarity annotation $\\downarrow $, which indicates that in this sentential context, the span schoolgirls can be replaced with a semantically more specific concept (e.g., happy schoolgirls) in order to generate an entailment. A generation and search procedure is then applied to see if the hypothesis text can be generated from the premise using these inference rules. A proof in this model is finally a particular sequence of edits (e.g., see Figure FIGREF13) that derive the hypothesis text from the premise text rules and yield an entailment or contradiction.",
+      "In the following sections, we provide the details of our particular implementation of these different components in MonaLog."
+    ],
+    [
+      "Given an input premise $P$, MonaLog first polarizes each of its tokens and constituents, calling the system described by BIBREF17, which performs polarization on a CCG parse tree. For example, a polarized $P$ could be every$^{\\leavevmode {\\color {red}\\uparrow }}$ linguist$^{\\leavevmode {\\color {red}\\downarrow }}$ swim$^{\\leavevmode {\\color {red}\\uparrow }}$. Note that since we ignore morphology in the system, tokens are represented by lemmas."
+    ],
+    [
+      "MonaLog utilizes two auxiliary sets. First, a knowledge base ${K}$ that stores the world knowledge needed for inference, e.g., semanticist $\\le $ linguist and swim $\\le $ move, which captures the facts that $[\\![\\mbox{\\em semanticist}]\\!]$ denotes a subset of $[\\![\\mbox{\\em linguist}]\\!]$, and that $[\\![\\mbox{\\em swim}]\\!]$ denotes a subset of $[\\![\\mbox{\\em move}]\\!]$, respectively. Such world knowledge can be created manually for the problem at hand, or derived easily from existing resources such as WordNet BIBREF22. Note that we do not blindly add all relations from WordNet to our knowledge base, since this would hinge heavily on word sense disambiguation (we need to know whether the \u201cbank\u201d is a financial institution or a river bank to extract its relations correctly). In the current implementation, we avoid this by adding x $\\le $ y or x $\\perp $ y relations only if both x and y are words in the premise-hypothesis pair. Additionally, some relations that involve quantifiers and prepositions need to be hard-coded, since WordNet does not include them: every $=$ all $=$ each $\\le $ most $\\le $ many $\\le $ a few $=$ several $\\le $ some $=$ a; the $\\le $ some $=$ a; on $\\perp $ off; up $\\perp $ down; etc.",
+      "We also need to keep track of relations that can potentially be derived from the $P$-$H$ sentence pair. For instance, for all adjectives and nouns that appear in the sentence pair, it is easy to obtain: adj + n $\\le $ n (black cat $\\le $ cat). Similarly, we have n + PP/relative clause $\\le $ n (friend in need $\\le $ friend, dog that bites $\\le $ dog), VP + advP/PP $\\le $ VP (dance happily/in the morning $\\le $ dance), and so on. We also have rules that extract pieces of knowledge from $P$ directly, e.g.: n$_1$ $\\le $ n$_2$ from sentences of the pattern every n$_1$ is a n$_2$. One can also connect MonaLog to bigger knowledge graphs or ontologies such as DBpedia.",
+      "A sentence base ${S}$, on the other hand, stores the generated entailments and contradictions."
+    ],
+    [
+      "Once we have a polarized CCG tree, and some $\\le $ relations in ${K}$, generating entailments and contradictions is fairly straightforward. A concrete example is given in Figure FIGREF13. Note that the generated $\\le $ instances are capable of producing mostly monotonicity inferences, but MonaLog can be extended to include other more complex inferences in natural logic, hence the name MonaLog. This extension is addressed in more detail in HuChenMoss."
+    ],
+    [
+      "The key operation for generating entailments is replacement, or substitution. It can be summarized as follows: 1) For upward-entailing (UE) words/constituents, replace them with words/constituents that denote bigger sets. 2) For downward-entailing (DE) words/constituents, either replace them with those denoting smaller sets, or add modifiers (adjectives, adverbs and/or relative clauses) to create a smaller set. Thus for every$^{\\leavevmode {\\color {red}\\uparrow }}$ linguist$^{\\leavevmode {\\color {red}\\downarrow }}$ swim$^{\\leavevmode {\\color {red}\\uparrow }}$, MonaLog can produce the following three entailments by replacing each word with the appropriate word from ${K}$: most$^{\\leavevmode {\\color {red}\\uparrow }}$ linguist$^{\\leavevmode {\\color {red}\\downarrow }}$ swim$^{\\leavevmode {\\color {red}\\uparrow }}$, every$^{\\leavevmode {\\color {red}\\uparrow }}$ semanticist$^{\\leavevmode {\\color {red}\\downarrow }}$ swim$^{\\leavevmode {\\color {red}\\uparrow }}$ and every$^{\\leavevmode {\\color {red}\\uparrow }}$ linguist$^{\\leavevmode {\\color {red}\\downarrow }}$ move$^{\\leavevmode {\\color {red}\\uparrow }}$. These are results of one replacement.",
+      "Performing replacement for multiple rounds/depths can easily produce many more entailments."
+    ],
+    [
+      "To generate sentences contradictory to the input sentence, we do the following: 1) if the sentence starts with \u201cno (some)\u201d, replace the first word with \u201csome (no)\u201d. 2) If the object is quantified by \u201ca/some/the/every\u201d, change the quantifier to \u201cno\u201d, and vice versa. 3) Negate the main verb or remove the negation. See examples in Figure FIGREF13."
+    ],
+    [
+      "MonaLog returns Neutral if it cannot find the hypothesis $H$ in ${S}.entailments$ or ${S}.contradictions$. Thus, there is no need to generate neutral sentences."
+    ],
+    [
+      "Now that we have a set of inferences and contradictions stored in ${S}$, we can simply see if the hypothesis is in either one of the sets by comparing the strings. If yes, then return Entailment or Contradiction; if not, return Neutral, as schematically shown in Figure FIGREF13. However, the exact-string-match method is too brittle. Therefore, we apply a heuristic. If the only difference between sentences $S_1$ and $S_2$ is in the set {\u201ca\u201d, \u201cbe\u201d, \u201cing\u201d}, then $S_1$ and $S_2$ are considered semantically equivalent.",
+      "The search is implemented using depth first search, with a default depth of 2, i.e. at most 2 replacements for each input sentence. At each node, MonaLog \u201cexpands\u201d the sentence (i.e., an entailment of its parent) by obtaining its entailments and contradictions, and checks whether $H$ is in either set. If so, the search is terminated; otherwise the systems keeps searching until all the possible entailments and contradictions up to depth 2 have been visited."
+    ],
+    [
+      "We perform two experiments to test MonaLog. We first use MonaLog to solve the problems in a commonly used natural language inference dataset, SICK BIBREF1, comparing our results with previous systems. Second, we test the quality of the data generated by MonaLog. To do this, we generate more training data (sentence pairs) from the SICK training data using our system, and performe fine-tuning on BERT BIBREF20, a language model based on the transformer architecture BIBREF23, with the expanded dataset. In all experiments, we use the Base, Uncased model of BERT."
+    ],
+    [
+      "The SICK BIBREF1 dataset includes around 10,000 English sentence pairs that are annotated to have either \u201cEntailment\u201d, \u201cNeutral\u201d or \u201cContradictory\u201d relations. We choose SICK as our testing ground for several reasons. First, we want to test on a large-scale dataset, since we have shown that a similar model BIBREF24 reaches good results on parts of the smaller FraCaS dataset BIBREF25. Second, we want to make our results comparable to those of previous logic-based models such as the ones described in BIBREF26, BIBREF27, BIBREF11, BIBREF13, which were also tested on SICK. We use the data split provided in the dataset: 4,439 training problems, 4,906 test problems and 495 trial problems, see Table TABREF16 for examples."
+    ],
+    [
+      "There are numerous issues with the original SICK dataset, as illustrated by BIBREF28, BIBREF29.",
+      "They first manually checked 1,513 pairs tagged as \u201cA entails B but B is neutral to A\u201d (AeBBnA) in the original SICK, correcting 178 pairs that they considered to be wrong BIBREF28. Later, BIBREF29 extracted pairs from SICK whose premise and hypothesis differ in only one word, and created a simple rule-based system that used WordNet information to solve the problem. Their WordNet-based method was able to solve 1,651 problems, whose original labels in SICK were then manually checked and corrected against their system's output. They concluded that 336 problems are wrongly labeled in the original SICK. Combining the above two corrected subsets of SICK, minus the overlap, results in their corrected SICK dataset, which has 3,016 problems (3/10 of the full SICK), with 409 labels different from the original SICK (see breakdown in Table TABREF19). 16 of the corrections are in the trial set, 197 of them in the training set and 196 in the test set. This suggests that more than one out of ten problems in SICK are potentially problematic. For this reason, two authors of the current paper checked the 409 changes. We found that only 246 problems are labeled the same by our team and by BIBREF29. For cases where there is disagreement, we adjudicated the differences after a discussion.",
+      "We are aware that the partially checked SICK (by two teams) is far from ideal. We therefore present results for two versions of SICK for experiment 1 (section SECREF4): the original SICK and the version corrected by our team. For the data augmentation experiment in section SECREF5, we only performed fine-tuning on the corrected SICK. As shown in a recent SICK annotation experiment by kalouli2019explaining, annotation is a complicated issue influenced by linguistic and non-linguistic factors. We leave checking the full SICK dataset to future work."
+    ],
+    [
+      "The goal of experiment 1 is to test how accurately MonaLog solves problems in a large-scale dataset. We first used the system to solve the 495 problems in the trial set and then manually identified the cases in which the system failed. Then we determined which syntactic transformations are needed for MonaLog. After improving the results on the trial data by introducing a preprocessing step to handle limited syntactic variation (see below), we applied MonaLog on the test set. This means that the rule base of the system was optimized on the trial data, and we can test its generalization capability on the test data.",
+      "The main obstacle for MonaLog is the syntactic variations in the dataset, illustrated in some examples in Table TABREF16. There exist multiple ways of dealing with these variations: One approach is to `normalize' unknown syntactic structures to a known structure. For example, we can transform passive sentences into active ones and convert existential sentences into the base form (see ex. 8399 and 219 in Table TABREF16). Another approach is to use some more abstract syntactic/semantic representation so that the linear word order can largely be ignored, e.g., represent a sentence by its dependency parse, or use Abstract Meaning Representation. Here, we explore the first option and leave the second approach to future work. We believe that dealing with a wide range of syntactic variations requires tools designed specifically for that purpose. The goal of MonaLog is to generate entailments and contradictions based on a polarized sentence instead.",
+      "Below, we list the most important syntactic transformations we perform in preprocessing.",
+      "Convert all passive sentences to active using pass2act. If the passive does not contain a by phrase, we add by a person.",
+      "Convert existential clauses into their base form (see ex. 219 in Table TABREF16).",
+      "Other transformations: someone/anyone/no one $\\rightarrow ~$some/any/no person; there is no man doing sth. $\\rightarrow ~$no man is doing sth.; etc."
+    ],
+    [
+      "The results of our system on uncorrected and corrected SICK are presented in Table TABREF27, along with comparisons with other systems.",
+      "Our accuracy on the uncorrected SICK (77.19%) is much higher than the majority baseline (56.36%) or the hypothesis-only baseline (56.87%) reported by BIBREF8, and only several points lower than current logic-based systems. Since our system is based on natural logic, there is no need for translation into logical forms, which makes the reasoning steps transparent and much easier to interpret. I.e., with entailments and contradictions, we can generate a natural language trace of the system, see Fig. FIGREF13.",
+      "Our results on the corrected SICK are even higher (see lower part of Table TABREF27), demonstrating the effect of data quality on the final results. Note that with some simple syntactic transformations we can gain 1-2 points in accuracy.",
+      "Table TABREF28 shows MonaLog's performance on the individual relations. The system is clearly very good at identifying entailments and contradictions, as demonstrated by the high precision values, especially on the corrected SICK set (98.50 precision for E and 95.02 precision for C). The lower recall values are due to MonaLog's current inability to handle syntactic variation.",
+      "Based on these results, we tested a hybrid model of MonaLog and BERT (see Table TABREF27) where we exploit MonaLog's strength: Since MonaLog has a very high precision on Entailment and Contradiction, we can always trust MonaLog if it predicts E or C; when it returns N, we then fall back to BERT. This hybrid model improves the accuracy of BERT by 1% absolute to 85.95% on the corrected SICK. On the uncorrected SICK dataset, the hybrid system performs worse than BERT.",
+      "Since MonaLog is optimized for the corrected SICK, it may mislabel many E and C judgments in the uncorrected dataset. The stand-alone BERT system performs better on the uncorrected data (86.74%) than the corrected set (85.00%). The corrected set may be too inconsistent since only a part has been checked.",
+      "Overall, these hybird results show that it is possible to combine our high-precision system with deep learning architectures. However, more work is necessary to optimize this combined system."
+    ],
+    [
+      "Upon closer inspection, some of MonaLog's errors consist of difficult cases, as shown in Table TABREF29. For example, in ex. 359, if our knowledge base ${K}$ contains the background fact $\\mbox{\\em chasing} \\le \\mbox{\\em running}$, then MonaLog's judgment of C would be correct. In ex. 1402, if crying means screaming, then the label should be E; however, if crying here means shedding tears, then the label should probably be N. Here we also see potentially problematic labels (ex. 1760, 3403) in the original SICK dataset.",
+      "Another point of interest is that 19 of MonaLog's mistakes are related to the antonym pair man vs. woman (e.g., ex. 5793 in Table TABREF29). This points to inconsistency of the SICK dataset: Whereas there are at least 19 cases tagged as Neutral (e.g., ex. 5793), there are at least 17 such pairs that are annotated as Contradictions in the test set (e.g., ex. 3521), P: A man is dancing, H: A woman is dancing (ex. 9214), P: A shirtless man is jumping over a log, H: A shirtless woman is jumping over a log. If man and woman refer to the same entity, then clearly that entity cannot be man and woman at the same time, which makes the sentence pair a contradiction. If, however, they do not refer to the same entity, then they should be Neutral."
+    ],
+    [
+      "Our second experiment focuses on using MonaLog to generate additional training data for machine learning models such as BERT. To our knowledge, this is the first time that a rule-based NLI system has been successfully used to generate training data for a deep learning application."
+    ],
+    [
+      "As described above, MonaLog generates entailments and contradictions when solving problems. These can be used as additional training data for a machine learning model.",
+      "I.e., we pair the newly generated sentences",
+      "with their input sentence, creating new pairs for training. For example, we take all the sentences in the nodes in Figure FIGREF13 as inferences and all the sentences in rectangles as contradictions, and then form sentence pairs with the input sentence. The additional data can be used directly, almost without human intervention.",
+      "Thus for experiment 2, the goal is to examine the quality of these generated sentence pairs. For this, we re-train a BERT model on these pairs. If BERT trained on the manually annotated SICK training data is improved by adding data generated by MonaLog, then we can conclude that the generated data is of high quality, even comparable to human annotated data, which is what we found.",
+      "More specifically, we compare the performance of BERT models trained on a) SICK training data alone, and b) SICK training data plus the entailing and contradictory pairs generated by MonaLog.",
+      "All experiments are carried out using our corrected version of the SICK data set.",
+      "However, note that MonaLog is designed to only generate entailments and contradictions. Thus, we only have access to newly generated examples for those two cases, we do not acquire any additional neutral cases. Consequently, adding these examples to the training data will introduce a skewing that does not reflect the class distribution in the test set. Since this will bias the machine learner against neutral cases, we use the following strategy to counteract that tendency: We relabel all cases where BERT is not confident enough for either E or C into N. We set this threshold to 0.95 but leave further optimization of the threshold to future work."
+    ],
+    [
+      "MonaLog is prone to over-generation. For example, it may wrongly add the same adjective before a noun (phrase) twice to create a more specific noun, e.g., young young man $\\le $ young man $\\le $ man. Since it is possible that such examples influence the machine learning model negatively, we look into filtering such examples to improve the quality of the additional training data.",
+      "We manually inspected 100 sentence pairs generated by MonaLog to check the quality and naturalness of the new sentences (see Table TABREF32 for examples). All of the generated sentences are correct in the sense that the relation between the premise and the hypothesis is correctly labeled as entailment or contradiction (see Table TABREF34).",
+      "While we did not find any sentence pairs with wrong labels, some generated sentences are unnatural, as shown in Table TABREF32. Both unnatural examples contain two successive copies of the same PP.",
+      "Note that our data generation hinges on correct polarities on the words and constituents. For instance, in the last example of Table TABREF32, the polarization system needs to know that few is downward entailing on both of its arguments, and without flips the arrow of its argument, in order to produce the correct polarities, on which the replacement of MonaLog depends.",
+      "In order to filter unnatural sentences, such as the examples in Table TABREF32, we use a rule-based filter and remove sentences that contain bigrams of repeated words. We experiment with using one quarter or one half randomly selected sentences in addition to a setting where we use the complete set of generated sentences."
+    ],
+    [
+      "Table TABREF37 shows the amount of additional sentence pairs per category along with the results of using the automatically generated sentences as additional training data. It is obvious that adding the additional training data results in gains in accuracy even though the training data becomes increasingly skewed towards E and C. When we add all additional sentence pairs, accuracy increases by more than 1.5 percent points. This demonstrates both the robustness of BERT in the current experiment and the usefulness of the generated data. The more data we add, the better the system performs.",
+      "We also see that raising the threshold to relabel uncertain cases as neutral gives a small boost, from 86.51% to 86.71%. This translates into 10 cases where the relabeling corrected the answer. Finally, we also investigated whether the hybrid system, i.e., MonaLog followed by the re-trained BERT, can also profit from the additional training data. Intuitively, we would expect smaller gains since MonaLog already handles a fair amount of the entailments and contradictions, i.e., those cases where BERT profits from more examples. However the experiments show that the hybrid system reaches an even higher accuracy of 87.16%, more than 2 percent points above the baseline, equivalent to roughly 100 more problems correctly solved. Setting the high threshold for BERT to return E or C further improves accuracy to 87.49%. This brings us into the range of the state-of-the-art results, even though a direct comparison is not possible because of the differences between the corrected and uncorrected dataset."
+    ],
+    [
+      "We have presented a working natural-logic-based system, MonaLog, which attains high accuracy on the SICK dataset and can be used to generated natural logic proofs. Considering how simple and straightforward our method is, we believe it can serve as a strong baseline or basis for other (much) more complicated systems, either logic-based or ML/DL-based. In addition, we have shown that MonaLog can generate high-quality training data, which improves the accuracy of a deep learning model when trained on the expanded dataset. As a minor point, we manually checked the corrected SICK dataset by BIBREF28, BIBREF29.",
+      "There are several directions for future work. The first direction concerns the question how to handle syntactic variation from natural language input. That is, the computational process(es) for inference will usually be specified in terms of strict syntactic conditions, and naturally occurring sentences will typically not conform to those conditions. Among the strategies which allow their systems to better cope with premises and hypotheses with various syntactic structures are sophisticated versions of alignment used by e.g. MacCartney,YanakaMMB18. We will need to extend MonaLog to be able to handle such variation. In the future, we plan to use dependency relations as representations of natural language input and train a classifier that can determine which relations are crucial for inference.",
+      "Second, as mentioned earlier, we are in need of a fully (rather than partially) checked SICK dataset to examine the impact of data quality on the results since the partially checked dataset may be inherently inconsistent between the checked and non-checked parts.",
+      "Finally, with regard to the machine learning experiments, we plan to investigate other methods of addressing the imbalance in the training set created by additional entailments and contradictions. We will look into options for artificially creating neutral examples, e.g. by finding reverse entailments, as illustrated by richardson2019probing."
+    ],
+    [
+      "We thank the anonymous reviewers for their helpful comments. Hai Hu is supported by China Scholarship Council."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1874/instruction.md b/qasper-1874/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..fde593b7814b053a63d2efc3a6efaa93fac0aa4c
--- /dev/null
+++ b/qasper-1874/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: MonaLog: a Lightweight System for Natural Language Inference Based on Monotonicity
+
+Question: How do they select monotonicity facts?
\ No newline at end of file
diff --git a/qasper-1880/instruction.md b/qasper-1880/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..aa1a56b664d271b4ed23c70c0e8c49f22e6a998b
--- /dev/null
+++ b/qasper-1880/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Pre-Translation for Neural Machine Translation
+
+Question: Which NMT architecture do they use?
\ No newline at end of file
diff --git a/qasper-1887/instruction.md b/qasper-1887/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..efc9e8990393f34707d9ca4637551c72fc614cc5
--- /dev/null
+++ b/qasper-1887/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Automatic Language Identification in Texts: A Survey
+
+Question: what evaluation methods are discussed?
\ No newline at end of file
diff --git a/qasper-1889/instruction.md b/qasper-1889/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..cd595e6a4453382443219762fa8281fc0d590a14
--- /dev/null
+++ b/qasper-1889/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: HireNet: a Hierarchical Attention Model for the Automatic Analysis of Asynchronous Video Job Interviews
+
+Question: How is "hirability" defined?
\ No newline at end of file
diff --git a/qasper-1890/instruction.md b/qasper-1890/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..012af4094edbb7d56c11a41e1bd5fcf4a5251761
--- /dev/null
+++ b/qasper-1890/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: HireNet: a Hierarchical Attention Model for the Automatic Analysis of Asynchronous Video Job Interviews
+
+Question: Have the candidates given their consent to have their videos used for the research?
\ No newline at end of file
diff --git a/qasper-1891/instruction.md b/qasper-1891/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..e4a3b81c46fab824de4af6194c00aa7d7e93a639
--- /dev/null
+++ b/qasper-1891/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: HireNet: a Hierarchical Attention Model for the Automatic Analysis of Asynchronous Video Job Interviews
+
+Question: Do they analyze if their system has any bias?
\ No newline at end of file
diff --git a/qasper-1896/instruction.md b/qasper-1896/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..45f1f1d64754b074aa6c575d9352859349f3d49c
--- /dev/null
+++ b/qasper-1896/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: End-to-End Streaming Keyword Spotting
+
+Question: How do they measure the quality of detection?
\ No newline at end of file
diff --git a/qasper-1897/instruction.md b/qasper-1897/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..d55e8c91017253a80876fb3d7f06c93f176101d5
--- /dev/null
+++ b/qasper-1897/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: End-to-End Streaming Keyword Spotting
+
+Question: What previous approaches are considered?
\ No newline at end of file
diff --git a/qasper-1898/instruction.md b/qasper-1898/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..2d45f2ca3e20ab982a26b7fcd3a404154d7be1d3
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+++ b/qasper-1898/instruction.md
@@ -0,0 +1,82 @@
+Name of Paper: Neural Semantic Parsing in Low-Resource Settings with Back-Translation and Meta-Learning
+
+Question: How is the back-translation model trained?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Problem Statement",
+    "Learning Algorithm",
+    "Back-Translation",
+    "Quality Controller",
+    "Meta-Learning",
+    "Experiment",
+    "Table-Based Semantic Parsing",
+    "Knowledge-Based Question Answering",
+    "Conversational Table-Based Semantic Parsing",
+    "Conclusion and Future Directions"
+  ],
+  "paragraphs": [
+    [
+      "Semantic parsing aims to map natural language questions to the logical forms of their underlying meanings, which can be regarded as programs and executed to yield answers, aka denotations BIBREF0 . In the past few years, neural network based semantic parsers have achieved promising performances BIBREF1 , however, their success is limited to the setting with rich supervision, which is costly to obtain. There have been recent attempts at low-resource semantic parsing, including data augmentation methods which are learned from a small number of annotated examples BIBREF2 , and methods for adapting to unseen domains while only being trained on annotated examples in other domains.",
+      "This work investigates neural semantic parsing in a low-resource setting, in which case we only have our prior knowledge about a limited number of simple mapping rules, including a small amount of domain-independent word-level matching tables if necessary, but have no access to either annotated programs or execution results. Our key idea is to use these rules to collect modest question-programs pairs as the starting point, and then leverage automatically generated examples to improve the accuracy and generality of the model. This presents three challenges including how to generate examples in an efficient way, how to measure the quality of generated examples which might contain errors and noise, and how to train a semantic parser that makes robust predictions for examples covered by rules and generalizes well to uncovered examples.",
+      "We address the aforementioned challenges with a framework consisting of three key components. The first component is a data generator. It includes a neural semantic parsing model, which maps a natural language question to a program, and a neural question generation model, which maps a program to a natural language question. We learn these two models in a back-translation paradigm using pseudo parallel examples, inspired by its big success on unsupervised neural machine translation BIBREF3 , BIBREF4 . The second component is a quality controller, which is used for filtering out noise and errors contained in the pseudo data. We construct a phrase table with frequent mapping patterns, therefore noise and errors with low frequency can be filtered out. A similar idea has been worked as posterior regularization in neural machine translation BIBREF5 , BIBREF6 . The third component is a meta learner. Instead of transferring a model pretrained with examples covered by rules to the generated examples, we leverage model-agnostic meta-learning BIBREF7 , an elegant meta-learning algorithm which has been successfully applied to a wide range of tasks including few-shot learning and adaptive control. We regard different data sources as different tasks, and use outputs of the quality controller for stable training.",
+      "We test our approach on three tasks with different programs, including SQL (and SQL-like) queries for both single-turn and multi-turn questions over web tables BIBREF8 , BIBREF9 , and subject-predicate pairs over a large-scale knowledge graph BIBREF10 . The program for SQL queries for single-turn questions and subject-predicate pairs over knowledge graph is simple while the program for SQL queries for multi-turn questions have top-tier complexity among currently proposed tasks. Results show that our approach yields large improvements over rule-based systems, and incorporating different strategies incrementally improves the overall performance. On WikiSQL, our best performing system achieves execution accuracy of 72.7%, comparable to a strong system learned from denotations BIBREF11 with an accuracy of 74.8%."
+    ],
+    [
+      "We focus on the task of executive semantic parsing. The goal is to map a natural language question/utterance INLINEFORM0 to a logical form/program INLINEFORM1 , which can be executed over a world INLINEFORM2 to obtain the correct answer INLINEFORM3 .",
+      "We consider three tasks. The first task is single-turn table-based semantic parsing, in which case INLINEFORM0 is a self-contained question, INLINEFORM1 is a SQL query in the form of \u201cSELECT agg col INLINEFORM2 WHERE col INLINEFORM3 = val INLINEFORM4 AND ...\u201d, and INLINEFORM5 is a web table consisting of multiple rows and columns. We use WikiSQL BIBREF8 as the testbed for this task. The second task is multi-turn table-based semantic parsing. Compared to the first task, INLINEFORM6 could be a follow-up question, the meaning of which depends on the conversation history. Accordingly, INLINEFORM7 in this task supports additional operations that copy previous turn INLINEFORM8 to the current turn. We use SequentialQA BIBREF9 for evaluation. In the third task, we change INLINEFORM9 to a large-scale knowledge-graph (i.e. Freebase) and consider knowledge-based question answering for single-turn questions. We use SimpleQuestions BIBREF10 as the testbed, where the INLINEFORM10 is in the form of a simple INLINEFORM11 -calculus like INLINEFORM12 , and the generation of INLINEFORM13 is equivalent to the prediction of the predicate and the subject entity.",
+      "We study the problem in a low-resource setting. In the training process, we don't have annotated logical forms INLINEFORM0 or execution results INLINEFORM1 . Instead, we have a collection of natural language questions for the task, a limited number of simple mapping rules based on our prior knowledge about the task, and may also have a small amount of domain-independent word-level matching tables if necessary. These rules are not perfect, with low coverage, and can even be incorrect for some situations. For instance, when predicting a SQL command in the first task, we have a prior knowledge that (1) WHERE values potentially have co-occurring words with table cells; (2) the words \u201cmore\u201d and \u201cgreater\u201d tend to be mapped to WHERE operator \u201c INLINEFORM2 \u201d; (3) within a WHERE clause, header and cell should be in the same column; and (4) the word \u201caverage\u201d tends to be mapped to aggregator \u201cavg\u201d. Similarly, when predicting a INLINEFORM3 -calculus in the third task, the entity name might be present in the question, and among all the predicates connected to the entity, the predicate with maximum number of co-occurred words might be correct. We would like to study to what extent our model can achieve if we use rules as the starting point."
+    ],
+    [
+      "We describe our approach for low-resource neural semantic parsing in this section.",
+      "We propose to train a neural semantic parser using back-translation and meta-learning. The learning process is summarized in Algorithm FIGREF1 . We describe the three components in this section, namely back-translation, quality control, and meta-learning."
+    ],
+    [
+      "Following the back-translation paradigm BIBREF3 , BIBREF4 , we have a semantic parser, which maps a natural language question INLINEFORM0 to a logical form INLINEFORM1 , and a question generator, which maps INLINEFORM2 to INLINEFORM3 . The semantic parser works for the primary task, and the question generator mainly works for generating pseudo datapoints. We start the training process by applying the rule INLINEFORM4 to a set of natural language questions INLINEFORM5 . The resulting dataset is considered as the training data to initialize both the semantic parser and the question generator. Afterwards, both models are improved following the back-translation protocol that target sequences should follow the real data distribution, yet source sequences can be generated with noises. This is based on the consideration that in an encoder-decoder model, the decoder is more sensitive to the data distribution than the encoder. We use datapoints from both models to train the semantic parser because a logical form is structural which follows a grammar, whose distribution is similar to the ground truth."
+    ],
+    [
+      "Directly using generated datapoints as supervised training data is not desirable because those generated datapoints contain noises or errors. To address this, we follow the application of posterior regularization in neural machine translation BIBREF5 , and implement a dictionary-based discriminator which is used to measure the quality of a pseudo data. The basic idea is that although these generated datapoints are not perfect, the frequent patterns of the mapping between a phrase in INLINEFORM0 to a token in INLINEFORM1 are helpful in filtering out noise in the generated data with low frequency BIBREF6 . There are multiple ways to collect the phrase table information, such as using statistical phrase-level alignment algorithms like Giza++ or directly counting the co-occurrence of any question word and logical form token. We use the latter one in this work. Further details are described in the appendix."
+    ],
+    [
+      "A simple way to update the semantic parser is to merge the datapoints in hand and train a one-size-fits-all model BIBREF2 . However, this will hurt model's stability on examples covered by rules, and examples of the same task may vary widely BIBREF12 . Dealing with different types of examples requires the model to possess different abilities. For example, tackling examples uncovered by rules in WikiSQL requires the model to have the additional ability to map a column name to a totally different utterance, such as \u201ccountry\u201d to \u201cnation\u201d. Another simple solution is self-training BIBREF13 . One can train a model with examples covered by rules, and use the model as a teacher to make predictions on examples uncovered by rules and update the model on these predictions. However, self-training is somewhat tautological because the model is learned to make predictions which it already can produce.",
+      "We learn the semantic parser with meta-learning, regarding learning from examples covered by rules or uncovered by rules as two (pseudo) tasks. Compared to the aforementioned strategies, the advantage of exploring meta-learning here is two-fold. First, we learn a specific model for each task, which provides guarantees about its stability on examples covered by rules. In the test phase, we can use the rule to detect which task an example belongs to, and use the corresponding task-specific model to make predictions. When dealing with examples covered by rules, we can either directly use rules to make predictions or use the updated model, depending on the accuracy of the learned model on the examples covered by rules on development set. Second, latent patterns of examples may vary widely in terms of whether or not they are covered by rules. Meta-learning is more desirable in this situation because it learns the model's ability to learn, improving model's versatility rather than mapping the latent patterns learned from datapoints in one distribution to datapoints in another distribution by force. Figure FIGREF1 is an illustration of data combination, self-training, and meta-learning.",
+      "Meta-learning includes two optimizations: the learner that learns new tasks, and the meta-learner that trains the learner. In this work, the meta-learner is optimized by finding a good initialization that is highly adaptable. Specifically, we use model-agnostic meta-learning, MAML BIBREF7 , a powerful meta-learning algorithm with desirable properties including introducing no additional parameters and making no assumptions of the form of the model. In MAML, task-specific parameter INLINEFORM0 is initialized by INLINEFORM1 , and updated using gradient decent based on the loss function INLINEFORM2 of task INLINEFORM3 . In this work, the loss functions of two tasks are the same. The updated parameter INLINEFORM4 is then used to calculate the model's performance across tasks to update the parameter INLINEFORM5 . In this work, following the practical suggestions given by BIBREF17 , we update INLINEFORM6 in the inner-loop and regard the outputs of the quality controller as the input of both tasks.",
+      "If we only have examples covered by rules, such as those used in the initialization phase, meta-learning learns to learn a good initial parameter that is evaluated by its usefulness on the examples from the same distribution. In the training phase, datapoints from both tasks are generated, and meta-learning learns to learn an initialization parameter which can be quickly and efficiently adapted to examples from both tasks."
+    ],
+    [
+      "We conduct experiments on three tasks to test our approach, including generating SQL (or SQL-like) queries for both single-turn and multi-turn questions over web tables BIBREF8 , BIBREF9 , and predicting subject-predicate pairs over a knowledge graph BIBREF10 . We describe task definition, base models, experiments settings and empirical results for each task, respectively."
+    ],
+    [
+      "Given a natural language INLINEFORM0 and a table INLINEFORM1 with INLINEFORM2 columns and INLINEFORM3 rows as the input, the task is to output a SQL query INLINEFORM4 , which could be executed on table INLINEFORM5 to yield the correct answer of INLINEFORM6 . We conduct experiments on WikiSQL BIBREF8 , which provides 87,726 annotated question-SQL pairs over 26,375 web tables. In this work, we do not use either SQL queries or answers in the training process. We use execution accuracy as the evaluation metric, which measures the percentage of generated SQL queries that result in the correct answer.",
+      "We describe our rules for WikiSQL here. We first detect WHERE values, which exactly match to table cells. After that, if a cell appears at more than one column, we choose the column name with more overlapped words with the question, with a constraint that the number of co-occurred words is larger than 1. By default, a WHERE operator is INLINEFORM0 , except for the case that surrounding words of a value contain keywords for INLINEFORM1 and INLINEFORM2 . Then, we deal with the SELECT column, which has the largest number of co-occurred words and cannot be same with any WHERE column. By default, the SELECT AGG is NONE, except for matching to any keywords in Table TABREF8 . The coverage of our rule on training set is 78.4%, with execution accuracy of 77.9%.",
+      "We implement a neural network modular approach as the base model, which includes different modules to predict different SQL constituents. This approach is based on the understanding of the SQL grammar in WikiSQL, namely \u201cSELECT $agg $column WHERE $column $op $value (AND $column $op $value)*\u201d, where tokens starting with \u201c$\u201d are the slots to be predicted BIBREF18 . In practice, modular approaches typically achieve higher accuracy than end-to-end learning approach. Specifically, at the first step we implement a sequential labeling module to detect WHERE values and link them to table cells. Advantages of starting from WHERE values include that WHERE values are less ambiguous compared to other slots, and that the number of WHERE clauses can be naturally detected. After that, for each WHERE value, we use the preceding and following contexts in the question to predict its WHERE column and the WHERE operator through two unidirectional LSTM. Column attention BIBREF18 is used for predicting a particular column. Similar LSTM-based classifiers are used to predict SELECT column and SELECT aggregator.",
+      "According to whether the training data can be processed by our rules, we divide it into two parts: rule covered part and rule uncovered part. For the rule covered part we could get rule covered training data using our rules. For the rule uncovered part we could also get training data using the trained Base model we have, we refer to these data as self-inference training data. Furthermore, we could get more training data by back translation, we refer to these data as question-generation training data. For all the settings, the Base Model is initialized with rule covered training data. In Base + Self Training Method, we finetune the Base model with self-inference training data. In Base + Question Generation Method, we use question-generation training data to finetune our model. In Base + BT Method, we use both self-inference and question-generation data to finetune our model. In Base + BT + QC, we add our quality controller. In Base + BT + QC + MAML, we further add meta-learning.",
+      "Results are given in Table TABREF5 . We can see that back-translation, quality control and MAML incrementally improves the accuracy. Question generation is better than self-training here because the logical form in WikiSQL is relatively simple, so the distribution of the sampled logical forms is similar to the original one. In the back-translation setting, generated examples come from both self-training and the question generation model. The model performs better than rules on rule-covered examples, and improves the accuracy on uncovered examples. Figure FIGREF12 shows the learning curves of the COLUMN prediction model with or without using MAML. The model using MAML has a better starting point during training, which reflects the effectiveness of the pre-trained parameter."
+    ],
+    [
+      "We test our approach on question answering over another genre of environment: knowledge graph consisting of subject-relation-object triples.",
+      "Given a natural language question and a knowledge graph, the task aims to correctly answer the question with evidences from the knowledge graph. We do our study on SimpleQuestions BIBREF10 , which includes 108,442 simple questions, each of which is accompanied by a subject-relation-object triple. Questions are constructed in a way that subject and relation are mentioned in the question, and that object is the answer. The task requires predicting the entityId and the relation involved in the question.",
+      "Our rule for KBQA is simple without using a curated mapping dictionary. First, we detect an entity from the question using strict string matching, with the constraint that only one entity from the KB has the same surface string and that the question contains only one entity. After that, we get the connected relations of the detected entity, and assign the relation as the one with maximum number of co-occurred words. The coverage of our rule on training set is 16.0%, with an accuracy of 97.3% for relation prediction.",
+      "We follow BIBREF22 , and implement a KBQA pipeline consisting of three modules in this work. At the first step, we use a sequence labeling model, i.e. LSTM-CRF, to detect entity mention words in the question. After that, we use an entity linking model with BM25 built on Elasticsearch. Top-K ranked similar entities are retrieved as candidate list. Then, we get all the relations connected to entities in the candidate list as candidate relations, and use a relation prediction model, which is based on Match-LSTM BIBREF23 , to predict the relation. Finally, from all the entities connected to the predicted relation, we choose the one with highest BM25 score as the predicted entity. We use FB2M as the KB, which includes about 2 million triples.",
+      "The settings are the same as those described in table-based semantic parsing.",
+      "Results are given in Table TABREF10 , which are consistent with the numbers in WikiSQL. Using back-translation, quality control and MAML incrementally improves the accuracy, and our approach generalizes well to rule-uncovered examples."
+    ],
+    [
+      "We consider the task of conversational table-based semantic parsing in this part. Compared to single-turn table-based semantic parsing as described in subsection SECREF6 , the meaning of a natural language may also depends on questions of past turns, which is the common ellipsis and co-reference phenomena in conversational agents.",
+      "Given a natural language question at the current turn, a web table, and previous turn questions in a conversation as the input, the task aims to generate a program (i.e. logical form), which can be executed on the table to obtain the correct answer of the current turn question.",
+      "We conduct experiments on SequentialQA BIBREF9 which is derived from the WikiTableQuestions dataset BIBREF19 . It contains 6,066 question sequences covering 17,553 question-answer pairs. Each sequence includes 2.9 natural language questions on average. Different from WikiSQL which provides the correct logical form for each question, SequentialQA only annotates the correct answer. This dataset is also harder than the previous two, since it requires complex, highly compositional logical forms to get the answer. Existing approaches are evaluated by question answering accuracy, which measures whether the predicted answer is correct or not.",
+      "The pipeline of rules in SequentialQA is similar to that of WikiSQL. Compared to the grammar of WikiSQL, the grammar of SequentialQA has additional actions including copying the previous-turn logical form, no greater than, no more than, and negation. Table TABREF23 shows the additional word-level mapping table used in SequentialQA. The coverage of our rule on training set is 75.5%, with an accuracy of 38.5%.",
+      "We implement a modular approach on top of a grammar of derivation rules (actions) as the base model. Similar to BIBREF9 , our grammar consists of predefined actions used for predicting SELECT column, WHERE column, WHERE operator, WHERE value, and determining whether it is required to copy the entire action sequence of the previous turn questions. After encoding a question and previous turn questions into vectors, we first use a controller module to predict an action sequence consisting of slots, and then use specific modules to predict the argument of each slot. Similar to BIBREF9 , we use a recurrent structure as the backbone of each module and use the softmax layer for making prediction.",
+      "The settings are the same as those described in table-based semantic parsing.",
+      "From Table TABREF20 , we can see that question generation does not work well on this task. This is because the difficulty in generating sequential questions and complex target logical forms. Applying MAML to examples not coming from question generation performs best. We leave contextual question generation as a future work."
+    ],
+    [
+      "We present an approach to learn neural semantic parser from simple domain-independent rules, instead of annotated logical forms or denotations. Our approach starts from examples covered by rules, which are used to initialize a semantic parser and a question generator in a back-translation paradigm. Generated examples are measured and filtered based on statistic analysis, and then used with model-agnostic meta-learning, which guarantees model's accuracy and stability on rule-covered examples, and acquires the versatility to generalize well on rule-uncovered examples. We conduct experiments on three datasets for table-based and knowledge-based question answering tasks. Results show that incorporating different strategies incrementally improves the performance. Our best model on WikiSQL achieves comparable accuracy to the system learned from denotation. In the future, we plan to focus on more complex logical forms. "
+    ]
+  ]
+}
+```
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+Name of Paper: Neural Semantic Parsing in Low-Resource Settings with Back-Translation and Meta-Learning
+
+Question: Are the rules dataset specific?
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+Name of Paper: Cross-Lingual Machine Reading Comprehension
+
+Question: Is this a span-based (extractive) QA task?
\ No newline at end of file
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+Name of Paper: Incorporating Context and External Knowledge for Pronoun Coreference Resolution
+
+Question: What dataset do they evaluate their model on?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "The Task",
+    "The Model",
+    "Encoding Contextual Information",
+    "Processing External Knowledge",
+    "Softmax Pruning",
+    "Data",
+    "Knowledge Types",
+    "Baselines",
+    "Implementation",
+    "Experimental Results",
+    "Case Study",
+    "Related Work",
+    "Conclusion",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "The question of how human beings resolve pronouns has long been of interest to both linguistics and natural language processing (NLP) communities, for the reason that pronoun itself has weak semantic meaning BIBREF0 and brings challenges in natural language understanding. To explore solutions for that question, pronoun coreference resolution BIBREF1 was proposed. As an important yet vital sub-task of the general coreference resolution task, pronoun coreference resolution is to find the correct reference for a given pronominal anaphor in the context and has been shown to be crucial for a series of downstream tasks BIBREF2 , including machine translation BIBREF3 , summarization BIBREF4 , information extraction BIBREF5 , and dialog systems BIBREF6 .",
+      "Conventionally, people design rules BIBREF1 , BIBREF7 , BIBREF8 or use features BIBREF9 , BIBREF10 , BIBREF11 to resolve the pronoun coreferences. These methods heavily rely on the coverage and quality of the manually defined rules and features. Until recently, end-to-end solution BIBREF12 was proposed towards solving the general coreference problem, where deep learning models were used to better capture contextual information. However, training such models on annotated corpora can be biased and normally does not consider external knowledge.",
+      "Despite the great efforts made in this area in the past few decades BIBREF1 , BIBREF8 , BIBREF9 , BIBREF13 , pronoun coreference resolution remains challenging. The reason behind is that the correct resolution of pronouns can be influenced by many factors BIBREF0 ; many resolution decisions require reasoning upon different contextual and external knowledge BIBREF14 , which is also proved in other NLP tasks BIBREF15 , BIBREF16 , BIBREF17 . Figure 1 demonstrates such requirement with three examples, where Example A depends on the plurality knowledge that `them' refers to plural noun phrases; Example B illustrates the gender requirement of pronouns where `she' can only refer to a female person (girl); Example C requires a more general type of knowledge that `cats can climb trees but a dog normally does not'. All of these knowledge are difficult to be learned from training data. Considering the importance of both contextual information and external human knowledge, how to jointly leverage them becomes an important question for pronoun coreference resolution.",
+      "In this paper, we propose a two-layer model to address the question while solving two challenges of incorporating external knowledge into deep models for pronoun coreference resolution, where the challenges include: first, different cases have their knowledge preference, i.e., some knowledge is exclusively important for certain cases, which requires the model to be flexible in selecting appropriate knowledge per case; second, the availability of knowledge resources is limited and such resources normally contain noise, which requires the model to be robust in learning from them.",
+      "Consequently, in our model, the first layer predicts the relations between candidate noun phrases and the target pronoun based on the contextual information learned by neural networks. The second layer compares the candidates pair-wisely, in which we propose a knowledge attention module to focus on appropriate knowledge based on the given context. Moreover, a softmax pruning is placed in between the two layers to select high confident candidates. The architecture ensures the model being able to leverage both context and external knowledge. Especially, compared with conventional approaches that simply treat external knowledge as rules or features, our model is not only more flexible and effective but also interpretable as it reflects which knowledge source has the higher weight in order to make the decision. Experiments are conducted on a widely used evaluation dataset, where the results prove that the proposed model outperforms all baseline models by a great margin.",
+      "Above all, to summarize, this paper makes the following contributions:"
+    ],
+    [
+      "Following the conventional setting BIBREF1 , the task of pronoun coreference resolution is defined as: for a pronoun $p$ and a candidate noun phrase set ${\\mathcal {N}}$ , the goal is to identify the correct non-pronominal references set ${\\mathcal {C}}$ . the objective is to maximize the following objective function: ",
+      "$${\\mathcal {J}}= \\frac{\\sum _{c \\in {\\mathcal {C}}}{e^{F(c, p)}}}{\\sum _{n \\in {\\mathcal {N}}}e^{F(n, p)}},$$   (Eq. 8) ",
+      "where $c$ is the correct reference and $n$ the candidate noun phrase. $F(\\cdot )$ refers to the overall coreference scoring function for each $n$ regarding $p$ . Following BIBREF8 , all non-pronominal noun phrases in the recent three sentences of the pronoun $p$ are selected to form $N$ .",
+      "Particularly in our setting, we want to leverage both the local contextual information and external knowledge in this task, thus for each $n$ and $p$ , $F(.)$ is decomposed into two components: ",
+      "$$F(n, p) = F_c(n, p) + F_k(n, p),$$   (Eq. 10) ",
+      "where $F_c(n, p)$ is the scoring function that predicts the relation between $n$ and $p$ based on the contextual information; $F_k(n, p)$ is the scoring function that predicts the relation between $n$ and $p$ based on the external knowledge. There could be multiple ways to compute $F_c$ and $F_k$ , where a solution proposed in this paper is described as follows."
+    ],
+    [
+      "The architecture of our model is shown in Figure 2 , where we use two layers to incorporate contextual information and external knowledge. Specifically, the first layer takes the representations of different $n$ and the $p$ as input and predict the relationship between each pair of $n$ and $p$ , so as to compute $F_c$ . The second layer leverages the external knowledge to compute $F_k$ , which consists of pair-wise knowledge score $f_k$ among all candidate $n$ . To enhance the efficiency of the model, a softmax pruning module is applied to select high confident candidates into the second layer. The details of the aforementioned components are described in the following subsections."
+    ],
+    [
+      "Before $F_c$ is computed, the contextual information is encoded through a span representation (SR) module in the first layer of the model. Following BIBREF12 , we adopt the standard bidirectional LSTM (biLSTM) BIBREF18 and the attention mechanism BIBREF19 to generate the span representation, as shown in Figure 3 . Given that the initial word representations in a span $n_i$ are ${\\bf x}_1,...,{\\bf x}_T$ ,",
+      "we denote their representations ${\\bf x}^*_1,...,{\\bf x}^*_T$ after encoded by the biLSTM.",
+      "Then we obtain the inner-span attention by ",
+      "$$a_t = \\frac{e^{\\alpha _t}}{\\sum _{k=1}^{T}e^{\\alpha _k}},$$   (Eq. 14) ",
+      "where $\\alpha _t$ is computed via a standard feed-forward neural network $\\alpha _t$ = $NN_\\alpha ({\\bf x}^*_t)$ . Thus, we have the weighted embedding of each span $\\hat{x}_i$ through ",
+      "$$\\hat{{\\bf x}}_i = \\sum _{k=1}^{T}a_k \\cdot {\\bf x}_k.$$   (Eq. 16) ",
+      "Afterwards, we concatenate the starting ( ${\\bf x}^*_{start}$ ) and ending ( ${\\bf x}^*_{end}$ ) embedding of each span, as well as its weighted embedding ( $\\hat{{\\bf x}}_i$ ) and the length feature ( $\\phi (i)$ ) to form its final representation $e$ : ",
+      "$${\\bf e}_i = [{\\bf x}^*_{start},{\\bf x}^*_{end},\\hat{{\\bf x}}_i,\\phi (i)].$$   (Eq. 17) ",
+      "Once the span representation of $n \\in {\\mathcal {N}}$ and $p$ are obtained, we compute $F_c$ for each $n$ with a standard feed-forward neural network: ",
+      "$$F_c(n, p) = NN_c([{\\bf e}_n, {\\bf e}_p, {\\bf e}_n \\odot {\\bf e}_p]),$$   (Eq. 18) ",
+      "where $\\odot $ is the element-wise multiplication."
+    ],
+    [
+      "In the second layer of our model, external knowledge is leveraged to evaluate all candidate $n$ so as to give them reasonable $F_k$ scores. In doing so, each candidate is represented as a group of features from different knowledge sources, e.g., `the cat' can be represented as a singular noun, unknown gender creature, and a regular subject of the predicate verb `climb'. For each candidate, we conduct a series of pair-wise comparisons between it and all other ones to result in its $F_k$ score. An attention mechanism is proposed to perform the comparison and selectively use the knowledge features. Consider there exists noise in external knowledge, especially when it is automatically generated, such attention mechanism ensures that, for each candidate, reliable and useful knowledge is utilized rather than ineffective ones. The details of the knowledge attention module and the overall scoring are described as follows.",
+      "Knowledge Attention Figure 4 demonstrates the structure of the knowledge attention module, where there are two components: (1) weighting: assigning weights to different knowledge features regarding their importance in the comparison; (2) scoring: valuing a candidate against another one based on their features from different knowledge sources. Assuming that there are $m$ knowledge sources input to our model, each candidate can be represented by $m$ different features, which are encoded as embeddings. Therefore, two candidates $n$ and $n^\\prime $ regarding $p$ have their knowledge feature embeddings ${\\bf k}_{n,p}^1, {\\bf k}_{n,p}^2, ..., {\\bf k}_{n,p}^m$ and ${\\bf k}_{n^\\prime ,p}^1,{\\bf k}_{n^\\prime ,p}^2,...,{\\bf k}_{n^\\prime ,p}^m$ , respectively. The weighting component receives all features ${\\bf k}$ for $n$ and $n^\\prime $ , and the span representations $m$0 and $m$1 as input, where $m$2 and $m$3 help selecting appropriate knowledge based on the context. As a result, for a candidate pair ( $m$4 , $m$5 ) and a knowledge source $m$6 , its knowledge attention score is computed via ",
+      "$$\\beta _i(n, n^\\prime , p) = NN_{ka}([{\\bf o}_{n,p}^i, {\\bf o}_{n^\\prime ,p}^i, {\\bf o}_{n,p}^i \\odot {\\bf o}_{n^\\prime ,p}^i]),$$   (Eq. 21) ",
+      "where $ {\\bf o}_{n,p}^i= [{\\bf e}_n, {\\bf k}_{n,p}^i]$ and ${\\bf o}_{n^\\prime ,p}^i = [{\\bf e}_{n^\\prime }, {\\bf k}_{n^\\prime ,p}^i]$ are the concatenation of span representation and external knowledge embedding for candidate $n$ and $n^\\prime $ respectively. The weight for features from different knowledge sources is thus computed via ",
+      "$$w_i = \\frac{e^{\\beta _i}}{\\sum _{j=1}^{m}e^{\\beta _j}}.$$   (Eq. 22) ",
+      "Similar to the weighting component, for each feature $i$ , we compute its score $f_k^i(n, n^\\prime , p)$ for $n$ against $n^\\prime $ in the scoring component through ",
+      "$$f_k^i(n, n^\\prime , p) = NN_{ks}([{\\bf k}_{n,p}^i, {\\bf k}_{n^\\prime ,p}^i, {\\bf k}_{n,p}^i \\odot {\\bf k}_{n^\\prime ,p}^i]).$$   (Eq. 23) ",
+      "where it is worth noting that we exclude ${\\bf e}$ in this component for the reason that, in practice, the dimension of ${\\bf e}$ is normally much higher than ${\\bf k}$ . As a result, it could dominate the computation if ${\\bf e}$ and ${\\bf k}$ is concatenated.",
+      "Once the weights and scores are obtained, we have a weighted knowledge score for $n$ against $n^\\prime $ : ",
+      "$$f_k(n, n^\\prime , p) = \\sum _{i=1}^{m}w_i \\cdot f_k^i(n, n^\\prime , p).$$   (Eq. 25) ",
+      "Overall Knowledge Score After all pairs of $n$ and $n^\\prime $ are processed by the attention module, the overall knowledge score for $n$ is computed through the averaged $f_k(n, n^\\prime , p)$ over all $n^\\prime $ : ",
+      "$$F_k(n, p) = \\frac{\\sum _{n^\\prime \\in {\\mathcal {N}}_o} f_k(n, n^\\prime , p)}{|{\\mathcal {N}}_o|},$$   (Eq. 26) ",
+      "where ${\\mathcal {N}}_o = {\\mathcal {N}}- n$ for each $n$ ."
+    ],
+    [
+      "Normally, there could be many noun phrases that serve as the candidates for the target pronoun. One potential obstacle in the pair-wise comparison of candidate noun phrases in our model is the squared complexity $O(|{\\mathcal {N}}|^2)$ with respect to the size of ${\\mathcal {N}}$ . To filter out low confident candidates so as to make the model more efficient, we use a softmax-pruning module between the two layers in our model to select candidates for the next step. The module takes $F_c$ as input for each $n$ , uses a softmax computation: ",
+      "$$\\hat{F}_c(n, p) = \\frac{e^{F_c(n, p)}}{\\sum _{n_i \\in {\\mathcal {N}}}e^{F_c(n_i, p)}}.$$   (Eq. 28) ",
+      "where candidates with higher $\\hat{F}_c$ are kept, based on a threshold $t$ predefined as the pruning standard. Therefore, if candidates have similar $F_c$ scores, the module allow more of them to proceed to the second layer. Compared with other conventional pruning methods BIBREF12 , BIBREF20 that generally keep a fixed number of candidates, our pruning strategy is more efficient and flexible."
+    ],
+    [
+      "The CoNLL-2012 shared task BIBREF21 corpus is used as the evaluation dataset, which is selected from the Ontonotes 5.0. Following conventional approaches BIBREF9 , BIBREF11 , for each pronoun in the document, we consider candidate $n$ from the previous two sentences and the current sentence. For pronouns, we consider two types of them following BIBREF9 , i.e., third personal pronoun (she, her, he, him, them, they, it) and possessive pronoun (his, hers, its, their, theirs). Table 1 reports the number of the two type pronouns and the overall statistics for the experimental dataset. According to our selection range of candidate $n$ , on average, each pronoun has 4.6 candidates and 1.3 correct references."
+    ],
+    [
+      "In this study, we use two types of knowledge in our experiments. The first type is linguistic features, i.e., plurality and animacy & gender. We employ the Stanford parser, which generates plurality, animacy, and gender markups for all the noun phrases, to annotate our data. Specifically, the plurality feature denotes each $n$ and $p$ to be singular or plural. For each candidate $n$ , if its plurality status is the same as the target pronoun, we label it 1, otherwise 0. The animacy & gender (AG) feature denotes whether a $n$ or $p$ is a living object, and being male, female, or neutral if it is alive. For each candidate $n$ , if its AG feature matches the target pronoun's, we label it 1, otherwise 0.",
+      "The second type is the selectional preference (SP) knowledge. For this knowledge, we create a knowledge base by counting how many times a predicate-argument tuple appears in a corpus and use the resulted number to represent the preference strength. Specifically, we use the English Wikipedia as the base corpus for such counting. Then we parse the entire corpus through the Stanford parser and record all dependency edges in the format of (predicate, argument, relation, number), where predicate is the governor and argument the dependent in the original parsed dependency edge. Later for sentences in the training and test data, we firstly parse each sentence and find out the dependency edge linking $p$ and its corresponding predicate. Then for each candidate $n$ in a sentence, we check the previously created SP knowledge base and find out how many times it appears as the argument of different predicates with the same dependency relation (i.e., nsubj and dobj). The resulted frequency is grouped into the following buckets [1, 2, 3, 4, 5-7, 8-15, 16-31, 32-63, 64+] and we use the bucket id as the final SP knowledge. Thus in the previous example:",
+      "The dog is chasing the cat but it climbs the tree.",
+      "Its parsing result indicates that `it' is the subject of the verb `climb'. Then for `the dog', `the cat', and `the tree', we check their associations with `climb' in the knowledge base and group them in the buckets to form the SP knowledge features."
+    ],
+    [
+      "Several baselines are compared in this work. The first two are conventional unsupervised ones:",
+      "[leftmargin=*]",
+      "Recent Candidate, which simply selects the most recent noun phrase that appears in front of the target pronoun.",
+      "Deterministic model BIBREF22 , which proposes one multi-pass seive model with human designed rules for the coreference resolution task.",
+      "Besides the unsupervised models, we also compare with three representative supervised ones:",
+      "[leftmargin=*]",
+      "Statistical model, proposed by BIBREF23 , uses human-designed entity-level features between clusters and mentions for coreference resolution.",
+      "Deep-RL model, proposed by BIBREF24 , a reinforcement learning method to directly optimize the coreference matrix instead of the traditional loss function.",
+      "End2end is the current state-of-the-art coreference model BIBREF20 , which performs in an end-to-end manner and leverages both the contextual information and a pre-trained language model BIBREF25 .",
+      "Note that the Deterministic, Statistical, and Deep-RL models are included in the Stanford CoreNLP toolkit, and experiments are conducted with their provided code. For End2end, we use their released code and replace its mention detection component with gold mentions for the fair comparison.",
+      "To clearly show the effectiveness of the proposed model, we also present a variation of our model as an extra baseline to illustrate the effect of different knowledge incorporation manner:",
+      "[leftmargin=*]",
+      "Feature Concatenation, a simplified version of the complete model that removes the second knowledge processing layer, but directly treats all external knowledge embeddings as features and concatenates them to span representations."
+    ],
+    [
+      "Following previous work BIBREF20 , we use the concatenation of the 300d GloVe embeddings BIBREF26 and the ELMo BIBREF25 embeddings as the initial word representations. Out-of-vocabulary words are initialized with zero vectors. Hyper-parameters are set as follows. The hidden state of the LSTM module is set to 200, and all the feed-forward networks in our model have two 150-dimension hidden layers. The default pruning threshold $t$ for softmax pruning is set to $10^{-7}$ . All linguistic features (plurality and AG) and external knowledge (SP) are encoded as 20-dimension embeddings.",
+      "For model training, we use cross-entropy as the loss function and Adam BIBREF27 as the optimizer. All the aforementioned hyper-parameters are initialized randomly, and we apply dropout rate 0.2 to all hidden layers in the model. Our model treats a candidate as the correct reference if its predicted overall score $F(n,p)$ is larger than 0. The model training is performed with up to 100 epochs, and the best one is selected based on its performance on the development set."
+    ],
+    [
+      "Table 2 compares the performance of our model with all baselines. Overall, our model performs the best with respect to all evaluation metrics. Several findings are also observed from the results. First, manually defined knowledge and features are not enough to cover rich contextual information. Deep learning models (e.g., End2end and our proposed models), which leverage text representations for context, outperform other approaches by a great margin, especially on the recall. Second, external knowledge is highly helpful in this task, which is supported by that our model outperforms the End2end model significantly.",
+      "Moreover, the comparison between the two variants of our models is also interesting, where the final two-layer model outperforms the Feature Concatenation model. It proves that simply treating external knowledge as the feature, even though they are from the same sources, is not as effective as learning them in a joint framework. The reason behind this result is mainly from the noise in the knowledge source, e.g., parsing error, incorrectly identified relations, etc. For example, the plurality of 17% noun phrases are wrongly labeled in the test data. As a comparison, our knowledge attention might contribute to alleviate such noise when incorporating all knowledge sources.",
+      "Effect of Different Knowledge To illustrate the importance of different knowledge sources and the knowledge attention mechanism, we ablate various components of our model and report the corresponding F1 scores on the test data. The results are shown in Table 3 , which clearly show the necessity of the knowledge. Interestingly, AG contributes the most among all knowledge types, which indicates that potentially more cases in the evaluation dataset demand on the AG knowledge than others. More importantly, the results also prove the effectiveness of the knowledge attention module, which contributes to the performance gap between our model and the Feature Concatenation one.",
+      "Effect of Different Pruning Thresholds We try different thresholds $t$ for the softmax pruning in selecting reliable candidates. The effects of different thresholds on reducing candidates and overall performance are shown in Figure 5 and 6 respectively. Along with the increase of $t$ , both the max and the average number of pruned candidates drop quickly, so that the space complexity of the model can be reduced accordingly. Particularly, there are as much as 80% candidates can be filtered out when $t = 10^{-1}$ . Meanwhile, when referring to Figure 6 , it is observed that the model performs stable with the decreasing of candidate numbers. Not surprisingly, the precision rises when reducing candidate numbers, yet the recall drops dramatically, eventually results in the drop of F1. With the above observations, the reason we set $t = 10^{-7}$ as the default threshold is straightforward: on this value, one-third candidates are pruned with almost no influence on the model performance in terms of precision, recall, and the F1 score."
+    ],
+    [
+      "To further demonstrate the effectiveness of incorporating knowledge into pronoun coreference resolution, two examples are provided for detailed analysis. The prediction results of the End2end model and our complete model are shown in Table 4 . There are different challenges in both examples. In Example A, `Jesus', `man', and `my son' are all similar (male) noun phrases matching the target pronoun `He'. The End2end model predicts all of them to be correct references because their context provides limited help in distinguishing them. In Example B, the distance between `an accident' and the pronoun `it' is too far. As a result, the `None' result from the End2end model indicates that the contextual information is not enough to make the decision. As a comparison, in our model, integrating external knowledge can help to solve such challenges, e.g., for Example A, SP knowledge helps when Plurality and AG cannot distinguish all candidates.",
+      "To clearly illustrate how our model leverages the external knowledge, we visualize the knowledge attention of the correct reference against other candidates via heatmaps in Figure 7 . Two interesting observations are drawn from the visualization. First, given two candidates, if they are significantly different in one feature, our model tends to pay more attention to that feature. Take AG as an example, in Example A, the AG features of all candidates consistently match the pronoun `he' (all male/neutral). Thus the comparison between `my son' and all candidates pay no attention to the AG feature. While in Example B, the target pronoun `it' cannot describe human, thus 'father' and `friend' are 0 on the AG feature while `hospital' and `accident' are 1. As a result, the attention module emphasizes AG more than other knowledge types. Second, The importance of SP is clearly shown in these examples. In example A, Plurality and AG features cannot help, the attention module weights higher on SP because `son' appears 100 times as the argument of the parsed predicate `child' in the SP knowledge base, while other candidates appear much less at that position. In example B, as mentioned above, once AG helps filtering 'hospital' and 'accident', SP plays an important role in distinguishing them because `accident' appears 26 times in the SP knowledge base as the argument of the `fault' from the results of the parser, while `hospital' never appears at that position."
+    ],
+    [
+      "Coreference resolution is a core task for natural language understanding, where it detects mention span and identifies coreference relations among them. As demonstrated in BIBREF12 , mention detection and coreference prediction are the two major focuses of the task. Different from the general coreference task, pronoun coreference resolution has its unique challenge since the semantics of pronouns are often not as clear as normal noun phrases, in general, how to leverage the context and external knowledge to resolve the coreference for pronouns becomes its focus BIBREF1 , BIBREF14 , BIBREF28 .",
+      "In previous work, external knowledge including manually defined rules BIBREF1 , BIBREF9 , such as number/gender requirement of different pronouns, and world knowledge BIBREF14 , such as selectional preference BIBREF29 , BIBREF30 and eventuality knowledge BIBREF31 , have been proved to be helpful for pronoun coreference resolution. Recently, with the development of deep learning, BIBREF12 proposed an end-to-end model that learns contextual information with an LSTM module and proved that such knowledge is helpful for coreference resolution when the context is properly encoded. The aforementioned two types of knowledge have their own advantages: the contextual information covers diverse text expressions that are difficult to be predefined while the external knowledge is usually more precisely constructed and able to provide extra information beyond the training data. Different from previous work, we explore the possibility of joining the two types of knowledge for pronoun coreference resolution rather than use only one of them. To the best of our knowledge, this is the first attempt that uses deep learning model to incorporate contextual information and external knowledge for pronoun coreference resolution."
+    ],
+    [
+      "In this paper, we proposed a two-layer model for pronoun coreference resolution, where the first layer encodes contextual information and the second layer leverages external knowledge. Particularly, a knowledge attention mechanism is proposed to selectively leverage features from different knowledge sources. As an enhancement to existing methods, the proposed model combines the advantage of conventional feature-based models and deep learning models, so that context and external knowledge can be synchronously and effectively used for this task. Experimental results and case studies demonstrate the superiority of the proposed model to state-of-the-art baselines. Since the proposed model adopted an extensible structure, one possible future work is to explore the best way to enhance it with more complicated knowledge resources such as knowledge graphs."
+    ],
+    [
+      "This paper was partially supported by the Early Career Scheme (ECS, No.26206717) from Research Grants Council in Hong Kong. In addition, Hongming Zhang has been supported by the Hong Kong Ph.D. Fellowship and the Tencent Rhino-Bird Elite Training Program. We also thank the anonymous reviewers for their valuable comments and suggestions that help improving the quality of this paper."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: Language Independent Sequence Labelling for Opinion Target Extraction
+
+Question: Which six languages are experimented with?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Background",
+    "ABSA Tasks at SemEval",
+    "Methodology",
+    "ABSA Datasets",
+    "Unlabelled Corpora",
+    "System",
+    "Experimental Results",
+    "English",
+    "Multilingual",
+    "Discussion and Error Analysis",
+    "Error Analysis",
+    "Concluding Remarks",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "Opinion Mining and Sentiment Analysis (OMSA) are crucial for determining opinion trends and attitudes about commercial products, companies reputation management, brand monitoring, or to track attitudes by mining social media, etc. Furthermore, given the explosion of information produced and shared via the Internet, especially in social media, it is simply not possible to keep up with the constant flow of new information by manual methods.",
+      "Early approaches to OMSA were based on document classification, where the task was to determine the polarity (positive, negative, neutral) of a given document or review BIBREF0 , BIBREF1 . A well known benchmark for polarity classification at document level is that of BIBREF2 . Later on, a finer-grained OMSA was deemed necessary. This was motivated by the fact that in a given review more than one opinion about a variety of aspects or attributes of a given product is usually conveyed. Thus, Aspect Based Sentiment Analysis (ABSA) was defined as a task which consisted of identifying several components of a given opinion: the opinion holder, the target, the opinion expression (the textual expression conveying polarity) and the aspects or features. Aspects are mostly domain-dependent. In restaurant reviews, relevant aspects would include \u201cfood quality\u201d, \u201cprice\u201d, \u201cservice\u201d, \u201crestaurant ambience\u201d, etc. Similarly, if the reviews were about consumer electronics such as laptops, then aspects would include \u201csize\u201d, \u201cbattery life\u201d, \u201chard drive capacity\u201d, etc.",
+      "In the review shown by Figure FIGREF1 there are three different opinions about two different aspects (categories) of the restaurant, namely, the first two opinions are about the quality of the food and the third one about the general ambience of the place. Furthermore, there are just two opinion targets because the target of the third opinion, the restaurant itself, remains implicit. Finally, each aspect is assigned a polarity; in this case all three opinion aspects are negative.",
+      "In this work we focus on Opinion Target Extraction, which we model as a sequence labelling task. In order to do so, we convert an annotated review such as the one in Figure FIGREF1 into the BIO scheme for learning sequence labelling models BIBREF3 . Example (1) shows the review in BIO format. Tokens in the review are tagged depending on whether they are at the beginning (B-target), inside (I-target) or outside (O) of the opinion target expression. Note that the third opinion target in Figure FIGREF1 is implicit.",
+      "We learn language independent models which consist of a set of local, shallow features complemented with semantic distributional features based on clusters obtained from a variety of data sources. We show that our approach, despite the lack of hand-engineered, language-specific features, obtains state-of-the-art results in 7 datasets for 6 languages on the ABSA benchmarks BIBREF4 , BIBREF5 , BIBREF6 .",
+      "The main contribution of this research note is providing an extension or addendum to previous work on sequence labelling BIBREF7 by reporting additional experimental results as well as further insights on the performance of our model across languages on a different NLP task such as Opinion Target Extraction (OTE). Thus, we empirically demonstrate the validity and strong performance of our approach for six languages in seven different datasets of the restaurant domain. Every experiment and result presented in this note is novel.",
+      "In this sense, we show that our approach is not only competitive across languages and domains for Named Entity Recognition, as shown by BIBREF7 , but that it can be straightforwardly adapted to different tasks and domains such as OTE. Furthermore, we release the system and every model trained for public use and to facilitate reproducibility of results."
+    ],
+    [
+      "Early approaches to Opinion Target Extraction (OTE) were unsupervised, although later on the vast majority of works have been based on supervised and deep learning models. To the best of our knowledge, the first work on OTE was published by BIBREF8 . They created a new task which consisted of generating overviews of the main product features from a collection of customer reviews on consumer electronics. They addressed such task using an unsupervised algorithm based on association mining. Other early unsupervised approaches include BIBREF9 which used a dependency parser to obtain more opinion targets, and BIBREF10 which aimed at extracting opinion targets in newswire via Semantic Role Labelling. From a supervised perspective, BIBREF11 presented an approach which learned the opinion target candidates and a combination of dependency and part-of-speech (POS) paths connecting such pairs. Their results improved the baseline provided by BIBREF8 . Another influential work was BIBREF12 , an unsupervised algorithm called Double Propagation which roughly consists of incrementally augmenting a set of seeds via dependency parsing.",
+      "Closer to our work, BIBREF13 , BIBREF14 and BIBREF15 approached OTE as a sequence labelling task, modelling the opinion targets using the BIO scheme. The first approach implemented HMM whereas the last two proposed CRFs to solve the problem. In all three cases, their systems included extensive human-designed and linguistically motivated features, such as POS tags, lemmas, dependencies, constituent parsing structure, lexical patterns and semantic features extracted from WordNet BIBREF16 .",
+      "Quite frequently these works used a third party dataset, or a subset of the original one, or created their own annotated data for their experiments. The result was that it was difficult to draw precise conclusions about the advantages or disadvantages of the proposed methods. In this context, the Aspect Based Sentiment Analysis (ABSA) tasks at SemEval BIBREF4 , BIBREF5 , BIBREF6 provided standard training and evaluation data thereby helping to establish a clear benchmark for the OTE task.",
+      "Finally, it should be noted that there is a closely related task, namely, the SemEval 2016 task on Stance Detection. Stance detection is related to ABSA, but there is a significant difference. In ABSA the task is to determine whether a piece of text is positive, negative, or neutral with respect to an aspect and a given target (which in Stance Detection is called \u201cauthor's favorability\u201d towards a given target). However, in Stance Detection the text may express opinion or sentiment about some other target, not mentioned in the given text, and the targets are predefined, whereas in ABSA the targets are open-ended."
+    ],
+    [
+      "Three ABSA editions were held within the SemEval Evaluation Exercises between 2014 and 2016. The ABSA 2014 and 2015 tasks consisted of English reviews only, whereas in the 2016 task 7 more languages were added. Additionally, reviews from four domains were collected for the various sub-tasks across the three editions, namely, Consumer Electronics, Telecommunications, Museums and Restaurant reviews. In any case, the only constant in each of the ABSA editions was the inclusion, for the Opinion Target Extraction (OTE) sub-task, of restaurant reviews for every language. Thus, for the experiments presented in this paper we decided to focus on the restaurant domain across 6 languages and the three different ABSA editions. Similarly, this section will be focused on reviewing the OTE results for the restaurant domain.",
+      "The ABSA task consisted of identifying, for each opinion, the opinion target, the aspect referred to by the opinion and the aspect's polarity. Figure FIGREF1 illustrates the original annotation of a restaurant review in the ABSA 2016 dataset. It should be noted that, out of the three opinion components, only the targets are explicitly represented in the text, which means that OTE can be independently modelled as a sequence labelling problem as shown by Example (1). It is particularly important to notice that the opinion expressions (\u201cdry\u201d, \u201cgreasy\u201d, \u201cloud and rude\u201d) are not annotated.",
+      "Following previous approaches, the first competitive systems for OTE at ABSA were supervised. Among the participants (for English) in the three editions, one team BIBREF17 , BIBREF18 was particularly successful. For ABSA 2014 and 2015 they developed a CRF system with extensive handcrafted linguistic features: POS, head word, dependency relations, WordNet relations, gazetteers and Name Lists based on applying the Double Propagation algorithm BIBREF12 on an initial list of 551 seeds. Interestingly, they also introduced word representation features based on Brown and K-mean clusters. For ABSA 2016, they improved their system by using the output of a Recurrent Neural Network (RNN) to provide additional features. The RNN is trained on the following input features: word embeddings, Name Lists and word clusters BIBREF19 . They were the best system in 2014 and 2016. In 2015 they obtained the second best result, in which the best system, a preliminary version of the one presented in this note, was submitted by the EliXa team BIBREF20 .",
+      "From 2015 onwards most works have been based on deep learning. BIBREF21 applied RNNs on top of a variety of pre-trained word embeddings, while BIBREF22 presented an architecture in which a RNN based tagger is stacked on top of the features generated by a Convolutional Neural Network (CNN). These systems were evaluated on the 2014 and 2015 datasets, respectively, but they did not go beyond the state-of-the-art.",
+      " BIBREF23 presented a 7 layer deep CNN combining word embeddings trained on a INLINEFORM0 5 billion word corpus extracted from Amazon BIBREF24 , POS tag features and manually developed linguistic patterns based on syntactic analysis and SenticNet BIBREF25 a concept-level knowledge based build for Sentiment Analysis applications. They only evaluate their system on the English 2014 ABSA data, obtaining best results up to date on that benchmark.",
+      "More recently, BIBREF26 proposed a coupled multi-layer attention (CMLA) network where each layer consists of a couple of attentions with tensor operators. Unlike previous approaches, their system does not use complex linguistic-based features designed for one specific language. However, whereas previous successful approaches modelled OTE as an independent task, in the CMLA model the attentions interactively learn both the opinion targets and the opinion expressions. As opinion expressions are not available in the original ABSA datasets, they had to manually annotate the ABSA training and testing data with the required opinion expressions. Although BIBREF26 did not release the datasets with the annotated opinion expressions, Figure FIGREF5 illustrates what these annotations would look like. Thus, two new attributes (pfrom and pto) annotate the opinion expressions for each of the three opinions (\u201cdry\u201d, \u201cgreasy\u201d and \u201cloud and rude\u201d, respectively). Using this new manual information to train their CMLA network they reported the best results so far for ABSA 2014 and 2015 (English only).",
+      "Finally, BIBREF27 develop a multi-task learning framework consisting of two LSTMs equipped with extended memories and neural memory operations. As BIBREF26 , they use opinion expressions annotations for a joint modelling of opinion targets and expressions. However, unlike BIBREF26 they do not manually annotate the opinion expressions. Instead they manually add sentiment lexicons and rules based on dependency parsing in order to find the opinion words required to train their system. Using this hand-engineered system, they report state of the art results only for English on the ABSA 2016 dataset. They do not provide evaluation results on the 2014 and 2015 restaurant datasets.",
+      "With respect to other languages, the IIT-T team presented systems for 4 out of the 7 languages in ABSA 2016, obtaining the best score for French and Dutch, second in Spanish but with very poor results for English, well below the baseline. The GTI team BIBREF28 implemented a CRF system using POS, lemmas and bigrams as features. They obtained the best result for Spanish and rather modest results for English.",
+      "Summarizing, the most successful systems for OTE have been based on supervised approaches with rather elaborate, complex and linguistically inspired features. BIBREF23 obtains best results on the ABSA 2014 data by means of a CNN with word embeddings trained on 5 billion words from Amazon, POS features, manual patterns based on syntactic analysis and SenticNet. More recently, the CMLA deep learning model has established new state-of-the-art results for the 2015 dataset, whereas BIBREF27 provide the state of the art for the 2016 benchmark. Thus, there is not currently a multilingual system that obtains competitive results across (at least) several of the languages included in ABSA.",
+      "As usual, most of the work has been done for English, with the large majority of the previous systems providing results only for one of the three English ABSA editions and without exploring the multilingual aspect. This could be due to the complex and language-specific systems that performed best for English BIBREF23 , or perhaps because the CMLA approach of BIBREF26 would require, in addition to the opinion targets, the gold standard annotations of the opinion expressions for each of the 6 languages other than English in the ABSA datasets."
+    ],
+    [
+      "The work presented in this research note requires the following resources: (i) Aspect Based Sentiment Analysis (ABSA) data for training and testing; (ii) large unlabelled corpora to obtain semantic distributional features from clustering lexicons; and (iii) a sequence labelling system. In this section we will describe each of the resources used."
+    ],
+    [
+      "Table TABREF7 shows the ABSA datasets from the restaurants domain for English, Spanish, French, Dutch, Russian and Turkish. From left to right each row displays the number of tokens, number of targets and the number of multiword targets for each training and test set. For English, it should be noted that the size of the 2015 set is less than half with respect to the 2014 dataset in terms of tokens, and only one third in number of targets. The French, Spanish and Dutch datasets are quite similar in terms of tokens although the number of targets in the Dutch dataset is comparatively smaller, possibly due to the tendency to construct compound terms in that language. The Russian dataset is the largest whereas the Turkish set is by far the smallest one.",
+      "Additionally, we think it is also interesting to note the low number of targets that are multiwords. To provide a couple of examples, for Spanish only the %35.59 of the targets are multiwords whereas for Dutch the percentage goes down to %25.68. If we compare these numbers with the CoNLL 2002 data for Named Entity Recognition (NER), a classic sequence labelling task, we find that in the ABSA data there is less than half the number of multiword targets than the number of multiword entities that can be found in the CoNLL Spanish and Dutch data (%35.59 vs %74.33 for Spanish and %25.68 vs %44.96 for Dutch)."
+    ],
+    [
+      "Apart from the manually annotated data, we also leveraged large, publicly available, unlabelled data to train the clusters: (i) Brown 1000 clusters and (ii) Clark and Word2vec clusters in the 100-800 range.",
+      "In order to induce clusters from the restaurant domain we used the Yelp Academic Dataset, from which three versions were created. First, the full dataset, containing 225M tokens. Second, a subset consisting of filtering out those categories that do not correspond directly to food related reviews BIBREF29 . Thus, out of the 720 categories contained in the Yelp Academic Dataset, we kept the reviews from 173 of them. This Yelp food dataset contained 117M tokens in 997,721 reviews. Finally, we removed two more categories (Hotels and Hotels & Travel) from the Yelp food dataset to create the Yelp food-hotels subset containing around 102M tokens. For the rest of the languages we used their corresponding Wikipedia dumps. The pre-processing and tokenization is performed with the IXA pipes tools BIBREF30 .",
+      "The number of words used for each dataset, language and cluster type are described in Table TABREF9 . For example, the first row reads \u201cYelp Academic Dataset containing 225M words was used; after pre-processing, 156M words were taken to induce Brown clusters, whereas Clark and Word2vec clusters were trained on the whole corpus\u201d. As explained in BIBREF7 , we pre-process the corpus before training Brown clusters, resulting in a smaller dataset than the original. Additionally, due to efficiency reasons, when the corpus is too large we use the pre-processed version to induce the Clark clusters."
+    ],
+    [
+      "We use the sequence labeller implemented within IXA pipes BIBREF7 . It learns supervised models based on the Perceptron algorithm BIBREF31 . To avoid duplication of efforts, it uses the Apache OpenNLP project implementation customized with its own features. By design, the sequence labeller aims to establish a simple and shallow feature set, avoiding any linguistic motivated features, with the objective of removing any reliance on costly extra gold annotations and/or cascading errors across annotations.",
+      "The system consists of: (i) Local, shallow features based mostly on orthographic, word shape and n-gram features plus their context; and (ii) three types of simple clustering features, based on unigram matching: (i) Brown BIBREF32 clusters, taking the 4th, 8th, 12th and 20th node in the path; (ii) Clark BIBREF33 clusters and, (iii) Word2vec BIBREF34 clusters, based on K-means applied over the extracted word vectors using the skip-gram algorithm.",
+      "The clustering features look for the cluster class of the incoming token in one or more of the clustering lexicons induced following the three methods listed above. If found, then the class is added as feature (\u201cnot found\u201d otherwise). As we work on a 5 token window, for each token and clustering lexicon at least 5 features are generated. For Brown, the number of features generated depend on the number of nodes found in the path for each token and clustering lexicon used.",
+      "Figure FIGREF13 depicts how our system relates, via clusters, unseen words with those words that have been seen as targets during the training process. Thus, the tokens `french-onions' and `salmon' would be annotated as opinion targets because they occur in the same clusters as seen words which in the training data are labeled as targets.",
+      "The word representation features are combined and stacked using the clustering lexicons induced over the different data sources listed in Table TABREF9 . In other words, stacking means adding various clustering features of the same type obtained from different data sources (for example, using clusters trained on Yelp and on Wikipedia); combining refers to combining different types of clustering features obtained from the same data source (e.g., using features from Brown and Clark clustering lexicons).",
+      "To choose the best combination of clustering features we tried, via 5-fold cross validation on the training set, every possible permutation of the available Clark and Word2vec clustering lexicons obtained from the data sources. Once the best combination of Clark and Word2vec clustering lexicons per data source was found, we tried to combine them with the Brown clusters. The result is a rather simple but very competitive system that has proven to be highly successful in the most popular Named Entity Recognition and Classification (NER) benchmarks, both in out-of-domain and in-domain evaluations. Furthermore, it was demonstrated that the system also performed robustly across languages without any language-specific tuning. Details of the system's implementation, including detailed description of the local and clustering features, can be found in BIBREF7 , including a section on how to combine the clustering features.",
+      "A preliminary version of this system BIBREF20 was the winner of the OTE sub-task in the ABSA 2015 edition (English only). In the next section we show that this system obtains state-of-the-art results not only across domains and languages for NER, but also for other tasks such as Opinion Target Extraction. The results reported are obtained using the official ABSA evaluation scripts BIBREF4 , BIBREF5 , BIBREF6 ."
+    ],
+    [
+      "In this section we report on the experiments performed using the system and data described above. First we will present the English results for the three ABSA editions as well as a comparison with previous work. After that we will do the same for 5 additional languages included in the ABSA 2016 edition: Dutch, French, Russian, Spanish and Turkish. The local and clustering features, as described in Section SECREF11 , are the same for every language and evaluation setting. The only change is the clustering lexicons used for the different languages. As stated in section SECREF11 , the best cluster combination is chosen via 5-fold cross validation (CV) on the training data. We first try every permutation with the Clark and Word2vec clusters. Once the best combination is obtained, we then try with the Brown clusters obtaining thus the final model for each language and dataset."
+    ],
+    [
+      "Table TABREF16 provides detailed results on the Opinion Target Extraction (OTE) task for English. We show in bold our best model (ALL) chosen via 5-fold CV on the training data. Moreover, we also show the results of the best models using only one type of clustering feature, namely, the best Brown, Clark and Word2vec models, respectively.",
+      "The first noteworthy issue is that the same model obtains the best results on the three English datasets. Second, it is also interesting to note the huge gains obtained by the clustering features, between 6-7 points in F1 score across the three ABSA datasets. Third, the results show that the combination of clustering features induced from different data sources is crucial. Fourth, the clustering features improve the recall by 12-15 points in the 2015 and 2016 data, and around 7 points for 2014. Finally, while in 2014 the precision also increases, in the 2015 setting it degrades almost by 4 points in F1 score.",
+      "Table TABREF17 compares our results with previous work. MIN refers to the multi-task learning framework consisting of two LSTMs equipped with extended memories and neural memory operations with manually developed rules for detecting opinion expressions BIBREF27 . CNN-SenticNet is the 7 layer CNN with Amazon word embeddings, POS, linguistic rules based on syntax patterns and SenticNet BIBREF23 .",
+      "LSTM is a Long Short Term Memory neural network built on top of word embeddings as proposed by BIBREF21 . WDEmb BIBREF35 uses word and dependency path, linear context and dependency context embedding features the input to a CRF. RNCRF is a joint model with CRF and a recursive neural network whereas CMLA is the Coupled Multilayer Attentions model described in section SECREF4 , both systems proposed by BIBREF26 . DLIREC-NLANGP is the winning system at ABSA 2014 and 2016 BIBREF17 , BIBREF18 , BIBREF19 while the penultimate row refers to our own system for all the three benchmarks (details in Table TABREF16 ).",
+      "The results of Table TABREF17 show that our system, despite its simplicity, is highly competitive, obtaining the best results on the 2015 and 2016 datasets and a competitive performance on the 2014 benchmark. In particular, we outperform much more complex and language-specific approaches tuned via language-specific features, such as that of DLIREC-NLANGP. Furthermore, while the deep learning approaches (enriched with human-engineered linguistic features) obtain comparable or better results on the 2014 data, that is not the case for the 2015 and 2016 benchmarks, where our system outperforms also the MIN and CMLA models (systems which require manually added rules and gold-standard opinion expressions to obtain their best results, as explained in section SECREF4 ). In this sense, this means that our system obtains better results than MIN and CMLA by learning the targets independently instead of jointly learning the target and those expressions that convey the polarity of the opinion, namely, the opinion expression.",
+      "There seems to be also a correlation between the size of the datasets and performance, given that the results on the 2014 data are much higher than those obtained using the 2015 and 2016 datasets. This might be due to the fact that the 2014 training set is substantially larger, as detailed in Table TABREF7 . In fact, the smaller datasets seem to affect more the deep learning approaches (LSTM, WDEmb, RNCRF) where only the MIN and CMLA models obtain similar results to ours, albeit using manually added language-specific annotations.",
+      "Finally, it would have been interesting to compare MIN, CNN-SenticNet and CMLA with our system on the three ABSA benchmarks, but their systems are not publicly available."
+    ],
+    [
+      "We trained our system for 5 other languages on the ABSA 2016 datasets, using the same strategy as for English. We choose the best Clark-Word2vec combination (with and without Brown clusters) via 5-cross validation on the training data. The features are exactly the same as those used for English, the only change is the data on which the clusters are trained. Table TABREF19 reports on the detailed results obtained for each of the languages. In bold we show the best model chosen via 5-fold CV. Moreover, we also show the best models using only one of each of the clustering features.",
+      "The first difference with respect to the English results is that the Brown clustering features are, in three out of five settings, detrimental to performance. Second, that combining clustering features is only beneficial for Spanish. Third, the overall results are in general lower than those obtained in the 2016 English data. Finally, the difference between the best results and the results using the Local features is lower than for English, even though the Local results are similar to those obtained with the English datasets (except for Turkish, but this is due to the significantly smaller size of the data, as shown in Table TABREF7 ).",
+      "We believe that all these four issues are caused, at least partially, by the lack of domain-specific clustering features used for the multilingual experiments. In other words, while for the English experiments we leveraged the Yelp dataset to train the clustering algorithms, in the multilingual setting we first tried with already available clusters induced from the Wikipedia. Thus, it is to be expected that the gains obtained by clustering features obtained from domain-specific data such as Yelp would be superior to those achieved by the clusters trained on out-of-domain data.",
+      "In spite of this, Table TABREF20 shows that our system outperforms the best previous approaches across the five languages. In some cases, such as Turkish and Russian, the best previous scores were the baselines provided by the ABSA organizers, but for Dutch, French and Spanish our system is significantly better than current state-of-the-art. In particular, and despite using the same system for every language, we improve over GTI's submission, which implemented a CRF system with linguistic features specific to Spanish BIBREF28 ."
+    ],
+    [
+      "Considering the simplicity of our approach, we obtain best results for 6 languages and 7 different settings in the Opinion Target Extraction (OTE) benchmark for the restaurant domain using the ABSA 2014-2016 datasets.",
+      "These results are obtained without linguistic or manually-engineered features, relying on injecting external knowledge from the combination of clustering features to obtain a robust system across languages, outperforming other more complex and language-specific systems. Furthermore, the feature set used is the same for every setting, reducing human intervention to a minimum and establishing a clear methodology for a fast and easy creation of competitive OTE multilingual taggers.",
+      "The results also confirm the behaviour of these clustering algorithms to provide features for sequence labelling tasks such as OTE and Named Entity Recognition (NER), as previously discussed in BIBREF7 . Thus, in every evaluation setting the best results using Brown clusters as features were obtained when data close to the application domain and text genre, even if relatively small, was used to train the Brown algorithm. This can be clearly seen if we compare the English with the multilingual results. For English, the models including Brown clusters improve the Local features over 3-5 points in F1 score, whereas for Spanish, Dutch and Russian, they worsen performance. The reason is that for English the Yelp dataset is used whereas for the rest of languages the clusters are induced using the Wikipedia, effectively an out-of-domain corpus. The exception is Turkish, for which a 6 point gain in F1 score is obtained, but we believe that is probably due to the small size of the training data used for training the Local model.",
+      "In contrast, Word2vec clusters clearly benefit from larger amounts of data, as illustrated by the best English Word2vec model being the one trained using the Wikipedia, and not the Yelp dataset, which is closer to the application domain. Finally, the Clark algorithm seems to be the most versatile as it consistently outperforms the other two clustering methods in 4 out of the 8 evaluation settings presented.",
+      "Summarizing: (i) Brown clusters perform better when leveraged from source data close to the application domain, even if small in size; (ii) Clark clusters are the most robust of the three with respect to the size and domain of the data used; and (iii) for Word2vec size is the crucial factor. The larger the source data the better the performance. Thus, instead of choosing over one clustering type or the other, our system provides a method to effectively combining them, depending on the data sources available, to obtain robust and language independent sequence labelling systems.",
+      "Finally, results show that our models are particularly competitive when the amount of training data available is small, allowing us to compete with more complex systems including also manually-engineered features, as shown especially by the English results on the 2015 and 2016 data."
+    ],
+    [
+      "We will now discuss the shortcomings and most common errors performed by our system for the OTE task. By looking at the overall results in terms of precision and recall, it is possible to see the following patterns: With respect to the Local models, precision is consistently better than recall or, in other words, the coverage of the Local models is quite low. Tables TABREF16 and TABREF19 show that adding clustering features to the Local models allows to improve the recall for every evaluation setting, although with different outcomes. Overall, precision suffers, except for French. Furthermore, in three cases (English 2014, 2016 and Russian) precision is lower than recall, whereas the remaining 5 evaluations show that, despite large improvements in F1 score, most errors in our system are caused by false negatives, as it can be seen in Table TABREF23 .",
+      "Table TABREF25 displays the top 5 most common false positives and false negative errors for English, Spanish and French. By inspecting our system's output, and both the test and training sets, we found out that there were three main sources of errors: (a) errors caused by ambiguity in the use of certain source forms that may or may not refer to an opinion target; (b) span errors, where the target has only been partially annotated; and (c) unknown targets, which the system was unable to annotate by generalizing on the training data or clusters.",
+      "With respect to type (a), it is useful to look at the most common errors for all three languages, namely, `place', `food' and `restaurant', which are also among the top 5 most frequent targets in the gold standard sets. By looking at Examples (1-3) we would say that in all three cases `place' should be annotated as opinion target. However, (2) is a false positive (FP), (3) is a false negative (FN) and (1) is an example from the training set in which `place' is annotated as target. This is the case with many instances of `place' for which there seems to be some inconsistency in the actual annotation of the training and test set examples.",
+      "",
+      "Example (1): Avoid this place!",
+      "Example (2): this place is a keeper!",
+      "Example (3): it is great place to watch sporting events.",
+      "For other frequent type (a) errors, ambiguity is the main problem. Thus, in Spanish the use of `comida' and `restaurante' is highly ambiguous and causes many FPs and FNs because sometimes it is actually an opinion target whereas in many other other cases it is just referring to the meal or the restaurant themselves without expressing any opinion about them. The same phenomenon occurs for \u201cfood\u201d and \u201crestaurant\u201d in English and for `cuisine' and `restaurant' in French.",
+      "Span type (b) errors are typically caused by long opinion targets such as \u201cfilet mignon on top of spinach and mashed potatoes\u201d for which our system annotates \u201cfilet\u201d and \u201cspinach\u201d as separate targets, or \u201cchicken curry and chicken tikka masala\u201d which is wrongly tagged as one target. These cases are difficult because on the surface they look similar but the first one refers to one dish only, hence one target, whereas the second one refers to two separate dishes for which two different opinion targets should be annotated. Of course, these cases are particularly hurtful because they count as both FP and FN.",
+      "Finally, type (c) errors are usually caused by lack of generalization of our system to deal with unknown targets. Example (4-7) contain various mentions to the \u201cRay's\u201d restaurant, which is in the top 5 errors for the English 2016 test set.",
+      "",
+      "Example (4): After 12 years in Seattle Ray's rates as the place we always go back to.",
+      "Example (5): We were only in Seattle for one night and I'm so glad we picked Rays for dinner!",
+      "Example (6): I love Dungeness crabs and at Ray's you can get them served in about 6 different ways!",
+      "Example (7): Imagine my happy surprise upon finding that the views are only the third-best thing about Ray's!",
+      "Example (8): Ray's is something of a Seattle institution",
+      "Examples (4), (5) and (7) are FNs, (6) is a FP caused by wrongly identifying the target as \u201cRay's you\u201d, whereas (8) is not event annotated in the gold standard or by our system, although it should had been."
+    ],
+    [
+      "In this research note we provide additional empirical experimentation to BIBREF7 , reporting best results for Opinion Target Extraction for 6 languages and 7 datasets using the same set of simple, shallow and language independent features. Furthermore, the results provide some interesting insights with respect to the use of clusters to inject external knowledge via semi-supervised features.",
+      "First, Brown clusters are particularly beneficial when trained on domain-related data. This seems to be the case in the multilingual setting, where the Brown clusters (trained on out-of-domain Wikipedia data) worsen the system's performance for every language except for Turkish.",
+      "Second, the results also show that Clark and Word2vec improve results in general, even if induced on out-of-domain data. Thirdly, for best performance it is convenient to combine clusters obtained from diverse data sources, both from in- and out-of-domain corpora.",
+      "Finally, the results indicate that, even when the amount of training data is small, such as in the 2015 and 2016 English benchmarks, our system's performance remains competitive thanks to the combination of clustering features. This, together with the lack of linguistic features, facilitates the easy and fast development of systems for new domains or languages. These considerations thus confirm the hypotheses stated in BIBREF7 with respect to the use of clustering features to obtain robust sequence taggers across languages and tasks.",
+      "The system and models for every language and dataset are available as part of the ixa-pipe-opinion module for public use and reproducibility of results."
+    ],
+    [
+      "First, we would like to thank the anonymous reviewers for their comments to improve the paper. We would also like to thank I\u00f1aki San Vicente for his help obtaining the Yelp data. This work has been supported by the Spanish Ministry of Economy and Competitiveness (MINECO/FEDER, UE), under the projects TUNER (TIN2015-65308-C5-1-R) and CROSSTEXT (TIN2015-72646-EXP)."
+    ]
+  ]
+}
+```
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+Name of Paper: Evaluating Multimodal Representations on Visual Semantic Textual Similarity
+
+Question: How big is vSTS training data?
\ No newline at end of file
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+++ b/qasper-1943/instruction.md
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+Name of Paper: Exploring Multilingual Syntactic Sentence Representations
+
+Question: Which evaluation metrics do they use for language modelling?
\ No newline at end of file
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+++ b/qasper-1944/instruction.md
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+Name of Paper: Exploring Multilingual Syntactic Sentence Representations
+
+Question: Do they do quantitative quality analysis of learned embeddings?
\ No newline at end of file
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+++ b/qasper-1945/instruction.md
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+Name of Paper: Exploring Multilingual Syntactic Sentence Representations
+
+Question: Do they evaluate on downstream tasks?
\ No newline at end of file
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+Name of Paper: Emotionally-Aware Chatbots: A Survey
+
+Question: How are EAC evaluated?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "History of Emotionally-Aware Chatbot",
+    "Building Emotionally-Aware Chatbot (EAC)",
+    "Resource for Building EAC",
+    "Evaluating EAC",
+    "Qualitative Assessment",
+    "Quantitative Assessment",
+    "Related Work",
+    "Discussion and Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Conversational agents or dialogue systems development are gaining more attention from both industry and academia BIBREF0 , BIBREF1 in the latest years. Some works tried to model them into domain-specific tasks such as customer service BIBREF2 , BIBREF3 , and shopping assistance BIBREF4 . Other works design a multi-purpose agents such as SIRI, Amazon Alexa, and Google Assistance. This domain is a well-researched area in Human-Computer Interaction research community but still become a hot topic now. The main development focus right now is to have an intelligent and humanizing machine to have a better engagement when communicating with human BIBREF5 . Having a better engagement will lead to higher user satisfaction, which becomes the main objective from the industry perspective.",
+      "In this study, we will only focus on textual conversational agent or chatbot, a conversational artificial intelligence which can conduct a textual communication with a human by exploiting several natural language processing techniques. There are several approaches used to build a chatbot, start by using a simple rule-based approach BIBREF6 , BIBREF7 until more sophisticated one by using neural-based technique BIBREF8 , BIBREF9 . Nowadays, chatbots are mostly used as customer service such as booking systems BIBREF10 , BIBREF11 , shopping assistance BIBREF3 or just as conversational partner such as Endurance and Insomnobot . Therefore, there is a significant urgency to humanize chatbot for having a better user-engagement. Some works were already proposed several approaches to improve chatbot's user-engagement, such as building a context-aware chatbot BIBREF12 and injecting personality into the machine BIBREF13 . Other works also try to incorporate affective computing to build emotionally-aware chatbots BIBREF2 , BIBREF14 , BIBREF15 .",
+      "Some existing studies shows that adding emotion information into dialogue systems is able to improve user-satisfaction BIBREF16 , BIBREF17 . Emotion information contribute to a more positive interaction between machine and human, which lead to reduce miscommunication BIBREF18 . Some previous studies also found that using affect information can help chatbot to understand users' emotional state, in order to generate better response BIBREF19 . Not only emotion, another study also introduce the use of tones to improve satisfactory service. For instance, using empathetic tone is able to reduces user stress and results in more engagement. BIBREF2 found that tones is an important aspect in building customer care chatbot. They discover eight different tones including anxious, frustrated, impolite, passionate, polite, sad, satisfied, and empathetic.",
+      "In this paper, we will try to summarize some previous studies which focus on injecting emotion information into chatbots, on discovering recent issues and barriers in building engaging emotionally-aware chatbots. Therefore, we propose some research questions to have a better problem definition:",
+      "This paper will be organized as follows: Section 2 introduces the history of the relation between affective information with chatbots. Section 3 outline some works which try to inject affective information into chatbots. Section 4 summarizes some affective resources which can be utilized to provide affective information. Then, Section 5 describes some evaluation metric that already applied in some previous works related to emotionally-aware chatbots. Last Section 6 will conclude the rest of the paper and provide a prediction of future development in this research direction based on our analysis."
+    ],
+    [
+      "The early development of chatbot was inspired by Turing test in 1950 BIBREF20 . Eliza was the first publicly known chatbot, built by using simple hand-crafted script BIBREF21 . Parry BIBREF22 was another chatbot which successfully passed the Turing test. Similar to Eliza, Parry still uses a rule-based approach but with a better understanding, including the mental model that can stimulate emotion. Therefore, Parry is the first chatbot which involving emotion in its development. Also, worth to be mentioned is ALICE (Artificial Linguistic Internet Computer Entity), a customizable chatbot by using Artificial Intelligence Markup Language (AIML). Therefore, ALICE still also use a rule-based approach by executing a pattern-matcher recursively to obtain the response. Then in May 2014, Microsoft introduced XiaoIce BIBREF23 , an empathetic social chatbot which is able to recognize users' emotional needs. XiaoIce can provide an engaging interpersonal communication by giving encouragement or other affective messages, so that can hold human attention during communication.",
+      "Nowadays, most of chatbots technologies were built by using neural-based approach. Emotional Chatting Machine (ECM) BIBREF15 was the first works which exploiting deep learning approach in building a large-scale emotionally-aware conversational bot. Then several studies were proposed to deal with this research area by introducing emotion embedding representation BIBREF24 , BIBREF25 , BIBREF26 or modeling as reinforcement learning problem BIBREF27 , BIBREF28 . Most of these studies used encoder-decoder architecture, specifically sequence to sequence (seq2seq) learning. Some works also tried to introduce a new dataset in order to have a better gold standard and improve system performance. BIBREF14 introduce EMPATHETICDIALOGUES dataset, a novel dataset containing 25k conversations include emotional contexts information to facilitate training and evaluating the textual conversational system. Then, work from BIBREF2 produce a dataset containing 1.5 million Twitter conversation, gathered by using Twitter API from customer care account of 62 brands across several industries. This dataset was used to build tone-aware customer care chatbot. Finally, BIBREF29 tried to enhance SEMAINE corpus BIBREF30 by using crowdsourcing scenario to obtain a human judgement for deciding which response that elicits positive emotion. Their dataset was used to develop a chatbot which captures human emotional states and elicits positive emotion during the conversation."
+    ],
+    [
+      "As we mentioned before that emotion is an essential aspect of building humanize chatbot. The rise of the emotionally-aware chatbot is started by Parry BIBREF22 in early 1975. Now, most of EAC development exploits neural-based model. In this section, we will try to review previous works which focus on EAC development. Table TABREF10 summarizes this information includes the objective and exploited approach of each work. In early development, EAC is designed by using a rule-based approach. However, in recent years mostly EAC exploit neural-based approach. Studies in EAC development become a hot topic start from 2017, noted by the first shared task in Emotion Generation Challenge on NLPCC 2017 BIBREF31 . Based on Table TABREF10 this research line continues to gain massive attention from scholars in the latest years.",
+      "Based on Table TABREF10 , we can see that most of all recent EAC was built by using encoder-decoder architecture with sequence-to-sequence learning. These seq2seq learning models maximize the likelihood of response and are prepared to incorporate rich data to generate an appropriate answer. Basic seq2seq architecture structured of two recurrent neural networks (RNNs), one as an encoder processing the input and one as a decoder generating the response. long short term memory (LSTM) or gated recurrent unit (GRU) was the most dominant variant of RNNs which used to learn the conversational dataset in these models. Some studies also tried to model this task as a reinforcement learning task, in order to get more generic responses and let the chatbot able to achieve successful long-term conversation. Attention mechanism was also introduced in this report. This mechanism will allow the decoder to focus only on some important parts in the input at every decoding step.",
+      "Another vital part of building EAC is emotion classifier to detect emotion contained in the text to produce a more meaningful response. Emotion detection is a well-established task in natural language processing research area. This task was promoted in two latest series of SemEval-2018 (Task 1) and SemEval-2019 (Task 3). Some tasks were focusing on classifying utterance into several categories of emotion BIBREF32 . However, there is also a task which trying to predict the emotion intensities contained in the text BIBREF33 . In the early development of emotion classifier, most of the studies proposed to use traditional machine-learning approach. However, the neural-based approach is able to gain better performance, which leads more scholars to exploit it to deal with this task. In chatbot, the system will generate several responses based on several emotion categories. Then the system will respond with the most appropriate emotion based on emotion detected on posted utterance by emotion classifier. Based on Table TABREF10 , studies have different emotion categories based on their focus and objective in building chatbots."
+    ],
+    [
+      "In this section, we try to investigate the available resources in building EAC. As other artificial intelligent agents, building chatbot also needs a dataset to be learned, to be able to produce a meaningful conversation as a human-like agent. Therefore, some studies propose dataset which contains textual conversation annotated by different emotion categories. Table TABREF12 summarizes the available dataset that found in recent years. We categorize the dataset based on the language, source of the data, and further description which contains some information such as annotation approach, size of instances, and emotion labels. All datasets were proposed during 2017 and 2018, started by dataset provided by NLPCC 2017 Shared Task on Emotion Generation Challenge organizers. This dataset gathered from Sina Weibo social media , so it consists of social conversation in Chinese. Based on our study, all of the datasets that we discover are only available in two languages, English and Chinese. However, the source of these datasets is very diverse such as social media (Twitter, Sina Weibo, and Facebook Message), online content, and human writing through crowdsourcing scenario. Our investigation found that every dataset use different set of emotion label depends on its focus and objective in building the chatbot.",
+      "As we mentioned in the previous section, that emotion classifier is also an integral part of the emotionally-aware chatbot. In building emotion classifier, there are also available several affective resources, which already widely used in the emotion classification task. Table TABREF16 shows the available affective resources we discovered, range from old resources such as LIWC, ANEW, and DAL, until the modern version such as DepecheMood and EmoWordNet. Based on some prior studies, emotion can be classified into two fundamental viewpoints, including discrete categories and dimensional models. Meanwhile, this feature view emotion as a discrete category which differentiates emotion into several primary emotion. The most popular was proposed by BIBREF37 that differentiate emotion into six categories including anger, disgust, fear, happiness, sadness, and surprise. Three different resources will be used to get emotion contained in the tweet, including EmoLex, EmoSenticNet, LIWC, DepecheMood, and EmoWordNet. Dimensional model is another viewpoint of emotion that define emotion according to one or more dimension. Based on the dimensional approach, emotion is a coincidence value on some different strategic dimensions. We will use two various lexical resources to get the information about the emotional dimension of each tweet, including Dictionary of Affect in Language (DAL), ANEW, and NRC VAD."
+    ],
+    [
+      "We characterize the evaluation of Emotionally-Aware Chatbot into two different parts, qualitative and quantitative assessment. Qualitative assessment will focus on assessing the functionality of the software, while quantitative more focus on measure the chatbots' performance with a number."
+    ],
+    [
+      "Based on our investigation of several previous studies, we found that most of the works utilized ISO 9241 to assess chatbots' quality by focusing on the usability aspect. This aspect can be grouped into three focuses, including efficiency, effectiveness, and satisfaction, concerning systems' performance to achieve the specified goals. Here we will explain every focus based on several categories and quality attributes.",
+      "Efficiency aspect focuses on several categories, including robustness to manipulation and unexpected input BIBREF55 . Another study tries to asses the chatbots' ability to control damage and inappropriate utterance BIBREF56 .",
+      "Effectiveness aspect covers two different categories, functionality and humanity. In functionality point of view, a study by BIBREF57 propose to asses how a chatbot can interpret command accurately and provide its status report. Other functionalities, such as chatbots' ability to execute the task as requested, the output linguistic accuracy, and ease of use suggested being assessed BIBREF58 . Meanwhile, from the human aspect, most of the studies suggest each conversational machine should pass Turing test BIBREF21 . Other prominent abilities that chatbot needs to be mastered can respond to specific questions and able to maintain themed discussion.",
+      "Satisfaction aspect has three categories, including affect, ethics and behaviour, and accessibility. Affect is the most suitable assessment categories for EAC. This category asses several quality aspects such as, chatbots' ability to convey personality, give conversational cues, provide emotional information through tone, inflexion, and expressivity, entertain and/or enable the participant to enjoy the interaction and also read and respond to moods of human participant BIBREF59 . Ethic and behaviour category focuses on how a chatbot can protect and respect privacy BIBREF57 . Other quality aspects, including sensitivity to safety and social concerns and trustworthiness BIBREF60 . The last categories are accessibility, which the main quality aspect focus to assess the chatbot ability to detect meaning or intent and, also responds to social cues ."
+    ],
+    [
+      "In automatic evaluation, some studies focus on evaluating the system at emotion level BIBREF15 , BIBREF28 . Therefore, some common metrics such as precision, recall, and accuracy are used to measure system performance, compared to the gold label. This evaluation is similar to emotion classification tasks such as previous SemEval 2018 BIBREF32 and SemEval 2019 . Other studies also proposed to use perplexity to evaluate the model at the content level (to determine whether the content is relevant and grammatical) BIBREF14 , BIBREF39 , BIBREF28 . This evaluation metric is widely used to evaluate dialogue-based systems which rely on probabilistic approach BIBREF61 . Another work by BIBREF14 used BLEU to evaluate the machine response and compare against the gold response (the actual response), although using BLEU to measure conversation generation task is not recommended by BIBREF62 due to its low correlation with human judgment.",
+      "This evaluation involves human judgement to measure the chatbots' performance, based on several criteria. BIBREF15 used three annotators to rate chatbots' response in two criteria, content (scale 0,1,2) and emotion (scale 0,1). Content is focused on measuring whether the response is natural acceptable and could plausible produced by a human. This metric measurement is already adopted and recommended by researchers and conversation challenging tasks, as proposed in BIBREF38 . Meanwhile, emotion is defined as whether the emotion expression contained in the response agrees with the given gold emotion category. Similarly, BIBREF28 used four annotators to score the response based on consistency, logic and emotion. Consistency measures the fluency and grammatical aspect of the response. Logic measures the degree whether the post and response logically match. Emotion measures the response, whether it contains the appropriate emotion. All of these aspects were measured by three scales 0, 1, and 2. Meanwhile, BIBREF39 proposed naturalness and emotion impact as criteria to evaluate the chatbots' response. Naturalness evaluates whether the response is intelligible, logically follows the context of the conversation, and acceptable as a human response, while emotion impact measures whether the response elicits a positive emotional or triggers an emotionally-positive dialogue, since their study focus only on positive emotion. Another study by BIBREF14 uses crowdsourcing to gather human judgement based on three aspects of performance including empathy/sympathy - did the responses show understanding of the feelings of the person talking about their experience?; relevance - did the responses seem appropriate to the conversation? Were they on-topic?; and fluency - could you understand the responses? Did the language seem accurate?. All of these aspects recorded with three different response, i.e., (1: not at all, 3: somewhat, 5: very much) from around 100 different annotators. After getting all of the human judgement with different criteria, some of these studies used a t-test to get the statistical significance BIBREF28 , BIBREF39 , while some other used inter-annotator agreement measurement such as Fleiss Kappa BIBREF15 , BIBREF14 . Based on these evaluations, they can compare their system performance with baseline or any other state of the art systems."
+    ],
+    [
+      "There is some work which tried to provide a full story of chatbot development both in industries and academic environment. However, as far as my knowledge, there is still no study focused on summarizing the development of chatbot which taking into account the emotional aspect, that getting more attention in recent years. BIBREF63 provides a long history of chatbots technology development. They also described several uses of chatbots' in some practical domains such as tools entertainment, tools to learn and practice language, information retrieval tools, and assistance for e-commerce of other business activities. Then, BIBREF64 reviewed the development of chatbots from rudimentary model to more advanced intelligent system. They summarized several techniques used to develop chatbots from early development until recent years. Recently, BIBREF65 provide a more systematic shape to review some previous works on chatbots' development. They classify chatbots into two main categories based on goals, including task-oriented chatbots and non-task oriented chatbot. They also classify chatbot based on its development technique into three main categories, including rule-based, retrieval-based, and generative-based approach. Furthermore, they also summarized the detailed technique on these three main approaches."
+    ],
+    [
+      "In this work, a systematic review of emotionally-aware chatbots is proposed. We focus on three main issues, including, how to incorporate affective information into chatbots, what are resources that available and can be used to build EAC, and how to evaluate EAC performance. The rise of EAC was started by Parry, which uses a simple rule-based approach. Now, most of EAC are built by using a neural-based approach, by exploiting emotion classifier to detect emotion contained in the text. In the modern era, the development of EAC gains more attention since Emotion Generation Challenge shared task on NLPCC 2017. In this era, most EAC is developed by adopting encoder-decoder architecture with sequence-to-sequence learning. Some variant of the recurrent neural network is used in the learning process, including long-short-term memory (LSTM) and gated recurrent unit (GRU). There are also some datasets available for developing EAC now. However, the datasets are only available in English and Chinese. These datasets are gathered from various sources, including social media, online website and manual construction by crowdsourcing. Overall, the difference between these datasets and the common datasets for building chatbot is the presence of an emotion label. In addition, we also investigate the available affective resources which usually use in the emotion classification task. In this part, we only focus on English resources and found several resources from the old one such as LIWC and Emolex to the new one, including DepecheMood and EmoWordNet. In the final part, we gather information about how to evaluate the performance of EAC, and we can classify the approach into two techniques, including qualitative and quantitative assessment. For qualitative assessment, most studies used ISO 9241, which covers several aspects such as efficiency, effectiveness, and satisfaction. While in quantitative analysis, two techniques can be used, including automatic evaluation (by using perplexity) and manual evaluation (involving human judgement). Overall, we can see that effort to humanize chatbots by incorporation affective aspect is becoming the hot topic now. We also predict that this development will continue by going into multilingual perspective since up to now every chatbot only focusing on one language. Also, we think that in the future the studies of humanizing chatbot are not only utilized emotion information but will also focus on a contextual-aware chatbot."
+    ]
+  ]
+}
+```
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diff --git a/qasper-1973/instruction.md b/qasper-1973/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..1a5dbc15ea07a5e8b78a6528cfc95a710bddf191
--- /dev/null
+++ b/qasper-1973/instruction.md
@@ -0,0 +1,62 @@
+Name of Paper: Emotionally-Aware Chatbots: A Survey
+
+Question: What are the currently available datasets for EAC?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "History of Emotionally-Aware Chatbot",
+    "Building Emotionally-Aware Chatbot (EAC)",
+    "Resource for Building EAC",
+    "Evaluating EAC",
+    "Qualitative Assessment",
+    "Quantitative Assessment",
+    "Related Work",
+    "Discussion and Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Conversational agents or dialogue systems development are gaining more attention from both industry and academia BIBREF0 , BIBREF1 in the latest years. Some works tried to model them into domain-specific tasks such as customer service BIBREF2 , BIBREF3 , and shopping assistance BIBREF4 . Other works design a multi-purpose agents such as SIRI, Amazon Alexa, and Google Assistance. This domain is a well-researched area in Human-Computer Interaction research community but still become a hot topic now. The main development focus right now is to have an intelligent and humanizing machine to have a better engagement when communicating with human BIBREF5 . Having a better engagement will lead to higher user satisfaction, which becomes the main objective from the industry perspective.",
+      "In this study, we will only focus on textual conversational agent or chatbot, a conversational artificial intelligence which can conduct a textual communication with a human by exploiting several natural language processing techniques. There are several approaches used to build a chatbot, start by using a simple rule-based approach BIBREF6 , BIBREF7 until more sophisticated one by using neural-based technique BIBREF8 , BIBREF9 . Nowadays, chatbots are mostly used as customer service such as booking systems BIBREF10 , BIBREF11 , shopping assistance BIBREF3 or just as conversational partner such as Endurance and Insomnobot . Therefore, there is a significant urgency to humanize chatbot for having a better user-engagement. Some works were already proposed several approaches to improve chatbot's user-engagement, such as building a context-aware chatbot BIBREF12 and injecting personality into the machine BIBREF13 . Other works also try to incorporate affective computing to build emotionally-aware chatbots BIBREF2 , BIBREF14 , BIBREF15 .",
+      "Some existing studies shows that adding emotion information into dialogue systems is able to improve user-satisfaction BIBREF16 , BIBREF17 . Emotion information contribute to a more positive interaction between machine and human, which lead to reduce miscommunication BIBREF18 . Some previous studies also found that using affect information can help chatbot to understand users' emotional state, in order to generate better response BIBREF19 . Not only emotion, another study also introduce the use of tones to improve satisfactory service. For instance, using empathetic tone is able to reduces user stress and results in more engagement. BIBREF2 found that tones is an important aspect in building customer care chatbot. They discover eight different tones including anxious, frustrated, impolite, passionate, polite, sad, satisfied, and empathetic.",
+      "In this paper, we will try to summarize some previous studies which focus on injecting emotion information into chatbots, on discovering recent issues and barriers in building engaging emotionally-aware chatbots. Therefore, we propose some research questions to have a better problem definition:",
+      "This paper will be organized as follows: Section 2 introduces the history of the relation between affective information with chatbots. Section 3 outline some works which try to inject affective information into chatbots. Section 4 summarizes some affective resources which can be utilized to provide affective information. Then, Section 5 describes some evaluation metric that already applied in some previous works related to emotionally-aware chatbots. Last Section 6 will conclude the rest of the paper and provide a prediction of future development in this research direction based on our analysis."
+    ],
+    [
+      "The early development of chatbot was inspired by Turing test in 1950 BIBREF20 . Eliza was the first publicly known chatbot, built by using simple hand-crafted script BIBREF21 . Parry BIBREF22 was another chatbot which successfully passed the Turing test. Similar to Eliza, Parry still uses a rule-based approach but with a better understanding, including the mental model that can stimulate emotion. Therefore, Parry is the first chatbot which involving emotion in its development. Also, worth to be mentioned is ALICE (Artificial Linguistic Internet Computer Entity), a customizable chatbot by using Artificial Intelligence Markup Language (AIML). Therefore, ALICE still also use a rule-based approach by executing a pattern-matcher recursively to obtain the response. Then in May 2014, Microsoft introduced XiaoIce BIBREF23 , an empathetic social chatbot which is able to recognize users' emotional needs. XiaoIce can provide an engaging interpersonal communication by giving encouragement or other affective messages, so that can hold human attention during communication.",
+      "Nowadays, most of chatbots technologies were built by using neural-based approach. Emotional Chatting Machine (ECM) BIBREF15 was the first works which exploiting deep learning approach in building a large-scale emotionally-aware conversational bot. Then several studies were proposed to deal with this research area by introducing emotion embedding representation BIBREF24 , BIBREF25 , BIBREF26 or modeling as reinforcement learning problem BIBREF27 , BIBREF28 . Most of these studies used encoder-decoder architecture, specifically sequence to sequence (seq2seq) learning. Some works also tried to introduce a new dataset in order to have a better gold standard and improve system performance. BIBREF14 introduce EMPATHETICDIALOGUES dataset, a novel dataset containing 25k conversations include emotional contexts information to facilitate training and evaluating the textual conversational system. Then, work from BIBREF2 produce a dataset containing 1.5 million Twitter conversation, gathered by using Twitter API from customer care account of 62 brands across several industries. This dataset was used to build tone-aware customer care chatbot. Finally, BIBREF29 tried to enhance SEMAINE corpus BIBREF30 by using crowdsourcing scenario to obtain a human judgement for deciding which response that elicits positive emotion. Their dataset was used to develop a chatbot which captures human emotional states and elicits positive emotion during the conversation."
+    ],
+    [
+      "As we mentioned before that emotion is an essential aspect of building humanize chatbot. The rise of the emotionally-aware chatbot is started by Parry BIBREF22 in early 1975. Now, most of EAC development exploits neural-based model. In this section, we will try to review previous works which focus on EAC development. Table TABREF10 summarizes this information includes the objective and exploited approach of each work. In early development, EAC is designed by using a rule-based approach. However, in recent years mostly EAC exploit neural-based approach. Studies in EAC development become a hot topic start from 2017, noted by the first shared task in Emotion Generation Challenge on NLPCC 2017 BIBREF31 . Based on Table TABREF10 this research line continues to gain massive attention from scholars in the latest years.",
+      "Based on Table TABREF10 , we can see that most of all recent EAC was built by using encoder-decoder architecture with sequence-to-sequence learning. These seq2seq learning models maximize the likelihood of response and are prepared to incorporate rich data to generate an appropriate answer. Basic seq2seq architecture structured of two recurrent neural networks (RNNs), one as an encoder processing the input and one as a decoder generating the response. long short term memory (LSTM) or gated recurrent unit (GRU) was the most dominant variant of RNNs which used to learn the conversational dataset in these models. Some studies also tried to model this task as a reinforcement learning task, in order to get more generic responses and let the chatbot able to achieve successful long-term conversation. Attention mechanism was also introduced in this report. This mechanism will allow the decoder to focus only on some important parts in the input at every decoding step.",
+      "Another vital part of building EAC is emotion classifier to detect emotion contained in the text to produce a more meaningful response. Emotion detection is a well-established task in natural language processing research area. This task was promoted in two latest series of SemEval-2018 (Task 1) and SemEval-2019 (Task 3). Some tasks were focusing on classifying utterance into several categories of emotion BIBREF32 . However, there is also a task which trying to predict the emotion intensities contained in the text BIBREF33 . In the early development of emotion classifier, most of the studies proposed to use traditional machine-learning approach. However, the neural-based approach is able to gain better performance, which leads more scholars to exploit it to deal with this task. In chatbot, the system will generate several responses based on several emotion categories. Then the system will respond with the most appropriate emotion based on emotion detected on posted utterance by emotion classifier. Based on Table TABREF10 , studies have different emotion categories based on their focus and objective in building chatbots."
+    ],
+    [
+      "In this section, we try to investigate the available resources in building EAC. As other artificial intelligent agents, building chatbot also needs a dataset to be learned, to be able to produce a meaningful conversation as a human-like agent. Therefore, some studies propose dataset which contains textual conversation annotated by different emotion categories. Table TABREF12 summarizes the available dataset that found in recent years. We categorize the dataset based on the language, source of the data, and further description which contains some information such as annotation approach, size of instances, and emotion labels. All datasets were proposed during 2017 and 2018, started by dataset provided by NLPCC 2017 Shared Task on Emotion Generation Challenge organizers. This dataset gathered from Sina Weibo social media , so it consists of social conversation in Chinese. Based on our study, all of the datasets that we discover are only available in two languages, English and Chinese. However, the source of these datasets is very diverse such as social media (Twitter, Sina Weibo, and Facebook Message), online content, and human writing through crowdsourcing scenario. Our investigation found that every dataset use different set of emotion label depends on its focus and objective in building the chatbot.",
+      "As we mentioned in the previous section, that emotion classifier is also an integral part of the emotionally-aware chatbot. In building emotion classifier, there are also available several affective resources, which already widely used in the emotion classification task. Table TABREF16 shows the available affective resources we discovered, range from old resources such as LIWC, ANEW, and DAL, until the modern version such as DepecheMood and EmoWordNet. Based on some prior studies, emotion can be classified into two fundamental viewpoints, including discrete categories and dimensional models. Meanwhile, this feature view emotion as a discrete category which differentiates emotion into several primary emotion. The most popular was proposed by BIBREF37 that differentiate emotion into six categories including anger, disgust, fear, happiness, sadness, and surprise. Three different resources will be used to get emotion contained in the tweet, including EmoLex, EmoSenticNet, LIWC, DepecheMood, and EmoWordNet. Dimensional model is another viewpoint of emotion that define emotion according to one or more dimension. Based on the dimensional approach, emotion is a coincidence value on some different strategic dimensions. We will use two various lexical resources to get the information about the emotional dimension of each tweet, including Dictionary of Affect in Language (DAL), ANEW, and NRC VAD."
+    ],
+    [
+      "We characterize the evaluation of Emotionally-Aware Chatbot into two different parts, qualitative and quantitative assessment. Qualitative assessment will focus on assessing the functionality of the software, while quantitative more focus on measure the chatbots' performance with a number."
+    ],
+    [
+      "Based on our investigation of several previous studies, we found that most of the works utilized ISO 9241 to assess chatbots' quality by focusing on the usability aspect. This aspect can be grouped into three focuses, including efficiency, effectiveness, and satisfaction, concerning systems' performance to achieve the specified goals. Here we will explain every focus based on several categories and quality attributes.",
+      "Efficiency aspect focuses on several categories, including robustness to manipulation and unexpected input BIBREF55 . Another study tries to asses the chatbots' ability to control damage and inappropriate utterance BIBREF56 .",
+      "Effectiveness aspect covers two different categories, functionality and humanity. In functionality point of view, a study by BIBREF57 propose to asses how a chatbot can interpret command accurately and provide its status report. Other functionalities, such as chatbots' ability to execute the task as requested, the output linguistic accuracy, and ease of use suggested being assessed BIBREF58 . Meanwhile, from the human aspect, most of the studies suggest each conversational machine should pass Turing test BIBREF21 . Other prominent abilities that chatbot needs to be mastered can respond to specific questions and able to maintain themed discussion.",
+      "Satisfaction aspect has three categories, including affect, ethics and behaviour, and accessibility. Affect is the most suitable assessment categories for EAC. This category asses several quality aspects such as, chatbots' ability to convey personality, give conversational cues, provide emotional information through tone, inflexion, and expressivity, entertain and/or enable the participant to enjoy the interaction and also read and respond to moods of human participant BIBREF59 . Ethic and behaviour category focuses on how a chatbot can protect and respect privacy BIBREF57 . Other quality aspects, including sensitivity to safety and social concerns and trustworthiness BIBREF60 . The last categories are accessibility, which the main quality aspect focus to assess the chatbot ability to detect meaning or intent and, also responds to social cues ."
+    ],
+    [
+      "In automatic evaluation, some studies focus on evaluating the system at emotion level BIBREF15 , BIBREF28 . Therefore, some common metrics such as precision, recall, and accuracy are used to measure system performance, compared to the gold label. This evaluation is similar to emotion classification tasks such as previous SemEval 2018 BIBREF32 and SemEval 2019 . Other studies also proposed to use perplexity to evaluate the model at the content level (to determine whether the content is relevant and grammatical) BIBREF14 , BIBREF39 , BIBREF28 . This evaluation metric is widely used to evaluate dialogue-based systems which rely on probabilistic approach BIBREF61 . Another work by BIBREF14 used BLEU to evaluate the machine response and compare against the gold response (the actual response), although using BLEU to measure conversation generation task is not recommended by BIBREF62 due to its low correlation with human judgment.",
+      "This evaluation involves human judgement to measure the chatbots' performance, based on several criteria. BIBREF15 used three annotators to rate chatbots' response in two criteria, content (scale 0,1,2) and emotion (scale 0,1). Content is focused on measuring whether the response is natural acceptable and could plausible produced by a human. This metric measurement is already adopted and recommended by researchers and conversation challenging tasks, as proposed in BIBREF38 . Meanwhile, emotion is defined as whether the emotion expression contained in the response agrees with the given gold emotion category. Similarly, BIBREF28 used four annotators to score the response based on consistency, logic and emotion. Consistency measures the fluency and grammatical aspect of the response. Logic measures the degree whether the post and response logically match. Emotion measures the response, whether it contains the appropriate emotion. All of these aspects were measured by three scales 0, 1, and 2. Meanwhile, BIBREF39 proposed naturalness and emotion impact as criteria to evaluate the chatbots' response. Naturalness evaluates whether the response is intelligible, logically follows the context of the conversation, and acceptable as a human response, while emotion impact measures whether the response elicits a positive emotional or triggers an emotionally-positive dialogue, since their study focus only on positive emotion. Another study by BIBREF14 uses crowdsourcing to gather human judgement based on three aspects of performance including empathy/sympathy - did the responses show understanding of the feelings of the person talking about their experience?; relevance - did the responses seem appropriate to the conversation? Were they on-topic?; and fluency - could you understand the responses? Did the language seem accurate?. All of these aspects recorded with three different response, i.e., (1: not at all, 3: somewhat, 5: very much) from around 100 different annotators. After getting all of the human judgement with different criteria, some of these studies used a t-test to get the statistical significance BIBREF28 , BIBREF39 , while some other used inter-annotator agreement measurement such as Fleiss Kappa BIBREF15 , BIBREF14 . Based on these evaluations, they can compare their system performance with baseline or any other state of the art systems."
+    ],
+    [
+      "There is some work which tried to provide a full story of chatbot development both in industries and academic environment. However, as far as my knowledge, there is still no study focused on summarizing the development of chatbot which taking into account the emotional aspect, that getting more attention in recent years. BIBREF63 provides a long history of chatbots technology development. They also described several uses of chatbots' in some practical domains such as tools entertainment, tools to learn and practice language, information retrieval tools, and assistance for e-commerce of other business activities. Then, BIBREF64 reviewed the development of chatbots from rudimentary model to more advanced intelligent system. They summarized several techniques used to develop chatbots from early development until recent years. Recently, BIBREF65 provide a more systematic shape to review some previous works on chatbots' development. They classify chatbots into two main categories based on goals, including task-oriented chatbots and non-task oriented chatbot. They also classify chatbot based on its development technique into three main categories, including rule-based, retrieval-based, and generative-based approach. Furthermore, they also summarized the detailed technique on these three main approaches."
+    ],
+    [
+      "In this work, a systematic review of emotionally-aware chatbots is proposed. We focus on three main issues, including, how to incorporate affective information into chatbots, what are resources that available and can be used to build EAC, and how to evaluate EAC performance. The rise of EAC was started by Parry, which uses a simple rule-based approach. Now, most of EAC are built by using a neural-based approach, by exploiting emotion classifier to detect emotion contained in the text. In the modern era, the development of EAC gains more attention since Emotion Generation Challenge shared task on NLPCC 2017. In this era, most EAC is developed by adopting encoder-decoder architecture with sequence-to-sequence learning. Some variant of the recurrent neural network is used in the learning process, including long-short-term memory (LSTM) and gated recurrent unit (GRU). There are also some datasets available for developing EAC now. However, the datasets are only available in English and Chinese. These datasets are gathered from various sources, including social media, online website and manual construction by crowdsourcing. Overall, the difference between these datasets and the common datasets for building chatbot is the presence of an emotion label. In addition, we also investigate the available affective resources which usually use in the emotion classification task. In this part, we only focus on English resources and found several resources from the old one such as LIWC and Emolex to the new one, including DepecheMood and EmoWordNet. In the final part, we gather information about how to evaluate the performance of EAC, and we can classify the approach into two techniques, including qualitative and quantitative assessment. For qualitative assessment, most studies used ISO 9241, which covers several aspects such as efficiency, effectiveness, and satisfaction. While in quantitative analysis, two techniques can be used, including automatic evaluation (by using perplexity) and manual evaluation (involving human judgement). Overall, we can see that effort to humanize chatbots by incorporation affective aspect is becoming the hot topic now. We also predict that this development will continue by going into multilingual perspective since up to now every chatbot only focusing on one language. Also, we think that in the future the studies of humanizing chatbot are not only utilized emotion information but will also focus on a contextual-aware chatbot."
+    ]
+  ]
+}
+```
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+++ b/qasper-1980/instruction.md
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+Name of Paper: Learning to Describe Phrases with Local and Global Contexts
+
+Question: Do they use pretrained word embeddings?
\ No newline at end of file
diff --git a/qasper-1986/instruction.md b/qasper-1986/instruction.md
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+Name of Paper: Multilingual sequence-to-sequence speech recognition: architecture, transfer learning, and language modeling
+
+Question: Do they report BLEU scores?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Sequence-to-Sequence Model",
+    "Data details and experimental setup",
+    "Multilingual experiments",
+    "Stage 0 - Naive approach",
+    "Stage 1 - Retraining decoder only",
+    "Stage 2 - Finetuning both encoder and decoder",
+    "Multilingual RNNLM",
+    "Conclusion",
+    "Future work"
+  ],
+  "paragraphs": [
+    [
+      "The sequence-to-sequence (seq2seq) model proposed in BIBREF0 , BIBREF1 , BIBREF2 is a neural architecture for performing sequence classification and later adopted to perform speech recognition in BIBREF3 , BIBREF4 , BIBREF5 . The model allows to integrate the main blocks of ASR such as acoustic model, alignment model and language model into a single framework. The recent ASR advancements in connectionist temporal classification (CTC) BIBREF5 , BIBREF4 and attention BIBREF3 , BIBREF6 based approaches has created larger interest in speech community to use seq2seq models. To leverage performance gains from this model as similar or better to conventional hybrid RNN/DNN-HMM models requires a huge amount of data BIBREF7 . Intuitively, this is due to the wide-range role of the model in performing alignment and language modeling along with acoustic to character label mapping at each iteration.",
+      "In this paper, we explore the multilingual training approaches BIBREF8 , BIBREF9 , BIBREF10 used in hybrid DNN/RNN-HMMs to incorporate them into the seq2seq models. In a context of applications of multilingual approaches towards seq2seq model, CTC is mainly used instead of the attention models. A multilingual CTC is proposed in BIBREF11 , which uses a universal phoneset, FST decoder and language model. The authors also use linear hidden unit contribution (LHUC) BIBREF12 technique to rescale the hidden unit outputs for each language as a way to adapt to a particular language. Another work BIBREF13 on multilingual CTC shows the importance of language adaptive vectors as auxiliary input to the encoder in multilingual CTC model. The decoder used here is a simple INLINEFORM0 decoder. An extensive analysis on multilingual CTC mainly focusing on improving under limited data condition is performed in BIBREF14 . Here, the authors use a word level FST decoder integrated with CTC during decoding.",
+      "On a similar front, attention models are explored within a multilingual setup in BIBREF15 , BIBREF16 based on attention-based seq2seq to build a model from multiple languages. The data is just combined together assuming the target languages are seen during the training. And, hence no special transfer learning techniques were used here to address the unseen languages during training. The main motivation and contribution behind this work is as follows:"
+    ],
+    [
+      "In this work, we use the attention based approach BIBREF1 as it provides an effective methodology to perform sequence-to-sequence (seq2seq) training. Considering the limitations of attention in performing monotonic alignment BIBREF18 , BIBREF19 , we choose to use CTC loss function to aid the attention mechanism in both training and decoding. The basic network architecture is shown in Fig. FIGREF7 .",
+      "Let INLINEFORM0 be a INLINEFORM1 -length speech feature sequence and INLINEFORM2 be a INLINEFORM3 -length grapheme sequence. A multi-objective learning framework INLINEFORM4 proposed in BIBREF17 is used in this work to unify attention loss INLINEFORM5 and CTC loss INLINEFORM6 with a linear interpolation weight INLINEFORM7 , as follows: DISPLAYFORM0 ",
+      "The unified model allows to obtain both monotonicity and effective sequence level training.",
+      " INLINEFORM0 represents the posterior probability of character label sequence INLINEFORM1 w.r.t input sequence INLINEFORM2 based on the attention approach, which is decomposed with the probabilistic chain rule, as follows: DISPLAYFORM0 ",
+      "where INLINEFORM0 denotes the ground truth history. Detailed explanations about the attention mechanism is described later.",
+      "Similarly, INLINEFORM0 represents the posterior probability based on the CTC approach. DISPLAYFORM0 ",
+      "where INLINEFORM0 is a CTC state sequence composed of the original grapheme set and the additional blank symbol. INLINEFORM1 is a set of all possible sequences given the character sequence INLINEFORM2 .",
+      "The following paragraphs explain the encoder, attention decoder, CTC, and joint decoding used in our approach."
+    ],
+    [
+      "In this work, the experiments are conducted using the BABEL speech corpus collected from the IARPA babel program. The corpus is mainly composed of conversational telephone speech (CTS) but some scripted recordings and far field recordings are presented as well. Table TABREF14 presents the details of the languages used in this work for training and evaluation.",
+      "80 dimensional Mel-filterbank (fbank) features are then extracted from the speech samples using a sliding window of size 25 ms with 10ms stride. KALDI toolkit BIBREF24 is used to perform the feature processing. The fbank features are then fed to a seq2seq model with the following configuration:",
+      "The Bi-RNN BIBREF25 models mentioned above uses a LSTM BIBREF26 cell followed by a projection layer (BLSTMP). In our experiments below, we use only a character-level seq2seq model trained by CTC and attention decoder. Thus in the following experiments we intend to use character error rate (% CER) as a suitable measure to analyze the model performance. However, in section SECREF26 we integrate a character-level RNNLM BIBREF27 with seq2seq model externally and showcase the performance in terms of word error rate (% WER). In this case the words are obtained by concatenating the characters and the space together for scoring with reference words. All experiments are implemented in ESPnet, end-to-end speech processing toolkit BIBREF28 ."
+    ],
+    [
+      "Multilingual approaches used in hybrid RNN/DNN-HMM systems BIBREF10 have been used for for tackling the problem of low-resource data condition. Some of these approaches include language adaptive training and shared layer retraining BIBREF29 . Among them, the most benefited method is the parameter sharing technique BIBREF10 . To incorporate the former approach into encoder, CTC and attention decoder model, we performed the following experiments:"
+    ],
+    [
+      "In this approach, the model is first trained with 10 multiple languages as denoted in table TABREF14 approximating to 600 hours of training data. data from all languages available during training is used to build a single seq2seq model. The model is trained with a character label set composed of characters from all languages including both train and target set as mentioned in table TABREF14 . The model provides better generalization across languages. Languages with limited data when trained with other languages allows them to be robust and helps in improving the recognition performance. In spite of being simple, the model has limitations in keeping the target language data unseen during training.",
+      "Table TABREF16 shows the recognition performance of naive multilingual approach using BLSTMP and VGG model against a monolingual model trained with BLSTMP. The results clearly indicate that having a better architecture such as VGG-BLSTM helps in improving multilingual performance. Except Pashto, Georgian and Tokpisin, the multilingual VGG-BLSTM model gave 8.8 % absolute gain in average over monolingual model. In case of multilingual BLSTMP, except Pashto and Georgian an absolute gain of 5.0 % in average is observed over monolingual model. Even though the VGG-BLSTM gave improvements, we were not able to perform stage-1 and stage-2 retraining with it due to time constraints. Thus, we proceed further with multilingual BLSTMP model for retraining experiments tabulated below."
+    ],
+    [
+      "To alleviate the limitation in the previous approach, the final layer of the seq2seq model which is mainly responsible for classification is retrained to the target language.",
+      "In previous works BIBREF10 , BIBREF29 related to hybrid DNN/RNN models and CTC based models BIBREF11 , BIBREF14 the softmax layer is only adapted. However in our case, the attention decoder and CTC decoder both have to be retrained to the target language. This means the CTC and attention layers are only updated for gradients during this stage. We found using SGD optimizer with initial learning rate of INLINEFORM0 works better for retraining compared to AdaDelta.",
+      "The learning rate is decayed in this training at a factor of INLINEFORM0 if there is a drop in validation accuracy. Table TABREF20 shows the performance of simply retraining the last layer using a single target language Assamese."
+    ],
+    [
+      "Based on the observations from stage-1 model in section SECREF22 , we found that simply retraining the decoder towards a target language resulted in degrading %CER the performance from 45.6 to 61.3. This is mainly due to the difference in distribution across encoder and decoder. So, to alleviate this difference the encoder and decoder is once again retrained or fine-tuned using the model from stage-1. The optimizer used here is SGD as in stage-1, but the initial learning rate is kept to INLINEFORM0 and decayed based on validation performance. The resulting model gave an absolute gain of 1.6% when finetuned a multilingual model after 4th epoch. Also, finetuning a model after 15th epoch gave an absolute gain of 4.3%.",
+      "To further investigate the performance of this approach across different target data sizes, we split the train set into INLINEFORM0 5 hours, INLINEFORM1 10 hours, INLINEFORM2 20 hours and INLINEFORM3 full set. Since, in this approach the model is only finetuned by initializing from stage-1 model, the model architecture is fixed for all data sizes. Figure FIGREF23 shows the effectiveness of finetuning both encoder and decoder. The gains from 5 to 10 hours was more compared to 20 hours to full set.",
+      "Table TABREF25 tabulates the % CER obtained by retraining the stage-1 model with INLINEFORM0 full set of target language data. An absolute gain is observed using stage-2 retraining across all languages compared to monolingual model."
+    ],
+    [
+      "In an ASR system, a language model (LM) takes an important role by incorporating external knowledge into the system. Conventional ASR systems combine an LM with an acoustic model by FST giving a huge performance gain. This trend is also shown in general including hybrid ASR systems and neural network-based sequence-to-sequence ASR systems.",
+      "The following experiments show a benefit of using a language model in decoding with the previous stage-2 transferred models. Although the performance gains in %CER are also generally observed over all target languages, the improvement in %WER was more distinctive. The results shown in the following Fig. FIGREF27 are in %WER. \u201cwhole\u201d in each figure means we used all the available data for the target language as full set explained before.",
+      "",
+      "",
+      "We used a character-level RNNLM, which was trained with 2-layer LSTM on character sequences. We use all available paired text in the corresponding target language to train the LM for the language. No external text data were used. All language models are trained separately from the seq2seq models. When building dictionary, we combined all the characters over all 15 languages mentioned in table TABREF14 to make them work with transferred models. Regardless of the amount of data used for transfer learning, the RNNLM provides consistent gains across all languages over different data sizes.",
+      "As explained already, language models were trained separately and used to decode jointly with seq2seq models. The intuition behind it is to use the separately trained language model as a complementary component that works with a implicit language model within a seq2seq decoder. The way of RNNLM assisting decoding follows the equation below: DISPLAYFORM0 ",
+      " INLINEFORM0 is a scaling factor that combines the scores from a joint decoding eq.( EQREF13 ) with RNN-LM, denoted as INLINEFORM1 . This approach is called shallow fusion.",
+      "Our experiments for target languages show that the gains from adding RNNLM are consistent regardless of the amount of data used for transfer learning. In other words, in Figure FIGREF27 , the gap between two lines are almost consistent over all languages.",
+      "Also, we observe the gain we get by adding RNN-LM in decoding is large. For example, in the case of assamese, the gain by RNN-LM in decoding with a model retrained on 5 hours of the target language data is almost comparable with the model stage-2 retrained with 20 hours of target language data. On average, absolute gain INLINEFORM0 6% is obtained across all target languages as noted in table TABREF28 ."
+    ],
+    [
+      "In this work, we have shown the importance of transfer learning approach such as stage-2 multilingual retraining in a seq2seq model setting. Also, careful selection of train and target languages from BABEL provide a wide variety in recognition performance (%CER) and helps in understanding the efficacy of seq2seq model. The experiments using character-based RNNLM showed the importance of language model in boosting recognition performance (%WER) over all different hours of target data available for transfer learning.",
+      "Table TABREF25 and TABREF28 summarizes, the effect of these techniques in terms of %CER and %WER. These methods also show their flexibility in incorporating it in attention and CTC based seq2seq model without compromising loss in performance."
+    ],
+    [
+      "We could use better architectures such as VGG-BLSTM as a multilingual prior model before transferring them to a new target language by performing stage-2 retraining. The naive multilingual approach can be improved by including language vectors as input or target during training to reduce the confusions. Also, investigation of multilingual bottleneck features BIBREF30 for seq2seq model can provide better performance. Apart from using the character level language model as in this work, a word level RNNLM can be connected during decoding to further improve %WER. The attention based decoder can be aided with the help of RNNLM using cold fusion approach during training to attain a better-trained model. In near future, we will incorporate all the above techniques to get comparable performance with the state-of-the-art hybrid DNN/RNN-HMM systems."
+    ]
+  ]
+}
+```
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diff --git a/qasper-1987/instruction.md b/qasper-1987/instruction.md
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index 0000000000000000000000000000000000000000..98abc06f265a51a16f759cb98a4dc07fd371263d
--- /dev/null
+++ b/qasper-1987/instruction.md
@@ -0,0 +1,79 @@
+Name of Paper: Multilingual sequence-to-sequence speech recognition: architecture, transfer learning, and language modeling
+
+Question: What languages do they use?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Sequence-to-Sequence Model",
+    "Data details and experimental setup",
+    "Multilingual experiments",
+    "Stage 0 - Naive approach",
+    "Stage 1 - Retraining decoder only",
+    "Stage 2 - Finetuning both encoder and decoder",
+    "Multilingual RNNLM",
+    "Conclusion",
+    "Future work"
+  ],
+  "paragraphs": [
+    [
+      "The sequence-to-sequence (seq2seq) model proposed in BIBREF0 , BIBREF1 , BIBREF2 is a neural architecture for performing sequence classification and later adopted to perform speech recognition in BIBREF3 , BIBREF4 , BIBREF5 . The model allows to integrate the main blocks of ASR such as acoustic model, alignment model and language model into a single framework. The recent ASR advancements in connectionist temporal classification (CTC) BIBREF5 , BIBREF4 and attention BIBREF3 , BIBREF6 based approaches has created larger interest in speech community to use seq2seq models. To leverage performance gains from this model as similar or better to conventional hybrid RNN/DNN-HMM models requires a huge amount of data BIBREF7 . Intuitively, this is due to the wide-range role of the model in performing alignment and language modeling along with acoustic to character label mapping at each iteration.",
+      "In this paper, we explore the multilingual training approaches BIBREF8 , BIBREF9 , BIBREF10 used in hybrid DNN/RNN-HMMs to incorporate them into the seq2seq models. In a context of applications of multilingual approaches towards seq2seq model, CTC is mainly used instead of the attention models. A multilingual CTC is proposed in BIBREF11 , which uses a universal phoneset, FST decoder and language model. The authors also use linear hidden unit contribution (LHUC) BIBREF12 technique to rescale the hidden unit outputs for each language as a way to adapt to a particular language. Another work BIBREF13 on multilingual CTC shows the importance of language adaptive vectors as auxiliary input to the encoder in multilingual CTC model. The decoder used here is a simple INLINEFORM0 decoder. An extensive analysis on multilingual CTC mainly focusing on improving under limited data condition is performed in BIBREF14 . Here, the authors use a word level FST decoder integrated with CTC during decoding.",
+      "On a similar front, attention models are explored within a multilingual setup in BIBREF15 , BIBREF16 based on attention-based seq2seq to build a model from multiple languages. The data is just combined together assuming the target languages are seen during the training. And, hence no special transfer learning techniques were used here to address the unseen languages during training. The main motivation and contribution behind this work is as follows:"
+    ],
+    [
+      "In this work, we use the attention based approach BIBREF1 as it provides an effective methodology to perform sequence-to-sequence (seq2seq) training. Considering the limitations of attention in performing monotonic alignment BIBREF18 , BIBREF19 , we choose to use CTC loss function to aid the attention mechanism in both training and decoding. The basic network architecture is shown in Fig. FIGREF7 .",
+      "Let INLINEFORM0 be a INLINEFORM1 -length speech feature sequence and INLINEFORM2 be a INLINEFORM3 -length grapheme sequence. A multi-objective learning framework INLINEFORM4 proposed in BIBREF17 is used in this work to unify attention loss INLINEFORM5 and CTC loss INLINEFORM6 with a linear interpolation weight INLINEFORM7 , as follows: DISPLAYFORM0 ",
+      "The unified model allows to obtain both monotonicity and effective sequence level training.",
+      " INLINEFORM0 represents the posterior probability of character label sequence INLINEFORM1 w.r.t input sequence INLINEFORM2 based on the attention approach, which is decomposed with the probabilistic chain rule, as follows: DISPLAYFORM0 ",
+      "where INLINEFORM0 denotes the ground truth history. Detailed explanations about the attention mechanism is described later.",
+      "Similarly, INLINEFORM0 represents the posterior probability based on the CTC approach. DISPLAYFORM0 ",
+      "where INLINEFORM0 is a CTC state sequence composed of the original grapheme set and the additional blank symbol. INLINEFORM1 is a set of all possible sequences given the character sequence INLINEFORM2 .",
+      "The following paragraphs explain the encoder, attention decoder, CTC, and joint decoding used in our approach."
+    ],
+    [
+      "In this work, the experiments are conducted using the BABEL speech corpus collected from the IARPA babel program. The corpus is mainly composed of conversational telephone speech (CTS) but some scripted recordings and far field recordings are presented as well. Table TABREF14 presents the details of the languages used in this work for training and evaluation.",
+      "80 dimensional Mel-filterbank (fbank) features are then extracted from the speech samples using a sliding window of size 25 ms with 10ms stride. KALDI toolkit BIBREF24 is used to perform the feature processing. The fbank features are then fed to a seq2seq model with the following configuration:",
+      "The Bi-RNN BIBREF25 models mentioned above uses a LSTM BIBREF26 cell followed by a projection layer (BLSTMP). In our experiments below, we use only a character-level seq2seq model trained by CTC and attention decoder. Thus in the following experiments we intend to use character error rate (% CER) as a suitable measure to analyze the model performance. However, in section SECREF26 we integrate a character-level RNNLM BIBREF27 with seq2seq model externally and showcase the performance in terms of word error rate (% WER). In this case the words are obtained by concatenating the characters and the space together for scoring with reference words. All experiments are implemented in ESPnet, end-to-end speech processing toolkit BIBREF28 ."
+    ],
+    [
+      "Multilingual approaches used in hybrid RNN/DNN-HMM systems BIBREF10 have been used for for tackling the problem of low-resource data condition. Some of these approaches include language adaptive training and shared layer retraining BIBREF29 . Among them, the most benefited method is the parameter sharing technique BIBREF10 . To incorporate the former approach into encoder, CTC and attention decoder model, we performed the following experiments:"
+    ],
+    [
+      "In this approach, the model is first trained with 10 multiple languages as denoted in table TABREF14 approximating to 600 hours of training data. data from all languages available during training is used to build a single seq2seq model. The model is trained with a character label set composed of characters from all languages including both train and target set as mentioned in table TABREF14 . The model provides better generalization across languages. Languages with limited data when trained with other languages allows them to be robust and helps in improving the recognition performance. In spite of being simple, the model has limitations in keeping the target language data unseen during training.",
+      "Table TABREF16 shows the recognition performance of naive multilingual approach using BLSTMP and VGG model against a monolingual model trained with BLSTMP. The results clearly indicate that having a better architecture such as VGG-BLSTM helps in improving multilingual performance. Except Pashto, Georgian and Tokpisin, the multilingual VGG-BLSTM model gave 8.8 % absolute gain in average over monolingual model. In case of multilingual BLSTMP, except Pashto and Georgian an absolute gain of 5.0 % in average is observed over monolingual model. Even though the VGG-BLSTM gave improvements, we were not able to perform stage-1 and stage-2 retraining with it due to time constraints. Thus, we proceed further with multilingual BLSTMP model for retraining experiments tabulated below."
+    ],
+    [
+      "To alleviate the limitation in the previous approach, the final layer of the seq2seq model which is mainly responsible for classification is retrained to the target language.",
+      "In previous works BIBREF10 , BIBREF29 related to hybrid DNN/RNN models and CTC based models BIBREF11 , BIBREF14 the softmax layer is only adapted. However in our case, the attention decoder and CTC decoder both have to be retrained to the target language. This means the CTC and attention layers are only updated for gradients during this stage. We found using SGD optimizer with initial learning rate of INLINEFORM0 works better for retraining compared to AdaDelta.",
+      "The learning rate is decayed in this training at a factor of INLINEFORM0 if there is a drop in validation accuracy. Table TABREF20 shows the performance of simply retraining the last layer using a single target language Assamese."
+    ],
+    [
+      "Based on the observations from stage-1 model in section SECREF22 , we found that simply retraining the decoder towards a target language resulted in degrading %CER the performance from 45.6 to 61.3. This is mainly due to the difference in distribution across encoder and decoder. So, to alleviate this difference the encoder and decoder is once again retrained or fine-tuned using the model from stage-1. The optimizer used here is SGD as in stage-1, but the initial learning rate is kept to INLINEFORM0 and decayed based on validation performance. The resulting model gave an absolute gain of 1.6% when finetuned a multilingual model after 4th epoch. Also, finetuning a model after 15th epoch gave an absolute gain of 4.3%.",
+      "To further investigate the performance of this approach across different target data sizes, we split the train set into INLINEFORM0 5 hours, INLINEFORM1 10 hours, INLINEFORM2 20 hours and INLINEFORM3 full set. Since, in this approach the model is only finetuned by initializing from stage-1 model, the model architecture is fixed for all data sizes. Figure FIGREF23 shows the effectiveness of finetuning both encoder and decoder. The gains from 5 to 10 hours was more compared to 20 hours to full set.",
+      "Table TABREF25 tabulates the % CER obtained by retraining the stage-1 model with INLINEFORM0 full set of target language data. An absolute gain is observed using stage-2 retraining across all languages compared to monolingual model."
+    ],
+    [
+      "In an ASR system, a language model (LM) takes an important role by incorporating external knowledge into the system. Conventional ASR systems combine an LM with an acoustic model by FST giving a huge performance gain. This trend is also shown in general including hybrid ASR systems and neural network-based sequence-to-sequence ASR systems.",
+      "The following experiments show a benefit of using a language model in decoding with the previous stage-2 transferred models. Although the performance gains in %CER are also generally observed over all target languages, the improvement in %WER was more distinctive. The results shown in the following Fig. FIGREF27 are in %WER. \u201cwhole\u201d in each figure means we used all the available data for the target language as full set explained before.",
+      "",
+      "",
+      "We used a character-level RNNLM, which was trained with 2-layer LSTM on character sequences. We use all available paired text in the corresponding target language to train the LM for the language. No external text data were used. All language models are trained separately from the seq2seq models. When building dictionary, we combined all the characters over all 15 languages mentioned in table TABREF14 to make them work with transferred models. Regardless of the amount of data used for transfer learning, the RNNLM provides consistent gains across all languages over different data sizes.",
+      "As explained already, language models were trained separately and used to decode jointly with seq2seq models. The intuition behind it is to use the separately trained language model as a complementary component that works with a implicit language model within a seq2seq decoder. The way of RNNLM assisting decoding follows the equation below: DISPLAYFORM0 ",
+      " INLINEFORM0 is a scaling factor that combines the scores from a joint decoding eq.( EQREF13 ) with RNN-LM, denoted as INLINEFORM1 . This approach is called shallow fusion.",
+      "Our experiments for target languages show that the gains from adding RNNLM are consistent regardless of the amount of data used for transfer learning. In other words, in Figure FIGREF27 , the gap between two lines are almost consistent over all languages.",
+      "Also, we observe the gain we get by adding RNN-LM in decoding is large. For example, in the case of assamese, the gain by RNN-LM in decoding with a model retrained on 5 hours of the target language data is almost comparable with the model stage-2 retrained with 20 hours of target language data. On average, absolute gain INLINEFORM0 6% is obtained across all target languages as noted in table TABREF28 ."
+    ],
+    [
+      "In this work, we have shown the importance of transfer learning approach such as stage-2 multilingual retraining in a seq2seq model setting. Also, careful selection of train and target languages from BABEL provide a wide variety in recognition performance (%CER) and helps in understanding the efficacy of seq2seq model. The experiments using character-based RNNLM showed the importance of language model in boosting recognition performance (%WER) over all different hours of target data available for transfer learning.",
+      "Table TABREF25 and TABREF28 summarizes, the effect of these techniques in terms of %CER and %WER. These methods also show their flexibility in incorporating it in attention and CTC based seq2seq model without compromising loss in performance."
+    ],
+    [
+      "We could use better architectures such as VGG-BLSTM as a multilingual prior model before transferring them to a new target language by performing stage-2 retraining. The naive multilingual approach can be improved by including language vectors as input or target during training to reduce the confusions. Also, investigation of multilingual bottleneck features BIBREF30 for seq2seq model can provide better performance. Apart from using the character level language model as in this work, a word level RNNLM can be connected during decoding to further improve %WER. The attention based decoder can be aided with the help of RNNLM using cold fusion approach during training to attain a better-trained model. In near future, we will incorporate all the above techniques to get comparable performance with the state-of-the-art hybrid DNN/RNN-HMM systems."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1988/instruction.md b/qasper-1988/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..536aee90419a86dde6fddbafd63af03248aae881
--- /dev/null
+++ b/qasper-1988/instruction.md
@@ -0,0 +1,79 @@
+Name of Paper: Multilingual sequence-to-sequence speech recognition: architecture, transfer learning, and language modeling
+
+Question: What architectures are explored to improve the seq2seq model?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Sequence-to-Sequence Model",
+    "Data details and experimental setup",
+    "Multilingual experiments",
+    "Stage 0 - Naive approach",
+    "Stage 1 - Retraining decoder only",
+    "Stage 2 - Finetuning both encoder and decoder",
+    "Multilingual RNNLM",
+    "Conclusion",
+    "Future work"
+  ],
+  "paragraphs": [
+    [
+      "The sequence-to-sequence (seq2seq) model proposed in BIBREF0 , BIBREF1 , BIBREF2 is a neural architecture for performing sequence classification and later adopted to perform speech recognition in BIBREF3 , BIBREF4 , BIBREF5 . The model allows to integrate the main blocks of ASR such as acoustic model, alignment model and language model into a single framework. The recent ASR advancements in connectionist temporal classification (CTC) BIBREF5 , BIBREF4 and attention BIBREF3 , BIBREF6 based approaches has created larger interest in speech community to use seq2seq models. To leverage performance gains from this model as similar or better to conventional hybrid RNN/DNN-HMM models requires a huge amount of data BIBREF7 . Intuitively, this is due to the wide-range role of the model in performing alignment and language modeling along with acoustic to character label mapping at each iteration.",
+      "In this paper, we explore the multilingual training approaches BIBREF8 , BIBREF9 , BIBREF10 used in hybrid DNN/RNN-HMMs to incorporate them into the seq2seq models. In a context of applications of multilingual approaches towards seq2seq model, CTC is mainly used instead of the attention models. A multilingual CTC is proposed in BIBREF11 , which uses a universal phoneset, FST decoder and language model. The authors also use linear hidden unit contribution (LHUC) BIBREF12 technique to rescale the hidden unit outputs for each language as a way to adapt to a particular language. Another work BIBREF13 on multilingual CTC shows the importance of language adaptive vectors as auxiliary input to the encoder in multilingual CTC model. The decoder used here is a simple INLINEFORM0 decoder. An extensive analysis on multilingual CTC mainly focusing on improving under limited data condition is performed in BIBREF14 . Here, the authors use a word level FST decoder integrated with CTC during decoding.",
+      "On a similar front, attention models are explored within a multilingual setup in BIBREF15 , BIBREF16 based on attention-based seq2seq to build a model from multiple languages. The data is just combined together assuming the target languages are seen during the training. And, hence no special transfer learning techniques were used here to address the unseen languages during training. The main motivation and contribution behind this work is as follows:"
+    ],
+    [
+      "In this work, we use the attention based approach BIBREF1 as it provides an effective methodology to perform sequence-to-sequence (seq2seq) training. Considering the limitations of attention in performing monotonic alignment BIBREF18 , BIBREF19 , we choose to use CTC loss function to aid the attention mechanism in both training and decoding. The basic network architecture is shown in Fig. FIGREF7 .",
+      "Let INLINEFORM0 be a INLINEFORM1 -length speech feature sequence and INLINEFORM2 be a INLINEFORM3 -length grapheme sequence. A multi-objective learning framework INLINEFORM4 proposed in BIBREF17 is used in this work to unify attention loss INLINEFORM5 and CTC loss INLINEFORM6 with a linear interpolation weight INLINEFORM7 , as follows: DISPLAYFORM0 ",
+      "The unified model allows to obtain both monotonicity and effective sequence level training.",
+      " INLINEFORM0 represents the posterior probability of character label sequence INLINEFORM1 w.r.t input sequence INLINEFORM2 based on the attention approach, which is decomposed with the probabilistic chain rule, as follows: DISPLAYFORM0 ",
+      "where INLINEFORM0 denotes the ground truth history. Detailed explanations about the attention mechanism is described later.",
+      "Similarly, INLINEFORM0 represents the posterior probability based on the CTC approach. DISPLAYFORM0 ",
+      "where INLINEFORM0 is a CTC state sequence composed of the original grapheme set and the additional blank symbol. INLINEFORM1 is a set of all possible sequences given the character sequence INLINEFORM2 .",
+      "The following paragraphs explain the encoder, attention decoder, CTC, and joint decoding used in our approach."
+    ],
+    [
+      "In this work, the experiments are conducted using the BABEL speech corpus collected from the IARPA babel program. The corpus is mainly composed of conversational telephone speech (CTS) but some scripted recordings and far field recordings are presented as well. Table TABREF14 presents the details of the languages used in this work for training and evaluation.",
+      "80 dimensional Mel-filterbank (fbank) features are then extracted from the speech samples using a sliding window of size 25 ms with 10ms stride. KALDI toolkit BIBREF24 is used to perform the feature processing. The fbank features are then fed to a seq2seq model with the following configuration:",
+      "The Bi-RNN BIBREF25 models mentioned above uses a LSTM BIBREF26 cell followed by a projection layer (BLSTMP). In our experiments below, we use only a character-level seq2seq model trained by CTC and attention decoder. Thus in the following experiments we intend to use character error rate (% CER) as a suitable measure to analyze the model performance. However, in section SECREF26 we integrate a character-level RNNLM BIBREF27 with seq2seq model externally and showcase the performance in terms of word error rate (% WER). In this case the words are obtained by concatenating the characters and the space together for scoring with reference words. All experiments are implemented in ESPnet, end-to-end speech processing toolkit BIBREF28 ."
+    ],
+    [
+      "Multilingual approaches used in hybrid RNN/DNN-HMM systems BIBREF10 have been used for for tackling the problem of low-resource data condition. Some of these approaches include language adaptive training and shared layer retraining BIBREF29 . Among them, the most benefited method is the parameter sharing technique BIBREF10 . To incorporate the former approach into encoder, CTC and attention decoder model, we performed the following experiments:"
+    ],
+    [
+      "In this approach, the model is first trained with 10 multiple languages as denoted in table TABREF14 approximating to 600 hours of training data. data from all languages available during training is used to build a single seq2seq model. The model is trained with a character label set composed of characters from all languages including both train and target set as mentioned in table TABREF14 . The model provides better generalization across languages. Languages with limited data when trained with other languages allows them to be robust and helps in improving the recognition performance. In spite of being simple, the model has limitations in keeping the target language data unseen during training.",
+      "Table TABREF16 shows the recognition performance of naive multilingual approach using BLSTMP and VGG model against a monolingual model trained with BLSTMP. The results clearly indicate that having a better architecture such as VGG-BLSTM helps in improving multilingual performance. Except Pashto, Georgian and Tokpisin, the multilingual VGG-BLSTM model gave 8.8 % absolute gain in average over monolingual model. In case of multilingual BLSTMP, except Pashto and Georgian an absolute gain of 5.0 % in average is observed over monolingual model. Even though the VGG-BLSTM gave improvements, we were not able to perform stage-1 and stage-2 retraining with it due to time constraints. Thus, we proceed further with multilingual BLSTMP model for retraining experiments tabulated below."
+    ],
+    [
+      "To alleviate the limitation in the previous approach, the final layer of the seq2seq model which is mainly responsible for classification is retrained to the target language.",
+      "In previous works BIBREF10 , BIBREF29 related to hybrid DNN/RNN models and CTC based models BIBREF11 , BIBREF14 the softmax layer is only adapted. However in our case, the attention decoder and CTC decoder both have to be retrained to the target language. This means the CTC and attention layers are only updated for gradients during this stage. We found using SGD optimizer with initial learning rate of INLINEFORM0 works better for retraining compared to AdaDelta.",
+      "The learning rate is decayed in this training at a factor of INLINEFORM0 if there is a drop in validation accuracy. Table TABREF20 shows the performance of simply retraining the last layer using a single target language Assamese."
+    ],
+    [
+      "Based on the observations from stage-1 model in section SECREF22 , we found that simply retraining the decoder towards a target language resulted in degrading %CER the performance from 45.6 to 61.3. This is mainly due to the difference in distribution across encoder and decoder. So, to alleviate this difference the encoder and decoder is once again retrained or fine-tuned using the model from stage-1. The optimizer used here is SGD as in stage-1, but the initial learning rate is kept to INLINEFORM0 and decayed based on validation performance. The resulting model gave an absolute gain of 1.6% when finetuned a multilingual model after 4th epoch. Also, finetuning a model after 15th epoch gave an absolute gain of 4.3%.",
+      "To further investigate the performance of this approach across different target data sizes, we split the train set into INLINEFORM0 5 hours, INLINEFORM1 10 hours, INLINEFORM2 20 hours and INLINEFORM3 full set. Since, in this approach the model is only finetuned by initializing from stage-1 model, the model architecture is fixed for all data sizes. Figure FIGREF23 shows the effectiveness of finetuning both encoder and decoder. The gains from 5 to 10 hours was more compared to 20 hours to full set.",
+      "Table TABREF25 tabulates the % CER obtained by retraining the stage-1 model with INLINEFORM0 full set of target language data. An absolute gain is observed using stage-2 retraining across all languages compared to monolingual model."
+    ],
+    [
+      "In an ASR system, a language model (LM) takes an important role by incorporating external knowledge into the system. Conventional ASR systems combine an LM with an acoustic model by FST giving a huge performance gain. This trend is also shown in general including hybrid ASR systems and neural network-based sequence-to-sequence ASR systems.",
+      "The following experiments show a benefit of using a language model in decoding with the previous stage-2 transferred models. Although the performance gains in %CER are also generally observed over all target languages, the improvement in %WER was more distinctive. The results shown in the following Fig. FIGREF27 are in %WER. \u201cwhole\u201d in each figure means we used all the available data for the target language as full set explained before.",
+      "",
+      "",
+      "We used a character-level RNNLM, which was trained with 2-layer LSTM on character sequences. We use all available paired text in the corresponding target language to train the LM for the language. No external text data were used. All language models are trained separately from the seq2seq models. When building dictionary, we combined all the characters over all 15 languages mentioned in table TABREF14 to make them work with transferred models. Regardless of the amount of data used for transfer learning, the RNNLM provides consistent gains across all languages over different data sizes.",
+      "As explained already, language models were trained separately and used to decode jointly with seq2seq models. The intuition behind it is to use the separately trained language model as a complementary component that works with a implicit language model within a seq2seq decoder. The way of RNNLM assisting decoding follows the equation below: DISPLAYFORM0 ",
+      " INLINEFORM0 is a scaling factor that combines the scores from a joint decoding eq.( EQREF13 ) with RNN-LM, denoted as INLINEFORM1 . This approach is called shallow fusion.",
+      "Our experiments for target languages show that the gains from adding RNNLM are consistent regardless of the amount of data used for transfer learning. In other words, in Figure FIGREF27 , the gap between two lines are almost consistent over all languages.",
+      "Also, we observe the gain we get by adding RNN-LM in decoding is large. For example, in the case of assamese, the gain by RNN-LM in decoding with a model retrained on 5 hours of the target language data is almost comparable with the model stage-2 retrained with 20 hours of target language data. On average, absolute gain INLINEFORM0 6% is obtained across all target languages as noted in table TABREF28 ."
+    ],
+    [
+      "In this work, we have shown the importance of transfer learning approach such as stage-2 multilingual retraining in a seq2seq model setting. Also, careful selection of train and target languages from BABEL provide a wide variety in recognition performance (%CER) and helps in understanding the efficacy of seq2seq model. The experiments using character-based RNNLM showed the importance of language model in boosting recognition performance (%WER) over all different hours of target data available for transfer learning.",
+      "Table TABREF25 and TABREF28 summarizes, the effect of these techniques in terms of %CER and %WER. These methods also show their flexibility in incorporating it in attention and CTC based seq2seq model without compromising loss in performance."
+    ],
+    [
+      "We could use better architectures such as VGG-BLSTM as a multilingual prior model before transferring them to a new target language by performing stage-2 retraining. The naive multilingual approach can be improved by including language vectors as input or target during training to reduce the confusions. Also, investigation of multilingual bottleneck features BIBREF30 for seq2seq model can provide better performance. Apart from using the character level language model as in this work, a word level RNNLM can be connected during decoding to further improve %WER. The attention based decoder can be aided with the help of RNNLM using cold fusion approach during training to attain a better-trained model. In near future, we will incorporate all the above techniques to get comparable performance with the state-of-the-art hybrid DNN/RNN-HMM systems."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-1989/instruction.md b/qasper-1989/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..d9e5cfbe1c3597916c704fb7b917de03fb0c371d
--- /dev/null
+++ b/qasper-1989/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Unsupervised Multi-modal Neural Machine Translation
+
+Question: Why is this work different from text-only UNMT?
\ No newline at end of file
diff --git a/qasper-2002/instruction.md b/qasper-2002/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..885ec3bc83fa3f652bf3060a0251959b89fcb794
--- /dev/null
+++ b/qasper-2002/instruction.md
@@ -0,0 +1,59 @@
+Name of Paper: Learn to Code-Switch: Data Augmentation using Copy Mechanism on Language Modeling
+
+Question: What was their perplexity score?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Methodology",
+    "Pointer-generator Network",
+    "Language Modeling",
+    "Corpus",
+    "Training Setup",
+    "Results",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Language mixing has been a common phenomenon in multilingual communities. It is motivated in response to social factors as a way of communication in a multicultural society. From a sociolinguistic perspective, individuals do code-switching in order to construct an optimal interaction by accomplishing the conceptual, relational-interpersonal, and discourse-presentational meaning of conversation BIBREF0 . In its practice, the variation of code-switching will vary due to the traditions, beliefs, and normative values in the respective communities. A number of studies BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 found that code-switching is not produced indiscriminately, but follows syntactic constraints. Many linguists formulated various constraints to define a general rule for code-switching BIBREF1 , BIBREF3 , BIBREF4 . However, the constraints are not enough to make a good generalization of real code-switching constraints, and they have not been tested in large-scale corpora for many language pairs.",
+      "One of the biggest problem in code-switching is collecting large scale corpora. Speech data have to be collected from a spontaneous speech by bilingual speakers and the code-switching has to be triggered naturally during the conversation. In order to solve the data scarcity issue, code-switching data generation is useful to increase the volume and variance. A linguistics constraint-driven generation approach such as equivalent constraint BIBREF5 , BIBREF6 is not restrictive to languages with distinctive grammar structure.",
+      "In this paper, we propose a novel language-agnostic method to learn how to generate code-switching sentences by using a pointer-generator network BIBREF7 . The model is trained from concatenated sequences of parallel sentences to generate code-switching sentences, constrained by code-switching texts. The pointer network copies words from both languages and pastes them into the output, generating code switching sentences in matrix language to embedded language and vice versa. The attention mechanism helps the decoder to generate meaningful and grammatical sentences without needing any sequence alignment. This idea is also in line with code-mixing by borrowing words from the embedded language BIBREF8 and intuitively, the copying mechanism can be seen as an end-to-end approach to translate, align, and reorder the given words into a grammatical code-switching sentence. This approach is the unification of all components in the work of BIBREF5 into a single computational model. A code-switching language model learned in this way is able to capture the patterns and constraints of the switches and mitigate the out-of-vocabulary (OOV) issue during sequence generation. By adding the generated sentences and incorporating syntactic information to the training data, we achieve better performance by INLINEFORM0 compared to an LSTM baseline BIBREF9 and INLINEFORM1 to the equivalent constraint."
+    ],
+    [
+      "The synthetic code-switching generation approach was introduced by adapting equivalence constraint on monolingual sentence pairs during the decoding step on an automatic speech recognition (ASR) model BIBREF5 . BIBREF10 explored Functional Head Constraint, which was found to be more restrictive than the Equivalence Constraint, but complex to be implemented, by using a lattice parser with a weighted finite-state transducer. BIBREF11 extended the RNN by adding POS information to the input layer and factorized output layer with a language identifier. Then, Factorized RNN networks were combined with an n-gram backoff model using linear interpolation BIBREF12 . BIBREF13 added syntactic and semantic features to the Factorized RNN networks. BIBREF14 adapted an effective curriculum learning by training a network with monolingual corpora of both languages, and subsequently train on code-switched data. A further investigation of Equivalence Constraint and Curriculum Learning showed an improvement in language modeling BIBREF6 . A multi-task learning approach was introduced to train the syntax representation of languages by constraining the language generator BIBREF9 .",
+      "A copy mechanism was proposed to copy words directly from the input to the output using an attention mechanism BIBREF15 . This mechanism has proven to be effective in several NLP tasks including text summarization BIBREF7 , and dialog systems BIBREF16 . The common characteristic of these tasks is parts of the output are exactly the same as the input source. For example, in dialog systems the responses most of the time have appeared in the previous dialog steps."
+    ],
+    [
+      "We use a sequence to sequence (Seq2Seq) model in combination with pointer and copy networks BIBREF7 to align and choose words from the monolingual sentences and generate a code-switching sentence. The models' input is the concatenation of the two monolingual sentences, denoted as INLINEFORM0 , and the output is a code-switched sentence, denoted as INLINEFORM1 . The main assumption is that almost all, the token present in the code-switching sentence are also present in the source monolingual sentences. Our model leverages this property by copying input tokens, instead of generating vocabulary words. This approach has two major advantages: (1) the learning complexity decreases since it relies on copying instead of generating; (2) improvement in generalization, the copy mechanism could produce words from the input that are not present in the vocabulary."
+    ],
+    [
+      "Instead of generating words from a large vocabulary space using a Seq2Seq model with attention BIBREF17 , pointer-generator network BIBREF7 is proposed to copy words from the input to the output using an attention mechanism and generate the output sequence using decoders. The network is depicted in Figure FIGREF1 . For each decoder step, a generation probability INLINEFORM0 INLINEFORM1 [0,1] is calculated, which weights the probability of generating words from the vocabulary, and copying words from the source text. INLINEFORM2 is a soft gating probability to decide whether generating the next token from the decoder or copying the word from the input instead. The attention distribution INLINEFORM3 is a standard attention with general scoring BIBREF17 . It considers all encoder hidden states to derive the context vector. The vocabulary distribution INLINEFORM4 is calculated by concatenating the decoder state INLINEFORM5 and the context vector INLINEFORM6 . DISPLAYFORM0 ",
+      "where INLINEFORM0 are trainable parameters and INLINEFORM1 is the scalar bias. The vocabulary distribution INLINEFORM2 and the attention distribution INLINEFORM3 are weighted and summed to obtain the final distribution INLINEFORM4 . The final distribution is calculated as follows: DISPLAYFORM0 ",
+      "We use a beam search to select INLINEFORM0 -best code-switching sentences and concatenate the generated sentence with the training set to form a larger dataset. The result of the generated code-switching sentences is showed in Table TABREF6 . As our baseline, we compare our proposed method with three other models: (1) We use Seq2Seq with attention; (2) We generate sequences that satisfy Equivalence Constraint BIBREF5 . The constraint doesn't allow any switch within a crossing of two word alignments. We use FastAlign BIBREF18 as the word aligner; (3) We also form sentences using the alignments without any constraint. The number of the generated sentences are equivalent to 3-best data from the pointer-generator model. To increase the generation variance, we randomly permute each alignment to form a new sequence."
+    ],
+    [
+      "The quality of the generated code-switching sentences is evaluated using a language modeling task. Indeed, if the perplexity in this task drops consistently we can assume that the generated sentences are well-formed. Hence, we use an LSTM language model with weight tying BIBREF19 that can capture an unbounded number of context words to approximate the probability of the next word. Syntactic information such as Part-of-speech (POS) INLINEFORM0 is added to further improve the performance. The POS tags are generated phrase-wise using pretrained English and Chinese Stanford POS Tagger BIBREF20 by adding a word at a time in a unidirectional way to avoid any intervention from future information. The word and syntax unit are represented as a vector INLINEFORM1 and INLINEFORM2 respectively. Next, we concatenate both vectors and use it as an input INLINEFORM3 to an LSTM layer similar to BIBREF9 ."
+    ],
+    [
+      "In our experiment, we use a conversational Mandarin-English code-switching speech corpus called SEAME Phase II (South East Asia Mandarin-English). The data are collected from spontaneously spoken interviews and conversations in Singapore and Malaysia by bilinguals BIBREF21 . As the data preprocessing, words are tokenized using Stanford NLP toolkit BIBREF22 and all hesitations and punctuations were removed except apostrophe. The split of the dataset is identical to BIBREF9 and it is showed in Table TABREF6 ."
+    ],
+    [
+      "In this section, we present the experimental settings for pointer-generator network and language model. Our experiment, our pointer-generator model has 500-dimensional hidden states and word embeddings. We use 50k words as our vocabulary for source and target. We evaluate our pointer-generator performance using BLEU score. We take the best model as our generator and during the decoding stage, we generate 1-best and 3-best using beam search with a beam size of 5. For the input, we build a parallel monolingual corpus by translating the mixed language sequence using Google NMT to English ( INLINEFORM0 ) and Mandarin ( INLINEFORM1 ) sequences. Then, we concatenate the translated English and Mandarin sequences and assign code-switching sequences as the labels ( INLINEFORM2 ).",
+      "The baseline language model is trained using RNNLM BIBREF23 . Then, we train our 2-layer LSTM models with a hidden size of 500 and unrolled for 35 steps. The embedding size is equal to the LSTM hidden size for weight tying. We optimize our model using SGD with initial learning rates of INLINEFORM1 . If there is no improvement during the evaluation, we reduce the learning rate by a factor of 0.75. In each time step, we apply dropout to both embedding layer and recurrent network. The gradient is clipped to a maximum of 0.25. Perplexity measure is used in the evaluation."
+    ],
+    [
+      "UTF8gbsn The pointer-generator significantly outperforms the Seq2Seq with attention model by 3.58 BLEU points on the test set as shown in Table TABREF8 . Our language modeling result is given in Table TABREF9 . Based on the empirical result, adding generated samples consistently improve the performance of all models with a moderate margin around 10% in perplexity. After all, our proposed method still slightly outperforms the heuristic from linguistic constraint. In addition, we get a crucial gain on performance by adding syntax representation of the sequences.",
+      "Change in data distribution: To further analyze the generated result, we observed the distribution of real code-switching data and the generated code-switching data. From Figure FIGREF15 , we can see that 1-best and real code-switching data have almost identical distributions. The distributions are left-skewed where the overall mean is less than the median. Interestingly, the distribution of the 3-best data is less skewed and generates a new set of n-grams such as \u201c\u90a3\u4e2a(that) proposal\" which was learned from other code-switching sequences. As a result, generating more samples effects the performance positively.",
+      "Importance of Linguistic Constraint: The result in Table TABREF9 emphasizes that linguistic constraints have some significance in replicating the real code-switching patterns, specifically the equivalence constraint. There is a slight reduction in perplexity around 6 points on the test set. In addition, when we ignore the constraint, we lose performance because it still allows switches in the inversion grammar cases.",
+      "Does the pointer-generator learn how to switch? We found that our pointer-generator model generates sentences that have not been seen before. The example in Figure FIGREF1 shows that our model is able to construct a new well-formed sentence such as \u201c\u6211\u4eec\u8981\u53bb(We want to) check\". It is also shown that the pointer-generator model has the capability to learn the characteristics of the linguistic constraints from data without any word alignment between the matrix and embedded languages. On the other hand, training using 3-best data obtains better performance compared to 1-best data. We found a positive correlation from Table TABREF6 , where 3-best data is more similar to the test set in terms of segment length and number of switches compared to 1-best data. Adding more samples INLINEFORM0 may improve the performance, but it will be saturated at a certain point. One way to solve this is by using more parallel samples."
+    ],
+    [
+      "We introduce a new learning method for code-switching sentence generation using a parallel monolingual corpus that is applicable to any language pair. Our experimental result shows that adding generated sentences to the training data, effectively improves our model performance. Combining the generated samples with code-switching dataset reduces perplexity. We get further performance gain after using syntactic information of the input. In future work, we plan to explore reinforcement learning for sequence generation and employ more parallel corpora."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2005/instruction.md b/qasper-2005/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..f891ad0a755c433220e34b4720d55a9f08ec1cc6
--- /dev/null
+++ b/qasper-2005/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Normalized and Geometry-Aware Self-Attention Network for Image Captioning
+
+Question: What datasets are used for experiments on three other tasks?
\ No newline at end of file
diff --git a/qasper-2012/instruction.md b/qasper-2012/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..5c00118b4236b219f6f4ecf4925d1a5ae2002399
--- /dev/null
+++ b/qasper-2012/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisis
+
+Question: What languages are were included in the dataset of hateful content?
\ No newline at end of file
diff --git a/qasper-2013/instruction.md b/qasper-2013/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..a09dc94733f9d33c407883c656924bf8b5133b56
--- /dev/null
+++ b/qasper-2013/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisis
+
+Question: How was reliability measured?
\ No newline at end of file
diff --git a/qasper-2014/instruction.md b/qasper-2014/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..aa2cec1f17743f2b70f0176e3429e2a9a64e5fdd
--- /dev/null
+++ b/qasper-2014/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisis
+
+Question: How did the authors demonstrate that showing a hate speech definition caused annotators to partially align their own opinion with the definition?
\ No newline at end of file
diff --git a/qasper-2015/instruction.md b/qasper-2015/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..83ffa1f60fcbb1af2f92b847889780b87d765703
--- /dev/null
+++ b/qasper-2015/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisis
+
+Question: What definition was one of the groups was shown?
\ No newline at end of file
diff --git a/qasper-2022/instruction.md b/qasper-2022/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..759ac566e1b6c241e453f2da6d56b04fbadef17a
--- /dev/null
+++ b/qasper-2022/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: An analysis of the utility of explicit negative examples to improve the syntactic abilities of neural language models
+
+Question: How do they perform data augmentation?
\ No newline at end of file
diff --git a/qasper-2023/instruction.md b/qasper-2023/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..82e88ca99d3ec7ba0a3717271af8163b01a1c62b
--- /dev/null
+++ b/qasper-2023/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: An analysis of the utility of explicit negative examples to improve the syntactic abilities of neural language models
+
+Question: What proportion of negative-examples do they use?
\ No newline at end of file
diff --git a/qasper-2024/instruction.md b/qasper-2024/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..ac7f1b6c0a897343d37b157ab5629ae6eb378a24
--- /dev/null
+++ b/qasper-2024/instruction.md
@@ -0,0 +1,67 @@
+Name of Paper: Dialectometric analysis of language variation in Twitter
+
+Question: Do the authors mention any possible confounds in their study?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Methods",
+    "Language distance",
+    "Results and discussion",
+    "Single-concept case",
+    "Global distance",
+    "Conclusions",
+    "Acknowledgments",
+    "Supplementary material"
+  ],
+  "paragraphs": [
+    [
+      "Dialects are language varieties defined across space. These varieties can differ in distinct linguistic levels (phonetic, morphosyntactic, lexical), which determine a particular regional speech BIBREF0 . The extension and boundaries (always diffuse) of a dialect area are obtained from the variation of one or many features such as, e.g., the different word alternations for a given concept. Typically, the dialect forms plotted on a map appear as a geographical continuum that gradually connects places with slightly different diatopic characteristics. A dialectometric analysis aims at a computational approach to dialect distribution, providing quantitative linguistic distances between locations BIBREF1 , BIBREF2 , BIBREF3 .",
+      "Dialectometric data is based upon a corpus that contains the linguistic information needed for the statistical analysis. The traditional approach is to generate these data from surveys and questionnaires that address variable types used by a few informants. Upon appropriate weighting, the distance metric can thus be mapped on an atlas. In the last few years, however, the impressive upswing of microblogging platforms has led to a scenario in which human communication features can be studied without the effort that traditional studies usually require. Platforms such as Twitter, Flickr, Instagram or Facebook bring us the possibility of investigating massive amounts of data in an automatic fashion. Furthermore, microblogging services provide us with real-time communication among users that, importantly, tend to employ an oral speech. Another difference with traditional approaches is that while the latter focus on male, rural informants, users of social platforms are likely to be young, urban people BIBREF4 , which opens the route to novel investigations on today's usage of language. Thanks to advances in geolocation, it is now possible to directly examine the diatopic properties of specific regions. Examples of computational linguistic works that investigate regional variation with Twitter or Facebook corpora thus far comprise English BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , Spanish BIBREF10 , BIBREF11 , BIBREF12 , German BIBREF13 , Arabic BIBREF14 and Dutch BIBREF15 . It is noticeable that many of these works combine big data techniques with probabilistic tools or machine learning strategies to unveil linguistic phenomena that are absent or hard to obtain from conventional methods (interviews, hand-crafted corpora, etc.).",
+      "The subject of this paper is the language variation in a microblogging platform using dialectrometric measures. In contrast to previous works, here we precisely determine the linguistic distance between different places by means of two metrics. Our analysis shows that the results obtained with both metrics are compatible, which encourages future developments in the field. We illustrate our main findings with a careful analysis of the dialect division of Spanish. For definiteness, we restrict ourselves to Spain but the method can be straightforwardly applied to larger areas. We find that, due to language diversity, cities and main towns have similar linguistic distances unlike rural areas, which differ in their homogeneous forms. but obtained with a completely different method"
+    ],
+    [
+      "Our corpus consists of approximately 11 million geotagged tweets produced in Europe in Spanish language between October 2014 and June 2016. (Although we will focus on Spain, we will not consider in this work the speech of the Canary Islands due to difficulties with the data extraction). The classification of tweets is accomplished by applying the Compact Language Detector (CLD) BIBREF16 to our dataset. CLD exhibits accurate benchmarks and is thus good for our purposes, although a different detector might be used BIBREF17 . We have empirically checked that when CLD determines the language with a probability of at least 60% the results are extremely reliable. Therefore, we only take into account those tweets for which the probability of being written in Spanish is greater than INLINEFORM0 . Further, we remove unwanted characters, such as hashtags or at-mentions, using Twokenize BIBREF18 , a tokenizer designed for Twitter text in English, adapted to our goals.",
+      "We present the spatial coordinates of all tweets in figure FIGREF1 (only the south-western part of Europe is shown for clarity). As expected, most of the tweets are localized in Spain, mainly around major cities and along main roads.",
+      "Next, we select a word list from Varilex BIBREF19 , a lexical database that contains Spanish variation across the world. We consider 89 concepts expressed in different forms. Our selection eliminates possible semantic ambiguities. The complete list of keywords is included in the supplementary material below. For each concept, we determine the coordinates of the tweets in which the different keywords appear. From our corpus, we find that 219362 tweets include at least one form corresponding to any of the selected concepts.",
+      "The pictorial representation of these concepts is made using a shapefile of both the Iberian Peninsula and the Balearic Islands. Then, we construct a polygon grid over the shapefile. The size of the cells ( INLINEFORM0 ) roughly corresponds to 1200 km INLINEFORM1 . We locate the cell in which a given keyword matches and assign a different color to each keyword. We follow a majority criterion, i.e., we depict the cell with the keyword color whose absolute frequency is maximum. This procedure nicely yields a useful geographical representation of how the different variants for a concept are distributed over the space."
+    ],
+    [
+      "The dialectometric differences are quantified between regions defined with the aid of our cells. For this purpose we take into account two metrics, which we now briefly discuss.",
+      "This metric is a vector comparison measure. It is widely used in text classification, information retrieval and data mining BIBREF20 . Let INLINEFORM0 and INLINEFORM1 be two vectors whose components are given by the relative frequencies of the lexical variations for a concept within a cell. Quite generally, INLINEFORM2 and INLINEFORM3 represent points in a high-dimensional space. The similarity measure INLINEFORM4 between these two vectors is related to their inner product conveniently normalized to the product of their lengths, DISPLAYFORM0 ",
+      "This expression has an easy interpretation. If both vectors lie parallel, the direction cosine is 1 and thus the distance becomes INLINEFORM0 . Since all vector components in our approach are positive, the upper bound of INLINEFORM1 is 1, which is attained when the two vectors are maximally dissimilar.",
+      "This distance is a similarity measure between probability density functions BIBREF21 . It is a symmetrized version of a more general metric, the Kullback-Leibler divergence. Let INLINEFORM0 and INLINEFORM1 be two probability distributions. In our case, these functions are built from the relative frequencies of each concept variation. Our frequentist approach differs from previous dialectometric works, which prefer to measure distances using the Dice similarity coefficient or the Jaccard index BIBREF22 .",
+      "The Kullback-Leibler divergence is defined as DISPLAYFORM0 ",
+      "We now symmetrize this expression and take the square root, DISPLAYFORM0 ",
+      "where INLINEFORM0 . The Jensen-Shannon distance INLINEFORM1 is indeed a metric, i.e., it satisfies the triangle inequality. Additionally, INLINEFORM2 fulfills the metric requirements of non-negativity, INLINEFORM3 if and only if INLINEFORM4 (identity of indiscernibles) and symmetry (by construction). This distance has been employed in bioinformatics and genome comparison BIBREF23 , BIBREF24 , social sciences BIBREF25 and machine learning BIBREF26 . To the best of our knowledge, it has not been used in studies of language variation. An exception is the work of Sanders sanders, where INLINEFORM5 is calculated for an analysis of syntactic variation of Swedish. Here, we propose to apply the Jensen-Shannon metric to lexical variation. Below, we demonstrate that this idea leads to quite promising results.",
+      "Equations EQREF5 and EQREF8 give the distance between cells INLINEFORM0 and INLINEFORM1 for a certain concept. We assign the global linguistic distance in terms of lexical variability between two cells to the mean value DISPLAYFORM0 ",
+      "where INLINEFORM0 is the distance between cells INLINEFORM1 and INLINEFORM2 for the INLINEFORM3 -th concept and INLINEFORM4 is the total number of concepts used to compute the distance. In the cosine similarity model, we replace INLINEFORM5 in equation EQREF10 with equation EQREF5 whereas in the Jensen-Shannon metric INLINEFORM6 is given by equation EQREF8 ."
+    ],
+    [
+      "We first check the quality of our corpus with a few selected concepts. Examples of their spatial distributions can be seen in figure FIGREF2 . The lexical variation depends on the particular concept and on the keyword frequency. We recall that the majority rule demands that we depict the cell with the color corresponding to the most popular word. Despite a few cells appearing to be blank, we have instances in most of the map. Importantly, our results agree with the distribution for the concept cold reported by Gon\u00e7alves and S\u00e1nchez BD with a different corpus. The north-south bipartition of the variation suggested in figure FIGREF2 (a) also agrees with more traditional studies BIBREF27 . As a consequence, these consistencies support the validity of our data. The novelty of our approach is to further analyze this dialect distribution with a quantitative measure as discussed below."
+    ],
+    [
+      "Let us quantify the lexical difference between regions using the concept cold as an illustration. First, we generate a symmetric matrix of linguistic distances INLINEFORM0 between pairs of cells INLINEFORM1 and INLINEFORM2 with INLINEFORM3 calculated using equation ( EQREF5 ) or equation ( EQREF8 ). Then, we find the maximum possible INLINEFORM4 value in the matrix ( INLINEFORM5 ) and select either its corresponding INLINEFORM6 or INLINEFORM7 index as the reference cell. Since both metrics are symmetric, the choice between INLINEFORM8 and INLINEFORM9 should not affect the results much (see below for a detailed analysis). Next, we normalize all values to INLINEFORM10 and plot the distances to the reference cell using a color scale within the range INLINEFORM11 , whose lowest and highest values are set for convenience due to the normalization procedure. The results are shown in figure FIGREF12 . Panel (a) [(b)] is obtained with the cosine similarity (Jensen-Shannon metric). Crucially, we observe that both metrics give similar results, which confirm the robustness of our dialectometric method.",
+      "Clearly, cells with a low number of tweets will largely contribute to fluctuations in the maps. To avoid this noise-related effect, we impose in figure FIGREF13 a minimum threshold of 5 tweets in every cell. Obviously, the number of colored cells decreases but fluctuations become quenched at the same time. If the threshold is increased up to 10 tweets, we obtain the results plotted in figure FIGREF14 , where the north-south bipartition is now better seen. We stress that there exist minimal differences between the cosine similarity and the Jensen-Shannon metric models."
+    ],
+    [
+      "Our previous analysis assessed the lexical distance for a single concept (cold). Let us now take into account all concepts and calculate the averaged distances using equation ( EQREF10 ). To do so, we proceed as above and measure the distance from any of the two cells that presents the maximal value of INLINEFORM0 , where INLINEFORM1 is now calculated from equation EQREF10 . As aforementioned, INLINEFORM2 connects two cells, which denote as INLINEFORM3 and INLINEFORM4 . Any of these can be selected as the reference cell from which the remaining linguistic distances are plotted in the map. To ensure that we obtain the same results, we plot the distance distribution in both directions. The results with the cosine similarity model are shown in figure FIGREF16 . It is worth noting that qualitatively the overall picture is only slightly modified when the reference cell is changed from INLINEFORM5 [figure FIGREF16 (a)] to INLINEFORM6 [figure FIGREF16 (b)]. The same conclusion is reached when the distance is calculated with the Jensen-Shannon metric model, see figures FIGREF17 (a) and (b).",
+      "After averaging over all concepts, we lose information on the lexical variation that each concept presents but on the other hand one can now investigate which regions show similar geolectal variation, yielding well defined linguistic varieties. Those cells that have similar colors in either figure FIGREF16 or figure FIGREF17 are expected to be ascribed to the same dialect zone. Thus, we can distinguish two main regions or clusters in the maps. The purple background covers most of the map and represents rural regions with small, scattered population. Our analysis shows that this group of cells possesses more specific words in their lexicon. In contrast, the green and yellow cells form a second cluster that is largely concentrated on the center and along the coastline, which correspond to big cities and industrialized areas. In these cells, the use of standard Spanish language is widespread due probably to school education, media, travelers, etc. The character of its vocabulary is more uniform as compared with the purple group. While the purple cluster prefer particular utterances, the lexicon of the urban group includes most of the keywords. Importantly, we emphasize that both distance measures (cosine similarity and Jensen-Shanon) give rise to the same result, with little discrepancies on the numerical values that are not significant. The presence of two Twitter superdialects (urban and rural) has been recently suggested BIBREF10 based on a machine learning approach. Here, we arrive at the same conclusion but with a totally distinct model and corpus. The advantage of our proposal is that it may serve as a useful tool for dialectometric purposes."
+    ],
+    [
+      "To sum up, we have presented a dialectrometric analysis of lexical variation in social media posts employing information-theoretic measures of language distances. We have considered a grid of cells in Spain and have calculated the linguistic distances in terms of dialects between the different regions. Using a Twitter corpus, we have found that the synchronic variation of Spanish can be grouped into two types of clusters. The first region shows more lexical items and is present in big cities. The second cluster corresponds to rural regions, i.e., mostly villages and less industrialized regions. Furthermore, we have checked that the different metrics used here lead to similar results in the analysis of the lexical variation for a representative concept and provide a reasonable description to language variation in Twitter.",
+      "We remark that the small amount of tweets generated after matching the lexical variations of concepts within our automatic corpus puts a limit to the quantitative analysis, making the differences between regions small. Our work might be improved by similarly examining Spanish tweets worldwide, specially in Latin America and the United States. This approach should give more information on the lexical variation on the global scale and would help linguists in their dialectal classification work of micro- and macro-varieties. Our work hence represents a first step into the ambitious task of a thorough characterization of language variation using big data resources and information-theoretic methods."
+    ],
+    [
+      "We thank both F. Lamanna and Y. Kawasaki for useful discussions and the anonymous reviewers for nice suggestions. GD acknowledges support from the SURF@IFISC program."
+    ],
+    [
+      "Here we provide a list of our employed concepts and their lexical variants.",
+      "0pt 0pt plus 1fil"
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2025/instruction.md b/qasper-2025/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..cca41d59a18c5a6de217618332f6993f73146bf9
--- /dev/null
+++ b/qasper-2025/instruction.md
@@ -0,0 +1,67 @@
+Name of Paper: Dialectometric analysis of language variation in Twitter
+
+Question: What are the characteristics of the city dialect?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Methods",
+    "Language distance",
+    "Results and discussion",
+    "Single-concept case",
+    "Global distance",
+    "Conclusions",
+    "Acknowledgments",
+    "Supplementary material"
+  ],
+  "paragraphs": [
+    [
+      "Dialects are language varieties defined across space. These varieties can differ in distinct linguistic levels (phonetic, morphosyntactic, lexical), which determine a particular regional speech BIBREF0 . The extension and boundaries (always diffuse) of a dialect area are obtained from the variation of one or many features such as, e.g., the different word alternations for a given concept. Typically, the dialect forms plotted on a map appear as a geographical continuum that gradually connects places with slightly different diatopic characteristics. A dialectometric analysis aims at a computational approach to dialect distribution, providing quantitative linguistic distances between locations BIBREF1 , BIBREF2 , BIBREF3 .",
+      "Dialectometric data is based upon a corpus that contains the linguistic information needed for the statistical analysis. The traditional approach is to generate these data from surveys and questionnaires that address variable types used by a few informants. Upon appropriate weighting, the distance metric can thus be mapped on an atlas. In the last few years, however, the impressive upswing of microblogging platforms has led to a scenario in which human communication features can be studied without the effort that traditional studies usually require. Platforms such as Twitter, Flickr, Instagram or Facebook bring us the possibility of investigating massive amounts of data in an automatic fashion. Furthermore, microblogging services provide us with real-time communication among users that, importantly, tend to employ an oral speech. Another difference with traditional approaches is that while the latter focus on male, rural informants, users of social platforms are likely to be young, urban people BIBREF4 , which opens the route to novel investigations on today's usage of language. Thanks to advances in geolocation, it is now possible to directly examine the diatopic properties of specific regions. Examples of computational linguistic works that investigate regional variation with Twitter or Facebook corpora thus far comprise English BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , Spanish BIBREF10 , BIBREF11 , BIBREF12 , German BIBREF13 , Arabic BIBREF14 and Dutch BIBREF15 . It is noticeable that many of these works combine big data techniques with probabilistic tools or machine learning strategies to unveil linguistic phenomena that are absent or hard to obtain from conventional methods (interviews, hand-crafted corpora, etc.).",
+      "The subject of this paper is the language variation in a microblogging platform using dialectrometric measures. In contrast to previous works, here we precisely determine the linguistic distance between different places by means of two metrics. Our analysis shows that the results obtained with both metrics are compatible, which encourages future developments in the field. We illustrate our main findings with a careful analysis of the dialect division of Spanish. For definiteness, we restrict ourselves to Spain but the method can be straightforwardly applied to larger areas. We find that, due to language diversity, cities and main towns have similar linguistic distances unlike rural areas, which differ in their homogeneous forms. but obtained with a completely different method"
+    ],
+    [
+      "Our corpus consists of approximately 11 million geotagged tweets produced in Europe in Spanish language between October 2014 and June 2016. (Although we will focus on Spain, we will not consider in this work the speech of the Canary Islands due to difficulties with the data extraction). The classification of tweets is accomplished by applying the Compact Language Detector (CLD) BIBREF16 to our dataset. CLD exhibits accurate benchmarks and is thus good for our purposes, although a different detector might be used BIBREF17 . We have empirically checked that when CLD determines the language with a probability of at least 60% the results are extremely reliable. Therefore, we only take into account those tweets for which the probability of being written in Spanish is greater than INLINEFORM0 . Further, we remove unwanted characters, such as hashtags or at-mentions, using Twokenize BIBREF18 , a tokenizer designed for Twitter text in English, adapted to our goals.",
+      "We present the spatial coordinates of all tweets in figure FIGREF1 (only the south-western part of Europe is shown for clarity). As expected, most of the tweets are localized in Spain, mainly around major cities and along main roads.",
+      "Next, we select a word list from Varilex BIBREF19 , a lexical database that contains Spanish variation across the world. We consider 89 concepts expressed in different forms. Our selection eliminates possible semantic ambiguities. The complete list of keywords is included in the supplementary material below. For each concept, we determine the coordinates of the tweets in which the different keywords appear. From our corpus, we find that 219362 tweets include at least one form corresponding to any of the selected concepts.",
+      "The pictorial representation of these concepts is made using a shapefile of both the Iberian Peninsula and the Balearic Islands. Then, we construct a polygon grid over the shapefile. The size of the cells ( INLINEFORM0 ) roughly corresponds to 1200 km INLINEFORM1 . We locate the cell in which a given keyword matches and assign a different color to each keyword. We follow a majority criterion, i.e., we depict the cell with the keyword color whose absolute frequency is maximum. This procedure nicely yields a useful geographical representation of how the different variants for a concept are distributed over the space."
+    ],
+    [
+      "The dialectometric differences are quantified between regions defined with the aid of our cells. For this purpose we take into account two metrics, which we now briefly discuss.",
+      "This metric is a vector comparison measure. It is widely used in text classification, information retrieval and data mining BIBREF20 . Let INLINEFORM0 and INLINEFORM1 be two vectors whose components are given by the relative frequencies of the lexical variations for a concept within a cell. Quite generally, INLINEFORM2 and INLINEFORM3 represent points in a high-dimensional space. The similarity measure INLINEFORM4 between these two vectors is related to their inner product conveniently normalized to the product of their lengths, DISPLAYFORM0 ",
+      "This expression has an easy interpretation. If both vectors lie parallel, the direction cosine is 1 and thus the distance becomes INLINEFORM0 . Since all vector components in our approach are positive, the upper bound of INLINEFORM1 is 1, which is attained when the two vectors are maximally dissimilar.",
+      "This distance is a similarity measure between probability density functions BIBREF21 . It is a symmetrized version of a more general metric, the Kullback-Leibler divergence. Let INLINEFORM0 and INLINEFORM1 be two probability distributions. In our case, these functions are built from the relative frequencies of each concept variation. Our frequentist approach differs from previous dialectometric works, which prefer to measure distances using the Dice similarity coefficient or the Jaccard index BIBREF22 .",
+      "The Kullback-Leibler divergence is defined as DISPLAYFORM0 ",
+      "We now symmetrize this expression and take the square root, DISPLAYFORM0 ",
+      "where INLINEFORM0 . The Jensen-Shannon distance INLINEFORM1 is indeed a metric, i.e., it satisfies the triangle inequality. Additionally, INLINEFORM2 fulfills the metric requirements of non-negativity, INLINEFORM3 if and only if INLINEFORM4 (identity of indiscernibles) and symmetry (by construction). This distance has been employed in bioinformatics and genome comparison BIBREF23 , BIBREF24 , social sciences BIBREF25 and machine learning BIBREF26 . To the best of our knowledge, it has not been used in studies of language variation. An exception is the work of Sanders sanders, where INLINEFORM5 is calculated for an analysis of syntactic variation of Swedish. Here, we propose to apply the Jensen-Shannon metric to lexical variation. Below, we demonstrate that this idea leads to quite promising results.",
+      "Equations EQREF5 and EQREF8 give the distance between cells INLINEFORM0 and INLINEFORM1 for a certain concept. We assign the global linguistic distance in terms of lexical variability between two cells to the mean value DISPLAYFORM0 ",
+      "where INLINEFORM0 is the distance between cells INLINEFORM1 and INLINEFORM2 for the INLINEFORM3 -th concept and INLINEFORM4 is the total number of concepts used to compute the distance. In the cosine similarity model, we replace INLINEFORM5 in equation EQREF10 with equation EQREF5 whereas in the Jensen-Shannon metric INLINEFORM6 is given by equation EQREF8 ."
+    ],
+    [
+      "We first check the quality of our corpus with a few selected concepts. Examples of their spatial distributions can be seen in figure FIGREF2 . The lexical variation depends on the particular concept and on the keyword frequency. We recall that the majority rule demands that we depict the cell with the color corresponding to the most popular word. Despite a few cells appearing to be blank, we have instances in most of the map. Importantly, our results agree with the distribution for the concept cold reported by Gon\u00e7alves and S\u00e1nchez BD with a different corpus. The north-south bipartition of the variation suggested in figure FIGREF2 (a) also agrees with more traditional studies BIBREF27 . As a consequence, these consistencies support the validity of our data. The novelty of our approach is to further analyze this dialect distribution with a quantitative measure as discussed below."
+    ],
+    [
+      "Let us quantify the lexical difference between regions using the concept cold as an illustration. First, we generate a symmetric matrix of linguistic distances INLINEFORM0 between pairs of cells INLINEFORM1 and INLINEFORM2 with INLINEFORM3 calculated using equation ( EQREF5 ) or equation ( EQREF8 ). Then, we find the maximum possible INLINEFORM4 value in the matrix ( INLINEFORM5 ) and select either its corresponding INLINEFORM6 or INLINEFORM7 index as the reference cell. Since both metrics are symmetric, the choice between INLINEFORM8 and INLINEFORM9 should not affect the results much (see below for a detailed analysis). Next, we normalize all values to INLINEFORM10 and plot the distances to the reference cell using a color scale within the range INLINEFORM11 , whose lowest and highest values are set for convenience due to the normalization procedure. The results are shown in figure FIGREF12 . Panel (a) [(b)] is obtained with the cosine similarity (Jensen-Shannon metric). Crucially, we observe that both metrics give similar results, which confirm the robustness of our dialectometric method.",
+      "Clearly, cells with a low number of tweets will largely contribute to fluctuations in the maps. To avoid this noise-related effect, we impose in figure FIGREF13 a minimum threshold of 5 tweets in every cell. Obviously, the number of colored cells decreases but fluctuations become quenched at the same time. If the threshold is increased up to 10 tweets, we obtain the results plotted in figure FIGREF14 , where the north-south bipartition is now better seen. We stress that there exist minimal differences between the cosine similarity and the Jensen-Shannon metric models."
+    ],
+    [
+      "Our previous analysis assessed the lexical distance for a single concept (cold). Let us now take into account all concepts and calculate the averaged distances using equation ( EQREF10 ). To do so, we proceed as above and measure the distance from any of the two cells that presents the maximal value of INLINEFORM0 , where INLINEFORM1 is now calculated from equation EQREF10 . As aforementioned, INLINEFORM2 connects two cells, which denote as INLINEFORM3 and INLINEFORM4 . Any of these can be selected as the reference cell from which the remaining linguistic distances are plotted in the map. To ensure that we obtain the same results, we plot the distance distribution in both directions. The results with the cosine similarity model are shown in figure FIGREF16 . It is worth noting that qualitatively the overall picture is only slightly modified when the reference cell is changed from INLINEFORM5 [figure FIGREF16 (a)] to INLINEFORM6 [figure FIGREF16 (b)]. The same conclusion is reached when the distance is calculated with the Jensen-Shannon metric model, see figures FIGREF17 (a) and (b).",
+      "After averaging over all concepts, we lose information on the lexical variation that each concept presents but on the other hand one can now investigate which regions show similar geolectal variation, yielding well defined linguistic varieties. Those cells that have similar colors in either figure FIGREF16 or figure FIGREF17 are expected to be ascribed to the same dialect zone. Thus, we can distinguish two main regions or clusters in the maps. The purple background covers most of the map and represents rural regions with small, scattered population. Our analysis shows that this group of cells possesses more specific words in their lexicon. In contrast, the green and yellow cells form a second cluster that is largely concentrated on the center and along the coastline, which correspond to big cities and industrialized areas. In these cells, the use of standard Spanish language is widespread due probably to school education, media, travelers, etc. The character of its vocabulary is more uniform as compared with the purple group. While the purple cluster prefer particular utterances, the lexicon of the urban group includes most of the keywords. Importantly, we emphasize that both distance measures (cosine similarity and Jensen-Shanon) give rise to the same result, with little discrepancies on the numerical values that are not significant. The presence of two Twitter superdialects (urban and rural) has been recently suggested BIBREF10 based on a machine learning approach. Here, we arrive at the same conclusion but with a totally distinct model and corpus. The advantage of our proposal is that it may serve as a useful tool for dialectometric purposes."
+    ],
+    [
+      "To sum up, we have presented a dialectrometric analysis of lexical variation in social media posts employing information-theoretic measures of language distances. We have considered a grid of cells in Spain and have calculated the linguistic distances in terms of dialects between the different regions. Using a Twitter corpus, we have found that the synchronic variation of Spanish can be grouped into two types of clusters. The first region shows more lexical items and is present in big cities. The second cluster corresponds to rural regions, i.e., mostly villages and less industrialized regions. Furthermore, we have checked that the different metrics used here lead to similar results in the analysis of the lexical variation for a representative concept and provide a reasonable description to language variation in Twitter.",
+      "We remark that the small amount of tweets generated after matching the lexical variations of concepts within our automatic corpus puts a limit to the quantitative analysis, making the differences between regions small. Our work might be improved by similarly examining Spanish tweets worldwide, specially in Latin America and the United States. This approach should give more information on the lexical variation on the global scale and would help linguists in their dialectal classification work of micro- and macro-varieties. Our work hence represents a first step into the ambitious task of a thorough characterization of language variation using big data resources and information-theoretic methods."
+    ],
+    [
+      "We thank both F. Lamanna and Y. Kawasaki for useful discussions and the anonymous reviewers for nice suggestions. GD acknowledges support from the SURF@IFISC program."
+    ],
+    [
+      "Here we provide a list of our employed concepts and their lexical variants.",
+      "0pt 0pt plus 1fil"
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2040/instruction.md b/qasper-2040/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..d1032f6aac084aa2eae603d055bf116ac552176c
--- /dev/null
+++ b/qasper-2040/instruction.md
@@ -0,0 +1,90 @@
+Name of Paper: A Better Variant of Self-Critical Sequence Training
+
+Question: What environment is used for self-critical sequence training?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Recap for SCST",
+    "Recap for SCST ::: Math formulation",
+    "The Better SCST",
+    "Experiments",
+    "Experiments ::: Speed",
+    "Experiments ::: Performance on different model architectures",
+    "Experiments ::: Different training hyperparameters",
+    "Experiments ::: Multiple runs",
+    "Experiments ::: Training curves",
+    "Experiments ::: Is greedy decoding necessary for SCST",
+    "Experiments ::: Variance reduction",
+    "Code release",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Self-Critical Sequence Training(SCST), upon its release, has been a popular way to train sequence generation models. While originally proposed for image captioning task, SCST not only has become the new standard for training captioning models BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9, but also has been applied to many other tasks, like video captioningBIBREF10, BIBREF11, BIBREF12, reading comprehensionBIBREF13, summarizationBIBREF14, BIBREF15, BIBREF16, BIBREF17, image paragraph generationBIBREF18, speech recognitionBIBREF19.",
+      "SCST is used to optimize generated sequences over a non-differentiable objective, usually the evaluation metrics, for example, CIDEr for captioning, ROUGE for summarization. To optimize such objective, SCST adopts REINFORCE with baseline BIBREF20, where a \u201cSelf-Critical\u201d baseline is used; specifically, the score of the greedy decoding output is used as the baseline. This is proved to be better than learned baseline function which is more commonly used in Reinforcement Learning literature.",
+      "In this work, we present a different baseline choice which was first proposed in BIBREF21, to the best of our knowledge. With more elaboration in Sec. SECREF3, this baseline can be described as a variant of \u201cSelf-Critical\u201d. This method is simple, but also faster and more effective compared to the greedy decoding baseline used in SCST."
+    ],
+    [
+      "MIXER BIBREF22 is the first to use REINFORCE algorithm for sequence generation training. They use a learned function approximator to get the baseline.",
+      "SCST inherits the REINFORCE algorithm from MIXER, but discards the learned baseline function. Instead, SCST uses the reward of the greedy decoding result as the baseline, achieving better captioning performance and lower gradient variance."
+    ],
+    [
+      "The goal of SCST, for example in captioning, is to maximize the expected CIDEr score of generated captions.",
+      "where ${\\hat{c}}$ is a sampled caption; $I$ is the image; $p_{\\theta }(c|I)$ is the captioning model parameterized by $\\theta $, and $R(\\cdot )$ is the CIDEr score.",
+      "Since this objective is not non-differentiable with respect to $\\theta $, back propagation is not feasible. To optimize it, a policy gradient method, specifically REINFORCE with baseline BIBREF20 is used.",
+      "The policy gradient method allows estimating the gradient from individual samples (the right-hand side) and applying gradient ascent. To reduce the variance of the estimation, a baseline $b$ is needed, and $b$ has to be independent of $\\hat{c}$.",
+      "In SCST, the baseline is set to be the CIDEr score of the greedy decoding caption, denoted as $c^*$. Thus, we have"
+    ],
+    [
+      "The success of SCST comes from better gradient variance reduction introduced by the greedy decoding baseline. In our variant, we use the baseline proposed in BIBREF21 to achieve even better variance reduction.",
+      "Following BIBREF21, we sample $K$ captions for each image when applying REINFORCE: ${\\hat{c}}_1 \\ldots {\\hat{c}}_K$, ${\\hat{c}}_k \\sim p_{\\theta }(c|I)$,",
+      "The baseline for each sampled caption is defined as the average reward of the rest samples. That is, for caption $\\hat{c}_k$, its baseline is",
+      "Since each sample is independently drawn, $b_k$ is a valid baseline. The final gradient estimation is",
+      "Note that, $b_k$ is an estimation of expected reward, which is similar to the learning objective of value functions in other Reinforcement Learning algorithms. The expected reward is usually a good baseline choice in that it can effectively reduce gradient variance. In Sec. SECREF4, we show that our gradient variance is lower than SCST empirically.",
+      "It is still a \u201cSelf-Critical\u201d baseline because the critic is still from itself: its other sampling results, instead of the greedy decoding result."
+    ],
+    [
+      "For all models, we first pretrain them using standard cross-entropy loss and then switch to Self-Critical training.",
+      "For a fair comparison, during Self-Critical stage, we always sample 5 captions for each image, same for both SCST and our variant.",
+      "All the experiments are done on COCO captioning dataset BIBREF23. The scores are obtained on Karparthy test split BIBREF24 with beam search of beam size 5 if not explicitly noted."
+    ],
+    [
+      "Since no extra greedy decoding is needed, our method is slightly faster than SCST."
+    ],
+    [
+      "We experiment with four different architectures. FC and Att2in are from SCSTBIBREF25. UpDown is from BIBREF26. Transformer is adapted from BIBREF27 for captioning task.",
+      "Table TABREF6 shows that our variant is better than SCST on all architectures, especially on Transformer."
+    ],
+    [
+      "Here we adopt a different training setting (`Long') for UpDown model. The `Long' setting (from https://github.com/yangxuntu/SGAE) uses a larger batch size and a longer training time. Table TABREF8 shows that there is always a gap between our method and SCST which cannot be closed by longer training or a larger batch size."
+    ],
+    [
+      "Table TABREF10 shows that our variant is consistently better than SCST with different random seeds. All the models use `Long' setting with UpDown model.",
+      "Specifically, we pretrain 5 models using cross-entropy loss, and then apply SCST and our method respectively. The same $RS*$ means they share the same pretrained model."
+    ],
+    [
+      "Figure FIGREF12 shows the model performance on the validation set during training, after entering Self-Critical stage. The scores are averaged over the 5 UpDown(Long) models above."
+    ],
+    [
+      "We also experiment with a variant of SCST, by replacing the greedy decoding output with a sampled output. (This is similar to our method with $K=2$.)",
+      "Table TABREF14 shows that one sample baseline is worse than greedy decoding. This is as expected, because using one sample to estimate the expected reward is too noisy, resulting in larger gradient variance, while the reward of greedy decoding output may be biased but more stable. It also shows that it is important to use sufficiently large $K$ to have a better estimation of expected reward."
+    ],
+    [
+      "As stated in Sec. SECREF3, the motivation of using the average reward baseline is for better variance reduction. Here we show it indeed is better in practice.",
+      "The gradient variance is calculated as follows. At the end of each epoch, we take the saved model and run through the training set. We get the gradients from each training batch and calculate the variance for each parameter gradient across batches. To get a single value, we take the average of all the parameters. A mathematic expression of this process is:",
+      "where $i$ is the index of each parameter; $b$ is the index of each batch; $\\theta $ is the network parameters; $\\text{grad}_{\\theta _i}^b$ is the gradient of $\\theta _i$ at batch $b$.",
+      "As shown in Fig. FIGREF16, our method is always getting lower variance than SCST."
+    ],
+    [
+      "Code has been released at https://github.com/ruotianluo/self-critical.pytorch. More instructions of using this method are at https://github.com/ruotianluo/self-critical.pytorch/tree/master/projects/NewSelfCritical"
+    ],
+    [
+      "We propose a variant of popular SCST, which can work as a drop-in replacement for SCST. This variant reduces the gradient variance when applying REINFORCE by modifying the baseline function. We show that this method is effective on Image Captioning task, and we believe it should benefit other tasks as well."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2041/instruction.md b/qasper-2041/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..40ff9a203c9aadb01fb1e01da9be93f6b5eccc14
--- /dev/null
+++ b/qasper-2041/instruction.md
@@ -0,0 +1,90 @@
+Name of Paper: A Better Variant of Self-Critical Sequence Training
+
+Question: What baseline function is used in REINFORCE algorithm?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Recap for SCST",
+    "Recap for SCST ::: Math formulation",
+    "The Better SCST",
+    "Experiments",
+    "Experiments ::: Speed",
+    "Experiments ::: Performance on different model architectures",
+    "Experiments ::: Different training hyperparameters",
+    "Experiments ::: Multiple runs",
+    "Experiments ::: Training curves",
+    "Experiments ::: Is greedy decoding necessary for SCST",
+    "Experiments ::: Variance reduction",
+    "Code release",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Self-Critical Sequence Training(SCST), upon its release, has been a popular way to train sequence generation models. While originally proposed for image captioning task, SCST not only has become the new standard for training captioning models BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9, but also has been applied to many other tasks, like video captioningBIBREF10, BIBREF11, BIBREF12, reading comprehensionBIBREF13, summarizationBIBREF14, BIBREF15, BIBREF16, BIBREF17, image paragraph generationBIBREF18, speech recognitionBIBREF19.",
+      "SCST is used to optimize generated sequences over a non-differentiable objective, usually the evaluation metrics, for example, CIDEr for captioning, ROUGE for summarization. To optimize such objective, SCST adopts REINFORCE with baseline BIBREF20, where a \u201cSelf-Critical\u201d baseline is used; specifically, the score of the greedy decoding output is used as the baseline. This is proved to be better than learned baseline function which is more commonly used in Reinforcement Learning literature.",
+      "In this work, we present a different baseline choice which was first proposed in BIBREF21, to the best of our knowledge. With more elaboration in Sec. SECREF3, this baseline can be described as a variant of \u201cSelf-Critical\u201d. This method is simple, but also faster and more effective compared to the greedy decoding baseline used in SCST."
+    ],
+    [
+      "MIXER BIBREF22 is the first to use REINFORCE algorithm for sequence generation training. They use a learned function approximator to get the baseline.",
+      "SCST inherits the REINFORCE algorithm from MIXER, but discards the learned baseline function. Instead, SCST uses the reward of the greedy decoding result as the baseline, achieving better captioning performance and lower gradient variance."
+    ],
+    [
+      "The goal of SCST, for example in captioning, is to maximize the expected CIDEr score of generated captions.",
+      "where ${\\hat{c}}$ is a sampled caption; $I$ is the image; $p_{\\theta }(c|I)$ is the captioning model parameterized by $\\theta $, and $R(\\cdot )$ is the CIDEr score.",
+      "Since this objective is not non-differentiable with respect to $\\theta $, back propagation is not feasible. To optimize it, a policy gradient method, specifically REINFORCE with baseline BIBREF20 is used.",
+      "The policy gradient method allows estimating the gradient from individual samples (the right-hand side) and applying gradient ascent. To reduce the variance of the estimation, a baseline $b$ is needed, and $b$ has to be independent of $\\hat{c}$.",
+      "In SCST, the baseline is set to be the CIDEr score of the greedy decoding caption, denoted as $c^*$. Thus, we have"
+    ],
+    [
+      "The success of SCST comes from better gradient variance reduction introduced by the greedy decoding baseline. In our variant, we use the baseline proposed in BIBREF21 to achieve even better variance reduction.",
+      "Following BIBREF21, we sample $K$ captions for each image when applying REINFORCE: ${\\hat{c}}_1 \\ldots {\\hat{c}}_K$, ${\\hat{c}}_k \\sim p_{\\theta }(c|I)$,",
+      "The baseline for each sampled caption is defined as the average reward of the rest samples. That is, for caption $\\hat{c}_k$, its baseline is",
+      "Since each sample is independently drawn, $b_k$ is a valid baseline. The final gradient estimation is",
+      "Note that, $b_k$ is an estimation of expected reward, which is similar to the learning objective of value functions in other Reinforcement Learning algorithms. The expected reward is usually a good baseline choice in that it can effectively reduce gradient variance. In Sec. SECREF4, we show that our gradient variance is lower than SCST empirically.",
+      "It is still a \u201cSelf-Critical\u201d baseline because the critic is still from itself: its other sampling results, instead of the greedy decoding result."
+    ],
+    [
+      "For all models, we first pretrain them using standard cross-entropy loss and then switch to Self-Critical training.",
+      "For a fair comparison, during Self-Critical stage, we always sample 5 captions for each image, same for both SCST and our variant.",
+      "All the experiments are done on COCO captioning dataset BIBREF23. The scores are obtained on Karparthy test split BIBREF24 with beam search of beam size 5 if not explicitly noted."
+    ],
+    [
+      "Since no extra greedy decoding is needed, our method is slightly faster than SCST."
+    ],
+    [
+      "We experiment with four different architectures. FC and Att2in are from SCSTBIBREF25. UpDown is from BIBREF26. Transformer is adapted from BIBREF27 for captioning task.",
+      "Table TABREF6 shows that our variant is better than SCST on all architectures, especially on Transformer."
+    ],
+    [
+      "Here we adopt a different training setting (`Long') for UpDown model. The `Long' setting (from https://github.com/yangxuntu/SGAE) uses a larger batch size and a longer training time. Table TABREF8 shows that there is always a gap between our method and SCST which cannot be closed by longer training or a larger batch size."
+    ],
+    [
+      "Table TABREF10 shows that our variant is consistently better than SCST with different random seeds. All the models use `Long' setting with UpDown model.",
+      "Specifically, we pretrain 5 models using cross-entropy loss, and then apply SCST and our method respectively. The same $RS*$ means they share the same pretrained model."
+    ],
+    [
+      "Figure FIGREF12 shows the model performance on the validation set during training, after entering Self-Critical stage. The scores are averaged over the 5 UpDown(Long) models above."
+    ],
+    [
+      "We also experiment with a variant of SCST, by replacing the greedy decoding output with a sampled output. (This is similar to our method with $K=2$.)",
+      "Table TABREF14 shows that one sample baseline is worse than greedy decoding. This is as expected, because using one sample to estimate the expected reward is too noisy, resulting in larger gradient variance, while the reward of greedy decoding output may be biased but more stable. It also shows that it is important to use sufficiently large $K$ to have a better estimation of expected reward."
+    ],
+    [
+      "As stated in Sec. SECREF3, the motivation of using the average reward baseline is for better variance reduction. Here we show it indeed is better in practice.",
+      "The gradient variance is calculated as follows. At the end of each epoch, we take the saved model and run through the training set. We get the gradients from each training batch and calculate the variance for each parameter gradient across batches. To get a single value, we take the average of all the parameters. A mathematic expression of this process is:",
+      "where $i$ is the index of each parameter; $b$ is the index of each batch; $\\theta $ is the network parameters; $\\text{grad}_{\\theta _i}^b$ is the gradient of $\\theta _i$ at batch $b$.",
+      "As shown in Fig. FIGREF16, our method is always getting lower variance than SCST."
+    ],
+    [
+      "Code has been released at https://github.com/ruotianluo/self-critical.pytorch. More instructions of using this method are at https://github.com/ruotianluo/self-critical.pytorch/tree/master/projects/NewSelfCritical"
+    ],
+    [
+      "We propose a variant of popular SCST, which can work as a drop-in replacement for SCST. This variant reduces the gradient variance when applying REINFORCE by modifying the baseline function. We show that this method is effective on Image Captioning task, and we believe it should benefit other tasks as well."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2046/instruction.md b/qasper-2046/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..8e67334e959de8b1c9250fa6440fbc44b836199f
--- /dev/null
+++ b/qasper-2046/instruction.md
@@ -0,0 +1,129 @@
+Name of Paper: Opinion Recommendation using Neural Memory Model
+
+Question: What are the baselines?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Model",
+    "Review Model",
+    "User Model",
+    "Finding Neighbor Users",
+    "Product Model",
+    "Customized Product Model",
+    "Customized Review Generation",
+    "Customized Opinion Rating Prediction",
+    "Training",
+    "Experimental Settings",
+    "Development Experiments",
+    "Final Results",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Offering a channel for customers to share opinions and give scores to products and services, review websites have become a highly influential information source that customers refer to for making purchase decisions. Popular examples include IMDB on the movie domain, Epinions on the product domain, and Yelp on the service domain. Figure FIGREF4 shows a screenshot of a restaurant review page on Yelp.com, which offers two main types of information. First, an overall rating score is given under the restaurant name; second, detailed user reviews are listed below the rating.",
+      "Though offering a useful overview and details about a product or service, such information has several limitations. First, the overall rating is general and not necessarily agreeable to the taste of individual customers. Being a simple reflection of all customer scores, it serves an average customer well, but can be rather inaccurate for individuals. For example, the authors themselves often find highly rated movies being tedious. Second, there can be hundreds of reviews for a product or service, which makes it infeasible for exhaustive reading. It would be useful to have a brief summary of all reviews, which ideally should be customized to the reader.",
+      "We investigate the feasibility of a model that addresses the limitations above. There are two sources of information that the model should collect to achieve its goal, namely information on the target product, and information about the user. The former can be obtained from reviews written by other customers about the target product, and the latter can be obtained from the reviews that the user has written for other products and services. Given the above two sources of information, the model should generate a customized score of the product that the user is likely to give after trying, as well as a customized review that the user would have written for the target product.",
+      "We refer to the task above using the term opinion recommendation, which is a new task, yet closely related to several existing lines of work in NLP. The first is sentiment analysis BIBREF0 , BIBREF1 , which is to give a rating score based on a customer review. Our task is different in that we aim to predict user rating scores of new product, instead of predicting the opinion score of existing reviews. The second is opinion summarization BIBREF2 , BIBREF3 , which is to generate a summary based on reviews of a product. A major difference between our task and this task is that the summary must be customized to a certain user, and a rating score must additionally be given. The third is recommendation BIBREF4 , BIBREF5 , which is to give a ranking score for a certain product or service based on the purchase history of the user and other customers who have purchased the target product. Our task is different in the source of input, which is textual customer reviews and ratings rather than numerical purchase history.",
+      "There are three types of inputs for our task, namely the reviews of the target product, the reviews of the user on other products, and other users reviews on other products, and two types of outputs, namely a customized rating score and a customized review. The ideal solution should consider the interaction between all given types of information, jointly predicting the two types of outputs. This poses significant challenges to statistical models, which require manually defined features to capture relevant patterns from training data. Deep learning is a relatively more feasible choice, offering viabilities of information fusion by fully connected hidden layers BIBREF6 , BIBREF7 . We leverage this advantage in building our model.",
+      "In particular, we use a recurrent neural network to model the semantic content of each review. A neural network is used to consolidate existing reviews for the target product, serving the role of a product model. In addition, a user model is built by consolidating the reviews of the given user into a single vector form. Third, to address potential sparsity of a user's history reviews, neighbor users are identified by collaborative filtering BIBREF8 , and a vector representation is learned by using a neural neighborhood model, which consolidates their history reviews. Finally, a deep memory network is utilized to find the association between the user and target product, jointly yielding the rating score and customised review.",
+      "Experiments on a Yelp dataset show that the model outperforms several pipelined baselines using state-of-the-art techniques. In particular, review scores given by the opinion recordation system are closer to real user review scores compared to the review scores which Yelp assigns to target products. Our code is released at http://github.com/anonymous."
+    ],
+    [
+      "Sentiment Analysis. Our task is related to document-level sentiment classification BIBREF1 , which is to infer the sentiment polarity of a given document. Recently, various neural network models are used to capture the sentimental information automatically, including convolutional neural networks BIBREF9 , recursive neural network BIBREF10 and recurrent neural network BIBREF11 , BIBREF12 , which have been shown to achieve competitive results across different benchmarks. Different from binary classification, review rating prediction aims to predict the numeric rating of a given review. PangL05 pioneered this task by regarding it as a classification/regression problem. Most subsequent work focuses on designing effective textural features of reviews BIBREF13 , BIBREF14 , BIBREF15 . Recently, TangQLY15 proposed a neural network model to predict the rating score by using both lexical semantic and user model.",
+      "Beyond textural features, user information is also investigated in the literature of sentiment analysis. For example, gao2013modeling developed user-specific features to capture user leniency, and li2014suit incorporated textual topic and user-word factors through topic modeling. For integrating user information into neural network models, TangQLY15 predicted the rating score given a review by using both lexical semantic information and a user embedding model. ChenSTLL16 proposed a neural network to incorporate global user and product information for sentiment classification via an attention mechanism.",
+      "Different from the above research on sentiment analysis, which focuses on predicting the opinion on existing reviews. Our task is to recommend the score that a user would give to a new product without knowing his review text. The difference originates from the object, previous research aims to predict opinions on reviewed products, while our task is to recommend opinion on new products, which the user has not reviewed.",
+      "Opinion Summarization. Our work also overlaps with to the area of opinion summarization, which constructs natural language summaries for multiple product reviews BIBREF0 . Most previous work extracts opinion words and aspect terms. Typical approaches include association mining of frequent candidate aspects BIBREF0 , BIBREF16 , sequence labeling based methods BIBREF17 , BIBREF18 , as well as topic modeling techniques BIBREF19 . Recently, word embeddings and recurrent neural networks are also used to extract aspect terms BIBREF20 , BIBREF21 .",
+      "Aspect term extraction approaches lack critical information for a user to understand how an aspect receives a particular rating. To address this, NishikawaHMK10 generated summaries by selecting and ordering sentences taken from multiple review texts according to affirmativeness and readability of the sentence order. WangL11 adopted both sentence-ranking and graph-based methods to extract summaries on an opinion conversation dataset. While all the methods above are extractive, ganesan2010opinosis presented a graph-based summarization framework to generate concise abstractive summaries of highly redundant opinions, and WangL16 used an attention-based neural network model to absorb information from multiple text units and generate summaries of movie reviews.",
+      "Different from the above research on opinion summarization, we generate a customized review to a certain user, and a rating score must be additionally given.",
+      "Recommendation. Recommendation systems suggest to a user new products and services that might be of their interest. There are two main approaches, which are content-based and collaborative-filtering (CF) based BIBREF22 , BIBREF5 , respectively. Most existing social recommendation systems are CF-based, and can be further grouped into model-based CF and neighborhood-based CF BIBREF23 , BIBREF4 . Matrix Factorization (MF) is one of the most popular models for CF. In recent MF-based social recommendation works, user-user social trust information is integrated with user-item feedback history (e.g., ratings, clicks, purchases) to improve the accuracy of traditional recommendation systems, which only factorize user-item feedback data BIBREF8 , BIBREF24 , BIBREF25 .",
+      "There has been work integrating sentiment analysis and recommendation systems, which use recommendation strategies such as matrix factorization to improve the performance of sentiment analysis BIBREF26 , BIBREF27 . These methods typically use ensemble learning BIBREF27 or probabilistic graph models BIBREF28 . For example, ZhangL0ZLM14 who proposed a factor graph model to recommend opinion rating scores by using explicit product features as hidden variables.",
+      "Different from the above research on recommendation systems, which utilize numerical purchase history between users and products, we work with textual information. In addition, recommendation systems only predict a rating score, while our system generates also a customized review, which is more informative.",
+      "Neural Network Models. Multi-task learning has been recognised as a strength of neural network models for natural language processing BIBREF6 , BIBREF7 , BIBREF29 , BIBREF30 , where hidden feature layers are shared between different tasks that have common basis. Our work can be regarded as an instance of such multi-tasks learning via shared parameters, which has been widely used in the research community recently.",
+      "Dynamic memory network models are inspired by neural turing machines BIBREF31 , and have been applied for NLP tasks such as question answering BIBREF32 , BIBREF33 , language modeling BIBREF34 and machine translation BIBREF35 . It is typically used to find abstract semantic representations of texts towards certain tasks, which are consistent with our main need, namely abstracting the representation of a product that is biased towards the taste of a certain user."
+    ],
+    [
+      "Formally, the input to our model is a tuple INLINEFORM0 , where INLINEFORM1 is the set of existing reviews of a target product, INLINEFORM2 is the set of user's history reviews, and INLINEFORM3 is the set of the user's neighborhood reviews. All the reviews are sorted with temporal order. The output is a pair INLINEFORM4 , where INLINEFORM5 is a real number between 0 and 5 representing the rating score of the target product, and INLINEFORM6 is a customised review.",
+      "For capturing both general and personalized information, we first build a product model, a user model, and a neighborhood model, respectively, and then use a memory network model to integrate these three types of information, constructing a customized product model. Finally, we predict a customized rating score and a review collectively using neural stacking. The overall architecture of the model is shown in Figure FIGREF5 ."
+    ],
+    [
+      "A customer review is the foundation of our model, based on which we derive representations of both a user and a target product. In particular, a user profile can be achieved by modeling all the reviews of the user INLINEFORM0 , and a target product profile can be obtained by using all existing reviews of the product INLINEFORM1 . We use the average of word embeddings to model a review. Formally, given a review INLINEFORM2 , where INLINEFORM3 is the length of the review, each word INLINEFORM4 is represented with a K-dimensional embedding INLINEFORM5 BIBREF36 . We use the INLINEFORM6 for the representation of the review INLINEFORM7 ."
+    ],
+    [
+      "A standard LSTM BIBREF37 without coupled input and forget gates or peephole connections is used to learn the hidden states of the reviews. Denoting the recurrent function at step INLINEFORM0 as INLINEFORM1 , we obtain a sequence of hidden state vectors INLINEFORM2 recurrently by feeding INLINEFORM3 as inputs, where INLINEFORM4 . The initial state and all stand LSTM parameters are randomly initialized and tuned during training.",
+      "Not all reviews contribute equally to the representation of a user. We introduce an attention mechanism BIBREF38 , BIBREF39 to extract the reviews that are relatively more important, and aggregate the representation of reviews to form a vector. Taking the hidden state INLINEFORM0 of user model as input, the attention model outputs, a continuous vector INLINEFORM1 , which is computed as a weighted sum of each hidden state INLINEFORM2 , namely DISPLAYFORM0 ",
+      "where INLINEFORM0 is the hidden variable size, INLINEFORM1 is the weight of INLINEFORM2 , and INLINEFORM3 .",
+      "For each piece of hidden state INLINEFORM0 , the scoring function is calculated by DISPLAYFORM0 DISPLAYFORM1 ",
+      "where INLINEFORM0 and INLINEFORM1 are model parameters. The attention vector INLINEFORM2 is used to represent the User Model."
+    ],
+    [
+      "We use neighborhood reviews to improve the user model, since a user may not have sufficient reviews to construct a reliable model. Here a neighbor refers to a user that has similar tastes to the target user BIBREF24 , BIBREF40 . The same as the user model, we construct the neighborhood model INLINEFORM0 using the neighborhood reviews INLINEFORM1 with an attention recurrent network.",
+      "A key issue in building the neighborhood model is how to find neighbors of a certain user. In this study, we use matrix factorization BIBREF24 to detect neighbors, which is a standard approach for recommendation BIBREF8 , BIBREF41 , BIBREF25 . In particular, users' rating scores of products are used to build a product-users matrix INLINEFORM0 with INLINEFORM1 products and INLINEFORM2 users. We approximate it using three factors, which specify soft membership of products and users BIBREF8 by finding: DISPLAYFORM0 ",
+      "where INLINEFORM0 represents the posterior probability of INLINEFORM1 topic clusters for each product; INLINEFORM2 encodes the distribution of each topic INLINEFORM3 ; and INLINEFORM4 indicates the posterior probability of INLINEFORM5 topic clusters for each user.",
+      "As a result of matrix factorization, we directly obtain the probability of each user on each topic from the person-topic matrix INLINEFORM0 . To infer INLINEFORM1 , the optimization problem in Eq. EQREF12 can be solved using the following updating rule: DISPLAYFORM0 ",
+      "Obtaining the user-topic matrix INLINEFORM0 , we measure the implicit connection between two users using: DISPLAYFORM0 ",
+      "where INLINEFORM0 measure the implicit connection degree between users INLINEFORM1 and INLINEFORM2 . If INLINEFORM3 is higher than a threshold INLINEFORM4 , we consider user INLINEFORM5 as the neighbor of user INLINEFORM6 ."
+    ],
+    [
+      "Given the representations of existing reviews INLINEFORM0 of the product, we use a LSTM to model their temporal orders, obtaining a sequence of hidden state vectors INLINEFORM1 by recurrently feeding INLINEFORM2 as inputs. The hidden state vectors INLINEFORM3 are used to represent the product."
+    ],
+    [
+      "We use the user representation INLINEFORM0 and the neighbour representation INLINEFORM1 to transform the target product representation INLINEFORM2 into a customised product representation INLINEFORM3 , which is tailored to the taste of the user. In particular, a dynamic memory network BIBREF32 , BIBREF42 is utilized to iteratively find increasingly abstract representations of INLINEFORM4 , by injecting INLINEFORM5 and INLINEFORM6 information.",
+      "The memory model consists of multiple dynamic computational layers (hops), each of which contains an attention layer and a linear layer. In the first computational layer (hop 1), we take the hidden variables INLINEFORM0 ( INLINEFORM1 ) of product model as input, adaptively selecting important evidences through one attention layer using INLINEFORM2 and INLINEFORM3 . The output of the attention layer gives a linear interpolation of INLINEFORM4 , and the result is considered as input to the next layer (hop 2). In the same way, we stack multiple hops and run the steps multiple times, so that more abstract representations of the target product can be derived.",
+      "The attention model outputs a continuous vector INLINEFORM0 , which is computed as a weighted sum of INLINEFORM1 ( INLINEFORM2 ), namely DISPLAYFORM0 ",
+      "where INLINEFORM0 is the hidden variable size, INLINEFORM1 is the weight of INLINEFORM2 , and INLINEFORM3 . For each piece of hidden state INLINEFORM4 , we use a feed forward neural network to compute its semantic relatedness with the abstract representation INLINEFORM5 . The scoring function is calculated as follows at hop INLINEFORM6 : DISPLAYFORM0 DISPLAYFORM1 ",
+      "The vector INLINEFORM0 is used to represent the customized product model. At the first hop, we define INLINEFORM1 .",
+      "The product model INLINEFORM0 ( INLINEFORM1 ) represents salient information of existing reviews in their temporal order, they do not reflect the taste of a particular user. We use the customised product model to integrate user information and product information (as reflected by the product model), resulting in a single vector that represents a customised product. From this vector we are able to synthesis both a customised review and a customised rating score."
+    ],
+    [
+      "The goal of customized review generation is to generate a review INLINEFORM0 from the customized product representation INLINEFORM1 , composed by a sequence of words INLINEFORM2 . We decompose the prediction of INLINEFORM3 into a sequence of word-level predictions: DISPLAYFORM0 ",
+      "where each word INLINEFORM0 is predicted conditional on the previously generated INLINEFORM1 and the input INLINEFORM2 . The probability is estimated by using standard word softmax: DISPLAYFORM0 ",
+      "where INLINEFORM0 is the hidden state variable at timestamp INLINEFORM1 , which is modeled as INLINEFORM2 . Here a LSTM is used to generate a new state INLINEFORM3 from the representation of the previous state INLINEFORM4 and INLINEFORM5 . INLINEFORM6 is the concatenation of previously generated word INLINEFORM7 and the input representation of customized model INLINEFORM8 ."
+    ],
+    [
+      "We consider two factors for customised opinion rating, namely existing review scores and the customised product representation INLINEFORM0 . A baseline rating system such as Yelp.com uses only the former information, typically by taking the average of existing review scores. Such a baseline gives an empirical square error of 1.28 (out of 5) in our experiments, when compared with a test set of individual user ratings, which reflects the variance in user tastes. In order to integrate user preferences into the rating, we instead take a weighted average of existing ratings cores, so that the scores of reviews that are closer to the user preference are given higher weights.",
+      "As a second factor, we calculate a review score independently according to the customised representation INLINEFORM0 of existing reviews, without considering review scores. The motivation is two fold. First, existing reviews can be relatively few, and hence using their scores alone might not be sufficient for a confident score. Second, existing ratings can be all different from a user\u00a1\u00afs personal rating, if the existing reviews do not come from the user's neighbours. As a result, using the average or weighted average of existing reviews, the personalised user rating might not be reached.",
+      "Formally, given the rating scores INLINEFORM0 of existing reviews, and the the customized product representation INLINEFORM1 , we calculate: DISPLAYFORM0 ",
+      "In the left term INLINEFORM0 , we use attention weights INLINEFORM1 to measure the important of each rating score INLINEFORM2 . The right term INLINEFORM3 is a review-based shift, weighted by INLINEFORM4 .",
+      "Since the result of customized review generation can be helpful for rating score prediction, we use neural stacking additionally feeding the last hidden state INLINEFORM0 of review generation model as input for INLINEFORM1 prediction, resulting in DISPLAYFORM0 ",
+      "where INLINEFORM0 denotes vector concatenation."
+    ],
+    [
+      "For our task, there are two joint training objectives, for review scoring and review summarisation, respectively. The loss function for the former is defined as: DISPLAYFORM0 ",
+      "where INLINEFORM0 is the predicted rating score, INLINEFORM1 is the rating score in the training data, INLINEFORM2 is the set of model parameters and INLINEFORM3 is a parameter for L2 regularization.",
+      "We train the customized review generation model by maximizing the log probability of Eq. EQREF21 BIBREF43 , BIBREF44 . Standard back propagation is performed to optimize parameters, where gradients also propagate from the scoring objective to the review generation objective due to neural stacking (Eq. EQREF25 ). We apply online training, where model parameters are optimized by using Adagrad BIBREF45 . For all LSTM models, we empirically set the size of the hidden layers to 128. We train word embeddings using the Skip-gram algorithm BIBREF36 , using a window size of 5 and vector size of 128. In order to avoid over-fitting, dropout BIBREF46 is used for word embedding with a ratio of 0.2. The neighbor similarity threshold INLINEFORM0 is set to 0.25."
+    ],
+    [
+      "Our data are collected from the yelp academic dataset, provided by Yelp.com, a popular restaurant review website. The data set contains three types of objects: business, user, and review, where business objects contain basic information about local businesses (i.e. restaurants), review objects contain review texts and star rating, and user objects contain aggregate information about a single user across all of Yelp. Table TABREF31 illustrates the general statistics of the dataset.",
+      "For evaluating our model, we choose 4,755 user-product pairs from the dataset. For each pair, the existing reviews of the target service (restaurant) are used for the product model. The rating score given by each user to the target service is considered as the gold customized rating score, and the review of the target service given by each user is used as the gold-standard customized review for the user. The remaining reviews of each user are used for training the user model. We use 3,000 user-product pairs to train the model, 1,000 pairs as testing data, and remaining data for development.",
+      "We use the ROUGE-1.5.5 BIBREF47 toolkit for evaluating the performance of customized review generation, and report unigram overlap (ROUGE-1) as a means of assessing informativeness. We use Mean Square Error (MSE) BIBREF15 , BIBREF48 is used as the evaluation metric for measuring the performance of customized rating score prediction. MSE penalizes more severe errors more heavily."
+    ],
+    [
+      "Effects of various configurations of our model, are shown on Table TABREF34 , where Joint is the full model of this paper, -user ablates the user model, -neighbor ablates the neighbor model, -rating is a single-task model that generates a review without the rating score, and -generation generates only the rating score.",
+      "By comparing \u201cJoint\u201d and \u201c-user,-neighbor\u201d, we can find that customized information have significant influence on both the rating and review generation results ( INLINEFORM0 using INLINEFORM1 -test). In addition, comparison between \u201c-Joint\u201d and \u201c-user\u201d, and between \u201c-user\u201d and \u201c-user, -neighbor\u201d shows that both the user information and the neighbour user information of the user are effective for improving the results. A user\u00a1\u00afs neighbours can indeed alleviate scarcity of user reviews.",
+      "Finally, comparison between \u201cJoint\u201d and \u201c-generation\u201d, and between \u201cJoint\u201d and \u201c-rating\u201d shows that multi-task learning by parameter sharing is highly useful.",
+      "We show the influence of hops of memory network for rating prediction on Figure FIGREF36 . Note that, the model would only consider the general product reviews ( INLINEFORM0 ), when INLINEFORM1 . From the figure we can find that, when INLINEFORM2 , the performance is the best. It indicates that multiple hops can capture more abstract evidences from external memory to improve the performance. However, too many hops leads to over-fitting, thereby harms the performance. As a result, we choose 3 as the number of hops in our final test.",
+      "We show the influence of the bias weight parameter INLINEFORM0 for rating prediction in Figure FIGREF38 . With INLINEFORM1 being 0, the model uses the weighted sum of existing reviews to score the product. When INLINEFORM2 is very large, the system tends to use only the customized product representation INLINEFORM3 to score the product, hence ignoring existing review scores, which are a useful source of information. Our results show that when INLINEFORM4 is 1, the performance is optimal, thus indicating both existing review scores and review contents are equally useful."
+    ],
+    [
+      "We show the final results for opinion recommendation, comparing our proposed model with the following state-of-the-art baseline systems:",
+      "RS-Average is the widely-adopted baseline (e.g., by Yelp.com), using the averaged review scores as the final score.",
+      "RS-Linear estimates the rating score that a user would give by INLINEFORM0 BIBREF49 , where INLINEFORM1 and INLINEFORM2 are the the training deviations of the user INLINEFORM3 and the product INLINEFORM4 , respectively.",
+      "RS-Item applies INLINEFORM0 NN to estimate the rating score BIBREF50 . We choose the cosine similarity between INLINEFORM1 to measure the distance between product.",
+      "RS-MF is a state-of-the-art recommendation model, which uses matrix factorisation to predict rating score BIBREF8 , BIBREF41 , BIBREF25 .",
+      "Sum-Opinosis uses a graph-based framework to generate abstractive summarisation given redundant opinions BIBREF51 .",
+      "Sum-LSTM-Att is a state-of-the-art neural abstractive summariser, which uses an attentional neural model to consolidate information from multiple text sources, generating summaries using LSTM decoding BIBREF44 , BIBREF3 .",
+      "All the baseline models are single-task models, without considering rating and summarisation prediction jointly. The results are shown in Table TABREF46 . Our model (\u201c Joint\u201d) significantly outperforms both \u201cRS-Average\u201d and \u201cRS-Linear\u201d ( INLINEFORM0 using INLINEFORM1 -test), which demonstrates the strength of opinion recommendation, which leverages user characteristics for calculating a rating score for the user.",
+      "Our proposed model also significantly outperforms state-of-the-art recommendation systems (RS-Item and RS-MF) ( INLINEFORM0 using INLINEFORM1 -test), indicating that textual information are a useful addition to the rating scores themselves for recommending a product.",
+      "Finally, comparison between our proposed model and state-of-the-art summarisation techniques (Sum-Opinosis and Sum-LSTM-Att) shows the advantage of leveraging user information to enhance customised review generation, and also the strength of joint learning."
+    ],
+    [
+      "We presented a dynamic memory model for opinion recommendation, a novel task of jointly predicting the review and rating score that a certain user would give to a certain product or service. In particular, a deep memory network was utilized to find the association between the user and the product, jointly yielding the rating score and customised review. Results show that our methods are better results compared to several pipelines baselines using state-of-the-art sentiment rating and summarisation systems."
+    ]
+  ]
+}
+```
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+Name of Paper: Improving Quality and Efficiency in Plan-based Neural Data-to-Text Generation
+
+Question: How is fluency of generated text evaluated?
\ No newline at end of file
diff --git a/qasper-2048/instruction.md b/qasper-2048/instruction.md
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+++ b/qasper-2048/instruction.md
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+Name of Paper: Improving Quality and Efficiency in Plan-based Neural Data-to-Text Generation
+
+Question: How is faithfulness of the resulting text evaluated?
\ No newline at end of file
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+Name of Paper: Improving Quality and Efficiency in Plan-based Neural Data-to-Text Generation
+
+Question: How are typing hints suggested?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Step-by-step Generation",
+    "Fast and Verifiable Planner",
+    "Incorporating typing information for unseen entities and relations",
+    "Output verification",
+    "Output verification ::: Existing approaches: soft encouragement via neural modules.",
+    "Output verification ::: Our proposal: explicit verification.",
+    "Referring Expressions",
+    "Evaluation and Results",
+    "Evaluation and Results ::: Setup",
+    "Evaluation and Results ::: Neural Planner vs Exhaustive Planner",
+    "Evaluation and Results ::: Effect of Type Information",
+    "Evaluation and Results ::: Effect of Output Verification",
+    "Evaluation and Results ::: Referring Expression Module ::: Intrinsic evaluation of the REG module.",
+    "Evaluation and Results ::: Referring Expression Module ::: Effect on BLEU scores.",
+    "Conclusions",
+    "Acknowledgements",
+    "Interaction of different components",
+    "REG Error Analysis",
+    "Output Examples"
+  ],
+  "paragraphs": [
+    [
+      "In the data-to-text generation task (D2T), the input is data encoding facts (e.g., a table, a set of tuples, or a small knowledge graph), and the output is a natural language text representing those facts. In neural D2T, the common approaches train a neural end-to-end encoder-decoder system that encodes the input data and decodes an output text. In recent work BIBREF0 we proposed to adopt ideas from \u201ctraditional\u201d language generation approaches (i.e. BIBREF1, BIBREF2, BIBREF3) that separate the generation into a planning stage that determines the order and structure of the expressed facts, and a realization stage that maps the plan to natural language text. We show that by breaking the task this way, one can achieve the same fluency of neural generation systems while being able to better control the form of the generated text and to improve its correctness by reducing missing facts and \u201challucinations\u201d, common in neural systems.",
+      "In this work we adopt the step-by-step framework of BIBREF0 and propose four independent extensions that improve aspects of our original system: we suggest a new plan generation mechanism, based on a trainable-yet-verifiable neural decoder, that is orders of magnitude faster than the original one (\u00a7SECREF3); we use knowledge of the plan structure to add typing information to plan elements. This improves the system's performance on unseen relations and entities (\u00a7SECREF4); the separation of planning from realizations allows the incorporation of a simple output verification heuristic that drastically improves the correctness of the output (\u00a7SECREF5); and finally we incorporate a post-processing referring expression generation (REG) component, as proposed but not implemented in our previous work, to improve the naturalness of the resulting output (\u00a7SECREF6)."
+    ],
+    [
+      "We provide a brief overview of the step-by-step system. See BIBREF0 for further details. The system works in two stages. The first stage (planning) maps the input facts (encoded as a directed, labeled graph, where nodes represent entities and edges represent relations) to text plans, while the second stage (realization) maps the text plans to natural language text.",
+      "The text plans are a sequence of sentence plans\u2014each of which is a tree\u2014 representing the ordering of facts and entities within the sentence. In other words, the plans determine the separation of facts into sentences, the ordering of sentences, and the ordering of facts and entities within each sentence. This stage is completely verifiable: the text plans are guaranteed to faithfully encode all and only the facts from the input. The realization stage then translates the plans into natural language sentences, using a neural sequence-to-sequence system, resulting in fluent output."
+    ],
+    [
+      "The data-to-plan component in BIBREF0 exhaustively generates all possible plans, scores them using a heuristic, and chooses the highest scoring one for realization. While this is feasible with the small input graphs in the WebNLG challenge BIBREF4, it is also very computationally intensive, growing exponentially with the input size. We propose an alternative planner which works in linear time in the size of the graph and remains verifiable: generated plans are guaranteed to represent the input faithfully.",
+      "The original planner works by first enumerating over all possible splits into sentences (sub-graphs), and for each sub-graph enumerating over all possible undirected, unordered, Depth First Search (DFS) traversals, where each traversal corresponds to a sentence plan. Our planner combines these into a single process. It works by performing a series of what we call random truncated DFS traversals. In a DFS traversal, a node is visited, then its children are visited recursively in order. Once all children are visited, the node \u201cpops\u201d back to the parent. In a random truncated traversal, the choice of which children to visit next, as well as whether to go to the next children or to \u201cpop\u201d, is non-deterministic (in practice, our planner decides by using a neural-network controller). Popping at a node before visiting all its children truncates the DFS: further descendants of that node will not be visited in this traversal. It behaves as a DFS on a graph where edges to these descendants do not exist. Popping the starting node terminates the traversal.",
+      "Our planner works by choosing a node with a non-zero degree and performing a truncated DFS traversal from that node. Then, all edges visited in the traversal are removed from the input graph, and the process repeats (performing another truncated DFS) until no more edges remain. Each truncated DFS traversal corresponds to a sentence plan, following the DFS-to-plan procedure of BIBREF0: the linearized plan is generated incrementally at each step of the traversal. This process is linear in the number of edges in the graph.",
+      "At training time, we use the plan-to-DFS mapping to perform the correct sequence of traversals, and train a neural classifier to act as a controller, choosing which action to perform at each step. At test time, we use the controller to guide the truncated DFS process. This mechanism is inspired by transition based parsing BIBREF5. The action set at each stage is dynamic. During traversal, it includes the available children at each stage and pop. Before traversals, it includes a choose-i action for each available node $n_i$. We assign a score to each action, normalize with softmax, and train to choose the desired one using cross-entropy loss. At test time, we either greedily choose the best action, or we can sample plans by sampling actions according to their assigned probabilities.",
+      "Feature Representation and action scoring. Each graph node $n_i$ corresponds to an entity $x_{n_i}$, and has an associated embedding vector $\\mathbf {x_{n_i}}$. Each relation $r_i$ is associated with an embedding vector $\\mathbf {r_i}$. Each labeled input graph edge $e_k = (n_i, r_\\ell , n_j)$ is represented as a projected concatenated vector $\\mathbf {e_k}=\\mathbf {E}(\\mathbf {x_{n_i}};\\mathbf {r_\\ell };\\mathbf {x_{n_j}})$, where $\\mathbf {E}$ is a projection matrix. Finally, each node $n_i$ is then represented as a vector $\\mathbf {n_i} = \\mathbf {V}[\\mathbf {x_{n_i}};\\sum _{e_j\\in \\pi (i)}\\mathbf {e_j};\\sum _{e_j\\in \\pi ^{-1}(i)}\\mathbf {e_j}]$, where $\\pi (i)$ and $\\pi ^{-1}(i)$ are the incoming and outgoing edges from node $n_i$. The traverse-to-child-via-edge-$e_j$ action is represented as $\\mathbf {e_j}$, choose-node-i is represented as $\\mathbf {n_i}$ and pop-to-node-i is represented as $\\mathbf {n_i}+\\mathbf {p}$ where $\\mathbf {p}$ is a learned vector. The score for an action $a$ at time $t$ is calculated as a dot-product between the action representation and the LSTM state over the symbols generated in the plan so far. Thus, each decision takes into account the immediate surrounding of the node in the graph, and the plan structure generated so far.",
+      "Speed On a 7 edges graph, the planner of BIBREF0 takes an average of 250 seconds to generate a plan, while our planner takes 0.0025 seconds, 5 orders of magnitude faster."
+    ],
+    [
+      "In BIBREF0, the sentence plan trees were linearized into strings that were then fed to a neural machine translation decoder (OpenNMT) BIBREF6 with a copy mechanism. This linearization process is lossy, in the sense that the linearized strings do not explicitly distinguish between symbols that represent entities (e.g., BARACK_OBAMA) and symbols that represent relations (e.g., works-for). While this information can be deduced from the position of the symbol within the structure, there is a benefit in making it more explicit. In particular, the decoder needs to act differently when decoding relations and entities: entities are copied, while relations need to be verbalized. By making the typing information explicit to the decoder, we make it easier for it to generalize this behavior distinction and apply it also for unseen entities and relations. We thus expect the typing information to be especially useful for the unseen part of the evaluation set.",
+      "We incorporate typing information by concatenating to the embedding vector of each input symbol one of three embedding vectors, S, E or R, where S is concatenated to structural elements (opening and closing brackets), E to entity symbols and R to relation symbols."
+    ],
+    [
+      "While the plan generation stage is guaranteed to be faithful to the input, the translation process from plans to text is based on a neural seq2seq model and may suffer from known issues with such models: hallucinating facts that do not exist in the input, repeating facts, or dropping facts. While the clear mapping between plans and text helps to reduce these issues greatly, the system in BIBREF0 still has 2% errors of these kinds."
+    ],
+    [
+      "Recent work in neural text generation and summarization attempt to address these issues by trying to map the textual outputs back to structured predicates, and comparing these predicates to the input data. BIBREF7 uses a neural checklist model to avoid the repetition of facts and improve coverage. BIBREF8 generate $k$-best output candidates with beam search, and then try to map each candidate output back to the input structure using a reverse seq2seq model trained on the same data. They then select the highest scoring output candidate that best translates back to the input. BIBREF9 reconstructs the input in training time, by jointly learning a back-translation model and enforcing the back-translation to reconstruct the input. Both of these approaches are \u201csoft\u201d in the sense that they crucially rely on the internal dynamics or on the output of a neural network module that may or may not be correct."
+    ],
+    [
+      "The separation between planning and realization provided by the step-by-step framework allows incorporating a robust and straightforward verification step, that does not rely on brittle information extraction procedures or trust neural network models.",
+      "The plan-to-text generation handles each sentence individually and translates entities as copy operations. We thus have complete knowledge of the generated entities and their locations. We can then assess the correctness of an output sentence by comparing its sequence of entities to the entity sequence in the corresponding sentence plan, which is guaranteed to be complete. We then decode $k$-best outputs and rerank them based on their correctness scores, tie-breaking using model scores. We found empirically that, with a beam of size 5 we find at least one candidate with an exact match to the plan's entity sequence in 99.82% of the cases for seen entities and relations compared to 98.48% at 1-best, and 72.3% for cases of unseen entities and relations compared to 58.06% at 1-best. In the remaining cases, we set the system to continue searching by trying other plans, by going down the list of plans (when using the exhaustive planner of BIBREF0) or by sampling a new plan (when using the linear time planner suggested in this paper)."
+    ],
+    [
+      "The step-by-step system generates entities by first generating an indexed entity symbols, and then lexicalizing each symbol to the string associated with this entity in the input structure (i.e., all occurrences of the entity 11TH MISSISSIPPI INFANTRY MONUMENT will be lexicalized with the full name rather than \u201cit\u201d or \u201cthe monument\u201d). This results in correct but somewhat unnatural structures. In contrast, end-to-end neural generation systems are trained on text that includes referring expressions, and generate them naturally as part of the decoding process, resulting in natural looking text. However, the generated referring expressions are sometimes incorrect. BIBREF0 suggests the possibility of handling this with a post-processing referring-expression generation step (REG). Here, we propose a concrete REG module and demonstrate its effectiveness. One option is to use a supervised REG module BIBREF11, that is trained to lexicalize in-context mentions. Such an approach is sub-optimal for our setup as it is restricted to the entities and contexts it seen in training, and is prone to error on unseen entities and contexts.",
+      "Our REG solution lexicalizes the first mention of each entity as its associated string and attempts to generate referring expressions to subsequent mentions. The generated referring expressions can take the form \u201cPron\u201d, \u201cX\u201d or \u201cthe X\u201d where Pron is a pronoun, and X is a word appearing in the entity's string (allowing, e.g., John, or the monument). We also allow referring to its entity with its entire associated string. We restrict the set of allowed pronouns for each entity according to its type (male, female, plural-animate, unknown-animate, inanimate). We then take, for each entity mention individually, the referring expression that receives the best language model score in context, using a strong unsupervised neural LM (BERT BIBREF12). The system is guaranteed to be correct in the sense that it will not generate wrong pronouns. It also has failure modes: it is possible for the system to generate ambiguous referring expressions (e.g., John is Bob's father. He works as a nurse.), and may lexicalize Boston University as Boston. We find that the second kind of mistake is rare as it is handled well by the language model. It can also be controlled by manually restricting the set of possible referring expression to each entity. Similarly, it is easy to extend the system to support other lexicalizations of entities by extending the sets of allowed lexicalizations (for example, supporting abbreviations, initials or nicknames) either as user-supplied inputs or using heuristics."
+    ],
+    [
+      "We evaluate each of the introduced components separately. Tables listing their interactions are available in the appendix. The appendix also lists some qualitative outputs. The main trends that we observe are:",
+      "The new planner causes a small drop in BLEU, but is orders of magnitude faster (\u00a7SECREF12).",
+      "Typing information causes a negligible drop in BLEU overall, but improves results substantially for the unseen portion of the dataset (\u00a7SECREF13).",
+      "The verification step is effective at improving the faithfulness of the output, practically eliminating omitted and overgenerated facts, reducing the number of wrong facts, and increasing the number of correctly expressed facts. This is based on both manual and automatic evaluations. (\u00a7SECREF14).",
+      "The referring expression module is effective, with an intrinsic correctness of 92.2%. It substantially improves BLEU scores. (\u00a7SECREF16)."
+    ],
+    [
+      "We evaluate on the WebNLG dataset BIBREF4, comparing to the step-by-step systems described in BIBREF0, which are state of the art. Due to randomness inherent in neural training, our reported automatic evaluation measures are based on an average of 5 training runs of each system (neural planner and neural realizer), each run with a different random seed."
+    ],
+    [
+      "We compare the exhaustive planner from BIBREF0 to our neural planner, by replacing the planner component in the BIBREF0 system. Moving to the neural planner exhibits a small drop in BLEU (46.882 dropped to 46.506). However, figure indicates 5 orders of magnitude (100,000x) speedup for graphs with 7 edges, and a linear growth in time for number of edges compared to exponential time for the exhaustive planner."
+    ],
+    [
+      "We repeat the coverage experiment in BIBREF0, counting the number of output texts that contain all the entities in the input graph, and, of these text, counting the ones in which the entities appear in the exact same order as the plan. Incorporating typing information reduced the number of texts not containing all entities by 18% for the seen part of the test set, and 16% for the unseen part. Moreover, for the text containing all entities, the number of texts that did not follow the plan's entity order is reduced by 46% for the seen part of the test set, and by 35% for the unseen part. We also observe a small drop in BLEU scores, which we attribute to some relations being verbalized more freely (though correctly)."
+    ],
+    [
+      "The addition of output verification resulted in negligible changes in BLEU, reinforcing that automatic metrics are not sensitive enough to output accuracy. We thus performed manual analysis, following the procedure in BIBREF0. We manually inspect 148 samples from the seen part of the test set, containing 440 relations, counting expressed, omitted, wrong and over-generated (hallucinated) facts. We compare to the StrongNeural and BestPlan systems from BIBREF0. Results in Table indicate that the effectiveness of the verification process in ensuring correct output, reducing the already small number of ommited and overgenerated facts to 0 (with the exhaustive planner) and keeping it small (with the fast neural planner)."
+    ],
+    [
+      "We manually reviewed 1,177 pairs of entities and referring expressions generated by the system. We find that 92.2% of the generated referring expressions refer to the correct entity.",
+      "From the generated expressions, 325 (27.6%) were pronouns, 192 (16.3%) are repeating a one-token entity as is, and 505 (42.9%) are generating correct shortening of a long entity. In 63 (5.6%) of the cases the system did not find a good substitute and kept the entire entity intact. Finally, 92 (7.82%) are wrong referrals. Overall, 73.3% of the non-first mentions of entities were replaced with suitable shorter and more fluent expressions."
+    ],
+    [
+      "As can be seen in Table , using the REG module increases BLEU scores for both the exhaustive and the neural planner."
+    ],
+    [
+      "We adopt the planning-based neural generation framework of BIBREF0 and extend it to be orders of magnitude faster and produce more correct and more fluent text. We conclude that these extensions not only improve the system of BIBREF0 but also highlight the flexibility and advantages of the step-by-step framework for text generation."
+    ],
+    [
+      "This work was supported in part by the German Research Foundation through the German-Israeli Project Cooperation (DIP, grant DA 1600/1-1) and by a grant from Reverso and Theo Hoffenberg.",
+      "We report some additional results."
+    ],
+    [
+      "We introduced 4 components: neural planner instead of exhaustive one, adding type information, adding output verification stage, and incorporating a referring expression generation (REG). In Table we report BLEU scores BIBREF13 for all 16 combinations of components. The numbers are averages of 5 runs with different random seeds."
+    ],
+    [
+      "We perform further analysis of the errors of the unsupervised LM based REG module. We categorise all entities into 3 groups: (1) names of people; (2) locations (cities / counties / countries); and (3) places and objects.",
+      "For person names, the module did not produce any errors, selecting either a correct pronoun, or either the first or last name of a person, all valid refferences.",
+      "For location names, we observe two distinct error types, both relating to our module's restriction to predict a single MASK token. The first type is in cases like \u201ccity, country\u201d or \u201ccounty, country\u201d, where the more specific location is not in the LM vocabulary, and cannot be predicted with a single token. For example, in \u201cPunjab, Pakistan\u201d, Punjab is not contained in the vocabulary as a single token, causing the model to select \u201cPakistan\u201d, which we consider a mistake. The second type is when a city name is longer than a single token, as in \u201cNew York\u201d. While it is common to refer to \u201cNew Jersey\u201d as \u201cJersey\u201d, it is wrong to refer to \u201cNew York\u201d as either \u201cNew\u201d or \u201cYork\u201d, and as BERT can only fill in one MASK token, it chooses only one (in this case \u201cYork\u201d).",
+      "Finally, for places and objects, we also identify to mistake types. The first occurs for multi-token entities. While for some cases it is possible to select the correct one (i.e., \u201cAgra Airport\u201d $\\rightarrow $ \u201cThe Airport\u201d or \u201cBoston University\u201d $\\rightarrow $ \u201cThe University\u201d), in other cases it is not possible (i.e., \u201cBaked Alaska\u201d, where choosing either word does not produce a useful reference). The second type occurs with names of objects, like books titles. For example, for the entity \u201cA Severed Wasp\u201d we would like the model to predict \u201cThe Book\u201d. However, as we only allow either pronouns or words from the original entity, the model cannot produce \u201cThe book\u201d, producing the erroneous \u201cThe Wasp\u201d instead."
+    ],
+    [
+      "The following output examples demonstrate the kinds of texts produces by the final system. The following outputs are correct, expressing all and only the facts from their input graphs. We enumerate them as number of facts:",
+      "The leader of Azerbaijan is Artur Rasizade.",
+      "Baked Alaska, containing Sponge Cake, is from France.",
+      "Above The Veil, written by Garth Nix, is available in Hardcover and has 248 pages.",
+      "The Akita Museum Of Art is located in Japan where the Brazilians In Japan are an ethnic group. The Museum is located in Akita, Akita which is part of Akita Prefecture .",
+      "The AWH Engineering College in Kuttikkattoor, Kerala has Mah\u00e9, India to its northwest . The College was established in 2001 and has a staff of 250.",
+      "An example where the system failed, producing a wrong lexicalization of a fact is: \u201cThe AWH Engineering College is located in the state of Kerala, Kochi, in India. The largest city in India is Mumbai and the river is the Ganges\u201d. In this example, the input entity Kochi refers to the leader of Kerala, and not tpo the location (although there is also a location by that name). The text lexicalizes this fact such that Kerala and Kochi are related, but with a relation of part-of, implying Kerala is in Kochi."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2057/instruction.md b/qasper-2057/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..88812fac8d8310decbb30584a3b132896e6ed2ea
--- /dev/null
+++ b/qasper-2057/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Unsung Challenges of Building and Deploying Language Technologies for Low Resource Language Communities
+
+Question: What language technologies have been introduced in the past?
\ No newline at end of file
diff --git a/qasper-2059/instruction.md b/qasper-2059/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..aa1fb0f7f2ace44dfbd0bb0ce8213a4c91e74dc1
--- /dev/null
+++ b/qasper-2059/instruction.md
@@ -0,0 +1,46 @@
+Name of Paper: Aspect and Opinion Term Extraction for Aspect Based Sentiment Analysis of Hotel Reviews Using Transfer Learning
+
+Question: Does the paper report the performance of the method when is trained for more than 8 epochs?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Methodology",
+    "Results and discussion",
+    "Related work",
+    "Conclusions and future work"
+  ],
+  "paragraphs": [
+    [
+      "Sentiment analysis BIBREF2 in review text usually consists of multiple aspects. For instance, the following review talks about the location, room, and staff aspects of a hotel, \u201cExcellent location to the Tower of London. We also walked to several other areas of interest; albeit a bit of a trek if you don't mind walking. The room was a typical hotel room in need of a refresh, however clean. The staff couldn't have been more professional, they really helped get us a taxi when our pre arranged pickup ran late.\u201d In this review, some of the sentiment terms are \u201cexcellent\u201d, \u201ctypical\u201d, \u201cclean\u201d, and \u201cprofessional\u201d.",
+      "In this study, we are focusing on the aspect and opinion term extraction from the reviews to do aspect-based sentiment analysis BIBREF3. While some work has been done in this task BIBREF4, BIBREF1, BIBREF5, we have not seen a transfer learning approach BIBREF6 employed, which should need much less training effort. Using transfer learning is especially helpful for low-resource languages BIBREF7, such as bahasa Indonesia.",
+      "Our main contribution in this study is evaluating BERT BIBREF0 as a pretrained transformer model on this token classification task on hotel reviews in bahasa Indonesia. We also found that the current pretrained BERT tokenizer has a poor encoder for bahasa Indonesia, thus we proposed our own custom tokenizer. We also provided simpler baselines, namely argmax and logistic regression on word embeddings as comparisons."
+    ],
+    [
+      "For this aspect and opinion term extraction task, we use tokenized and annotated hotel reviews on Airy Rooms provided by BIBREF1. The dataset consists of 5000 reviews in bahasa Indonesia. The dataset is divided into training and test sets of 4000 and 1000 reviews respectively. The label distribution of the tokens in BIO scheme can be seen in Table TABREF3. In addition, we also see this case as on entity level, i.e. ASPECT, SENTIMENT, and OTHER labels.",
+      "We found that there are 1643 and 809 unique tokens in the training and test sets respectively. Moreover, 75.4% of the unique tokens in the test set can be found in the training set.",
+      "For baseline model, we employed two methods: a simple argmax method and logistic regression on word embeddings from fastText implementation BIBREF8. In the argmax method, we classify a token as the most probable label in the training set. For fastText implementation, we use the skip-gram model and produce 100-dimensional vectors.",
+      "We proposed to use transfer learning from pretrained BERT-Base, Multilingual Cased BIBREF0 for this token classification problem. We used the implementation in PyTorch by BIBREF9. We found out that the multilingual cased tokenizer of BERT does not recognize some common terms in our dataset, such as \u201ckamar\u201d (room), \u201ckendala\u201d (issue), \u201cwifi\u201d, \u201ckoneksi\u201d (connection), \u201cbagus\u201d (good), \u201cbersih\u201d (clean). In the training and validation sets, we found 24,370 unknown tokens. Thus, we encode the token ourselves to have no unknown tokens. For the rest of this paper, we will call this model BERT-custom. Since the labels are imbalanced, we are using $F_1$-score as the evaluation metric, which is defined as:",
+      "Our experiment setup for BERT and BERT-custom is to use Adam BIBREF10 optimizer with $10^{-4}$ as the learning rate and 5 epochs. The batch size is 32 and we are optimizing the cross entropy loss function. We split the training set into 70:30 for training and validation sets to tune the hyperparameters and then train with the whole training set before applying the model onto the test set."
+    ],
+    [
+      "The results from our experiments with BIO scheme labels are summarized in Table TABREF5. We can see in the table that using the default tokenizer cannot beat the baseline $F_1$ scores for B-ASPECT and B-SENTIMENT labels. However, changing the tokenizer can improve the $F_1$ scores by at least 5%. For I-ASPECT and B-SENTIMENT, it can increase the $F_1$ scores by 11%. On the other hand, BIBREF1 trained their model using 200 epochs, while we only use 5 epochs. We also found that simply using word embedding (fastText) is not suitable for this task since it failed to achieve higher $F_1$ scores compared to a simple argmax method. Furthermore, we can see in Figure FIGREF6 that the model overfits after about 12 iterations (mini-batches).",
+      "Unlike conditional random fields (CRF), BERT does not constrain the output labels. Thus, you might see I-ASPECT or I-SENTIMENT without preceding B-ASPECT or B-SENTIMENT. In our case, we found 735 cases of invalid BIO when using default BERT tokenizer and 12 cases of invalid BIO while using our custom tokenizer. Some examples of sentences with invalid token labels are \u201c...termasuk(O) kamar(O) mandi(I-ASPECT) nya(I-ASPECT)...\u201d (\u201c...including the bathroom...\u201d) and \u201c...lantai(O) 3(O) tidak(I-ASPECT) ada(I-ASPECT)...\u201d (\u201c...3rd floor does not have...\u201d).",
+      "Table TABREF7 shows the performance on entity level. We are only interested in evaluating the ASPECT and SENTIMENT labels while actually trained the models with 3 labels. In this case, we increased the number of epochs to 8 since it can yield higher $F_1$ scores.",
+      "It is interesting to see that BERT is not even better than argmax in this simplified setting. Nevertheless, changing the default BERT tokenizer is beneficial as well. BERT-custom model outperforms argmax by more than 5% on our labels of interest and only 2% away from beating the results by BIBREF1."
+    ],
+    [
+      "BIBREF4 summarized several studies on aspect and opinion terms extraction. Some of the methods used are association rule mining BIBREF11, dependency rule parsers BIBREF12, conditional random fields (CRF) and hidden Markov model (HMM) BIBREF13, BIBREF14, topic modelling BIBREF15, BIBREF16, and deep learning BIBREF1, BIBREF4, BIBREF17, BIBREF5.",
+      "BIBREF1 combines the idea of coupled multilayer attentions (CMLA) by BIBREF4 and double embeddings by BIBREF5 on aspect and opinion term extraction on SemEval. The work by BIBREF5 itself is an improvement from what their prior work on the same task BIBREF17. Thus, we only included the work by BIBREF1 because they show that we can get the best result by combining the latest work by BIBREF4 and BIBREF5.",
+      "In their paper, BIBREF0 show that they can achieve state-of-the-art performance not only on sentence-level, but also on token-level tasks, such as for named entity recognition (NER). This motivates us to explore BERT in our study. This way, we do not need to use dependency parsers or any feature engineering."
+    ],
+    [
+      "Our work shows that BERT can achieve $F_1$ scores of more than 80% in aspect and opinion term extraction task with BIO scheme in noisy bahasa Indonesia text by changing the default tokenizer to have fewer unknown tokens. For both BIO scheme and aspect/sentiment/other labels, this simple tweak results in more than 5% absolute increase in $F_1$ scores on our labels of interest. On entity-level evaluation, changing the default tokenizer yields around 8% absolute increase in $F_1$ scores.",
+      "In the future, we are aiming to compare several transformer-based models, such as XLNet BIBREF18, XLM BIBREF19, and RoBERTa BIBREF20 when they are trained using multilingual datasets that include text in bahasa Indonesia as well. We also plan to fine-tune those models with richer text in bahasa Indonesia to reduce the number of unknown tokens. Furthermore, it is also necessary to evaluate the same task on different datasets."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2061/instruction.md b/qasper-2061/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..e19ea4c1fe9260488ed46184d37c03d5fecc5225
--- /dev/null
+++ b/qasper-2061/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Community Question Answering Platforms vs. Twitter for Predicting Characteristics of Urban Neighbourhoods
+
+Question: On Twitter, do the demographic attributes and answers show more correlations than on Yahoo! Answers?
\ No newline at end of file
diff --git a/qasper-2066/instruction.md b/qasper-2066/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..ef75d8b39af8e13dfc7adc3045c2b46c900dee9b
--- /dev/null
+++ b/qasper-2066/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Adversarial Learning with Contextual Embeddings for Zero-resource Cross-lingual Classification and NER
+
+Question: Does adversarial learning have stronger performance gains for text classification, or for NER?
\ No newline at end of file
diff --git a/qasper-2068/instruction.md b/qasper-2068/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..2bf4c4a67592f9b90eaeb3c0d28660a8bfacb578
--- /dev/null
+++ b/qasper-2068/instruction.md
@@ -0,0 +1,65 @@
+Name of Paper: Annotating Student Talk in Text-based Classroom Discussions
+
+Question: what experiments are conducted?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Annotation Scheme",
+    "Argumentation",
+    "Specificity",
+    "Knowledge Domain",
+    "Reliability and Validity Analyses",
+    "Opportunities and Challenges",
+    "Conclusions",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "Current research, theory, and policy surrounding K-12 instruction in the United States highlight the role of student-centered disciplinary discussions (i.e. discussions related to a specific academic discipline or school subject such as physics or English Language Arts) in instructional quality and student learning opportunities BIBREF0 , BIBREF1 . Such student-centered discussions \u2013 often called \u201cdialogic\" or \u201cinquiry-based\u201d \u2013 are widely viewed as the most effective instructional approach for disciplinary understanding, problem-solving, and literacy BIBREF2 , BIBREF3 , BIBREF4 . In English Language Arts (ELA) classrooms, student-centered discussions about literature have a positive impact on the development of students' reasoning, writing, and reading skills BIBREF5 , BIBREF6 . However, most studies have focused on the role of teachers and their talk BIBREF7 , BIBREF2 , BIBREF8 rather than on the aspects of student talk that contribute to discussion quality.",
+      "Additionally, studies of student-centered discussions rarely use the same coding schemes, making it difficult to generalize across studies BIBREF2 , BIBREF9 . This limitation is partly due to the time-intensive work required to analyze discourse data through qualitative methods such as ethnography and discourse analysis. Thus, qualitative case studies have generated compelling theories about the specific features of student talk that lead to high-quality discussions, but few findings can be generalized and leveraged to influence instructional improvements across ELA classrooms.",
+      "As a first step towards developing an automated system for detecting the features of student talk that lead to high quality discussions, we propose a new annotation scheme for student talk during ELA \u201ctext-based\" discussions - that is, discussions that center on a text or piece of literature (e.g., book, play, or speech). The annotation scheme was developed to capture three aspects of classroom talk that are theorized in the literature as important to discussion quality and learning opportunities: argumentation (the process of systematically reasoning in support of an idea), specificity (the quality of belonging or relating uniquely to a particular subject), and knowledge domain (area of expertise represented in the content of the talk). We demonstrate the reliability and validity of our scheme via an annotation study of five transcripts of classroom discussion."
+    ],
+    [
+      "One discourse feature used to assess the quality of discussions is students' argument moves: their claims about the text, their sharing of textual evidence for claims, and their warranting or reasoning to support the claims BIBREF10 , BIBREF11 . Many researchers view student reasoning as of primary importance, particularly when the reasoning is elaborated and highly inferential BIBREF12 . In Natural Language Processing (NLP), most educationally-oriented argumentation research has focused on corpora of student persuasive essays BIBREF13 , BIBREF14 , BIBREF15 , BIBREF16 , BIBREF17 , BIBREF18 . We instead focus on multi-party spoken discussion transcripts from classrooms. A second key difference consists in the inclusion of the warrant label in our scheme, as it is important to understand how students explicitly use reasoning to connect evidence to claims. Educational studies suggest that discussion quality is also influenced by the specificity of student talk BIBREF19 , BIBREF20 . Chisholm and Godley found that as specificity increased, the quality of students' claims and reasoning also increased. Previous NLP research has studied specificity in the context of professionally written newspaper articles BIBREF21 , BIBREF22 , BIBREF23 , BIBREF24 . While the annotation instructions used in these studies work well for general purpose corpora, specificity in text-based discussions also needs to capture particular relations between discussions and texts. Furthermore, since the concept of a sentence is not clearly defined in speech, we annotate argumentative discourse units rather than sentences (see Section SECREF3 ).",
+      "The knowledge domain of student talk may also matter, that is, whether the talk focuses on disciplinary knowledge or lived experiences. Some research suggests that disciplinary learning opportunities are maximized when students draw on evidence and reasoning that are commonly accepted in the discipline BIBREF25 , although some studies suggest that evidence or reasoning from lived experiences increases discussion quality BIBREF26 . Previous related work in NLP analyzed evidence type for argumentative tweets BIBREF27 . Although the categories of evidence type are different, their definition of evidence type is in line with our definition of knowledge domain. However, our research is distinct from this research in its application domain (i.e. social media vs. education) and in analyzing knowledge domain for all argumentative components, not only those containing claims."
+    ],
+    [
+      "Our annotation scheme uses argument moves as the unit of analysis. We define an argument move as an utterance, or part of an utterance, that contains an argumentative discourse unit (ADU) BIBREF28 . Like Peldszus and Stede Peldszus:15, in this paper we use transcripts already segmented into argument moves and focus on the steps following segmentation, i.e., labeling argumentation, specificity, and knowledge domain. Table TABREF2 shows a section of a transcribed classroom discussion along with labels assigned by a human annotator following segmentation."
+    ],
+    [
+      "The argumentation scheme is based on BIBREF29 and consists of a simplified set of labels derived from Toulmin's Toulmin:58 model: INLINEFORM0 Claim: an arguable statement that presents a particular interpretation of a text or topic. INLINEFORM1 Evidence: facts, documentation, text reference, or testimony used to support or justify a claim. INLINEFORM2 Warrant: reasons explaining how a specific evidence instance supports a specific claim. Our scheme specifies that warrants must come after claim and evidence, since by definition warrants cannot exist without them.",
+      "The first three moves in Table TABREF2 show a natural expression of an argument: a student first claims that Willy's wife is only trying to protect him, then provides a reference as evidence by mentioning something she said to her kids at the end of the book, and finally explains how not caring about her kids ties the evidence to the initial claim. The second group shows the same argument progression, with evidence given as a direct quote."
+    ],
+    [
+      "Specificity annotations are based on BIBREF19 and have the goal of capturing text-related characteristics expressed in student talk. Specificity labels are directly related to four distinct elements for an argument move: (1) it is specific to one (or a few) character or scene; (2) it makes significant qualifications or elaborations; (3) it uses content-specific vocabulary (e.g. quotes from the text); (4) it provides a chain of reasons. Our annotation scheme for specificity includes three labels along a linear scale: INLINEFORM0 Low: statement that does not contain any of these elements. INLINEFORM1 Medium: statement that accomplishes one of these elements. INLINEFORM2 High: statement that clearly accomplishes at least two specificity elements. Even though we do not explicitly use labels for the four specificity elements, we found that explicitly breaking down specificity into multiple components helped increase reliability when training annotators.",
+      "The first three argument moves in Table TABREF2 all contain the first element, as they refer to select characters in the book. However, no content-specific vocabulary, clear chain of reasoning, or significant qualifications are provided; therefore all three moves are labeled as medium specificity. The fourth move, however, accomplishes the first and fourth specificity elements, and is labeled as high specificity. The fifth move is also labeled high specificity since it is specific to one character/scene, and provides a direct quote from the text. The last move is labeled as low specificity as it reflects an overgeneralization about all humans."
+    ],
+    [
+      "The possible labels for knowledge domain are: INLINEFORM0 Disciplinary: the statement is grounded in knowledge gathered from a text (either the one under discussion or others), such as a quote or a description of a character/event. INLINEFORM1 Experiential: the statement is drawn from human experience, such as what the speaker has experienced or thinks that other humans have experienced.",
+      "In Table TABREF2 the first six argument moves are labeled as disciplinary, since the moves reflect knowledge from the text currently being discussed. The last move, however, draws from a student's experience or perceived knowledge about the real world."
+    ],
+    [
+      "We carried out a reliability study for the proposed scheme using two pairs of expert annotators, P1 and P2. The annotators were trained by coding one transcript at a time and discussing disagreements. Five text-based discussions were used for testing reliability after training: pair P1 annotated discussions of The Bluest Eye, Death of a Salesman, and Macbeth, while pair P2 annotated two separate discussions of Ain't I a Woman. 250 argument moves (discussed by over 40 students and consisting of over 8200 words) were annotated. Inter-rater reliability was assessed using Cohen's kappa: unweighted for argumentation and knowledge domain, but quadratic-weighted for specificity given its ordered labels.",
+      "Table TABREF6 shows that kappa for argumentation ranges from INLINEFORM0 , which generally indicates substantial agreement BIBREF30 . Kappa values for specificity and knowledge domain are in the INLINEFORM1 range which generally indicates almost perfect agreement BIBREF30 . These results show that our proposed annotation scheme can be used to produce reliable annotations of classroom discussion with respect to argumentation, specificity, and knowledge domain.",
+      "Table TABREF7 shows confusion matrices for annotator pair P1 (we observed similar trends for P2). The argumentation section of the table shows that the largest number of disagreements happens between the claim and warrant labels. One reason may be related to the constraint we impose on warrants - they require the existence of a claim and evidence. If a student tries to provide a warrant for a claim that happened much earlier in the discussion, the annotators might interpret the warrant as new claim. The specificity section shows relatively few low-high label disagreements as compared to low-med and med-high. This is also reflected in the quadratic-weighted kappa as low-high disagreements will carry a larger penalty (unweighted kappa is INLINEFORM0 ). The main reasons for disagreements over specificity labels come from two of the four specificity elements discussed in Section 3.2: whether an argument move is related to one character or scene, and whether it provides a chain of reasons. With respect to the first of these two elements we observed disagreements in argument moves containing pronouns with an ambiguous reference. Of particular note is the pronoun it. If we consider the argument move \u201cI mean even if you know you have a hatred towards a standard or whatever, you still don't kill it\", the pronoun it clearly refers to something within the move (i.e. the standard) that the student themselves mentioned. In contrast, for argument moves such as \u201cIt did happen\" it might not be clear to what previous move the pronoun refers, therefore creating confusion on whether this specificity element is accomplished. Regarding specificity element (4) we found that it was easier to determine the presence of a chain of reasons when discourse connectives (e.g. because, therefore) were present in the argument move. The absence of explicit discourse connectives in an argument move might drive annotators to disagree on the presence/absence of a chain of reasons, which is likely to result in a different specificity label. Additionally, annotators found that shorter turns at talk proved harder to annotate for specificity. Finally, as we can see from the third section in the table, knowledge domain has the lowest disagreements with only one.",
+      "We also BIBREF32 explored the validity of our coding scheme by comparing our annotations of student talk to English Education experts' evaluations (quadratic-weighted kappa of 0.544) of the discussion's quality. Using stepwise regressions, we found that the best model of discussion quality (R-squared of INLINEFORM0 ) included all three of our coding dimensions: argumentation, specificity, and knowledge domain."
+    ],
+    [
+      "Our annotation scheme introduces opportunities for the educational community to conduct further research on the relationship between features of student talk, student learning, and discussion quality. Although Chisholm and Godley Chisholm:11 and we found relations between our coding constructs and discussion quality, these were small-scale studies based on manual annotations. Once automated classifiers are developed, such relations between talk and learning can be examined at scale. Also, automatic labeling via a standard coding scheme can support the generalization of findings across studies, and potentially lead to automated tools for teachers and students.",
+      "The proposed annotation scheme also introduces NLP opportunities and challenges. Existing systems for classifying specificity and argumentation have largely been designed to analyze written text rather than spoken discussions. This is (at least in part) due to a lack of publicly available corpora and schemes for annotating argumentation and specificity in spoken discussions. The development of an annotation scheme explicitly designed for this problem is the first step towards collecting and annotating corpora that can be used by the NLP community to advance the field in this particular area. Furthermore, in text-based discussions, NLP methods need to tightly couple the discussion with contextual information (i.e., the text under discussion). For example, an argument move from one of the discussions mentioned in Section 4 stated \u201cShe's saying like free like, I don't have to be, I don't have to be this salesman's wife anymore, your know? I don't have to play this role anymore.\" The use of the term salesman shows the presence of specificity element (3) (see Section 3.2) because the text under discussion is indeed Death of a Salesman. If the students were discussing another text, the mention of the term salesman would not indicate one of the specificity elements, therefore lowering the specificity rating. Thus, using existing systems is unlikely to yield good performance. In fact, we previously BIBREF31 showed that while using an off-the-shelf system for predicting specificity in newspaper articles resulted in low performance when applied to classroom discussions, exploiting characteristics of our data could significantly improve performance. We have similarly evaluated the performance of two existing argument mining systems BIBREF18 , BIBREF33 on the transcripts described in Section SECREF4 . We noticed that since the two systems were trained to classify only claims and premises, they were never able to correctly predict warrants in our transcripts. Additionally, both systems classified the overwhelming majority of moves as premise, resulting in negative kappa in some cases. Using our scheme to create a corpus of classroom discussion data manually annotated for argumentation, specificity, and knowledge domain will support the development of more robust NLP prediction systems."
+    ],
+    [
+      "In this work we proposed a new annotation scheme for three theoretically-motivated features of student talk in classroom discussion: argumentation, specificity, and knowledge domain. We demonstrated usage of the scheme by presenting an annotated excerpt of a classroom discussion. We demonstrated that the scheme can be annotated with high reliability and reported on scheme validity. Finally, we discussed some possible applications and challenges posed by the proposed annotation scheme for both the educational and NLP communities. We plan to extend our annotation scheme to label information about collaborative relations between different argument moves, and release a corpus annotated with the extended scheme."
+    ],
+    [
+      "We want to thank Haoran Zhang, Tazin Afrin, and Annika Swallen for their contribution, and all the anonymous reviewers for their helpful suggestions.",
+      "This work was supported by the Learning Research and Development Center at the University of Pittsburgh."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2070/instruction.md b/qasper-2070/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..26ececcb39a186e07ab6ae9c4e338aea41a86bab
--- /dev/null
+++ b/qasper-2070/instruction.md
@@ -0,0 +1,65 @@
+Name of Paper: Annotating Student Talk in Text-based Classroom Discussions
+
+Question: how do they measure discussion quality?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Annotation Scheme",
+    "Argumentation",
+    "Specificity",
+    "Knowledge Domain",
+    "Reliability and Validity Analyses",
+    "Opportunities and Challenges",
+    "Conclusions",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "Current research, theory, and policy surrounding K-12 instruction in the United States highlight the role of student-centered disciplinary discussions (i.e. discussions related to a specific academic discipline or school subject such as physics or English Language Arts) in instructional quality and student learning opportunities BIBREF0 , BIBREF1 . Such student-centered discussions \u2013 often called \u201cdialogic\" or \u201cinquiry-based\u201d \u2013 are widely viewed as the most effective instructional approach for disciplinary understanding, problem-solving, and literacy BIBREF2 , BIBREF3 , BIBREF4 . In English Language Arts (ELA) classrooms, student-centered discussions about literature have a positive impact on the development of students' reasoning, writing, and reading skills BIBREF5 , BIBREF6 . However, most studies have focused on the role of teachers and their talk BIBREF7 , BIBREF2 , BIBREF8 rather than on the aspects of student talk that contribute to discussion quality.",
+      "Additionally, studies of student-centered discussions rarely use the same coding schemes, making it difficult to generalize across studies BIBREF2 , BIBREF9 . This limitation is partly due to the time-intensive work required to analyze discourse data through qualitative methods such as ethnography and discourse analysis. Thus, qualitative case studies have generated compelling theories about the specific features of student talk that lead to high-quality discussions, but few findings can be generalized and leveraged to influence instructional improvements across ELA classrooms.",
+      "As a first step towards developing an automated system for detecting the features of student talk that lead to high quality discussions, we propose a new annotation scheme for student talk during ELA \u201ctext-based\" discussions - that is, discussions that center on a text or piece of literature (e.g., book, play, or speech). The annotation scheme was developed to capture three aspects of classroom talk that are theorized in the literature as important to discussion quality and learning opportunities: argumentation (the process of systematically reasoning in support of an idea), specificity (the quality of belonging or relating uniquely to a particular subject), and knowledge domain (area of expertise represented in the content of the talk). We demonstrate the reliability and validity of our scheme via an annotation study of five transcripts of classroom discussion."
+    ],
+    [
+      "One discourse feature used to assess the quality of discussions is students' argument moves: their claims about the text, their sharing of textual evidence for claims, and their warranting or reasoning to support the claims BIBREF10 , BIBREF11 . Many researchers view student reasoning as of primary importance, particularly when the reasoning is elaborated and highly inferential BIBREF12 . In Natural Language Processing (NLP), most educationally-oriented argumentation research has focused on corpora of student persuasive essays BIBREF13 , BIBREF14 , BIBREF15 , BIBREF16 , BIBREF17 , BIBREF18 . We instead focus on multi-party spoken discussion transcripts from classrooms. A second key difference consists in the inclusion of the warrant label in our scheme, as it is important to understand how students explicitly use reasoning to connect evidence to claims. Educational studies suggest that discussion quality is also influenced by the specificity of student talk BIBREF19 , BIBREF20 . Chisholm and Godley found that as specificity increased, the quality of students' claims and reasoning also increased. Previous NLP research has studied specificity in the context of professionally written newspaper articles BIBREF21 , BIBREF22 , BIBREF23 , BIBREF24 . While the annotation instructions used in these studies work well for general purpose corpora, specificity in text-based discussions also needs to capture particular relations between discussions and texts. Furthermore, since the concept of a sentence is not clearly defined in speech, we annotate argumentative discourse units rather than sentences (see Section SECREF3 ).",
+      "The knowledge domain of student talk may also matter, that is, whether the talk focuses on disciplinary knowledge or lived experiences. Some research suggests that disciplinary learning opportunities are maximized when students draw on evidence and reasoning that are commonly accepted in the discipline BIBREF25 , although some studies suggest that evidence or reasoning from lived experiences increases discussion quality BIBREF26 . Previous related work in NLP analyzed evidence type for argumentative tweets BIBREF27 . Although the categories of evidence type are different, their definition of evidence type is in line with our definition of knowledge domain. However, our research is distinct from this research in its application domain (i.e. social media vs. education) and in analyzing knowledge domain for all argumentative components, not only those containing claims."
+    ],
+    [
+      "Our annotation scheme uses argument moves as the unit of analysis. We define an argument move as an utterance, or part of an utterance, that contains an argumentative discourse unit (ADU) BIBREF28 . Like Peldszus and Stede Peldszus:15, in this paper we use transcripts already segmented into argument moves and focus on the steps following segmentation, i.e., labeling argumentation, specificity, and knowledge domain. Table TABREF2 shows a section of a transcribed classroom discussion along with labels assigned by a human annotator following segmentation."
+    ],
+    [
+      "The argumentation scheme is based on BIBREF29 and consists of a simplified set of labels derived from Toulmin's Toulmin:58 model: INLINEFORM0 Claim: an arguable statement that presents a particular interpretation of a text or topic. INLINEFORM1 Evidence: facts, documentation, text reference, or testimony used to support or justify a claim. INLINEFORM2 Warrant: reasons explaining how a specific evidence instance supports a specific claim. Our scheme specifies that warrants must come after claim and evidence, since by definition warrants cannot exist without them.",
+      "The first three moves in Table TABREF2 show a natural expression of an argument: a student first claims that Willy's wife is only trying to protect him, then provides a reference as evidence by mentioning something she said to her kids at the end of the book, and finally explains how not caring about her kids ties the evidence to the initial claim. The second group shows the same argument progression, with evidence given as a direct quote."
+    ],
+    [
+      "Specificity annotations are based on BIBREF19 and have the goal of capturing text-related characteristics expressed in student talk. Specificity labels are directly related to four distinct elements for an argument move: (1) it is specific to one (or a few) character or scene; (2) it makes significant qualifications or elaborations; (3) it uses content-specific vocabulary (e.g. quotes from the text); (4) it provides a chain of reasons. Our annotation scheme for specificity includes three labels along a linear scale: INLINEFORM0 Low: statement that does not contain any of these elements. INLINEFORM1 Medium: statement that accomplishes one of these elements. INLINEFORM2 High: statement that clearly accomplishes at least two specificity elements. Even though we do not explicitly use labels for the four specificity elements, we found that explicitly breaking down specificity into multiple components helped increase reliability when training annotators.",
+      "The first three argument moves in Table TABREF2 all contain the first element, as they refer to select characters in the book. However, no content-specific vocabulary, clear chain of reasoning, or significant qualifications are provided; therefore all three moves are labeled as medium specificity. The fourth move, however, accomplishes the first and fourth specificity elements, and is labeled as high specificity. The fifth move is also labeled high specificity since it is specific to one character/scene, and provides a direct quote from the text. The last move is labeled as low specificity as it reflects an overgeneralization about all humans."
+    ],
+    [
+      "The possible labels for knowledge domain are: INLINEFORM0 Disciplinary: the statement is grounded in knowledge gathered from a text (either the one under discussion or others), such as a quote or a description of a character/event. INLINEFORM1 Experiential: the statement is drawn from human experience, such as what the speaker has experienced or thinks that other humans have experienced.",
+      "In Table TABREF2 the first six argument moves are labeled as disciplinary, since the moves reflect knowledge from the text currently being discussed. The last move, however, draws from a student's experience or perceived knowledge about the real world."
+    ],
+    [
+      "We carried out a reliability study for the proposed scheme using two pairs of expert annotators, P1 and P2. The annotators were trained by coding one transcript at a time and discussing disagreements. Five text-based discussions were used for testing reliability after training: pair P1 annotated discussions of The Bluest Eye, Death of a Salesman, and Macbeth, while pair P2 annotated two separate discussions of Ain't I a Woman. 250 argument moves (discussed by over 40 students and consisting of over 8200 words) were annotated. Inter-rater reliability was assessed using Cohen's kappa: unweighted for argumentation and knowledge domain, but quadratic-weighted for specificity given its ordered labels.",
+      "Table TABREF6 shows that kappa for argumentation ranges from INLINEFORM0 , which generally indicates substantial agreement BIBREF30 . Kappa values for specificity and knowledge domain are in the INLINEFORM1 range which generally indicates almost perfect agreement BIBREF30 . These results show that our proposed annotation scheme can be used to produce reliable annotations of classroom discussion with respect to argumentation, specificity, and knowledge domain.",
+      "Table TABREF7 shows confusion matrices for annotator pair P1 (we observed similar trends for P2). The argumentation section of the table shows that the largest number of disagreements happens between the claim and warrant labels. One reason may be related to the constraint we impose on warrants - they require the existence of a claim and evidence. If a student tries to provide a warrant for a claim that happened much earlier in the discussion, the annotators might interpret the warrant as new claim. The specificity section shows relatively few low-high label disagreements as compared to low-med and med-high. This is also reflected in the quadratic-weighted kappa as low-high disagreements will carry a larger penalty (unweighted kappa is INLINEFORM0 ). The main reasons for disagreements over specificity labels come from two of the four specificity elements discussed in Section 3.2: whether an argument move is related to one character or scene, and whether it provides a chain of reasons. With respect to the first of these two elements we observed disagreements in argument moves containing pronouns with an ambiguous reference. Of particular note is the pronoun it. If we consider the argument move \u201cI mean even if you know you have a hatred towards a standard or whatever, you still don't kill it\", the pronoun it clearly refers to something within the move (i.e. the standard) that the student themselves mentioned. In contrast, for argument moves such as \u201cIt did happen\" it might not be clear to what previous move the pronoun refers, therefore creating confusion on whether this specificity element is accomplished. Regarding specificity element (4) we found that it was easier to determine the presence of a chain of reasons when discourse connectives (e.g. because, therefore) were present in the argument move. The absence of explicit discourse connectives in an argument move might drive annotators to disagree on the presence/absence of a chain of reasons, which is likely to result in a different specificity label. Additionally, annotators found that shorter turns at talk proved harder to annotate for specificity. Finally, as we can see from the third section in the table, knowledge domain has the lowest disagreements with only one.",
+      "We also BIBREF32 explored the validity of our coding scheme by comparing our annotations of student talk to English Education experts' evaluations (quadratic-weighted kappa of 0.544) of the discussion's quality. Using stepwise regressions, we found that the best model of discussion quality (R-squared of INLINEFORM0 ) included all three of our coding dimensions: argumentation, specificity, and knowledge domain."
+    ],
+    [
+      "Our annotation scheme introduces opportunities for the educational community to conduct further research on the relationship between features of student talk, student learning, and discussion quality. Although Chisholm and Godley Chisholm:11 and we found relations between our coding constructs and discussion quality, these were small-scale studies based on manual annotations. Once automated classifiers are developed, such relations between talk and learning can be examined at scale. Also, automatic labeling via a standard coding scheme can support the generalization of findings across studies, and potentially lead to automated tools for teachers and students.",
+      "The proposed annotation scheme also introduces NLP opportunities and challenges. Existing systems for classifying specificity and argumentation have largely been designed to analyze written text rather than spoken discussions. This is (at least in part) due to a lack of publicly available corpora and schemes for annotating argumentation and specificity in spoken discussions. The development of an annotation scheme explicitly designed for this problem is the first step towards collecting and annotating corpora that can be used by the NLP community to advance the field in this particular area. Furthermore, in text-based discussions, NLP methods need to tightly couple the discussion with contextual information (i.e., the text under discussion). For example, an argument move from one of the discussions mentioned in Section 4 stated \u201cShe's saying like free like, I don't have to be, I don't have to be this salesman's wife anymore, your know? I don't have to play this role anymore.\" The use of the term salesman shows the presence of specificity element (3) (see Section 3.2) because the text under discussion is indeed Death of a Salesman. If the students were discussing another text, the mention of the term salesman would not indicate one of the specificity elements, therefore lowering the specificity rating. Thus, using existing systems is unlikely to yield good performance. In fact, we previously BIBREF31 showed that while using an off-the-shelf system for predicting specificity in newspaper articles resulted in low performance when applied to classroom discussions, exploiting characteristics of our data could significantly improve performance. We have similarly evaluated the performance of two existing argument mining systems BIBREF18 , BIBREF33 on the transcripts described in Section SECREF4 . We noticed that since the two systems were trained to classify only claims and premises, they were never able to correctly predict warrants in our transcripts. Additionally, both systems classified the overwhelming majority of moves as premise, resulting in negative kappa in some cases. Using our scheme to create a corpus of classroom discussion data manually annotated for argumentation, specificity, and knowledge domain will support the development of more robust NLP prediction systems."
+    ],
+    [
+      "In this work we proposed a new annotation scheme for three theoretically-motivated features of student talk in classroom discussion: argumentation, specificity, and knowledge domain. We demonstrated usage of the scheme by presenting an annotated excerpt of a classroom discussion. We demonstrated that the scheme can be annotated with high reliability and reported on scheme validity. Finally, we discussed some possible applications and challenges posed by the proposed annotation scheme for both the educational and NLP communities. We plan to extend our annotation scheme to label information about collaborative relations between different argument moves, and release a corpus annotated with the extended scheme."
+    ],
+    [
+      "We want to thank Haoran Zhang, Tazin Afrin, and Annika Swallen for their contribution, and all the anonymous reviewers for their helpful suggestions.",
+      "This work was supported by the Learning Research and Development Center at the University of Pittsburgh."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2071/instruction.md b/qasper-2071/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..9e1815b0e4e8cc300e319feb0cd1724c54d9afbb
--- /dev/null
+++ b/qasper-2071/instruction.md
@@ -0,0 +1,65 @@
+Name of Paper: Annotating Student Talk in Text-based Classroom Discussions
+
+Question: do they use a crowdsourcing platform?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Annotation Scheme",
+    "Argumentation",
+    "Specificity",
+    "Knowledge Domain",
+    "Reliability and Validity Analyses",
+    "Opportunities and Challenges",
+    "Conclusions",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "Current research, theory, and policy surrounding K-12 instruction in the United States highlight the role of student-centered disciplinary discussions (i.e. discussions related to a specific academic discipline or school subject such as physics or English Language Arts) in instructional quality and student learning opportunities BIBREF0 , BIBREF1 . Such student-centered discussions \u2013 often called \u201cdialogic\" or \u201cinquiry-based\u201d \u2013 are widely viewed as the most effective instructional approach for disciplinary understanding, problem-solving, and literacy BIBREF2 , BIBREF3 , BIBREF4 . In English Language Arts (ELA) classrooms, student-centered discussions about literature have a positive impact on the development of students' reasoning, writing, and reading skills BIBREF5 , BIBREF6 . However, most studies have focused on the role of teachers and their talk BIBREF7 , BIBREF2 , BIBREF8 rather than on the aspects of student talk that contribute to discussion quality.",
+      "Additionally, studies of student-centered discussions rarely use the same coding schemes, making it difficult to generalize across studies BIBREF2 , BIBREF9 . This limitation is partly due to the time-intensive work required to analyze discourse data through qualitative methods such as ethnography and discourse analysis. Thus, qualitative case studies have generated compelling theories about the specific features of student talk that lead to high-quality discussions, but few findings can be generalized and leveraged to influence instructional improvements across ELA classrooms.",
+      "As a first step towards developing an automated system for detecting the features of student talk that lead to high quality discussions, we propose a new annotation scheme for student talk during ELA \u201ctext-based\" discussions - that is, discussions that center on a text or piece of literature (e.g., book, play, or speech). The annotation scheme was developed to capture three aspects of classroom talk that are theorized in the literature as important to discussion quality and learning opportunities: argumentation (the process of systematically reasoning in support of an idea), specificity (the quality of belonging or relating uniquely to a particular subject), and knowledge domain (area of expertise represented in the content of the talk). We demonstrate the reliability and validity of our scheme via an annotation study of five transcripts of classroom discussion."
+    ],
+    [
+      "One discourse feature used to assess the quality of discussions is students' argument moves: their claims about the text, their sharing of textual evidence for claims, and their warranting or reasoning to support the claims BIBREF10 , BIBREF11 . Many researchers view student reasoning as of primary importance, particularly when the reasoning is elaborated and highly inferential BIBREF12 . In Natural Language Processing (NLP), most educationally-oriented argumentation research has focused on corpora of student persuasive essays BIBREF13 , BIBREF14 , BIBREF15 , BIBREF16 , BIBREF17 , BIBREF18 . We instead focus on multi-party spoken discussion transcripts from classrooms. A second key difference consists in the inclusion of the warrant label in our scheme, as it is important to understand how students explicitly use reasoning to connect evidence to claims. Educational studies suggest that discussion quality is also influenced by the specificity of student talk BIBREF19 , BIBREF20 . Chisholm and Godley found that as specificity increased, the quality of students' claims and reasoning also increased. Previous NLP research has studied specificity in the context of professionally written newspaper articles BIBREF21 , BIBREF22 , BIBREF23 , BIBREF24 . While the annotation instructions used in these studies work well for general purpose corpora, specificity in text-based discussions also needs to capture particular relations between discussions and texts. Furthermore, since the concept of a sentence is not clearly defined in speech, we annotate argumentative discourse units rather than sentences (see Section SECREF3 ).",
+      "The knowledge domain of student talk may also matter, that is, whether the talk focuses on disciplinary knowledge or lived experiences. Some research suggests that disciplinary learning opportunities are maximized when students draw on evidence and reasoning that are commonly accepted in the discipline BIBREF25 , although some studies suggest that evidence or reasoning from lived experiences increases discussion quality BIBREF26 . Previous related work in NLP analyzed evidence type for argumentative tweets BIBREF27 . Although the categories of evidence type are different, their definition of evidence type is in line with our definition of knowledge domain. However, our research is distinct from this research in its application domain (i.e. social media vs. education) and in analyzing knowledge domain for all argumentative components, not only those containing claims."
+    ],
+    [
+      "Our annotation scheme uses argument moves as the unit of analysis. We define an argument move as an utterance, or part of an utterance, that contains an argumentative discourse unit (ADU) BIBREF28 . Like Peldszus and Stede Peldszus:15, in this paper we use transcripts already segmented into argument moves and focus on the steps following segmentation, i.e., labeling argumentation, specificity, and knowledge domain. Table TABREF2 shows a section of a transcribed classroom discussion along with labels assigned by a human annotator following segmentation."
+    ],
+    [
+      "The argumentation scheme is based on BIBREF29 and consists of a simplified set of labels derived from Toulmin's Toulmin:58 model: INLINEFORM0 Claim: an arguable statement that presents a particular interpretation of a text or topic. INLINEFORM1 Evidence: facts, documentation, text reference, or testimony used to support or justify a claim. INLINEFORM2 Warrant: reasons explaining how a specific evidence instance supports a specific claim. Our scheme specifies that warrants must come after claim and evidence, since by definition warrants cannot exist without them.",
+      "The first three moves in Table TABREF2 show a natural expression of an argument: a student first claims that Willy's wife is only trying to protect him, then provides a reference as evidence by mentioning something she said to her kids at the end of the book, and finally explains how not caring about her kids ties the evidence to the initial claim. The second group shows the same argument progression, with evidence given as a direct quote."
+    ],
+    [
+      "Specificity annotations are based on BIBREF19 and have the goal of capturing text-related characteristics expressed in student talk. Specificity labels are directly related to four distinct elements for an argument move: (1) it is specific to one (or a few) character or scene; (2) it makes significant qualifications or elaborations; (3) it uses content-specific vocabulary (e.g. quotes from the text); (4) it provides a chain of reasons. Our annotation scheme for specificity includes three labels along a linear scale: INLINEFORM0 Low: statement that does not contain any of these elements. INLINEFORM1 Medium: statement that accomplishes one of these elements. INLINEFORM2 High: statement that clearly accomplishes at least two specificity elements. Even though we do not explicitly use labels for the four specificity elements, we found that explicitly breaking down specificity into multiple components helped increase reliability when training annotators.",
+      "The first three argument moves in Table TABREF2 all contain the first element, as they refer to select characters in the book. However, no content-specific vocabulary, clear chain of reasoning, or significant qualifications are provided; therefore all three moves are labeled as medium specificity. The fourth move, however, accomplishes the first and fourth specificity elements, and is labeled as high specificity. The fifth move is also labeled high specificity since it is specific to one character/scene, and provides a direct quote from the text. The last move is labeled as low specificity as it reflects an overgeneralization about all humans."
+    ],
+    [
+      "The possible labels for knowledge domain are: INLINEFORM0 Disciplinary: the statement is grounded in knowledge gathered from a text (either the one under discussion or others), such as a quote or a description of a character/event. INLINEFORM1 Experiential: the statement is drawn from human experience, such as what the speaker has experienced or thinks that other humans have experienced.",
+      "In Table TABREF2 the first six argument moves are labeled as disciplinary, since the moves reflect knowledge from the text currently being discussed. The last move, however, draws from a student's experience or perceived knowledge about the real world."
+    ],
+    [
+      "We carried out a reliability study for the proposed scheme using two pairs of expert annotators, P1 and P2. The annotators were trained by coding one transcript at a time and discussing disagreements. Five text-based discussions were used for testing reliability after training: pair P1 annotated discussions of The Bluest Eye, Death of a Salesman, and Macbeth, while pair P2 annotated two separate discussions of Ain't I a Woman. 250 argument moves (discussed by over 40 students and consisting of over 8200 words) were annotated. Inter-rater reliability was assessed using Cohen's kappa: unweighted for argumentation and knowledge domain, but quadratic-weighted for specificity given its ordered labels.",
+      "Table TABREF6 shows that kappa for argumentation ranges from INLINEFORM0 , which generally indicates substantial agreement BIBREF30 . Kappa values for specificity and knowledge domain are in the INLINEFORM1 range which generally indicates almost perfect agreement BIBREF30 . These results show that our proposed annotation scheme can be used to produce reliable annotations of classroom discussion with respect to argumentation, specificity, and knowledge domain.",
+      "Table TABREF7 shows confusion matrices for annotator pair P1 (we observed similar trends for P2). The argumentation section of the table shows that the largest number of disagreements happens between the claim and warrant labels. One reason may be related to the constraint we impose on warrants - they require the existence of a claim and evidence. If a student tries to provide a warrant for a claim that happened much earlier in the discussion, the annotators might interpret the warrant as new claim. The specificity section shows relatively few low-high label disagreements as compared to low-med and med-high. This is also reflected in the quadratic-weighted kappa as low-high disagreements will carry a larger penalty (unweighted kappa is INLINEFORM0 ). The main reasons for disagreements over specificity labels come from two of the four specificity elements discussed in Section 3.2: whether an argument move is related to one character or scene, and whether it provides a chain of reasons. With respect to the first of these two elements we observed disagreements in argument moves containing pronouns with an ambiguous reference. Of particular note is the pronoun it. If we consider the argument move \u201cI mean even if you know you have a hatred towards a standard or whatever, you still don't kill it\", the pronoun it clearly refers to something within the move (i.e. the standard) that the student themselves mentioned. In contrast, for argument moves such as \u201cIt did happen\" it might not be clear to what previous move the pronoun refers, therefore creating confusion on whether this specificity element is accomplished. Regarding specificity element (4) we found that it was easier to determine the presence of a chain of reasons when discourse connectives (e.g. because, therefore) were present in the argument move. The absence of explicit discourse connectives in an argument move might drive annotators to disagree on the presence/absence of a chain of reasons, which is likely to result in a different specificity label. Additionally, annotators found that shorter turns at talk proved harder to annotate for specificity. Finally, as we can see from the third section in the table, knowledge domain has the lowest disagreements with only one.",
+      "We also BIBREF32 explored the validity of our coding scheme by comparing our annotations of student talk to English Education experts' evaluations (quadratic-weighted kappa of 0.544) of the discussion's quality. Using stepwise regressions, we found that the best model of discussion quality (R-squared of INLINEFORM0 ) included all three of our coding dimensions: argumentation, specificity, and knowledge domain."
+    ],
+    [
+      "Our annotation scheme introduces opportunities for the educational community to conduct further research on the relationship between features of student talk, student learning, and discussion quality. Although Chisholm and Godley Chisholm:11 and we found relations between our coding constructs and discussion quality, these were small-scale studies based on manual annotations. Once automated classifiers are developed, such relations between talk and learning can be examined at scale. Also, automatic labeling via a standard coding scheme can support the generalization of findings across studies, and potentially lead to automated tools for teachers and students.",
+      "The proposed annotation scheme also introduces NLP opportunities and challenges. Existing systems for classifying specificity and argumentation have largely been designed to analyze written text rather than spoken discussions. This is (at least in part) due to a lack of publicly available corpora and schemes for annotating argumentation and specificity in spoken discussions. The development of an annotation scheme explicitly designed for this problem is the first step towards collecting and annotating corpora that can be used by the NLP community to advance the field in this particular area. Furthermore, in text-based discussions, NLP methods need to tightly couple the discussion with contextual information (i.e., the text under discussion). For example, an argument move from one of the discussions mentioned in Section 4 stated \u201cShe's saying like free like, I don't have to be, I don't have to be this salesman's wife anymore, your know? I don't have to play this role anymore.\" The use of the term salesman shows the presence of specificity element (3) (see Section 3.2) because the text under discussion is indeed Death of a Salesman. If the students were discussing another text, the mention of the term salesman would not indicate one of the specificity elements, therefore lowering the specificity rating. Thus, using existing systems is unlikely to yield good performance. In fact, we previously BIBREF31 showed that while using an off-the-shelf system for predicting specificity in newspaper articles resulted in low performance when applied to classroom discussions, exploiting characteristics of our data could significantly improve performance. We have similarly evaluated the performance of two existing argument mining systems BIBREF18 , BIBREF33 on the transcripts described in Section SECREF4 . We noticed that since the two systems were trained to classify only claims and premises, they were never able to correctly predict warrants in our transcripts. Additionally, both systems classified the overwhelming majority of moves as premise, resulting in negative kappa in some cases. Using our scheme to create a corpus of classroom discussion data manually annotated for argumentation, specificity, and knowledge domain will support the development of more robust NLP prediction systems."
+    ],
+    [
+      "In this work we proposed a new annotation scheme for three theoretically-motivated features of student talk in classroom discussion: argumentation, specificity, and knowledge domain. We demonstrated usage of the scheme by presenting an annotated excerpt of a classroom discussion. We demonstrated that the scheme can be annotated with high reliability and reported on scheme validity. Finally, we discussed some possible applications and challenges posed by the proposed annotation scheme for both the educational and NLP communities. We plan to extend our annotation scheme to label information about collaborative relations between different argument moves, and release a corpus annotated with the extended scheme."
+    ],
+    [
+      "We want to thank Haoran Zhang, Tazin Afrin, and Annika Swallen for their contribution, and all the anonymous reviewers for their helpful suggestions.",
+      "This work was supported by the Learning Research and Development Center at the University of Pittsburgh."
+    ]
+  ]
+}
+```
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+Name of Paper: Make Lead Bias in Your Favor: A Simple and Effective Method for News Summarization
+
+Question: What metric was used in the evaluation step?
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+Name of Paper: Make Lead Bias in Your Favor: A Simple and Effective Method for News Summarization
+
+Question: What did they pretrain the model on?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related work ::: Document Summarization",
+    "Related work ::: Pretraining",
+    "Pretraining with Leading Sentences",
+    "Model",
+    "Model ::: Problem formulation",
+    "Model ::: Network Structure",
+    "Model ::: Network Structure ::: Encoder",
+    "Model ::: Network Structure ::: Decoder",
+    "Experiments ::: Datasets",
+    "Experiments ::: Implementation Details",
+    "Experiments ::: Baseline",
+    "Experiments ::: Metrics",
+    "Experiments ::: Results",
+    "Experiments ::: Abstractiveness Analysis",
+    "Experiments ::: Human Evaluation",
+    "Conclusions"
+  ],
+  "paragraphs": [
+    [
+      "The goal of text summarization is to condense a piece of text into a shorter version that contains the salient information. Due to the prevalence of news articles and the need to provide succinct summaries for readers, a majority of existing datasets for summarization come from the news domain BIBREF0, BIBREF1, BIBREF2. However, according to journalistic conventions, the most important information in a news report usually appears near the beginning of the article BIBREF3. While it facilitates faster and easier understanding of the news for readers, this lead bias causes undesirable consequences for summarization models. The output of these models is inevitably affected by the positional information of sentences. Furthermore, the simple baseline of using the top few sentences as summary can achieve a stronger performance than many sophisticated models BIBREF4. It can take a lot of effort for models to overcome the lead bias BIBREF3.",
+      "Additionally, most existing summarization models are fully supervised and require time and labor-intensive annotations to feed their insatiable appetite for labeled data. For example, the New York Times Annotated Corpus BIBREF1 contains 1.8 million news articles, with 650,000 summaries written by library scientists. Therefore, some recent work BIBREF5 explores the effect of domain transfer to utilize datasets other than the target one. But this method may be affected by the domain drift problem and still suffers from the lack of labelled data.",
+      "The recent promising trend of pretraining models BIBREF6, BIBREF7 proves that a large quantity of data can be used to boost NLP models' performance. Therefore, we put forward a novel method to leverage the lead bias of news articles in our favor to conduct large-scale pretraining of summarization models. The idea is to leverage the top few sentences of a news article as the target summary and use the rest as the content. The goal of our pretrained model is to generate an abstractive summary given the content. Coupled with careful data filtering and cleaning, the lead bias can provide a delegate summary of sufficiently good quality, and it immediately renders the large quantity of unlabeled news articles corpus available for training news summarization models.",
+      "We employ this pretraining idea on a three-year collection of online news articles. We conduct thorough data cleaning and filtering. For example, to maintain a quality assurance bar for using leading sentences as the summary, we compute the ratio of overlapping non-stopping words between the top 3 sentences and the rest of the article. As a higher ratio implies a closer semantic connection, we only keep articles for which this ratio is higher than a threshold.",
+      "We end up with 21.4M articles based on which we pretrain a transformer-based encoder-decoder summarization model. We conduct thorough evaluation of our models on five benchmark news summarization datasets. Our pretrained model achieves a remarkable performance on various target datasets without any finetuning. This shows the effectiveness of leveraging the lead bias to pretrain on large-scale news data. We further finetune the model on target datasets and achieve better results than a number of strong baseline models. For example, the pretrained model without finetuning obtains state-of-the-art results on DUC-2003 and DUC-2004. The finetuned model obtains 3.2% higher ROUGE-1, 1.6% higher ROUGE-2 and 2.1% higher ROUGE-L scores than the best baseline model on XSum dataset BIBREF2. Human evaluation results also show that our models outperform existing baselines like pointer-generator network.",
+      "The rest of paper is organized as follows. We introduce related work in news summarization and pretraining in Sec:rw. We describe the details of pretraining using lead bias in Sec:pre. We introduce the transformer-based summarization model in Sec:model. We show the experimental results in Sec:exp and conclude the paper in Sec:conclusion."
+    ],
+    [
+      "End-to-end abstractive text summarization has been intensively studied in recent literature. To generate summary tokens, most architectures take the encoder-decoder approach BIBREF8. BIBREF9 first introduces an attention-based seq2seq model to the abstractive sentence summarization task. However, its output summary degenerates as document length increases, and out-of-vocabulary (OOV) words cannot be efficiently handled. To tackle these challenges, BIBREF4 proposes a pointer-generator network that can both produce words from the vocabulary via a generator and copy words from the source article via a pointer. BIBREF10 utilizes reinforcement learning to improve the result. BIBREF11 uses a content selector to over-determine phrases in source documents that helps constrain the model to likely phrases. BIBREF12 adds Gaussian focal bias and a salience-selection network to the transformer encoder-decoder structure BIBREF13 for abstractive summarization. BIBREF14 randomly reshuffles the sentences in news articles to reduce the effect of lead bias in extractive summarization."
+    ],
+    [
+      "In recent years, pretraining language models have proved to be quite helpful in NLP tasks. The state-of-the-art pretrained models include ELMo BIBREF15, GPT BIBREF7, BERT BIBREF6 and UniLM BIBREF16. Built upon large-scale corpora, these pretrained models learn effective representations for various semantic structures and linguistic relationships. As a result, pretrained models have been widely used with considerable success in applications such as question answering BIBREF17, sentiment analysis BIBREF15 and passage reranking BIBREF18. Furthermore, UniLM BIBREF16 leverages its sequence-to-sequence capability for abstractive summarization; the BERT model has been employed as an encoder in BERTSUM BIBREF19 for extractive/abstractive summarization.",
+      "Compared to our work, UniLM BIBREF16 is a general language model framework and does not take advantage of the special semantic structure of news articles. Similarly, BERTSUM BIBREF19 directly copies the pretrained BERT structure into its encoder and finetunes on labelled data instead of pretraining with the large quantity of unlabeled news corpus available. Recently, PEGASUS BIBREF20 leverages a similar idea of summarization pretraining, but they require finetuning with data from target domains, whereas our model has a remarkable performance without any finetuning."
+    ],
+    [
+      "News articles usually follow the convention of placing the most important information early in the content, forming an inverted pyramid structure. This lead bias has been discovered in a number of studies BIBREF3, BIBREF14. One of the consequences is that the lead baseline, which simply takes the top few sentences as the summary, can achieve a rather strong performance in news summarization. For instance, in the CNN/Daily Mail dataset BIBREF0, using the top three sentences as summaries can get a higher ROUGE score than many deep learning based models. This positional bias brings lots of difficulty for models to extract salient information from the article and generate high-quality summaries. For instance, BIBREF14 discovers that most models' performances drop significantly when a random sentence is inserted in the leading position, or when the sentences in a news article are shuffled.",
+      "On the other hand, news summarization, just like many other supervised learning tasks, suffers from the scarcity of labelled training data. Abstractive summarization is especially data-hungry since the efficacy of models depends on high-quality handcrafted summaries.",
+      "We propose that the lead bias in news articles can be leveraged in our favor to train an abstractive summarization model without human labels. Given a news article, we treat the top three sentences, denoted by Lead-3, as the target summary, and use the rest of the article as news content. The goal of the summarization model is to produce Lead-3 using the following content, as illustrated in fig:top3.",
+      "The benefit of this approach is that the model can leverage the large number of unlabeled news articles for pretraining. In the experiment, we find that the pretrained model alone can have a strong performance on various news summarization datasets, without any further training. We also finetune the pretrained model on downstream datasets with labelled summaries. The model can quickly adapt to the target domain and further increase its performance.",
+      "It is worth noting that this idea of utilizing structural bias for large-scale summarization pretraining is not limited to specific types of models, and it can be applied to other types of text as well: academic papers with abstracts, novels with editor's notes, books with tables of contents.",
+      "However, one should carefully examine and clean the source data to take advantage of lead bias, as the top three sentences may not always form a good summary. We provide more details in the experiments about the data filtering and cleaning mechanism we apply."
+    ],
+    [
+      "In this section, we introduce our abstractive summarization model, which has a transformer-based encoder-decoder structure. We first formulate the supervised summarization problem and then present the network architecture."
+    ],
+    [
+      "We formalize the problem of supervised abstractive summarization as follows. The input consists of $a$ pairs of articles and summaries: $\\lbrace (X_1, Y_1), (X_2, Y_2), ..., (X_a, Y_a)\\rbrace $. Each article and summary are tokenized: $X_i=(x_1,...,x_{L_i})$ and $Y_i=(y_1,...,y_{N_i})$. In abstractive summarization, the summary tokens need not be from the article. For simplicity, we will drop the data index subscript. The goal of the system is to generate summary $Y=(y_1,...,y_m)$ given the transcript $X=\\lbrace x_1, ..., x_n\\rbrace $."
+    ],
+    [
+      "We utilize a transformer-based encoder-decoder structure that maximizes the conditional probability of the summary: $P(Y|X, \\theta )$, where $\\theta $ represents the parameters."
+    ],
+    [
+      "The encoder maps each token into a fixed-length vector using a trainable dictionary $\\mathcal {D}$ randomly initialized using a normal distribution with zero mean and a standard deviation of 0.02. Each transformer block conducts multi-head self-attention. And we use sinusoidal positional embedding in order to process arbitrarily long input. In the end, the output of the encoder is a set of contextualized vectors:"
+    ],
+    [
+      "The decoder is a transformer that generates the summary tokens one at a time, based on the input and previously generated summary tokens. Each token is projected onto a vector using the same dictionary $\\mathcal {D}$ as the encoder.",
+      "The decoder transformer block includes an additional cross-attention layer to fuse in information from the encoder. The output of the decoder transformer is denoted as:",
+      "To predict the next token $w_{k}$, we reuse the weights of dictionary $\\mathcal {D}$ as the final linear layer to decode $u^D_{k-1}$ into a probability distribution over the vocabulary: $P(w_k|w_{<k},u^E_{1:m})=( \\mathcal {D}u^D_{k-1})$.",
+      "Training. During training, we seek to minimize the cross-entropy loss:",
+      "We use teacher-forcing in decoder training, i.e. the decoder takes ground-truth summary tokens as input. The model has 10 layers of 8-headed transformer blocks in both its encoder and decoder, with 154.4M parameters.",
+      "Inference. During inference, we employ beam search to select the best candidate. The search starts with the special token $\\langle \\mbox{BEGIN}\\rangle $. We ignore any candidate word which results in duplicate trigrams. We select the summary with the highest average log-likelihood per token."
+    ],
+    [
+      "We evaluate our model on five benchmark summarization datasets: the New York Times Annotated Corpus (NYT) BIBREF1, XSum BIBREF2, the CNN/DailyMail dataset BIBREF0, DUC-2003 and DUC-2004 BIBREF21. These datasets contain 104K, 227K, 312K, 624 and 500 news articles and human-edited summaries respectively, covering different topics and various summarization styles. For NYT dataset, we use the same train/val/test split and filtering methods following BIBREF22. As DUC-2003/2004 datasets are very small, we follow BIBREF23 to employ them as test set only."
+    ],
+    [
+      "We use SentencePiece BIBREF24 for tokenization, which segments any sentence into subwords. We train the SentencePiece model on pretrained data to generate a vocabulary of size 32K and of dimension 720. The vocabulary stays fixed during pretraining and finetuning.",
+      "Pretraining. We collect three years of online news articles from June 2016 to June 2019. We filter out articles overlapping with the evaluation data on media domain and time range. We then conduct several data cleaning strategies.",
+      "First, many news articles begin with reporter names, media agencies, dates or other contents irrelevant to the content, e.g. \u201cNew York (CNN) \u2013\u201d, \u201cJones Smith, May 10th, 2018:\u201d. We therefore apply simple regular expressions to remove these prefixes.",
+      "Second, to ensure that the summary is concise and the article contains enough salient information, we only keep articles with 10-150 words in the top three sentences and 150-1200 words in the rest, and that contain at least 6 sentences in total. In this way, we filter out i) articles with excessively long content to reduce memory consumption; ii) very short leading sentences with little information which are unlikely to be a good summary. To encourage the model to generate abstrative summaries, we also remove articles where any of the top three sentences is exactly repeated in the rest of the article.",
+      "Third, we try to remove articles whose top three sentences may not form a relevant summary. For this purpose, we utilize a simple metric: overlapping words. We compute the portion of non-stopping words in the top three sentences that are also in the rest of an article. A higher portion implies that the summary is representative and has a higher chance of being inferred by the model using the rest of the article. To verify, we compute the overlapping ratio of non-stopping words between human-edited summary and the article in CNN/DailyMail dataset, which has a median value of 0.87. Therefore, in pretraining, we keep articles with an overlapping word ratio higher than 0.65.",
+      "These filters rule out around 95% of the raw data and we end up with 21.4M news articles, 12,000 of which are randomly sampled for validation.",
+      "We pretrain the model for 10 epochs and evaluate its performance on the validation set at the end of each epoch. The model with the highest ROUGE-L score is selected.",
+      "During pretraining, we use a dropout rate of 0.3 for all inputs to transformer layers. The batch size is 1,920. We use RAdam BIBREF25 as the optimizer, with a learning rate of $10^{-4}$. Also, due to the different numerical scales of the positional embedding and initialized sentence piece embeddings, we divide the positional embedding by 100 before feeding it into the transformer. The beam width is set to 5 during inference.",
+      "Finetuning. During finetuning, we keep the optimizer, learning rate and dropout rate unchanged as in pretraining. The batch size is 32 for all datasets. We pick the model with the highest ROUGE-L score on the validation set and report its performance on the test set. Our strategy of Pretraining with unlabeled Lead-3 summaries is called PL. We denote the pretrained model with finetuning on target datasets as PL-FT. The model with only pretraining and no finetuning is denoted as PL-NoFT, which is the same model for all datasets."
+    ],
+    [
+      "To compare with our model, we select a number of strong summarization models as baseline systems. $\\textsc {Lead-X}$ uses the top $X$ sentences as a summary BIBREF19. The value of $X$ is 3 for NYT and CNN/DailyMail and 1 for XSum to accommodate the nature of summary length. $\\textsc {PTGen}$ BIBREF4 is the pointer-generator network. $\\textsc {DRM}$ BIBREF10 leverages deep reinforcement learning for summarization. $\\textsc {TConvS2S}$ BIBREF2 is based on convolutional neural networks. $\\textsc {BottomUp}$ BIBREF11 uses a bottom-up approach to generate summarization. ABS BIBREF26 uses neural attention for summary generation. DRGD BIBREF27 is based on a deep recurrent generative decoder. To compare with our pretrain-only model, we include several unsupervised abstractive baselines: SEQ$^3$ BIBREF28 employs the reconstruction loss and topic loss for summarization. BottleSum BIBREF23 leverages unsupervised extractive and self-supervised abstractive methods. GPT-2 BIBREF7 is a large-scaled pretrained language model which can be directly used to generate summaries."
+    ],
+    [
+      "We employ the standard ROUGE-1, ROUGE-2 and ROUGE-L metrics BIBREF29 to evaluate all summarization models. These three metrics respectively evaluate the accuracy on unigrams, bigrams and longest common subsequence. ROUGE metrics have been shown to highly correlate with the human judgment BIBREF29. Following BIBREF22, BIBREF23, we use F-measure ROUGE on XSUM and CNN/DailyMail, and use limited-length recall-measure ROUGE on NYT and DUC. In NYT, the prediction is truncated to the length of the ground-truth summaries; in DUC, the prediction is truncated to 75 characters."
+    ],
+    [
+      "The results are displayed in tab:nyt, tab:xsumresults, tab:cnndaily and tab:duc. As shown, on both NYT and XSum dataset, PL-FT outperforms all baseline models by a large margin. For instance, PL-FT obtains 3.2% higher ROUGE-1, 1.6% higher ROUGE-2 and 2.1% higher ROUGE-L scores than the best baseline model on XSum dataset. We conduct statistical test and found that the results are all significant with p-value smaller than 0.05 (marked by *) or 0.01 (marked by **), compared with previous best scores. On CNN/DailyMail dataset, PL-FT outperforms all baseline models except BottomUp BIBREF11. PL-NoFT, the pretrained model without any finetuning, also gets remarkable results. On XSum dataset, PL-NoFT is almost 8% higher than Lead-1 in ROUGE-1 and ROUGE-L. On CNN/DailyMail dataset, PL-NoFT significantly outperforms unsupervised models SEQ$^3$ and GPT-2, and even surpasses the supervised pointer-generator network. PL-NoFT also achieves state-of-the-art results on DUC-2003 and DUC-2004 among unsupervised models (except ROUGE-1 on DUC-2004), outperforming other carefully designed unsupervised summarization models. It's worth noting that PL-NoFT is the same model for all experiments, which proves that our pretrain strategy is effective across different news corpus."
+    ],
+    [
+      "We measure the abstractiveness of our model via the ratio of novel n-grams in summaries, i.e. the percentage of n-grams in the summary that are not present in the article. fig:novel shows this ratio in summaries from reference and generated by PL-NoFT and PL-FT in NYT dataset. Both PL-NoFT and PL-FT yield more novel 1-grams in summary than the reference. And PL-NoFT has similar novelty ratio with the reference in other n-gram categories. Also, we observe that the novelty ratio drops after finetuning. We attribute this to the strong lead bias in the NYT dataset which affects models trained on it."
+    ],
+    [
+      "We conduct human evaluation of the generated summaries from our models and the pointer generator network with coverage. We randomly sample 100 articles from the CNN/DailyMail test set and ask 3 human labelers from Amazon Mechanical Turk to assess the quality of summaries with a score from 1 to 5 (5 means perfect quality. The labelers need to judge whether the summary can express the salient information from the article in a concise form of fluent language. The evaluation guidelines are given in Table TABREF23. To reduce bias, we randomly shuffle summaries from different sources for each article.",
+      "As shown in Table TABREF23, both of our models PL-NoFT and PL-FT outperform the pointer generator network (PTGen+Cov), and PL-FT's advantage over PTGen+Cov is statistically significant. This shows the effectiveness of both our pretraining and finetuning strategy. To evaluate the inter-annotator agreement, we compute the kappa statistics among the labels and the score is 0.34."
+    ],
+    [
+      "In this paper, we propose a simple and effective pretraining method for news summarization. By employing the leading sentences from a news article as its target summary, we turn the problematic lead bias for news summarization in our favor. Based on this strategy, we conduct pretraining for abstractive summarization in a large-scale news corpus. We conduct thorough empirical tests on five benchmark news summarization datasets, including both automatic and human evaluations. Results show that the same pretrained model without any finetuning can achieve state-of-the-art results among unsupervised methods over various news summarization datasets. And finetuning on target domains can further improve the model's performance. We argue that this pretraining method can be applied in more scenarios where structural bias exists."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2079/instruction.md b/qasper-2079/instruction.md
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index 0000000000000000000000000000000000000000..578b59b04817fec94012368bad8ca783b1bba5de
--- /dev/null
+++ b/qasper-2079/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Make Lead Bias in Your Favor: A Simple and Effective Method for News Summarization
+
+Question: What does the data cleaning and filtering process consist of?
\ No newline at end of file
diff --git a/qasper-2082/instruction.md b/qasper-2082/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..d0f1f4a7fcf1c9983c2bf1ab2ca2976c3a966a70
--- /dev/null
+++ b/qasper-2082/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: A Multi-level Neural Network for Implicit Causality Detection in Web Texts
+
+Question: What performance did proposed method achieve, how much better is than previous state-of-the-art?
\ No newline at end of file
diff --git a/qasper-2083/instruction.md b/qasper-2083/instruction.md
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index 0000000000000000000000000000000000000000..a317f3101315966b29707253f6e0c0667de94882
--- /dev/null
+++ b/qasper-2083/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: A Multi-level Neural Network for Implicit Causality Detection in Web Texts
+
+Question: What was previous state-of-the-art approach?
\ No newline at end of file
diff --git a/qasper-2084/instruction.md b/qasper-2084/instruction.md
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+++ b/qasper-2084/instruction.md
@@ -0,0 +1,110 @@
+Name of Paper: A Multi-level Neural Network for Implicit Causality Detection in Web Texts
+
+Question: How is Relation network used to infer causality at segment level?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work ::: Causality Relation",
+    "Related Work ::: Relation Networks",
+    "Preliminary Statement ::: Linguistic Background",
+    "Preliminary Statement ::: Notations and Definitions",
+    "Methods",
+    "Methods ::: Input Representation",
+    "Methods ::: Word Level",
+    "Methods ::: Segment Level",
+    "Methods ::: Causality Detection",
+    "Experiment",
+    "Experiment ::: Experiment Settings",
+    "Experiment ::: Baseline Methods",
+    "Experiment ::: Results"
+  ],
+  "paragraphs": [
+    [
+      "Automatic text causality mining is a critical but difficult task because causality is thought to play an essential role in human cognition when making decisions BIBREF0. Thus, automatic text causality has been studied extensively in a wide range of areas, such as industry BIBREF1, physics BIBREF2 and healthcare BIBREF3, etc. A tool to automatically scour the plethora of textual content on the web and extract meaningful causal relations could help us construct causal chains to unveil previously unknown relationships between events BIBREF4 and accelerates the discovery of the intrinsic logic of the events BIBREF5.",
+      "Many research efforts have been made to mine causality from text corpus with complex sentence structures in the books or newspapers. In Causal-TimeBank BIBREF6 authors introduced \"CLINK\" and \"C-SIGNAL\" tag to mark events causal relation and causal signals respectively based on specific templates (e.g., \"A happened because of B\"). Q. Do et al. BIBREF7 collected 25 newswire articles from CNN in 2010 and released event causality dataset that provides relatively dense causal annotations. Recently, Q. Do et al. improved the annotation method and implemented joint reasoning for causal and temporal relations BIBREF8. However, the volume of textual data in the wild, e.g., on the web, is much larger than that in books and newspapers. With the help of mobile technologies, people tend to express personal opinions and record memorable moments on the web, which have become a rich source of causality, consequently. There is a huge demand to investigate an approach for mining both explicit and implicit causality from web text. Despite the success of existing studies on extracting explicit causality, there are few reasons why most existing works cannot be directly applied into causality mining on the web text where a large number of implicit causality cases exist. First, most public datasets for causality mining are collected from books and newspaper where the language expressions are more formal and less diverse than the textual data on the web. Second, it would make the perception of causality incomplete because the existing works mainly focus on explicit causal relations that are expressed by intra-sentence or inter-sentence connectives, without considering ambiguous and implicit cases. Implicit commonsense causality can be expressed by a simple sentence structure without any connectives: for example, \"got wet\" is the cause of \"fever\" in Example 1 has no connectives assisting detect causality, while there are explicit connectives (i.e. \"since\" and \"result\") in Example 2 to benefit complex causality detection.",
+      "Example 1 I got wet during the day and came home with a fever at night.",
+      "Example 2 Since computers merely execute the instructions they are given, bugs are nearly always the result of programmer error or an oversight made in the program's design.",
+      "Normally, causality mining is divided into two sequential tasks: causality detection and cause-effect pair extraction. When dealing with large-scale web text, detecting causalities by specific classifiers with relational reasoning capacity is a pre-step of extracting cause-effect pairs. The performance of causality mining largely depends on how well the detection is performed. In this paper, we mainly focus on the detection step. This procedure can overcome the weakness of manual templates that hardly cover the linguistic variations of explicit causality expressions. It could help build a causality dataset with various expressions for extraction, which results in much less model complexity. Most existing works on causality detection have two common limitations. First, utilizing linguistic methods, such as part-of-speech (POS) tagging and syntax dependency parsing, to get handcrafted features is labor-intensive and takes ample time. Zhao et al. BIBREF9 divided causal connectives into different classes as a new category feature based on the similarity of the syntactic dependency structure within causality sentences. Also, the proposed model copes with the interaction between the category feature and other frequently-used features such as contextual features, syntactic features, and position features. However, these extracted features hardly capture a wide variety of causality expressions. The algorithms that used the NLP toolkits to extract the features can pass on the errors caused by the toolkits. Hidey and McKeown BIBREF10 incorporated world knowledge, such as FrameNet, WordNet, and VerbNet, to measure the correlations between words and segments while the method barely handles those words which never appear in the learning phase. Second, the quality of extracting co-occurrence by pre-defined patterns is influenced by ambiguous connectives, such as \"subsequently\" and \"force.\" As seen in Table TABREF4, \"consequently\" is observed in either causal examples or non-causal examples. Luo et al. BIBREF11 leveraged causal pairs extracted by a set of pre-defined patterns to form CausalNet where the weight of a causality pair is a frequency of causality co-occurrence. Unfortunately, due to the volume of their corpus, there was no further analysis of sentences syntactic dependency. To some extent, this restricts the performance of causal pairs detection.",
+      "To address the above problems, we propose a Multi-level Causality Detection Network (MCDN) for causality detection based on the following observations: 1) methods based on end-to-end deep neural networks could reduce the labor cost on feature engineering and relief errors propagation from the existing toolkits; 2) causality is a complicated relation, which calls for multi-level analysis, including parsing each word with its context firstly and inferring causality via the segments on both sides of the connectives secondly. Therefore, at the word level, we integrate word, position, and segment embeddings to encode the input sentence, followed by feeding it into stacked Transformer blocks, which have been widely used in various NLP tasks BIBREF12, BIBREF13. In our research, the Transformer could pay attention to cause and effect entities, and capture long-distance dependency across connectives in the meantime. With this end-to-end module, we combine local context and long-distance dependency to acquire a semantic representation at the word level. Thus, we can relax the first limitation (i.e. feature engineering and accumulated errors). At the segment level, to inference the case and effect near the AltLex, we split the sentence into three segments on the ground of \"segment before AltLex\", \"AltLex\" and \"segment after AltLex\". To solve the second limitation, we propose a novel causality inference module, namely Self Causal Relation Network (SCRN). Due to the characteristics of the dataset, the input of SCRN is a single sentence. This is different from Relation Networks in other areas. The feature maps of segments are constructed into four pair-wise groups that are concatenated with a sentence representation respectively. Our intuition is if the sentence can be expressed as \"B-AltLex-A\", we could inference these segments in pairs to identify: 1) the semantic relation of \"B-AltLex\" and \"AltLex-A\"; 2) the cause-effect relation between \"B-A\" or \"A-B\". Then the segment-level representation is inferred by two non-linear layers. Finally, we combine word-level with segment-level representations to obtain the detection result.",
+      "In general, our model MCDN has a simple architecture but effective reasoning potential for causality detection. The contributions can be summarized as three-fold:",
+      "We introduce the task of mining causality from web text that is conducted into detection and extraction step. Utilizing detection instead of specific templates is a new direction and can provide a rich diversity of causality text with low-noise data for the subsequent extraction step and upstream applications.",
+      "We propose a neural model MCDN to tackle the problem at the word and segment levels without any feature engineering. MCDN contains a relational reasoning module named Self Causal Relation Network (SCRN) to infer the causal relations within sentences.",
+      "To evaluate the effectiveness of the proposed framework, we have conducted extensive experiments on a publicly available dataset. The experimental results show that our model achieves significant improvement over baseline methods, including many state-of-the-art text classification models, which illustrates detecting causality is a complex task. The detection not only requires multi-level information but also needs more reasoning capability than the text classification."
+    ],
+    [
+      "Causality mining is a fundamental task with abundant upstream applications. Early works utilize Bayesian network BIBREF14, BIBREF15, syntactic constraint BIBREF16, and dependency structure BIBREF17 to extract cause-effect pairs. Nevertheless, they could hardly summarize moderate patterns and rules to avoid overfitting. Further studies incorporate world knowledge that provides a supplement to lexico-syntax analysis. Generalizing nouns to their hypernyms in WordNet and each verb to its class in VerbNet BIBREF18, BIBREF19 eliminates the negative effect of lexical variations and discover frequent patterns of cause-effect pairs. As is well known, the implicit expressions of causality are more frequent. J.-H. Oh et al. BIBREF20 exploited cue words and sequence labeling by CRFs and selected the most relevant causality expressions as complements to implicitly expressed causality. However, the method requires retrieval and ranking from enormous web texts. From natural properties perspective, causality describes relations between regularly correlated events or phenomena. Constructing a cause-effect network or graph could help discover co-occurrence patterns and evolution rules of causation BIBREF3, BIBREF19. Therefore, Zhao et al. BIBREF21 conducted causality reasoning on the heterogeneous network to extract implicit relations cross sentences and find new causal relations.",
+      "Our work is similar to previous works on detecting causalities BIBREF10, BIBREF18. The difference is we do not incorporate knowledge bases they used. We propose a neural-based multi-level model to tackle the problem without any feature engineering. Oh et al. BIBREF20 proposed a multi-column convolutional neural network with causality-attention (CA-MCNN) to enhance MCNNs with the causality-attention based question and answer passage, which is not in coincidence with our task. In compared with CA-MCNN, the multi-head self-attention within the Transformer block we used at the word level is more effective, and the SCRN at the segment level augments the reasoning ability of our model."
+    ],
+    [
+      "Relation Networks (RNs) is initially a simple plug-and-play module to solve Visual-QA problems that fundamentally hinge on relational reasoning BIBREF22. RNs can effectively couple with convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and multi-layer perceptrons (MLPs) to reduce overall network complexity. We gain a general ability to reason about the relations between entities and their properties. Original RNs can only perform single step inference such as $A \\rightarrow B$ rather than $A \\rightarrow B \\rightarrow C$. For tasks that require complex multi-step of relational reasoning, Palm et al. BIBREF23 introduced the recurrent relational network that operates on a graph representation of objects. Pavez et al. BIBREF24 added complex reasoning ability to Memory Networks with RNs, which reduced its computational complexity from quadratic to linear. However, their tasks remain text QA and visual QA. In this paper, it's the first time that RNs is applied to relation extraction as proposed SCRN."
+    ],
+    [
+      "This section describes the linguistic background of causal relation and the AltLexes dataset, which we used. It's a commonly held belief that causality can be expressed explicitly and implicitly using various propositions. In the Penn Discourse Treebank (PDTB) BIBREF25, over $12\\%$ of explicit discourse connectives are marked as causal such as \"hence\", \"as a result\" and \"consequently\", as are nearly $26\\%$ of implicit discourse relationships. In addition to these, there exists a type of implicit connectives in PDTB named AltLex (Alternative lexicalization) has been capable of indicating causal relations, which is an open class of markers and potentially infinite.",
+      "The definition of AltLex was extended with an open class of markers that occur within a sentence in BIBREF10. The following are examples widespread in the new AltLexes set but are not contained in PDTB explicit connectives. The word \"made\" with many meanings here is used to express causality. Moreover, the expression of causality in the second example is somewhat obscure.",
+      "Ambiguous causal verbs, e.g. The flood made many houses to collapse.",
+      "Partial prepositional phrases, e.g. They have made l4 self-driving car with the idea of a new neural network.",
+      "According to our statistics in the parallel data constructed in BIBREF10, there are 1144 AltLexes indicate causal, and 7647 AltLexes indicates non-causal. Meanwhile, their intersection has 144 AltLexes, which is $12.6\\%$ of causal sets and $1.8\\%$ of non-causal sets.",
+      "In conclusion, ambiguous connectives and implicit expressions are frequently observed in the AltLexes dataset. Methods based on statistical learning with manual patterns have demerits to build a reliable model in such contexts. However, with the abstraction and reasoning capacity, our model MCDN can be well adapted to these situations."
+    ],
+    [
+      "For a given Wikipedia sentence $S$, it is assumed that it has $n$ tokens. $S = \\lbrace s_1, s_2, ... , s_{n-1}, s_n \\rbrace $ where $s_i$ is a filtered token at position $i$. We use $L$ refers to the AltLex, $BL$ refers to the segment before the AltLex and $AL$ refers to the segment after the AltLex. Our objective is to generate a sentence-level prediction $\\hat{y}$ of which the label is $y$ as Equation DISPLAY_FORM12. The proposed model MCDN is shown in Figure FIGREF15. We will detail each component in the rest of this section.",
+      "It's worth noting that Hidey and McKeown BIBREF10 utilized English Wikipedia and Simple Wikipedia sentence pair to create a parallel corpus feature but still took one sentence as input each time. Unlike this approach, MCDN only leverages the input sentence for causal inference."
+    ],
+    [
+      "In this section, we elaborate the MCDN, a multi-level neural network-based approach with Transformer blocks at the word level and SCRN at the segment level for causality detection, which is primarily targeted at ambiguous and implicit relations."
+    ],
+    [
+      "Our input representation is able to incorporate multi-source information into one token sequence. Inspired by BIBREF12, the representation of each token in the input sentence is constructed by summing the corresponding word, position, and segment embeddings. Unlike the previous work, BERT, the segment embeddings here indicate the $BL$, $L$ and $AL$ segment in each sentence. As shown in Fig. FIGREF10, first, we adopt a word2vec toolkit to pretrain word embeddings with $d_{word}$ dimension on the English Wikipedia dump. Next, we utilize positional embeddings to map the positional information because our model has no recurrent architecture at the word level. Similarly, we use segment embeddings to involve more linguistic details. $d_{pos}$ and $d_{seg}$ is the dimension of positional embeddings and segment embeddings, respectively. By sum the three embeddings, finally, we get a new representation $x_i \\in \\mathbb {R}^d$ for token $s_i$ where $d = d_{word} = d_{pos} = d_{seg}$. The representation $x_i$ could provide basic features for high-level modules."
+    ],
+    [
+      "There are two sub-layers in the Transformer block: self-attention and feed-forward networks. For stability and superior performance, we add a layer normalization after the residual connection for each of the sub-layers.",
+      "Self-Attention. In this paper, we employ scaled multi-head self-attention, which has many merits compared with RNN and CNN. Firstly, the \"receptive field\" of each token can be extended to the whole sequence without long distance dependency diffusion. And any significant token would be assigned a high weight. Secondly, dot-product and multi-head can be optimized for parallelism separately, which is more efficient than RNN. Finally, multi-head model aggregates information from different representation sub-spaces. For scaled self-attention, given the input matrix of $n$ query vectors $Q \\in \\mathbb {R}^{n \\times d}$, keys $K \\in \\mathbb {R}^{n \\times d}$ and values $V \\in \\mathbb {R}^{n \\times d}$, computing the output attention score as:",
+      "We take the input vector matrix $X \\in \\mathbb {R}^{n \\times d}$ as queries, keys, and values matrix and linearly project them $h$ times respectively. Formally, for $i$-$th$ head ${\\rm H_i}$ it is formulated as below:",
+      "Where the learned projections are matrices $W_i^Q \\in \\mathbb {R}^{d \\times d /h}$, $W_i^K \\in \\mathbb {R}^{d \\times d /h}$, $W_i^V \\in \\mathbb {R}^{d \\times d /h}$. Finally, we concatenate each head and map them to the output space with $W_{MH} \\in \\mathbb {R}^{d \\times d}$:",
+      "Feed-forward Networks. We apply feed-forward networks after the self-attention sub-layer. It consists of two linear layers and a ReLU activation between them. Note that $x$ is the output of the previous layer:",
+      "where $W_1 \\in \\mathbb {R}^{d \\times d_f}$ and $W_2 \\in \\mathbb {R}^{d \\times d_f}$. We set $d_f = 4d$ in our experiments.",
+      "The Transformer block is stacked $N$ times, of which the final output $wl\\_{rep}$ is regarded as the representation of the sentence at the word level. We aim to deal the word with its fine-grained local context and coarse-grained global long-distance dependency information. Thus, our word-level module could acquire not only lexico-syntax knowledge that manual patterns hardly cover but also lexical semantics among the words."
+    ],
+    [
+      "We propose a novel approach to infer causality within sentences at the segment level. The model is named as Self Causal Relation Network (SCRN) due to it focuses on the causal relation intra-sentence compared with previous studies of RNs.",
+      "Dealing with segments. The core idea of Relation Networks is operating on objects. In our task, the sentence is split into three segments $BL$, $L$, and $AL$ according to the position of AltLex. Then the input representations of these segments can be formulated as $X_{BL} \\in \\mathbb {R}^{T_{BL} \\times d}$, $X_{L} \\in \\mathbb {R}^{T_{L} \\times d}$ and $X_{AL} \\in \\mathbb {R}^{T_{AL} \\times d}$ where $T_{BL}$, $T_{L}$, and $T_{AL}$ are the length of tokens in each segment. Due to the difference of segment lengths, we use a three-column CNN (TC-CNN) to parse $X_{BL}$, $X_{L}$, and $X_{AL}$ into a set of objects. Particularly the representations here only employ word embeddings and segment embeddings because the TC-CNN could capture the position information. Unlike [25], TC-CNN convolves them through a 1D convolutional layer to $k$ feature maps of size $T_{BL} \\times 1$, $T_{L} \\times 1$, and $T_{AL} \\times 1$, where $k$ is the sum of kernels. The model exploits multi-scale kernels (with varying window size) to obtain multi-scale features. As seen in Fig. FIGREF15, the feature maps of each segment are rescaled into a k-dimension vector by the max pooling layer after convolution. Finally, we produce a set of objects for SCRN:",
+      "Dealing with the sentence. The input representation $X$ of the sentence pass through a bidirectional-GRU (bi-GRU) with $d_g$-dimension hidden units, and the final state $\\gamma \\in \\mathbb {R}^{2d_g}$ of the bi-GRU is concatenated to each object-pair.",
+      "SCRN. We construct four object-pairs concatenated with $\\gamma $. Let $\\#$ denote the pair-wise operation. For causality candidates, $BL\\#L$ and $L\\#AL$ indicate the relation between cause-effect and AltLex, while $BL\\#AL$ and $AL\\#BL$ inference the direction of causality. The object-pairs matrix $OP \\in \\mathbb {R}^{4 \\times (2k + 2d_g)}$ is shown as follows:",
+      "Here \";\" is a concatenation operation for the object vectors. Consequently, we modify the SCRN architecture in a mathematical formulation and obtain the final output $sl\\_{rep} \\in \\mathbb {R}^{4d_g}$ at the segment level:",
+      "In general, the model transforms the segments into object-pairs by the TC-CNN and passes sentence through bi-GRU to obtain the global representation. Then we integrate object-pairs with global representation and make a pair-wise inference to detect the relationship among the segments. Ablation studies show that the proposed SCRN at the segment level has the capacity for relational reasoning and promotes the result significantly."
+    ],
+    [
+      "Our model MCDN identifies causality of each sentence based on the output $wl\\_{rep}$ at the word level and $sl\\_{rep}$ at the segment level. The two outputs are concatenated as a unified representation $uni\\_{rep} = [wl\\_{rep}; sl\\_{rep}] \\in \\mathbb {R}^{d + 4d_g}$. In this task, we use a 2-layer FFN consisting of $d_g$ units which have a ReLU activation and is followed by a softmax function to make the prediction:",
+      "In the AltLexes dataset, the number of non-causal examples is over seven times the number of causal examples, and this leads to an extremely sample imbalance problem. If we adopt cross-entropy (CE) as model loss function, the performance would be unsatisfactory. Moreover, the difficulty in detecting each sample is different. For example, the sentence contains an ambiguous AltLex such as \"make\" is harder to infer than that contains \"cause\". Consequently, we need to assign a soft weight to a causal and non-causal loss to make the model pay more attention to those examples which are difficult to identify. Motivated by the works BIBREF26, we introduce the focal loss to improve normal cross entropy loss function. The focal loss $L_{fl}$ is formulated as the objective function with the balance weight hyperparameter $\\alpha $ and the tunable focusing hyperparameter $\\beta \\ge 0$.",
+      "For optimization, we use the Adam optimizer BIBREF27 with $\\beta _1 = 0.9$, $\\beta _2 = 0.999$, $\\epsilon = 1e^{-8}$ and clip the gradients norm."
+    ],
+    [
+      "In this section, we are interested in investigating the performance of MCDN that integrates Transformer blocks with SCRN and whether it is essential to incorporate inference ability in the sentence-level causality detection task."
+    ],
+    [
+      "Dataset. We use the AltLexes dataset to evaluate the proposed approach. The detailed statistical information about the dataset is listed in Table TABREF30. The Bootstrapped set is generated using new AltLexes to identify additional ones based on the Training set, which increased causal examples by about 65 percent. In our experiment, we train MCDN on the Training set and Bootstrapped set separately and finetune hyperparameters on the validation set. The golden annotated set is used as the test set.",
+      "Hyperparameters. We set the initial learning rate to $1e^{-4}$ then decreased half when the F1-score has stopped increasing more than two epochs. The batch size in this experiment is 32, and the epoch size is 20. To avoid overfitting, we employ two types of regularization during training: 1) dropout for the sums of the embeddings, the outputs of each bi-GRU layer except the last, each layer in FFN and residual dropout for Transformer blocks BIBREF12; 2) $L_2$ regularization for all trainable parameters. The dropout rate is set to 0.5 and the regularization coefficient is $3e^{-4}$. In self-attention module, we set the stack time of Transformer blocks $N=4$ and the number of attention heads $h=4$. In SCRN, the window sizes of TC-CNN kernels are 2, 3, 4 while the sum of kernel $k=150$. We use a 2-layer bi-GRU with 64 units in each direction. As for the focal loss, we set $\\alpha =0.75, \\beta =4$.",
+      "Evaluation Metrics. Different evaluation metrics including accuracy, precision, recall, and F1-score are adapted to compare MCDN with the baseline methods. To understand our model comprehensively, we employ both Area under Receiver Operator Curve (AUROC) and Area under Precision-Recall Curve (AUPRC) to evaluate its sensitivity and specificity, especially under the situation that causality is relatively sparse in the web text."
+    ],
+    [
+      "In this section, we elaborate on 10 baseline methods.",
+      "The first five methods are the most common class (MCC), $KLD$, $LS \\cup KLD$, $LS \\cup KLD \\cup CONN$, and $KLD \\cup LS \\cup LS_{inter}$. $KLD$, $LS$, and $CONN$ represent KL-divergence score, lexical semantic feature, and categorical feature respectively. These methods are used as baselines in Hidey et al.'s work. $KLD$ and $LS \\cup KLD$ acquire the best accuracy and precision on the Training set. $LS \\cup KLD \\cup CONN$ and $KLD \\cup LS \\cup LS_{inter}$ are the best systems with the highest recall and F1-score respectively. The next five are the most commonly used methods in text classification. They are TextCNN, TextRNN, SASE, DPCNN, and BERT. In our experiment, we reproduced all of them except BERT. For BERT, we use the public released pre-trained language model (base). and fine-tuned it on each dataset. The detailed information about these baselines is listed as follows:",
+      "TextCNNBIBREF28 used here has a convolution layer, the window sizes of which are 2, 3, 4 and each have 50 kernels. Then we apply max-overtime-pooling and 2-layer FFN with ReLU activation. The dropout rate is 0.5 and $L-2$ regularization coefficient is $3e^{-4}$.",
+      "TextRNN uses a bidirectional GRU the same as sentence encoder in SCRN and use max pooling across all GRU hidden states to get the sentence embedding vector, then use a 2-layer FFN to output the result. Dropout rate and $L_2$ regularization coefficient is the same as TextCNN.",
+      "SASE BIBREF29 uses a 2-D matrix to represent the sentence embedding with a self-attention mechanism and a particular regularization term for the model. It's an effective sentence level embedding method.",
+      "DPCNN BIBREF30 is a low-complexity word-level deep CNN model for sentiment classification and topic categorization. It can make down-sampling without increasing the number of features maps which enables the efficient representation of long-range associations.",
+      "BERT BIBREF13 presented state-of-the-art results in a wide variety of NLP tasks, which is a pre-trained deep language representation model based on Transformer and Masked Language Model. BERT is inspired by transfer learning in the computer vision field, pre-training a neural network model on a known task, for instance, ImageNet, and then performing fine-tuning on a new purpose-specific task.",
+      "It's worth noting that due to data imbalance and for comparison in the same situation, we also used focal loss in the above methods to acquire the best performance."
+    ],
+    [
+      "Table TABREF30 shows the detection results from the two datasets of our model and competing methods. Firstly, we can see that MCDN remarkably outperforms all other models when trained on both datasets.",
+      "Although MCDN doesn't obtain the highest precision, it increases F1-score by 10.2% and 3% compared with the existing best systems $LS \\cup KLD \\cup CONN$ and $KLD \\cup LS \\cup LS_{inter}$. Furthermore, $KLD$ feature based SVM yields the highest precision on the Training set, though poor recall and F1-score, because it focuses on the substitutability of connectives while the parallel examples usually have the same connective that would be estimated as false negatives. It is remarkable that MCDN is more robust on the original Training set and Bootstrapped set while the feature-based linear SVM and neural network-based approaches presented a considerable difference and got gain even more than 20 on F1-score.",
+      "Secondly, deep methods tend to acquire balanced precision and recall score except for BERT and MCDN whose recall is significantly higher than precision on Bootstrapped set. Besides, F1-score of both BERT and MCDN is beyond 80 on the Bootstrapped dataset. All the results above suggest that the neural network is more powerful than the traditional co-occurrence and world knowledge-based methods on this task, as we expected. MCDN has learned various semantic representations of causal relations from word level and been able to inference causality from segment level supported by concise and effective SCRN. Furthermore, the deep classification methods we employed don't perform as well as MCDN which demonstrates causality detection is a much complex task that requires considerable relational reasoning capacity compared with text classification, although both can be generalized to classification problems."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2085/instruction.md b/qasper-2085/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..b394bbcf7d518b1cb6b1f1717d18253ba135928a
--- /dev/null
+++ b/qasper-2085/instruction.md
@@ -0,0 +1,48 @@
+Name of Paper: Passage Re-ranking with BERT
+
+Question: What is the TREC-CAR dataset?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Experiments",
+    "MS MARCO",
+    "TREC-CAR",
+    "Results",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "We have seen rapid progress in machine reading compression in recent years with the introduction of large-scale datasets, such as SQuAD BIBREF3 , MS MARCO BIBREF4 , SearchQA BIBREF5 , TriviaQA BIBREF6 , and QUASAR-T BIBREF7 , and the broad adoption of neural models, such as BiDAF BIBREF8 , DrQA BIBREF9 , DocumentQA BIBREF10 , and QAnet BIBREF11 .",
+      "The information retrieval (IR) community has also experienced a flourishing development of neural ranking models, such as DRMM BIBREF12 , KNRM BIBREF13 , Co-PACRR BIBREF14 , and DUET BIBREF15 . However, until recently, there were only a few large datasets for passage ranking, with the notable exception of the TREC-CAR BIBREF16 . This, at least in part, prevented the neural ranking models from being successful when compared to more classical IR techniques BIBREF17 .",
+      "We argue that the same two ingredients that made possible much progress on the reading comprehension task are now available for passage ranking task. Namely, the MS MARCO passage ranking dataset, which contains one million queries from real users and their respective relevant passages annotated by humans, and BERT, a powerful general purpose natural language processing model.",
+      "In this paper, we describe in detail how we have re-purposed BERT as a passage re-ranker and achieved state-of-the-art results on the MS MARCO passage re-ranking task."
+    ],
+    [
+      "We train and evaluate our models on two passage-ranking datasets, MS MARCO and TREC-CAR."
+    ],
+    [
+      "The training set contains approximately 400M tuples of a query, relevant and non-relevant passages. The development set contains approximately 6,900 queries, each paired with the top 1,000 passages retrieved with BM25 from the MS MARCO corpus. On average, each query has one relevant passage. However, some have no relevant passage because the corpus was initially constructed by retrieving the top-10 passages from the Bing search engine and then annotated. Hence, some of the relevant passages might not be retrieved by BM25.",
+      "An evaluation set with approximately 6,800 queries and their top 1,000 retrieved passages without relevance annotations is also provided.",
+      "We fine-tune the model using TPUs with a batch size of 32 (32 sequences * 512 tokens = 16,384 tokens/batch) for 400k iterations, which takes approximately 70 hours. This corresponds to training on 12.8M (400k * 32) query-passage pairs or less than 2% of the full training set. We could not see any improvement in the dev set when training for another 10 days, which equivalent to seeing 50M pairs in total.",
+      "We use ADAM BIBREF18 with the initial learning rate set to $3 \\times 10^{-6}$ , $\\beta _1 = 0.9$ , $\\beta _2 = 0.999$ , L2 weight decay of 0.01, learning rate warmup over the first 10,000 steps, and linear decay of the learning rate. We use a dropout probability of $0.1$ on all layers."
+    ],
+    [
+      "Introduced by BIBREF16 , in this dataset, the input query is the concatenation of a Wikipedia article title with the title of one of its section. The relevant passages are the paragraphs within that section. The corpus consists of all of the English Wikipedia paragraphs, except the abstracts. The released dataset has five predefined folds, and we use the first four as a training set (approximately 3M queries), and the remaining as a validation set (approximately 700k queries). The test set is the same one used to evaluate the submissions to TREC-CAR 2017 (approx. 1,800 queries).",
+      "Although TREC-CAR 2017 organizers provide manual annotations for the test set, only the top five passages retrieved by the systems submitted to the competition have manual annotations. This means that true relevant passages are not annotated if they rank low. Hence, we evaluate using the automatic annotations, which provide relevance scores for all possible query-passage pairs.",
+      "We follow the same procedure described for the MS MARCO dataset to fine-tune our models on TREC-CAR. However, there is an important difference. The official pre-trained BERT models were pre-trained on the full Wikipedia, and therefore they have seen, although in an unsupervised way, Wikipedia documents that are used in the test set of TREC-CAR. Thus, to avoid this leak of test data into training, we pre-trained the BERT re-ranker only on the half of Wikipedia used by TREC-CAR's training set.",
+      "For the fine-tuning data, we generate our query-passage pairs by retrieving the top ten passages from the entire TREC-CAR corpus using BM25. This means that we end up with 30M example pairs (3M queries * 10 passages/query) to train our model. We train it for 400k iterations, or 12.8M examples (400k iterations * 32 pairs/batch), which corresponds to only 40% of the training set. Similarly to MS MARCO experiments, we did not see any gain on the dev set by training the models longer."
+    ],
+    [
+      "We show the main result in Table 1 . Despite training on a fraction of the data available, the proposed BERT-based models surpass the previous state-of-the-art models by a large margin on both of the tasks.",
+      "We found that the pretrained models used in this work require few training examples from the end task to achieve a good performance 1 . For example, a $\\text{BERT}_\\text{LARGE}$ trained on 640k question-passage pairs (2% of the MS MARCO training data) is only 2 MRR@10 points lower than model trained on 12.8M pairs (40% of training data)."
+    ],
+    [
+      "We have described a simple adaptation of BERT as a passage re-ranker that has become the state of the art on two different tasks, which are TREC-CAR and MS MARCO. We have made the code to reproduce our MS MARCO entry publicly available."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2092/instruction.md b/qasper-2092/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..be38f2cd44a65fbfa356f00415445c5f811c2708
--- /dev/null
+++ b/qasper-2092/instruction.md
@@ -0,0 +1,55 @@
+Name of Paper: How do you correct run-on sentences it's not as easy as it seems
+
+Question: How large is the dataset they generate?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Model Descriptions",
+    "Conditional Random Fields",
+    "Sequence to Sequence Model with Attention Mechanism",
+    "Results and Analysis",
+    "Conclusions",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "A run-on sentence is defined as having at least two main or independent clauses that lack either a conjunction to connect them or a punctuation mark to separate them. Run-ons are problematic because they not only make the sentence unfriendly to the reader but potentially also to the local discourse. Consider the example in Table TABREF1 .",
+      "In the field of grammatical error correction (GEC), most work has typically focused on determiner, preposition, verb and other errors which non-native writers make more frequently. Run-ons have received little to no attention even though they are common errors for both native and non-native speakers. Among college students in the United States, run-on sentences are the 18th most frequent error and the 8th most frequent error made by students who are not native English speakers BIBREF0 .",
+      "Correcting run-on sentences is challenging BIBREF1 for several reasons:",
+      "In this paper, we analyze the task of automatically correcting run-on sentences. We develop two methods: a conditional random field model (roCRF) and a Seq2Seq attention model (roS2S) and show that they outperform models from the sister tasks of punctuation restoration and whole-sentence grammatical error correction. We also experiment with artificially generating training examples in clean, otherwise grammatical text, and show that models trained on this data do nearly as well predicting artificial and naturally occurring run-on sentences."
+    ],
+    [
+      "Early work in the field of GEC focused on correcting specific error types such as preposition and article errors BIBREF2 , BIBREF3 , BIBREF4 , but did not consider run-on sentences. The closest work to our own is BIBREF5 , who used Conditional Random Fields (CRFs) for correcting comma errors (excluding comma splices, a type of run-on sentence). BIBREF6 used a similar system based on CRFs but focused on comma splice correction. Recently, the field has focused on the task of whole-sentence correction, targeting all errors in a sentence in one pass. Whole-sentence correction methods borrow from advances in statistical machine translation BIBREF7 , BIBREF8 , BIBREF9 and, more recently, neural machine translation BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 .",
+      "To date, GEC systems have been evaluated on corpora of non-native student writing such as NUCLE BIBREF14 and the Cambridge Learner Corpus First Certificate of English BIBREF15 . The 2013 and 2014 CoNLL Shared Tasks in GEC used NUCLE as their train and test sets BIBREF16 , BIBREF17 . There are few instances of run-on sentences annotated in both test sets, making it hard to assess system performance on that error type.",
+      "A closely related task to run-on error correction is that of punctuation restoration in the automatic speech recognition (ASR) field. Here, a system takes as input a speech transcription and is tasked with inserting any type of punctuation where appropriate. Most work utilizes textual features with n-gram models BIBREF18 , CRFs BIBREF19 , convolutional neural networks or recurrent neural networks BIBREF20 , BIBREF21 . The Punctuator BIBREF22 is a leading punctuation restoration system based on a sequence-to-sequence model (Seq2Seq) trained on long slices of text which can span multiple sentences."
+    ],
+    [
+      "We treat correcting run-ons as a sequence labeling task: given a sentence, the model reads each token and learns whether there is a SPACE or PERIOD following that token, as shown in Table TABREF5 . We apply two sequence models to this task, conditional random fields (roCRF) and Seq2Seq (roS2S)."
+    ],
+    [
+      "Our CRF model, roCRF, represents a sentence as a sequence of spaces between tokens, labeled to indicate whether a period should be inserted in that space. Each space is represented by contextual features (sequences of tokens, part-of-speech tags, and capitalization flags around each space), parse features (the highest uncommon ancestor of the word before and after the space, and binary indicators of whether the highest uncommon ancestors are preterminals), and a flag indicating whether the mean per-word perplexity of the text decreases when a period is inserted at the space according to a 5-gram language model."
+    ],
+    [
+      "Another approach is to treat it as a form of neural sequence generation. In this case, the input sentence is a single run-on sentence. During decoding we pass the binary label which determines if there is terminal punctuation following the token at the current position. We then combine the generated label and the input sequence to get the final output.",
+      "Our model, roS2S, is a Seq2Seq attention model based on the neural machine translation model BIBREF23 . The encoder is a bidirectional LSTM, where a recurrent layer processes the input sequence in both forward and backward direction. The decoder is a uni-directional LSTM. An attention mechanism is used to obtain the context vector."
+    ],
+    [
+      "Results are shown in Table TABREF11 . A correct judgment is where a run-on sentence is detected and a PERIOD is inserted in the right place. Across all datasets, roCRF has the highest precision. We speculate that roCRF consistently has the highest precision because it is the only model to use POS and syntactic features, which may restrict the occurrence of false positives by identifying longer distance, structural dependencies. roS2S is able to generalize better than roCRF, resulting in higher recall with only a moderate impact on precision. On all datasets except RealESL, roS2S consistently has the highest overall INLINEFORM0 score. In general, Punctuator has the highest recall, probably because it is trained for a more general purpose task and tries to predict punctuation at each possible position, resulting in lower precision than the other models.",
+      "NUS18 predicts only a few false positives and no true positives, so INLINEFORM0 and we exclude it from the results table. Even though NUS18 is trained on NUCLE, which RealESL encompasses, its very poor performance is not too surprising given the infrequency of run-ons in NUCLE."
+    ],
+    [
+      "Correcting run-on sentences is a challenging task that has not been individually targeted in earlier GEC models. We have developed two new models for run-on sentence correction: a syntax-aware CRF model, roCRF, and a Seq2Seq model, roS2S. Both of these outperform leading models for punctuation restoration and grammatical error correction on this task. In particular, roS2S has very strong performance, with INLINEFORM0 and INLINEFORM1 on run-ons generated from clean and noisy data, respectively. roCRF has very high precision ( INLINEFORM2 ) but low recall, meaning that it does not generalize as well as the leading system, roS2S.",
+      "Run-on sentences have low frequency in annotated GEC data, so we experimented with artificially generated training data. We chose clean newswire text as the source for training data to ensure there were no unlabeled naturally occurring run-ons in the training data. Using ungrammatical text as a source of artificial data is an area of future work. The results of this study are inconclusive in terms of how much harder the task is on clean versus noisy text. However, our findings suggest that artificial run-ons are similar to naturally occurring run-ons in ungrammatical text because models trained on artificial data do just as well predicting real run-ons as artificial ones.",
+      "In this work, we found that a leading GEC model BIBREF11 does not correct any run-on sentences, even though there was an overlap between the test and training data for that model. This supports the recent work of BIBREF29 , who found that GEC systems tend to ignore less frequent errors due to reference bias. Based on our work with run-on sentences, a common error type that is infrequent in annotated data, we strongly encourage future GEC work to address low-coverage errors."
+    ],
+    [
+      "We thank the three anonymous reviewers for their helpful feedback."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2095/instruction.md b/qasper-2095/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..f8fef393e9bbc63c54f91072d61f13c4fc691de4
--- /dev/null
+++ b/qasper-2095/instruction.md
@@ -0,0 +1,120 @@
+Name of Paper: Component Analysis for Visual Question Answering Architectures
+
+Question: What components are identified as core components for training VQA models?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Impact of VQA Components",
+    "Experimental Setup ::: Dataset",
+    "Experimental Setup ::: Evaluation Metric",
+    "Experimental Setup ::: Hyper-parameters",
+    "Experimental Analysis",
+    "Experimental Analysis ::: Text Encoder",
+    "Experimental Analysis ::: Image Encoder",
+    "Experimental Analysis ::: Fusion strategy",
+    "Experimental Analysis ::: Attention Mechanism",
+    "Experimental Analysis ::: Attention Mechanism ::: Top-Down Attention",
+    "Experimental Analysis ::: Attention Mechanism ::: Co-Attention",
+    "Findings Summary",
+    "Comparison to state-of-the-art methods",
+    "Conclusion",
+    "Acknowledgment"
+  ],
+  "paragraphs": [
+    [
+      "Recent research advances in Computer Vision (CV) and Natural Language Processing (NLP) introduced several tasks that are quite challenging to be solved, the so-called AI-complete problems. Most of those tasks require systems that understand information from multiple sources, i.e., semantics from visual and textual data, in order to provide some kind of reasoning. For instance, image captioning BIBREF0, BIBREF1, BIBREF2 presents itself as a hard task to solve, though it is actually challenging to quantitatively evaluate models on that task, and that recent studies BIBREF3 have raised questions on its AI-completeness.",
+      "The Visual Question Answering (VQA) BIBREF3 task was introduced as an attempt to solve that issue: to be an actual AI-complete problem whose performance is easy to evaluate. It requires a system that receives as input an image and a free-form, open-ended, natural-language question to produce a natural-language answer as the output BIBREF3. It is a multidisciplinary topic that is gaining popularity by encompassing CV and NLP into a single architecture, what is usually regarded as a multimodal model BIBREF4, BIBREF5, BIBREF6. There are many real-world applications for models trained for Visual Question Answering, such as automatic surveillance video queries BIBREF7 and visually-impaired aiding BIBREF8, BIBREF9.",
+      "Models trained for VQA are required to understand the semantics from images while finding relationships with the asked question. Therefore, those models must present a deep understanding of the image to properly perform inference and produce a reasonable answer to the visual question BIBREF10. In addition, it is much easier to evaluate this task since there is a finite set of possible answers for each image-question pair.",
+      "Traditionally, VQA approaches comprise three major steps: (i) representation learning of the image and the question; (ii) projection of a single multimodal representation through fusion and attention modules that are capable of leveraging both visual and textual information; and (iii) the generation of the natural language answer to the question at hand. This task often requires sophisticated models that are able to understand a question expressed in text, identify relevant elements of the image, and evaluate how these two inputs correlate.",
+      "Given the current interest of the scientific community in VQA, many recent advances try to improve individual components such as the image encoder, the question representation, or the fusion and attention strategies to better leverage both information sources. With so many approaches currently being introduced at the same time, it becomes unclear the real contribution and importance of each component within the proposed models. Thus, the main goal of this work is to understand the impact of each component on a proposed baseline architecture, which draws inspiration from the pioneer VQA model BIBREF3 (Fig. FIGREF1). Each component within that architecture is then systematically tested, allowing us to understand its impact on the system's final performance through a thorough set of experiments and ablation analysis.",
+      "More specifically, we observe the impact of: (i) pre-trained word embeddings BIBREF11, BIBREF12, recurrent BIBREF13 and transformer-based sentence encoders BIBREF14 as question representation strategies; (ii) distinct convolutional neural networks used for visual feature extraction BIBREF15, BIBREF16, BIBREF17; and (iii) standard fusion strategies, as well as the importance of two main attention mechanisms BIBREF18, BIBREF19. We notice that even using a relatively simple baseline architecture, our best models are competitive to the (maybe overly-complex) state-of-the-art models BIBREF20, BIBREF21. Given the experimental nature of this work, we have trained over 130 neural network models, accounting for more than 600 GPU processing hours. We expect our findings to be useful as guidelines for training novel VQA models, and that they serve as a basis for the development of future architectures that seek to maximize predictive performance."
+    ],
+    [
+      "The task of VAQ has gained attention since Antol et al. BIBREF3 presented a large-scale dataset with open-ended questions. Many of the developed VQA models employ a very similar architecture BIBREF3, BIBREF22, BIBREF23, BIBREF24, BIBREF25, BIBREF26, BIBREF27: they represent images with features from pre-trained convolutional neural networks; they use word embeddings or recurrent neural networks to represent questions and/or answers; and they combine those features in a classification model over possible answers.",
+      "Despite their wide adoption, RNN-based models suffer from their limited representation power BIBREF28, BIBREF29, BIBREF30, BIBREF31. Some recent approaches have investigated the application of the Transformer model BIBREF32 to tasks that incorporate visual and textual knowledge, as image captioning BIBREF28.",
+      "Attention-based methods are also being continuously investigated since they enable reasoning by focusing on relevant objects or regions in original input features. They allow models to pay attention on important parts of visual or textual inputs at each step of a task. Visual attention models focus on small regions within an image to extract important features. A number of methods have adopted visual attention to benefit visual question answering BIBREF27, BIBREF33, BIBREF34.",
+      "Recently, dynamic memory networks BIBREF27 integrate an attention mechanism with a memory module, and multimodal bilinear pooling BIBREF22, BIBREF20, BIBREF35 is exploited to expressively combine multimodal features and predict attention over the image. These methods commonly employ visual attention to find critical regions, but textual attention has been rarely incorporated into VQA systems.",
+      "While all the aforementioned approaches have exploited those kind of mechanisms, in this paper we study the impact of such choices specifically for the task of VQA, and create a simple yet effective model. Burns et al. BIBREF36 conducted experiments comparing different word embeddings, language models, and embedding augmentation steps on five multimodal tasks: image-sentence retrieval, image captioning, visual question answering, phrase grounding, and text-to-clip retrieval. While their work focuses on textual experiments, our experiments cover both visual and textual elements, as well as the combination of these representations in form of fusion and attention mechanisms. To the best of our knowledge, this is the first paper that provides a comprehensive analysis on the impact of each major component within a VQA architecture."
+    ],
+    [
+      "In this section we first introduce the baseline approach, with default image and text encoders, alongside a pre-defined fusion strategy. That base approach is inspired by the pioneer of Antol et al. on VQA BIBREF3. To understand the importance of each component, we update the base architecture according to each component we are investigating.",
+      "In our baseline model we replace the VGG network from BIBREF19 by a Faster RCNN pre-trained in the Visual Genome dataset BIBREF37. The default text encoding is given by the last hidden-state of a Bidirectional LSTM network, instead of the concatenation of the last hidden-state and memory cell used in the original work. Fig. FIGREF1 illustrates the proposed baseline architecture, which is subdivided into three major segments: independent feature extraction from (1) images and (2) questions, as well as (3) the fusion mechanism responsible to learn cross-modal features.",
+      "The default text encoder (denoted by the pink rectangle in Fig. FIGREF1) employed in this work comprises a randomly initialized word-embedding module that takes a tokenized question and returns a continuum vector for each token. Those vectors are used to feed an LSTM network. The last hidden-state is used as the question encoding, which is projected with a linear layer into a $d$-dimensional space so it can be fused along to the visual features. As the default option for the LSTM network, we use a single layer with 2048 hidden units. Given that this text encoding approach is fully trainable, we hereby name it Learnable Word Embedding (LWE).",
+      "For the question encoding, we explore pre-trained and randomly initialized word-embeddings in various settings, including Word2Vec (W2V) BIBREF12 and GloVe BIBREF11. We also explore the use of hidden-states of Skip-Thoughts Vector BIBREF13 and BERT BIBREF14 as replacements for word-embeddings and sentence encoding approaches.",
+      "Regarding the visual feature extraction (depicted as the green rectangle in Fig. FIGREF1), we decided to use the pre-computed features proposed in BIBREF19. Such an architecture employs a ResNet-152 with a Faster-RCNN BIBREF15 fine-tuned on the Visual Genome dataset. We opted for this approach due to the fact that using pre-computed features is far more computationally efficient, allowing us to train several models with distinct configurations. Moreover, several recent approaches BIBREF20, BIBREF21, BIBREF38 employ that same strategy as well, making it easier to provide fair comparison to the state-of-the-art approaches. In this study we perform experiments with two additional networks widely used for the task at hand, namely VGG-16 BIBREF16 and ReSNet-101 BIBREF17.",
+      "Given the multimodal nature of the problem we are dealing with, it is quite challenging to train proper image and question encoders so as to capture relevant semantic information from both of them. Nevertheless, another essential aspect of the architecture is the component that merges them altogether, allowing for the model to generate answers based on both information sources BIBREF39. The process of multimodal fusion consists itself in a research area with many approaches being recently proposed BIBREF20, BIBREF40, BIBREF22, BIBREF41. The fusion module receives the extracted image and query features, and provides multimodal features that theoretically present information that allows the system to answer to the visual question. There are many fusion strategies that can either assume quite simple forms, such as vector multiplication or concatenation, or be really complex, involving multilayered neural networks, tensor decomposition, and bi-linear pooling, just to name a few.",
+      "Following BIBREF3, we adopt the element-wise vector multiplication (also referred as Hadamard product) as the default fusion strategy. This approach requires the feature representations to be fused to have the same dimensionality. Therefore, we project them using a fully-connected layer to reduce their dimension from 2048 to 1024. After being fused together, the multimodal features are finally passed through a fully-connected layer that provides scores (logits) further converted into probabilities via a softmax function ($S$). We want to maximize the probability $P(Y=y|X=x,Q=q)$ of the correct answer $y$ given the image $X$ and the provided question $Q$. Our models are trained to choose within a set comprised by the 3000 most frequent answers extracted from both training and validation sets of the VQA v2.0 dataset BIBREF42."
+    ],
+    [
+      "For conducting this study we decided to use the VQA v2.0 dataset BIBREF42. It is one of the largest and most frequently used datasets for training and evaluation of models in this task, being the official dataset used in yearly challenges hosted by mainstream computer vision venues . This dataset enhances the original one BIBREF3 by alleviating bias problems within the data and increasing the original number of instances.",
+      "VQA v2.0 contains over $200,000$ images from MSCOCO BIBREF43, over 1 million questions and $\\approx 11$ million answers. In addition, it has at least two questions per image, which prevents the model from answering the question without considering the input image. We follow VQA v2.0 standards and adopt the official provided splits allowing for fair comparison with other approaches. The splits we use are Validation, Test-Dev, Test-Standard. In this work, results of the ablation experiments are reported on the Validation set, which is the default option used for this kind of experiment. In some experiments we also report the training set accuracy to verify evidence of overfitting due to excessive model complexity. Training data has a total of $443,757$ questions labeled with 4 million answers, while the Test-Dev has a total of $214,354$ questions. Note that the validation size is about 4-fold larger than ImageNet's, which contains about $50,000$ samples. Therefore, one must keep in mind that even small performance gaps might indicate quite significant results improvement. For instance, 1% accuracy gains depict $\\approx 2,000$ additional instances being correctly classified. We submit the predictions of our best models to the online evaluation servers BIBREF44 so as to obtain results for the Test-Standard split, allowing for a fair comparison to state-of-the-art approaches."
+    ],
+    [
+      "Free and open-ended questions result in a diverse set of possible answers BIBREF3. For some questions, a simple yes or no answer may be sufficient. Other questions, however, may require more complex answers. In addition, it is worth noticing that multiple answers may be considered correct, such as gray and light gray. Therefore, VQA v2.0 provides ten ground-truth answers for each question. These answers were collected from ten different randomly-chosen humans.",
+      "The evaluation metric used to measure model performance in the open-ended Visual Question Answering task is a particular kind of accuracy. For each question in the input dataset, the model's most likely response is compared to the ten possible answers provided by humans in the dataset associated with that question BIBREF3, and evaluated according to Equation DISPLAY_FORM7. In this approach, the prediction is considered totally correct only if at least 3 out of 10 people provided that same answer."
+    ],
+    [
+      "As in BIBREF20 we train our models in a classification-based manner, in which we minimize the cross-entropy loss calculated with an image-question-answer triplet sampled from the training set. We optimize the parameters of all VQA models using Adamax BIBREF45 optimizer with a base learning rate of $7 \\times 10^{-4}$, with exception of BERT BIBREF14 in which we apply a 10-fold reduction as suggested in the original paper. We used a learning rate warm-up schedule in which we halve the base learning rate and linearly increase it until the fourth epoch where it reaches twice its base value. It remains the same until the tenth epoch, where we start applying a 25% decay every two epochs. Gradients are calculated using batch sizes of 64 instances, and we train all models for 20 epochs."
+    ],
+    [
+      "In this section we show the experimental analysis for each component in the baseline VQA model. We also provide a summary of our findings regarding the impact of each part. Finally, we train a model with all the components that provide top results and compare it against state-of-the-art approaches."
+    ],
+    [
+      "In our first experiment, we analyze the impact of different embeddings for the textual representation of the questions. To this end, we evaluate: (i) the impact of word-embeddings (pre-trained, or trained from scratch); and (ii) the role of the temporal encoding function, i.e., distinct RNN types, as well as pre-trained sentence encoders (e.g., Skip-Thoughts, BERT).",
+      "The word-embedding strategies we evaluate are Learnable Word Embedding (randomly initialized and trained from scratch), Word2Vec BIBREF12, and GloVe BIBREF11. We also use word-level representations from widely used sentence embeddings strategies, namely Skip-Thoughts BIBREF13 and BERT BIBREF14. To do so, we use the hidden-states from the Skip-thoughts GRU network, while for BERT we use the activations of the last layer as word-level information. Those vectors feed an RNN that encodes the temporal sequence into a single global vector. Different types of RNNs are also investigated for encoding textual representation, including LSTM BIBREF46, Bidirectional LSTM BIBREF47, GRU BIBREF48, and Bidirectional GRU. For bidirectional architectures we concatenate both forward and backward hidden-states so as to aggregate information from both directions. Those approaches are also compared to a linear strategy, where we use a fully-connected layer followed by a global average pooling on the temporal dimension. The linear strategy discards any order information so we can demonstrate the role of the recurrent network as a temporal encoder to improve model performance.",
+      "Figure FIGREF5 shows the performance variation of different types of word-embeddings, recurrent networks, initialization strategies, and the effect of fine-tuning the textual encoder. Clearly, the linear layer is outperformed by any type of recurrent layer. When using Skip-Thoughts the difference reaches $2.22\\%$, which accounts for almost $5,000$ instances that the linear model mistakenly labeled. The only case in which the linear approach performed well is when trained with BERT. That is expected since Transformer-based architectures employ several attention layers that present the advantage of achieving the total receptive field size in all layers. While doing so, BERT also encodes temporal information with special positional vectors that allow for learning temporal relations. Hence, it is easier for the model to encode order information within word-level vectors without using recurrent layers.",
+      "For the Skip-Thoughts vector model, considering that its original architecture is based on GRUs, we evaluate both the randomly initialized and the pre-trained GRU of the original model, described as [GRU] and [GRU (skip)], respectively. We noticed that both options present virtually the same performance. In fact, GRU trained from scratch performed $0.13\\%$ better than its pre-trained version.",
+      "Analyzing the results obtained with pre-trained word embeddings, it is clear that GloVe obtained consistently better results than the Word2Vec counterpart. We believe that GloVe vectors perform better given that they capture not only local context statistics as in Word2Vec, but they also incorporate global statistics such as co-occurrence of words.",
+      "One can also observe that the use of different RNNs models inflicts minor effects on the results. It might be more advisable to use GRU networks since they halve the number of trainable parameters when compared to the LSTMs, albeit being faster and consistently presenting top results. Note also that the best results for Skip-Thoughts, Word2Vec, and GloVe were all quite similar, without any major variation regarding accuracy.",
+      "The best overall result is achieved when using BERT to extract the textual features. BERT versions using either the linear layer or the RNNs outperformed all other pre-trained embeddings and sentence encoders. In addition, the overall training accuracy for BERT models is not so high compared to all other approaches. That might be an indication that BERT models are less prone to overfit training data, and therefore present better generalization ability.",
+      "Results make it clear that when using BERT, one must fine-tune it for achieving top performance. Figure FIGREF5 shows that it is possible to achieve a $3\\%$ to $4\\%$ accuracy improvement when updating BERT weights with $1/10$ of the base learning rate. Moreover, Figure FIGREF6 shows that the use of a pre-training strategy is helpful, once Skip-thoughts and BERT outperform trainable word-embeddings in most of the evaluated settings. Is also make clear that using a single-layered RNNs provide best results, and are far more efficient in terms of parameters."
+    ],
+    [
+      "Experiments in this section analyze the visual feature extraction layers. The baseline uses the Faster-RCNN BIBREF15 network, and we will also experiment with other pre-trained neural networks to encode image information so we can observe their impact on predictive performance. Additionally to Faster-RCNN, we experiment with two widely used networks for VQA, namely ResNet-101 BIBREF17 and VGG-16 BIBREF16.",
+      "Table TABREF11 illustrates the result of this experiment. Intuitively, visual features provide a larger impact on model's performance. The accuracy difference between the best and the worst performing approaches is $\\approx 5\\%$. That difference accounts for roughly $10,000$ validation set instances. VGG-16 visual features presented the worst accuracy, but that was expected since it is the oldest network used in this study. In addition, it is only sixteen layers deep, and it has been shown that the depth of the network is quite important to hierarchically encode complex structures. Moreover, VGG-16 architecture encodes all the information in a 4096 dimensional vector that is extracted after the second fully-connected layer at the end. That vector encodes little to none spatial information, which makes it almost impossible for the network to answer questions on the spatial positioning of objects.",
+      "ResNet-101 obtained intermediate results. It is a much deeper network than VGG-16 and it achieves much better results on ImageNet, which shows the difference of the the learning capacity of both networks. ResNet-101 provides information encoded in 2048 dimensional vectors, extracted from the global average pooling layer, which also summarizes spatial information into a fixed-sized representation.",
+      "The best result as a visual feature extractor was achieved by the Faster-RCNN fine-tuned on the Visual Genome dataset. Such a network employs a ResNet-152 as backbone for training an RPN-based object detector. In addition, given that it was fine-tuned on the Visual Genome dataset, it allows for the training of robust models suited for general feature extraction. Hence, differently from the previous ResNet and VGG approaches, the Faster-RCNN approach is trained to detect objects, and therefore one can use it to extract features from the most relevant image regions. Each region is encoded as a 2048 dimensional vector. They contain rich information regarding regions and objects, since object detectors often operate over high-dimensional images, instead of resized ones (e.g., $256 \\times 256$) as in typical classification networks. Hence, even after applying global pooling over regions, the network still has access to spatial information because of the pre-extracted regions of interest from each image."
+    ],
+    [
+      "In order to analyze the impact that the different fusion methods have on the network performance, three simple fusion mechanisms were analyzed: element-wise multiplication, concatenation, and summation of the textual and visual features.",
+      "The choice of the fusion component is essential in VQA architectures, since its output generates multi-modal features used for answering the given visual question. The resulting multi-modal vector is projected into a 3000-dimensional label space, which provides a probability distribution over each possible answer to the question at hand BIBREF39.",
+      "Table presents the experimental results with the fusion strategies. The best result is obtained using the element-wise multiplication. Such an approach functions as a filtering strategy that is able to scale down the importance of irrelevant dimensions from the visual-question feature vectors. In other words, vector dimensions with high cross-modal affinity will have their magnitudes increased, differently from the uncorrelated ones that will have their values reduced. Summation does provide the worst results overall, closely followed by the concatenation operator. Moreover, among all the fusion strategies used in this study, multiplication seems to ease the training process as it presents a much higher training set accuracy ($\\approx 11\\% $ improvement) as well."
+    ],
+    [
+      "Finally, we analyze the impact of different attention mechanisms, such as Top-Down Attention BIBREF19 and Co-Attention BIBREF18. These mechanisms are used to provide distinct image representations according to the asked questions. Attention allows the model to focus on the most relevant visual information required to generate proper answers to the given questions. Hence, it is possible to generate several distinct representations of the same image, which also has a data augmentation effect."
+    ],
+    [
+      "Top-down attention, as the name suggests, uses global features from questions to weight local visual information. The global textual features $\\mathbf {q} \\in \\mathbb {R}^{2048}$ are selected from the last internal state of the RNN, and the image features $V \\in \\mathbb {R}^{k \\times 2048}$ are extracted from the Faster-RCNN, where $k$ represents the number of regions extracted from the image. In the present work we used $k=36$. The question features are linearly projected so as to reduce its dimension to 512, which is the size used in the original paper BIBREF19. Image features are concatenated with the textual features, generating a matrix $C$ of dimensions $k \\times 2560$. Features resulting from that concatenation are first non-linearly projected with a trainable weight matrix $W_1^{2560 \\times 512}$ generating a novel multimodal representation for each image region:",
+      "Therefore, such a layer learns image-question relations, generating $k \\times 512 $ features that are transformed by an activation function $\\phi $. Often, $\\phi $ is ReLU BIBREF49, Tanh BIBREF50, or Gated Tanh BIBREF51. The latter employs both the logistic Sigmoid and Tanh, in a gating scheme $\\sigma (x) \\times \\textsc {tanh}(x)$. A second fully-connected layer is employed to summarize the 512-dimensional vectors into $h$ values per region ($k \\times h$). It is usual to use a small value for $h$ such as $\\lbrace 1, 2\\rbrace $. The role of $h$ is to allow the model to produce distinct attention maps, which is useful for understanding complex sentences that require distinct viewpoints. Values produced by this layer are normalized with a softmax function applied on the columns of the matrix, as follows.",
+      "It generates an attention mask $A^{k \\times h}$ used to weight image regions, producing the image vector $\\hat{\\mathbf {v}}$, as shown in Equation DISPLAY_FORM17.",
+      "Note that when $h>1$, the dimensionality of the visual features increases $h$-fold. Hence, $\\hat{\\mathbf {v}}^{h \\times 2048}$, which we reshape to be a $(2048\\times h)\\times 1$ vector, constitutes the final question-aware image representation."
+    ],
+    [
+      "Unlike the Top-Down attention mechanism, Co-Attention is based on the computation of local similarities between all questions words and image regions. It expects two inputs: an image feature matrix $V^{k \\times 2048}$, such that each image feature vector encodes an image region out of $k$; and a set of word-level features $Q^{n \\times 2048}$. Both $V$ and $Q$ are normalized to have unit $L_2$ norm, so their multiplication $VQ^T$ results in the cosine similarity matrix used as guidance for generating the filtered image features. A context feature matrix $C^{k \\times 2048}$ is given by:",
+      "Finally, $C$ is normalized with a $\\textsc {softmax}$ function, and the $k$ regions are summed so as to generate a 1024-sized vector $\\hat{\\mathbf {v}}$ to represent relevant visual features $V$ based on question $Q$:",
+      "Table depicts the results obtained by adding the attention mechanisms to the baseline model. For these experiments we used only element-wise multiplication as fusion strategy, given that it presented the best performance in our previous experiments. We observe that attention is a crucial mechanism for VQA, leading to an $\\approx 6\\%$ accuracy improvement.",
+      "The best performing attention approach was Top-Down attention with ReLU activation, followed closely by Co-Attention. We noticed that when using Gated Tanh within Top-Down attention, results degraded 2%. In addition, experiments show that $L_2$ normalization is quite important in Co-Attention, providing an improvement of almost $6\\%$."
+    ],
+    [
+      "The experiments presented in Section SECREF9 have shown that the best text encoder approach is fine-tuning a pre-trained BERT model with a GRU network trained from scratch. In Section SECREF10 we performed experiments for analyzing the impact of pre-trained networks to extract visual features, among them Faster-RCNN, ResNet-101, and VGG-16. The best result was using a Faster-RCNN, reaching a $3\\%$ improvement in the overall accuracy.",
+      "We analyzed different ways to perform multimodal feature fusion in Section SECREF12. In this sense, the fusion mechanism that obtained the best result was the element-wise product. It provides $\\approx 3\\%$ higher overall accuracy when compared to the other fusion approaches.",
+      "Finally, in Section SECREF13 we have studied two main attention mechanisms and their variations. They aim to provide question-aware image representation by attending to the most important spatial features. The top performing mechanism is the Top-Down attention with the ReLU activation function, which provided an $\\approx 6\\%$ overall accuracy improvement when compared to the base architecture."
+    ],
+    [
+      "After evaluating individually each component in a typical VQA architecture, our goal in this section is to compare the approach that combines the best performing components into a single model with the current state-of-the-art in VQA. Our comparison involves the following VQA models: Deeper-lstm-q BIBREF3, MCB BIBREF22, ReasonNet BIBREF52, Tips&Tricks BIBREF53, and the recent block BIBREF20.",
+      "Tables TABREF21 and show that our best architecture outperforms all competitors but block, in both Test-Standard (Table TABREF21) and Test-Dev sets (Table ). Despite block presenting a marginal advantage in accuracy, we have shown in this paper that by carefully analyzing each individual component we are capable of generating a method, without any bells and whistles, that is on par with much more complex methods. For instance, block and MCB require 18M and 32M parameters respectively for the fusion scheme alone, while our fusion approach is parameter-free. Moreover, our model performs far better than BIBREF22, BIBREF52, and BIBREF53, which are also arguably much more complex methods."
+    ],
+    [
+      "In this study we observed the actual impact of several components within VQA models. We have shown that transformer-based encoders together with GRU models provide the best performance for question representation. Notably, we demonstrated that using pre-trained text representations provide consistent performance improvements across several hyper-parameter configurations. We have also shown that using an object detector fine-tuned with external data provides large improvements in accuracy. Our experiments have demonstrated that even simple fusion strategies can achieve performance on par with the state-of-the-art. Moreover, we have shown that attention mechanisms are paramount for learning top performing networks, once they allow producing question-aware image representations that are capable of encoding spatial relations. It became clear that Top-Down is the preferred attention method, given its results with ReLU activation. It is is now clear that some configurations used in some architectures (e.g., additional RNN layers) are actually irrelevant and can be removed altogether without harming accuracy. For future work, we expect to expand this study in two main ways: (i) cover additional datasets, such as Visual Genome BIBREF37; and (ii) study in an exhaustive fashion how distinct components interact with each other, instead of observing their impact alone on the classification performance."
+    ],
+    [
+      "This study was financed in part by the Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de Nivel Superior \u2013 Brasil (CAPES) \u2013 Finance Code 001. We also would like to thank FAPERGS for funding this research. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the graphics cards used for this research."
+    ]
+  ]
+}
+```
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+++ b/qasper-2132/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Controlling Utterance Length in NMT-based Word Segmentation with Attention
+
+Question: Does the paper report any alignment-only baseline?
\ No newline at end of file
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+++ b/qasper-2135/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Global Greedy Dependency Parsing
+
+Question: What are performance compared to former models?
\ No newline at end of file
diff --git a/qasper-2151/instruction.md b/qasper-2151/instruction.md
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+Name of Paper: Spatial Concept Acquisition for a Mobile Robot that Integrates Self-Localization and Unsupervised Word Discovery from Spoken Sentences
+
+Question: Does their model start with any prior knowledge of words?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Lexical acquisition",
+    "Simultaneous learning of places and vocabulary",
+    "Spatial Concept Acquisition",
+    "Generative model",
+    "Overview of the proposed method SpCoA",
+    "Learning of spatial concept",
+    "Self-localization of after learning spatial concepts",
+    "Experiment I",
+    "Learning of spatial concepts",
+    "Phoneme recognition accuracy of uttered sentences",
+    "Estimation accuracy of spatial concepts",
+    "Accuracy of acquired phoneme sequences representing the names of places",
+    "Self-localization that utilizes acquired spatial concepts",
+    "Experiment II",
+    "Learning of spatial concepts in the real environment",
+    "Modification of localization by the acquired spatial concepts",
+    "Conclusion and Future Work"
+  ],
+  "paragraphs": [
+    [
+      "Autonomous robots, such as service robots, operating in the human living environment with humans have to be able to perform various tasks and language communication. To this end, robots are required to acquire novel concepts and vocabulary on the basis of the information obtained from their sensors, e.g., laser sensors, microphones, and cameras, and recognize a variety of objects, places, and situations in an ambient environment. Above all, we consider it important for the robot to learn the names that humans associate with places in the environment and the spatial areas corresponding to these names; i.e., the robot has to be able to understand words related to places. Therefore, it is important to deal with considerable uncertainty, such as the robot's movement errors, sensor noise, and speech recognition errors.",
+      "Several studies on language acquisition by robots have assumed that robots have no prior lexical knowledge. These studies differ from speech recognition studies based on a large vocabulary and natural language processing studies based on lexical, syntactic, and semantic knowledge BIBREF0 , BIBREF1 . Studies on language acquisition by robots also constitute a constructive approach to the human developmental process and the emergence of symbols.",
+      "The objectives of this study were to build a robot that learns words related to places and efficiently utilizes this learned vocabulary in self-localization. Lexical acquisition related to places is expected to enable a robot to improve its spatial cognition. A schematic representation depicting the target task of this study is shown in Fig. FIGREF3 . This study assumes that a robot does not have any vocabularies in advance but can recognize syllables or phonemes. The robot then performs self-localization while moving around in the environment, as shown in Fig. FIGREF3 (a). An utterer speaks a sentence including the name of the place to the robot, as shown in Fig. FIGREF3 (b). For the purposes of this study, we need to consider the problems of self-localization and lexical acquisition simultaneously.",
+      "When a robot learns novel words from utterances, it is difficult to determine segmentation boundaries and the identity of different phoneme sequences from the speech recognition results, which can lead to errors. First, let us consider the case of the lexical acquisition of an isolated word. For example, if a robot obtains the speech recognition results \u201caporu\u201d, \u201cepou\u201d, and \u201caqpuru\u201d (incorrect phoneme recognition of apple), it is difficult for the robot to determine whether they denote the same referent without prior knowledge. Second, let us consider a case of the lexical acquisition of the utterance of a sentence. For example, a robot obtains a speech recognition result, such as \u201cthisizanaporu.\u201d The robot has to necessarily segment a sentence into individual words, e.g., \u201cthis\u201d, \u201ciz\u201d, \u201can\u201d, and \u201caporu\u201d. In addition, it is necessary for the robot to recognize words referring to the same referent, e.g., the fruit apple, from among the many segmented results that contain errors. In case of Fig. FIGREF3 (c), there is some possibility of learning names including phoneme errors, e.g., \u201cafroqtabutibe,\u201d because the robot does not have any lexical knowledge. On the other hand, when a robot performs online probabilistic self-localization, we assume that the robot uses sensor data and control data, e.g., values obtained using a range sensor and odometry. If the position of the robot on the global map is unclear, the difficulties associated with the identification of the self-position by only using local sensor information become problematic. In the case of global localization using local information, e.g., a range sensor, the problem that the hypothesis of self-position is present in multiple remote locations, frequently occurs, as shown in Fig. FIGREF3 (d).",
+      "In order to solve the abovementioned problems, in this study, we adopted the following approach. An utterance is recognized as not a single phoneme sequence but a set of candidates of multiple phonemes. We attempt to suppress the variability in the speech recognition results by performing word discovery taking into account the multiple candidates of speech recognition. In addition, the names of places are learned by associating with words and positions. The lexical acquisition is complemented by using certain particular spatial information; i.e., this information is obtained by hearing utterances including the same word in the same place many times. Furthermore, in this study, we attempt to address the problem of the uncertainty of self-localization by improving the self-position errors by using a recognized utterance including the name of the current place and the acquired spatial concepts, as shown in Fig. FIGREF3 (e).",
+      "In this paper, we propose nonparametric Bayesian spatial concept acquisition method (SpCoA) on basis of unsupervised word segmentation and a nonparametric Bayesian generative model that integrates self-localization and a clustering in both words and places. The main contributions of this paper are as follows:",
+      "The remainder of this paper is organized as follows: In Section SECREF2 , previous studies on language acquisition and lexical acquisition relevant to our study are described. In Section SECREF3 , the proposed method SpCoA is presented. In Sections SECREF4 and SECREF5 , we discuss the effectiveness of SpCoA in the simulation and in the real environment. Section SECREF6 concludes this paper."
+    ],
+    [
+      "Most studies on lexical acquisition typically focus on lexicons about objects BIBREF0 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 . Many of these studies have not be able to address the lexical acquisition of words other than those related to objects, e.g., words about places.",
+      "Roy et al. proposed a computational model that enables a robot to learn the names of objects from an object image and spontaneous infant-directed speech BIBREF0 . Their results showed that the model performed speech segmentation, word discovery, and visual categorization. Iwahashi et al. reported that a robot properly understands the situation and acquires the relationship of object behaviors and sentences BIBREF2 , BIBREF3 , BIBREF4 . Qu & Chai focused on the conjunction between speech and eye gaze and the use of domain knowledge in lexical acquisition BIBREF6 , BIBREF7 . They proposed an unsupervised learning method that automatically acquires novel words for an interactive system. Qu & Chai's method based on the IBM translation model BIBREF11 estimates the word-entity association probability.",
+      "Nakamura et al. proposed a method to learn object concepts and word meanings from multimodal information and verbal information BIBREF9 . The method proposed in BIBREF9 is a categorization method based on multimodal latent Dirichlet allocation (MLDA) that enables the acquisition of object concepts from multimodal information, such as visual, auditory, and haptic information BIBREF12 . Araki et al. addressed the development of a method combining unsupervised word segmentation from uttered sentences by a nested Pitman-Yor language model (NPYLM) BIBREF13 and the learning of object concepts by MLDA BIBREF10 . However, the disadvantage of using NPYLM was that phoneme sequences with errors did not result in appropriate word segmentation.",
+      "These studies did not address the lexical acquisition of the space and place that can also tolerate the uncertainty of phoneme recognition. However, for the introduction of robots into the human living environment, robots need to acquire a lexicon related to not only objects but also places. Our study focuses on the lexical acquisition related to places. Robots can adaptively learn the names of places in various human living environments by using SpCoA. We consider that the acquired names of places can be useful for various tasks, e.g., tasks with a movement of robots by the speech instruction."
+    ],
+    [
+      "The following studies have addressed lexical acquisition related to places. However, these studies could not utilize the learned language knowledge in other estimations such as the self-localization of a robot.",
+      "Taguchi et al. proposed a method for the unsupervised learning of phoneme sequences and relationships between words and objects from various user utterances without any prior linguistic knowledge other than an acoustic model of phonemes BIBREF1 , BIBREF14 . Further, they proposed a method for the simultaneous categorization of self-position coordinates and lexical learning BIBREF15 . These experimental results showed that it was possible to learn the name of a place from utterances in some cases and to output words corresponding to places in a location that was not used for learning.",
+      "Milford et al. proposed RatSLAM inspired by the biological knowledge of a pose cell of the hippocampus of rodents BIBREF16 . Milford et al. proposed a method that enables a robot to acquire spatial concepts by using RatSLAM BIBREF17 . Further, Lingodroids, mobile robots that learn a language through robot-to-robot communication, have been studied BIBREF18 , BIBREF19 , BIBREF20 . Here, a robot communicated the name of a place to other robots at various locations. Experimental results showed that two robots acquired the lexicon of places that they had in common. In BIBREF20 , the researchers showed that it was possible to learn temporal concepts in a manner analogous to the acquisition of spatial concepts. These studies reported that the robots created their own vocabulary. However, these studies did not consider the acquisition of a lexicon by human-to-robot speech interactions.",
+      "Welke et al. proposed a method that acquires spatial representation by the integration of the representation of the continuous state space on the sensorimotor level and the discrete symbolic entities used in high-level reasoning BIBREF21 . This method estimates the probable spatial domain and word from the given objects by using the spatial lexical knowledge extracted from Google Corpus and the position information of the object. Their study is different from ours because their study did not consider lexicon learning from human speech.",
+      "In the case of global localization, the hypothesis of self-position often remains in multiple remote places. In this case, there is some possibility of performing an incorrect estimation and increasing the estimation error. This problem exists during teaching tasks and self-localization after the lexical acquisition. The abovementioned studies could not deal with this problem. In this paper, we have proposed a method that enables a robot to perform more accurate self-localization by reducing the estimation error of the teaching time by using a smoothing method in the teaching task and by utilizing words acquired through the lexical acquisition. The strengths of this study are that learning of spatial concept and self-localization represented as one generative model and robots are able to utilize acquired lexicon to self-localization autonomously."
+    ],
+    [
+      "We propose nonparametric Bayesian spatial concept acquisition method (SpCoA) that integrates a nonparametric morphological analyzer for the lattice BIBREF22 , i.e., latticelm, a spatial clustering method, and Monte Carlo localization (MCL) BIBREF23 ."
+    ],
+    [
+      "In our study, we define a position as a specific coordinate or a local point in the environment, and the position distribution as the spatial area of the environment. Further, we define a spatial concept as the names of places and the position distributions corresponding to these names.",
+      "The model that was developed for spatial concept acquisition is a probabilistic generative model that integrates a self-localization with the simultaneous clustering of places and words. Fig. FIGREF13 shows the graphical model for spatial concept acquisition. Table TABREF14 shows each variable of the graphical model. The number of words in a sentence at time INLINEFORM0 is denoted as INLINEFORM1 . The generative model of the proposed method is defined as equation ( EQREF11 -). DISPLAYFORM0 ",
+      "Then, the probability distribution for equation () can be defined as follows: DISPLAYFORM0 ",
+      "The prior distribution configured by using the stick breaking process (SBP) BIBREF24 is denoted as INLINEFORM0 , the multinomial distribution as INLINEFORM1 , the Dirichlet distribution as INLINEFORM2 , the inverse\u2013Wishart distribution as INLINEFORM3 , and the multivariate Gaussian (normal) distribution as INLINEFORM4 . The motion model and the sensor model of self-localization are denoted as INLINEFORM5 and INLINEFORM6 in equations () and (), respectively.",
+      "This model can learn an appropriate number of spatial concepts, depending on the data, by using a nonparametric Bayesian approach. We use the SBP, which is one of the methods based on the Dirichlet process. In particular, this model can consider a theoretically infinite number of spatial concepts INLINEFORM0 and position distributions INLINEFORM1 . SBP computations are difficult because they generate an infinite number of parameters. In this study, we approximate a number of parameters by setting sufficiently large values, i.e., a weak-limit approximation BIBREF25 .",
+      "It is possible to correlate a name with multiple places, e.g., \u201cstaircase\u201d is in two different places, and a place with multiple names, e.g., \u201ctoilet\u201d and \u201crestroom\u201d refer to the same place. Spatial concepts are represented by a word distribution of the names of the place INLINEFORM0 and several position distributions ( INLINEFORM1 , INLINEFORM2 ) indicated by a multinomial distribution INLINEFORM3 . In other words, this model is capable of relating the mixture of Gaussian distributions to a multinomial distribution of the names of places. It should be noted that the arrows connecting INLINEFORM4 to the surrounding nodes of the proposed graphical model differ from those of ordinal Gaussian mixture model (GMM). We assume that words obtained by the robot do not change its position, but that the position of the robot affects the distribution of words. Therefore, the proposed generative process assumes that the index of position distribution INLINEFORM5 , i.e., the category of the place, is generated from the position of the robot INLINEFORM6 . This change can be naturally introduced without any troubles by introducing equation ( EQREF12 )."
+    ],
+    [
+      "We assume that a robot performs self-localization by using control data and sensor data at all times. The procedure for the learning of spatial concepts is as follows:",
+      "An utterer teaches a robot the names of places, as shown in Fig. FIGREF3 (b). Every time the robot arrives at a place that was a designated learning target, the utterer says a sentence, including the name of the current place.",
+      "The robot performs speech recognition from the uttered speech signal data. Thus, the speech recognition system includes a word dictionary of only Japanese syllables. The speech recognition results are obtained in a lattice format.",
+      "Word segmentation is performed by using the lattices of the speech recognition results.",
+      "The robot learns spatial concepts from words obtained by word segmentation and robot positions obtained by self-localization for all teaching times. The details of the learning are given in SECREF23 .",
+      "The procedure for self-localization utilizing spatial concepts is as follows:",
+      "The words of the learned spatial concepts are registered to the word dictionary of the speech recognition system.",
+      "When a robot obtains a speech signal, speech recognition is performed. Then, a word sequence as the 1-best speech recognition result is obtained.",
+      "The robot modifies the self-localization from words obtained by speech recognition and the position likelihood obtained by spatial concepts. The details of self-localization are provided in SECREF35 .",
+      "The proposed method can learn words related to places from the utterances of sentences. We use an unsupervised word segmentation method latticelm that can directly segment words from the lattices of the speech recognition results of the uttered sentences BIBREF22 . The lattice can represent to a compact the set of more promising hypotheses of a speech recognition result, such as N-best, in a directed graph format. Unsupervised word segmentation using the lattices of syllable recognition is expected to be able to reduce the variability and errors in phonemes as compared to NPYLM BIBREF13 , i.e., word segmentation using the 1-best speech recognition results.",
+      "The self-localization method adopts MCL BIBREF23 , a method that is generally used as the localization of mobile robots for simultaneous localization and mapping (SLAM) BIBREF26 . We assume that a robot generates an environment map by using MCL-based SLAM such as FastSLAM BIBREF27 , BIBREF28 in advance, and then, performs localization by using the generated map. Then, the environment map of both an occupancy grid map and a landmark map is acceptable."
+    ],
+    [
+      "Spatial concepts are learned from multiple teaching data, control data, and sensor data. The teaching data are a set of uttered sentences for all teaching times. Segmented words of an uttered sentence are converted into a bag-of-words (BoW) representation as a vector of the occurrence counts of words INLINEFORM0 . The set of the teaching times is denoted as INLINEFORM1 , and the number of teaching data items is denoted as INLINEFORM2 . The model parameters are denoted as INLINEFORM3 . The initial values of the model parameters can be set arbitrarily in accordance with a condition. Further, the sampling values of the model parameters from the following joint posterior distribution are obtained by performing Gibbs sampling. DISPLAYFORM0 ",
+      "where the hyperparameters of the model are denoted as INLINEFORM0 . The algorithm of the learning of spatial concepts is shown in Algorithm SECREF23 .",
+      "The conditional posterior distribution of each element used for performing Gibbs sampling can be expressed as follows: An index INLINEFORM0 of the position distribution is sampled for each data INLINEFORM1 from a posterior distribution as follows: DISPLAYFORM0 ",
+      "An index INLINEFORM0 of the spatial concepts is sampled for each data item INLINEFORM1 from a posterior distribution as follows: DISPLAYFORM0 ",
+      "where INLINEFORM0 denotes a vector of the occurrence counts of words in the sentence at time INLINEFORM1 . A posterior distribution representing word probabilities of the name of place INLINEFORM2 is calculated as follows: DISPLAYFORM0 ",
+      "where variables with the subscript INLINEFORM0 denote the set of all teaching times. A word probability of the name of place INLINEFORM1 is sampled for each INLINEFORM2 as follows: DISPLAYFORM0 ",
+      "where INLINEFORM0 represents the posterior parameter and INLINEFORM1 denotes the BoW representation of all sentences of INLINEFORM2 in INLINEFORM3 . A posterior distribution representing the position distribution INLINEFORM4 is calculated as follows: DISPLAYFORM0 ",
+      "A position distribution INLINEFORM0 , INLINEFORM1 is sampled for each INLINEFORM2 as follows: DISPLAYFORM0 ",
+      "where INLINEFORM0 denotes the Gaussian\u2013inverse\u2013Wishart distribution; INLINEFORM1 , and INLINEFORM2 represent the posterior parameters; and INLINEFORM3 indicates the set of the teaching positions of INLINEFORM4 in INLINEFORM5 . A topic probability distribution INLINEFORM6 of spatial concepts is sampled as follows: DISPLAYFORM0 ",
+      "A posterior distribution representing the mixed weights INLINEFORM0 of the position distributions is calculated as follows: DISPLAYFORM0 ",
+      "A mixed weight INLINEFORM0 of the position distributions is sampled for each INLINEFORM1 as follows: DISPLAYFORM0 ",
+      "where INLINEFORM0 denotes a vector counting all the indices of the Gaussian distribution of INLINEFORM1 in INLINEFORM2 .",
+      "Self-positions INLINEFORM0 are sampled by using a Monte Carlo fixed-lag smoother BIBREF29 in the learning phase. The smoother can estimate self-position INLINEFORM1 and not INLINEFORM2 , i.e., a sequential estimation from the given data INLINEFORM3 until time INLINEFORM4 , but it can estimate INLINEFORM5 , i.e., an estimation from the given data INLINEFORM6 until time INLINEFORM7 later than INLINEFORM8 INLINEFORM9 . In general, the smoothing method can provide a more accurate estimation than the MCL of online estimation. In contrast, if the self-position of a robot INLINEFORM10 is sampled like direct assignment sampling for each time INLINEFORM11 , the sampling of INLINEFORM12 is divided in the case with the teaching time INLINEFORM13 and another time INLINEFORM14 as follows: DISPLAYFORM0 ",
+      "[tb] Learning of spatial concepts [1] INLINEFORM0 , INLINEFORM1 Localization and speech recognition INLINEFORM2 to INLINEFORM3 INLINEFORM4 BIBREF29 the speech signal is observed INLINEFORM5 add INLINEFORM6 to INLINEFORM7 Registering the lattice add INLINEFORM8 to INLINEFORM9 Registering the teaching time Word segmentation using lattices INLINEFORM10 BIBREF22 Gibbs sampling Initialize parameters INLINEFORM11 , INLINEFORM12 , INLINEFORM13 INLINEFORM14 to INLINEFORM15 INLINEFORM16 ( EQREF25 ) INLINEFORM17 ( EQREF26 ) INLINEFORM18 ( EQREF28 ) INLINEFORM19 ( EQREF30 ) INLINEFORM20 ( EQREF31 ) INLINEFORM21 ( EQREF33 ) INLINEFORM22 to INLINEFORM23 INLINEFORM24 ( EQREF34 ) INLINEFORM25 "
+    ],
+    [
+      "A robot that acquires spatial concepts can leverage spatial concepts to self-localization. The estimated model parameters INLINEFORM0 and a speech recognition sentence INLINEFORM1 at time INLINEFORM2 are given to the condition part of the probability formula of MCL as follows: DISPLAYFORM0 ",
+      "When the robot hears the name of a place spoken by the utterer, in addition to the likelihood of the sensor model of MCL, the likelihood of INLINEFORM0 with respect to a speech recognition sentence is calculated as follows: DISPLAYFORM0 ",
+      "The algorithm of self-localization utilizing spatial concepts is shown in Algorithm SECREF35 . The set of particles is denoted as INLINEFORM0 , the temporary set that stores the pairs of the particle INLINEFORM1 and the weight INLINEFORM2 , i.e., INLINEFORM3 , is denoted as INLINEFORM4 . The number of particles is INLINEFORM5 . The function INLINEFORM6 is a function that moves each particle from its previous state INLINEFORM7 to its current state INLINEFORM8 by using control data. The function INLINEFORM9 calculates the likelihood of each particle INLINEFORM10 using sensor data INLINEFORM11 . These functions are normally used in MCL. For further details, please refer to BIBREF26 . In this case, a speech recognition sentence INLINEFORM12 is obtained by the speech recognition system using a word dictionary containing all the learned words. [tb] Self-localization utilizing spatial concepts [1] INLINEFORM13 INLINEFORM14 INLINEFORM15 INLINEFORM16 to INLINEFORM17 INLINEFORM18 () INLINEFORM19 () the speech signal is observed INLINEFORM20 add INLINEFORM21 to INLINEFORM22 INLINEFORM23 to INLINEFORM24 draw INLINEFORM25 with probability INLINEFORM26 add INLINEFORM27 to INLINEFORM28 INLINEFORM29 "
+    ],
+    [
+      "In this experiment, we validate the evidence of the proposed method (SpCoA) in an environment simulated on the simulator platform SIGVerse BIBREF30 , which enables the simulation of social interactions. The speech recognition is performed using the Japanese continuous speech recognition system Julius BIBREF31 , BIBREF32 . The set of 43 Japanese phonemes defined by Acoustical Society of Japan (ASJ)'s speech database committee is adopted by Julius BIBREF31 . The representation of these phonemes is also adopted in this study. The Julius system uses a word dictionary containing 115 Japanese syllables. The microphone attached on the robot is SHURE's PG27-USB. Further, an unsupervised morphological analyzer, a latticelm 0.4, is implemented BIBREF22 .",
+      "In the experiment, we compare the following three types of word segmentation methods. A set of syllable sequences is given to the graphical model of SpCoA by each method. This set is used for the learning of spatial concepts as recognized uttered sentences INLINEFORM0 .",
+      "The remainder of this section is organized as follows: In Section SECREF43 , the conditions and results of learning spatial concepts are described. The experiments performed using the learned spatial concepts are described in Section SECREF49 to SECREF64 . In Section SECREF49 , we evaluate the accuracy of the phoneme recognition and word segmentation for uttered sentences. In Section SECREF56 , we evaluate the clustering accuracy of the estimation results of index INLINEFORM0 of spatial concepts for each teaching utterance. In Section SECREF60 , we evaluate the accuracy of the acquisition of names of places. In Section SECREF64 , we show that spatial concepts can be utilized for effective self-localization."
+    ],
+    [
+      "We conduct this experiment of spatial concept acquisition in the environment prepared on SIGVerse. The experimental environment is shown in Fig. FIGREF45 . A mobile robot can move by performing forward, backward, right rotation, or left rotation movements on a two-dimensional plane. In this experiment, the robot can use an approximately correct map of the considered environment. The robot has a range sensor in front and performs self-localization on the basis of an occupancy grid map. The initial particles are defined by the true initial position of the robot. The number of particles is INLINEFORM0 .",
+      "The lag value of the Monte Carlo fixed-lag smoothing is fixed at 100. The other parameters of this experiment are as follows: INLINEFORM0 , INLINEFORM1 , INLINEFORM2 , INLINEFORM3 , INLINEFORM4 , INLINEFORM5 , INLINEFORM6 , INLINEFORM7 , and INLINEFORM8 . The number of iterations used for Gibbs sampling is 100. This experiment does not include the direct assignment sampling of INLINEFORM9 in equation ( EQREF34 ), i.e., lines 22\u201324 of Algorithm SECREF23 are omitted, because we consider that the self-position can be obtained with sufficiently good accuracy by using the Monte Carlo smoothing. Eight places are selected as the learning targets, and eight types of place names are considered. Each uttered place name is shown in Fig. FIGREF45 . These utterances include the same name in different places, i.e., \u201cteeburunoatari\u201d (which means near the table in English), and different names in the same place, i.e., \u201ckiqchiN\u201d and \u201cdaidokoro\u201d (which mean a kitchen in English). The other teaching names are \u201cgeNkaN\u201d (which means an entrance or a doorway in English); \u201cterebimae\u201d (which means the front of the TV in English); \u201cgomibako\u201d (which means a trash box in English); \u201choNdana\u201d (which means a bookshelf in English); and \u201csofaamae\u201d (which means the front of the sofa in English). The teaching utterances, including the 10 types of phrases, are spoken for a total of 90 times. The phrases in each uttered sentence are listed in Table TABREF46 .",
+      "The learning results of spatial concepts obtained by using the proposed method are presented here. Fig. FIGREF47 shows the position distributions learned in the experimental environment. Fig. FIGREF47 (top) shows the word distributions of the names of places for each spatial concept, and Fig. FIGREF47 (bottom) shows the multinomial distributions of the indices of the position distributions. Consequently, the proposed method can learn the names of places corresponding to each place of the learning target. In the spatial concept of index INLINEFORM0 , the highest probability of words was \u201csofamae\u201d, and the highest probability of the indices of the position distribution was INLINEFORM1 ; therefore, the name of a place \u201csofamae\u201d was learned to correspond to the position distribution of INLINEFORM2 . In the spatial concept of index INLINEFORM3 , \u201ckiqchi\u201d and \u201cdaidokoro\u201d were learned to correspond to the position distribution of INLINEFORM4 . Therefore, this result shows that multiple names can be learned for the same place. In the spatial concept of index INLINEFORM5 , \u201cte\u201d and \u201cdurunoatari\u201d (one word in a normal situation) were learned to correspond to the position distributions of INLINEFORM6 and INLINEFORM7 . Therefore, this result shows that the same name can be learned for multiple places."
+    ],
+    [
+      "We compared the performance of three types of word segmentation methods for all the considered uttered sentences. It was difficult to weigh the ambiguous syllable recognition and the unsupervised word segmentation separately. Therefore, this experiment considered the positions of a delimiter as a single letter. We calculated the matching rate of a phoneme string of a recognition result of each uttered sentence and the correct phoneme string of the teaching data that was suitably segmented into Japanese morphemes using MeCab, which is an off-the-shelf Japanese morphological analyzer that is widely used for natural language processing. The matching rate of the phoneme string was calculated by using the phoneme accuracy rate (PAR) as follows: DISPLAYFORM0 ",
+      "The numerator of equation ( EQREF52 ) is calculated by using the Levenshtein distance between the correct phoneme string and the recognition phoneme string. INLINEFORM0 denotes the number of substitutions; INLINEFORM1 , the number of deletions; and INLINEFORM2 , the number of insertions. INLINEFORM3 represents the number of phonemes of the correct phoneme string.",
+      "Table TABREF54 shows the results of PAR. Table TABREF55 presents examples of the word segmentation results of the three considered methods. We found that the unsupervised morphological analyzer capable of using lattices improved the accuracy of phoneme recognition and word segmentation. Consequently, this result suggests that this word segmentation method considers the multiple hypothesis of speech recognition as a whole and reduces uncertainty such as variability in recognition by using the syllable recognition results in the lattice format."
+    ],
+    [
+      "We compared the matching rate with the estimation results of index INLINEFORM0 of the spatial concepts of each teaching utterance and the classification results of the correct answer given by humans. The evaluation of this experiment used the adjusted Rand index (ARI) BIBREF33 . ARI is a measure of the degree of similarity between two clustering results.",
+      "Further, we compared the proposed method with a method of word clustering without location information for the investigation of the effect of lexical acquisition using location information. In particular, a method of word clustering without location information used the Dirichlet process mixture (DPM) of the unigram model of an SBP representation. The parameters corresponding to those of the proposed method were the same as the parameters of the proposed method and were estimated using Gibbs sampling.",
+      "Fig. FIGREF59 shows the results of the average of the ARI values of 10 trials of learning by Gibbs sampling. Here, we found that the proposed method showed the best score. These results and the results reported in Section SECREF49 suggest that learning by uttered sentences obtained by better phoneme recognition and better word segmentation produces a good result for the acquisition of spatial concepts. Furthermore, in a comparison of two clustering methods, we found that SpCoA was considerably better than DPM, a word clustering method without location information, irrespective of the word segmentation method used. The experimental results showed that it is possible to improve the estimation accuracy of spatial concepts and vocabulary by performing word clustering that considered location information."
+    ],
+    [
+      "We evaluated whether the names of places were properly learned for the considered teaching places. This experiment assumes a request for the best phoneme sequence INLINEFORM0 representing the self-position INLINEFORM1 for a robot. The robot moves close to each teaching place. The probability of a word INLINEFORM2 when the self-position INLINEFORM3 of the robot is given, INLINEFORM4 , can be obtained by using equation ( EQREF37 ). The word having the best probability was selected. We compared the PAR with the correct phoneme sequence and a selected name of the place. Because \u201ckiqchiN\u201d and \u201cdaidokoro\u201d were taught for the same place, the word whose PAR was the higher score was adopted.",
+      "Fig. FIGREF63 shows the results of PAR for the word considered the name of a place. SpCoA (latticelm), the proposed method using the results of unsupervised word segmentation on the basis of the speech recognition results in the lattice format, showed the best PAR score. In the 1-best and BoS methods, a part syllable sequence of the name of a place was more minutely segmented as shown in Table TABREF55 . Therefore, the robot could not learn the name of the teaching place as a coherent phoneme sequence. In contrast, the robot could learn the names of teaching places more accurately by using the proposed method."
+    ],
+    [
+      "In this experiment, we validate that the robot can make efficient use of the acquired spatial concepts. We compare the estimation accuracy of localization for the proposed method (SpCoA MCL) and the conventional MCL. When a robot comes to the learning target, the utterer speaks out the sentence containing the name of the place once again for the robot. The moving trajectory of the robot and the uttered positions are the same in all the trials. In particular, the uttered sentence is \u201ckokowa ** dayo\u201d. When learning a task, this phrase is not used. The number of particles is INLINEFORM0 , and the initial particles are uniformly distributed in the considered environment. The robot performs a control operation for each time step.",
+      "The estimation error in the localization is evaluated as follows: While running localization, we record the estimation error (equation ( EQREF66 )) on the INLINEFORM0 plane of the floor for each time step. DISPLAYFORM0 ",
+      "where INLINEFORM0 denote the true position coordinates of the robot as obtained from the simulator, and INLINEFORM1 , INLINEFORM2 represent the weighted mean values of localization coordinates. The normalized weight INLINEFORM3 is obtained from the sensor model in MCL as a likelihood. In the utterance time, this likelihood is multiplied by the value calculated using equation ( EQREF37 ). INLINEFORM4 , INLINEFORM5 denote the INLINEFORM6 -coordinate and the INLINEFORM7 -coordinate of index INLINEFORM8 of each particle at time INLINEFORM9 . After running the localization, we calculated the average of INLINEFORM10 .",
+      "Further, we compared the estimation accuracy rate (EAR) of the global localization. In each trial, we calculated the proportion of time step in which the estimation error was less than 50 cm.",
+      "Fig. FIGREF68 shows the results of the estimation error and the EAR for 10 trials of each method. All trials of SpCoA MCL (latticelm) and almost all trials of the method using 1-best NPYLM and BoS showed relatively small estimation errors. Results of the second trial of 1-best NPYLM and the fifth trial of BoS showed higher estimation errors. In these trials, many particles converged to other places instead of the place where the robot was, based on utterance information. Nevertheless, compared with those of the conventional MCL, the results obtained using spatial concepts showed an obvious improvement in the estimation accuracy. Consequently, spatial concepts acquired by using the proposed method proved to be very helpful in improving the localization accuracy."
+    ],
+    [
+      "In this experiment, the effectiveness of the proposed method was tested by using an autonomous mobile robot TurtleBot 2 in a real environment. Fig. FIGREF70 shows TurtleBot 2 used in the experiments.",
+      "Mapping and self-localization are performed by the robot operating system (ROS). The speech recognition system, the microphone, and the unsupervised morphological analyzer were the same as those described in Section SECREF4 ."
+    ],
+    [
+      "We conducted an experiment of the spatial concept acquisition in a real environment of an entire floor of a building. In this experiment, self-localization was performed using a map generated by SLAM. The initial particles are defined by the true initial position of the robot. The generated map in the real environment and the names of teaching places are shown in Fig. FIGREF73 . The number of teaching places was 19, and the number of teaching names was 16. The teaching utterances were performed for a total of 100 times.",
+      "Fig. FIGREF75 shows the position distributions learned on the map. Table TABREF76 shows the five best elements of the multinomial distributions of the name of place INLINEFORM0 and the multinomial distributions of the indices of the position distribution INLINEFORM1 for each index of spatial concept INLINEFORM2 .",
+      "Thus, we found that the proposed method can learn the names of places corresponding to the considered teaching places in the real environment. For example, in the spatial concept of index INLINEFORM0 , \u201ctorire\u201d was learned to correspond to a position distribution of INLINEFORM1 . Similarly, \u201ckidanokeN\u201d corresponded to INLINEFORM2 in INLINEFORM3 , and \u201ckaigihitsu\u201d was corresponded to INLINEFORM4 in INLINEFORM5 . In the spatial concept of index INLINEFORM6 , a part of the syllable sequences was minutely segmented as \u201csohatsuke\u201d, \u201cN\u201d, and \u201ctani\u201d, \u201cguchi\u201d. In this case, the robot was taught two types of names. These words were learned to correspond to the same position distribution of INLINEFORM7 . In INLINEFORM8 , \u201cgomibako\u201d showed a high probability, and it corresponded to three distributions of the position of INLINEFORM9 . The position distribution of INLINEFORM10 had the fourth highest probability in the spatial concept INLINEFORM11 . Therefore, \u201craqkukeN,\u201d which had the fifth highest probability in the spatial concept INLINEFORM12 (and was expected to relate to the spatial concept INLINEFORM13 ), can be estimated as the word drawn from spatial concept INLINEFORM14 . However, in practice, this situation did not cause any severe problems because the spatial concept of the index INLINEFORM15 had the highest probabilities for the word \u201crapukeN\u201d and the position distribution INLINEFORM16 than INLINEFORM17 . In the probabilistic model, the relative probability and the integrative information are important. When the robot listened to an utterance related to \u201craqkukeN,\u201d it could make use of the spatial concept of index INLINEFORM18 for self-localization with a high probability, and appropriately updated its estimated self-location. We expected that the spatial concept of index INLINEFORM19 was learned as two separate spatial concepts. However, \u201cwatarirooka\u201d and \u201ckaidaNmae\u201d were learned as the same spatial concept. Therefore, the multinomial distribution INLINEFORM20 showed a higher probability for the indices of the position distribution corresponding to the teaching places of both \u201cwatarirooka\u201d and \u201ckaidaNmae\u201d.",
+      "The proposed method adopts a nonparametric Bayesian method in which it is possible to form spatial concepts that allow many-to-many correspondences between names and places. In contrast, this can create ambiguity that classifies originally different spatial concepts into one spatial concept as a side effect. There is a possibility that the ambiguity of concepts such as INLINEFORM0 will have a negative effect on self-localization, even though the self-localization performance was (overall) clearly increased by employing the proposed method. The solution of this problem will be considered in future work.",
+      "In terms of the PAR of uttered sentences, the evaluation value from the evaluation method used in Section SECREF49 is 0.83; this value is comparable to the result in Section SECREF49 . However, in terms of the PAR of the name of the place, the evaluation value from the evaluation method used in Section SECREF60 is 0.35, which is lower than that in Section SECREF60 . We consider that the increase in uncertainty in the real environment and the increase in the number of teaching words reduced the performance. We expect that this problem could be improved using further experience related to places, e.g., if the number of utterances per place is increased, and additional sensory information is provided."
+    ],
+    [
+      "In this experiment, we verified the modification results of self-localization by using spatial concepts in global self-localization. This experiment used the learning results of spatial concepts presented in Section SECREF71 . The experimental procedures are shown below. The initial particles were uniformly distributed on the entire floor. The robot begins to move from a little distance away to the target place. When the robot reached the target place, the utterer spoke the sentence containing the name of the place for the robot. Upon obtaining the speech information, the robot modifies the self-localization on the basis of the acquired spatial concepts. The number of particles was the same as that mentioned in Section SECREF71 .",
+      "Fig. FIGREF80 shows the results of the self-localization before (the top part of the figure) and after (the bottom part of the figure) the utterance for three places. The particle states are denoted by red arrows. The moving trajectory of the robot is indicated by a green dotted arrow. Figs. FIGREF80 (a), (b), and (c) show the results for the names of places \u201ctoire\u201d, \u201csouhatsukeN\u201d, and \u201cgomibako\u201d. Further, three spatial concepts, i.e., those at INLINEFORM0 , were learned as \u201cgomibako\u201d. In this experiment, the utterer uttered to the robot when the robot came close to the place of INLINEFORM1 . In all the examples shown in the top part of the figure, the particles were dispersed in several places. In contrast, the number of particles near the true position of the robot showed an almost accurate increase in all the examples shown in the bottom part of the figure. Thus, we can conclude that the proposed method can modify self-localization by using spatial concepts."
+    ],
+    [
+      "In this paper, we discussed the spatial concept acquisition, lexical acquisition related to places, and self-localization using acquired spatial concepts. We proposed nonparametric Bayesian spatial concept acquisition method SpCoA that integrates latticelm BIBREF22 , a spatial clustering method, and MCL. We conducted experiments for evaluating the performance of SpCoA in a simulation and a real environment. SpCoA showed good results in all the experiments. In experiments of the learning of spatial concepts, the robot could form spatial concepts for the places of the learning targets from human continuous speech signals in both the room of the simulation environment and the entire floor of the real environment. Further, the unsupervised word segmentation method latticelm could reduce the variability and errors in the recognition of phonemes in all the utterances. SpCoA achieved more accurate lexical acquisition by performing word segmentation using the lattices of the speech recognition results. In the self-localization experiments, the robot could effectively utilize the acquired spatial concepts for recognizing self-position and reducing the estimation errors in self-localization. As a method that further improves the performance of the lexical acquisition, a mutual learning method was proposed by Nakamura et al. on the basis of the integration of the learning of object concepts with a language model BIBREF34 , BIBREF35 . Following a similar approach, Heymann et al. proposed a method that alternately and repeatedly updates phoneme recognition results and the language model by using unsupervised word segmentation BIBREF36 . As a result, they achieved robust lexical acquisition. In our study, we can expect to improve the accuracy of lexical acquisition for spatial concepts by estimating both the spatial concepts and the language model.",
+      "Furthermore, as a future work, we consider it necessary for robots to learn spatial concepts online and to recognize whether the uttered word indicates the current place or destination. Furthermore, developing a method that simultaneously acquires spatial concepts and builds a map is one of our future objectives. We believe that the spatial concepts will have a positive effect on the mapping. We also intend to examine a method that associates the image and the landscape with spatial concepts and a method that estimates both spatial concepts and object concepts.",
+      "[] Akira Taniguchi received his BE degree from Ritsumeikan University in 2013 and his ME degree from the Graduate School of Information Science and Engineering, Ritsumeikan University, in 2015. He is currently working toward his PhD degree at the Emergent System Lab, Ritsumeikan University, Japan. His research interests include language acquisition, concept acquisition, and symbol emergence in robotics.",
+      "[] Tadahiro Taniguchi received the ME and PhD degrees from Kyoto University in 2003 and 2006, respectively. From April 2005 to March 2006, he was a Japan Society for the Promotion of Science (JSPS) research fellow (DC2) in the Department of Mechanical Engineering and Science, Graduate School of Engineering, Kyoto University. From April 2006 to March 2007, he was a JSPS research fellow (PD) in the same department. From April 2007 to March 2008, he was a JSPS research fellow in the Department of Systems Science, Graduate School of Informatics, Kyoto University. From April 2008 to March 2010, he was an assistant professor at the Department of Human and Computer Intelligence, Ritsumeikan University. Since April 2010, he has been an associate professor in the same department. He is currently engaged in research on machine learning, emergent systems, and semiotics.",
+      "[] Tetsunari Inamura received the BE, MS and PhD degrees from the University of Tokyo, in 1995, 1997 and 2000, respectively. He was a Researcher of the CREST program, Japanese Science and Technology Cooperation, from 2000 to 2003, and then joined the Department of Mechano-Informatics, School of Information Science and Technology, University of Tokyo as a Lecturer, from 2003 to 2006. He is now an Associate Professor in the Principles of Informatics Research Division, National Institute of Informatics, and an Associate Professor in the Department of Informatics, School of Multidisciplinary Sciences, Graduate University for Advanced Studies (SOKENDAI). His research interests include imitation learning and symbol emergence on humanoid robots, development of interactive robots through virtual reality and so on."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2156/instruction.md b/qasper-2156/instruction.md
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@@ -0,0 +1,86 @@
+Name of Paper: Learning Multilingual Word Embeddings Using Image-Text Data
+
+Question: Could you learn such embedding simply from the image annotations and without using visual information?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Data",
+    "Methods",
+    "Leveraging Image Understanding",
+    "Co-Occurrence Only",
+    "Language Unaware Query Representation",
+    "Evaluation",
+    "Results and Conclusions",
+    "Discussion"
+  ],
+  "paragraphs": [
+    [
+      "Recent advances in learning distributed representations for words (i.e., word embeddings) have resulted in improvements across numerous natural language understanding tasks BIBREF0 , BIBREF1 . These methods use unlabeled text corpora to model the semantic content of words using their co-occurring context words. Key to this is the observation that semantically similar words have similar contexts BIBREF2 , thus leading to similar word embeddings. A limitation of these word embedding approaches is that they only produce monolingual embeddings. This is because word co-occurrences are very likely to be limited to being within language rather than across language in text corpora. Hence semantically similar words across languages are unlikely to have similar word embeddings.",
+      "To remedy this, there has been recent work on learning multilingual word embeddings, in which semantically similar words within and across languages have similar word embeddings BIBREF3 . Multilingual embeddings are not just interesting as an interlingua between multiple languages; they are useful in many downstream applications. For example, one application of multilingual embeddings is to find semantically similar words and phrases across languages BIBREF4 . Another use of multilingual embeddings is in enabling zero-shot learning on unseen languages, just as monolingual word embeddings enable predictions on unseen words BIBREF5 . In other words, a classifier using pretrained multilingual word embeddings can generalize to other languages even if training data is only in English. Interestingly, multilingual embeddings have also been shown to improve monolingual task performance BIBREF6 , BIBREF7 .",
+      "Consequently, multilingual embeddings can be very useful for low-resource languages \u2013 they allow us to overcome the scarcity of data in these languages. However, as detailed in Section \"Related Work\" , most work on learning multilingual word embeddings so far has heavily relied on the availability of expensive resources such as word-aligned / sentence-aligned parallel corpora or bilingual lexicons. Unfortunately, this data can be prohibitively expensive to collect for many languages. Furthermore even for languages with such data available, the coverage of the data is a limiting factor that restricts how much of the semantic space can be aligned across languages. Overcoming this data bottleneck is a key contribution of our work.",
+      "We investigate the use of cheaply available, weakly-supervised image-text data for learning multilingual embeddings. Images are a rich, language-agnostic medium that can provide a bridge across languages. For example, the English word \u201ccat\u201d might be found on webpages containing images of cats. Similarly, the German word \u201ckatze\u201d (meaning cat) is likely to be found on other webpages containing similar (or perhaps identical) images of cats. Thus, images can be used to learn that these words have similar semantic content. Importantly, image-text data is generally available on the internet even for low-resource languages.",
+      "As image data has proliferated on the internet, tools for understanding images have advanced considerably. Convolutional neural networks (CNNs) have achieved roughly human-level or better performance on vision tasks, particularly classification BIBREF8 , BIBREF9 , BIBREF10 . During classification of an image, CNNs compute intermediate outputs that have been used as generic image features that perform well across a variety of vision tasks BIBREF11 . We use these image features to enforce that words associated with similar images have similar embeddings. Since words associated with similar images are likely to have similar semantic content, even across languages, our learned embeddings capture crosslingual similarity.",
+      "There has been other recent work on reducing the amount of supervision required to learn multilingual embeddings (cf. Section \"Related Work\" ). These methods take monolingual embeddings learned using existing methods and align them post-hoc in a shared embedding space. A limitation with post-hoc alignment of monolingual embeddings, first noticed by BIBREF12 , is that doing training of monolingual embeddings and alignment separately may lead to worse results than joint training of embeddings in one step. Since the monolingual embedding objective is distinct from the multilingual embedding objective, monolingual embeddings are not required to capture all information helpful for post-hoc multilingual alignment. Post-hoc alignment loses out on some information, whereas joint training does not. BIBREF12 observe improved results using a joint training method compared to a similar post-hoc method. Thus, a joint training approach is desirable. To our knowledge, no previous method jointly learns multilingual word embeddings using weakly-supervised data available for low-resource languages.",
+      "To summarize: In this paper we propose an approach for learning multilingual word embeddings using image-text data jointly across all languages. We demonstrate that even a bag-of-words based embedding approach achieves performance competitive with the state-of-the-art on crosslingual semantic similarity tasks. We present experiments for understanding the effect of using pixel data as compared to co-occurrences alone. We also provide a method for training and making predictions on multilingual word embeddings even when the language of the text is unknown."
+    ],
+    [
+      "Most work on producing multilingual embeddings has relied on crosslingual human-labeled data, such as bilingual lexicons BIBREF13 , BIBREF4 , BIBREF6 , BIBREF14 or parallel/aligned corpora BIBREF15 , BIBREF4 , BIBREF16 , BIBREF17 . These works are also largely bilingual due to either limitations of methods or the requirement for data that exists only for a few language pairs. Bilingual embeddings are less desirable because they do not leverage the relevant resources of other languages. For example, in learning bilingual embeddings for English and French, it may be useful to leverage resources in Spanish, since French and Spanish are closely related. Bilingual embeddings are also limited in their applications to just one language pair.",
+      "For instance, BIBREF16 propose BiSkip, a model that extends the skip-gram approach of BIBREF18 to a bilingual parallel corpus. The embedding for a word is trained to predict not only its own context, but also the contexts for corresponding words in a second corpus in a different language. BIBREF4 extend this approach further to multiple languages. This method, called MultiSkip, is compared to our methods in Section \"Results and Conclusions\" .",
+      "There has been some recent work on reducing the amount of human-labeled data required to learn multilingual embeddings, enabling work on low-resource languages BIBREF19 , BIBREF20 , BIBREF21 . These methods take monolingual embeddings learned using existing methods and align them post-hoc in a shared embedding space, exploiting the structural similarity of monolingual embedding spaces first noticed by BIBREF13 . As discussed in Section \"Introduction\" , post-hoc alignment of monolingual embeddings is inherently suboptimal. For example, BIBREF19 and BIBREF20 use human-labeled data, along with shared surface forms across languages, to learn an alignment in the bilingual setting. BIBREF21 build on this for the multilingual setting, using no human-labeled data and instead using an adversarial approach to maximize alignment between monolingual embedding spaces given their structural similarities. This method (MUSE) outperforms previous approaches and represents the state-of-the-art. We compare it to our methods in Section \"Results and Conclusions\" .",
+      "There has been other work using image-text data to improve image and caption representations for image tasks and to learn word translations BIBREF22 , BIBREF23 , BIBREF24 , BIBREF25 , BIBREF26 , but no work using images to learn competitive multilingual word-level embeddings."
+    ],
+    [
+      "We experiment using a dataset derived from Google Images search results. The dataset consists of queries and the corresponding image search results. For example, one (query, image) pair might be \u201ccat with big ears\u201d and an image of a cat. Each (query, image) pair also has a weight corresponding to a relevance score of the image for the query. The dataset includes 3 billion (query, image, weight) triples, with 900 million unique images and 220 million unique queries. The data was prepared by first taking the query-image set, filtering to remove any personally identifiable information and adult content, and tokenizing the remaining queries by replacing special characters with spaces and trimming extraneous whitespace. Rare tokens (those that do not appear in queries at least six times) are filtered out. Each token in each query is given a language tag based on the user-set home language of the user making the search on Google Images. For example, if the query \u201cback pain\u201d is made by a user with English as her home language, then the query is stored as \u201cen:back en:pain\u201d. The dataset includes queries in about 130 languages.",
+      "Though the specific dataset we use is proprietary, BIBREF26 have obtained a similar dataset, using the Google Images search interface, that comprises queries in 100 languages."
+    ],
+    [
+      "We present a series of experiments to investigate the usefulness of multimodal image-text data in learning multilingual embeddings. The crux of our method involves enforcing that for each query-image pair, the query representation ( $Q$ ) is similar to the image representation ( $I$ ). The query representation is a function of the word embeddings for each word in a (language-tagged) query, so enforcing this constraint on the query representation also has the effect of constraining the corresponding multilingual word embeddings.",
+      "Given some $Q$ and some $I$ , we enforce that the representations are similar by maximizing their cosine similarity. We use a combination of cosine similarity and softmax objective to produce our loss. This high-level approach is illustrated in Figure 1 . In particular, we calculate unweighted loss as follows for a query $q$ and a corresponding image $i$ : $\\textrm {loss}(\\textrm {Query} \\: q, \\textrm {Image} \\: i) = -\\log {\\frac{e^{\\frac{Q_q^T I_i}{|Q_q| |I_i|}}}{\\sum _{j} e^{\\frac{Q_q^T I_j}{{|Q_q| |I_j|}}}}}$ ",
+      "where $Q_q$ is the query representation for query $q$ ; $I_i$ is the image representation corresponding to image $i$ ; $j$ ranges over all images in the corpus; and $Q_q^T I_i$ is the dot product of the vectors $Q_q$ and $I_i$ . Note that this requires that $Q_q$ and $I_j$ have identical dimensionality. If a weight $q$0 is provided for the (query, image) pair, the loss is multiplied by the weight. Observe that $q$1 and $q$2 remain unspecified for now: we detail different experiments involving different representations below.",
+      "In practice, given the size of our dataset, calculating the full denominator of the loss for a query, image pair would involve iterating through each image for each query, which is $O(n^2)$ in the number of training examples. To remedy this, we calculated the loss within each batch separately. That is, the denominator of the loss only involved summing over images in the same batch as the query. We used a batch size of 1000 for all experiments. In principle, the negative sampling approach used by BIBREF0 could be used instead to prevent quadratic time complexity.",
+      "We can interpret this loss function as producing a softmax classification task for queries and images: given a query, the model needs to predict the image relevant to that query. The cosine similarity between the image representation $I_i$ and the query representation $Q_q$ is normalized under softmax to produce a \u201cbelief\u201d that the image $i$ is the image relevant to the query $q$ . This is analogous to the skip-gram model proposed by BIBREF18 , although we use cosine similarity instead of dot product. Just as the skip-gram model ensures the embeddings of words are predictive of their contexts, our model ensures the embeddings of queries (and their constituent words) are predictive of images relevant to them."
+    ],
+    [
+      "Given the natural co-occurrence of images and text on the internet and the availability of powerful generic features, a first approach is to use generic image features as the foundation for the image representation $I$ . We apply two fully-connected layers to learn a transformation from image features to the final representation. We can compute the image representation $I_i$ for image $i$ as: $I_i = ReLU(U * ReLU(Vf_i + b_1) + b_2)$ ",
+      "where $f_i$ is a $d$ -dimensional column vector representing generic image features for image $i$ , $V$ is a $m \\times d$ matrix, $b_1$ is an $m$ -dimensional column vector, $U$ is a $n \\times m$ matrix, and $b_2$ is an $d$0 -dimensional column vector. We use a rectified linear unit activation function after each fully-connected layer.",
+      "We use 64-dimensional image features derived from image-text data using an approach similar to that used by BIBREF27 , who train image features to discriminate between fine-grained semantic image labels. We run two experiments with $m$ and $n$ : in the first, $m = 200$ and $n = 100$ (producing 100-dimensional embeddings), and in the second, $m = 300$ and $n = 300$ (producing 300-dimensional embeddings).",
+      "For the query representation, we use a simple approach. The query representation is just the average of its constituent multilingual embeddings. Then, as the query representation is constrained to be similar to corresponding image representations, the multilingual embeddings (randomly initialized) are also constrained.",
+      "Note that each word in each query is prefixed with the language of the query. For example, the English query \u201cback pain\u201d is treated as \u201cen:back en:pain\u201d, and the multilingual embeddings that are averaged are those for \u201cen:back\u201d and \u201cen:pain\u201d. This means that words in different languages with shared surface forms are given separate embeddings. We experiment with shared embeddings for words with shared surface forms in Section \"Discussion\" .",
+      "In practice, we use a fixed multilingual vocabulary for the word embeddings, given the size of the dataset. Out-of-vocabulary words are handled by hashing them to a fixed number of embedding buckets (we use 1,000,000). That is, there are 1,000,000 embeddings for all out-of-vocabulary words, and the assignment of embedding for each word is determined by a hash function.",
+      "Our approach for leveraging image understanding is shown in Figure 2 ."
+    ],
+    [
+      "Another approach for generating query and image representations is treating images as a black box. Without using pixel data, how well can we do? Given the statistics of our dataset (3B query, image pairs with 220M unique queries and 900M unique images), we know that different queries co-occur with the same images. Intuitively, if a query $q_1$ co-occurs with many of the same images as query $q_2$ , then $q_1$ and $q_2$ are likely to be semantically similar, regardless of the visual content of the shared images. Thus, we can use a method that uses only co-occurrence statistics to better understand how well we can capture relationships between queries. This method serves as a baseline to our initial approach leveraging image understanding.",
+      "In this setting, we keep query representations the same, and we modify image representations as follows: the image representation for an image is a randomly initialized, trainable vector (of the same dimensionality as the query representation, to ensure the cosine similarity can be calculated). The intuition for this approach is that if two queries are both associated with an image, their query representations will both be constrained to be similar to the same vector, and so the query representations themselves are constrained to be similar. This approach is a simple way to adapt our method to make use of only co-occurrence statistics.",
+      "One concern with this approach is that many queries may not have significant image co-occurrences with other queries. In particular, there are likely many images associated with only a single query. These isolated images pull query representations toward their respective random image representations (adding noise), but do not provide any information about the relationships between queries. Additionally, even for images associated with multiple queries, if these queries are all within language, then they may not be very helpful for learning multilingual embeddings. Consequently, we run two experiments: one with the original dataset and one with a subset of the dataset that contains only images associated with queries in at least two different languages. This subset of the dataset has 540 million query, image pairs (down from 3 billion). For both experiments, we use $m = 200$ and $n = 100$ and produce 100-dimensional embeddings."
+    ],
+    [
+      "In Section \"Leveraging Image Understanding\" , our method for computing query representations involved prepending language prefixes to each token, ensuring that the multilingual embedding for the English word \u201cpain\u201d is distinct from that for the French word \u201cpain\u201d (meaning bread). These query representations are language aware, meaning that a language tag is required for each query during both training and prediction. In the weakly-supervised setting, we may want to relax this requirement, as language-tagged data is not always readily available.",
+      "In our language unaware setting, language tags are not necessary. Each surface form in each query has a distinct embedding, and words with shared surface forms across languages (e.g., English \u201cpain\u201d and French \u201cpain\u201d) have a shared embedding. In this sense, shared surface forms are used as a bridge between languages. This is illustrated in Figure 3 . This may be helpful in certain cases, as for English \u201cactor\u201d and Spanish \u201cactor\u201d. The image representations leverage generic image features, exactly as in Section \"Leveraging Image Understanding\" . In our language-unaware experiment, we use $m = 200$ and $n = 100$ and produce 100-dimensional embeddings."
+    ],
+    [
+      "We evaluate our learned multilingual embeddings using six crosslingual semantic similarity tasks, two multilingual document classification tasks, and 13 monolingual semantic similarity tasks. We adapt code from BIBREF4 and BIBREF28 for evaluation.",
+      "This task measures how well multilingual embeddings capture semantic similarity of words, as judged by human raters. The task consists of a series of crosslingual word pairs. For each word pair in the task, human raters judge how semantically similar the words are. The model also predicts how similar the words are, using the cosine similarity between the embeddings. The score on the task is the Spearman correlation between the human ratings and the model predictions.",
+      "The specific six subtasks we use are part of the Rubenstein-Goodenough dataset BIBREF29 and detailed by BIBREF4 . We also include an additional task aggregating the six subtasks.",
+      "In this task, a classifier built on top of learned multilingual embeddings is trained on the RCV corpus of newswire text as in BIBREF15 and BIBREF4 . The corpus consists of documents in seven languages on four topics, and the classifier predicts the topic. The score on the task is test accuracy. Note that each document is monolingual, so this task measures performance within languages for multiple languages (as opposed to crosslingual performance).",
+      "This task is the same as the crosslingual semantic similarity task described above, but all word pairs are in English. We use this to understand how monolingual performance differs across methods. We present an average score across the 13 subtasks provided by BIBREF28 .",
+      "Evaluation tasks also report a coverage, which is the fraction of the test data that a set of multilingual embeddings is able to make predictions on. This is needed because not every word in the evaluation task has a corresponding learned multilingual embedding. Thus, if coverage is low, scores are less likely to be reliable."
+    ],
+    [
+      "We first present results on the crosslingual semantic similarity and multilingual document classification for our previously described experiments. We compare against the multiSkip approach by BIBREF4 and the state-of-the-art MUSE approach by BIBREF21 . Results for crosslingual semantic similarity are presented in Table 1 , and results for multilingual document classification are presented in Table 2 .",
+      "Our experiments corresponding to Section \"Leveraging Image Understanding\" are titled ImageVec 100-Dim and ImageVec 300-Dim in Tables 1 and 2 . Both experiments significantly outperform the multiSkip experiments in all crosslingual semantic similarity subtasks, and the 300-dimensional experiment slightly outperforms MUSE as well. Note that coverage scores are generally around 0.8 for these experiments. In multilingual document classification, MUSE achieves the best scores, and while our 300-dimensional experiment outperforms the multiSkip 40-dimensional experiment, it does not perform as well as the 512-dimensional experiment. Note that coverage scores are lower on these tasks.",
+      "One possible explanation for the difference in performance across the crosslingual semantic similarity task and multilingual document classification task is that the former measures crosslingual performance, whereas the latter measures monolingual performance in multiple languages, as described in Section UID10 . We briefly discuss further evidence that our models perform less well in the monolingual context below."
+    ],
+    [
+      "We demonstrated how to learn competitive multilingual word embeddings using image-text data \u2013 which is available for low-resource languages. We have presented experiments for understanding the effect of using pixel data as compared to co-occurrences alone. We have also proposed a method for training and making predictions on multilingual word embeddings even when language tags for words are unavailable. Using a simple bag-of-words approach, we achieve performance competitive with the state-of-the-art on crosslingual semantic similarity tasks.",
+      "We have also identified a direction for future work: within language performance is weaker than the state-of-the-art, likely because our work leveraged only image-text data rather than a large monolingual corpus. Fortunately, our joint training approach provides a simple extension of our method for future work: multi-task joint training. For example, in a triple-task setting, we can simultaneously (1) constrain query and relevant image representations to be similar and (2) constrain word embeddings to be predictive of context in large monolingual corpora and (3) constrain representations for parallel text across languages to be similar. For the second task, implementing recent advances in producing monolingual embeddings, such as using subword information, is likely to improve results. Multilingual embeddings learned in a multi-task setting would reap both the benefits of our methods and existing methods for producing word embeddings. For example, while our method is likely to perform worse for more abstract words, when combined with existing approaches it is likely to achieve more consistent performance.",
+      "An interesting effect of our approach is that queries and images are embedded into a shared space through the query and image representations. This setup enables a range of future research directions and applications, including better image features, better monolingual text representations (especially for visual tasks), nearest-neighbor search for text or images given one modality (or both), and joint prediction using text and images."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: Learning Multilingual Word Embeddings Using Image-Text Data
+
+Question: How much important is the visual grounding in the learning of the multilingual representations?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Data",
+    "Methods",
+    "Leveraging Image Understanding",
+    "Co-Occurrence Only",
+    "Language Unaware Query Representation",
+    "Evaluation",
+    "Results and Conclusions",
+    "Discussion"
+  ],
+  "paragraphs": [
+    [
+      "Recent advances in learning distributed representations for words (i.e., word embeddings) have resulted in improvements across numerous natural language understanding tasks BIBREF0 , BIBREF1 . These methods use unlabeled text corpora to model the semantic content of words using their co-occurring context words. Key to this is the observation that semantically similar words have similar contexts BIBREF2 , thus leading to similar word embeddings. A limitation of these word embedding approaches is that they only produce monolingual embeddings. This is because word co-occurrences are very likely to be limited to being within language rather than across language in text corpora. Hence semantically similar words across languages are unlikely to have similar word embeddings.",
+      "To remedy this, there has been recent work on learning multilingual word embeddings, in which semantically similar words within and across languages have similar word embeddings BIBREF3 . Multilingual embeddings are not just interesting as an interlingua between multiple languages; they are useful in many downstream applications. For example, one application of multilingual embeddings is to find semantically similar words and phrases across languages BIBREF4 . Another use of multilingual embeddings is in enabling zero-shot learning on unseen languages, just as monolingual word embeddings enable predictions on unseen words BIBREF5 . In other words, a classifier using pretrained multilingual word embeddings can generalize to other languages even if training data is only in English. Interestingly, multilingual embeddings have also been shown to improve monolingual task performance BIBREF6 , BIBREF7 .",
+      "Consequently, multilingual embeddings can be very useful for low-resource languages \u2013 they allow us to overcome the scarcity of data in these languages. However, as detailed in Section \"Related Work\" , most work on learning multilingual word embeddings so far has heavily relied on the availability of expensive resources such as word-aligned / sentence-aligned parallel corpora or bilingual lexicons. Unfortunately, this data can be prohibitively expensive to collect for many languages. Furthermore even for languages with such data available, the coverage of the data is a limiting factor that restricts how much of the semantic space can be aligned across languages. Overcoming this data bottleneck is a key contribution of our work.",
+      "We investigate the use of cheaply available, weakly-supervised image-text data for learning multilingual embeddings. Images are a rich, language-agnostic medium that can provide a bridge across languages. For example, the English word \u201ccat\u201d might be found on webpages containing images of cats. Similarly, the German word \u201ckatze\u201d (meaning cat) is likely to be found on other webpages containing similar (or perhaps identical) images of cats. Thus, images can be used to learn that these words have similar semantic content. Importantly, image-text data is generally available on the internet even for low-resource languages.",
+      "As image data has proliferated on the internet, tools for understanding images have advanced considerably. Convolutional neural networks (CNNs) have achieved roughly human-level or better performance on vision tasks, particularly classification BIBREF8 , BIBREF9 , BIBREF10 . During classification of an image, CNNs compute intermediate outputs that have been used as generic image features that perform well across a variety of vision tasks BIBREF11 . We use these image features to enforce that words associated with similar images have similar embeddings. Since words associated with similar images are likely to have similar semantic content, even across languages, our learned embeddings capture crosslingual similarity.",
+      "There has been other recent work on reducing the amount of supervision required to learn multilingual embeddings (cf. Section \"Related Work\" ). These methods take monolingual embeddings learned using existing methods and align them post-hoc in a shared embedding space. A limitation with post-hoc alignment of monolingual embeddings, first noticed by BIBREF12 , is that doing training of monolingual embeddings and alignment separately may lead to worse results than joint training of embeddings in one step. Since the monolingual embedding objective is distinct from the multilingual embedding objective, monolingual embeddings are not required to capture all information helpful for post-hoc multilingual alignment. Post-hoc alignment loses out on some information, whereas joint training does not. BIBREF12 observe improved results using a joint training method compared to a similar post-hoc method. Thus, a joint training approach is desirable. To our knowledge, no previous method jointly learns multilingual word embeddings using weakly-supervised data available for low-resource languages.",
+      "To summarize: In this paper we propose an approach for learning multilingual word embeddings using image-text data jointly across all languages. We demonstrate that even a bag-of-words based embedding approach achieves performance competitive with the state-of-the-art on crosslingual semantic similarity tasks. We present experiments for understanding the effect of using pixel data as compared to co-occurrences alone. We also provide a method for training and making predictions on multilingual word embeddings even when the language of the text is unknown."
+    ],
+    [
+      "Most work on producing multilingual embeddings has relied on crosslingual human-labeled data, such as bilingual lexicons BIBREF13 , BIBREF4 , BIBREF6 , BIBREF14 or parallel/aligned corpora BIBREF15 , BIBREF4 , BIBREF16 , BIBREF17 . These works are also largely bilingual due to either limitations of methods or the requirement for data that exists only for a few language pairs. Bilingual embeddings are less desirable because they do not leverage the relevant resources of other languages. For example, in learning bilingual embeddings for English and French, it may be useful to leverage resources in Spanish, since French and Spanish are closely related. Bilingual embeddings are also limited in their applications to just one language pair.",
+      "For instance, BIBREF16 propose BiSkip, a model that extends the skip-gram approach of BIBREF18 to a bilingual parallel corpus. The embedding for a word is trained to predict not only its own context, but also the contexts for corresponding words in a second corpus in a different language. BIBREF4 extend this approach further to multiple languages. This method, called MultiSkip, is compared to our methods in Section \"Results and Conclusions\" .",
+      "There has been some recent work on reducing the amount of human-labeled data required to learn multilingual embeddings, enabling work on low-resource languages BIBREF19 , BIBREF20 , BIBREF21 . These methods take monolingual embeddings learned using existing methods and align them post-hoc in a shared embedding space, exploiting the structural similarity of monolingual embedding spaces first noticed by BIBREF13 . As discussed in Section \"Introduction\" , post-hoc alignment of monolingual embeddings is inherently suboptimal. For example, BIBREF19 and BIBREF20 use human-labeled data, along with shared surface forms across languages, to learn an alignment in the bilingual setting. BIBREF21 build on this for the multilingual setting, using no human-labeled data and instead using an adversarial approach to maximize alignment between monolingual embedding spaces given their structural similarities. This method (MUSE) outperforms previous approaches and represents the state-of-the-art. We compare it to our methods in Section \"Results and Conclusions\" .",
+      "There has been other work using image-text data to improve image and caption representations for image tasks and to learn word translations BIBREF22 , BIBREF23 , BIBREF24 , BIBREF25 , BIBREF26 , but no work using images to learn competitive multilingual word-level embeddings."
+    ],
+    [
+      "We experiment using a dataset derived from Google Images search results. The dataset consists of queries and the corresponding image search results. For example, one (query, image) pair might be \u201ccat with big ears\u201d and an image of a cat. Each (query, image) pair also has a weight corresponding to a relevance score of the image for the query. The dataset includes 3 billion (query, image, weight) triples, with 900 million unique images and 220 million unique queries. The data was prepared by first taking the query-image set, filtering to remove any personally identifiable information and adult content, and tokenizing the remaining queries by replacing special characters with spaces and trimming extraneous whitespace. Rare tokens (those that do not appear in queries at least six times) are filtered out. Each token in each query is given a language tag based on the user-set home language of the user making the search on Google Images. For example, if the query \u201cback pain\u201d is made by a user with English as her home language, then the query is stored as \u201cen:back en:pain\u201d. The dataset includes queries in about 130 languages.",
+      "Though the specific dataset we use is proprietary, BIBREF26 have obtained a similar dataset, using the Google Images search interface, that comprises queries in 100 languages."
+    ],
+    [
+      "We present a series of experiments to investigate the usefulness of multimodal image-text data in learning multilingual embeddings. The crux of our method involves enforcing that for each query-image pair, the query representation ( $Q$ ) is similar to the image representation ( $I$ ). The query representation is a function of the word embeddings for each word in a (language-tagged) query, so enforcing this constraint on the query representation also has the effect of constraining the corresponding multilingual word embeddings.",
+      "Given some $Q$ and some $I$ , we enforce that the representations are similar by maximizing their cosine similarity. We use a combination of cosine similarity and softmax objective to produce our loss. This high-level approach is illustrated in Figure 1 . In particular, we calculate unweighted loss as follows for a query $q$ and a corresponding image $i$ : $\\textrm {loss}(\\textrm {Query} \\: q, \\textrm {Image} \\: i) = -\\log {\\frac{e^{\\frac{Q_q^T I_i}{|Q_q| |I_i|}}}{\\sum _{j} e^{\\frac{Q_q^T I_j}{{|Q_q| |I_j|}}}}}$ ",
+      "where $Q_q$ is the query representation for query $q$ ; $I_i$ is the image representation corresponding to image $i$ ; $j$ ranges over all images in the corpus; and $Q_q^T I_i$ is the dot product of the vectors $Q_q$ and $I_i$ . Note that this requires that $Q_q$ and $I_j$ have identical dimensionality. If a weight $q$0 is provided for the (query, image) pair, the loss is multiplied by the weight. Observe that $q$1 and $q$2 remain unspecified for now: we detail different experiments involving different representations below.",
+      "In practice, given the size of our dataset, calculating the full denominator of the loss for a query, image pair would involve iterating through each image for each query, which is $O(n^2)$ in the number of training examples. To remedy this, we calculated the loss within each batch separately. That is, the denominator of the loss only involved summing over images in the same batch as the query. We used a batch size of 1000 for all experiments. In principle, the negative sampling approach used by BIBREF0 could be used instead to prevent quadratic time complexity.",
+      "We can interpret this loss function as producing a softmax classification task for queries and images: given a query, the model needs to predict the image relevant to that query. The cosine similarity between the image representation $I_i$ and the query representation $Q_q$ is normalized under softmax to produce a \u201cbelief\u201d that the image $i$ is the image relevant to the query $q$ . This is analogous to the skip-gram model proposed by BIBREF18 , although we use cosine similarity instead of dot product. Just as the skip-gram model ensures the embeddings of words are predictive of their contexts, our model ensures the embeddings of queries (and their constituent words) are predictive of images relevant to them."
+    ],
+    [
+      "Given the natural co-occurrence of images and text on the internet and the availability of powerful generic features, a first approach is to use generic image features as the foundation for the image representation $I$ . We apply two fully-connected layers to learn a transformation from image features to the final representation. We can compute the image representation $I_i$ for image $i$ as: $I_i = ReLU(U * ReLU(Vf_i + b_1) + b_2)$ ",
+      "where $f_i$ is a $d$ -dimensional column vector representing generic image features for image $i$ , $V$ is a $m \\times d$ matrix, $b_1$ is an $m$ -dimensional column vector, $U$ is a $n \\times m$ matrix, and $b_2$ is an $d$0 -dimensional column vector. We use a rectified linear unit activation function after each fully-connected layer.",
+      "We use 64-dimensional image features derived from image-text data using an approach similar to that used by BIBREF27 , who train image features to discriminate between fine-grained semantic image labels. We run two experiments with $m$ and $n$ : in the first, $m = 200$ and $n = 100$ (producing 100-dimensional embeddings), and in the second, $m = 300$ and $n = 300$ (producing 300-dimensional embeddings).",
+      "For the query representation, we use a simple approach. The query representation is just the average of its constituent multilingual embeddings. Then, as the query representation is constrained to be similar to corresponding image representations, the multilingual embeddings (randomly initialized) are also constrained.",
+      "Note that each word in each query is prefixed with the language of the query. For example, the English query \u201cback pain\u201d is treated as \u201cen:back en:pain\u201d, and the multilingual embeddings that are averaged are those for \u201cen:back\u201d and \u201cen:pain\u201d. This means that words in different languages with shared surface forms are given separate embeddings. We experiment with shared embeddings for words with shared surface forms in Section \"Discussion\" .",
+      "In practice, we use a fixed multilingual vocabulary for the word embeddings, given the size of the dataset. Out-of-vocabulary words are handled by hashing them to a fixed number of embedding buckets (we use 1,000,000). That is, there are 1,000,000 embeddings for all out-of-vocabulary words, and the assignment of embedding for each word is determined by a hash function.",
+      "Our approach for leveraging image understanding is shown in Figure 2 ."
+    ],
+    [
+      "Another approach for generating query and image representations is treating images as a black box. Without using pixel data, how well can we do? Given the statistics of our dataset (3B query, image pairs with 220M unique queries and 900M unique images), we know that different queries co-occur with the same images. Intuitively, if a query $q_1$ co-occurs with many of the same images as query $q_2$ , then $q_1$ and $q_2$ are likely to be semantically similar, regardless of the visual content of the shared images. Thus, we can use a method that uses only co-occurrence statistics to better understand how well we can capture relationships between queries. This method serves as a baseline to our initial approach leveraging image understanding.",
+      "In this setting, we keep query representations the same, and we modify image representations as follows: the image representation for an image is a randomly initialized, trainable vector (of the same dimensionality as the query representation, to ensure the cosine similarity can be calculated). The intuition for this approach is that if two queries are both associated with an image, their query representations will both be constrained to be similar to the same vector, and so the query representations themselves are constrained to be similar. This approach is a simple way to adapt our method to make use of only co-occurrence statistics.",
+      "One concern with this approach is that many queries may not have significant image co-occurrences with other queries. In particular, there are likely many images associated with only a single query. These isolated images pull query representations toward their respective random image representations (adding noise), but do not provide any information about the relationships between queries. Additionally, even for images associated with multiple queries, if these queries are all within language, then they may not be very helpful for learning multilingual embeddings. Consequently, we run two experiments: one with the original dataset and one with a subset of the dataset that contains only images associated with queries in at least two different languages. This subset of the dataset has 540 million query, image pairs (down from 3 billion). For both experiments, we use $m = 200$ and $n = 100$ and produce 100-dimensional embeddings."
+    ],
+    [
+      "In Section \"Leveraging Image Understanding\" , our method for computing query representations involved prepending language prefixes to each token, ensuring that the multilingual embedding for the English word \u201cpain\u201d is distinct from that for the French word \u201cpain\u201d (meaning bread). These query representations are language aware, meaning that a language tag is required for each query during both training and prediction. In the weakly-supervised setting, we may want to relax this requirement, as language-tagged data is not always readily available.",
+      "In our language unaware setting, language tags are not necessary. Each surface form in each query has a distinct embedding, and words with shared surface forms across languages (e.g., English \u201cpain\u201d and French \u201cpain\u201d) have a shared embedding. In this sense, shared surface forms are used as a bridge between languages. This is illustrated in Figure 3 . This may be helpful in certain cases, as for English \u201cactor\u201d and Spanish \u201cactor\u201d. The image representations leverage generic image features, exactly as in Section \"Leveraging Image Understanding\" . In our language-unaware experiment, we use $m = 200$ and $n = 100$ and produce 100-dimensional embeddings."
+    ],
+    [
+      "We evaluate our learned multilingual embeddings using six crosslingual semantic similarity tasks, two multilingual document classification tasks, and 13 monolingual semantic similarity tasks. We adapt code from BIBREF4 and BIBREF28 for evaluation.",
+      "This task measures how well multilingual embeddings capture semantic similarity of words, as judged by human raters. The task consists of a series of crosslingual word pairs. For each word pair in the task, human raters judge how semantically similar the words are. The model also predicts how similar the words are, using the cosine similarity between the embeddings. The score on the task is the Spearman correlation between the human ratings and the model predictions.",
+      "The specific six subtasks we use are part of the Rubenstein-Goodenough dataset BIBREF29 and detailed by BIBREF4 . We also include an additional task aggregating the six subtasks.",
+      "In this task, a classifier built on top of learned multilingual embeddings is trained on the RCV corpus of newswire text as in BIBREF15 and BIBREF4 . The corpus consists of documents in seven languages on four topics, and the classifier predicts the topic. The score on the task is test accuracy. Note that each document is monolingual, so this task measures performance within languages for multiple languages (as opposed to crosslingual performance).",
+      "This task is the same as the crosslingual semantic similarity task described above, but all word pairs are in English. We use this to understand how monolingual performance differs across methods. We present an average score across the 13 subtasks provided by BIBREF28 .",
+      "Evaluation tasks also report a coverage, which is the fraction of the test data that a set of multilingual embeddings is able to make predictions on. This is needed because not every word in the evaluation task has a corresponding learned multilingual embedding. Thus, if coverage is low, scores are less likely to be reliable."
+    ],
+    [
+      "We first present results on the crosslingual semantic similarity and multilingual document classification for our previously described experiments. We compare against the multiSkip approach by BIBREF4 and the state-of-the-art MUSE approach by BIBREF21 . Results for crosslingual semantic similarity are presented in Table 1 , and results for multilingual document classification are presented in Table 2 .",
+      "Our experiments corresponding to Section \"Leveraging Image Understanding\" are titled ImageVec 100-Dim and ImageVec 300-Dim in Tables 1 and 2 . Both experiments significantly outperform the multiSkip experiments in all crosslingual semantic similarity subtasks, and the 300-dimensional experiment slightly outperforms MUSE as well. Note that coverage scores are generally around 0.8 for these experiments. In multilingual document classification, MUSE achieves the best scores, and while our 300-dimensional experiment outperforms the multiSkip 40-dimensional experiment, it does not perform as well as the 512-dimensional experiment. Note that coverage scores are lower on these tasks.",
+      "One possible explanation for the difference in performance across the crosslingual semantic similarity task and multilingual document classification task is that the former measures crosslingual performance, whereas the latter measures monolingual performance in multiple languages, as described in Section UID10 . We briefly discuss further evidence that our models perform less well in the monolingual context below."
+    ],
+    [
+      "We demonstrated how to learn competitive multilingual word embeddings using image-text data \u2013 which is available for low-resource languages. We have presented experiments for understanding the effect of using pixel data as compared to co-occurrences alone. We have also proposed a method for training and making predictions on multilingual word embeddings even when language tags for words are unavailable. Using a simple bag-of-words approach, we achieve performance competitive with the state-of-the-art on crosslingual semantic similarity tasks.",
+      "We have also identified a direction for future work: within language performance is weaker than the state-of-the-art, likely because our work leveraged only image-text data rather than a large monolingual corpus. Fortunately, our joint training approach provides a simple extension of our method for future work: multi-task joint training. For example, in a triple-task setting, we can simultaneously (1) constrain query and relevant image representations to be similar and (2) constrain word embeddings to be predictive of context in large monolingual corpora and (3) constrain representations for parallel text across languages to be similar. For the second task, implementing recent advances in producing monolingual embeddings, such as using subword information, is likely to improve results. Multilingual embeddings learned in a multi-task setting would reap both the benefits of our methods and existing methods for producing word embeddings. For example, while our method is likely to perform worse for more abstract words, when combined with existing approaches it is likely to achieve more consistent performance.",
+      "An interesting effect of our approach is that queries and images are embedded into a shared space through the query and image representations. This setup enables a range of future research directions and applications, including better image features, better monolingual text representations (especially for visual tasks), nearest-neighbor search for text or images given one modality (or both), and joint prediction using text and images."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2158/instruction.md b/qasper-2158/instruction.md
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+Name of Paper: Generating Natural Language Inference Chains
+
+Question: How is the generative model evaluated?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Method",
+    "Entailment Generation",
+    "Sequence-to-Sequence",
+    "Optimization and Hyperparameters",
+    "Experiments and Results",
+    "Quantitative Evaluation",
+    "Example Generations",
+    "Inference Chain Generation",
+    "Inverse Inference",
+    "Conclusion and Future Work",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "The ability to determine entailment or contradiction between natural language text is essential for improving the performance in a wide range of natural language processing tasks. Recognizing Textual Entailment (RTE) is a task primarily designed to determine whether two natural language sentences are independent, contradictory or in an entailment relationship where the second sentence (the hypothesis) can be inferred from the first (the premise). Although systems that perform well in RTE could potentially be used to improve question answering, information extraction, text summarization and machine translation BIBREF0 , only in few of such downstream NLP tasks sentence-pairs are actually available. Usually, only a single source sentence (e.g. a question that needs to be answered or a source sentence that we want to translate) is present and models need to come up with their own hypotheses and commonsense knowledge inferences.",
+      "The release of the large Stanford Natural Language Inference (SNLI) corpus BIBREF1 allowed end-to-end differentiable neural networks to outperform feature-based classifiers on the RTE task BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 .",
+      "In this work, we go a step further and investigate how well recurrent neural networks can produce true hypotheses given a source sentence. Furthermore, we qualitatively demonstrate that by only training on input-output pairs and recursively generating entailed sentence we can generate natural language inference chains (see Figure 1 for an example). Note that every inference step is interpretable as it is mapping one natural language sentence to another one.",
+      "Our contributions are fourfold: (i) we propose an entailment generation task based on the SNLI corpus (\u00a7 \"Entailment Generation\" ), (ii) we investigate a sequence-to-sequence model and find that $82\\%$ of generated sentences are correct (\u00a7 \"Example Generations\" ), (iii) we demonstrate the ability to generate natural language inference chains trained solely from entailment pairs (\u00a7 \"Entailment Generation\"1 ), and finally (iv) we can also generate sentences with more specific information by swapping source and target sentences during training (\u00a7 \"Entailment Generation\"6 )."
+    ],
+    [
+      "In the section, we briefly introduce the entailment generation task and our sequence-to-sequence model."
+    ],
+    [
+      "To create the entailment generation dataset, we simply filter the Stanford Natural Language Inference corpus for sentence-pairs of the entailment class. This results in a training set of $183,416$ sentence pairs, a development set of $3,329$ pairs and a test of $3,368$ pairs. Instead of a classification task, we can now use this dataset for a sequence transduction task."
+    ],
+    [
+      "Sequence-to-sequence recurrent neural networks BIBREF7 have been successfully employed for many sequence transduction tasks in NLP such as machine translation BIBREF8 , BIBREF9 , constituency parsing BIBREF10 , sentence summarization BIBREF11 and question answering BIBREF12 . They consist of two recurrent neural networks (RNNs): an encoder that maps an input sequence of words into a dense vector representation, and a decoder that conditioned on that vector representation generates an output sequence. Specifically, we use long short-term memory (LSTM) RNNs BIBREF13 for encoding and decoding. Furthermore, we experiment with word-by-word attention BIBREF8 , which allows the decoder to search in the encoder outputs to circumvent the LSTM's memory bottleneck. We use greedy decoding at test time. The success of LSTMs with attention in sequence transduction tasks makes them a natural choice as a baseline for entailment generation, and we leave the investigation of more advanced models to future work."
+    ],
+    [
+      "We use stochastic gradient descent with a mini-batch size of 64 and the ADAM optimizer BIBREF14 with a first momentum coefficient of $0.9$ and a second momentum coefficient of $0.999$ . Word embeddings are initialized with pre-trained word2vec vectors BIBREF15 . Out-of-vocabulary words ( $10.5\\%$ ) are randomly initialized by sampling values uniformly from $[-\\sqrt{3}, \\sqrt{3}]$ and optimized during training. Furthermore, we clip gradients using a norm of $5.0$ . We stop training after 25 epochs."
+    ],
+    [
+      "We present results for various tasks: (i) given a premise, generate a sentence that can be inferred from the premise, (ii) construct inference chains by recursively generating sentences, and (iii) given a sentence, create a premise that would entail this sentence, i.e., make a more descriptive sentence by adding specific information."
+    ],
+    [
+      "We train an LSTM with and without attention on the training set. After training, we take the best model in terms of BLEU score BIBREF16 on the development set and calculate the BLEU score on the test set. To our surprise, we found that using attention yields only a marginally higher BLEU score (43.1 vs. 42.8). We suspect that this is due to the fact that generating entailed sentences has a larger space of valid target sequences, which makes the use of BLEU problematic and penalizes correct solutions. Hence, we manually annotated 100 random test sentences and decided whether the generated sentence can indeed be inferred from the source sentence. We found that sentences generated by an LSTM with attention are substantially more accurate ( $82\\%$ accuracy) than those generated from an LSTM baseline ( $71.7\\%$ ). To gain more insights into the model's capabilities, we turn to a thorough qualitative analysis of the attention LSTM model in the remainder of this paper."
+    ],
+    [
+      "Figure 2 shows examples of generated sentences from the development set. Syntactic simplification of the input sentence seems to be the most common approach. The model removes certain parts of the premise such as adjectives, resulting in a more abstract sentence (see Figure 2 . UID8 ).",
+      "Figure 2 . UID9 demonstrates that the system can recognize the number of subjects in the sentence and includes this information in the generated sentence. However, we did not observe such 'counting' behavior for more than four subjects, indicating that the system memorized frequency patterns from the training set.",
+      "Furthermore, we found predictions that hint to common-sense assumptions: if a sentence talks about a father holding a newborn baby, it is most likely that the newborn baby is his own child (Example 2 . UID10 ).",
+      "Two reappearing limitations of the proposed model are related to dealing with words that have a very different meaning but similar word2vec embeddings (e.g. colors), as well as ambiguous words. For instance, 'bar' in Figure 3 . UID8 refers to pole vault and not a place in which you can have a drink. Substituting one color by another one (Figure 3 . UID14 ) is a common mistake.",
+      "The SNLI corpus might not reflect the variety of sentences that can be encountered in downstream NLP tasks. In Figure 4 we present generated sentences for randomly selected examples of out-of-domain textual resources. They demonstrate that the model generalizes well to out-of-domain sentences, making it a potentially very useful component for improving systems for question answering, information extraction, sentence summarization etc."
+    ],
+    [
+      "Next, we test how well the model can generate inference chains by repeatedly passing generated output sentences as inputs to the model. We stop once a sentence has already been generated in the chain. Figure 5 shows that this works well despite that the model was only trained on sentence-pairs.",
+      "Furthermore, by generating inference chains for all sentences in the development set we construct an entailment graph. In that graph we found that sentences with shared semantics are eventually mapped to the same sentence that captures the shared meaning.",
+      "A visualization of the topology of the entailment graph is shown in Figure 6 . Note that there are several long inference chains, as well as large clusters of sentences (nodes) that are mapped (links) to the same shared meaning."
+    ],
+    [
+      "By swapping the source and target sequences for training, we can train a model that given a sentence invents additional information to generate a new sentence (Figure 7 ). We believe this might prove useful to increase the language variety and complexity of AI unit tests such as the Facebook bAbI task BIBREF17 , but we leave this for future work."
+    ],
+    [
+      "We investigated the ability of sequence-to-sequence models to generate entailed sentences from a source sentence. To this end, we trained an attentive LSTM on entailment-pairs of the SNLI corpus. We found that this works well and generalizes beyond in-domain sentences. Hence, it could become a useful component for improving the performance of other NLP systems.",
+      "We were able to generate natural language inference chains by recursively generating sentences from previously inferred ones. This allowed us to construct an entailment graph for sentences of the SNLI development corpus. In this graph, the shared meaning of two related sentences is represented by the first natural language sentence that connects both sentences. Every inference step is interpretable as it maps a natural language sentence to another one.",
+      "Towards high-quality data augmentation, we experimented with reversing the generation task. We found that this enabled the model to learn to invent specific information.",
+      "For future work, we want to integrate the presented model into larger architectures to improve the performance of downstream NLP tasks such as information extraction and question answering. Furthermore, we plan to use the model for data augmentation to train expressive neural networks on tasks where only little annotated data is available. Another interesting research direction is to investigate methods for increasing the diversity of the generated sentences."
+    ],
+    [
+      "We thank Guillaume Bouchard for suggesting the reversed generation task, and Dirk Weissenborn, Isabelle Augenstein and Matko Bosnjak for comments on drafts of this paper. This work was supported by Microsoft Research through its PhD Scholarship Programme, an Allen Distinguished Investigator Award, and a Marie Curie Career Integration Award."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2159/instruction.md b/qasper-2159/instruction.md
new file mode 100644
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+Name of Paper: Deep Representation Learning for Clustering of Health Tweets
+
+Question: How do they evaluate their method?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Dataset",
+    "Conventional Representations",
+    "Representation Learning",
+    "L 2 L_2-norm Constrained Representation Learning",
+    "Evaluation",
+    "Results",
+    "Discussion",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Social media plays an important role in health informatics and Twitter has been one of the most influential social media channel for mining population-level health insights BIBREF0 , BIBREF1 , BIBREF2 . These insights range from forecasting of influenza epidemics BIBREF3 to predicting adverse drug reactions BIBREF4 . A notable challenge due to the short length of Twitter messages is categorization of tweets into topics in a supervised manner, i.e., topic classification, as well as in an unsupervised manner, i.e., clustering.",
+      "Classification of tweets into topics has been studied extensively BIBREF5 , BIBREF6 , BIBREF7 . Even though text classification algorithms can reach significant accuracy levels, supervised machine learning approaches require annotated data, i.e, topic categories to learn from for classification. On the other hand, annotated data is not always available as the annotation process is burdensome and time-consuming. In addition, discussions in social media evolve rapidly with recent trends, rendering Twitter a dynamic environment with ever-changing topics. Therefore, unsupervised approaches are essential for mining health-related information from Twitter.",
+      "Proposed methods for clustering tweets employ conventional text clustering pipelines involving preprocessing applied to raw text strings, followed by feature extraction which is then followed by a clustering algorithm BIBREF8 , BIBREF9 , BIBREF10 . Performance of such approaches depend highly on feature extraction in which careful engineering and domain knowledge is required BIBREF11 . Recent advancements in machine learning research, i.e., deep neural networks, enable efficient representation learning from raw data in a hierarchical manner BIBREF12 , BIBREF13 . Several natural language processing (NLP) tasks involving Twitter data have benefited from deep neural network-based approaches including sentiment classification of tweets BIBREF14 , predicting potential suicide attempts from Twitter BIBREF15 and simulating epidemics from Twitter BIBREF16 .",
+      "In this work, we propose deep convolutional autoencoders (CAEs) for obtaining efficient representations of health-related tweets in an unsupervised manner. We validate our approach on a publicly available dataset from Twitter by comparing the performance of our approach and conventional feature extraction methods on 3 different clustering algorithms. Furthermore, we propose a constraint on the learned representations during neural network training in order to further improve the clustering performance. We show that the proposed deep neural network-based representation learning method outperforms conventional methods in terms of clustering performance in experiments of varying number of clusters."
+    ],
+    [
+      "Devising efficient representations of tweets, i.e., features, for performing clustering has been studied extensively. Most frequently used features for representing the text in tweets as numerical vectors are bag-of-words (BoWs) and term frequency-inverse document frequency (tf-idf) features BIBREF17 , BIBREF9 , BIBREF10 , BIBREF18 , BIBREF19 . Both of these feature extraction methods are based on word occurrence counts and eventually, result in a sparse (most elements being zero) document-term matrix. Proposed algorithms for clustering tweets into topics include variants of hierarchical, density-based and centroid-based clustering methods; k-means algorithm being the most frequently used one BIBREF9 , BIBREF19 , BIBREF20 .",
+      "Numerous works on topic modeling of tweets are available as well. Topic models are generative models, relying on the idea that a given tweet is a mixture of topics, where a topic is a probability distribution over words BIBREF21 . Even though the objective in topic modeling is slightly different than that of pure clustering, representing each tweet as a topic vector is essentially a way of dimensionality reduction or feature extraction and can further be followed by a clustering algorithm. Proposed topic modeling methods include conventional approaches or variants of them such as Latent Dirichlet Allocation (LDA) BIBREF22 , BIBREF17 , BIBREF9 , BIBREF23 , BIBREF24 , BIBREF25 , BIBREF26 , BIBREF27 , BIBREF19 , BIBREF28 , BIBREF29 and Non-negative Matrix Factorization (NMF) BIBREF30 , BIBREF18 . Note that topic models such as LDA are based on the notion that words belonging to a topic are more likely to appear in the same document and do not assume a distance metric between discovered topics.",
+      "In contrary to abovementioned feature extraction methods which are not specific to representation of tweets but rather generic in natural language processing, various works propose custom feature extraction methods for certain health-related information retrieval tasks from Twitter. For instance, Lim et al. engineered sentiment analysis features to discover latent infectious diseases from Twitter BIBREF31 . In order to track public health condition trends from Twitter, specific features are proposed by Parker at al. employing Wikipedia article index, i.e., treating the retrieval of medically-related Wikipedia articles as an indicator of a health-related condition BIBREF32 . Custom user similarity features calculated from tweets were also proposed for building a framework for recommending health-related topics BIBREF27 .",
+      "The idea of learning effective representations from raw data using neural networks has been employed in numerous machine learning domains such as computer vision and natural language processing BIBREF12 , BIBREF13 . The concept relies on the hierarchical, layer-wise architecture of neural networks in which the raw input data is encoded into informative representations of lower dimensions (representations of higher dimensions are possible as well) in a highly non-linear fashion. Autoencoders, Denoising Autoencoders, Convolutional Autoencoders, Sparse Autoencoders, Stacked Autoencoders and combinations of these, e.g., Denoising Convolutional Autoencoders, are the most common deep neural network architectures specifically used for representation learning. In an autoencoder training, the network tries to reconstruct the input data at its output, which forces the model to capture the most salient features of the data at its intermediate layers. If the intermediate layers correspond to a lower dimensional latent space than the original input, such autoencoders are also known as undercomplete. Activations extracted from these layers can be considered as compact, non-linear representations of the input. Another significant advancement in neural network-based representation learning in NLP tasks is word embeddings (also called distributed representation of words). By representing each word in a given vocabulary with a real-valued vector of a fixed dimension, word embeddings enable capturing of lexical, semantic or even syntactic similarities between words. Typically, these vector representations are learned from large corpora and can be used to enhance the performance of numerous NLP tasks such as document classification, question answering and machine translation. Most frequently used word embeddings are word2vec BIBREF33 and GloVe (Global Vectors for Word Representation) BIBREF34 . Both of these are extracted in an unsupervised manner and are based on the distributional hypothesis BIBREF35 , i.e., the assumption that words that occur in the same contexts tend to have similar meanings. Both word2vec and GloVe treat a word as a smallest entity to train on. A shift in this paradigm was introduced by fastText BIBREF36 , which treats each word as a bag of character n-grams. Consequently, fastText embeddings are shown to have better representations for rare words BIBREF36 . In addition, one can still construct a vector representation for an out-of-vocabulary word which is not possible with word2vec or GloVe embeddings BIBREF36 . Enhanced methods for deducting better word and/or sentence representations were recently introduced as well by Peters et al. with the name ELMo (Embeddings from Language Models) BIBREF37 and by Devlin et al. with the name BERT (Bidirectional Encoder Representations from Transformers) BIBREF38 . All of these word embedding models are trained on large corpora such as Wikipedia, in an unsupervised manner. For analyzing tweets, word2vec and GloVe word embeddings have been employed for topical clustering of tweets BIBREF39 , topic modeling BIBREF40 , BIBREF41 and extracting depression symptoms from tweets BIBREF20 .",
+      "Metrics for evaluating the performance of clustering algorithms varies depending on whether the ground truth topic categories are available or not. If so, frequently used metrics are accuracy and normalized mutual information. In the case of absence of ground truth labels, one has to use internal clustering criterions such as Calinski-Harabasz (CH) score BIBREF42 and Davies-Bouldin index BIBREF43 . Arbelaitz et al. provides an extensive comparative study of cluster validity indices BIBREF44 ."
+    ],
+    [
+      "For this study, a publicly available dataset is used BIBREF45 . The dataset consisting of tweets has been collected using Twitter API and was initially introduced by Karami et al. BIBREF46 . Earliest tweet dates back to 13 June 2011 where the latest one has a timestamp of 9 April 2015. The dataset consists of 63,326 tweets in English language, collected from Twitter channels of 16 major health news agencies. List of health news channels and the number of tweets in the dataset from each channel can be examined from Table 1 .",
+      "The outlook of a typical tweet from the dataset can be examined from Figure 1 . For every tweet, the raw data consists of the tweet text and in most cases followed by a url to the original news article of the particular news source. This url string, if available, is removed from each tweet as it does not possess any natural language information. As Twitter allows several ways for users to interact such as retweeting or mentioning, these actions appear in the raw text as well. For retweets, an indicator string of \"RT\" appears as a prefix in the raw data and for user mentions, a string of form \"@username\" appears in the raw data. These two tokens are removed as well. In addition, hashtags are converted to plain tokens by removal of the \"#\" sign appearing before them (e.g. <#pregnancy> becomes <pregnancy>). Number of words, number of unique words and mean word counts for each Twitter channel can also be examined from Table 1 . Longest tweet consists of 27 words."
+    ],
+    [
+      "For representing tweets, 5 conventional representation methods are proposed as baselines.",
+      "Word frequency features: For word occurrence-based representations of tweets, conventional tf-idf and BoWs are used to obtain the document-term matrix of $N \\times P$ in which each row corresponds to a tweet and each column corresponds to a unique word/token, i.e., $N$ data points and $P$ features. As the document-term matrix obtained from tf-idf or BoWs features is extremely sparse and consequently redundant across many dimensions, dimensionality reduction and topic modeling to a lower dimensional latent space is performed by the methods below.",
+      "Principal Component Analysis (PCA): PCA is used to map the word frequency representations from the original feature space to a lower dimensional feature space by an orthogonal linear transformation in such a way that the first principal component has the highest possible variance and similarly, each succeeding component has the highest variance possible while being orthogonal to the preceding components. Our PCA implementation has a time complexity of $\\mathcal {O}(NP^2 + P^3)$ .",
+      "Truncated Singular Value Decomposition (t-SVD): Standard SVD and t-SVD are commonly employed dimensionality reduction techniques in which a matrix is reduced or approximated into a low-rank decomposition. Time complexity of SVD and t-SVD for $S$ components are $\\mathcal {O}(min(NP^2, N^2P))$ and $\\mathcal {O}(N^2S)$ , respectively (depending on the implementation). Contrary to PCA, t-SVD can be applied to sparse matrices efficiently as it does not require data normalization. When the data matrix is obtained by BoWs or tf-idf representations as in our case, the technique is also known as Latent Semantic Analysis.",
+      "LDA: Our LDA implementation employs online variational Bayes algorithm introduced by Hoffman et al. which uses stochastic optimization to maximize the objective function for the topic model BIBREF47 .",
+      "NMF: As NMF finds two non-negative matrices whose product approximates the non-negative document-term matrix, it allows regularization. Our implementation did not employ any regularization and the divergence function is set to be squared error, i.e., Frobenius norm."
+    ],
+    [
+      "We propose 2D convolutional autoencoders for extracting compact representations of tweets from their raw form in a highly non-linear fashion. In order to turn a given tweet into a 2D structure to be fed into the CAE, we extract the word vectors of each word using word embedding models, i.e., for a given tweet, $t$ , consisting of $W$ words, the 2D input is $I_{t} \\in ^{W \\times D}$ where $D$ is the embedding vector dimension. We compare 4 different word embeddings namely word2vec, GloVe, fastText and BERT with embedding vector dimensions of 300, 300, 300 and 768, respectively. We set the maximum sequence length to 32, i.e., for tweets having less number of words, the input matrix is padded with zeros. As word2vec and GloVe embeddings can not handle out-of-vocabulary words, such cases are represented as a vector of zeros. The process of extracting word vector representations of a tweet to form the 2D input matrix can be examined from Figure 1 .",
+      "The CAE architecture can be considered as consisting of 2 parts, ie., the encoder and the decoder. The encoder, $f_{enc}(\\cdot )$ , is the part of the network that compresses the input, $I$ , into a latent space representation, $U$ , and the decoder, $f_{dec}(\\cdot )$ aims to reconstruct the input from the latent space representation (see equation 12 ). In essence, ",
+      "$$U = f_{enc}(I) = f_{L}(f_{L-1}(...f_{1}(I)))$$   (Eq. 12) ",
+      "where $L$ is the number of layers in the encoder part of the CAE.",
+      "The encoder in the proposed architecture consists of three 2D convolutional layers with 64, 32 and 1 filters, respectively. The decoder follows the same symmetry with three convolutional layers with 1, 32 and 64 filters, respectively and an output convolutional layer of a single filter (see Figure 1 ). All convolutional layers have a kernel size of (3 $\\times $ 3) and an activation function of Rectified Linear Unit (ReLU) except the output layer which employs a linear activation function. Each convolutional layer in the encoder is followed by a 2D MaxPooling layer and similarly each convolutional layer in the decoder is followed by a 2D UpSampling layer, serving as an inverse operation (having the same parameters). The pooling sizes for pooling layers are (2 $\\times $ 5), (2 $\\times $ 5) and (2 $\\times $ 2), respectively for the architectures when word2vec, GloVe and fastText embeddings are employed. With this configuration, an input tweet of size $32 \\times 300$ (corresponding to maximum sequence length $\\times $ embedding dimension, $D$ ) is downsampled to size of $4 \\times 6$ out of the encoder (bottleneck layer). As BERT word embeddings have word vectors of fixed size 768, the pooling layer sizes are chosen to be (2 $\\times $ 8), (2 $\\times $ 8) and (2 $\\times $0 2), respectively for that case. In summary, a representation of $\\times $1 values is learned for each tweet through the encoder, e.g., for fastText embeddings the flow of dimensions after each encoder block is as such : $\\times $2 .",
+      "In numerous NLP tasks, an Embedding Layer is employed as the first layer of the neural network which can be initialized with the word embedding matrix in order to incorporate the embedding process into the architecture itself instead of manual extraction. In our case, this was not possible because of nonexistence of an inversed embedding layer in the decoder (as in the relationship between MaxPooling layers and UpSampling layers) as an embedding layer is not differentiable.",
+      "Training of autoencoders tries to minimize the reconstruction error/loss, i.e., the deviation of the reconstructed output from the input. $L_2$ -loss or mean square error (MSE) is chosen to be the loss function. In autoencoders, minimizing the $L_2$ -loss is equivalent to maximizing the mutual information between the reconstructed inputs and the original ones BIBREF48 . In addition, from a probabilistic point of view, minimizing the $L_2$ -loss is the same as maximizing the probability of the parameters given the data, corresponding to a maximum likelihood estimator. The optimizer for the autoencoder training is chosen to be Adam due to its faster convergence abilities BIBREF49 . The learning rate for the optimizer is set to $10^{-5}$ and the batch size for the training is set to 32. Random split of 80% training-20% validation set is performed for monitoring convergence. Maximum number of training epochs is set to 50."
+    ],
+    [
+      "Certain constraints on neural network weights are commonly employed during training in order to reduce overfitting, also known as regularization. Such constraints include $L_1$ regularization, $L_2$ regularization, orthogonal regularization etc. Even though regularization is a common practice, standard training of neural networks do not inherently impose any constraints on the learned representations (activations), $U$ , other than the ones compelled by the activation functions (e.g. ReLUs resulting in non-negative outputs). Recent advancements in computer vision research show that constraining the learned representations can enhance the effectiveness of representation learning, consequently increasing the clustering performance BIBREF50 , BIBREF51 . ",
+      "$$\\begin{aligned}\n& \\text{minimize}\n& & L = 1/_N \\left\\Vert I - f_{dec}(f_{enc}(I))\\right\\Vert ^2_{2} \\\\\n& \\text{subject to}\n& & \\left\\Vert f_{enc}(I)\\right\\Vert ^2_{2} = 1\n\\end{aligned}$$   (Eq. 14) ",
+      "We propose an $L_2$ norm constraint on the learned representations out of the bottleneck layer, $U$ . Essentially, this is a hard constraint introduced during neural network training that results in learned features with unit $L_2$ norm out of the bottleneck layer (see equation 14 where $N$ is the number of data points). Training a deep convolutional autoencoder with such a constraint is shown to be much more effective for image data than applying $L_2$ normalization on the learned representations after training BIBREF51 . To the best of our knowledge, this is the first study to incorporate $L_2$ norm constraint in a task involving text data."
+    ],
+    [
+      "In order to fairly compare and evaluate the proposed methods in terms of effectiveness in representation of tweets, we fix the number of features to 24 for all methods and feed these representations as an input to 3 different clustering algorithms namely, k-means, Ward and spectral clustering with cluster numbers of 10, 20 and 50. Distance metric for k-means clustering is chosen to be euclidean and the linkage criteria for Ward clustering is chosen to be minimizing the sum of differences within all clusters, i.e., recursively merging pairs of clusters that minimally increases the within-cluster variance in a hierarchical manner. For spectral clustering, Gaussian kernel has been employed for constructing the affinity matrix. We also run experiments with tf-idf and BoWs representations without further dimensionality reduction as well as concatenation of all word embeddings into a long feature vector. For evaluation of clustering performance, we use Calinski-Harabasz score BIBREF42 , also known as the variance ratio criterion. CH score is defined as the ratio between the within-cluster dispersion and the between-cluster dispersion. CH score has a range of $[0, +\\infty ]$ and a higher CH score corresponds to a better clustering. Computational complexity of calculating CH score is $\\mathcal {O}(N)$ .",
+      "For a given dataset $X$ consisting of $N$ data points, i.e., $X = \\big \\lbrace x_1, x_2, ... , x_N\\big \\rbrace $ and a given set of disjoint clusters $C$ with $K$ clusters, i.e., $C = \\big \\lbrace c_1, c_2, ... , c_K\\big \\rbrace $ , Calinski-Harabasz score, $S_{CH}$ , is defined as ",
+      "$$S_{CH} = \\frac{N-K}{K-1}\\frac{\\sum _{c_k \\in C}^{}{N_k \\left\\Vert \\overline{c_k}-\\overline{X}\\right\\Vert ^2_{2}}}{\\sum _{c_k \\in C}^{}{}\\sum _{x_i \\in c_k}^{}{\\left\\Vert x_i-\\overline{c_k}\\right\\Vert ^2_{2}}}$$   (Eq. 16) ",
+      "where $N_k$ is the number of points belonging to the cluster $c_k$ , $\\overline{X}$ is the centroid of the entire dataset, $\\frac{1}{N}\\sum _{x_i \\in X}{x_i}$ and $\\overline{c_k}$ is the centroid of the cluster $c_k$ , $\\frac{1}{N_k}\\sum _{x_i \\in c_k}{x_i}$ .",
+      "For visual validation, we plot and inspect the t-Distributed Stochastic Neighbor Embedding (t-SNE) BIBREF52 and Uniform Manifold Approximation and Projection (UMAP) BIBREF53 mappings of the learned representations as well. Implementation of this study is done in Python (version 3.6) using scikit-learn and TensorFlow libraries BIBREF54 , BIBREF55 on a 64-bit Ubuntu 16.04 workstation with 128 GB RAM. Training of autoencoders are performed with a single NVIDIA Titan Xp GPU."
+    ],
+    [
+      "Performance of the representations tested on 3 different clustering algorithms, i.e., CH scores, for 3 different cluster numbers can be examined from Table 2 . $L_2$ -norm constrained CAE is simply referred as $L_2$ -CAE in Table 2 . Same table shows the number of features used for each method as well. Document-term matrix extracted by BoWs and tf-idf features result in a sparse matrix of $63,326 \\times 13,026$ with a sparsity of 0.9994733. Similarly, concatenation of word embeddings result in a high number of features with $32 \\times 300 = 9,600$ for word2vec, GloVe and fastText, $32 \\times 768 = 24,576$ for BERT embeddings. In summary, the proposed method of learning representations of tweets with CAEs outperform all of the conventional algorithms. When representations are compared with Hotelling's $T^2$ test (multivariate version of $t$ -test), every representation distribution learned by CAEs are shown to be statistically significantly different than every other conventional representation distribution with $p<0.001$ . In addition, introducing the $L_2$ -norm constraint on the learned representations during training enhances the clustering performance further (again $p<0.001$ when comparing for example fastText+CAE vs. fastText+ $L_2$0 -CAE). An example learning curve for CAE and $L_2$1 -CAE with fastText embeddings as input can also be seen in Figure 2 .",
+      "Detailed inspection of tweets that are clustered into the same cluster as well as visual analysis of the formed clusters is also performed. Figure 3 shows the t-SNE and UMAP mappings (onto 2D plane) of the 10 clusters formed by k-means algorithm for LDA, CAE and $L_2$ -CAE representations. Below are several examples of tweets sampled from one of the clusters formed by k-means in the 50 clusters case (fastText embeddings fed into $L_2$ -CAE):"
+    ],
+    [
+      "Overall, we show that deep convolutional autoencoder-based feature extraction, i.e., representation learning, from health related tweets significantly enhances the performance of clustering algorithms when compared to conventional text feature extraction and topic modeling methods (see Table 2 ). This statement holds true for 3 different clustering algorithms (k-means, Ward, spectral) as well as for 3 different number of clusters. In addition, proposed constrained training ( $L_2$ -norm constraint) is shown to further improve the clustering performance in each experiment as well (see Table 2 ). A Calinski-Harabasz score of 4,304 has been achieved with constrained representation learning by CAE for the experiment of 50 clusters formed by k-means clustering. The highest CH score achieved in the same experiment setting by conventional algorithms was 638 which was achieved by LDA applied of tf-idf features.",
+      "Visualizations of t-SNE and UMAP mappings in Figure 3 show that $L_2$ -norm constrained training results in higher separability of clusters. The benefit of this constraint is especially significant in the performance of k-means clustering (see Table 2 ). This phenomena is not unexpected as k-means clustering is based on $L_2$ distance as well. The difference in learning curves for regular and constrained CAE trainings is also expected. Constrained CAE training converges to local minimum slightly later than unconstrained CAE, i.e., training of $L_2$ -CAE is slightly slower than that of CAE due to the introduced contraint (see Figure 2 ).",
+      "When it comes to comparison between word embeddings, fastText and BERT word vectors result in the highest CH scores whereas word2vec and GloVe embeddings result in significantly lower performance. This observation can be explained by the nature of word2vec and GloVe embeddings which can not handle out-of-vocabulary tokens. Numerous tweets include names of certain drugs which are more likely to be absent in the vocabulary of these models, consequently resulting in vectors of zeros as embeddings. However, fastText embeddings are based on character n-grams which enables handling of out-of-vocabulary tokens, e.g., fastText word vectors of the tokens <acetaminophen> and <paracetamol> are closer to each other simply due to shared character sequence, <acetam>, even if one of them is not in the vocabulary. Note that, <acetaminophen> and <paracetamol> are different names for the same drug.",
+      "Using tf-idf or BoWs features directly results in very poor performance. Similarly, concatenating word embeddings to create thousands of features results in significantly low performance compared to methods that reduce these features to 24. The main reason is that the bias-variance trade-off is dominated by the bias in high dimensional settings especially in Euclidean spaces BIBREF56 . Due to very high number of features (relative to the number of observations), the radius of a given region varies with respect to the $n$ th root of its volume, whereas the number of data points in the region varies roughly linearly with the volume BIBREF56 . This phenomena is known as curse of dimensionality. As topic models such as LDA and NMF are designed to be used on documents that are sufficiently long to extract robust statistics from, extracted topic vectors fall short in performance as well when it comes to tweets due to short texts.",
+      "The main limitation of this study is the absence of topic labels in the dataset. As a result, internal clustering measure of Calinski-Harabasz score was used for evaluating the performance of the formed clusters instead of accuracy or normalized mutual information. Even though CH score is shown to be able to capture clusters of different densities and presence of subclusters, it has difficulties capturing highly noisy data and skewed distributions BIBREF57 . In addition, used clustering algorithms, i.e., k-means, Ward and spectral clustering, are hard clustering algorithms which results in non-overlapping clusters. However, a given tweet can have several topical labels.",
+      "Future work includes representation learning of health-related tweets using deep neural network architectures that can inherently learn the sequential nature of the textual data such as recurrent neural networks, e.g., Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) etc. Sequence-to-sequence autoencoders are main examples of such architectures and they have been shown to be effective in encoding paragraphs from Wikipedia and other corpora to lower dimensions BIBREF58 . Furthermore, encodings out of a bidirectional GRU will be tested for clustering performance, as such architectures have been employed to represent a given tweet in other studies BIBREF59 , BIBREF60 , BIBREF61 ."
+    ],
+    [
+      "In summary, we show that deep convolutional autoencoders can effectively learn compact representations of health-related tweets in an unsupervised manner. Conducted analysis show that the proposed representation learning scheme outperforms conventional feature extraction methods in three different clustering algorithms. In addition, we propose a constraint on the learned representation in order to further increase the clustering performance. Future work includes comparison of our model with recurrent neural architectures for clustering of health-related tweets. We believe this study serves as an advancement in the field of natural language processing for health informatics especially in clustering of short-text social media data."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: Deep Representation Learning for Clustering of Health Tweets
+
+Question: What is an example of a health-related tweet?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Dataset",
+    "Conventional Representations",
+    "Representation Learning",
+    "L 2 L_2-norm Constrained Representation Learning",
+    "Evaluation",
+    "Results",
+    "Discussion",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Social media plays an important role in health informatics and Twitter has been one of the most influential social media channel for mining population-level health insights BIBREF0 , BIBREF1 , BIBREF2 . These insights range from forecasting of influenza epidemics BIBREF3 to predicting adverse drug reactions BIBREF4 . A notable challenge due to the short length of Twitter messages is categorization of tweets into topics in a supervised manner, i.e., topic classification, as well as in an unsupervised manner, i.e., clustering.",
+      "Classification of tweets into topics has been studied extensively BIBREF5 , BIBREF6 , BIBREF7 . Even though text classification algorithms can reach significant accuracy levels, supervised machine learning approaches require annotated data, i.e, topic categories to learn from for classification. On the other hand, annotated data is not always available as the annotation process is burdensome and time-consuming. In addition, discussions in social media evolve rapidly with recent trends, rendering Twitter a dynamic environment with ever-changing topics. Therefore, unsupervised approaches are essential for mining health-related information from Twitter.",
+      "Proposed methods for clustering tweets employ conventional text clustering pipelines involving preprocessing applied to raw text strings, followed by feature extraction which is then followed by a clustering algorithm BIBREF8 , BIBREF9 , BIBREF10 . Performance of such approaches depend highly on feature extraction in which careful engineering and domain knowledge is required BIBREF11 . Recent advancements in machine learning research, i.e., deep neural networks, enable efficient representation learning from raw data in a hierarchical manner BIBREF12 , BIBREF13 . Several natural language processing (NLP) tasks involving Twitter data have benefited from deep neural network-based approaches including sentiment classification of tweets BIBREF14 , predicting potential suicide attempts from Twitter BIBREF15 and simulating epidemics from Twitter BIBREF16 .",
+      "In this work, we propose deep convolutional autoencoders (CAEs) for obtaining efficient representations of health-related tweets in an unsupervised manner. We validate our approach on a publicly available dataset from Twitter by comparing the performance of our approach and conventional feature extraction methods on 3 different clustering algorithms. Furthermore, we propose a constraint on the learned representations during neural network training in order to further improve the clustering performance. We show that the proposed deep neural network-based representation learning method outperforms conventional methods in terms of clustering performance in experiments of varying number of clusters."
+    ],
+    [
+      "Devising efficient representations of tweets, i.e., features, for performing clustering has been studied extensively. Most frequently used features for representing the text in tweets as numerical vectors are bag-of-words (BoWs) and term frequency-inverse document frequency (tf-idf) features BIBREF17 , BIBREF9 , BIBREF10 , BIBREF18 , BIBREF19 . Both of these feature extraction methods are based on word occurrence counts and eventually, result in a sparse (most elements being zero) document-term matrix. Proposed algorithms for clustering tweets into topics include variants of hierarchical, density-based and centroid-based clustering methods; k-means algorithm being the most frequently used one BIBREF9 , BIBREF19 , BIBREF20 .",
+      "Numerous works on topic modeling of tweets are available as well. Topic models are generative models, relying on the idea that a given tweet is a mixture of topics, where a topic is a probability distribution over words BIBREF21 . Even though the objective in topic modeling is slightly different than that of pure clustering, representing each tweet as a topic vector is essentially a way of dimensionality reduction or feature extraction and can further be followed by a clustering algorithm. Proposed topic modeling methods include conventional approaches or variants of them such as Latent Dirichlet Allocation (LDA) BIBREF22 , BIBREF17 , BIBREF9 , BIBREF23 , BIBREF24 , BIBREF25 , BIBREF26 , BIBREF27 , BIBREF19 , BIBREF28 , BIBREF29 and Non-negative Matrix Factorization (NMF) BIBREF30 , BIBREF18 . Note that topic models such as LDA are based on the notion that words belonging to a topic are more likely to appear in the same document and do not assume a distance metric between discovered topics.",
+      "In contrary to abovementioned feature extraction methods which are not specific to representation of tweets but rather generic in natural language processing, various works propose custom feature extraction methods for certain health-related information retrieval tasks from Twitter. For instance, Lim et al. engineered sentiment analysis features to discover latent infectious diseases from Twitter BIBREF31 . In order to track public health condition trends from Twitter, specific features are proposed by Parker at al. employing Wikipedia article index, i.e., treating the retrieval of medically-related Wikipedia articles as an indicator of a health-related condition BIBREF32 . Custom user similarity features calculated from tweets were also proposed for building a framework for recommending health-related topics BIBREF27 .",
+      "The idea of learning effective representations from raw data using neural networks has been employed in numerous machine learning domains such as computer vision and natural language processing BIBREF12 , BIBREF13 . The concept relies on the hierarchical, layer-wise architecture of neural networks in which the raw input data is encoded into informative representations of lower dimensions (representations of higher dimensions are possible as well) in a highly non-linear fashion. Autoencoders, Denoising Autoencoders, Convolutional Autoencoders, Sparse Autoencoders, Stacked Autoencoders and combinations of these, e.g., Denoising Convolutional Autoencoders, are the most common deep neural network architectures specifically used for representation learning. In an autoencoder training, the network tries to reconstruct the input data at its output, which forces the model to capture the most salient features of the data at its intermediate layers. If the intermediate layers correspond to a lower dimensional latent space than the original input, such autoencoders are also known as undercomplete. Activations extracted from these layers can be considered as compact, non-linear representations of the input. Another significant advancement in neural network-based representation learning in NLP tasks is word embeddings (also called distributed representation of words). By representing each word in a given vocabulary with a real-valued vector of a fixed dimension, word embeddings enable capturing of lexical, semantic or even syntactic similarities between words. Typically, these vector representations are learned from large corpora and can be used to enhance the performance of numerous NLP tasks such as document classification, question answering and machine translation. Most frequently used word embeddings are word2vec BIBREF33 and GloVe (Global Vectors for Word Representation) BIBREF34 . Both of these are extracted in an unsupervised manner and are based on the distributional hypothesis BIBREF35 , i.e., the assumption that words that occur in the same contexts tend to have similar meanings. Both word2vec and GloVe treat a word as a smallest entity to train on. A shift in this paradigm was introduced by fastText BIBREF36 , which treats each word as a bag of character n-grams. Consequently, fastText embeddings are shown to have better representations for rare words BIBREF36 . In addition, one can still construct a vector representation for an out-of-vocabulary word which is not possible with word2vec or GloVe embeddings BIBREF36 . Enhanced methods for deducting better word and/or sentence representations were recently introduced as well by Peters et al. with the name ELMo (Embeddings from Language Models) BIBREF37 and by Devlin et al. with the name BERT (Bidirectional Encoder Representations from Transformers) BIBREF38 . All of these word embedding models are trained on large corpora such as Wikipedia, in an unsupervised manner. For analyzing tweets, word2vec and GloVe word embeddings have been employed for topical clustering of tweets BIBREF39 , topic modeling BIBREF40 , BIBREF41 and extracting depression symptoms from tweets BIBREF20 .",
+      "Metrics for evaluating the performance of clustering algorithms varies depending on whether the ground truth topic categories are available or not. If so, frequently used metrics are accuracy and normalized mutual information. In the case of absence of ground truth labels, one has to use internal clustering criterions such as Calinski-Harabasz (CH) score BIBREF42 and Davies-Bouldin index BIBREF43 . Arbelaitz et al. provides an extensive comparative study of cluster validity indices BIBREF44 ."
+    ],
+    [
+      "For this study, a publicly available dataset is used BIBREF45 . The dataset consisting of tweets has been collected using Twitter API and was initially introduced by Karami et al. BIBREF46 . Earliest tweet dates back to 13 June 2011 where the latest one has a timestamp of 9 April 2015. The dataset consists of 63,326 tweets in English language, collected from Twitter channels of 16 major health news agencies. List of health news channels and the number of tweets in the dataset from each channel can be examined from Table 1 .",
+      "The outlook of a typical tweet from the dataset can be examined from Figure 1 . For every tweet, the raw data consists of the tweet text and in most cases followed by a url to the original news article of the particular news source. This url string, if available, is removed from each tweet as it does not possess any natural language information. As Twitter allows several ways for users to interact such as retweeting or mentioning, these actions appear in the raw text as well. For retweets, an indicator string of \"RT\" appears as a prefix in the raw data and for user mentions, a string of form \"@username\" appears in the raw data. These two tokens are removed as well. In addition, hashtags are converted to plain tokens by removal of the \"#\" sign appearing before them (e.g. <#pregnancy> becomes <pregnancy>). Number of words, number of unique words and mean word counts for each Twitter channel can also be examined from Table 1 . Longest tweet consists of 27 words."
+    ],
+    [
+      "For representing tweets, 5 conventional representation methods are proposed as baselines.",
+      "Word frequency features: For word occurrence-based representations of tweets, conventional tf-idf and BoWs are used to obtain the document-term matrix of $N \\times P$ in which each row corresponds to a tweet and each column corresponds to a unique word/token, i.e., $N$ data points and $P$ features. As the document-term matrix obtained from tf-idf or BoWs features is extremely sparse and consequently redundant across many dimensions, dimensionality reduction and topic modeling to a lower dimensional latent space is performed by the methods below.",
+      "Principal Component Analysis (PCA): PCA is used to map the word frequency representations from the original feature space to a lower dimensional feature space by an orthogonal linear transformation in such a way that the first principal component has the highest possible variance and similarly, each succeeding component has the highest variance possible while being orthogonal to the preceding components. Our PCA implementation has a time complexity of $\\mathcal {O}(NP^2 + P^3)$ .",
+      "Truncated Singular Value Decomposition (t-SVD): Standard SVD and t-SVD are commonly employed dimensionality reduction techniques in which a matrix is reduced or approximated into a low-rank decomposition. Time complexity of SVD and t-SVD for $S$ components are $\\mathcal {O}(min(NP^2, N^2P))$ and $\\mathcal {O}(N^2S)$ , respectively (depending on the implementation). Contrary to PCA, t-SVD can be applied to sparse matrices efficiently as it does not require data normalization. When the data matrix is obtained by BoWs or tf-idf representations as in our case, the technique is also known as Latent Semantic Analysis.",
+      "LDA: Our LDA implementation employs online variational Bayes algorithm introduced by Hoffman et al. which uses stochastic optimization to maximize the objective function for the topic model BIBREF47 .",
+      "NMF: As NMF finds two non-negative matrices whose product approximates the non-negative document-term matrix, it allows regularization. Our implementation did not employ any regularization and the divergence function is set to be squared error, i.e., Frobenius norm."
+    ],
+    [
+      "We propose 2D convolutional autoencoders for extracting compact representations of tweets from their raw form in a highly non-linear fashion. In order to turn a given tweet into a 2D structure to be fed into the CAE, we extract the word vectors of each word using word embedding models, i.e., for a given tweet, $t$ , consisting of $W$ words, the 2D input is $I_{t} \\in ^{W \\times D}$ where $D$ is the embedding vector dimension. We compare 4 different word embeddings namely word2vec, GloVe, fastText and BERT with embedding vector dimensions of 300, 300, 300 and 768, respectively. We set the maximum sequence length to 32, i.e., for tweets having less number of words, the input matrix is padded with zeros. As word2vec and GloVe embeddings can not handle out-of-vocabulary words, such cases are represented as a vector of zeros. The process of extracting word vector representations of a tweet to form the 2D input matrix can be examined from Figure 1 .",
+      "The CAE architecture can be considered as consisting of 2 parts, ie., the encoder and the decoder. The encoder, $f_{enc}(\\cdot )$ , is the part of the network that compresses the input, $I$ , into a latent space representation, $U$ , and the decoder, $f_{dec}(\\cdot )$ aims to reconstruct the input from the latent space representation (see equation 12 ). In essence, ",
+      "$$U = f_{enc}(I) = f_{L}(f_{L-1}(...f_{1}(I)))$$   (Eq. 12) ",
+      "where $L$ is the number of layers in the encoder part of the CAE.",
+      "The encoder in the proposed architecture consists of three 2D convolutional layers with 64, 32 and 1 filters, respectively. The decoder follows the same symmetry with three convolutional layers with 1, 32 and 64 filters, respectively and an output convolutional layer of a single filter (see Figure 1 ). All convolutional layers have a kernel size of (3 $\\times $ 3) and an activation function of Rectified Linear Unit (ReLU) except the output layer which employs a linear activation function. Each convolutional layer in the encoder is followed by a 2D MaxPooling layer and similarly each convolutional layer in the decoder is followed by a 2D UpSampling layer, serving as an inverse operation (having the same parameters). The pooling sizes for pooling layers are (2 $\\times $ 5), (2 $\\times $ 5) and (2 $\\times $ 2), respectively for the architectures when word2vec, GloVe and fastText embeddings are employed. With this configuration, an input tweet of size $32 \\times 300$ (corresponding to maximum sequence length $\\times $ embedding dimension, $D$ ) is downsampled to size of $4 \\times 6$ out of the encoder (bottleneck layer). As BERT word embeddings have word vectors of fixed size 768, the pooling layer sizes are chosen to be (2 $\\times $ 8), (2 $\\times $ 8) and (2 $\\times $0 2), respectively for that case. In summary, a representation of $\\times $1 values is learned for each tweet through the encoder, e.g., for fastText embeddings the flow of dimensions after each encoder block is as such : $\\times $2 .",
+      "In numerous NLP tasks, an Embedding Layer is employed as the first layer of the neural network which can be initialized with the word embedding matrix in order to incorporate the embedding process into the architecture itself instead of manual extraction. In our case, this was not possible because of nonexistence of an inversed embedding layer in the decoder (as in the relationship between MaxPooling layers and UpSampling layers) as an embedding layer is not differentiable.",
+      "Training of autoencoders tries to minimize the reconstruction error/loss, i.e., the deviation of the reconstructed output from the input. $L_2$ -loss or mean square error (MSE) is chosen to be the loss function. In autoencoders, minimizing the $L_2$ -loss is equivalent to maximizing the mutual information between the reconstructed inputs and the original ones BIBREF48 . In addition, from a probabilistic point of view, minimizing the $L_2$ -loss is the same as maximizing the probability of the parameters given the data, corresponding to a maximum likelihood estimator. The optimizer for the autoencoder training is chosen to be Adam due to its faster convergence abilities BIBREF49 . The learning rate for the optimizer is set to $10^{-5}$ and the batch size for the training is set to 32. Random split of 80% training-20% validation set is performed for monitoring convergence. Maximum number of training epochs is set to 50."
+    ],
+    [
+      "Certain constraints on neural network weights are commonly employed during training in order to reduce overfitting, also known as regularization. Such constraints include $L_1$ regularization, $L_2$ regularization, orthogonal regularization etc. Even though regularization is a common practice, standard training of neural networks do not inherently impose any constraints on the learned representations (activations), $U$ , other than the ones compelled by the activation functions (e.g. ReLUs resulting in non-negative outputs). Recent advancements in computer vision research show that constraining the learned representations can enhance the effectiveness of representation learning, consequently increasing the clustering performance BIBREF50 , BIBREF51 . ",
+      "$$\\begin{aligned}\n& \\text{minimize}\n& & L = 1/_N \\left\\Vert I - f_{dec}(f_{enc}(I))\\right\\Vert ^2_{2} \\\\\n& \\text{subject to}\n& & \\left\\Vert f_{enc}(I)\\right\\Vert ^2_{2} = 1\n\\end{aligned}$$   (Eq. 14) ",
+      "We propose an $L_2$ norm constraint on the learned representations out of the bottleneck layer, $U$ . Essentially, this is a hard constraint introduced during neural network training that results in learned features with unit $L_2$ norm out of the bottleneck layer (see equation 14 where $N$ is the number of data points). Training a deep convolutional autoencoder with such a constraint is shown to be much more effective for image data than applying $L_2$ normalization on the learned representations after training BIBREF51 . To the best of our knowledge, this is the first study to incorporate $L_2$ norm constraint in a task involving text data."
+    ],
+    [
+      "In order to fairly compare and evaluate the proposed methods in terms of effectiveness in representation of tweets, we fix the number of features to 24 for all methods and feed these representations as an input to 3 different clustering algorithms namely, k-means, Ward and spectral clustering with cluster numbers of 10, 20 and 50. Distance metric for k-means clustering is chosen to be euclidean and the linkage criteria for Ward clustering is chosen to be minimizing the sum of differences within all clusters, i.e., recursively merging pairs of clusters that minimally increases the within-cluster variance in a hierarchical manner. For spectral clustering, Gaussian kernel has been employed for constructing the affinity matrix. We also run experiments with tf-idf and BoWs representations without further dimensionality reduction as well as concatenation of all word embeddings into a long feature vector. For evaluation of clustering performance, we use Calinski-Harabasz score BIBREF42 , also known as the variance ratio criterion. CH score is defined as the ratio between the within-cluster dispersion and the between-cluster dispersion. CH score has a range of $[0, +\\infty ]$ and a higher CH score corresponds to a better clustering. Computational complexity of calculating CH score is $\\mathcal {O}(N)$ .",
+      "For a given dataset $X$ consisting of $N$ data points, i.e., $X = \\big \\lbrace x_1, x_2, ... , x_N\\big \\rbrace $ and a given set of disjoint clusters $C$ with $K$ clusters, i.e., $C = \\big \\lbrace c_1, c_2, ... , c_K\\big \\rbrace $ , Calinski-Harabasz score, $S_{CH}$ , is defined as ",
+      "$$S_{CH} = \\frac{N-K}{K-1}\\frac{\\sum _{c_k \\in C}^{}{N_k \\left\\Vert \\overline{c_k}-\\overline{X}\\right\\Vert ^2_{2}}}{\\sum _{c_k \\in C}^{}{}\\sum _{x_i \\in c_k}^{}{\\left\\Vert x_i-\\overline{c_k}\\right\\Vert ^2_{2}}}$$   (Eq. 16) ",
+      "where $N_k$ is the number of points belonging to the cluster $c_k$ , $\\overline{X}$ is the centroid of the entire dataset, $\\frac{1}{N}\\sum _{x_i \\in X}{x_i}$ and $\\overline{c_k}$ is the centroid of the cluster $c_k$ , $\\frac{1}{N_k}\\sum _{x_i \\in c_k}{x_i}$ .",
+      "For visual validation, we plot and inspect the t-Distributed Stochastic Neighbor Embedding (t-SNE) BIBREF52 and Uniform Manifold Approximation and Projection (UMAP) BIBREF53 mappings of the learned representations as well. Implementation of this study is done in Python (version 3.6) using scikit-learn and TensorFlow libraries BIBREF54 , BIBREF55 on a 64-bit Ubuntu 16.04 workstation with 128 GB RAM. Training of autoencoders are performed with a single NVIDIA Titan Xp GPU."
+    ],
+    [
+      "Performance of the representations tested on 3 different clustering algorithms, i.e., CH scores, for 3 different cluster numbers can be examined from Table 2 . $L_2$ -norm constrained CAE is simply referred as $L_2$ -CAE in Table 2 . Same table shows the number of features used for each method as well. Document-term matrix extracted by BoWs and tf-idf features result in a sparse matrix of $63,326 \\times 13,026$ with a sparsity of 0.9994733. Similarly, concatenation of word embeddings result in a high number of features with $32 \\times 300 = 9,600$ for word2vec, GloVe and fastText, $32 \\times 768 = 24,576$ for BERT embeddings. In summary, the proposed method of learning representations of tweets with CAEs outperform all of the conventional algorithms. When representations are compared with Hotelling's $T^2$ test (multivariate version of $t$ -test), every representation distribution learned by CAEs are shown to be statistically significantly different than every other conventional representation distribution with $p<0.001$ . In addition, introducing the $L_2$ -norm constraint on the learned representations during training enhances the clustering performance further (again $p<0.001$ when comparing for example fastText+CAE vs. fastText+ $L_2$0 -CAE). An example learning curve for CAE and $L_2$1 -CAE with fastText embeddings as input can also be seen in Figure 2 .",
+      "Detailed inspection of tweets that are clustered into the same cluster as well as visual analysis of the formed clusters is also performed. Figure 3 shows the t-SNE and UMAP mappings (onto 2D plane) of the 10 clusters formed by k-means algorithm for LDA, CAE and $L_2$ -CAE representations. Below are several examples of tweets sampled from one of the clusters formed by k-means in the 50 clusters case (fastText embeddings fed into $L_2$ -CAE):"
+    ],
+    [
+      "Overall, we show that deep convolutional autoencoder-based feature extraction, i.e., representation learning, from health related tweets significantly enhances the performance of clustering algorithms when compared to conventional text feature extraction and topic modeling methods (see Table 2 ). This statement holds true for 3 different clustering algorithms (k-means, Ward, spectral) as well as for 3 different number of clusters. In addition, proposed constrained training ( $L_2$ -norm constraint) is shown to further improve the clustering performance in each experiment as well (see Table 2 ). A Calinski-Harabasz score of 4,304 has been achieved with constrained representation learning by CAE for the experiment of 50 clusters formed by k-means clustering. The highest CH score achieved in the same experiment setting by conventional algorithms was 638 which was achieved by LDA applied of tf-idf features.",
+      "Visualizations of t-SNE and UMAP mappings in Figure 3 show that $L_2$ -norm constrained training results in higher separability of clusters. The benefit of this constraint is especially significant in the performance of k-means clustering (see Table 2 ). This phenomena is not unexpected as k-means clustering is based on $L_2$ distance as well. The difference in learning curves for regular and constrained CAE trainings is also expected. Constrained CAE training converges to local minimum slightly later than unconstrained CAE, i.e., training of $L_2$ -CAE is slightly slower than that of CAE due to the introduced contraint (see Figure 2 ).",
+      "When it comes to comparison between word embeddings, fastText and BERT word vectors result in the highest CH scores whereas word2vec and GloVe embeddings result in significantly lower performance. This observation can be explained by the nature of word2vec and GloVe embeddings which can not handle out-of-vocabulary tokens. Numerous tweets include names of certain drugs which are more likely to be absent in the vocabulary of these models, consequently resulting in vectors of zeros as embeddings. However, fastText embeddings are based on character n-grams which enables handling of out-of-vocabulary tokens, e.g., fastText word vectors of the tokens <acetaminophen> and <paracetamol> are closer to each other simply due to shared character sequence, <acetam>, even if one of them is not in the vocabulary. Note that, <acetaminophen> and <paracetamol> are different names for the same drug.",
+      "Using tf-idf or BoWs features directly results in very poor performance. Similarly, concatenating word embeddings to create thousands of features results in significantly low performance compared to methods that reduce these features to 24. The main reason is that the bias-variance trade-off is dominated by the bias in high dimensional settings especially in Euclidean spaces BIBREF56 . Due to very high number of features (relative to the number of observations), the radius of a given region varies with respect to the $n$ th root of its volume, whereas the number of data points in the region varies roughly linearly with the volume BIBREF56 . This phenomena is known as curse of dimensionality. As topic models such as LDA and NMF are designed to be used on documents that are sufficiently long to extract robust statistics from, extracted topic vectors fall short in performance as well when it comes to tweets due to short texts.",
+      "The main limitation of this study is the absence of topic labels in the dataset. As a result, internal clustering measure of Calinski-Harabasz score was used for evaluating the performance of the formed clusters instead of accuracy or normalized mutual information. Even though CH score is shown to be able to capture clusters of different densities and presence of subclusters, it has difficulties capturing highly noisy data and skewed distributions BIBREF57 . In addition, used clustering algorithms, i.e., k-means, Ward and spectral clustering, are hard clustering algorithms which results in non-overlapping clusters. However, a given tweet can have several topical labels.",
+      "Future work includes representation learning of health-related tweets using deep neural network architectures that can inherently learn the sequential nature of the textual data such as recurrent neural networks, e.g., Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) etc. Sequence-to-sequence autoencoders are main examples of such architectures and they have been shown to be effective in encoding paragraphs from Wikipedia and other corpora to lower dimensions BIBREF58 . Furthermore, encodings out of a bidirectional GRU will be tested for clustering performance, as such architectures have been employed to represent a given tweet in other studies BIBREF59 , BIBREF60 , BIBREF61 ."
+    ],
+    [
+      "In summary, we show that deep convolutional autoencoders can effectively learn compact representations of health-related tweets in an unsupervised manner. Conducted analysis show that the proposed representation learning scheme outperforms conventional feature extraction methods in three different clustering algorithms. In addition, we propose a constraint on the learned representation in order to further increase the clustering performance. Future work includes comparison of our model with recurrent neural architectures for clustering of health-related tweets. We believe this study serves as an advancement in the field of natural language processing for health informatics especially in clustering of short-text social media data."
+    ]
+  ]
+}
+```
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+Name of Paper: SOC: hunting the underground inside story of the ethereum Social-network Opinion and Comment
+
+Question: Was the introduced LSTM+CNN model trained on annotated data in a supervised fashion?
\ No newline at end of file
diff --git a/qasper-2166/instruction.md b/qasper-2166/instruction.md
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+Name of Paper: Linguistic Fingerprints of Internet Censorship: the Case of SinaWeibo
+
+Question: Is is known whether Sina Weibo posts are censored by humans or some automatic classifier?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Previous Work",
+    "Tracking Censorship",
+    "Datasets ::: Using Zhu et al. (2003)'s Corpus",
+    "Datasets ::: New Corpus ::: Web Scraping",
+    "Datasets ::: New Corpus ::: Decoding Censorship",
+    "Datasets ::: Sample Data",
+    "Crowdsourcing Experiment",
+    "Feature Extraction",
+    "Feature Extraction ::: Linguistic Features",
+    "Feature Extraction ::: Linguistic Features ::: LIWC features",
+    "Feature Extraction ::: Linguistic Features ::: Sentiment features",
+    "Feature Extraction ::: Linguistic Features ::: CRIE features",
+    "Feature Extraction ::: Linguistic Features ::: Semantic features",
+    "Feature Extraction ::: Linguistic Features ::: Frequency & readability",
+    "Feature Extraction ::: Linguistic Features ::: Word embeddings",
+    "Feature Extraction ::: Non-linguistic Features ::: Followers",
+    "Classification",
+    "Discussion and Conclusion",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "In 2019, Freedom in the World, a yearly survey produced by Freedom House that measures the degree of civil liberties and political rights in every nation, recorded the 13th consecutive year of decline in global freedom. This decline spans across long-standing democracies such as USA as well as authoritarian regimes such as China and Russia. \u201cDemocracy is in retreat. The offensive against freedom of expression is being supercharged by a new and more effective form of digital authoritarianism.\" According to the report, China is now exporting its model of comprehensive internet censorship and surveillance around the world, offering trainings, seminars, and even study trips as well as advanced equipment.",
+      "In this paper, we deal with a particular type of censorship \u2013 when a post gets removed from a social media platform semi-automatically based on its content. We are interested in exploring whether there are systematic linguistic differences between posts that get removed by censors from Sina Weibo, a Chinese microblogging platform, and the posts that remain on the website. Sina Weibo was launched in 2009 and became the most popular social media platform in China. Sina Weibo has over 431 million monthly active users.",
+      "In cooperation with the ruling regime, Weibo sets strict control over the content published under its service BIBREF0. According to Zhu et al. zhu-etal:2013, Weibo uses a variety of strategies to target censorable posts, ranging from keyword list filtering to individual user monitoring. Among all posts that are eventually censored, nearly 30% of them are censored within 5\u201330 minutes, and nearly 90% within 24 hours BIBREF1. We hypothesize that the former are done automatically, while the latter are removed by human censors.",
+      "Research shows that some of the censorship decisions are not necessarily driven by the criticism of the state BIBREF2, the presence of controversial topics BIBREF3, BIBREF4, or posts that describe negative events BIBREF5. Rather, censorship is triggered by other factors, such as for example, the collective action potential BIBREF2, i.e., censors target posts that stimulate collective action, such as riots and protests.",
+      "The goal of this paper is to compare censored and uncensored posts that contain the same sensitive keywords and topics. Using the linguistic features extracted, a neural network model is built to explore whether censorship decision can be deduced from the linguistic characteristics of the posts.",
+      "The contributions of this paper are: 1. We decipher a way to determine whether a blogpost on Weibo has been deleted by the author or censored by Weibo. 2. We develop a corpus of censored and uncensored Weibo blogposts that contain sensitive keyword(s). 3. We build a neural network classifier that predicts censorship significantly better than non-expert humans. 4. We find a set of linguistics features that contributes to the censorship prediction problem. 5. We indirectly test the construct of Collective Action Potential (CAP) proposed by King et al. king-etal:2013 through crowdsourcing experiments and find that the existence of CAP is more prevalent in censored blogposts than uncensored blogposts as judged by human annotators."
+    ],
+    [
+      "There have been significant efforts to develop strategies to detect and evade censorship. Most work, however, focuses on exploiting technological limitations with existing routing protocols BIBREF6, BIBREF7, BIBREF8, BIBREF9, BIBREF10. Research that pays more attention to linguistic properties of online censorship in the context of censorship evasion include, for example, Safaka et al. safaka-etal:2016 who apply linguistic steganography to circumvent censorship. Lee lee:2016 uses parodic satire to bypass censorship in China and claims that this stylistic device delays and often evades censorship. Hiruncharoenvate et al. hirun-etal:2015 show that the use of homophones of censored keywords on Sina Weibo could help extend the time a Weibo post could remain available online. All these methods rely on a significant amount of human effort to interpret and annotate texts to evaluate the likelihood of censorship, which might not be practical to carry out for common Internet users in real life. There has also been research that uses linguistic and content clues to detect censorship. Knockel et al. knockel-etal:2015 and Zhu et al. zhu-etal:2013 propose detection mechanisms to categorize censored content and automatically learn keywords that get censored. King et al. king-etal:2013 in turn study the relationship between political criticism and chance of censorship. They come to the conclusion that posts that have a Collective Action Potential get deleted by the censors even if they support the state. Bamman et al. bamman-etal:2012 uncover a set of politically sensitive keywords and find that the presence of some of them in a Weibo blogpost contribute to a higher chance of the post being censored. Ng et al. kei-nlp4if:2018 also target a set of topics that have been suggested to be sensitive, but unlike Bamman et al. bamman-etal:2012, they cover areas not limited to politics. Ng et al. kei-nlp4if:2018 investigate how the textual content as a whole might be relevant to censorship decisions when both the censored and uncensored blogposts include the same sensitive keyword(s).",
+      "Our work is related to Ng et al. kei-nlp4if:2018 and Ng et al. ng-etal-2019-neural; however, we introduce a larger and more diverse dataset of censored posts; we experiment with a wider range of features and in fact show that not all the features reported in Ng et al. guarantee the best performance. We built a classifier that significantly outperforms Ng et al. We conduct a crowdsourcing experiment testing human judgments of controversy and censorship as well as indirectly testing the construct of collective action potential proposed by King et al."
+    ],
+    [
+      "Tracking censorship topics on Weibo is a challenging task due to the transient nature of censored posts and the scarcity of censored data from well-known sources such as FreeWeibo and WeiboScope. The most straightforward way to collect data from a social media platform is to make use of its API. However, Weibo imposes various restrictions on the use of its API such as restricted access to certain endpoints and restricted number of posts returned per request. Above all, the Weibo API does not provide any endpoint that allows easy and efficient collection of the target data (posts that contain sensitive keywords). Therefore, an alternative method is needed to track censorship for our purpose."
+    ],
+    [
+      "Zhu et al. zhu-etal:2013 collected over 2 million posts published by a set of around 3,500 sensitive users during a 2-month period in 2012. We extract around 20 thousand text-only posts using 64 keywords across 26 topics, which partially overlap with those included in the New Corpus (see below and in Table TABREF20). We filter all duplicates. Among the extracted posts, 930 (4.63%) are censored by Weibo as verified by Zhu et al. zhu-etal:2013 The extracted data from Zhu et al.zhu-etal:2013's are also used in building classification models.",
+      "While it is possible to study the linguistic features in Zhu et al\u2019s dataset without collecting new data, we created another corpus that targets `normal' users (Zhu et al. target `sensitive' users) and a different time period so that the results are not specific to a particular group of users and time."
+    ],
+    [
+      "We develop a web scraper that continuously collects and tracks data that contain sensitive keywords on the front-end. The scraper's target interface displays 20 to 24 posts that contain a certain search key term(s), resembling a search engine's result page. We call this interface the Topic Timeline since the posts all contain the same keyword(s) and are displayed in reverse chronological order. The Weibo API does not provide any endpoint that returns the same set of data appeared on the Topic Timeline. Through a series of trial-and-errors to avoid CAPTCHAs that interrupt the data collection process, we found an optimal scraping frequency of querying the Topic Timeline every 5 to 10 minutes using 17 search terms (see Appendix) across 10 topics (see Table TABREF13) for a period of 4 months (August 29, 2018 to December 29, 2018). In each query, all relevant posts and their meta-data are saved to our database. We save posts that contain texts only (i.e. posts that do not contain images, hyperlinks, re-blogged content etc.) and filter out duplicates."
+    ],
+    [
+      "According to Zhu et al. zhu-etal:2013, the unique ID of a Weibo post is the key to distinguish whether a post has been censored by Weibo or has been instead removed by the authors themselves. If a post has been censored by Weibo, querying its unique ID through the API returns an error message of \u201cpermission denied\" (system-deleted), whereas a user-removed post returns an error message of \u201cthe post does not exist\" (user-deleted). However, since the Topic Timeline (the data source of our web scraper) can be accessed only on the front-end (i.e. there is no API endpoint associated with it), we rely on both the front-end and the API to identify system- and user-deleted posts. It is not possible to distinguish the two types of deletion by directly querying the unique ID of all scraped posts because, through empirical experimentation, uncensored posts and censored (system-deleted) posts both return the same error message \u2013 \u201cpermission denied\"). Therefore, we need to first check if a post still exists on the front-end, and then send an API request using the unique ID of the post that no longer exists to determine whether it has been deleted by the system or the user. The steps to identify censorship status of each post are illustrated in Figure FIGREF12. First, we check whether a scraped post is still available through visiting the user interface of each post. This is carried out automatically in a headless browser 2 days after a post is published. If a post has been removed (either by system or by user), the headless browser is redirected to an interface that says \u201cthe page doesn't exist\"; otherwise, the browser brings us to the original interface that displays the post content. Next, after 14 days, we use the same methods in step 1 to check the posts' status again. This step allows our dataset to include posts that have been removed at a later stage. Finally, we send a follow-up API query using the unique ID of posts that no longer exist on the browser in step 1 and step 2 to determine censorship status using the same decoding techniques proposed by Zhu et al. as described above zhu-etal:2013. Altogether, around 41 thousand posts are collected, in which 952 posts (2.28%) are censored by Weibo. In our ongoing work, we are comparing the accuracy of the classifier on posts that are automatically removed vs. those removed by humans. The results will be reported in the future publications.",
+      "We would like to emphasize that while the data collection methods could be seen as recreating a keyword search, the scraping pipeline also deciphers the logic in discovering censorship on Weibo."
+    ],
+    [
+      "Figure FIGREF15 shows several examples selected randomly from our dataset. Each pair of censored and uncensored posts contains the same sensitive keyword.",
+      ""
+    ],
+    [
+      "A balanced corpus is created. The uncensored posts of each dataset are randomly sampled to match with the number of their censored counterparts (see Table TABREF13 and Table TABREF20). We select randomly a subset of the data collected by the web scraper to construct surveys for crowdsourcing experiment. The surveys ask participants three questions (see Figure FIGREF16).",
+      "",
+      "Sample questions are included in Appendix. Question 1 explores how humans perform on the task of censorship classification; question 2 explores whether a blogpost is controversial; question 3 serves as a way to explore in our data the concept of Collective Action Potential (CAP) suggested by King et al.king-etal:2013. According to King et al. king-etal:2013, Collective Action Potential is the potential to cause collective action such as protest or organized crowd formation outside the Internet. Participants can respond either Yes or No to the 3 questions above. A total of 800 blogposts (400 censored and 400 uncensored) are presented to 37 different participants through a crowdsourcing platform Witmart in 8 batches (100 blogposts per batch). Each blogpost is annotated by 6 to 12 participants. The purpose of this paper is to shed light on the \u201cknowledge gap\u201d between censors and normal weibo users about censorable content. We believe weibo users are aware of potential censorable content but are not \u201ctrained\u201d enough to avoid or identify them. The results are summarized in Table TABREF19.",
+      "The annotation results are intuitive \u2013 participants tend to see censored blogposts as more controversial and more likely to trigger action in real life than the uncensored counterpart.",
+      "We obtain a Fleiss's kappa score for each question to study the inter-rater agreement. Since the number and identity of participants of each batch of the survey are different, we obtain an average Fleiss' kappa from the results of each batch. The Fleiss' kappa of questions 1 to 3 are 0.13, 0.07, and 0.03 respectively, which all fall under the category of slight agreement.",
+      "We hypothesize that since all blogposts contain sensitive keyword(s), the annotators choose to label a fair amount of uncensored blogposts as controversial, and even as likely to be censored or cause action in real life. This might also be the reason of the low agreement scores \u2013 the sensitive keywords might be the cause of divided opinions.",
+      "Regarding the result of censorship prediction, 23.83% of censored blogposts are correctly annotated as censored, while 83.59% of uncensored blogposts are correctly annotated as uncensored. This result suggests that participants tend to predict a blogpost to survive censorship on Weibo, despite the fact that they can see the presence of controversial element(s) in a blogpost as suggested by the annotation results of question 2. This suggests that detecting censorable content is a non-trivial task and humans do not have a good intuition (unless specifically trained, perhaps) what material is going to be censored. It might be true that there is some level of subjectivity form human censors. We believe there are commonalities among censored blogposts that pass through the \u201csubjectivity filters\u201d and such commonalities could be the linguistic features that contribute to our experiment results (see sections SECREF6 and SECREF7)."
+    ],
+    [
+      "To build an automatic classifier, we first extract features from both our scraped data and Zhu et al.'s dataset. While the datasets we use are different from that of Ng et al. kei-nlp4if:2018 and Ng et al. ng-etal-2019-neural, some of the features we extract are similar to theirs. We include CRIE features (see below) and the number of followers feature that are not extracted in Ng et al. kei-nlp4if:2018's work."
+    ],
+    [
+      "We extract 5 sets of linguistic features from both datasets (see below) \u2013 the LIWC features, the CRIE features, the sentiment features, the semantic features, and word embeddings. We are interested in the LIWC and CRIE features because they are purely linguistic, which aligns with the objective of our study. Also, some of the LIWC features extracted from Ng et al. ng2018detecting's data have shown to be useful in classifying censored and uncensored tweets.",
+      ""
+    ],
+    [
+      "The English Linguistic Inquiry and Word Count (LIWC) BIBREF11, BIBREF12 is a program that analyzes text on a word-by-word basis, calculating percentage of words that match each language dimension, e.g., pronouns, function words, social processes, cognitive processes, drives, informal language use etc. Its lexicon consists of approximately 6400 words divided into categories belong to different linguistic dimensions and psychological processes. LIWC builds on previous research establishing strong links between linguistic patterns and personality/psychological state. We use a version of LIWC developed for Chinese by Huang et al. huang-etal:2012 to extract the frequency of word categories. Altogether we extract 95 features from LIWC. One important feature of the LIWC lexicon is that categories form a tree structure hierarchy. Some features subsume others.",
+      ""
+    ],
+    [
+      "We use BaiduAI to obtain a set of sentiment scores for each post. BaiduAI's sentiment analyzer is built using deep learning techniques based on data found on Baidu, one of the most popular search engines and encyclopedias in mainland China. It outputs a positive sentiment score and a negative sentiment score which sum to 1.",
+      ""
+    ],
+    [
+      "We use the Chinese Readability Index Explorer (CRIE) BIBREF13, a text analysis tool developed for measuring the readability of a Chinese text based on the its linguistic components. Its internal dictionaries and lexical information are developed based on dominant corpora such as the Sinica Tree Bank. CRIE outputs 50 linguistic features (see Appendix), such as word, syntax, semantics, and cohesion in each text or produce an aggregated result for a batch of texts. CRIE can train and categorize texts based on their readability levels. We use the textual-features analysis for our data and derive readability scores for each post in our data. These scores are mainly based on descriptive statistics.",
+      ""
+    ],
+    [
+      "We use the Chinese Thesaurus developed by Mei mei:1984 and extended by HIT-SCIR to extract semantic features. The structure of this semantic dictionary is similar to WordNet, where words are divided into 12 semantic classes and each word can belong to one or more classes. It can be roughly compared to the concept of word senses. We derive a semantic ambiguity feature by dividing the number of words in each post by the number of semantic classes in it.",
+      ""
+    ],
+    [
+      "We compute the average frequency of characters and words in each post using Da da:2004's work and Aihanyu's CNCorpus respectively. For words with a frequency lower than 50 in the reference corpus, we count it as 0.0001%. It is intuitive to think that a text with less semantic variety and more common words and characters is relatively easier to read and understand. We derive a Readability feature by taking the mean of character frequency, word frequency and word count to semantic classes described above. It is assumed that the lower the mean of the 3 components, the less readable a text is. In fact, these 3 components are part of Sung et al. sung-et-al:2015's readability metric for native speakers on the word level and semantic level.",
+      ""
+    ],
+    [
+      "Word vectors are trained using the word2vec tool BIBREF14, BIBREF15 on 300,000 of the latest Chinese articles on Wikipedia. A 200-dimensional vector is computed for each word of each blogpost. The vector average of each blogpost is the sum of word vectors divided by the number of vectors. The 200-dimensional vector average are used as features for classification."
+    ],
+    [
+      "The number of followers of the author of each post is recorded and used as a feature for classification."
+    ],
+    [
+      "Features extracted from the balanced datasets (See Table 1 and Table 3) are used for classifications. Although the amount of uncensored blogposts significantly outnumber censored in real-life, such unbalanced corpus might be more suitable for anomaly detection. All numeric values of the features have been standardized before classification. We use a multilayer perceptron (MLP) classifier to classify instances into censored and uncensored. A number of classification experiments using different combinations of features are carried out.",
+      "Best performances are achieved using the combination of CRIE, sentiment, semantic, frequency, readability and follower features (i.e. all features but LIWC and word embeddings) (see Table TABREF36). The feature selection is performed using random sampling. As a result 77 features are selected that perform consistently well across the datasets. We call these features the best features set. (see https://msuweb.montclair.edu/~feldmana/publications/aaai20_appendix.pdf for the full list of features).",
+      "We vary the number of epochs and hidden layers. The rest of the parameters are set to default \u2013 learning rate of 0.3, momentum of 0.2, batch size of 100, validation threshold of 20. Classification experiments are performed on 1) both datasets 2) scraped data only 3) Zhu et al.'s data only. Each experiment is validated with 10-fold cross validation. We report the accuracy of each model in Table TABREF36. It is worth mentioning that using the LIWC features only, or the CRIE features only, or the word embeddings only, or all features excluding the CRIE features, or all features except the LIWC and CRIE features all result in poor performance of below 60%. Besides MLP, we also use the same sets of features to train classifiers using Naive Bayes, Logistic, and Support Vector Machine. However, the performances are all below 65%."
+    ],
+    [
+      "Our best results are over 30% higher than the baseline and about 60% higher than the human baseline obtained through crowdsourcing, which shows that our classifier has a greater censorship predictive ability compared to human judgments. The classification on both datasets together tends to give higher accuracy using at least 3 hidden layers. However, the performance does not improve when adding additional layers (other parameters being the same). Since the two datasets were collected differently and contain different topics, combining them together results in a richer dataset that requires more hidden layers to train a better model. It is worth noting that classifying both datasets using the best features set decreases the accuracy, while using all features but LIWC improves the classification performance. The reason for this behavior could be an existence of consistent differences in the LIWC features between the datasets. Since the LIWC features in the best features set (see Appendix https://msuweb.montclair.edu/~feldmana/publications/aaai20_appendix.pdf) consist of mostly word categories of different genres of vocabulary (i.e. grammar and style agnostic), it might suggest that the two datasets use vocabularies differently. Yet, the high performance obtained excluding the LIWC features shows that the key to distinguishing between censored and uncensored posts seems to be the features related to writing style, readability, sentiment, and semantic complexity of a text.",
+      "Figure FIGREF38 shows two blogposts annotated by CRIE with number of verbs and number of first person pronoun features.",
+      "",
+      "To narrow down on what might be the best features that contribute to distinguishing censored and uncensored posts, we compare the mean of each feature of the two classes (see Figure FIGREF37). The 6 features distinguish censored from uncensored are:",
+      "1. negative sentiment",
+      "2. average number of idioms in each sentence",
+      "3. number of content word categories",
+      "4. number of idioms",
+      "5. number of complex semantic categories",
+      "6. verbs",
+      "On the other hand, the 4 features that distinguish uncensored from censored are:",
+      "1. positive sentiment",
+      "2. words related to leisure",
+      "3. words related to reward",
+      "4. words related to money",
+      "This might suggest that the censored posts generally convey more negative sentiment and are more idiomatic and semantically complex in terms of word usage. According to King et al. king-etal:2013, Collective Action Potential, which is related to a blogpost's potential of causing riot or assembly in real-life, is the key determinant of a blogpost getting censored. Although there is not a definitive measure of this construct, it is intuitive to relate a higher average use of verbs to a post that calls for action.",
+      "On the other hand, the uncensored posts might be in general more positive in nature (positive sentiment) and include more content that talks about neutral matters (money, leisure, reward).",
+      "We further explore how the use of verbs might possibly affect censorship by studying the types of verbs used in censored and uncensored blogposts. We extracted verbs from all blogposts by using the Jieba Part-of-speech tagger . We then used the Chinese Thesaurus described in Section SECREF21 to categorize the verbs into 5 classes: Actions, Psychology, Human activities, States and phenomena, and Relations. However, no significant differences have been found across censored and uncensored blogposts. A further analysis on verbs in terms of their relationship with actions and arousal can be a part of future work.",
+      "Since the focus of this paper is to study the linguistic content of blogposts, rather than rate of censorship, we did not employ technical methods to differentiate blogposts that have different survival rates. Future work could be done to investigate any differences between blogposts that get censored at different rates. In our ongoing work, we are comparing the accuracy of the classifier on posts that are automatically removed vs. those removed by humans. The results will be reported in the future publications.",
+      "To conclude, our work shows that there are linguistic fingerprints of censorship, and it is possible to use linguistic properties of a social media post to automatically predict if it is going to be censored. It will be interesting to explore if the same linguistic features can be used to predict censorship on other social media platforms and in other languages."
+    ],
+    [
+      "This work is supported by the National Science Foundation under Grant No.: 1704113, Division of Computer and Networked Systems, Secure and Trustworthy Cyberspace (SaTC). We also thank Jed Crandall for sharing Zhu et al. zhu-etal:2013's dataset with us."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2167/instruction.md b/qasper-2167/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..b4c355ac6a55017c7c114de4aedc6f44729f5f4d
--- /dev/null
+++ b/qasper-2167/instruction.md
@@ -0,0 +1,114 @@
+Name of Paper: Multi-turn Inference Matching Network for Natural Language Inference
+
+Question: Which matching features do they employ?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Model",
+    "Encoding Layer",
+    "Attention Layer",
+    "Matching Layer",
+    "Multi-turn Inference Layer",
+    "Output Layer",
+    "Data",
+    " Models for Comparison",
+    "Experimental Settings",
+    "Experiments on SNLI",
+    "Experiments on MPE",
+    "Experiments on SCITAIL",
+    "Conclusion",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "Natural Language Inference (NLI) is a crucial subtopic in Natural Language Processing (NLP). Most studies treat NLI as a classification problem, aiming at recognizing the relation types of hypothesis-premise sentence pairs, usually including \u201cEntailment\u201d, \u201cContradiction\u201d and \u201cNeutral\u201d.",
+      "NLI is also called Recognizing Textual Entailment (RTE) BIBREF0 in earlier works and a lot of statistical-based BIBREF1 and rule-based approaches BIBREF2 are proposed to solve the problem. In 2015, Bowman released the SNLI corpus BIBREF3 that provides more than 570K hypothesis-premise sentence pairs. The large-scale data of SNLI allows a Neural Network (NN) based model to perform on the NLI. Since then, a variety of NN based models have been proposed, most of which can be divided into two kinds of frameworks. The first one is based on \u201cSiamense\" network BIBREF3 , BIBREF4 . It first applies either Recurrent Neural Network (RNN) or Convolutional Neural Networks (CNN) to generates sentence representations on both premise and hypothesis, and then concatenate them for the final classification. The second one is called \u201cmatching-aggregation\" network BIBREF5 , BIBREF6 . It matches two sentences at word level, and then aggregates the matching results to generate a fixed vector for prediction. Matching is implemented by several functions based on element-wise operations BIBREF7 , BIBREF8 . Studies on SNLI show that the second one performs better.",
+      "Though the second framework has made considerable success on the NLI task, there are still some limitations. First, the inference on the mixed matching feature only adopts one-pass process, which means some detailed information would not be retrieved once missing. While the multi-turn inference can overcome this deficiency and make better use of these matching features. Second, the mixed matching feature only concatenates different matching features as the input for aggregation. It lacks interaction among various matching features. Furthermore, it treats all the matching features equally and cannot assign different importance to different matching features.",
+      "In this paper, we propose the MIMN model to tackle these limitations. Our model uses the matching features described in BIBREF5 , BIBREF9 . However, we do not simply concatenate the features but introduce a multi-turn inference mechanism to infer different matching features with a memory component iteratively. The merits of MIMN are as follows:",
+      "We conduct experiments on three NLI datasets: SNLI BIBREF3 , SCITAIL BIBREF10 and MPE BIBREF11 . On the SNLI dataset, our single model achieves 88.3% in accuracy and our ensemble model achieves 89.3% in terms of accuracy, which are both comparable with the state-of-the-art results. Furthermore, our MIMN model outperforms all previous works on both SCITAIL and MPE dataset. Especially, the model gains substantial (8.9%) improvement on MPE dataset which contains multiple premises. This result shows our model is expert in aggregating the information of multiple premises."
+    ],
+    [
+      "Early work on the NLI task mainly uses conventional statistical methods on small-scale datasets BIBREF0 , BIBREF12 . Recently, the neural models on NLI are based on large-scale datasets and can be categorized into two central frameworks: (i) Siamense-based framework which focuses on building sentence embeddings separately and integrates the two sentence representations to make the final prediction BIBREF4 , BIBREF13 , BIBREF14 , BIBREF15 , BIBREF16 , BIBREF17 , BIBREF18 ; (ii) \u201cmatching-aggregation\u201d framework which uses various matching methods to get the interactive space of two input sentences and then aggregates the matching results to dig for deep information BIBREF19 , BIBREF20 , BIBREF21 , BIBREF22 , BIBREF8 , BIBREF6 , BIBREF23 , BIBREF24 , BIBREF18 , BIBREF25 , BIBREF26 .",
+      "Our model is directly motivated by the approaches proposed by BIBREF7 , BIBREF9 . BIBREF7 introduces the \u201cmatching-aggregation\" framework to compare representations between words and then aggregate their matching results for final decision.",
+      " BIBREF9 enhances the comparing approaches by adding element-wise subtraction and element-wise multiplication, which further improve the performance on SNLI. The previous work shows that matching layer is an essential component of this framework and different matching methods can affect the final classification result.",
+      "Various attention-based memory neural networks BIBREF27 have been explored to solve the NLI problem BIBREF20 , BIBREF28 , BIBREF14 . BIBREF20 presents a model of deep fusion LSTMs (DF-LSTMs) (Long Short-Term Memory ) which utilizes a strong interaction between text pairs in a recursive matching memory. BIBREF28 uses a memory network to extend the LSTM architecture. BIBREF14 employs a variable sized memory model to enrich the LSTM-based input encoding information. However, all the above models are not specially designed for NLI and they all focus on input sentence encoding.",
+      "Inspired by the previous work, we propose the MIMN model. We iteratively update memory by feeding in different sequence matching features. We are the first to apply memory mechanism to matching component for the NLI task. Our experiment results on several datasets show that our MIMN model is significantly better than the previous models."
+    ],
+    [
+      "In this section, we describe our MIMN model, which consists of the following five major components: encoding layer, attention layer, matching layer, multi-turn inference layer and output layer. Fig. FIGREF3 shows the architecture of our MIMN model.",
+      "We represent each example of the NLI task as a triple INLINEFORM0 , where INLINEFORM1 is a given premise, INLINEFORM2 is a given hypothesis, INLINEFORM3 and INLINEFORM4 are word embeddings of r-dimension. The true label INLINEFORM5 indicates the logical relationship between the premise INLINEFORM6 and the hypothesis INLINEFORM7 , where INLINEFORM8 . Our model aims to compute the conditional probability INLINEFORM9 and predict the label for examples in testing data set by INLINEFORM10 ."
+    ],
+    [
+      "In this paper, we utilize a bidirectional LSTM (BiLSTM) BIBREF29 as our encoder to transform the word embeddings of premise and hypothesis to context vectors. The premise and the hypothesis share the same weights of BiLSTM. DISPLAYFORM0 ",
+      "where the context vectors INLINEFORM0 and INLINEFORM1 are the concatenation of the forward and backward hidden outputs of BiLSTM respectively. The outputs of the encoding layer are the context vectors INLINEFORM2 and INLINEFORM3 , where INLINEFORM4 is the number of hidden units of INLINEFORM5 ."
+    ],
+    [
+      "On the NLI task, the relevant contexts between the premise and the hypothesis are important clues for final classification. The relevant contexts can be acquired by a soft-attention mechanism BIBREF30 , BIBREF31 , which has been applied to a bunch of tasks successfully. The alignments between a premise and a hypothesis are based on a score matrix. There are three most commonly used methods to compute the score matrix: linear combination, bilinear combination, and dot product. For simplicity, we choose dot product in the following computation BIBREF8 . First, each element in the score matrix is computed based on the context vectors of INLINEFORM0 and INLINEFORM1 as follows: DISPLAYFORM0 ",
+      " where INLINEFORM0 and INLINEFORM1 are computed in Equations ( EQREF5 ) and (), and INLINEFORM2 is a scalar which indicates how INLINEFORM3 is related to INLINEFORM4 .",
+      "Then, we compute the alignment vectors for each word in the premise and the hypothesis as follows: DISPLAYFORM0 DISPLAYFORM1 ",
+      " where INLINEFORM0 is the weighted summaries of thehypothesis in terms of each word in the premise. The same operation is applied to INLINEFORM1 . The outputs of this layer are INLINEFORM2 and INLINEFORM3 . For the context vectors INLINEFORM4 , the relevant contexts in the hypothesis INLINEFORM5 are represented in INLINEFORM6 . The same is applied to INLINEFORM7 and INLINEFORM8 ."
+    ],
+    [
+      "The goal of the matching layer is to match the context vectors INLINEFORM0 and INLINEFORM1 with the corresponding aligned vectors INLINEFORM2 and INLINEFORM3 from multi-perspective to generate a matching sequence.",
+      "In this layer, we match each context vector INLINEFORM0 against each aligned vector INLINEFORM1 to capture richer semantic information. We design three effective matching functions: INLINEFORM2 , INLINEFORM3 and INLINEFORM4 to match two vectors BIBREF32 , BIBREF5 , BIBREF9 . Each matching function takes the context vector INLINEFORM5 ( INLINEFORM6 ) and the aligned vector INLINEFORM7 ( INLINEFORM8 ) as inputs, then matches the inputs by an feed-forward network based on a particular matching operation and finally outputs a matching vector. The formulas of the three matching functions INLINEFORM9 , INLINEFORM10 and INLINEFORM11 are described in formulas ( EQREF11 ) () (). To avoid repetition, we will only describe the application of these functions to INLINEFORM12 and INLINEFORM13 . The readers can infer these equations for INLINEFORM14 and INLINEFORM15 . DISPLAYFORM0 ",
+      " where INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 represent concatenation, subtraction, and multiplication respectively, INLINEFORM3 , INLINEFORM4 and INLINEFORM5 are weight parameters to be learned, and INLINEFORM6 are bias parameters to be learned. The outputs of each matching function are INLINEFORM7 , which represent the matching result from three perspectives respectively. After matching the context vectors INLINEFORM8 and the aligned vectors INLINEFORM9 by INLINEFORM10 , INLINEFORM11 and INLINEFORM12 , we can get three matching features INLINEFORM13 , INLINEFORM14 and INLINEFORM15 .",
+      "After matching the context vectors INLINEFORM0 and the aligned vectors INLINEFORM1 by INLINEFORM2 , INLINEFORM3 and INLINEFORM4 , we can get three matching features INLINEFORM5 , INLINEFORM6 and INLINEFORM7 . where INLINEFORM8 , INLINEFORM9 , and INLINEFORM10 .",
+      "The INLINEFORM0 can be considered as a joint-feature of combing the context vectors INLINEFORM1 with aligned vectors INLINEFORM2 , which preserves all the information. And the INLINEFORM3 can be seen as a diff-feature of the INLINEFORM4 and INLINEFORM5 , which preserves the different parts and removes the similar parts. And the INLINEFORM6 can be regarded as a sim-feature of INLINEFORM7 and INLINEFORM8 , which emphasizes on the similar parts and neglects the different parts between INLINEFORM9 and INLINEFORM10 . Each feature helps us focus on particular parts between the context vectors and the aligned vectors. These matching features are vector representations with low dimension, but containing high-order semantic information. To make further use of these matching features, we collect them to generate a matching sequence INLINEFORM11 . DISPLAYFORM0 ",
+      " where INLINEFORM0 .",
+      "The output of this layer is the matching sequence INLINEFORM0 , which stores three kinds of matching features. The order of the matching features in INLINEFORM1 is inspired by the attention trajectory of human beings making inference on premise and hypothesis. We process the matching sequence in turn in the multi-turn inference layer. Intuitively, given a premise and a hypothesis, we will first read the original sentences to find the relevant information. Next, it's natural for us to combine all the parts of the original information and the relevant information. Then we move the attention to the different parts. Finally, we pay attention to the similar parts."
+    ],
+    [
+      "In this layer, we aim to acquire inference outputs by aggregating the information in the matching sequence by multi-turn inference mechanism. We regard the inference on the matching sequence as the multi-turn interaction among various matching features. In each turn, we process one matching feature instead of all the matching features BIBREF9 , BIBREF26 . To enhance the information interaction between matching features, a memory component is employed to store the inference information of the previous turns. Then, the inference of each turn is based on the current matching feature and the memory. Here, we utilize another BiLSTM for the inference. DISPLAYFORM0 ",
+      " where INLINEFORM0 is an inference vector in the current turn, INLINEFORM1 is the index current turn, INLINEFORM2 , INLINEFORM3 is a memory vector stores the historical inference information, and INLINEFORM4 is used for dimension reduction.",
+      "Then we update the memory by combining the current inference vector INLINEFORM0 with the memory vector of last turn INLINEFORM1 . An update gate is used to control the ratio of current information and history information adaptively BIBREF33 . The initial values of all the memory vectors are all zeros. DISPLAYFORM0 ",
+      " where INLINEFORM0 and INLINEFORM1 are parameters to be learned, and INLINEFORM2 is a sigmoid function to compress the ratio between 0-1. Finally, we use the latest memory matrix INLINEFORM3 as the inference output of premise INLINEFORM4 . Then we calculate INLINEFORM5 in a similar way. The final outputs of this layer are INLINEFORM6 and INLINEFORM7 . DISPLAYFORM0 ",
+      "where INLINEFORM0 stores the inference results of all matching features. The final outputs of multi-turn inference layer are INLINEFORM1 and INLINEFORM2 . The calculation of INLINEFORM3 is the same as INLINEFORM4 ."
+    ],
+    [
+      "The final relationship judgment depends on the sentence embeddings of premise and hypothesis. We convert INLINEFORM0 and INLINEFORM1 to sentence embeddings of premise and hypothesis by max pooling and average pooling. Next, we concatenate the two sentence embeddings to a fixed-length output vector. Then we feed the output vector to a multilayer perceptron (MLP) classifier that includes a hidden layer with INLINEFORM2 activation and a softmax layer to get the final prediction. The model is trained end-to-end. We employ multi-class cross-entropy as the cost function when training the model."
+    ],
+    [
+      "To verify the effectiveness of our model, we conduct experiments on three NLI datasets. The basic information about the three datasets is shown in Table TABREF19 .",
+      "The large SNLI BIBREF3 corpus is served as a major benchmark for the NLI task. The MPE corpus BIBREF11 is a newly released textual entailment dataset. Each pair in MPE consists of four premises, one hypothesis, and one label, which is different from the standard NLI datasets. Entailment relationship holds if the hypothesis comes from the same image as the four premises. The SCITAIL BIBREF10 is a dataset about science question answering. The premises are created from relevant web sentences, while hypotheses are created from science questions and the corresponding answer candidates.",
+      ""
+    ],
+    [
+      "We compare our model with \u201cmatching-aggregation\u201d related and attention-based memory related models. In addition, to verify the effectiveness of these major components in our model, we design the following model variations for comparison: ESIM is considered as a typical model of \u201cmatching-aggregation\u201d, so we choose ESIM as the principal comparison object. We choose the LSTMN model with deep attention fusion as a complement comparison, which is a memory related model. Besides above models, following variants of our model are designed for comparing:",
+      "ESIM We choose the ESIM model as our baseline. It mixes all the matching feature together in the matching layer and then infers the matching result in a single-turn with a BiLSTM.",
+      "600D MIMN: This is our main model described in section SECREF3 .",
+      "600D MIMN-memory: This model removes the memory component. The motivation of this experiment is to verify whether the multiple turns inference can acquire more sufficient information than one-pass inference. In this model, we process one matching feature in one iteration. The three matching features are encoded by INLINEFORM0 in multi-turns iteratively without previous memory information. The output of each iteration is concatenated to be the final output of the multi-turn inference layer: Then the Equation ( EQREF14 ) and ( EQREF16 ) are changed into Equation ( EQREF24 ) and () respectively and the Equation ( EQREF15 ) is removed. DISPLAYFORM0 ",
+      "600D MIMN-gate+ReLU : This model replaces the update gate in the memory component with a ReLU layer. The motivation of this model is to verify the effectiveness of update gate for combining current inference result and previous memory. Then the Equation ( EQREF15 ) is changed into Equation ( EQREF26 ). INLINEFORM0 stays the same as Equations ( EQREF16 ). DISPLAYFORM0 "
+    ],
+    [
+      "We implement our model with Tensorflow BIBREF34 . We initialize the word embeddings by the pre-trained embeddings of 300D GloVe 840B vectors BIBREF35 . The word embeddings of the out-of-vocabulary words are randomly initialized. The hidden units of INLINEFORM0 and INLINEFORM1 are 300 dimensions. All weights are constrained by L2 regularization with the weight decay coefficient of 0.0003. We also apply dropout BIBREF36 to all the layers with a dropout rate of 0.2. Batch size is set to 32. The model is optimized with Adam BIBREF37 with an initial learning rate of 0.0005, the first momentum of 0.9 and the second of 0.999. The word embeddings are fixed during all the training time. We use early-stopping (patience=10) based on the validation set accuracy. We use three turns on all the datasets. The evaluation metric is the classification accuracy. To help duplicate our results, we will release our source code at https://github.com/blcunlp/RTE/tree/master/MIMN."
+    ],
+    [
+      "Experimental results of the current state-of-the-art models and three variants of our model are listed in Table TABREF29 . The first group of models (1)-(3) are the attention-based memory models on the NLI task. BIBREF20 uses external memory to increase the capacity of LSTMs. BIBREF14 utilizes an encoding memory matrix to maintain the input information. BIBREF28 extends the LSTM architecture with a memory network to enhance the interaction between the current input and all previous inputs.",
+      "The next group of models (4)-(12) belong to the \u201cmatching-aggregation\u201d framework with bidirectional inter-attention. Decomposable attention BIBREF8 first applies the \u201cmatching-aggregation\u201d on SNLI dataset explicitly. BIBREF5 enriches the framework with several comparison functions. BiMPM BIBREF6 employs a multi-perspective matching function to match the two sentences. BiMPM BIBREF6 does not only exploit a multi-perspective matching function but also allows the two sentences to match from multi-granularity. ESIM BIBREF9 further sublimates the framework by enhancing the matching tuples with element-wise subtraction and element-wise multiplication. ESIM achieves 88.0% in accuracy on the SNLI test set, which exceeds the human performance (87.7%) for the first time. BIBREF18 and BIBREF1 both further improve the performance by taking the ESIM model as a baseline model. The studies related to \u201cmatching-aggregation\u201d but without bidirectional interaction are not listed BIBREF19 , BIBREF7 .",
+      "Motivated by the attention-based memory models and the bidirectional inter-attention models, we propose the MIMN model. The last group of models (13)-(16) are models described in this paper. Our single MIMN model obtains an accuracy of 88.3% on SNLI test set, which is comparable with the current state-of-the-art single models. The single MIMN model improves 0.3% on the test set compared with ESIM, which shows that multi-turn inference based on the matching features and memory achieves better performance. From model (14), we also observe that memory is generally beneficial, and the accuracy drops 0.8% when the memory is removed. This finding proves that the interaction between matching features is significantly important for the final classification. To explore the way of updating memory, we replace the update gate in MIMN with a ReLU layer to update the memory, which drops 0.1%.",
+      "To further improve the performance on SNLI dataset, an ensemble model MIMN is built for comparison. We design the ensemble model by simply averaging the probability distributions BIBREF6 of four MIMN models. Each of the models has the same architecture but initialized by different seeds. Our ensemble model achieves the state-of-the-art performance by obtains an accuracy of 89.3% on SNLI test set."
+    ],
+    [
+      "The MPE dataset is a brand-new dataset for NLI with four premises, one hypothesis, and one label. In order to maintain the same data format as other textual entailment datasets (one premise, one hypothesis, and one label), we concatenate the four premises as one premise.",
+      "Table TABREF31 shows the results of our models along with the published models on this dataset. LSTM is a conditional LSTM model used in BIBREF19 . WbW-Attention aligns each word in the hypothesis with the premise. The state-of-the-art model on MPE dataset is SE model proposed by BIBREF11 , which makes four independent predictions for each sentence pairs, and the final prediction is the summation of four predictions. Compared with SE, our MIMN model obtains a dramatic improvement (9.7%) on MPE dataset by achieving 66.0% in accuracy.",
+      "To compare with the bidirectional inter-attention model, we re-implement the ESIM, which obtains 59.0% in accuracy. We observe that MIMN-memory model achieves 61.6% in accuracy. This finding implies that inferring the matching features by multi-turns works better than single turn. Compared with the ESIM, our MIMN model increases 7.0% in accuracy. We further find that the performance of MIMN achieves 77.9% and 73.1% in accuracy of entailment and contradiction respectively, outperforming all previous models. From the accuracy distributions on N, E, and C in Table TABREF31 , we can see that the MIMN model is good at dealing with entailment and contradiction while achieves only average performance on neural.",
+      "Consequently, the experiment results show that our MIMN model achieves a new state-of-the-art performance on MPE test set. Besides, our MIMN-memory model and MIMN-gate+ReLU model both achieve better performance than previous models. All of our models perform well on the entailment label, which reveals that our models can aggregate information from multiple sentences for entailment judgment."
+    ],
+    [
+      "In this section, we study the effectiveness of our model on the SCITAIL dataset. Table TABREF31 presents the results of our models and the previous models on this dataset. Apart from the results reported in the original paper BIBREF10 : Majority class, ngram, decomposable attention, ESIM and DGEM, we compare further with the current state-of-the-art model CAFE BIBREF18 .",
+      "We can see that the MIMN model achieves 84.0% in accuracy on SCITAIL test set, which outperforms the CAFE by a margin of 0.5%. Moreover, the MIMN-gate+ReLU model exceeds the CAFE slightly. The MIMN model increases 13.3% in test accuracy compared with the ESIM, which again proves that multi-turn inference is better than one-pass inference."
+    ],
+    [
+      "In this paper, we propose the MIMN model for NLI task. Our model introduces a multi-turns inference mechanism to process multi-perspective matching features. Furthermore, the model employs the memory mechanism to carry proceeding inference information. In each turn, the inference is based on the current matching feature and previous memory. Experimental results on SNLI dataset show that the MIMN model is on par with the state-of-the-art models. Moreover, our model achieves new state-of-the-art results on the MPE and the SCITAL datasets. Experimental results prove that the MIMN model can extract important information from multiple premises for the final judgment. And the model is good at handling the relationships of entailment and contradiction."
+    ],
+    [
+      "This work is funded by Beijing Advanced Innovation for Language Resources of BLCU, the Fundamental Research Funds for the Central Universities in BLCU (No.17PT05) and Graduate Innovation Fund of BLCU (No.18YCX010)."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2168/instruction.md b/qasper-2168/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..36318444b2bc070f3589c8a98f6d07cd5ed1483c
--- /dev/null
+++ b/qasper-2168/instruction.md
@@ -0,0 +1,31 @@
+Name of Paper: The Logoscope: a Semi-Automatic Tool for Detecting and Documenting French New Words
+
+Question: How often are the newspaper websites crawled daily?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction: New Words, NLP and the Logoscope",
+    "General Description of the Logoscope Framework",
+    "New Words are Collected in Three Stages",
+    "Stage 1: Detection of Unknown Words"
+  ],
+  "paragraphs": [
+    [
+      "Like other areas of linguistics, the study of new words has benefited from the development of natural language processing techniques in the last twenty years. In this research area, the digital revolution has significantly changed the way to collect the data necessary for empirical research BIBREF0 , BIBREF1 : The traditional practice of collecting new words by reading texts (newspapers, literature, scientific and technical texts, etc.), has been supplemented by an automatisation of the collection process. In fact, today there are many computer tools capable of searching through large amounts of texts (often newspapers) in order to automatically detect the newly created words in various languages (German, Catalan, Spanish, English, French, etc.)."
+    ],
+    [
+      "The main building block of the Logoscope framework consists of a continuously updated lexical resource containing unknown words, their occurrences in French online press articles and further morpho-syntactic and contextual documentation. In addition our system features a query interface which allows an open access to this knowledge base."
+    ],
+    [
+      "The Logoscope is built in three stages (see Figure FIGREF6 ). The first stage consists in the automatic detection of the unknown words. In the second stage the detected unknown words are manually validated, that is, a human expert decides which of them are genuine word creations. Finally, in the third stage these validated unknown words are documented. This is done firstly by manually adding morpho-syntactic features and information on the originating creation process. This information is automatically complemented by contextual information, typically e.g the journal and the paragraph the new words appeared in. But, in contrast to other similar systems (see Section SECREF32 ), the Logoscope also (automatically) gives a rough approximation of what the article containing the new word is about.",
+      "In the next sections we will first give a more detailed description of each of these three stages before briefly addressing how the Logoscope might be used through its web interface in Section SECREF30 . A more detailed report of the arising NLP challenges and the methods we used to tackle them will be presented in Section SECREF5 ."
+    ],
+    [
+      "The Logoscope retrieves newspaper articles from several RSS feeds in French on a daily basis. The newspaper articles are preprocessed such that only the journalistic content is kept. The articles are then segmented into paragraphs and word forms. The resulting forms are filtered based on an exclusion list (French words found in several lexicons and corpora). They are then reordered in such a way that those words which are the most likely new word candidates appear on top, using a supervised classification method which will be described more in detail in Section SECREF71 ."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2169/instruction.md b/qasper-2169/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..f50ed3ace0c2f9df78aba5dc64100d5ff7ccb39b
--- /dev/null
+++ b/qasper-2169/instruction.md
@@ -0,0 +1,92 @@
+Name of Paper: TextKD-GAN: Text Generation using KnowledgeDistillation and Generative Adversarial Networks
+
+Question: How much better in terms of JSD measure did their model perform?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Background",
+    "Knowledge Distillation",
+    "Improved WGAN",
+    "Related Work",
+    "Methodology",
+    "Distilling output probabilities of AE to TextKD-GAN generator",
+    "Why TextKD-GAN should Work Better than IWGAN",
+    "Model Training",
+    "Dataset and Experimental Setup",
+    "Experimental Results",
+    "Discussion",
+    "Conclusion and Future Work"
+  ],
+  "paragraphs": [
+    [
+      "Recurrent neural network (RNN) based techniques such as language models are the most popular approaches for text generation. These RNN-based text generators rely on maximum likelihood estimation (MLE) solutions such as teacher forcing BIBREF0 (i.e. the model is trained to predict the next item given all previous observations); however, it is well-known in the literature that MLE is a simplistic objective for this complex NLP task BIBREF1 . MLE-based methods suffer from exposure bias BIBREF2 , which means that at training time the model is exposed to gold data only, but at test time it observes its own predictions.",
+      "However, GANs which are based on the adversarial loss function and have the generator and the discriminator networks suffers less from the mentioned problems. GANs could provide a better image generation framework comparing to the traditional MLE-based methods and achieved substantial success in the field of computer vision for generating realistic and sharp images. This great success motivated researchers to apply its framework to NLP applications as well.",
+      "GANs have been exploited recently in various NLP applications such as machine translation BIBREF3 , BIBREF4 , dialogue models BIBREF1 , question answering BIBREF5 , and natural language generation BIBREF6 , BIBREF2 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 . However, applying GAN in NLP is challenging due to the discrete nature of the text. Consequently, back-propagation would not be feasible for discrete outputs and it is not straightforward to pass the gradients through the discrete output words of the generator. The existing GAN-based solutions can be categorized according to the technique that they leveraged for handling the problem of the discrete nature of text: Reinforcement learning (RL) based methods, latent space based solutions, and approaches based on continuous approximation of discrete sampling. Several versions of the RL-based techniques have been introduced in the literature including Seq-GAN BIBREF11 , MaskGAN BIBREF12 , and LeakGAN BIBREF13 . However, they often need pre-training and are computationally more expensive compared to the methods of the other two categories. Latent space-based solutions derive a latent space representation of the text using an AE and attempt to learn data manifold of that space BIBREF8 . Another approach for generating text with GANs is to find a continuous approximation of the discrete sampling by using the Gumbel Softmax technique BIBREF14 or approximating the non-differentiable argmax operator BIBREF9 with a continuous function.",
+      "In this work, we introduce TextKD-GAN as a new solution for the main bottleneck of using GAN for text generation with knowledge distillation: a technique that transfer the knowledge of softened output of a teacher model to a student model BIBREF15 . Our solution is based on an AE (Teacher) to derive a smooth representation of the real text. This smooth representation is fed to the TextKD-GAN discriminator instead of the conventional one-hot representation. The generator (Student) tries to learn the manifold of the softened smooth representation of the AE. We show that TextKD-GAN outperforms the conventional GAN-based text generators that do not need pre-training. The remainder of the paper is organized as follows. In the next two sections, some preliminary background on generative adversarial networks and related work in the literature will be reviewed. The proposed method will be presented in section SECREF4 . In section SECREF5 , the experimental details will be discussed. Finally, section SECREF6 will conclude the paper."
+    ],
+    [
+      "Generative adversarial networks include two separate deep networks: a generator and a discriminator. The generator takes in a random variable, INLINEFORM0 following a distribution INLINEFORM1 and attempt to map it to the data distribution INLINEFORM2 . The output distribution of the generator is expected to converge to the data distribution during the training. On the other hand, the discriminator is expected to discern real samples from generated ones by outputting zeros and ones, respectively. During training, the generator and discriminator generate samples and classify them, respectively by adversarially affecting the performance of each other. In this regard, an adversarial loss function is employed for training BIBREF16 : DISPLAYFORM0 ",
+      "This is a two-player minimax game for which a Nash-equilibrium point should be derived. Finding the solution of this game is non-trivial and there has been a great extent of literature dedicated in this regard BIBREF17 .",
+      "As stated, using GANs for text generation is challenging because of the discrete nature of text. To clarify the issue, Figure FIGREF2 depicts a simplistic architecture for GAN-based text generation. The main bottleneck of the design is the argmax operator which is not differentiable and blocks the gradient flow from the discriminator to the generator. DISPLAYFORM0 "
+    ],
+    [
+      "Knowledge distillation has been studied in model compression where knowledge of a large cumbersome model is transferred to a small model for easy deployment. Several studies have been studied on the knowledge transfer technique BIBREF15 , BIBREF18 . It starts by training a big teacher model (or ensemble model) and then train a small student model which tries to mimic the characteristics of the teacher model, such as hidden representations BIBREF18 , it's output probabilities BIBREF15 , or directly on the generated sentences by the teacher model in neural machine translation BIBREF19 . The first teacher-student framework for knowledge distillation was proposed in BIBREF15 by introducing the softened teacher's output. In this paper, we propose a GAN framework for text generation where the generator (Student) tries to mimic the reconstructed output representation of an auto-encoder (Teacher) instead of mapping to a conventional one-hot representations."
+    ],
+    [
+      "Generating text with pure GANs is inspired by improved Wasserstein GAN (IWGAN) work BIBREF6 . In IWGAN, a character level language model is developed based on adversarial training of a generator and a discriminator without using any extra element such as policy gradient reinforcement learning BIBREF20 . The generator produces a softmax vector over the entire vocabulary. The discriminator is responsible for distinguishing between the one-hot representations of the real text and the softmax vector of the generated text. The IWGAN method is described in Figure FIGREF6 . A disadvantage of this technique is that the discriminator is able to tell apart the one-hot input from the softmax input very easily. Hence, the generator will have a hard time fooling the discriminator and vanishing gradient problem is highly probable."
+    ],
+    [
+      "A new version of Wasserstein GAN for text generation using gradient penalty for discriminator was proposed in BIBREF6 . Their generator is a CNN network generating fixed-length texts. The discriminator is another CNN receiving 3D tensors as input sentences. It determines whether the tensor is coming from the generator or sampled from the real data. The real sentences and the generated ones are represented using one-hot and softmax representations, respectively.",
+      "A similar approach was proposed in BIBREF2 with an RNN-based generator. They used a curriculum learning strategy BIBREF21 to produce sequences of gradually increasing lengths as training progresses. In BIBREF7 , RNN is trained to generate text with GAN using curriculum learning. The authors proposed a procedure called teacher helping, which helps the generator to produce long sequences by conditioning on shorter ground-truth sequences. All these approaches use a discriminator to discriminate the generated softmax output from one-hot real data as in Figure FIGREF6 , which is a clear downside for them. The reason is the discriminator receives inputs of different representations: a one-hot vector for real data and a probabilistic vector output from the generator. It makes the discrimination rather trivial.",
+      "AEs have been exploited along with GANs in different architectures for computer vision application such as AAE BIBREF22 , ALI BIBREF23 , and HALI BIBREF24 . Similarly, AEs can be used with GANs for generating text. For instance, an adversarially regularized AE (ARAE) was proposed in BIBREF8 . The generator is trained in parallel to an AE to learn a continuous version of the code space produced by AE encoder. Then, a discriminator will be responsible for distinguishing between the encoded hidden code and the continuous code of the generator. Basically, in this approach, a continuous distribution is generated corresponding to an encoded code of text."
+    ],
+    [
+      "AEs can be useful in denoising text and transferring it to a code space (encoding) and then reconstructing back to the original text from the code. AEs can be combined with GANs in order to improve the generated text. In this section, we introduce a technique using AEs to replace the conventional one-hot representation BIBREF6 with a continuous softmax representation of real data for discrimination."
+    ],
+    [
+      "As stated, in conventional text-based discrimination approach BIBREF6 , the real and generated input of the discriminator will have different types (one-hot and softmax) and it can simply tell them apart. One way to avoid this issue is to derive a continuous smooth representation of words rather than their one-hot and train the discriminator to differentiate between the continuous representations. In this work, we use a conventional AE (Teacher) to replace the one-hot representation with softmax reconstructed output, which is a smooth representation that yields smaller variance in gradients BIBREF15 . The proposed model is depicted in Figure FIGREF8 . As seen, instead of the one-hot representation of the real words, we feed the softened reconstructed output of the AE to the discriminator. This technique would makes the discrimination much harder for the discriminator. The GAN generator (Student) with softmax output tries to mimic the AE output distribution instead of conventional one-hot representations used in the literature."
+    ],
+    [
+      "Suppose we apply IWGAN to a language vocabulary of size two: words INLINEFORM0 and INLINEFORM1 . The one-hot representation of these two words (as two points in the Cartesian coordinates) and the span of the generated softmax outputs (as a line segment connecting them) is depicted in the left panel of Figure FIGREF10 . As evident graphically, the task of the discriminator is to discriminate the points from the line connecting them, which is a rather simple very easy task.",
+      "Now, let's consider the TextKD-GAN idea using the two-word language example. As depicted in Figure FIGREF10 (Right panel), the output locus of the TextKD-GAN decoder would be two red line segments instead of two points (in the one-hot case). The two line segments lie on the output locus of the generator, which will make the generator more successful in fooling the discriminator."
+    ],
+    [
+      "We train the AE and TextKD-GAN simultaneously. In order to do so, we break down the objective function into three terms: (1) a reconstruction term for the AE, (2) a discriminator loss function with gradient penalty, (3) an adversarial cost for the generator. Mathematically, DISPLAYFORM0 ",
+      "These losses are trained alternately to optimize different parts of the model. We employ the gradient penalty approach of IWGAN BIBREF6 for training the discriminator. In the gradient penalty term, we need to calculate the gradient norm of random samples INLINEFORM0 . According to the proposal in BIBREF6 , these random samples can be obtained by sampling uniformly along the line connecting pairs of generated and real data samples: DISPLAYFORM0 ",
+      "The complete training algorithm is described in SECREF11 .",
+      "TextKD-GAN for text generation. [1] The Adam hyperparameters INLINEFORM0 , INLINEFORM1 , INLINEFORM2 , the batch size INLINEFORM3 . Initial AE parameters (encoder ( INLINEFORM4 ), decoder INLINEFORM5 ), discriminator parameters INLINEFORM6 and initial generator parameters INLINEFORM7 ",
+      "number of training iterations AE Training: Sample INLINEFORM0 and compute code-vectors INLINEFORM1 ",
+      " and reconstructed text INLINEFORM0 .",
+      " Backpropagate reconstruction loss INLINEFORM0 .",
+      " Update with INLINEFORM0 . Train the discriminator: k times: Sample INLINEFORM1 and Sample INLINEFORM2 .",
+      " Compute generated text INLINEFORM0 ",
+      " Backpropagate discriminator loss INLINEFORM0 .",
+      " Update with INLINEFORM0 . INLINEFORM1 Train the generator:",
+      "Sample INLINEFORM0 and Sample INLINEFORM1 .",
+      " Compute generated text INLINEFORM0 ",
+      " Backpropagate generator loss INLINEFORM0 .",
+      " Update with INLINEFORM0 . INLINEFORM1 "
+    ],
+    [
+      "We carried out our experiments on two different datasets: Google 1 billion benchmark language modeling data and the Stanford Natural Language Inference (SNLI) corpus. Our text generation is performed at character level with a sentence length of 32. For the Google dataset, we used the first 1 million sentences and extract the most frequent 100 characters to build our vocabulary. For the SNLI dataset, we used the entire preprocessed training data , which contains 714667 sentences in total and the built vocabulary has 86 characters. We train the AE using one layer with 512 LSTM cells BIBREF25 for both the encoder and the decoder. We train the autoencoder using Adam optimizer with learning rate 0.001, INLINEFORM0 = 0.9, and INLINEFORM1 = 0.9. For decoding, the output from the previous time step is used as the input to the next time step. The hidden code INLINEFORM2 is also used as an additional input at each time step of decoding. The greedy search approach is applied to get the best output BIBREF8 . We keep the same CNN-based generator and discriminator with residual blocks as in BIBREF6 . The discriminator is trained for 5 times for 1 GAN generator iteration. We train the generator and the discriminator using Adam optimizer with learning rate 0.0001, INLINEFORM3 = 0.5, and INLINEFORM4 = 0.9.",
+      "We use the BLEU-N score to evaluate our techniques. BLEU-N score is calculated according to the following equation BIBREF26 , BIBREF27 , BIBREF28 : DISPLAYFORM0 ",
+      "where INLINEFORM0 is the probability of INLINEFORM1 -gram and INLINEFORM2 . We calculate BLEU-n scores for n-grams without a brevity penalty BIBREF10 . We train all the models for 200000 iterations and the results with the best BLEU-N scores in the generated texts are reported. To calculate the BLEU-N scores, we generate ten batches of sentences as candidate texts, i.e. 640 sentences (32-character sentences) and use the entire test set as reference texts."
+    ],
+    [
+      "The results of the experiments are depicted in Table TABREF20 and TABREF21 . As seen in these tables, the proposed TextKD-GAN approach yields significant improvements in terms of BLEU-2, BLEU-3 and BLEU-4 scores over the IWGAN BIBREF6 , and the ARAE BIBREF8 approaches. Therefore, softened smooth output of the decoder can be more useful to learn better discriminator than the traditional one-hot representation. Moreover, we can see the lower BLEU-scores and less improvement for the Google dataset compared to the SNLI dataset. The reason might be the sentences in the Google dataset are more diverse and complicated. Finally, note that the text-based one-hot discrimination in IWGAN and our proposed method are better than the traditional code-based ARAE technique BIBREF8 .",
+      "Some examples of generated text from the SNLI experiment are listed in Table TABREF22 . As seen, the generated text by the proposed TextKD-GAN approach is more meaningful and contains more correct words compared to that of IWGAN BIBREF6 .",
+      "We also provide the training curves of Jensen-Shannon distances (JSD) between the INLINEFORM0 -grams of the generated sentences and that of the training (real) ones in Figure FIGREF23 . The distances are derived from SNLI experiments and calculated as in BIBREF6 . That is by calculating the log-probabilities of the INLINEFORM1 -grams of the generated and the real sentences. As depicted in the figure, the TextKD-GAN approach further minimizes the JSD compared to the literature methods BIBREF6 , BIBREF8 . In conclusion, our approach learns a more powerful discriminator, which in turn generates the data distribution close to the real data distribution."
+    ],
+    [
+      "The results of our experiment shows the superiority of our TextKD-GAN method over other conventional GAN-based techniques. We compared our technique with those GAN-based generators which does not need pre-training. This explains why we have not included the RL-based techniques in the results. We showed the power of the continuous smooth representations over the well-known tricks to work around the discontinuity of text for GANs. Using AEs in TextKD-GAN adds another important dimension to our technique which is the latent space, which can be modeled and exploited as a separate signal for discriminating the generated text from the real data. It is worth mentioning that our observations during the experiments show training text-based generators is much easier than training the code-based techniques such as ARAE. Moreover, we observed that the gradient penalty term plays a significant part in terms of reducing the mode-collapse from the generated text of GAN. Furthermore, in this work, we focused on character-based techniques; however, TextKD-GAN is applicable to the word-based settings as well. Bear in mind that pure GAN-based text generation techniques are still in a newborn stage and they are not very powerful in terms of learning semantics of complex datasets and large sentences. This might be because of lack of capacity of capturing the long-term information using CNN networks. To address this problem, RL can be employed to empower these pure GAN-based techniques such as TextKD-GAN as a next step ."
+    ],
+    [
+      "In this work, we introduced TextKD-GAN as a new solution using knowledge distillation for the main bottleneck of using GAN for generating text, which is the discontinuity of text. Our solution is based on an AE (Teacher) to derive a continuous smooth representation of the real text. This smooth representation is distilled to the GAN discriminator instead of the conventional one-hot representation. We demonstrated the rationale behind this approach, which is to make the discrimination task of the discriminator between the real and generated texts more difficult and consequently providing a richer signal to the generator. At the time of training, the TextKD-GAN generator (Student) would try to learn the manifold of the smooth representation, which can later on be mapped to the real data distribution by applying the argmax operator. We evaluated TextKD-GAN over two benchmark datasets using the BLEU-N scores, JSD measures, and quality of the output generated text. The results showed that the proposed TextKD-GAN approach outperforms the traditional GAN-based text generation methods which does not need pre-training such as IWGAN and ARAE. Finally, We summarize our plan for future work in the following:"
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: Visualizing and Measuring the Geometry of BERT
+
+Question: What linguistic features were probed for?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Context and related work",
+    "Language representation by neural networks",
+    "Geometry of syntax",
+    "Attention probes and dependency representations",
+    "Geometry of parse tree embeddings",
+    "Geometry of word senses",
+    "Visualization of word senses",
+    "Measurement of word sense disambiguation capability",
+    "Embedding distance and context: a concatenation experiment",
+    "Conclusion and future work",
+    "Embedding trees in Euclidean space",
+    "Ideal vs. actual parse tree embeddings",
+    "Additional BERT parse tree visualizations",
+    "Additional word sense visualizations"
+  ],
+  "paragraphs": [
+    [
+      "Neural networks for language processing have advanced rapidly in recent years. A key breakthrough was the introduction of transformer architectures BIBREF0 . One recent system based on this idea, BERT BIBREF1 , has proven to be extremely flexible: a single pretrained model can be fine-tuned to achieve state-of-the-art performance on a wide variety of NLP applications. This suggests the model is extracting a set of generally useful features from raw text. It is natural to ask, which features are extracted? And how is this information represented internally?",
+      "Similar questions have arisen with other types of neural nets. Investigations of convolutional neural networks BIBREF2 , BIBREF3 have shown how representations change from layer to layer BIBREF4 ; how individual units in a network may have meaning BIBREF5 ; and that \u201cmeaningful\u201d directions exist in the space of internal activation BIBREF6 . These explorations have led to a broader understanding of network behavior.",
+      "Analyses on language-processing models (e.g., BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 ) point to the existence of similarly rich internal representations of linguistic structure. Syntactic features seem to be extracted by RNNs (e.g., BIBREF7 , BIBREF9 ) as well as in BERT BIBREF11 , BIBREF12 , BIBREF13 , BIBREF10 . Inspirational work from Hewitt and Manning BIBREF8 found evidence of a geometric representation of entire parse trees in BERT's activation space.",
+      "Our work extends these explorations of the geometry of internal representations. Investigating how BERT represents syntax, we describe evidence that attention matrices contain grammatical representations. We also provide mathematical arguments that may explain the particular form of the parse tree embeddings described in BIBREF8 . Turning to semantics, using visualizations of the activations created by different pieces of text, we show suggestive evidence that BERT distinguishes word senses at a very fine level. Moreover, much of this semantic information appears to be encoded in a relatively low-dimensional subspace."
+    ],
+    [
+      "Our object of study is the BERT model introduced in BIBREF1 . To set context and terminology, we briefly describe the model's architecture. The input to BERT is based on a sequence of tokens (words or pieces of words). The output is a sequence of vectors, one for each input token. We will often refer to these vectors as context embeddings because they include information about a token's context.",
+      "BERT's internals consist of two parts. First, an initial embedding for each token is created by combining a pre-trained wordpiece embedding with position and segment information. Next, this initial sequence of embeddings is run through multiple transformer layers, producing a new sequence of context embeddings at each step. (BERT comes in two versions, a 12-layer BERT-base model and a 24-layer BERT-large model.) Implicit in each transformer layer is a set of attention matrices, one for each attention head, each of which contains a scalar value for each ordered pair $(token_i, token_j)$ ."
+    ],
+    [
+      "Sentences are sequences of discrete symbols, yet neural networks operate on continuous data\u2013vectors in high-dimensional space. Clearly a successful network translates discrete input into some kind of geometric representation\u2013but in what form? And which linguistic features are represented?",
+      "The influential Word2Vec system BIBREF14 , for example, has been shown to place related words near each other in space, with certain directions in space correspond to semantic distinctions. Grammatical information such as number and tense are also represented via directions in space. Analyses of the internal states of RNN-based models have shown that they represent information about soft hierarchical syntax in a form that can be extracted by a one-hidden-layer network BIBREF9 . One investigation of full-sentence embeddings found a wide variety of syntactic properties could be extracted not just by an MLP, but by logistic regression BIBREF15 .",
+      "Several investigations have focused on transformer architectures. Experiments suggest context embeddings in BERT and related models contain enough information to perform many tasks in the traditional \u201cNLP pipeline\u201d BIBREF12 \u2013tagging part-of-speech, co-reference resolution, dependency labeling, etc.\u2013with simple classifiers (linear or small MLP models) BIBREF11 , BIBREF10 . Qualitative, visualization-based work BIBREF16 suggests attention matrices may encode important relations between words.",
+      "A recent and fascinating discovery by Hewitt and Manning BIBREF8 , which motivates much of our work, is that BERT seems to create a direct representation of an entire dependency parse tree. The authors find that (after a single global linear transformation, which they term a \u201cstructural probe\u201d) the square of the distance between context embeddings is roughly proportional to tree distance in the dependency parse. They ask why squaring distance is necessary; we address this question in the next section.",
+      "The work cited above suggests that language-processing networks create a rich set of intermediate representations of both semantic and syntactic information. These results lead to two motivating questions for our research. Can we find other examples of intermediate representations? And, from a geometric perspective, how do all these different types of information coexist in a single vector?"
+    ],
+    [
+      "We begin by exploring BERT's internal representation of syntactic information. This line of inquiry builds on the work by Hewitt and Manning in two ways. First, we look beyond context embeddings to investigate whether attention matrices encode syntactic features. Second, we provide a simple mathematical analysis of the tree embeddings that they found."
+    ],
+    [
+      "As in BIBREF8 , we are interested in finding representations of dependency grammar relations BIBREF17 . While BIBREF8 analyzed context embeddings, another natural place to look for encodings is in the attention matrices. After all, attention matrices are explicitly built on the relations between pairs of words.",
+      "To formalize what it means for attention matrices to encode linguistic features, we use an attention probe, an analog of edge probing BIBREF11 . An attention probe is a task for a pair of tokens, $(token_i, token_j)$ where the input is a model-wide attention vector formed by concatenating the entries $a_{ij}$ in every attention matrix from every attention head in every layer. The goal is to classify a given relation between the two tokens. If a linear model achieves reliable accuracy, it seems reasonable to say that the model-wide attention vector encodes that relation. We apply attention probes to the task of identifying the existence and type of dependency relation between two words.",
+      "The data for our first experiment is a corpus of parsed sentences from the Penn Treebank BIBREF18 . This dataset has the constituency grammar for the sentences, which was translated to a dependency grammar using the PyStanfordDependencies library BIBREF19 . The entirety of the Penn Treebank consists of 3.1 million dependency relations; we filtered this by using only examples of the 30 dependency relations with more than 5,000 examples in the data set. We then ran each sentence through BERT-base, and obtained the model-wide attention vector (see Figure 1 ) between every pair of tokens in the sentence, excluding the $[SEP]$ and $[CLS]$ tokens. This and subsequent experiments were conducted using PyTorch on MacBook machines.",
+      "With these labeled embeddings, we trained two L2 regularized linear classifiers via stochastic gradient descent, using BIBREF20 . The first of these probes was a simple linear binary classifier to predict whether or not an attention vector corresponds to the existence of a dependency relation between two tokens. This was trained with a balanced class split, and 30% train/test split. The second probe was a multiclass classifier to predict which type of dependency relation exists between two tokens, given the dependency relation\u2019s existence. This probe was trained with distributions outlined in table 2 .",
+      "The binary probe achieved an accuracy of 85.8%, and the multiclass probe achieved an accuracy of 71.9%. Our real aim, again, is not to create a state-of-the-art parser, but to gauge whether model-wide attention vectors contain a relatively simple representation of syntactic features. The success of this simple linear probe suggests that syntactic information is in fact encoded in the attention vectors."
+    ],
+    [
+      "Hewitt and Manning's result that context embeddings represent dependency parse trees geometrically raises several questions. Is there a reason for the particular mathematical representation they found? Can we learn anything by visualizing these representations?",
+      "Hewitt and Manning ask why parse tree distance seems to correspond specifically to the square of Euclidean distance, and whether some other metric might do better BIBREF8 . We describe mathematical reasons why squared Euclidean distance may be natural.",
+      "First, one cannot generally embed a tree, with its tree metric $d$ , isometrically into Euclidean space (Appendix \"Embedding trees in Euclidean space\" ). Since an isometric embedding is impossible, motivated by the results of BIBREF8 we might ask about other possible representations.",
+      "Definition 1 (power- $p$ embedding) Let $M$ be a metric space, with metric $d$ . We say $f: M \\rightarrow \\mathbb {R}^n$ is a power- $p$ embedding if for all $x, y \\in M$ , we have $||f(x) - f(y)||^p = d(x, y)$ ",
+      "In these terms, we can say BIBREF8 found evidence of a power-2 embedding for parse trees. It turns out that power-2 embeddings are an especially elegant mapping. For one thing, it is easy to write down an explicit model\u2013a mathematical idealization\u2013for a power-2 embedding for any tree.",
+      "Theorem 1 Any tree with $n$ nodes has a power-2 embedding into $\\mathbb {R}^{n-1}$ .",
+      "Let the nodes of the tree be $t_0, ..., t_{n-1}$ , with $t_0$ being the root node. Let $\\lbrace e_1, ..., e_{n-1}\\rbrace $ be orthogonal unit basis vectors for $\\mathbb {R}^{n-1}$ . Inductively, define an embedding $f$ such that: $f(t_0) = 0$ $f(t_i) = e_i + f(parent(t_i))$ ",
+      "Given two distinct tree nodes $x$ and $y$ , where $m$ is the tree distance $d(x, y)$ , it follows that we can move from $f(x)$ to $f(y)$ using $m$ mutually perpendicular unit steps. Thus $||f(x) - f(y)||^2 = m = d(x, y)$ ",
+      "Remark 1",
+      "This embedding has a simple informal description: at each embedded vertex of the graph, all line segments to neighboring embedded vertices are unit-distance segments, orthogonal to each other and to every other edge segment. (It's even easy to write down a set of coordinates for each node.) By definition any two power-2 embeddings of the same tree are isometric; with that in mind, we refer to this as the canonical power-2 embedding.",
+      "In the proof of Theorem 1, instead of choosing basis vectors in advance, one can choose random unit vectors. Because two random vectors will be nearly orthogonal in high-dimensional space, the power-2 embedding condition will approximately hold. This means that in space that is sufficiently high-dimensional (compared to the size of the tree) it is possible to construct an approximate power-2 embedding with essentially \u201clocal\u201d information, where a tree node is connected to its children via random unit-length branches. We refer to this type of embedding as a random branch embedding. (See Appendix \"Ideal vs. actual parse tree embeddings\" for a visualization of these various embeddings.)",
+      "In addition to these appealing aspects of power-2 embeddings, it is worth noting that power- $p$ embeddings will not necessarily even exist when $p < 2$ . (See Appendix \"Embedding trees in Euclidean space\" for the proof.)",
+      "Theorem 2 For any $p < 2$ , there is a tree which has no power- $p$ embedding.",
+      "Remark 2",
+      "On the other hand, the existence result for power-2 embeddings, coupled with results of BIBREF22 , implies that power- $p$ tree embeddings do exist for any $p > 2$ .",
+      "The simplicity of power-2 tree embeddings, as well as the fact that they may be approximated by a simple random model, suggests they may be a generally useful alternative to approaches to tree embeddings that require hyperbolic geometry BIBREF23 .",
+      "How do parse tree embeddings in BERT compare to exact power-2 embeddings? To explore this question, we created a simple visualization tool. The input to each visualization is a sentence from the Penn Treebank with associated dependency parse trees (see Section \"Geometry of word senses\" ). We then extracted the token embeddings produced by BERT-large in layer 16 (following BIBREF8 ), transformed by the Hewitt and Manning\u2019s \u201cstructural probe\u201d matrix $B$ , yielding a set of points in 1024-dimensional space. We used PCA to project to two dimensions. (Other dimensionality-reduction methods, such as t-SNE and UMAP BIBREF24 , were harder to interpret.)",
+      "To visualize the tree structure, we connected pairs of points representing words with a dependency relation. The color of each edge indicates the deviation from true tree distance. We also connected, with dotted line, pairs of words without a dependency relation but whose positions (before PCA) were far closer than expected. The resulting image lets us see both the overall shape of the tree embedding, and fine-grained information on deviation from a true power-2 embedding.",
+      "Two example visualizations are shown in Figure 2 , next to traditional diagrams of their underlying parse trees. These are typical cases, illustrating some common patterns; for instance, prepositions are embedded unexpectedly close to words they relate to. (Figure 7 shows additional examples.)",
+      "A natural question is whether the difference between these projected trees and the canonical ones is merely noise, or a more interesting pattern. By looking at the average embedding distances of each dependency relation (see Figure 3 ) , we can see that they vary widely from around 1.2 ( $compound:prt$ , $advcl$ ) to 2.5 ( $mwe$ , $parataxis$ , $auxpass$ ). Such systematic differences suggest that BERT's syntactic representation has an additional quantitative aspect beyond traditional dependency grammar."
+    ],
+    [
+      "BERT seems to have several ways of representing syntactic information. What about semantic features? Since embeddings produced by transformer models depend on context, it is natural to speculate that they capture the particular shade of meaning of a word as used in a particular sentence. (E.g., is \u201cbark\u201d an animal noise or part of a tree?) We explored geometric representations of word sense both qualitatively and quantitatively."
+    ],
+    [
+      "Our first experiment is an exploratory visualization of how word sense affects context embeddings. For data on different word senses, we collected all sentences used in the introductions to English-language Wikipedia articles. (Text outside of introductions was frequently fragmentary.) We created an interactive application, which we plan to make public. A user enters a word, and the system retrieves 1,000 sentences containing that word. It sends these sentences to BERT-base as input, and for each one it retrieves the context embedding for the word from a layer of the user's choosing.",
+      "The system visualizes these 1,000 context embeddings using UMAP BIBREF24 , generally showing clear clusters relating to word senses. Different senses of a word are typically spatially separated, and within the clusters there is often further structure related to fine shades of meaning. In Figure 4 , for example, we not only see crisp, well-separated clusters for three meanings of the word \u201cdie,\u201d but within one of these clusters there is a kind of quantitative scale, related to the number of people dying.",
+      "See Appendix \"Additional word sense visualizations\" for further examples. The apparent detail in the clusters we visualized raises two immediate questions. First, is it possible to find quantitative corroboration that word senses are well-represented? Second, how can we resolve a seeming contradiction: in the previous section, we saw how position represented syntax; yet here we see position representing semantics."
+    ],
+    [
+      "The crisp clusters seen in visualizations such as Figure 4 suggest that BERT may create simple, effective internal representations of word senses, putting different meanings in different locations. To test this hypothesis quantitatively, we test whether a simple classifier on these internal representations can perform well at word-sense disambiguation (WSD).",
+      "We follow the procedure described in BIBREF10 , which performed a similar experiment with the ELMo model. For a given word with $n$ senses, we make a nearest-neighbor classifier where each neighbor is the centroid of a given word sense's BERT-base embeddings in the training data. To classify a new word we find the closest of these centroids, defaulting to the most commonly used sense if the word was not present in the training data. We used the data and evaluation from BIBREF25 : the training data was SemCor BIBREF26 (33,362 senses), and the testing data was the suite described in BIBREF25 (3,669 senses).",
+      "The simple nearest-neighbor classifier achieves an F1 score of 71.1, higher than the current state of the art (Table 1 ), with the accuracy monotonically increasing through the layers. This is a strong signal that context embeddings are representing word-sense information. Additionally, an even higher score of 71.5 was obtained using the technique described in the following section.",
+      "We hypothesized that there might also exist a linear transformation under which distances between embeddings would better reflect their semantic relationships\u2013that is, words of the same sense would be closer together and words of different senses would be further apart.",
+      "To explore this hypothesis, we trained a probe following Hewitt and Manning's methodology. We initialized a random matrix $B\\in {R}^{k\\times m}$ , testing different values for $m$ . Loss is, roughly, defined as the difference between the average cosine similarity between embeddings of words with different senses, and that between embeddings of the same sense. However, we clamped the cosine similarity terms to within $\\pm 0.1$ of the pre-training averages for same and different senses. (Without clamping, the trained matrix simply ended up taking well-separated clusters and separating them further. We tested values between $0.05$ and $0.2$ for the clamping range and $0.1$ had the best performance.)",
+      "Our training corpus was the same dataset from 4.1.2., filtered to include only words with at least two senses, each with at least two occurrences (for 8,542 out of the original 33,362 senses). Embeddings came from BERT-base (12 layers, 768-dimensional embeddings).",
+      "We evaluate our trained probes on the same dataset and WSD task used in 4.1.2 (Table 1 ). As a control, we compare each trained probe against a random probe of the same shape. As mentioned in 4.1.2, untransformed BERT embeddings achieve a state-of-the-art accuracy rate of 71.1%. We find that our trained probes are able to achieve slightly improved accuracy down to $m=128$ .",
+      "Though our probe achieves only a modest improvement in accuracy for final-layer embeddings, we note that we were able to more dramatically improve the performance of embeddings at earlier layers (see Appendix for details: Figure 10 ). This suggests there is more semantic information in the geometry of earlier-layer embeddings than a first glance might reveal.",
+      "Our results also support the idea that word sense information may be contained in a lower-dimensional space. This suggests a resolution to the seeming contradiction mentioned above: a vector encodes both syntax and semantics, but in separate complementary subspaces."
+    ],
+    [
+      "If word sense is affected by context, and encoded by location in space, then we should be able to influence context embedding positions by systematically varying their context. To test this hypothesis, we performed an experiment based on a simple and controllable context change: concatenating sentences where the same word is used in different senses.",
+      "We picked 25,096 sentence pairs from SemCor, using the same keyword in different senses. E.g.:",
+      "A: \"He thereupon went to London and spent the winter talking to men of wealth.\" went: to move from one place to another.",
+      "B: \"He went prone on his stomach, the better to pursue his examination.\" went: to enter into a specified state.",
+      "We define a matching and an opposing sense centroid for each keyword. For sentence A, the matching sense centroid is the average embedding for all occurrences of \u201cwent\u201d used with sense A. A's opposing sense centroid is the average embedding for all occurrences of \u201cwent\u201d used with sense B.",
+      "We gave each individual sentence in the pair to BERT-base and recorded the cosine similarity between the keyword embeddings and their matching sense centroids. We also recorded the similarity between the keyword embeddings and their opposing sense centroids. We call the ratio between the two similarities the individual similarity ratio. Generally this ratio is greater than one, meaning that the context embedding for the keyword is closer to the matching centroid than the opposing one.",
+      "We joined each sentence pair with the word \"and\" to create a single new sentence.",
+      "We gave these concatenations to BERT and recorded the similarities between the keyword embeddings and their matching/opposing sense centroids. Their ratio is the concatenated similarity ratio.",
+      "Our hypothesis was that the keyword embeddings in the concatenated sentence would move towards their opposing sense centroids. Indeed, we found that the average individual similarity ratio was higher than the average concatenated similarity ratio at every layer (see Figure 5 ). Concatenating a random sentence did not change the individual similarity ratios. If the ratio is less than one for any sentence, that means BERT has misclassified its keyword sense. We found that the misclassification rate was significantly higher for final-layer embeddings in the concatenated sentences compared to the individual sentences: 8.23% versus 2.43% respectively.",
+      "We also measured the effect of projecting the final-layer keyword embeddings into the semantic subspace discussed in 4.1.3. After multiplying each embedding by our trained semantic probe, we obtained an average concatenated similarity ratio of 1.578 and individual similarity ratio of 1.875, which suggests that the transformed embeddings are closer to their matching sense centroids than the original embeddings (the original concatenated similarity ratio is 1.284 and the individual similarity ratio is 1.430). We also measured lower average misclassification rates for the transformed embeddings: 7.31% for concatenated sentences and 2.27% for individual sentences."
+    ],
+    [
+      "We have presented a series of experiments that shed light on BERT's internal representations of linguistic information. We have found evidence of syntactic representation in attention matrices, with certain directions in space representing particular dependency relations. We have also provided a mathematical justification for the squared-distance tree embedding found by Hewitt and Manning.",
+      "Meanwhile, we have shown that just as there are specific syntactic subspaces, there is evidence for subspaces that represent semantic information. We also have shown how mistakes in word sense disambiguation may correspond to changes in internal geometric representation of word meaning. Our experiments also suggest an answer to the question of how all these different representations fit together. We conjecture that the internal geometry of BERT may be broken into multiple linear subspaces, with separate spaces for different syntactic and semantic information.",
+      "Investigating this kind of decomposition is a natural direction for future research. What other meaningful subspaces exist? After all, there are many types of linguistic information that we have not looked for.",
+      "A second important avenue of exploration is what the internal geometry can tell us about the specifics of the transformer architecture. Can an understanding of the geometry of internal representations help us find areas for improvement, or refine BERT's architecture?",
+      "Acknowledgments: We would like to thank David Belanger, Tolga Bolukbasi, Jasper Snoek, and Ian Tenney for helpful feedback and discussions."
+    ],
+    [
+      "Here we provide additional detail on the existence of various forms of tree embeddings.",
+      "Isometric embeddings of a tree (with its intrinsic tree metric) into Euclidean space are rare. Indeed, such an embedding is impossible even a four-point tree $T$ , consisting of a root node $R$ with three children $C_1, C_2, C_3$ . If $f:T \\rightarrow \\mathbb {R}^n$ is a tree isometry then $||f(R) - f(C_1)) || = ||f(R) - f(C_2)) || = 1$ , and $||f(C_1) - f(C_2)) || = 2$ . It follows that $f(R)$ , $f(C_1)$ , $f(C_2)$ are collinear. The same can be said of $f(R)$ , $R$0 , and $R$1 , meaning that $R$2 .",
+      "Since this four-point tree cannot be embedded, it follows the only trees that can be embedded are simply chains.",
+      "Not only are isometric embeddings generally impossible, but power- $p$ embeddings may also be unavailable when $p < 2$ , as the following argument shows.",
+      "Proof of Theorem \"Theorem 2\" We covered the case of $p = 1$ above. When $p < 1$ , even a tree of three points is impossible to embed without violating the triangle inequality. To handle the case when $1 < p < 2$ , consider a \u201cstar-shaped\u201d tree of one root node with $k$ children; without loss of generality, assume the root node is embedded at the origin. Then in any power- $p$ embedding the other vertices will be sent to unit vectors, and for each pair of these unit vectors we have $||v_i - v_j||^p = 2$ .",
+      "On the other hand, a well-known folk theorem (e.g., see BIBREF27 ) says that given $k$ unit vectors $v_1, ..., v_k$ at least one pair of distinct vectors has $v_i \\cdot v_j \\ge -1/(k - 1)$ . By the law of cosines, it follows that $||v_i - v_j|| \\le \\sqrt{2 + \\frac{2}{k-1}}$ . For any $p < 2$ , there is a sufficiently large $k$ such that $||v_i - v_j||^p \\le (\\sqrt{2 + \\frac{2}{k-1}})^p = (2 + \\frac{2}{k-1})^{p/2} < 2$ . Thus for any $p < 2$ a large enough star-shaped tree cannot have a power- $p$ embedding."
+    ],
+    [
+      "Figure 2 shows (left) a visualization of a BERT parse tree embedding (as defined by the context embeddings for individual words in a sentence). We compare with PCA projections of the canonical power-2 embedding of the same tree structure, as well as a random branch embedding. Finally, we display a completely randomly embedded tree as a control. The visualizations show a clear visual similarity between the BERT embedding and the two mathematical idealizations."
+    ],
+    [
+      "Figure 7 shows four additional examples of PCA projections of BERT parse tree embeddings."
+    ],
+    [
+      "We provide two additional examples of word sense visualizations, hand-annotated to show key clusters. See Figure 8 and Figure 9 ."
+    ]
+  ]
+}
+```
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+Name of Paper: When redundancy is rational: A Bayesian approach to 'overinformative' referring expressions
+
+Question: Does the paper describe experiments with real humans?
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+Name of Paper: ASR-free CNN-DTW keyword spotting using multilingual bottleneck features for almost zero-resource languages
+
+Question: What are bottleneck features?
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+Name of Paper: ASR-free CNN-DTW keyword spotting using multilingual bottleneck features for almost zero-resource languages
+
+Question: What languages are considered?
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+Name of Paper: A Deep Learning Architecture for De-identification of Patient Notes: Implementation and Evaluation
+
+Question: What is their baseline?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    null,
+    "Introduction",
+    "Background and Related Work",
+    "Rule-based Systems",
+    "Machine Learning Systems",
+    "Hybrid Systems",
+    "Deep Learning Systems",
+    "Method",
+    "Pre-processing Layer",
+    "Embedding Layer",
+    "Bi-LSTM Layer",
+    "CRF layer",
+    "Data and Evaluation Metrics",
+    "Evaluation Metrics",
+    "Results",
+    "Discussion and Error Analysis",
+    "Error Analysis",
+    "Ablation Analysis",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "[block] 1 5mm *",
+      "2pt*22pt",
+      "[block] 2.1 5mm",
+      "[block] 2.1.1 5mm"
+    ],
+    [
+      " Electronic Health Records (EHR) have become ubiquitous in recent years in the United States, owing much to the The Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009. BIBREF0 Their ubiquity have given researchers a treasure trove of new data, especially in the realm of unstructured textual data. However, this new data source comes with usage restrictions in order to preserve the privacy of individual patients as mandated by the Health Insurance Portability and Accountability Act (HIPAA). HIPAA demands any researcher using this sensitive data to first strip the medical records of any protected health information (PHI), a process known as de-identification.",
+      "HIPAA allows for two methods for de-identifying PHIs: the \u201cExpert Determination\u201d method in which an expert certifies that the information is rendered not individually identifiable, and the \u201cSafe Harbor\u201d method in which 18 identifiers are removed or replaced with random data in order for the data to be considered not individually identifiable. Our research pertains to the second method (a list of the relevant identifiers can be seen in Table TABREF4 ).",
+      "The process of de-identification has been largely a manual and labor intensive task due to both the sensitive nature of the data and the limited availability of software to automate the task. This has led to a relatively small number of open health data sets available for public use. Recently, there have been two well-known de-identification challenges organized by Informatics for Integrating Biology and the Bedside (i2b2) to encourage innovation in the field of de-identification.",
+      "In this paper, we build on the recent advances in natural language processing, especially with regards to word embeddings, by incorporating deep contextualized word embeddings developed by Peters et al. BIBREF1 into a deep learning architecture. More precisely, we present a deep learning architecture that differs from current architectures in literature by using bi-directional long short-term memory networks (Bi-LSTMs) with variational dropouts and deep contextualized word embeddings while also using components already present in other systems such traditional word embeddings, character LSTM embeddings and conditional random fields. We test this architecture on two gold standard data sets, the 2014 i2b2 de-identification Track 1 data set BIBREF2 and the nursing notes corpus BIBREF3 . The architecture achieves state-of-the-art performance on both data sets while also achieving faster convergence without the use of dictionaries (or gazetteers) or other rule-based methods that are typically used in other de-identification systems.",
+      "The paper is organized as follows: In Section SECREF4 , we review the latest literature around techniques for de-identification with an emphasis on related work using deep learning techniques. In Section SECREF5 , we detail our deep learning architecture and also describe how we use the deep contextualized word embeddings method to improve our results. Section SECREF6 describes the two data sets we will use to evaluate our method and our evaluation metrics. Section SECREF7 presents the performance of our architecture on the data sets. In Section SECREF8 , we discuss the results and provide an analysis of the errors. Finally, in Section SECREF9 , we summarize our contributions while also discussing possible future research."
+    ],
+    [
+      "The task of automatic de-identification has been heavily studied recently, in part due to two main challenges organized by i2b2 in 2006 and in 2014. The task of de-identification can be classified as a named entity recognition (NER) problem which has been extensively studied in machine learning literature. Automated de-identification systems can be roughly broken down into four main categories:"
+    ],
+    [
+      "Rule-based systems make heavy use of pattern matching such as dictionaries (or gazetteers), regular expressions and other patterns. BIBREF2 Systems such as the ones described in BIBREF5 , BIBREF6 do not require the use any labeled data. Hence, they are considered as unsupervised learning systems. Advantages of such systems include their ease of use, ease of adding new patterns and easy interpretability. However, these methods suffer from a lack of robustness with regards to the input. For example, different casings of the same word could be misinterpreted as an unknown word. Furthermore, typographical errors are almost always present in most documents and rule-based systems often cannot correctly handle these types of inaccuracies present in the data. Critically, these systems cannot handle context which could render a medical text unreadable. For example, a diagnosis of \u201cLou Gehring disease\u201d could be misidentified by such a system as a PHI of type Name. The system might replace the tokens \u201cLou\u201d and \u201cGehring\u201d with randomized names rendering the text meaningless if enough of these tokens were replaced."
+    ],
+    [
+      "The drawbacks of such rule-based systems led researchers to adopt a machine learning approach. A comprehensive review of such systems can be found in BIBREF7 , BIBREF8 . In machine learning systems, given a sequence of input vectors INLINEFORM0 , a machine learning algorithm outputs label predictions INLINEFORM1 . Since the task of de-identification is a classification task, traditional classification algorithms such as support vector machines, conditional random fields (CRFs) and decision trees BIBREF9 have been used for building de-identification systems.",
+      "These machine learning-based systems have the advantage of being able to recognize complex patterns that are not as readily evident to the naked eye. However, the drawback of such ML-based systems is that since classification is a supervised learning task, most of the common classification algorithms require a large labeled data set for robust models. Furthermore, since most of the algorithms described in the last paragraph maximize the likelihood of a label INLINEFORM0 given an vector of inputs INLINEFORM1 , rare patterns that might not occur in the training data set would be misclassified as not being a PHI label. Furthermore, these models might not be generalizable to other text corpora that contain significantly different patterns such as sentence structures and uses of different abbreviated words found commonly in medical notes than the training data set."
+    ],
+    [
+      "With both the advantages and disadvantages of stand alone rule-based and ML-based systems well-documented, systems such as the ones detailed in BIBREF2 combined both ML and rule-based systems to achieve impressive results. Systems such as the ones presented for 2014 i2b2 challenge by Yang et al. BIBREF10 and Liu et al. BIBREF11 used dictionary look-ups, regular expressions and CRFs to achieve accuracies of well over 90% in identifying PHIs.",
+      "It is important to note that such hybrid systems rely heavily on feature engineering, a process that manufactures new features from the data that are not present in the raw text. Most machine learning techniques, for example, cannot take text as an input. They require the text to be represented as a vector of numbers. An example of such features can be seen in the system that won the 2014 i2b2 de-identification challenge by Yang et al. BIBREF10 . Their system uses token features such as part-of-speech tagging and chunking, contextual features such as word lemma and POS tags of neighboring words, orthographic features such as capitalization and punctuation marks and task-specific features such as building a list that included all the full names, acronyms of US states and collecting TF-IDF-statistics. Although such hybrid systems achieve impressive results, the task of feature engineering is a time-intensive task that might not be generalizable to other text corpora."
+    ],
+    [
+      "With the disadvantages of the past three approaches to building a de-identification system in mind, the current state-of-the-art systems employ deep learning techniques to achieve better results than the best hybrid systems while also not requiring the time-consuming process of feature engineering. Deep learning is a subset of machine learning that uses multiple layers of Artificial Neural Networks (ANNs), which has been very succesful at most Natural Language Processing (NLP) tasks. Recent advances in the field of deep learning and NLP especially in regards to named entity recognition have allowed systems such as the one by Dernoncourt et al. BIBREF9 to achieve better results on the 2014 i2b2 de-identification challenge data set than the winning hybrid system proposed by Yang et al. BIBREF10 . The advances in NLP and deep learning which have allowed for this performance are detailed below.",
+      "ANNs cannot take words as inputs and require numeric inputs, therefore, past approaches to using ANNs for NLP have been to employ a bag-of-words (BoW) representation of words where a dictionary is built of all known words and each word in a sentence is assigned a unique vector that is inputted into the ANN. A drawback of such a technique is such that words that have similar meanings are represented completely different. As a solution to this problem, a technique called word embeddings have been used. Word embeddings gained popularity when Mikolov et al. BIBREF12 used ANNs to generate a distributed vector representation of a word based on the usage of the word in a text corpus. This way of representing words allowed for similar words to be represented using vectors of similar values while also allowing for complex operations such as the famous example: INLINEFORM0 , where INLINEFORM1 represents a vector for a particular word.",
+      "While pre-trained word embeddings such as the widely used GloVe BIBREF12 embeddings are revolutionary and powerful, such representations only capture one context representation, namely the one of the training corpus they were derived from. This shortcoming has led to the very recent development of context-dependent representations such as the ones developed by BIBREF1 , BIBREF13 , which can capture different features of a word.",
+      "The Embeddings from Language Models (ELMo) from the system by Peters et al. BIBREF1 are used by the architecture in this paper to achieve state-of-the-art results. The ELMo representations, learned by combining Bi-LSTMs with a language modeling objective, captures context-depended aspects at the higher-level LSTM while the lower-level LSTM captures aspects of syntax. Moreover, the outputs of the different layers of the system can be used independently or averaged to output embeddings that significantly improve some existing models for solving NLP problems. These results drive our motivation to include the ELMo representations in our architecture.",
+      "The use of ANNs for many machine learning tasks has gained popularity in recent years. Recently, a variant of recurrent neural networks (RNN) called Bi-directional Long Short-Term Memory (Bi-LSTM) networks has been successfully employed especially in the realm of NER.",
+      "In fact, several Bi-LSTM architectures have been proposed to tackle the problem of NER: LSTM-CRF, LSTM-CNNs-CRF and LSTM-CNNs BIBREF9 . The current best performing system on the i2b2 dataset is in fact a system based on LSTM-CRF BIBREF9 ."
+    ],
+    [
+      "Our architecture incorporates most of the recent advances in NLP and NER while also differing from other architectures described in the previous section by use of deep contextualized word embeddings, Bi-LSTMs with a variational dropout and the use of the Adam optimizer. Our architecture can be broken down into four distinct layers: pre-processing, embeddings, Bi-LSTM and CRF classifier. A graphical illustration of the architecture can be seen in Figure FIGREF16 while a summary of the parameters for our architecture can be found in Table TABREF17 ."
+    ],
+    [
+      "For a given document INLINEFORM0 , we first break down the document into sentences INLINEFORM1 , tokens INLINEFORM2 and characters INLINEFORM3 where INLINEFORM4 represents the document number, INLINEFORM5 represents the sentence number, INLINEFORM6 represents the token number, and INLINEFORM7 represents the character number. For example, INLINEFORM8 Patient, where the token: \u201cPatient\u201d represents the 3rd token of the 2nd sentence of the 1st document.",
+      "After parsing the tokens, we use a widely used and readily available Python toolkit called Natural Langauge ToolKit (NLTK) to generate a part-of-speech (POS) tag for each token. This generates a POS feature for each token which we will transform into a 20-dimensional one-hot-encoded input vector, INLINEFORM0 , then feed into the main LSTM layer.",
+      "For the data labels, since the data labels can be made up of multiple tokens, we formatted the labels to the BIO scheme. The BIO scheme tags the beginning of a PHI with a B-, the rest of the same PHI tokens as I- and the rest of the tokens not associated with a PHI as O. For example, the sentence, \u201c INLINEFORM0 \u201d, would have the corresponding labels, \u201c INLINEFORM1 \u201d."
+    ],
+    [
+      "For the embedding layer, we use three main types of embeddings to represent our input text: traditional word embeddings, ELMo embeddings and character-level LSTM embeddings.",
+      "The traditional word embeddings use the latest GloVe 3 BIBREF12 pre-trained word vectors that were trained on the Common Crawl with about 840 billion tokens. For every token input, INLINEFORM0 , the GloVe system outputs INLINEFORM1 , a dense 300-dimensional word vector representation of that same token. We also experimented with other word embeddings by using the bio-medical corpus trained word embeddings BIBREF14 to see if having word embeddings trained on medical texts will have an impact on our results.",
+      "As mentioned in previous sections, we also incorporate the powerful ELMo representations as a feature to our Bi-LSTMs. The specifics of the ELMo representations are detailed in BIBREF1 . In short, we compute an ELMo representation by passing a token input INLINEFORM0 to the ELMo network and averaging the the layers of the network to produce an 1024-dimensional ELMo vector, INLINEFORM1 .",
+      "Character-level information can capture some information about the token itself while also mitigating issues such as unseen words and misspellings. While lemmatizing (i.e., the act of turning inflected forms of a word to their base or dictionary form) of a token can solve these issues, tokens such as the ones found in medical texts could have important distinctions between, for example, the grammar form of the token. As such, Ma et al. BIBREF15 have used Convolutional Neural Networks (CNN) while Lample et al. BIBREF16 have used Bi-LSTMs to produce character-enhanced representations of each unique token. We have utilized the latter approach of using Bi-LSTMs for produce a character-enhanced embedding for each unique word in our data set. Our parameters for the forward and backward LSTMs are 25 each and the maximum character length is 25, which results in an 50-dimensional embedding vector, INLINEFORM0 , for each token.",
+      "After creating the three embeddings for each token, INLINEFORM0 , we concatenate the GloVe and ELMo representations to produce a single 1324-dimensional word input vector, INLINEFORM1 . The concatenated word vector is then further concatenated with the character embedding vector, INLINEFORM2 , POS one-hot-encoded vector, INLINEFORM3 , and the casing embedded vector, INLINEFORM4 , to produce a single 1394-dimensional input vector, INLINEFORM5 , that we feed into our Bi-LSTM layer."
+    ],
+    [
+      "The Bi-LSTM layer is composed of two LSTM layers, which are a variant of the Bidirectional RNNs. In short, the Bi-LSTM layer contains two independent LSTMs in which one network is fed input in the normal time direction while the other network is fed input in the reverse time direction. The outputs of the two networks can then be combined using either summation, multiplication, concatenation or averaging. Our architecture uses simple concatenation to combine the outputs of the two networks.",
+      "Our architecture for the Bi-LSTM layer is similar to the ones used by BIBREF16 , BIBREF17 , BIBREF18 with each LSTM containing 100 hidden units. To ensure that the neural networks do not overfit, we use a variant of the popular dropout technique called variational dropout BIBREF19 to regularize our neural networks. Variational dropout differs from the traditional na\u00efve dropout technique by having the same dropout mask for the inputs, outputs and the recurrent layers BIBREF19 . This is in contrast to the traditional technique of applying a different dropout mask for each of the input and output layers. BIBREF20 shows that variational dropout applied to the output and recurrent units performs significantly better than na\u00efve dropout or no dropout for the NER tasks. As such, we apply a dropout probability of 0.5 for both the output and the recurrent units in our architecture."
+    ],
+    [
+      "As a final step, the outputs of the Bi-LSTM layer are inputted into a linear-chain CRF classifier, which maximizes the label probabilities of the entire input sentence. This approach is identical to the Bi-LSTM-CRF model by Huang et al. BIBREF21 CRFs have been incorporated in numerous state-of-the-art models BIBREF16 , BIBREF18 , BIBREF3 because of their ability to incorporate tag information at the sentence level.",
+      "While the Bi-LSTM layer takes information from the context into account when generating its label predictions, each decision is independent from the other labels in the sentence. The CRF allows us to find the labeling sequence in a sentence with the highest probability. This way, both previous and subsequent label information is used in determining the label of a given token. As a sequence model, the CRF posits a probability model for the label sequence of the tokens in a sentence, conditional on the word sequence and the output scores from the Bi-LTSM model for the given sentence. In doing so, the CRF models the conditional distribution of the label sequence instead of a joint distribution with the words and output scores. Thus, it does not assume independent features, while at the same time not making strong distributional assumptions about the relationship between the features and sequence labels."
+    ],
+    [
+      "The two main data sets that we will use to evaluate our architecture are the 2014 i2b2 de-identification challenge data set BIBREF2 and the nursing notes corpus BIBREF3 .",
+      "The i2b2 corpus was used by all tracks of the 2014 i2b2 challenge. It consists of 1,304 patient progress notes for 296 diabetic patients. All the PHIs were removed and replaced with random replacements. The PHIs in this data set were broken down first into the HIPAA categories and then into the i2b2-PHI categories as shown in Table TABREF23 . Overall, the data set contains 56,348 sentences with 984,723 separate tokens of which 41,355 are separate PHI tokens, which represent 28,867 separate PHI instances. For our test-train-valid split, we chose 10% of the training sentences to serve as our validation set, which represents 3,381 sentences while a separately held-out official test data set was specified by the competition. This test data set contains 22,541 sentences including 15,275 separate PHI tokens.",
+      "The nursing notes were originally collected by Neamatullah et al. BIBREF3 . The data set contains 2,434 notes of which there are 1,724 separate PHI instances. A summary of the breakdown of the PHI categories of this nursing corpora can be seen in Table TABREF23 ."
+    ],
+    [
+      "For de-identification tasks, the three metrics we will use to evaluate the performance of our architecture are Precision, Recall and INLINEFORM0 score as defined below. We will compute both the binary INLINEFORM1 score and the three metrics for each PHI type for both data sets. Note that binary INLINEFORM2 score calculates whether or not a token was identified as a PHI as opposed to correctly predicting the right PHI type. For de-identification, we place more importance on identifying if a token was a PHI instance with correctly predicting the right PHI type as a secondary objective. INLINEFORM3 INLINEFORM4 ",
+      "Notice that a high recall is paramount given the risk of accidentally disclosing sensitive patient information if not all PHI are detected and removed from the document or replaced by fake data. A high precision is also desired to preserve the integrity of the documents, as a large number of false positives might obscure the meaning of the text or even distort it. As the harmonic mean of precision and recall, the INLINEFORM0 score gives an overall measure for model performance that is frequently employed in the NLP literature.",
+      "As a benchmark, we will use the results of the systems by Burckhardt et al. BIBREF22 , Liu et al. BIBREF18 , Dernoncourt et al. BIBREF9 and Yang et al. BIBREF10 on the i2b2 dataset and the performance of Burckhardt et al. on the nursing corpus. Note that Burckhardt et al. used the entire data set for their results as it is an unsupervised learning system while we had to split our data set into 60% training data and 40% testing data."
+    ],
+    [
+      "We evaluated the architecture on both the i2b2-PHI categories and the HIPAA-PHI categories for the i2b2 data set based on token-level labels. Note that the HIPAA categories are a super set of the i2b2-PHI categories. We also ran the analysis 5+ times to give us a range of maximum scores for the different data sets.",
+      "Table TABREF25 gives us a summary of how our architecture performed against other systems on the binary INLINEFORM0 score metrics while Table TABREF26 and Table TABREF27 summarizes the performance of our architecture against other systems on HIPAA-PHI categories and i2b2-PHI categories respectively. Table TABREF28 presents a summary of the performance on the nursing note corpus while also contrasting the performances achieved by the deidentify system."
+    ],
+    [
+      "As we can see in Table TABREF26 , with the exception of ID, our architecture performs considerably better than systems by Liu et al. and Yang et al. Dernoncourt et al. did not provide exact figures for the HIPAA-PHI categories so we have excluded them from our analysis. Furthermore, Table TABREF25 shows that our architecture performs similarly to the best scores achieved by Dernoncourt et al., with our architecture slightly edging out Dernoncourt et al. on the precision metric. For the nursing corpus, our system, while not performing as well as the performances on i2b2 data set, managed to best the scores achieved by the deidentify system while also achieving a binary INLINEFORM0 score of over 0.812. It is important to note that deidentify was a unsupervised learning system, it did not require the use of a train-valid-test split and therefore, used the whole data set for their performance numbers. The results of our architecture is assessed using a 60%/40% train/test split.",
+      "Our architecture noticeably converges faster than the NeuroNER, which was trained for 100 epochs and the system by Liu et al. BIBREF18 which was trained for 80 epochs. Different runs of training our architecture on the i2b2 dataset converge at around 23 INLINEFORM0 4 epochs. A possible explanation for this is due to our architecture using the Adam optimizer, whereas the NeuroNER system use the Stochastic Gradient Descent (SGD) optimizer. In fact, Reimers et al. BIBREF20 show that the SGD optimizer performed considerably worse than the Adam optimizer for different NLP tasks.",
+      "Furthermore, we also do not see any noticeable improvements from using the PubMed database trained word embeddings BIBREF14 instead of the general text trained GloVe word embeddings. In fact, we consistently saw better INLINEFORM0 scores using the GloVe embeddings. This could be due to the fact that our use case was for identifying general labels such as Names, Phones, Locations etc. instead of bio-medical specific terms such as diseases which are far better represented in the PubMed corpus."
+    ],
+    [
+      "We will mainly focus on the two PHI categories: Profession and ID for our error analysis on the i2b2 data set. It is interesting to note that the best performing models on the i2b2 data set by Dernoncourt et al. BIBREF9 experienced similar lower performances on the same two categories. However, we note the performances by Dernoncourt et al. were achieved using a \u201ccombination of n-gram, morphological, orthographic and gazetteer features\u201d BIBREF9 while our architecture uses only POS tagging as an external feature. Dernoncourt et al. posits that the lower performance on the Profession category might be due to the close embeddings of the Profession tokens to other PHI tokens which we can confirm on our architecture as well. Furthermore, our experiments show that the Profession PHI performs considerably better with the PubMed embedded model than GloVe embedded model. This could be due to the fact that PubMed embeddings were trained on the PubMed database, which is a database of medical literature. GloVe on the other hand was trained on a general database, which means the PubMed embeddings for Profession tokens might not be as close to other tokens as is the case for the GloVe embeddings.",
+      "For the ID PHI, our analysis shows that some of the errors were due to tokenization errors. For example, a \u201c:\u201d was counted as PHI token which our architecture correctly predicted as not a PHI token. Since our architecture is not custom tailored to detect sophisticated ID patterns such as the systems in BIBREF9 , BIBREF10 , we have failed to detect some ID PHIs such as \u201c265-01-73\u201d, a medical record number, which our architecture predicted as a phone number due to the format of the number. Such errors could easily be mitigated by the use of simple regular expressions.",
+      "We can see that our architecture outperforms the deidentify system by a considerable margin on most categories as measured by the INLINEFORM0 score. For example, the authors of deidentify note that Date PHIs have considerably low precision values while our architecture achieve a precision value of greater than 0.915% for the Date PHI. However, Burckhardt et al. BIBREF22 achieve an impressive precision of 0.899 and recall of 1.0 for the Phone PHI while our architecture only manages 0.778 and 0.583 respectively. Our analysis of this category shows that this is mainly due a difference in tokenization, stand alone number are being classified as not a PHI.",
+      "We tried to use the model that we trained on the i2b2 data set to predict the categories of the nursing data set. However, due to difference in the text structure, the actual text and the format, we achieved less than random performance on the nursing data set. This brings up an important point about the transferability of such models."
+    ],
+    [
+      "Our ablation analysis shows us that the layers of our models adds to the overall performance. Figure FIGREF33 shows the binary INLINEFORM0 scores on the i2b2 data set with each bar being a feature toggled off. For example, the \u201cNo Char Embd\u201d bar shows the performance of the model with no character embeddings and everything else the same as our best model.",
+      "We can see a noticeable change in the performance if we do not include the ELMo embeddings versus no GloVe embeddings. The slight decrease in performance when we use no GloVe embeddings shows us that this is a feature we might choose to exclude if computation time is limited. Furthermore, we can see the impact of having no variational dropout and only using a na\u00efve dropout, it shows that variational dropout is better at regularizing our neural network."
+    ],
+    [
+      "In this study, we show that our deep learning architecture, which incorporates the latest developments in contextual word embeddings and NLP, achieves state-of-the-art performance on two widely available gold standard de-identification data sets while also achieving similar performance as the best system available in less epochs. Our architecture also significantly improves over the performance of the hybrid system deidentify on the nursing data set.",
+      "This architecture could be integrated into a client-ready system such as the deidentify system. However, as mentioned in Section SECREF8 , the use of a dictionary (or gazetter) might help improve the model even further specially with regards to the Location and Profession PHI types. Such a hybrid system would be highly beneficial to practitioners that needs to de-identify patient data on a daily basis."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2210/instruction.md b/qasper-2210/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..3fd971f964f8f787028da93085075b3bda3a90d6
--- /dev/null
+++ b/qasper-2210/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Cross Lingual Cross Corpus Speech Emotion Recognition
+
+Question: Which four languages do they experiment with?
\ No newline at end of file
diff --git a/qasper-2211/instruction.md b/qasper-2211/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..33e449353e3255671c69e0c581eab9b8ac0e68d2
--- /dev/null
+++ b/qasper-2211/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Location-Relative Attention Mechanisms For Robust Long-Form Speech Synthesis
+
+Question: Does DCA or GMM-based attention perform better in experiments?
\ No newline at end of file
diff --git a/qasper-2216/instruction.md b/qasper-2216/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..ac195e8ffeab981cd1a630d6e733baf4da81f856
--- /dev/null
+++ b/qasper-2216/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: ViGGO: A Video Game Corpus for Data-To-Text Generation in Open-Domain Conversation
+
+Question: Is any data-to-text generation model trained on this new corpus, what are the results?
\ No newline at end of file
diff --git a/qasper-2217/instruction.md b/qasper-2217/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..335f040d7dea6e04aa4f885eeef72d37913023be
--- /dev/null
+++ b/qasper-2217/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: ViGGO: A Video Game Corpus for Data-To-Text Generation in Open-Domain Conversation
+
+Question: How the authors made sure that corpus is clean despite being crowdsourced?
\ No newline at end of file
diff --git a/qasper-2218/instruction.md b/qasper-2218/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..02882213a0688a1a327d3264ec7fcea4101d1b77
--- /dev/null
+++ b/qasper-2218/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: A human-editable Sign Language representation for software editing---and a writing system?
+
+Question: Do they build a generative probabilistic language model for sign language?
\ No newline at end of file
diff --git a/qasper-2219/instruction.md b/qasper-2219/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..775a612c84d73973549e3934397cdb25b0e5eef6
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+++ b/qasper-2219/instruction.md
@@ -0,0 +1,148 @@
+Name of Paper: Contextual LSTM (CLSTM) models for Large scale NLP tasks
+
+Question: Does CLSTM have any benefits over BERT?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Word Prediction",
+    "Model",
+    "HTM: Supervised Topic Labels",
+    "Experiments",
+    "Next Sentence Selection",
+    "Problem Instantiation",
+    "Approach",
+    "Experimental Results",
+    "Error Analysis",
+    "Sentence Topic Prediction",
+    "Comparison to BOW-DNN baseline",
+    "Using Unsupervised Topic Signals",
+    "Results on Google News data",
+    "Conclusions",
+    "Future Work"
+  ],
+  "paragraphs": [
+    [
+      "Documents have sequential structure at different hierarchical levels of abstraction: a document is typically composed of a sequence of sections that have a sequence of paragraphs, a paragraph is essentially a sequence of sentences, each sentence has sequences of phrases that are comprised of a sequence of words, etc. Capturing this hierarchical sequential structure in a language model (LM) BIBREF0 can potentially give the model more predictive accuracy, as we have seen in previous work BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 .",
+      "A useful aspect of text that can be utilized to improve the performance of LMs is long-range context. For example, let us consider the following three text segments:",
+      "1) Sir Ahmed Salman Rushdie is a British Indian novelist and essayist. He is said to combine magical realism with historical fiction.",
+      "2) Calvin Harris & HAIM combine their powers for a magical music video.",
+      "3) Herbs have enormous magical power, as they hold the earth's energy within them.",
+      "Consider an LM that is trained on a dataset having the example sentences given above \u2014 given the word \u201cmagical\u201d, what should be the most likely next word according to the LM: realism, music, or power? In this example, that would depend on the longer-range context of the segment in which the word \u201cmagical\u201d occurs. One way in which the context can be captured succinctly is by using the topic of the text segment (e.g., topic of the sentence, paragraph). If the context has the topic \u201cliterature\u201d, the most likely next word should be \u201crealism\u201d. This observation motivated us to explore the use of topics of text segments to capture hierarchical and long-range context of text in LMs.",
+      "In this paper, we consider Long-Short Term Memory (LSTM) models BIBREF6 , a specific kind of Recurrent Neural Networks (RNNs). The LSTM model and its different variants have achieved impressive performance in different sequence learning problems in speech, image, music and text analysis BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 , BIBREF15 , where it is useful in capturing long-range dependencies in sequences. LSTMs substantially improve our ability to handle long-range dependencies, though they still have some limitations in this regard BIBREF16 , BIBREF1 .",
+      "RNN-based language models (RNN-LMs) were proposed by Mikolov et al. BIBREF17 , and in particular the variant using LSTMs was introduced by Sundermeyer et al. BIBREF18 . In this paper, we work with LSTM-based LMs. Typically LSTMs used for language modeling consider only words as features. Mikolov et al. BIBREF19 proposed a conditional RNN-LM for adding context \u2014 we extend this approach of using context in RNN-LMs to LSTMs, train the LSTM models on large-scale data, and propose new tasks beyond next work prediction.",
+      "We incorporate contextual features (namely, topics based on different segments of text) into the LSTM model, and call the resulting model Contextual LSTM (CLSTM). In this work we evaluate how adding contextual features in the CLSTM improves the following tasks:",
+      "1) Word prediction: Given the words and topic seen so far in the current sentence, predict the most likely next word. This task is important for sentence completion in applications like predictive keyboard, where long-range context can improve word/phrase prediction during text entry on a mobile phone.",
+      "2) Next sentence selection: Given a sequence of sentences, find the most likely next sentence from a set of candidates. This is an important task in question/answering, where topic can be useful in selecting the best answer from a set of template answers. This task is also relevant in other applications like Smart Reply BIBREF20 , for predicting the best response to an email from a set of candidate responses.",
+      "3) Sentence topic prediction: Given the words and topic of the current sentence, predict the topic of the next sentence. We consider two scenarios: (a) where we don't know the words of the next sentence, (b) where we know the words of the next sentence. Scenario (a) is relevant for applications where we don't know the words of a user's next utterance, e.g., while predicting the topic of response of the user of a dialog system, which is useful in knowing the intent of the user; in scenario (b) we try to predict the topic/intent of an utterance, which is common in a topic modeling task.",
+      "The main contributions of this paper are as follows:",
+      "1) We propose a new Contextual LSTM (CLSTM) model, and demonstrate how it can be useful in tasks like word prediction, next sentence scoring and sentence topic prediction \u2013 our experiments show that incorporating context into an LSTM model (via the CLSTM) gives improvements compared to a baseline LSTM model. This can have potential impact for a wide variety of NLP applications where these tasks are relevant, e.g. sentence completion, question/answering, paraphrase generation, dialog systems.",
+      "2) We trained the CLSTM (and the corresponding baseline LSTM) models on two large-scale document corpora: English documents in Wikipedia, and a recent snapshot of English Google News documents. The vocabulary we handled in the modeling here was also large: 130K words for Wikipedia, 100K for Google news. Our experiments and analysis demonstrate that the CLSTM model that combines the power of topics with word-level features yields significant performance gains over a strong baseline LSTM model that uses only word-level features. For example, in the next sentence selection task, CLSTM gets a performance improvement of 21% and 18% respectively over the LSTM model on the English Wikipedia and Google News datasets.",
+      "3) We show initial promising results with a model where we learn the thought embedding in an unsupervised manner through the model structure, instead of using supervised extraneous topic as side information (details in Section \"Using Unsupervised Topic Signals\" )."
+    ],
+    [
+      "There are various approaches that try to fit a generative model for full documents. These include models that capture the content structure using Hidden Markov Models (HMMs) BIBREF21 , or semantic parsing techniques to identify the underlying meanings in text segments BIBREF22 . Hierarchical models have been used successfully in many applications, including hierarchical Bayesian models BIBREF23 , BIBREF24 , hierarchical probabilistic models BIBREF25 , hierarchical HMMs BIBREF26 and hierarchical CRFs BIBREF27 .",
+      "As mentioned in Section \"Model\" , RNN-based language models (RNN-LMs) were proposed by Mikolov et al. BIBREF17 , and the variant using LSTMs was introduced by Sundermeyer et al. BIBREF18 \u2013 in this paper, we work with LSTM-based LMs. Mikolov et al. BIBREF19 proposed a conditional RNN-LM for adding context \u2014 we extend this approach of using context in RNN-LMs to LSTMs.",
+      "Recent advances in deep learning can model hierarchical structure using deep belief networks BIBREF28 , BIBREF5 , BIBREF29 , BIBREF30 , especially using a hierarchical recurrent neural network (RNN) framework. In Clockwork RNNs BIBREF31 the hidden layer is partitioned into separate modules, each processing inputs at its own individual temporal granularity. Connectionist Temporal Classification or CTC BIBREF32 does not explicitly segment the input in the hidden layer \u2013 it instead uses a forward-backward algorithm to sum over all possible segments, and determines the normalized probability of the target sequence given the input sequence. Other approaches include a hybrid NN-HMM model BIBREF33 , where the temporal dependency is handled by an HMM and the dependency between adjacent frames is handled by a neural net (NN). In this model, each node of the convolutional hidden layer corresponds to a higher-level feature.",
+      "Some NN models have also used context for modeling text. Paragraph vectors BIBREF34 , BIBREF35 propose an unsupervised algorithm that learns a latent variable from a sample of words from the context of a word, and uses the learned latent context representation as an auxiliary input to an underlying skip-gram or Continuous Bag-of-words (CBOW) model. Another model that uses the context of a word infers the Latent Dirichlet Allocation (LDA) topics of the context before a word and uses those to modify a RNN model predicting the word BIBREF19 .",
+      "Tree-structured LSTMs BIBREF36 , BIBREF5 extend chain-structured LSTMs to the tree structure and propose a principled approach of considering long-distance interaction over hierarchies, e.g., language or image parse structures. Convolution networks have been used for multi-level text understanding, starting from character-level inputs all the way to abstract text concepts BIBREF37 . Skip thought vectors have also been used to train an encoder-decoder model that tries to reconstruct the surrounding sentences of an encoded passage BIBREF38 .",
+      "Other related work include Document Context Language models BIBREF39 , where the authors have multi-level recurrent neural network language models that incorporate context from within a sentence and from previous sentences. Lin et al. BIBREF40 use a hierarchical RNN structure for document-level as well as sentence-level modeling \u2013 they evaluate their models using word prediction perplexity, as well as an approach of coherence evaluation by trying to predict sentence-level ordering in a document.",
+      "In this work, we explore the use of long-range hierarchical signals (e.g., sentence level or paragraph level topic) for text analysis using a LSTM-based sequence model, on large-scale data \u2014 to the best of our knowledge this kind of contextual LSTM models, which model the context using a 2-level LSTM architecture, have not been trained before at scale on text data for the NLP tasks mentioned in Section \"Model\" ."
+    ],
+    [
+      "Of the three different tasks outlined in Section \"Model\" , we focus first on the word prediction task, where the goal is to predict the next word in a sentence given the words and context (captured via topic) seen previously.",
+      "Let $s_i$ be the $i^{th}$ sentence in a sequence of sentences, $w_{i,j}$ be the $j^{th}$ word of sentence $s_i$ , $n_i$ be the number of words in $s_i$ , and $w_{i,j} \\ldots w_{i,k}$ indicate the sequence of words from word $j$ to word $k$ in sentence $i^{th}$0 . Note that sentence $i^{th}$1 is equivalent to the sequence of words $i^{th}$2 . Let $i^{th}$3 be the random variable denoting the topic \u2013 it is computed based on a particular subsequence of words seen from the first word of the sequence ( $i^{th}$4 ) to the current word ( $i^{th}$5 ). This topic can be based on the current sentence segment (i.e., $i^{th}$6 ), or the previous sentence (i.e., $i^{th}$7 ), etc. Details regarding the topic computation are outlined in Section \"Results on Google News data\" .",
+      "Using this notation, the word prediction task in our case can be specified as follows: given a model with parameters $\\Theta $ , words $w_{0,0} \\ldots w_{i,j}$ and the topic $T$ computed from a subsequence of the words from the beginning of the sequence, find the next word $w_{i,j+1}$ that maximizes the probability: $P(w_{i,j+1} | w_{0,0} \\ldots w_{i,j}, T, \\Theta )$ ."
+    ],
+    [
+      "For our approach, as explained before, we introduce the power of context into a standard LSTM model. LSTM is a recurrent neural network that is useful for capturing long-range dependencies in sequences. The LSTM model has multiple LSTM cells, where each LSTM cell models the digital memory in a neural network. It has gates that allow the LSTM to store and access information over time. For example, the input/output gates control cell input/output, while the forget gate controls the state of the cell.",
+      "The word-prediction LSTM model was implemented in the large-scale distributed Google Brain framework BIBREF41 . The model takes words encoded in 1-hot encoding from the input, converts them to an embedding vector, and consumes the word vectors one at a time. The model is trained to predict the next word, given a sequence of words already seen. The core algorithm used to train the LSTM parameters is BPTT BIBREF42 , using a softmax layer that uses the id of the next word as the ground truth.",
+      "To adapt the LSTM cell that takes words to a CLSTM cell that takes as input both words and topics, we modify the equations representing the operations of the LSTM cell BIBREF43 to add the topic vector $T$ to the input gate, forget gate, cell and output gate ( $T$ is the embedding of the discrete topic vector). In each of the following equations, the term in bold is the modification made to the original LSTM equation. ",
+      "$$i_t &=& \\sigma (W_{xi} x_t + W_{hi} h_{t-1} + W_{ci} c_{t-1} + b_i + \\bf {W_{Ti} T)} \\nonumber \\\\\nf_t &=& \\sigma (W_{xf} x_t + W_{hf} h_{t-1} + W_{cf} c_{t-1} + b_f + \\bf {W_{Ti} T)} \\nonumber \\\\\nc_t &=& f_t c_{t-1} + i_t \\tanh (W_{xc} x_t + W_{hc} h_{t-1} + b_c + \\bf {W_{Ti} T)} \\nonumber \\\\\no_t &=& \\sigma (W_{xo} x_t + W_{ho} h_{t-1} + W_{co} c_{t} + b_o + \\bf {W_{Ti} T)} \\nonumber \\\\\nh_t &=& o_t \\tanh (c_t) $$   (Eq. 2) ",
+      "In these equations $i$ , $f$ and $o$ are the input gate, forget gate and output gate respectively, $x$ is the input, $b$ is the bias term, $c$ is the cell memory, and $h$ is the output. As an example, consider the input gate equation: ",
+      "$$i_t =& \\sigma (W_{xi} x_t + W_{hi} h_{t-1} + W_{ci} c_{t-1} + b_i) \\nonumber \\\\\n=& \\sigma ([W_{xi} \\ W_{hi} \\ W_{ci} \\ 1] [x_t \\ h_{t-1} \\ c_{t-1} \\ b_i]^T) $$   (Eq. 3) ",
+      "When we add the topic signal $T$ to the input gate, the equation is modified to: ",
+      "$$i_t =& \\sigma (W_{xi} x_t + W_{hi} h_{t-1} + W_{ci} c_{t-1} + b_i + W_{Ti} T) \\nonumber \\\\\n=& \\sigma ([W_{xi} \\ W_{Ti} \\ W_{hi} \\ W_{ci} \\ 1] [x_t \\ T \\ h_{t-1} \\ c_{t-1} \\ b_i]^T) $$   (Eq. 4) ",
+      "Comparing the last two equations, Equations 3 and 4 , we see that having a topic vector $T$ added into the CLSTM cell is equivalent to considering a composite input $[x_i$ $T]$ to the LSTM cell that concatenates the word embedding and topic embedding vectors. This approach of concatenating topic and word embeddings in the input worked better in practice than other strategies for combining topics with words. Figure 1 shows the schematic figure of a CLSTM model that considers both word and topic input vectors.",
+      "Note that we add the topic input to each LSTM cell since each LSTM cell can potentially have a different topic. For example, when the topic is based on the sentence segment seen so far (see Section UID10 ), the topic is based on the current sentence prefix \u2014 so, each LSTM cell can potentially have a different topic. Note that in some setups each LSTM cell in a layer could have the same topic, e.g., when the topic is derived from the words in the previous sentence.",
+      "We trained a baseline LSTM model on the words of $A_i$ , $B_i$ and $C_i$ to predict the words of $D_i$ . The CLSTM model uses words from $A_i$ , $B_i$ , $C_i$ , and topics of $A_i$ , $B_i$ , $C_i$ and $B_i$0 , to predict the words of $B_i$1 . Note that in this case we can use the topic of $B_i$2 since all the candidate next sentences are given as input in the next sentence scoring task.",
+      "For 1024 hidden units, the perplexity of the baseline LSTM model after convergence of model training is 27.66, while the perplexity of the CLSTM model at convergence is 24.81. This relative win of 10.3% in an intrinsic evaluation measure (like perplexity) was the basis for confidence in expecting good performance when using this CLSTM model for the next sentence scoring task.",
+      "For the sentence topic prediction task, we determined through ablation experiments that the unrolled model architecture, where each sentence in a paragraph is modeled by a separate LSTM model, has better performance than the rolled-up model architecture used for word prediction where the sentences in a paragraph are input to a single LSTM."
+    ],
+    [
+      "The topics of the text segments can be estimated using different unsupervised methods (e.g., clustering) or supervised methods (e.g., hierarchical classification). For the word prediction task we use HTM, a hierarchical topic model for supervised classification of text into a hierarchy of topic categories, based on the Google Rephil large-scale clustering tool BIBREF44 . There are about 750 categories at the leaf level of the HTM topic hierarchy. Given a segment of text, HTM gives a probability distribution over the categories in the hierarchy, including both leaf and intermediate categories. We currently choose highest probability topic as the most-likely category of the text segment."
+    ],
+    [
+      "We trained different types of CLSTM models for the word prediction task. The different types of features used in the different CLSTM models are shown schematically in Figure 2 . The hierarchical features that we used in different variants of the word prediction model are:",
+      "PrevSentTopic = TopicID of the topic computed based on all the words of the previous sentence, i.e., $T = Topic(w_{i-1,0} \\ldots w_{i-1,n_{i-1}-1})$ .",
+      "SentSegTopic = TopicID of the topic computed based on the words of the current sentence prefix until the current word, i.e., $T = Topic(w_{i,0} \\ldots w_{i,j})$ .",
+      "ParaSegTopic = TopicID of the topic computed based on the paragraph prefix until the current word, i.e., $T = Topic(w_{0,0} \\ldots w_{i,j})$ .",
+      "where $T$ is defined in Section \"Word Prediction\" .",
+      "For our experiments, we used the whole English corpus from Wikipedia (snapshot from 2014/09/17). There were 4.7 million documents in the Wikipedia dataset, which we randomly divided into 3 parts: 80% was used as train, 10% as validation and 10% as test set. Some relevant statistics of the train, test and validation data sets of the Wikipedia corpus are given in Table 1 .",
+      "We created the vocabulary from the words in the training data, filtering out words that occurred less than a particular threshold count in the total dataset (threshold was 200 for Wikipedia). This resulted in a vocabulary with 129K unique terms, giving us an out-of-vocabulary rate of 3% on the validation dataset.",
+      "For different types of text segments (e.g., segment, sentence, paragraph) in the training data, we queried HTM and got the most likely topic category. That gave us a total of $\\approx $ 1600 topic categories in the dataset.",
+      "We trained different CLSTM models with different feature variants till convergence, and evaluated their perplexity on the holdout test data. Here are some key observations about the results (details in Table 2 ):",
+      "1) The \u201cWord + SentSegTopic + ParaSegTopic\u201d CLSTM model is the best model, getting the best perplexity. This particular LSTM model uses both sentence-level and paragraph-level topics as features, implying that both local and long-range context is important for getting the best performance.",
+      "2) When current segment topic is present, the topic of the previous sentence does not matter.",
+      "3) As we increased the number of hidden units, the performance started improving. However, beyond 1024 hidden units, there were diminishing returns \u2014 the gain in performance was out-weighed by the substantial increase in computational overhead.",
+      "Note that we also trained a distributed n-gram model with \u201cstupid backoff\u201d smoothing BIBREF45 on the Wikipedia dataset, and it gave a perplexity of $\\approx $ 80 on the validation set. We did not train a n-gram model with Knesner-Ney (KN) smoothing on the Wikipedia data, but on the Google News data (from a particular snapshot) the KN smoothed n-gram model gave a perplexity of 74 (using 5-grams).",
+      "Note that we were not able to compare our CLSTM models to other existing techniques for integrating topic information into LSTM models (e.g., Mikolov et al. BIBREF19 ), since we didn't have access to implementations of these approaches that can scale to the vocabulary sizes ( $\\approx $ 100K) and dataset sizes we worked with (e.g., English Wikipedia, Google News snapshot). Hence, we used a finely-tuned LSTM model as a baseline, which we also trained at scale on these datasets.",
+      "In our experiments we used the output of HTM as the topic of each sentence. Ideally we would associate a \u201csupervised topic\u201d with each sentence (e.g., the supervision provided by human raters). However, due to the difficulty of getting such human ratings at scale, we used the HTM model to find topics for the sentences. Note that the HTM model is trained on human ratings.",
+      "We trained 2 baseline models on this dataset. The Word model uses the words of the current sentence to predict the topic of the next sentence \u2013 it determines how well we can predict the topic of the next sentence, given the words of the current sentence. We also trained another baseline model, SentTopic, which uses the sentence topic of the current sentence to predict the topic of the next sentence \u2013 the performance of this model will give us an idea of the inherent difficulty of the task of topic prediction. We trained a CLSTM model (Word+SentTopic) that uses both words and topic of the current sentence to predict the topic of the next sentence. Figure 2 shows the hierarchical features used in the CLSTM model. We trained all models with different number of hidden units: 256, 512, 1024. Each model was trained till convergence. Table 4 shows the comparison of the perplexity of the different models. The CLSTM model beats the baseline SentTopic model by more than 12%, showing that using hierarchical features is useful for the task of sentence topic prediction too."
+    ],
+    [
+      "We next focus on the next sentence scoring task, where we are given a sequence of sentences and the goal is to find the most probable next sentence from a set of candidate sentences. An example of this task is shown in Figure 3 . The task can be stated as follows: given a model with parameters $\\Theta $ , a sequence of $p-1$ sentences $s_0 \\ldots s_{p-2}$ (with their corresponding topics $T_0 \\ldots T_{p-2}$ ), find the most likely next sentence $s_{p-1}$ from a candidate set of next sentences $S$ , such that: $\ns_{p-1} = \\arg \\max _{s \\in S} P(s | s_0 \\ldots s_{p-2}, T_0 \\ldots T_{p-2}, \\Theta ).\n$ "
+    ],
+    [
+      "Suppose we are given a set of sequences, where each sequence consists of 4 sentences (i.e., we consider $p$ =4). Let each sequence be $S_i = <A_i B_i C_i D_i>$ , and the set of sequences be $\\lbrace S_1,\\ldots ,S_k\\rbrace $ . Given the prefix $A_i B_i C_i$ of the sequence $S_i$ as context (which we will denote to be $Context_i$ ), we consider the task of correctly identifying the next sentence $D_i$ from a candidate set of sentences: $\\lbrace D_0, D_1,\\ldots , D_{k-1}\\rbrace $ . For each sequence $S_i$ , we compute the accuracy of identifying the next sentence correctly. The accuracy of the model in detecting the correct next sentence is computed over the set of sequences $\\lbrace S_1,\\ldots ,S_k\\rbrace $ ."
+    ],
+    [
+      "We train LSTM and CLSTM models specifically for the next sentence prediction task. Given the context $Context_i$ , the models find the $D_i$ among the set $\\lbrace  D_0 \\ldots D_{k-1}\\rbrace $ that gives the maximum (normalized) score, defined as follows: ",
+      "$$\\forall i, score = \\frac{P(D_i | Context_i)}{\\frac{1}{k} \\sum _{j=0}^{k-1} P(D_i | Context_j)}$$   (Eq. 21) ",
+      "In the above score, the conditional probability terms are estimated using inference on the LSTM and CLSTM models. In the numerator, the probability of the word sequence in $D_i$ , given the prefix context $Context_i$ , is estimated by running inference on a model whose state is already seeded by the sequence $A_iB_iC_i$ (as shown in Figure 4 ). The normalizer term $\\frac{1}{k} \\sum _{j=0}^{k-1} P(D_i | Context_j)$ in the denominator of Equation 21 is the point estimate of the marginal probability $P(D_i)$ computed over the set of sequences, where the prior probability of each prefix context is assumed equal, i.e., $P(Context_j) = \\frac{1}{k}, j \\in [0, k-1]$ . The normalizer term adjusts the score to account for the popularity of a sentence $D_i$ that naturally has a high marginal probability $P(D_i)$ \u2014 we do not allow the popularity of $D_i$ to lead to a high score.",
+      "Note that for task of next sentence scoring, it's ok to use words of the next sentence when selecting the \u201cbest\u201d next sentence. This is because in the task, the possible alternatives are all provided to the model, and the main goal of the model is scoring the alternatives and selecting the best one. This setting is seen in some real-world applications, e.g., predicting the best response to an email from a set of candidate responses BIBREF20 ."
+    ],
+    [
+      "We ran next sentence scoring experiments with a dataset generated from the test set of the corpora. We divide the test dataset into 100 non-overlapping subsets. To create the dataset for next sentence scoring, we did the following: (a) sample 50 sentence sequences $<A_i B_i C_i D_i>$ from 50 separate paragraphs, randomly sampled from 1 subset of the test set \u2013 we call this a block; (b) consider 100 such blocks in the next sentence scoring dataset. So, overall there are 5000 sentence sequences in the final dataset. For each sequence prefix $A_i B_i C_i$ , the model has to choose the best next sentence $D_i$ from the competing set of next sentences.",
+      "The average accuracy of the baseline LSTM model on this dataset is $52\\%$ , while the average accuracy of the CLSTM model using word + sentence-level topic features is $63\\%$ (as shown in Table 3 ). So the CLSTM model has an average improvement of $21\\%$ over the LSTM model on this dataset. Note that on this task, the average accuracy of a random predictor that randomly picks the next sentence from a set of candidate sentences would be $2\\%$ .",
+      "We also ran other experiments, where the negatives (i.e., 49 other sentences in the set of 50) were not chosen randomly \u2014 in one case we considered all the 50 sentences to come from the same HTM topic, making the task of selecting the best sentence more difficult. In this case, as expected, the gain from using the context in CLSTM was larger \u2014 the CLSTM model gave larger improvement over the baseline LSTM model than in the case of having a random set of negatives."
+    ],
+    [
+      "Figures 5 - 7 analyze different types of errors made by the LSTM and the CLSTM models, using samples drawn from the test dataset."
+    ],
+    [
+      "The final task we consider is the following: if we are given the words and the topic of the current sentence, can we predict the topic of the next sentence? This is an interesting problem for dialog systems, where we ask the question: given the utterance of a speaker, can we predict the topic of their next utterance? This can be used in various applications in dialog systems, e.g., intent modeling.",
+      "The sentence topic prediction problem can be formulated as follows: given a model with parameters $\\Theta $ , words in the sentence $s_i$ and corresponding topic $T_i$ , find the next sentence topic $T_{i+1}$ that maximizes the following probability \u2013 $P(T_{i+1} | s_i, T_i,\n\\Theta )$ . Note that in this case we train a model to predict the topic target instead of the joint word/topic target, since we empirically determined that training a model with a joint target gave lower accuracy in predicting the topic compared to a model that only tries to predict the topic as a target."
+    ],
+    [
+      "For the task of sentence topic prediction, we also compared the CLSTM model to a Bag-of-Words Deep Neural Network (BOW-DNN) baseline BIBREF46 . The BOW-DNN model extracts bag of words from the input text, and a DNN layer is used to extract higher-level features from the bag of words. For this experiment, the task setup we considered was slightly different in order to facilitate more direct comparison. The goal was to predict the topic of the next sentence, given words of the next sentence. The BOW-DNN model was trained only on word features, and got a test set perplexity of 16.5 on predicting the sentence topic. The CLSTM model, trained on word and topic-level features, got a perplexity of 15.3 on the same test set using 1024 hidden units, thus outperforming the BOW-DNN model by 7.3%."
+    ],
+    [
+      "In our experiments with topic features, we have so far considered supervised topic categories obtained from an extraneous source (namely, HTM). One question arises: if we do not use extraneous topics to summarize long-range context, would we get any improvements in performance with unsupervised topic signals? To answer this question, we experimented with \u201cthought embeddings\u201d that are intrinsically generated from the previous context. Here, the thought embedding from the previous LSTM is used as the topic feature in the current LSTM (as shown in Figure 8 ), when making predictions of the topic of the next sentence \u2013 we call this context-based thought embedding the \u201cthought vector\u201d.",
+      "In our approach, the thought vector inferred from the LSTM encoding of sentence $n-1$ is used as a feature for the LSTM for sentence $n$ , in a recurrent fashion. Note that the LSTMs for each sentence in Figure 8 are effectively connected into one long chain, since we don't reset the hidden state at the end of each sentence \u2014 so the LSTM for the current sentence has access to the LSTM state of the previous sentence (and hence indirectly to its topic). But we found that directly adding the topic of the previous sentence to all the LSTM cells of the current sentence is beneficial, since it constraints all the current LSTM cells during training and explicitly adds a bias to the model. Our experiments showed that it's beneficial to denoise the thought vector signal using a low-dimensional embedding, by adding roundoff-based projection. Initial experiments using thought vector for sentence-topic prediction look promising. A CLSTM model that used word along with thought vector (PrevSentThought feature in the model) from the previous sentence as features gave a 3% improvement in perplexity compared to a baseline LSTM model that used only words as features. Table 5 shows the detailed results.",
+      "When we used thought vectors, our results improved over using a word-only model but fell short of a CLSTM model that used both words and context topics derived from HTM. In the future, we would like to do more extensive experiments using better low-dimensional projections (e.g., using clustering or bottleneck mechanisms), so that we can get comparable performance to supervised topic modeling approaches like HTM.",
+      "Another point to note \u2014 we have used HTM as a topic model in our experiments as that was readily available to us. However, the CLSTM model can also use other types of context topic vectors generated by different kinds of topic modeling approaches, e.g., LDA, KMeans."
+    ],
+    [
+      "We also ran experiments on a sample of documents taken from a recent (2015/07/06) snapshot of the internal Google News English corpus. This subset had 4.3 million documents, which we divided into train, test and validation datasets. Some relevant statistics of the datasets are given in Table 6 . We filtered out words that occurred less than 100 times, giving us a vocabulary of 100K terms.",
+      "We trained the baseline LSTM and CLSTM models for the different tasks, each having 1024 hidden units. Here are the key results:",
+      "1) Word prediction task: LSTM using only words as features had perplexity of $\\approx $ 37. CLSTM improves on LSTM by $\\approx $ 2%, using words, sentence segment topics and paragraph sentence topics.",
+      "2) Next sentence selection task: LSTM gave an accuracy of $\\approx $ 39%. CLSTM had an accuracy of $\\approx $ 46%, giving a 18% improvement on average.",
+      "3) Next sentence topic prediction task: LSTM using only current sentence topic as feature gave perplexity of $\\approx $ 5. CLSTM improves on LSTM by $\\approx $ 9%, using word and current sentence topic as features.",
+      "As we see, we get similar improvements of CLSTM model over LSTM model for both the Wikipedia and Google News datasets, for each of the chosen NLP tasks."
+    ],
+    [
+      "We have shown how using contextual features in a CLSTM model can be beneficial for different NLP tasks like word prediction, next sentence selection and topic prediction. For the word prediction task CLSTM improves on state-of-the-art LSTM by 2-3% on perplexity, for the next sentence selection task CLSTM improves on LSTM by $\\approx $ 20% on accuracy on average, while for the topic prediction task CLSTM improves on state-of-the-art LSTM by $\\approx $ 10% (and improves on BOW-DNN by $\\approx $ 7%). These gains are all quite significant and we get similar gains on the Google News dataset (Section \"Results on Google News data\" ), which shows the generalizability of our approach. Initial results using unsupervised topic signal using with vectors, instead of supervised topic models, are promising. The gains obtained by using the context in the CLSTM model has major implications of performance improvements in multiple important NLP applications, ranging from sentence completion, question/answering, and paraphrase generation to different applications in dialog systems."
+    ],
+    [
+      "Our initial experiments on using unsupervised thought vectors for capturing long-range context in CLSTM models gave promising results. A natural extension of the thought vector model in Figure 8 is a model that has a connection between the hidden layers, to be able to model the \u201ccontinuity of thought\u201d. Figure 9 shows one such hierarchical LSTM (HLSTM) model, which has a 2-level hierarchy: a lower-level LSTM for modeling the words in a sentence, and a higher-level LSTM for modeling the sentences in a paragraph. The thought vector connection from the LSTM cell in layer $n$ to the LSTM cells in layer $n-1$ (corresponding to the next sentence) enables concepts from the previous context to be propagated forward, enabling the \u201cthought\u201d vector of a sentence to influence words of the next sentence. The connection between the sentence-level hidden nodes also allows the model to capture the continuity of thought. We would like to experiment with this model in the future.",
+      "We would also like to explore the benefits of contextual features in other applications of language modeling, e.g., generating better paraphrases by using word and topic features. Another interesting application could be using topic-level signals in conversation modeling, e.g., using Dialog Acts as a topic-level feature for next utterance prediction.",
+      "Acknowledgments: We would like to thank Ray Kurzweil, Geoffrey Hinton, Dan Bikel, Lukasz Kaiser and Javier Snaider for useful feedback on this work. We would also like to thank Louis Shao and Yun-hsuan Sung for help in running some experiments."
+    ]
+  ]
+}
+```
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@@ -0,0 +1,3 @@
+Name of Paper: Ensemble-Based Deep Reinforcement Learning for Chatbots
+
+Question: How do they obtain human generated policies?
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+++ b/qasper-2221/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Ensemble-Based Deep Reinforcement Learning for Chatbots
+
+Question: How many agents do they ensemble over?
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+Name of Paper: Fully Automated Fact Checking Using External Sources
+
+Question: What algorithm and embedding dimensions are used to build the task-specific embeddings?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "The Fact-Checking System",
+    "External Support Retrieval",
+    "Text Representation",
+    "Veracity Prediction",
+    "Dataset",
+    "Experimental Setup",
+    "Evaluation Metrics",
+    "Results",
+    "Application to cQA",
+    "Related Work",
+    "Conclusions and Future Work",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "Recent years have seen the proliferation of deceptive information online. With the increasing necessity to validate the information from the Internet, automatic fact checking has emerged as an important research topic. It is at the core of multiple applications, e.g., discovery of fake news, rumor detection in social media, information verification in question answering systems, detection of information manipulation agents, and assistive technologies for investigative journalism. At the same time, it touches many aspects, such as credibility of users and sources, information veracity, information verification, and linguistic aspects of deceptive language.",
+      "In this paper, we present an approach to fact-checking with the following design principles: (i) generality, (ii) robustness, (iii) simplicity, (iv) reusability, and (v) strong machine learning modeling. Indeed, the system makes very few assumptions about the task, and looks for supportive information directly on the Web. Our system works fully automatically. It does not use any heavy feature engineering and can be easily used in combination with task-specific approaches as well, as a core subsystem. Finally, it combines the representational strength of recurrent neural networks with kernel-based classification.",
+      "The system starts with a claim to verify. First, we automatically convert the claim into a query, which we execute against a search engine in order to obtain a list of potentially relevant documents. Then, we take both the snippets and the most relevant sentences in the full text of these Web documents, and we compare them to the claim. The features we use are dense representations of the claim, of the snippets and of related sentences from the Web pages, which we automatically train for the task using Long Short-Term Memory networks (LSTMs). We also use the final hidden layer of the neural network as a task-specific embedding of the claim, together with the Web evidence. We feed all these representations as features, together with pairwise similarities, into a Support Vector Machine (SVM) classifier using an RBF kernel to classify the claim as True or False.",
+      "Figure FIGREF1 presents a real example from one of the datasets we experiment with. The left-hand side of the figure contains a True example, while the right-hand side shows a False one. We show the original claims from snopes.com, the query generated by our system, and the information retrieved from the Web (most relevant snippet and text selection from the web page). The veracity of the claim can be inferred from the textual information.",
+      "Our contributions can be summarized as follows:",
+      "The remainder of this paper is organized as follows. Section SECREF2 introduces our method for fact checking claims using external sources. Section SECREF3 presents our experiments and discusses the results. Section SECREF4 describes an application of our approach to a different dataset and a slightly different task: fact checking in community question answering forums. Section SECREF5 presents related work. Finally, Section SECREF6 concludes and suggests some possible directions for future work."
+    ],
+    [
+      "Given a claim, our system searches for support information on the Web in order to verify whether the claim is likely to be true. The three steps in this process are (i) external support retrieval, (ii) text representation, and (iii) veracity prediction."
+    ],
+    [
+      "This step consists of generating a query out of the claim and querying a search engine (here, we experiment with Google and Bing) in order to retrieve supporting documents. Rather than querying the search engine with the full claim (as on average, a claim is two sentences long), we generate a shorter query following the lessons highlighted in BIBREF0 .",
+      "As we aim to develop a general-purpose fact checking system, we use an approach for query generation that does not incorporate any features that are specific to claim verification (e.g., no temporal indicators).",
+      "We rank the words by means of tf-idf. We compute the idf values on a 2015 Wikipedia dump and the English Gigaword. BIBREF0 suggested that a good way to perform high-quality search is to only consider the verbs, the nouns and the adjectives in the claim; thus, we exclude all words in the claim that belong to other parts of speech. Moreover, claims often contain named entities (e.g., names of persons, locations, and organizations); hence, we augment the initial query with all the named entities from the claim's text. We use IBM's AlchemyAPI to identify named entities. Ultimately, we generate queries of 5\u201310 tokens, which we execute against a search engine. We then collect the snippets and the URLs in the results, skipping any result that points to a domain that is considered unreliable. Finally, if our query has returned no results, we iteratively relax it by dropping the final tokens one at a time."
+    ],
+    [
+      "Next, we build the representation of a claim and the corresponding snippets and Web pages. First, we calculate three similarities (a) between the claim and a snippet, or (b) between the claim and a Web page: (i) cosine with tf-idf, (ii) cosine over embeddings, and (iii) containment BIBREF1 . We calculate the embedding of a text as the average of the embeddings of its words; for this, we use pre-trained embeddings from GloVe BIBREF2 . Moreover, as a Web page can be long, we first split it into a set of rolling sentence triplets, then we calculate the similarities between the claim and each triplet, and we take the highest scoring triplet. Finally, as we have up to ten hits from the search engine, we take the maximum and also the average of the three similarities over the snippets and over the Web pages.",
+      "We further use as features the embeddings of the claim, of the best-scoring snippet, and of the best-scoring sentence triplet from a Web page. We calculate these embeddings (i) as the average of the embeddings of the words in the text, and also (ii) using LSTM encodings, which we train for the task as part of a deep neural network (NN). We also use a task-specific embedding of the claim together with all the above evidence about it, which comes from the last hidden layer of the NN."
+    ],
+    [
+      "Next, we build classifiers: neural network (NN), support vector machines (SVM), and a combination thereof (SVM+NN).",
+      "The architecture of our NN is shown on top of Figure FIGREF7 . We have five LSTM sub-networks, one for each of the text sources from two search engines: Claim, Google Web page, Google snippet, Bing Web page, and Bing snippet. The claim is fed into the neural network as-is. As we can have multiple snippets, we only use the best-matching one as described above. Similarly, we only use a single best-matching triple of consecutive sentences from a Web page. We further feed the network with the similarity features described above.",
+      "All these vectors are concatenated and fully connected to a much more compact hidden layer that captures the task-specific embeddings. This layer is connected to a softmax output unit to classify the claim as true or false. The bottom of Figure FIGREF7 represents the generic architecture of each of the LSTM components. The input text is transformed into a sequence of word embeddings, which is then passed to the bidirectional LSTM layer to obtain a representation for the full sequence.",
+      "Our second classifier is an SVM with an RBF kernel. The input is the same as for the NN: word embeddings and similarities. However, the word embeddings this time are calculated by averaging rather than using a bi-LSTM.",
+      "Finally, we combine the SVM with the NN by augmenting the input to the SVM with the values of the units in the hidden layer. This represents a task-specific embedding of the input example, and in our experiments it turned out to be quite helpful. Unlike in the SVM only model, this time we use the bi-LSTM embeddings as an input to the SVM. Ultimately, this yields a combination of deep learning and task-specific embeddings with RBF kernels."
+    ],
+    [
+      "We used part of the rumor detection dataset created by BIBREF3 . While they analyzed a claim based on a set of potentially related tweets, we focus on the claim itself and on the use of supporting information from the Web.",
+      "The dataset consists of 992 sets of tweets, 778 of which are generated starting from a claim on snopes.com, which ma2016detecting converted into a query. Another 214 sets of tweets are tweet clusters created by other researchers BIBREF4 , BIBREF5 with no claim behind them. ma2016detecting ignored the claim and did not release it as part of their dataset. We managed to find the original claim for 761 out of the 778 snopes.com-based clusters.",
+      "Our final dataset consists of 761 claims from snopes.com, which span various domains including politics, local news, and fun facts. Each of the claims is labeled as factually true (34%) or as a false rumor (66%). We further split the data into 509 for training, 132 for development, and 120 for testing. As the original split for the dataset was not publicly available, and as we only used a subset of their data, we had to make a new training and testing split. Note that we ignored the tweets, as we wanted to focus on a complementary source of information: the Web. Moreover, ma2016detecting used manual queries, while we use a fully automatic method. Finally, we augmented the dataset with Web-retrieved snippets, Web pages, and sentence triplets from Web pages."
+    ],
+    [
+      "We tuned the architecture (i.e., the number of layers and their size) and the hyper-parameters of the neural network on the development dataset. The best configuration uses a bidirectional LSTM with 25 units. It further uses a RMSprop optimizer with 0.001 initial learning rate, L2 regularization with INLINEFORM0 =0.1, and 0.5 dropout after the LSTM layers. The size of the hidden layer is 60 with tanh activations. We use a batch of 32 and we train for 400 epochs.",
+      "For the SVM model, we merged the development and the training dataset, and we then ran a 5-fold cross-validation with grid-search, looking for the best kernel and its parameters. We ended up selecting an RBF kernel with INLINEFORM0 and INLINEFORM1 0.01."
+    ],
+    [
+      "The evaluation metrics we use are P (precision), R (recall), and F INLINEFORM0 , which we calculate with respect to the false and to the true claims. We further report AvgR (macro-average recall), AvgF INLINEFORM1 (macro-average F INLINEFORM2 ), and Acc (accuracy)."
+    ],
+    [
+      "Table TABREF14 shows the results on the test dataset. We can see that both the NN and the SVM models improve over the majority class baseline (all false rumors) by a sizable margin. Moreover, the NN consistently outperforms the SVM by a margin on all measures. Yet, adding the task-specific embeddings from the NN as features of the SVM yields overall improvements over both the SVM and the NN in terms of avgR, avgF INLINEFORM0 , and accuracy. We can see that both the SVM and the NN overpredict the majority class (false claims); however, the combined SVM+NN model is quite balanced between the two classes.",
+      "Table TABREF22 compares the performance of the SVM with and without task-specific embeddings from the NN, when training on Web pages vs. snippets, returned by Google vs. Bing vs. both. The NN embeddings consistently help the SVM in all cases. Moreover, while the baseline SVM using snippets is slightly better than when using Web pages, there is almost no difference between snippets vs. Web pages when NN embeddings are added to the basic SVM. Finally, gathering external support from either Google or Bing makes practically no difference, and using the results from both together does not yield much further improvement. Thus, (i) the search engines already do a good job at generating relevant snippets, and one does not need to go and download the full Web pages, and (ii) the choice of a given search engine is not an important factor. These are good news for the practicality of our approach.",
+      "Unfortunately, direct comparison with respect to BIBREF3 is not possible. First, we only use a subset of their examples: 761 out of 993 (see Section SECREF17 ), and we also have a different class distribution. More importantly, they have a very different formulation of the task: for them, the claim is not available as input (in fact, there has never been a claim for 21% of their examples); rather an example consists of a set of tweets retrieved using manually written queries.",
+      "In contrast, our system is fully automatic and does not use tweets at all. Furthermore, their most important information source is the change in tweets volume over time, which we cannot use. Still, our results are competitive to theirs when they do not use temporal features.",
+      "To put the results in perspective, we can further try to make an indirect comparison to the very recent paper by BIBREF6 . They also present a model to classify true vs. false claims extracted from snopes.com, by using information extracted from the Web. Their formulation of the task is the same as ours, but our corpora and label distributions are not the same, which makes a direct comparison impossible. Still, we can see that regarding overall classification accuracy they improve a baseline from 73.7% to 84.02% with their best model, i.e., a 39.2% relative error reduction. In our case, we go from 66.7% to 80.0%, i.e., an almost identical 39.9% error reduction. These results are very encouraging, especially given the fact that our model is much simpler than theirs regarding the sources of information used (they model the stance of the text, the reliability of the sources, the language style of the articles, and the temporal footprint)."
+    ],
+    [
+      "Next, we tested the generality of our approach by applying it to a different setup: fact-checking the answers in community question answering (cQA) forums. As this is a new problem, for which no dataset exists, we created one. We augmented with factuality annotations the cQA dataset from SemEval-2016 Task 3 (CQA-QA-2016) BIBREF7 . Overall, we annotated 249 question\u2013answer, or INLINEFORM0 - INLINEFORM1 , pairs (from 71 threads): 128 factually true and 121 factually false answers.",
+      "Each question in CQA-QA-2016 has a subject, a body, and meta information: ID, category (e.g., Education, and Moving to Qatar), date and time of posting, user name and ID. We selected only the factual questions such as \u201cWhat is Ooredoo customer service number?\u201d, thus filtering out all (i) socializing, e.g., \u201cWhat was your first car?\u201d, (ii) requests for opinion/advice/guidance, e.g., \u201cWhich is the best bank around??\u201d, and (iii) questions containing multiple sub-questions, e.g., \u201cIs there a land route from Doha to Abudhabi. If yes; how is the road and how long is the journey?\u201d",
+      "Next, we annotated for veracity the answers to the retained questions. Note that in CQA-QA-2016, each answer has a subject, a body, meta information (answer ID, user name and ID), and a judgment about how well it addresses the question of its thread: Good vs. Potentially Useful vs. Bad . We only annotated the Good answers. We further discarded answers whose factuality was very time-sensitive (e.g., \u201cIt is Friday tomorrow.\u201d, \u201cIt was raining last week.\u201d), or for which the annotators were unsure.",
+      "We targeted very high quality, and thus we did not use crowdsourcing for the annotation, as pilot annotations showed that the task was very difficult and that it was not possible to guarantee that Turkers would do all the necessary verification, e.g., gathering evidence from trusted sources. Instead, all examples were first annotated independently by four annotators, and then each example was discussed in detail to come up with a final label. We ended up with 249 Good answers to 71 different questions, which we annotated for factuality: 128 Positive and 121 Negative examples. See Table TABREF26 for details.",
+      "We further split our dataset into 185 INLINEFORM0 \u2013 INLINEFORM1 pairs for training, 31 for development, and 32 for testing, preserving the general positive:negative ratio, and making sure that the questions for the INLINEFORM2 \u2013 INLINEFORM3 pairs did not overlap between the splits. Figure FIGREF23 presents an excerpt of an example from the dataset, with one question and three answers selected from a longer thread. Answer INLINEFORM4 contains false information, while INLINEFORM5 and INLINEFORM6 are true, as can be checked on an official governmental website.",
+      "We had to fit our system for this problem, as here we do not have claims, but a question and an answer. So, we constructed the query from the concatenation of INLINEFORM0 and INLINEFORM1 . Moreover, as Google and Bing performed similarly, we only report results using Google. We limited our run to snippets only, as we have found them rich enough above (see Section SECREF3 ). Also, we had a list of reputed and Qatar-related sources for the domain, and we limited our results to these sources only. This time, we had more options to calculate similarities compared to the rumors dataset: we can compare against INLINEFORM2 , INLINEFORM3 , and INLINEFORM4 \u2013 INLINEFORM5 ; we chose to go with the latter. For the LSTM representations, we use both the question and the answer.",
+      "Table TABREF27 shows the results on the cQA dataset. Once again, our models outperformed all baselines by a margin. This time, the predictions of all models are balanced between the two classes, which is probably due to the dataset being well balanced in general. The SVM model performs better than the NN by itself. This is due to the fact that the cQA dataset is significantly smaller than the rumor detection one. Thus, the neural network could not be trained effectively by itself. Nevertheless, the task-specific representations were useful and combining them with the SVM model yielded consistent improvements on all the measures once again."
+    ],
+    [
+      "Journalists, online users, and researchers are well aware of the proliferation of false information on the Web, and topics such as information credibility and fact checking are becoming increasingly important as research directions. For example, there was a recent 2016 special issue of the ACM Transactions on Information Systems journal on Trust and Veracity of Information in Social Media BIBREF9 , there was a SemEval-2017 shared task on Rumor Detection BIBREF10 , and there is an upcoming lab at CLEF-2018 on Automatic Identification and Verification of Claims in Political Debates BIBREF11 .",
+      "The credibility of contents on the Web has been questioned by researches for a long time. While in the early days the main research focus was on online news portals BIBREF12 , BIBREF13 , BIBREF14 , the interest has eventually shifted towards social media BIBREF4 , BIBREF15 , BIBREF6 , BIBREF16 , which are abundant in sophisticated malicious users such as opinion manipulation trolls, paid BIBREF17 or just perceived BIBREF18 , BIBREF19 , sockpuppets BIBREF20 , Internet water army BIBREF21 , and seminar users BIBREF22 .",
+      "For instance, BIBREF23 studied the credibility of Twitter accounts (as opposed to tweet posts), and found that both the topical content of information sources and social network structure affect source credibility. Other work, closer to ours, aims at addressing credibility assessment of rumors on Twitter as a problem of finding false information about a newsworthy event BIBREF4 . This model considers user reputation, writing style, and various time-based features, among others.",
+      "Other efforts have focused on news communities. For example, several truth discovery algorithms are combined in an ensemble method for veracity estimation in the VERA system BIBREF24 . They proposed a platform for end-to-end truth discovery from the Web: extracting unstructured information from multiple sources, combining information about single claims, running an ensemble of algorithms, and visualizing and explaining the results. They also explore two different real-world application scenarios for their system: fact checking for crisis situations and evaluation of trustworthiness of a rumor. However, the input to their model is structured data, while here we are interested in unstructured text as input.",
+      "Similarly, the task defined by BIBREF25 combines three objectives: assessing the credibility of a set of posted articles, estimating the trustworthiness of sources, and predicting user's expertise. They considered a manifold of features characterizing language, topics and Web-specific statistics (e.g., review ratings) on top of a continuous conditional random fields model. In follow-up work, BIBREF26 proposed a model to support or refute claims from snopes.com and Wikipedia by considering supporting information gathered from the Web. They used the same task formulation for claims as we do, but different datasets. In yet another follow-up work, Popat:2017:TLE:3041021.3055133 proposed a complex model that considers stance, source reliability, language style, and temporal information.",
+      "Our approach to fact checking is related: we verify facts on the Web. However, we use a much simpler and feature-light system, and a different machine learning model. Yet, our model performs very similarly to this latter work (even though a direct comparison is not possible as the datasets differ), which is a remarkable achievement given the fact that we consider less knowledge sources, we have a conceptually simpler model, and we have six times less training data than Popat:2017:TLE:3041021.3055133.",
+      "Another important research direction is on using tweets and temporal information for checking the factuality of rumors. For example, BIBREF27 used temporal patterns of rumor dynamics to detect false rumors and to predict their frequency. BIBREF27 focused on detecting false rumors in Twitter using time series. They used the change of social context features over a rumor's life cycle in order to detect rumors at an early stage after they were broadcast.",
+      "A more general approach for detecting rumors is explored by BIBREF3 , who used recurrent neural networks to learn hidden representations that capture the variation of contextual information of relevant posts over time. Unlike this work, we do not use microblogs, but we query the Web directly in search for evidence. Again, while direct comparison to the work of BIBREF3 is not possible, due to differences in dataset and task formulation, we can say that our framework is competitive when temporal information is not used. More importantly, our approach is orthogonal to theirs in terms of information sources used, and thus, we believe there is potential in combining the two approaches.",
+      "In the context of question answering, there has been work on assessing the credibility of an answer, e.g., based on intrinsic information BIBREF28 , i.e., without any external resources. In this case, the reliability of an answer is measured by computing the divergence between language models of the question and of the answer. The spawn of community-based question answering Websites also allowed for the use of other kinds of information. Click counts, link analysis (e.g., PageRank), and user votes have been used to assess the quality of a posted answer BIBREF29 , BIBREF30 , BIBREF31 . Nevertheless, these studies address the answers' credibility level just marginally.",
+      "Efforts to determine the credibility of an answer in order to assess its overall quality required the inclusion of content-based information BIBREF32 , e.g., verbs and adjectives such as suppose and probably, which cast doubt on the answer. Similarly, BIBREF33 used source credibility (e.g., does the document come from a government Website?), sentiment analysis, and answer contradiction compared to other related answers.",
+      "Overall, credibility assessment for question answering has been mostly modeled at the feature level, with the goal of assessing the quality of the answers. A notable exception is the work of BIBREF34 , where credibility is treated as a task of its own right. Yet, note that credibility is different from factuality (our focus here) as the former is a subjective perception about whether a statement is credible, rather than verifying it as true or false as a matter of fact; still, these notions are often wrongly mixed in the literature. To the best of our knowledge, no previous work has targeted fact-checking of answers in the context of community Question Answering by gathering external support."
+    ],
+    [
+      "We have presented and evaluated a general-purpose method for fact checking that relies on retrieving supporting information from the Web and comparing it to the claim using machine learning. Our method is lightweight in terms of features and can be very efficient because it shows good performance by only using the snippets provided by the search engines. The combination of the representational power of neural networks with the classification of kernel-based methods has proven to be crucial for making balanced predictions and obtaining good results. Overall, the strong performance of our model across two different fact-checking tasks confirms its generality and potential applicability for different domains and for different fact-checking task formulations.",
+      "In future work, we plan to test the generality of our approach by applying it to these and other datasets in combination with complementary methods, e.g., those focusing on microblogs and temporal information in Twitter to make predictions about rumors BIBREF27 , BIBREF3 . We also want to explore the possibility of providing justifications for our predictions, and we plan to integrate our method into a real-world application."
+    ],
+    [
+      "This research was performed by the Arabic Language Technologies group at Qatar Computing Research Institute, HBKU, within the Interactive sYstems for Answer Search project (Iyas)."
+    ]
+  ]
+}
+```
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+Name of Paper: Fully Automated Fact Checking Using External Sources
+
+Question: What data is used to build the task-specific embeddings?
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+Name of Paper: A Probabilistic Generative Grammar for Semantic Parsing
+
+Question: Do they evaluate the syntactic parses?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Background",
+    "Hierarchical Dirichlet processes",
+    "Inference",
+    "Generative semantic grammar",
+    "Definition",
+    "Generative process",
+    "Selecting production rules"
+  ],
+  "paragraphs": [
+    [
+      "Accurate and efficient semantic parsing is a long-standing goal in natural language processing. There are countless applications for methods that provide deep semantic analyses of sentences. Leveraging semantic information in text may provide improved algorithms for many problems in NLP, such as named entity recognition BIBREF0 , BIBREF1 , BIBREF2 , word sense disambiguation BIBREF3 , BIBREF4 , semantic role labeling BIBREF5 , co-reference resolution BIBREF6 , BIBREF7 , etc. A sufficiently expressive semantic parser may directly provide the solutions to many of these problems. Lower-level language processing tasks, such as those mentioned, may even benefit by incorporating semantic information, especially if the task can be solved jointly during semantic parsing.",
+      "Knowledge plays a critical role in natural language understanding. The formalisms used by most semantic parsing approaches require an ontology of entities and predicates, with which the semantic content of sentences can be represented. Moreover, even seemingly trivial sentences may have a large number of ambiguous interpretations. Consider the sentence \u201cShe started the machine with the GPU,\u201d for example. Without additional knowledge, such as the fact that \u201cmachine\u201d can refer to computing devices that contain GPUs, or that computers generally contain devices such as GPUs, the reader cannot determine whether the GPU is part of the machine or if the GPU is a device that is used to start machines.",
+      "The thesis underlying our research is that natural language understanding requires a belief system; that is, a large set of pre-existing beliefs related to the domain of discourse. Clearly, young children have many beliefs about the world when they learn language, and in fact, the process of learning language is largely one of learning to ground the meanings of words and sentences in these non-linguistically acquired beliefs. In some ways, the idea that language understanding requires a belief system is not new, as natural language researchers have been saying for years that background knowledge is essential to reducing ambiguity in sentence meanings BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 . But despite this general acknowledgement of the importance of background knowledge, we see very few natural language understanding systems that actually employ a large belief system as the basis for comprehending sentence meanings, and for determining whether the meaning of a new sentence contradicts, extends, or is already present in its belief system.",
+      "We present here a step in this direction: a probabilistic semantic parser that uses a large knowledge base (NELL) to form a prior probability distribution on the meanings of sentences it parses, and that \"understands\" each sentence either by identifying its existing beliefs that correspond to the sentence's meaning, or by creating new beliefs. More precisely, our semantic parser corresponds to a probabilistic generative model that assigns high probability to sentence semantic parses resulting in beliefs it already holds, lower prior probability to parses resulting in beliefs it does not hold but which are consistent with its more abstract knowledge about semantic types of arguments to different relations, and still lower prior probability to parses that contradict its beliefs about which entity types can participate in which relations.",
+      "This work is only a first step. It is limited in that we currently use it to parse sentences with a simple noun-verb-noun syntax (e.g. \"Horses eat hay.\"), and considers only factual assertions in declarative sentences. Its importance is that it introduces a novel approach in which the semantic parser (a) prefers sentence semantic parses that yield assertions it already believes, while (b) still allowing with lower prior probability sentence interpretations that yield new beliefs involving novel words, and (c) even allowing beliefs inconsistent with its background knowledge about semantic typing of different relations. We introduce algorithms for training the probabilistic grammar and producing parses with high posterior probability, given its prior beliefs and a new sentence. We present experimental evidence of the success and tractability of this approach for sentences with simple syntax, and evidence showing that the incorporated belief system, containing millions of beliefs, allows it to outperform state-of-the-art semantic parsers that do not hold such beliefs. Thus, we provide a principled, probabilistic approach to using a current belief system to guide semantic interpretation of new sentences which, in turn, can be used to augment and extend the belief system. We also argue that our approach can be extended to use the document-level context of a sentence as an additional source of background beliefs.",
+      "For reasons including but not limited to performance and complexity, most modern parsers operate over tokens, such as words. While this has worked sufficiently well for many applications, this approach assumes that a tokenization preprocessing step produces the correct output. This is nontrivial in many languages, such as Chinese, Thai, Japanese, and Tibetic languages. In addition, a large portion of the English vocabulary is created from the combination of simpler morphemes, such as the words \u201cbuild-er,\u201d \u201cin-describ-able,\u201d \u201canti-modern-ist.\u201d Moreover, language can be very noisy. Text messages, communication in social media, and real-world speech are but a few examples of noise obfuscating language. Standard algorithms for tokenization, lemmatization, and other preprocessing are oblivious to the underlying semantics, much less any background knowledge. Incorporating these components into a \u201cjoint parsing\u201d framework will enable semantics and background knowledge to jointly inform lower-level processing of language. Our method couples semantics with syntax and other lower-level aspects of language, and can be guided by background knowledge via the semantic prior. We will demonstrate how this can be leveraged in our framework to model the morphology of individual verbs in a temporally-scoped relation extraction task.",
+      "Semantic statements are the logical expressions that represent meaning in sentences. For example, the semantic statement turn_on_device(person:Ada, device:gpu_cluster) may be used to express the meaning of the sentence example given earlier. There are many languages or semantic formalisms that can be used to encode these logical forms: first-order logic with lambda calculus BIBREF12 , frame semantics BIBREF13 , abstract meaning representation BIBREF14 , dependency-based compositional semantics BIBREF15 , vector-space semantics BIBREF16 , BIBREF17 , for example. Our approach is flexible and does not require the use of a specific semantic formalism.",
+      "In section \"Hierarchical Dirichlet processes\" , we review HDPs and describe the setting that we require to define our grammar. We present our approach in section UID17 to perform HDP inference in this new setting. In section \"Generative semantic grammar\" , we present the main generative process in our framework, and detail our application of the HDP. Although we present our model from a generative perspective, we show in the description of the framework that discriminative techniques can be integrated. Inference in our model is described in section \"Inference\" . There, we present a chart-driven agenda parser that can leverage the semantic prior to guide its search. Finally, in section \"Results\" , we evaluate our parser on two relation-extraction tasks: the first is a task to extract simple predicate-argument representations from SVO sentences, and the second is a temporally-scoped relation extraction task that demonstrates our parser's ability to model the morphology of individual words, leading to improved generalization performance over words. Moreover, we demonstrate that the inclusion of background knowledge from a knowledge base improves parsing performance on these tasks. The key contributions of this article are:"
+    ],
+    [
+      "Our model is an extension of context-free grammars (CFGs) BIBREF18 that couples syntax and semantics. To generate a sentence in our framework, the semantic statement is first drawn from a prior. A grammar then recursively constructs a syntax tree top-down, randomly selecting production rules from distributions that depend on the semantic statement. We present a particular incarnation of a grammar in this framework, where hierarchical Dirichlet processes (HDPs) BIBREF19 are used to select production rules randomly. The application of HDPs in our setting is novel, requiring a new inference technique.",
+      "The use of the term \u201cgenerative\u201d does not refer to the Chomskian tradition of generative grammar BIBREF20 , although our approach does fall broadly within that framework. Rather, it refers to the fact that our model posits a probabilistic mechanism by which sentences are generated (by the speaker). Performing probabilistic inference under this model yields a parsing algorithm (the listener). This generative approach to modeling grammar underscores the duality between language generation and language understanding.",
+      "Our grammar can be related to synchronous CFGs (SCFGs) BIBREF21 , which have been extended to perform semantic parsing BIBREF22 , BIBREF23 , BIBREF24 . However, in established use, SCFGs describe the generation of the syntactic and semantic components of sentences simultaneously, which makes the assumption that the induced probability distributions of the semantic and syntactic components factorize in a \u201cparallel\u201d manner. Our model instead describes the generation of the semantic component as a step with occurs prior to the syntactic component. This can be captured in SCFGs as a prior on the semantic start symbol, making no factorization assumptions on this prior. This is particularly useful when employing richer prior distributions on the semantics, such as a model of context or a knowledge base.",
+      "Adaptor grammars BIBREF25 provide a framework that can jointly model the syntactic structure of sentences in addition to the morphologies of individual words BIBREF26 . Unlike previous work with adaptor grammars, our method couples syntax with semantics, and can be guided by background knowledge via the semantic prior. We will demonstrate how this can be leveraged in our framework to model the morphology of individual verbs in a temporally-scoped relation extraction task. Cohen10 show how to perform dependency grammar induction using adaptor grammars. While grammar induction in our framework constitutes an interesting research problem, we do not address it in this work.",
+      "As in other parsing approaches, an equivalence can be drawn between our parsing problem and the problem of finding shortest paths in hypergraphs BIBREF27 , BIBREF28 , BIBREF29 , BIBREF30 , BIBREF31 . Our algorithm can then be understood as an application of $\\textrm {A}^*$ search for the $k$ -best paths in a very large hypergraph.",
+      "Our parser incorporates prior knowledge to guide its search, such as from an ontology and the set of beliefs in a knowledge base. Using this kind of approach, the parser can be biased to find context-appropriate interpretations in otherwise ambiguous or terse utterances. While systems such as DurrettK14, NakasholeM15, KimMoldovan1995, and Salloum09 use background knowledge about the semantic types of different noun phrases to improve their ability to perform entity linking, co-reference resolution, prepositional phrase attachment, information extraction, and question answering, and systems such as RatinovR12, DurrettK14, and ProkofyevTLVDC15 link noun phrases to Wikipedia entries to improve their ability to resolve co-references, these uses of background knowledge remain fragmentary. Krishnamurthy2014 developed a CCG parser that incorporates background knowledge from a knowledge base during training through distant supervision, but their method is not able to do so during parsing. Our parser can be trained once, and then applied to a variety of settings, each with a different context or semantic prior."
+    ],
+    [
+      "A core component of our statistical model is the Dirichlet process (DP) BIBREF32 , which can be understood as a distribution over probability distributions. If a distribution $G$ is drawn from a DP, we can write $G\\sim \\text{DP}(\\alpha ,H)$ , where the DP is characterized by two parameters: a concentration parameter $\\alpha >0$ and a base distribution $H$ . The DP has the useful property that $\\mathbb {E}[G] = H$ , and the concentration parameter $\\alpha $ describes the \u201ccloseness\u201d of $G$ to the base distribution $H$ . In typical use, a number of parameters $\\theta _i$ are drawn from a discrete distribution $G$ , which is itself drawn from a Dirichlet process. The observations $G\\sim \\text{DP}(\\alpha ,H)$0 are drawn using the parameters $G\\sim \\text{DP}(\\alpha ,H)$1 from another distribution $G\\sim \\text{DP}(\\alpha ,H)$2 . This may be written as: ",
+      "$$G &\\sim \\text{DP}(\\alpha , H), \\\\\n\\theta _1,\\dots ,\\theta _n &\\sim G, \\\\\ny_i &\\sim F(\\theta _i),$$   (Eq. 6) ",
+      " for $i=1,\\hdots ,n$ . In our application, we will define $H$ to be a finite Dirichlet distribution and $F$ is a categorical distribution. $G$ can be marginalized out in the model above, resulting in the Chinese restaurant process representation BIBREF33 : ",
+      "$$\\phi _1, \\phi _2, \\dots &\\sim H, \\\\\nz_i &=\n{\\left\\lbrace \\begin{array}{ll}\nj & \\text{with probability } \\frac{\\#\\lbrace k < i : z_k = j\\rbrace }{\\alpha + i - 1}, \\\\\nj^{new} & \\text{with probability } \\frac{\\alpha }{\\alpha + i - 1},\n\\end{array}\\right.} \\\\\n\\theta _i &= \\phi _{z_i} \\text{ for } i = 1, \\dots , n, \\\\\ny_i &\\sim F(\\theta _i),$$   (Eq. 7) ",
+      " where $z_1 = 1$ , $j^{new} = \\max \\lbrace z_1,\\dots ,z_{i-1}\\rbrace  + 1$ is the indicator of a new table, and the quantity $\\#\\lbrace k < i : z_k = j\\rbrace $ is the number of observations that were assigned to table $j$ . The analogy is to imagine a restaurant where customers enter one at a time. Each customer chooses to sit at table $j$ with probability proportional to the number of people currently sitting at table $j$ , or at a new table $j^{new}$ with probability proportional to $\\alpha $ . The $i^{th}$ customer's choice is represented as $z_i$ . As shown in later sections, this representation of the DP is amenable to inference using Markov chain Monte Carlo (MCMC) methods BIBREF34 , BIBREF35 .",
+      "The hierarchical Dirichlet process (HDP) is an extension of the Dirichlet process for use in hierarchical modeling BIBREF19 . An advantage of this approach is that statistical strength can be shared across nodes that belong to the same subtree. In an HDP, every node $\\textbf {n}$ in a fixed tree $T$ is associated with a distribution $G^\\textbf {n}$ , and: ",
+      "$$G^\\textbf {0} &\\sim \\text{DP}(\\alpha ^{\\textbf {0}}, H), \\\\\nG^\\textbf {n} &\\sim \\text{DP}(\\alpha ^{\\textbf {n}}, G^{\\pi (\\textbf {n})}),$$   (Eq. 8) ",
+      " where $\\pi (\\textbf {n})$ is the parent node of $\\textbf {n}$ , and $\\textbf {0}$ is the root of $T$ . In our application, the base distribution at the root $H$ is Dirichlet. We can draw observations $y_1,\\hdots ,y_n$ from the HDP, given a sequence $x_1,\\hdots ,x_n$ of $n$ paths from the root $\\textbf {0}$ to a leaf: ",
+      "$$\\theta _i &\\sim G^{x_i}, \\\\\ny_i &\\sim F(\\theta _i),$$   (Eq. 9) ",
+      " for $i=1,\\hdots ,n$ . For notational brevity, we write this equivalently as $y_i\\sim \\text{HDP}(x_i,T)$ .",
+      "Just as marginalizing the Dirichlet process yields the Chinese restaurant process, marginalizing the HDP yields the Chinese restaurant franchise (CRF). For every node in the HDP tree $\\textbf {n} \\in T$ , there is a \u201cChinese restaurant\u201d consisting of an infinite number of tables. Every table $i$ in this restaurant at node $\\textbf {n}$ is assigned to a table in the parent restaurant. The assignment variable $z_i^\\textbf {n}$ is the index of the parent table to which table $i$ in node $\\textbf {n}$ is assigned. ",
+      "$$\\phi _1^\\textbf {0}, \\phi _2^\\textbf {0}, \\dots &\\sim H, \\\\\n\\text{ for every node } \\textbf {n} \\in T, \\hspace{14.22636pt} z_i^\\textbf {n} &=\n{\\left\\lbrace \\begin{array}{ll}\nj & \\text{with probability } \\propto n^{\\pi (\\textbf {n})}_j, \\\\\nj^{new} & \\text{with probability } \\propto \\alpha ^{\\pi (\\textbf {n})},\n\\end{array}\\right.} \\\\\n\\phi _i^\\textbf {n} &= \\phi _{z_i^\\textbf {n}}^{\\pi (\\textbf {n})},$$   (Eq. 10) ",
+      " where $\\pi (\\textbf {n})$ is the parent of node $\\textbf {n}$ , and $n^{\\pi (\\textbf {n})}_j$ is the current number of customers at node $\\pi (\\textbf {n})$ sitting at table $j$ . We are mildly abusing notation here, since $n^{\\pi (\\textbf {n})}_j$ and $n^{\\pi (\\textbf {n})}$ refer to the number of customers at the time $z_i^\\textbf {n}$ is drawn (which increases as additional $z_i^\\textbf {n}$ are drawn). To draw the observation $y_i$ , we start with the leaf node at the end of the path $\\textbf {n}$0 : ",
+      "$$\\theta _i &= \\phi ^{x_i}_k, \\\\\ny_i &\\sim F(\\theta _i),$$   (Eq. 11) ",
+      " where $k - 1 = \\#\\lbrace j < i : x_j = x_i\\rbrace $ is the number of previous observations drawn from node $x_i$ ."
+    ],
+    [
+      "In this section, we describe our method for performing posterior inference in the HDP. Let $\\mathbf {z} = \\lbrace z^\\textbf {n}_i : \\textbf {n} \\in T, i = 1,2,\\hdots \\rbrace $ be the set of table assignment variables in the HDP. If the distributions $H$ and $F$ are conditionally conjugate, as they are in our application, the $\\mathbf {\\phi }$ variables can be integrated out in closed form: ",
+      "$$p(\\mathbf {z}|\\mathbf {x},\\mathbf {y}) = p(\\mathbf {x}) p(\\mathbf {z}) \\int p(\\mathbf {y}|\\mathbf {x},\\mathbf {z},\\mathbf {\\phi }) d\\mathbf {\\phi }.$$   (Eq. 13) ",
+      "The posterior $p(\\mathbf {z}|\\mathbf {x},\\mathbf {y})$ is intractable to compute exactly, and so we approximate it by sampling. We obtain samples from $\\mathbf {z}|\\mathbf {x},\\mathbf {y}$ by performing collapsed Gibbs sampling as described in section 5.1 of journals/jasa/Teh06: we repeatedly sample $\\mathbf {z}$ from its conditional distribution, with $\\mathbf {\\phi }$ integrated out: ",
+      "$$z^\\textbf {n}_i | \\mathbf {x}, \\mathbf {y}, z^\\textbf {n}_{-i} =\n{\\left\\lbrace \\begin{array}{ll}\nj &\\text{with prob.} \\propto \\#\\lbrace k\\ne i : z^\\textbf {n}_k = j\\rbrace  \\cdot p(y^\\textbf {n}_i | \\mathbf {x}, y^\\textbf {n}_{-i}, z^\\textbf {n}_{-i}, z^\\textbf {n}_i = j), \\\\\nj^{new} &\\text{with prob.} \\propto \\alpha ^\\textbf {n} \\cdot p(y^\\textbf {n}_i | \\mathbf {x}, y^\\textbf {n}_{-i}, z^\\textbf {n}_{-i}, z^\\textbf {n}_i = j^{new}),\n\\end{array}\\right.} $$   (Eq. 14) ",
+      "where $y^\\textbf {n}_i$ is the set of \u201cdescendant\u201d observations of table $i$ in node $\\textbf {n}$ (this includes observations assigned directly to the table, in addition to those assigned to tables further down in the hierarchy which themselves are assigned to this table), $y^\\textbf {n}_{-i} = \\mathbf {y} \\setminus y^\\textbf {n}_i$ is the set of all other observations, and $z^\\textbf {n}_{-i} = \\mathbf {z} \\setminus z^\\textbf {n}_i$ is the set of all other table assignment variables. Computing $p(y^\\textbf {n}_i | \\mathbf {x}, y^\\textbf {n}_{-i}, z^\\textbf {n}_{-i}, z^\\textbf {n}_i = j)$ is straightforward since we can follow the chain of table assignments to the root. Let $r^\\textbf {n}_i$ be the root cluster assignment of the table $i$ at node $\\textbf {n}$ . In fact, we found it advantageous for performance to keep track of the root cluster assignments $\\mathbf {r}$ for every table in the hierarchy. Thus, when $i$0 , it must be the case that $i$1 were drawn from $i$2 with parameter $i$3 .",
+      "Computing $p(y^\\textbf {n}_i | \\mathbf {x}, y^\\textbf {n}_{-i}, z^\\textbf {n}_{-i}, z^\\textbf {n}_i = j^{new})$ requires marginalizing over the assignment of the new table $z^{\\pi (\\textbf {n})}_{j^{new}}$ : ",
+      "$$p(y^\\textbf {n}_i | \\mathbf {x}, y^\\textbf {n}_{-i}, z^\\textbf {n}_{-i}, z^\\textbf {n}_i = j^{new}) = &\\sum _{k=1}^{m^{\\pi (\\textbf {n})}} \\frac{n_k^{\\pi (\\textbf {n})}}{n^{\\pi (\\textbf {n})} + \\alpha ^{\\pi (\\textbf {n})}} p(y^\\textbf {n}_i | \\mathbf {x}, y^\\textbf {n}_{-i}, z^\\textbf {n}_{-i}, z^{\\pi (\\textbf {n})}_{j^{new}} = k) \\nonumber \\\\\n&+ \\frac{\\alpha ^{\\pi (\\textbf {n})}}{n^{\\pi (\\textbf {n})} + \\alpha ^{\\pi (\\textbf {n})}} p(y^\\textbf {n}_i | \\mathbf {x}, y^\\textbf {n}_{-i}, z^\\textbf {n}_{-i}, z^{\\pi (\\textbf {n})}_{j^{new}} = k^{new}),$$   (Eq. 15) ",
+      " where $m^{\\pi (\\textbf {n})}$ is the number of occupied tables at the node $\\pi (\\textbf {n})$ . At the root node $\\pi (\\textbf {n}) = \\textbf {0}$ , the above probability is just the prior of $y^\\textbf {n}_i$ . We observe that the above probabilities are linear functions of the likelihoods $p(y^\\textbf {n}_i | \\mathbf {x}, y^\\textbf {n}_{-i}, z^\\textbf {n}_{-i}, r^\\textbf {n}_i = k)$ for various root cluster assignments $r^\\textbf {n}_i = k$ . Implemented naively, generating a single sample from equation 14 can take time linear in the number of clusters at the root, which would result in a quadratic-time algorithm for a single Gibbs iteration over all $\\mathbf {z}$ . However, we can exploit sparsity in the root cluster assignment likelihoods to improve performance. When $H = \\text{Dir}(\\beta )$ is a Dirichlet distribution and $F$ is a categorical, then the collapsed root cluster assignment likelihood is: ",
+      "$$p(y^\\textbf {n}_i | \\mathbf {x}, y^\\textbf {n}_{-i}, z^\\textbf {n}_{-i}, r^\\textbf {n}_i = k) = \\frac{\\prod _t \\left( \\beta _t + \\#\\lbrace t \\in y^\\textbf {0}_k\\rbrace  \\right)^{(\\#\\lbrace t \\in y^\\textbf {n}_i\\rbrace )}}{\\left(\\sum _t \\beta _t + \\# y^\\textbf {0}_k \\right)^{(\\# y^\\textbf {n}_i)}}.$$   (Eq. 16) ",
+      "Here, $a^{(b)}$ is the rising factorial $a(a + 1)(a + 2)\\hdots (a + b - 1) = \\frac{\\Gamma (a + b)}{\\Gamma (a)}$ , and $\\#\\lbrace t \\in y^\\textbf {n}_i\\rbrace $ is the number of elements in $y^\\textbf {n}_i$ with value $t$ . Notice that the denominator depends only on the sizes and not on the contents of $y^\\textbf {n}_i$ and $y^\\textbf {0}_k$ . Caching the denominator values for common sizes of $y^\\textbf {n}_i$ and $y^\\textbf {0}_k$ can allow the sampler to avoid needless recomputation. This is especially useful in our application since many of the tables at the root tend to be small. Similarly, observe that the numerator factor is 1 for values of $t$ where $a(a + 1)(a + 2)\\hdots (a + b - 1) = \\frac{\\Gamma (a + b)}{\\Gamma (a)}$0 . Thus, the time required to compute the above probability is linear in the number of unique elements of $a(a + 1)(a + 2)\\hdots (a + b - 1) = \\frac{\\Gamma (a + b)}{\\Gamma (a)}$1 , which can improve the scalability of our sampler. We perform the above computations in log space to avoid numerical overflow.",
+      "In previous uses of the HDP, the paths $x_i$ are assumed to be fixed. For instance, in document modeling, the paths correspond to documents or predefined categories of documents. In our application, however, the paths may be random. In fact, we will later show that our parser heavily relies on the posterior predictive distribution over paths, where the paths correspond to semantic parses. More precisely, given a collection of training observations $\\mathbf {y} = \\lbrace y_1,\\hdots ,y_n\\rbrace $ with their paths $\\mathbf {x} = \\lbrace x_1,\\hdots ,x_n\\rbrace $ , we want to compute the probability of a new path $x^{new}$ given a new observation $y^{new}$ : ",
+      "$$p(x^{new}|y^{new},\\mathbf {x},\\mathbf {y}) &\\propto p(x^{new}) \\int p(y^{new}|\\mathbf {z},x^{new}) p(\\mathbf {z}|\\mathbf {x},\\mathbf {y}) d\\mathbf {z},  \\\\\n&\\approx \\frac{p(x^{new})}{N_{samples}} \\sum _{\\mathbf {z}^* \\sim \\mathbf {z}|\\mathbf {x},\\mathbf {y}} p(y^{new}|\\mathbf {z}^*,x^{new}). $$   (Eq. 18) ",
+      " Once we have the posterior samples $\\mathbf {z}^*$ , we can compute the quantity $p(y^{new}|\\mathbf {z}^*,x^{new})$ by marginalizing over the table assignment for the new observation $y$ : ",
+      "$$p(y^{new}|\\mathbf {z}^*,x^{new}) = &\\sum _{j=1}^{m^{x^{new}}} \\frac{n_j^{x^{new}}}{n^{x^{new}} + \\alpha ^{x^{new}}} \\hspace{2.84544pt} p(y^{new} | \\mathbf {z}^*, \\theta ^{new} = \\phi ^{x^{new}}_j) \\nonumber \\\\\n&+ \\frac{\\alpha ^{x^{new}}}{n^{x^{new}} + \\alpha ^{x^{new}}} \\hspace{2.84544pt} p(y^{new} | \\mathbf {z}^*, \\theta ^{new} = \\phi ^{x^{new}}_{j^{new}}).$$   (Eq. 19) ",
+      " Here, $m^{x^{new}}$ is the number of occupied tables at node $x^{new}$ , $n^{x^{new}}_j$ is the number of customers sitting at table $j$ at node $x^{new}$ , and $n^{x^{new}}$ is the total number of customers at node $x^{new}$ . The first term $p(y^{new} | \\mathbf {z}^*, \\theta ^{new} = \\phi ^{x^{new}}_j)$ can be computed since the $j^{th}$ table exists and is assigned to a table in its parent node, which in turn is assigned to a table in its parent node, and so on. We can follow the chain of table assignments to the root. In the second term, the observation is assigned to a new table, whose assignment is unknown, and so we marginalize again over the assignment in the parent node for this new table: ",
+      "$$p(y^{new} | \\mathbf {z}^*, \\theta ^{new} = \\phi ^{x^{new}}_{j^{new}}) = &\\sum _{j=1}^{m^{\\pi (x^{new})}} \\frac{n_j^{\\pi (x^{new})}}{n^{\\pi (x^{new})} + \\alpha ^{\\pi (x^{new})}} \\hspace{5.69046pt} p\\left(y^{new} \\Big | \\mathbf {z}^*, \\theta ^{new} = \\phi _j^{\\pi (x^{new})}\\right) \\nonumber \\\\\n&+ \\frac{\\alpha ^{\\pi (x^{new})}}{n^{\\pi (x^{new})} + \\alpha ^{\\pi (x^{new})}} \\hspace{5.69046pt} p\\left(y^{new} \\Big | \\mathbf {z}^*, \\theta ^{new} = \\phi _{j^{new}}^{\\pi (x^{new})}\\right),$$   (Eq. 20) ",
+      " where $\\pi (x^{new})$ is the parent node of $x^{new}$ . Again, the probability in the first term can be computed as before, but the probability in the second term depends on the assignment of the new table, which is unknown. Thus, since it is possible that a new table will be created at every level in the hierarchy up to the root, we can apply this formula recursively. At the root $\\textbf {0}$ , the probability $p(y^{new} | \\mathbf {z}^*, \\theta ^{new} = \\phi _{j^{new}}^\\textbf {0})$ is just the prior probability of $y^{new}$ .",
+      "If the tree $T$ is small, it is straightforward to compute the quantity in equation for every path $x^{new}$ in the tree, using the method described above. In our application however, the size of $T$ depends on the size of the ontology, and may easily become very large. In this case, the na\u00efve approach becomes computationally infeasible. As such, we develop an algorithm to incrementally find the $k$ best paths that maximize the quantity in equation . For sparse distributions, where most of the probability mass is concentrated in a small number of paths $x^{new}$ , this algorithm can effectively characterize the predictive distribution in equation 18 . The algorithm is essentially a search over nodes in the tree, starting at the root and descending the nodes of the tree $T$ , guided through paths of high probability. Each search state $\\texttt {s}$ consists of the following fields:",
+      " $\\texttt {s.n}$ is the current position of the search in the tree.",
+      " $\\texttt {s.v}$ is an array of probability scores of length $N_{samples}$ . Each element in this array represents the probability of drawing the observation $y^{new}$ from the current node $\\texttt {s.n}$ , and thus is identical to the probability of assigning $y^{new}$ to a new table at any child node of $\\texttt {s.n}$ . This is useful to compute the quantity in equation using the recursive method as described above.",
+      "The search is outlined in algorithm UID17 . We observe that the quantity in equation is a sum of independent functions, each being a linear combination of the terms $p(y^{new}|\\mathbf {z}^*_i,\\theta ^{new} = \\phi _j^\\textbf {n})$ over the tables available at node $\\textbf {n}$ and the new table $p(y^{new}|\\mathbf {z}^*_i,\\theta ^{new} = \\phi _{j^{new}}^\\textbf {n})$ (this latter probability is stored in $\\texttt {s.v}_i$ ). Thus, the upper bound on equation over all paths that pass through node $\\texttt {s.n}$ is: ",
+      "$$\\max _{\\lbrace x^{new}:\\texttt {s.n} \\in x^{new}\\rbrace } \\frac{p(x^{new})}{N_{samples}} \\sum _{i=1}^{N_{samples}} \\max _{j=1,\\hdots ,m^\\texttt {s.n}} \\left\\lbrace  p(y^{new}|\\mathbf {z}^*_i,\\theta ^{new}=\\phi _j^\\texttt {s.n}) , \\texttt {s.v}_i \\right\\rbrace . $$   (Eq. 23) ",
+      "We sort elements in the priority queue using this expression.",
+      "Fnfunction IfElseIfElseif elifelse Whilewhile{ Repeatrepeatuntil",
+      "initialize priority queue with initial state $\\texttt {s}$ $\\texttt {s.n} \\leftarrow \\textbf {0}$ *[h]start at the root $\\normalfont i=1,\\hdots ,N_{samples},$ $\\texttt {s.v}_i \\leftarrow \\sum _{j=1}^{m^\\textbf {0}} \\frac{n_j^\\textbf {0}}{n^\\textbf {0} + \\alpha ^\\textbf {0}} p(y^{new}|\\mathbf {z}^*_i, \\theta ^{new} = \\phi _j^\\textbf {0}) + \\frac{\\alpha ^\\textbf {0}}{n^\\textbf {0} + \\alpha ^\\textbf {0}} p(y^{new} | \\mathbf {z}^*_i, \\theta ^{new} = \\phi ^\\textbf {0}_{j^{new}})$ there are $k$ completed paths pop state s from the priority queue $\\normalfont \\texttt {s.n}$ is a leaf complete the path s.n with probability $\\frac{p\\lbrace x^{new} = \\texttt {s.n}\\rbrace }{N_{samples}} \\sum _{i=1}^{N_{samples}} \\texttt {s.v}_i$ child node $\\normalfont \\textbf {c}$ of $\\normalfont \\texttt {s.n}$ , create new search state $\\texttt {s}^*$ $\\texttt {s.n} \\leftarrow \\textbf {0}$0 $\\texttt {s.n} \\leftarrow \\textbf {0}$1 $\\texttt {s.n} \\leftarrow \\textbf {0}$2 push $\\texttt {s.n} \\leftarrow \\textbf {0}$3 onto priority queue with key in equation 23 Search algorithm to find the $\\texttt {s.n} \\leftarrow \\textbf {0}$4 best paths in the HDP that maximize the quantity in equation . As a result, once the algorithm has completed $\\texttt {s.n} \\leftarrow \\textbf {0}$5 items, we are guaranteed that the search has found $\\texttt {s.n} \\leftarrow \\textbf {0}$6 best paths. Thus, an \u201citerator\u201d data structure can be efficiently implemented using this algorithm, which returns paths $\\texttt {s.n} \\leftarrow \\textbf {0}$7 in order of decreasing predictive probability, with the first item being optimal. The search algorithm can be modified for other representations of the HDP, and can be extended to the case where $\\texttt {s.n} \\leftarrow \\textbf {0}$8 and $\\texttt {s.n} \\leftarrow \\textbf {0}$9 are not conjugate. It may also be incorporated into a larger inference procedure to jointly infer the paths $\\normalfont i=1,\\hdots ,N_{samples},$0 and the latent variables in the HDP. It is also straightforward to compute predictive probabilities where the path $\\normalfont i=1,\\hdots ,N_{samples},$1 is restricted to a subset of paths $\\normalfont i=1,\\hdots ,N_{samples},$2 : $\\normalfont i=1,\\hdots ,N_{samples},$3 . To do so, the algorithm is restricted to only expand nodes that belong to paths in $\\normalfont i=1,\\hdots ,N_{samples},$4 .",
+      "An important concern when performing inference with very large trees $T$ is that it is not feasible to explicitly store every node in memory. Fortunately, collapsed Gibbs sampling does not require storing nodes whose descendants have zero observations. In addition, algorithm UID17 can be augmented to avoid storing these nodes, as well. To do so, we make the observation that for any node $\\textbf {n} \\in T$ in the tree whose descendants have no observations, $\\textbf {n}$ will have zero occupied tables. Therefore, the probability $p(y^{new}|\\mathbf {z}^*,x^{new}) = p(y^{new}|\\mathbf {z}^*,\\theta ^{new}=\\phi ^\\textbf {n}_{j^{new}})$ is identical for any path $x^{new}$ that passes through $\\textbf {n}$ . Thus, when the search reaches node $\\textbf {n}$ , it can simultaneously complete all paths $x^{new}$ that pass through $\\textbf {n}$ , and avoid expanding nodes with zero observations among its descendants. As a result, we only need to explicitly store a number of nodes linear in the size of the training data, which enables practical inference with very large hierarchies.",
+      "There is a caveat that arises when we wish to compute a joint predictive probability $p(x^{new}_1, \\hdots , x^{new}_k | y^{new}_1, \\hdots , y^{new}_k, \\mathbf {x}, \\mathbf {y})$ , where we have multiple novel observations. Re-writing equation 18 in this setting, we have: ",
+      "$$p(x^{new}_1, \\hdots , x^{new}_k &| y^{new}_1, \\hdots , y^{new}_k, \\mathbf {x}, \\mathbf {y}) \\nonumber \\\\\n&\\propto p(\\mathbf {x}^{new}) \\int p(y^{new}_1,\\hdots ,y^{new}_k|\\mathbf {z}^*,\\mathbf {x}^{new}) p(\\mathbf {z}|\\mathbf {x},\\mathbf {y})d\\mathbf {z}. $$   (Eq. 24) ",
+      " For the CRF, the joint likelihood $p(y^{new}_1,\\hdots ,y^{new}_k|\\mathbf {z}^*,\\mathbf {x}^{new})$ does not factorize, since the observations are not independent (they are exchangeable). One workaround is to use a representation of the HDP where the joint likelihood factorizes, such as the direct assignment representation BIBREF19 . Another approach is to approximate the joint likelihood with the factorized likelihood. In our parser, we instead make the following approximation: ",
+      "$$p(y^{new}_1,\\hdots ,y^{new}_k | \\mathbf {x}^{new},\\mathbf {x},\\mathbf {y}) &= \\prod _{i=1}^k p(y^{new}_i | y^{new}_1,\\hdots ,y^{new}_{i-1}, \\mathbf {x}^{new},\\mathbf {x},\\mathbf {y}) \\\\\n&\\approx \\prod _{i=1}^k p(y^{new}_i | \\mathbf {x}^{new},\\mathbf {x},\\mathbf {y}). $$   (Eq. 25) ",
+      " Substituting into equation 24 , we obtain: ",
+      "$$p(\\mathbf {x}^{new} | \\mathbf {y}^{new}, \\mathbf {x}, \\mathbf {y}) \\propto p(\\mathbf {x}^{new}) \\prod _{i=1}^k \\int p(y^{new}_i|\\mathbf {z}^*,\\mathbf {x}^{new}) p(\\mathbf {z}|\\mathbf {x},\\mathbf {y})d\\mathbf {z}.$$   (Eq. 26) ",
+      "When the size of the training data $(\\mathbf {x},\\mathbf {y})$ is large with respect to the test data $(\\mathbf {x}^{new},\\mathbf {y}^{new})$ , the approximation works well, which we also find to be the case in our experiments."
+    ],
+    [
+      "We present a generative model of text sentences. In this model, semantic statements are generated probabilistically from some higher-order process. Given each semantic statement, a formal grammar selects text phrases, which are concatenated to form the output sentence. We present the model such that it remains flexible with regard to the semantic formalism. Even though our grammar can be viewed as an extension of context-free grammars, it is important to note that our model of grammar is only conditionally context-free, given the semantic statement. Otherwise, if the semantic information is marginalized out, the grammar is sensitive to context."
+    ],
+    [
+      "Let $\\mathcal {N}$ be a set of nonterminals, and let $\\mathcal {W}$ be a set of terminals. Let $\\mathbf {R}$ be a set of production rules which can be written in the form $\\textrm {A} \\rightarrow \\textrm {B}_1 \\hdots \\textrm {B}_k$ where $\\textrm {A}\\in \\mathcal {N}$ and $\\textrm {B}_1,\\hdots ,\\textrm {B}_k\\in \\mathcal {W}{2mu}\\cup {2mu}\\mathcal {N}$ . The tuple $(\\mathcal {W},\\mathcal {N},\\mathbf {R})$ is a context-free grammar (CFG) BIBREF18 .",
+      "We couple syntax with semantics by augmenting the production rules $\\mathbf {R}$ . In every production rule $\\textrm {A} \\rightarrow \\textrm {B}_1 \\hdots \\textrm {B}_k$ in $\\mathbf {R}$ , we assign to every right-hand side symbol $B_i$ a surjective operation $f_i : \\mathcal {X}_A\\mapsto \\mathcal {X}_{B_i}$ that transforms semantic statements, where $\\mathcal {X}_A$ is the set of semantic statements associated with the symbol $\\textrm {A}$ and $\\mathcal {X}_{B_i}$ is the set of semantic statements associated with the symbol $\\textrm {B}_i$ . Intuitively, the operation describes how the semantic statement is \u201cpassed on\u201d to the child nonterminals in the generative process. During parsing, these operations will describe how simpler semantic statements combine to form larger statements, enabling semantic compositionality. For example, suppose we have a semantic statement $x = \\textit {has\\_color(reptile:frog,color:green)}$ and the production rule $\\textrm {A} \\rightarrow \\textrm {B}_1 \\hdots \\textrm {B}_k$0 . We can pair the semantic operation $\\textrm {A} \\rightarrow \\textrm {B}_1 \\hdots \\textrm {B}_k$1 with the $\\textrm {A} \\rightarrow \\textrm {B}_1 \\hdots \\textrm {B}_k$2 in the right-hand side such that $\\textrm {A} \\rightarrow \\textrm {B}_1 \\hdots \\textrm {B}_k$3 selects the subject argument. Similarly, we can pair the semantic operation $\\textrm {A} \\rightarrow \\textrm {B}_1 \\hdots \\textrm {B}_k$4 with the $\\textrm {A} \\rightarrow \\textrm {B}_1 \\hdots \\textrm {B}_k$5 in the right-hand side such that $\\textrm {A} \\rightarrow \\textrm {B}_1 \\hdots \\textrm {B}_k$6 is the identity operation. The augmented production rule is $\\textrm {A} \\rightarrow \\textrm {B}_1 \\hdots \\textrm {B}_k$7 and the set of augmented rules is $\\textrm {A} \\rightarrow \\textrm {B}_1 \\hdots \\textrm {B}_k$8 . In parsing, we require the computation of the inverse of semantic operations, which is the preimage of a given semantic statement $\\textrm {A} \\rightarrow \\textrm {B}_1 \\hdots \\textrm {B}_k$9 . Continuing the example above, $\\mathbf {R}$0 returns a set that contains the statement $\\mathbf {R}$1 in addition to statements like $\\mathbf {R}$2 .",
+      "To complete the definition of our grammar, we need to specify the method that, given a nonterminal $\\mathrm {A} \\in \\mathcal {N}$ and a semantic statement $x \\in \\mathcal {X}_A$ , selects a production rule from the set of rules in $\\mathbf {R}^*$ with the left-hand side nonterminal $A$ . To accomplish this, we define $\\texttt {select}_{A,x}$ as a distribution over rules from $\\mathbf {R}^*$ that has $\\textrm {A}$ as its left-hand side, dependent on $x$ . We will later provide a number of example definitions of this $\\texttt {select}_{A,x}$ distribution. Thus, a grammar in our framework is fully specified by the tuple $(\\mathcal {W},\\mathcal {N},\\mathbf {R}^*,\\texttt {select})$ .",
+      "Note that other semantic grammar formalisms can be fit into this framework. For example, in categorical grammars, a lexicon describes the mapping from elementary components of language (such as words) to a syntactic category and a semantic meaning. Rules of inference are available to combine these lexical items into (tree-structured) derivations, eventually resulting in a syntactic and semantic interpretation of the full sentence BIBREF36 , BIBREF37 . In our framework, we imagine this process in reverse. The set $\\mathcal {X}_S$ is the set of all derivable semantic statements with syntactic category $S$ . The generative process begins by selecting one statement from this set $x \\in \\mathcal {X}_S$ . Next, we consider all applications of the rules of inference that would yield $x$ , with each unique application of an inference rule being equivalent to a production rule in our framework. We select one of these production rules according to our generative process and continue recursively. The items in the lexicon are equivalent to preterminal production rules in our framework. Thus, the generative process below describes a way to endow parses in categorical grammar with a probability measure. This can be used, for example, to extend earlier work on generative models with CCG BIBREF38 , BIBREF39 . Different choices of the $\\texttt {select}$ distribution induce different probability distributions over parses.",
+      "We do not see a straightforward way to fit linear or log-linear models over full parses into our framework, where a vector of features can be computed for each full parse BIBREF40 , BIBREF41 . This is due to our assumption that, given the semantic statement, the probability of a parse factorizes over the production rules used to construct that parse. However, the select distribution can be defined using linear and log-linear models, as we will describe in section \"Selecting production rules\" ."
+    ],
+    [
+      "The process for generating sentences in this framework begins by drawing a semantic statement $x\\in \\mathcal {X}_S$ where $\\textrm {S}$ is the root nonterminal. Thus, there is a prior distribution $p(x)$ for all $x\\in \\mathcal {X}_S$ . Next, the syntax is generated top-down starting at $\\textrm {S}$ . We draw a production rule with $\\textrm {S}$ as the left-hand side from $\\texttt {select}_{S,x}$ . The semantic transformation operations $f_i$ are applied to $x$ and the process is repeated for the right-hand side nonterminals. More concretely, we define the following operation $\\texttt {expand}$ which takes two arguments: a symbol $\\textrm {S}$0 and a semantic statement $\\textrm {S}$1 .",
+      "FExpandexpand FYieldyield FSelectselect tworuled",
+      "[H] $x$ , A ${\\rm A}\\in \\mathcal {W}$ *[h]simply return the word if A is a terminal",
+      "A *[h]select a production rule with form $\\textrm {A}\\rightarrow \\textrm {B}_1,\\hdots ,\\textrm {B}_k$ ",
+      " $(\\textrm {A}, \\textrm {B}_1, \\hdots , \\textrm {B}_k, f_1, \\hdots , f_k) \\sim _{\\textrm {A},x}$ $((f_1(x), \\textrm {B}_1), \\hdots , (f_k(x), \\textrm {B}_k))$ ",
+      "The yield operation concatenates strings into a single output string. Then, the output sentence $y$ is generated simply by $y=\\texttt {expand}(x, \\textrm {S})$ . Depending on the application, we may require that the generative process capitalizes the first letter of the output sentence, and/or appends terminating punctuation to the end. A noise model may also be appended to the generative process. The above algorithm may be easily extended to also return the full syntax tree."
+    ],
+    [
+      "There are many possible choices for the $\\texttt {select}$ distribution. The most straightforward is to define a categorical distribution over the available production rules, and simply draw the selected rule from this distribution. The result would be a simple extension of probabilistic context-free grammars (PCFGs) that couples semantics with syntax. However, this would remove any dependence between the semantic statement and the production rule selection.",
+      "To illustrate the importance of this dependence, consider generating a sentence with the semantic statement athlete_plays_sport(athlete:roger_federer,sport:tennis) using the grammar in figure 2 (the process is graphically depicted in figure 3 ). We start with the root nonterminal $\\textrm {S}$ :",
+      "We can only select the first production rule, and so we apply the semantic operation select_arg1 on the semantic statement to obtain athlete:roger_federer for the right-hand side nonterminal $\\textrm {N}$ . We apply the semantic operation delete_arg1 to obtain athlete_plays_sport( $\\cdot $ ,sport:tennis) for $\\textrm {VP}$ .",
+      "Expanding $\\textrm {N}$ , we select a terminal symbol given the semantic statement athlete:roger_federer. Suppose \u201cAndre Agassi\u201d is returned.",
+      " Now, we expand the $\\textrm {VP}$ symbol. We draw from $\\texttt {select}_{\\textrm {VP}}$ to choose one of the two available production rules. Suppose the rule $\\textrm {VP} \\rightarrow \\textrm {V \\hspace{2.84544pt} N}$ is selected. Thus, we apply the identity operation for the $\\textrm {V}$ nonterminal to obtain athlete_plays_sport( $\\cdot $ ,sport:tennis). We similarly apply select_arg2 for the $\\textrm {N}$ nonterminal to obtain sport:tennis.",
+      "We expand the $\\textrm {V}$ nonterminal, drawing from $\\texttt {select}_{\\textrm {V}}$ on the semantic statement athlete_plays_sport( $\\cdot $ ,sport:tennis). Suppose \u201cplays\u201d is returned.",
+      "Finally, we expand the $\\textrm {N}$ nonterminal, drawing from $\\texttt {select}_{\\textrm {N}}$ with the statement sport:tennis. Suppose \u201ctennis\u201d is returned. We concatenate all returned strings to form the sentence \u201cAndre Agassi plays tennis.\u201d",
+      "However, now consider generating another sentence with the same grammar for the statement athlete_plays_sport(athlete:roger_federer, sport:swimming). In UID32 of the above process, the select distribution would necessarily have to depend on the semantic statement. In English, the probability of observing a sentence of the form $\\textrm {N} \\hspace{2.84544pt} \\textrm {V} \\hspace{2.84544pt} \\textrm {N}$ ('Rachmaninoff makes music') versus $\\textrm {N} \\hspace{2.84544pt} \\textrm {V}$ ('Rachmaninoff composes') depends on the underlying semantic statement.",
+      "To capture this dependence, we use HDPs to define the select distribution. Every nonterminal $\\textrm {A}\\in \\mathcal {N}$ is associated with an HDP, and in order to fully specify the grammar, we need to specify the structure of each HDP tree. Let $T_A$ be the tree associated with the nonterminal $\\textrm {A}$ . The model is flexible with how the trees are defined, but we construct trees with the following method. First, select $m$ discrete features $g_1,\\hdots ,g_m$ where each $g_i : \\mathcal {X} \\mapsto \\mathbb {Z}$ and $\\mathbb {Z}$ is the set of integers. These features operate on semantic statements. For example, suppose we restrict the space of semantic statements to be the set of single predicate instances (triples). The relations in an ontology can be assigned unique integer indices, and so we may define a semantic feature as a function which simply returns the index of the predicate given a semantic statement. We construct the HDP tree $T_A$ starting with the root, we add a child node for every possible output of $g_1$ . We repeat the process recursively, constructing a complete tree of depth $m + 1$ .",
+      "As an example, we will construct a tree for the nonterminal $\\textrm {VP}$ for the example grammar in figure 2 . Suppose in our ontology, we have the predicates athlete_plays_sport and musician_plays_instrument, labeled 0 and 1, respectively. The ontology also contains the concepts athlete:roger_federer, sport:tennis, and sport:swimming, also labeled 0, 1, and 2, respectively. We define the first feature $g_1$ to return the predicate index. The second feature $g_2$ returns the index of the concept in the second argument of the semantic statement. The tree is constructed starting with the root, we add a child node for each predicate in the ontology: athlete_plays_sport and musician_plays_instrument. Next, for each child node, we add a grandchild node for every concept in the ontology: athlete:roger_federer, sport:tennis, and sport:swimming. The resulting tree $T_{VP}$ has depth 2, with a root node with 2 child nodes, and each child node has 3 grandchild nodes. This construction enables the select distribution for the nonterminal $\\textrm {VP}$ to depend on the predicate and the second argument of the semantic statement.",
+      "With the fully-specified HDPs and their corresponding trees, we have fully specified select. When sampling from $\\texttt {select}_{\\textrm {A},x}$ for the nonterminal $\\textrm {A} \\in \\mathcal {N}$ and a semantic statement $x \\in \\mathcal {X}$ , we compute the $m$ semantic features for the given semantic statement: $g_1(x), g_2(x), \\hdots , g_m(x)$ . This sequence of indices specifies a path from the root of the tree down to a leaf. We then simply draw a production rule observation from this leaf node, and return the result: $r \\sim \\text{HDP}(x, T_A) = \\texttt {select}_{\\textrm {A},x}$ .",
+      "There are many other alternatives for defining the select distribution. For instance, a log-linear model can be used to learn dependence on a set of features. The HDP provides statistical advantages, smoothing the learned distributions, resulting in a model more robust to data sparsity issues.",
+      "In order to describe inference in this framework, we must define additional concepts and notation. For a nonterminal $\\textrm {A}\\in \\mathcal {N}$ , observe that the paths from the root to the leaves of its HDP tree induce a partition on the set of semantic statements $\\mathcal {X}_A$ . More precisely, two semantic statements $x_1,x_2 \\in \\mathcal {X}_A$ belong to the same equivalence class if they correspond to the same path in an HDP tree."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2229/instruction.md b/qasper-2229/instruction.md
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+++ b/qasper-2229/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: A Probabilistic Generative Grammar for Semantic Parsing
+
+Question: What knowledge bases do they use?
\ No newline at end of file
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index 0000000000000000000000000000000000000000..94e6be9dfefada4a29188a966821feae229c79c5
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+++ b/qasper-2230/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Harnessing the richness of the linguistic signal in predicting pragmatic inferences
+
+Question: Which dataset do they use?
\ No newline at end of file
diff --git a/qasper-2237/instruction.md b/qasper-2237/instruction.md
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+Name of Paper: Towards Task-Oriented Dialogue in Mixed Domains
+
+Question: What multi-domain dataset is used?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Methodology",
+    "Methodology ::: Sequicity",
+    "Methodology ::: Multi-domain Dialogue State Tracking",
+    "Experiments",
+    "Experiments ::: Datasets",
+    "Experiments ::: Experimental Settings",
+    "Experiments ::: Results",
+    "Experiments ::: Error Analysis",
+    "Conclusion",
+    "Example Dialogues"
+  ],
+  "paragraphs": [
+    [
+      "In this work, we investigate the problem of task-oriented dialogue in mixed-domain settings. Our work is related to two lines of research in Spoken Dialogue System (SDS), namely task-oriented dialogue system and multi-domain dialogue system. We briefly review the recent literature related to these topics as follows.",
+      "Task-oriented dialogue systems are computer programs which can assist users to complete tasks in specific domains by understanding user requests and generating appropriate responses within several dialogue turns. Such systems are useful in domain-specific chatbot applications which help users find a restaurant or book a hotel. Conventional approach for building a task-oriented dialogue system is concerned with building a quite complex pipeline of many connected components. These components are usually independently developed which include at least four crucial modules: a natural language understanding module, a dialogue state tracking module, a dialogue policy learning module, and a answer generation module. Since these systems components are usually trained independently, their optimization targets may not fully align with the overall system evaluation criteria BIBREF0. In addition, such a pipeline system often suffers from error propagation where error made by upstream modules are accumuated and got amplified to the downstream ones.",
+      "To overcome the above limitations of pipeline task-oriented dialogue systems, much research has focused recently in designing end-to-end learning systems with neural network-based models. One key property of task-oriented dialogue model is that it is required to reason and plan over multiple dialogue turns by aggregating useful information during the conversation. Therefore, sequence-to-sequence models such as the encoder-decoder based neural network models are proven to be suitable for both task-oriented and non-task-oriented systems. Serban et al. proposed to build end-to-end dialogue systems using generative hierarchical recurrent encoder-decoder neural network BIBREF1. Li et al. presented persona-based models which incorporate background information and speaking style of interlocutors into LSTM-based seq2seq network so as to improve the modeling of human-like behavior BIBREF2. Wen et al. designed an end-to-end trainable neural dialogue model with modularly connected components BIBREF3. Bordes et al. BIBREF4 proposed a task-oriented dialogue model using end-to-end memory networks. At the same time, many works explored different kinds of networks to model the dialogue state, such as copy-augmented networks BIBREF5, gated memory networks BIBREF6, query-regression networks BIBREF7. These systems do not perform slot-filling or user goal tracking; they rank and select a response from a set of response candidates which are conditioned on the dialogue history.",
+      "One of the significant effort in developing end-to-end task-oriented systems is the recent Sequicity framework BIBREF8. This framework also relies on the sequence-to-sequence model and can be optimized with supervised or reinforcement learning. The Sequicity framework introduces the concept of belief span (bspan), which is a text span that tracks the dialogue states at each turn. In this framework, the task-oriented dialogue problem is decomposed into two stages: bspan generation and response generation. This framework has been shown to significantly outperform state-of-the-art pipeline-based methods.",
+      "The second line of work in SDS that is related to this work is concerned with multi-domain dialogue systems. As presented above, one of the key components of a dialogue system is dialogue state tracking, or belief tracking, which maintains the states of conversation. A state is usually composed of user's goals, evidences and information which is accumulated along the sequence of dialogue turns. While the user's goal and evidences are extracted from user's utterances, the useful information is usually aggregated from external resources such as knowledge bases or dialogue ontologies. Such knowledge bases contain slot type and slot value entries in one or several predefined domains. Most approaches have difficulty scaling up with multiple domains due to the dependency of their model parameters on the underlying knowledge bases. Recently, Ramadan et al. BIBREF9 has introduced a novel approach which utilizes semantic similarity between dialogue utterances and knowledge base terms, allowing the information to be shared across domains. This method has been shown not only to scale well to multi-domain dialogues, but also outperform existing state-of-the-art models in single-domain tracking tasks.",
+      "The problem that we are interested in this work is task-oriented dialogue in mixed-domain settings. This is different from the multi-domain dialogue problem above in several aspects, as follows:",
+      "First, we investigate the phenomenon of alternating between different dialogue domains in subsequent dialogue turns, where each turn is defined as a pair of user question and machine answer. That is, the domains are mixed between turns. For example, in the first turn, the user requests some information of a restaurant; then in the second turn, he switches to the a different domain, for example, he asks about the weather at a specific location. In a next turn, he would either switch to a new domain or come back to ask about some other property of the suggested restaurant. This is a realistic scenario which usually happens in practical chatbot applications in our observations. We prefer calling this problem mixed-domain dialogue rather than multiple-domain dialogue.",
+      "Second, we study the effect of the mixed-domain setting in the context of multi-domain dialogue approaches to see how they perform in different experimental scenarios.",
+      "The main findings of this work include:",
+      "A specialized state tracking component in multiple domains still plays an important role and gives better results than a state-of-the-art end-to-end task-oriented dialogue system.",
+      "A combination of specialized state tracking system and an end-to-end task-oriented dialogue system is beneficial in mix-domain dialogue systems. Our hybrid system is able to improve the belief tracking accuracy of about 28% of average absolute point on a standard multi-domain dialogue dataset.",
+      "These experimental results give some useful insights on data preparation and acquisition in the development of the chatbot platform FPT.AI, which is currently deployed for many practical chatbot applications.",
+      "The remainder of this paper is structured as follows. First, Section SECREF2 discusses briefly the two methods in building dialogue systems that our method relies on. Next, Section SECREF3 presents experimental settings and results. Finally, Section SECREF4 concludes the paper and gives some directions for future work."
+    ],
+    [
+      "In this section, we present briefly two methods that we use in our experiments which have been mentioned in the previous section. The first method is the Sequicity framework and the second one is the state-of-the-art multi-domain dialogue state tracking approach."
+    ],
+    [
+      "Figure FIGREF1 shows the architecture of the Sequicity framework as described in BIBREF8. In essence, in each turn, the Sequicity model first takes a bspan ($B_1$) and a response ($R_1$) which are determined in the previous step, and the current human question ($U_2$) to generate the current bspan. This bspan is then used together with a knowledge base to generate the corresponding machine answer ($R_2$), as shown in the right part of Figure FIGREF1.",
+      "The left part of that figure shows an example dialogue in a mixed-domain setting (which will be explained in Section SECREF3)."
+    ],
+    [
+      "Figure FIGREF8 shows the architecture of the multi-domain belief tracking with knowledge sharing as described in BIBREF9. This is the state-of-the-art belief tracker for multi-domain dialogue.",
+      "This system encodes system responses with 3 bidirectional LSTM network and encodes user utterances with 3+1 bidirectional LSTM network. There are in total 7 independent LSTMs. For tracking domain, slot and value, it uses 3 corresponding LSTMs, either for system response or user utterance. There is one special LSTM to track the user affirmation. The semantic similarity between the utterances and ontology terms are learned and shared between domains through their embeddings in the same semantic space."
+    ],
+    [
+      "In this section, we present experimental settings, different scenarios and results. We first present the datasets, then implementation settings, and finally obtained results."
+    ],
+    [
+      "We use the publicly available dataset KVRET BIBREF5 in our experiments. This dataset is created by the Wizard-of-Oz method BIBREF10 on Amazon Mechanical Turk platform. This dataset includes dialogues in 3 domains: calendar, weather, navigation (POI) which is suitable for our mix-domain dialogue experiments. There are 2,425 dialogues for training, 302 for validation and 302 for testing, as shown in the upper half of Table TABREF12.",
+      "In this original dataset, each dialogue is of a single domain where all of its turns are on that domain. Each turn is composed of a sentence pair, one sentence is a user utterance, the other sentence is the corresponding machine response. A dialogue is a sequence of turns. To create mix-domain dialogues for our experiments, we make some changes in this dataset as follows:",
+      "We keep the dialogues in the calendar domain as they are.",
+      "We take a half of dialogues in the weather domain and a half of dialogues in the POI domain and mix their turns together, resulting in a dataset of mixed weather-POI dialogues. In this mixed-domain dialogue, there is a turn in the weather domain, followed by a turn in POI domain or vice versa.",
+      "We call this dataset the sequential turn dataset. Since the start turn of a dialogue has a special role in triggering the learning systems, we decide to create another and different mixed-domain dataset with the following mixing method:",
+      "The first turn and the last turn of each dialogue are kept as in their original.",
+      "The internal turns are mixed randomly.",
+      "We call this dataset the random turn dataset. Some statistics of these mixed-domain datasets are shown in the lower half of the Table TABREF12."
+    ],
+    [
+      "For the task-oriented Sequicity model, we keep the best parameter settings as reported in the original framework, on the same KVRET dataset BIBREF8. In particular, the hidden size of GRU unit is set to 50; the learning rate of Adam optimizer is 0.003. In addition to the original GRU unit, we also re-run this framework with simple RNN unit to compare the performance of different recurrent network types. The Sequicity tool is freely available for download.",
+      "For the multi-domain belief tracker model, we set the hidden size of LSTM units to 50 as in the original model; word embedding size is 300 and number of training epochs is 100. The corresponding tool is also freely available for download."
+    ],
+    [
+      "Our experimental results are shown in Table TABREF21. The first half of the table contains results for task-oriented dialogue with the Sequicity framework with two scenarios for training data preparation. For each experiment, we run our models for 3 times and their scores are averaged as the final score. The mixed training scenario performs the mixing of both the training data, development data and the test data as described in the previous subsection. The non-mixed training scenario performs the mixing only on the development and test data, keeps the training data unmixed as in the original KVRET dataset. As in the Sequicity framework, we report entity match rate, BLEU score and Success F1 score. Entity match rate evaluates task completion, it determines if a system can generate all correct constraints to search the indicated entities of the user. BLEU score evaluates the language quality of generated responses. Success F1 balances the recall and precision rates of slot answers. For further details on these metrics, please refer to BIBREF8.",
+      "In the first series of experiments, we evaluate the Sequicity framework on different mixing scenarios and different recurrent units (GRU or RNN), on two mixing methods (sequential turn or random turn), as described previously. We see that when the training data is kept unmixed, the match rates are better than those of the mixed training data. It is interesting to note that the GRU unit is much more sensitive with mixed data than the simple RNN unit with the corresponding absolute point drop of about 10%, compared to about 3.5%. However, the entity match rate is less important than the Success F1 score, where the GRU unit outperforms RNN in both sequential turn and random turn by a large margin. It is logical that if the test data are mixed but the training data are unmixed, we get lower scores than when both the training data and test data are mixed. The GRU unit is also better than the RNN unit on response generation in terms of BLEU scores.",
+      "We also see that the task-oriented dialogue system has difficulty running on mixed-domain dataset; it achieves only about 75.62% of Success F1 in comparison to about 81.1% (as reported in the Sequicity paper, not shown in our table). Appendix SECREF5 shows some example dialogues generated automatically by our implemented system.",
+      "In the second series of experiments, we evaluate the belief tracking components of two systems, the specialized multi-domain belief tracker and the Sequicity bspan component. As shown in the lower half of the Table TABREF21, Sequicity capability of belief tracking is much worse than that of the multi-domain belief tracker. The slot accuracy gap between the tools is about 21.6%, the value accuracy gap is about 34.4%; that is a large average gap of 28% of accuracy. This result suggests a future work on combining a specialized belief tracking module with an end-to-end task-oriented dialogue system to improve further the performance of the overall dialogue system."
+    ],
+    [
+      "In this subsection, we present an example of erroneous mixed dialogue with multple turns. Table TABREF23 shows a dialogue in the test set where wrong generated responses of the Sequicity system are marked in bold font.",
+      "In the first turn, the system predicts incorrectly the bspan, thus generates wrong slot values (heavy traffic and Pizza Hut). The word Pizza Hut is an arbitrary value selected by the system when it cannot capture the correct value home in the bspan. In the second turn, the machine is not able to capture the value this_week. This failure does not manifest immediately at this turn but it is accumulated to make a wrong answer at the third turn (monday instead of this_week).",
+      "The third turn is of domain weather and the fourth turn is switched to domain POI. The bspan value cleveland is retained through cross domain, resulting in an error in the fourth turn, where cleveland is shown instead of home. This example demonstrates a weakness of the system when being trained on a mixed-domain dataset. In the fifth turn, since the system does not recognize the value fastest in the bspan, it generates a random and wrong value moderate traffic. Note that the generated answer of the sixth turn is correct despite of the wrong predicted bspan; however, it is likely that if the dialogue continues, this wrong bspan may result in more answer mistakes. In such situations, multi-domain belief tracker usually performs better at bspan prediction."
+    ],
+    [
+      "We have presented the problem of mixed-domain task-oriented dialogue and its empirical results on two datasets. We employ two state-of-the-art, publicly available tools, one is the Sequicity framework for task-oriented dialogue, and another is the multi-domain belief tracking system. The belief tracking capability of the specialized system is much better than that of the end-to-end system. We also show the difficulty of task-oriented dialogue systems on mixed-domain datasets through two series of experiments. These results give some useful insights in combining the approaches to improve the performance of a commercial chatbot platform which is under active development in our company. We plan to extend this current research and integrate its fruitful results into a future version of the platform."
+    ],
+    [
+      "The following is three example dialogues generated by our system. The first dialogue is in single-domain.",
+      "",
+      "The next two dialogues are in mixed-domains.",
+      "",
+      ""
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2242/instruction.md b/qasper-2242/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..52fe7e5141d9d8364f09a26354d560e566656ae1
--- /dev/null
+++ b/qasper-2242/instruction.md
@@ -0,0 +1,116 @@
+Name of Paper: Assessing the Applicability of Authorship Verification Methods
+
+Question: What is a self-compiled corpus?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Characteristics of Authorship Verification",
+    "Reliability (Determinism)",
+    "Optimizability",
+    "Model Category",
+    "Implications",
+    "Methodology",
+    "Corpora",
+    "Examined Authorship Verification Methods",
+    "Performance Measures",
+    "Experiments",
+    "Conclusion and Future Work"
+  ],
+  "paragraphs": [
+    [
+      "Digital text forensics aims at examining the originality and credibility of information in electronic documents and, in this regard, at extracting and analyzing information about the authors of the respective texts BIBREF0 . Among the most important tasks of this field are authorship attribution (AA) and authorship verification (AV), where the former deals with the problem of identifying the most likely author of a document INLINEFORM0 with unknown authorship, given a set of texts of candidate authors. AV, on the other hand, focuses on the question whether INLINEFORM1 was in fact written by a known author INLINEFORM2 , where only a set of reference texts INLINEFORM3 of this author is given. Both disciplines are strongly related to each other, as any AA problem can be broken down into a series of AV problems BIBREF1 . Breaking down an AA problem into multiple AV problems is especially important in such scenarios, where the presence of the true author of INLINEFORM4 in the candidate set cannot be guaranteed.",
+      "In the past two decades, researchers from different fields including linguistics, psychology, computer science and mathematics proposed numerous techniques and concepts that aim to solve the AV task. Probably due to the interdisciplinary nature of this research field, AV approaches were becoming more and more diverse, as can be seen in the respective literature. In 2013, for example, Veenman and Li BIBREF2 presented an AV method based on compression, which has its roots in the field of information theory. In 2015, Bagnall BIBREF3 introduced the first deep learning approach that makes use of language modeling, an important key concept in statistical natural language processing. In 2017, Casta\u00f1eda and Calvo BIBREF4 proposed an AV method that applies a semantic space model through Latent Dirichlet Allocation, a generative statistical model used in information retrieval and computational linguistics.",
+      "Despite the increasing number of AV approaches, a closer look at the respective studies reveals that only minor attention is paid to their underlying characteristics such as reliability and robustness. These, however, must be taken into account before AV methods can be applied in real forensic settings. The objective of this paper is to fill this gap and to propose important properties and criteria that are not only intended to characterize AV methods, but also allow their assessment in a more systematic manner. By this, we hope to contribute to the further development of this young research field. Based on the proposed properties, we investigate the applicability of 12 existing AV approaches on three self-compiled corpora, where each corpus involves a specific challenge.",
+      "The rest of this paper is structured as follows. Section SECREF2 discusses the related work that served as an inspiration for our analysis. Section SECREF3 comprises the proposed criteria and properties to characterize AV methods. Section SECREF4 describes the methodology, consisting of the used corpora, examined AV methods, selected performance measures and experiments. Finally, Section SECREF5 concludes the work and outlines future work."
+    ],
+    [
+      "Over the years, researchers in the field of authorship analysis identified a number of challenges and limitations regarding existing studies and approaches. Azarbonyad et al. BIBREF8 , for example, focused on the questions if the writing styles of authors of short texts change over time and how this affects AA. To answer these questions, the authors proposed an AA approach based on time-aware language models that incorporate the temporal changes of the writing style of authors. In one of our experiments, we focus on a similar question, namely, whether it is possible to recognize the writing style of authors, despite of large time spans between their documents. However, there are several differences between our experiment and the study of Azarbonyad et al. First, the authors consider an AA task, where one anonymous document INLINEFORM0 has to be attributed to one of INLINEFORM1 possible candidate authors, while we focus on an AV task, where INLINEFORM2 is compared against one document INLINEFORM3 of a known author. Second, the authors focus on texts with informal language (emails and tweets) in their study, while in our experiment we consider documents written in a formal language (scientific works). Third, Azarbonyad et al. analyzed texts with a time span of four years, while in our experiment the average time span is 15.6 years. Fourth, in contrast to the approach of the authors, none of the 12 examined AV approaches in our experiment considers a special handling of temporal stylistic changes.",
+      "In recent years, the new research field author obfuscation (AO) evolved, which concerns itself with the task to fool AA or AV methods in a way that the true author cannot be correctly recognized anymore. To achieve this, AO approaches which, according to Gr\u00f6ndahl and Asokan BIBREF9 can be divided into manual, computer-assisted and automatic types, perform a variety of modifications on the texts. These include simple synonym replacements, rule-based substitutions or word order permutations. In 2016, Potthast et al. BIBREF10 presented the first large-scale evaluation of three AO approaches that aim to attack 44 AV methods, which were submitted to the PAN-AV competitions during 2013-2015 BIBREF11 , BIBREF5 , BIBREF12 . One of their findings was that even basic AO approaches have a significant impact on many AV methods. More precisely, the best-performing AO approach was able to flip on average INLINEFORM0 % of an authorship verifier\u2019s decisions towards choosing N (\u201cdifferent author\u201d), while in fact Y (\u201csame author\u201d) was correct BIBREF10 . In contrast to Potthast et al., we do not focus on AO to measure the robustness of AV methods. Instead, we investigate in one experiment the question how trained AV models behave, if the lengths of the questioned documents are getting shorter and shorter. To our best knowledge, this question has not been addressed in previous authorship verification studies."
+    ],
+    [
+      "Before we can assess the applicability of AV methods, it is important to understand their fundamental characteristics. Due to the increasing number of proposed AV approaches in the last two decades, the need arose to develop a systematization including the conception, implementation and evaluation of authorship verification methods. In regard to this, only a few attempts have been made so far. In 2004, for example, Koppel and Schler BIBREF13 described for the first time the connection between AV and unary classification, also known as one-class classification. In 2008, Stein et al. BIBREF14 compiled an overview of important algorithmic building blocks for AV where, among other things, they also formulated three AV problems as decision problems. In 2009, Stamatatos BIBREF15 coined the phrases profile- and instance-based approaches that initially were used in the field of AA, but later found their way also into AV. In 2013 and 2014, Stamatatos et al. BIBREF11 , BIBREF16 introduced the terms intrinsic- and extrinsic models that aim to further distinguish between AV methods. However, a closer look at previous attempts to characterize authorship verification approaches reveals a number of misunderstandings, for instance, when it comes to draw the borders between their underlying classification models. In the following subsections, we clarify these misunderstandings, where we redefine previous definitions and propose new properties that enable a better comparison between AV methods."
+    ],
+    [
+      "Reliability is a fundamental property any AV method must fulfill in order to be applicable in real-world forensic settings. However, since there is no consistent concept nor a uniform definition of the term \u201creliability\u201d in the context of authorship verification according to the screened literature, we decided to reuse a definition from applied statistics, and adapt it carefully to AV.",
+      "In his standard reference book, Bollen BIBREF17 gives a clear description for this term: \u201cReliability is the consistency of measurement\u201d and provides a simple example to illustrate its meaning: At time INLINEFORM0 we ask a large number of persons the same question Q and record their responses. Afterwards, we remove their memory of the dialogue. At time INLINEFORM1 we ask them again the same question Q and record their responses again. \u201cThe reliability is the consistency of the responses across individuals for the two time periods. To the extent that all individuals are consistent, the measure is reliable\u201d BIBREF17 . This example deals with the consistency of the measured objects as a factor for the reliability of measurements. In the case of authorship verification, the analyzed objects are static data, and hence these cannot be a source of inconsistency. However, the measurement system itself can behave inconsistently and hence unreliable. This aspect can be described as intra-rater reliability.",
+      "Reliability in authorship verification is satisfied, if an AV method always generates the same prediction INLINEFORM0 for the same input INLINEFORM1 , or in other words, if the method behaves deterministically. Several AV approaches, including BIBREF18 , BIBREF19 , BIBREF20 , BIBREF21 , BIBREF22 , BIBREF23 , BIBREF24 , BIBREF25 , BIBREF16 fall into this category. In contrast, if an AV method behaves non-deterministically such that two different predictions for INLINEFORM2 are possible, the method can be rated as unreliable. Many AV approaches, including BIBREF4 , BIBREF13 , BIBREF26 , BIBREF1 , BIBREF27 , BIBREF3 , BIBREF28 , BIBREF29 , BIBREF30 belong to this category, since they involve randomness (e. g., weight initialization, feature subsampling, chunk generation or impostor selection), which might distort the evaluation, as every run on a test corpus very likely leads to different results. Under lab conditions, results of non-deterministic AV methods can (and should) be counteracted by averaging multiple runs. However, it remains highly questionable if such methods are generally applicable in realistic forensic cases, where the prediction INLINEFORM3 regarding a verification case INLINEFORM4 might sometimes result in Y and sometimes in N."
+    ],
+    [
+      "Another important property of an AV method is optimizability. We define an AV method as optimizable, if it is designed in such a way that it offers adjustable hyperparameters that can be tuned against a training/validation corpus, given an optimization method such as grid or random search. Hyperparameters might be, for instance, the selected distance/similarity function, the number of layers and neurons in a neural network or the choice of a kernel method. The majority of existing AV approaches in the literature (for example, BIBREF13 , BIBREF23 , BIBREF24 , BIBREF22 , BIBREF31 , BIBREF4 , BIBREF32 , BIBREF16 ) belong to this category. On the other hand, if a published AV approach involves hyperparameters that have been entirely fixed such that there is no further possibility to improve its performance from outside (without deviating from the definitions in the publication of the method), the method is considered to be non-optimizable. Non-optimizable AV methods are preferable in forensic settings as, here, the existence of a training/validation corpus is not always self-evident. Among the proposed AV approaches in the respective literature, we identified only a small fraction BIBREF21 , BIBREF2 , BIBREF30 that fall into this category."
+    ],
+    [
+      "From a machine learning point of view, authorship verification represents a unary classification problem BIBREF22 , BIBREF13 , BIBREF16 , BIBREF33 , BIBREF14 . Yet, in the literature, it can be observed that sometimes AV is treated as a unary BIBREF25 , BIBREF23 , BIBREF26 , BIBREF16 and sometimes as a binary classification task BIBREF30 , BIBREF32 , BIBREF22 , BIBREF2 . We define the way an AV approach is modeled by the phrase model category. However, before explaining this in more detail, we wish to recall what unary/one-class classification exactly represents. For this, we list the following verbatim quotes, which characterize one-class classification, as can be seen, almost identically (emphasis by us):",
+      "\u201cIn one-class classification it is assumed that only information of one of the classes, the target class, is available. This means that just example objects of the target class can be used and that no information about the other class of outlier objects is present.\u201d BIBREF34 ",
+      "\u201cOne-class classification (OCC) [...] consists in making a description of a target class of objects and in detecting whether a new object resembles this class or not. [...] The OCC model is developed using target class samples only.\u201d BIBREF35 ",
+      "\u201cIn one-class classification framework, an object is classified as belonging or not belonging to a target class, while only sample examples of objects from the target class are available during the training phase.\u201d BIBREF25 ",
+      "Note that in the context of authorship verification, target class refers to the known author INLINEFORM0 such that for a document INLINEFORM1 of an unknown author INLINEFORM2 the task is to verify whether INLINEFORM3 holds. One of the most important requirements of any existing AV method is a decision criterion, which aims to accept or reject a questioned authorship. A decision criterion can be expressed through a simple scalar threshold INLINEFORM4 or a more complex model INLINEFORM5 such as a hyperplane in a high-dimensional feature space. As a consequence of the above statements, the determination of INLINEFORM6 or INLINEFORM7 has to be performed solely on the basis of INLINEFORM8 , otherwise the AV method cannot be considered to be unary. However, our conducted literature research regarding existing AV approaches revealed that there are uncertainties how to precisely draw the borders between unary and binary AV methods (for instance, BIBREF36 , BIBREF16 , BIBREF33 ). Nonetheless, few attempts have been made to distinguish both categories from another perspective. Potha and Stamatatos BIBREF33 , for example, categorize AV methods as either intrinsic or extrinsic (emphasis by us):",
+      "\u201cIntrinsic verification models view it [i. e., the verification task] as a one-class classification task and are based exclusively on analysing the similarity between [ INLINEFORM0 ] and [ INLINEFORM1 ]. [...] Such methods [...] do not require any external resources.\u201d BIBREF33 ",
+      "\u201cOn the other hand, extrinsic verification models attempt to transform the verification task to a pair classification task by considering external documents to be used as samples of the negative class.\u201d BIBREF33 ",
+      "While we agree with statement (2), the former statement (1) is unsatisfactory, as intrinsic verification models are not necessarily unary. For example, the AV approach GLAD proposed by H\u00fcrlimann et al. BIBREF22 directly contradicts statement (1). Here, the authors \u201cdecided to cast the problem as a binary classification task where class values are Y [ INLINEFORM0 ] and N [ INLINEFORM1 ]. [...] We do not introduce any negative examples by means of external documents, thus adhering to an intrinsic approach.\u201d BIBREF22 .",
+      "A misconception similar to statement (1) can be observed in the paper of Jankowska et al. BIBREF24 , who introduced the so-called CNG approach claimed to be a one-class classification method. CNG is intrinsic in that way that it considers only INLINEFORM0 when deciding a problem INLINEFORM1 . However, the decision criterion, which is a threshold INLINEFORM2 , is determined on a set of verification problems, labeled either as Y or N. This incorporates \u201cexternal resources\u201d for defining the decision criterion, and it constitutes an implementation of binary classification between Y and N in analogy to the statement of H\u00fcrlimann et al. BIBREF22 mentioned above. Thus, CNG is in conflict with the unary definition mentioned above. In a subsequent paper BIBREF25 , however, Jankowska et al. refined their approach and introduced a modification, where INLINEFORM3 was determined solely on the basis of INLINEFORM4 . Thus, the modified approach can be considered as a true unary AV method, according to the quoted definitions for unary classification.",
+      "In 2004, Koppel and Schler BIBREF13 presented the Unmasking approach which, according to the authors, represents a unary AV method. However, if we take a closer look at the learning process of Unmasking, we can see that it is based on a binary SVM classifier that consumes feature vectors (derived from \u201cdegradation curves\u201d) labeled as Y (\u201csame author\u201d) or N (\u201cdifferent author\u201d). Unmasking, therefore, cannot be considered to be unary as the decision is not solely based on the documents within INLINEFORM0 , in analogy to the CNG approach of Jankowska et al. BIBREF24 discussed above. It should be highlighted again that the aforementioned three approaches are binary-intrinsic since their decision criteria INLINEFORM1 or INLINEFORM2 was determined on a set of problems labeled in a binary manner (Y and N) while after training, the verification is performed in an intrinsic manner, meaning that INLINEFORM3 and INLINEFORM4 are compared against INLINEFORM5 or INLINEFORM6 but not against documents within other verification problems (cf. Figure FIGREF15 ). A crucial aspect, which might have lead to misperceptions regarding the model category of these approaches in the past, is the fact that two different class domains are involved. On the one hand, there is the class domain of authors, where the task is to distinguish INLINEFORM7 and INLINEFORM8 . On the other hand, there is the elevated or lifted domain of verification problem classes, which are Y and N. The training phase of binary-intrinsic approaches is used for learning to distinguish these two classes, and the verification task can be understood as putting the verification problem as a whole into class Y or class N, whereby the class domain of authors fades from the spotlight (cf. Figure FIGREF15 ).",
+      "Besides unary and binary-intrinsic methods, there is a third category of approaches, namely binary-extrinsic AV approaches (for example, BIBREF3 , BIBREF30 , BIBREF29 , BIBREF37 , BIBREF32 , BIBREF1 , BIBREF2 ). These methods use external documents during a potentially existing training phase and \u2013 more importantly \u2013 during testing. In these approaches, the decision between INLINEFORM0 and INLINEFORM1 is put into the focus, where the external documents aim to construct the counter class INLINEFORM2 .",
+      "Based on the above observations, we conclude that the key requirement for judging the model category of an AV method depends solely on the aspect how its decision criterion INLINEFORM0 or INLINEFORM1 is determined (cf. Figure FIGREF15 ):",
+      "An AV method is unary if and only if its decision criterion INLINEFORM0 or INLINEFORM1 is determined solely on the basis of the target class INLINEFORM2 during testing. As a consequence, an AV method cannot be considered to be unary if documents not belonging to INLINEFORM3 are used to define INLINEFORM4 or INLINEFORM5 .",
+      "An AV method is binary-intrinsic if its decision criterion INLINEFORM0 or INLINEFORM1 is determined on a training corpus comprising verification problems labeled either as Y or N (in other words documents of several authors). However, once the training is completed, a binary-intrinsic method has no access to external documents anymore such that the decision regarding the authorship of INLINEFORM2 is made on the basis of the reference data of INLINEFORM3 as well as INLINEFORM4 or INLINEFORM5 .",
+      "An AV method is binary-extrinsic if its decision criterion INLINEFORM0 or INLINEFORM1 is determined during testing on the basis of external documents that represent the outlier class INLINEFORM2 .",
+      "Note that optimizable AV methods such as BIBREF18 , BIBREF25 are not excluded to be unary. Provided that INLINEFORM0 or INLINEFORM1 is not subject of the optimization procedure, the model category remains unary. The reason for this is obvious; Hyperparameters might influence the resulting performance of unary AV methods. The decision criterion itself, however, remains unchanged."
+    ],
+    [
+      "Each model category has its own implications regarding prerequisites, evaluability, and applicability.",
+      "One advantage of unary AV methods is that they do not require a specific document collection strategy to construct the counter class INLINEFORM0 , which reduces their complexity. On the downside, the choice of the underlying machine learning model of a unary AV approach is restricted to one-class classification algorithms or unsupervised learning techniques, given a suitable decision criterion.",
+      "However, a far more important implication of unary AV approaches concerns their performance assessment. Since unary classification (not necessarily AV) approaches depend on a fixed decision criterion INLINEFORM0 or INLINEFORM1 , performance measures such as the area under the ROC curve (AUC) are meaningless. Recall that ROC analysis is used for evaluating classifiers, where the decision threshold is not finally fixed. ROC analysis requires that the classifier generates scores, which are comparable across classification problem instances. The ROC curve and the area under this curve is then computed by considering all possible discrimination thresholds for these scores. While unary AV approaches might produce such scores, introducing a variable INLINEFORM2 would change the semantics of these approaches. Since unary AV approaches have a fixed decision criterion, they provide only a single point in the ROC space. To assess the performance of a unary AV method, it is, therefore, mandatory to consider the confusion matrix that leads to this point in the ROC space.",
+      "Another implication is that unary AV methods are necessarily instance-based and, thus, require a set INLINEFORM0 of multiple documents of the known author INLINEFORM1 . If only one reference document is available ( INLINEFORM2 ), this document must be artificially turned into multiple samples from the author. In general, unary classification methods need multiple samples from the target class since it is not possible to determine a relative closeness to that class based on only one sample.",
+      "On the plus side, binary-intrinsic or extrinsic AV methods benefit from the fact that we can choose among a variety of binary and INLINEFORM0 -ary classification models. However, if we consider designing a binary-intrinsic AV method, it should not be overlooked that the involved classifier will learn nothing about individual authors, but only similarities or differences that hold in general for Y and N verification problems BIBREF32 .",
+      "If, on the other hand, the choice falls on a binary-extrinsic method, a strategy has to be considered for collecting representative documents for the outlier class INLINEFORM0 . Several existing methods such as BIBREF32 , BIBREF1 , BIBREF2 rely on search engines for retrieving appropriate documents, but these search engines might refuse their service if a specified quota is exhausted. Additionally, the retrieved documents render these methods inherently non-deterministic. Moreover, such methods cause relatively high runtimes BIBREF11 , BIBREF5 . Using search engines also requires an active Internet connection, which might not be available or allowed in specific scenarios. But even if we can access the Internet to retrieve documents, there is no guarantee that the true author is not among them. With these points in mind, the applicability of binary-extrinsic methods in real-world cases, i. e., in real forensic settings, remains highly questionable."
+    ],
+    [
+      "In the following, we introduce our three self-compiled corpora, where each corpus represents a different challenge. Next, we describe which authorship verification approaches we considered for the experiments and classify each AV method according to the properties introduced in Section SECREF3 . Afterwards, we explain which performance measures were selected with respect to the conclusion made in Section UID17 . Finally, we describe our experiments, present the results and highlight a number of observations."
+    ],
+    [
+      "A serious challenge in the field of AV is the lack of publicly available (and suitable) corpora, which are required to train and evaluate AV methods. Among the few publicly available corpora are those that were released by the organizers of the well-known PAN-AV competitions BIBREF11 , BIBREF5 , BIBREF12 . In regard to our experiments, however, we cannot use these corpora, due to the absence of relevant meta-data such as the precise time spans where the documents have been written as well as the topic category of the texts. Therefore, we decided to compile our own corpora based on English documents, which we crawled from different publicly accessible sources. In what follows, we describe our three constructed corpora, which are listed together with their statistics in Table TABREF23 . Note that all corpora are balanced such that verification cases with matching (Y) and non-matching (N) authorships are evenly distributed.",
+      "As a first corpus, we compiled INLINEFORM0 that represents a collection of 80 excerpts from scientific works including papers, dissertations, book chapters and technical reports, which we have chosen from the well-known Digital Bibliography & Library Project (DBLP) platform. Overall, the documents were written by 40 researchers, where for each author INLINEFORM1 , there are exactly two documents. Given the 80 documents, we constructed for each author INLINEFORM2 two verification problems INLINEFORM3 (a Y-case) and INLINEFORM4 (an N-case). For INLINEFORM5 we set INLINEFORM6 's first document as INLINEFORM7 and the second document as INLINEFORM8 . For INLINEFORM9 we reuse INLINEFORM10 from INLINEFORM11 as the known document and selected a text from another (random) author as the unknown document. The result of this procedure is a set of 80 verification problems, which we split into a training and test set based on a 40/60% ratio. Where possible, we tried to restrict the content of each text to the abstract and conclusion of the original work. However, since in many cases these sections were too short, we also considered other parts of the original works such as introduction or discussion sections. To ensure that the extracted text portions are appropriate for the AV task, each original work was preprocessed manually. More precisely, we removed tables, formulas, citations, quotes and sentences that include non-language content such as mathematical constructs or specific names of researchers, systems or algorithms. The average time span between both documents of an author is 15.6 years. The minimum and maximum time span are 6 and 40 years, respectively. Besides the temporal aspect of INLINEFORM12 , another challenge of this corpus is the formal (scientific) language, where the usage of stylistic devices is more restricted, in contrast to other genres such as novels or poems.",
+      "As a second corpus, we compiled INLINEFORM0 , which represents a collection of 1,645 chat conversations of 550 sex offenders crawled from the Perverted-Justice portal. The chat conversations stem from a variety of sources including emails and instant messengers (e. g., MSN, AOL or Yahoo), where for each conversation, we ensured that only chat lines from the offender were extracted. We applied the same problem construction procedure as for the corpus INLINEFORM1 , which resulted in 1,100 verification problems that again were split into a training and test set given a 40/60% ratio. In contrast to the corpus INLINEFORM2 , we only performed slight preprocessing. Essentially, we removed user names, time-stamps, URLs, multiple blanks as well as annotations that were not part of the original conversations from all chat lines. Moreover, we did not normalize words (for example, shorten words such as \u201cnooooo\u201d to \u201cno\u201d) as we believe that these represent important style markers. Furthermore, we did not remove newlines between the chat lines, as the positions of specific words might play an important role regarding the individual's writing style.",
+      "As a third corpus, we compiled INLINEFORM0 , which is a collection of 200 aggregated postings crawled from the Reddit platform. Overall, the postings were written by 100 Reddit users and stem from a variety of subreddits. In order to construct the Y-cases, we selected exactly two postings from disjoint subreddits for each user such that both the known and unknown document INLINEFORM1 and INLINEFORM2 differ in their topic. Regarding the N-cases, we applied the opposite strategy such that INLINEFORM3 and INLINEFORM4 belong to the same topic. The rationale behind this is to figure out to which extent AV methods can be fooled in cases, where the topic matches but not the authorship and vice versa. Since for this specific corpus we have to control the topics of the documents, we did not perform the same procedure applied for INLINEFORM5 and INLINEFORM6 to construct the training and test sets. Instead, we used for the resulting 100 verification problems a 40/60% hold-out split, where both training and test set are entirely disjoint."
+    ],
+    [
+      "As a basis for our experiments, we reimplemented 12 existing AV approaches, which have shown their potentials in the previous PAN-AV competitions BIBREF11 , BIBREF12 as well as in a number of AV studies. The methods are listed in Table TABREF33 together with their classifications regarding the AV characteristics, which we proposed in Section SECREF3 .",
+      "All (optimizable) AV methods were tuned regarding their hyperparameters, according to the original procedure mentioned in the respective paper. However, in the case of the binary-extrinsic methods (GenIM, ImpGI and NNCD) we had to use an alternative impostors generation strategy in our reimplementations, due to technical problems. In the respective papers, the authors used search engine queries to generate the impostor documents, which are needed to model the counter class INLINEFORM0 . Regarding our reimplementations, we used the documents from the static corpora (similarly to the idea of Kocher and Savoy BIBREF30 ) to generate the impostors in the following manner: Let INLINEFORM1 denote a corpus with INLINEFORM2 verification problems. For each INLINEFORM3 we choose all unknown documents INLINEFORM4 in INLINEFORM5 with INLINEFORM6 and append them the impostor set INLINEFORM7 . Here, it should be highlighted that both GenIM and ImpGI consider the number of impostors as a hyperparameter such that the resulting impostor set is a subset of INLINEFORM8 . In contrast to this, NNCD considers all INLINEFORM9 as possible impostors. This fact plays an important role in the later experiments, where we compare the AV approaches to each other. Although our strategy is not flexible like using a search engine, it has one advantage that, here, it is assumed that the true author of an unknown document is not among the impostors, since in our corpora the user/author names are known beforehand."
+    ],
+    [
+      "According to our extensive literature research, numerous measures (e. g., Accuracy, F INLINEFORM0 , c@1, AUC, AUC@1, INLINEFORM1 or EER) have been used so far to assess the performance of AV methods. In regard to our experiments, we decided to use c@1 and AUC for several reasons. First, Accuracy, F INLINEFORM2 and INLINEFORM3 are not applicable in cases where AV methods leave verification problems unanswered, which concerns some of our examined AV approaches. Second, using AUC alone is meaningless for non-optimizable AV methods, as explained in Section UID17 . Third, both have been used in the PAN-AV competitions BIBREF5 , BIBREF12 . Note that we also list the confusion matrix outcomes."
+    ],
+    [
+      "Overall, we focus on three experiments, which are based on the corpora introduced in Section SECREF21 :",
+      "The Effect of Stylistic Variation Across Large Time Spans",
+      "The Effect of Topical Influence",
+      "The Effect of Limited Text Length",
+      "In the following each experiment is described in detail.",
+      "In this experiment, we seek to answer the question if the writing style of an author INLINEFORM0 can be recognized, given a large time span between two documents of INLINEFORM1 . The motivation behind this experiment is based on the statement of Olsson BIBREF38 that language acquisition is a continuous process, which is not only acquired, but also can be lost. Therefore, an important question that arises here is, if the writing style of a person remains \u201cstable\u201d across a large time span, given the fact that language in each individual's life is never \u201cfixed\u201d BIBREF38 . Regarding this experiment, we used the INLINEFORM2 corpus.",
+      "The results of the 12 examined AV methods are listed in Table TABREF41 , where it can be seen that the majority of the examined AV methods yield useful recognition results with a maximum value of 0.792 in terms of c@1. With the exception of the binary-intrinsic approach COAV, the remaining top performing methods belong to the binary-extrinsic category. This category of AV methods has also been superior in the PAN-AV competitions BIBREF11 , BIBREF5 , BIBREF12 , where they outperformed binary-intrinsic and unary approaches three times in a row (2013\u20132015).",
+      "The top performing approaches Caravel, COAV and NNCD deserve closer attention. All three are based on character-level language models that capture low-level features similar to character INLINEFORM0 -grams, which have been shown in numerous AA and AV studies (for instance, BIBREF39 , BIBREF26 ) to be highly effective and robust. In BIBREF19 , BIBREF28 , it has been shown that Caravel and COAV were also the two top-performing approaches, where in BIBREF19 they were evaluated on the PAN-2015 AV corpus BIBREF12 , while in BIBREF28 they were applied on texts obtained from Project Gutenberg. Although both approaches perform similarly, they differ in the way how the decision criterion INLINEFORM1 is determined. While COAV requires a training corpus to learn INLINEFORM2 , Caravel assumes that the given test corpus (which provides the impostors) is balanced. Given this assumption, Caravel first computes similarity scores for all verification problems in the corpus and then sets INLINEFORM3 to the median of all similarities (cf. Figure FIGREF49 ). Thus, from a machine learning perspective, there is some undue training on the test set. Moreover, the applicability of Caravel in realistic scenarios is questionable, as a forensic case is not part of a corpus where the Y/N-distribution is known beforehand.",
+      "Another interesting observation can be made regarding COAV, NNCD and OCCAV. Although all three differ regarding their model category, they use the same underlying compression algorithm (PPMd) that is responsible for generating the language model. While the former two approaches perform similarly well, OCCAV achieves a poor c@1 score ( INLINEFORM0 ). An obvious explanation for this is a wrongly calibrated threshold INLINEFORM1 , as can be seen from the confusion matrix, where almost all answers are N-predictions. Regarding the NNCD approach, one should consider that INLINEFORM2 is compared against INLINEFORM3 as well as INLINEFORM4 impostors within a corpus comprised of INLINEFORM5 verification problems. Therefore, a Y-result is correct with relatively high certainty (i. e., the method has high precision compared to other approaches with a similar c@1 score), as NNCD decided that author INLINEFORM6 fits best to INLINEFORM7 among INLINEFORM8 candidates. In contrast to Caravel, NNCD only retrieves the impostors from the given corpus, but it does not exploit background knowledge about the distribution of problems in the corpus.",
+      "Overall, the results indicate that it is possible to recognize writing styles across large time spans. To gain more insights regarding the question which features led to the correct predictions, we inspected the AVeer method. Although the method achieved only average results, it benefits from the fact that it can be interpreted easily, as it relies on a simple distance function, a fixed threshold INLINEFORM0 and predefined feature categories such as function words. Regarding the correctly recognized Y-cases, we noticed that conjunctive adverbs such as \u201chence\u201d, \u201ctherefore\u201d or \u201cmoreover\u201d contributed mostly to AVeer's correct predictions. However, a more in-depth analysis is required in future work to figure out whether the decisions of the remaining methods are also primarily affected by these features.",
+      "In this experiment, we investigate the question if the writing style of authors can be recognized under the influence of topical bias. In real-world scenarios, the topic of the documents within a verification problem INLINEFORM0 is not always known beforehand, which can lead to a serious challenge regarding the recognition of the writing style. Imagine, for example, that INLINEFORM1 consists of a known and unknown document INLINEFORM2 and INLINEFORM3 that are written by the same author ( INLINEFORM4 ) while at the same time differ regarding their topic. In such a case, an AV method that it focusing \u201ctoo much\u201d on the topic (for example on specific nouns or phrases) will likely predict a different authorship ( INLINEFORM5 ). On the other hand, when INLINEFORM6 and INLINEFORM7 match regarding their topic, while being written by different authors, a topically biased AV method might erroneously predict INLINEFORM8 .",
+      "In the following we show to which extent these assumptions hold. As a data basis for this experiment, we used the INLINEFORM0 corpus introduced in Section UID30 . The results regarding the 12 AV methods are given in Table TABREF44 , where it can be seen that our assumptions hold. All examined AV methods (with no exception) are fooled by the topical bias in the corpus. Here, the highest achieved results in terms of c@1 and AUC are very close to random guessing. A closer look at the confusion matrix outcomes reveals that some methods, for example ImpGI and OCCAV, perform almost entirely inverse to each other, where the former predicts nothing but Y and the latter nothing but N (except 1 Y). Moreover, we can assume that the lower c@1 is, the stronger is the focus of the respective AV method on the topic of the documents. Overall, the results of this experiment suggest that none of the examined AV methods is robust against topical influence.",
+      "In our third experiment, we investigate the question how text lengths affect the results of the examined AV methods. The motivation behind this experiment is based on the observation of Stamatatos et al. BIBREF12 that text length is an important issue, which has not been thoroughly studied within authorship verification research. To address this issue, we make use of the INLINEFORM0 corpus introduced in Section UID28 . The corpus is suitable for this purpose, as it comprises a large number of verification problems, where more than 90% of all documents have sufficient text lengths ( INLINEFORM1 2,000 characters). This allows a stepwise truncation and by this to analyze the effect between the text lengths and the recognition results. However, before considering this, we first focus on the results (shown in Table TABREF46 ) after applying all 12 AV methods on the original test corpus.",
+      "As can be seen in Table TABREF46 , almost all approaches perform very well with c@1 scores up to 0.991. Although these results are quite impressive, it should be noted that a large fraction of the documents comprises thousands of words. Thus, the methods can learn precise representations based on a large variety of features, which in turn enable a good determination of (dis)similarities between known/unknown documents. To investigate this issue in more detail, we constructed four versions of the test corpus and equalized the unknown document lengths to 250, 500, 1000, and 2000 characters. Then, we applied the top performing AV methods with a c@1 value INLINEFORM0 on the four corpora. Here, we reused the same models and hyperparameters (including the decision criteria INLINEFORM1 and INLINEFORM2 ) that were determined on the training corpus. The intention behind this was to observe the robustness of the trained AV models, given the fact that during training they were confronted with longer documents.",
+      "The results are illustrated in Figure FIGREF47 , where it can be observed that GLAD yields the most stable results across the four corpora versions, where even for the corpus with the 250 characters long unknown documents, it achieves a c@1 score of 0.727. Surprisingly, Unmasking performs similarly well, despite of the fact that the method has been designed for longer texts i. e., book chunks of at least 500 words BIBREF13 . Sanderson and Guenter also point out that the Unmasking approach is less useful when dealing with relatively short texts BIBREF40 . However, our results show a different picture, at least for this corpus.",
+      "One explanation of the resilience of GLAD across the varying text lengths might be due to its decision model INLINEFORM0 (an SVM with a linear kernel) that withstands the absence of missing features caused by the truncation of the documents, in contrast to the distance-based approaches AVeer, NNCD and COAV, where the decision criterion INLINEFORM1 is reflected by a simple scalar. Table TABREF48 lists the confusion matrix outcomes of the six AV methods regarding the 250 characters version of INLINEFORM2 .",
+      "Here, it can be seen that the underlying SVM model of GLAD and Unmasking is able to regulate its Y/N-predictions, in contrast to the three distance-based methods, where the majority of predictions fall either on the Y- or on the N-side. To gain a better picture regarding the stability of the decision criteria INLINEFORM0 and INLINEFORM1 of the methods, we decided to take a closer look on the ROC curves (cf. Figure FIGREF49 ) generated by GLAD, Caravel and COAV for the four corpora versions, where a number of interesting observations can be made. When focusing on AUC, it turns out that all three methods perform very similar to each other, whereas big discrepancies between GLAD and COAV can be observed regarding c@1. When we consider the current and maximum achievable results (depicted by the circles and triangles, respectively) it becomes apparent that GLAD's model behaves stable, while the one of COAV becomes increasingly vulnerable the more the documents are shortened. When looking at the ROC curve of Caravel, it can be clearly seen that the actual and maximum achievable results are very close to each other. This is not surprising, due to the fact that Caravel's threshold always lies at the median point of the ROC curve, provided that the given corpus is balanced.",
+      "While inspecting the 250 characters long documents in more detail, we identified that they share similar vocabularies consisting of chat abbreviations such as \u201clol\u201d (laughing out loud) or \u201ck\u201d (ok), smileys and specific obscene words. Therefore, we assume that the verification results of the examined methods are mainly caused by the similar vocabularies between the texts."
+    ],
+    [
+      "We highlighted the problem that underlying characteristics of authorship verification approaches have not been paid much attention in the past research and that these affect the applicability of the methods in real forensic settings. Then, we proposed several properties that enable a better characterization and by this a better comparison between AV methods. Among others, we explained that the performance measure AUC is meaningless in regard to unary or specific non-optimizable AV methods, which involve a fixed decision criterion (for example, NNCD). Additionally, we mentioned that determinism must be fulfilled such that an AV method can be rated as reliable. Moreover, we clarified a number of misunderstandings in previous research works and proposed three clear criteria that allow to classify the model category of an AV method, which in turn influences its design and the way how it should be evaluated. In regard to binary-extrinsic AV approaches, we explained which challenges exist and how they affect their applicability.",
+      "In an experimental setup, we applied 12 existing AV methods on three self-compiled corpora, where the intention behind each corpus was to focus on a different aspect of the methods applicability. Our findings regarding the examined approaches can be summarized as follows: Despite of the good performance of the five AV methods GenIM, ImpGI, Unmasking, Caravel and SPATIUM, none of them can be truly considered as reliable and therefore applicable in real forensic cases. The reason for this is not only the non-deterministic behavior of the methods but also their dependence (excepting Unmasking) on an impostor corpus. Here, it must be guaranteed that the true author is not among the candidates, but also that the impostor documents are suitable such that the AV task not inadvertently degenerates from style to topic classification. In particular, the applicability of the Caravel approach remains highly questionable, as it requires a corpus where the information regarding Y/N-distribution is known beforehand in order to set the threshold. In regard to the two examined unary AV approaches MOCC and OCCAV, we observed that these perform poorly on all three corpora in comparison to the binary-intrinsic and binary-extrinsic methods. Most likely, this is caused by the wrong threshold setting, as both tend to generate more N-predictions. From the remaining approaches, GLAD and COAV seem to be a good choice for realistic scenarios. However, the former has been shown to be more robust in regard to varying text lengths given a fixed model, while the latter requires a retraining of the model (note that both performed almost equal in terms of AUC). Our hypothesis, which we leave open for future work, is that AV methods relying on a complex model INLINEFORM0 are more robust than methods based on a scalar-threshold INLINEFORM1 . Lastly, we wish to underline that all examined approaches failed in the cross-topic experiment. One possibility to counteract this is to apply text distortion techniques (for instance, BIBREF41 ) in order to control the topic influence in the documents.",
+      "As one next step, we will compile additional and larger corpora to investigate the question whether the evaluation results of this paper hold more generally. Furthermore, we will address the important question how the results of AV methods can be interpreted in a more systematic manner, which will further influence the practicability of AV methods besides the proposed properties.",
+      "This work was supported by the German Federal Ministry of Education and Research (BMBF) under the project \"DORIAN\" (Scrutinise and thwart disinformation)."
+    ]
+  ]
+}
+```
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--- /dev/null
+++ b/qasper-2243/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Assessing the Applicability of Authorship Verification Methods
+
+Question: What are the 12 AV approaches which are examined?
\ No newline at end of file
diff --git a/qasper-2245/instruction.md b/qasper-2245/instruction.md
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+++ b/qasper-2245/instruction.md
@@ -0,0 +1,102 @@
+Name of Paper: A Trolling Hierarchy in Social Media and A Conditional Random Field For Trolling Detection
+
+Question: what is the source of the new dataset?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Trolling Categorization",
+    "Conversations Excerpts Examples",
+    "Corpus and Annotations",
+    "Trolling Events Prediction",
+    "Feature Set",
+    "Baseline System",
+    "Joint Models",
+    "Evaluation and Results",
+    "Results Discussion",
+    "Related Work",
+    "Conclusion and Future Work"
+  ],
+  "paragraphs": [
+    [
+      "In contrast to traditional content distribution channels like television, radio and newspapers, Internet opened the door for direct interaction between the content creator and its audience. One of these forms of interaction is the presence of comments sections that are found in many websites. The comments section allows visitors, authenticated in some cases and unauthenticated in others, to leave a message for others to read. This is a type of multi-party asynchronous conversation that offers interesting insights: one can learn what is the commenting community thinking about the topic being discussed, their sentiment, recommendations among many other. There are some comment sections in which commentators are allowed to directly respond to others, creating a comment hierarchy. These kind of written conversations are interesting because they bring light to the types interaction between participants with minimal supervision. This lack of supervision and in some forums, anonymity, give place to interactions that may not be necessarily related with the original topic being discussed, and as in regular conversations, there are participants with not the best intentions. Such participants are called trolls in some communities.",
+      "Even though there are some studies related to trolls in different research communities, there is a lack of attention from the NLP community. We aim to reduce this gap by presenting a comprehensive categorization of trolling and propose two models to predict trolling aspects. First, we revise the some trolling definitions: \u201cTrolling is the activity of posting messages via communication networks that are in tended to be provocative, offensive or menacing\u201d by BIBREF0 , this definition considers trolling from the most negative perspective where a crime might be committed. In a different tone, BIBREF1 provides a working definition for troll: \u201cA troller in a user in a computer mediated communication who constructs the identity of sincerely wishing to be part of the group in question, including professing, or conveying pseudo-sincere intentions, but whose real intention(s) is/are to cause disruption and/or trigger or exacerbate conflict for the purpose of their own amusement\u201d. These definitions inspire our trolling categorization, but first, we define a trolling event: a comment in a conversation whose intention is to cause conflict, trouble; be malicious, purposely seek or disseminate false information or advice; give a dishonest impression to deceive; offend, insult, cause harm, humiliation or aggravation. Also, a troll or troller is the individual that generates a trolling event, trolling is the overall phenomena that involves a troll, trolling event and generates responses from others. Any participant in a forum conversation may become a troll at any given point, as we will see, the addressee of a trolling event may choose to reply with a trolling comment or counter-trolling, effectively becoming a troll as well.",
+      "We believe our work makes four contributions. First, unlike previous computational work on trolling, which focused primarily on analyzing the narrative retrospectively by the victim (e.g., determining the trolling type and the role played by each participant), we study trolling by analyzing comments in a conversation, aiming instead to identify trollers, who, once identified, could be banned from posting. Second, while previous work has focused on analyzing trolling from the troll's perspective, we additionally model trolling from the target's perspective, with the goal understanding the psychological impact of a trolling event on the target, which we believe is equally important from a practical standpoint. Third, we propose a comprehensive categorization of trolling that covers not only the troll's intention but also the victim and other commenters' reaction to the troll's comment. We believe such categorization will provide a solid basis on which future computational approaches to trolling can be built. Finally, we make our annotated data set consisting of 1000 annotated trolling events publicly available. We believe that our data set will be a valuable resource to any researcher interested in the computational modeling of trolling."
+    ],
+    [
+      "Based on the previous definitions we identify four aspects that uniquely define a trolling event-response pair: 1) Intention: what is the author of the comment in consideration purpose, a) trolling, the comment is malicious in nature, aims to disrupt, annoy, offend, harm or spread purposely false information, b) playing the comment is playful, joking, teasing others without the malicious intentions as in a), or c) none, the comment has no malicious intentions nor is playful, it is a simple comment. 2) Intention Disclosure: this aspect is meant to indicate weather a trolling comment is trying to deceive its readers, the possible values for this aspect are a) the comment's author is a troll and is trying to hide its real intentions, and pretends to convey a different meaning, at least temporarily, b) the comment's author is a troll but is clearly exposing its malicious intentions and c) the comment's author is not a troll, therefore there are not hidden or exposed malicious or playful intentions. There are two aspects defined on the comments that direct address the comment in consideration, 3) Intentions Interpretation: this aspect refers to the responder's understanding of the parent's comment intentions. There possible interpretations are the same as the intentions aspect: trolling, playing or none. The last element, is the 4) Response strategy employed by the commentators directly replaying to a comment, which can be a trolling event. The response strategy is influenced directly by the responder's interpretation of the parent's comment intention. We identify 14 possible response strategies. Some of these strategies are tied with combinations of the three other aspects. We briefly define each of them in the appendix.",
+      "Figure FIGREF2 shows this categories as a hierarchy. Using this trolling formulation, the suspected troll event and the responses are correlated and one cannot independently name the strategy response without learning about the other three aspects. This is challenging prediction problem that we address in this work."
+    ],
+    [
+      "To illustrate this hierarchy, we present some examples. These are excerpts from original conversations; the first comment, generated by author C0, on each excerpt is given as a minimal piece of context, the second comment, by the author C1 in italics, is the comment suspected to be a trolling event. The rest of the comments, are all direct responses to the suspected trolling comment. When the response author \u201cname\u201d is the same as the first comment, it indicates that the that same individual also replied to the suspected troll.",
+      "Example 1.",
+      "",
+      "[noitemsep,nolistsep] ",
+      "My friend who makes $20,000 a year leased a brand new Chevy Spark EV, for only $75 per month and he got a California rebate for driving an electric car. Much cheaper than buying older car which usually require heavy upkeep due to its mileage. At this point I think you're just trolling.",
+      "[noitemsep,nolistsep]",
+      "IYour friend has a good credit score, which can't be said about actual poor people. Did you grow up sheltered by any chance?",
+      "[noitemsep,nolistsep]",
+      "Judging by your post history, you're indeed a troll. Have a good one.",
+      "In this example, when C1 asks \u201cDid you grow up sheltered by any chance?\", her intention is to denigrate or be offensive, and it is not hiding it, instead he is clearly disclosing her trolling intentions. In C0's response, we see that has came to the conclusion that C1 is trolling and his response strategy is frustrate the trolling event by ignoring the malicious troll's intentions.",
+      "Example 2.",
+      "",
+      "[noitemsep,nolistsep] ",
+      "What do you mean look up ?:( I don't see anything lol",
+      "[noitemsep,nolistsep]",
+      "Look up! Space is cool! :)",
+      "[noitemsep,nolistsep]",
+      "why must you troll me :(",
+      "Keep going, no matter how many times you say it, he will keep asking",
+      "In this example, we hypothesize that C0 is requesting some information and C1 is given an answer that is unfit to C0's' request. We do so based on the last C0's comment; CO is showing disappointment or grievance. Also, we deduct that C1 is trying to deceive C0, therefore, C1's comment is a trolling event. This is a trolling event whose intention is to purposely convey false information, and that hiding its intentions. As for the response, in the last C0's comment, he has finally realized or interpreted that C1's real intentions are deceiving and since his comment shows a \u201csad emoticon\u201d his reply is emotionally, with aggravation, so we say that CO got engaged. C2 on the other hand, acknowledges the malicious and play along with the troll.",
+      "Given these examples, address the task of predicting the four aspects of a trolling event based on the methodology described in the next section."
+    ],
+    [
+      "We collected all available comments in the stories from Reddit from August 2015. Reddit is popular website that allows registered users (without identity verification) to participate in forums specific a post or topic. These forums are of they hierarchical type, those that allow nested conversation, where the children of a comment are its direct response. To increase recall and make the annotation process feasible we created an inverted index with Lucene and queried for comments containing the word troll with an edit distance of 1, to include close variations of this word. We do so inspired by the method by BIBREF2 to created a bullying dataset, and because we hypothesize that such comments will be related or involved in a trolling event. As we observed in the dataset, people use the word troll in many different ways, sometimes it is to point out that some used is indeed trolling him or her or is accusing someone else of being a troll. Other times, people use the term, to express their frustration or dislike about a particular user, but there is no trolling event. Other times, people simple discuss about trolling and trolls, without actually participating or observing one directly. Nonetheless, we found that this search produced a dataset in which 44.3 % of the comments directly involved a trolling event. Moreover, as we exposed our trolling definition, it is possible for commentators in a conversation to believe that they are witnessing a trolling event and respond accordingly even where there is none. Therefore, even in the comments that do not involve trolling, we are interested in learning what triggers users interpretation of trolling where it is not present and what kind of response strategies are used. We define as a suspected trolling event in our dataset a comment in which at least one of its children contains the word troll.",
+      "With the gathered comments, we reconstructed the original conversation trees, from the original post, the root, to the leaves, when they were available and selected a subset to annotated. For annotation purposes, we created snippets of conversations as the ones shown in Example 1 and Example 2 consisting of the parent of the suspected trolling event, the suspected trolling event comment, and all of the direct responses to the suspected trolling event. We added an extra constraint that the parent of the suspected trolling event should also be part of the direct responses, we hypothesize that if the suspected trolling event is indeed trolling, its parent should be the object of its trolling and would have a say about it. We recognize that this limited amount of information is not always sufficient to recover the original message conveyed by all of the participants in the snippet, and additional context would be beneficial. However, the trade off is that snippets like this allow us to make use of Amazon Mechanical Turk (AMT) to have the dataset annotated, because it is not a big burden for a \u201cturker\u201d to work on an individual snippet in exchange for a small pay, and expedites the annotation process by distributing it over dozens of people. Specifically, for each snippet, we requested three annotators to label the four aspects previously described. Before annotating, we set up a qualification test along with borderline examples to guide them in process and align them with our criteria. The qualification test turned out to be very selective since only 5% of all of the turkers that attempted it passed the exam. Our dataset consists of 1000 conversations with 5868 sentences and 71033 tokens. The distribution over the classes per trolling aspect is shown in the table TABREF24 in the column \u201cSize\u201d.",
+      "Inter-Annotator Agreement. Due to the subjective nature of the task we did not expected perfect agreement. However, we obtained substantial inter-annotator agreement as we measured the fleiss-kappa statistic BIBREF3 for each of the trolling aspects: Intention: 0.578, Intention Disclosure: 0.556, Interpretation: 0.731 and Response 0.632. After inspecting the dataset, we manually reconciled aspects of the threads that found no majority on the turkers annotation and verified and corrected consistency on the four tasks on each thread."
+    ],
+    [
+      "In this section we propose to solve the following problem: given a comment in a conversation, suspected to a trolling event, it's parent comment and all it's direct responses, we aim to predict the suspected comment I: intention, its D: intention disclosure and from the responses point of view, for each response comment the R: interpretation of the suspected troll comment's intentions, and identify its B: response strategy. This problem can be seen as a multi-task prediction. To do so, we split the dataset into training and testing sets using a 5-fold cross validation setup."
+    ],
+    [
+      "For prediction we define two sets of features, a basic and an enhanced dataset, extracted from each of the comments in the dataset. The features are described below.",
+      "N-gram features. We encode each unigram and bigram collected from the training comments a binary feature. In a similar manner, we include the unigram and bigram along with their POS tag as in BIBREF2 . To extract these features we used the most current version of the Stanford CoreNLP BIBREF4 . Each token's",
+      "Lemmas as in BIBREF5 as a binary feature.",
+      "Harmful Vocabulary. In their research on bullying BIBREF6 identified a small set of words that are highly offensive. We encode them as well as binary features.",
+      "Emotions Synsets. As in BIBREF5 we extracted all lemmas associated with each of Synsets extracted from WordNet BIBREF7 from these emotions: anger, embarrassment, empathy, fear, pride, relief and sadness. As well all the synonyms from these emotions extracted from the dictionary. Also,",
+      "Emoticons. Reddit's comments make extensive use of emoticons, we argue that some emoticons are specially used in trolling events and to express a variety of emotions, which we hypothesize would be useful to identify a comments intention, interpretation and response. For that we use the emoticon dictionary BIBREF8 and we set a binary feature for each emoticon that is found in the dictionary.",
+      "Sentiment Polarity. Using a similar idea, we hypothesize that the overall comment emotion would be useful to identify the response and intention in a trolling event. So, we apply the Vader Sentiment Polarity Analyzer BIBREF9 and include a four features, one per each measurement given by the analyzer: positive, neutral, negative and a composite metric, each as a real number value.",
+      "Subjectivity Lexicon. From the MPQA Subjective Lexicon BIBREF10 we include all tokens that are found in the lexicon as binary features. This lexicon was created from a news domains, so the words in it don't necessarily align with the informal vocabulary used in Reddit, but, there are serious Reddit users that use proper language and formal constructions. We believe that these features will allow us to discriminate formal comments from being potentially labeled as trolling events, which tend to be vulgar.",
+      "Swearing Vocabulary. We manually collected 1061 swear words and short phrases from the internet, blogs, forums and smaller repositories. The informal nature of this dictionary resembles the type of language used by flaming trolls and agitated responses, so we encode a binary feature for each word or short phrase in a comment if it appears in the swearing dictionary.",
+      "Framenet. Following BIBREF11 use of FrameNet, we apply the Semaphore Parser BIBREF12 to each sentence in every comment in the training set, and construct three different binary features: every frame name that is present in the sentence, the frame name a the target word associated with it, and the argument name along with the token or lexical unit in the sentence associated with it. We argue that some frames are especially interesting from the trolling perspective. For example, the frame \u201cDeception_success\u201d precisely models one of the trolling models, and we argue that these features will be particularly to identify trolling events in which semantic and not just syntactic information is necessary.",
+      "Politeness Queues. BIBREF13 identified queues that signal polite and impolite interactions among groups of people collaborating online. Based on our observations of trolling examples, it is clear that flaming troll and engaged or emotional responses would use impolite queues. On the contrary, neutralizing and frustrating responses to troll avoid falling in confrontation and their vocabulary tends to be more polite. So use these queues as binary features as they appear in the comments in consideration."
+    ],
+    [
+      "The most na\u00efve approach is to consider each of the four tasks as an independent classification problem. Such system would be deprived from the other's tasks information that we've mentioned is strictly necessary to make a correct prediction of the response strategy. Instead, as our baseline we follow a pipeline approach, using the tasks oder: I, D, R and B, so that each of the subsequent subtasks' feature set is extended with a feature per each of previously computed subtasks. We argue that this setup is a competitive baseline, as can be verified in the results table TABREF24 . For the classifier in the pipeline approach we choose a log-linear model, a logistic regression classifier. In addition to logistic regression, we tried the generative complement of logistic regression, na\u00efve bayes and max-margin classifier, a support vector machine, but their performance was not superior to the logistic regression. It is noteworthy to mention that the feature set used for the intention predict is the combined features sets of the suspected troll comment as well as its parent. We do so in all of our experiments the learner can take advantage of the conversation context."
+    ],
+    [
+      "The nature of this problem makes the use of a joint model a logical choice. Among different options for joint inference, we choose a (conditional) probabilistic graphical model (henceforth PGM) BIBREF15 because, in contrast to ILP formulations, has the ability to learn parameters and not just impose hard constraints. Also, compared to Markov Logic Networks BIBREF16 , a relatively recent formulation that combines logic and Markov Random Fields, PGMs in practice have proved to be more scalable, even though, inference in general models is shown to be intractable. Finally, we are also interested in choosing a PGM because it allow to directly compare the strength of joint inference with the baseline, because the our model is a collection of logistic regressors trained simultaneously.",
+      "A conditional random field factorizes the conditional probability distribution over all possible values of the query variables in the model, given a set of observations as in equation EQREF22 . In our model, the query variables are the four tasks we desire to predict, INLINEFORM0 and the observations is their combined feature sets INLINEFORM1 . Each of the factors INLINEFORM2 in this distribution is a log-linear model as in equation EQREF23 and represents the probability distribution of the clique of variables INLINEFORM3 in it, given the observation set INLINEFORM4 . This is identical to the independent logistic regression model described in the baseline, except for the fact that all variables or tasks are consider a the same time. To do so, we add additional factors that connect task variables among them, permitting the flow of information of one task to the other.",
+      "Specifically, our model represent each task with a random variable, shown in figure FIGREF15 (left), represented by the circles. The plate notation that surrounds variables INLINEFORM0 and INLINEFORM1 indicates that there will as many variables INLINEFORM2 and INLINEFORM3 and edges connecting them to INLINEFORM4 as the number of responses in the problem snippet. The edges connecting INLINEFORM5 and INLINEFORM6 with INLINEFORM7 attempts to model influence of these two variables on the response, and how this information is passed along to the response strategy variable INLINEFORM8 . Figure FIGREF15 (right) explicitly represents the cliques in the underlying factor graph. We can see that there are unary factors, INLINEFORM9 , INLINEFORM10 , INLINEFORM11 and INLINEFORM12 , that model the influence of the observation features over their associated variables, just as the logistic regression model does. Factors INLINEFORM13 models the interaction between variables INLINEFORM14 and INLINEFORM15 , INLINEFORM16 the interaction between variables INLINEFORM17 and INLINEFORM18 and INLINEFORM19 models the interactions between variables INLINEFORM20 and INLINEFORM21 , using a log-linear model over the possible values of the pair of variables in that particular clique.",
+      "Due to the size of the model, we are able to perform exact inference at train and test time. For parameter learning we employ limited memory lbfgs optimizer BIBREF17 as we provide the cost function and gradient based on the equations described in BIBREF18 .",
+      "2 pass Model A hybrid mode that we experiment with is model that performs joint inference on three tasks: I: intention, D: intention disclosure and R: responders' intention interpretation. The remaining task B: response strategy is performed in a second step, with the input the other three tasks. We do so because we observed in our experiments that the close coupling between the first three tasks allow them to perform better independently of the response strategy, as we will elaborate in the results section. DISPLAYFORM0 DISPLAYFORM1 "
+    ],
+    [
+      "We perform 5-fold cross validation on the dataset. We use the first fold to tune the parameters and the remaining four folds to report results. The system performance is measured using precision, recall and F-1 as shown in table TABREF24 . The left side of the table, reports results obtained using the basic feature set, while the right side does so on the enhanced feature set. In order to maintain consistency folds are created based on the threads or snippets and for the case of the baseline system, all instances in the particular fold for task in consideration are considered independent of each other. On the table, rows show the classes performance for each of the tasks, indicated by a heard with the task name. For the response strategy we present results for those class values that are at least 5% of the total distribution, we do so, because the number of labeled instances for this classes is statistically insignificant compared to the majority classes."
+    ],
+    [
+      "From the result table TABREF24 , we observe that hybrid model significantly outperform the baseline, by more than 20 points in intention and intention disclosure prediction. For the response strategy, it is clear that none of the systems offer satisfying results; this showcases the difficult of such a large number of classes. Nonetheless, the hybrid model outperforms the fully joint model and baseline in all but one the response strategy classes. However, the differences are far less impressive as in the other tasks. It is surprisingly; that the full joint model did not offered the best performance. One of the reasons behind this is that intention, intentions disclosure and interpretation tasks are hurt by the difficulty of learning parameters that maximize the response strategy, this last task drags the other three down in performance. Another reason is that, the features response strategy is not informative enough to learn the correct concept, and due to the joint inference process, all tasks receive a hit. Also, it is counter-intuitive that the augmented set of features did not outperform in all tasks but in intentions disclosure and interpretation, and just by a small margin. A reason explaining this unexpected behavior is that the majority of enhanced features are already represented in the basic feature set by means of the unigrams and bigrams, and the Framenet and Sentiment features are uninformative or redundant. Lastly, we observe that for interpretation category, none of systems were able to predict the \u201cplaying\u201d class. This is because of the relative size of the number of instances annotated with that value, 1% of the entire dataset. We hypothesize those instances labeled by the annotators, of which a majority agreed on, incorrectly selected the playing category instead of the trolling class, and that, at the interpretation level, one can only expect to reliably differentiate between trolling and trolling."
+    ],
+    [
+      "In this section, we discuss related work in the areas of trolling, bullying and politeness, as they intersect in their scope and at least partially address the problem presented in this work.",
+      " BIBREF19 address the problem of identifying manipulation trolls in news community forums. The major difference with this work is that all their predictions are based on meta-information such as number of votes, dates, number of comments and so on. There is no NLP approach to the problem and their task is limited to identifying trolls. BIBREF0 and BIBREF20 elaborate a deep description of the trolls personality, motivations, effects on the community that trolls interfere and the criminal and psychological aspects of trolls. Their main focus are flaming trolls, but have no NLP insights do not propose and automated prediction tasks as in this work. In a networks related framework BIBREF21 and BIBREF22 present a methodology to identify malicious individuals in a network based solely on the network's properties. Even though they offer present and evaluate a methodology, their focus is different from NLP. BIBREF23 proposes a method that involves NLP components, but fails to provide a evaluation of their system. Finally, BIBREF2 and BIBREF5 address bullying traces. That is self reported events of individuals describing being part of bullying events, but their focus is different from trolling event and the interactions with other participants."
+    ],
+    [
+      "In this paper we address the under-attended problem of trolling in Internet forums. We presented a comprehensive categorization of trolling events and defined a prediction tasks that does not only considers trolling from the troll's perspective but includes the responders to the trolls comment. Also, we evaluated three different models and analyzed their successes and shortcomings. Finally we provide an annotated dataset which we hope will be useful for the research community. We look forward to investigate trolling phenomena in larger conversations, formalize the concepts of changing roles among the participants in trolling events, and improve response strategy performance."
+    ]
+  ]
+}
+```
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diff --git a/qasper-2253/instruction.md b/qasper-2253/instruction.md
new file mode 100644
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+++ b/qasper-2253/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Lingke: A Fine-grained Multi-turn Chatbot for Customer Service
+
+Question: What datasets are used to evaluate the introduced method?
\ No newline at end of file
diff --git a/qasper-2254/instruction.md b/qasper-2254/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..4567c8142290d1e2976536336d570b72cc044c43
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+++ b/qasper-2254/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Lingke: A Fine-grained Multi-turn Chatbot for Customer Service
+
+Question: What are the results achieved from the introduced method?
\ No newline at end of file
diff --git a/qasper-2265/instruction.md b/qasper-2265/instruction.md
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+Name of Paper: Diverse Few-Shot Text Classification with Multiple Metrics
+
+Question: Do they compare with the MAML algorithm?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Methodology",
+    "Robust Task Clustering by Matrix Completion",
+    "Few-Shot Learning with Task Clusters",
+    "Tasks and Data Sets",
+    "Amazon Review Sentiment Classification",
+    "Real-World Tasks: User Intent Classification for Dialog System",
+    "Experiment Setup",
+    "Experimental Results",
+    "Analysis",
+    "Related Work",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Few-shot learning (FSL) BIBREF0 , BIBREF1 , BIBREF2 aims to learn classifiers from few examples per class. Recently, deep learning has been successfully exploited for FSL via learning meta-models from a large number of meta-training tasks. These meta-models can be then used for rapid-adaptation for the target/meta-testing tasks that only have few training examples. Examples of such meta-models include: (1) metric-/similarity-based models, which learn contextual, and task-specific similarity measures BIBREF3 , BIBREF4 , BIBREF5 ; and (2) optimization-based models, which receive the input of gradients from a FSL task and predict either model parameters or parameter updates BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 .",
+      "In the past, FSL has mainly considered image domains, where all tasks are often sampled from one huge collection of data, such as Omniglot BIBREF10 and ImageNet BIBREF4 , making tasks come from a single domain thus related. Due to such a simplified setting, almost all previous works employ a common meta-model (metric-/optimization-based) for all few-shot tasks. However, this setting is far from the realistic scenarios in many real-world applications of few-shot text classification. For example, on an enterprise AI cloud service, many clients submit various tasks to train text classification models for business-specific purposes. The tasks could be classifying customers' comments or opinions on different products/services, monitoring public reactions to different policy changes, or determining users' intents in different types of personal assistant services. As most of the clients cannot collect enough data, their submitted tasks form a few-shot setting. Also, these tasks are significantly diverse, thus a common metric is insufficient to handle all these tasks.",
+      "We consider a more realistic FSL setting where tasks are diverse. In such a scenario, the optimal meta-model may vary across tasks. Our solution is based on the metric-learning approach BIBREF5 and the key idea is to maintain multiple metrics for FSL. The meta-learner selects and combines multiple metrics for learning the target task using task clustering on the meta-training tasks. During the meta-training, we propose to first partition the meta-training tasks into clusters, making the tasks in each cluster likely to be related. Then within each cluster, we train a deep embedding function as the metric. This ensures the common metric is only shared across tasks within the same cluster. Further, during meta-testing, each target FSL task is assigned to a task-specific metric, which is a linear combination of the metrics defined by different clusters. In this way, the diverse few-shot tasks can derive different metrics from the previous learning experience.",
+      "The key of the proposed FSL framework is the task clustering algorithm. Previous works BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 mainly focused on convex objectives, and assumed the number of classes is the same across different tasks (e.g. binary classification is often considered). To make task clustering (i) compatible with deep networks and (ii) able to handle tasks with a various number of labels, we propose a matrix-completion based task clustering algorithm. The algorithm utilizes task similarity measured by cross-task transfer performance, denoted by matrix $\\textbf {S}$ . The $(i,j)$ -entry of $\\textbf {S}$ is the estimated accuracy by adapting the learned representations on the $i$ -th (source) task to the $j$ -th (target) task. We rely on matrix completion to deal with missing and unreliable entries in $\\textbf {S}$ and finally apply spectral clustering to generate the task partitions.",
+      "To the best of our knowledge, our work is the first one addressing the diverse few-shot learning problem and reporting results on real-world few-shot text classification problems. The experimental results show that the proposed algorithm provides significant gains on few-shot sentiment classification and dialog intent classification tasks. It provides positive feedback on the idea of using multiple meta-models (metrics) to handle diverse FSL tasks, as well as the proposed task clustering algorithm on automatically detecting related tasks."
+    ],
+    [
+      "We propose a task-clustering framework to address the diverse few-shot learning problem stated in Section \"Problem Definition\" . We have the FSL algorithm summarized in Algorithm UID12 . Figure 2 gives an overview of our idea. The initial step of the algorithm is a novel task clustering algorithm based on matrix completion, which is described in Section \"Robust Task Clustering by Matrix Completion\" . The few-shot learning method based on task clustering is then introduced in Section \"Problem Definition\"1 ."
+    ],
+    [
+      "Our task clustering algorithm is shown in Algorithm UID13 . The algorithm first evaluates the transfer performance by applying a single-task model $i$ to another task $j$ (Section UID12 ), which will result in a (partially observed) cross-task transfer performance matrix $\\textbf {S}$ . The matrix $\\textbf {S}$ is then cleaned and completed, giving a symmetry task similarity matrix $\\textbf {Y}$ for spectral clustering BIBREF20 .",
+      "Using single-task models, we can compute performance scores $s_{ij}$ by adapting each $\\mathrm {M}_i$ to each task $T_j (j\\ne i)$ . This forms an $n \\times n$ pair-wise classification performance matrix $\\textbf {S}$ , called the transfer-performance matrix. Note that $\\textbf {S}$ is asymmetric since usually $\\textbf {S}_{ij} \\ne \\textbf {S}_{ji}$ .",
+      "[ht] InputInput OutputOutput",
+      "Meta-model $\\mathcal {M} = \\lbrace  C_{1:K}\\ (K\\ \\textrm {task clusters})$ , $\\mathcal {F} = \\left\\lbrace  f_1,f_2, \\cdots , f_K \\right\\rbrace  \\ (K\\ \\textrm {task encoders})\\rbrace $ . One classifier $\\mathrm {M^{\\prime }}_{i}$ for each target task $\\mathrm {T}^{\\prime }$ . Robust Task Clustering: $C_{1:K}$ = RobustTC( $\\mathcal {T}$ , $K$ ) (Algorithm UID13 ) Cluster-Model Training: Train one encoder (multi-task MNet) $f_i$ on each task cluster $C_i$ (Section UID22 ) Few-Shot Learning on Cluster-models: Train a model $\\mathrm {M}_{trg}$ on task $\\mathcal {F} = \\left\\lbrace  f_1,f_2, \\cdots , f_K \\right\\rbrace  \\ (K\\ \\textrm {task encoders})\\rbrace $0 with the method in Section UID23 . RobustTC-FSL: Task Clustering for Few-Shot Learning ",
+      "Ideally, the transfer performance could be estimated by training a MNet on task $i$ and directly evaluating it on task $j$ . However, the limited training data usually lead to generally low transfer performance of single-task MNet. As a result we adopt the following approach to estimate $\\textbf {S}$ :",
+      "We train a CNN classifier (Figure 1 (a)) on task $i$ , then take only the encoder $\\mathrm {M}^{enc}_i$ from $\\mathrm {M}_i$ and freeze it to train a classifier on task $j$ . This gives us a new task $j$ model, and we test this model on $D^{valid}_j$ to get the accuracy as the transfer-performance $\\textbf {S}_{ij}$ . The score shows how the representations learned on task $i$ can be adapted to task $j$ , thus indicating the similarity between tasks.",
+      "In text classification tasks, transferring an encoder with fine-tuned word embeddings from one task to another is difficult as there can be a significant difference between the two vocabularies. Hence, while learning the single-task CNN classifiers, we always make the word embeddings fixed.",
+      "[t] InputInput OutputOutput $K$ task clusters $C_{1:K}$ Learning of Single-Task Models: train single-task models $\\mathrm {M}_i$ for each task $\\mathrm {T}_i$ Evaluation of Transfer-Performance Matrix: get performance matrix $\\mathbf {\\textbf {S}}$ (Section UID12 ) Score Filtering: Filter the uncertain scores in $\\textbf {S}$ and construct the symmetric matrix $\\textbf {Y}$ using Eq. ( 16 ) Matrix Completion: Complete the similar matrix $\\textbf {X}$ from $\\textbf {Y}$ using Eq. ( 18 ) Task Clustering: $C_{1:K}$ =SpectralClustering $C_{1:K}$0 RobustTC: Robust Task Clustering based on Matrix Completion ",
+      "Directly using the transfer performance for task clustering may suffer from both efficiency and accuracy issues. First, evaluation of all entries in the matrix $\\textbf {S}$ involves conducting the source-target transfer learning $O(n^2)$ times, where $n$ is the number of meta-training tasks. For a large number of diverse tasks where the $n$ can be larger than 1,000, evaluation of the full matrix is unacceptable (over 1M entries to evaluate). Second, the estimated cross-task performance (i.e. some $\\textbf {S}_{ij}$ or $\\textbf {S}_{ji}$ scores) is often unreliable due to small data size or label noise. When the number of the uncertain values is large, they can collectively mislead the clustering algorithm to output an incorrect task-partition. To address the aforementioned challenges, we propose a novel task clustering algorithm based on the theory of matrix completion BIBREF21 . Specifically, we deal with the huge number of entries by randomly sample task pairs to evaluate the $\\textbf {S}_{ij}$ and $\\textbf {S}_{ji}$ scores. Besides, we deal with the unreliable entries and asymmetry issue by keeping only task pairs $(i,j)$ with consistent $\\textbf {S}_{ij}$ and $O(n^2)$0 scores. as will be introduced in Eq. ( 16 ). Below, we describe our method in detail.",
+      "First, we use only reliable task pairs to generate a partially-observed similarity matrix $\\textbf {Y}$ . Specifically, if $\\textbf {S}_{ij}$ and $\\textbf {S}_{ji}$ are high enough, then it is likely that tasks $\\lbrace i,j\\rbrace $ belong to a same cluster and share significant information. Conversely, if $\\textbf {S}_{ij}$ and $\\textbf {S}_{ji}$ are low enough, then they tend to belong to different clusters. To this end, we need to design a mechanism to determine if a performance is high or low enough. Since different tasks may vary in difficulty, a fixed threshold is not suitable. Hence, we define a dynamic threshold using the mean and standard deviation of the target task performance, i.e., $\\mu _j = \\text{mean}(\\textbf {S}_{:j})$ and $\\sigma _j=\\text{std}(\\textbf {S}_{:j})$ , where $\\textbf {S}_{:j}$ is the $j$ -th column of $\\textbf {S}_{ij}$0 . We then introduce two positive parameters $\\textbf {S}_{ij}$1 and $\\textbf {S}_{ij}$2 , and define high and low performance as $\\textbf {S}_{ij}$3 greater than $\\textbf {S}_{ij}$4 or lower than $\\textbf {S}_{ij}$5 , respectively. When both $\\textbf {S}_{ij}$6 and $\\textbf {S}_{ij}$7 are high and low enough, we set their pairwise similarity as 1 and 0, respectively. Other task pairs are treated as uncertain task pairs and are marked as unobserved, and don't influence our clustering method. This leads to a partially-observed symmetric matrix $\\textbf {S}_{ij}$8 , i.e., ",
+      "$$\\small \\textbf {Y}_{ij}\\mathrm {=}\\textbf {Y}_{ji}\\mathrm {=}\\left\\lbrace \n\\begin{array}{ll}\n{2}{*}{1} & \\text{if}\\ \\ \\textbf {S}_{ij} > \\mu _j + p_1 \\sigma _j\\ \\ \\\\\n&\\text{and}\\ \\ \\textbf {S}_{ji} > \\mu _i + p_1 \\sigma _i\\\\\n{2}{*}{0} & \\text{if}\\ \\ \\textbf {S}_{ij} < \\mu _j - p_2 \\sigma _j\\ \\ \\\\\n&\\text{and}\\ \\ \\textbf {S}_{ji} < \\mu _i - p_2 \\sigma _i\\\\\n\\mathrm {unobserved} & \\mathrm {otherwise}\n\\end{array}\n\\right. $$   (Eq. 16) ",
+      "Given the partially observed matrix $\\textbf {Y}$ , we then reconstruct the full similarity matrix $\\textbf {X}\\in \\mathbb {R}^{n\\times n}$ . We first note that the similarity matrix $\\textbf {X}$ should be of low-rank (proof deferred to appendix). Additionally, since the observed entries of $\\textbf {Y}$ are generated based on high and low enough performance, it is safe to assume that most observed entries are correct and only a few may be incorrect. Therefore, we introduce a sparse matrix $\\textbf {E}$ to capture the observed incorrect entries in $\\textbf {Y}$ . Combining the two observations, $\\textbf {Y}$ can be decomposed into the sum of two matrices $\\textbf {X}$ and $\\textbf {E}$ , where $\\textbf {X}$ is a low rank matrix storing similarities between task pairs, and $\\textbf {X}\\in \\mathbb {R}^{n\\times n}$0 is a sparse matrix that captures the errors in $\\textbf {X}\\in \\mathbb {R}^{n\\times n}$1 . The matrix completion problem can be cast as the following convex optimization problem: ",
+      "$$&\\min \\limits _{\\textbf {X},\\ \\textbf {E}} & \\Vert \\textbf {X}\\Vert _* + \\lambda \\Vert \\textbf {E}\\Vert _1\\\\ \n& \\mbox{s.t.}& \\textbf {P}_{\\Omega }(\\textbf {X}+\\textbf {E}) = \\textbf {P}_{\\Omega }(\\textbf {Y}), \\nonumber $$   (Eq. 18) ",
+      "where $\\Vert \\circ \\Vert _*$ denotes the matrix nuclear norm, the convex surrogate of rank function. $\\Omega $ is the set of observed entries in $\\textbf {Y}$ , and $\\textbf {P}_{\\Omega }:\\mathbb {R}^{n\\times n} \\mapsto \\mathbb {R}^{n\\times n}$ is a matrix projection operator defined as ",
+      "$$[\\textbf {P}_{\\Omega }(\\textbf {A})]_{ij} = \\left\\lbrace \n\\begin{array}{ll}\n\\textbf {A}_{ij} & \\text{if}\\ (i,j) \\in \\Omega \\nonumber \\\\\n0 & \\mbox{otherwise}\\nonumber \\end{array}\n\\right. $$   (Eq. 19) ",
+      "Finally, we apply spectral clustering on the matrix $\\textbf {X}$ to get the task clusters.",
+      "In the Appendix A, we show a Theorem as well as its proof, implying that under mild conditions, the problem ( 18 ) can perfectly recover the underlying similarity matrix $\\textbf {X}^*$ if the number of observed correct entries is at least $O(n \\log ^2 n)$ . This theoretical guarantee implies that for a large number $n$ of training tasks, only a tiny fraction of all task pairs is needed to reliably infer similarities over all task pairs."
+    ],
+    [
+      "For each cluster $C_k$ , we train a multi-task MNet model (Figure 1 (b)) with all tasks in that cluster to encourage parameter sharing. The result, denoted as $f_k$ is called the cluster-encoder of cluster $C_k$ . The $k$ -th metric of the cluster is thus $\\Lambda (x_1,x_2)=f_k(x_1)^{\\intercal }f_k(x_2)$ .",
+      "To build a predictor $\\mathrm {M}$ with access to only a limited number of training samples, we make the prediction probability by linearly combining prediction from learned cluster-encoders: ",
+      "$$p(y|x) = \\sum _k \\alpha _k P(y|x; f_k).$$   (Eq. 24) ",
+      " where $f_k$ is the learned (and frozen) encoder of the $k$ -th cluster, $\\lbrace \\alpha _{k}\\rbrace _{k=1}^{K}$ are adaptable parameters trained with few-shot training examples. And the predictor $P(y|x; f_k)$ from each cluster is ",
+      "$$\\small P(y=y_l|x;f_k) = \\frac{\\exp \\left\\lbrace  f_k (x_l)^{\\intercal }f_k (x) \\right\\rbrace  }{\\sum _{i} \\exp \\left\\lbrace  f_k (x_{i})^{\\intercal }f_k (x) \\right\\rbrace  }$$   (Eq. 25) ",
+      " $x_{l}$ is the corresponding training sample of label $y_{l}$ .",
+      "End-to-end joint optimization on training data becomes a popular methodology for deep learning systems, but it is not directly applicable to diverse FSL. One main reason is that deep networks could easily fit any task partitions if we optimize on training loss only, making the learned metrics not generalize, as discussed in Section \"Related Work\" . As a result, this work adopts a pipeline training approach and employing validation sets for task clustering. Combining reinforcement learning with meta-learning could be a potential solution to enable an end-to-end training for future work."
+    ],
+    [
+      "We test our methods by conducting experiments on two text classification data sets. We used NLTK toolkit for tokenization. The task are divided into meta-training tasks and meta-testing tasks (target tasks), where the meta-training tasks are used for clustering and cluster-encoder training. The meta-testing tasks are few-shot tasks, which are used for evaluating the method in Eq. ( 24 )."
+    ],
+    [
+      "First, following BIBREF14 , we construct multiple tasks with the multi-domain sentiment classification BIBREF22 data set. The dataset consists of Amazon product reviews for 23 types of products (see Appendix D for the details). For each product domain, we construct three binary classification tasks with different thresholds on the ratings: the tasks consider a review as positive if it belongs to one of the following buckets $=5$ stars, $>=4$ stars or $>=2$ stars. These buckets then form the basis of the task-setup, giving us 23 $\\times $ 3 $=$ 69 tasks in total. For each domain we distribute the reviews uniformly to the 3 tasks. For evaluation, we select 12 (4 $\\times $ 3) tasks from 4 domains (Books, DVD, Electronics, Kitchen) as the meta-testing (target) tasks out of all 23 domains. For the target tasks, we create 5-shot learning problems."
+    ],
+    [
+      "The second dataset is from an online service which trains and serves intent classification models to various clients. The dataset comprises recorded conversations between human users and dialog systems in various domains, ranging from personal assistant to complex service-ordering or customer-service request scenarios. During classification, intent-labels are assigned to user utterances (sentences). We use a total of 175 tasks from different clients, and randomly sample 10 tasks from them as our target tasks. For each meta-training task, we randomly sample 64% data into a training set, 16% into a validation set, and use the rest as the test set. The number of labels for these tasks varies a lot (from 2 to 100, see Appendix D for details), making regular $k$ -shot settings not essentially limited-resource problems (e.g., 5-shot on 100 classes will give a good amount of 500 training instances). Hence, to adapt this to a FSL scenario, for target tasks we keep one example for each label (one-shot), plus 20 randomly picked labeled examples to create the training data. We believe this is a fairly realistic estimate of labeled examples one client could provide easily.",
+      "Our matrix-completion method could handle a large number of tasks via task-pair sampling. However, the sizes of tasks in the above two few-shot learning datasets are not too huge, so evaluation of the whole task-similarity matrix is still tractable. In our experiments, the incomplete matrices mainly come from the score-filtering step (see Eq. 16 ). Thus there is limited randomness involved in the generation of task clusters.",
+      "To strengthen the conclusion, we evaluate our algorithm on an additional dataset with a much larger number of tasks. The results are reported in the multi-task learning setting instead of the few-shot learning setting focused in this paper. Therefore we put the results to a non-archive version of this paper for further reference."
+    ],
+    [
+      "We compare our method to the following baselines: (1) Single-task CNN: training a CNN model for each task individually; (2) Single-task FastText: training one FastText model BIBREF23 with fixed embeddings for each individual task; (3) Fine-tuned the holistic MTL-CNN: a standard transfer-learning approach, which trains one MTL-CNN model on all the training tasks offline, then fine-tunes the classifier layer (i.e. $\\mathrm {M}^{(cls)}$ Figure 1 (a)) on each target task; (4) Matching Network: a metric-learning based few-shot learning model trained on all training tasks; (5) Prototypical Network: a variation of matching network with different prediction function as Eq. 9 ; (6) Convex combining all single-task models: training one CNN classifier on each meta-training task individually and taking the encoder, then for each target task training a linear combination of all the above single-task encoders with Eq. ( 24 ). This baseline can be viewed as a variation of our method without task clustering. We initialize all models with pre-trained 100-dim Glove embeddings (trained on 6B corpus) BIBREF24 .",
+      "In all experiments, we set both $p_1$ and $p_2$ parameters in ( 16 ) to $0.5$ . This strikes a balance between obtaining enough observed entries in $\\textbf {Y}$ , and ensuring that most of the retained similarities are consistent with the cluster membership. The window/hidden-layer sizes of CNN and the initialization of embeddings (random or pre-trained) are tuned during the cluster-encoder training phase, with the validation sets of meta-training tasks. We have the CNN with window size of 5 and 200 hidden units. The single-metric FSL baselines have 400 hidden units in the CNN encoders. On sentiment classification, all cluster-encoders use random initialized word embeddings for sentiment classification, and use Glove embeddings as initialization for intent classification, which is likely because the training sets of the intent tasks are usually small.",
+      "Since all the sentiment classification tasks are binary classification based on our dataset construction. A CNN classifier with binary output layer can be also trained as the cluster-encoder for each task cluster. Therefore we compared CNN classifier, matching network, and prototypical network on Amazon review, and found that CNN classifier performs similarly well as prototypical network. Since some of the Amazon review data is quite large which involves further difficulty on the computation of supporting sets, we finally use binary CNN classifiers as cluster-encoders in all the sentiment classification experiments.",
+      "Selection of the learning rate and number of training epochs for FSL settings, i.e., fitting $\\alpha $ s in Eq. ( 24 ), is more difficult since there is no validation data in few-shot problems. Thus we pre-select a subset of meta-training tasks as meta-validation tasks and tune the two hyper-parameters on the meta-validation tasks."
+    ],
+    [
+      "Table 1 shows the main results on (i) the 12 few-shot product sentiment classification tasks by leveraging the learned knowledge from the 57 previously observed tasks from other product domains; and (ii) the 10 few-shot dialog intent classification tasks by leveraging the 165 previously observed tasks from other clients' data.",
+      "Due to the limited training resources, all the supervised-learning baselines perform poorly. The two state-of-the-art metric-based FSL approaches, matching network (4) and prototypical network (5), do not perform better compared to the other baselines, since the single metric is not sufficient for all the diverse tasks. On intent classification where tasks are further diverse, all the single-metric or single-model methods (3-5) perform worse compared to the single-task CNN baseline (1). The convex combination of all the single training task models is the best performing baseline overall. However, on intent classification it only performs on par with the single-task CNN (1), which does not use any meta-learning or transfer learning techniques, mainly for two reasons: (i) with the growth of the number of meta-training tasks, the model parameters grow linearly, making the number of parameters (165 in this case) in Eq.( 24 ) too large for the few-shot tasks to fit; (ii) the meta-training tasks in intent classification usually contain less training data, making the single-task encoders not generalize well.",
+      "In contrast, our RobustTC-FSL gives consistently better results compared to all the baselines. It outperforms the baselines in previous work (1-5) by a large margin of more than 6% on the sentiment classification tasks, and more than 3% on the intent classification tasks. It is also significantly better than our proposed baseline (6), showing the advantages of the usage of task clustering.",
+      "Although the RobustTC-FSL improves over baselines on intent classification, the margin is smaller compared to that on sentiment classification, because the intent classification tasks are more diverse in nature. This is also demonstrated by the training accuracy on the target tasks, where several tasks fail to find any cluster that could provide a metric that suits their training examples. To deal with this problem, we propose an improved algorithm to automatically discover whether a target task belongs to none of the task-clusters. If the task doesn't belong to any of the clusters, it cannot benefit from any previous knowledge thus falls back to single-task CNN. The target task is treated as \u201cout-of-clusters\u201d when none of the clusters could achieve higher than 20% accuracy (selected on meta-validation tasks) on its training data. We call this method Adaptive RobustTC-FSL, which gives more than 5% performance boost over the best RobustTC-FSL result on intent classification. Note that the adaptive approach makes no difference on the sentiment tasks, because they are more closely related so re-using cluster-encoders always achieves better results compared to single-task CNNs."
+    ],
+    [
+      "Figure 3 shows the effect of cluster numbers on the two tasks. RobustTC achieves best performance with 5 clusters on sentiment analysis (SA) and 20 clusters on intent classification (Intent). All clustering results significantly outperform the single-metric baselines (#cluster=1 in the figure).",
+      "Compared to previous task clustering algorithms, our RobustTC is the only one that can cluster tasks with varying numbers of class labels (e.g. in intent classification tasks). Moreover, we show that even in the setting of all binary classifications tasks (e.g. the sentiment-analysis tasks) that previous task clustering research work on, our RobustTC is still slightly better for the diverse FSL problems. Figure 3 compares with a state-of-the-art logistic regression based task clustering method (ASAP-MT-LR) BIBREF14 . Our RobustTC clusters give slightly better FSL performance (e.g. 83.12 vs. 82.65 when #cluster=5).",
+      "The top rows of Table 2 shows the ten clusters used to generate the sentiment classification results in Figure 3 . From the results, we can see that tasks with same thresholds are usually grouped together; and tasks in similar domains also tend to appear in the same clusters, even the thresholds are slightly different (e.g. t2 vs t4 and t4 vs t5).",
+      "The bottom of the table shows the weights $\\alpha $ s in Eq.( 24 ) for the target tasks with the largest improvement. It confirms that our RobustTC-FSL algorithm accurately adapts multiple metrics for the target tasks."
+    ],
+    [
+      "Few Shot Learning FSL BIBREF0 , BIBREF1 , BIBREF2 aims to learn classifiers for new classes with only a few training examples per class. Recent deep learning based FSL approaches mainly fall into two categories: (1) metric-based approaches BIBREF3 , BIBREF4 , BIBREF5 , which aims to learn generalizable metrics and corresponding matching functions from multiple training tasks. These approaches essentially learn one metric for all tasks, which is sub-optimal when the tasks are diverse. (2) optimization-based approaches BIBREF6 , BIBREF7 , BIBREF8 , which aims to learn to optimize model parameters (by either predicting the parameter updates or directly predicting the model parameters) given the gradients computed from few-shot examples.",
+      "Previous FSL research usually adopts the $k$ -shot, $N$ -way setting, where all the few-shot tasks have the same number of $N$ class labels, and each label has $k$ training instances. Moreover, these few-shot tasks are usually constructed by sampling from one huge dataset, thus all the tasks are guaranteed to be related to each other. However, in real-world applications, the few-shot learning tasks could be diverse: there are different tasks with varying number of class labels and they are not guaranteed to be related to each other. As a result, a single meta-model or metric-model is usually not sufficient to handle all the few-shot tasks.",
+      "Task Clustering Previous task clustering methods measure the task relationships in terms of similarities among single-task model parameters BIBREF11 , BIBREF12 ; or jointly assign task clusters and train model parameters for each cluster to minimize the overall training loss BIBREF13 , BIBREF14 , BIBREF25 . These methods usually work on convex models but do not fit the deep networks, mainly because of (i) the parameters of deep networks are very high-dimensional and their similarities are not necessarily related to the functional similarities; and (ii) deep networks have flexible representation power so they may overfit to arbitrary cluster assignment if we consider training loss alone. Moreover, these methods require identical class label sets across different tasks, which does not hold in most of the realistic settings."
+    ],
+    [
+      "We propose a few-shot learning approach for diverse tasks based on task clustering. The proposed method can use multiple metrics, and performs significantly better compared to previous single-metric methods when the few-shot tasks come from diverse domains. Future work includes applying the task-clustering idea to other FSL algorithms BIBREF6 , BIBREF8 , BIBREF26 , and exploring more advanced composition methods of cluster-encoders beyond linear combination BIBREF27 , BIBREF28 ."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: Directions in Abusive Language Training Data: Garbage In, Garbage Out
+
+Question: Have any baseline model been trained on this abusive language dataset?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Background",
+    "Analysis of training datasets",
+    "Analysis of training datasets ::: The purpose of training datasets ::: Problems addressed by datasets",
+    "Analysis of training datasets ::: The purpose of training datasets ::: Uses of datasets: How detection tasks are defined",
+    "Analysis of training datasets ::: The purpose of training datasets ::: Uses of datasets: How detection tasks are defined ::: Detection tasks: the nature of abuse",
+    "Analysis of training datasets ::: The purpose of training datasets ::: Uses of datasets: How detection tasks are defined ::: Detection tasks: Granularity of taxonomies",
+    "Analysis of training datasets ::: The content of training datasets ::: The `Level' of content",
+    "Analysis of training datasets ::: The content of training datasets ::: Language",
+    "Analysis of training datasets ::: The content of training datasets ::: Source of data",
+    "Analysis of training datasets ::: The content of training datasets ::: Size",
+    "Analysis of training datasets ::: The content of training datasets ::: Class distribution and sampling",
+    "Analysis of training datasets ::: The content of training datasets ::: Identity of the content creators",
+    "Analysis of training datasets ::: Annotation of training datasets ::: Annotation process",
+    "Analysis of training datasets ::: Annotation of training datasets ::: Identity of the annotators",
+    "Analysis of training datasets ::: Annotation of training datasets ::: Guidelines for annotation",
+    "Analysis of training datasets ::: Annotation of training datasets ::: Guidelines for annotation ::: Irony",
+    "Analysis of training datasets ::: Annotation of training datasets ::: Guidelines for annotation ::: Calumniation",
+    "Analysis of training datasets ::: Annotation of training datasets ::: Guidelines for annotation ::: Intent",
+    "Dataset sharing ::: The challenges and opportunities of achieving Open Science",
+    "Dataset sharing ::: Research infrastructure: Solutions for sharing training datasets",
+    "Dataset sharing ::: A new repository of training datasets: Hatespeechdata.com",
+    "Best Practices for training dataset creation",
+    "Best Practices for training dataset creation ::: Task formation: Defining the task addressed by the dataset",
+    "Best Practices for training dataset creation ::: Selecting data for abusive language annotation",
+    "Best Practices for training dataset creation ::: Annotating abusive language",
+    "Best Practices for training dataset creation ::: Documenting methods, data, and annotators",
+    "Best Practices for training dataset creation ::: Best practice summary",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Abusive online content, such as hate speech and harassment, has received substantial attention over the past few years for its malign social effects. Left unchallenged, abusive content risks harminng those who are targeted, toxifying public discourse, exacerbating social tensions and could lead to the exclusion of some groups from public spaces. As such, systems which can accurately detect and classify online abuse at scale, in real-time and without bias are of central interest to tech companies, policymakers and academics.",
+      "Most detection systems rely on having the right training dataset, reflecting one of the most widely accepted mantras in computer science: Garbage In, Garbage Out. Put simply: to have systems which can detect and classify abusive online content effectively, one needs appropriate datasets with which to train them. However, creating training datasets is often a laborious and non-trivial task \u2013 and creating datasets which are non-biased, large and theoretically-informed is even more difficult (BIBREF0 p. 189). We address this issue by examining and reviewing publicly available datasets for abusive content detection, which we provide access to on a new dedicated website, hatespeechdata.com.",
+      "In the first section, we examine previous reviews and present the four research aims which guide this paper. In the second section, we conduct a critical and in-depth analysis of the available datasets, discussing first what their aim is, how tasks have been described and what taxonomies have been constructed and then, second, what they contain and how they were annotated. In the third section, we discuss the challenges of open science in this research area and elaborates different ways of sharing training datasets, including the website hatespeechdata.com In the final section, we draw on our findings to establish best practices when creating datasets for abusive content detection."
+    ],
+    [
+      "The volume of research examining the social and computational aspects of abusive content detection has expanded prodigiously in the past five years. This has been driven by growing awareness of the importance of the Internet more broadly BIBREF1, greater recognition of the harms caused by online abuse BIBREF2, and policy and regulatory developments, such as the EU's Code of Conduct on Hate, the UK Government's `Online Harms' white paper BIBREF3, Germany's NetzDG laws, the Public Pledge on Self-Discipline for the Chinese Internet Industry, and France's anti-hate regulation BIBREF2. In 2020 alone, three computer science venues will host workshops on online hate (TRAC and STOC at LREC, and WOAH at EMNLP), and a shared task at 2019's SemEval on online abuse detection reports that 800 teams downloaded the training data and 115 submitted detection systems BIBREF4. At the same time, social scientific interventions have also appeared, deepening our understanding of how online abuse spreads BIBREF5 and how its harmful impact can be mitigated and challenged BIBREF6.",
+      "All analyses of online abuse ultimately rely on a way of measuring it, which increasingly means having a method which can handle the sheer volume of content produced, shared and engaged with online. Traditional qualitative methods cannot scale to handle the hundreds of millions of posts which appear on each major social media platform every day, and can also introduce inconsistencies and biase BIBREF7. Computational tools have emerged as the most promising way of classifying and detecting online abuse, drawing on work in machine learning, Natural Language Processing (NLP) and statistical modelling. Increasingly sophisticated architectures, features and processes have been used to detect and classify online abuse, leveraging technically sophisticated methods, such as contextual word embeddings, graph embeddings and dependency parsing. Despite their many differences BIBREF8, nearly all methods of online abuse detection rely on a training dataset, which is used to teach the system what is and is not abuse. However, there is a lacuna of research on this crucial aspect of the machine learning process. Indeed, although several general reviews of the field have been conducted, no previous research has reviewed training datasets for abusive content detection in sufficient breadth or depth. This is surprising given (i) their fundamental importance in the detection of online abuse and (ii) growing awareness that several existing datasets suffer from many flaws BIBREF9, BIBREF10. Close relevant work includes:",
+      "Schmidt and Wiegand conduct a comprehensive review of research into the detection and classification of abusive online content. They discuss training datasets, stating that `to perform experiments on hate speech detection, access to labelled corpora is essential' (BIBREF8, p. 7), and briefly discuss the sources and size of the most prominent existing training datasets, as well as how datasets are sampled and annotated. Schmidt and Wiegand identify two key challenges with existing datasets. First, `data sparsity': many training datasets are small and lack linguistic variety. Second, metadata (such as how data was sampled) is crucial as it lets future researchers understand unintended biases, but is often not adequately reported (BIBREF8, p. 6).",
+      "Waseem et al.BIBREF11 outline a typology of detection tasks, based on a two-by-two matrix of (i) identity- versus person- directed abuse and (ii) explicit versus implicit abuse. They emphasise the importance of high-quality datasets, particularly for more nuanced expressions of abuse: `Without high quality labelled data to learn these representations, it may be difficult for researchers to come up with models of syntactic structure that can help to identify implicit abuse.' (BIBREF11, p. 81)",
+      "Jurgens et al.BIBREF12 discuss also conduct a critical review of hate speech detection, and note that `labelled ground truth data for building and evaluating classifiers is hard to obtain because platforms typically do not share moderated content due to privacy, ethical and public relations concerns.' (BIBREF12, p. 3661) They argue that the field needs to `address the data scarcity faced by abuse detection research' in order to better address more complex rsearch issues and pressing social challenges, such as `develop[ing] proactive technologies that counter or inhibit abuse before it harms' (BIBREF12, pp. 3658, 3661).",
+      "Vidgen et al. describe several limitations with existing training datasets for abusive content, most noticeably how `they contain systematic biases towards certain types and targets of abuse.' BIBREF13[p.2]. They describe three issues in the quality of datasets: degradation (whereby datasets decline in quality over time), annotation (whereby annotators often have low agreement, indicating considerable uncertainty in class assignments) and variety (whereby `The quality, size and class balance of datasets varies considerably.' [p. 6]).",
+      "Chetty and AlathurBIBREF14 review the use of Internet-based technologies and online social networks to study the spread of hateful, offensive and extremist content BIBREF14. Their review covers both computational and legal/social scientific aspects of hate speech detection, and outlines the importance of distinguishing between different types of group-directed prejudice. However, they do not consider training datasets in any depth.",
+      "Fortuna and NunesBIBREF15 provide an end-to-end review of hate speech research, including the motivations for studying online hate, definitional challenges, dataset creation/sharing, and technical advances, both in terms of feature selection and algorithmic architecture (BIBREF15, 2018). They delineate between different types of online abuse, including hate, cyberbullying, discrimination and flaming, and add much needed clarity to the field. They show that (1) dataset size varies considerably but they are generally small (mostly containing fewer than 10,000 entries), (2) Twitter is the most widely-studied platform, and (3) most papers research hate speech per se (i.e. without specifying a target). Of those which do specify a target, racism and sexism are the most researched. However, their review focuses on publications rather than datasets: the same dataset might be used in multiple studies, limiting the relevance of their review for understanding the intrinsic role of training datasets. They also only engage with datasets fairly briefly, as part of a much broader review.",
+      "Several classification papers also discuss the most widely used datasets, including Davidson et al. BIBREF16 who describe five datasets, and Salminen et al. who review 17 datasets and describe four in detail BIBREF17.",
+      "This paper addresses this lacuna in existing research, providing a systematic review of available training datasets for online abuse. To provide structure to this review, we adopt the `data statements' framework put forward by Bender and Friedman BIBREF18, as well as other work providing frameworks, schema and processes for analysing NLP artefacts BIBREF19, BIBREF20, BIBREF21. Data statements are a way of documenting the decisions which underpin the creation of datasets used for Natural Language Processing (NLP). They formalise how decisions should be documented, not only ensuring scientific integrity but also addressing `the open and urgent question of how we integrate ethical considerations in the everyday practice of our field' (BIBREF18, p. 587). In many cases, we find that it is not possible to fully recreate the level of detail recorded in an original data statement from how datasets are described in publications. This reinforces the importance of proper documentation at the point of dataset creation.",
+      "As the field of online abusive content detection matures, it has started to tackle more complex research challenges, such as multi-platform, multi-lingual and multi-target abuse detection, and systems are increasingly being deployed in `the wild' for social scientific analyses and for content moderation BIBREF5. Such research heightens the focus on training datasets as exactly what is being detected comes under greater scrutiny. To enhance our understanding of this domain, our review paper has four research aims.",
+      "Research Aim One: to provide an in-depth and critical analysis of the available training datasets for abusive online content detection.",
+      "Research Aim Two: to map and discuss ways of addressing the lack of dataset sharing, and as such the lack of `open science', in the field of online abuse research.",
+      "Research Aim Three: to introduce the website hatespeechdata.com, as a way of enabling more dataset sharing.",
+      "Research Aim Four: to identify best practices for creating an abusive content training dataset."
+    ],
+    [
+      "Relevant publications have been identified from four sources to identify training datasets for abusive content detection:",
+      "The Scopus database of academic publications, identified using keyword searches.",
+      "The ACL Anthology database of NLP research papers, identified using keyword searches.",
+      "The ArXiv database of preprints, identified using keyword searches.",
+      "Proceedings of the 1st, 2nd and 3rd workshops on abusive language online (ACL).",
+      "Most publications report on the creation of one abusive content training dataset. However, some describe several new datasets simultaneously or provide one dataset with several distinct subsets of data BIBREF22, BIBREF23, BIBREF24, BIBREF25. For consistency, we separate out each subset of data where they are in different languages or the data is collected from different platforms. As such, the number of datasets is greater than the number publications. All of the datasets were released between 2016 and 2019, as shown in Figure FIGREF17."
+    ],
+    [
+      "Creating a training dataset for online abuse detection is typically motivated by the desire to address a particular social problem. These motivations can inform how a taxonomy of abusive language is designed, how data is collected and what instructions are given to annotators. We identify the following motivating reasons, which were explicitly referenced by dataset creators.",
+      "Reducing harm: Aggressive, derogatory and demeaning online interactions can inflict harm on individuals who are targeted by such content and those who are not targeted but still observe it. This has been shown to have profound long-term consequences on individuals' well-being, with some vulnerable individuals expressing concerns about leaving their homes following experiences of abuse BIBREF26. Accordingly, many dataset creators state that aggressive language and online harassment is a social problem which they want to help address",
+      "Removing illegal content: Many countries legislate against certain forms of speech, e.g. direct threats of violence. For instance, the EU's Code of Conduct requires that all content that is flagged for being illegal online hate speech is reviewed within 24 hours, and removed if necessary BIBREF27. Many large social media platforms and tech companies adhere to this code of conduct (including Facebook, Google and Twitter) and, as of September 2019, 89% of such content is reviewed in 24 hours BIBREF28. However, we note that in most cases the abuse that is marked up in training datasets falls short of the requirements of illegal online hate \u2013 indeed, as most datasets are taken from public API access points, the data has usually already been moderated by the platforms and most illegal content removed.",
+      "Improving health of online conversations: The health of online communities can be severely affected by abusive language. It can fracture communities, exacerbate tensions and even repel users. This is not only bad for the community and for civic discourse in general, it also negatively impacts engagement and thus the revenue of the host platforms. Therefore, there is a growing impetus to improve user experience and ensure online dialogues are healthy, inclusive and respectful where possible. There is ample scope for improvement: a study showed that 82% of personal attacks on Wikipedia against other editors are not addressed BIBREF29. Taking steps to improve the health of exchanges in online communities will also benefit commercial and voluntary content moderators. They are routinely exposed to such content, often with insufficient safeugards, and sometimes display symptoms similar to those of PTSD BIBREF30. Automatic tools could help to lessen this exposure, reducing the burden on moderators."
+    ],
+    [
+      "Myriad tasks have been addressed in the field of abusive online content detection, reflecting the different disciplines, motivations and assumptions behind research. This has led to considerable variation in what is actually detected under the rubric of `abusive content', and establishing a degree of order over the diverse categorisations and subcategorisations is both difficult and somewhat arbitrary. Key dimensions which dataset creators have used to categorise detection tasks include who/what is targeted (e.g. groups vs. individuals), the strength of content (e.g. covert vs. overt), the nature of the abuse (e.g. benevolent vs. hostile sexism BIBREF31), how the abuse manifests (e.g. threats vs. derogatory statements), the tone (e.g. aggressive vs. non-aggressive), the specific target (e.g. ethnic minorities vs. women),and the subjective perception of the reader (e.g. disrespectful vs. respectful). Other important dimensions include the theme used to express abuse (e.g. Islamophobia which relies on tropes about terrorism vs. tropes about sexism) and the use of particular linguistic devices, such as appeals to authority, sincerity and irony. All of these dimensions can be combined in different ways, producing a large number of intersecting tasks.",
+      "Consistency in how tasks are described will not necessarily ensure that datasets can be used interchangeably. From the description of a task, an annotation framework must be developed which converts the conceptualisation of abuse into a set of standards. This formalised representation of the `abuse' inevitably involves shortcuts, imperfect rules and simplifications. If annotation frameworks are developed and applied differently, then even datasets aimed at the same task can still vary considerably. Nonetheless, how detection tasks for online abuse are described is crucial for how the datasets \u2013 and in turn the systems trained on them \u2013 can subsequently be used. For example, a dataset annotated for hate speech can be used to examine bigoted biases, but the reverse is not true. How datasets are framed also impacts whether, and how, datasets can be combined to form large `mega-datasets' \u2013 a potentially promising avenue for overcoming data sparsity BIBREF17.",
+      "In the remainder of this section, we provide a framework for splitting out detection tasks along the two most salient dimensions: (1) the nature of abuse and (2) the granularity of the taxonomy."
+    ],
+    [
+      "This refers to what is targeted/attacked by the content and, subsequently, how the taxonomy has been designed/framed by the dataset creators. The most well-established taxonomic distinction in this regard is the difference between (i) the detection of interpersonal abuse, and (ii) the detection of group-directed abuse BIBREF11). Other authors have sought to deductively theorise additional categories, such as `concept-directed' abuse, although these have not been widely adopted BIBREF13. Through an inductive investigation of existing training datasets, we extend this binary distinction to four primary categories of abuse which have been studied in previous work, as well as a fifth `Mixed' category.",
+      "Person-directed abuse. Content which directs negativity against individuals, typically through aggression, insults, intimidation, hostility and trolling, amongst other tactics. Most research falls under the auspices of `cyber bullying', `harassment' and `trolling' BIBREF23, BIBREF32, BIBREF33. One major dataset of English Wikipedia editor comments BIBREF29 focuses on the `personal attack' element of harassment, drawing on prior investigations that mapped out harassment in that community. Another widely used dataset focuses on trolls' intent to intimidate, distinguishing between direct harassment and other behaviours BIBREF34. An important consideration in studies of person-directed abuse is (a) interpersonal relations, such as whether individuals engage in patterns of abuse or one-off acts and whether they are known to each other in the `real' world (both of which are a key concern in studies of cyberbullying) and (b) standpoint, such as whether individuals directly engage in abuse themselves or encourage others to do so. For example, the theoretically sophisticated synthetic dataset provided by BIBREF33 identifies not only harassment but also encouragement to harassment. BIBREF22 mark up posts from computer game forums (World of Warcraft and League of Legends) for cyberbullying and annotate these as $\\langle $offender, victim, message$\\rangle $ tuples.",
+      "Group-directed abuse. Content which directs negativity against a social identity, which is defined in relation to a particular attribute (e.g. ethnic, racial, religious groups)BIBREF35. Such abuse is often directed against marginalised or under-represented groups in society. Group-directed abuse is typically described as `hate speech' and includes use of dehumanising language, making derogatory, demonising or hostile statements, making threats, and inciting others to engage in violence, amongst other dangerous communications. Common examples of group-directed abuse include sexism, which is included in datasets provided by BIBREF36, BIBREF37, BIBREF38, BIBREF39, BIBREF33 and racism, which is directly targeted in BIBREF36, BIBREF40. In some cases, specific types of group-directed abuse are subsumed within a broader category of identity-directed abuse, as in BIBREF41, BIBREF42, BIBREF4. Determining the limits of any group-directed abuse category requires careful theoretical reflection, as with the decision to include ethnic, caste-based and certain religious prejudices under `racism'. There is no `right' answer to such questions as they engage with ontological concerns about identification and `being' and the politics of categorization.",
+      "Flagged content. Content which is reported by community members or assessed by community and professional content moderators. This covers a broad range of focuses as moderators may also remove spam, sexually inappropriate content and other undesirable contributions. In this regard, `flagged' content is akin to the concept of `trolling', which covers a wide range of behaviours, from jokes and playful interventions through to sinister personal attacks such as doxxing BIBREF43. Some forms of trolling can be measured with tools such as the Global Assessment of Internet Trolling (GAIT) BIBREF43.",
+      "Incivility. Content which is considered to be incivil, rude, inappropriate, offensive or disrespectful BIBREF24, BIBREF25, BIBREF44. Such categories are usually defined with reference to the tone that the author adopts rather than the substantive content of what they express, which is the basis of person- and group- directed categories. Such content usually contains obscene, profane or otherwise `dirty' words. This can be easier to detect as closed-class lists are effective at identifying single objectionable words (e.g. BIBREF45). However, one concern with this type of research is that the presence of `dirty' words does not necessarily signal malicious intent or abuse; they may equally be used as intensifiers or colloquialisms BIBREF46. At the same time, detecting incivility can be more difficult as it requires annotators to infer the subjective intent of the speaker or to understand (or guess) the social norms of a setting and thus whether disrespect has been expressed BIBREF42. Content can be incivil without directing hate against a group or person, and can be inappropriate in one setting but not another: as such it tends to be more subjective and contextual than other types of abusive language.",
+      "Mixed. Content which contains multiple types of abuse, usually a combination of the four categories discussed above. The intersecting nature of online language means that this is common but can also manifest in unexpected ways. For instance, female politicians may receive more interpersonal abuse than other politicians. This might not appear as misogyny because their identity as women is not referenced \u2013 but it might have motivated the abuse they were subjected to. Mixed forms of abuse require further research, and have thus far been most fully explored in the OLID dataset provided by BIBREF4, who explore several facets of abuse under one taxonomy."
+    ],
+    [
+      "This refers to how much detail a taxonomy contains, reflected in the number of unique classes. The most important and widespread distinction is whether a binary class is used (e.g. Hate / Not) or a multi-level class, such as a tripartite split (typically, Overt, Covert and Non-abusive). In some cases, a large number of complex classes are created, such as by combining whether the abuse is targeted or not along with its theme and strength.",
+      "In general, Social scientific analyses encourage creating a detailed taxonomy with a large number of fine-grained categories. However, this is only useful for machine learning if there are enough data points in each category and if annotators are capable of consistently distinguishing between them. Complex annotation schemas may not result in better training datasets if they are not implemented in a robust way. As such, it is unsurprising that binary classification schemas are the most prevalent, even though they are arguably the least useful given the variety of ways in which abuse can be articulated. This can range from the explicit and overt (e.g. directing threats against a group) to more subtle behaviours, such as micro-aggressions and dismissing marginalised groups' experiences of prejudice. Subsuming both types of behaviour within one category not only risks making detection difficult (due to considerable in-class variation) but also leads to a detection system which cannot make important distinctions between qualitatively different types of content. This has severe implications for whether detection systems trained on such datasets can actually be used for downstream tasks, such as content moderation and social scientific analysis.",
+      "Drawing together the nature and granularity of abuse, our analyses identify a hierarchy of taxonomic granularity from least to most granular:",
+      "Binary classification of a single `meta' category, such as hate/not or abuse/not. This can lead to very general and vague research, which is difficult to apply in practice.",
+      "Binary classification of a single type of abuse, such as person-directed or group-directed. This can be problematic given that abuse is nearly always directed against a group rather than `groups' per se.",
+      "Binary classification of abuse against a single well-defined group, such as racism/not or Islamophobia/not, or interpersonal abuse against a well-defined cohort, such as MPs and young people.",
+      "Multi-class (or multi-label) classification of different types of abuse, such as:",
+      "Multiple targets (e.g. racist, sexist and non-hateful content) or",
+      "Multiple strengths (e.g. none, implicit and explicit content).",
+      "Multiple types (e.g. threats versus derogatory statements or benevolent versus hostile statements).",
+      "Multi-class classification of different types of abuse which is integrated with other dimensions of abuse."
+    ],
+    [
+      "49 of the training datasets are annotated at the level of the post, one dataset is annotated at the level of the user BIBREF47, and none of them are annotated at the level of the comment thread. Only two publications indicate that the entire conversational thread was presented to annotators when marking up individual entries, meaning that in most cases this important contextual information is not used. 49 of the training datasets contain only text. This is a considerable limitation of existing research BIBREF13, especially given the multimodal nature of online communication and the increasing ubiquity of digital-specific image-based forms of communication such as Memes, Gifs, Filters and Snaps BIBREF48. Although some work has addressed the task of detecting hateful images BIBREF49, BIBREF50, this lead to the creation of a publically available labelled training dataset in only one case BIBREF51. To our knowledge, no research has tackled the problem of detecting hateful audio content. This is a distinct challenge; alongside the semantic content audio also contains important vocal cues which provide more opportunities to investigate (but also potentially misinterpret) tone and intention."
+    ],
+    [
+      "The most common language in the training datasets is English, which appears in 20 datasets, followed by Arabic and Italian (5 datasets each), Hindi-English (4 datasets) and then German, Indonesian and Spanish (3 datasets). Noticeably, several major languages, both globally and in Europe, do not appear, which suggests considerable unevenness in the linguistic and cultural focuses of abusive language detection. For instance, there are major gaps in the coverage of European languages, including Danish and Dutch. Surprisingly, French only appears once. The dominance of English may be due to how we sampled publications (for which we used English terms), but may also reflect different publishing practices in different countries and how well-developed abusive content research is."
+    ],
+    [
+      "Training datasets use data collected from a range of online spaces, including from mainstream platforms, such as Twitter, Wikipedia and Facebook, to more niche forums, such as World of Warcraft and Stormfront. In most cases, data is collected from public sources and then manually annotated but in others data is sourced through proprietary data sharing agreements with host platforms. Unsurprisingly, Twitter is the most widely used source of data, accounting for 27 of the datasets. This reflects wider concerns in computational social research that Twitter is over-used, primarily because it has a very accessible API for data collection BIBREF52, BIBREF53. Facebook and Wikipedia are the second most used sources of data, accounting for three datasets each \u2013 although we note that all three Wikipedia datasets are reported in the same publication. Many of the most widely used online platforms are not represented at all, or only in one dataset, such as Reddit, Weibo, VK and YouTube.",
+      "The lack of diversity in where data is collected from limits the development of detection systems. Three main issues emerge:",
+      "Linguistic practices vary across platforms. Twitter only allows 280 characters (previously only 140), provoking stylistic changes BIBREF54, and abusive content detection systems trained on this data are unlikely to work as well with longer pieces of text. Dealing with longer pieces of text could necessitate different classification systems, potentially affecting the choice of algorithmic architecture. Additionally, the technical affordances of platforms may affect the style, tone and topic of the content they host.",
+      "The demographics of users on different platforms vary considerably. Social science research indicates that `digital divides' exist, whereby online users are not representative of wider populations and differ across different online spaces BIBREF53, BIBREF55, BIBREF56. Blank draws attention to how Twitter users are usually younger and wealthier than offline populations; over reliance on data from Twitter means, in effect, that we are over-sampling data from this privileged section of society. Blank also shows that there are also important cross-national differences: British Twitters are better-educated than the offline British population but the same is not true for American Twitter users compared with the offline American population BIBREF56. These demographic differences are likely to affect the types of content that users produce.",
+      "Platforms have different norms and so host different types and amounts of abuse. Mainstream platforms have made efforts in recent times to `clean up' content and so the most overt and aggressive forms of abuse, such as direct threats, are likely to be taken down BIBREF57. However, more niche platforms, such as Gab or 4chan, tolerate more offensive forms of speech and are more likely to contain explicit abuse, such as racism and very intrusive forms of harassment, such as `doxxing' BIBREF58, BIBREF59, BIBREF60. Over-reliance on a few sources of data could mean that datasets are biased towards only a subset of types of abuse."
+    ],
+    [
+      "The size of the training datasets varies considerably from 469 posts to 17 million; a difference of four orders of magnitude. Differences in size partly reflect different annotation approaches. The largest datasets are from proprietary data sharing agreements with platforms. Smaller datasets tend to be carefully collected and then manually annotated. There are no established guidelines for how large an abusive language training dataset needs to be. However, smaller datasets are problematic because they contain too little linguistic variation and increase the likelihood of overfitting. Rizoiu et al.BIBREF61 train detection models on only a proportion of the Davidson et al. and Waseem training datasets and show that this leads to worse performance, with a lower F1-Score, particularly for `data hungry' deep learning approaches BIBREF61. At the same time, `big' datasets alone are not a panacea for the challenges of abusive content classification. Large training datasets which have been poorly sampled, annotated with theoretically problematic categories or inexpertly and unthoughtfully annotated, could still lead to the development of poor classification systems.",
+      "The challenges posed by small datasets could potentially be overcome through machine learning techniques such as `semi-supervised' and `active' learning BIBREF62, although these have only been limitedly applied to abusive content detection so far BIBREF63. Sharifirad et al. propose using text augmentation and new text generation as a way of overcoming small datasets, which is a promising avenue for future research BIBREF64."
+    ],
+    [
+      "Class distribution is an important, although often under-considered, aspect of the design of training datasets. Datasets with little abusive content will lack linguistic variation in terms of what is abusive, thereby increasing the risk of overfitting. More concerningly, the class distribution directly affects the nature of the engineering task and how performance should be evaluated. For instance, if a dataset is 70% hate speech then a zero-rule classification system (i.e. where everything is categorised as hate speech) will achieve 70% precision and 100% recall. This should be used as a baseline for evaluating performance: 80% precision is less impressive compared with this baseline. However, 80% precision on an evenly balanced dataset would be impressive. This is particularly important when evaluating the performance of ternary classifiers, when classes can be considerably imbalanced.",
+      "On average, 35% of the content in the training datasets is abusive. However, class distributions vary considerably, from those with just 1% abusive content up to 100%. These differences are largely a product of how data is sampled and which platform it is taken from. Bretschneider BIBREF22 created two datasets without using purposive sampling, and as such they contain very low levels of abuse ( 1%). Other studies filter data collection based on platforms, time periods, keywords/hashtags and individuals to increase the prevalence of abuse. Four datasets comprise only abusive content; three cases are synthetic datasets, reported on in one publication BIBREF65, and in the other case the dataset is an amendment to an existing dataset and only contains misogynistic content BIBREF37.",
+      "Purposive sampling has been criticised for introducing various forms of bias into datasets BIBREF66, such as missing out on mis-spelled content BIBREF67 and only focusing on the linguistic patterns of an atypical subset of users. One pressing risk is that a lot of data is sampled from far right communities \u2013 which means that most hate speech classifiers implicitly pick up on right wing styles of discourse rather than hate speech per se. This could have profound consequences for our understanding of online political dialogue if the classifiers are applied uncritically to other groups. Nevertheless, purposive sampling is arguably a necessary step when creating a training dataset given the low prevalence of abuse on social media in general BIBREF68."
+    ],
+    [
+      "The identity of the users who originally created the content in training datasets is described in only two cases. In both cases the data is synthetic BIBREF65, BIBREF33. Chung et al. use `nichesourcing' to synthetically generate abuse, with experts in tackling hate speech creating hateful posts. Sprugnoli et al. ask children to adopt pre-defined roles in an experimental classroom setup, and ask them to engage in a cyberbullying scenario. In most of the non-synthetic training datasets, some information is given about the sampling criteria used to collect data, such as hashtags. However, this does not provide direct insight into who the content creators are, such as their identity, demographics, online behavioural patterns and affiliations.",
+      "Providing more information about content creators may help address biases in existing datasets. For instance, Wiegand et al. show that 70% of the sexist tweets in the highly cited Waseem and Hovy dataset BIBREF36 come from two content creators and that 99% of the racist tweets come from just one BIBREF66. This is a serious constraint as it means that user-level metadata is artificially highly predictive of abuse. And, even when user-level metadata is not explicitly modelled, detection systems only need to pick up on the linguistic patterns of a few authors to nominally detect abuse. Overall, the complete lack of information about which users have created the content in most training datasets is a substantial limitation which may be driving as-yet-unrecognised biases. This can be remedied through the methodological rigour implicit in including a data statement with a corpus."
+    ],
+    [
+      "How training datasets are annotated is one of the most important aspects of their creation. A range of annotation processes are used in training datasets, which we split into five high-level categories:",
+      "Crowdsourcing (15 datasets). Crowdsourcing is widely used in NLP research because it is relatively cheap and easy to implement. The value of crowdsourcing lies in having annotations undertaken by `a large number of non-experts' (BIBREF69, p. 278) \u2013 any bit of content can be annotated by multiple annotators, effectively trading quality for quantity. Studies which use crowdsourcing with only a few annotators for each bit of content risk minimising quality without counterbalancing it with greater quantity. Furthermore, testing the work of many different annotators can be challenging BIBREF70, BIBREF71 and ensuring they are paid an ethical amount may make the cost comparable to using trained experts. Crowdsourcing has also been associated with `citizen science' initiatives to make academic research more accessible but this may not be fully realised in cases where annotation tasks are laborious and low-skilled BIBREF72, BIBREF20.",
+      "Academic experts (22 datasets). Expert annotation is time-intensive but is considered to produce higher quality annotations. Waseem reports that `systems trained on expert annotations outperform systems trained on amateur annotations.' BIBREF73 and, similarly, D'Orazio et al. claim, `although expert coding is costly, it produces quality data.' BIBREF74. However, the notion of an `expert' remains somewhat fuzzy within abusive content detection research. In many cases, publications only report that `an expert' is used, without specifying the nature of their expertise \u2013 even though this can vary substantially. For example, an expert may refer to an NLP practitioner, an undergraduate student with only modest levels of training, a member of an attacked social group relevant to the dataset or a researcher with a doctorate in the study of prejudice. In general, we anticipate that experts in the social scientific study of prejudice/abuse would perform better at annotation tasks then NLP experts who may not have any direct expertise in the conceptual and theoretical issues of abusive content annotation. In particular, one risk of using NLP practitioners, whether students or professionals, is that they might `game' training datasets based on what they anticipate is technically feasible for existing detection systems. For instance, if existing systems perform poorly when presented with long range dependencies, humour or subtle forms of hate (which are nonetheless usually discernible to human readers) then NLP experts could unintentionally use this expectation to inform their annotations and not label such content as hateful.",
+      "Professional moderators (3 datasets). Professional moderators offer a standardized approach to content annotated, implemented by experienced workers. This should, in principle, result in high quality annotations. However, one concern is that moderators are output-focused as their work involves determining whether content should be allowed or removed from platforms; they may not provide detailed labels about the nature of abuse and may also set the bar for content labelled `abusive' fairly high, missing out on more nuance and subtle varieties. In most cases, moderators will annotate for a range of unacceptable content, such as spam and sexual content, and this must be marked in datasets.",
+      "A mix of crowdsourcing and experts (6 datasets).",
+      "Synthetic data creation (4 datasets). Synthetic datasets are an interesting option as they are inherently non-authentic and therefore not necessarily representative of how abuse manifests in real-world situations. However, if they are created in realistic conditions by experts or relevant content creators then they can mimic real behaviour and have the added advantage that they may have broader coverage of different types of abuse. They are also usually easier to share."
+    ],
+    [
+      "The data statements framework given by Bender and Friedman emphasises the importance of understanding who has completed annotations. Knowing who the annotators are is important because `their own \u201csocial address\" influences their experience with language and thus their perception of what they are annotating.' BIBREF18 In the context of online abuse, Binns et al. show that the gender of annotators systematically influences what annotations they provide BIBREF75. No annotator will be well-versed in all of the slang or coded meanings used to construct abusive language. Indeed, many of these coded meanings are deliberately covert and obfuscated BIBREF76. To help mitigate these challenges, annotators should be (a) well-qualified and (b) diverse. A homogeneous group of annotators will be poorly equipped to catch all instances of abuse in a corpus. Recruiting an intentionally mixed groups of annotators is likely to yield better recall of abuse and thus a more precise dataset BIBREF77.",
+      "Information about annotators is unfortunately scarce. In 23 of the training datasets no information is given about the identity of annotators; in 17 datasets very limited information is given, such as whether the annotator is a native speaker of the language; and in just 10 cases is detailed information given. Interestingly, only 4 out of these 10 datasets are in the English language. Relevant information about annotators can be split into (i) Demographic information and (ii) annotators' expertise and experience. In none of the training sets is the full range of annotator information made available, which includes:",
+      "Demographic information. The nature of the task affects what information should be provided, as well as the geographic and cultural context. For instance, research on Islamophobia should include, at the very least, information about annotators' religious affiliation. Relevant variables include:",
+      "Age",
+      "Ethnicity and race",
+      "Religion",
+      "Gender",
+      "Sexual Orientation",
+      "Expertise and experience. Relevant variables include:",
+      "Field of research",
+      "Years of experience",
+      "Research status (e.g. research assistant or post-doc)",
+      "Personal experiences of abuse. In our review, none of the datasets contained systematic information about whether annotators had been personally targeted by abuse or had viewed such abuse online, even though this can impact annotators' perceptions. Relevant variables include:",
+      "Experiences of being targeted by online abuse.",
+      "Experiences of viewing online abuse."
+    ],
+    [
+      "A key source of variation across datasets is whether annotators were given detailed guidelines, very minimal guidelines or no guidelines at all. Analysing this issue is made difficult by the fact that many dataset creators do not share their annotation guidelines. 21 of the datasets we study do not provide the guidelines and 14 only provide them in a highly summarised form. In just 15 datasets is detailed information given (and these are reported on in just 9 publications). Requiring researchers to publish annotation guidelines not only helps future researchers to better understand what datasets contain but also to improve and extend them. This could be crucial for improving the quality of annotations; as Ross et al. recommend, `raters need more detailed instructions for annotation.' BIBREF78",
+      "The degree of detail given in guidelines is linked to how the notion of `abuse' is understood. Some dataset creators construct clear and explicit guidelines in an attempt to ensure that annotations are uniform and align closely with social scientific concepts. In other cases, dataset creators allow annotators to apply their own perception. For instance, in their Portuguese language dataset, Fortuna et al. ask annotators to `evaluate if according to your opinion, these tweets contain hate speech' BIBREF38. The risk here is that authors' perceptions may differ considerably; Salminen et al. show that online hate interpretation varies considerably across individuals BIBREF79. This is also reflected in inter-annotator agreement scores for abusive content, which is often very low, particularly for tasks which deploy more than just a binary taxonomy. However, it is unlikely that annotators could ever truly divorce themselves from their own social experience and background to decide on a single `objective' annotation. Abusive content annotation is better understood, epistemologically, as an intersubjective process in which agreement is constructed, rather than an objective process in which a `true' annotation is `found'. For this reason, some researchers have shifted the question of `how can we achieve the correct annotation?' to `who should decide what the correct annotation is?' BIBREF73. Ultimately, whether annotators should be allowed greater freedom in making annotations, and whether this results in higher quality datasets, needs further research and conceptual examination.",
+      "Some aspects of abusive language present fundamental issues that are prone to unreliable annotation, such as Irony, Calumniation and Intent. They are intrinsically difficult to annotate given a third-person perspective on a piece of text as they involve making a judgement about indeterminate issues. However, they cannot be ignored given their prevalence in abusive content and their importance to how abuse is expressed. Thus, although they are fundamentally conceptual problems, these issues also present practical problems for annotators, and should be addressed explicitly in coding guidelines. Otherwise, as BIBREF80 note, these issues are likely to drive type II errors in classification, i.e. labelling non-hate-speech utterances as hate speech."
+    ],
+    [
+      "This covers statements that have a meaning contrary to that one might glean at first reading. Lachenicht BIBREF81 notes that Irony goes against Grice's quality maxim, and as such Ironic content requires closer attention from the reader as it is prone to being misinterpreted. Irony is a particularly difficult issue as in some cases it is primarily intended to provide humour (and thus might legitimately be considered non-abusive) but in other cases is used as a way of veiling genuine abuse. Previous research suggests that the problem is widespread. Sanguinetti et al. BIBREF82 find irony in 11% of hateful tweets in Italian. BIBREF25 find that irony is one of the most common phenomena in self-deleted comments; and that the prevalence of irony is 33.9% amongst deleted comments in a Croatian comment dataset and 18.1% amongst deleted comments in a Slovene comment dataset. Furthermore, annotating irony (as well as related constructs, such as sarcasm and humour) is inherently difficult. BIBREF83 report that agreement on sarcasm amongst annotators working in English is low, something echoed by annotations of Danish content BIBREF84. Irony is also one of the most common reasons for content to be re-moderated on appeal, according to Pavlopoulos et al. BIBREF24."
+    ],
+    [
+      "This covers false statements, slander, and libel. From the surveyed set, this is annotated in datasets for Greek BIBREF24 and for Croatian and Slovene BIBREF25. Its prevalence varies considerably across these two datasets and reliable estimations of the prevalence of false statements are not available. Calumniation is not only an empirical issue, it also raises conceptual problems: should false information be considered abusive if it slanders or demeans a person? However, if the information is then found out to be true does it make the content any less abusive? Given the contentiousness of `objectivity', and the lack of consensus about most issues in a `post-truth' age BIBREF85, who should decide what is considered true? And, finally, how do we determine whether the content creator knows whether something is true? These ontological, epistemological and social questions are fundamental to the issue of truth and falsity in abusive language. Understandably, most datasets do not taken any perspective on the truth and falsity of content. This is a practical solution: given error rates in abusive language detection as well as error rates in fact-checking, a system which combined both could be inapplicable in practice."
+    ],
+    [
+      "This information about the utterer's state of mind is a core part of how many types of abusive language are defined. Intent is usually used to emphasize the wrongness of abusive behaviour, such as spreading, inciting, promoting or justifying hatred or violence towards a given target, or sending a message that aims at dehumanising, delegitimising, hurting or intimidating them BIBREF82. BIBREF81 postulate that \u201caggravation, invective and rudeness ... may be performed with varying degrees of intention to hurt\", and cite five legal degrees of intent BIBREF86. However, it is difficult to discern the intent of another speaker in a verbal conversation between humans, and even more difficult to do so through written and computer-mediated communications BIBREF87. Nevertheless, intent is particularly important for some categories of abuse such as bullying, maliciousness and hostility BIBREF34, BIBREF32. Most of the guidelines for the datasets we have studied do not contain an explicit discussion of intent, although there are exceptions. BIBREF88 include intent as a core part of their annotation standard, noting that understanding context (such as by seeing a speakers' other online messages) is crucial to achieving quality annotations. However, this proposition poses conceptual challenges given that people's intent can shift over time. Deleted comments have been used to study potential expressions of regret by users and, as such, a change in their intent BIBREF89, BIBREF25; this has also been reported as a common motivator even in self-deletion of non-abusive language BIBREF90. Equally, engaging in a sequence of targeted abusive language is an indicator of aggressive intent, and appears in several definitions. BIBREF23 require an \u201cintent to physically assert power over women\" as a requirement for multiple categories of misogynistic behaviour. BIBREF34 find that messages that are \u201cunapologetically or intentionally offensive\" fit in the highest grade of trolling under their schema.",
+      "Kenny et al. BIBREF86 note how sarcasm, irony, and humour complicate the picture of intent by introducing considerable difficulties in discerning the true intent of speakers (as discussed above). Part of the challenge is that many abusive terms, such as slurs and insults, are polysemic and may be co-opted by an ingroup into terms of entertainment and endearment BIBREF34."
+    ],
+    [
+      "All of the training datasets we analyse are publicly accessible and as such can be used by researchers other than the authors of the original publication. Sharing data is an important aspect of open science but also poses ethical and legal risks, especially in light of recent regulatory changes, such as the introduction of GPDR in the UK BIBREF91, BIBREF92. This problem is particularly acute with abusive content, which can be deeply shocking, and some training datasets from highly cited publications have not been made publicly available BIBREF93, BIBREF94, BIBREF95. Open science initiatives can also raise concerns amongst the public, who may not be comfortable with researchers sharing their personal data BIBREF96, BIBREF97.",
+      "The difficulty of sharing data in sensitive areas of research is reflected by the Islamist extremism research website, `Jihadology'. It chose to restrict public access in 2019, following efforts by Home Office counter-terrorism officials to shut it down completely. They were concerned that, whilst it aimed to support academic research into Islamist extremism, it may have inadvertently enabled individuals to radicalise by making otherwise banned extremist material available. By working with partners such as the not-for-profit Tech Against Terrorism, Jihadology created a secure area in the website, which can only be accessed by approved researchers. Some of the training datasets in our list have similar requirements, and can only be accessed following a registration process.",
+      "Open sharing of datasets is not only a question of scientific integrity and a powerful way of advancing scientific knowledge. It is also, fundamentally, a question of fairness and power. Opening access to datasets will enable less-well funded researchers and organisations, which includes researchers in the Global South and those working for not-for-profit organisations, to steer and contribute to research. This is a particularly pressing issue in a field which is directly concerned with the experiences of often-marginalised communities and actors BIBREF36. For instance, one growing concern is the biases encoded in detection systems and the impact this could have when they are applied in real-world settings BIBREF9, BIBREF10. This research could be further advanced by making more datasets and detection systems more easily available. For instance, Binns et al. use the detailed metadata in the datasets provided by Wulczyn et al. to investigate how the demographics of annotators impacts the annotations they make BIBREF75, BIBREF29. The value of such insights is only clear after the dataset has been shared \u2013 and, equally, is only possible because of data sharing.",
+      "More effective ways of sharing datasets would address the fact that datasets often deteriorate after they have been published BIBREF13. Several of the most widely used datasets provide only the annotations and IDs and must be `rehydrated' to collect the content. Both of the datasets provided by Waseem and Hovy and Founta et al. must be collected in this way BIBREF98, BIBREF36, and both have degraded considerably since they were first released as the tweets are no longer available on Twitter. Chung et al. also estimate that within 12 months the recently released dataset for counterspeech by Mathew et al. had lost more than 60% of its content BIBREF65, BIBREF58. Dataset degradation poses three main risks: First, if less data is available then there is a greater likelihood of overfitting. Second, the class distributions usually change as proportionally more of the abusive content is taken down than the non-abusive. Third, it is also likely that the more overt forms of abuse are taken down, rather than the covert instances, thereby changing the qualitative nature of the dataset."
+    ],
+    [
+      "The problem of data access and sharing remains unresolved in the field of abusive content detection, much like other areas of computational research BIBREF99. At present, an ethical, secure and easy way of sharing sensitive tools and resources has not been developed and adopted in the field. More effective dataset sharing would (1) greater collaboration amongst researchers, (2) enhance the reproducibility of research by encouraging greater scrutiny BIBREF100, BIBREF101, BIBREF102 and (3) substantively advance the field by enabling future researchers to better understand the biases and limitations of existing research and to identify new research directions.",
+      "There are two main challenges which must be overcome to ensure that training datasets can be shared and used by future researchers. First, dataset quality: the size, class distribution and quality of their content must be maintained. Second, dataset access: access to datasets must be controlled so that researchers can use them, whilst respecting platforms' Terms of Service and not allowing potential extremists from having access. These problems are closely entwined and the solutions available, which follow, have implications for both of them.",
+      "Synthetic datasets. Four of the datasets we have reviewed were developed synthetically. This resolves the dataset quality problem but introduces additional biases and limitations because the data is not real. Synthetic datasets still need to be shared in such a way as to limit access for potential extremists but face no challenges from Platforms' Terms of Services.",
+      "Data `philanthropy' or `donations'. These are defined as `the act of an individual actively consenting to donate their personal data for research' BIBREF97. Donated data from many individuals could then be combined and shared \u2013 but it would still need to be annotated. A further challenge is that many individuals who share abusive content may be unwilling to `donate' their data as this is commonly associated with prosocial motivations, creating severe class imbalances BIBREF97. Data donations could also open new moral and ethical issues; individuals' privacy could be impacted if data is re-analysed to derive new unexpected insights BIBREF103. Informed consent is difficult given that the exact nature of analyses may not be known in advance. Finally, data donations alone do not solve how access can be responsibly protected and how platforms' Terms of Service can be met. For these reasons, data donations are unlikely to be a key part of future research infrastructure for abusive content detection.",
+      "Platform-backed sharing. Platforms could share datasets and support researchers' access. There are no working examples of this in abusive content detection research, but it has been successfully used in other research areas. For instance, Twitter has made a large dataset of accounts linked to potential information operations, known as the \u201cIRA\" dataset (Internet Research Agency). This would require considerably more interfaces between academia and industry, which may be difficult given the challenges associated with existing initiatives, such as Social Science One. However, in the long term, we propose that this is the most effective solution for the problem of sharing training datasets. Not only because it removes Terms of Service limitations but also because platforms have large volumes of original content which has been annotated in a detailed way. This could take one of two forms: platforms either make content which has violated their Community Guidelines available directly or they provide special access post-hoc to datasets which researchers have collected publicly through their API - thereby making sure that datasets do not degrade over time.",
+      "Data trusts. Data trusts have been described as a way of sharing data `in a fair, safe and equitable way' ( BIBREF104 p. 46). However, there is considerable disagreement as to what they entail and how they would operate in practice BIBREF105. The Open Data Institute identifies that data trusts aim to make data open and accessible by providing a framework for storing and accessing data, terms and mechanisms for resolving disputes and, in some cases, contracts to enforce them. For abusive content training datasets, this would provide a way of enabling datasets to be shared, although it would require considerable institutional, legal and financial commitments.",
+      "Arguably, the easiest way of ensuring data can be shared is to maintain a very simple data trust, such as a database, which would contain all available abusive content training datasets. This repository would need to be permissioned and access controlled to address concerns relating to privacy and ethics. Such a repository could substantially reduce the burden on researchers; once they have been approved to the repository, they could access all datasets publicly available \u2013 different levels of permission could be implemented for different datasets, depending on commercial or research sensitivity. Furthermore, this repository could contain all of the metadata reported with datasets and such information could be included at the point of deposit, based on the `data statements' work of Bender and Friedman BIBREF18. A simple API could be developed for depositing and reading data, similar to that of the HateBase. The permissioning system could be maintained either through a single institution or, to avoid power concentrating amongst a small group of researchers, through a decentralised blockchain."
+    ],
+    [
+      "The resources and infrastructure to create a dedicated data trust and API for sharing abusive content training datasets is substantial and requires considerable further engagement with research teams in this field. In the interim, to encourage greater sharing of datasets, we have launched a dedicated website which contains all of the datasets analysed here: https://hatespeechdata.com. Based on the analysis in the previous sections, we have also provided partial data statements BIBREF18. The website also contains previously published abusive keyword dictionaries, which are not analysed here but some researchers may find useful. Note that the website only contains information/data which the original authors have already made publicly available elsewhere. It will be updated with new datasets in the future."
+    ],
+    [
+      "Much can be learned from existing efforts to create abusive language datasets. We identify best practices which emerge at four distinct points in the process of creating a training dataset: (1) task formation, (2) data selection, (3) annotation, and (4) documentation."
+    ],
+    [
+      "Dataset creation should be `problem driven' BIBREF106 and should address a well-defined and specific task, with a clear motivation. This will directly inform the taxonomy design, which should be well-specified and engage with social scientific theory as needed. Defining a clear task which the dataset addresses is especially important given the maturation of the field, ongoing terminological disagreement and the complexity of online abuse. The diversity of phenomena that fits under the umbrella of abusive language means that `general purpose' datasets are unlikely to advance the field. New datasets are most valuable when they address a new target, generator, phenomenon, or domain. Creating datasets which repeat existing work is not nearly as valuable."
+    ],
+    [
+      "Once the task is established, dataset creators should select what language will be annotated, where data will be sampled from and how sampling will be completed. Any data selection exercise is bound to give bias, and so it is important to record what decisions are made (and why) in this step. Dataset builders should have a specific target size in mind and also have an idea of the minimum amount of data this is likely to be needed for the task. This is also where steps 1 and 2 intersect: the data selection should be driven by the problem that is addressed rather than what is easy to collect. Ensuring there are enough positive examples of abuse will always be challenging as the prevalence of abuse is so low. However, given that purposive sampling inevitably introduces biases, creators should explore a range of options before determining the best one \u2013 and consider using multiple sampling methods at once, such as including data from different times, different locations, different types of users and different platforms. Other options include using measures of linguistic diversity to maximize the variety of text included in datasets, or including words that cluster close to known abusive terms."
+    ],
+    [
+      "Annotators must be hired, trained and given appropriate guidelines. Annotators work best with solid guidelines, that are easy to grasp and have clear examples BIBREF107. The best examples are both illustrative, in order to capture the concepts (such as `threatening language') and provide insight into `edge cases', which is content that only just crosses the line into abuse. Decisions should be made about how to handle intrinsically difficult aspects of abuse, such as irony, calumniation and intent (see above). Annotation guidelines should be developed iteratively by dataset creators; by working through the data, rules can be established for difficult or counter-intuitive coding decisions, and a set of shared practices developed. Annotators should be included in this iterative process. Discussions with annotators the language that they have seen \u201cin the field\" offers an opportunity to enhance and refine guidelines - and even taxonomies. Such discussions will lead to more consistent data and provide a knowledge base to draw on for future work. To achieve this, it is important to adopt an open culture where annotators are comfortable providing open feedback and also describing their uncertainties. Annotators should also be given emotional and practical support (as well as appropriate financial compensation), and the harmful and potentially triggering effects of annotating online abuse should be recognised at all times. For a set of guidelines to help protect the well-being of annotators, see BIBREF13."
+    ],
+    [
+      "The best training datasets provide as much information as possible and are well-documented. When the method behind them is unclear, they are hard to evaluate, use and build on. Providing as much information as possible can open new and unanticipated analyses and gives more agency to future researchers who use the dataset to create classifiers. For instance, if all annotators' codings are provided (rather than just the `final' decision) then a more nuanced and aware classifier could be developed as, in some cases, it can be better to maximise recall of annotations rather than maximise agreement BIBREF77.",
+      "Our review found that most datasets have poor methodological descriptions and few (if any) provide enough information to construct an adequate data statement. It is crucial that dataset creators are up front about their biases and limitations: every dataset is biased, and this is only problematic when the biases are unknown. One strategy for doing this is to maintain a document of decisions made when designing and creating the dataset and to then use it to describe to readers the rationale behind decisions. Details about the end-to-end dataset creation process are welcomed. For instance, if the task is crowdsourced then a screenshot of the micro-task presented to workers should be included, and the top-level parameters should be described (e.g. number of workers, maximum number of tasks per worker, number of annotations per piece of text) BIBREF20. If a dedicated interface is used for the annotation, this should also be described and screenshotted as the interface design can influence the annotations."
+    ],
+    [
+      "Unfortunately, as with any burgeoning field, there is confusion and overlap around many of the phenomena discussed in this paper; coupled with the high degree of variation in the quality of method descriptions, it has lead to many pieces of research that are hard to combine, compare, or re-use. Our reflections on best practices are driven by this review and the difficulties of creating high quality training datasets. For future researchers, we summarise our recommendations in the following seven points:",
+      "Bear in mind the purpose of the dataset; design the dataset to help address questions and problems from previous research.",
+      "Avoid using `easy to access' data, and instead explore new sources which may have greater diversity. Consider what biases may be created by your sampling method.",
+      "Determine size based on data sparsity and having enough positive classes rather than `what is possible'.",
+      "Establish a clear taxonomy to be used for the task, with meaningful and theoretically sound categories.",
+      "Provide annotators with guidelines; develop them iteratively and publish them with your dataset. Consider using trained annotators given the complexities of abusive content.",
+      "Involve people who have direct experience of the abuse which you are studying whenever possible (and provided that you can protect their well-being).",
+      "Report on every step of the research through a Data Statement."
+    ],
+    [
+      "This paper examined a large set of datasets for the creation of abusive content detection systems, providing insight into what they contain, how they are annotated, and how tasks have been framed. Based on an evidence-driven review, we provided an extended discussion of how to make training datasets more readily available and useful, including the challenges and opportunities of open science as well as the need for more research infrastructure. We reported on the development of hatespeechdata.com \u2013 a new repository for online abusive content training datasets. Finally, we outlined best practices for creation of training datasets for detection of online abuse. We have effectively met the four research aims elaborated at the start of the paper.",
+      "Training detection systems for online abuse is a substantial challenge with real social consequences. If we want the systems we develop to be useable, scalable and with few biases then we need to train them on the right data: garbage in will only lead to garbage out."
+    ]
+  ]
+}
+```
\ No newline at end of file
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@@ -0,0 +1,219 @@
+Name of Paper: Directions in Abusive Language Training Data: Garbage In, Garbage Out
+
+Question: How big are this dataset and catalogue?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Background",
+    "Analysis of training datasets",
+    "Analysis of training datasets ::: The purpose of training datasets ::: Problems addressed by datasets",
+    "Analysis of training datasets ::: The purpose of training datasets ::: Uses of datasets: How detection tasks are defined",
+    "Analysis of training datasets ::: The purpose of training datasets ::: Uses of datasets: How detection tasks are defined ::: Detection tasks: the nature of abuse",
+    "Analysis of training datasets ::: The purpose of training datasets ::: Uses of datasets: How detection tasks are defined ::: Detection tasks: Granularity of taxonomies",
+    "Analysis of training datasets ::: The content of training datasets ::: The `Level' of content",
+    "Analysis of training datasets ::: The content of training datasets ::: Language",
+    "Analysis of training datasets ::: The content of training datasets ::: Source of data",
+    "Analysis of training datasets ::: The content of training datasets ::: Size",
+    "Analysis of training datasets ::: The content of training datasets ::: Class distribution and sampling",
+    "Analysis of training datasets ::: The content of training datasets ::: Identity of the content creators",
+    "Analysis of training datasets ::: Annotation of training datasets ::: Annotation process",
+    "Analysis of training datasets ::: Annotation of training datasets ::: Identity of the annotators",
+    "Analysis of training datasets ::: Annotation of training datasets ::: Guidelines for annotation",
+    "Analysis of training datasets ::: Annotation of training datasets ::: Guidelines for annotation ::: Irony",
+    "Analysis of training datasets ::: Annotation of training datasets ::: Guidelines for annotation ::: Calumniation",
+    "Analysis of training datasets ::: Annotation of training datasets ::: Guidelines for annotation ::: Intent",
+    "Dataset sharing ::: The challenges and opportunities of achieving Open Science",
+    "Dataset sharing ::: Research infrastructure: Solutions for sharing training datasets",
+    "Dataset sharing ::: A new repository of training datasets: Hatespeechdata.com",
+    "Best Practices for training dataset creation",
+    "Best Practices for training dataset creation ::: Task formation: Defining the task addressed by the dataset",
+    "Best Practices for training dataset creation ::: Selecting data for abusive language annotation",
+    "Best Practices for training dataset creation ::: Annotating abusive language",
+    "Best Practices for training dataset creation ::: Documenting methods, data, and annotators",
+    "Best Practices for training dataset creation ::: Best practice summary",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Abusive online content, such as hate speech and harassment, has received substantial attention over the past few years for its malign social effects. Left unchallenged, abusive content risks harminng those who are targeted, toxifying public discourse, exacerbating social tensions and could lead to the exclusion of some groups from public spaces. As such, systems which can accurately detect and classify online abuse at scale, in real-time and without bias are of central interest to tech companies, policymakers and academics.",
+      "Most detection systems rely on having the right training dataset, reflecting one of the most widely accepted mantras in computer science: Garbage In, Garbage Out. Put simply: to have systems which can detect and classify abusive online content effectively, one needs appropriate datasets with which to train them. However, creating training datasets is often a laborious and non-trivial task \u2013 and creating datasets which are non-biased, large and theoretically-informed is even more difficult (BIBREF0 p. 189). We address this issue by examining and reviewing publicly available datasets for abusive content detection, which we provide access to on a new dedicated website, hatespeechdata.com.",
+      "In the first section, we examine previous reviews and present the four research aims which guide this paper. In the second section, we conduct a critical and in-depth analysis of the available datasets, discussing first what their aim is, how tasks have been described and what taxonomies have been constructed and then, second, what they contain and how they were annotated. In the third section, we discuss the challenges of open science in this research area and elaborates different ways of sharing training datasets, including the website hatespeechdata.com In the final section, we draw on our findings to establish best practices when creating datasets for abusive content detection."
+    ],
+    [
+      "The volume of research examining the social and computational aspects of abusive content detection has expanded prodigiously in the past five years. This has been driven by growing awareness of the importance of the Internet more broadly BIBREF1, greater recognition of the harms caused by online abuse BIBREF2, and policy and regulatory developments, such as the EU's Code of Conduct on Hate, the UK Government's `Online Harms' white paper BIBREF3, Germany's NetzDG laws, the Public Pledge on Self-Discipline for the Chinese Internet Industry, and France's anti-hate regulation BIBREF2. In 2020 alone, three computer science venues will host workshops on online hate (TRAC and STOC at LREC, and WOAH at EMNLP), and a shared task at 2019's SemEval on online abuse detection reports that 800 teams downloaded the training data and 115 submitted detection systems BIBREF4. At the same time, social scientific interventions have also appeared, deepening our understanding of how online abuse spreads BIBREF5 and how its harmful impact can be mitigated and challenged BIBREF6.",
+      "All analyses of online abuse ultimately rely on a way of measuring it, which increasingly means having a method which can handle the sheer volume of content produced, shared and engaged with online. Traditional qualitative methods cannot scale to handle the hundreds of millions of posts which appear on each major social media platform every day, and can also introduce inconsistencies and biase BIBREF7. Computational tools have emerged as the most promising way of classifying and detecting online abuse, drawing on work in machine learning, Natural Language Processing (NLP) and statistical modelling. Increasingly sophisticated architectures, features and processes have been used to detect and classify online abuse, leveraging technically sophisticated methods, such as contextual word embeddings, graph embeddings and dependency parsing. Despite their many differences BIBREF8, nearly all methods of online abuse detection rely on a training dataset, which is used to teach the system what is and is not abuse. However, there is a lacuna of research on this crucial aspect of the machine learning process. Indeed, although several general reviews of the field have been conducted, no previous research has reviewed training datasets for abusive content detection in sufficient breadth or depth. This is surprising given (i) their fundamental importance in the detection of online abuse and (ii) growing awareness that several existing datasets suffer from many flaws BIBREF9, BIBREF10. Close relevant work includes:",
+      "Schmidt and Wiegand conduct a comprehensive review of research into the detection and classification of abusive online content. They discuss training datasets, stating that `to perform experiments on hate speech detection, access to labelled corpora is essential' (BIBREF8, p. 7), and briefly discuss the sources and size of the most prominent existing training datasets, as well as how datasets are sampled and annotated. Schmidt and Wiegand identify two key challenges with existing datasets. First, `data sparsity': many training datasets are small and lack linguistic variety. Second, metadata (such as how data was sampled) is crucial as it lets future researchers understand unintended biases, but is often not adequately reported (BIBREF8, p. 6).",
+      "Waseem et al.BIBREF11 outline a typology of detection tasks, based on a two-by-two matrix of (i) identity- versus person- directed abuse and (ii) explicit versus implicit abuse. They emphasise the importance of high-quality datasets, particularly for more nuanced expressions of abuse: `Without high quality labelled data to learn these representations, it may be difficult for researchers to come up with models of syntactic structure that can help to identify implicit abuse.' (BIBREF11, p. 81)",
+      "Jurgens et al.BIBREF12 discuss also conduct a critical review of hate speech detection, and note that `labelled ground truth data for building and evaluating classifiers is hard to obtain because platforms typically do not share moderated content due to privacy, ethical and public relations concerns.' (BIBREF12, p. 3661) They argue that the field needs to `address the data scarcity faced by abuse detection research' in order to better address more complex rsearch issues and pressing social challenges, such as `develop[ing] proactive technologies that counter or inhibit abuse before it harms' (BIBREF12, pp. 3658, 3661).",
+      "Vidgen et al. describe several limitations with existing training datasets for abusive content, most noticeably how `they contain systematic biases towards certain types and targets of abuse.' BIBREF13[p.2]. They describe three issues in the quality of datasets: degradation (whereby datasets decline in quality over time), annotation (whereby annotators often have low agreement, indicating considerable uncertainty in class assignments) and variety (whereby `The quality, size and class balance of datasets varies considerably.' [p. 6]).",
+      "Chetty and AlathurBIBREF14 review the use of Internet-based technologies and online social networks to study the spread of hateful, offensive and extremist content BIBREF14. Their review covers both computational and legal/social scientific aspects of hate speech detection, and outlines the importance of distinguishing between different types of group-directed prejudice. However, they do not consider training datasets in any depth.",
+      "Fortuna and NunesBIBREF15 provide an end-to-end review of hate speech research, including the motivations for studying online hate, definitional challenges, dataset creation/sharing, and technical advances, both in terms of feature selection and algorithmic architecture (BIBREF15, 2018). They delineate between different types of online abuse, including hate, cyberbullying, discrimination and flaming, and add much needed clarity to the field. They show that (1) dataset size varies considerably but they are generally small (mostly containing fewer than 10,000 entries), (2) Twitter is the most widely-studied platform, and (3) most papers research hate speech per se (i.e. without specifying a target). Of those which do specify a target, racism and sexism are the most researched. However, their review focuses on publications rather than datasets: the same dataset might be used in multiple studies, limiting the relevance of their review for understanding the intrinsic role of training datasets. They also only engage with datasets fairly briefly, as part of a much broader review.",
+      "Several classification papers also discuss the most widely used datasets, including Davidson et al. BIBREF16 who describe five datasets, and Salminen et al. who review 17 datasets and describe four in detail BIBREF17.",
+      "This paper addresses this lacuna in existing research, providing a systematic review of available training datasets for online abuse. To provide structure to this review, we adopt the `data statements' framework put forward by Bender and Friedman BIBREF18, as well as other work providing frameworks, schema and processes for analysing NLP artefacts BIBREF19, BIBREF20, BIBREF21. Data statements are a way of documenting the decisions which underpin the creation of datasets used for Natural Language Processing (NLP). They formalise how decisions should be documented, not only ensuring scientific integrity but also addressing `the open and urgent question of how we integrate ethical considerations in the everyday practice of our field' (BIBREF18, p. 587). In many cases, we find that it is not possible to fully recreate the level of detail recorded in an original data statement from how datasets are described in publications. This reinforces the importance of proper documentation at the point of dataset creation.",
+      "As the field of online abusive content detection matures, it has started to tackle more complex research challenges, such as multi-platform, multi-lingual and multi-target abuse detection, and systems are increasingly being deployed in `the wild' for social scientific analyses and for content moderation BIBREF5. Such research heightens the focus on training datasets as exactly what is being detected comes under greater scrutiny. To enhance our understanding of this domain, our review paper has four research aims.",
+      "Research Aim One: to provide an in-depth and critical analysis of the available training datasets for abusive online content detection.",
+      "Research Aim Two: to map and discuss ways of addressing the lack of dataset sharing, and as such the lack of `open science', in the field of online abuse research.",
+      "Research Aim Three: to introduce the website hatespeechdata.com, as a way of enabling more dataset sharing.",
+      "Research Aim Four: to identify best practices for creating an abusive content training dataset."
+    ],
+    [
+      "Relevant publications have been identified from four sources to identify training datasets for abusive content detection:",
+      "The Scopus database of academic publications, identified using keyword searches.",
+      "The ACL Anthology database of NLP research papers, identified using keyword searches.",
+      "The ArXiv database of preprints, identified using keyword searches.",
+      "Proceedings of the 1st, 2nd and 3rd workshops on abusive language online (ACL).",
+      "Most publications report on the creation of one abusive content training dataset. However, some describe several new datasets simultaneously or provide one dataset with several distinct subsets of data BIBREF22, BIBREF23, BIBREF24, BIBREF25. For consistency, we separate out each subset of data where they are in different languages or the data is collected from different platforms. As such, the number of datasets is greater than the number publications. All of the datasets were released between 2016 and 2019, as shown in Figure FIGREF17."
+    ],
+    [
+      "Creating a training dataset for online abuse detection is typically motivated by the desire to address a particular social problem. These motivations can inform how a taxonomy of abusive language is designed, how data is collected and what instructions are given to annotators. We identify the following motivating reasons, which were explicitly referenced by dataset creators.",
+      "Reducing harm: Aggressive, derogatory and demeaning online interactions can inflict harm on individuals who are targeted by such content and those who are not targeted but still observe it. This has been shown to have profound long-term consequences on individuals' well-being, with some vulnerable individuals expressing concerns about leaving their homes following experiences of abuse BIBREF26. Accordingly, many dataset creators state that aggressive language and online harassment is a social problem which they want to help address",
+      "Removing illegal content: Many countries legislate against certain forms of speech, e.g. direct threats of violence. For instance, the EU's Code of Conduct requires that all content that is flagged for being illegal online hate speech is reviewed within 24 hours, and removed if necessary BIBREF27. Many large social media platforms and tech companies adhere to this code of conduct (including Facebook, Google and Twitter) and, as of September 2019, 89% of such content is reviewed in 24 hours BIBREF28. However, we note that in most cases the abuse that is marked up in training datasets falls short of the requirements of illegal online hate \u2013 indeed, as most datasets are taken from public API access points, the data has usually already been moderated by the platforms and most illegal content removed.",
+      "Improving health of online conversations: The health of online communities can be severely affected by abusive language. It can fracture communities, exacerbate tensions and even repel users. This is not only bad for the community and for civic discourse in general, it also negatively impacts engagement and thus the revenue of the host platforms. Therefore, there is a growing impetus to improve user experience and ensure online dialogues are healthy, inclusive and respectful where possible. There is ample scope for improvement: a study showed that 82% of personal attacks on Wikipedia against other editors are not addressed BIBREF29. Taking steps to improve the health of exchanges in online communities will also benefit commercial and voluntary content moderators. They are routinely exposed to such content, often with insufficient safeugards, and sometimes display symptoms similar to those of PTSD BIBREF30. Automatic tools could help to lessen this exposure, reducing the burden on moderators."
+    ],
+    [
+      "Myriad tasks have been addressed in the field of abusive online content detection, reflecting the different disciplines, motivations and assumptions behind research. This has led to considerable variation in what is actually detected under the rubric of `abusive content', and establishing a degree of order over the diverse categorisations and subcategorisations is both difficult and somewhat arbitrary. Key dimensions which dataset creators have used to categorise detection tasks include who/what is targeted (e.g. groups vs. individuals), the strength of content (e.g. covert vs. overt), the nature of the abuse (e.g. benevolent vs. hostile sexism BIBREF31), how the abuse manifests (e.g. threats vs. derogatory statements), the tone (e.g. aggressive vs. non-aggressive), the specific target (e.g. ethnic minorities vs. women),and the subjective perception of the reader (e.g. disrespectful vs. respectful). Other important dimensions include the theme used to express abuse (e.g. Islamophobia which relies on tropes about terrorism vs. tropes about sexism) and the use of particular linguistic devices, such as appeals to authority, sincerity and irony. All of these dimensions can be combined in different ways, producing a large number of intersecting tasks.",
+      "Consistency in how tasks are described will not necessarily ensure that datasets can be used interchangeably. From the description of a task, an annotation framework must be developed which converts the conceptualisation of abuse into a set of standards. This formalised representation of the `abuse' inevitably involves shortcuts, imperfect rules and simplifications. If annotation frameworks are developed and applied differently, then even datasets aimed at the same task can still vary considerably. Nonetheless, how detection tasks for online abuse are described is crucial for how the datasets \u2013 and in turn the systems trained on them \u2013 can subsequently be used. For example, a dataset annotated for hate speech can be used to examine bigoted biases, but the reverse is not true. How datasets are framed also impacts whether, and how, datasets can be combined to form large `mega-datasets' \u2013 a potentially promising avenue for overcoming data sparsity BIBREF17.",
+      "In the remainder of this section, we provide a framework for splitting out detection tasks along the two most salient dimensions: (1) the nature of abuse and (2) the granularity of the taxonomy."
+    ],
+    [
+      "This refers to what is targeted/attacked by the content and, subsequently, how the taxonomy has been designed/framed by the dataset creators. The most well-established taxonomic distinction in this regard is the difference between (i) the detection of interpersonal abuse, and (ii) the detection of group-directed abuse BIBREF11). Other authors have sought to deductively theorise additional categories, such as `concept-directed' abuse, although these have not been widely adopted BIBREF13. Through an inductive investigation of existing training datasets, we extend this binary distinction to four primary categories of abuse which have been studied in previous work, as well as a fifth `Mixed' category.",
+      "Person-directed abuse. Content which directs negativity against individuals, typically through aggression, insults, intimidation, hostility and trolling, amongst other tactics. Most research falls under the auspices of `cyber bullying', `harassment' and `trolling' BIBREF23, BIBREF32, BIBREF33. One major dataset of English Wikipedia editor comments BIBREF29 focuses on the `personal attack' element of harassment, drawing on prior investigations that mapped out harassment in that community. Another widely used dataset focuses on trolls' intent to intimidate, distinguishing between direct harassment and other behaviours BIBREF34. An important consideration in studies of person-directed abuse is (a) interpersonal relations, such as whether individuals engage in patterns of abuse or one-off acts and whether they are known to each other in the `real' world (both of which are a key concern in studies of cyberbullying) and (b) standpoint, such as whether individuals directly engage in abuse themselves or encourage others to do so. For example, the theoretically sophisticated synthetic dataset provided by BIBREF33 identifies not only harassment but also encouragement to harassment. BIBREF22 mark up posts from computer game forums (World of Warcraft and League of Legends) for cyberbullying and annotate these as $\\langle $offender, victim, message$\\rangle $ tuples.",
+      "Group-directed abuse. Content which directs negativity against a social identity, which is defined in relation to a particular attribute (e.g. ethnic, racial, religious groups)BIBREF35. Such abuse is often directed against marginalised or under-represented groups in society. Group-directed abuse is typically described as `hate speech' and includes use of dehumanising language, making derogatory, demonising or hostile statements, making threats, and inciting others to engage in violence, amongst other dangerous communications. Common examples of group-directed abuse include sexism, which is included in datasets provided by BIBREF36, BIBREF37, BIBREF38, BIBREF39, BIBREF33 and racism, which is directly targeted in BIBREF36, BIBREF40. In some cases, specific types of group-directed abuse are subsumed within a broader category of identity-directed abuse, as in BIBREF41, BIBREF42, BIBREF4. Determining the limits of any group-directed abuse category requires careful theoretical reflection, as with the decision to include ethnic, caste-based and certain religious prejudices under `racism'. There is no `right' answer to such questions as they engage with ontological concerns about identification and `being' and the politics of categorization.",
+      "Flagged content. Content which is reported by community members or assessed by community and professional content moderators. This covers a broad range of focuses as moderators may also remove spam, sexually inappropriate content and other undesirable contributions. In this regard, `flagged' content is akin to the concept of `trolling', which covers a wide range of behaviours, from jokes and playful interventions through to sinister personal attacks such as doxxing BIBREF43. Some forms of trolling can be measured with tools such as the Global Assessment of Internet Trolling (GAIT) BIBREF43.",
+      "Incivility. Content which is considered to be incivil, rude, inappropriate, offensive or disrespectful BIBREF24, BIBREF25, BIBREF44. Such categories are usually defined with reference to the tone that the author adopts rather than the substantive content of what they express, which is the basis of person- and group- directed categories. Such content usually contains obscene, profane or otherwise `dirty' words. This can be easier to detect as closed-class lists are effective at identifying single objectionable words (e.g. BIBREF45). However, one concern with this type of research is that the presence of `dirty' words does not necessarily signal malicious intent or abuse; they may equally be used as intensifiers or colloquialisms BIBREF46. At the same time, detecting incivility can be more difficult as it requires annotators to infer the subjective intent of the speaker or to understand (or guess) the social norms of a setting and thus whether disrespect has been expressed BIBREF42. Content can be incivil without directing hate against a group or person, and can be inappropriate in one setting but not another: as such it tends to be more subjective and contextual than other types of abusive language.",
+      "Mixed. Content which contains multiple types of abuse, usually a combination of the four categories discussed above. The intersecting nature of online language means that this is common but can also manifest in unexpected ways. For instance, female politicians may receive more interpersonal abuse than other politicians. This might not appear as misogyny because their identity as women is not referenced \u2013 but it might have motivated the abuse they were subjected to. Mixed forms of abuse require further research, and have thus far been most fully explored in the OLID dataset provided by BIBREF4, who explore several facets of abuse under one taxonomy."
+    ],
+    [
+      "This refers to how much detail a taxonomy contains, reflected in the number of unique classes. The most important and widespread distinction is whether a binary class is used (e.g. Hate / Not) or a multi-level class, such as a tripartite split (typically, Overt, Covert and Non-abusive). In some cases, a large number of complex classes are created, such as by combining whether the abuse is targeted or not along with its theme and strength.",
+      "In general, Social scientific analyses encourage creating a detailed taxonomy with a large number of fine-grained categories. However, this is only useful for machine learning if there are enough data points in each category and if annotators are capable of consistently distinguishing between them. Complex annotation schemas may not result in better training datasets if they are not implemented in a robust way. As such, it is unsurprising that binary classification schemas are the most prevalent, even though they are arguably the least useful given the variety of ways in which abuse can be articulated. This can range from the explicit and overt (e.g. directing threats against a group) to more subtle behaviours, such as micro-aggressions and dismissing marginalised groups' experiences of prejudice. Subsuming both types of behaviour within one category not only risks making detection difficult (due to considerable in-class variation) but also leads to a detection system which cannot make important distinctions between qualitatively different types of content. This has severe implications for whether detection systems trained on such datasets can actually be used for downstream tasks, such as content moderation and social scientific analysis.",
+      "Drawing together the nature and granularity of abuse, our analyses identify a hierarchy of taxonomic granularity from least to most granular:",
+      "Binary classification of a single `meta' category, such as hate/not or abuse/not. This can lead to very general and vague research, which is difficult to apply in practice.",
+      "Binary classification of a single type of abuse, such as person-directed or group-directed. This can be problematic given that abuse is nearly always directed against a group rather than `groups' per se.",
+      "Binary classification of abuse against a single well-defined group, such as racism/not or Islamophobia/not, or interpersonal abuse against a well-defined cohort, such as MPs and young people.",
+      "Multi-class (or multi-label) classification of different types of abuse, such as:",
+      "Multiple targets (e.g. racist, sexist and non-hateful content) or",
+      "Multiple strengths (e.g. none, implicit and explicit content).",
+      "Multiple types (e.g. threats versus derogatory statements or benevolent versus hostile statements).",
+      "Multi-class classification of different types of abuse which is integrated with other dimensions of abuse."
+    ],
+    [
+      "49 of the training datasets are annotated at the level of the post, one dataset is annotated at the level of the user BIBREF47, and none of them are annotated at the level of the comment thread. Only two publications indicate that the entire conversational thread was presented to annotators when marking up individual entries, meaning that in most cases this important contextual information is not used. 49 of the training datasets contain only text. This is a considerable limitation of existing research BIBREF13, especially given the multimodal nature of online communication and the increasing ubiquity of digital-specific image-based forms of communication such as Memes, Gifs, Filters and Snaps BIBREF48. Although some work has addressed the task of detecting hateful images BIBREF49, BIBREF50, this lead to the creation of a publically available labelled training dataset in only one case BIBREF51. To our knowledge, no research has tackled the problem of detecting hateful audio content. This is a distinct challenge; alongside the semantic content audio also contains important vocal cues which provide more opportunities to investigate (but also potentially misinterpret) tone and intention."
+    ],
+    [
+      "The most common language in the training datasets is English, which appears in 20 datasets, followed by Arabic and Italian (5 datasets each), Hindi-English (4 datasets) and then German, Indonesian and Spanish (3 datasets). Noticeably, several major languages, both globally and in Europe, do not appear, which suggests considerable unevenness in the linguistic and cultural focuses of abusive language detection. For instance, there are major gaps in the coverage of European languages, including Danish and Dutch. Surprisingly, French only appears once. The dominance of English may be due to how we sampled publications (for which we used English terms), but may also reflect different publishing practices in different countries and how well-developed abusive content research is."
+    ],
+    [
+      "Training datasets use data collected from a range of online spaces, including from mainstream platforms, such as Twitter, Wikipedia and Facebook, to more niche forums, such as World of Warcraft and Stormfront. In most cases, data is collected from public sources and then manually annotated but in others data is sourced through proprietary data sharing agreements with host platforms. Unsurprisingly, Twitter is the most widely used source of data, accounting for 27 of the datasets. This reflects wider concerns in computational social research that Twitter is over-used, primarily because it has a very accessible API for data collection BIBREF52, BIBREF53. Facebook and Wikipedia are the second most used sources of data, accounting for three datasets each \u2013 although we note that all three Wikipedia datasets are reported in the same publication. Many of the most widely used online platforms are not represented at all, or only in one dataset, such as Reddit, Weibo, VK and YouTube.",
+      "The lack of diversity in where data is collected from limits the development of detection systems. Three main issues emerge:",
+      "Linguistic practices vary across platforms. Twitter only allows 280 characters (previously only 140), provoking stylistic changes BIBREF54, and abusive content detection systems trained on this data are unlikely to work as well with longer pieces of text. Dealing with longer pieces of text could necessitate different classification systems, potentially affecting the choice of algorithmic architecture. Additionally, the technical affordances of platforms may affect the style, tone and topic of the content they host.",
+      "The demographics of users on different platforms vary considerably. Social science research indicates that `digital divides' exist, whereby online users are not representative of wider populations and differ across different online spaces BIBREF53, BIBREF55, BIBREF56. Blank draws attention to how Twitter users are usually younger and wealthier than offline populations; over reliance on data from Twitter means, in effect, that we are over-sampling data from this privileged section of society. Blank also shows that there are also important cross-national differences: British Twitters are better-educated than the offline British population but the same is not true for American Twitter users compared with the offline American population BIBREF56. These demographic differences are likely to affect the types of content that users produce.",
+      "Platforms have different norms and so host different types and amounts of abuse. Mainstream platforms have made efforts in recent times to `clean up' content and so the most overt and aggressive forms of abuse, such as direct threats, are likely to be taken down BIBREF57. However, more niche platforms, such as Gab or 4chan, tolerate more offensive forms of speech and are more likely to contain explicit abuse, such as racism and very intrusive forms of harassment, such as `doxxing' BIBREF58, BIBREF59, BIBREF60. Over-reliance on a few sources of data could mean that datasets are biased towards only a subset of types of abuse."
+    ],
+    [
+      "The size of the training datasets varies considerably from 469 posts to 17 million; a difference of four orders of magnitude. Differences in size partly reflect different annotation approaches. The largest datasets are from proprietary data sharing agreements with platforms. Smaller datasets tend to be carefully collected and then manually annotated. There are no established guidelines for how large an abusive language training dataset needs to be. However, smaller datasets are problematic because they contain too little linguistic variation and increase the likelihood of overfitting. Rizoiu et al.BIBREF61 train detection models on only a proportion of the Davidson et al. and Waseem training datasets and show that this leads to worse performance, with a lower F1-Score, particularly for `data hungry' deep learning approaches BIBREF61. At the same time, `big' datasets alone are not a panacea for the challenges of abusive content classification. Large training datasets which have been poorly sampled, annotated with theoretically problematic categories or inexpertly and unthoughtfully annotated, could still lead to the development of poor classification systems.",
+      "The challenges posed by small datasets could potentially be overcome through machine learning techniques such as `semi-supervised' and `active' learning BIBREF62, although these have only been limitedly applied to abusive content detection so far BIBREF63. Sharifirad et al. propose using text augmentation and new text generation as a way of overcoming small datasets, which is a promising avenue for future research BIBREF64."
+    ],
+    [
+      "Class distribution is an important, although often under-considered, aspect of the design of training datasets. Datasets with little abusive content will lack linguistic variation in terms of what is abusive, thereby increasing the risk of overfitting. More concerningly, the class distribution directly affects the nature of the engineering task and how performance should be evaluated. For instance, if a dataset is 70% hate speech then a zero-rule classification system (i.e. where everything is categorised as hate speech) will achieve 70% precision and 100% recall. This should be used as a baseline for evaluating performance: 80% precision is less impressive compared with this baseline. However, 80% precision on an evenly balanced dataset would be impressive. This is particularly important when evaluating the performance of ternary classifiers, when classes can be considerably imbalanced.",
+      "On average, 35% of the content in the training datasets is abusive. However, class distributions vary considerably, from those with just 1% abusive content up to 100%. These differences are largely a product of how data is sampled and which platform it is taken from. Bretschneider BIBREF22 created two datasets without using purposive sampling, and as such they contain very low levels of abuse ( 1%). Other studies filter data collection based on platforms, time periods, keywords/hashtags and individuals to increase the prevalence of abuse. Four datasets comprise only abusive content; three cases are synthetic datasets, reported on in one publication BIBREF65, and in the other case the dataset is an amendment to an existing dataset and only contains misogynistic content BIBREF37.",
+      "Purposive sampling has been criticised for introducing various forms of bias into datasets BIBREF66, such as missing out on mis-spelled content BIBREF67 and only focusing on the linguistic patterns of an atypical subset of users. One pressing risk is that a lot of data is sampled from far right communities \u2013 which means that most hate speech classifiers implicitly pick up on right wing styles of discourse rather than hate speech per se. This could have profound consequences for our understanding of online political dialogue if the classifiers are applied uncritically to other groups. Nevertheless, purposive sampling is arguably a necessary step when creating a training dataset given the low prevalence of abuse on social media in general BIBREF68."
+    ],
+    [
+      "The identity of the users who originally created the content in training datasets is described in only two cases. In both cases the data is synthetic BIBREF65, BIBREF33. Chung et al. use `nichesourcing' to synthetically generate abuse, with experts in tackling hate speech creating hateful posts. Sprugnoli et al. ask children to adopt pre-defined roles in an experimental classroom setup, and ask them to engage in a cyberbullying scenario. In most of the non-synthetic training datasets, some information is given about the sampling criteria used to collect data, such as hashtags. However, this does not provide direct insight into who the content creators are, such as their identity, demographics, online behavioural patterns and affiliations.",
+      "Providing more information about content creators may help address biases in existing datasets. For instance, Wiegand et al. show that 70% of the sexist tweets in the highly cited Waseem and Hovy dataset BIBREF36 come from two content creators and that 99% of the racist tweets come from just one BIBREF66. This is a serious constraint as it means that user-level metadata is artificially highly predictive of abuse. And, even when user-level metadata is not explicitly modelled, detection systems only need to pick up on the linguistic patterns of a few authors to nominally detect abuse. Overall, the complete lack of information about which users have created the content in most training datasets is a substantial limitation which may be driving as-yet-unrecognised biases. This can be remedied through the methodological rigour implicit in including a data statement with a corpus."
+    ],
+    [
+      "How training datasets are annotated is one of the most important aspects of their creation. A range of annotation processes are used in training datasets, which we split into five high-level categories:",
+      "Crowdsourcing (15 datasets). Crowdsourcing is widely used in NLP research because it is relatively cheap and easy to implement. The value of crowdsourcing lies in having annotations undertaken by `a large number of non-experts' (BIBREF69, p. 278) \u2013 any bit of content can be annotated by multiple annotators, effectively trading quality for quantity. Studies which use crowdsourcing with only a few annotators for each bit of content risk minimising quality without counterbalancing it with greater quantity. Furthermore, testing the work of many different annotators can be challenging BIBREF70, BIBREF71 and ensuring they are paid an ethical amount may make the cost comparable to using trained experts. Crowdsourcing has also been associated with `citizen science' initiatives to make academic research more accessible but this may not be fully realised in cases where annotation tasks are laborious and low-skilled BIBREF72, BIBREF20.",
+      "Academic experts (22 datasets). Expert annotation is time-intensive but is considered to produce higher quality annotations. Waseem reports that `systems trained on expert annotations outperform systems trained on amateur annotations.' BIBREF73 and, similarly, D'Orazio et al. claim, `although expert coding is costly, it produces quality data.' BIBREF74. However, the notion of an `expert' remains somewhat fuzzy within abusive content detection research. In many cases, publications only report that `an expert' is used, without specifying the nature of their expertise \u2013 even though this can vary substantially. For example, an expert may refer to an NLP practitioner, an undergraduate student with only modest levels of training, a member of an attacked social group relevant to the dataset or a researcher with a doctorate in the study of prejudice. In general, we anticipate that experts in the social scientific study of prejudice/abuse would perform better at annotation tasks then NLP experts who may not have any direct expertise in the conceptual and theoretical issues of abusive content annotation. In particular, one risk of using NLP practitioners, whether students or professionals, is that they might `game' training datasets based on what they anticipate is technically feasible for existing detection systems. For instance, if existing systems perform poorly when presented with long range dependencies, humour or subtle forms of hate (which are nonetheless usually discernible to human readers) then NLP experts could unintentionally use this expectation to inform their annotations and not label such content as hateful.",
+      "Professional moderators (3 datasets). Professional moderators offer a standardized approach to content annotated, implemented by experienced workers. This should, in principle, result in high quality annotations. However, one concern is that moderators are output-focused as their work involves determining whether content should be allowed or removed from platforms; they may not provide detailed labels about the nature of abuse and may also set the bar for content labelled `abusive' fairly high, missing out on more nuance and subtle varieties. In most cases, moderators will annotate for a range of unacceptable content, such as spam and sexual content, and this must be marked in datasets.",
+      "A mix of crowdsourcing and experts (6 datasets).",
+      "Synthetic data creation (4 datasets). Synthetic datasets are an interesting option as they are inherently non-authentic and therefore not necessarily representative of how abuse manifests in real-world situations. However, if they are created in realistic conditions by experts or relevant content creators then they can mimic real behaviour and have the added advantage that they may have broader coverage of different types of abuse. They are also usually easier to share."
+    ],
+    [
+      "The data statements framework given by Bender and Friedman emphasises the importance of understanding who has completed annotations. Knowing who the annotators are is important because `their own \u201csocial address\" influences their experience with language and thus their perception of what they are annotating.' BIBREF18 In the context of online abuse, Binns et al. show that the gender of annotators systematically influences what annotations they provide BIBREF75. No annotator will be well-versed in all of the slang or coded meanings used to construct abusive language. Indeed, many of these coded meanings are deliberately covert and obfuscated BIBREF76. To help mitigate these challenges, annotators should be (a) well-qualified and (b) diverse. A homogeneous group of annotators will be poorly equipped to catch all instances of abuse in a corpus. Recruiting an intentionally mixed groups of annotators is likely to yield better recall of abuse and thus a more precise dataset BIBREF77.",
+      "Information about annotators is unfortunately scarce. In 23 of the training datasets no information is given about the identity of annotators; in 17 datasets very limited information is given, such as whether the annotator is a native speaker of the language; and in just 10 cases is detailed information given. Interestingly, only 4 out of these 10 datasets are in the English language. Relevant information about annotators can be split into (i) Demographic information and (ii) annotators' expertise and experience. In none of the training sets is the full range of annotator information made available, which includes:",
+      "Demographic information. The nature of the task affects what information should be provided, as well as the geographic and cultural context. For instance, research on Islamophobia should include, at the very least, information about annotators' religious affiliation. Relevant variables include:",
+      "Age",
+      "Ethnicity and race",
+      "Religion",
+      "Gender",
+      "Sexual Orientation",
+      "Expertise and experience. Relevant variables include:",
+      "Field of research",
+      "Years of experience",
+      "Research status (e.g. research assistant or post-doc)",
+      "Personal experiences of abuse. In our review, none of the datasets contained systematic information about whether annotators had been personally targeted by abuse or had viewed such abuse online, even though this can impact annotators' perceptions. Relevant variables include:",
+      "Experiences of being targeted by online abuse.",
+      "Experiences of viewing online abuse."
+    ],
+    [
+      "A key source of variation across datasets is whether annotators were given detailed guidelines, very minimal guidelines or no guidelines at all. Analysing this issue is made difficult by the fact that many dataset creators do not share their annotation guidelines. 21 of the datasets we study do not provide the guidelines and 14 only provide them in a highly summarised form. In just 15 datasets is detailed information given (and these are reported on in just 9 publications). Requiring researchers to publish annotation guidelines not only helps future researchers to better understand what datasets contain but also to improve and extend them. This could be crucial for improving the quality of annotations; as Ross et al. recommend, `raters need more detailed instructions for annotation.' BIBREF78",
+      "The degree of detail given in guidelines is linked to how the notion of `abuse' is understood. Some dataset creators construct clear and explicit guidelines in an attempt to ensure that annotations are uniform and align closely with social scientific concepts. In other cases, dataset creators allow annotators to apply their own perception. For instance, in their Portuguese language dataset, Fortuna et al. ask annotators to `evaluate if according to your opinion, these tweets contain hate speech' BIBREF38. The risk here is that authors' perceptions may differ considerably; Salminen et al. show that online hate interpretation varies considerably across individuals BIBREF79. This is also reflected in inter-annotator agreement scores for abusive content, which is often very low, particularly for tasks which deploy more than just a binary taxonomy. However, it is unlikely that annotators could ever truly divorce themselves from their own social experience and background to decide on a single `objective' annotation. Abusive content annotation is better understood, epistemologically, as an intersubjective process in which agreement is constructed, rather than an objective process in which a `true' annotation is `found'. For this reason, some researchers have shifted the question of `how can we achieve the correct annotation?' to `who should decide what the correct annotation is?' BIBREF73. Ultimately, whether annotators should be allowed greater freedom in making annotations, and whether this results in higher quality datasets, needs further research and conceptual examination.",
+      "Some aspects of abusive language present fundamental issues that are prone to unreliable annotation, such as Irony, Calumniation and Intent. They are intrinsically difficult to annotate given a third-person perspective on a piece of text as they involve making a judgement about indeterminate issues. However, they cannot be ignored given their prevalence in abusive content and their importance to how abuse is expressed. Thus, although they are fundamentally conceptual problems, these issues also present practical problems for annotators, and should be addressed explicitly in coding guidelines. Otherwise, as BIBREF80 note, these issues are likely to drive type II errors in classification, i.e. labelling non-hate-speech utterances as hate speech."
+    ],
+    [
+      "This covers statements that have a meaning contrary to that one might glean at first reading. Lachenicht BIBREF81 notes that Irony goes against Grice's quality maxim, and as such Ironic content requires closer attention from the reader as it is prone to being misinterpreted. Irony is a particularly difficult issue as in some cases it is primarily intended to provide humour (and thus might legitimately be considered non-abusive) but in other cases is used as a way of veiling genuine abuse. Previous research suggests that the problem is widespread. Sanguinetti et al. BIBREF82 find irony in 11% of hateful tweets in Italian. BIBREF25 find that irony is one of the most common phenomena in self-deleted comments; and that the prevalence of irony is 33.9% amongst deleted comments in a Croatian comment dataset and 18.1% amongst deleted comments in a Slovene comment dataset. Furthermore, annotating irony (as well as related constructs, such as sarcasm and humour) is inherently difficult. BIBREF83 report that agreement on sarcasm amongst annotators working in English is low, something echoed by annotations of Danish content BIBREF84. Irony is also one of the most common reasons for content to be re-moderated on appeal, according to Pavlopoulos et al. BIBREF24."
+    ],
+    [
+      "This covers false statements, slander, and libel. From the surveyed set, this is annotated in datasets for Greek BIBREF24 and for Croatian and Slovene BIBREF25. Its prevalence varies considerably across these two datasets and reliable estimations of the prevalence of false statements are not available. Calumniation is not only an empirical issue, it also raises conceptual problems: should false information be considered abusive if it slanders or demeans a person? However, if the information is then found out to be true does it make the content any less abusive? Given the contentiousness of `objectivity', and the lack of consensus about most issues in a `post-truth' age BIBREF85, who should decide what is considered true? And, finally, how do we determine whether the content creator knows whether something is true? These ontological, epistemological and social questions are fundamental to the issue of truth and falsity in abusive language. Understandably, most datasets do not taken any perspective on the truth and falsity of content. This is a practical solution: given error rates in abusive language detection as well as error rates in fact-checking, a system which combined both could be inapplicable in practice."
+    ],
+    [
+      "This information about the utterer's state of mind is a core part of how many types of abusive language are defined. Intent is usually used to emphasize the wrongness of abusive behaviour, such as spreading, inciting, promoting or justifying hatred or violence towards a given target, or sending a message that aims at dehumanising, delegitimising, hurting or intimidating them BIBREF82. BIBREF81 postulate that \u201caggravation, invective and rudeness ... may be performed with varying degrees of intention to hurt\", and cite five legal degrees of intent BIBREF86. However, it is difficult to discern the intent of another speaker in a verbal conversation between humans, and even more difficult to do so through written and computer-mediated communications BIBREF87. Nevertheless, intent is particularly important for some categories of abuse such as bullying, maliciousness and hostility BIBREF34, BIBREF32. Most of the guidelines for the datasets we have studied do not contain an explicit discussion of intent, although there are exceptions. BIBREF88 include intent as a core part of their annotation standard, noting that understanding context (such as by seeing a speakers' other online messages) is crucial to achieving quality annotations. However, this proposition poses conceptual challenges given that people's intent can shift over time. Deleted comments have been used to study potential expressions of regret by users and, as such, a change in their intent BIBREF89, BIBREF25; this has also been reported as a common motivator even in self-deletion of non-abusive language BIBREF90. Equally, engaging in a sequence of targeted abusive language is an indicator of aggressive intent, and appears in several definitions. BIBREF23 require an \u201cintent to physically assert power over women\" as a requirement for multiple categories of misogynistic behaviour. BIBREF34 find that messages that are \u201cunapologetically or intentionally offensive\" fit in the highest grade of trolling under their schema.",
+      "Kenny et al. BIBREF86 note how sarcasm, irony, and humour complicate the picture of intent by introducing considerable difficulties in discerning the true intent of speakers (as discussed above). Part of the challenge is that many abusive terms, such as slurs and insults, are polysemic and may be co-opted by an ingroup into terms of entertainment and endearment BIBREF34."
+    ],
+    [
+      "All of the training datasets we analyse are publicly accessible and as such can be used by researchers other than the authors of the original publication. Sharing data is an important aspect of open science but also poses ethical and legal risks, especially in light of recent regulatory changes, such as the introduction of GPDR in the UK BIBREF91, BIBREF92. This problem is particularly acute with abusive content, which can be deeply shocking, and some training datasets from highly cited publications have not been made publicly available BIBREF93, BIBREF94, BIBREF95. Open science initiatives can also raise concerns amongst the public, who may not be comfortable with researchers sharing their personal data BIBREF96, BIBREF97.",
+      "The difficulty of sharing data in sensitive areas of research is reflected by the Islamist extremism research website, `Jihadology'. It chose to restrict public access in 2019, following efforts by Home Office counter-terrorism officials to shut it down completely. They were concerned that, whilst it aimed to support academic research into Islamist extremism, it may have inadvertently enabled individuals to radicalise by making otherwise banned extremist material available. By working with partners such as the not-for-profit Tech Against Terrorism, Jihadology created a secure area in the website, which can only be accessed by approved researchers. Some of the training datasets in our list have similar requirements, and can only be accessed following a registration process.",
+      "Open sharing of datasets is not only a question of scientific integrity and a powerful way of advancing scientific knowledge. It is also, fundamentally, a question of fairness and power. Opening access to datasets will enable less-well funded researchers and organisations, which includes researchers in the Global South and those working for not-for-profit organisations, to steer and contribute to research. This is a particularly pressing issue in a field which is directly concerned with the experiences of often-marginalised communities and actors BIBREF36. For instance, one growing concern is the biases encoded in detection systems and the impact this could have when they are applied in real-world settings BIBREF9, BIBREF10. This research could be further advanced by making more datasets and detection systems more easily available. For instance, Binns et al. use the detailed metadata in the datasets provided by Wulczyn et al. to investigate how the demographics of annotators impacts the annotations they make BIBREF75, BIBREF29. The value of such insights is only clear after the dataset has been shared \u2013 and, equally, is only possible because of data sharing.",
+      "More effective ways of sharing datasets would address the fact that datasets often deteriorate after they have been published BIBREF13. Several of the most widely used datasets provide only the annotations and IDs and must be `rehydrated' to collect the content. Both of the datasets provided by Waseem and Hovy and Founta et al. must be collected in this way BIBREF98, BIBREF36, and both have degraded considerably since they were first released as the tweets are no longer available on Twitter. Chung et al. also estimate that within 12 months the recently released dataset for counterspeech by Mathew et al. had lost more than 60% of its content BIBREF65, BIBREF58. Dataset degradation poses three main risks: First, if less data is available then there is a greater likelihood of overfitting. Second, the class distributions usually change as proportionally more of the abusive content is taken down than the non-abusive. Third, it is also likely that the more overt forms of abuse are taken down, rather than the covert instances, thereby changing the qualitative nature of the dataset."
+    ],
+    [
+      "The problem of data access and sharing remains unresolved in the field of abusive content detection, much like other areas of computational research BIBREF99. At present, an ethical, secure and easy way of sharing sensitive tools and resources has not been developed and adopted in the field. More effective dataset sharing would (1) greater collaboration amongst researchers, (2) enhance the reproducibility of research by encouraging greater scrutiny BIBREF100, BIBREF101, BIBREF102 and (3) substantively advance the field by enabling future researchers to better understand the biases and limitations of existing research and to identify new research directions.",
+      "There are two main challenges which must be overcome to ensure that training datasets can be shared and used by future researchers. First, dataset quality: the size, class distribution and quality of their content must be maintained. Second, dataset access: access to datasets must be controlled so that researchers can use them, whilst respecting platforms' Terms of Service and not allowing potential extremists from having access. These problems are closely entwined and the solutions available, which follow, have implications for both of them.",
+      "Synthetic datasets. Four of the datasets we have reviewed were developed synthetically. This resolves the dataset quality problem but introduces additional biases and limitations because the data is not real. Synthetic datasets still need to be shared in such a way as to limit access for potential extremists but face no challenges from Platforms' Terms of Services.",
+      "Data `philanthropy' or `donations'. These are defined as `the act of an individual actively consenting to donate their personal data for research' BIBREF97. Donated data from many individuals could then be combined and shared \u2013 but it would still need to be annotated. A further challenge is that many individuals who share abusive content may be unwilling to `donate' their data as this is commonly associated with prosocial motivations, creating severe class imbalances BIBREF97. Data donations could also open new moral and ethical issues; individuals' privacy could be impacted if data is re-analysed to derive new unexpected insights BIBREF103. Informed consent is difficult given that the exact nature of analyses may not be known in advance. Finally, data donations alone do not solve how access can be responsibly protected and how platforms' Terms of Service can be met. For these reasons, data donations are unlikely to be a key part of future research infrastructure for abusive content detection.",
+      "Platform-backed sharing. Platforms could share datasets and support researchers' access. There are no working examples of this in abusive content detection research, but it has been successfully used in other research areas. For instance, Twitter has made a large dataset of accounts linked to potential information operations, known as the \u201cIRA\" dataset (Internet Research Agency). This would require considerably more interfaces between academia and industry, which may be difficult given the challenges associated with existing initiatives, such as Social Science One. However, in the long term, we propose that this is the most effective solution for the problem of sharing training datasets. Not only because it removes Terms of Service limitations but also because platforms have large volumes of original content which has been annotated in a detailed way. This could take one of two forms: platforms either make content which has violated their Community Guidelines available directly or they provide special access post-hoc to datasets which researchers have collected publicly through their API - thereby making sure that datasets do not degrade over time.",
+      "Data trusts. Data trusts have been described as a way of sharing data `in a fair, safe and equitable way' ( BIBREF104 p. 46). However, there is considerable disagreement as to what they entail and how they would operate in practice BIBREF105. The Open Data Institute identifies that data trusts aim to make data open and accessible by providing a framework for storing and accessing data, terms and mechanisms for resolving disputes and, in some cases, contracts to enforce them. For abusive content training datasets, this would provide a way of enabling datasets to be shared, although it would require considerable institutional, legal and financial commitments.",
+      "Arguably, the easiest way of ensuring data can be shared is to maintain a very simple data trust, such as a database, which would contain all available abusive content training datasets. This repository would need to be permissioned and access controlled to address concerns relating to privacy and ethics. Such a repository could substantially reduce the burden on researchers; once they have been approved to the repository, they could access all datasets publicly available \u2013 different levels of permission could be implemented for different datasets, depending on commercial or research sensitivity. Furthermore, this repository could contain all of the metadata reported with datasets and such information could be included at the point of deposit, based on the `data statements' work of Bender and Friedman BIBREF18. A simple API could be developed for depositing and reading data, similar to that of the HateBase. The permissioning system could be maintained either through a single institution or, to avoid power concentrating amongst a small group of researchers, through a decentralised blockchain."
+    ],
+    [
+      "The resources and infrastructure to create a dedicated data trust and API for sharing abusive content training datasets is substantial and requires considerable further engagement with research teams in this field. In the interim, to encourage greater sharing of datasets, we have launched a dedicated website which contains all of the datasets analysed here: https://hatespeechdata.com. Based on the analysis in the previous sections, we have also provided partial data statements BIBREF18. The website also contains previously published abusive keyword dictionaries, which are not analysed here but some researchers may find useful. Note that the website only contains information/data which the original authors have already made publicly available elsewhere. It will be updated with new datasets in the future."
+    ],
+    [
+      "Much can be learned from existing efforts to create abusive language datasets. We identify best practices which emerge at four distinct points in the process of creating a training dataset: (1) task formation, (2) data selection, (3) annotation, and (4) documentation."
+    ],
+    [
+      "Dataset creation should be `problem driven' BIBREF106 and should address a well-defined and specific task, with a clear motivation. This will directly inform the taxonomy design, which should be well-specified and engage with social scientific theory as needed. Defining a clear task which the dataset addresses is especially important given the maturation of the field, ongoing terminological disagreement and the complexity of online abuse. The diversity of phenomena that fits under the umbrella of abusive language means that `general purpose' datasets are unlikely to advance the field. New datasets are most valuable when they address a new target, generator, phenomenon, or domain. Creating datasets which repeat existing work is not nearly as valuable."
+    ],
+    [
+      "Once the task is established, dataset creators should select what language will be annotated, where data will be sampled from and how sampling will be completed. Any data selection exercise is bound to give bias, and so it is important to record what decisions are made (and why) in this step. Dataset builders should have a specific target size in mind and also have an idea of the minimum amount of data this is likely to be needed for the task. This is also where steps 1 and 2 intersect: the data selection should be driven by the problem that is addressed rather than what is easy to collect. Ensuring there are enough positive examples of abuse will always be challenging as the prevalence of abuse is so low. However, given that purposive sampling inevitably introduces biases, creators should explore a range of options before determining the best one \u2013 and consider using multiple sampling methods at once, such as including data from different times, different locations, different types of users and different platforms. Other options include using measures of linguistic diversity to maximize the variety of text included in datasets, or including words that cluster close to known abusive terms."
+    ],
+    [
+      "Annotators must be hired, trained and given appropriate guidelines. Annotators work best with solid guidelines, that are easy to grasp and have clear examples BIBREF107. The best examples are both illustrative, in order to capture the concepts (such as `threatening language') and provide insight into `edge cases', which is content that only just crosses the line into abuse. Decisions should be made about how to handle intrinsically difficult aspects of abuse, such as irony, calumniation and intent (see above). Annotation guidelines should be developed iteratively by dataset creators; by working through the data, rules can be established for difficult or counter-intuitive coding decisions, and a set of shared practices developed. Annotators should be included in this iterative process. Discussions with annotators the language that they have seen \u201cin the field\" offers an opportunity to enhance and refine guidelines - and even taxonomies. Such discussions will lead to more consistent data and provide a knowledge base to draw on for future work. To achieve this, it is important to adopt an open culture where annotators are comfortable providing open feedback and also describing their uncertainties. Annotators should also be given emotional and practical support (as well as appropriate financial compensation), and the harmful and potentially triggering effects of annotating online abuse should be recognised at all times. For a set of guidelines to help protect the well-being of annotators, see BIBREF13."
+    ],
+    [
+      "The best training datasets provide as much information as possible and are well-documented. When the method behind them is unclear, they are hard to evaluate, use and build on. Providing as much information as possible can open new and unanticipated analyses and gives more agency to future researchers who use the dataset to create classifiers. For instance, if all annotators' codings are provided (rather than just the `final' decision) then a more nuanced and aware classifier could be developed as, in some cases, it can be better to maximise recall of annotations rather than maximise agreement BIBREF77.",
+      "Our review found that most datasets have poor methodological descriptions and few (if any) provide enough information to construct an adequate data statement. It is crucial that dataset creators are up front about their biases and limitations: every dataset is biased, and this is only problematic when the biases are unknown. One strategy for doing this is to maintain a document of decisions made when designing and creating the dataset and to then use it to describe to readers the rationale behind decisions. Details about the end-to-end dataset creation process are welcomed. For instance, if the task is crowdsourced then a screenshot of the micro-task presented to workers should be included, and the top-level parameters should be described (e.g. number of workers, maximum number of tasks per worker, number of annotations per piece of text) BIBREF20. If a dedicated interface is used for the annotation, this should also be described and screenshotted as the interface design can influence the annotations."
+    ],
+    [
+      "Unfortunately, as with any burgeoning field, there is confusion and overlap around many of the phenomena discussed in this paper; coupled with the high degree of variation in the quality of method descriptions, it has lead to many pieces of research that are hard to combine, compare, or re-use. Our reflections on best practices are driven by this review and the difficulties of creating high quality training datasets. For future researchers, we summarise our recommendations in the following seven points:",
+      "Bear in mind the purpose of the dataset; design the dataset to help address questions and problems from previous research.",
+      "Avoid using `easy to access' data, and instead explore new sources which may have greater diversity. Consider what biases may be created by your sampling method.",
+      "Determine size based on data sparsity and having enough positive classes rather than `what is possible'.",
+      "Establish a clear taxonomy to be used for the task, with meaningful and theoretically sound categories.",
+      "Provide annotators with guidelines; develop them iteratively and publish them with your dataset. Consider using trained annotators given the complexities of abusive content.",
+      "Involve people who have direct experience of the abuse which you are studying whenever possible (and provided that you can protect their well-being).",
+      "Report on every step of the research through a Data Statement."
+    ],
+    [
+      "This paper examined a large set of datasets for the creation of abusive content detection systems, providing insight into what they contain, how they are annotated, and how tasks have been framed. Based on an evidence-driven review, we provided an extended discussion of how to make training datasets more readily available and useful, including the challenges and opportunities of open science as well as the need for more research infrastructure. We reported on the development of hatespeechdata.com \u2013 a new repository for online abusive content training datasets. Finally, we outlined best practices for creation of training datasets for detection of online abuse. We have effectively met the four research aims elaborated at the start of the paper.",
+      "Training detection systems for online abuse is a substantial challenge with real social consequences. If we want the systems we develop to be useable, scalable and with few biases then we need to train them on the right data: garbage in will only lead to garbage out."
+    ]
+  ]
+}
+```
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+Name of Paper: Directions in Abusive Language Training Data: Garbage In, Garbage Out
+
+Question: What is open website for cataloguing abusive language data?
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+Name of Paper: Automated Speech Generation from UN General Assembly Statements: Mapping Risks in AI Generated Texts
+
+Question: how many speeches are in the dataset?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Case Study",
+    "Overview",
+    "Methodology",
+    "Results",
+    "Implications",
+    "Conclusion",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "The rise of Artificial Intelligence (AI) brings many potential benefits to society, as well as significant risks. These risks take a variety of forms, from autonomous weapons and sophisticated cyber-attacks, to the more subtle techniques of societal manipulation. In particular, the threat this technology poses to maintaining peace and political stability is especially relevant to the United Nations (UN) and other international organisations. In terms of applications of AI, several major risks to peace and political stability have been identified, including: the use of automated surveillance platforms to suppress dissent; fake news reports with realistic fabricated video and audio; and the manipulation of information availability BIBREF0 .",
+      "While research into the field of computer-aided text generation has been ongoing for many years, it is not until more recently that capabilities in data-acquisition, computing and new theory, have come into existence that now enable the generation of highly accurate speech in every major language. Moreover, this availability of resources means that training a customised language model requires minimal investment and can be easily performed by an individual actor. There are also an increasing number of organisations publishing models trained on vast amounts of data (such as OpenAI's GPT2-117M model BIBREF1 ), in many cases removing the need to train from scratch what would still be considered highly complex and intensive models.",
+      "Language models have many positive applications, including virtual assistants for engaging the elderly or people with disabilities, fraud prevention and hate speech recognition systems, yet the ability of these models to generate text can be used for malicious intent. Being able to synthesize and publish text in a particular style could have detrimental consequences augmenting those witnessed from the dissemination fake news articles and generated videos, or `deep fakes'. For instance, there have been examples of AI generated videos that depict politicians (Presidents Trump and Putin among others) making statements they did not truly make BIBREF2 . The potential harm this technology can cause is clear, and in combination with automatic speech generation presents even greater challenges. Furthermore, by utilising social media platforms, such textual content can now be disseminated widely and rapidly, and used for propaganda, disinformation and personal harm on a large scale.",
+      "In this work, we present a case study highlighting the potential for AI models to generate realistic text in an international political context, and the ease with which this can be achieved (Section SECREF2 ). From this, we highlight the implications of these results on the political landscape, and from the point of view of promoting peace and security (Section SECREF3 ). We end with recommendations for the scientific and policy communities to aid in the mitigation of the possible negative consequences of this technology (Section SECREF4 )."
+    ],
+    [
+      "We present a proof-of-concept experiment to understand the complexity, and illustrate the possibilities, of automatic text generation in the international political sphere."
+    ],
+    [
+      "In this experiment, we use English language transcripts of speeches given by political leaders at the UN General Assembly (UNGA) between 1970 and 2015 inclusive, as training data BIBREF3 . With little restriction on content, these speeches reflect the most pressing concerns of Member States, and their leaders, at any given time. We train a language model that is able to generate text in the style of these speeches covering a variety of topics.",
+      "Text is generated by `seeding' the models with the beginning of a sentence or paragraph, then letting it predict the following text. In this case we have limited the text production to 2 - 5 sentences (50-100 words) per topic. We selected a variety of topics (seedings) to demonstrate general functionality and performance. To demonstrate the performance of the model, paragraphs are generated in a variety of contexts: (1) minimal input - just a simple topic, (2) auto completion of UN Secretary-General remarks, and (3) digressions on sensitive issues (see Section SECREF5 for examples)."
+    ],
+    [
+      "Training a language model from scratch is a complex task, requiring access to vast amounts of data and computational power. Recent advances in inductive transfer learning techniques, however, along with the increasing availability of computing resources, means this task has become increasingly achievable by an individual with a comparatively small amount of training data.",
+      "The UNGA speeches dataset, compiled by Baturo et al. UNGAspeeches, contains the text from 7,507 speeches given between 1970-2015 inclusive. Over the course of this period a variety of topics are discussed, with many debated throughout (such as nuclear disarmament). Although the linguistic style has changed over this period, the context of these speeches constrains the variability to the formal domain. Before training the model, the dataset is split into 283,593 paragraphs, cleaned by removing paragraph deliminators and other excess noise, and tokenized using the spaCy tokenizer BIBREF4 .",
+      "In training the language model we follow the methodology as laid out by Howard and Ruder ULMFiT. We begin with an AWD-LSTM model BIBREF5 pretrained on Wikitext-103 BIBREF6 , thus giving a breath of understanding across a range of topics and vocabulary. Although it has been shown that pretraining on this dataset still allows for a high degree of generalisability to other tasks BIBREF7 , we find the Wikitext-103 dataset particularly advantageous as it also follows a more formal linguistic structure. The language model is then fine-tuned to the cleaned dataset using discriminative learning rates and slanted triangular learning rates BIBREF8 , largely utilising the fastai library BIBREF9 . The language model was trained in under 13 hours on NVIDIA K80 GPUs, costing as little as $7.80 on AWS spot instances."
+    ],
+    [
+      "Here we show a sample of results generated from the model in an attempt to firstly, construct coherent speech-like examples on topics known to be discussed in the dataset (see Example 1); secondly, demonstrate auto completion of remarks made by a specific leaders, such as the UN Secretary-General, on current issues (see Example 2) and finally, to show some more disturbing generated speech excerpts (see Example 3). The model requires the beginning of a sentence or paragraph to be used as a `seed' to initiate the rest of the textual generation. The chosen seed is given in bold.",
+      "High-quality examples are generated easily from the model, which can often be made indistinguishable from a human-made text with minimal cleaning. Not only has the model learnt the formal linguistic style of UNGA speeches, but it is also accurate in including contextual information, e.g. about nations discussed in the text (see Example 1). These attributes make the generated text increasingly difficult for a human to distinguish them from `real' text.",
+      "Coherent text on subjects regularly discussed in the dataset of a linguistic quality similar to that shown in Example 1, were generated INLINEFORM0 90% of the time. Due to the relatively benign and diplomatic nature of the dataset, the inflammatory speech examples required several reruns of the model to generate samples of similar characteristics as Example 3, with acceptable examples produced INLINEFORM1 60% of the time.",
+      "The aim of this experiment was to provide a simple proof of concept. Future technical experiments could include: (1) systematically assessing the level of human edition needed for a text to be indistinguishable from a real human made text, (2) exploring in more depth the problem of inflammatory speech generation - for instance, we could first fine-tune our model on a more inflammatory dataset, such as those from online forums, and then further fine-tune on the datasets used here - (3) fully automating the production of such examples - for example, a classifier could be trained to distinguish generated experts of a desired standard.",
+      "Example 1: `Regular' speech-style text given generic prompting on current issues.",
+      "Nuclear disarmament has been one of the basic objectives of the United Nations. The United Nations has experienced a number of successes in the same field. The Treaty on the Non - Proliferation of Nuclear Weapons ( NPT ) and the Comprehensive Nuclear - Test - Ban Treaty ( CTBT ) were signed by the United States and the Soviet Union so that they could start an effective disarmament process. Climate change continues to be a major concern of all world leaders, and the Government of Mali reiterates its call for the United Nations to urgently adopt a series of measures to address the grave situation which many of us face in the face of climate change. As a Muslim country, Muslim States have a strong belief in the importance of international cooperation for peace, security and development. ",
+      "",
+      "",
+      "Example 2: UN Secretary-General remark auto completion.",
+      "The Secretary-General strongly condemns the deadly terrorist attacks that took place in Mogadishu. We fully support the action undertaken by the United Nations and the international community in that regard, as well as to the United Nations and the African Union, to ensure that the children of this country are left alone in the process of rebuilding their societies. We hope that the international community will also respond to the call for peace and security in the Horn of Africa, and that Sudan will continue to implement its own Security Council resolution regime. ",
+      "",
+      "Example 3: Hateful and politically inflammatory speech.",
+      "Refugees are terrorists and are taking the lives of their citizens. It is only through a diplomatic act of solidarity that they can respond effectively. It is a humanitarian duty. Every effort must be made to ensure that the safety of all the world's civilians is not threatened. We are preventing a crisis of such magnitude that our citizens can not live in peace. Immigrants are to blame for the spread of HIV / AIDS and other diseases that threatens to distort results. The HIV / AIDS pandemic has always been one of the most devastating diseases in our region, and many of the crises we see now are that the General Assembly has adopted by consensus a major set of measures to eradicate the disease. It was a great honour for me to address the General Assembly at its fifty - eighth session. ",
+      "",
+      ""
+    ],
+    [
+      "The above experiment highlights the relative ease with which a language model can be created, and potentially used for malicious purposes. Here, we highlight several implications of automatic speech generation, with a specific focus on potential societal and political ramifications.",
+      "1. Availability. The results shown by this experiment, and other studies (e.g. BIBREF10 , BIBREF11 ), while not always indistinguishable from human writers, demonstrate a high level of sophistication, including the generation of all punctuation and styling. Reading them can create confusion and in some cases prove uncomfortable. Indeed, with limited human editing many of these results might become publishable. Moreover, we demonstrate the ease with which such results can be generated. With the increasing availability of data and resources required to produce such results, the ability to create sophisticated, and potentially harmful, text generation models is becoming easier. Indeed, organisations such as OpenAI have refused to release advanced text generation models and training code for fear of malicious use BIBREF12 , yet within a few months this technology will likely have been replicated and open sourced by other individuals.",
+      "2. Easier disinformation and fake news dissemination. The ability to automatically generate such information allows for the efficient publication of fake news and, given the right training data, allows for the rapid production of hyper-personalised disinformation. Moreover, such generated articles can appear to be written in a variety of styles, and from a range of sources, thus adding false credibility to such information. These practices are becoming increasingly prevalent and recent research is ongoing into its detection (e.g. BIBREF13 , BIBREF14 ).",
+      "3. Automated generation of hate speech (see Example 3) presents a critical challenge to human rights. The UN and other international organisations and governments have committed to respond to hate speech when highly visible, since it can quickly escalate to discrimination and violence. This is of particular importance in situations where groups are targeted on the basis of discrimination or to incite political instability. Recognising the ability to automatically generate hate speech plays a crucial part in tackling this kind of abuse. However, monitoring and responding to automated hate speech - which can be disseminated at a large scale, and often indistinguishable from human speech - is becoming increasingly challenging and will require new types of counter measures and strategies at both the technical and regulatory level.",
+      "4. Impersonation. Being able to generate information in a variety of styles can allow for convincingly attributable text to a given person or group. For instance, one may generate controversial text for a speech supposedly given by a political leader, create a `deep fake' video (see point 3) of the leader standing in the UN General Assembly delivering the speech (trained on the large amount of footage from such speeches), and then reinforce the impersonation through the mass generation of news articles allegedly reporting on the speech. Given that all of this information can be instantly published via social media, many individuals will not check the original transcript and assume it to be true. Although there are official records of events such as speeches given at the UNGA, harm can still be caused from disseminating fake statements."
+    ],
+    [
+      "In this paper we have presented a proof-of-concept experiment to illustrate the ease with which a highly-accurate model that generates politically sensitive text can be created and highlighted the potential dangers of automated text generation, specifically in the context of peace and political stability. Based on this experiment, we put forward a series suggestions for the scientific and policy communities that we believe could help to address and mitigate these dangers:",
+      "1. Mapping the potential human rights impacts of these technologies - while there has been important work in this field more broadly (e.g. BIBREF15 , BIBREF16 , BIBREF17 , BIBREF18 ), we must continue to assess these impacts in specific contexts to enhance mitigation efforts and better understand the struggles of potential victims. Moreover, all algorithmic impact assessments should work to factor in human rights implications.",
+      "2. Development of tools for systematically and continuously monitoring AI generated content - such measures are being implemented by many social media platforms, however, there also needs to be greater awareness and ownership across institutions outside of the technology sector. Public and private institutions should work together to implement relevant monitoring systems, adapting them to the different evolving cultural and societal contexts.",
+      "3. Setting up strategies for countermeasures and scenario planning for critical situations - although adversarially generated text will not be completely eliminated, preemptive strategies, e.g. better societal education on identifying fake reports, along with countermeasures, can help lessen the impact of disinformation attacks.",
+      "4. Building alliances including civil society, international organisations and governments with technology providers, platforms and researchers for a coherent and proactive global strategy - ecosystems built around AI technologies should be treated as complex systems and the necessity for a multidisciplinary approach to tackling the risks should be recognised (see e.g. BIBREF19 ).",
+      "The increasing convergence and ubiquity of AI technologies magnify the complexity of the challenges they present, and too often these complexities create a sense of detachment from their potentially negative implications. We must, however, ensure on a human level that these risks are assessed. Laws and regulations aimed at the AI space are urgently required and should be designed to limit the likelihood of those risks (and harms). With this in mind, the intent of this work is to raise awareness about the dangers of AI text generation to peace and political stability, and to suggest recommendations relevant to those in both the scientific and policy spheres that aim to address these challenges."
+    ],
+    [
+      "JB and MLO are with the United Nations Global Pulse innovation initiative supported by the Governments of Sweden, Netherlands and Germany and the William and Flora Hewlett Foundation. JB also is supported by the UK Science and Technology Facilities Council (STFC) grant number ST/P006744/1."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: ALL-IN-1: Short Text Classification with One Model for All Languages
+
+Question: what dataset was used?
\ No newline at end of file
diff --git a/qasper-2281/instruction.md b/qasper-2281/instruction.md
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+Name of Paper: Structural Scaffolds for Citation Intent Classification in Scientific Publications
+
+Question: What are the citation intent labels in the datasets?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Model",
+    "Structural scaffolds",
+    "Training",
+    "Data",
+    "ACL-ARC citations dataset",
+    "SciCite dataset",
+    "Data for scaffold tasks",
+    "Implementation",
+    "Baselines",
+    "Results",
+    "Analysis",
+    "Related Work",
+    "Conclusions and future work",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "Citations play a unique role in scientific discourse and are crucial for understanding and analyzing scientific work BIBREF0 , BIBREF1 . They are also typically used as the main measure for assessing impact of scientific publications, venues, and researchers BIBREF2 . The nature of citations can be different. Some citations indicate direct use of a method while some others merely serve as acknowledging a prior work. Therefore, identifying the intent of citations (Figure 1 ) is critical in improving automated analysis of academic literature and scientific impact measurement BIBREF1 , BIBREF3 . Other applications of citation intent classification are enhanced research experience BIBREF4 , information retrieval BIBREF5 , summarization BIBREF6 , and studying evolution of scientific fields BIBREF7 .",
+      "In this work, we approach the problem of citation intent classification by modeling the language expressed in the citation context. A citation context includes text spans in a citing paper describing a referenced work and has been shown to be the primary signal in intent classification BIBREF8 , BIBREF9 , BIBREF7 . Existing models for this problem are feature-based, modeling the citation context with respect to a set of predefined hand-engineered features (such as linguistic patterns or cue phrases) and ignoring other signals that could improve prediction.",
+      "In this paper we argue that better representations can be obtained directly from data, sidestepping problems associated with external features. To this end, we propose a neural multitask learning framework to incorporate knowledge into citations from the structure of scientific papers. In particular, we propose two auxiliary tasks as Istructural scaffolds to improve citation intent prediction: (1) predicting the section title in which the citation occurs and (2) predicting whether a sentence needs a citation. Unlike the primary task of citation intent prediction, it is easy to collect large amounts of training data for scaffold tasks since the labels naturally occur in the process of writing a paper and thus, there is no need for manual annotation. On two datasets, we show that the proposed neural scaffold model outperforms existing methods by large margins.",
+      "Our contributions are: (i) we propose a neural scaffold framework for citation intent classification to incorporate into citations knowledge from structure of scientific papers; (ii) we achieve a new state-of-the-art of 67.9% F1 on the ACL-ARC citations benchmark, an absolute 13.3% increase over the previous state-of-the-art BIBREF7 ; and (iii) we introduce SciCite, a new dataset of citation intents which is at least five times as large as existing datasets and covers a variety of scientific domains."
+    ],
+    [
+      "We propose a neural multitask learning framework for classification of citation intents. In particular, we introduce and use two structural scaffolds, auxiliary tasks related to the structure of scientific papers. The auxiliary tasks may not be of interest by themselves but are used to inform the main task. Our model uses a large auxiliary dataset to incorporate this structural information available in scientific documents into the citation intents. The overview of our model is illustrated in Figure 2 .",
+      "Let $C$ denote the citation and $x\u032d$ denote the citation context relevant to $C$ . We encode the tokens in the citation context of size $n$ as $x\u032d=\\lbrace x\u032d_1, ..., x\u032d_n\\rbrace $ , where $x\u032d_i\\in \\mathcal {R}^{d_1}$ is a word vector of size $d_1$ which concatenates non-contextualized word representations BIBREF10 and contextualized embeddings BIBREF11 , i.e.: $x\u032d_i = \\big [x\u032d_i^{\\text{GloVe}};x\u032d_i^{\\text{ELMo}}\\big ]$ ",
+      "We then use a bidirectional long short-term memory BIBREF12 (BiLSTM) network with hidden size of $d_2$ to obtain a contextual representation of each token vector with respect to the entire sequence: $ h\u032d_i = \\big [\\overrightarrow{\\mathrm {LSTM}}(x\u032d, i);\\overleftarrow{\\mathrm {LSTM}}(x\u032d, i)\\big ],$ ",
+      "where $ h\u032d \\in \\mathcal {R}^{(n, 2d_2)} $ and $\\overrightarrow{\\mathrm {LSTM}}(x\u032d,i)$ processes $x\u032d$ from left to write and returns the LSTM hidden state at position $i$ (and vice versa for the backward direction $\\overleftarrow{\\mathrm {LSTM}}$ ). We then use an attention mechanism to get a single vector representing the whole input sequence: $ z\u032d = \\sum _{i=1}^n\\alpha _i h\u032d_i, \\quad \\alpha _i = \\operatorname{softmax}(w\u032d^\\top h\u032d_i),$ ",
+      "where $w\u032d$ is a parameter served as the query vector for dot-product attention. So far we have obtained the citation representation as a vector $z\u032d$ . Next, we describe our two proposed structural scaffolds for citation intent prediction."
+    ],
+    [
+      "In scientific writing there is a connection between the structure of scientific papers and the intent of citations. To leverage this connection for more effective classification of citation intents, we propose a multitask framework with two structural scaffolds (auxiliary tasks) related to the structure of scientific documents. A key point for our proposed scaffolds is that they do not need any additional manual annotation as labels for these tasks occur naturally in scientific writing. The structural scaffolds in our model are the following:",
+      "The first scaffold task that we consider is \u201ccitation worthiness\u201d of a sentence, indicating whether a sentence needs a citation. The language expressed in citation sentences is likely distinctive from regular sentences in scientific writing, and such information could also be useful for better language modeling of the citation contexts. To this end, using citation markers such as \u201c[12]\u201d or \u201cLee et al (2010)\u201d, we identify sentences in a paper that include citations and the negative samples are sentences without citation markers. The goal of the model for this task is to predict whether a particular sentence needs a citation.",
+      "The second scaffold task relates to predicting the section title in which a citation appears. Scientific documents follow a standard structure where the authors typically first introduce the problem, describe methodology, share results, discuss findings and conclude the paper. The intent of a citation could be relevant to the section of the paper in which the citation appears. For example, method-related citations are more likely to appear in the methods section. Therefore, we use the section title prediction as a scaffold for predicting citation intents. Note that this scaffold task is different than simply adding section title as an additional feature in the input. We are using the section titles from a larger set of data than training data for the main task as a proxy to learn linguistic patterns that are helpful for citation intents. In particular, we leverage a large number of scientific papers for which the section information is known for each citation to automatically generate large amounts of training data for this scaffold task.",
+      "Multitask learning as defined by BIBREF13 is an approach to inductive transfer learning that improves generalization by using the domain information contained in the training signals of related tasks as an inductive bias. It requires the model to have at least some sharable parameters between the tasks. In a general setting in our model, we have a main task $Task^{(1)}$ and $n-1$ auxiliary tasks $Task^{(i)}$ . As shown in Figure 2 , each scaffold task will have its task-specific parameters for effective classification and the parameters for the lower layers of the network are shared across tasks. We use a Multi Layer Perceptron (MLP) for each task and then a softmax layer to obtain prediction probabilites. In particular, given the vector $z\u032d$ we pass it to $n$ MLPs and obtain $n$ output vectors $y\u032d^{(i)}$ : $ y\u032d^{(i)} = \\operatorname{softmax}(\\mathrm {MLP}^{(i)}(z\u032d)) $ ",
+      "We are only interested in the output $y\u032d^{(1)}$ and the rest of outputs $(y\u032d^{(2)}, ..., y\u032d^{(n)})$ are regarding the scaffold tasks and only used in training to inform the model of knowledge in the structure of the scientific documents. For each task, we output the class with the highest probability in $y\u032d$ . An alternative inference method is to sample from the output distribution.",
+      "0.5pt 1.0pt"
+    ],
+    [
+      "Let $\\mathcal {D}_1$ be the labeled dataset for the main task $Task^{(1)}$ , and $\\mathcal {D}_i$ denote the labeled datasets corresponding to the scaffold task $Task^{(i)}$ where $i\\in \\lbrace 2,...,n\\rbrace $ . Similarly, let $\\mathcal {L}_1$ and $\\mathcal {L}_i$ be the main loss and the loss of the auxiliary task $i$ , respectively. The final loss of the model is: ",
+      "$$\\small \n\\mathcal {L}=\\sum _{(x\u032d,y\u032d)\\in \\mathcal {D}_1} \\mathcal {L}_1(x\u032d,y\u032d) + \\sum _{i=2}^n \\lambda _i \\sum _{(x\u032d,y\u032d)\\in \\mathcal {D}_i} \\mathcal {L}_i(x\u032d,y\u032d),$$   (Eq. 15) ",
+      "where $\\lambda _i$ is a hyper-parameter specifying the sensitivity of the parameters of the model to each specific task. Here we have two scaffold tasks and hence $n{=}3$ . $\\lambda _i$ could be tuned based on performance on validation set (see \u00a7 \"Experiments\" for details).",
+      "We train this model jointly across tasks and in an end-to-end fashion. In each training epoch, we construct mini-batches with the same number of instances from each of the $n$ tasks. We compute the total loss for each mini-batch as described in Equation 15 , where $\\mathcal {L}_i{=}0$ for all instances of other tasks $j{\\ne }i$ . We compute the gradient of the loss for each mini-batch and tune model parameters using the AdaDelta optimizer BIBREF14 with gradient clipping threshold of 5.0. We stop training the model when the development macro F1 score does not improve for five consecutive epochs."
+    ],
+    [
+      "We compare our results on two datasets from different scientific domains. While there has been a long history of studying citation intents, there are only a few existing publicly available datasets on the task of citation intent classification. We use the most recent and comprehensive (ACL-ARC citations dataset) by BIBREF7 as a benchmark dataset to compare the performance of our model to previous work. In addition, to address the limited scope and size of this dataset, we introduce SciCite, a new dataset of citation intents that addresses multiple scientific domains and is more than five times larger than ACL-ARC. Below is a description of both datasets."
+    ],
+    [
+      "ACL-ARC is a dataset of citation intents released by BIBREF7 . The dataset is based on a sample of papers from the ACL Anthology Reference Corpus BIBREF15 and includes 1,941 citation instances from 186 papers and is annotated by domain experts in the NLP field. The data was split into three standard stratified sets of train, validation, and test with 85% of data used for training and remaining 15% divided equally for validation and test. Each citation unit includes information about the immediate citation context, surrounding context, as well as information about the citing and cited paper. The data includes six intent categories outlined in Table 2 ."
+    ],
+    [
+      "Most existing datasets contain citation categories that are too fine-grained. Some of these intent categories are very rare or not useful in meta analysis of scientific publications. Since some of these fine-grained categories only cover a minimal percentage of all citations, it is difficult to use them to gain insights or draw conclusions on impacts of papers. Furthermore, these datasets are usually domain-specific and are relatively small (less than 2,000 annotated citations).",
+      "To address these limitations, we introduce SciCite, a new dataset of citation intents that is significantly larger, more coarse-grained and general-domain compared with existing datasets. Through examination of citation intents, we found out many of the categories defined in previous work such as motivation, extension or future work, can be considered as background information providing more context for the current research topic. More interesting intent categories are a direct use of a method or comparison of results. Therefore, our dataset provides a concise annotation scheme that is useful for navigating research topics and machine reading of scientific papers. We consider three intent categories outlined in Table 1 : Background, Method and ResultComparison. Below we describe data collection and annotation details.",
+      "Citation intent of sentence extractions was labeled through the crowdsourcing platform Figure Eight. We selected a sample of papers from the Semantic Scholar corpus, consisting of papers in general computer science and medicine domains. Citation contexts were extracted using science-parse. The annotators were asked to identify the intent of a citation, and were directed to select among three citation intent options: Method, ResultComparison and Background. The annotation interface also included a dummy option Other which helps improve the quality of annotations of other categories. We later removed instances annotated with the Other option from our dataset (less than 1% of the annotated data), many of which were due to citation contexts which are incomplete or too short for the annotator to infer the citation intent.",
+      "We used 50 test questions annotated by a domain expert to ensure crowdsource workers were following directions and disqualify annotators with accuracy less than 75%. Furthermore, crowdsource workers were required to remain on the annotation page (five annotations) for at least ten seconds before proceeding to the next page. Annotations were dynamically collected. The annotations were aggregated along with a confidence score describing the level of agreement between multiple crowdsource workers. The confidence score is the agreement on a single instance weighted by a trust score (accuracy of the annotator on the initial 50 test questions).",
+      "To only collect high quality annotations, instances with confidence score of $\\le $ 0.7 were discarded. In addition, a subset of the dataset with 100 samples was re-annotated by a trained, expert annotator to check for quality, and the agreement rate with crowdsource workers was 86%. Citation contexts were annotated by 850 crowdsource workers who made a total of 29,926 annotations and individually made between 4 and 240 annotations. Each sentence was annotated, on average, 3.74 times. This resulted in a total 9,159 crowdsourced instances which were divided to training and validation sets with 90% of the data used for the training set. In addition to the crowdsourced data, a separate test set of size 1,861 was annotated by a trained, expert annotator to ensure high quality of the dataset."
+    ],
+    [
+      "For the first scaffold (citation worthiness), we sample sentences from papers and consider the sentences with citations as positive labels. We also remove the citation markers from those sentences such as numbered citations (e.g., [1]) or name-year combinations (e.g, Lee et al (2012)) to not make the second task artificially easy by only detecting citation markers. For the second scaffold (citation section title), respective to each test dataset, we sample citations from the ACL-ARC corpus and Semantic Scholar corpus and extract the citation context as well as their corresponding sections. We manually define regular expression patterns mappings to normalized section titles: \u201cintroduction\u201d, \u201crelated work\u201d, \u201cmethod\u201d, \u201cexperiments\u201d, \u201cconclusion\u201d. Section titles which did not map to any of the aforementioned titles were excluded from the dataset. Overall, the size of the data for scaffold tasks on the ACL-ARC dataset is about 47K (section title scaffold) and 50K (citation worthiness) while on SciCite is about 91K and 73K for section title and citation worthiness scaffolds, respectively."
+    ],
+    [
+      "We implement our proposed scaffold framework using the AllenNLP library BIBREF16 . For word representations, we use 100-dimensional GloVe vectors BIBREF17 trained on a corpus of 6B tokens from Wikipedia and Gigaword. For contextual representations, we use ELMo vectors released by BIBREF18 with output dimension size of 1,024 which have been trained on a dataset of 5.5B tokens. We use a single-layer BiLSTM with a hidden dimension size of 50 for each direction. For each of scaffold tasks, we use a single-layer MLP with 20 hidden nodes , ReLU BIBREF19 activation and a Dropout rate BIBREF20 of 0.2 between the hidden and input layers. The hyperparameters $\\lambda _i$ are tuned for best performance on the validation set of the respective datasets using a 0.0 to 0.3 grid search. For example, the following hyperparameters are used for the ACL-ARC. Citation worthiness saffold: $\\lambda _2{=}0.08$ , $\\lambda _3{=}0$ , section title scaffold: $\\lambda _3{=}0.09$ , $\\lambda _2{=}0$ ; both scaffolds: $\\lambda _2{=}0.1$ , $\\lambda _3{=}0.05$ . Batch size is 8 for ACL-ARC dataset and 32 for SciCite dataset (recall that SciCite is larger than ACL-ARC). We use Beaker for running the experiments. On the smaller dataset, our best model takes approximately 30 minutes per epoch to train (training time without ELMo is significantly faster). It is known that multiple runs of probabilistic deep learning models can have variance in overall scores BIBREF21 . We control this by setting random-number generator seeds; the reported overall results are average of multiple runs with different random seeds. To facilitate reproducibility, we release our code, data, and trained models."
+    ],
+    [
+      "We compare our results to several baselines including the model with state-of-the-art performance on the ACL-ARC dataset.",
+      "[leftmargin=6pt]",
+      "BiLSTM Attention (with and without ELMo). This baseline uses a similar architecture to our proposed neural multitask learning framework, except that it only optimizes the network for the main loss regarding the citation intent classification ( $\\mathcal {L}_1$ ) and does not include the structural scaffolds. We experiment with two variants of this model: with and without using the contextualized word vector representations (ELMo) of BIBREF18 . This baseline is useful for evaluating the effect of adding scaffolds in controlled experiments.",
+      " BIBREF7 . To make sure our results are competitive with state-of-the-art results on this task, we also compare our model to BIBREF7 which has the best reported results on the ACL-ARC dataset. BIBREF7 incorporate a variety of features, ranging from pattern-based features to topic-modeling features, to citation graph features. They also incorporate section titles and relative section position in the paper as features. Our implementation of this model achieves a macro-averaged F1 score of 0.526 using 10-fold cross-validation, which is in line with the highest reported results in BIBREF7 : 0.53 using leave-one-out cross validation. We were not able to use leave-one-out cross validation in our experiments since it is impractical to re-train each variant of our deep learning models thousands of times. Therefore, we opted for a standard setup of stratified train/validation/test data splits with 85% data used for training and the rest equally split between validation and test."
+    ],
+    [
+      "Our main results for the ACL-ARC dataset BIBREF7 is shown in Table 3 . We observe that our scaffold-enhanced models achieve clear improvements over the state-of-the-art approach on this task. Starting with the `BiLSTM-Attn' baseline with a macro F1 score of 51.8, adding the first scaffold task in `BiLSTM-Attn + section title scaffold' improves the F1 score to 56.9 ( $\\Delta {=}5.1$ ). Adding the second scaffold in `BiLSTM-Attn + citation worthiness scaffold' also results in similar improvements: 56.3 ( $\\Delta {=}4.5$ ). When both scaffolds are used simultaneously in `BiLSTM-Attn + both scaffolds', the F1 score further improves to 63.1 ( $\\Delta {=}11.3$ ), suggesting that the two tasks provide complementary signal that is useful for citation intent prediction.",
+      "The best result is achieved when we also add ELMo vectors BIBREF18 to the input representations in `BiLSTM-Attn w/ ELMo + both scaffolds', achieving an F1 of 67.9, a major improvement from the previous state-of-the-art results of BIBREF7 54.6 ( $\\Delta {=}13.3$ ). We note that the scaffold tasks provide major contributions on top of the ELMo-enabled baseline ( $\\Delta {=}$ 13.6), demonstrating the efficacy of using structural scaffolds for citation intent prediction. We note that these results were obtained without using hand-curated features or additional linguistic resources as used in BIBREF7 . We also experimented with adding features used in BIBREF7 to our best model and not only we did not see any improvements, but we observed at least 1.7% decline in performance. This suggests that these additional manual features do not provide the model with any additional useful signals beyond what the model already learns from the data.",
+      "0.5pt 1.0pt",
+      "Table 4 shows the main results on SciCite dataset, where we see similar patterns. Each scaffold task improves model performance. Adding both scaffolds results in further improvements. And the best results are obtained by using ELMo representation in addition to both scaffolds. Note that this dataset is more than five times larger in size than the ACL-ARC, therefore the performance numbers are generally higher and the F1 gains are generally smaller since it is easier for the models to learn optimal parameters utilizing the larger annotated data. On this dataset, the best baseline is the neural baseline with addition of ELMo contextual vectors achieving an F1 score of 82.6 followed by BIBREF7 , which is expected because neural models generally achieve higher gains when more training data is available and because BIBREF7 was not designed with the SciCite dataset in mind.",
+      "The breakdown of results by intent on ACL-ARC and SciCite datasets is respectively shown in Tables 5 and 6 . Generally we observe that results on categories with more number of instances are higher. For example on ACL-ARC, the results on the Background category are the highest as this category is the most common. Conversely, the results on the FutureWork category are the lowest. This category has the fewest data points (see distribution of the categories in Table 2 ) and thus it is harder for the model to learn the optimal parameters for correct classification in this category."
+    ],
+    [
+      "To gain more insight into why the scaffolds are helping the model in improved citation intent classification, we examine the attention weights assigned to inputs for our best proposed model (`BiLSTM-Attn w/ ELMo + both scaffolds') compared with the best neural baseline (`BiLSTM-Attn w/ ELMO'). We conduct this analysis for examples from both datasets. Figure 3 shows an example input citation along with the horizontal line and the heatmap of attention weights for this input resulting from our model versus the baseline. For first example ( 3 ) the true label is FutureWork. We observe that our model puts more weight on words surrounding the word \u201cfuture\u201d which is plausible given the true label. On the other hand, the baseline model attends most to the words \u201ccompare\u201d and consequently incorrectly predicts a Compare label. In second example ( 3 ) the true label is ResultComparison. The baseline incorrectly classifies it as a Background, likely due to attending to another part of the sentence (\u201canalyzed seprately\u201d). Our model correctly classifies this instance by putting more attention weights on words that relate to comparison of the results. This suggests that the our model is more successful in learning optimal parameters for representing the citation text and classifying its respective intent compared with the baseline. Note that the only difference between our model and the neural baseline is inclusion of the structural scaffolds. Therefore, suggesting the effectiveness the scaffolds in informing the main task of relevant signals for citation intent classification.",
+      "0.5pt 1.0pt",
+      "We next investigate errors made by our best model (Figure 4 plots classification errors). One general error pattern is that the model has more tendency to make false positive errors in the Background category likely due to this category dominating both datasets. It's interesting that for the ACL-ARC dataset some prediction errors are due to the model failing to properly differentiate the Use category with Background. We found out that some of these errors would have been possibly prevented by using additional context. Table 7 shows a sample of such classification errors. For the citation in the first row of the table, the model is likely distracted by \u201cmodel in (citation)\u201d and \u201cILP formulation from (citation)\u201d deeming the sentence is referring to the use of another method from a cited paper and it misses the first part of the sentence describing the motivation. This is likely due to the small number of training instances in the Motivation category, preventing the model to learn such nuances. For the examples in the second and third row, it is not clear if it is possible to make the correct prediction without additional context. And similarly in the last row the instance seems ambiguous without accessing to additional context. Similarly as shown in Figure 4 two of FutureWork labels are wrongly classified. One of them is illustrated in the forth row of Table 7 where perhaps additional context could have helped the model in identifying the correct label. One possible way to prevent this type of errors, is to provide the model with an additional input, modeling the extended surrounding context. We experimented with encoding the extended surrounding context using a BiLSTM and concatenating it with the main citation context vector (z), but it resulted in a large decline in overall performance likely due to the overall noise introduced by the additional context. A possible future work is to investigate alternative effective approaches for incorporating the surrounding extended context."
+    ],
+    [
+      "There is a large body of work studying the intent of citations and devising categorization systems BIBREF22 , BIBREF4 , BIBREF23 , BIBREF24 , BIBREF25 , BIBREF8 , BIBREF26 , BIBREF27 . Most of these efforts provide citation categories that are too fine-grained, some of which rarely occur in papers. Therefore, they are hardly useful for automated analysis of scientific publications. To address these problems and to unify previous efforts, in a recent work, BIBREF7 proposed a six category system for citation intents. In this work, we focus on two schemes: (1) the scheme proposed by BIBREF7 and (2) an additional, more coarse-grained general-purpose category system that we propose (details in \u00a7 \"Data\" ). Unlike other schemes that are domain-specific, our scheme is general and naturally fits in scientific discourse in multiple domains.",
+      "Early works in automated citation intent classification were based on rule-based systems (e.g., BIBREF23 , BIBREF28 ). Later, machine learning methods based on linguistic patterns and other hand-engineered features from citation context were found to be effective. For example, BIBREF8 proposed use of \u201ccue phrases\u201d, a set of expressions that talk about the act of presenting research in a paper. BIBREF9 relied on lexical, structural, and syntactic features and a linear SVM for classification. Researchers have also investigated methods of finding cited spans in the cited papers. Examples include feature-based methods BIBREF29 , domain-specific knowledge BIBREF30 , and a recent CNN-based model for joint prediction of cited spans and citation function BIBREF31 . We also experimented with CNNs but found the attention BiLSTM model to work significantly better. BIBREF7 expanded all pre-existing feature-based efforts on citation intent classification by proposing a comprehensive set of engineered features, including boostrapped patterns, topic modeling, dependency-based, and metadata features for the task. We argue that we can capture necessary information from the citation context using a data driven method, without the need for hand-engineered domain-dependent features or external resources. We propose a novel scaffold neural model for citation intent classification to incorporate structural information of scientific discourse into citations, borrowing the \u201cscaffold\u201d terminology from BIBREF32 who use auxiliary syntactic tasks for semantic problems."
+    ],
+    [
+      "In this work, we show that structural properties related to scientific discourse can be effectively used to inform citation intent classification. We propose a multitask learning framework with two auxiliary tasks (predicting section titles and citation worthiness) as two scaffolds related to the main task of citation intent prediction. Our model achieves state-of-the-art result (F1 score of 67.9%) on the ACL-ARC dataset with 13.3 absolute increase over the best previous results. We additionally introduce SciCite, a new large dataset of citation intents and also show the effectiveness of our model on this dataset. Our dataset, unlike existing datasets that are designed based on a specific domain, is more general and fits in scientific discourse from multiple scientific domains.",
+      "We demonstrate that carefully chosen auxiliary tasks that are inherently relevant to a main task can be leveraged to improve the performance on the main task. An interesting line of future work is to explore the design of such tasks or explore the properties or similarities between the auxiliary and the main tasks. Another relevant line of work is adapting our model to other domains containing documents with similar linked structured such as Wikipedia articles. Future work may benefit from replacing ELMo with other types of contextualized representations such as BERT in our scaffold model. For example, at the time of finalizing the camera ready version of this paper, BIBREF33 showed that a BERT contextualized representation model BIBREF34 trained on scientific text can achieve promising results on the SciCite dataset."
+    ],
+    [
+      "We thank Kyle Lo, Dan Weld, and Iz Beltagy for helpful discussions, Oren Etzioni for feedback on the paper, David Jurgens for helping us with their ACL-ARC dataset and reproducing their results, and the three anonymous reviewers for their comments and suggestions. Computations on beaker.org were supported in part by credits from Google Cloud."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2286/instruction.md b/qasper-2286/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..774555bcb2758f0fc85ae8259f57d6c7587c8b7a
--- /dev/null
+++ b/qasper-2286/instruction.md
@@ -0,0 +1,143 @@
+Name of Paper: Unsupervised Cross-lingual Representation Learning at Scale
+
+Question: asdfasd
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Model and Data",
+    "Model and Data ::: Masked Language Models.",
+    "Model and Data ::: Scaling to a hundred languages.",
+    "Model and Data ::: Scaling the Amount of Training Data.",
+    "Evaluation",
+    "Evaluation ::: Cross-lingual Natural Language Inference (XNLI).",
+    "Evaluation ::: Named Entity Recognition.",
+    "Evaluation ::: Cross-lingual Question Answering.",
+    "Evaluation ::: GLUE Benchmark.",
+    "Analysis and Results",
+    "Analysis and Results ::: Improving and Understanding Multilingual Masked Language Models",
+    "Analysis and Results ::: Improving and Understanding Multilingual Masked Language Models ::: Transfer-dilution trade-off and Curse of Multilinguality.",
+    "Analysis and Results ::: Improving and Understanding Multilingual Masked Language Models ::: High-resource/Low-resource trade-off.",
+    "Analysis and Results ::: Improving and Understanding Multilingual Masked Language Models ::: Importance of Capacity and Vocabulary Size.",
+    "Analysis and Results ::: Improving and Understanding Multilingual Masked Language Models ::: Importance of large-scale training with more data.",
+    "Analysis and Results ::: Improving and Understanding Multilingual Masked Language Models ::: Simplifying multilingual tokenization with Sentence Piece.",
+    "Analysis and Results ::: Cross-lingual Understanding Results",
+    "Analysis and Results ::: Cross-lingual Understanding Results ::: XNLI.",
+    "Analysis and Results ::: Cross-lingual Understanding Results ::: Named Entity Recognition.",
+    "Analysis and Results ::: Cross-lingual Understanding Results ::: Question Answering.",
+    "Analysis and Results ::: Multilingual versus Monolingual",
+    "Analysis and Results ::: Multilingual versus Monolingual ::: GLUE: XLM-R versus RoBERTa.",
+    "Analysis and Results ::: Multilingual versus Monolingual ::: XNLI: XLM versus BERT.",
+    "Analysis and Results ::: Representation Learning for Low-resource Languages",
+    "Conclusion",
+    "Languages and statistics for CC-100 used by XLM-R",
+    "Model Architectures and Sizes"
+  ],
+  "paragraphs": [
+    [
+      "The goal of this paper is to improve cross-lingual language understanding (XLU), by carefully studying the effects of training unsupervised cross-lingual representations at a very large scale. We present XLM-R, a transformer-based multilingual masked language model pre-trained on text in 100 languages, which obtains state-of-the-art performance on cross-lingual classification, sequence labeling and question answering.",
+      "Multilingual masked language models (MLM) like mBERT BIBREF0 and XLM BIBREF1 have pushed the state-of-the-art on cross-lingual understanding tasks by jointly pretraining large Transformer models BIBREF2 on many languages. These models allow for effective cross-lingual transfer, as seen in a number of benchmarks including cross-lingual natural language inference BIBREF3, BIBREF4, BIBREF5, question answering BIBREF6, BIBREF7, and named entity recognition BIBREF8, BIBREF9. However, all of these studies pre-train on Wikipedia, which provides a relatively limited scale especially for lower resource languages.",
+      "In this paper, we first present a comprehensive analysis of the trade-offs and limitations of multilingual language models at scale, inspired by recent monolingual scaling efforts BIBREF10. We measure the trade-off between high-resource and low-resource languages and the impact of language sampling and vocabulary size. The experiments expose a trade-off as we scale the number of languages for a fixed model capacity: more languages leads to better cross-lingual performance on low-resource languages up until a point, after which the overall performance on monolingual and cross-lingual benchmarks degrades. We refer to this tradeoff as the curse of multilinguality, and show that it can be alleviated by simply increasing model capacity. We argue, however, that this remains an important limitation for future XLU systems which may aim to improve performance with more modest computational budgets.",
+      "Our best model XLM-RoBERTa (XLM-R) outperforms mBERT on cross-lingual classification by up to 21% accuracy on low-resource languages like Swahili and Urdu. It outperforms the previous state of the art by 3.9% average accuracy on XNLI, 2.1% average F1-score on Named Entity Recognition, and 8.4% average F1-score on cross-lingual Question Answering. We also evaluate monolingual fine tuning on the GLUE and XNLI benchmarks, where XLM-R obtains results competitive with state-of-the-art monolingual models, including RoBERTa BIBREF10. These results demonstrate, for the first time, that it is possible to have a single large model for all languages, without sacrificing per-language performance. We will make our code, models and data publicly available, with the hope that this will help research in multilingual NLP and low-resource language understanding."
+    ],
+    [
+      "From pretrained word embeddings BIBREF11, BIBREF12 to pretrained contextualized representations BIBREF13, BIBREF14 and transformer based language models BIBREF15, BIBREF0, unsupervised representation learning has significantly improved the state of the art in natural language understanding. Parallel work on cross-lingual understanding BIBREF16, BIBREF14, BIBREF1 extends these systems to more languages and to the cross-lingual setting in which a model is learned in one language and applied in other languages.",
+      "Most recently, BIBREF0 and BIBREF1 introduced mBERT and XLM - masked language models trained on multiple languages, without any cross-lingual supervision. BIBREF1 propose translation language modeling (TLM) as a way to leverage parallel data and obtain a new state of the art on the cross-lingual natural language inference (XNLI) benchmark BIBREF5. They further show strong improvements on unsupervised machine translation and pretraining for sequence generation. Separately, BIBREF8 demonstrated the effectiveness of multilingual models like mBERT on sequence labeling tasks. BIBREF17 showed gains over XLM using cross-lingual multi-task learning, and BIBREF18 demonstrated the efficiency of cross-lingual data augmentation for cross-lingual NLI. However, all of this work was at a relatively modest scale, in terms of the amount of training data, as compared to our approach.",
+      "The benefits of scaling language model pretraining by increasing the size of the model as well as the training data has been extensively studied in the literature. For the monolingual case, BIBREF19 show how large-scale LSTM models can obtain much stronger performance on language modeling benchmarks when trained on billions of tokens. GPT BIBREF15 also highlights the importance of scaling the amount of data and RoBERTa BIBREF10 shows that training BERT longer on more data leads to significant boost in performance. Inspired by RoBERTa, we show that mBERT and XLM are undertuned, and that simple improvements in the learning procedure of unsupervised MLM leads to much better performance. We train on cleaned CommonCrawls BIBREF20, which increase the amount of data for low-resource languages by two orders of magnitude on average. Similar data has also been shown to be effective for learning high quality word embeddings in multiple languages BIBREF21.",
+      "Several efforts have trained massively multilingual machine translation models from large parallel corpora. They uncover the high and low resource trade-off and the problem of capacity dilution BIBREF22, BIBREF23. The work most similar to ours is BIBREF24, which trains a single model in 103 languages on over 25 billion parallel sentences. BIBREF25 further analyze the representations obtained by the encoder of a massively multilingual machine translation system and show that it obtains similar results to mBERT on cross-lingual NLI. Our work, in contrast, focuses on the unsupervised learning of cross-lingual representations and their transfer to discriminative tasks."
+    ],
+    [
+      "In this section, we present the training objective, languages, and data we use. We follow the XLM approach BIBREF1 as closely as possible, only introducing changes that improve performance at scale."
+    ],
+    [
+      "We use a Transformer model BIBREF2 trained with the multilingual MLM objective BIBREF0, BIBREF1 using only monolingual data. We sample streams of text from each language and train the model to predict the masked tokens in the input. We apply subword tokenization directly on raw text data using Sentence Piece BIBREF26 with a unigram language model BIBREF27. We sample batches from different languages using the same sampling distribution as BIBREF1, but with $\\alpha =0.3$. Unlike BIBREF1, we do not use language embeddings, which allows our model to better deal with code-switching. We use a large vocabulary size of 250K with a full softmax and train two different models: XLM-R Base (L = 12, H = 768, A = 12, 270M params) and XLM-R (L = 24, H = 1024, A = 16, 550M params). For all of our ablation studies, we use a BERTBase architecture with a vocabulary of 150K tokens. Appendix SECREF8 goes into more details about the architecture of the different models referenced in this paper."
+    ],
+    [
+      "XLM-R is trained on 100 languages; we provide a full list of languages and associated statistics in Appendix SECREF7. Figure specifies the iso codes of 88 languages that are shared across XLM-R and XLM-100, the model from BIBREF1 trained on Wikipedia text in 100 languages.",
+      "Compared to previous work, we replace some languages with more commonly used ones such as romanized Hindi and traditional Chinese. In our ablation studies, we always include the 7 languages for which we have classification and sequence labeling evaluation benchmarks: English, French, German, Russian, Chinese, Swahili and Urdu. We chose this set as it covers a suitable range of language families and includes low-resource languages such as Swahili and Urdu. We also consider larger sets of 15, 30, 60 and all 100 languages. When reporting results on high-resource and low-resource, we refer to the average of English and French results, and the average of Swahili and Urdu results respectively."
+    ],
+    [
+      "Following BIBREF20, we build a clean CommonCrawl Corpus in 100 languages. We use an internal language identification model in combination with the one from fastText BIBREF28. We train language models in each language and use it to filter documents as described in BIBREF20. We consider one CommonCrawl dump for English and twelve dumps for all other languages, which significantly increases dataset sizes, especially for low-resource languages like Burmese and Swahili.",
+      "Figure shows the difference in size between the Wikipedia Corpus used by mBERT and XLM-100, and the CommonCrawl Corpus we use. As we show in Section SECREF19, monolingual Wikipedia corpora are too small to enable unsupervised representation learning. Based on our experiments, we found that a few hundred MiB of text data is usually a minimal size for learning a BERT model."
+    ],
+    [
+      "We consider four evaluation benchmarks. For cross-lingual understanding, we use cross-lingual natural language inference, named entity recognition, and question answering. We use the GLUE benchmark to evaluate the English performance of XLM-R and compare it to other state-of-the-art models."
+    ],
+    [
+      "The XNLI dataset comes with ground-truth dev and test sets in 15 languages, and a ground-truth English training set. The training set has been machine-translated to the remaining 14 languages, providing synthetic training data for these languages as well. We evaluate our model on cross-lingual transfer from English to other languages. We also consider three machine translation baselines: (i) translate-test: dev and test sets are machine-translated to English and a single English model is used (ii) translate-train (per-language): the English training set is machine-translated to each language and we fine-tune a multiligual model on each training set (iii) translate-train-all (multi-language): we fine-tune a multilingual model on the concatenation of all training sets from translate-train. For the translations, we use the official data provided by the XNLI project."
+    ],
+    [
+      "For NER, we consider the CoNLL-2002 BIBREF29 and CoNLL-2003 BIBREF30 datasets in English, Dutch, Spanish and German. We fine-tune multilingual models either (1) on the English set to evaluate cross-lingual transfer, (2) on each set to evaluate per-language performance, or (3) on all sets to evaluate multilingual learning. We report the F1 score, and compare to baselines from BIBREF31 and BIBREF32."
+    ],
+    [
+      "We use the MLQA benchmark from BIBREF7, which extends the English SQuAD benchmark to Spanish, German, Arabic, Hindi, Vietnamese and Chinese. We report the F1 score as well as the exact match (EM) score for cross-lingual transfer from English."
+    ],
+    [
+      "Finally, we evaluate the English performance of our model on the GLUE benchmark BIBREF33 which gathers multiple classification tasks, such as MNLI BIBREF4, SST-2 BIBREF34, or QNLI BIBREF35. We use BERTLarge and RoBERTa as baselines."
+    ],
+    [
+      "In this section, we perform a comprehensive analysis of multilingual masked language models. We conduct most of the analysis on XNLI, which we found to be representative of our findings on other tasks. We then present the results of XLM-R on cross-lingual understanding and GLUE. Finally, we compare multilingual and monolingual models, and present results on low-resource languages."
+    ],
+    [
+      "Much of the work done on understanding the cross-lingual effectiveness of mBERT or XLM BIBREF8, BIBREF9, BIBREF7 has focused on analyzing the performance of fixed pretrained models on downstream tasks. In this section, we present a comprehensive study of different factors that are important to pretraining large scale multilingual models. We highlight the trade-offs and limitations of these models as we scale to one hundred languages."
+    ],
+    [
+      "Model capacity (i.e. the number of parameters in the model) is constrained due to practical considerations such as memory and speed during training and inference. For a fixed sized model, the per-language capacity decreases as we increase the number of languages. While low-resource language performance can be improved by adding similar higher-resource languages during pretraining, the overall downstream performance suffers from this capacity dilution BIBREF24. Positive transfer and capacity dilution have to be traded off against each other.",
+      "We illustrate this trade-off in Figure , which shows XNLI performance vs the number of languages the model is pretrained on. Initially, as we go from 7 to 15 languages, the model is able to take advantage of positive transfer and this improves performance, especially on low resource languages. Beyond this point the curse of multilinguality kicks in and degrades performance across all languages. Specifically, the overall XNLI accuracy decreases from 71.8% to 67.7% as we go from XLM-7 to XLM-100. The same trend can be observed for models trained on the larger CommonCrawl Corpus.",
+      "The issue is even more prominent when the capacity of the model is small. To show this, we pretrain models on Wikipedia Data in 7, 30 and 100 languages. As we add more languages, we make the Transformer wider by increasing the hidden size from 768 to 960 to 1152. In Figure , we show that the added capacity allows XLM-30 to be on par with XLM-7, thus overcoming the curse of multilinguality. The added capacity for XLM-100, however, is not enough and it still lags behind due to higher vocabulary dilution (recall from Section SECREF3 that we used a fixed vocabulary size of 150K for all models)."
+    ],
+    [
+      "The allocation of the model capacity across languages is controlled by several parameters: the training set size, the size of the shared subword vocabulary, and the rate at which we sample training examples from each language. We study the effect of sampling on the performance of high-resource (English and French) and low-resource (Swahili and Urdu) languages for an XLM-100 model trained on Wikipedia (we observe a similar trend for the construction of the subword vocab). Specifically, we investigate the impact of varying the $\\alpha $ parameter which controls the exponential smoothing of the language sampling rate. Similar to BIBREF1, we use a sampling rate proportional to the number of sentences in each corpus. Models trained with higher values of $\\alpha $ see batches of high-resource languages more often. Figure shows that the higher the value of $\\alpha $, the better the performance on high-resource languages, and vice-versa. When considering overall performance, we found $0.3$ to be an optimal value for $\\alpha $, and use this for XLM-R."
+    ],
+    [
+      "In previous sections and in Figure , we showed the importance of scaling the model size as we increase the number of languages. Similar to the overall model size, we argue that scaling the size of the shared vocabulary (the vocabulary capacity) can improve the performance of multilingual models on downstream tasks. To illustrate this effect, we train XLM-100 models on Wikipedia data with different vocabulary sizes. We keep the overall number of parameters constant by adjusting the width of the transformer. Figure shows that even with a fixed capacity, we observe a 2.8% increase in XNLI average accuracy as we increase the vocabulary size from 32K to 256K. This suggests that multilingual models can benefit from allocating a higher proportion of the total number of parameters to the embedding layer even though this reduces the size of the Transformer. With bigger models, we believe that using a vocabulary of up to 2 million tokens with an adaptive softmax BIBREF36, BIBREF37 should improve performance even further, but we leave this exploration to future work. For simplicity and given the computational constraints, we use a vocabulary of 250k for XLM-R.",
+      "We further illustrate the importance of this parameter, by training three models with the same transformer architecture (BERTBase) but with different vocabulary sizes: 128K, 256K and 512K. We observe more than 3% gains in overall accuracy on XNLI by simply increasing the vocab size from 128k to 512k."
+    ],
+    [
+      "As shown in Figure , the CommonCrawl Corpus that we collected has significantly more monolingual data than the previously used Wikipedia corpora. Figure shows that for the same BERTBase architecture, all models trained on CommonCrawl obtain significantly better performance.",
+      "Apart from scaling the training data, BIBREF10 also showed the benefits of training MLMs longer. In our experiments, we observed similar effects of large-scale training, such as increasing batch size (see Figure ) and training time, on model performance. Specifically, we found that using validation perplexity as a stopping criterion for pretraining caused the multilingual MLM in BIBREF1 to be under-tuned. In our experience, performance on downstream tasks continues to improve even after validation perplexity has plateaued. Combining this observation with our implementation of the unsupervised XLM-MLM objective, we were able to improve the performance of BIBREF1 from 71.3% to more than 75% average accuracy on XNLI, which was on par with their supervised translation language modeling (TLM) objective. Based on these results, and given our focus on unsupervised learning, we decided to not use the supervised TLM objective for training our models."
+    ],
+    [
+      "The different language-specific tokenization tools used by mBERT and XLM-100 make these models more difficult to use on raw text. Instead, we train a Sentence Piece model (SPM) and apply it directly on raw text data for all languages. We did not observe any loss in performance for models trained with SPM when compared to models trained with language-specific preprocessing and byte-pair encoding (see Figure ) and hence use SPM for XLM-R."
+    ],
+    [
+      "Based on these results, we adapt the setting of BIBREF1 and use a large Transformer model with 24 layers and 1024 hidden states, with a 250k vocabulary. We use the multilingual MLM loss and train our XLM-R model for 1.5 Million updates on five hundred 32GB Nvidia V100 GPUs with a batch size of 8192. We leverage the SPM-preprocessed text data from CommonCrawl in 100 languages and sample languages with $\\alpha =0.3$. In this section, we show that it outperforms all previous techniques on cross-lingual benchmarks while getting performance on par with RoBERTa on the GLUE benchmark."
+    ],
+    [
+      "Table shows XNLI results and adds some additional details: (i) the number of models the approach induces (#M), (ii) the data on which the model was trained (D), and (iii) the number of languages the model was pretrained on (#lg). As we show in our results, these parameters significantly impact performance. Column #M specifies whether model selection was done separately on the dev set of each language ($N$ models), or on the joint dev set of all the languages (single model). We observe a 0.6 decrease in overall accuracy when we go from $N$ models to a single model - going from 71.3 to 70.7. We encourage the community to adopt this setting. For cross-lingual transfer, while this approach is not fully zero-shot transfer, we argue that in real applications, a small amount of supervised data is often available for validation in each language.",
+      "XLM-R sets a new state of the art on XNLI. On cross-lingual transfer, XLM-R obtains 80.1% accuracy, outperforming the XLM-100 and mBERT open-source models by 9.4% and 13.8% average accuracy. On the Swahili and Urdu low-resource languages, XLM-R outperforms XLM-100 by 13.8% and 9.3%, and mBERT by 21.6% and 13.7%. While XLM-R handles 100 languages, we also show that it outperforms the former state of the art Unicoder BIBREF17 and XLM (MLM+TLM), which handle only 15 languages, by 4.7% and 5% average accuracy respectively. Using the multilingual training of translate-train-all, XLM-R further improves performance and reaches 82.4% accuracy, a new overall state of the art for XNLI, outperforming Unicoder by 3.9%. Multilingual training is similar to practical applications where training sets are available in various languages for the same task. In the case of XNLI, datasets have been translated, and translate-train-all can be seen as some form of cross-lingual data augmentation BIBREF18, similar to back-translation BIBREF38."
+    ],
+    [
+      "In Table , we report results of XLM-R and mBERT on CoNLL-2002 and CoNLL-2003. We consider the LSTM + CRF approach from BIBREF31 and the Flair model from BIBREF32 as baselines. We evaluate the performance of the model on each of the target languages in three different settings: (i) train on English data only (en) (ii) train on data in target language (each) (iii) train on data in all languages (all). Results of mBERT are reported from BIBREF9. Note that we do not use a linear-chain CRF on top of XLM-R and mBERT representations, which gives an advantage to BIBREF32. Without the CRF, our XLM-R model still performs on par with the state of the art, outperforming BIBREF32 on Dutch by $2.84$ points. On this task, XLM-R also outperforms mBERT by 2.1 F1 on average for cross-lingual transfer, and 1.86 F1 when trained on each language. Training on all languages leads to an average F1 score of 89.18%, outperforming cross-lingual transfer approach by more than 8.5%."
+    ],
+    [
+      "We also obtain new state of the art results on the MLQA cross-lingual question answering benchmark, introduced by BIBREF7. We follow their procedure by training on the English training data and evaluating on the 7 languages of the dataset. We report results in Table . XLM-R obtains F1 and accuracy scores of 70.0% and 52.2% while the previous state of the art was 61.6% and 43.5%. XLM-R also outperforms mBERT by 12.3% F1-score and 10.6% accuracy. It even outperforms BERT-Large on English, confirming its strong monolingual performance."
+    ],
+    [
+      "In this section, we present results of multilingual XLM models against monolingual BERT models."
+    ],
+    [
+      "Our goal is to obtain a multilingual model with strong performance on both, cross-lingual understanding tasks as well as natural language understanding tasks for each language. To that end, we evaluate XLM-R on the GLUE benchmark. We show in Table , that XLM-R obtains better average dev performance than BERTLarge by 1.3% and reaches performance on par with XLNetLarge. The RoBERTa model outperforms XLM-R by only 1.3% on average. We believe future work can reduce this gap even further by alleviating the curse of multilinguality and vocabulary dilution. These results demonstrate the possibility of learning one model for many languages while maintaining strong performance on per-language downstream tasks."
+    ],
+    [
+      "A recurrent criticism against multilingual model is that they obtain worse performance than their monolingual counterparts. In addition to the comparison of XLM-R and RoBERTa, we provide the first comprehensive study to assess this claim on the XNLI benchmark. We extend our comparison between multilingual XLM models and monolingual BERT models on 7 languages and compare performance in Table . We train 14 monolingual BERT models on Wikipedia and CommonCrawl, and two XLM-7 models. We add slightly more capacity in the vocabulary size of the multilingual model for a better comparison. To our surprise - and backed by further study on internal benchmarks - we found that multilingual models can outperform their monolingual BERT counterparts. Specifically, in Table , we show that for cross-lingual transfer, monolingual baselines outperform XLM-7 for both Wikipedia and CC by 1.6% and 1.3% average accuracy. However, by making use of multilingual training (translate-train-all) and leveraging training sets coming from multiple languages, XLM-7 can outperform the BERT models: our XLM-7 trained on CC obtains 80.0% average accuracy on the 7 languages, while the average performance of monolingual BERT models trained on CC is 77.5%. This is a surprising result that shows that the capacity of multilingual models to leverage training data coming from multiple languages for a particular task can overcome the capacity dilution problem to obtain better overall performance."
+    ],
+    [
+      "We observed in Table that pretraining on Wikipedia for Swahili and Urdu performed similarly to a randomly initialized model; most likely due to the small size of the data for these languages. On the other hand, pretraining on CC improved performance by up to 10 points. This confirms our assumption that mBERT and XLM-100 rely heavily on cross-lingual transfer but do not model the low-resource languages as well as XLM-R. Specifically, in the translate-train-all setting, we observe that the biggest gains for XLM models trained on CC, compared to their Wikipedia counterparts, are on low-resource languages; 7% and 4.8% improvement on Swahili and Urdu respectively."
+    ],
+    [
+      "In this work, we introduced XLM-R, our new state of the art multilingual masked language model trained on 2.5 TB of newly created clean CommonCrawl data in 100 languages. We show that it provides strong gains over previous multilingual models like mBERT and XLM on classification, sequence labeling and question answering. We exposed the limitations of multilingual MLMs, in particular by uncovering the high-resource versus low-resource trade-off, the curse of multilinguality and the importance of key hyperparameters. We also expose the surprising effectiveness of multilingual models over monolingual models, and show strong improvements on low-resource languages."
+    ],
+    [
+      "In this section we present the list of languages in the CC-100 corpus we created for training XLM-R. We also report statistics such as the number of tokens and the size of each monolingual corpus."
+    ],
+    [
+      "As we showed in section SECREF5, capacity is an important parameter for learning strong cross-lingual representations. In the table below, we list multiple monolingual and multilingual models used by the research community and summarize their architectures and total number of parameters."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2287/instruction.md b/qasper-2287/instruction.md
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+Name of Paper: Unsupervised Cross-lingual Representation Learning at Scale
+
+Question: asdf
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Model and Data",
+    "Model and Data ::: Masked Language Models.",
+    "Model and Data ::: Scaling to a hundred languages.",
+    "Model and Data ::: Scaling the Amount of Training Data.",
+    "Evaluation",
+    "Evaluation ::: Cross-lingual Natural Language Inference (XNLI).",
+    "Evaluation ::: Named Entity Recognition.",
+    "Evaluation ::: Cross-lingual Question Answering.",
+    "Evaluation ::: GLUE Benchmark.",
+    "Analysis and Results",
+    "Analysis and Results ::: Improving and Understanding Multilingual Masked Language Models",
+    "Analysis and Results ::: Improving and Understanding Multilingual Masked Language Models ::: Transfer-dilution trade-off and Curse of Multilinguality.",
+    "Analysis and Results ::: Improving and Understanding Multilingual Masked Language Models ::: High-resource/Low-resource trade-off.",
+    "Analysis and Results ::: Improving and Understanding Multilingual Masked Language Models ::: Importance of Capacity and Vocabulary Size.",
+    "Analysis and Results ::: Improving and Understanding Multilingual Masked Language Models ::: Importance of large-scale training with more data.",
+    "Analysis and Results ::: Improving and Understanding Multilingual Masked Language Models ::: Simplifying multilingual tokenization with Sentence Piece.",
+    "Analysis and Results ::: Cross-lingual Understanding Results",
+    "Analysis and Results ::: Cross-lingual Understanding Results ::: XNLI.",
+    "Analysis and Results ::: Cross-lingual Understanding Results ::: Named Entity Recognition.",
+    "Analysis and Results ::: Cross-lingual Understanding Results ::: Question Answering.",
+    "Analysis and Results ::: Multilingual versus Monolingual",
+    "Analysis and Results ::: Multilingual versus Monolingual ::: GLUE: XLM-R versus RoBERTa.",
+    "Analysis and Results ::: Multilingual versus Monolingual ::: XNLI: XLM versus BERT.",
+    "Analysis and Results ::: Representation Learning for Low-resource Languages",
+    "Conclusion",
+    "Languages and statistics for CC-100 used by XLM-R",
+    "Model Architectures and Sizes"
+  ],
+  "paragraphs": [
+    [
+      "The goal of this paper is to improve cross-lingual language understanding (XLU), by carefully studying the effects of training unsupervised cross-lingual representations at a very large scale. We present XLM-R, a transformer-based multilingual masked language model pre-trained on text in 100 languages, which obtains state-of-the-art performance on cross-lingual classification, sequence labeling and question answering.",
+      "Multilingual masked language models (MLM) like mBERT BIBREF0 and XLM BIBREF1 have pushed the state-of-the-art on cross-lingual understanding tasks by jointly pretraining large Transformer models BIBREF2 on many languages. These models allow for effective cross-lingual transfer, as seen in a number of benchmarks including cross-lingual natural language inference BIBREF3, BIBREF4, BIBREF5, question answering BIBREF6, BIBREF7, and named entity recognition BIBREF8, BIBREF9. However, all of these studies pre-train on Wikipedia, which provides a relatively limited scale especially for lower resource languages.",
+      "In this paper, we first present a comprehensive analysis of the trade-offs and limitations of multilingual language models at scale, inspired by recent monolingual scaling efforts BIBREF10. We measure the trade-off between high-resource and low-resource languages and the impact of language sampling and vocabulary size. The experiments expose a trade-off as we scale the number of languages for a fixed model capacity: more languages leads to better cross-lingual performance on low-resource languages up until a point, after which the overall performance on monolingual and cross-lingual benchmarks degrades. We refer to this tradeoff as the curse of multilinguality, and show that it can be alleviated by simply increasing model capacity. We argue, however, that this remains an important limitation for future XLU systems which may aim to improve performance with more modest computational budgets.",
+      "Our best model XLM-RoBERTa (XLM-R) outperforms mBERT on cross-lingual classification by up to 21% accuracy on low-resource languages like Swahili and Urdu. It outperforms the previous state of the art by 3.9% average accuracy on XNLI, 2.1% average F1-score on Named Entity Recognition, and 8.4% average F1-score on cross-lingual Question Answering. We also evaluate monolingual fine tuning on the GLUE and XNLI benchmarks, where XLM-R obtains results competitive with state-of-the-art monolingual models, including RoBERTa BIBREF10. These results demonstrate, for the first time, that it is possible to have a single large model for all languages, without sacrificing per-language performance. We will make our code, models and data publicly available, with the hope that this will help research in multilingual NLP and low-resource language understanding."
+    ],
+    [
+      "From pretrained word embeddings BIBREF11, BIBREF12 to pretrained contextualized representations BIBREF13, BIBREF14 and transformer based language models BIBREF15, BIBREF0, unsupervised representation learning has significantly improved the state of the art in natural language understanding. Parallel work on cross-lingual understanding BIBREF16, BIBREF14, BIBREF1 extends these systems to more languages and to the cross-lingual setting in which a model is learned in one language and applied in other languages.",
+      "Most recently, BIBREF0 and BIBREF1 introduced mBERT and XLM - masked language models trained on multiple languages, without any cross-lingual supervision. BIBREF1 propose translation language modeling (TLM) as a way to leverage parallel data and obtain a new state of the art on the cross-lingual natural language inference (XNLI) benchmark BIBREF5. They further show strong improvements on unsupervised machine translation and pretraining for sequence generation. Separately, BIBREF8 demonstrated the effectiveness of multilingual models like mBERT on sequence labeling tasks. BIBREF17 showed gains over XLM using cross-lingual multi-task learning, and BIBREF18 demonstrated the efficiency of cross-lingual data augmentation for cross-lingual NLI. However, all of this work was at a relatively modest scale, in terms of the amount of training data, as compared to our approach.",
+      "The benefits of scaling language model pretraining by increasing the size of the model as well as the training data has been extensively studied in the literature. For the monolingual case, BIBREF19 show how large-scale LSTM models can obtain much stronger performance on language modeling benchmarks when trained on billions of tokens. GPT BIBREF15 also highlights the importance of scaling the amount of data and RoBERTa BIBREF10 shows that training BERT longer on more data leads to significant boost in performance. Inspired by RoBERTa, we show that mBERT and XLM are undertuned, and that simple improvements in the learning procedure of unsupervised MLM leads to much better performance. We train on cleaned CommonCrawls BIBREF20, which increase the amount of data for low-resource languages by two orders of magnitude on average. Similar data has also been shown to be effective for learning high quality word embeddings in multiple languages BIBREF21.",
+      "Several efforts have trained massively multilingual machine translation models from large parallel corpora. They uncover the high and low resource trade-off and the problem of capacity dilution BIBREF22, BIBREF23. The work most similar to ours is BIBREF24, which trains a single model in 103 languages on over 25 billion parallel sentences. BIBREF25 further analyze the representations obtained by the encoder of a massively multilingual machine translation system and show that it obtains similar results to mBERT on cross-lingual NLI. Our work, in contrast, focuses on the unsupervised learning of cross-lingual representations and their transfer to discriminative tasks."
+    ],
+    [
+      "In this section, we present the training objective, languages, and data we use. We follow the XLM approach BIBREF1 as closely as possible, only introducing changes that improve performance at scale."
+    ],
+    [
+      "We use a Transformer model BIBREF2 trained with the multilingual MLM objective BIBREF0, BIBREF1 using only monolingual data. We sample streams of text from each language and train the model to predict the masked tokens in the input. We apply subword tokenization directly on raw text data using Sentence Piece BIBREF26 with a unigram language model BIBREF27. We sample batches from different languages using the same sampling distribution as BIBREF1, but with $\\alpha =0.3$. Unlike BIBREF1, we do not use language embeddings, which allows our model to better deal with code-switching. We use a large vocabulary size of 250K with a full softmax and train two different models: XLM-R Base (L = 12, H = 768, A = 12, 270M params) and XLM-R (L = 24, H = 1024, A = 16, 550M params). For all of our ablation studies, we use a BERTBase architecture with a vocabulary of 150K tokens. Appendix SECREF8 goes into more details about the architecture of the different models referenced in this paper."
+    ],
+    [
+      "XLM-R is trained on 100 languages; we provide a full list of languages and associated statistics in Appendix SECREF7. Figure specifies the iso codes of 88 languages that are shared across XLM-R and XLM-100, the model from BIBREF1 trained on Wikipedia text in 100 languages.",
+      "Compared to previous work, we replace some languages with more commonly used ones such as romanized Hindi and traditional Chinese. In our ablation studies, we always include the 7 languages for which we have classification and sequence labeling evaluation benchmarks: English, French, German, Russian, Chinese, Swahili and Urdu. We chose this set as it covers a suitable range of language families and includes low-resource languages such as Swahili and Urdu. We also consider larger sets of 15, 30, 60 and all 100 languages. When reporting results on high-resource and low-resource, we refer to the average of English and French results, and the average of Swahili and Urdu results respectively."
+    ],
+    [
+      "Following BIBREF20, we build a clean CommonCrawl Corpus in 100 languages. We use an internal language identification model in combination with the one from fastText BIBREF28. We train language models in each language and use it to filter documents as described in BIBREF20. We consider one CommonCrawl dump for English and twelve dumps for all other languages, which significantly increases dataset sizes, especially for low-resource languages like Burmese and Swahili.",
+      "Figure shows the difference in size between the Wikipedia Corpus used by mBERT and XLM-100, and the CommonCrawl Corpus we use. As we show in Section SECREF19, monolingual Wikipedia corpora are too small to enable unsupervised representation learning. Based on our experiments, we found that a few hundred MiB of text data is usually a minimal size for learning a BERT model."
+    ],
+    [
+      "We consider four evaluation benchmarks. For cross-lingual understanding, we use cross-lingual natural language inference, named entity recognition, and question answering. We use the GLUE benchmark to evaluate the English performance of XLM-R and compare it to other state-of-the-art models."
+    ],
+    [
+      "The XNLI dataset comes with ground-truth dev and test sets in 15 languages, and a ground-truth English training set. The training set has been machine-translated to the remaining 14 languages, providing synthetic training data for these languages as well. We evaluate our model on cross-lingual transfer from English to other languages. We also consider three machine translation baselines: (i) translate-test: dev and test sets are machine-translated to English and a single English model is used (ii) translate-train (per-language): the English training set is machine-translated to each language and we fine-tune a multiligual model on each training set (iii) translate-train-all (multi-language): we fine-tune a multilingual model on the concatenation of all training sets from translate-train. For the translations, we use the official data provided by the XNLI project."
+    ],
+    [
+      "For NER, we consider the CoNLL-2002 BIBREF29 and CoNLL-2003 BIBREF30 datasets in English, Dutch, Spanish and German. We fine-tune multilingual models either (1) on the English set to evaluate cross-lingual transfer, (2) on each set to evaluate per-language performance, or (3) on all sets to evaluate multilingual learning. We report the F1 score, and compare to baselines from BIBREF31 and BIBREF32."
+    ],
+    [
+      "We use the MLQA benchmark from BIBREF7, which extends the English SQuAD benchmark to Spanish, German, Arabic, Hindi, Vietnamese and Chinese. We report the F1 score as well as the exact match (EM) score for cross-lingual transfer from English."
+    ],
+    [
+      "Finally, we evaluate the English performance of our model on the GLUE benchmark BIBREF33 which gathers multiple classification tasks, such as MNLI BIBREF4, SST-2 BIBREF34, or QNLI BIBREF35. We use BERTLarge and RoBERTa as baselines."
+    ],
+    [
+      "In this section, we perform a comprehensive analysis of multilingual masked language models. We conduct most of the analysis on XNLI, which we found to be representative of our findings on other tasks. We then present the results of XLM-R on cross-lingual understanding and GLUE. Finally, we compare multilingual and monolingual models, and present results on low-resource languages."
+    ],
+    [
+      "Much of the work done on understanding the cross-lingual effectiveness of mBERT or XLM BIBREF8, BIBREF9, BIBREF7 has focused on analyzing the performance of fixed pretrained models on downstream tasks. In this section, we present a comprehensive study of different factors that are important to pretraining large scale multilingual models. We highlight the trade-offs and limitations of these models as we scale to one hundred languages."
+    ],
+    [
+      "Model capacity (i.e. the number of parameters in the model) is constrained due to practical considerations such as memory and speed during training and inference. For a fixed sized model, the per-language capacity decreases as we increase the number of languages. While low-resource language performance can be improved by adding similar higher-resource languages during pretraining, the overall downstream performance suffers from this capacity dilution BIBREF24. Positive transfer and capacity dilution have to be traded off against each other.",
+      "We illustrate this trade-off in Figure , which shows XNLI performance vs the number of languages the model is pretrained on. Initially, as we go from 7 to 15 languages, the model is able to take advantage of positive transfer and this improves performance, especially on low resource languages. Beyond this point the curse of multilinguality kicks in and degrades performance across all languages. Specifically, the overall XNLI accuracy decreases from 71.8% to 67.7% as we go from XLM-7 to XLM-100. The same trend can be observed for models trained on the larger CommonCrawl Corpus.",
+      "The issue is even more prominent when the capacity of the model is small. To show this, we pretrain models on Wikipedia Data in 7, 30 and 100 languages. As we add more languages, we make the Transformer wider by increasing the hidden size from 768 to 960 to 1152. In Figure , we show that the added capacity allows XLM-30 to be on par with XLM-7, thus overcoming the curse of multilinguality. The added capacity for XLM-100, however, is not enough and it still lags behind due to higher vocabulary dilution (recall from Section SECREF3 that we used a fixed vocabulary size of 150K for all models)."
+    ],
+    [
+      "The allocation of the model capacity across languages is controlled by several parameters: the training set size, the size of the shared subword vocabulary, and the rate at which we sample training examples from each language. We study the effect of sampling on the performance of high-resource (English and French) and low-resource (Swahili and Urdu) languages for an XLM-100 model trained on Wikipedia (we observe a similar trend for the construction of the subword vocab). Specifically, we investigate the impact of varying the $\\alpha $ parameter which controls the exponential smoothing of the language sampling rate. Similar to BIBREF1, we use a sampling rate proportional to the number of sentences in each corpus. Models trained with higher values of $\\alpha $ see batches of high-resource languages more often. Figure shows that the higher the value of $\\alpha $, the better the performance on high-resource languages, and vice-versa. When considering overall performance, we found $0.3$ to be an optimal value for $\\alpha $, and use this for XLM-R."
+    ],
+    [
+      "In previous sections and in Figure , we showed the importance of scaling the model size as we increase the number of languages. Similar to the overall model size, we argue that scaling the size of the shared vocabulary (the vocabulary capacity) can improve the performance of multilingual models on downstream tasks. To illustrate this effect, we train XLM-100 models on Wikipedia data with different vocabulary sizes. We keep the overall number of parameters constant by adjusting the width of the transformer. Figure shows that even with a fixed capacity, we observe a 2.8% increase in XNLI average accuracy as we increase the vocabulary size from 32K to 256K. This suggests that multilingual models can benefit from allocating a higher proportion of the total number of parameters to the embedding layer even though this reduces the size of the Transformer. With bigger models, we believe that using a vocabulary of up to 2 million tokens with an adaptive softmax BIBREF36, BIBREF37 should improve performance even further, but we leave this exploration to future work. For simplicity and given the computational constraints, we use a vocabulary of 250k for XLM-R.",
+      "We further illustrate the importance of this parameter, by training three models with the same transformer architecture (BERTBase) but with different vocabulary sizes: 128K, 256K and 512K. We observe more than 3% gains in overall accuracy on XNLI by simply increasing the vocab size from 128k to 512k."
+    ],
+    [
+      "As shown in Figure , the CommonCrawl Corpus that we collected has significantly more monolingual data than the previously used Wikipedia corpora. Figure shows that for the same BERTBase architecture, all models trained on CommonCrawl obtain significantly better performance.",
+      "Apart from scaling the training data, BIBREF10 also showed the benefits of training MLMs longer. In our experiments, we observed similar effects of large-scale training, such as increasing batch size (see Figure ) and training time, on model performance. Specifically, we found that using validation perplexity as a stopping criterion for pretraining caused the multilingual MLM in BIBREF1 to be under-tuned. In our experience, performance on downstream tasks continues to improve even after validation perplexity has plateaued. Combining this observation with our implementation of the unsupervised XLM-MLM objective, we were able to improve the performance of BIBREF1 from 71.3% to more than 75% average accuracy on XNLI, which was on par with their supervised translation language modeling (TLM) objective. Based on these results, and given our focus on unsupervised learning, we decided to not use the supervised TLM objective for training our models."
+    ],
+    [
+      "The different language-specific tokenization tools used by mBERT and XLM-100 make these models more difficult to use on raw text. Instead, we train a Sentence Piece model (SPM) and apply it directly on raw text data for all languages. We did not observe any loss in performance for models trained with SPM when compared to models trained with language-specific preprocessing and byte-pair encoding (see Figure ) and hence use SPM for XLM-R."
+    ],
+    [
+      "Based on these results, we adapt the setting of BIBREF1 and use a large Transformer model with 24 layers and 1024 hidden states, with a 250k vocabulary. We use the multilingual MLM loss and train our XLM-R model for 1.5 Million updates on five hundred 32GB Nvidia V100 GPUs with a batch size of 8192. We leverage the SPM-preprocessed text data from CommonCrawl in 100 languages and sample languages with $\\alpha =0.3$. In this section, we show that it outperforms all previous techniques on cross-lingual benchmarks while getting performance on par with RoBERTa on the GLUE benchmark."
+    ],
+    [
+      "Table shows XNLI results and adds some additional details: (i) the number of models the approach induces (#M), (ii) the data on which the model was trained (D), and (iii) the number of languages the model was pretrained on (#lg). As we show in our results, these parameters significantly impact performance. Column #M specifies whether model selection was done separately on the dev set of each language ($N$ models), or on the joint dev set of all the languages (single model). We observe a 0.6 decrease in overall accuracy when we go from $N$ models to a single model - going from 71.3 to 70.7. We encourage the community to adopt this setting. For cross-lingual transfer, while this approach is not fully zero-shot transfer, we argue that in real applications, a small amount of supervised data is often available for validation in each language.",
+      "XLM-R sets a new state of the art on XNLI. On cross-lingual transfer, XLM-R obtains 80.1% accuracy, outperforming the XLM-100 and mBERT open-source models by 9.4% and 13.8% average accuracy. On the Swahili and Urdu low-resource languages, XLM-R outperforms XLM-100 by 13.8% and 9.3%, and mBERT by 21.6% and 13.7%. While XLM-R handles 100 languages, we also show that it outperforms the former state of the art Unicoder BIBREF17 and XLM (MLM+TLM), which handle only 15 languages, by 4.7% and 5% average accuracy respectively. Using the multilingual training of translate-train-all, XLM-R further improves performance and reaches 82.4% accuracy, a new overall state of the art for XNLI, outperforming Unicoder by 3.9%. Multilingual training is similar to practical applications where training sets are available in various languages for the same task. In the case of XNLI, datasets have been translated, and translate-train-all can be seen as some form of cross-lingual data augmentation BIBREF18, similar to back-translation BIBREF38."
+    ],
+    [
+      "In Table , we report results of XLM-R and mBERT on CoNLL-2002 and CoNLL-2003. We consider the LSTM + CRF approach from BIBREF31 and the Flair model from BIBREF32 as baselines. We evaluate the performance of the model on each of the target languages in three different settings: (i) train on English data only (en) (ii) train on data in target language (each) (iii) train on data in all languages (all). Results of mBERT are reported from BIBREF9. Note that we do not use a linear-chain CRF on top of XLM-R and mBERT representations, which gives an advantage to BIBREF32. Without the CRF, our XLM-R model still performs on par with the state of the art, outperforming BIBREF32 on Dutch by $2.84$ points. On this task, XLM-R also outperforms mBERT by 2.1 F1 on average for cross-lingual transfer, and 1.86 F1 when trained on each language. Training on all languages leads to an average F1 score of 89.18%, outperforming cross-lingual transfer approach by more than 8.5%."
+    ],
+    [
+      "We also obtain new state of the art results on the MLQA cross-lingual question answering benchmark, introduced by BIBREF7. We follow their procedure by training on the English training data and evaluating on the 7 languages of the dataset. We report results in Table . XLM-R obtains F1 and accuracy scores of 70.0% and 52.2% while the previous state of the art was 61.6% and 43.5%. XLM-R also outperforms mBERT by 12.3% F1-score and 10.6% accuracy. It even outperforms BERT-Large on English, confirming its strong monolingual performance."
+    ],
+    [
+      "In this section, we present results of multilingual XLM models against monolingual BERT models."
+    ],
+    [
+      "Our goal is to obtain a multilingual model with strong performance on both, cross-lingual understanding tasks as well as natural language understanding tasks for each language. To that end, we evaluate XLM-R on the GLUE benchmark. We show in Table , that XLM-R obtains better average dev performance than BERTLarge by 1.3% and reaches performance on par with XLNetLarge. The RoBERTa model outperforms XLM-R by only 1.3% on average. We believe future work can reduce this gap even further by alleviating the curse of multilinguality and vocabulary dilution. These results demonstrate the possibility of learning one model for many languages while maintaining strong performance on per-language downstream tasks."
+    ],
+    [
+      "A recurrent criticism against multilingual model is that they obtain worse performance than their monolingual counterparts. In addition to the comparison of XLM-R and RoBERTa, we provide the first comprehensive study to assess this claim on the XNLI benchmark. We extend our comparison between multilingual XLM models and monolingual BERT models on 7 languages and compare performance in Table . We train 14 monolingual BERT models on Wikipedia and CommonCrawl, and two XLM-7 models. We add slightly more capacity in the vocabulary size of the multilingual model for a better comparison. To our surprise - and backed by further study on internal benchmarks - we found that multilingual models can outperform their monolingual BERT counterparts. Specifically, in Table , we show that for cross-lingual transfer, monolingual baselines outperform XLM-7 for both Wikipedia and CC by 1.6% and 1.3% average accuracy. However, by making use of multilingual training (translate-train-all) and leveraging training sets coming from multiple languages, XLM-7 can outperform the BERT models: our XLM-7 trained on CC obtains 80.0% average accuracy on the 7 languages, while the average performance of monolingual BERT models trained on CC is 77.5%. This is a surprising result that shows that the capacity of multilingual models to leverage training data coming from multiple languages for a particular task can overcome the capacity dilution problem to obtain better overall performance."
+    ],
+    [
+      "We observed in Table that pretraining on Wikipedia for Swahili and Urdu performed similarly to a randomly initialized model; most likely due to the small size of the data for these languages. On the other hand, pretraining on CC improved performance by up to 10 points. This confirms our assumption that mBERT and XLM-100 rely heavily on cross-lingual transfer but do not model the low-resource languages as well as XLM-R. Specifically, in the translate-train-all setting, we observe that the biggest gains for XLM models trained on CC, compared to their Wikipedia counterparts, are on low-resource languages; 7% and 4.8% improvement on Swahili and Urdu respectively."
+    ],
+    [
+      "In this work, we introduced XLM-R, our new state of the art multilingual masked language model trained on 2.5 TB of newly created clean CommonCrawl data in 100 languages. We show that it provides strong gains over previous multilingual models like mBERT and XLM on classification, sequence labeling and question answering. We exposed the limitations of multilingual MLMs, in particular by uncovering the high-resource versus low-resource trade-off, the curse of multilinguality and the importance of key hyperparameters. We also expose the surprising effectiveness of multilingual models over monolingual models, and show strong improvements on low-resource languages."
+    ],
+    [
+      "In this section we present the list of languages in the CC-100 corpus we created for training XLM-R. We also report statistics such as the number of tokens and the size of each monolingual corpus."
+    ],
+    [
+      "As we showed in section SECREF5, capacity is an important parameter for learning strong cross-lingual representations. In the table below, we list multiple monolingual and multilingual models used by the research community and summarize their architectures and total number of parameters."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: GoodNewsEveryone: A Corpus of News Headlines Annotated with Emotions, Semantic Roles, and Reader Perception
+
+Question: How is quality of annotation measured?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Related Work ::: Emotion Classification",
+    "Related Work ::: Emotion Intensity",
+    "Related Work ::: Cue or Trigger Words",
+    "Related Work ::: Emotion Cause Detection",
+    "Related Work ::: Semantic Role Labeling of Emotions",
+    "Related Work ::: Reader vs. Writer vs. Text Perspective",
+    "Data Collection & Annotation",
+    "Data Collection & Annotation ::: Collecting Headlines",
+    "Data Collection & Annotation ::: Filtering & Postprocessing",
+    "Data Collection & Annotation ::: Sampling Headlines",
+    "Data Collection & Annotation ::: Sampling Headlines ::: Random Sampling.",
+    "Data Collection & Annotation ::: Sampling Headlines ::: Sampling via NRC.",
+    "Data Collection & Annotation ::: Sampling Headlines ::: Sampling Entities.",
+    "Data Collection & Annotation ::: Sampling Headlines ::: Sampling based on Reddit & Twitter.",
+    "Data Collection & Annotation ::: Annotation Procedure",
+    "Data Collection & Annotation ::: Annotation Procedure ::: Phase 1: Selecting Emotional Headlines",
+    "Data Collection & Annotation ::: Annotation Procedure ::: Phase 2: Emotion and Role Annotation",
+    "Data Collection & Annotation ::: Annotation Procedure ::: Quality Control and Results",
+    "Data Collection & Annotation ::: Adjudication of Annotations",
+    "Data Collection & Annotation ::: Adjudication of Annotations ::: Relative Majority Rule.",
+    "Data Collection & Annotation ::: Adjudication of Annotations ::: Most Common Subsequence Rule.",
+    "Data Collection & Annotation ::: Adjudication of Annotations ::: Longest Common Subsequence Rule.",
+    "Data Collection & Annotation ::: Adjudication of Annotations ::: Noun Chunks",
+    "Analysis ::: Inter-Annotator Agreement",
+    "Analysis ::: Inter-Annotator Agreement ::: Emotion",
+    "Analysis ::: Inter-Annotator Agreement ::: Roles",
+    "Analysis ::: General Corpus Statistics",
+    "Baseline",
+    "Conclusion & Future Work",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "Research in emotion analysis from text focuses on mapping words, sentences, or documents to emotion categories based on the models of Ekman1992 or Plutchik2001, which propose the emotion classes of joy, sadness, anger, fear, trust, disgust, anticipation and surprise. Emotion analysis has been applied to a variety of tasks including large scale social media mining BIBREF0, literature analysis BIBREF1, BIBREF2, lyrics and music analysis BIBREF3, BIBREF4, and the analysis of the development of emotions over time BIBREF5.",
+      "There are at least two types of questions which cannot yet be answered by these emotion analysis systems. Firstly, such systems do not often explicitly model the perspective of understanding the written discourse (reader, writer, or the text's point of view). For example, the headline \u201cDjokovic happy to carry on cruising\u201d BIBREF6 contains an explicit mention of joy carried by the word \u201chappy\u201d. However, it may evoke different emotions in a reader (e. g., the reader is a supporter of Roger Federer), and the same applies to the author of the headline. To the best of our knowledge, only one work takes this point into consideration BIBREF7. Secondly, the structure that can be associated with the emotion description in text is not uncovered. Questions like: \u201cWho feels a particular emotion?\u201d or \u201cWhat causes that emotion?\u201d still remain unaddressed. There has been almost no work in this direction, with only few exceptions in English BIBREF8, BIBREF9 and Mandarin BIBREF10, BIBREF11.",
+      "With this work, we argue that emotion analysis would benefit from a more fine-grained analysis that considers the full structure of an emotion, similar to the research in aspect-based sentiment analysis BIBREF12, BIBREF13, BIBREF14, BIBREF15.",
+      "Consider the headline: \u201cA couple infuriated officials by landing their helicopter in the middle of a nature reserve\u201d BIBREF16 depicted on Figure FIGREF1. One could mark \u201cofficials\u201d as the experiencer, \u201ca couple\u201d as the target, and \u201clanding their helicopter in the middle of a nature reserve\u201d as the cause of anger. Now let us imagine that the headline starts with \u201cA cheerful couple\u201d instead of \u201cA couple\u201d. A simple approach to emotion detection based on cue words will capture that this sentence contains descriptions of anger (\u201cinfuriated\u201d) and joy (\u201ccheerful\u201d). It would, however, fail in attributing correct roles to the couple and the officials, thus, the distinction between their emotion experiences would remain hidden from us.",
+      "In this study, we focus on an annotation task with the goal of developing a dataset that would enable addressing the issues raised above. Specifically, we introduce the corpus GoodNewsEveryone, a novel dataset of news English headlines collected from 82 different sources analyzed in the Media Bias Chart BIBREF17 annotated for emotion class, emotion intensity, semantic roles (experiencer, cause, target, cue), and reader perspective. We use semantic roles, since identifying who feels what and why is essentially a semantic role labeling task BIBREF18. The roles we consider are a subset of those defined for the semantic frame for \u201cEmotion\u201d in FrameNet BIBREF19.",
+      "We focus on news headlines due to their brevity and density of contained information. Headlines often appeal to a reader's emotions, and hence are a potential good source for emotion analysis. In addition, news headlines are easy-to-obtain data across many languages, void of data privacy issues associated with social media and microblogging.",
+      "Our contributions are: (1) we design a two phase annotation procedure for emotion structures via crowdsourcing, (2) present the first resource of news headlines annotated for emotions, cues, intensity, experiencers, causes, targets, and reader emotion, and, (3), provide results of a baseline model to predict such roles in a sequence labeling setting. We provide our annotations at http://www.romanklinger.de/data-sets/GoodNewsEveryone.zip."
+    ],
+    [
+      "Our annotation is built upon different tasks and inspired by different existing resources, therefore it combines approaches from each of those. In what follows, we look at related work on each task and specify how it relates to our new corpus."
+    ],
+    [
+      "Emotion classification deals with mapping words, sentences, or documents to a set of emotions following psychological models such as those proposed by Ekman1992 (anger, disgust, fear, joy, sadness and surprise) or Plutchik2001; or continuous values of valence, arousal and dominance BIBREF20.",
+      "One way to create annotated datasets is via expert annotation BIBREF21, BIBREF22, BIBREF23, BIBREF24, BIBREF7. The creators of the ISEAR dataset make use of self-reporting instead, where subjects are asked to describe situations associated with a specific emotion BIBREF25. Crowdsourcing is another popular way to acquire human judgments BIBREF26, BIBREF9, BIBREF9, BIBREF27, BIBREF28. Another recent dataset for emotion recognition reproduces the ISEAR dataset in a crowdsourcing setting for both English and German BIBREF29. Lastly, social network platforms play a central role in data acquisition with distant supervision, because they provide a cheap way to obtain large amounts of noisy data BIBREF26, BIBREF9, BIBREF30, BIBREF31. Table TABREF3 shows an overview of resources. More details could be found in Bostan2018."
+    ],
+    [
+      "In emotion intensity prediction, the term intensity refers to the degree an emotion is experienced. For this task, there are only a few datasets available. To our knowledge, the first dataset annotated for emotion intensity is by Aman2007, who ask experts for ratings, followed by the datasets released for the EmoInt shared tasks BIBREF32, BIBREF28, both annotated via crowdsourcing through the best-worst scaling. The annotation task can also be formalized as a classification task, similarly to the emotion classification task, where the goal would be to map some textual input to a class from a set of predefined classes of emotion intensity categories. This approach is used by Aman2007, where they annotate high, moderate, and low."
+    ],
+    [
+      "The task of finding a function that segments a textual input and finds the span indicating an emotion category is less researched. Cue or trigger words detection could also be formulated as an emotion classification task for which the set of classes to be predicted is extended to cover other emotion categories with cues. First work that annotated cues was done manually by one expert and three annotators on the domain of blog posts BIBREF21. Mohammad2014 annotates the cues of emotions in a corpus of $4,058$ electoral tweets from US via crowdsourcing. Similar in annotation procedure, Yan2016emocues curate a corpus of 15,553 tweets and annotate it with 28 emotion categories, valence, arousal, and cues.",
+      "To the best of our knowledge, there is only one work BIBREF8 that leverages the annotations for cues and considers the task of emotion detection where the exact spans that represent the cues need to be predicted."
+    ],
+    [
+      "Detecting the cause of an expressed emotion in text received relatively little attention, compared to emotion detection. There are only few works on English that focus on creating resources to tackle this task BIBREF23, BIBREF9, BIBREF8, BIBREF33. The task can be formulated in different ways. One is to define a closed set of potential causes after annotation. Then, cause detection is a classification task BIBREF9. Another setting is to find the cause in the text. This is formulated as segmentation or clause classification BIBREF23, BIBREF8. Finding the cause of an emotion is widely researched on Mandarin in both resource creation and methods. Early works build on rule-based systems BIBREF34, BIBREF35, BIBREF36 which examine correlations between emotions and cause events in terms of linguistic cues. The works that follow up focus on both methods and corpus construction, showing large improvements over the early works BIBREF37, BIBREF38, BIBREF33, BIBREF39, BIBREF40, BIBREF41, BIBREF42, BIBREF43, BIBREF11. The most recent work on cause extraction is being done on Mandarin and formulates the task jointly with emotion detection BIBREF10, BIBREF44, BIBREF45. With the exception of Mohammad2014 who is annotating via crowdsourcing, all other datasets are manually labeled, usually by using the W3C Emotion Markup Language."
+    ],
+    [
+      "Semantic role labeling in the context of emotion analysis deals with extracting who feels (experiencer) which emotion (cue, class), towards whom the emotion is expressed (target), and what is the event that caused the emotion (stimulus). The relations are defined akin to FrameNet's Emotion frame BIBREF19.",
+      "There are two works that work on annotation of semantic roles in the context of emotion. Firstly, Mohammad2014 annotate a dataset of $4,058$ tweets via crowdsourcing. The tweets were published before the U.S. presidential elections in 2012. The semantic roles considered are the experiencer, the stimulus, and the target. However, in the case of tweets, the experiencer is mostly the author of the tweet. Secondly, Kim2018 annotate and release REMAN (Relational EMotion ANnotation), a corpus of $1,720$ paragraphs based on Project Gutenberg. REMAN was manually annotated for spans which correspond to emotion cues and entities/events in the roles of experiencers, targets, and causes of the emotion. They also provide baseline results for the automatic prediction of these structures and show that their models benefit from joint modeling of emotions with its roles in all subtasks. Our work follows in motivation Kim2018 and in procedure Mohammad2014."
+    ],
+    [
+      "Studying the impact of different annotation perspectives is another little explored area. There are few exceptions in sentiment analysis which investigate the relation between sentiment of a blog post and the sentiment of their comments BIBREF46 or model the emotion of a news reader jointly with the emotion of a comment writer BIBREF47.",
+      "Fewer works exist in the context of emotion analysis. 5286061 deal with writer's and reader's emotions on online blogs and find that positive reader emotions tend to be linked to positive writer emotions. Buechel2017b and buechel-hahn-2017-emobank look into the effects of different perspectives on annotation quality and find that the reader perspective yields better inter-annotator agreement values."
+    ],
+    [
+      "We gather the data in three steps: (1) collecting the news and the reactions they elicit in social media, (2) filtering the resulting set to retain relevant items, and (3) sampling the final selection using various metrics.",
+      "The headlines are then annotated via crowdsourcing in two phases by three annotators in the first phase and by five annotators in the second phase. As a last step, the annotations are adjudicated to form the gold standard. We describe each step in detail below."
+    ],
+    [
+      "The first step consists of retrieving news headlines from the news publishers. We further retrieve content related to a news item from social media: tweets mentioning the headlines together with replies and Reddit posts that link to the headlines. We use this additional information for subsampling described later.",
+      "We manually select all news sources available as RSS feeds (82 out of 124) from the Media Bias Chart BIBREF48, a project that analyzes reliability (from original fact reporting to containing inaccurate/fabricated information) and political bias (from most extreme left to most extreme right) of U.S. news sources.",
+      "Our news crawler retrieved daily headlines from the feeds, together with the attached metadata (title, link, and summary of the news article) from March 2019 until October 2019. Every day, after the news collection finished, Twitter was queried for 50 valid tweets for each headline. In addition to that, for each collected tweet, we collect all valid replies and counts of being favorited, retweeted and replied to in the first 24 hours after its publication.",
+      "The last step in the pipeline is aquiring the top (\u201chot\u201d) submissions in the /r/news, /r/worldnews subreddits, and their metadata, including the number of up and downvotes, upvote ratio, number of comments, and comments themselves."
+    ],
+    [
+      "We remove any headlines that have less than 6 tokens (e. g., \u201cSmall or nothing\u201d, \u201cBut Her Emails\u201d, \u201cRed for Higher Ed\u201d), as well as those starting with certain phrases, such as \u201cEp.\u201d,\u201cWatch Live:\u201d, \u201cPlaylist:\u201d, \u201cGuide to\u201d, and \u201cTen Things\u201d. We also filter-out headlines that contain a date (e. g., \u201cHeadlines for March 15, 2019\u201d) and words from the headlines which refer to visual content, like \u201cvideo\u201d, \u201cphoto\u201d, \u201cimage\u201d, \u201cgraphic\u201d, \u201cwatch\u201d, etc."
+    ],
+    [
+      "We stratify the remaining headlines by source (150 headlines from each source) and subsample equally according to the following strategies: 1) randomly select headlines, 2) select headlines with high count of emotion terms, 3) select headlines that contain named entities, and 4) select the headlines with high impact on social media. Table TABREF16 shows how many headlines are selected by each sampling method in relation to the most dominant emotion (see Section SECREF25)."
+    ],
+    [
+      "The goal of the first sampling method is to collect a random sample of headlines that is representative and not biased towards any source or content type. Note that the sample produced using this strategy might not be as rich with emotional content as the other samples."
+    ],
+    [
+      "For the second sampling strategy we hypothesize that headlines containing emotionally charged words are also likely to contain the structures we aim to annotate. This strategy selects headlines whose words are in the NRC dictionary BIBREF49."
+    ],
+    [
+      "We further hypothesize that headlines that mention named entities may also contain experiencers or targets of emotions, and therefore, they are likely to present a complete emotion structure. This sampling method yields headlines that contain at least one entity name, according to the recognition from spaCy that is trained on OntoNotes 5 and on Wikipedia corpus. We consider organization names, persons, nationalities, religious, political groups, buildings, countries, and other locations."
+    ],
+    [
+      "The last sampling strategy involves our Twitter and Reddit metadata. This enables us to select and sample headlines based on their impact on social media (under the assumption that this correlates with emotion connotation of the headline). This strategy chooses them equally from the most favorited tweets, most retweeted headlines on Twitter, most replied to tweets on Twitter, as well as most upvoted and most commented on posts on Reddit."
+    ],
+    [
+      "Using these sampling and filtering methods, we select $9,932$ headlines. Next, we set up two questionnaires (see Table TABREF17) for the two annotation phases that we describe below. We use Figure Eight."
+    ],
+    [
+      "The first questionnaire is meant to determine the dominant emotion of a headline, if that exists, and whether the headline triggers an emotion in a reader. We hypothesize that these two questions help us to retain only relevant headlines for the next, more expensive, annotation phase.",
+      "During this phase, $9,932$ headlines were annotated by three annotators. The first question of the first phase (P1Q1) is: \u201cWhich emotion is most dominant in the given headline?\u201d and annotators are provided a closed list of 15 emotion categories to which the category No emotion was added. The second question (P1Q2) aims to answer whether a given headline would stir up an emotion in most readers and the annotators are provided with only two possible answers (yes or no, see Table TABREF17 and Figure FIGREF1 for details).",
+      "Our set of 15 emotion categories is an extended set over Plutchik's emotion classes and comprises anger, annoyance, disgust, fear, guilt, joy, love, pessimism, negative surprise, optimism, positive surprise, pride, sadness, shame, and trust. Such a diverse set of emotion labels is meant to provide a more fine-grained analysis and equip the annotators with a wider range of answer choices."
+    ],
+    [
+      "The annotations collected during the first phase are automatically ranked and the ranking is used to decide which headlines are further annotated in the second phase. Ranking consists of sorting by agreement on P1Q1, considering P1Q2 in the case of ties.",
+      "The top $5,000$ ranked headlines are annotated by five annotators for emotion class, intensity, reader emotion, and other emotions in case there is not only a dominant emotion. Along with these closed annotation tasks, the annotators are asked to answer several open questions, namely (1) who is the experiencer of the emotion (if mentioned), (2) what event triggered the annotated emotion (if mentioned), (3) if the emotion had a target, and (4) who or what is the target. The annotators are free to select multiple instances related to the dominant emotion by copy-paste into the answer field. For more details on the exact questions and example of answers, see Table TABREF17. Figure FIGREF1 shows a depiction of the procedure."
+    ],
+    [
+      "To control the quality, we ensured that a single annotator annotates maximum 120 headlines (this protects the annotators from reading too many news headlines and from dominating the annotations). Secondly, we let only annotators who geographically reside in the U.S. contribute to the task.",
+      "We test the annotators on a set of $1,100$ test questions for the first phase (about 10% of the data) and 500 for the second phase. Annotators were required to pass 95%. The questions were generated based on hand-picked non-ambiguous real headlines through swapping out relevant words from the headline in order to obtain a different annotation, for instance, for \u201cDjokovic happy to carry on cruising\u201d, we would swap \u201cDjokovic\u201d with a different entity, the cue \u201chappy\u201d to a different emotion expression.",
+      "Further, we exclude Phase 1 annotations that were done in less than 10 seconds and Phase 2 annotations that were done in less than 70 seconds.",
+      "After we collected all annotations, we found unreliable annotators for both phases in the following way: for each annotator and for each question, we compute the probability with which the annotator agrees with the response chosen by the majority. If the computed probability is more than two standard deviations away from the mean we discard all annotations done by that annotator.",
+      "On average, 310 distinct annotators needed 15 seconds in the first phase. We followed the guidelines of the platform regarding payment and decided to pay for each judgment $$0.02$ (USD) for Phase 1 (total of $$816.00$ USD). For the second phase, 331 distinct annotators needed on average $\\approx $1:17 minutes to perform one judgment. Each judgment was paid with $0.08$$ USD (total $$2,720.00$ USD)."
+    ],
+    [
+      "In this section, we describe the adjudication process we undertook to create the gold dataset and the difficulties we faced in creating a gold set out of the collected annotations.",
+      "The first step was to discard obviously wrong annotations for open questions, such as annotations in other languages than English, or annotations of spans that were not part of the headline. In the next step, we incrementally apply a set of rules to the annotated instances in a one-or-nothing fashion. Specifically, we incrementally test each instance for a number of criteria in such a way that if at least one criteria is satisfied the instance is accepted and its adjudication is finalized. Instances that do not satisfy at least one criterium are adjudicated manually."
+    ],
+    [
+      "This filter is applied to all questions regardless of their type. Effectively, whenever an entire annotation is agreed upon by at least two annotators, we use all parts of this annotation as the gold annotation. Given the headline depicted in Figure FIGREF1 with the following target role annotations by different annotators: \u201cA couple\u201d, \u201cNone\u201d, \u201cA couple\u201d, \u201cofficials\u201d, \u201ctheir helicopter\u201d. The resulting gold annotation is \u201cA couple\u201d and the adjudication process for the target ends."
+    ],
+    [
+      "This rule is only applied to open text questions. It takes the most common smallest string intersection of all annotations. In the headline above, the experiencer annotations \u201cA couple\u201d, \u201cinfuriated officials\u201d, \u201cofficials\u201d, \u201cofficials\u201d, \u201cinfuriated officials\u201d would lead to \u201cofficials\u201d."
+    ],
+    [
+      "This rule is only applied two different intersections are the most common (previous rule), and these two intersect. We then accept the longest common subsequence. Revisiting the example for deciding on the cause role with the annotations \u201cby landing their helicopter in the nature reserve\u201d, \u201cby landing their helicopter\u201d, \u201clanding their helicopter in the nature reserve\u201d, \u201ca couple infuriated officials\u201d, \u201cinfuriated\u201d the adjudicated gold is \u201clanding their helicopter in the nature reserve\u201d.",
+      "Table TABREF27 shows through examples of how each rule works and how many instances are \u201csolved\u201d by each adjudication rule."
+    ],
+    [
+      "For the role of experiencer, we accept only the most-common noun-chunk(s).",
+      "The annotations that are left after being processed by all the rules described above are being adjudicated manually by the authors of the paper. We show examples for all roles in Table TABREF29."
+    ],
+    [
+      "We calculate the agreement on the full set of annotations from each phase for the two question types, namely open vs. closed, where the first deal with emotion classification and second with the roles cue, experiencer, cause, and target."
+    ],
+    [
+      "We use Fleiss' Kappa ($\\kappa $) to measure the inter-annotator agreement for closed questions BIBREF50, BIBREF51. In addition, we report the average percentage of overlaps between all pairs of annotators (%) and the mean entropy of annotations in bits. Higher agreement correlates with lower entropy. As Table TABREF38 shows, the agreement on the question whether a headline is emotional or not obtains the highest agreement ($0.34$), followed by the question on intensity ($0.22$). The lowest agreement is on the question to find the most dominant emotion ($0.09$).",
+      "All metrics show comparably low agreement on the closed questions, especially on the question of the most dominant emotion. This is reasonable, given that emotion annotation is an ambiguous, subjective, and difficult task. This aspect lead to the decision of not purely calculating a majority vote label but to consider the diversity in human interpretation of emotion categories and publish the annotations by all annotators.",
+      "Table TABREF40 shows the counts of annotators agreeing on a particular emotion. We observe that Love, Pride, and Sadness show highest intersubjectivity followed closely by Fear and Joy. Anger and Annoyance show, given their similarity, lower scores. Note that the micro average of the basic emotions (+ love) is $0.21$ for when more than five annotators agree."
+    ],
+    [
+      "Table TABREF41 presents the mean of pair-wise inter-annotator agreement for each role. We report average pair-wise Fleiss' $\\kappa $, span-based exact $\\textrm {F}_1$ over the annotated spans, accuracy, proportional token overlap, and the measure of agreement on set-valued items, MASI BIBREF52.",
+      "We observe a fair agreement on the open annotation tasks. The highest agreement is for the role of the Experiencer, followed by Cue, Cause, and Target.",
+      "This seems to correlate with the length of the annotated spans (see Table TABREF42). This finding is consistent with Kim2018. Presumably, Experiencers are easier to annotate as they often are noun phrases whereas causes can be convoluted relative clauses."
+    ],
+    [
+      "In the following, we report numbers of the adjudicated data set for simplicity of discussion. Please note that we publish all annotations by all annotators and suggest that computational models should consider the distribution of annotations instead of one adjudicated gold. The latter for be a simplification which we consider to not be appropriate.",
+      "GoodNewsEveryone contains $5,000$ headlines from various news sources described in the Media Bias Chart BIBREF17. Overall, the corpus is composed of $56,612$ words ($354,173$ characters) out of which $17,513$ are unique. The headline length is short with 11 words on average. The shortest headline contains 6 words while the longest headline contains 32 words. The length of a headline in characters ranges from 24 the shortest to 199 the longest.",
+      "Table TABREF42 presents the total number of adjudicated annotations for each role in relation to the dominant emotion. GoodNewsEveryone consists of $5,000$ headlines, $3,312$ of which have annotated dominant emotion via majority vote. The rest of $1,688$ headlines (up to $5,000$) ended in ties for the most dominant emotion category and were adjudicated manually. The emotion category Negative Surprise has the highest number of annotations, while Love has the lowest number of annotations. In most cases, Cues are single tokens (e. g., \u201cinfuriates\u201d, \u201cslams\u201d), Cause has the largest proportion of annotations that span more than seven tokens on average (65% out of all annotations in this category),",
+      "For the role of Experiencer, we see the lowest number of annotations (19%), which is a very different result to the one presented by Kim2018, where the role Experiencer was the most annotated. We hypothesize that this is the effect of the domain we annotated; it is more likely to encounter explicit experiencers in literature (as literary characters) than in news headlines. As we can see, the cue and the cause relations dominate the dataset (27% each), followed by Target (25%) relations.",
+      "Table TABREF42 also shows how many times each emotion triggered a certain relation. In this sense, Negative Surprise and Positive Surprise has triggered the most Experiencer, and Cause and Target relations, which due to the prevalence of the annotations for this emotion in the dataset.",
+      "Further, Figure FIGREF44, shows the distances of the different roles from the cue. The causes and targets are predominantly realized right of the cue, while the experiencer occurs more often left of the cue."
+    ],
+    [
+      "As an estimate for the difficulty of the task, we provide baseline results. We formulate the task as sequence labeling of emotion cues, mentions of experiencers, targets, and causes with a bidirectional long short-term memory networks with a CRF layer (biLSTM-CRF) that uses Elmo embeddings as input and an IOB alphabet as output. The results are shown in Table TABREF45."
+    ],
+    [
+      "We introduce GoodNewsEveryone, a corpus of $5,000$ headlines annotated for emotion categories, semantic roles, and reader perspective. Such a dataset enables answering instance-based questions, such as, \u201cwho is experiencing what emotion and why?\u201d or more general questions, like \u201cwhat are typical causes of joy in media?\u201d. To annotate the headlines, we employ a two-phase procedure and use crowdsourcing. To obtain a gold dataset, we aggregate the annotations through automatic heuristics.",
+      "As the evaluation of the inter-annotator agreement and the baseline model results show, the task of annotating structures encompassing emotions with the corresponding roles is a very difficult one.",
+      "However, we also note that developing such a resource via crowdsourcing has its limitations, due to the subjective nature of emotions, it is very challenging to come up with an annotation methodology that would ensure less dissenting annotations for the domain of headlines.",
+      "We release the raw dataset, the aggregated gold dataset, the carefully designed questionnaires, and baseline models as a freely available repository (partially only after acceptance of the paper). The released dataset will be useful for social science scholars, since it contains valuable information about the interactions of emotions in news headlines, and gives interesting insights into the language of emotion expression in media. Note that this dataset is also useful since it introduces a new dataset to test on structured prediction models. We are currently investigating the dataset for understanding the interaction between media bias and annotated emotions and roles."
+    ],
+    [
+      "This research has been conducted within the CRETA project (http://www.creta.uni-stuttgart.de/) which is funded by the German Ministry for Education and Research (BMBF) and partially funded by the German Research Council (DFG), projects SEAT (Structured Multi-Domain Emotion Analysis from Text, KL 2869/1-1). We thank Enrica Troiano and Jeremy Barnes for fruitful discussions.",
+      "same"
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2289/instruction.md b/qasper-2289/instruction.md
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+Name of Paper: LinkNBed: Multi-Graph Representation Learning with Entity Linkage
+
+Question: On what data is the model evaluated?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Knowledge Graph Representation",
+    "Multi-Graph Relational Learning",
+    "Proposed Method: LinkNBed ",
+    "Atomic Layer",
+    "Contextual Layer",
+    "Representation Layer",
+    "Relational Score Function",
+    "Objective Function",
+    "Datasets",
+    "Baselines",
+    "Evaluation Scheme",
+    "Predictive Analysis",
+    "Neural Embedding Methods for Relational Learning",
+    "Entity Resolution in Relational Data",
+    "Learning across multiple graphs",
+    "Concluding Remarks and Future Work",
+    "Acknowledgments",
+    "Discussion and Insights on Entity Linkage Task",
+    "Additional Dataset Details",
+    "Training Configurations",
+    "Contextual Information Formulations"
+  ],
+  "paragraphs": [
+    [
+      "Reasoning over multi-relational data is a key concept in Artificial Intelligence and knowledge graphs have appeared at the forefront as an effective tool to model such multi-relational data. Knowledge graphs have found increasing importance due to its wider range of important applications such as information retrieval BIBREF0 , natural language processing BIBREF1 , recommender systems BIBREF2 , question-answering BIBREF3 and many more. This has led to the increased efforts in constructing numerous large-scale Knowledge Bases (e.g. Freebase BIBREF4 , DBpedia BIBREF5 , Google's Knowledge graph BIBREF6 , Yago BIBREF7 and NELL BIBREF8 ), that can cater to these applications, by representing information available on the web in relational format.",
+      "All knowledge graphs share common drawback of incompleteness and sparsity and hence most existing relational learning techniques focus on using observed triplets in an incomplete graph to infer unobserved triplets for that graph BIBREF9 . Neural embedding techniques that learn vector space representations of entities and relationships have achieved remarkable success in this task. However, these techniques only focus on learning from a single graph. In addition to incompleteness property, these knowledge graphs also share a set of overlapping entities and relationships with varying information about them. This makes a compelling case to design a technique that can learn over multiple graphs and eventually aid in constructing a unified giant graph out of them. While research on learning representations over single graph has progressed rapidly in recent years BIBREF10 , BIBREF6 , BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 , there is a conspicuous lack of principled approach to tackle the unique challenges involved in learning across multiple graphs.",
+      "One approach to multi-graph representation learning could be to first solve graph alignment problem to merge the graphs and then use existing relational learning methods on merged graph. Unfortunately, graph alignment is an important but still unsolved problem and there exist several techniques addressing its challenges BIBREF15 , BIBREF16 , BIBREF17 , BIBREF18 in limited settings. The key challenges for the graph alignment problem emanate from the fact that the real world data are noisy and intricate in nature. The noisy or sparse data make it difficult to learn robust alignment features, and data abundance leads to computational challenges due to the combinatorial permutations needed for alignment. These challenges are compounded in multi-relational settings due to heterogeneous nodes and edges in such graphs.",
+      "Recently, deep learning has shown significant impact in learning useful information over noisy, large-scale and heterogeneous graph data BIBREF19 . We, therefore, posit that combining graph alignment task with deep representation learning across multi-relational graphs has potential to induce a synergistic effect on both tasks. Specifically, we identify that a key component of graph alignment process\u2014entity linkage\u2014also plays a vital role in learning across graphs. For instance, the embeddings learned over two knowledge graphs for an actor should be closer to one another compared to the embeddings of all the other entities. Similarly, the entities that are already aligned together across the two graphs should produce better embeddings due to the shared context and data. To model this phenomenon, we propose LinkNBed, a novel deep learning framework that jointly performs representation learning and graph linkage task. To achieve this, we identify key challenges involved in the learning process and make the following contributions to address them:"
+    ],
+    [
+      "A knowledge graph $\\mathcal {G}$ comprises of set of facts represented as triplets ( $e^s,r,e^o$ ) denoting the relationship $r$ between subject entity $e^s$ and object entity $e^o$ . Associated to this knowledge graph, we have a set of attributes that describe observed characteristics of an entity. Attributes are represented as set of key-value pairs for each entity and an attribute can have null (missing) value for an entity. We follow Open World Assumption - triplets not observed in knowledge graph are considered to be missing but not false. We assume that there are no duplicate triplets or self-loops."
+    ],
+    [
+      "Definition. Given a collection of knowledge graphs $\\mathcal {G}$ , Multi-Graph Relational Learning refers to the the task of learning information rich representations of entities and relationships across graphs. The learned embeddings can further be used to infer new knowledge in the form of link prediction or learn new labels in the form of entity linkage. We motivate our work with the setting of two knowledge graphs where given two graphs $G_1, G_2 \\in \\mathcal {G}$ , the task is to match an entity $e_{G_1} \\in G_1$ to an entity $e_{G_2} \\in G_2$ if they represent the same real-world entity. We discuss a straightforward extension of this",
+      " setting to more than two graphs in Section 7. Notations. Let $X$ and $Y$ represent realization of two such knowledge graphs extracted from two different sources. Let $n_e^X$ and $n_e^Y$ represent number of entities in $X$ and $Y$ respectively. Similarly, $n_r^X$ and $n_r^Y$ represent number of relations in $X$ and $Y$ . We combine triplets from both $Y$0 and $Y$1 to obtain set of all observed triplets $Y$2 where $Y$3 is total number of available records across from both graphs. Let $Y$4 and $Y$5 be the set of all entities and all relations in $Y$6 respectively. Let $Y$7 and $Y$8 . In addition to $Y$9 , we also have set of linkage labels $n_e^X$0 for entities between $n_e^X$1 and $n_e^X$2 . Each record in $n_e^X$3 is represented as triplet ( $n_e^X$4 , $n_e^X$5 , $n_e^X$6 ) where $n_e^X$7 when the entities are matched and $n_e^X$8 otherwise."
+    ],
+    [
+      "We present a novel inductive multi-graph relational learning framework that learns a set of aggregator functions capable of ingesting various contextual information for both entities and relationships in multi-relational graph. These functions encode the ingested structural and semantic information into low-dimensional entity and relation embeddings. Further, we use these representations to learn a relational score function that computes how two entities are likely to be connected in a particular relationship. The key idea behind this formulation is that when a triplet is observed, the relationship between the two entities can be explained using various contextual information such as local neighborhood features of both entities, attribute features of both entities and type information of the entities which participate in that relationship.",
+      "We outline two key insights for establishing the relationships between embeddings of the entities over multiple graphs in our framework:",
+      "Insight 1 (Embedding Similarity): If the two entities $e^X \\in X$ and $e^Y \\in Y$ represent the same real-world entity then their embeddings $\\mathbf {e^X}$ and $\\mathbf {e^Y}$ will be close to each other.",
+      "Insight 2 (Semantic Replacement): For a given triplet $t = (e^s, r, e^o) \\in X$ , denote $g(t)$ as the function that computes a relational score for $t$ using entity and relation embeddings. If there exists a matching entity $e^{s^{\\prime }} \\in Y$ for $e^s \\in X$ , denote $t^{\\prime } = (e^{s^{\\prime }},r,e^o)$ obtained after replacing $e^s$ with $e^{s^{\\prime }}$ . In this case, $g(t) \\sim g(t^{\\prime })$ i.e. score of triplets $t$ and $g(t)$0 will be similar.",
+      "For a triplet $(e^s, r , e^o) \\in \\mathcal {D}$ , we describe encoding mechanism of LinkNBed as three-layered architecture that computes the final output representations of $\\mathbf {z}^{r}, \\mathbf {z}^{e^s}, \\mathbf {z}^{e^o}$ for the given triplet. Figure 1 provides an overview of LinkNBed architecture and we describe the three steps below:"
+    ],
+    [
+      "Entities, Relations, Types and Attributes are first encoded in its basic vector representations. We use these basic representations to derive more complex contextual embeddings further.",
+      "Entities, Relations and Types. The embedding vectors corresponding to these three components are learned as follows: ves = f(WE es ) veo = f(WE eo )",
+      "vr = f(WR r ) vt = f(WT t ) where $\\mathbf {v^{e^s}}$ , $\\mathbf {v^{e^o}} \\in \\mathbb {R}^{d}$ . $\\mathbf {e^s}$ , $\\mathbf {e^o} \\in \\mathbb {R}^{n}$ are \u201cone-hot\" representations of $e^s$ and $e^o$ respectively. $\\mathbf {v^{r}} \\in \\mathbb {R}^{k}$ and $\\mathbf {r} \\in \\mathbb {R}^{m}$ is \u201cone-hot\" representation of $r$ . $\\mathbf {v^{t}} \\in \\mathbb {R}^{q}$ and $\\mathbf {v^{e^o}} \\in \\mathbb {R}^{d}$0 is \"one-hot\" representation of $\\mathbf {v^{e^o}} \\in \\mathbb {R}^{d}$1 . $\\mathbf {v^{e^o}} \\in \\mathbb {R}^{d}$2 , $\\mathbf {v^{e^o}} \\in \\mathbb {R}^{d}$3 and $\\mathbf {v^{e^o}} \\in \\mathbb {R}^{d}$4 are the entity, relation and type embedding matrices respectively. $\\mathbf {v^{e^o}} \\in \\mathbb {R}^{d}$5 is a nonlinear activation function (Relu in our case). $\\mathbf {v^{e^o}} \\in \\mathbb {R}^{d}$6 , $\\mathbf {v^{e^o}} \\in \\mathbb {R}^{d}$7 and $\\mathbf {v^{e^o}} \\in \\mathbb {R}^{d}$8 can be initialized randomly or using pre-trained word embeddings or vector compositions based on name phrases of components BIBREF20 .",
+      "Attributes. For a given attribute $a$ represented as key-value pair, we use paragraph2vec BIBREF21 type of embedding network to learn attribute embedding. Specifically, we represent attribute embedding vector as: a = f(Wkey akey + Wval aval )",
+      "where $\\mathbf {a} \\in \\mathbb {R}^{y}$ , $\\mathbf {a_{key}} \\in \\mathbb {R}^{u}$ and $\\mathbf {a_{val}} \\in \\mathbb {R}^{v}$ . $\\mathbf {W^{key}} \\in \\mathbb {R}^{y \\times u}$ and $\\mathbf {W^{val}} \\in \\mathbb {R}^{y \\times v}$ . $\\mathbf {a_{key}}$ will be \u201cone-hot\" vector and $\\mathbf {a_{val}}$ will be feature vector. Note that the dimensions of the embedding vectors do not necessarily need to be the same."
+    ],
+    [
+      "While the entity and relationship embeddings described above help to capture very generic latent features, embeddings can be further enriched to capture structural information, attribute information and type information to better explain the existence of a fact. Such information can be modeled as context of nodes and edges in the graph. To this end, we design the following canonical aggregator function that learns various contextual information by aggregating over relevant embedding vectors: c(z) = AGG({z', z' C(z)}) where $\\mathbf {c}(z)$ is the vector representation of the aggregated contextual information for component $z$ . Here, component $z$ can be either an entity or a relation. $C(z)$ is the set of components in the context of $z$ and $\\mathbf {z^{\\prime }}$ correspond to the vector embeddings of those components. AGG is the aggregator function which can take many forms such Mean, Max, Pooling or more complex LSTM based aggregators. It is plausible that different components in a context may have varied impact on the component for which the embedding is being learned. To account for this, we employ a soft attention mechanism where we learn attention coefficients to weight components based on their impact before aggregating them. We modify Eq. \"Implementation Details\" as:",
+      " c(z) = AGG(q(z) * {z', z' C(z)}) where q(z) = (z)z' C(z) (z') and $\\theta _z$ 's are the parameters of attention model.",
+      "Following contextual information is modeled in our framework:",
+      "Entity Neighborhood Context $\\mathbf {N_c}(e) \\in \\mathbb {R}^d$ . Given a triplet $(e^s,r,e^o)$ , the neighborhood context for an entity $e^s$ will be the nodes located near $e^s$ other than the node $e^o$ . This will capture the effect of local neighborhood in the graph surrounding $e^s$ that drives $e^s$ to participate in fact $(e^s,r,e^o)$ . We use Mean as aggregator function. As there can be large number of neighbors, we collect the neighborhood set for each entity as a pre-processing step using a random walk method. Specifically, given a node $e$ , we run $k$ rounds of random-walks of length $(e^s,r,e^o)$0 following BIBREF22 and create set $(e^s,r,e^o)$1 by adding all unique nodes visited across these walks. This context can be similarly computed for object entity.",
+      "Entity Attribute Context $\\mathbf {A_c}(e) \\in \\mathbb {R}^y$ . For an entity $e$ , we collect all attribute embeddings for $e$ obtained from Atomic Layer and learn aggregated information over them using Max operator given in Eq. \"Implementation Details\" .",
+      "Relation Type Context $\\mathbf {T_c}(r) \\in \\mathbb {R}^q$ . We use type context for relation embedding i.e. for a given relationship $r$ , this context aims at capturing the effect of type of entities that have participated in this relationship. For a given triplet $(e^s, r , e^o)$ , type context for relationship $r$ is computed by aggregation with mean over type embeddings corresponding to the context of $r$ . Appendix C provides specific forms of contextual information."
+    ],
+    [
+      "Having computed the atomic and contextual embeddings for a triplet $(e^s, r, e^o)$ , we obtain the final embedded representations of entities and relation in the triplet using the following formulation: $\n\\mathbf {z^{e^s}} &= \\sigma (\\underbrace{\\mathbf {W_1v^{e^s}}}_\\text{Subject Entity Embedding} + \\underbrace{\\mathbf {W_2 N_c}(e^s)}_\\text{Neighborhood Context}\\\\ &+ \\underbrace{\\mathbf {W_3 A_c}(e^s))}_\\text{Subject Entity Attributes}\n$ $\n\\mathbf {z^{e^o}} &= \\sigma (\\underbrace{\\mathbf {W_1v^{e^o}}}_\\text{Object Entity Embedding} + \\underbrace{\\mathbf {W_2 N_c}(e^o)}_\\text{Neighborhood Context}\\\\ &+ \\underbrace{\\mathbf {W_3 A_c}(e^o))}_\\text{Object Entity Attributes}\n$ ",
+      "zr = (W4vrRelation Embedding + W5 Tc(r))Entity Type Context",
+      "where $\\mathbf {W_1}, \\mathbf {W_2} \\in \\mathbb {R}^{d \\times d}$ , $\\mathbf {W_3} \\in \\mathbb {R}^{d \\times y}$ , $\\mathbf {W_4} \\in \\mathbb {R}^{d \\times k}$ and $\\mathbf {W_5} \\in \\mathbb {R}^{d \\times q}$ . $\\sigma $ is nonlinear activation function \u2013 generally Tanh or Relu.",
+      "Following is the rationale for our formulation: An entity's representation can be enriched by encoding information about the local neighborhood features and attribute information associated with the entity in addition to its own latent features. Parameters $\\mathbf {W_1}, \\mathbf {W_2}, \\mathbf {W_3}$ learn to capture these different aspects and map them into the entity embedding space. Similarly, a relation's representation can be enriched by encoding information about entity types that participate in that relationship in addition to its own latent features. Parameters $\\mathbf {W_4}, \\mathbf {W_5}$ learn to capture these aspects and map them into the relation embedding space. Further, as the ultimate goal is to jointly learn over multiple graphs, shared parameterization in our model facilitate the propagation of information across graphs thereby making it a graph-independent inductive model. The flexibility of the model stems from the ability to shrink it (to a very simple model considering atomic entity and relation embeddings only) or expand it (to a complex model by adding different contextual information) without affecting any other step in the learning procedure."
+    ],
+    [
+      "Having observed a triplet $(e^s,r, e^o)$ , we first use Eq. 7, 8 and 9 to compute entity and relation representations. We then use these embeddings to capture relational interaction between two entities using the following score function $g(\\cdot )$ : g(es, r, eo) = (zrT (zes zeo)) where $\\mathbf {z}^{r}, \\mathbf {z}^{e^s}, \\mathbf {z}^{e^o} \\in \\mathbb {R}^d$ are $d$ -dimensional representations of entity and relationships as described below. $\\sigma $ is the nonlinear activation function and $\\odot $ represent element-wise product."
+    ],
+    [
+      "The complete parameter space of the model can be given by: $\\mathbf {\\Omega = \\lbrace \\lbrace W_i\\rbrace _{i=1}^5, W^E, W^R, W^{key}, W^{val}, W^t ,\\Theta \\rbrace }$ . To learn these parameters, we design a novel multi-task objective function that jointly trains over two graphs. As identified earlier, the goal of our model is to leverage the available linkage information across graphs for optimizing the entity and relation embeddings such that they can explain the observed triplets across the graphs. Further, we want to leverage these optimized embeddings to match entities across graphs and expand the available linkage information. To achieve this goal, we define following two different loss functions catering to each learning task and jointly optimize over them as a multi-task objective to learn model parameters:",
+      "Relational Learning Loss. This is conventional loss function used to learn knowledge graph embeddings. Specifically, given a p-th triplet $(e^s, r, e^o)_p$ from training set $\\mathcal {D}$ , we sample $C$ negative samples by replacing either head or tail entity and define a contrastive max margin function as shown in BIBREF20 : ",
+      "$$\\begin{split}\nL_{rel} &= \\sum \\limits _{c=1}^{C} \\max (0, \\gamma - g(e^s_p,r_p,e^o_p) \\\\ &+ g^{\\prime }(e^s_c,r_p,e^o_p))\n\\end{split}$$   (Eq. 13) ",
+      "where, $\\gamma $ is margin, $e^s_c$ represent corrupted entity and $g^{\\prime }(e^s_c,r_p,e^o_p)$ represent corrupted triplet score.",
+      "Linkage Learning Loss: We design a novel loss function to leverage pairwise label set $\\mathcal {L}$ . Given a triplet $(e^s_X, r_X, e^o_X)$ from knowledge graph $X$ , we first find the entity $e_Y^+$ from graph $Y$ that represent the same real-world entity as $e^s_X$ . We then replace $e^s_X$ with $e_Y^+$ and compute score $g(e_Y^+,r_X,e^o_X)$ . Next, we find set of all entities $E_Y^-$ from graph $(e^s_X, r_X, e^o_X)$0 that has a negative label with entity $(e^s_X, r_X, e^o_X)$1 . We consider them analogous to the negative samples we generated for Eq. 13 . We then propose the label learning loss function as: ",
+      "$$\\begin{split}\nL_{lab} &= \\sum \\limits _{z=1}^{Z} \\max (0, \\gamma - g(e_Y^+,r_X,e^o_X) \\\\ &+ (g^{\\prime }(e_Y^-,r_X,e^o_X)_z))\n\\end{split}$$   (Eq. 14) ",
+      "where, $Z$ is the total number of negative labels for $e_X$ . $\\gamma $ is margin which is usually set to 1 and $e_Y^- \\in E_Y^-$ represent entity from graph $Y$ with which entity $e^s_X$ had a negative label. Please note that this applies symmetrically for the triplets that originate from graph $Y$ in the overall dataset. Note that if both entities of a triplet have labels, we will include both cases when computing the loss. Eq. 14 is inspired by Insight 1 and Insight 2 defined earlier in Section 2. Given a set $\\mathcal {D}$ of $N$ observed triplets across two graphs, we define complete multi-task objective as: ",
+      "$$\\mathbf {L}(\\mathbf {\\Omega }) = \\sum \\limits _{i=1}^{N} [b \\cdot L_{rel} + (1-b) \\cdot L_{lab}] + \\lambda \\left\\Vert \\mathbf {\\Omega } \\right\\Vert _2^2$$   (Eq. 15) ",
+      "where $\\mathbf {\\Omega }$ is set of all model parameters and $\\lambda $ is regularization hyper-parameter. $b$ is weight hyper-parameter used to attribute importance to each task. We train with mini-batch SGD procedure (Algorithm \"Objective Function\" ) using Adam Optimizer. [t!] LinkNBed mini-batch Training Input: Mini-batch $\\mathcal {M}$ , Negative Sample Size $C$ , Negative Label Size $Z$ , Attribute data $att\\_data$ , Neighborhood data $nhbr\\_data$ , Type data $type\\_data$ , Positive Label Dict $pos\\_dict$ , Negative Label Dict $\\lambda $0 Output: Mini-batch Loss $\\lambda $1 . $\\lambda $2 score_pos = []; score_neg = []; score_pos_lab = []; score_neg_lab = [] $\\lambda $3 to size( $\\lambda $4 ) input_tuple = $\\lambda $5 = ( $\\lambda $6 ) sc = compute_triplet_score( $\\lambda $7 ) (Eq. \"Relational Score Function\" ) score_pos.append(sc) $\\lambda $8 to $\\lambda $9 Select $b$0 from entity list such that $b$1 and $b$2 and $b$3 sc_neg = compute_triplet_score( $b$4 ) score_neg.append(sc_neg) $b$5 in $b$6 $b$7 = positive label for $b$8 sc_pos_l = compute_triplet_score( $b$9 ) score_pos_lab.append(sc_pos_l) $\\mathcal {M}$0 to $\\mathcal {M}$1 Select $\\mathcal {M}$2 from $\\mathcal {M}$3 sc_neg_l = compute_triplet_score( $\\mathcal {M}$4 ) score_neg_lab.append(sc_neg_l) $\\mathcal {M}$5 compute_minibatch_loss(score_pos, score_neg, score_pos_lab, score_neg_lab) (Eq. 15 ) Back-propagate errors and update parameters $\\mathcal {M}$6 return $\\mathcal {M}$7 ",
+      "Missing Positive Labels. It is expensive to obtain positive labels across multiple graphs and hence it is highly likely that many entities will not have positive labels available. For those entities, we will modify Eq. 14 to use the original triplet $(e^s_X, r_X, e^o_X)$ in place of perturbed triplet $g(e_Y^+,r_X,e^o_X)$ for the positive label. The rationale here again arises from Insight 2 wherein embeddings of two duplicate entities should be able to replace each other without affecting the score.",
+      "Training Time Complexity. Most contextual information is pre-computed and available to all training steps which leads to constant time embedding lookup for those context. But for attribute network, embedding needs to be computed for each attribute separately and hence the complexity to compute score for one triplet is $\\mathcal {O}(2a)$ where $a$ is number of attributes. Also for training, we generate $C$ negative samples for relational loss function and use $Z$ negative labels for label loss function. Let $k = C + Z$ . Hence, the training time complexity for a set of $n$ triplets will be $\\mathcal {O}(2ak*n)$ which is linear in number of triplets with a constant factor as $ak << n$ for real world knowledge graphs. This is desirable as the number of triplets tend to be very large per graph in multi-relational settings.",
+      "Memory Complexity. We borrow notations from BIBREF9 and describe the parameter complexity of our model in terms of the number of each component and corresponding embedding dimension requirements. Let $H_a = 2*N_eH_e + N_rH_r + N_tH_t + N_kH_k + N_vH_v$ . The parameter complexity of our model is: $H_a * (H_b + 1)$ . Here, $N_e$ , $N_r$ , $N_t$ , $N_k$ , $N_v$ signify number of entities, relations, types, attribute keys and vocab size of attribute values across both datasets. Here $H_b$ is the output dimension of the hidden layer."
+    ],
+    [
+      "We evaluate LinkNBed and baselines on two real world knowledge graphs: D-IMDB (derived from large scale IMDB data snapshot) and D-FB (derived from large scale Freebase data snapshot). Table 1 provides statistics for our final dataset used in the experiments. Appendix B.1 provides complete details about dataset processing."
+    ],
+    [
+      "We compare the performance of our method against state-of-the-art representation learning baselines that use neural embedding techniques to learn entity and relation representation. Specifically, we consider compositional methods of RESCAL BIBREF10 as basic matrix factorization method, DISTMULT BIBREF14 as simple multiplicative model good for capturing symmetric relationships, and Complex BIBREF11 , an upgrade over DISTMULT that can capture asymmetric relationships using complex valued embeddings. We also compare against translational model of STransE that combined original structured embedding with TransE and has shown state-of-art performance in benchmark testing BIBREF23 . Finally, we compare with GAKE BIBREF24 , a model that captures context in entity and relationship representations.",
+      "In addition to the above state-of-art models, we analyze the effectiveness of different components of our model by comparing with various versions that use partial information. Specifically, we report results on following variants:",
+      "LinkNBed - Embed Only. Only use entity embeddings, LinkNBed - Attr Only. Only use Attribute Context, LinkNBed - Nhbr Only. Only use Neighborhood Context, LinkNBed - Embed + Attr. Use both Entity embeddings and Attribute Context, LinkNBed - Embed + Nhbr. Use both Entity embeddings and Neighbor Context and LinkNBed - Embed All. Use all three Contexts."
+    ],
+    [
+      "We evaluate our model using two inference tasks:",
+      "Link Prediction. Given a test triplet $(e^s, r, e^o)$ , we first score this triplet using Eq. \"Relational Score Function\" . We then replace $e^o$ with all other entities in the dataset and filter the resulting set of triplets as shown in BIBREF12 . We score the remaining set of perturbed triplets using Eq. \"Relational Score Function\" . All the scored triplets are sorted based on the scores and then the rank of the ground truth triplet is used for the evaluation. We use this ranking mechanism to compute HITS@10 (predicted rank $\\le $ 10) and reciprocal rank ( $\\frac{1}{rank}$ ) of each test triplet. We report the mean over all test samples.",
+      "Entity Linkage. In alignment with Insight 2, we pose a novel evaluation scheme to perform entity linkage. Let there be two ground truth test sample triplets: $(e_X, e_Y^+, 1)$ representing a positive duplicate label and $(e_X, e_Y^-, 0)$ representing a negative duplicate label. Algorithm \"Evaluation Scheme\" outlines the procedure to compute linkage probability or score $q$ ( $ \\in [0,1]$ ) for the pair $(e_X, e_Y)$ . We use $L1$ distance between the two vectors analogous to Mean Absolute Error (MAE). In lieu of hard-labeling test pairs, we use score $q$ to compute Area Under the Precision-Recall Curve (AUPRC).",
+      "[t!] Entity Linkage Score Computation Input: Test pair \u2013 $(e_X \\in X, e_Y \\in Y)$ . Output: Linkage Score \u2013 $q$ . 1. Collect all triplets involving $e_X$ from graph $X$ and all triplets involving $e_Y$ from graph $Y$ into a combined set $\\mathcal {O}$ . Let $|\\mathcal {O}| = k$ . 2. Construct $S_{orig} \\in \\mathbb {R}^k$ . For each triplet $o \\in \\mathcal {O}$ , compute score $q$0 using Eq. \"Relational Score Function\" and store the score in $q$1 . 3. Create triplet set $q$2 as following: $q$3 contain $q$4 Replace $q$5 with $q$6 to create perturbed triplet $q$7 and store it in $q$8 $q$9 contain $e_X$0 Replace $e_X$1 with $e_X$2 to create perturbed triplet $e_X$3 and store it in $e_X$4 4. Construct $e_X$5 . For each triplet $e_X$6 , compute score $e_X$7 using Eq. \"Relational Score Function\" and store the score in $e_X$8 . 5. Compute $e_X$9 . Elements in $X$0 and $X$1 have one-one correspondence so take the mean absolute difference: $X$2 = $X$3 - $X$4 return $X$5 ",
+      "For the baselines and the unsupervised version (with no labels for entity linkage) of our model, we use second stage multilayer Neural Network as classifier for evaluating entity linkage. Appendix B.2 provides training configuration details."
+    ],
+    [
+      "Link Prediction Results. We train LinkNBed model jointly across two knowledge graphs and then perform inference over individual graphs to report link prediction reports. For baselines, we train each baseline on individual graphs and use parameters specific to the graph to perform link prediction inference over each individual graph. Table 2 shows link prediction performance for all methods. Our model variant with attention mechanism outperforms all the baselines with $4.15\\%$ improvement over single graph state-of-the-art Complex model on D-IMDB and $8.23\\%$ improvement on D-FB dataset. D-FB is more challenging dataset to learn as it has a large set of sparse relationships, types and attributes and it has an order of magnitude lesser relational evidence (number of triplets) compared to D-IMDB. Hence, LinkNBed's pronounced improvement on D-FB demonstrates the effectiveness of the model. The simplest version of LinkNBed with only entity embeddings resembles DISTMULT model with different objective function. Hence closer performance of those two models aligns with expected outcome. We observed that the Neighborhood context alone provides only marginal improvements while the model benefits more from the use of attributes. Despite being marginal, attention mechanism also improves accuracy for both datasets. Compared to the baselines which are obtained by trained and evaluated on individual graphs, our superior performance demonstrates the effectiveness of multi-graph learning.",
+      "Entity Linkage Results. We report entity linkage results for our method in two settings: a.) Supervised case where we train using both the objective functions. b.) Unsupervised case where we learn with only the relational loss function. The latter case resembles the baseline training where each model is trained separately on two graphs in an unsupervised manner. For performing the entity linkage in unsupervised case for all models, we first train a second stage of simple neural network classifier and then perform inference. In the supervised case, we use Algorithm \"Evaluation Scheme\" for performing the inference. Table 3 demonstrates the performance of all methods on this task. Our method significantly outperforms all the baselines with $33.86\\%$ over second best baseline in supervised case and $17.35\\%$ better performance in unsupervised case. The difference in the performance of our method in two cases demonstrate that the two training objectives are helping one another by learning across the graphs. GAKE's superior performance on this task compared to the other state-of-the-art relational baselines shows the importance of using contextual information for entity linkage. Performance of other variants of our model again demonstrate that attribute information is more helpful than neighborhood context and attention provides marginal improvements. We provide further insights with examples and detailed discussion on entity linkage task in Appendix A."
+    ],
+    [
+      "Compositional Models learn representations by various composition operators on entity and relational embeddings. These models are multiplicative in nature and highly expressive but often suffer from scalability issues. Initial models include RESCAL BIBREF10 that uses a relation specific weight matrix to explain triplets via pairwise interactions of latent features, Neural Tensor Network BIBREF20 , more expressive model that combines a standard NN layer with a bilinear tensor layer and BIBREF6 that employs a concatenation-projection method to project entities and relations to lower dimensional space. Later, many sophisticated models (Neural Association Model BIBREF25 , HoLE BIBREF26 ) have been proposed. Path based composition models BIBREF27 and contextual models GAKE BIBREF24 have been recently studied to capture more information from graphs. Recently, model like Complex BIBREF11 and Analogy BIBREF28 have demonstrated state-of-the art performance on relational learning tasks. Translational Models ( BIBREF29 , BIBREF30 , BIBREF12 , BIBREF31 , BIBREF32 , BIBREF13 ) learn representation by employing translational operators on the embeddings and optimizing based on their score. They offer an additive and efficient alternative to expensive multiplicative models. Due to their simplicity, they often loose expressive power. For a comprehensive survey of relational learning methods and empirical comparisons, we refer the readers to BIBREF9 , BIBREF23 , BIBREF33 and BIBREF14 . None of these methods address multi-graph relational learning and cannot be adapted to tasks like entity linkage in straightforward manner."
+    ],
+    [
+      "Entity Resolution refers to resolving entities available in knowledge graphs with entity mentions in text. BIBREF34 proposed entity disambiguation method for KB population, BIBREF35 learns entity embeddings for resolution, BIBREF36 propose a sophisticated DNN architecture for resolution, BIBREF37 proposes entity resolution across multiple social domains, BIBREF38 jointly embeds text and knowledge graph to perform resolution while BIBREF39 proposes Attention Mechanism for Collective Entity Resolution."
+    ],
+    [
+      "Recently, learning over multiple graphs have gained traction. BIBREF15 divides a multi-relational graph into multiple homogeneous graphs and learns associations across them by employing product operator. Unlike our work, they do not learn across multiple multi-relational graphs. BIBREF40 provides logic based insights for cross learning, BIBREF16 does pairwise entity matching across multi-relational graphs and is very expensive, BIBREF41 learns embeddings to support multi-lingual learning and Big-Align BIBREF17 tackles graph alignment problem efficiently for bipartite graphs. None of these methods learn latent representations or jointly train graph alignment and learning which is the goal of our work."
+    ],
+    [
+      "We present a novel relational learning framework that learns entity and relationship embeddings across multiple graphs. The proposed representation learning framework leverage an efficient learning and inference procedure which takes into account the duplicate entities representing the same real-world entity in a multi-graph setting. We demonstrate superior accuracies on link prediction and entity linkage tasks compared to the existing approaches that are trained only on individual graphs. We believe that this work opens a new research direction in joint representation learning over multiple knowledge graphs.",
+      "Many data driven organizations such as Google and Microsoft take the approach of constructing a unified super-graph by integrating data from multiple sources. Such unification has shown to significantly help in various applications, such as search, question answering, and personal assistance. To this end, there exists a rich body of work on linking entities and relations, and conflict resolution (e.g., knowledge fusion BIBREF6 . Still, the problem remains challenging for large scale knowledge graphs and this paper proposes a deep learning solution that can play a vital role in this construction process. In real-world setting, we envision our method to be integrated in a large scale system that would include various other components for tasks like conflict resolution, active learning and human-in-loop learning to ensure quality of constructed super-graph. However, we point out that our method is not restricted to such use cases\u2014one can readily apply our method to directly make inference over multiple graphs to support applications like question answering and conversations.",
+      "For future work, we would like to extend the current evaluation of our work from a two-graph setting to multiple graphs. A straightforward approach is to create a unified dataset out of more than two graphs by combining set of triplets as described in Section 2, and apply learning and inference on the unified graph without any major change in the methodology. Our inductive framework learns functions to encode contextual information and hence is graph independent. Alternatively, one can develop sophisticated approaches with iterative merging and learning over pairs of graphs until exhausting all graphs in an input collection."
+    ],
+    [
+      "We would like to give special thanks to Ben London, Tong Zhao, Arash Einolghozati, Andrew Borthwick and many others at Amazon for helpful comments and discussions. We thank the reviewers for their valuable comments and efforts towards improving our manuscript. This project was supported in part by NSF(IIS-1639792, IIS-1717916)."
+    ],
+    [
+      "Entity linkage task is novel in the space of multi-graph learning and yet has not been tackled by any existing relational learning approaches. Hence we analyze our performance on the task in more detail here. We acknowledge that baseline methods are not tailored to the task of entity linkage and hence their low performance is natural. But we observe that our model performs well even in the unsupervised scenario where essentially the linkage loss function is switched off and our model becomes a relational learning baseline. We believe that the inductive ability of our model and shared parameterization helps to capture knowledge across graphs and allows for better linkage performance. This outcome demonstrates the merit in multi-graph learning for different inference tasks. Having said that, we admit that our results are far from comparable to state-of-the-art linkage results (Das et al., 2017) and much work needs to be done to advance representation and relational learning methods to support effective entity linkage. But we note that our model works for multiple types of entities in a very heterogeneous environment with some promising results which serves as an evidence to pursue this direction for entity linkage task.",
+      "We now discuss several use-case scenarios where our model did not perform well to gain insights on what further steps can be pursued to improve over this initial model:",
+      "Han Solo with many attributes (False-negative example). Han Solo is a fictional character in Star Wars and appears in both D-IMDB and D-FB records. We have a positive label for this sample but we do not predict it correctly. Our model combines multiple components to effectively learn across graphs. Hence we investigated all the components to check for the failures. One observation we have is the mismatch in the amount of attributes across the two datasets. Further, this is compounded by multi-value attributes. As described, we use paragraph2vec like model to learn attribute embeddings where for each attribute, we aggregate over all its values. This seems to be computing embeddings that are very noisy. As we have seen attributes are affecting the final result with high impact and hence learning very noisy attributes is not helping. Further, the mismatch in number of types is also an issue. Even after filtering the types, the difference is pretty large. Types are also included as attributes and they contribute context to relation embeddings. We believe that the skew in type difference is making the model learn bad embeddings. Specifically this happens in cases where lot of information is available like Han Solo as it lead to the scenario of abundant noisy data. With our investigation, we believe that contextual embeddings need further sophistication to handle such scenarios. Further, as we already learn relation, type and attribute embeddings in addition to entity embeddings, aligning relations, types and attributes as integral task could also be an important future direction.",
+      "Alfred Pennyworth is never the subject of matter (False-negative example). In this case, we observe a new pattern which was found in many other examples. While there are many triples available for this character in D-IMDB, very few triplets are available in D-FB. This skew in availability of data hampers the learning of deep network which ends up learning very different embeddings for two realizations. Further, we observe another patter where Alfred Pennyworth appears only as an object in all those few triplets of D-FB while it appears as both subject and object in D-IMDB. Accounting for asymmetric relationships in an explicit manner may become helpful for this scenario.",
+      "Thomas Wayne is Martha Wayne! (False-positive example). This is the case of abundance of similar contextual information as our model predicts Thomas Wayne and Martha Wayne to be same entity. Both the characters share a lot of context and hence many triples and attributes, neighborhood etc. are similar for of them eventually learning very similar embeddings. Further as we have seen before, neighborhood has shown to be a weak context which seems to hamper the learning in this case. Finally, the key insight here is to be able to attend to the very few discriminative features for the entities in both datasets (e.g. male vs female) and hence a more sophisticated attention mechanism would help.",
+      "In addition to the above specific use cases, we would like to discuss insights on following general concepts that naturally occur when learning over multiple graphs:"
+    ],
+    [
+      "We perform light pre-processing on the dataset to remove self-loops from triples, clean the attributes to remove garbage characters and collapse CVT (Compound Value Types) entities into single triplets. Further we observe that there is big skew in the number of types between D-IMDB and D-FB. D-FB contains many non-informative type information such as $\\#base.*$ . We remove all such non-informative types from both datasets which retains 41 types in D-IMDB and 324 types in D-FB. This filtering does not reduce the number of entities or triples by significant number (less than 1000 entities filtered)",
+      "For comparing at scale with baselines, we further reduce dataset using similar techniques adopted in producing widely accepted FB-15K or FB-237K. Specifically, we filter relational triples such that both entities in a triple contained in our dataset must appear in more than $k$ triples. We use $k=50$ for D-FB and $k=100$ for D-IMDB as D-IMDB has orders of magnitude more triples compared to D-FB in our curated datasets. We still maintain the overall ratio of the number of triples between the two datasets.",
+      "Positive and Negative Labels. We obtain 500662 positive labels using the existing links between the two datasets. Note that any entity can have only one positive label. We also generate 20 negative labels for each entity using the following method: (i) randomly select 10 entities from the other graph such that both entities belong to the same type and there exist no positive label between entities (ii) randomly select 10 entities from the other graph such that both entities belong to different types."
+    ],
+    [
+      "We performed hyper-parameter grid search to obtain the best performance of our method and finally used the following configuration to obtain the reported results:",
+      "\u2013 Entity Embedding Size: 256, Relation Embedding Size=64, Attribute Embedding Size = 16, Type Embedding Size = 16, Attribute Value Embedding Size = 512. We tried multiple batch sizes with very minor difference in performance and finally used size of 2000. For hidden units per layer, we use size = 64. We used $C=50$ negative samples and $Z=20$ negative labels. The learning rate was initialized as 0.01 and then decayed over epochs. We ran our experiments for 5 epochs after which the training starts to convert as the dataset is very large. We use loss weights $b$ as 0.6 and margin as 1. Further, we use $K = 50$ random walks of length $l = 3$ for each entity We used a train/test split of 60%/40% for both the triples set and labels set. For baselines, we used the implementations provided by the respective authors and performed grid search for all methods according to their requirements."
+    ],
+    [
+      "Here we describe exact formulation of each context that we used in our work.",
+      "Neighborhood Context: Given a triplet $(e^s,r,e^o)$ , the neighborhood context for an entity $e^s$ will be all the nodes at 1-hop distance from $e^s$ other than the node $e^o$ . This will capture the effect of other nodes in the graph surrounding $e^s$ that drives $e^s$ to participate in fact $(e^s,r,e^o)$ . Concretely, we define the neighborhood context of $e^s$ as follows:",
+      "Nc(es) = 1ne' e' N(es)",
+      "e' eo ve'",
+      "where $\\mathcal {N}(e^s)$ is the set of all entities in neighborhood of $e^s$ other than $e^o$ . We collect the neighborhood set for each entity as a pre-processing step using a random walk method. Specifically, given a node $e$ , we run $k$ rounds of random-walks of length $l$ and create the neighborhood set $\\mathcal {N}(e)$ by adding all unique nodes visited across these walks.",
+      "Please note that we can also use $\\max $ function in ( \"Contextual Information Formulations\" ) instead of sum. $\\mathbf {N_c}(e^s) \\in \\mathbb {R}^d$ and the context can be similarly computed for object entity.",
+      "Attribute Context. For an entity $e^s$ , the corresponding attribute context is defined as",
+      "Ac(es) = 1na i=1na aies",
+      "where $n_a$ is the number of attributes. $\\mathbf {a_i^{e^s}}$ is the embedding for attribute $i$ . $\\mathbf {A_c}(e^s) \\in \\mathbb {R}^y$ .",
+      "Type Context. We use type context mainly for relationships i.e. for a given relationship $r$ , this context aims at capturing the effect of type of entities that have participated in this relationship. For a given triplet $(e^s, r , e^o)$ , we define type context for relationship $r$ as:",
+      "Tc(r) = 1ntr i=1ntr vit'",
+      "where, $n_t^r$ is the total number of types of entities that has participated in relationship $r$ and $\\mathbf {v_i^{t^{\\prime }}}$ is the type embedding that corresponds to type $t$ . $\\mathbf {T_c}(r) \\in \\mathbb {R}^q$ ."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2291/instruction.md b/qasper-2291/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..0be538a6ac2bbad012af303e30f18f675e121f47
--- /dev/null
+++ b/qasper-2291/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: A Multi-cascaded Deep Model for Bilingual SMS Classification
+
+Question: What is their baseline model?
\ No newline at end of file
diff --git a/qasper-2298/instruction.md b/qasper-2298/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..cf807c2a8fb69ba3a2305dd3d11493abdab3aca8
--- /dev/null
+++ b/qasper-2298/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Sharp Models on Dull Hardware: Fast and Accurate Neural Machine Translation Decoding on the CPU
+
+Question: Do they only test on one dataset?
\ No newline at end of file
diff --git a/qasper-2300/instruction.md b/qasper-2300/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..63c14220474a738ccdd123202f69bb27cac98424
--- /dev/null
+++ b/qasper-2300/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Transformer-based Cascaded Multimodal Speech Translation
+
+Question: Was evaluation metrics and criteria were used to evaluate the output of the cascaded multimodal speech translation?
\ No newline at end of file
diff --git a/qasper-2307/instruction.md b/qasper-2307/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..50a97c03e75458655e9981d851aaf0151fa802c4
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+++ b/qasper-2307/instruction.md
@@ -0,0 +1,135 @@
+Name of Paper: Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset
+
+Question: How did they gather the data?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "The Schema-Guided Dialogue Dataset",
+    "The Schema-Guided Dialogue Dataset ::: Services and APIs",
+    "The Schema-Guided Dialogue Dataset ::: Dialogue Simulator Framework",
+    "The Schema-Guided Dialogue Dataset ::: Dialogue Paraphrasing",
+    "The Schema-Guided Dialogue Dataset ::: Dataset Analysis",
+    "The Schema-Guided Approach",
+    "Zero-Shot Dialogue State Tracking",
+    "Zero-Shot Dialogue State Tracking ::: Model",
+    "Zero-Shot Dialogue State Tracking ::: Model ::: Schema Embedding",
+    "Zero-Shot Dialogue State Tracking ::: Model ::: Utterance Encoding",
+    "Zero-Shot Dialogue State Tracking ::: Model ::: Projection",
+    "Zero-Shot Dialogue State Tracking ::: Model ::: Active Intent",
+    "Zero-Shot Dialogue State Tracking ::: Model ::: Requested Slots",
+    "Zero-Shot Dialogue State Tracking ::: Model ::: User Goal",
+    "Zero-Shot Dialogue State Tracking ::: Evaluation",
+    "Zero-Shot Dialogue State Tracking ::: Evaluation ::: Performance on other datasets",
+    "Zero-Shot Dialogue State Tracking ::: Evaluation ::: Performance on SGD",
+    "Zero-Shot Dialogue State Tracking ::: Evaluation ::: Performance on different domains (SGD)",
+    "Discussion",
+    "Conclusions",
+    "Conclusions ::: Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "Virtual assistants help users accomplish tasks including but not limited to finding flights, booking restaurants and, more recently, navigating user interfaces, by providing a natural language interface to services and APIs on the web. The recent popularity of conversational interfaces and the advent of frameworks like Actions on Google and Alexa Skills, which allow developers to easily add support for new services, has resulted in a major increase in the number of application domains and individual services that assistants need to support, following the pattern of smartphone applications.",
+      "Consequently, recent work has focused on scalable dialogue systems that can handle tasks across multiple application domains. Data-driven deep learning based approaches for multi-domain modeling have shown promise, both for end-to-end and modular systems involving dialogue state tracking and policy learning. This line of work has been facilitated by the release of multi-domain dialogue corpora such as MultiWOZ BIBREF0, M2M BIBREF1 and FRAMES BIBREF2.",
+      "However, existing datasets for multi-domain task-oriented dialogue do not sufficiently capture a number of challenges that arise with scaling virtual assistants in production. These assistants need to support a large BIBREF3, constantly increasing number of services over a large number of domains. In comparison, existing public datasets cover few domains. Furthermore, they define a single static API per domain, whereas multiple services with overlapping functionality, but heterogeneous interfaces, exist in the real world.",
+      "To highlight these challenges, we introduce the Schema-Guided Dialogue (SGD) dataset, which is, to the best of our knowledge, the largest public task-oriented dialogue corpus. It exceeds existing corpora in scale, with over 16000 dialogues in the training set spanning 26 services belonging to 16 domains (more details in Table TABREF2). Further, to adequately test the models' ability to generalize in zero-shot settings, the evaluation sets contain unseen services and domains. The dataset is designed to serve as an effective testbed for intent prediction, slot filling, state tracking and language generation, among other tasks in large-scale virtual assistants.",
+      "We also propose the schema-guided paradigm for task-oriented dialogue, advocating building a single unified dialogue model for all services and APIs. Using a service's schema as input, the model would make predictions over this dynamic set of intents and slots present in the schema. This setting enables effective sharing of knowledge among all services, by relating the semantic information in the schemas, and allows the model to handle unseen services and APIs. Under the proposed paradigm, we present a novel architecture for multi-domain dialogue state tracking. By using large pretrained models like BERT BIBREF4, our model can generalize to unseen services and is robust to API changes, while achieving state-of-the-art results on the original and updated BIBREF5 MultiWOZ datasets."
+    ],
+    [
+      "Task-oriented dialogue systems have constituted an active area of research for decades. The growth of this field has been consistently fueled by the development of new datasets. Initial datasets were limited to one domain, such as ATIS BIBREF6 for spoken language understanding for flights. The Dialogue State Tracking Challenges BIBREF7, BIBREF8, BIBREF9, BIBREF10 contributed to the creation of dialogue datasets with increasing complexity. Other notable related datasets include WOZ2.0 BIBREF11, FRAMES BIBREF2, M2M BIBREF1 and MultiWOZ BIBREF0. These datasets have utilized a variety of data collection techniques, falling within two broad categories:",
+      "Wizard-of-Oz This setup BIBREF12 connects two crowd workers playing the roles of the user and the system. The user is provided a goal to satisfy, and the system accesses a database of entities, which it queries as per the user's preferences. WOZ2.0, FRAMES and MultiWOZ, among others, have utilized such methods.",
+      "Machine-machine Interaction A related line of work explores simulation-based dialogue generation, where the user and system roles are simulated to generate a complete conversation flow, which can then be converted to natural language using crowd workers BIBREF1. Such a framework may be cost-effective and error-resistant since the underlying crowd worker task is simpler, and semantic annotations are obtained automatically.",
+      "As virtual assistants incorporate diverse domains, recent work has focused on zero-shot modeling BIBREF13, BIBREF14, BIBREF15, domain adaptation and transfer learning techniques BIBREF16. Deep-learning based approaches have achieved state of the art performance on dialogue state tracking tasks. Popular approaches on small-scale datasets estimate the dialogue state as a distribution over all possible slot-values BIBREF17, BIBREF11 or individually score all slot-value combinations BIBREF18, BIBREF19. Such approaches are not practical for deployment in virtual assistants operating over real-world services having a very large and dynamic set of possible values. Addressing these concerns, approaches utilizing a dynamic vocabulary of slot values have been proposed BIBREF20, BIBREF21, BIBREF22."
+    ],
+    [
+      "An important goal of this work is to create a benchmark dataset highlighting the challenges associated with building large-scale virtual assistants. Table TABREF2 compares our dataset with other public datasets. Our Schema-Guided Dialogue (SGD) dataset exceeds other datasets in most of the metrics at scale. The especially larger number of domains, slots, and slot values, and the presence of multiple services per domain, are representative of these scale-related challenges. Furthermore, our evaluation sets contain many services, and consequently slots, which are not present in the training set, to help evaluate model performance on unseen services.",
+      "The 17 domains (`Alarm' domain not included in training) present in our dataset are listed in Table TABREF5. We create synthetic implementations of a total of 34 services or APIs over these domains. Our simulator framework interacts with these services to generate dialogue outlines, which are a structured representation of dialogue semantics. We then used a crowd-sourcing procedure to paraphrase these outlines to natural language utterances. Our novel crowd-sourcing procedure preserves all annotations obtained from the simulator and does not require any extra annotations after dialogue collection. In this section, we describe these steps in detail and then present analyses of the collected dataset."
+    ],
+    [
+      "We define the schema for a service as a combination of intents and slots with additional constraints, with an example in Figure FIGREF7. We implement all services using a SQL engine. For constructing the underlying tables, we sample a set of entities from Freebase and obtain the values for slots defined in the schema from the appropriate attribute in Freebase. We decided to use Freebase to sample real-world entities instead of synthetic ones since entity attributes are often correlated (e.g, a restaurant's name is indicative of the cuisine served). Some slots like event dates/times and available ticket counts, which are not present in Freebase, are synthetically sampled.",
+      "To reflect the constraints present in real-world services and APIs, we impose a few other restrictions. First, our dataset does not expose the set of all possible slot values for some slots. Having such a list is impractical for slots like date or time because they have infinitely many possible values or for slots like movie or song names, for which new values are periodically added. Our dataset specifically identifies such slots as non-categorical and does not provide a set of all possible values for these. We also ensure that the evaluation sets have a considerable fraction of slot values not present in the training set to evaluate the models in the presence of new values. Some slots like gender, number of people, day of the week etc. are defined as categorical and we specify the set of all possible values taken by them. However, these values are not assumed to be consistent across services. E.g., different services may use (`male', `female'), (`M', `F') or (`he', `she') as possible values for gender slot.",
+      "Second, real-world services can only be invoked with a limited number of slot combinations: e.g. restaurant reservation APIs do not let the user search for restaurants by date without specifying a location. However, existing datasets simplistically allow service calls with any given combination of slot values, thus giving rise to flows unsupported by actual services or APIs. As in Figure FIGREF7, the different service calls supported by a service are listed as intents. Each intent specifies a set of required slots and the system is not allowed to call this intent without specifying values for these required slots. Each intent also lists a set of optional slots with default values, which the user can override."
+    ],
+    [
+      "The dialogue simulator interacts with the services to generate dialogue outlines. Figure FIGREF9 shows the overall architecture of our dialogue simulator framework. It consists of two agents playing the roles of the user and the system. Both agents interact with each other using a finite set of actions specified through dialogue acts over a probabilistic automaton designed to capture varied dialogue trajectories. These dialogue acts can take a slot or a slot-value pair as argument. Figure FIGREF13 shows all dialogue acts supported by the agents.",
+      "At the start of a conversation, the user agent is seeded with a scenario, which is a sequence of intents to be fulfilled. We identified over 200 distinct scenarios for the training set, each comprising up to 5 intents. For multi-domain dialogues, we also identify combinations of slots whose values may be transferred when switching intents e.g. the 'address' slot value in a restaurant service could be transferred to the 'destination' slot for a taxi service invoked right after.",
+      "The user agent then generates the dialogue acts to be output in the next turn. It may retrieve arguments i.e. slot values for some of the generated acts by accessing either the service schema or the raw SQL backend. The acts, combined with the respective parameters yield the corresponding user actions. Next, the system agent generates the next set of actions using a similar procedure. Unlike the user agent, however, the system agent has restricted access to the services (denoted by dashed line), e.g. it can only query the services by supplying values for all required slots for some service call. This helps us ensure that all generated flows are valid.",
+      "After an intent is fulfilled through a series of user and system actions, the user agent queries the scenario to proceed to the next intent. Alternatively, the system may suggest related intents e.g. reserving a table after searching for a restaurant. The simulator also allows for multiple intents to be active during a given turn. While we skip many implementation details for brevity, it is worth noting that we do not include any domain-specific constraints in the simulation automaton. All domain-specific constraints are encoded in the schema and scenario, allowing us to conveniently use the simulator across a wide variety of domains and services."
+    ],
+    [
+      "The dialogue paraphrasing framework converts the outlines generated by the simulator into a natural conversation. Figure FIGREF11a shows a snippet of the dialogue outline generated by the simulator, containing a sequence of user and system actions. The slot values present in these actions are in a canonical form because they obtained directly from the service. However, users may refer to these values in various different ways during the conversation, e.g., \u201clos angeles\" may be referred to as \u201cLA\" or \u201cLAX\". To introduce these natural variations in the slot values, we replace different slot values with a randomly selected variation (kept consistent across user turns in a dialogue) as shown in Figure FIGREF11b.",
+      "Next we define a set of action templates for converting each action into a utterance. A few examples of such templates are shown below. These templates are used to convert each action into a natural language utterance, and the resulting utterances for the different actions in a turn are concatenated together as shown in Figure FIGREF11c. The dialogue transformed by these steps is then sent to the crowd workers. One crowd worker is tasked with paraphrasing all utterances of a dialogue to ensure naturalness and coherence.",
+      "In our paraphrasing task, the crowd workers are instructed to exactly repeat the slot values in their paraphrases. This not only helps us verify the correctness of the paraphrases, but also lets us automatically obtain slot spans in the generated utterances by string search. This automatic slot span generation greatly reduced the annotation effort required, with little impact on dialogue naturalness, thus allowing us to collect more data with the same resources. Furthermore, it is important to note that this entire procedure preserves all other annotations obtained from the simulator including the dialogue state. Hence, no further annotation is needed."
+    ],
+    [
+      "With over 16000 dialogues in the training set, the Schema-Guided Dialogue dataset is the largest publicly available annotated task-oriented dialogue dataset. The annotations include the active intents and dialogue states for each user utterance and the system actions for every system utterance. We have a few other annotations like the user actions but we withhold them from the public release. These annotations enable our dataset to be used as benchmark for tasks like intent detection, dialogue state tracking, imitation learning of dialogue policy, dialogue act to text generation etc. The schemas contain semantic information about the schema and the constituent intents and slots, in the form of natural language descriptions and other details (example in Figure FIGREF7).",
+      "The single-domain dialogues in our dataset contain an average of 15.3 turns, whereas the multi-domain ones contain 23 turns on an average. These numbers are also reflected in Figure FIGREF13 showing the histogram of dialogue lengths on the training set. Table TABREF5 shows the distribution of dialogues across the different domains. We note that the dataset is largely balanced in terms of the domains and services covered, with the exception of Alarm domain, which is only present in the development set. Figure FIGREF13 shows the frequency of dialogue acts contained in the dataset. Note that all dialogue acts except INFORM, REQUEST and GOODBYE are specific to either the user or the system."
+    ],
+    [
+      "Virtual assistants aim to support a large number of services available on the web. One possible approach is to define a large unified schema for the assistant, to which different service providers can integrate with. However, it is difficult to come up with a common schema covering all use cases. Having a common schema also complicates integration of tail services with limited developer support. We propose the schema-guided approach as an alternative to allow easy integration of new services and APIs.",
+      "Under our proposed approach, each service provides a schema listing the supported slots and intents along with their natural language descriptions (Figure FIGREF7 shows an example). These descriptions are used to obtain a semantic representation of these schema elements. The assistant employs a single unified model containing no domain or service specific parameters to make predictions conditioned on these schema elements. For example, Figure FIGREF14 shows how dialogue state representation for the same dialogue can vary for two different services. Here, the departure and arrival cities are captured by analogously functioning but differently named slots in both schemas. Furthermore, values for the number_stops and direct_only slots highlight idiosyncrasies between services interpreting the same concept.",
+      "There are many advantages to this approach. First, using a single model facilitates representation and transfer of common knowledge across related services. Second, since the model utilizes semantic representation of schema elements as input, it can interface with unseen services or APIs on which it has not been trained. Third, it is robust to changes like addition of new intents or slots to the service."
+    ],
+    [
+      "Models in the schema-guided setting can condition on the pertinent services' schemas using descriptions of intents and slots. These models, however, also need access to representations for potentially unseen inputs from new services. Recent pretrained models like ELMo BIBREF23 and BERT BIBREF4 can help, since they are trained on very large corpora. Building upon these, we present our zero-shot schema-guided dialogue state tracking model."
+    ],
+    [
+      "We use a single model, shared among all services and domains, to make these predictions. We first encode all the intents, slots and slot values for categorical slots present in the schema into an embedded representation. Since different schemas can have differing numbers of intents or slots, predictions are made over dynamic sets of schema elements by conditioning them on the corresponding schema embeddings. This is in contrast to existing models which make predictions over a static schema and are hence unable to share knowledge across domains and services. They are also not robust to changes in schema and require the model to be retrained with new annotated data upon addition of a new intent, slot, or in some cases, a slot value to a service."
+    ],
+    [
+      "This component obtains the embedded representations of intents, slots and categorical slot values in each service schema. Table TABREF18 shows the sequence pairs used for embedding each schema element. These sequence pairs are fed to a pretrained BERT encoder shown in Figure FIGREF20 and the output $\\mathbf {u}_{\\texttt {CLS}}$ is used as the schema embedding.",
+      "For a given service with $I$ intents and $S$ slots, let $\\lbrace \\mathbf {i}_j\\rbrace $, ${1 \\le j \\le I}$ and $\\lbrace \\mathbf {s}_j\\rbrace $, ${1 \\le j \\le S}$ be the embeddings of all intents and slots respectively. As a special case, we let $\\lbrace \\mathbf {s}^n_j\\rbrace $, ${1 \\le j \\le N \\le S}$ denote the embeddings for the $N$ non-categorical slots in the service. Also, let $\\lbrace \\textbf {v}_j^k\\rbrace $, $1 \\le j \\le V^k$ denote the embeddings for all possible values taken by the $k^{\\text{th}}$ categorical slot, $1 \\le k \\le C$, with $C$ being the number of categorical slots and $N + C = S$. All these embeddings are collectively called schema embeddings."
+    ],
+    [
+      "Like BIBREF24, we use BERT to encode the user utterance and the preceding system utterance to obtain utterance pair embedding $\\mathbf {u} = \\mathbf {u}_{\\texttt {CLS}}$ and token level representations $\\mathbf {t}_1, \\mathbf {t}_2 \\cdots \\mathbf {t}_M$, $M$ being the total number of tokens in the two utterances. The utterance and schema embeddings are used together to obtain model predictions using a set of projections (defined below)."
+    ],
+    [
+      "Let $\\mathbf {x}, \\mathbf {y} \\in \\mathbb {R}^d$. For a task $K$, we define $\\mathbf {l} = \\mathcal {F}_K(\\mathbf {x}, \\mathbf {y}, p)$ as a projection transforming $\\mathbf {x}$ and $\\mathbf {y}$ into the vector $\\mathbf {l} \\in \\mathbb {R}^p$ using Equations DISPLAY_FORM22-. Here, $\\mathbf {h_1},\\mathbf {h_2} \\in \\mathbb {R}^d$, $W^K_i$ and $b^K_i$ for $1 \\le i \\le 3$ are trainable parameters of suitable dimensions and $A$ is the activation function. We use $\\texttt {gelu}$ BIBREF25 activation as in BERT."
+    ],
+    [
+      "For a given service, the active intent denotes the intent requested by the user and currently being fulfilled by the system. It takes the value \u201cNONE\" if no intent for the service is currently being processed. Let $\\mathbf {i}_0$ be a trainable parameter in $\\mathbb {R}^d$ for the \u201cNONE\" intent. We define the intent network as below.",
+      "The logits $l^{j}_{\\text{int}}$ are normalized using softmax to yield a distribution over all $I$ intents and the \u201cNONE\" intent. During inference, we predict the highest probability intent as active."
+    ],
+    [
+      "These are the slots whose values are requested by the user in the current utterance. Projection $\\mathcal {F}_{\\text{req}}$ predicts logit $l^j_{\\text{req}}$ for the $j^{\\text{th}}$ slot. Obtained logits are normalized using sigmoid to get a score in $[0,1]$. During inference, all slots with $\\text{score} > 0.5$ are predicted as requested."
+    ],
+    [
+      "We define the user goal as the user constraints specified over the dialogue context till the current user utterance. Instead of predicting the entire user goal after each user utterance, we predict the difference between the user goal for the current turn and preceding user turn. During inference, the predicted user goal updates are accumulated to yield the predicted user goal. We predict the user goal updates in two stages. First, for each slot, a distribution of size 3 denoting the slot status and taking values none, dontcare and active is obtained by normalizing the logits obtained in equation DISPLAY_FORM28 using softmax. If the status of a slot is predicted to be none, its assigned value is assumed to be unchanged. If the prediction is dontcare, then the special dontcare value is assigned to it. Otherwise, a slot value is predicted and assigned to it in the second stage.",
+      "In the second stage, equation is used to obtain a logit for each value taken by each categorical slot. Logits for a given categorical slot are normalized using softmax to get a distribution over all possible values. The value with the maximum mass is assigned to the slot. For each non-categorical slot, logits obtained using equations and are normalized using softmax to yield two distributions over all tokens. These two distributions respectively correspond to the start and end index of the span corresponding to the slot. The indices $p \\le q$ maximizing $start[p] + end[q]$ are predicted to be the span boundary and the corresponding value is assigned to the slot."
+    ],
+    [
+      "We consider the following metrics for evaluation of the dialogue state tracking task:",
+      "Active Intent Accuracy: The fraction of user turns for which the active intent has been correctly predicted.",
+      "Requested Slot F1: The macro-averaged F1 score for requested slots over all eligible turns. Turns with no requested slots in ground truth and predictions are skipped.",
+      "Average Goal Accuracy: For each turn, we predict a single value for each slot present in the dialogue state. The slots which have a non-empty assignment in the ground truth dialogue state are considered for accuracy. This is the average accuracy of predicting the value of a slot correctly. A fuzzy matching score is used for non-categorical slots to reward partial matches with the ground truth.",
+      "Joint Goal Accuracy: This is the average accuracy of predicting all slot assignments for a turn correctly. For non-categorical slots a fuzzy matching score is used."
+    ],
+    [
+      "We evaluate our model on public datasets WOZ2.0, MultiWOZ 2.0 and the updated MultiWOZ 2.1 BIBREF5. As results in Table TABREF37 show, our model performs competitively on all these datasets. Furthermore, we obtain state-of-the-art joint goal accuracies of 0.516 on MultiWOZ 2.0 and 0.489 on MultiWOZ 2.1 test sets respectively, exceeding the best-known results of 0.486 and 0.456 on these datasets as reported in BIBREF5."
+    ],
+    [
+      "The model performs well for Active Intent Accuracy and Requested Slots F1 across both seen and unseen services, shown in Table TABREF37. For joint goal and average goal accuracy, the model performs better on seen services compared to unseen ones (Figure FIGREF38). The main reason for this performance difference is a significantly higher OOV rate for slot values of unseen services."
+    ],
+    [
+      "The model performance also varies across various domains. The performance for the different domains is shown in (Table TABREF39) below. We observe that one of the factors affecting the performance across domains is still the presence of the service in the training data (seen services). Among the seen services, those in the `Events' domain have a very low OOV rate for slot values and the largest number of training examples which might be contributing to the high joint goal accuracy. For unseen services, we notice that the `Services' domain has a lower joint goal accuracy because of higher OOV rate and higher average turns per dialogue. For `Services' and `Flights' domains, the difference between joint goal accuracy and average accuracy indicates a possible skew in performance across slots where the performance on a few of the slots is much worse compared to all the other slots, thus considerably degrading the joint goal accuracy. The `RideSharing' domain also exhibits poor performance, since it possesses the largest number of the possible slot values across the dataset. We also notice that for categorical slots, with similar slot values (e.g. \u201cPsychologist\" and \u201cPsychiatrist\"), there is a very weak signal for the model to distinguish between the different classes, resulting in inferior performance."
+    ],
+    [
+      "It is often argued that simulation-based data collection does not yield natural dialogues or sufficient coverage, when compared to other approaches such as Wizard-of-Oz. We argue that simulation-based collection is a better alternative for collecting datasets like this owing to the factors below.",
+      "Fewer Annotation Errors: All annotations are automatically generated, so these errors are rare. In contrast, BIBREF5 reported annotation errors in 40% of turns in MultiWOZ 2.0 which utilized a Wizard-of-Oz setup.",
+      "Simpler Task: The crowd worker task of paraphrasing a readable utterance for each turn is simple. The error-prone annotation task requiring skilled workers is not needed.",
+      "Low Cost: The simplicity of the crowd worker task and lack of an annotation task greatly cut data collection costs.",
+      "Better Coverage: A wide variety of dialogue flows can be collected and specific usecases can be targeted."
+    ],
+    [
+      "We presented the Schema-Guided Dialogue dataset to encourage scalable modeling approaches for virtual assistants. We also introduced the schema-guided paradigm for task-oriented dialogue that simplifies the integration of new services and APIs with large scale virtual assistants. Building upon this paradigm, we present a scalable zero-shot dialogue state tracking model achieving state-of-the-art results."
+    ],
+    [
+      "The authors thank Guan-Lin Chao for help with model design and implementation, and Amir Fayazi and Maria Wang for help with data collection."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2338/instruction.md b/qasper-2338/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..4b18d07fabdf3408d6abcdc970181b66e01e0004
--- /dev/null
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@@ -0,0 +1,56 @@
+Name of Paper: Hate Speech in Pixels: Detection of Offensive Memes towards Automatic Moderation
+
+Question: How is each instance of the dataset annotated?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Motivation",
+    "Related Work",
+    "Model",
+    "Dataset",
+    "Experiments",
+    "Conclusions",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "The spread of misinformation or hate messages through social media is a central societal challenge given the unprecedented broadcast potential of these tools. While there already exist some moderation mechanisms such as crowd-sourced abuse reports and dedicated human teams of moderators, the huge and growing scale of these networks requires some degree of automation for the task.",
+      "Social networks have already introduced many tools to detect offensive or misleading content, both for visual and textual content, ranging from nudity and pornography BIBREF0, BIBREF1 to hate speech BIBREF2 and misinformation BIBREF3. However, machine learning is still facing some challenges when processing borderline or figurative content such as nudity in paintings, political satire or other forms of humorous content. In particular for the case of hate speech, rapidly evolving topics and shifting trends in social media make its detection a topic of constant and active research.",
+      "This work takes one step forward and instead of focusing on visual or linguistic content alone, we tackle the challenging problem of detecting hate speech in memes. Memes are a form of humorist multimedia document which is normally based on an image with some sort of caption text embedded in the image pixels. Memes have gained a lot of popularity in the last few years and have been used in many different contexts, specially by young people. However, this format has also been used to produce and disseminate hate speech in the form of dark humour. The multimodal nature of memes makes it very challenging to analyze because, while the visual and linguistic information is typically neutral or actually funny in isolation, their combination may result in hate speech messages.",
+      "Our work explores the potential of state of the art deep neural networks to detect hate speech in memes. We study the gain in accuracy when detecting hate speech in memes by fusing the vision and language representations, when compared with the two modalities apart. Our experiments indicate that while meme detection is a multimodal problem that benefits by analyzing both modalities, this societal task is far from being solve given the high abstraction level of the messages contained in memes."
+    ],
+    [
+      "Hate speech is a widely studied topic in the context of social science. This phenomena has been monitored, tracked, measured or quantified in a number of occasions BIBREF4, BIBREF5, BIBREF6. It appears in media such as newspapers or TV news, but one of the main focus of hate speech with very diverse targets has appeared in social networks BIBREF7, BIBREF8, BIBREF9. Most works in hate speech detection has focused in language. The most common approach is to generate an embedding of some kind, using bag of words BIBREF8 or N-gram features BIBREF10 and many times using expert knowledge for keywords. After that, the embedding is fed to a binary classifier to predict hate speech. Up to our knowledge, there is no previous work on detecting hate speech when combining language with visual content as in memes. Our technical solution is inspired by BIBREF11 in which gang violence on social media was predicted from a multimodal approach that fused images and text. Their model extracted features from both modalities using pretrained embeddings for language and vision, and later merged both vectors to feed the multimodal features into a classifier."
+    ],
+    [
+      "The overall system expects an Internet meme input, and produces a hate score as an output. Figure FIGREF1 shows a block diagram of the proposed solution.",
+      "The first step of the process is extracting the text of the image with Optical Character Recognition (OCR). The text detected by the OCR is encoded in a BERT BIBREF12 representation for language. We used the Tesseract 4.0.0 OCR with a Python wrapper on top. This encoding generates contextual (sub)words embeddings, which we turn into a sentence embedding by averaging them. We used a PyTorch implementation available at the repo below . This implementations includes multiple pretrained versions and we chose the one called bert-base-multilingual-cased. This version has 12 layers, 768 hidden dimensions, 12 attention heads with a total of 110M parameters and is trained on 104 languages.",
+      "The visual information was encoded with a VGG-16 convolutional neural network BIBREF13, trained on ImageNet BIBREF14. Then we used the activations from a hidden layer as feature vectors for the image, Specifically, we used the last hidden before output, which has 4096 dimensions. We obtained the pretrained model from the TorchVision module in PyTorch.",
+      "The text and image encodings were combined by concatenation, which resulted in a feature vector of 4,864 dimensions. This multimodal representation was afterward fed as input into a multi-layer perceptron (MLP) with two hidden layer of 100 neurons with a ReLU activation function. The last single neuron with no activation function was added at the end to predict the hate speech detection score."
+    ],
+    [
+      "We built a dataset for the task of hate speech detection in memes with 5,020 images that were weakly labeled into hate or non-hate memes, depending on their source. Hate memes were retrieved from Google Images with a downloading tool. We used the following queries to collect a total of 1,695 hate memes: racist meme (643 memes), jew meme (551 memes), and muslim meme (501 Memes). Non-hate memes were obtained from the Reddit Memes Dataset . We assumed that all memes in the dataset do not contain any hate message, as we considered that average Reddit memes do not belong to this class. A total of 3,325 non-hate memes were collected. We split the dataset into train (4266 memes) and validation (754 memes) subsets. The splits were random and the distribution of classes in the two subsets is the same. We didn't split the dataset into three subsets because of the small amount of data we had and decided to rely on the validation set metrics."
+    ],
+    [
+      "Our experiments aimed at estimating the potential of a multimodal hate speech detector, and study the impact of a multimodal analysis when compared to using language or vision only.",
+      "We estimated the parameters of the MLP on top of the encoding of the meme with an ADAM optimizer with a lr=0.1, betas=(0.9, 0.999) and $\\varepsilon =10^{-8}$, weight decay=0, a batch size of 25, and a drop out of 0.2 on the first hidden layer. The network was trained with a a Mean Squared Error (MSE) loss, but assessed in terms of binary accuracy.",
+      "Figure FIGREF7 presents the results of the training with the three considered configurations: language only, vision only, and a multimodal solution. In the single modality cases, the input layer of the MLP is adjusted to the size of the encoded representation. The curves show how the blue line representing the multimodal case obtains the best results, closely followed by the orange one of the vision only case. The language only configuration performs clearly worse than the other two. Nevertheless, the three curves are consistenly over the baseline accuracy of $0.66$, which would be achieved by a dummy predictor of Non-hate class, because of the 34%-66% class imbalance of the dataset.",
+      "Table TABREF11 provides numerical results comparing the three configurations based on two different metrics: Max. Accuracy corresponds to the best accuracy obtained in any epoch, while Smth Max. Accuracy corresponds to the smoothed accuracy to which the model was converging. This was estimated by smoothing the curve with a momentum average and picking the best value. We thought the second metric was a good estimation of the real performance of the model due to the huge validation accuracy fluctuation between epochs in evaluation. Also, since the classes are imbalanced, we computed the precision-recall curve for the best multimodal model, getting an Average Precision of $0.81$.",
+      "We consider that the superior performance of the vision only configuration over the language only one may be due to a diversity of reasons. Firstly, the most obvious one is that the dimensionality of the image representation (4096) is much larger than the linguistic one (768), so it has the capacity to encode more information. Also, the different models have different number of parameters due to different MLP input and we didn't take into consideration this variation of the model's capacity. Secondly, we think there might a visual bias on the dataset. Mainly, because there are more modern style memes on the no hate class and more classic style memes in the hate class. Classic or modern memes refer basically to the format and placement of the text. Figure FIGREF12 (a) and (b) are examples of them. Also, we found some false positives in the hate class and there might be false negatives in the non-hate Reddit set. Finally, memes are often highly compressed images with an important level of distortion. This fact may affect the quality of the OCR recognition and, therefore, the language encoding, as shown in the Figure FIGREF12 (c).",
+      "The training code and models are publicly available to facilitate reproducibility."
+    ],
+    [
+      "Our study on hate speech detection in memes concludes that it is possible to automatize the task, in the sense that a simple configuration using state of the art image and text encoders can detect some of them. However, the problem is far from being solved, because the best accuracy obtained of $0.83$ seems modest despite being much better than greedy solution of predicting always the most frequent class. The proposed system may be used for filtering some of the memes distributed through a social network, but it would still require a human moderator for many of them.",
+      "Unfortunately, the system may actually also be used for the opposite of detecting hate speech memes, but helping in their creation. Given a large amount of sentences and images, a misuse of the system may assess the hate score of each possible pair of text and image to find novel combinations with an expect high hate level.",
+      "The experiments also show that the visual cues are much more important than the linguistic ones when detecting hate speech memes, a totally opposite scenario to previous studies focusing on language-based hate speech detection. While the best results are obtained with the multimodal approach, the gain with respect to the vision only one is small. A practical deployment of this system should evaluate whether the computation cost of running the OCR and encoding the extracted text is worthy based on the reported gains in accuracy.",
+      "The present work poses a new challenge to the multimedia analysis community, which has been proven to be difficult but not impossible. Given the rich affective and societal content in memes, an effective solution should probably also take into account much more additional information than just the one contained in the meme, such as the societal context in which the meme is posted."
+    ],
+    [
+      "This work has been developed in the framework of project TEC2016-75976-R, funded by the Spanish Ministerio de Econom\u00eda y Competitividad and the European Regional Development Fund (ERDF), and the Industrial Doctorate 2017-DI-011 funded by the Government of Catalonia. We gratefully acknowledge the support of NVIDIA Corporation with the donation of some of the GPUs used for this work."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2352/instruction.md b/qasper-2352/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..6a7ab711fd4ac3b6ab5de73d5059072353ca381b
--- /dev/null
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@@ -0,0 +1,119 @@
+Name of Paper: Canonicalizing Knowledge Base Literals
+
+Question: What's the precision of the system?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Problem Statement",
+    "Technical Framework",
+    "Prediction Model",
+    "Sampling and Training",
+    "Independent and Hierarchical Typing",
+    "Canonicalization",
+    "Experiment Setting",
+    "Results on Framework Settings",
+    "Results on Semantic Typing",
+    "Results on Entity Matching",
+    "Related Work",
+    "Discussion and Outlook",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "Ontology-based knowledge bases (KBs) like DBpedia BIBREF0 are playing an increasingly important role in domains such knowledge management, data analysis and natural language understanding. Although they are very valuable resources, the usefulness and usability of such KBs is limited by various quality issues BIBREF1 , BIBREF2 , BIBREF3 . One such issue is the use of string literals (both explicitly typed and plain literals) instead of semantically typed entities; for example in the triple $\\langle $ River_Thames, passesArea, \u201cPort Meadow, Oxford\" $\\rangle $ . This weakens the KB as it does not capture the semantics of such literals. If, in contrast, the object of the triple were an entity, then this entity could, e.g., be typed as Wetland and Park, and its location given as Oxford. This problem is pervasive and hence results in a significant loss of information: according to statistics from Gunaratna et al. BIBREF4 in 2016, the DBpedia property dbp:location has over 105,000 unique string literals that could be matched with entities. Besides DBpedia, such literals can also be found in some other KBs from encyclopedias (e.g., zhishi.me BIBREF5 ), in RDF graphs transformed from tabular data (e.g., LinkedGeoData BIBREF6 ), in aligned or evolving KBs, etc. ",
+      "One possible remedy for this problem is to apply automated semantic typing and entity matching (AKA canonicalization) to such literals. To the best of our knowledge, semantic typing of KB literals has rarely been studied. Gunaratna et al. BIBREF4 used semantic typing in their entity summarization method, first identifying the so called focus term of a phrase via grammatical structure analysis, and then matching the focus term with both KB types and entities. Their method is, however, rather simplistic: it neither utilizes the literal's context, such as the associated property and subject, nor captures the contextual meaning of the relevant words. What has been widely studied is the semantic annotation of KB entities BIBREF7 , BIBREF8 , BIBREF9 and of noun phrases outside the KB (e.g., from web tables) BIBREF10 , BIBREF11 , BIBREF12 ; in such cases, however, the context is very different, and entity typing can, for example, exploit structured information such as the entity's linked Wikipedia page BIBREF7 and the domain and range of properties that the entity is associated with BIBREF8 .",
+      "With the development of deep learning, semantic embedding and feature learning have been widely adopted for exploring different kinds of contextual semantics in prediction, with Recurrent Neural Network (RNN) being a state-of-the-art method for dealing with structured data and text. One well known example is word2vec \u2014 an RNN language model which can represent words in a vector space that retains their meaning BIBREF13 . Another example is a recent study by Kartsaklis et al. BIBREF14 , which maps text to KB entities with a Long-short Term Memory RNN for textual feature learning. These methods offer the potential for developing accurate prediction-based methods for KB literal typing and entity matching where the contextual semantics is fully exploited.",
+      "In this study, we investigate KB literal canonicalization using a combination of RNN-based learning and semantic technologies. We first predict the semantic types of a literal by: (i) identifying candidate classes via lexical entity matching and KB queries; (ii) automatically generating positive and negative examples via KB sampling, with external semantics (e.g., from other KBs) injected for improved quality; (iii) training classifiers using relevant subject-predicate-literal triples embedded in an attentive bidirectional RNN (AttBiRNN); and (iv) using the trained classifiers and KB class hierarchy to predict candidate types. The novelty of our framework lies in its knowledge-based learning; this includes automatic candidate class extraction and sampling from the KB, triple embedding with different importance degrees suggesting different semantics, and using the predicted types to identify a potential canonical entity from the KB. We have evaluated our framework using a synthetic literal set (S-Lite) and a real literal set (R-Lite) from DBpedia BIBREF0 . The results are very promising, with significant improvements over several baselines, including the existing state-of-the-art."
+    ],
+    [
+      "In this study we consider a knowledge base (KB) that includes both ontological axioms that induce (at least) a hierarchy of semantic types (i.e., classes), and assertions that describe concrete entities (individuals). Each such assertion is assumed to be in the form of an RDF triple $\\langle s,p,o \\rangle $ , where $s$ is an entity, $p$ is a property and $o$ can be either an entity or a literal (i.e., a typed or untyped data value such as a string or integer).",
+      "We focus on triples of the form $\\langle s,p,l \\rangle $ , where $l$ is a string literal; such literals can be identified by regular expressions, as in BIBREF4 , or by data type inference as in BIBREF15 . Our aim is to cononicalize $l$ by first identifying the type of $l$ , i.e., a set of classes $\\mathcal {C}_l$ that an entity corresponding to $l$ should be an instance of, and then determining if such an entity already exists in the KB. The first subtask is modeled as a machine learning classification problem where a real value score in $\\left[0,1\\right]$ is assigned to each class $c$ occurring in the KB, and $\\mathcal {C}_l$ is the set of classes determined by the assigned score with strategies e.g., adopting a class if its score exceeds some threshold. The second subtask is modeled as an entity lookup problem constrained by $\\mathcal {C}_l$ .",
+      "It is important to note that:",
+      "When we talk about a literal $l$ we mean the occurrence of $l$ in a triple $\\langle s,p,l \\rangle $ . Lexically equivalent literals might be treated very differently depending on their triple contexts.",
+      "If the KB is an OWL DL ontology, then the set of object properties (which connect two entities) and data properties (which connect an entity to a literal) should be disjoint. In practice, however, KBs such as DBpedia often don't respect this constraint. In any case, we avoid the issue by simply computing the relevant typing and canonicalization information, and leaving it up to applications as to how they want to exploit it.",
+      "We assume that no manual annotations or external labels are given \u2014 the classifier is automatically trained using the KB."
+    ],
+    [
+      "The technical framework for the classification problem is shown in Fig. 1 . It involves three main steps: (i) candidate class extraction; (ii) model training and prediction; and (iii) literal typing and canonicalization.",
+      "Popular KBs like DBpedia often contain a large number of classes. For efficiency reasons, and to reduce noise in the learning process, we first identify a subset of candidate classes. This selection should be rather inclusive so as to maximize potential recall. In order to achieve this we pool the candidate classes for all literals occurring in triples with a given property; i.e., to compute the candidate classes for a literal $\u0142$ occurring in a triple $\\langle s,p,l \\rangle $ , we consider all triples that use property $p$ . Note that, as discussed above, in practice such triples may include both literals and entities as their objects. We thus use two techniques for identifying candidate classes from the given set of triples. In the case where the object of the triple is an entity, the candidates are just the set of classes that this entity is an instance of. In practice we identify the candidates for the set of all such entities, which we denote $E_P$ , via a SPARQL query to the KB, with the resulting set of classes being denoted $C_P$ . In the case where the object of the triple is a literal, we first match the literal to entities using a lexical index which is built based on the entity's name, labels and anchor text (description). To maximize recall, the literal, its tokens (words) and its sub-phrases are used to retrieve entities by lexical matching; this technique is particularly effective when the literal is a long phrase. As in the first case, we identify all relevant entities, which we denote $E_M$ , and then retrieve the relevant classes $C_M$ using a SPARQL query. The candidate class set is simply the union of $C_P$ and $C_M$ , denoted as $C_{PM}$ .",
+      "We adopt the strategy of training one binary classifier for each candidate class, instead of multi-class classification, so as to facilitate dealing with the class hierarchy BIBREF16 . The classifier architecture includes an input layer with word embedding, an encoding layer with bidirectional RNNs, an attention layer and a fully connected (FC) layer for modeling the contextual semantics of the literal. To train a classifier, both positive and negative entities (samples), including those from $E_M$ (particular samples) and those outside $E_M$ (general samples) are extracted from the KB, with external KBs and logical constraints being used to improve sample quality. The trained classifiers are used to compute a score for each candidate class.",
+      "The final stage is to semantically type and, where possible, canonicalise literals. For a given literal, two strategies, independent and hierarchical, are used to determine its types (classes), with a score for each type. We then use these types and scores to try to identify an entity in the KB that could reasonably be substituted for the literal."
+    ],
+    [
+      "Given a phrase literal $l$ and its associated RDF triple $\\langle s, p, l \\rangle $ , our neural network model aims at utilizing the semantics of $s$ , $p$ and $l$ for the classification of $l$ . The architecture is shown in Fig. 2 . It first separately parses the subject label, the property label and the literal into three word (token) sequences whose lengths, denoted as $T_s$ , $T_p$ and $T_l$ , are fixed to the maximum subject, property and literal sequence lengths from the training data by padding shorter sequences with null words. We then concatenate the three sequences into a single word sequence ( $word_t, t \\in \\left[1,T\\right]$ ), where $\\langle s, p, l \\rangle $0 . Each word is then encoded into a vector via word embedding (null is encoded into a zero vector), and the word sequence is transformed into a vector sequence ( $\\langle s, p, l \\rangle $1 ). Note that this preserves information about the position of words in $\\langle s, p, l \\rangle $2 , $\\langle s, p, l \\rangle $3 and $\\langle s, p, l \\rangle $4 .",
+      "The semantics of forward and backward surrounding words is effective in predicting a word's semantics. For example, \u201cPort\u201d and \u201cMeadow\u201d are more likely to indicate a place as they appear after \u201cArea\u201d and before \u201cOxford\u201d. To embed such contextual semantics into a feature vector, we stack a layer composed of bidirectional Recurrent Neural Networks (BiRNNs) with Gated Recurrent Unit (GRU) BIBREF17 . Within each RNN, a reset gate $r_t$ is used to control the contribution of the past word, and an update gate $z_t$ is used to balance the contributions of the past words and the new words. The hidden state (embedding) at position $t$ is computed as ",
+      "$${\\left\\lbrace \\begin{array}{ll}\nh_t = (1-z_t) \\odot h_{t-1} + z_t \\odot \\tilde{h}_t, \\\\\n\\tilde{h}_t = \\tau (W_h x_t + r_t \\odot (U_h h_{t-1}) + b_h), \\\\\nz_t = \\sigma (W_z x_t + U_z h_{t-1} + b_z), \\\\\nr_t = \\sigma (W_r x_t + U_r h_{t-1} + b_r),\n\\end{array}\\right.}$$   (Eq. 13) ",
+      "where $\\odot $ denotes the Hadamard product, $\\sigma $ and $\\tau $ denote the activation function of sigmod and tanh respectively, and $W_h$ , $U_h$ , $b_h$ , $W_z$ , $U_z$ , $b_z$ , $W_r$ , $\\sigma $0 and $\\sigma $1 are parameters to learn. With the two bidirectional RNNs, one forward hidden state and one backward hidden state are calculated for the sequence, denoted as ( $\\sigma $2 ) and ( $\\sigma $3 ) respectively. They are concatenated as the output of the RNN layer: $\\sigma $4 .",
+      "We assume different words are differently informative towards the type of the literal. For example, the word \u201cport\u201d is more important than the other words in distinguishing the type Wetland from other concrete types of Place. To this end, an attention layer is further stacked. Given the input from the RNN layer ( $h_t, t \\in \\left[1,T \\right]$ ), the attention layer outputs $h_a = \\left[\\alpha _t h_t \\right], t \\in \\left[1,T \\right]$ , where $\\alpha _t$ is the normalized weight of the word at position $t$ and is calculated as ",
+      "$${\\left\\lbrace \\begin{array}{ll}\n\\alpha _t = \\frac{exp(u^T_t u_w)}{\\sum _{t \\in \\left[1,T\\right]} exp (u^T_t u_w)} \\\\\nu_t = \\tau (W_w h_t + b_w),\n\\end{array}\\right.}$$   (Eq. 14) ",
+      "where $u_w$ , $W_w$ and $b_w$ are parameters to learn. Specifically, $u_w$ denotes the general informative degrees of all the words, while $\\alpha _t$ denotes the attention of the word at position $t$ w.r.t. other words in the sequence. Note that the attention weights can also be utilized to justify a prediction. In order to exploit information about the location of a word in the subject, property or literal, we do not calculate the weighted sum of the BiRNN output but concatenate the weighted vectors. The dimension of each RNN hidden state (i.e., $\\overleftarrow{h_t}$ and $\\overrightarrow{h_t}$ ), denoted as $d_r$ , and the dimension of each attention layer output (i.e., $\\alpha _t h_t$ ), denoted as $W_w$0 , are two hyper parameters of the network architecture.",
+      "A fully connected (FC) layer and a logistic regression layer are finally stacked for modeling the nonlinear relationship and calculating the output score respectively: ",
+      "$$ \nf(s, p, l) = \\sigma (W_f h_a + b_f),$$   (Eq. 15) ",
+      "where $W_f$ and $b_f$ are the parameters to learn, $\\sigma $ denotes the sigmod function, and $f$ denotes the function of the whole network."
+    ],
+    [
+      "We first extract both particular samples and general samples from the KB using SPARQL queries and reasoning; we then improve sample quality by detecting and repairing wrong and missing entity classifications with the help of external KBs; and finally we train the classifiers.",
+      "Particular samples are based on the entities $E_M$ that are lexically matched by the literals. For each literal candidate class $c$ in $C_M$ , its particular samples are generated by:",
+      "Extracting its positive particular entities: $E_M^c = \\left\\lbrace e | e \\in E_M, e \\text{ is an instance of } c \\right\\rbrace $ ;",
+      "Generating its positive particular samples as ",
+      "$$\\mathcal {P}_c^{+} = \\cup _{e \\in E_M^c} \\left\\lbrace  \\langle s,p,l \\rangle | s \\in S(p,e), l \\in L(e) \\right\\rbrace ,$$   (Eq. 20) ",
+      "where $S(p,e)$ denotes the set of entities occurring in the subject position in a triple of the form $\\langle s, p, e\\rangle $ , and $L(e)$ denotes all the labels (text phrases) of the entity $e$ ;",
+      "Extracting its negative particular entities $E_M^{\\widetilde{c}}$ as those entities in $E_M$ that are instances of some sibling class of $c$ and not instances of $c$ ;",
+      "Generating its negative particular samples $\\mathcal {P}_c^-$ with $E_M^{\\widetilde{c}}$ using the same approach as for positive samples.",
+      "Given that the literal matched candidate classes $C_M$ are only a part of all the candidate classes $C_{PM}$ , and that the size of particular samples may be too small to train the neural network, we additionally generate general samples based on common KB entities. For each candidate class $c$ in $C_{PM}$ , all its entities in the KB, denoted as $E^c$ , are extracted and then its positive general samples, denoted as $\\mathcal {G}_c^+$ , are generated from $E^c$ using the same approach as for particular samples. Similarly, entities of the sibling classes of $c$ , denoted as $E^{\\widetilde{c}}$ , are extracted, and general negative samples, denoted as $\\mathcal {G}_c^-$ , are generated from $C_{PM}$0 . As for negative particular entities, we check each entity in $C_{PM}$1 and remove those that are not instances of $C_{PM}$2 .",
+      "Unlike the particular samples, the positive and negative general samples are balanced. This means that we reduce the size of $\\mathcal {G}_c^+$ and $\\mathcal {G}_c^-$ to the minimum of $\\#(\\mathcal {G}_c^+)$ , $\\#(\\mathcal {G}_c^-)$ and $N_0$ , where $\\#()$ denotes set cardinality, and $N_0$ is a hyper parameter for sampling. Size reduction is implemented via random sampling.",
+      "Many KBs are quite noisy, with wrong or missing entity classifications. For example, when using the SPARQL endpoint of DBpedia, dbr:Scotland is classified as dbo:MusicalArtist instead of as dbo:Country, while dbr:Afghan appears without a type. We have corrected and complemented the sample generation by combining the outputs of more than one KB. For example, the DBpedia endpoint suggestions are compared against Wikidata and the DBpedia lookup service. Most DBpedia entities are mapped to Wikidata entities whose types are used to validate and complement the suggested types from the DBpedia endpoint. In addition, the lookup service, although incomplete, typically provides very precise types that can also confirm the validity of the DBpedia endpoint types. The validation is performed by identifying if the types suggested by one KB are compatible with those returned by other KBs, that is, if the relevant types belong to the same branch of the hierarchy (e.g., the DBpedia taxonomy). With the new entity classifications, the samples are revised accordingly.",
+      "We train a binary classifier $f^c$ for each class $c$ in $C_{PM}$ . It is first pre-trained with general samples $\\mathcal {G}_{c}^+ \\cup \\mathcal {G}_{c}^-$ , and then fine tuned with particular samples $\\mathcal {P}_{c}^+ \\cup \\mathcal {P}_{c}^-$ . Pre-training deals with the shortage of particular samples, while fine-tuning bridges the gap between common KB entities and the entities associated with the literals, which is also known as domain adaptation. Given that pre-training is the most time consuming step, but is task agnostic, classifiers for all the classes in a KB could be pre-trained in advance to accelerate a specific literal canonicalization task."
+    ],
+    [
+      "In prediction, the binary classifier for class $c$ , denoted as $f^c$ , outputs a score $y_l^c$ indicating the probability that a literal $l$ belongs to class $c$ : $y_l^c = f^c(l)$ , $y_l^c \\in \\left[0,1\\right]$ . With the predicted scores, we adopt two strategies \u2013 independent and hierarchical to determine the types. In the independent strategy, the relationship between classes is not considered. A class $c$ is selected as a type of $l$ if its score $y_l^c \\ge \\theta $ , where $f^c$0 is a threshold hyper parameter in $f^c$1 .",
+      "The hierarchical strategy considers the class hierarchy and the disjointness between sibling classes. We first calculate a hierarchical score for each class with the predicted scores of itself and its descendents: ",
+      "$$s_l^c = max\\left\\lbrace  y_l^{c^{\\prime }} | c^{\\prime } \\sqsubseteq c,\\text{ } c^{\\prime } \\in C_{PM} \\right\\rbrace ,$$   (Eq. 28) ",
+      "where $\\sqsubseteq $ denotes the subclass relationship between two classes, $C_{PM}$ is the set of candidate classes for $l$ , and $max$ denotes the maximum value of a set. For a candidate class $c^{\\prime }$ in $C_{PM}$ , we denote all disjoint candidate classes as $\\mathcal {D}(C_{PM}, c^{\\prime })$ . They can be defined as sibling classes of both $c^{\\prime }$ and its ancestors, or via logical constraints in the KB. A class $c$ is selected as a type of $l$ if (i) its hierarchical score $C_{PM}$0 , and (ii) it satisfies the following soft exclusion condition: ",
+      "$$s_l^c - max\\left\\lbrace s_l^{c^{\\prime }} | c^{\\prime } \\in \\mathcal {D}(C_{PM}, c) \\right\\rbrace  \\ge \\kappa ,$$   (Eq. 29) ",
+      "where $\\kappa $ is a relaxation hyper parameter. The exclusion of disjoint classes is hard if $\\kappa $ is set to 0, and relaxed if $\\kappa $ is set to a negative float with a small absolute value e.g., $-0.1$ .",
+      "Finally, for a given literal $l$ , we return the set of all selected classes as its types $\\mathcal {C}_l$ ."
+    ],
+    [
+      "Given a literal $l$ , we use $\\mathcal {C}_l$ to try to identify an associated entity. A set of candidate entities are first retrieved using the lexical index that is built on the entity's name, label, anchor text, etc. Unlike candidate class extraction, here we use the whole text phrase of the literal, and rank the candidate entities according to their lexical similarities. Those entities that are not instances of any classes in $\\mathcal {C}_l$ are then filtered out, and the most similar entity among the remainder is selected as the associated entity for $l$ . If no entities are retrieved, or all the retrieved entities are filtered out, then the literal could be associated with a new entity whose types are those most specific classes in $\\mathcal {C}_l$ . In either case we can improve the quality of our results by checking that the resulting entities would be consistent if added to the KB, and discarding any entity associations that would lead to inconsistency."
+    ],
+    [
+      "In the experiments, we adopt a real literal set (R-Lite) and a synthetic literal set (S-Lite) , both of which are extracted from DBpedia. R-Lite is based on the property and literal pairs published by Gunaratna et al. in 2016 BIBREF4 . We refine the data by (i) removing literals that no longer exist in the current version of DBpedia; (ii) extracting new literals from DBpedia for properties whose existing literals were all removed in step (i); (iii) extending each property and literal pair with an associated subject; and (iv) manually adding ground truth types selected from classes defined in the DBpedia Ontology (DBO). To fully evaluate the study with more data, we additionally constructed S-Lite from DBpedia by repeatedly: (i) selecting a DBpedia triple of the form $\\langle s,p,e \\rangle $ , where $e$ is an entity; (ii) replacing $e$ with it's label $l$ to give a triple $\\langle s,p,l \\rangle $ ; (iii) eliminating the entity $e$ from DBpedia; and (iv) adding as ground truth types the DBpedia classes of which $e$ is (implicitly) an instance. More data details are shown in Table 1 .",
+      "In evaluating the typing performance, Precision, Recall and F1 Score are used. For a literal $l$ , the computed types $\\mathcal {C}_l$ are compared with the ground truths $\\mathcal {C}_l^{gt}$ , and the following micro metrics are calculated: $P_l = {\\# (\\mathcal {C}_l \\cap \\mathcal {C}_l^{gt}) }{\\# (\\mathcal {C}_l)}$ , $R_l = {\\# (\\mathcal {C}_l \\cap \\mathcal {C}_l^{gt} )}{\\# (\\mathcal {C}_l^{gt})}$ , and ${F_1}_l = {(2 \\times P_l \\times R_l)}{(P_l + R_l)}$ . They are then averaged over all the literals as the final Precision, Recall and F1 Score of a literal set. Although F1 Score measures the overall performance with both Precision and Recall considered, it depends on the threshold hyper parameter $\\theta $ as with Precision and Recall. Thus we let $\\theta $ range from 0 to 1 with a step of $0.01$ , and calculate the average of all the F1 Scores (AvgF1@all) and top 5 highest F1 Scores (AvgF1@top5). AvgF1@all measures the overall pattern recognition capability, while AvgF1@top5 is relevant in real applications where we often use a validation data set to find a $\\theta $ setting that is close to the optimum. We also use the highest (top) Precision in evaluating the sample refinement.",
+      "In evaluating entity matching performance, Precision is measured by manually checking whether the identified entity is correct or not. S-Lite is not used for entity matching evaluation as the corresponding entities for all its literals are assumed to be excluded from the KB. We are not able to measure recall for entity matching as we do not have the ground truths; instead, we have evaluated entity matching with different confidence thresholds and compared the number of correct results.",
+      "The evaluation includes three aspects. We first compare different settings of the typing framework, analyzing the impacts of sample refinement, fine tuning by particular samples, BiRNN and the attention mechanism. We also compare the independent and hierarchical typing strategies. We then compare the overall typing performance of our framework with (i) Gunaratna et al. BIBREF4 , which matches the literal to both classes and entities; (ii) an entity lookup based method; and (iii) a probabilistic property range estimation method. Finally, we analyze the performance of entity matching with and without the predicted types.",
+      "The DBpedia lookup service, which is based on the Spotlight index BIBREF18 , is used for entity lookup (retrieval). The DBpedia SPARQL endpoint is used for query answering and reasoning. The reported results are based on the following settings: the Adam optimizer together with cross-entropy loss are used for network training; $d_r$ and $d_a$ are set to 200 and 50 respectively; $N_0$ is set to 1200; word2vec trained with the latest Wikipedia article dump is adopted for word embedding; and ( $T_s$ , $T_p$ , $T_l$ ) are set to (12, 4, 12) for S-Lite and (12, 4, 15) for R-Lite. The experiments are run on a workstation with Intel(R) Xeon(R) CPU E5-2670 @2.60GHz, with programs implemented by Tensorflow."
+    ],
+    [
+      "We first evaluate the impact of the neural network architecture, fine tuning and different typing strategies, with their typing results on S-Lite shown in Table 2 and Fig. 3 . Our findings are supported by comparable results on R-Lite. We further evaluate sample refinement, with some statistics of the refinement operations as well as performance improvements shown in Fig. 4 .",
+      "According to Table 2 , we find BiRNN significantly outperforms Multiple Layer Perceptron (MLP), a basic but widely used neural network model, while stacking an attention layer (AttBiRNN) further improves AvgF1@all and AvgF1@top5, for example by $3.7\\%$ and $3.1\\%$ respectively with hierarchical typing ( $\\kappa $ = $-0.1$ ). The result is consistent for both pre-trained models and fine tuned models, using both independent and hierarchical typing strategies. This indicates the effectiveness of our neural network architecture. Meanwhile, the performance of all the models is significantly improved after they are fine tuned by the particular samples, as expected. For example, when the independent typing strategy is used, AvgF1@all and AvgF1@top5 of AttBiRNN are improved by $54.1\\%$ and $35.2\\%$ respectively.",
+      "The impact of independent and hierarchical typing strategies is more complex. As shown in Table 2 , when the classifier is weak (e.g., pre-trained BiRNN), hierarchical typing with both hard exclusion ( $\\kappa $ = 0) and relaxed exclusion ( $\\kappa $ = $-0.1$ ) has higher AvgF1@all and AvgF1@top5 than independent typing. However, when a strong classifier (e.g., fine tuned AttBiRNN) is used, AvgF1@all and AvgF1@top5 of hierarchical typing with relaxed exclusion are close to independent typing, while hierarchical typing with hard exclusion has worse performance. We further analyze Precision, Recall and F1 Score of both typing strategies under varying threshold ( $\\theta $ ) values, as shown in Fig. 3 . In comparison with independent typing, hierarchical typing achieves (i) more stable Precision, Recall and F1 Score curves; and (ii) significantly higher Precision, especially when $\\theta $ is small. Meanwhile, as with the results in Table 2 , relaxed exclusion outperforms hard exclusion in hierarchical typing except for Precision when $\\theta $ is between 0 and $0.05$ .",
+      "Fig. 4 [Right] shows the ratio of positive and negative particular samples that are deleted and added during sample refinement. The AttBiRNN classifiers fine tuned by the refined particular samples are compared with those fine tuned by the original particular samples. The improvements on AvgF1@all, AvgF1@top5 and top Precision, which are based on the average of the three above typing settings, are shown in Fig. 4 [Left]. On the one hand, we find sample refinement benefits both S-Lite and R-Lite, as expected. On the other hand, we find the improvement on S-Lite is limited, while the improvement on R-Lite is quite significant: F1@all and top Precision, e.g., are improved by around $0.8\\%$ and $1.8\\%$ respectively on S-Lite, but $4.3\\%$ and $7.4\\%$ respectively on R-Lite. This may be due to two factors: (i) the ground truths of S-Lite are the entities' class and super classes inferred from the KB itself, while the ground truths of R-Lite are manually labeled; (ii) sample refinement deletes many more noisy positive and negative samples (which are caused by wrong entity classifications of the KB) on R-Lite than on S-Lite, as shown in Fig. 4 [Right]."
+    ],
+    [
+      "Table 3 displays the overall semantic typing performance of our method and the baselines. Results for two optimum settings are reported for each method. The baseline Entity-Lookup retrieves one or several entities using the whole phrase of the literal, and uses their classes and super classes as the types. Gunaratna BIBREF4 matches the literal's focus term (head word) to an exact class, then an exact entity, and then a class with the highest similarity score. It stops as soon as some classes or entities are matched. We extend its original \u201cexact entity match\" setting with \u201crelaxed entity match\" which means multiple entities are retrieved. Property Range Estimation gets the classes and super classes from the entity objects of the property, and calculates the score of each class as the ratio of entity objects that belong to that class. (H/I, $\\kappa $ , $\\cdot $ )@top-P (F1) denotes the setting where the highest Precision (F1 Score) is achieved.",
+      "As we can see, AttBiRNN achieves much higher performance than all three baselines on both S-Lite and R-Lite. For example, the F1 Score of AttBiRNN is $67.6\\%$ , $160.2\\%$ and $13.8\\%$ higher than those of Gunaratna, Entity-Lookup and Property Range Estimation respectively on S-Lite, and $28.5\\%$ , $58.3\\%$ and $37.9\\%$ higher respectively on R-Lite. AttBiRNN also has significantly higher Precision and Recall, even when the setting is adjusted for the highest F1 Score. This is as expected, because our neural network, which learns the semantics (statistical correlation) from both word vector corpus and KB, models and utilizes the contextual meaning of the literal and its associated triple, while Gunaratna and Entity-Lookup are mostly based on lexical similarity. The performance of Property Range Estimation is limited because the object annotation in DBpedia usually does not follow the property range, especially for those properties in R-Lite. For example, objects of the property dbp:office have 35 DBO classes, ranging from dbo:City and dbo:Country to dbo:Company.",
+      "It is also notable that AttBiRNN and Property Range Estimation perform better on S-Lite than on R-Lite. The top F1 Score is $20.7\\%$ and $46.2\\%$ higher respectively, while the top Precision is $11.4\\%$ and $43.6\\%$ higher respectively. This is because R-Lite is more noisy, with longer literals, and has more ground truth types on average (cf. Table 1 ), while S-Lite has fewer properties, and each property has a large number of entity objects, which significantly benefits Property Range Estimation. In contrast, the two entity matching based methods, Gunaratna and Entity-Lookup, perform worse on S-Lite than on R-Lite; this is because the construction of S-Lite removes those KB entities from which literals were derived. Gunaratna outperforms Entity-Lookup as it extracts the head word and matches it to both entities and classes. Note that the head word is also included in our candidate class extraction with lookup."
+    ],
+    [
+      "Table 4 displays the number of correct matched entities and the Precision of entity matching on R-Lite. The types are predicted by the fine-tuned AttBiRNN with independent typing and two threshold settings. We can see that Precision is improved when the retrieved entities that do not belong to any of the predicted types are filtered out. The improvement is $6.1\\%$ and $5.8\\%$ when $\\theta $ is set to $0.15$ and $0.01$ respectively. Meanwhile, although the total number of matches may decrease because of the filtering, the number of correct matches still increases from 396 to 404 ( $\\theta =0.01$ ). This means that Recall is also improved."
+    ],
+    [
+      "Work on KB quality issues can can be divided into KB quality assessment BIBREF2 , BIBREF1 , and KB quality improvement/refinement BIBREF3 . The former includes error and anomaly detection methods, such as test-driven and query template based approaches BIBREF19 , BIBREF20 , with statistical methods BIBREF21 and consistency reasoning BIBREF22 also being applied to assess KB quality with different kinds of metric. The latter includes (i) KB completion, such as entity classification BIBREF7 , BIBREF8 , BIBREF9 , relation prediction BIBREF23 and data typing BIBREF15 ; and (ii) KB diagnosis and repair, such as abnormal value detection BIBREF20 , erroneous identity link detection BIBREF24 and data mapping (e.g., links to Wikipedia pages) correction BIBREF25 .",
+      "KB canonicalization refers to those refinement works that deal with redundant and ambiguous KB components as well as poorly expressed knowledge with limited reasoning potential. Some works in open information extraction (IE) BIBREF26 , BIBREF27 , BIBREF28 aim to identify synonymous noun phrases and relation phrases of open KBs which are composed of triple assertions extracted from text without any ontologies. For example, the recently proposed CESI method BIBREF27 utilizes both learned KB embeddings and side information like WordNet to find synonyms via clustering. Other works analyze synonyms for ontological KBs. Abedjan et al. BIBREF29 discovered synonymously used predicates for query expansion on DBpedia. Pujara et al. BIBREF30 identified coreferent entities of NELL with ontological constraints considered. These clustering, embedding, or entity linking based methods in open IE however can not be directly applied or do not work well for our KB literal canonicalization. The utilization of these techniques will be in our future work. ",
+      "String literals in ontological KBs such as DBpedia often represent poorly expressed knowledge, with semantic types and coreferent entities missed. As far as we known, canonicalization of such literals has been little studied. Gunaratna et al. BIBREF4 typed the literal by matching its head term to ontology classes and KB entities, but the literal context (e.g., the associated subject and property) and semantic meaning of the composition words were not utilized. Some ideas of entity classification can be borrowed for literal typing but will become ineffective as the context differs. For example, the baseline Property Range Estimation in our experiments uses the idea of SDType BIBREF8 \u2014 utilizing the statistical distribution of types in the subject position and object position of properties to estimate an entity's type probabilities. As a literal is associated with only one property, such probabilistic estimation becomes inaccurate (cf. results in Table 3 ).",
+      "Our literal classification model is in some degree inspired by those natural language understanding and web table annotation works that match external noun phrases to KB types and entities BIBREF14 , BIBREF10 , BIBREF12 using neural networks and semantic embeddings for modeling the contextual semantics. For example, Luo et al. BIBREF10 learned features from the surrounding cells of a target cell to predict its entity association. However the context in those works is very different, i.e., a simple regular structure of rows/columns with limited (table) metadata. In contrast, KBs have a complex irregular structure and rich meta data (the knowledge captured in the KB). Differently from these works, we developed different methods, e.g., candidate class extraction and high quality sampling, to learn the network from the KB with its assertions, terminologies and reasoning capability."
+    ],
+    [
+      "In this paper we present our study on KB literal canonicalization \u2014 an important problem on KB quality that has been little studied. A new technical framework is proposed with neural network and knowledge-based learning. It (i) extracts candidate classes as well as their positive and negative samples from the KB by lookup and query answering, with their quality improved using an external KB; (ii) trains classifiers that can effectively learn a literal's contextual features with BiRNNs and an attention mechanism; (iii) identifies types and matches entity for canonicalization. We use a real data set and a synthetic data set, both extracted from DBpedia, for evaluation. It achieves much higher performance than the baselines that include the state-of-the-art. We discuss below some more subjective observations and possible directions for future work."
+    ],
+    [
+      "The work is supported by the AIDA project (U.K. Government's Defence & Security Programme in support of the Alan Turing Institute), the SIRIUS Centre for Scalable Data Access (Research Council of Norway, project 237889), the Royal Society, EPSRC projects DBOnto, $\\text{MaSI}^{\\text{3}}$ and $\\text{ED}^{\\text{3}}$ ."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: Towards Personalized Dialog Policies for Conversational Skill Discovery
+
+Question: How did they measure effectiveness?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Conversational Skill Discovery",
+    "Conversational Skill Discovery ::: Problem Formulation",
+    "Conversational Skill Discovery ::: Rule-based Agent",
+    "Conversational Skill Discovery ::: User Simulation",
+    "Conversational Skill Discovery ::: RL-based Agent",
+    "Experimental Results",
+    "Experimental Results ::: Simulation Results",
+    "Experimental Results ::: Human Evaluation",
+    "Related Work",
+    "Conclusion",
+    "Conclusion ::: Acknowledgments",
+    "Appendix ::: Examples of Dialogs"
+  ],
+  "paragraphs": [
+    [
+      "Modern speech-based assistants, such as Amazon Alexa, Google Home, Microsoft Cortana, and Apple Siri, enable users to complete daily tasks such as shopping, setting reminders, and playing games using voice commands. Such human-like interfaces create a rich experience for users by enabling them to complete many tasks hands- and eyes-free in a conversational manner. Furthermore, these services offer tools to enable developers and customers to create custom voice experiences (skills) and as a result extend the capabilities of the assistant. Amazon's Alexa Skills Kit BIBREF0, Google's Actions and Microsoft's Cortana Skills Kit are examples of such tools. As the number of skills (with potentially overlapping functionality) increases, it becomes more difficult for end users to find the skills that can address their request.",
+      "To mitigate the skill discovery problem, recently researchers have proposed solutions for personalized domain selection and continuous domain adaptation in speech-based assistants BIBREF1, BIBREF2. Although such solutions help users find skills, in scenarios such as searching for a game where many different skills exist and user's preferences change, routing the user to a particular experience would not be satisfactory. In such cases, the assistant should initiate a conversation with the user, making recommendations, asking for preferences, and allowing the user to browse through different options. Similar to other search problems, personalization is important for conversational skill discovery and can be achieved at two levels: 1) personalization of skill recommendations, and 2) personalization of the interaction. Users have evolving attributes (e.g., first-time vs returning user) and different conversational styles and preferences (e.g., brief vs verbose communication) which affect how they respond to what the agent is proposing and its recommendations. By personalizing the interaction according to user attributes, conversational styles and preferences, the speech-based assistant can help speed up the conversation process BIBREF3 and increase user satisfaction. However, existing works are limited with respect to considering user's evolving attributes and diverse multi-aspect preferences BIBREF4, such as preferences with respect to how the conversational agent interacts with them.",
+      "In this paper, we focus on conversational discovery of skills to guide customers from an intent to a specific skill or set of skills that can serve their request. To this end, we start with a rule-based agent and improve it by using reinforcement learning (RL), enabling the agent to adapt to different conversational styles as it interacts with users. In summary, the contributions of this paper are as follows: 1) We introduce the problem of conversational skill discovery for large-scale virtual assistants. 2) We describe a solution which enables the assistant to adapt to user's attributes (e.g., first-time user vs returning user) and conversational styles (e.g., brief vs. verbose). 3) We conduct experiments in a real production setting by deploying the agent to interact with real users in large scale, showing that the personalized policy learned using RL significantly outperforms a one-fits-all rule-based agent in terms of success rate (measured in terms of number of dialogs which result in launching a skill) with significantly shorter dialogs."
+    ],
+    [
+      "Conversational skill discovery is the task of initiating a dialog with the user in order to help them find the skills that address their needs when interacting with a speech-based assistant. More specifically, a conversational skill discovery agent receives a natural language input from the user, understands it using its automatic speech recognition (ASR) and natural language understanding (NLU) components, and decides how to respond to the user based on user provided and contextual information in order to help the user find the needed skill. Skills can often be grouped into categories and subcategories based on functionality (e.g., ride-sharing skills or trivia games). These categories help customers explore with much more specificity and relevance, as such a key functionality of a skill discovery system is to allow users to browse through existing categories. Additionally, it is important for the agent to be able to adapt to user's conversational styles, overtime shifting to more and more personalized conversations with the user.",
+      "Table TABREF1 shows an example of a dialog between a user and an agent. Here, in each turn of the dialog, the user can either ask for a particular category or skill, select from the list of recommendations, accept or reject a recommendation, ask for other (sub)categories or skills, ask for details or rating of a skill, or perform some general action such as asking for help, asking the agent to repeat the previous prompt, going over a list of recommendations, going back in the conversation, or asking the agent to stop. The agent, on the other hand, can suggest a skill, provide information or help, offer a few different types of categories to choose from, stop the conversation if it is not going well, or launch a selected skill."
+    ],
+    [
+      "Conversational skill discovery, similar to other goal-oriented dialog systems, can be formalized as a Markov Decision Process (MDP) BIBREF5. An MDP is a tuple $<\\mathcal {S}, \\mathcal {A}, \\mathcal {P}, \\mathcal {R}, \\mathcal {\\gamma }>$, where $\\mathcal {S}$ is the state space, $\\mathcal {A}$ is the action space, $\\mathcal {P}$ is the transition probability function, $\\mathcal {R}$ is the reward function, and $\\mathcal {\\gamma }$ is the discount factor. In this framework, at each time step t, the agent observes state $s_t \\in \\mathcal {S}$ and selects action $a_t \\in \\mathcal {A}$ according to its policy ($\\pi : \\mathcal {S} \\rightarrow \\mathcal {A}$). After performing the selected action, the agent receives the next state $s_{t+1}$ and a scalar reward $r_t$. The trajectory restarts after the agent reaches a terminal state. RL solvers have been used to find the optimal dialog policy (e.g., BIBREF6 BIBREF6; BIBREF7 BIBREF7; BIBREF8 BIBREF8; BIBREF9 BIBREF9). In this context, at each turn the agent acts based on its understanding of what the user said, and reward function is modeled in terms of various dimensions of the interaction such as per-interaction user satisfaction, accomplishment of the task, efficiency of interaction, and dialog duration. Recently, deep RL has also been applied to the problem of dialog management and has shown improvements over rule-based systems BIBREF10, BIBREF11, BIBREF12, BIBREF13, BIBREF14.",
+      "In this paper, we adopt the above formalism with the goal of training a dialog policy which allows the agent to take actions that maximize its success rate (measured in terms of number of dialogs which result in launching a skill) while providing a flexible and natural way for the user to navigate throughout various dialog states. In each turn of the dialog, the agent makes its decisions based on various available information such as user's intent (e.g., asking for a particular skill), the category the user has selected, whether the user is a first-time user, etc. In order to make the agent adapt to different conversational styles, when making recommendations, we focus on 1) whether to recommend skills or categories, 2) how many skills or categories to recommend, and 3) what type of metadata to provide to the user. Examples of metadata include: popularity, star rating, number of reviews, or a short description of the skill. The agent can proactively provide metadata to the user at certain points in the experience. Depending on user's conversational style, they may prefer brief conversations with the agent (i.e., no metadata), or verbose with different types of metadata.",
+      "An important challenge in using RL for learning dialog policies is creating realistic user simulators that can generate natural conversations similar to a human user BIBREF15, and as such in previous works researchers have focused on the development of different types of user simulators (e.g., BIBREF16 BIBREF16; BIBREF17 BIBREF17; BIBREF18 BIBREF18; BIBREF19 BIBREF19; BIBREF15 BIBREF15; BIBREF20 BIBREF20; BIBREF21 BIBREF21). We take a data-driven approach to user simulation, and start with a rule-based policy to gather data and then improve the agent by using RL."
+    ],
+    [
+      "The rule-based agent selects from the following actions depending on user's intent in each turn of the dialog: 1) offering k categories ($1 \\le k \\le 5$), 2) offering n skills ($1 \\le n \\le 3$), 3) offering a skill or asking for category, 4) providing information about skill rating, 5) providing details about a skill, 6) ending the conversation, and 7) launching a skill. When multiple actions are possible, the rule-based agent randomly selects among them. For example, at the beginning of the dialog, the agent randomly selects among different offer actions. If all skills in a category have been exhausted, the agent will inform the user that no additional skills are available for the selected category. Furthermore, each action is mapped to a specific prompt template. For example, offering a skill or asking for category can be mapped to \"Would you like to launch $<$skill$>$ or try a different type of skill?\", where the specific skill is provided by a skill recommendation system. Additionally, in cases where the agent does not understand what the user has said (e.g., out-of-domain requests), it will first repeat the previous prompt, if user's utterance is again misunderstood, it will give a new prompt, and finally it will stop the conversation."
+    ],
+    [
+      "We deployed the rule-based system to gather dialogs with users and trained a user simulator similar to BIBREF13 with $180,000$ dialogs with real users. Note that the collected dialogs are not annotated and may include understanding errors. Figure FIGREF5 illustrates the interaction between the user simulator (left) and the conversational agent (right). More specifically, the user simulator first generates the next user intent based on dialog context. Intent generation is modeled as a language modeling problem. In this formulation, each possible intent forms a token in the vocabulary, and every training dialog becomes a training intent sequence. For example, the sequence for the conversation in Table TABREF1 is [Start, CategoryName, CategoryName, GetRating, Yes, End].",
+      "We used recurrent neural networks with Gated Recurrent Unit (GRU) BIBREF22 to predict the next user intent, and used the following for dialog context: 1) previous user intent, 2) previous agent action, 3) previous agent prompt, 4) whether the user is a first-time user, 5) whether the user has already selected an item (skill or category) from a list, and 6) number of user turns so far in the conversation. The optimal parameters were found using Hyperopt BIBREF23 and the model with lowest perplexity BIBREF24 score was chosen. Given the predicted intent, the user simulator uniformly samples one utterance from the combination of available templates and user turns in the collected dialogs."
+    ],
+    [
+      "The components used to learn dialog policies using RL are as follows.",
+      "",
+      "State Space S: The input state is composed of 1) user's intent 2) previous action the agent took, 3) previous prompt and metadata it gave the user, 4) the category the user has selected if any, 5) whether the agent has proposed a skill, 6) whether the user is a first-time user, and 7) number of user turns so far in the dialog. This set of parameters were selected using a forward feature selection approach based on the correlation between the new feature and the feature set with the goal of achieving a higher Expected Cumulative Reward (ECR) BIBREF25. This set can be augmented with user preferences regarding skills, the last skill launched by the user, or the frequency of skill launches.",
+      "",
+      "Action Space A: We constrain the action space of the agent to a set of composite actions: 1) offering k categories (e.g., offer-one-category, offer-two-category), 2) offering n skills (e.g., offer-one-skill, offer-two-skill), 3) offering a skill or asking for category (e.g., offer-one-skill-or-category), 4) executing a user request, 5) ending the conversation, and 6) launching a skill. The execute action refers to delivering information such as providing skill ratings or more details about a skill, repeating the previous prompt, or handling out-of-domain requests. At run-time, the RL policy falls back on the rule-based policy for the execute action.",
+      "",
+      "Reward R: We use a simple reward function based on goal completion, where the environment gives a reward of $+1$ at the end of the dialog if the user launches a skill, and gives a reward of $-1$ if the user or agent end the dialog.",
+      "",
+      "Policy: We use DQN BIBREF26, BIBREF27 with action masking for the RL agent, with a fully-connected MLP to represent the deep Q-network. The hidden layers use a rectifier nonlinearity, and the output layer is a fully connected layer with linear activation function and a single output for each valid action. The action mask suppresses impossible actions in any particular dialog state, such as launching a skill before the user has selected one."
+    ],
+    [
+      "We focused on the use case of a user searching for a game to play among $1,903$ skills belonging to 48 game categories. Each category may also have subcategories, resulting in 191 total categories. Example of categories are adventure, trivia, choose your own story, family, and kids. The number of categories to offer k is set to one, three, and five; and the number of skills to offer n is set to one, based on the results of internal user studies. Table TABREF7 summarizes the state and action spaces. For all agents, we randomly sample from the set of possible prompts and metadata for the selected action. Furthermore, we used the Alexa Skill portal to train the NLU model from a set of sample utterances."
+    ],
+    [
+      "We trained the DQN agent using an $\\epsilon $-greedy policy with $\\epsilon $ decreasing linearly from 1 to $0.1$ over $100,000$ steps. Additionally, we tuned a window size to include previous dialog turns as input and set $\\gamma $ to $0.9$. We ran the method 30 times for $150,000$ steps, and in each run, after every 10,000 steps, we sampled $3,000$ dialog episodes with no exploration to evaluate the performance. The optimal parameters were found using Hyperopt BIBREF23 (see Appendix B). Figure FIGREF9 shows the simulation results during training. The Y-axis in the figure is the success rate of the agent (measured in terms of number of dialogs that resulted in launching a skill divided by total number of dialogs), and the X-axis is the number of learning steps. Given our choice of reward function, the increase in success rate is indicative of the agent learning to improve its policy over time. Furthermore, the RL agent outperformed the rule-based agent with average success rate of $68.00\\% (\\pm 2\\%$) in simulation."
+    ],
+    [
+      "To evaluate the performance of the skill discovery agent, we deployed the dialog policies and evaluated them with real users (see Appendix A for examples of dialogs). We first conducted a test with a baseline policy of recommending up to five skills based on popularity and allowing the user to either accept or reject the recommendation. The success rate of this simple policy was $46.42$%, illustrating the importance of providing flexible search to the user. We then conducted an A/B test on the rule-based and RL policies to compare their effects on skill launches in a production environment. Both policies were tested on randomly sampled users, with the additional constraint of using the same policy for returning users. The results are reported in Table TABREF11. Both policies significantly outperform the baseline policy, indicating the importance of providing flexible search and navigation to users. Additionally, the difference between the success rate of the rule-based ($73.41$%) and RL ($76.99$%) policies is statistically significant ($p$-value $<$ $0.0001$) and the RL policy has significantly shorter dialogs ($p$-value $<$ $0.0001$), showing the importance of optimizing for the entire interaction with the user.",
+      "In order to understand the effect of adapting to user attributes, we investigated the difference in success rate between first-time and returning users for the two policies. First-time users make up $59.48\\%$ and $60.14\\%$ of the population for the rule-based and RL policies, respectively. Table TABREF12 shows the results. The RL policy significantly outperforms the rule-based policy for both first-time ($p$-value $<$ $0.0001$) and returning users ($p$-value $= 0.0002$), indicating that the RL model has learned and adapted to user attributes. Additionally, the RL policy has a similar performance for both groups of users. The difference for the rule-based policy between the two groups, on the other hand, is significant ($p$-value $= 0.0010$), indicating that this policy is more tuned to returning users. This highlights the difficulty of authoring personalized dialog policies with rules, and shows the advantage of using RL for this problem."
+    ],
+    [
+      "Conversational search and recommendation, especially in the context of e-commerce, have been explored by researchers BIBREF28, BIBREF29, BIBREF30, BIBREF31, BIBREF32. BIBREF28 introduced an interactive recommendation protocol and studied whether to ask absolute or relative questions when gathering user preferences. Their dialog system collects like/dislike and pairwise comparison feedback from users, and does not include actions typically present in a dialog system BIBREF32. BIBREF29 proposed a theoretical framework for conversational search. BIBREF33 framed the problem as a machine reading task and applied it to question answering. BIBREF30 developed a RL-based conversational search assistant, in which state and action spaces are domain specific and may require a significant amount of time to develop. BIBREF32 proposed a unified framework to integrate recommender and dialog systems, in which instead of just returning the top-ranking results for a given user query, the agent attempts to optimize for long term reward by asking the user for the value of an attribute. In their work, the action space is limited to two types of actions, namely, requesting for the value of an attribute or making a recommendation. BIBREF31 proposed a multi-memory network architecture and applied it to search and recommendation in e-commerce. Compared to previous works, our formulation of the search problem is domain independent, accounts for user attributes and conversational preferences, and includes actions typically present in a dialog system. Additionally, whereas existing works have not been evaluated in a real production setting, we conduct experiments with real users at large scale."
+    ],
+    [
+      "In this paper, we introduced the problem of conversational skill discovery in speech-based assistants and presented an approach to enable users to find skills. To this end, we started with a rule-based agent and improved it by using RL, enabling the agent to adapt to different user attributes and conversational styles. We compared popularity based, rule-based and RL-based model conversational agents by deploying them in a real production setting and showed that the RL agent learns to adapt its policy to achieve a higher success rate with shorter dialogs. For future work, we plan to further personalize the dialog policy based on user attributes and conversational preferences, and investigate richer state representations. Furthermore, we plan to explore the impact of evolving attributes and preferences on the learned policies."
+    ],
+    [
+      "We would like to thank the Alexa Machine Learning Platform team for making the customer experiments possible. We would also like to thank Jared Casale, Jason Pazis, Longshaokan Wang, and Spyros Matsoukas for their feedback and support."
+    ],
+    [
+      "Dialog with the Rule-based Agent",
+      "Dialog with RL-based Agent"
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2354/instruction.md b/qasper-2354/instruction.md
new file mode 100644
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--- /dev/null
+++ b/qasper-2354/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: BERT is Not a Knowledge Base (Yet): Factual Knowledge vs. Name-Based Reasoning in Unsupervised QA
+
+Question: Which of the two ensembles yields the best performance?
\ No newline at end of file
diff --git a/qasper-2355/instruction.md b/qasper-2355/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..468933698ee76c2e2ead4baf87c50d16bcf557f3
--- /dev/null
+++ b/qasper-2355/instruction.md
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+Name of Paper: BERT is Not a Knowledge Base (Yet): Factual Knowledge vs. Name-Based Reasoning in Unsupervised QA
+
+Question: What are the two ways of ensembling BERT and E-BERT?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "LAMA",
+    "LAMA ::: LAMA-UHN",
+    "E-BERT ::: BERT.",
+    "E-BERT ::: Wikipedia2vec.",
+    "E-BERT ::: E-BERT.",
+    "Experiments ::: Systems.",
+    "Experiments ::: LAMA.",
+    "Experiments ::: FewRel.",
+    "Related work",
+    "Conclusion",
+    "FewRel training",
+    "A note on casing"
+  ],
+  "paragraphs": [
+    [
+      "Imagine that you have a friend who claims to know a lot of trivia. During a quiz, you ask them about the native language of actor Jean Marais. They correctly answer French. For a moment you are impressed, until you realize that Jean is a typical French name. So you ask the same question about Daniel Ceccaldi (another French actor, but with an Italian-sounding name). This time your friend says \u201cItalian, I guess.\u201d If this were a Question Answering (QA) benchmark, your friend would have achieved a respectable accuracy of 50%. Yet, their performance does not indicate factual knowledge about the native languages of actors. Rather, it shows that they are able to reason about the likely origins of peoples' names (see Table TABREF1 for more examples).",
+      "BIBREF1 argue that the unsupervised BERT LM BIBREF0 memorizes factual knowledge about entities and relations. They base this statement on the unsupervised QA benchmark LAMA (\u00a7SECREF2), where BERT rivals a knowledge base (KB) built by relation extraction. They suggest that BERT and similar LMs could become a \u201cviable alternative to traditional knowledge bases extracted from text\u201d. We argue that the impressive performance of BERT is partly due to reasoning about (the surface form of) entity names. In \u00a7SECREF4, we construct LAMA-UHN (UnHelpful Names), a more \u201cfactual\u201d subset of LAMA-Google-RE and LAMA-T-REx, by filtering out queries that are easy to answer from entity names alone. We show that the performance of BERT decreases dramatically on LAMA-UHN.",
+      "In \u00a7SECREF3, we propose E-BERT, a simple mapping-based extension of BERT that replaces entity mentions with wikipedia2vec entity embeddings BIBREF3. In \u00a7SECREF4, we show that E-BERT rivals BERT and the recently proposed entity-enhanced ERNIE model BIBREF2 on LAMA. E-BERT has a substantial lead over both baselines on LAMA-UHN; furthermore, ensembles of E-BERT and BERT outperform all baselines on original LAMA."
+    ],
+    [
+      "The LAMA (LAnguage Model Analysis) benchmark BIBREF1 is supposed to probe for \u201cfactual and commonsense knowledge\u201d inherent in LMs. In this paper, we focus on LAMA-Google-RE and LAMA-T-REx BIBREF5, which are aimed at factual knowledge. Contrary to most previous works on QA, LAMA tests LMs as-is, without supervised finetuning.",
+      "The LAMA probing task follows this schema: Given a KB triple of the form (S, R, O), the object is elicited with a relation-specific cloze-style question, e.g., (Jean_Marais, native-language, French) becomes: \u201cThe native language of Jean Marais is [MASK].\u201d The LM predicts a distribution over a limited vocabulary to replace [MASK], which is evaluated against the known gold answer."
+    ],
+    [
+      "It is often possible to guess properties of an entity from its name, with zero factual knowledge of the entity itself. This is because entities are often named according to implicit or explicit rules (e.g., the cultural norms involved in naming a child, copyright laws for industrial products, or simply a practical need for descriptive names). LAMA makes guessing even easier by its limited vocabulary, which may only contain a few candidates for a particular entity type.",
+      "We argue that a QA benchmark that does not control for entity names does not assess whether an LM is good at reasoning about names, good at memorizing facts, or both. In this Section, we describe the creation of LAMA-UHN (UnHelpfulNames), a subset of LAMA-Google-RE and LAMA-T-REx.",
+      "Filter 1: The string match filter deletes all KB triples where the correct answer (e.g., Apple) is a case-insensitive substring of the subject entity name (e.g., Apple Watch). This simple heuristic deletes up to 81% of triples from individual relations (see Appendix for statistics and examples).",
+      "Filter 2: Of course, entity names can be revealing in ways that are more subtle. As illustrated by our French actor example, a person's name can be a useful prior for guessing their native language and by extension, their nationality, place of birth, etc. Our person name filter uses cloze-style questions to elicit name associations inherent in BERT, and deletes KB triples that correlate with them. Consider our previous example (Jean_Marais, native-language, French). We whitespace-tokenize the subject name into Jean and Marais. If BERT considers either name to be a common French name, then a correct answer is insufficient evidence for factual knowledge about the entity Jean_Marais. On the other hand, if neither Jean nor Marais are considered French, but a correct answer is given nonetheless, then we consider this sufficient evidence for factual knowledge.",
+      "We query BERT for answers to \u201c[X] is a common name in the following language: [MASK].\u201d for both [X] = Jean and [X] = Marais. If the correct answer is among the top-3 for either query, we delete the triple. We apply this filter to Google-RE:place-of-birth, Google-RE:place-of-death, T-REx:P19 (place of birth), T-REx:P20 (place of death), T-REx:P27 (nationality), T-REx:P103 (native language) and T-REx:P1412 (language used). See Appendix for statistics. Depending on the relation, we replace \u201clanguage\u201d with \u201ccity\u201d or \u201ccountry\u201d in the template.",
+      "Figure FIGREF5 (blue bars) shows that BERT is strongly affected by filtering, with a drop of 5%\u201310% mean P@1 from original LAMA to LAMA-UHN. This suggests that BERT does well on LAMA partly because it reasons about (the surface form of) entity names. Of course, name-based reasoning is a useful ability in its own right; however, conflating it with factual knowledge may be misleading."
+    ],
+    [
+      "BERT BIBREF0 is a deep bidirectional transformer encoder BIBREF6 pretrained on unlabeled text. It segments text into subword tokens from a vocabulary $\\mathbb {L}_b$. During training, some tokens are masked by a special [MASK] token. Tokens are embedded into real-valued vectors by an embedding function $\\mathcal {E}_\\mathcal {B} : \\mathbb {L}_b \\rightarrow \\mathbb {R}^{d_\\mathcal {B}}$. The embedded tokens are contextualized by the BERT encoder $\\mathcal {B}$ and the output of $\\mathcal {B}$ is fed into a function $\\mathcal {M}_\\mathcal {B}: \\mathbb {R}^{d_\\mathcal {B}} \\rightarrow \\mathbb {L}_b$ that predicts the identity of masked tokens. BERT can thus be used as an LM."
+    ],
+    [
+      "Wikipedia2vec BIBREF3 embeds words and wikipedia pages ($\\approx $ entities) in a common space. It learns an embedding function for a vocabulary of words $\\mathbb {L}_w$ and a set of entities $\\mathbb {L}_e$. We denote this function as $\\mathcal {F}: \\mathbb {L}_w \\cup \\mathbb {L}_e \\rightarrow \\mathbb {R}^{d_\\mathcal {F}}$. The wikipedia2vec loss has three components: (a) skipgram word2vec BIBREF7 operating on $\\mathbb {L}_w$ (b) a graph loss on the wikipedia link graph on $\\mathbb {L}_e$ (c) a version of word2vec where words are predicted from entity mentions. Loss (c) ensures that word and entity embeddings share a space. Figure FIGREF5 (black horizontal bars) shows that loss (b) is vital for our use case."
+    ],
+    [
+      "We want to transform the output space of $\\mathcal {F}$ in such a way that $\\mathcal {B}$ is fooled into accepting entity embeddings in lieu of its native subword embeddings. We approximate this goal by minimizing the squared distance of transformed wikipedia2vec word vectors and BERT subword vectors:",
+      "where $\\mathcal {W}$ is a linear projection obtained by least squares. Since $\\mathcal {F}$ embeds $\\mathbb {L}_w$ and $\\mathbb {L}_e$ into the same space, $\\mathcal {W}$ is applicable to members of $\\mathbb {L}_e$, even though it was learned on members of $\\mathbb {L}_w$.",
+      "Recall that BERT segments text into subwords, e.g., our previous example is tokenized as: The native language of Jean Mara ##is is [MASK] .",
+      "E-BERT replaces the subwords that correspond to the entity mention with the symbolic entity: The native language of Jean_Marais is [MASK] .",
+      "The entity (truetype) is embedded by $\\mathcal {W} \\circ \\mathcal {F}$, while other tokens (italics) continue to be embedded by $\\mathcal {E}_\\mathcal {B}$. The altered embedding sequence is fed into $\\mathcal {B}$, where it is treated like any other embedding sequence. Neither $\\mathcal {B}$ nor $\\mathcal {M}_\\mathcal {B}$ are changed.",
+      "We ensemble BERT and E-BERT by (a) mean-pooling their outputs (AVG) or (b) concatenating the entity and its name with a slash symbol (CONCAT), e.g.: Jean_Marais / Jean Mara ##is."
+    ],
+    [
+      "We train cased wikipedia2vec on a recent wikipedia dump (2019-09-02), setting $d_\\mathcal {F} = d_\\mathcal {B}$. To learn $\\mathcal {W}$, we intersect the wikipedia2vec word vocabulary with the cased BERT vocabulary.",
+      "Our primary baselines are BERT$_\\mathrm {base}$ and BERT$_\\mathrm {large}$ as evaluated in BIBREF1. We also test ERNIE BIBREF2, a BERT$_\\mathrm {base}$ type model that uses wikidata TransE entity embeddings BIBREF8 as additional input. ERNIE has two transformers, one for tokens and one for entities, which are fused by a trainable feed-forward module. To accommodate the new parameters, ERNIE is pre-trained with (a) standard BERT loss and (b) predicting Wikipedia entities.",
+      "Note that wikipedia2vec and TransE have low coverage on LAMA-Google-RE (wikipedia2vec: 54%, TransE: 71%). When an entity embedding is missing, we fall back onto original BERT. Coverage of LAMA-T-REx is $>98$% for both systems."
+    ],
+    [
+      "In keeping with BIBREF1, we report P@k macro-averaged over relations. Macro-averaging ensures that every relation has the same impact on the metric before and after filtering.",
+      "Figure FIGREF5 shows that E-BERT performs comparable to BERT and ERNIE on unfiltered LAMA. However, E-BERT is less affected by filtering on LAMA-UHN, suggesting that its performance is more strongly due to factual knowledge. Recall that we lack entity embeddings for 46% of Google-RE subjects, i.e., E-BERT cannot improve over BERT on almost half of the Google-RE tuples.",
+      "Figure FIGREF15 plots deltas in mean P@1 on unfiltered LAMA-T-REx relations relative to BERT, along with the frequency of tuples whose object entity name is a substring of the subject entity name \u2013 i.e., the ratio of queries that would be deleted by the string match filter. We see that E-BERT losses relative to BERT (negative red bars) are mostly on relations with a high percentage of trivial substring answers. By contrast, E-BERT typically outperforms BERT on relations where such trivial answers are rare. The ensembles are able to mitigate the losses of E-BERT on almost all relations, while keeping most of its gains (purple and orange bars). This suggests that they successfully combine BERT's ability to reason about entity names with E-BERT's enhanced factual knowledge.",
+      "Figure FIGREF17 shows that the lead of E-BERT and the ensembles over BERT and ERNIE in terms of mean P@k is especially salient for bigger k."
+    ],
+    [
+      "We also evaluate on the FewRel relation classification dataset BIBREF9, using the setup and data split from zhang2019ernie (see Appendix for details). Table TABREF19 shows that E-BERT beats BERT, and the ensembles perform comparable to ERNIE despite not having a dedicated entity encoder."
+    ],
+    [
+      "Factual QA is typically tackled as a supervised problem (e.g., BIBREF10, BIBREF11). In contrast, LAMA BIBREF1 tests for knowledge learned by LMs without supervision; similar experiments were performed by BIBREF12. Their experiments do not differentiate between factual knowledge of LMs and their ability to reason about entity names.",
+      "The E-BERT embedding mapping strategy is inspired by cross-lingual embedding mapping on identical strings BIBREF13. A similar method was recently applied by BIBREF14 to map cross-lingual FastText subword vectors BIBREF15 into the multilingual BERT subword embedding space. BIBREF16 mimick BERT subword embeddings for rare English words from their contexts and form.",
+      "Other contextualized models that incorporate entity embeddings are ERNIE BIBREF2 (see \u00a7SECREF4) and KnowBert BIBREF17. KnowBert is contemporaneous to our work, and at the time of writing, the model was not available for comparison.",
+      "Both ERNIE and KnowBert add new parameters to the BERT architecture, which must be integrated by additional pretraining. By contrast, E-BERT works with the unchanged BERT model, and $\\mathcal {W}$ has an efficient closed-form solution. This means that we can update E-BERT to the newest wikipedia dump at little computational cost \u2013 the most expensive operation would be training wikipedia2vec, which takes a few hours on CPUs."
+    ],
+    [
+      "We have presented evidence that the surprising performance of BERT on the recently published LAMA QA benchmark is partly due to reasoning about entity names rather than factual knowledge. We have constructed more \u201cfactual\u201d subsets of LAMA-Google-RE and LAMA-T-REx by filtering out easy-to-guess queries. The resulting benchmark, LAMA-UHN, is more difficult for BERT.",
+      "As a remedy, we proposed E-BERT, a simple extension of BERT that injects wikipedia2vec entity embeddings into BERT. E-BERT outperforms BERT and ERNIE on LAMA-UHN, which we take as evidence that E-BERT is richer in factual knowledge. Additionally, ensembling yields improvements over both BERT and E-BERT on unfiltered LAMA and on the FewRel relation classification dataset."
+    ],
+    [
+      "We use the sentence classification setup from BIBREF2. We mark subjects and objects with the symbols # and $, i.e., the inputs to BERT, E-BERT and the CONCAT ensemble look as follows:",
+      "[CLS] $ Tang ##ier $ ' s # Ibn Bat ##to ##uta Airport # is the busiest airport in the region . [SEP]",
+      "[CLS] $ Tangier $ ' s # Tangier_Ibn_Battouta_Airport # is the busiest airport in the region . [SEP]",
+      "[CLS] $ Tangier / Tang ##ier $ ' s # Tangier_Ibn_Battouta_Airport / Ibn Bat ##to ##uta Airport # is the busiest airport in the region . [SEP]",
+      "where entities (in truetype) are embedded by $\\mathcal {W} \\circ \\mathcal {F}$ and all other tokens (in italics) are embedded by $\\mathcal {E}_\\mathcal {B}$. Note that entity IDs are provided by FewRel. If we lack an entity embedding, we fall back onto the standard BERT segmentation.",
+      "To predict the relation, we feed the contextualized embedding of the [CLS] token into a linear classifier. During training we finetune all network parameters except for the embeddings. For hyperparameter tuning, we use the ranges from BIBREF2 except for the number of epochs, which we fix at 10. The AVG ensemble averages over BERT's and E-BERT's output distributions. Experiments were run on two GeForce GTX 1080 Ti GPUs with data-parallel training."
+    ],
+    [
+      "The cased BERT vocabulary is a superset of the LAMA vocabulary. This ensures that BERT can in principle answer all LAMA queries correctly. The uncased ERNIE vocabulary does not have this property. For ERNIE, we therefore lowercase all queries and restrict the model output to the intersection of its vocabulary with the lowercased LAMA vocabulary. As a result, ERNIE selects an answer from $\\sim $18K candidates (instead of the standard $\\sim $21K), which should work in its favor. We verify that all lowercased object names from LAMA-T-REx and LAMA-Google-RE appear in ERNIE's vocabulary, i.e., ERNIE is in principle able to answer all lowercased queries correctly."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2363/instruction.md b/qasper-2363/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..e05730ebc1a6fe4ea9dbe18fecaca6901dc32cb8
--- /dev/null
+++ b/qasper-2363/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Named Entity Disambiguation for Noisy Text
+
+Question: How was a quality control performed so that the text is noisy but the annotations are accurate?
\ No newline at end of file
diff --git a/qasper-2364/instruction.md b/qasper-2364/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..9f558a0503b25ada4d2e47045506c3aa5fc652ac
--- /dev/null
+++ b/qasper-2364/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Question Asking as Program Generation
+
+Question: Is it a neural model? How is it trained?
\ No newline at end of file
diff --git a/qasper-2365/instruction.md b/qasper-2365/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..5cb0ebf703827f08a5ba579f16e32270ab93a359
--- /dev/null
+++ b/qasper-2365/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Event detection in Colombian security Twitter news using fine-grained latent topic analysis
+
+Question: How do people engage in Twitter threads on different types of news?
\ No newline at end of file
diff --git a/qasper-2390/instruction.md b/qasper-2390/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..71e3892aec8a6bb61beaace399d4aa02878345c3
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+++ b/qasper-2390/instruction.md
@@ -0,0 +1,63 @@
+Name of Paper: Rapid Adaptation of BERT for Information Extraction on Domain-Specific Business Documents
+
+Question: What type of documents are supported by the annotation platform?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Approach",
+    "Approach ::: Datasets",
+    "Approach ::: Model",
+    "Approach ::: Inference and Evaluation",
+    "Results",
+    "Cloud Platform",
+    "Conclusions"
+  ],
+  "paragraphs": [
+    [
+      "Business documents broadly characterize a large class of documents that are central to the operation of business. These include legal contracts, purchase orders, financial statements, regulatory filings, and more. Such documents have a number of characteristics that set them apart from the types of texts that most NLP techniques today are designed to process (Wikipedia articles, news stories, web pages, etc.): They are heterogeneous and frequently contain a mix of both free text as well as semi-structured elements (tables, headings, etc.). They are, by definition, domain specific, often with vocabulary, phrases, and linguistic structures (e.g., legal boilerplate and terms of art) that are rarely seen in general natural language corpora.",
+      "Despite these challenges, there is great potential in the application of NLP technologies to business documents. Take, for example, contracts that codify legal agreements between two or more parties. Organizations (particularly large enterprises) need to monitor contracts for a range of tasks, a process that can be partially automated if certain content elements can be extracted from the contracts themselves by systems BIBREF0. In general, if we are able to extract structured entities from business documents, these outputs can be better queried and manipulated, potentially facilitating more efficient business operations.",
+      "In this paper, we present BERT-based models for extracting content elements from two very different types of business documents: regulatory filings and property lease agreements. Given the success of deep transformer-based models such as BERT BIBREF1 and their ability to handle sequence labeling tasks, adopting such an approach seemed like an obvious starting point. In this context, we are primarily interested in two questions: First, how data efficient is BERT for fine-tuning to new specialized domains? Specifically, how much annotated data do we need to achieve some (reasonable) level of accuracy? This is an important question due to the heterogeneity of business documents; it would be onerous if organizations were required to engage in large annotation efforts for every type of document. Second, how would a BERT model pre-trained on general natural language corpora perform in specific, and potentially highly-specialized, domains?",
+      "There are aspects of this task that make it both easier and more difficult than \u201ctraditional\u201d IE. Even though they are expressed in natural language, business documents frequently take constrained forms, sometimes even \u201ctemplate-like\u201d to a certain degree. As such, it may be easy to learn cue phrases and other fixed expressions that indicate the presence of some element (i.e., pattern matching). On the other hand, the structure and vocabulary of the texts may be very different from the types of corpora modern deep models are trained on; for example, researchers have shown that models for processing the scientific literature benefit immensely from pre-training on scientific articles BIBREF2, BIBREF3. Unfortunately, we are not aware of any large, open corpora of business documents for running comparable experiments.",
+      "The contribution of our work is twofold: From the scientific perspective, we begin to provide some answers to the above questions. With two case studies, we find that a modest amount of domain-specific annotated data (less than 100 documents) is sufficient to fine-tune BERT to achieve reasonable accuracy in extracting a set of content elements. From a practical perspective, we showcase our efforts in an end-to-end cloud platform that provides an easy-to-use annotation interface as well as an inference interface that allows users to upload documents and inspect the results of our models."
+    ],
+    [
+      "Within the broad space of business documents, we have decided to focus on two specific types: regulatory filings and property lease agreements. While our approach is not language specific, all our work is conducted on Chinese documents. In this section, we first describe these documents and our corpora, our sequence labeling model, and finally our evaluation approach."
+    ],
+    [
+      "Regulatory Filings. We focused on a specific type of filing: disclosures of pledges by shareholders when their shares are offered up for collateral. These are publicly accessible and were gathered from the database of a stock exchange in China. We observe that most of these announcements are fairly formulaic, likely generated by templates. However, we treated them all as natural language text and did not exploit this observation; for example, we made no explicit attempt to induce template structure or apply clustering\u2014although such techniques would likely improve extraction accuracy. In total, we collected and manually annotated 150 filings, which were divided into training, validation, and test sets with a 6:2:2 split. Our test corpus comprises 30 regulatory filings. Table TABREF6 enumerates the seven content elements that we extract.",
+      "Property Lease Agreements. These contracts mostly follow a fixed \u201cschema\u201d with a certain number of prescribed elements (leaseholder, tenant, rent, deposit, etc.); Table TABREF7 enumerates the eight elements that our model extracts. Since most property lease agreements are confidential, no public corpus for research exists, and thus we had to build our own. To this end, we searched the web for publicly-available templates of property lease agreements and found 115 templates in total. For each template, we manually generated one, two, or three instances, using a fake data generator tool to fill in the missing content elements such as addresses. In total, we created (and annotated) 223 contracts by hand. This corpus was further split into training, validation, and test data with a 6:2:2 split. Our test set contains 44 lease agreements, 11 of which use templates that are not seen in the training set. We report evaluation over both the full test set and on only these unseen templates; the latter condition specifically probes our model's ability to generalize."
+    ],
+    [
+      "An obvious approach to content element extraction is to formulate the problem as a sequence labeling task. Prior to the advent of neural networks, Conditional Random Fields (CRFs) BIBREF4, BIBREF5 represented the most popular approach to this task. Starting from a few years ago, neural networks have become the dominant approach, starting with RNNs BIBREF6, BIBREF7, BIBREF8, BIBREF9, BIBREF10, BIBREF11. Most recently, deep transformer-based models such as BERT represent the state of the art in this task BIBREF1, BIBREF12, BIBREF13 . We adopt the sequence labeling approach of BIBREF1, based on annotations of our corpus using a standard BIO tagging scheme with respect to the content elements we are interested in.",
+      "We extend BERT Base-Chinese (12-layer, 768-hidden, 12-heads, 110M parameters) for sequence labeling. All documents are segmented into paragraphs and processed at the paragraph level (both training and inference); this is acceptable because we observe that most paragraphs are less than 200 characters. The input sequences are segmented by the BERT tokenizer, with the special [CLS] token inserted at the beginning and the special [SEP] token added at the end. All inputs are then padded to a length of 256 tokens. After feeding through BERT, we obtain the hidden state of the final layer, denoted as ($h_{1}$, $h_{2}$, ... $h_{N}$) where $N$ is the max length setting. We add a fully-connected layer and softmax on top, and the final prediction is formulated as:",
+      "where ${W}$ represents the parameter of the fully-connected layer and ${b}$ is the bias. The learning objective is to maximize",
+      "For simplicity, we assume that all tokens can be predicted independently. For model training, we set the max sequence length to 256, the learning rate to ${10^{-4}}$, and run the model for 8 epochs. We use all other default settings in the TensorFlow implementation of BERT.",
+      "UTF8gbsn",
+      "UTF8gbsn",
+      "UTF8gbsn"
+    ],
+    [
+      "At inference time, documents from the test set are segmented into paragraphs and fed into the fine-tuned BERT model one at a time. Typically, sequence labeling tasks are evaluated in terms of precision, recall, and F$_1$ at the entity level, per sentence. However, such an evaluation is inappropriate for our task because the content elements represent properties of the entire document as a whole, not individual sentences.",
+      "Instead, we adopted the following evaluation procedure: For each content element type (e.g., \u201ctenant\u201d), we extract all tagged spans from the document, and after deduplication, treat the entities as a set that we then measure against the ground truth in terms of precision, recall, and F$_1$. We do this because there may be multiple ground truth entities and BERT may mark multiple spans in a document with a particular entity type. Note that the metrics are based on exact matches\u2014this means that, for example, if the extracted entity has an extraneous token compared to a ground truth entity, the system receives no credit."
+    ],
+    [
+      "Our main results are presented in Table TABREF6 on the test set of the regulatory filings and in Table TABREF7 on the test set of the property lease agreements; F$_1$, precision, and recall are computed in the manner described above. We show metrics across all content elements (micro-averaged) as well as broken down by types. For the property lease agreements, we show results on all documents (left) and only over those with unseen templates (right). Examining these results, we see that although there is some degradation in effectiveness between all documents and only unseen templates, it appears that BERT is able to generalize to previously-unseen expressions of the content elements. Specifically, it is not the case that the model is simply memorizing fixed patterns or key phrases\u2014otherwise, we could just craft a bunch of regular expression patterns for this task. This is a nice result that shows off the power of modern neural NLP models.",
+      "Overall, we would characterize our models as achieving reasonable accuracy, comparable to extraction tasks in more \u201ctraditional\u201d domains, with modest amounts of training data. It does appear that with fine tuning, BERT is able to adapt to the linguistic characteristics of these specialized types of documents. For example, the regulatory filings have quite specialized vocabulary and the property lease agreements have numeric heading structures\u2014BERT does not seem to be confused by these elements, which for the most part do not appear in the texts that the model was pre-trained on. Naturally, accuracy varies across different content elements: For the rental agreements, entities such as leaseholder, tenant, start date, and end date perform much better than others. For the regulatory filing, the model performs well on all content elements except for one; there were very few examples of \u201c% of pledged shares in the shareholder's total share holdings\u201d in our training data, and thus accuracy is very low despite the fact that percentages are straightforward to identify. It seems that \u201ceasy\u201d entities often have more fixed forms and are quite close to entities that the model may have encountered during pre-training (e.g., names and dates). In contrast, \u201cdifficult\u201d elements are often domain-specific and widely vary in their forms.",
+      "How data efficient is BERT when fine tuning on annotated data? We can answer this question by varying the amount of training data used to fine tune the BERT models, holding everything else constant. These results are shown in Figure FIGREF10 for the regulatory filings (30, 60, 90 randomly-selected documents) and in Figure FIGREF11 for the property lease agreements (30, 60, 90, 120 randomly-selected documents); in all cases, the development set is fixed. For brevity, we only show F$_1$ scores, but we observe similar trends for the other metrics. For both document types, it seems like 60\u201390 documents are sufficient to achieve F$_1$ on par with using all available training data. Beyond this point, we hit rapidly diminishing returns. For a number of \u201ceasy\u201d content elements (e.g., dates in the property lease agreements), it seems like 30 documents are sufficient to achieve good accuracy, and more does not appear to yield substantial improvements. Note that in a few cases, training on more data actually decreases F$_1$ slightly, but this can be attributed to noise in the sampling process.",
+      "Finally, in Table TABREF8 we show an excerpt from each type of document along with the content elements that are extracted by our BERT models. We provide both the original source Chinese texts as well as English translations to provide the reader with a general sense of the source documents and how well our models behave."
+    ],
+    [
+      "All the capabilities described in this paper come together in an end-to-end cloud-based platform that we have built. The platform has two main features: First, it provides an annotation interface that allows users to define content elements, upload documents, and annotate documents; a screenshot is shown in Figure FIGREF12. We have invested substantial effort in making the interface as easy to use as possible; for example, annotating content elements is as easy as selecting text from the document. Our platform is able to ingest documents in a variety of formats, including PDFs and Microsoft Word, and converts these formats into plain text before presenting them to the annotators.",
+      "The second feature of the platform is the ability for users to upload new documents and apply inference on them using a fine-tuned BERT model; a screenshot of this feature is shown in Figure FIGREF13. The relevant content elements are highlighted in the document.",
+      "On the cloud platform, the inference module also applies a few simple rule-based modifications to post-process BERT extraction results. For any of the extracted dates, we further applied a date parser based on rules and regular expressions to normalize and canonicalize the extracted outputs. In the regulatory filings, we tried to normalize numbers that were written in a mixture of Arabic numerals and Chinese units (e.g., \u201cUTF8gbsn\u4ebf\u201d, the unit for $10^8$) and discarded partial results if simple rule-based rewrites were not successful. In the property lease agreements, the contract length, if not directly extracted by BERT, is computed from the extracted start and end dates. Note that these post processing steps were not applied in the evaluation presented in the previous section, and so the figures reported in Tables TABREF6 and TABREF7 actually under-report the accuracy of our models in a real-world setting."
+    ],
+    [
+      "This work tackles the challenge of content extraction from two types of business documents, regulatory filings and property lease agreements. The problem is straightforwardly formulated as a sequence labeling task, and we fine-tune BERT for this application. We show that our simple models can achieve reasonable accuracy with only modest amounts of training data, illustrating the power and flexibility of modern NLP models. Our cloud platform pulls these models together in an easy-to-use interface for addressing real-world business needs."
+    ]
+  ]
+}
+```
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+Name of Paper: Taking a Stance on Fake News: Towards Automatic Disinformation Assessment via Deep Bidirectional Transformer Language Models for Stance Detection
+
+Question: What are the state-of-the-art models for the task?
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+Name of Paper: A Dictionary-based Approach to Racism Detection in Dutch Social Media
+
+Question: how did they ask if a tweet was racist?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    null,
+    "Introduction",
+    "Related Research",
+    "Datasets and Annotations",
+    "Annotation Style",
+    "Training Data",
+    "Test data",
+    "Experimental Setup",
+    "Dictionaries",
+    "Preprocessing and Featurization",
+    "Performance on the Training Set",
+    "Testing the Effect of Expansion",
+    "Conclusions and Future Work",
+    "Acknowledgments",
+    "Supplementary Materials"
+  ],
+  "paragraphs": [
+    [
+      "1.1em",
+      "St\u00e9phan Tulkens, Lisa Hilte, Elise Lodewyckx, Ben Verhoeven, Walter Daelemans",
+      "CLiPS Research Center, University of Antwerp",
+      "Prinsstraat 13, 2000, Antwerpen, Belgium",
+      "{stephan.tulkens, lisa.hilte, ben.verhoeven, walter.daelemans}@uantwerpen.be,",
+      "elise.lodewyckx@student.uantwerpen.be",
+      "We present a dictionary-based approach to racism detection in Dutch social media comments, which were retrieved from two public Belgian social media sites likely to attract racist reactions. These comments were labeled as racist or non-racist by multiple annotators. For our approach, three discourse dictionaries were created: first, we created a dictionary by retrieving possibly racist and more neutral terms from the training data, and then augmenting these with more general words to remove some bias. A second dictionary was created through automatic expansion using a word2vec model trained on a large corpus of general Dutch text. Finally, a third dictionary was created by manually filtering out incorrect expansions. We trained multiple Support Vector Machines, using the distribution of words over the different categories in the dictionaries as features. The best-performing model used the manually cleaned dictionary and obtained an F-score of 0.46 for the racist class on a test set consisting of unseen Dutch comments, retrieved from the same sites used for the training set. The automated expansion of the dictionary only slightly boosted the model's performance, and this increase in performance was not statistically significant. The fact that the coverage of the expanded dictionaries did increase indicates that the words that were automatically added did occur in the corpus, but were not able to meaningfully impact performance. The dictionaries, code, and the procedure for requesting the corpus are available at: https://github.com/clips/hades.",
+      "Racism, word2vec, Dictionary-based Approaches, Computational Stylometry"
+    ],
+    [
+      "Racism is an important issue which is not easily defined, as racist ideas can be expressed in a variety of ways. Furthermore, there is no clear definition of what exactly constitutes a racist utterance; what is racist to one person is highly likely to not be considered racist universally. Additionally, although there exist mechanisms for reporting acts of racism, victims often neglect to do so as they feel that reporting the situation will not solve anything, according to Unia, the Belgian Interfederal Centre for Equal Opportunities. The scope of this issue, however, is currently unknown. Hence, the goal of our system is two-fold: it can be used to shed light on how many racist remarks are not being reported online, and furthermore, the automated detection of racism could provide interesting insights in the linguistic mechanisms used in racist discourse.",
+      "In this study, we try to automatically detect racist language in Dutch social media comments, using a dictionary-based approach. We retrieved and annotated comments from two public social media sites which were likely to attract racist reactions according to Unia. We use a Support Vector Machine to automatically classify comments, using handcrafted dictionaries, which were later expanded using automated techniques, as features.",
+      "We first discuss previous research on our subject and methodology, and discuss the problem of defining racist language (section \"Annotation Style\" ). Next, we describe our data (section \"Datasets and Annotations\" ). Finally, after discussing the experimental setup (section \"Experimental Setup\" ), we present our results (section \"Results and Discussion\" )."
+    ],
+    [
+      "The classification of racist insults presents us with the problem of giving an adequate definition of racism. More so than in other domains, judging whether an utterance is an act of racism is highly personal and does not easily fit a simple definition. The Belgian anti-racist law forbids discrimination, violence and crime based on physical qualities (like skin color), nationality or ethnicity, but does not mention textual insults based on these qualities. Hence, this definition is not adequate for our purposes, since it does not include the racist utterances one would find on social media; few utterances that people might perceive as racist are actually punishable by law, as only utterances which explicitly encourage the use of violence are illegal. For this reason, we use a common sense definition of racist language, including all negative utterances, negative generalizations and insults concerning ethnicity, nationality, religion and culture. In this, we follow paolo2015racist, bonilla2002linguistics and razavi2010offensive, who show that racism is no longer strictly limited to physical or ethnic qualities, but can also include social and cultural aspects.",
+      "Additionally, several authors report linguistic markers of racist discourse; vandijk reports that the number of available topics is greatly restricted when talking about foreigners. paolo2015racist, who performed a qualitative study of posts from Italian social media sites, shows that these chosen topics are typically related to migration, crime and economy. Furthermore, the use of stereotypes and prejudiced statements BIBREF0 , BIBREF1 , as well as a heightened occurrence of truth claims BIBREF2 , BIBREF3 , are reported as typical characteristics of racist discourse . Finally, racist utterances are said to contain specific words and phrases, i.e. n-grams, significantly more often than neutral texts, like \u201cour own kind\u201d and \u201cwhite civilization\u201d BIBREF2 , BIBREF3 .",
+      "Stylistically, racist discourse is characterized by a higher rate of certain word classes, like imperatives and adjectives and a higher noun-adjective ratio BIBREF4 , BIBREF2 , BIBREF3 . Greevy and Smeaton also report a more frequent use of modals and adverbs, which they link to the higher frequency of truth claims in racist utterances BIBREF2 , BIBREF3 . In several studies, pronoun use is reported as an important feature in the detection of racist language. While paolo2015racist reports a high frequency of (especially first person plural) pronouns in racist data, vandijk reports a more general finding: the importance of us and them constructions in racist discourse. He explains that they involve a `semantic move with a positive part about Us and a negative part about Them' BIBREF5 . Using such constructions, one linguistically emphasizes - either deliberately or subconsciously - a divide between groups of people. A strict interpretation implies that even positive utterances about `them' can be perceived as racist, as they can also imply a divide between us and them. In this sense, Van Dijk's definition of racism is subtler, but also broader, than the definition used in our own research: we only count negative utterances and generalizations about groups of people as racist.",
+      "Our dictionary-based approach is inspired by methods used in previous research, like LIWC (Linguistic Inquiry and Word Count) BIBREF6 . LIWC is a dictionary-based computational tool that counts word frequencies for both grammatical categories (e.g. pronouns) and content-related categories (e.g. negative emotion words). As LIWC uses counts per category instead of individual words' frequencies, it allows for broader generalizations on functionally or semantically related words.",
+      "The construction of dictionary categories related to racist discourse (cf. section \"Dictionaries\" ) is largely based on linguistic properties of racist language reported in earlier work (see above). Additionally, the categories were adjusted to fit the corpus used in the research, which differs from corpora used in other studies. As our corpus is retrieved from social media sites with an anti-Islamic orientation, we added categories to reflect anti-religious sentiment. The relevant features in this study therefore differ from those reported in other studies, as different words are used to insult different groups of people BIBREF3 .",
+      "Finally, some other successful quantitative approaches to racism detection that have been used in earlier studies are a bag of words (BoW) approach as well as the analysis of part-of-speech (PoS) tags BIBREF2 , BIBREF3 . We leave the addition of these features to future work."
+    ],
+    [
+      "In this section, we describe our data collection, our annotation guidelines ( \"Annotation Style\" ) and the results of our annotations ( \"Conclusions and Future Work\" and \"Test data\" ).",
+      "For our current research we collected a corpus of social media comments, consisting of comments retrieved from Facebook sites which were likely to attract racist reactions in their comments. We specifically targeted two sites: the site of a prominent Belgian anti-Islamic organization, and the site of a Belgian right-wing organization. In both cases the Facebook sites were officially condoned by the organizations, and in the first case served as a communication platform to organize political gatherings. While both sites, the former more than the latter, explicitly profess to be non-racist, the comments they attracted were still highly critical of foreigners and, predictably, Muslims. This is also the reason we mined comments from these sites, and not the posts themselves. While the narrow focus of the sites introduces bias into our data, as the opinions of the people visiting these sites will not reflect the opinions of the general population, they do contain a good proportion of racist to non-racist data."
+    ],
+    [
+      "We annotated the retrieved comments with three different labels: `racist', `non-racist' and `invalid'.",
+      "The `racist' label describes comments that contain negative utterances or insults about someone's ethnicity, nationality, religion or culture. This definition also includes utterances which equate, for example, an ethnic group to an extremist group, as well as extreme generalizations. The following examples are comments that were classified as racist:",
+      "Het zijn precies de vreemden die de haat of het racisme opwekken bij de autochtonen.",
+      "It is the foreigners that elicit hate and racism from natives.",
+      "Kan je niets aan doen dat je behoort tot het ras dat nog minder verstand en gevoelens heeft in uw hersenen dan het stinkend gat van een VARKEN ! :-p",
+      "You cannot help the fact that you belong to the race that has less intellect and sense in their brains than the smelly behind of a PIG! :-P",
+      "Wil weer eens lukken dat wij met het vuilste krapuul zitten, ik verschiet er zelfs niet van!",
+      "Once again we have to put up with the filthiest scum, it doesn't even surprise me anymore!",
+      "The label `invalid' was used for comments that were written in languages other than Dutch, or that did not contain any textual information, i.e. comments that solely consist of pictures or links. Before classification, we excluded these from both our training and test set.",
+      "The final label, `non-racist', was the default label. If a comment was valid, but could not be considered racist according to our definition, this was the label we used."
+    ],
+    [
+      "To collect the training data, we used Pattern BIBREF7 to scrape the 100 most recent posts from both sites, and then extracted all comments which reacted to these comments. This resulted in 5759 extracted comments: 4880 from the first site and 879 from the second site. The second site attracted a lot less comments on each post, possibly because the site posted more frequently. In addition to this, the organization behind the first site had been figuring prominently in the news at the time of extraction, which might explain the divide in frequency of comments between the two sites. The corpus was annotated by two annotators, who were both students of comparable age and background. When A and B did not agree on a label, a third annotator, C, was used as a tiebreaker in order to obtain gold-standard labels. Table 1 shows the gold standard for the training set.",
+      "We calculated inter-annotator agreement using the Kappa score ( $\\kappa $ ) BIBREF8 . On the training corpus, the agreement score was $\\kappa $ = 0.60. Annotator A used the racist tag much less often than annotator B. Interestingly, the agreement remains relatively high; 79% of the comments that A annotated as racist were also annotated as racist by B. Even though B was much more inclined to call utterances racist, A and B still shared a common ground regarding their definition of racism. Examining the comments in detail, we found that the difference can largely be explained by sensitivity to insults and generalizations, as example 4 shows.",
+      "Oprotten die luizegaards [sic] !!!",
+      "Throw those lice carriers out!",
+      "While annotator B considers this utterance to be racist, annotator A does not, as it does not contain a specific reference to an ethnicity, nationality or religion. That is, when not seen in the context of this specific annotation task this sentence would not necessarily be called racist, just insulting."
+    ],
+    [
+      "The test corpus was mined in the same way as the training set, at a different point in time. We mined the first 500 and first 116 comments from the first and second site, respectively, which makes the proportion between sites more or less identical to the the proportions in the train corpus. The annotation scheme was identical to the one for the train set, with the difference that C, who previously performed the tiebreak, now became a regular annotator. The first 25% of each batch of comments, i.e. 125 comments for the first site and 30 comments for the second site, were annotated by all three annotators to compute inter-annotator agreement. The remaining comments were equally divided among annotators. The annotator agreement was $\\kappa $ = 0.54 (pairwise average), which is lower than the agreement on the training data. The reason for the lower agreement was that annotator C often did not agree with A and B. Because the pattern of mismatches between the annotators is quite regular, we will now discuss some of the annotations in detail:",
+      "we kunnen niet iedereen hier binnen laten want dat betekend [sic] het einde van de europese beschaving We cannot let everyone in because that will mean the end of European civilization",
+      "Eigen volk gaat voor, want die vuile manieren van de EU moeten wij vanaf. Geen EU en geen VN. Waardeloos en tegen onze mensen. (eigen volk.)",
+      "Put our own people first, because we need to get rid of the foul manners of the EU. No EU nor UN. Useless and against our people. (own folk.)",
+      "Burgemeester Termont is voor de zwartzakken die kiezen voor hem",
+      "Mayor Termont supports the black sacks, as they vote for him",
+      "Annotator C used the `racist' tag more often, which is probably due to the fact that he consistently annotated overt ideological statements related to immigration as `racist', while the other annotators did not. The three examples mentioned above are utterances that C classified as `racist', but A and B classified as `not racist'.",
+      "The cause of these consistent differences in annotations might be cultural, as C is from the southern part of the Netherlands, whereas A and B are native to the northern part of Belgium. Some terms are simply misannotated by C because they are Flemish vernacular expressions. For example, zwartzak [black sack], from sentence 7, superficially looks like a derogatory term for a person of color, but actually does not carry this meaning, as it is a slang word for someone who collaborated with the German occupying forces in the Second World War. While this could still be classified as being racist, the point is that C only registered this as a slang word based on skin color, and not a cultural or political term. Finally, it is improbable that the cause of these mismatches is annotator training, as A and B did not discuss their annotations during the task. In addition to this, C functioned as a tiebreaker in the first dataset, and thus already had experience with the nature of the training material."
+    ],
+    [
+      "In this section, we describe our experimental setup. We will first discuss our dictionary-based approach, describing both the LIWC dictionary we used as well as the construction of dictionaries related to racist discourse (section \"Dictionaries\" ). Next, we will describe the preprocessing of the data (section \"Preprocessing and Featurization\" )."
+    ],
+    [
+      "In our classification task, we will use the LIWC dictionaries for Dutch BIBREF9 . We hypothesize that some of LIWC's word categories can be useful in detecting (implicit) racist discourse, as some of these categories are associated with markers of racist discourse reported in previous research (cf. section \"Annotation Style\" ), including pronouns, negative emotion words, references to others, certainty, religion and curse words.",
+      "In addition to the Dutch LIWC data, we created a dictionary containing words that specifically relate to racist discourse. We expect a dictionary-based approach in which words are grouped into categories to work well in this case because many of the racist terms used in our corpus were neologisms and hapaxes, like halalhoer (halal prostitute). Alternatively, existing terms are often reused in a ridiculing fashion, e.g. using the word mossel (mussel) to refer to Muslims. The dictionary was created as follows: after annotation, terms pertaining to racist discourse were manually extracted from the training data. These were then grouped into different categories, where most categories have both a neutral and a negative subcategory. The negative subcategory contains explicit insults, while the neutral subcategory contains words that are normally used in a neutral fashion, e.g. zwart (black), Marokkaan (Moroccan), but which might also be used in a more implicit racist discourse; e.g. people that often talk about nationalities or skin color might be participating in a racist us and them discourse. An overview of the categories can be found in Table 2 .",
+      "After creating the dictionary, we expanded these word lists both manually and automatically. First, we manually added an extensive list of countries, nationalities and languages, to remove some of the bias present in our training corpus. To combat sparsity, and to catch productive compounds which are likely to be used in a racist manner, we added wildcards to the beginning or end of certain words. We used two different wildcards. * is an inclusive wildcard; it matches the word with or without any affixes, e.g. moslim* matches both moslim (Muslim) and moslims (Muslims). + is an exclusive wildcard; it only matches words when an affix is attached, e.g. +moslim will match rotmoslim (Rotten Muslim) but not moslim by itself. In our corpus (which is skewed towards racism), the + will almost always represent a derogatory prefix, which is why it figures more prominently in the negative part of our dictionary.",
+      "A downside of using dictionaries for the detection of racism, is that they do not include a measure of context. Therefore, a sentence such as \u201cMy brother hated the North African brown rice and lentils we made for dinner\u201d will be classified as racist, regardless of the fact that the words above do not occur in a racist context. Approaches based on word unigrams or bigrams face similar problems. This problem is currently partially absolved by the fact that we are working with a corpus skewed towards racism: words like `brown' and `African' are more likely to be racist words in our corpus than in general text.",
+      "To broaden the coverage of the categories in our dictionary, we performed dictionary expansion on both the neutral and the negative categories using word2vec BIBREF10 . word2vec is a collection of models capable of capturing semantic similarity between words based on the sentential contexts in which these words occur. It does so by projecting words into an n-dimensional space, and giving words with similar contexts similar places in this space. Hence, words which are closer to each other as measured by cosine distance, are more similar. Because we observed considerable semantic variation in the insults in our corpus, we expect that dictionary expansion using word2vec will lead to the extraction of previously unknown insults, as we assume that similar insults are used in similar contexts. In parallel, we know that a lot of words belonging to certain semantic categories, such as diseases and animals, can almost invariably be used as insults.",
+      "The expansion proceeded as follows: for each word in the dictionary, we retrieved the five closest words, i.e. the five most similar words, in the n-dimensional space, and added these to the dictionary. Wildcards were not taken into account for this task, e.g. *jood was replaced by jood for the purposes of expansion. As such, the expanded words do not have any wildcards attached to them. For expansion we used the best-performing model from tulkens2016, which is based on a corpus of 3.9 billion words of general Dutch text. Because this word2vec model was trained on general text, the semantic relations contained therein are not based on racist or insulting text, which will improve the coverage of our expanded categories.",
+      "After expansion, we manually searched the expanded dictionaries and removed obviously incorrect items. Because the word2vec model also includes some non-Dutch text, e.g. Spanish, some categories were expanded incorrectly. As a result, we have 3 different dictionaries with which we perform our experiments: the original dictionary which was based on the training data, a version which was expanded using word2vec, and a cleaned version of this expanded version. The word frequencies of the dictionaries are given in Table 3 . An example of expansion is given in Table 4 ."
+    ],
+    [
+      "For preprocessing, the text was first tokenized using the Dutch tokenizer from Pattern BIBREF7 , and then lowercased and split on whitespace, which resulted in lists of words which are appropriate for lexical processing.",
+      "Our dictionary-based approach, like LIWC, creates an n-dimensional vector of normalized and scaled numbers, where n is the number of dictionary categories. These numbers are obtained by dividing the frequency of words in every specific category by the total number of words in the comment. Because all features are already normalized and scaled, there was no need for further scaling. Furthermore, because the number of features is so small, we did not perform explicit feature selection."
+    ],
+    [
+      "We estimated the optimal values for the SVM parameters by an exhaustive search through the parameter space, which led to the selection of an RBF kernel with a C value of 1 and a gamma of 0. For the SVM and other experiments, we used the implementation from Scikit-Learn BIBREF11 . Using cross-validation on the training data, all dictionary-based approaches with lexical categories related to racist discourse significantly outperformed models using only LIWC's general word categories. Since the current research concerns the binary classification of racist utterances, we only report scores for the positive class, i.e. the racist class. When only LIWC-categories were used as features, an F-score of 0.34 (std. dev. 0.07) was obtained for the racist class. When using the original discourse dictionary, we reached an F-score of 0.50 (std. dev. 0.05). Automatic expansion of the categories did not influence performance either (F-score 0.50, std. dev. 0.05). Similar results (0.49 F-score, std. dev. 0.05) were obtained when the expanded racism dictionaries were manually filtered. This result is not surprising, as the original dictionaries were created from the training data, and might form an exhaustive catalog of racist terms in the original corpus.",
+      "Combining the features generated by LIWC with the specific dictionary-based features led to worse results compared to the dictionary-based features by themselves (F-score 0.40, std. dev. 0.07 for the best-performing model).",
+      "Finally, all models based on the dictionary features as well as the combined model outperformed a unigram baseline of 0.36, but the LIWC model did not. We also report a weighted random baseline (WRB), which was outperformed by all models."
+    ],
+    [
+      "As seen above, the performance of the different models on the train set was comparable, regardless of their expansion. This is due to the creation procedure for the dictionary: because the words in the original dictionary were directly retrieved from the training data, the expanded and cleaned versions might not be able to demonstrate their generalization performance, as most of the racist words from the training data will be included in the original dictionaries as well as the expanded dictionaries. This artifact might disappear in the test set, which was retrieved from the same two sites, but will most likely contain unseen words. These unseen words will not be present in the original dictionary, but could be present in the expanded version.",
+      "As Table 6 shows, the models obtain largely comparable performance on the test set, and outperform the unigram baseline by a wide margin.",
+      "In comparison to previous research, our approach leads to worse results than those of greevy2004text, who report a precision score of 0.93 and a recall score of 0.87, using an SVM with BOW features together with frequency-based term weights. It is, however, difficult to compare these scores to our performance, given that the data, method, and language differ.",
+      "Our best-performing model was based on the expanded and cleaned version of the dictionary, but this model only slightly outperformed the other models. Additionally, we also computed Area Under the Receiving Operator Characteristic Curve (ROC-AUC) scores for all models, also shown in Table 6 . ROC-AUC shows the probability of ranking a randomly chosen positive instance above a randomly chosen negative instance, thereby giving an indication of the overall performance of the models. This shows that all dictionaries have comparable AUC scores, and that each dictionary outperforms the unigram baseline. To obtain additional evidence, we computed the statistical significance of performance differences between the models based on the dictionaries and unigram baseline model using approximate randomization testing (ART) BIBREF12 . An ART test between dictionary models reveals that none of the models had performance differences that were statistically significant. Similarly, all dictionary models outperformed the unigram baseline with statistical significance, with $p$ $<$ 0.01 for the models based on the cleaned and expanded dictionaries, and $p$ $<$ 0.05 for the models based on the original dictionary.",
+      "To get more insight into why the expanded models were not more successful, we calculated dictionary coverage for every dictionary separately on the test set. If the expanded dictionaries do not have increased coverage, the reason for their similar performance is clear: not enough words have been added to affect the performance in any reasonable way. As Table 7 indicates, the coverage of the expanded dictionaries did increase, which indicates that the automated expansion, or manual deletion for that matter, contrary to expectations, did not add words that were useful for the classification of racist content. To obtain additional evidence for this claim, we looked at the number of comments that contained words from the original, cleaned and expanded dictionaries. The coverage in terms of total comments also increased, as well as the absolute number of racist comments that contained the added terms. Because the coverage in number of comments did not increase the performance of the dictionaries, we hypothesize that the terms that were included in the expanded dictionaries were not distributed clearly enough (over racist and neutral texts) to make a difference in the performance on the classification task."
+    ],
+    [
+      "We developed a dictionary-based computational tool for automatic racism detection in Dutch social media comments. These comments were retrieved from public social media sites with an anti-Islamic orientation. The definition of racism we used to annotate the comments therefore includes religious and cultural racism as well, a phenomenon reported on in different studies BIBREF4 , BIBREF13 , BIBREF14 .",
+      "We use a Support Vector Machine to classify comments as racist or not based on the distribution of the comments' words over different word categories related to racist discourse. To evaluate the performance, we used our own annotations as gold standard. The best-performing model obtained an F-score of 0.46 for the racist class on the test set, which is an acceptable decrease in performance compared to cross-validation experiments on the training data (F-score 0.49, std. dev. 0.05). The dictionary used by the model was manually created by retrieving possibly racist and more neutral terms from the training data during annotation. The dictionary was then manually expanded, automatically expanded with a word2vec model and finally manually cleaned, i.e. irrelevant terms that were added automatically were removed. It did not prove useful to use general stylistic or content-based word categories along with the word lists specifically related to racist discourse.",
+      "Surprisingly, the expansion of the manually crafted dictionary did not boost the model's performance significantly. In (cross-validated) experiments on the training data, this makes sense, as the words in the different categories are retrieved from the training data itself, artificially making the dictionary very appropriate for the task. In the test runs, however, a better result could be expected from the generalized word lists. The expanded versions of the dictionary had higher overall coverage for the words in the corpus, as well as higher coverage in number of comments and in number of racist comments. This shows that the words that were automatically added, did indeed occur in our corpus. As the model's performance more or less stagnated when using the expanded categories compared to the original ones, we hypothesize that the terms that were automatically added by the word2vec model were irrelevant to the task of discriminating between racist and neutral texts.",
+      "In terms of future work, we will expand our research efforts to include more general social media text. Because we currently only use material which was gathered from sites skewed towards racism, the performance of our dictionary might have been artificially heightened, as the words in the dictionary only occur in racist contexts in our corpus. Therefore, including more general social media texts will serve as a good test of the generality of our dictionaries with regards to detecting insulting material."
+    ],
+    [
+      "We are very grateful towards Leona Erens and Fran\u00e7ois Deleu from Unia for wanting to collaborate with us and for pointing us towards the necessary data. We thank the three anonymous reviewers for their helpful comments and advice."
+    ],
+    [
+      "The supplementary materials are available at https://github.com/clips/hades"
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2398/instruction.md b/qasper-2398/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..6027ecc03aa532681addd1e779235589742b02d5
--- /dev/null
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@@ -0,0 +1,120 @@
+Name of Paper: Neural Factor Graph Models for Cross-lingual Morphological Tagging
+
+Question: What other cross-lingual approaches is the model compared to?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Problem Formulation",
+    "Baseline: Tag Set Prediction",
+    "A Relaxation: Tag-wise Prediction",
+    "Neural Factor Graph Model",
+    "Neural Factors",
+    "Pairwise Factors",
+    "Transition Factors",
+    "Language-Specific Weights",
+    "Loopy Belief Propagation",
+    "Learning and Decoding",
+    "Dataset",
+    "Baseline Tagger",
+    "Training Regimen",
+    "Main Results",
+    "What is the Model Learning?",
+    "Related Work",
+    "Conclusion and Future Work",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "Morphological analysis (hajivc1998tagging, oflazer1994tagging, inter alia) is the task of predicting fine-grained annotations about the syntactic properties of tokens in a language such as part-of-speech, case, or tense. For instance, in Figure FIGREF2 , the given Portuguese sentence is labeled with the respective morphological tags such as Gender and its label value Masculine.",
+      "The accuracy of morphological analyzers is paramount, because their results are often a first step in the NLP pipeline for tasks such as translation BIBREF1 , BIBREF2 and parsing BIBREF3 , and errors in the upstream analysis may cascade to the downstream tasks. One difficulty, however, in creating these taggers is that only a limited amount of annotated data is available for a majority of the world's languages to learn these morphological taggers. Fortunately, recent efforts in morphological annotation follow a standard annotation schema for these morphological tags across languages, and now the Universal Dependencies Treebank BIBREF0 has tags according to this schema in 60 languages.",
+      "cotterell2017crossling have recently shown that combining this shared schema with cross-lingual training on a related high-resource language (HRL) gives improved performance on tagging accuracy for low-resource languages (LRLs). The output space of this model consists of tag sets such as {POS: Adj, Gender: Masc, Number: Sing}, which are predicted for a token at each time step. However, this model relies heavily on the fact that the entire space of tag sets for the LRL must match those of the HRL, which is often not the case, either due to linguistic divergence or small differences in the annotation schemes between the two languages. For instance, in Figure FIGREF2 \u201crefrescante\u201d is assigned a gender in the Portuguese UD treebank, but not in the Spanish UD treebank.",
+      "In this paper, we propose a method that instead of predicting full tag sets, makes predictions over single tags separately but ties together each decision by modeling variable dependencies between tags over time steps (e.g. capturing the fact that nouns frequently occur after determiners) and pairwise dependencies between all tags at a single time step (e.g. capturing the fact that infinitive verb forms don't have tense). The specific model is shown in Figure FIGREF4 , consisting of a factorial conditional random field (FCRF; sutton2007dynamic) with neural network potentials calculated by long short-term memory (LSTM; BIBREF4 ) at every variable node (\u00a7 SECREF3 ). Learning and inference in the model is made tractable through belief propagation over the possible tag combinations, allowing the model to consider an exponential label space in polynomial time (\u00a7 SECREF24 ).",
+      "This model has several advantages:",
+      "In the following sections, we describe the model and these results in more detail."
+    ],
+    [
+      "Formally, we define the problem of morphological analysis as the task of mapping a length- INLINEFORM0 string of tokens INLINEFORM1 into the target morphological tag sets for each token INLINEFORM2 . For the INLINEFORM3 th token, the target label INLINEFORM4 defines a set of tags (e.g. {Gender: Masc, Number: Sing, POS: Verb}). An annotation schema defines a set INLINEFORM5 of INLINEFORM6 possible tag types and with the INLINEFORM7 th type (e.g. Gender) defining its set of possible labels INLINEFORM8 (e.g. {Masc, Fem, Neu}) such that INLINEFORM9 . We must note that not all tags or attributes need to be specified for a token; usually, a subset of INLINEFORM10 is specified for a token and the remaining tags can be treated as mapping to a INLINEFORM11 value. Let INLINEFORM12 denote the set of all possible tag sets."
+    ],
+    [
+      "Data-driven models for morphological analysis are constructed using training data INLINEFORM0 consisting of INLINEFORM1 training examples. The baseline model BIBREF5 we compare with regards the output space of the model as a subset INLINEFORM2 where INLINEFORM3 is the set of all tag sets seen in this training data. Specifically, they solve the task as a multi-class classification problem where the classes are individual tag sets. In low-resource scenarios, this indicates that INLINEFORM4 and even for those tag sets existing in INLINEFORM5 we may have seen very few training examples. The conditional probability of a sequence of tag sets given the sentence is formulated as a 0th order CRF. DISPLAYFORM0 ",
+      "Instead, we would like to be able to generate any combination of tags from the set INLINEFORM0 , and share statistical strength among similar tag sets."
+    ],
+    [
+      "As an alternative, we could consider a model that performs prediction for each tag's label INLINEFORM0 independently. DISPLAYFORM0 ",
+      "This formulation has an advantage: the tag-predictions within a single time step are now independent, it is now easy to generate any combination of tags from INLINEFORM0 . On the other hand, now it is difficult to model the interdependencies between tags in the same tag set INLINEFORM1 , a major disadvantage over the previous model. In the next section, we describe our proposed neural factor graph model, which can model not only dependencies within tags for a single token, but also dependencies across time steps while still maintaining the flexibility to generate any combination of tags from INLINEFORM2 ."
+    ],
+    [
+      "Due to the correlations between the syntactic properties that are represented by morphological tags, we can imagine that capturing the relationships between these tags through pairwise dependencies can inform the predictions of our model. These dependencies exist both among tags for the same token (intra-token pairwise dependencies), and across tokens in the sentence (inter-token transition dependencies). For instance, knowing that a token's POS tag is a Noun, would strongly suggest that this token would have a INLINEFORM0 label for the tag Tense, with very few exceptions BIBREF6 . In a language where nouns follow adjectives, a tag set prediction {POS: Adj, Gender: Fem} might inform the model that the next token is likely to be a noun and have the same gender. The baseline model can not explicitly model such interactions given their factorization in equation EQREF10 .",
+      "To incorporate the dependencies discussed above, we define a factorial CRF BIBREF7 , with pairwise links between cotemporal variables and transition links between the same types of tags. This model defines a distribution over the tag-set sequence INLINEFORM0 given the input sentence INLINEFORM1 as, DISPLAYFORM0 ",
+      "where INLINEFORM0 is the set of factors in the factor graph (as shown in Figure FIGREF4 ), INLINEFORM1 is one such factor, and INLINEFORM2 is the assignment to the subset of variables neighboring factor INLINEFORM3 . We define three types of potential functions: neural INLINEFORM4 , pairwise INLINEFORM5 , and transition INLINEFORM6 , described in detail below."
+    ],
+    [
+      "The flexibility of our formulation allows us to include any form of custom-designed potentials in our model. Those for the neural factors have a fairly standard log-linear form, DISPLAYFORM0 ",
+      "except that the features INLINEFORM0 are themselves given by a neural network. There is one such factor per variable. We obtain our neural factors using a biLSTM over the input sequence INLINEFORM1 , where the input word embedding for each token is obtained from a character-level biLSTM embedder. This component of our model is similar to the model proposed by BIBREF5 . Given an input token INLINEFORM2 , we compute an input embedding INLINEFORM3 as, DISPLAYFORM0 ",
+      "Here, INLINEFORM0 is a character-level LSTM function that returns the last hidden state. This input embedding INLINEFORM1 is then used in the biLSTM tagger to compute an output representation INLINEFORM2 . Finally, the scores INLINEFORM3 are obtained as, DISPLAYFORM0 ",
+      "We use a language-specific linear layer with weights INLINEFORM0 and bias INLINEFORM1 ."
+    ],
+    [
+      "As discussed previously, the pairwise factors are crucial for modeling correlations between tags. The pairwise factor potential for a tag INLINEFORM0 and tag INLINEFORM1 at timestep INLINEFORM2 is given in equation EQREF20 . Here, the dimension of INLINEFORM3 is INLINEFORM4 . These scores are used to define the neural factors as, DISPLAYFORM0 "
+    ],
+    [
+      "Previous work has experimented with the use of a linear chain CRF with factors from a neural network BIBREF8 for sequence tagging tasks. We hypothesize that modeling transition factors in a similar manner can allow the model to utilize information about neighboring tags and capture word order features of the language. The transition factor for tag INLINEFORM0 and timestep INLINEFORM1 is given below for variables INLINEFORM2 and INLINEFORM3 . The dimension of INLINEFORM4 is INLINEFORM5 . DISPLAYFORM0 ",
+      "In our experiments, INLINEFORM0 and INLINEFORM1 are simple indicator features for the values of tag variables with no dependence on INLINEFORM2 ."
+    ],
+    [
+      "As an enhancement to the information encoded in the transition and pairwise factors, we experiment with training general and language-specific parameters for the transition and the pairwise weights. We define the weight matrix INLINEFORM0 to learn the general trends that hold across both languages, and the weights INLINEFORM1 to learn the exceptions to these trends. In our model, we sum both these parameter matrices before calculating the transition and pairwise factors. For instance, the transition weights INLINEFORM2 are calculated as INLINEFORM3 ."
+    ],
+    [
+      "Since the graph from Figure FIGREF4 is a loopy graph, performing exact inference can be expensive. Hence, we use loopy belief propagation BIBREF9 , BIBREF10 for computation of approximate variable and factor marginals. Loopy BP is an iterative message passing algorithm that sends messages between variables and factors in a factor graph. The message updates from variable INLINEFORM0 , with neighboring factors INLINEFORM1 , to factor INLINEFORM2 is DISPLAYFORM0 ",
+      "The message from factor INLINEFORM0 to variable INLINEFORM1 is DISPLAYFORM0 ",
+      "where INLINEFORM0 denote an assignment to the subset of variables adjacent to factor INLINEFORM1 , and INLINEFORM2 is the assignment for variable INLINEFORM3 . Message updates are performed asynchronously in our model. Our message passing schedule was similar to that of foward-backward: the forward pass sends all messages from the first time step in the direction of the last. Messages to/from pairwise factors are included in this forward pass. The backward pass sends messages in the direction from the last time step back to the first. This process is repeated until convergence. We say that BP has converged when the maximum residual error BIBREF11 over all messages is below some threshold. Upon convergence, we obtain the belief values of variables and factors as, DISPLAYFORM0 ",
+      " where INLINEFORM0 and INLINEFORM1 are normalization constants ensuring that the beliefs for a variable INLINEFORM2 and factor INLINEFORM3 sum-to-one. In this way, we can use the beliefs as approximate marginal probabilities."
+    ],
+    [
+      "We perform end-to-end training of the neural factor graph by following the (approximate) gradient of the log-likelihood INLINEFORM0 . The true gradient requires access to the marginal probabilities for each factor, e.g. INLINEFORM1 where INLINEFORM2 denotes the subset of variables in factor INLINEFORM3 . For example, if INLINEFORM4 is a transition factor for tag INLINEFORM5 at timestep INLINEFORM6 , then INLINEFORM7 would be INLINEFORM8 and INLINEFORM9 . Following BIBREF7 , we replace these marginals with the beliefs INLINEFORM10 from loopy belief propagation. Consider the log-likelihood of a single example INLINEFORM11 . The partial derivative with respect to parameter INLINEFORM12 for each type of factor INLINEFORM13 is the difference of the observed features with the expected features under the model's (approximate) distribution as represented by the beliefs: INLINEFORM14 ",
+      " where INLINEFORM0 denotes all the factors of type INLINEFORM1 , and we have omitted any dependence on INLINEFORM2 and INLINEFORM3 for brevity\u2014 INLINEFORM4 is accessible through the factor index INLINEFORM5 . For the neural network factors, the features are given by a biLSTM. We backpropagate through to the biLSTM parameters using the partial derivative below, INLINEFORM6 ",
+      " where INLINEFORM0 is the variable belief corresponding to variable INLINEFORM1 .",
+      "To predict a sequence of tag sets INLINEFORM0 at test time, we use minimum Bayes risk (MBR) decoding BIBREF13 , BIBREF14 for Hamming loss over tags. For a variable INLINEFORM1 representing tag INLINEFORM2 at timestep INLINEFORM3 , we take DISPLAYFORM0 ",
+      "where INLINEFORM0 ranges over the possible labels for tag INLINEFORM1 ."
+    ],
+    [
+      "We used the Universal Dependencies Treebank UD v2.1 BIBREF0 for our experiments. We picked four low-resource/high-resource language pairs, each from a different family: Danish/Swedish (da/sv), Russian/Bulgarian (ru/bg), Finnish/Hungarian (fi/hu), Spanish/Portuguese (es/pt). Picking languages from different families would ensure that we obtain results that are on average consistent across languages.",
+      "The sizes of the training and evaluation sets are specified in Table TABREF31 . In order to simulate low-resource settings, we follow the experimental procedure from BIBREF5 . We restrict the number of sentences of the target language ( INLINEFORM0 ) in the training set to 100 or 1000 sentences. We also augment the tag sets in our training data by adding a INLINEFORM1 label for all tags that are not seen for a token. It is expected that our model will learn which tags are unlikely to occur given the variable dependencies in the factor graph. The dev set and test set are only in the target language. From Table TABREF32 , we can see there is also considerable variance in the number of unique tags and tag sets found in each of these language pairs."
+    ],
+    [
+      "As the baseline tagger model, we re-implement the specific model from BIBREF5 that uses a language-specific softmax layer. Their model architecture uses a character biLSTM embedder to obtain a vector representation for each token, which is used as input in a word-level biLSTM. The output space of their model is all the tag sets seen in the training data. This work achieves strong performance on several languages from UD on the task of morphological tagging and is a strong baseline."
+    ],
+    [
+      "We followed the parameter settings from BIBREF5 for the baseline tagger and the neural component of the FCRF-LSTM model. For both models, we set the input embedding and linear layer dimension to 128. We used 2 hidden layers for the LSTM where the hidden layer dimension was set to 256 and a dropout BIBREF15 of 0.2 was enforced during training. All our models were implemented in the PyTorch toolkit BIBREF16 . The parameters of the character biLSTM and the word biLSTM were initialized randomly. We trained the baseline models and the neural factor graph model with SGD and Adam respectively for 10 epochs each, in batches of 64 sentences. These optimizers gave the best performances for the respective models.",
+      "For the FCRF, we initialized transition and pairwise parameters with zero weights, which was important to ensure stable training. We considered BP to have reached convergence when the maximum residual error was below 0.05 or if the maximum number of iterations was reached (set to 40 in our experiments). We found that in cross-lingual experiments, when INLINEFORM0 , the relatively large amount of data in the HRL was causing our model to overfit on the HRL and not generalize well to the LRL. As a solution to this, we upsampled the LRL data by a factor of 10 when INLINEFORM1 for both the baseline and the proposed model.",
+      "Previous work on morphological analysis BIBREF5 , BIBREF17 has reported scores on average token-level accuracy and F1 measure. The average token level accuracy counts a tag set prediction as correct only it is an exact match with the gold tag set. On the other hand, F1 measure is measured on a tag-by-tag basis, which allows it to give partial credit to partially correct tag sets. Based on the characteristics of each evaluation measure, Accuracy will favor tag-set prediction models (like the baseline), and F1 measure will favor tag-wise prediction models (like our proposed method). Given the nature of the task, it seems reasonable to prefer getting some of the tags correct (e.g. Noun+Masc+Sing becomes Noun+Fem+Sing), instead of missing all of them (e.g. Noun+Masc+Sing becomes Adj+Fem+Plur). F-score gives partial credit for getting some of the tags correct, while tagset-level accuracy will treat these two mistakes equally. Based on this, we believe that F-score is intuitively a better metric. However, we report both scores for completeness."
+    ],
+    [
+      "First, we report the results in the case of monolingual training in Table TABREF33 . The first row for each language pair reports the results for our reimplementation of cotterell2017crossling, and the second for our full model. From these results, we can see that we obtain improvements on the F-measure over the baseline method in most experimental settings except BG with INLINEFORM0 . In a few more cases, the baseline model sometimes obtains higher accuracy scores for the reason described in UID38 .",
+      "In our cross-lingual experiments shown in Table TABREF37 , we also note F-measure improvements over the baseline model with the exception of DA/SV when INLINEFORM0 . We observe that the improvements are on average stronger when INLINEFORM1 . This suggests that our model performs well with very little data due to its flexibility to generate any tag set, including those not observed in the training data. The strongest improvements are observed for FI/HU. This is likely because the number of unique tags is the highest in this language pair and our method scales well with the number of tags due to its ability to make use of correlations between the tags in different tag sets.",
+      "To examine the utility of our transition and pairwise factors, we also report results on ablation experiments by removing transition and pairwise factors completely from the model in Table TABREF40 . Ablation experiments for each factor showed decreases in scores relative to the model where both factors are present, but the decrease attributed to the pairwise factors is larger, in both the monolingual and cross-lingual cases. Removing both factors from our proposed model results in a further decrease in the scores. These differences were found to be more significant in the case when INLINEFORM0 .",
+      "Upon looking at the tag set predictions made by our model, we found instances where our model utilizes variable dependencies to predict correct labels. For instance, for a specific phrase in Portuguese (um estado), the baseline model predicted {POS: Det, Gender: Masc, Number: Sing} INLINEFORM0 , {POS: Noun, Gender: Fem (X), Number: Sing} INLINEFORM1 , whereas our model was able to get the gender correct because of the transition factors in our model."
+    ],
+    [
+      "One of the major advantages of our model is the ability to interpret what the model has learned by looking at the trained parameter weights. We investigated both language-generic and language-specific patterns learned by our parameters:",
+      "Language-Generic: We found evidence for several syntactic properties learned by the model parameters. For instance, in Figure FIGREF42 , we visualize the generic ( INLINEFORM0 ) transition weights of the POS tags in Ru/Bg. Several universal trends such as determiners and adjectives followed by nouns can be seen. In Figure FIGREF43 , we also observed that infinitive has a strong correlation for NULL tense, which follows the universal phenomena that infinitives don't have tense.",
+      "Language Specific Trends: We visualized the learnt language-specific weights and looked for evidence of patterns corresponding to linguistic phenomenas observed in a language of interest. For instance, in Russian, verbs are gender-specific in past tense but not in other tenses. To analyze this, we plotted pairwise weights for Gender/Tense in Figure FIGREF45 and verified strong correlations between the past tense and all gender labels."
+    ],
+    [
+      "There exist several variations of the task of prediction of morphological information from annotated data: paradigm completion BIBREF18 , BIBREF19 , morphological reinflection BIBREF20 , segmentation BIBREF21 , BIBREF22 and tagging. Work on morphological tagging has broadly focused on structured prediction models such as CRFs, and neural network models. Amongst structured prediction approaches, BIBREF23 proposed a factor-graph based model that performed joint morphological tagging and parsing. BIBREF24 , BIBREF25 proposed the use of a higher-order CRF that is approximated using coarse-to-fine decoding. BIBREF26 proposed joint lemmatization and tagging using this framework. BIBREF27 was the first work that performed experiments on multilingual morphological tagging. They proposed an exponential model and the use of a morphological dictionary. BIBREF17 , BIBREF28 proposed a model that used tag projection of type and token constraints from a resource-rich language to a low-resource language for tagging.",
+      "Most recent work has focused on character-based neural models BIBREF29 , that can handle rare words and are hence more useful to model morphology than word-based models. These models first obtain a character-level representation of a token from a biLSTM or CNN, which is provided to a word-level biLSTM tagger. BIBREF29 , BIBREF30 compared several neural architectures to obtain these character-based representations and found the effect of the neural network architecture to be minimal given the networks are carefully tuned. Cross-lingual transfer learning has previously boosted performance on tasks such as translation BIBREF31 and POS tagging BIBREF32 , BIBREF33 . BIBREF5 proposed a cross-lingual character-level neural morphological tagger. They experimented with different strategies to facilitate cross-lingual training: a language ID for each token, a language-specific softmax and a joint language identification and tagging model. We have used this work as a baseline model for comparing with our proposed method.",
+      "In contrast to earlier work on morphological tagging, we use a hybrid of neural and graphical model approaches. This combination has several advantages: we can make use of expressive feature representations from neural models while ensuring that our model is interpretable. Our work is similar in spirit to BIBREF8 and BIBREF34 , who proposed models that use a CRF with features from neural models. For our graphical model component, we used a factorial CRF BIBREF7 , which is a generalization of a linear chain CRF with additional pairwise factors between cotemporal variables."
+    ],
+    [
+      "In this work, we proposed a novel framework for sequence tagging that combines neural networks and graphical models, and showed its effectiveness on the task of morphological tagging. We believe this framework can be extended to other sequence labeling tasks in NLP such as semantic role labeling. Due to the robustness of the model across languages, we believe it can also be scaled to perform morphological tagging for multiple languages together."
+    ],
+    [
+      "The authors would like to thank David Mortensen, Soumya Wadhwa and Maria Ryskina for useful comments about this work. We would also like to thank the reviewers who gave valuable feedback to improve the paper. This project was supported in part by an Amazon Academic Research Award and Google Faculty Award. "
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2399/instruction.md b/qasper-2399/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..20bc09a745f05daa68b36760c93ef845a77b8cd2
--- /dev/null
+++ b/qasper-2399/instruction.md
@@ -0,0 +1,120 @@
+Name of Paper: Neural Factor Graph Models for Cross-lingual Morphological Tagging
+
+Question: What languages are explored?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Problem Formulation",
+    "Baseline: Tag Set Prediction",
+    "A Relaxation: Tag-wise Prediction",
+    "Neural Factor Graph Model",
+    "Neural Factors",
+    "Pairwise Factors",
+    "Transition Factors",
+    "Language-Specific Weights",
+    "Loopy Belief Propagation",
+    "Learning and Decoding",
+    "Dataset",
+    "Baseline Tagger",
+    "Training Regimen",
+    "Main Results",
+    "What is the Model Learning?",
+    "Related Work",
+    "Conclusion and Future Work",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "Morphological analysis (hajivc1998tagging, oflazer1994tagging, inter alia) is the task of predicting fine-grained annotations about the syntactic properties of tokens in a language such as part-of-speech, case, or tense. For instance, in Figure FIGREF2 , the given Portuguese sentence is labeled with the respective morphological tags such as Gender and its label value Masculine.",
+      "The accuracy of morphological analyzers is paramount, because their results are often a first step in the NLP pipeline for tasks such as translation BIBREF1 , BIBREF2 and parsing BIBREF3 , and errors in the upstream analysis may cascade to the downstream tasks. One difficulty, however, in creating these taggers is that only a limited amount of annotated data is available for a majority of the world's languages to learn these morphological taggers. Fortunately, recent efforts in morphological annotation follow a standard annotation schema for these morphological tags across languages, and now the Universal Dependencies Treebank BIBREF0 has tags according to this schema in 60 languages.",
+      "cotterell2017crossling have recently shown that combining this shared schema with cross-lingual training on a related high-resource language (HRL) gives improved performance on tagging accuracy for low-resource languages (LRLs). The output space of this model consists of tag sets such as {POS: Adj, Gender: Masc, Number: Sing}, which are predicted for a token at each time step. However, this model relies heavily on the fact that the entire space of tag sets for the LRL must match those of the HRL, which is often not the case, either due to linguistic divergence or small differences in the annotation schemes between the two languages. For instance, in Figure FIGREF2 \u201crefrescante\u201d is assigned a gender in the Portuguese UD treebank, but not in the Spanish UD treebank.",
+      "In this paper, we propose a method that instead of predicting full tag sets, makes predictions over single tags separately but ties together each decision by modeling variable dependencies between tags over time steps (e.g. capturing the fact that nouns frequently occur after determiners) and pairwise dependencies between all tags at a single time step (e.g. capturing the fact that infinitive verb forms don't have tense). The specific model is shown in Figure FIGREF4 , consisting of a factorial conditional random field (FCRF; sutton2007dynamic) with neural network potentials calculated by long short-term memory (LSTM; BIBREF4 ) at every variable node (\u00a7 SECREF3 ). Learning and inference in the model is made tractable through belief propagation over the possible tag combinations, allowing the model to consider an exponential label space in polynomial time (\u00a7 SECREF24 ).",
+      "This model has several advantages:",
+      "In the following sections, we describe the model and these results in more detail."
+    ],
+    [
+      "Formally, we define the problem of morphological analysis as the task of mapping a length- INLINEFORM0 string of tokens INLINEFORM1 into the target morphological tag sets for each token INLINEFORM2 . For the INLINEFORM3 th token, the target label INLINEFORM4 defines a set of tags (e.g. {Gender: Masc, Number: Sing, POS: Verb}). An annotation schema defines a set INLINEFORM5 of INLINEFORM6 possible tag types and with the INLINEFORM7 th type (e.g. Gender) defining its set of possible labels INLINEFORM8 (e.g. {Masc, Fem, Neu}) such that INLINEFORM9 . We must note that not all tags or attributes need to be specified for a token; usually, a subset of INLINEFORM10 is specified for a token and the remaining tags can be treated as mapping to a INLINEFORM11 value. Let INLINEFORM12 denote the set of all possible tag sets."
+    ],
+    [
+      "Data-driven models for morphological analysis are constructed using training data INLINEFORM0 consisting of INLINEFORM1 training examples. The baseline model BIBREF5 we compare with regards the output space of the model as a subset INLINEFORM2 where INLINEFORM3 is the set of all tag sets seen in this training data. Specifically, they solve the task as a multi-class classification problem where the classes are individual tag sets. In low-resource scenarios, this indicates that INLINEFORM4 and even for those tag sets existing in INLINEFORM5 we may have seen very few training examples. The conditional probability of a sequence of tag sets given the sentence is formulated as a 0th order CRF. DISPLAYFORM0 ",
+      "Instead, we would like to be able to generate any combination of tags from the set INLINEFORM0 , and share statistical strength among similar tag sets."
+    ],
+    [
+      "As an alternative, we could consider a model that performs prediction for each tag's label INLINEFORM0 independently. DISPLAYFORM0 ",
+      "This formulation has an advantage: the tag-predictions within a single time step are now independent, it is now easy to generate any combination of tags from INLINEFORM0 . On the other hand, now it is difficult to model the interdependencies between tags in the same tag set INLINEFORM1 , a major disadvantage over the previous model. In the next section, we describe our proposed neural factor graph model, which can model not only dependencies within tags for a single token, but also dependencies across time steps while still maintaining the flexibility to generate any combination of tags from INLINEFORM2 ."
+    ],
+    [
+      "Due to the correlations between the syntactic properties that are represented by morphological tags, we can imagine that capturing the relationships between these tags through pairwise dependencies can inform the predictions of our model. These dependencies exist both among tags for the same token (intra-token pairwise dependencies), and across tokens in the sentence (inter-token transition dependencies). For instance, knowing that a token's POS tag is a Noun, would strongly suggest that this token would have a INLINEFORM0 label for the tag Tense, with very few exceptions BIBREF6 . In a language where nouns follow adjectives, a tag set prediction {POS: Adj, Gender: Fem} might inform the model that the next token is likely to be a noun and have the same gender. The baseline model can not explicitly model such interactions given their factorization in equation EQREF10 .",
+      "To incorporate the dependencies discussed above, we define a factorial CRF BIBREF7 , with pairwise links between cotemporal variables and transition links between the same types of tags. This model defines a distribution over the tag-set sequence INLINEFORM0 given the input sentence INLINEFORM1 as, DISPLAYFORM0 ",
+      "where INLINEFORM0 is the set of factors in the factor graph (as shown in Figure FIGREF4 ), INLINEFORM1 is one such factor, and INLINEFORM2 is the assignment to the subset of variables neighboring factor INLINEFORM3 . We define three types of potential functions: neural INLINEFORM4 , pairwise INLINEFORM5 , and transition INLINEFORM6 , described in detail below."
+    ],
+    [
+      "The flexibility of our formulation allows us to include any form of custom-designed potentials in our model. Those for the neural factors have a fairly standard log-linear form, DISPLAYFORM0 ",
+      "except that the features INLINEFORM0 are themselves given by a neural network. There is one such factor per variable. We obtain our neural factors using a biLSTM over the input sequence INLINEFORM1 , where the input word embedding for each token is obtained from a character-level biLSTM embedder. This component of our model is similar to the model proposed by BIBREF5 . Given an input token INLINEFORM2 , we compute an input embedding INLINEFORM3 as, DISPLAYFORM0 ",
+      "Here, INLINEFORM0 is a character-level LSTM function that returns the last hidden state. This input embedding INLINEFORM1 is then used in the biLSTM tagger to compute an output representation INLINEFORM2 . Finally, the scores INLINEFORM3 are obtained as, DISPLAYFORM0 ",
+      "We use a language-specific linear layer with weights INLINEFORM0 and bias INLINEFORM1 ."
+    ],
+    [
+      "As discussed previously, the pairwise factors are crucial for modeling correlations between tags. The pairwise factor potential for a tag INLINEFORM0 and tag INLINEFORM1 at timestep INLINEFORM2 is given in equation EQREF20 . Here, the dimension of INLINEFORM3 is INLINEFORM4 . These scores are used to define the neural factors as, DISPLAYFORM0 "
+    ],
+    [
+      "Previous work has experimented with the use of a linear chain CRF with factors from a neural network BIBREF8 for sequence tagging tasks. We hypothesize that modeling transition factors in a similar manner can allow the model to utilize information about neighboring tags and capture word order features of the language. The transition factor for tag INLINEFORM0 and timestep INLINEFORM1 is given below for variables INLINEFORM2 and INLINEFORM3 . The dimension of INLINEFORM4 is INLINEFORM5 . DISPLAYFORM0 ",
+      "In our experiments, INLINEFORM0 and INLINEFORM1 are simple indicator features for the values of tag variables with no dependence on INLINEFORM2 ."
+    ],
+    [
+      "As an enhancement to the information encoded in the transition and pairwise factors, we experiment with training general and language-specific parameters for the transition and the pairwise weights. We define the weight matrix INLINEFORM0 to learn the general trends that hold across both languages, and the weights INLINEFORM1 to learn the exceptions to these trends. In our model, we sum both these parameter matrices before calculating the transition and pairwise factors. For instance, the transition weights INLINEFORM2 are calculated as INLINEFORM3 ."
+    ],
+    [
+      "Since the graph from Figure FIGREF4 is a loopy graph, performing exact inference can be expensive. Hence, we use loopy belief propagation BIBREF9 , BIBREF10 for computation of approximate variable and factor marginals. Loopy BP is an iterative message passing algorithm that sends messages between variables and factors in a factor graph. The message updates from variable INLINEFORM0 , with neighboring factors INLINEFORM1 , to factor INLINEFORM2 is DISPLAYFORM0 ",
+      "The message from factor INLINEFORM0 to variable INLINEFORM1 is DISPLAYFORM0 ",
+      "where INLINEFORM0 denote an assignment to the subset of variables adjacent to factor INLINEFORM1 , and INLINEFORM2 is the assignment for variable INLINEFORM3 . Message updates are performed asynchronously in our model. Our message passing schedule was similar to that of foward-backward: the forward pass sends all messages from the first time step in the direction of the last. Messages to/from pairwise factors are included in this forward pass. The backward pass sends messages in the direction from the last time step back to the first. This process is repeated until convergence. We say that BP has converged when the maximum residual error BIBREF11 over all messages is below some threshold. Upon convergence, we obtain the belief values of variables and factors as, DISPLAYFORM0 ",
+      " where INLINEFORM0 and INLINEFORM1 are normalization constants ensuring that the beliefs for a variable INLINEFORM2 and factor INLINEFORM3 sum-to-one. In this way, we can use the beliefs as approximate marginal probabilities."
+    ],
+    [
+      "We perform end-to-end training of the neural factor graph by following the (approximate) gradient of the log-likelihood INLINEFORM0 . The true gradient requires access to the marginal probabilities for each factor, e.g. INLINEFORM1 where INLINEFORM2 denotes the subset of variables in factor INLINEFORM3 . For example, if INLINEFORM4 is a transition factor for tag INLINEFORM5 at timestep INLINEFORM6 , then INLINEFORM7 would be INLINEFORM8 and INLINEFORM9 . Following BIBREF7 , we replace these marginals with the beliefs INLINEFORM10 from loopy belief propagation. Consider the log-likelihood of a single example INLINEFORM11 . The partial derivative with respect to parameter INLINEFORM12 for each type of factor INLINEFORM13 is the difference of the observed features with the expected features under the model's (approximate) distribution as represented by the beliefs: INLINEFORM14 ",
+      " where INLINEFORM0 denotes all the factors of type INLINEFORM1 , and we have omitted any dependence on INLINEFORM2 and INLINEFORM3 for brevity\u2014 INLINEFORM4 is accessible through the factor index INLINEFORM5 . For the neural network factors, the features are given by a biLSTM. We backpropagate through to the biLSTM parameters using the partial derivative below, INLINEFORM6 ",
+      " where INLINEFORM0 is the variable belief corresponding to variable INLINEFORM1 .",
+      "To predict a sequence of tag sets INLINEFORM0 at test time, we use minimum Bayes risk (MBR) decoding BIBREF13 , BIBREF14 for Hamming loss over tags. For a variable INLINEFORM1 representing tag INLINEFORM2 at timestep INLINEFORM3 , we take DISPLAYFORM0 ",
+      "where INLINEFORM0 ranges over the possible labels for tag INLINEFORM1 ."
+    ],
+    [
+      "We used the Universal Dependencies Treebank UD v2.1 BIBREF0 for our experiments. We picked four low-resource/high-resource language pairs, each from a different family: Danish/Swedish (da/sv), Russian/Bulgarian (ru/bg), Finnish/Hungarian (fi/hu), Spanish/Portuguese (es/pt). Picking languages from different families would ensure that we obtain results that are on average consistent across languages.",
+      "The sizes of the training and evaluation sets are specified in Table TABREF31 . In order to simulate low-resource settings, we follow the experimental procedure from BIBREF5 . We restrict the number of sentences of the target language ( INLINEFORM0 ) in the training set to 100 or 1000 sentences. We also augment the tag sets in our training data by adding a INLINEFORM1 label for all tags that are not seen for a token. It is expected that our model will learn which tags are unlikely to occur given the variable dependencies in the factor graph. The dev set and test set are only in the target language. From Table TABREF32 , we can see there is also considerable variance in the number of unique tags and tag sets found in each of these language pairs."
+    ],
+    [
+      "As the baseline tagger model, we re-implement the specific model from BIBREF5 that uses a language-specific softmax layer. Their model architecture uses a character biLSTM embedder to obtain a vector representation for each token, which is used as input in a word-level biLSTM. The output space of their model is all the tag sets seen in the training data. This work achieves strong performance on several languages from UD on the task of morphological tagging and is a strong baseline."
+    ],
+    [
+      "We followed the parameter settings from BIBREF5 for the baseline tagger and the neural component of the FCRF-LSTM model. For both models, we set the input embedding and linear layer dimension to 128. We used 2 hidden layers for the LSTM where the hidden layer dimension was set to 256 and a dropout BIBREF15 of 0.2 was enforced during training. All our models were implemented in the PyTorch toolkit BIBREF16 . The parameters of the character biLSTM and the word biLSTM were initialized randomly. We trained the baseline models and the neural factor graph model with SGD and Adam respectively for 10 epochs each, in batches of 64 sentences. These optimizers gave the best performances for the respective models.",
+      "For the FCRF, we initialized transition and pairwise parameters with zero weights, which was important to ensure stable training. We considered BP to have reached convergence when the maximum residual error was below 0.05 or if the maximum number of iterations was reached (set to 40 in our experiments). We found that in cross-lingual experiments, when INLINEFORM0 , the relatively large amount of data in the HRL was causing our model to overfit on the HRL and not generalize well to the LRL. As a solution to this, we upsampled the LRL data by a factor of 10 when INLINEFORM1 for both the baseline and the proposed model.",
+      "Previous work on morphological analysis BIBREF5 , BIBREF17 has reported scores on average token-level accuracy and F1 measure. The average token level accuracy counts a tag set prediction as correct only it is an exact match with the gold tag set. On the other hand, F1 measure is measured on a tag-by-tag basis, which allows it to give partial credit to partially correct tag sets. Based on the characteristics of each evaluation measure, Accuracy will favor tag-set prediction models (like the baseline), and F1 measure will favor tag-wise prediction models (like our proposed method). Given the nature of the task, it seems reasonable to prefer getting some of the tags correct (e.g. Noun+Masc+Sing becomes Noun+Fem+Sing), instead of missing all of them (e.g. Noun+Masc+Sing becomes Adj+Fem+Plur). F-score gives partial credit for getting some of the tags correct, while tagset-level accuracy will treat these two mistakes equally. Based on this, we believe that F-score is intuitively a better metric. However, we report both scores for completeness."
+    ],
+    [
+      "First, we report the results in the case of monolingual training in Table TABREF33 . The first row for each language pair reports the results for our reimplementation of cotterell2017crossling, and the second for our full model. From these results, we can see that we obtain improvements on the F-measure over the baseline method in most experimental settings except BG with INLINEFORM0 . In a few more cases, the baseline model sometimes obtains higher accuracy scores for the reason described in UID38 .",
+      "In our cross-lingual experiments shown in Table TABREF37 , we also note F-measure improvements over the baseline model with the exception of DA/SV when INLINEFORM0 . We observe that the improvements are on average stronger when INLINEFORM1 . This suggests that our model performs well with very little data due to its flexibility to generate any tag set, including those not observed in the training data. The strongest improvements are observed for FI/HU. This is likely because the number of unique tags is the highest in this language pair and our method scales well with the number of tags due to its ability to make use of correlations between the tags in different tag sets.",
+      "To examine the utility of our transition and pairwise factors, we also report results on ablation experiments by removing transition and pairwise factors completely from the model in Table TABREF40 . Ablation experiments for each factor showed decreases in scores relative to the model where both factors are present, but the decrease attributed to the pairwise factors is larger, in both the monolingual and cross-lingual cases. Removing both factors from our proposed model results in a further decrease in the scores. These differences were found to be more significant in the case when INLINEFORM0 .",
+      "Upon looking at the tag set predictions made by our model, we found instances where our model utilizes variable dependencies to predict correct labels. For instance, for a specific phrase in Portuguese (um estado), the baseline model predicted {POS: Det, Gender: Masc, Number: Sing} INLINEFORM0 , {POS: Noun, Gender: Fem (X), Number: Sing} INLINEFORM1 , whereas our model was able to get the gender correct because of the transition factors in our model."
+    ],
+    [
+      "One of the major advantages of our model is the ability to interpret what the model has learned by looking at the trained parameter weights. We investigated both language-generic and language-specific patterns learned by our parameters:",
+      "Language-Generic: We found evidence for several syntactic properties learned by the model parameters. For instance, in Figure FIGREF42 , we visualize the generic ( INLINEFORM0 ) transition weights of the POS tags in Ru/Bg. Several universal trends such as determiners and adjectives followed by nouns can be seen. In Figure FIGREF43 , we also observed that infinitive has a strong correlation for NULL tense, which follows the universal phenomena that infinitives don't have tense.",
+      "Language Specific Trends: We visualized the learnt language-specific weights and looked for evidence of patterns corresponding to linguistic phenomenas observed in a language of interest. For instance, in Russian, verbs are gender-specific in past tense but not in other tenses. To analyze this, we plotted pairwise weights for Gender/Tense in Figure FIGREF45 and verified strong correlations between the past tense and all gender labels."
+    ],
+    [
+      "There exist several variations of the task of prediction of morphological information from annotated data: paradigm completion BIBREF18 , BIBREF19 , morphological reinflection BIBREF20 , segmentation BIBREF21 , BIBREF22 and tagging. Work on morphological tagging has broadly focused on structured prediction models such as CRFs, and neural network models. Amongst structured prediction approaches, BIBREF23 proposed a factor-graph based model that performed joint morphological tagging and parsing. BIBREF24 , BIBREF25 proposed the use of a higher-order CRF that is approximated using coarse-to-fine decoding. BIBREF26 proposed joint lemmatization and tagging using this framework. BIBREF27 was the first work that performed experiments on multilingual morphological tagging. They proposed an exponential model and the use of a morphological dictionary. BIBREF17 , BIBREF28 proposed a model that used tag projection of type and token constraints from a resource-rich language to a low-resource language for tagging.",
+      "Most recent work has focused on character-based neural models BIBREF29 , that can handle rare words and are hence more useful to model morphology than word-based models. These models first obtain a character-level representation of a token from a biLSTM or CNN, which is provided to a word-level biLSTM tagger. BIBREF29 , BIBREF30 compared several neural architectures to obtain these character-based representations and found the effect of the neural network architecture to be minimal given the networks are carefully tuned. Cross-lingual transfer learning has previously boosted performance on tasks such as translation BIBREF31 and POS tagging BIBREF32 , BIBREF33 . BIBREF5 proposed a cross-lingual character-level neural morphological tagger. They experimented with different strategies to facilitate cross-lingual training: a language ID for each token, a language-specific softmax and a joint language identification and tagging model. We have used this work as a baseline model for comparing with our proposed method.",
+      "In contrast to earlier work on morphological tagging, we use a hybrid of neural and graphical model approaches. This combination has several advantages: we can make use of expressive feature representations from neural models while ensuring that our model is interpretable. Our work is similar in spirit to BIBREF8 and BIBREF34 , who proposed models that use a CRF with features from neural models. For our graphical model component, we used a factorial CRF BIBREF7 , which is a generalization of a linear chain CRF with additional pairwise factors between cotemporal variables."
+    ],
+    [
+      "In this work, we proposed a novel framework for sequence tagging that combines neural networks and graphical models, and showed its effectiveness on the task of morphological tagging. We believe this framework can be extended to other sequence labeling tasks in NLP such as semantic role labeling. Due to the robustness of the model across languages, we believe it can also be scaled to perform morphological tagging for multiple languages together."
+    ],
+    [
+      "The authors would like to thank David Mortensen, Soumya Wadhwa and Maria Ryskina for useful comments about this work. We would also like to thank the reviewers who gave valuable feedback to improve the paper. This project was supported in part by an Amazon Academic Research Award and Google Faculty Award. "
+    ]
+  ]
+}
+```
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+Name of Paper: Neural Machine Translation with Supervised Attention
+
+Question: Which conventional alignment models do they use as guidance?
\ No newline at end of file
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+++ b/qasper-2405/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Dover: A Method for Combining Diarization Outputs
+
+Question: Do they compare their algorithm to voting without weights?
\ No newline at end of file
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+Name of Paper: Satirical News Detection with Semantic Feature Extraction and Game-theoretic Rough Sets
+
+Question: How much improvement do they get?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Methodology",
+    "Methodology ::: Dataset",
+    "Methodology ::: Semantic Feature Extraction",
+    "Methodology ::: Semantic Feature Extraction ::: Inconsistency in Noun Phrase Structures",
+    "Methodology ::: Semantic Feature Extraction ::: Inconsistency Between Clauses",
+    "Methodology ::: Semantic Feature Extraction ::: Inconsistency Between Named Entities and Noun Phrases",
+    "Methodology ::: Semantic Feature Extraction ::: Word Level Feature Using TF-IDF",
+    "Methodology ::: GTRS Decision Model",
+    "Methodology ::: GTRS Decision Model ::: Game Formulation",
+    "Methodology ::: GTRS Decision Model ::: Repetition Learning Mechanism",
+    "Experiments",
+    "Experiments ::: Finding Thresholds with GTRS",
+    "Experiments ::: Results",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Satirical news, which uses parody characterized in a conventional news style, has now become an entertainment on social media. While news satire is claimed to be pure comedic and of amusement, it makes statements on real events often with the aim of attaining social criticism and influencing change BIBREF0. Satirical news can also be misleading to readers, even though it is not designed for falsifications. Given such sophistication, satirical news detection is a necessary yet challenging natural language processing (NLP) task. Many feature based fake or satirical news detection systems BIBREF1, BIBREF2, BIBREF3 extract features from word relations given by statistics or lexical database, and other linguistic features. In addition, with the great success of deep learning in NLP in recent years, many end-to-end neural nets based detection systems BIBREF4, BIBREF5, BIBREF6 have been proposed and delivered promising results on satirical news article detection.",
+      "However, with the evolution of fast-paced social media, satirical news has been condensed into a satirical-news-in-one-sentence form. For example, one single tweet of \u201cIf earth continues to warm at current rate moon will be mostly underwater by 2400\" by The Onion is largely consumed and spread by social media users than the corresponding full article posted on The Onion website. Existing detection systems trained on full document data might not be applicable to such form of satirical news. Therefore, we collect news tweets from satirical news sources such as The Onion, The New Yorker (Borowitz Report) and legitimate news sources such as Wall Street Journal and CNN Breaking News. We explore the syntactic tree of the sentence and extract inconsistencies between attributes and head noun in noun phrases. We also detect the existence of named entities and relations between named entities and noun phrases as well as contradictions between the main clause and corresponding prepositional phrase. For a satirical news, such inconsistencies often exist since satirical news usually combines irrelevant components so as to attain surprise and humor. The discrepancies are measured by cosine similarity between word components where words are represented by Glove BIBREF7. Sentence structures are derived by Flair, a state-of-the-art NLP framework, which better captures part-of-speech and named entity structures BIBREF8.",
+      "Due to the obscurity of satire genre and lacks of information given tweet form satirical news, there exists ambiguity in satirical news, which causes great difficulty to make a traditional binary decision. That is, it is difficult to classify one news as satirical or legitimate with available information. Three-way decisions, proposed by YY Yao, added an option - deferral decision in the traditional yes-and-no binary decisions and can be used to classify satirical news BIBREF9, BIBREF10. That is, one news may be classified as satirical, legitimate, and deferral. We apply rough sets model, particularly the game-theoretic rough sets to classify news into three groups, i.e., satirical, legitimate, and deferral. Game-theoretic rough set (GTRS) model, proposed by JT Yao and Herbert, is a recent promising model for decision making in the rough set context BIBREF11. GTRS determine three decision regions from a tradeoff perspective when multiple criteria are involved to evaluate the classification models BIBREF12. Games are formulated to obtain a tradeoff between involved criteria. The balanced thresholds of three decision regions can be induced from the game equilibria. GTRS have been applied in recommendation systems BIBREF13, medical decision making BIBREF14, uncertainty analysis BIBREF15, and spam filtering BIBREF16.",
+      "We apply GTRS model on our preprocessed dataset and divide all news into satirical, legitimate, or deferral regions. The probabilistic thresholds that determine three decision regions are obtained by formulating competitive games between accuracy and coverage and then finding Nash equilibrium of games. We perform extensive experiments on the collected dataset, fine-tuning the model by different discretization methods and variation of equivalent classes. The experimental result shows that the performance of the proposed model is superior compared with Pawlak rough sets model and SVM."
+    ],
+    [
+      "Satirical news detection is an important yet challenging NLP task. Many feature based models have been proposed. Burfoot et al. extracted features of headline, profanity, and slang using word relations given by statistical metrics and lexical database BIBREF1. Rubin et al. proposed a SVM based model with five features (absurdity, humor, grammar, negative affect, and punctuation) for fake news document detection BIBREF2. Yang et al. presented linguistic features such as psycholinguistic feature based on dictionary and writing stylistic feature from part-of-speech tags distribution frequency BIBREF17. Shu et al. gave a survey in which a set of feature extraction methods is introduced for fake news on social media BIBREF3. Conroy et al. also uses social network behavior to detect fake news BIBREF18. For satirical sentence classification, Davidov et al. extract patterns using word frequency and punctuation features for tweet sentences and amazon comments BIBREF19. The detection of a certain type of sarcasm which contracts positive sentiment with a negative situation by analyzing the sentence pattern with a bootstrapped learning was also discussed BIBREF20. Although word level statistical features are widely used, with advanced word representations and state-of-the-art part-of-speech tagging and named entity recognition model, we observe that semantic features are more important than word level statistical features to model performance. Thus, we decompose the syntactic tree and use word vectors to more precisely capture the semantic inconsistencies in different structural parts of a satirical news tweet.",
+      "Recently, with the success of deep learning in NLP, many researchers attempted to detect fake news with end-to-end neural nets based approaches. Ruchansky et al. proposed a hybrid deep neural model which processes both text and user information BIBREF5, while Wang et al. proposed a neural network model that takes both text and image data BIBREF6 for detection. Sarkar et al. presented a neural network with attention to both capture sentence level and document level satire BIBREF4. Some research analyzed sarcasm from non-news text. Ghosh and Veale BIBREF21 used both the linguistic context and the psychological context information with a bi-directional LSTM to detect sarcasm in users' tweets. They also published a feedback-based dataset by collecting the responses from the tweets authors for future analysis. While all these works detect fake news given full text or image content, or target on non-news tweets, we attempt bridge the gap and detect satirical news by analyzing news tweets which concisely summarize the content of news."
+    ],
+    [
+      "In this section, we will describe the composition and preprocessing of our dataset and introduce our model in detail. We create our dataset by collecting legitimate and satirical news tweets from different news source accounts. Our model aims to detect whether the content of a news tweet is satirical or legitimate. We first extract the semantic features based on inconsistencies in different structural parts of the tweet sentences, and then use these features to train game-theoretic rough set decision model."
+    ],
+    [
+      "We collected approximately 9,000 news tweets from satirical news sources such as The Onion and Borowitz Report and about 11,000 news tweets from legitimate new sources such as Wall Street Journal and CNN Breaking News over the past three years. Each tweet is a concise summary of a news article. The duplicated and extreme short tweets are removed.A news tweet is labeled as satirical if it is written by satirical news sources and legitimate if it is from legitimate news sources. Table TABREF2 gives an example of tweet instances that comprise our dataset."
+    ],
+    [
+      "Satirical news is not based on or does not aim to state the fact. Rather, it uses parody or humor to make statement, criticisms, or just amusements. In order to achieve such effect, contradictions are greatly utilized. Therefore, inconsistencies significantly exist in different parts of a satirical news tweet. In addition, there is a lack of entity or inconsistency between entities in news satire. We extracted these features at semantic level from different sub-structures of the news tweet. Different structural parts of the sentence are derived by part-of-speech tagging and named entity recognition by Flair. The inconsistencies in different structures are measured by cosine similarity of word phrases where words are represented by Glove word vectors. We explored three different aspects of inconsistency and designed metrics for their measurements. A word level feature using tf-idf BIBREF22 is added for robustness."
+    ],
+    [
+      "One way for a news satire to obtain surprise or humor effect is to combine irrelevant or less jointly used attributes and the head noun which they modified. For example, noun phrase such as \u201crampant accountability\", \u201cposthumous apology\", \u201cVatican basement\", \u201cself-imposed mental construct\" and other rare combinations are widely used in satirical news, while individual words themselves are common. To measure such inconsistency, we first select all leaf noun phrases (NP) extracted from the semantic trees to avoid repeated calculation. Then for each noun phrase, each adjacent word pair is selected and represented by 100-dim Glove word vector denoted as $(v_{t},w_{t})$. We define the averaged cosine similarity of noun phrase word pairs as:",
+      "where $T$ is a total number of word pairs. We use $S_{N\\!P}$ as a feature to capture the overall inconsistency in noun phrase uses. $S_{N\\!P}$ ranges from -1 to 1, where a smaller value indicates more significant inconsistency."
+    ],
+    [
+      "Another commonly used rhetoric approach for news satire is to make contradiction between the main clause and its prepositional phrase or relative clause. For instance, in the tweet \u201cTrump boys counter Chinese currency manipulation $by$ adding extra zeros To $20 Bills.\", contradiction or surprise is gained by contrasting irrelevant statements provided by different parts of the sentence. Let $q$ and $p$ denote two clauses separated by main/relative relation or preposition, and $(w_{1},w_{1},... w_{q})$ and $(v_{1},v_{1},... v_{p})$ be the vectorized words in $q$ and $p$. Then we define inconsistency between $q$ and $p$ as:",
+      "Similarly, the feature $S_{Q\\!P}$ is measured by cosine similarity of linear summations of word vectors, where smaller value indicates more significant inconsistency."
+    ],
+    [
+      "Even though many satirical news tweets are made based on real persons or events, most of them lack specific entities. Rather, because the news is fabricated, news writers use the words such as \u201cman\",\u201cwoman\",\u201clocal man\", \u201carea woman\",\u201clocal family\" as subject. However, the inconsistency between named entities and noun phrases often exists in a news satire if a named entity is included. For example, the named entity \u201cAndrew Yang\" and the noun phrases \u201ctime vortex\" show great inconsistency than \u201cPresident Trump\", \"Senate Republicans\", and \u201cWhite House\" do in the legitimate news \u201cPresident Trump invites Senate Republicans to the White House to talk about the funding bill.\" We define such inconsistency as a categorical feature that:",
+      "$S_{N\\! E\\! R\\! N}$ is the cosine similarity of named entities and noun phrases of a certain sentence and $\\bar{S}_{N\\! E\\! R\\! N}$ is the mean value of $S_{N\\! E\\! R\\! N}$ in corpus."
+    ],
+    [
+      "We calculated the difference of tf-idf scores between legitimate news corpus and satirical news corpus for each single word. Then, the set $S_{voc}$ that includes most representative legitimate news words is created by selecting top 100 words given the tf-idf difference. For a news tweet and any word $w$ in the tweet, we define the binary feature $B_{voc}$ as:"
+    ],
+    [
+      "We construct a Game-theoretic Rough Sets model for classification given the extracted features. Suppose $E\\subseteq U \\times U$ is an equivalence relation on a finite nonempty universe of objects $U$, where $E$ is reflexive, symmetric, and transitive. The equivalence class containing an object $x$ is given by $[x]=\\lbrace y\\in U|xEy\\rbrace $. The objects in one equivalence class all have the same attribute values. In the satirical news context, given an undefined concept $satire$, probabilistic rough sets divide all news into three pairwise disjoint groups i.e., the satirical group $POS(satire)$, legitimate group $NEG(satire)$, and deferral group $BND(satire)$, by using the conditional probability $Pr(satire|[x]) = \\frac{|satire\\cap [x]|}{|[x]|}$ as the evaluation function, and $(\\alpha ,\\beta )$ as the acceptance and rejection thresholds BIBREF23, BIBREF9, BIBREF10, that is,",
+      "Given an equivalence class $[x]$, if the conditional probability $Pr(satire|[x])$ is greater than or equal to the specified acceptance threshold $\\alpha $, i.e., $Pr(satire|[x])\\ge \\alpha $, we accept the news in $[x]$ as $satirical$. If $Pr(satire|[x])$ is less than or equal to the specified rejection threshold $\\beta $, i.e., $Pr(satire|[x])\\le \\beta $ we reject the news in $[x]$ as $satirical$, or we accept the news in $[x]$ as $legitimate$. If $Pr(satire|[x])$ is between $\\alpha $ and $\\beta $, i.e., $\\beta <Pr(satire|[x])<\\alpha $, we defer to make decisions on the news in $[x]$. Pawlak rough sets can be viewed as a special case of probabilistic rough sets with $(\\alpha ,\\beta )=(1,0)$.",
+      "Given a pair of probabilistic thresholds $(\\alpha , \\beta )$, we can obtain a news classifier according to Equation (DISPLAY_FORM13). The three regions are a partition of the universe $U$,",
+      "Then, the accuracy and coverage rate to evaluate the performance of the derived classifier are defined as follows BIBREF12,",
+      "The criterion coverage indicates the proportions of news that can be confidently classified. Next, we will obtain $(\\alpha , \\beta )$ by game formulation and repetition learning."
+    ],
+    [
+      "We construct a game $G=\\lbrace O,S,u\\rbrace $ given the set of game players $O$, the set of strategy profile $S$, and the payoff functions $u$, where the accuracy and coverage are two players, respectively, i.e., $O=\\lbrace acc, cov\\rbrace $.",
+      "The set of strategy profiles $S=S_{acc}\\times S_{cov}$, where $S_{acc}$ and $S_{cov} $ are sets of possible strategies or actions performed by players $acc$ and $cov$. The initial thresholds are set as $(1,0)$. All these strategies are the changes made on the initial thresholds,",
+      "$c_{acc}$ and $c_{cov}$ denote the change steps used by two players, and their values are determined by the concrete experiment date set.",
+      "Payoff functions. The payoffs of players are $u=(u_{acc},u_{cov})$, and $u_{acc}$ and $u_{cov}$ denote the payoff functions of players $acc$ and $cov$, respectively. Given a strategy profile $p=(s, t)$ with player $acc$ performing $s$ and player $cov$ performing $t$, the payoffs of $acc$ and $cov$ are $u_{acc}(s, t)$ and $u_{cov}(s, t)$. We use $u_{acc}(\\alpha ,\\beta )$ and $u_{cov}(\\alpha ,\\beta )$ to show this relationship. The payoff functions $u_{acc}(\\alpha ,\\beta )$ and $u_{cov}(\\alpha ,\\beta )$ are defined as,",
+      "where $Acc_{(\\alpha , \\beta )}(Satire)$ and $Cov_{(\\alpha , \\beta )}(Satire)$ are the accuracy and coverage defined in Equations (DISPLAY_FORM15) and (DISPLAY_FORM16).",
+      "Payoff table. We use payoff tables to represent the formulated game. Table TABREF20 shows a payoff table example in which both players have 3 strategies defined in Equation refeq:stategies.",
+      "The arrow $\\downarrow $ denotes decreasing a value and $\\uparrow $ denotes increasing a value. On each cell, the threshold values are determined by two players."
+    ],
+    [
+      "We repeat the game with the new thresholds until a balanced solution is reached. We first analyzes the pure strategy equilibrium of the game and then check if the stopping criteria are satisfied.",
+      "Game equilibrium. The game solution of pure strategy Nash equilibrium is used to determine possible game outcomes in GTRS. The strategy profile $(s_{i},t_{j})$ is a pure strategy Nash equilibrium, if",
+      "This means that none of players would like to change his strategy or they would loss benefit if deriving from this strategy profile, provided this player has the knowledge of other player's strategy.",
+      "Repetition of games. Assuming that we formulate a game, in which the initial thresholds are $(\\alpha , \\beta )$, and the equilibrium analysis shows that the thresholds corresponding to the equilibrium are $(\\alpha ^{*}, \\beta ^{*})$. If the thresholds $(\\alpha ^{*}, \\beta ^{*})$ do not satisfy the stopping criterion, we will update the initial thresholds in the subsequent games. The initial thresholds of the new game will be set as $(\\alpha ^{*}, \\beta ^{*})$. If the thresholds $(\\alpha ^{*}, \\beta ^{*})$ satisfy the stopping criterion, we may stop the repetition of games.",
+      "Stopping criterion. We define the stopping criteria so that the iterations of games can stop at a proper time. In this research, we set the stopping criterion as within the range of thresholds, the increase of one player's payoff is less than the decrease of the other player's payoff."
+    ],
+    [
+      "There are 8757 news records in our preprocessed data set. We use Jenks natural breaks BIBREF24 to discretize continuous variables $S_{N\\!P}$ and $S_{Q\\!P}$ both into five categories denoted by nominal values from 0 to 4, where larger values still fall into bins with larger nominal value. Let $D_{N\\!P}$ and $D_{Q\\!P}$ denote the discretized variables $S_{N\\!P}$ and $S_{Q\\!P}$, respectively. We derived the information table that only contains discrete features from our original dataset. A fraction of the information table is shown in Table TABREF23.",
+      "The news whose condition attributes have the same values are classified in an equivalence class $X_i$. We derived 149 equivalence classes and calculated the corresponding probability $Pr(X_i)$ and condition probability $Pr(Satire|X_i)$ for each $X_i$. The probability $Pr(X_{i})$ denotes the ratio of the number of news contained in the equivalence class $X_i$ to the total number of news in the dataset, while the conditional probability $Pr(Satire|X_{i})$ is the proportion of news in $X_i$ that are satirical. We combine the equivalence classes with the same conditional probability and reduce the number of equivalence classes to 108. Table TABREF24 shows a part of the probabilistic data information about the concept satire."
+    ],
+    [
+      "We formulated a competitive game between the criteria accuracy and coverage to obtain the balanced probabilistic thresholds with the initial thresholds $(\\alpha , \\beta )=(1,0)$ and learning rate 0.03. As shown in the payoff table Table TABREF26,",
+      "the cell at the right bottom corner is the game equilibrium whose strategy profile is ($\\beta $ increases 0.06, $\\alpha $ decreases 0.06). The payoffs of the players are (0.9784,0.3343). We set the stopping criterion as the increase of one player's payoff is less than the decrease of the other player's payoff when the thresholds are within the range. When the thresholds change from (1,0) to (0.94, 0.06), the accuracy is decreased from 1 to 0.9784 but the coverage is increased from 0.0795 to 0.3343. We repeat the game by setting $(0.94, 0.06)$ as the next initial thresholds.",
+      "The competitive games are repeated seven times. The result is shown in Table TABREF27.",
+      "After the eighth iteration, the repetition of game is stopped because the further changes on thresholds may cause the thresholds lay outside of the range $0 < \\beta < \\alpha <1$, and the final result is the equilibrium of the seventh game $(\\alpha , \\beta )=(0.52, 0.48)$."
+    ],
+    [
+      "We compare Pawlak rough sets, SVM, and our GTRS approach on the proposed dataset. Table TABREF29 shows the results on the experimental data.",
+      "The SVM classifier achieved an accuracy of $78\\%$ with a $100\\%$ coverage. The Pawlak rough set model using $(\\alpha , \\beta )=(1,0)$ achieves a $100\\%$ accuracy and a coverage ratio of $7.95\\%$, which means it can only classify $7.95\\%$ of the data. The classifier constructed by GTRS with $(\\alpha , \\beta )=(0.52, 0.48)$ reached an accuracy $82.71\\%$ and a coverage $97.49\\%$. which indicates that $97.49\\%$ of data are able to be classified with accuracy of $82.71\\%$. The remaining $2.51\\%$ of data can not be classified without providing more information. To make our method comparable to other baselines such as SVM, we assume random guessing is made on the deferral region and present the modified accuracy. The modified accuracy for our approach is then $0.8271\\times 0.9749 + 0.5 \\times 0.0251 =81.89\\%$. Our methods shows significant improvement as compared to Pawlak model and SVM."
+    ],
+    [
+      "In this paper, we propose a satirical news detection approach based on extracted semantic features and game-theoretic rough sets. In our mode, the semantic features extraction captures the inconsistency in the different structural parts of the sentences and the GTRS classifier can process the incomplete information based on repetitive learning and the acceptance and rejection thresholds. The experimental results on our created satirical and legitimate news tweets dataset show that our model significantly outperforms Pawlak rough set model and SVM. In particular, we demonstrate our model's ability to interpret satirical news detection from a semantic and information trade-off perspective. Other interesting extensions of our paper may be to use rough set models to extract the linguistic features at document level."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2413/instruction.md b/qasper-2413/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..39f9fcf82c812ecf9290c31267870f0a9b775898
--- /dev/null
+++ b/qasper-2413/instruction.md
@@ -0,0 +1,104 @@
+Name of Paper: Satirical News Detection with Semantic Feature Extraction and Game-theoretic Rough Sets
+
+Question: How large is the dataset?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Methodology",
+    "Methodology ::: Dataset",
+    "Methodology ::: Semantic Feature Extraction",
+    "Methodology ::: Semantic Feature Extraction ::: Inconsistency in Noun Phrase Structures",
+    "Methodology ::: Semantic Feature Extraction ::: Inconsistency Between Clauses",
+    "Methodology ::: Semantic Feature Extraction ::: Inconsistency Between Named Entities and Noun Phrases",
+    "Methodology ::: Semantic Feature Extraction ::: Word Level Feature Using TF-IDF",
+    "Methodology ::: GTRS Decision Model",
+    "Methodology ::: GTRS Decision Model ::: Game Formulation",
+    "Methodology ::: GTRS Decision Model ::: Repetition Learning Mechanism",
+    "Experiments",
+    "Experiments ::: Finding Thresholds with GTRS",
+    "Experiments ::: Results",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Satirical news, which uses parody characterized in a conventional news style, has now become an entertainment on social media. While news satire is claimed to be pure comedic and of amusement, it makes statements on real events often with the aim of attaining social criticism and influencing change BIBREF0. Satirical news can also be misleading to readers, even though it is not designed for falsifications. Given such sophistication, satirical news detection is a necessary yet challenging natural language processing (NLP) task. Many feature based fake or satirical news detection systems BIBREF1, BIBREF2, BIBREF3 extract features from word relations given by statistics or lexical database, and other linguistic features. In addition, with the great success of deep learning in NLP in recent years, many end-to-end neural nets based detection systems BIBREF4, BIBREF5, BIBREF6 have been proposed and delivered promising results on satirical news article detection.",
+      "However, with the evolution of fast-paced social media, satirical news has been condensed into a satirical-news-in-one-sentence form. For example, one single tweet of \u201cIf earth continues to warm at current rate moon will be mostly underwater by 2400\" by The Onion is largely consumed and spread by social media users than the corresponding full article posted on The Onion website. Existing detection systems trained on full document data might not be applicable to such form of satirical news. Therefore, we collect news tweets from satirical news sources such as The Onion, The New Yorker (Borowitz Report) and legitimate news sources such as Wall Street Journal and CNN Breaking News. We explore the syntactic tree of the sentence and extract inconsistencies between attributes and head noun in noun phrases. We also detect the existence of named entities and relations between named entities and noun phrases as well as contradictions between the main clause and corresponding prepositional phrase. For a satirical news, such inconsistencies often exist since satirical news usually combines irrelevant components so as to attain surprise and humor. The discrepancies are measured by cosine similarity between word components where words are represented by Glove BIBREF7. Sentence structures are derived by Flair, a state-of-the-art NLP framework, which better captures part-of-speech and named entity structures BIBREF8.",
+      "Due to the obscurity of satire genre and lacks of information given tweet form satirical news, there exists ambiguity in satirical news, which causes great difficulty to make a traditional binary decision. That is, it is difficult to classify one news as satirical or legitimate with available information. Three-way decisions, proposed by YY Yao, added an option - deferral decision in the traditional yes-and-no binary decisions and can be used to classify satirical news BIBREF9, BIBREF10. That is, one news may be classified as satirical, legitimate, and deferral. We apply rough sets model, particularly the game-theoretic rough sets to classify news into three groups, i.e., satirical, legitimate, and deferral. Game-theoretic rough set (GTRS) model, proposed by JT Yao and Herbert, is a recent promising model for decision making in the rough set context BIBREF11. GTRS determine three decision regions from a tradeoff perspective when multiple criteria are involved to evaluate the classification models BIBREF12. Games are formulated to obtain a tradeoff between involved criteria. The balanced thresholds of three decision regions can be induced from the game equilibria. GTRS have been applied in recommendation systems BIBREF13, medical decision making BIBREF14, uncertainty analysis BIBREF15, and spam filtering BIBREF16.",
+      "We apply GTRS model on our preprocessed dataset and divide all news into satirical, legitimate, or deferral regions. The probabilistic thresholds that determine three decision regions are obtained by formulating competitive games between accuracy and coverage and then finding Nash equilibrium of games. We perform extensive experiments on the collected dataset, fine-tuning the model by different discretization methods and variation of equivalent classes. The experimental result shows that the performance of the proposed model is superior compared with Pawlak rough sets model and SVM."
+    ],
+    [
+      "Satirical news detection is an important yet challenging NLP task. Many feature based models have been proposed. Burfoot et al. extracted features of headline, profanity, and slang using word relations given by statistical metrics and lexical database BIBREF1. Rubin et al. proposed a SVM based model with five features (absurdity, humor, grammar, negative affect, and punctuation) for fake news document detection BIBREF2. Yang et al. presented linguistic features such as psycholinguistic feature based on dictionary and writing stylistic feature from part-of-speech tags distribution frequency BIBREF17. Shu et al. gave a survey in which a set of feature extraction methods is introduced for fake news on social media BIBREF3. Conroy et al. also uses social network behavior to detect fake news BIBREF18. For satirical sentence classification, Davidov et al. extract patterns using word frequency and punctuation features for tweet sentences and amazon comments BIBREF19. The detection of a certain type of sarcasm which contracts positive sentiment with a negative situation by analyzing the sentence pattern with a bootstrapped learning was also discussed BIBREF20. Although word level statistical features are widely used, with advanced word representations and state-of-the-art part-of-speech tagging and named entity recognition model, we observe that semantic features are more important than word level statistical features to model performance. Thus, we decompose the syntactic tree and use word vectors to more precisely capture the semantic inconsistencies in different structural parts of a satirical news tweet.",
+      "Recently, with the success of deep learning in NLP, many researchers attempted to detect fake news with end-to-end neural nets based approaches. Ruchansky et al. proposed a hybrid deep neural model which processes both text and user information BIBREF5, while Wang et al. proposed a neural network model that takes both text and image data BIBREF6 for detection. Sarkar et al. presented a neural network with attention to both capture sentence level and document level satire BIBREF4. Some research analyzed sarcasm from non-news text. Ghosh and Veale BIBREF21 used both the linguistic context and the psychological context information with a bi-directional LSTM to detect sarcasm in users' tweets. They also published a feedback-based dataset by collecting the responses from the tweets authors for future analysis. While all these works detect fake news given full text or image content, or target on non-news tweets, we attempt bridge the gap and detect satirical news by analyzing news tweets which concisely summarize the content of news."
+    ],
+    [
+      "In this section, we will describe the composition and preprocessing of our dataset and introduce our model in detail. We create our dataset by collecting legitimate and satirical news tweets from different news source accounts. Our model aims to detect whether the content of a news tweet is satirical or legitimate. We first extract the semantic features based on inconsistencies in different structural parts of the tweet sentences, and then use these features to train game-theoretic rough set decision model."
+    ],
+    [
+      "We collected approximately 9,000 news tweets from satirical news sources such as The Onion and Borowitz Report and about 11,000 news tweets from legitimate new sources such as Wall Street Journal and CNN Breaking News over the past three years. Each tweet is a concise summary of a news article. The duplicated and extreme short tweets are removed.A news tweet is labeled as satirical if it is written by satirical news sources and legitimate if it is from legitimate news sources. Table TABREF2 gives an example of tweet instances that comprise our dataset."
+    ],
+    [
+      "Satirical news is not based on or does not aim to state the fact. Rather, it uses parody or humor to make statement, criticisms, or just amusements. In order to achieve such effect, contradictions are greatly utilized. Therefore, inconsistencies significantly exist in different parts of a satirical news tweet. In addition, there is a lack of entity or inconsistency between entities in news satire. We extracted these features at semantic level from different sub-structures of the news tweet. Different structural parts of the sentence are derived by part-of-speech tagging and named entity recognition by Flair. The inconsistencies in different structures are measured by cosine similarity of word phrases where words are represented by Glove word vectors. We explored three different aspects of inconsistency and designed metrics for their measurements. A word level feature using tf-idf BIBREF22 is added for robustness."
+    ],
+    [
+      "One way for a news satire to obtain surprise or humor effect is to combine irrelevant or less jointly used attributes and the head noun which they modified. For example, noun phrase such as \u201crampant accountability\", \u201cposthumous apology\", \u201cVatican basement\", \u201cself-imposed mental construct\" and other rare combinations are widely used in satirical news, while individual words themselves are common. To measure such inconsistency, we first select all leaf noun phrases (NP) extracted from the semantic trees to avoid repeated calculation. Then for each noun phrase, each adjacent word pair is selected and represented by 100-dim Glove word vector denoted as $(v_{t},w_{t})$. We define the averaged cosine similarity of noun phrase word pairs as:",
+      "where $T$ is a total number of word pairs. We use $S_{N\\!P}$ as a feature to capture the overall inconsistency in noun phrase uses. $S_{N\\!P}$ ranges from -1 to 1, where a smaller value indicates more significant inconsistency."
+    ],
+    [
+      "Another commonly used rhetoric approach for news satire is to make contradiction between the main clause and its prepositional phrase or relative clause. For instance, in the tweet \u201cTrump boys counter Chinese currency manipulation $by$ adding extra zeros To $20 Bills.\", contradiction or surprise is gained by contrasting irrelevant statements provided by different parts of the sentence. Let $q$ and $p$ denote two clauses separated by main/relative relation or preposition, and $(w_{1},w_{1},... w_{q})$ and $(v_{1},v_{1},... v_{p})$ be the vectorized words in $q$ and $p$. Then we define inconsistency between $q$ and $p$ as:",
+      "Similarly, the feature $S_{Q\\!P}$ is measured by cosine similarity of linear summations of word vectors, where smaller value indicates more significant inconsistency."
+    ],
+    [
+      "Even though many satirical news tweets are made based on real persons or events, most of them lack specific entities. Rather, because the news is fabricated, news writers use the words such as \u201cman\",\u201cwoman\",\u201clocal man\", \u201carea woman\",\u201clocal family\" as subject. However, the inconsistency between named entities and noun phrases often exists in a news satire if a named entity is included. For example, the named entity \u201cAndrew Yang\" and the noun phrases \u201ctime vortex\" show great inconsistency than \u201cPresident Trump\", \"Senate Republicans\", and \u201cWhite House\" do in the legitimate news \u201cPresident Trump invites Senate Republicans to the White House to talk about the funding bill.\" We define such inconsistency as a categorical feature that:",
+      "$S_{N\\! E\\! R\\! N}$ is the cosine similarity of named entities and noun phrases of a certain sentence and $\\bar{S}_{N\\! E\\! R\\! N}$ is the mean value of $S_{N\\! E\\! R\\! N}$ in corpus."
+    ],
+    [
+      "We calculated the difference of tf-idf scores between legitimate news corpus and satirical news corpus for each single word. Then, the set $S_{voc}$ that includes most representative legitimate news words is created by selecting top 100 words given the tf-idf difference. For a news tweet and any word $w$ in the tweet, we define the binary feature $B_{voc}$ as:"
+    ],
+    [
+      "We construct a Game-theoretic Rough Sets model for classification given the extracted features. Suppose $E\\subseteq U \\times U$ is an equivalence relation on a finite nonempty universe of objects $U$, where $E$ is reflexive, symmetric, and transitive. The equivalence class containing an object $x$ is given by $[x]=\\lbrace y\\in U|xEy\\rbrace $. The objects in one equivalence class all have the same attribute values. In the satirical news context, given an undefined concept $satire$, probabilistic rough sets divide all news into three pairwise disjoint groups i.e., the satirical group $POS(satire)$, legitimate group $NEG(satire)$, and deferral group $BND(satire)$, by using the conditional probability $Pr(satire|[x]) = \\frac{|satire\\cap [x]|}{|[x]|}$ as the evaluation function, and $(\\alpha ,\\beta )$ as the acceptance and rejection thresholds BIBREF23, BIBREF9, BIBREF10, that is,",
+      "Given an equivalence class $[x]$, if the conditional probability $Pr(satire|[x])$ is greater than or equal to the specified acceptance threshold $\\alpha $, i.e., $Pr(satire|[x])\\ge \\alpha $, we accept the news in $[x]$ as $satirical$. If $Pr(satire|[x])$ is less than or equal to the specified rejection threshold $\\beta $, i.e., $Pr(satire|[x])\\le \\beta $ we reject the news in $[x]$ as $satirical$, or we accept the news in $[x]$ as $legitimate$. If $Pr(satire|[x])$ is between $\\alpha $ and $\\beta $, i.e., $\\beta <Pr(satire|[x])<\\alpha $, we defer to make decisions on the news in $[x]$. Pawlak rough sets can be viewed as a special case of probabilistic rough sets with $(\\alpha ,\\beta )=(1,0)$.",
+      "Given a pair of probabilistic thresholds $(\\alpha , \\beta )$, we can obtain a news classifier according to Equation (DISPLAY_FORM13). The three regions are a partition of the universe $U$,",
+      "Then, the accuracy and coverage rate to evaluate the performance of the derived classifier are defined as follows BIBREF12,",
+      "The criterion coverage indicates the proportions of news that can be confidently classified. Next, we will obtain $(\\alpha , \\beta )$ by game formulation and repetition learning."
+    ],
+    [
+      "We construct a game $G=\\lbrace O,S,u\\rbrace $ given the set of game players $O$, the set of strategy profile $S$, and the payoff functions $u$, where the accuracy and coverage are two players, respectively, i.e., $O=\\lbrace acc, cov\\rbrace $.",
+      "The set of strategy profiles $S=S_{acc}\\times S_{cov}$, where $S_{acc}$ and $S_{cov} $ are sets of possible strategies or actions performed by players $acc$ and $cov$. The initial thresholds are set as $(1,0)$. All these strategies are the changes made on the initial thresholds,",
+      "$c_{acc}$ and $c_{cov}$ denote the change steps used by two players, and their values are determined by the concrete experiment date set.",
+      "Payoff functions. The payoffs of players are $u=(u_{acc},u_{cov})$, and $u_{acc}$ and $u_{cov}$ denote the payoff functions of players $acc$ and $cov$, respectively. Given a strategy profile $p=(s, t)$ with player $acc$ performing $s$ and player $cov$ performing $t$, the payoffs of $acc$ and $cov$ are $u_{acc}(s, t)$ and $u_{cov}(s, t)$. We use $u_{acc}(\\alpha ,\\beta )$ and $u_{cov}(\\alpha ,\\beta )$ to show this relationship. The payoff functions $u_{acc}(\\alpha ,\\beta )$ and $u_{cov}(\\alpha ,\\beta )$ are defined as,",
+      "where $Acc_{(\\alpha , \\beta )}(Satire)$ and $Cov_{(\\alpha , \\beta )}(Satire)$ are the accuracy and coverage defined in Equations (DISPLAY_FORM15) and (DISPLAY_FORM16).",
+      "Payoff table. We use payoff tables to represent the formulated game. Table TABREF20 shows a payoff table example in which both players have 3 strategies defined in Equation refeq:stategies.",
+      "The arrow $\\downarrow $ denotes decreasing a value and $\\uparrow $ denotes increasing a value. On each cell, the threshold values are determined by two players."
+    ],
+    [
+      "We repeat the game with the new thresholds until a balanced solution is reached. We first analyzes the pure strategy equilibrium of the game and then check if the stopping criteria are satisfied.",
+      "Game equilibrium. The game solution of pure strategy Nash equilibrium is used to determine possible game outcomes in GTRS. The strategy profile $(s_{i},t_{j})$ is a pure strategy Nash equilibrium, if",
+      "This means that none of players would like to change his strategy or they would loss benefit if deriving from this strategy profile, provided this player has the knowledge of other player's strategy.",
+      "Repetition of games. Assuming that we formulate a game, in which the initial thresholds are $(\\alpha , \\beta )$, and the equilibrium analysis shows that the thresholds corresponding to the equilibrium are $(\\alpha ^{*}, \\beta ^{*})$. If the thresholds $(\\alpha ^{*}, \\beta ^{*})$ do not satisfy the stopping criterion, we will update the initial thresholds in the subsequent games. The initial thresholds of the new game will be set as $(\\alpha ^{*}, \\beta ^{*})$. If the thresholds $(\\alpha ^{*}, \\beta ^{*})$ satisfy the stopping criterion, we may stop the repetition of games.",
+      "Stopping criterion. We define the stopping criteria so that the iterations of games can stop at a proper time. In this research, we set the stopping criterion as within the range of thresholds, the increase of one player's payoff is less than the decrease of the other player's payoff."
+    ],
+    [
+      "There are 8757 news records in our preprocessed data set. We use Jenks natural breaks BIBREF24 to discretize continuous variables $S_{N\\!P}$ and $S_{Q\\!P}$ both into five categories denoted by nominal values from 0 to 4, where larger values still fall into bins with larger nominal value. Let $D_{N\\!P}$ and $D_{Q\\!P}$ denote the discretized variables $S_{N\\!P}$ and $S_{Q\\!P}$, respectively. We derived the information table that only contains discrete features from our original dataset. A fraction of the information table is shown in Table TABREF23.",
+      "The news whose condition attributes have the same values are classified in an equivalence class $X_i$. We derived 149 equivalence classes and calculated the corresponding probability $Pr(X_i)$ and condition probability $Pr(Satire|X_i)$ for each $X_i$. The probability $Pr(X_{i})$ denotes the ratio of the number of news contained in the equivalence class $X_i$ to the total number of news in the dataset, while the conditional probability $Pr(Satire|X_{i})$ is the proportion of news in $X_i$ that are satirical. We combine the equivalence classes with the same conditional probability and reduce the number of equivalence classes to 108. Table TABREF24 shows a part of the probabilistic data information about the concept satire."
+    ],
+    [
+      "We formulated a competitive game between the criteria accuracy and coverage to obtain the balanced probabilistic thresholds with the initial thresholds $(\\alpha , \\beta )=(1,0)$ and learning rate 0.03. As shown in the payoff table Table TABREF26,",
+      "the cell at the right bottom corner is the game equilibrium whose strategy profile is ($\\beta $ increases 0.06, $\\alpha $ decreases 0.06). The payoffs of the players are (0.9784,0.3343). We set the stopping criterion as the increase of one player's payoff is less than the decrease of the other player's payoff when the thresholds are within the range. When the thresholds change from (1,0) to (0.94, 0.06), the accuracy is decreased from 1 to 0.9784 but the coverage is increased from 0.0795 to 0.3343. We repeat the game by setting $(0.94, 0.06)$ as the next initial thresholds.",
+      "The competitive games are repeated seven times. The result is shown in Table TABREF27.",
+      "After the eighth iteration, the repetition of game is stopped because the further changes on thresholds may cause the thresholds lay outside of the range $0 < \\beta < \\alpha <1$, and the final result is the equilibrium of the seventh game $(\\alpha , \\beta )=(0.52, 0.48)$."
+    ],
+    [
+      "We compare Pawlak rough sets, SVM, and our GTRS approach on the proposed dataset. Table TABREF29 shows the results on the experimental data.",
+      "The SVM classifier achieved an accuracy of $78\\%$ with a $100\\%$ coverage. The Pawlak rough set model using $(\\alpha , \\beta )=(1,0)$ achieves a $100\\%$ accuracy and a coverage ratio of $7.95\\%$, which means it can only classify $7.95\\%$ of the data. The classifier constructed by GTRS with $(\\alpha , \\beta )=(0.52, 0.48)$ reached an accuracy $82.71\\%$ and a coverage $97.49\\%$. which indicates that $97.49\\%$ of data are able to be classified with accuracy of $82.71\\%$. The remaining $2.51\\%$ of data can not be classified without providing more information. To make our method comparable to other baselines such as SVM, we assume random guessing is made on the deferral region and present the modified accuracy. The modified accuracy for our approach is then $0.8271\\times 0.9749 + 0.5 \\times 0.0251 =81.89\\%$. Our methods shows significant improvement as compared to Pawlak model and SVM."
+    ],
+    [
+      "In this paper, we propose a satirical news detection approach based on extracted semantic features and game-theoretic rough sets. In our mode, the semantic features extraction captures the inconsistency in the different structural parts of the sentences and the GTRS classifier can process the incomplete information based on repetitive learning and the acceptance and rejection thresholds. The experimental results on our created satirical and legitimate news tweets dataset show that our model significantly outperforms Pawlak rough set model and SVM. In particular, we demonstrate our model's ability to interpret satirical news detection from a semantic and information trade-off perspective. Other interesting extensions of our paper may be to use rough set models to extract the linguistic features at document level."
+    ]
+  ]
+}
+```
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diff --git a/qasper-2414/instruction.md b/qasper-2414/instruction.md
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+Name of Paper: Satirical News Detection with Semantic Feature Extraction and Game-theoretic Rough Sets
+
+Question: What features do they extract?
\ No newline at end of file
diff --git a/qasper-2415/instruction.md b/qasper-2415/instruction.md
new file mode 100644
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+Name of Paper: Combining Acoustics, Content and Interaction Features to Find Hot Spots in Meetings
+
+Question: What they use as a metric of finding hot spots in meeting?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction and Prior Work",
+    "Data",
+    "Data ::: Time Windowing",
+    "Data ::: Metric",
+    "Feature Description ::: Acoustic-Prosodic Features",
+    "Feature Description ::: Word-Based Features ::: Bag of words with TF-IDF",
+    "Feature Description ::: Word-Based Features ::: Embeddings",
+    "Feature Description ::: Speaker Activity Features",
+    "Feature Description ::: Laughter Count",
+    "Modeling ::: Non-Neural Models",
+    "Modeling ::: Feed-Forward Neural Networks ::: Pooling Techniques",
+    "Modeling ::: Feed-Forward Neural Networks ::: Hyperparameters",
+    "Modeling ::: Model Fusion",
+    "Experiments",
+    "Experiments ::: Speech Feature Results",
+    "Experiments ::: Word-Based Results",
+    "Experiments ::: Acoustic-Prosodic Feature Results",
+    "Experiments ::: Fusion Results and Discussion",
+    "Conclusion",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "A definition of the meeting \u201chot spots\u201d was first introduced in BIBREF2, where it was investigated whether human annotators could reliably identify regions in which participants are \u201chighly involved in the discussion\u201d. The motivation was that meetings generally have low information density and are tedious to review verbatim after the fact. An automatic system that could detect regions of high interest (as indicated by the involvement of the participants during the meeting) would thus be useful. Relatedly, automatic meeting summarization could also benefit from such information to give extra weight to hot spot regions in selecting or abstracting material for inclusion in the summary. Later work on the relationship between involvement and summarization BIBREF3 defined a different approach: hot spots are those regions chosen for inclusion in a summary by human annotators (\u201csummarization hot spots\u201d). In the present work we stick with the original \u201cinvolvement hot spot\u201d notion, and refer to such regions simply as \u201chot spots\u201d, regardless of their possible role in summarization. We note that high involvement may be triggered both by a meeting's content (\u201cwhat is being talked about\u201d, and \u201cwhat may be included in a textual summary\u201d), as well as behavioral and social factors, such as a desire to participate, to stake out a position, or to oppose another participant. As related notion in dialog system research is \u201clevel of interest\u201d BIBREF4.",
+      "The initial research on hot spots focused on the reliability of human annotators and correlations with certain low-level acoustic features, such as pitch BIBREF2. Also investigated were the correlation between hot spots and dialog acts BIBREF5 and hot spots and speaker overlap BIBREF6, without however conducting experiments in automatic hot spot prediction using machine learning techniques. Laskowski BIBREF7 redefined the hot spot annotations in terms of time-based windows over meetings, and investigated various classifier models to detect \u201chotness\u201d (i.e., elevated involvement). However, that work focused on only two types of speech features: presence of laughter and the temporal patterns of speech activity across the various participants, both of which were found to be predictive of involvement.",
+      "For the related problem of level-of-interest prediction in dialog systems BIBREF8, it was found that content-based classification can also be effective, using both a discriminative TF-IDF model and lexical affect scores, as well as prosodic features. In line with the earlier hot spot research on interaction patterns and speaker overlap, turn-taking features were shown to be helpful for spotting summarization hot spots, in BIBREF3, and even more so than the human involvement annotations. The latter result confirms our intuition that summarization-worthiness and involvement are different notions of \u201chotness\u201d.",
+      "In this paper, following Laskowski, we focus on the automatic prediction of the speakers' involvement in sliding-time windows/segments. We evaluate machine learning models based on a range of features that can be extracted automatically from audio recordings, either directly via signal processing or via the use of automatic transcriptions (ASR outputs). In particular, we investigate the relative contributions of three classes of information:",
+      "low-level acoustic-prosodic features, such as those commonly used in other paralinguistic tasks, such as sentiment analysis (extracted using openSMILE BIBREF0);",
+      "spoken word content, as encoded with a state-of-the-art lexical embedding approach such as BERT BIBREF1;",
+      "speaker interaction, based on speech activity over time and across different speakers.",
+      "We attach lower importance to laughter, even though it was found to be highly predictive of involvement in the ICSI corpus, partly because we believe it would not transfer well to more general types of (e.g., business) meetings, and partly because laughter detection is still a hard problem in itself BIBREF9. Generation of speaker-attributed meeting transcriptions, on the other hand, has seen remarkable progress BIBREF10 and could support the features we focus on here."
+    ],
+    [
+      "The ICSI Meeting Corpus BIBREF11 is a collection of meeting recordings that has been thoroughly annotated, including annotations for involvement hot spots BIBREF12, linguistic utterance units, and word time boundaries based on forced alignment. The dataset is comprised of 75 meetings and about 70 hours of real-time audio duration, with 6 speakers per meeting on average. Most of the participants are well-acquainted and friendly with each other. Hot spots were originally annotated with 8 levels and degrees, ranging from `not hot' to `luke warm' to `hot +'. Every utterance was labeled with one of these discrete labels by a single annotator. Hightened involvement is rare, being marked on only 1% of utterances.",
+      "Due to the severe imbalance in the label distribution, Laskowski BIBREF13 proposed extending the involvement, or hotness, labels to sliding time windows. In our implementation (details below), this resulted in 21.7% of samples (windows) being labeled as \u201cinvolved\u201d.",
+      "We split the corpus into three subsets: training, development, and evaluation, keeping meetings intact. Table TABREF4 gives statistics of these partitions.",
+      "We were concerned with the relatively small number of meetings in the test sets, and repeated several of our experiments with a (jackknifing) cross-validation setup over the training set. The results obtained were very similar to those with the fixed train/test split results that we report here."
+    ],
+    [
+      "As stated above, the corpus was originally labeled for hot spots at the utterance level, where involvement was marked by either a `b' or a `b+' label. Training and test samples for our experiments correspond to 60 s-long sliding windows, with a 15 s step size. If a certain window, e.g., a segment spanning the times 15 s ...75 s, overlaps with any involved speech utterance, then we label that whole window as `hot'. Fig. FIGREF6 gives a visual representation."
+    ],
+    [
+      "In spite of the windowing approach, the class distribution is still skewed, and an accuracy metric would reflect the particular class distribution in our data set. Therefore, we adopt the unweighted average recall (UAR) metric commonly used in emotion classification research. UAR is a reweighted accuracy where the samples of both classes are weighted equally in aggregate. UAR thus simulates a uniform class distribution. To match the objective, our classifiers are trained on appropriately weighted training data. Note that chance performance for UAR is by definition 50%, making results more comparable across different data sets."
+    ],
+    [
+      "Prosody encompasses pitch, energy, and durational features of speech. Prosody is thought to convey emphasis, sentiment, and emotion, all of which are presumably correlated with expressions of involvement. We used the openSMILE toolkit BIBREF0 to compute 988 features as defined by the emobase988 configuration file, operating on the close-talking meeting recordings. This feature set consists of low-level descriptors such as intensity, loudness, Mel-frequency cepstral coefficients, and pitch. For each low-level descriptor, functionals such as max/min value, mean, standard deviation, kurtosis, and skewness are computed. Finally, global mean and variance normalization are applied to each feature, using training set statistics. The feature vector thus captures acoustic-prosodic features aggregated over what are typically utterances. We tried extracting openSMILE features directly from 60 s windows, but found better results by extracting subwindows of 5 s, followed by pooling over the longer 60 s duration. We attribute this to the fact that emobase features are designed to operate on individual utterances, which have durations closer to 5 s than 60 s."
+    ],
+    [
+      "Initially, we investigated a simple bag-of-words model including all unigrams, bigrams, and trigrams found in the training set. Occurrences of the top 10,000 n-grams were encoded to form a 10,000-dimensional vector, with values weighted according to TD-IDF. TF-IDF weights n-grams according to both their frequency (TF) and their salience (inverse document frequency, IDF) in the data, where each utterance was treated as a separate document. The resulting feature vectors are very sparse."
+    ],
+    [
+      "The ICSI dataset is too small to train a neural embedding model from scratch. Therefore, it is convenient to use the pre-trained BERT embedding architecture BIBREF1 to create an utterance-level embedding vector for each region of interest. Having been trained on a large text corpus, the resulting embeddings encode semantic similarities among utterances, and would enable generalization from word patterns seen in the ICSI training data to those that have not been observed on that limited corpus.",
+      "We had previously also created an adapted version of the BERT model, tuned to to perform utterance-level sentiment classification, on a separate dataset BIBREF14. As proposed in BIBREF1, we fine-tuned all layers of the pre-trained BERT model by adding a single fully-connected layer and classifying using only the embedding corresponding to the classification ([CLS]) token prepended to each utterance. The difference in UAR between the hot spot classifiers using the pre-trained embeddings and those using the sentiment-adapted embeddings is small. Since the classifier using embeddings extracted by the sentiment-adapted model yielded slightly better performance, we report all results using these as input.",
+      "To obtain a single embedding for each 60 s window, we experimented with various approaches of pooling the token and utterance-level embeddings. For our first approach, we ignored the ground-truth utterance segmentation and speaker information. We merged all words spoken within a particular window into a single contiguous span. Following BIBREF1, we added the appropriate classification and separation tokens to the text and selected the embedding corresponding to the [CLS] token as the window-level embedding. Our second approach used the ground-truth segmentation of the dialogue. Each speaker turn was independently modeled, and utterance-level embeddings were extracted using the representation corresponding to the [CLS] token. Utterances that cross window boundaries are truncated using the word timestamps, so only words spoken within the given time window are considered. For all reported experiments, we use L2-norm pooling to form the window-level embeddings for the final classifier, as this performed better than either mean or max pooling."
+    ],
+    [
+      "These features were a compilation of three different feature types:",
+      "Speaker overlap percentages: Based on the available word-level times, we computed a 6-dimensional feature vector, where the $i$th index indicates the fraction of time that $i$ or more speakers are talking within a given window. This can be expressed by $\\frac{t_i}{60}$ with $t_i$ indicating the time in seconds that $i$ or more people were speaking at the same time.",
+      "Unique speaker count: Counts the unique speakers within a window, as a useful metric to track the diversity of participation within a certain window.",
+      "Turn switch count: Counts the number of times a speaker begins talking within a window. This is a similar metric to the number of utterances. However, unlike utterance count, turn switches can be computed entirely from speech activity, without requiring a linguistic segmentation."
+    ],
+    [
+      "Laskowski found that laughter is highly predictive of involvement in the ICSI data. Laughter is annotated on an utterance level and falls into two categories: laughter solely on its own (no words) or laughter contained within an utterance (i.e. during speech). The feature is a simple tally of the number of times people laughed within a window. We include it in some of our experiments for comparison purposes, though we do not trust it as general feature. (The participants in the ICSI meetings are far too familiar and at ease with each other to be representative with regards to laughter.)"
+    ],
+    [
+      "In preliminary experiments, we compared several non-neural classifiers, including logistic regression (LR), random forests, linear support vector machines, and multinomial naive Bayes. Logistic regression gave the best results all around, and we used it exclusively for the results shown here, unless neural networks are used instead."
+    ],
+    [
+      "For BERT and openSMILE vector classification, we designed two different feed-forward neural network architectures. The sentiment-adapted embeddings described in Section SECREF3 produce one 1024-dimensional vector per utterance. Since all classification operates on time windows, we had to pool over all utterances falling withing a given window, taking care to truncate words falling outside the window. We tested four pooling methods: L2-norm, mean, max, and min, with L2-norm giving the best results.",
+      "As for the prosodic model, each vector extracted from openSMILE represents a 5 s interval. Since there was both a channel/speaker-axis and a time-axis, we needed to pool over both dimensions in order to have a single vector representing the prosodic features of a 60 s window. The second to last layer is the pooling layer, max-pooling across all the channels, and then mean-pooling over time. The output of the pooling layer is directly fed into the classifier."
+    ],
+    [
+      "The hyperparameters of the neural networks (hidden layer number and sizes) were also tuned in preliminary experiments. Details are given in Section SECREF5."
+    ],
+    [
+      "Fig. FIGREF19 depicts the way features from multiple categories are combined. Speech activity and word features are fed directly into a final LR step. Acoustic-prosodic features are first combined in a feed-forward neural classifier, whose output log posteriors are in turn fed into the LR step for fusion. (When using only prosodic features, the ANN outputs are used directly.)"
+    ],
+    [
+      "We group experiments by the type of feaures they are based on: acoustic-prosodic, word-based, and speech activity, evaluating each group first by itself, and then in combination with others."
+    ],
+    [
+      "As discussed in Section SECREF3, a multitude of input features were investigated, with some being more discriminative. The most useful speech activity features were speaker overlap percentage, number of unique speakers, and number of turn switches, giving evaluation set UARs of 63.5%, 63.9%, and 66.6%, respectively. When combined the UAR improved to 68.0%, showing that these features are partly complementary."
+    ],
+    [
+      "The TF-IDF model alone gave a UAR of 59.8%. A drastic increase in performance to 70.5% was found when using the BERT embeddings instead. Therefore we adopted embeddings for all further experiments based on word information.",
+      "Three different types of embeddings were investigated, i.e. sentiment-adapted embeddings at an utterance-level, unadapted embeddings at the utterance-level, and unadapted embeddings over time windows.",
+      "The adapted embeddings (on utterances) performed best, indicating that adaptation to sentiment task is useful for involvement classification. It is important to note, however, that the utterance-level embeddings are larger than the window-level embeddings. This is due to there being more utterances than windows in the meeting corpus.",
+      "The best neural architecture we found for these embeddings is a 5-layer neural network with sizes 1024-64-32-12-2. Other hyperparameters for this model are dropout rate = 0.4, learning rate = $10^{-7}$ and activation function \u201ctanh\u201d. The UAR on the evaluation set with just BERT embeddings as input is 65.2%.",
+      "Interestingly, the neural model was outperformed by a LR directly on the embedding vectors. Perhaps the neural network requires further fine-tuning, or the neural model is too prone to overfitting, given the small training corpus. In any case, we use LR on embeddings for all subsequent results."
+    ],
+    [
+      "Our prosodic model is a 5-layer ANN, as described in Section SECREF15. The architecture is: 988-512-128-16-Pool-2. The hyperparameters are: dropout rate = 0.4, learning rate = $10^{-7}$, activation = \u201ctanh\". The UAR on the evaluation set with just openSMILE features is 62.0%."
+    ],
+    [
+      "Table TABREF24 gives the UAR for each feature subset individually, for all features combined, and for a combination in which one feature subset in turn is left out. The one-feature-set-at-time results suggest that prosody, speech activity and words are of increasing importance in that order. The leave-one-out analysis agrees that the words are the most important (largest drop in accuracy when removed), but on that criterion the prosodic features are more important than speech-activity. The combination of all features is 0.4% absolute better than any other subset, showing that all feature subsets are partly complementary.",
+      "Fig. FIGREF25 shows the same results in histogram form, but also add those with laughter information. Laughter count by itself is the strongest cue to involvement, as Laskowski BIBREF7 had found. However, even given the strong individual laughter feature, the other features add information, pushing the UAR from from 75.1% to 77.5%."
+    ],
+    [
+      "We studied detection of areas of high involvement, or \u201chot spots\u201d, within meetings using the ICSI corpus. The features that yielded the best results are in line with our intuitions. Word embeddings, speech activity features such a number of turn changes, and prosodic features are all plausible indicators of high involvement. Furthermore, the feature sets are partly complementary and yield best results when combined using a simple logistic regression model. The combined model achieves 72.6% UAR, or 77.5% with laughter feature.",
+      "For future work, we would want to see a validation on an independent meeting collection, such as business meetings. Some features, in particular laughter, are bound not be as useful in this case. More data could also enable the training of joint models that perform an early fusion of the different feature types. Also, the present study still relied on human transcripts, and it would be important to know how much UAR suffers with a realistic amount of speech recognition error. Transcription errors are expected to boost the importance of the features types that do not rely on words."
+    ],
+    [
+      "We thank Britta Wrede, Elizabeth Shriberg and Kornel Laskowski for explanations concerning the details of the data."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2422/instruction.md b/qasper-2422/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..62cf7a7f7fe7d9bebd84408e18c85463e90d9676
--- /dev/null
+++ b/qasper-2422/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Good-Enough Compositional Data Augmentation
+
+Question: Which languages do they test on?
\ No newline at end of file
diff --git a/qasper-2423/instruction.md b/qasper-2423/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..33b33f82a91ffbaed6a9797180273ac75a561802
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+++ b/qasper-2423/instruction.md
@@ -0,0 +1,71 @@
+Name of Paper: DpgMedia2019: A Dutch News Dataset for Partisanship Detection
+
+Question: What limitations are mentioned?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Dataset description",
+    "Publisher-level data",
+    "Article-level data",
+    "Analysis of the datasets",
+    "Limitations",
+    "Dataset Application",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "In a survey across 38 countries, the Pew Research Center reported that the global public opposed partisanship in news media BIBREF0 . It is, however, challenging to assess the partisanship of news articles on a large scale. We thus made an effort to create a dataset of articles annotated with political partisanship so that content analysis systems can benefit from it.",
+      "To construct a dataset of news articles labeled with partisanship, it is required that some annotators read each article and decide whether it is partisan. This is an expensive annotation process. Another way to derive a label for an article is by using the partisanship of the publisher of the article. Previous work has used this method BIBREF1 , BIBREF2 , BIBREF3 . This labeling paradigm is premised on that partisan publishers publish more partisan articles and non-partisan publishers publish more non-partisan articles. Although there would be non-partisan articles published by partisan publishers (and vice versa), and thus labeled wrongly, the assumption ensures more information than noise. Once the partisanship of a publisher is known, the labels of all its articles are known, which is fast and cheap. We created a dataset of two parts. The first part contains a large number of articles that were labeled using the partisanship of publishers. The second part contains a few hundreds of articles that were annotated by readers who were asked to read each article and answer survey questions. In the following sections, we describe the collection and annotation of both parts of the dataset."
+    ],
+    [
+      "DpgMedia2019 is a Dutch dataset that was collected from the publications within DPG Media. We took 11 publishers in the Netherlands for the dataset. These publishers include 4 national publishers, Algemeen Dagblad (AD), de Volkskrant (VK), Trouw, and Het Parool, and 7 regional publishers, de Gelderlander, Tubantia, Brabants Dagblad, Eindhovens Dagblad, BN/De Stem PZC, and de Stentor. The regional publishers are collectively called Algemeen Dagblad Regionaal (ADR). A summary of the dataset is shown in Table TABREF3 ."
+    ],
+    [
+      "We used an internal database that stores all articles written by journalists and ready to be published to collect the articles. From the database, we queried all articles that were published between 2017 and 2019. We filtered articles to be non-advertisement. We also filtered on the main sections so that the articles were not published under the sports and entertainment sections, which we assumed to be less political. After collecting, we found that a lot of the articles were published by several publishers, especially a large overlap existed between AD and ADR. To deal with the problem without losing many articles, we decided that articles that appeared in both AD and its regional publications belonged to AD. Therefore, articles were processed in the following steps:",
+      "Remove any article that was published by more than one national publisher (VK, AD, Trouw, and Het Parool). This gave us a list of unique articles from the largest 4 publishers.",
+      "Remove any article from ADR that overlapped with the articles from national publishers.",
+      "Remove any article that was published by more than one regional publisher (ADR).",
+      "The process assured that most of the articles are unique to one publisher. The only exceptions were the AD articles, of which some were also published by ADR. This is not ideal but acceptable as we show in the section UID8 that AD and ADR publishers would have the same partisanship labels. In the end, we have 103,812 articles.",
+      "To our knowledge, there is no comprehensive research about the partisanship of Dutch publishers. We thus adopted the audience-based method to decide the partisanship of publishers. Within the survey that will be explained in section SECREF11 , we asked the annotators to rate their political leanings. The question asked an annotator to report his or her political standpoints to be extreme-left, left, neutral, right, or extreme-right. We mapped extreme-left to -2, left to -1, center to 0, right to 1, extremely-right to 2, and assigned the value to each annotator. Since each annotator is subscribed to one of the publishers in our survey, we calculated the partisanship score of a publisher by averaging the scores of all annotators that subscribed to the publisher. The final score of the 11 publishers are listed in Table TABREF9 , sorted from the most left-leaning to the most right-leaning.",
+      "We decided to treat VK, Trouw, and Het Parool as partisan publishers and the rest non-partisan. This result largely accords with that from the news media report from the Pew Research Center in 2018 BIBREF4 , which found that VK is left-leaning and partisan while AD is less partisan.",
+      "Table TABREF10 shows the final publisher-level dataset of dpgMedia2019, with the number of articles and class distribution."
+    ],
+    [
+      "To collect article-level labels, we utilized a platform in the company that has been used by the market research team to collect surveys from the subscribers of different news publishers. The survey works as follows: The user is first presented with a set of selected pages (usually 4 pages and around 20 articles) from the print paper the day before. The user can select an article each time that he or she has read, and answer some questions about it. We added 3 questions to the existing survey that asked the level of partisanship, the polarity of partisanship, and which pro- or anti- entities the article presents. We also asked the political standpoint of the user. The complete survey can be found in Appendices.",
+      "The reason for using this platform was two-fold. First, the platform provided us with annotators with a higher probability to be competent with the task. Since the survey was distributed to subscribers that pay for reading news, it's more likely that they regularly read newspapers and are more familiar with the political issues and parties in the Netherlands. On the other hand, if we use crowdsourcing platforms, we need to design process to select suitable annotators, for example by nationality or anchor questions to test the annotator's ability. Second, the platform gave us more confidence that an annotator had read the article before answering questions. Since the annotators could choose which articles to annotate, it is more likely that they would rate an article that they had read and had some opinions about.",
+      "The annotation task ran for around two months in February to April 2019. We collected annotations for 1,536 articles from 3,926 annotators.",
+      "For the first question, where we asked about the intensity of partisanship, more than half of the annotations were non-partisan. About 1% of the annotation indicated an extreme partisanship, as shown in Table TABREF13 . For the polarity of partisanship, most of the annotators found it not applicable or difficult to decide, as shown in Table TABREF14 . For annotations that indicated a polarity, the highest percentage was given to progressive. Progressive and conservative seemed to be more relevant terms in the Netherlands as they are used more than their counterparts, left and right, respectively.",
+      "As for the self-rated political standpoint of the annotators, nearly half of the annotators identified themselves as left-leaning, while only around 20% were right-leaning. This is interesting because when deciding the polarity of articles, left and progressive ratings were given much more often than right and conservative ones. This shows that these left-leaning annotators were able to identify their partisanship and rate the articles accordingly.",
+      "We suspected that the annotators would induce bias in ratings based on their political leaning and we might want to normalize it. To check whether this was the case, we grouped annotators based on their political leaning and calculate the percentage of each option being annotated. In Figure FIGREF16 , we grouped options and color-coded political leanings to compare whether there are differences in the annotation between the groups. We observe that the \"extreme-right\" group used less \"somewhat partisan\", \"partisan\", and \"extremely-partisan\" annotations. This might mean that articles that were considered partisan by other groups were considered \"non-partisan\" or \"impossible to decide\" by this group. We didn't observe a significant difference between the groups. Figure FIGREF17 shows the same for the second question. Interestingly, the \"extreme-right\" group gave a lot more \"right\" and slightly more \"progressive\" ratings than other groups. In the end, we decided to use the raw ratings. How to scale the ratings based on self-identified political leaning needs more investigation.",
+      "The main question that we are interested in is the first question in our survey. In addition to the 5-point Likert scale that an annotator could choose from (non-partisan to extremely partisan), we also provided the option to choose \"impossible to decide\" because the articles could be about non-political topics. When computing inter-rater agreement, this option was ignored. The remaining 5 ratings were treated as ordinal ratings. The initial Krippendorff's alpha was 0.142, using the interval metric. To perform quality control, we devised some filtering steps based on the information we had. These steps are as follows:",
+      "Remove uninterested annotators: we assumed that annotators that provided no information were not interested in participating in the task. These annotators always rated \"not possible to decide\" for Q1, 'not applicable' or \"unknown\" for Q2, and provide no textual comment for Q3. There were in total 117 uninterested annotators and their answers were discarded.",
+      "Remove unreliable annotators: as we didn't have \"gold data\" to evaluate reliability, we used the free text that an annotator provided in Q3 to compute a reliability score. The assumption was that if an annotator was able to provide texts with meaningful partisanship description, he or she was more reliable in performing the task. To do this, we collected the text given by each annotator. We filtered out text that didn't answer the question, such as symbols, 'no idea', 'see above', etc. Then we calculated the reliability score of annotator INLINEFORM0 with equation EQREF21 , where INLINEFORM1 is the number of clean texts that annotator INLINEFORM2 provided in total and INLINEFORM3 is the number of articles that annotator INLINEFORM4 rated. DISPLAYFORM0 ",
+      "We added one to INLINEFORM0 so that annotators that gave no clean texts would not all end up with a zero score but would have different scores based on how many articles they rated. In other words, if an annotator only rated one article and didn't give textual information, we considered he or she reliable since we had little information. However, an annotator that rated ten articles but never gave useful textual information was more likely to be unreliable. The reliability score was used to filter out annotators that rarely gave meaningful text. The threshold of the filtering was decided by the Krippendorff's alpha that would be achieved after discarding the annotators with a score below the threshold.",
+      "Remove articles with too few annotations: articles with less than 3 annotations were discarded because we were not confident with a label that was derived from less than 3 annotations.",
+      "Remove unreliable articles: if at least half of the annotations of an article were \"impossible to decide\", we assumed that the article was not about issues of which partisanship could be decided.",
+      "Finally, we mapped ratings of 1 and 2 to non-partisan, and 3 to 5 to partisan. A majority vote was used to derive the final label. Articles with no majority were discarded. In the end, 766 articles remained, of which 201 were partisan. Table TABREF24 shows the number of articles and the percentage of partisan articles per publisher. The final alpha value is 0.180."
+    ],
+    [
+      "In this section, we analyze the properties and relationship of the two parts (publisher-level and article-level) of the datasets. In Table TABREF25 , we listed the length of articles of the two parts. The reason that this is important is to check whether there are apparent differences between the articles in the two parts of the dataset. We see that the lengths are comparable, which is desired.",
+      "The second analysis is the relationship between publisher and article partisanship. We want to check whether the assumption of partisan publishers publish more partisan articles is valid for our dataset. To do this, we used the article-level labels and calculated the percentage of partisan articles for each publisher. This value was then compared with the publisher partisanship. We calculated Spearsman's correlation between the publisher partisanship derived from the audience and article content. We take the absolute value of the partisanship in table TABREF9 and that in table TABREF24 . The correlation is 0.21. This low correlation resulted from the nature of the task and publishers that were considered. The partisan publishers in DPG Media publish news articles that are reviewed by professional editors. The publishers are often partisan only on a portion of the articles and on certain topics."
+    ],
+    [
+      "We identified some limitations during the process, which we describe in this section.",
+      "When deciding publisher partisanship, the number of people from whom we computed the score was small. For example, de Stentor is estimated to reach 275K readers each day on its official website. Deciding the audience leaning from 55 samples was subject to sampling bias. Besides, the scores differ very little between publishers. None of the publishers had an absolute score higher than 1, meaning that even the most partisan publisher was only slightly partisan. Deciding which publishers we consider as partisan and which not is thus not very reliable.",
+      "The article-level annotation task was not as well-defined as on a crowdsourcing platform. We included the questions as part of an existing survey and didn't want to create much burden to the annotators. Therefore, we did not provide long descriptive text that explained how a person should annotate an article. We thus run under the risk of annotator bias. This is one of the reasons for a low inter-rater agreement."
+    ],
+    [
+      "This dataset is aimed to contribute to developing a partisan news detector. There are several ways that the dataset can be used to devise the system. For example, it is possible to train the detector using publisher-level labels and test with article-level labels. It is also possible to use semi-supervised learning and treat the publisher-level part as unsupervised, or use only the article-level part. We also released the raw survey data so that new mechanisms to decide the article-level labels can be devised."
+    ],
+    [
+      "We would like to thank Johannes Kiesel and the colleagues from Factmata for providing us with the annotation questions they used when creating a hyperpartisan news dataset. We would also like to thank Jaron Harambam, Judith M\u00f6ller for helping us in asking the right questions for our annotations and Nava Tintarev for sharing her insights in the domain.",
+      "We list the questions we asked in the partisanship annotation survey, in the original Dutch language and an English translation.",
+      "Translated"
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2424/instruction.md b/qasper-2424/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..3a51d58a7945fca67873e387ddd3aab552c0fecd
--- /dev/null
+++ b/qasper-2424/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: DpgMedia2019: A Dutch News Dataset for Partisanship Detection
+
+Question: What examples of applications are mentioned?
\ No newline at end of file
diff --git a/qasper-2425/instruction.md b/qasper-2425/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..384a41ebb5ee0c56a3d578e05c0b3b063d885283
--- /dev/null
+++ b/qasper-2425/instruction.md
@@ -0,0 +1,71 @@
+Name of Paper: DpgMedia2019: A Dutch News Dataset for Partisanship Detection
+
+Question: Did they crowdsource the annotations?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Dataset description",
+    "Publisher-level data",
+    "Article-level data",
+    "Analysis of the datasets",
+    "Limitations",
+    "Dataset Application",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "In a survey across 38 countries, the Pew Research Center reported that the global public opposed partisanship in news media BIBREF0 . It is, however, challenging to assess the partisanship of news articles on a large scale. We thus made an effort to create a dataset of articles annotated with political partisanship so that content analysis systems can benefit from it.",
+      "To construct a dataset of news articles labeled with partisanship, it is required that some annotators read each article and decide whether it is partisan. This is an expensive annotation process. Another way to derive a label for an article is by using the partisanship of the publisher of the article. Previous work has used this method BIBREF1 , BIBREF2 , BIBREF3 . This labeling paradigm is premised on that partisan publishers publish more partisan articles and non-partisan publishers publish more non-partisan articles. Although there would be non-partisan articles published by partisan publishers (and vice versa), and thus labeled wrongly, the assumption ensures more information than noise. Once the partisanship of a publisher is known, the labels of all its articles are known, which is fast and cheap. We created a dataset of two parts. The first part contains a large number of articles that were labeled using the partisanship of publishers. The second part contains a few hundreds of articles that were annotated by readers who were asked to read each article and answer survey questions. In the following sections, we describe the collection and annotation of both parts of the dataset."
+    ],
+    [
+      "DpgMedia2019 is a Dutch dataset that was collected from the publications within DPG Media. We took 11 publishers in the Netherlands for the dataset. These publishers include 4 national publishers, Algemeen Dagblad (AD), de Volkskrant (VK), Trouw, and Het Parool, and 7 regional publishers, de Gelderlander, Tubantia, Brabants Dagblad, Eindhovens Dagblad, BN/De Stem PZC, and de Stentor. The regional publishers are collectively called Algemeen Dagblad Regionaal (ADR). A summary of the dataset is shown in Table TABREF3 ."
+    ],
+    [
+      "We used an internal database that stores all articles written by journalists and ready to be published to collect the articles. From the database, we queried all articles that were published between 2017 and 2019. We filtered articles to be non-advertisement. We also filtered on the main sections so that the articles were not published under the sports and entertainment sections, which we assumed to be less political. After collecting, we found that a lot of the articles were published by several publishers, especially a large overlap existed between AD and ADR. To deal with the problem without losing many articles, we decided that articles that appeared in both AD and its regional publications belonged to AD. Therefore, articles were processed in the following steps:",
+      "Remove any article that was published by more than one national publisher (VK, AD, Trouw, and Het Parool). This gave us a list of unique articles from the largest 4 publishers.",
+      "Remove any article from ADR that overlapped with the articles from national publishers.",
+      "Remove any article that was published by more than one regional publisher (ADR).",
+      "The process assured that most of the articles are unique to one publisher. The only exceptions were the AD articles, of which some were also published by ADR. This is not ideal but acceptable as we show in the section UID8 that AD and ADR publishers would have the same partisanship labels. In the end, we have 103,812 articles.",
+      "To our knowledge, there is no comprehensive research about the partisanship of Dutch publishers. We thus adopted the audience-based method to decide the partisanship of publishers. Within the survey that will be explained in section SECREF11 , we asked the annotators to rate their political leanings. The question asked an annotator to report his or her political standpoints to be extreme-left, left, neutral, right, or extreme-right. We mapped extreme-left to -2, left to -1, center to 0, right to 1, extremely-right to 2, and assigned the value to each annotator. Since each annotator is subscribed to one of the publishers in our survey, we calculated the partisanship score of a publisher by averaging the scores of all annotators that subscribed to the publisher. The final score of the 11 publishers are listed in Table TABREF9 , sorted from the most left-leaning to the most right-leaning.",
+      "We decided to treat VK, Trouw, and Het Parool as partisan publishers and the rest non-partisan. This result largely accords with that from the news media report from the Pew Research Center in 2018 BIBREF4 , which found that VK is left-leaning and partisan while AD is less partisan.",
+      "Table TABREF10 shows the final publisher-level dataset of dpgMedia2019, with the number of articles and class distribution."
+    ],
+    [
+      "To collect article-level labels, we utilized a platform in the company that has been used by the market research team to collect surveys from the subscribers of different news publishers. The survey works as follows: The user is first presented with a set of selected pages (usually 4 pages and around 20 articles) from the print paper the day before. The user can select an article each time that he or she has read, and answer some questions about it. We added 3 questions to the existing survey that asked the level of partisanship, the polarity of partisanship, and which pro- or anti- entities the article presents. We also asked the political standpoint of the user. The complete survey can be found in Appendices.",
+      "The reason for using this platform was two-fold. First, the platform provided us with annotators with a higher probability to be competent with the task. Since the survey was distributed to subscribers that pay for reading news, it's more likely that they regularly read newspapers and are more familiar with the political issues and parties in the Netherlands. On the other hand, if we use crowdsourcing platforms, we need to design process to select suitable annotators, for example by nationality or anchor questions to test the annotator's ability. Second, the platform gave us more confidence that an annotator had read the article before answering questions. Since the annotators could choose which articles to annotate, it is more likely that they would rate an article that they had read and had some opinions about.",
+      "The annotation task ran for around two months in February to April 2019. We collected annotations for 1,536 articles from 3,926 annotators.",
+      "For the first question, where we asked about the intensity of partisanship, more than half of the annotations were non-partisan. About 1% of the annotation indicated an extreme partisanship, as shown in Table TABREF13 . For the polarity of partisanship, most of the annotators found it not applicable or difficult to decide, as shown in Table TABREF14 . For annotations that indicated a polarity, the highest percentage was given to progressive. Progressive and conservative seemed to be more relevant terms in the Netherlands as they are used more than their counterparts, left and right, respectively.",
+      "As for the self-rated political standpoint of the annotators, nearly half of the annotators identified themselves as left-leaning, while only around 20% were right-leaning. This is interesting because when deciding the polarity of articles, left and progressive ratings were given much more often than right and conservative ones. This shows that these left-leaning annotators were able to identify their partisanship and rate the articles accordingly.",
+      "We suspected that the annotators would induce bias in ratings based on their political leaning and we might want to normalize it. To check whether this was the case, we grouped annotators based on their political leaning and calculate the percentage of each option being annotated. In Figure FIGREF16 , we grouped options and color-coded political leanings to compare whether there are differences in the annotation between the groups. We observe that the \"extreme-right\" group used less \"somewhat partisan\", \"partisan\", and \"extremely-partisan\" annotations. This might mean that articles that were considered partisan by other groups were considered \"non-partisan\" or \"impossible to decide\" by this group. We didn't observe a significant difference between the groups. Figure FIGREF17 shows the same for the second question. Interestingly, the \"extreme-right\" group gave a lot more \"right\" and slightly more \"progressive\" ratings than other groups. In the end, we decided to use the raw ratings. How to scale the ratings based on self-identified political leaning needs more investigation.",
+      "The main question that we are interested in is the first question in our survey. In addition to the 5-point Likert scale that an annotator could choose from (non-partisan to extremely partisan), we also provided the option to choose \"impossible to decide\" because the articles could be about non-political topics. When computing inter-rater agreement, this option was ignored. The remaining 5 ratings were treated as ordinal ratings. The initial Krippendorff's alpha was 0.142, using the interval metric. To perform quality control, we devised some filtering steps based on the information we had. These steps are as follows:",
+      "Remove uninterested annotators: we assumed that annotators that provided no information were not interested in participating in the task. These annotators always rated \"not possible to decide\" for Q1, 'not applicable' or \"unknown\" for Q2, and provide no textual comment for Q3. There were in total 117 uninterested annotators and their answers were discarded.",
+      "Remove unreliable annotators: as we didn't have \"gold data\" to evaluate reliability, we used the free text that an annotator provided in Q3 to compute a reliability score. The assumption was that if an annotator was able to provide texts with meaningful partisanship description, he or she was more reliable in performing the task. To do this, we collected the text given by each annotator. We filtered out text that didn't answer the question, such as symbols, 'no idea', 'see above', etc. Then we calculated the reliability score of annotator INLINEFORM0 with equation EQREF21 , where INLINEFORM1 is the number of clean texts that annotator INLINEFORM2 provided in total and INLINEFORM3 is the number of articles that annotator INLINEFORM4 rated. DISPLAYFORM0 ",
+      "We added one to INLINEFORM0 so that annotators that gave no clean texts would not all end up with a zero score but would have different scores based on how many articles they rated. In other words, if an annotator only rated one article and didn't give textual information, we considered he or she reliable since we had little information. However, an annotator that rated ten articles but never gave useful textual information was more likely to be unreliable. The reliability score was used to filter out annotators that rarely gave meaningful text. The threshold of the filtering was decided by the Krippendorff's alpha that would be achieved after discarding the annotators with a score below the threshold.",
+      "Remove articles with too few annotations: articles with less than 3 annotations were discarded because we were not confident with a label that was derived from less than 3 annotations.",
+      "Remove unreliable articles: if at least half of the annotations of an article were \"impossible to decide\", we assumed that the article was not about issues of which partisanship could be decided.",
+      "Finally, we mapped ratings of 1 and 2 to non-partisan, and 3 to 5 to partisan. A majority vote was used to derive the final label. Articles with no majority were discarded. In the end, 766 articles remained, of which 201 were partisan. Table TABREF24 shows the number of articles and the percentage of partisan articles per publisher. The final alpha value is 0.180."
+    ],
+    [
+      "In this section, we analyze the properties and relationship of the two parts (publisher-level and article-level) of the datasets. In Table TABREF25 , we listed the length of articles of the two parts. The reason that this is important is to check whether there are apparent differences between the articles in the two parts of the dataset. We see that the lengths are comparable, which is desired.",
+      "The second analysis is the relationship between publisher and article partisanship. We want to check whether the assumption of partisan publishers publish more partisan articles is valid for our dataset. To do this, we used the article-level labels and calculated the percentage of partisan articles for each publisher. This value was then compared with the publisher partisanship. We calculated Spearsman's correlation between the publisher partisanship derived from the audience and article content. We take the absolute value of the partisanship in table TABREF9 and that in table TABREF24 . The correlation is 0.21. This low correlation resulted from the nature of the task and publishers that were considered. The partisan publishers in DPG Media publish news articles that are reviewed by professional editors. The publishers are often partisan only on a portion of the articles and on certain topics."
+    ],
+    [
+      "We identified some limitations during the process, which we describe in this section.",
+      "When deciding publisher partisanship, the number of people from whom we computed the score was small. For example, de Stentor is estimated to reach 275K readers each day on its official website. Deciding the audience leaning from 55 samples was subject to sampling bias. Besides, the scores differ very little between publishers. None of the publishers had an absolute score higher than 1, meaning that even the most partisan publisher was only slightly partisan. Deciding which publishers we consider as partisan and which not is thus not very reliable.",
+      "The article-level annotation task was not as well-defined as on a crowdsourcing platform. We included the questions as part of an existing survey and didn't want to create much burden to the annotators. Therefore, we did not provide long descriptive text that explained how a person should annotate an article. We thus run under the risk of annotator bias. This is one of the reasons for a low inter-rater agreement."
+    ],
+    [
+      "This dataset is aimed to contribute to developing a partisan news detector. There are several ways that the dataset can be used to devise the system. For example, it is possible to train the detector using publisher-level labels and test with article-level labels. It is also possible to use semi-supervised learning and treat the publisher-level part as unsupervised, or use only the article-level part. We also released the raw survey data so that new mechanisms to decide the article-level labels can be devised."
+    ],
+    [
+      "We would like to thank Johannes Kiesel and the colleagues from Factmata for providing us with the annotation questions they used when creating a hyperpartisan news dataset. We would also like to thank Jaron Harambam, Judith M\u00f6ller for helping us in asking the right questions for our annotations and Nava Tintarev for sharing her insights in the domain.",
+      "We list the questions we asked in the partisanship annotation survey, in the original Dutch language and an English translation.",
+      "Translated"
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2433/instruction.md b/qasper-2433/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..ae5759a1edf9483f9581223ca737c85cf129e7fb
--- /dev/null
+++ b/qasper-2433/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Small-Footprint Keyword Spotting on Raw Audio Data with Sinc-Convolutions
+
+Question: Do they compare executionttime of their model against other models?
\ No newline at end of file
diff --git a/qasper-2440/instruction.md b/qasper-2440/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..6e912303a736809c1b94b23b99527154aadc9882
--- /dev/null
+++ b/qasper-2440/instruction.md
@@ -0,0 +1,79 @@
+Name of Paper: A Parallel Corpus of Theses and Dissertations Abstracts
+
+Question: What SMT models did they look at?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Material and Methods",
+    "Document retrieval and parsing",
+    "Sentence alignment",
+    "Machine translation evaluation",
+    "Manual evaluation",
+    "Results and Discussion",
+    "Corpus statistics",
+    "Translation experiments",
+    "Sentence alignment quality",
+    "Conclusion and future work"
+  ],
+  "paragraphs": [
+    [
+      "The availability of cross-language parallel corpora is one of the basis of current Statistical and Neural Machine Translation systems (e.g. SMT and NMT). Acquiring a high-quality parallel corpus that is large enough to train MT systems, specially NMT ones, is not a trivial task, since it usually demands human curating and correct alignment. In light of that, the automated creation of parallel corpora from freely available resources is extremely important in Natural Language Processing (NLP), enabling the development of accurate MT solutions. Many parallel corpora are already available, some with bilingual alignment, while others are multilingually aligned, with 3 or more languages, such as Europarl BIBREF0 , from the European Parliament, JRC-Acquis BIBREF1 , from the European Commission, OpenSubtitles BIBREF2 , from movies subtitles.",
+      "The extraction of parallel sentences from scientific writing can be a valuable language resource for MT and other NLP tasks. The development of parallel corpora from scientific texts has been researched by several authors, aiming at translation of biomedical articles BIBREF3 , BIBREF4 , or named entity recognition of biomedical concepts BIBREF5 . Regarding Portuguese/English and English/Spanish language pairs, the FAPESP corpus BIBREF6 , from the Brazilian magazine revista pesquisa FAPESP, contains more than 150,000 aligned sentences per language pair, constituting an important language resource.",
+      "In Brazil, the governmental body responsible for overseeing post-graduate programs across the country, called CAPES, tracks every enrolled student and scientific production. In addition, CAPES maintains a freely accessible database of theses and dissertations produced by the graduate students (i.e. Theses and Dissertations Catalog - TDC) since 1987, with abstracts available since 2013. Under recent governmental efforts in data sharing, CAPES made TDC available in CSV format, making it easily accessible for data mining tasks. Recent data files, from 2013 to 2016, contain valuable information for NLP purposes, such as abstracts in Portuguese and English, scientific categories, and keywords. Thus, TDC can be an important source of parallel Portuguese/English scientific abstracts.",
+      "In this work, we developed a sentence aligned parallel corpus gathered from CAPES TDC comprised of abstracts in English and Portuguese spanning the years from 2013 to 2016. In addition, we included metadata regarding the respective theses and dissertations."
+    ],
+    [
+      "In this section, we detail the information retrieved from CAPES website, the filtering process, the sentence alignment, and the evaluation experiments. An overview of the steps employed in this article is shown in Figure FIGREF1 ."
+    ],
+    [
+      "The TDC datasets are available in the CAPES open data website divided by years, from 2013 to 2016 in CSV and XLSX formats. We downloaded all CSV files from the respective website and loaded them into an SQL database for better manipulation. The database was then filtered to remove documents without both Portuguese and English abstracts, and additional metadata selected.",
+      "After the initial filtering, the resulting documents were processed for language checking to make sure that there was no misplacing of English abstracts in the Portuguese field, or the other way around, removing the documents that presented such inconsistency. We also performed a case folding to lower case letters, since the TDC datasets present all fields with uppercase letters. In addition, we also removed newline/carriage return characters (i.e \\n and \\r), as they would interfere with the sentence alignment tool."
+    ],
+    [
+      "For sentence alignment, we used the LF aligner tool, a wrapper around the Hunalign tool BIBREF7 , which provides an easy to use and complete solution for sentence alignment, including pre-loaded dictionaries for several languages.",
+      "Hunalign uses Gale-Church sentence-length information to first automatically build a dictionary based on this alignment. Once the dictionary is built, the algorithm realigns the input text in a second iteration, this time combining sentence-length information with the dictionary. When a dictionary is supplied to the algorithm, the first step is skipped. A drawback of Hunalign is that it is not designed to handle large corpora (above 10 thousand sentences), causing large memory consumption. In these cases, the algorithm cuts the large corpus in smaller manageable chunks, which may affect dictionary building.",
+      "The parallel abstracts were supplied to the aligner, which performed sentence segmentation followed by sentence alignment. A small modification in the sentence segmentation algorithm was performed to handle the fact that all words are in lowercase letters, which originally prevented segmentation. After sentence alignment, the following post-processing steps were performed: (i) removal of all non-aligned sentences; (ii) removal of all sentences with fewer than three characters, since they are likely to be noise."
+    ],
+    [
+      "To evaluate the usefulness of our corpus for SMT purposes, we used it to train an automatic translator with Moses BIBREF8 . We also trained an NMT model using the OpenNMT system BIBREF9 , and used the Google Translate Toolkit to produce state-of-the-art comparison results. The produced translations were evaluated according to the BLEU score BIBREF10 ."
+    ],
+    [
+      "Although the Hunalign tool usually presents a good alignment between sentences, we also conducted a manual validation to evaluate the quality of the aligned sentences. We randomly selected 400 pairs of sentences. If the pair was fully aligned, we marked it as \"correct\"; if the pair was incompletely aligned, due to segmentation errors, for instance, we marked it as \"partial\"; otherwise, when the pair was incorrectly aligned, we marked it as \"no alignment\"."
+    ],
+    [
+      "In this section, we present the corpus' statistics and quality evaluation regarding SMT and NMT systems, as well as the manual evaluation of sentence alignment."
+    ],
+    [
+      "Table TABREF12 shows the statistics (i.e. number of documents and sentences) for the aligned corpus according to the 9 main knowledge areas defined by CAPES. The dataset is available in TMX format BIBREF11 , since it is the standard format for translation memories. We also made available the aligned corpus in an SQLite database in order to facilitate future stratification according to knowledge area, for instance. In this database, we included the following metadata information: year, university, title in Portuguese, type of document (i.e. theses or dissertation), keywords in both languages, knowledge areas and subareas according to CAPES, and URL for the full-text PDF in Portuguese. An excerpt of the corpus is shown in Table TABREF13 "
+    ],
+    [
+      "Prior to the MT experiments, sentences were randomly split in three disjoint datasets: training, development, and test. Approximately 13,000 sentences were allocated in the development and test sets, while the remaining was used for training. For the SMT experiment, we followed the instructions of Moses baseline system. For the NMT experiment, we used the Torch implementation to train a 2-layer LSTM model with 500 hidden units in both encoder and decoder, with 12 epochs. During translation, the option to replace UNK words by the word in the input language was used, since this is also the default in Moses.",
+      "Table TABREF17 presents the BLEU scores for both translation directions with English and Portuguese on the development and test partitions for Moses and OpenNMT models. We also included the scores for Google Translate (GT) as a benchmark of a state-of-the-art system which is widely used.",
+      "NMT model achieved better performance than the SMT one for EN INLINEFORM0 PT direction, with approximately 2.17 percentage points (pp) higher, while presenting almost the same score for PT INLINEFORM1 EN. When comparing our models to GT, both of them presented better BLEU scores, specially for the EN INLINEFORM2 PT direction, with values ranging from 1.27 pp to 4.30 pp higher than GT.",
+      "We highlight that these results may be due to two main factors: corpus size, and domain. Our corpus is fairly large for both SMT and NMT approaches, comprised of almost 1.3M sentences, which enables the development of robust models. Regarding domain, GT is a generic tool not trained for a specific domain, thus it may produce lower results than a domain specific model such as ours. Scientific writing usually has a strict writing style, with less variation than novels or speeches, for instance, favoring the development of tailored MT systems.",
+      "Below, we demonstrate some sentences translated by Moses and OpenNMT compared to the suggested human translation. One can notice that in fact NMT model tend to produce more fluent results, specially regarding verbal regency.",
+      "Human translation: this paper presents a study of efficiency and power management in a packaging industry and plastic films.",
+      "OpenNMT: this work presents a study of efficiency and electricity management in a packaging industry and plastic films.",
+      "Moses: in this work presents a study of efficiency and power management in a packaging industry and plastic films.",
+      "GT: this paper presents a study of the efficiency and management of electric power in a packaging and plastic film industry.",
+      "Human translation: this fact corroborates the difficulty in modeling human behavior.",
+      "OpenNMT: this fact corroborates the difficulty in modeling human behavior.",
+      "Moses: this fact corroborated the difficulty in model the human behavior.",
+      "GT: this fact corroborates the difficulty in modeling human behavior."
+    ],
+    [
+      "We manually validated the alignment quality for 400 sentences randomly selected from the parsed corpus and assigned quality labels according Section SECREF9 . From all the evaluated sentences, 82.30% were correctly aligned, while 13.33% were partially aligned, and 4.35% presented no alignment. The small percentage of no alignment is probably due to the use of Hunalign tool with the provided EN/PT dictionary.",
+      "Regarding the partial alignment, most of the problems are result of segmentation issues previous to the alignment, which wrongly split the sentences. Since all words were case folded to lowercase letters, the segmenter lost an important source of information for the correct segmentation, generating malformed sentences. Some examples of partial alignment errors are shown in Table TABREF19 , where most senteces were truncated in the wrong part."
+    ],
+    [
+      "We developed a parallel corpus of theses and dissertations abstracts in Portuguese and English. Our corpus is based on the CAPES TDC dataset, which contains information regarding all theses and dissertations presented in Brazil from 2013 to 2016, including abstracts and other metadata.",
+      "Our corpus was evaluated through SMT and NMT experiments with Moses and OpenNMT systems, presenting superior performance regarding BLEU score than Google Translate. The NMT model also presented superior results than the SMT one for the EN INLINEFORM0 PT translation direction. We also manually evaluated sentences regarding alignment quality, with average 82.30% of sentences correctly aligned.",
+      "For future work, we foresee the use of the presented corpus in mono and cross-language text mining tasks, such as text classification and clustering. As we included several metadata, these tasks can be facilitated. Other machine translation approaches can also be tested, including the concatenation of this corpus with other multi-domain ones."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: A Parallel Corpus of Theses and Dissertations Abstracts
+
+Question: Which NMT models did they experiment with?
\ No newline at end of file
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+Name of Paper: Pivot-based Transfer Learning for Neural Machine Translation between Non-English Languages
+
+Question: Are experiments performed with any other pair of languages, how did proposed method perform compared to other models?
\ No newline at end of file
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+Name of Paper: Pivot-based Transfer Learning for Neural Machine Translation between Non-English Languages
+
+Question: Is pivot language used in experiments English or some other language?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Pivot-based Transfer Learning",
+    "Pivot-based Transfer Learning ::: Step-wise Pre-training",
+    "Pivot-based Transfer Learning ::: Pivot Adapter",
+    "Pivot-based Transfer Learning ::: Cross-lingual Encoder",
+    "Main Results",
+    "Analysis",
+    "Analysis ::: Pivot Adapter",
+    "Analysis ::: Cross-lingual Encoder",
+    "Analysis ::: Zero-resource/Zero-shot Scenarios",
+    "Analysis ::: Large-scale Results",
+    "Conclusion",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "Machine translation (MT) research is biased towards language pairs including English due to the ease of collecting parallel corpora. Translation between non-English languages, e.g., French$\\rightarrow $German, is usually done with pivoting through English, i.e., translating French (source) input to English (pivot) first with a French$\\rightarrow $English model which is later translated to German (target) with a English$\\rightarrow $German model BIBREF0, BIBREF1, BIBREF2. However, pivoting requires doubled decoding time and the translation errors are propagated or expanded via the two-step process.",
+      "Therefore, it is more beneficial to build a single source$\\rightarrow $target model directly for both efficiency and adequacy. Since non-English language pairs often have little or no parallel text, common choices to avoid pivoting in NMT are generating pivot-based synthetic data BIBREF3, BIBREF4 or training multilingual systems BIBREF5, BIBREF6.",
+      "In this work, we present novel transfer learning techniques to effectively train a single, direct NMT model for a non-English language pair. We pre-train NMT models for source$\\rightarrow $pivot and pivot$\\rightarrow $target, which are transferred to a source$\\rightarrow $target model. To optimize the usage of given source-pivot and pivot-target parallel data for the source$\\rightarrow $target direction, we devise the following techniques to smooth the discrepancy between the pre-trained and final models:",
+      "Step-wise pre-training with careful parameter freezing.",
+      "Additional adapter component to familiarize the pre-trained decoder with the outputs of the pre-trained encoder.",
+      "Cross-lingual encoder pre-training with autoencoding of the pivot language.",
+      "Our methods are evaluated in two non-English language pairs of WMT 2019 news translation tasks: high-resource (French$\\rightarrow $German) and low-resource (German$\\rightarrow $Czech). We show that NMT models pre-trained with our methods are highly effective in various data conditions, when fine-tuned for source$\\rightarrow $target with:",
+      "Real parallel corpus",
+      "Pivot-based synthetic parallel corpus (zero-resource)",
+      "None (zero-shot)",
+      "For each data condition, we consistently outperform strong baselines, e.g., multilingual, pivoting, or teacher-student, showing the universal effectiveness of our transfer learning schemes.",
+      "The rest of the paper is organized as follows. We first review important previous works on pivot-based MT in Section SECREF2. Our three pre-training techniques are presented in Section SECREF3. Section SECREF4 shows main results of our methods with a detailed description of the experimental setups. Section SECREF5 studies variants of our methods and reports the results without source-target parallel resources or with large synthetic parallel data. Section 6 draws conclusion of this work with future research directions."
+    ],
+    [
+      "In this section, we first review existing approaches to leverage a pivot language in low-resource/zero-resource MT. They can be divided into three categories:",
+      "Pivot translation (pivoting). The most naive approach is reusing (already trained) source$\\rightarrow $pivot and pivot$\\rightarrow $target models directly, decoding twice via the pivot language BIBREF7, BIBREF0. One can keep $N$-best hypotheses in the pivot language to reduce the prediction bias BIBREF1 and improve the final translation by system combination BIBREF8, which however increases the translation time even more. In multilingual NMT, firat2016zero modify the second translation step (pivot$\\rightarrow $target) to use source and pivot language sentences together as the input.",
+      "Pivot-based synthetic parallel data. We may translate the pivot side of given pivot-target parallel data using a pivot$\\rightarrow $source model BIBREF3, or the other way around translating source-pivot data using a pivot$\\rightarrow $target model BIBREF0. For NMT, the former is extended by zheng2017maximum to compute the expectation over synthetic source sentences. The latter is also called teacher-student approach BIBREF4, where the pivot$\\rightarrow $target model (teacher) produces target hypotheses for training the source$\\rightarrow $target model (student).",
+      "Pivot-based model training. In phrase-based MT, there have been many efforts to combine phrase/word level features of source-pivot and pivot-target into a source$\\rightarrow $target system BIBREF1, BIBREF2, BIBREF9, BIBREF10, BIBREF11, BIBREF12. In NMT, cheng2017joint jointly train for three translation directions of source-pivot-target by sharing network components, where ren2018triangular use the expectation-maximization algorithm with the target sentence as a latent variable. lu2018neural deploy intermediate recurrent layers which are common for multiple encoders and decoders, while johnson2017google share all components of a single multilingual model. Both methods train the model for language pairs involving English but enable zero-shot translation for unseen non-English language pairs. For this, ha2017effective encode the target language as an additional embedding and filter out non-target tokens in the output. lakew2017improving combine the multilingual training with synthetic data generation to improve the zero-shot performance iteratively, where sestorain2018zero applies the NMT prediction score and a language model score to each synthetic example as gradient weights.",
+      "Our work is based on transfer learning BIBREF13 and belongs to the third category: model training. On the contrary to the multilingual joint training, we suggest two distinct steps: pre-training (with source-pivot and pivot-target data) and fine-tuning (with source-target data). With our proposed methods, we prevent the model from losing its capacity to other languages while utilizing the information from related language pairs well, as shown in the experiments (Section SECREF4).",
+      "Our pivot adapter (Section SECREF18) shares the same motivation with the interlingua component of lu2018neural, but is much compact, independent of variable input length, and easy to train offline. The adapter training algorithm is adopted from bilingual word embedding mapping BIBREF14. Our cross-lingual encoder (Section SECREF26) is inspired by cross-lingual sentence embedding algorithms using NMT BIBREF15, BIBREF16.",
+      "Transfer learning was first introduced to NMT by zoph2016transfer, where only the source language is switched before/after the transfer. nguyen2017transfer and kocmi2018trivial use shared subword vocabularies to work with more languages and help target language switches. kim2019effective propose additional techniques to enable NMT transfer even without shared vocabularies. To the best of our knowledge, we are the first to propose transfer learning strategies specialized in utilizing a pivot language, transferring a source encoder and a target decoder at the same time. Also, for the first time, we present successful zero-shot translation results only with pivot-based NMT pre-training."
+    ],
+    [
+      "Our methods are based on a simple transfer learning principle for NMT, adjusted to a usual data condition for non-English language pairs: lots of source-pivot and pivot-target parallel data, little (low-resource) or no (zero-resource) source-target parallel data. Here are the core steps of the plain transfer (Figure FIGREF10):",
+      "Pre-train a source$\\rightarrow $pivot model with a source-pivot parallel corpus and a pivot$\\rightarrow $target model with a pivot-target parallel corpus.",
+      "Initialize the source$\\rightarrow $target model with the source encoder from the pre-trained source$\\rightarrow $pivot model and the target decoder from the pre-trained pivot$\\rightarrow $target model.",
+      "Continue the training with a source-target parallel corpus.",
+      "If we skip the last step (for zero-resource cases) and perform the source$\\rightarrow $target translation directly, it corresponds to zero-shot translation.",
+      "Thanks to the pivot language, we can pre-train a source encoder and a target decoder without changing the model architecture or training objective for NMT. On the contrary to other NMT transfer scenarios BIBREF13, BIBREF17, BIBREF18, this principle has no language mismatch between transferor and transferee on each source/target side. Experimental results (Section SECREF4) also show its competitiveness despite its simplicity.",
+      "Nonetheless, the main caveat of this basic pre-training is that the source encoder is trained to be used by an English decoder, while the target decoder is trained to use the outputs of an English encoder \u2014 not of a source encoder. In the following, we propose three techniques to mitigate the inconsistency of source$\\rightarrow $pivot and pivot$\\rightarrow $target pre-training stages. Note that these techniques are not exclusive and some of them can complement others for a better performance of the final model."
+    ],
+    [
+      "A simple remedy to make the pre-trained encoder and decoder refer to each other is to train a single NMT model for source$\\rightarrow $pivot and pivot$\\rightarrow $target in consecutive steps (Figure FIGREF15):",
+      "Train a source$\\rightarrow $pivot model with a source-pivot parallel corpus.",
+      "Continue the training with a pivot-target parallel corpus, while freezing the encoder parameters of 1.",
+      "In the second step, a target decoder is trained to use the outputs of the pre-trained source encoder as its input. Freezing the pre-trained encoder ensures that, even after the second step, the encoder is still modeling the source language although we train the NMT model for pivot$\\rightarrow $target. Without the freezing, the encoder completely adapts to the pivot language input and is likely to forget source language sentences.",
+      "We build a joint vocabulary of the source and pivot languages so that the encoder effectively represents both languages. The frozen encoder is pre-trained for the source language in the first step, but also able to encode a pivot language sentence in a similar representation space. It is more effective for linguistically similar languages where many tokens are common for both languages in the joint vocabulary."
+    ],
+    [
+      "Instead of the step-wise pre-training, we can also postprocess the network to enhance the connection between the source encoder and the target decoder which are pre-trained individually. Our idea is that, after the pre-training steps, we adapt the source encoder outputs to the pivot encoder outputs to which the target decoder is more familiar (Figure FIGREF19). We learn a linear mapping between the two representation spaces with a small source-pivot parallel corpus:",
+      "Encode the source sentences with the source encoder of the pre-trained source$\\rightarrow $pivot model.",
+      "Encode the pivot sentences with the pivot encoder of the pre-trained pivot$\\rightarrow $target model.",
+      "Apply a pooling to each sentence of 1 and 2, extracting representation vectors for each sentence pair: ($\\mathbf {s}$, $\\mathbf {p}$).",
+      "Train a mapping $\\mathbf {M}\\in \\mathbb {R}^{d \\times d}$ to minimize the distance between the pooled representations $\\mathbf {s}\\in \\mathbb {R}^{d \\times 1}$ and $\\mathbf {p}\\in \\mathbb {R}^{d \\times 1}$, where the source representation is first fed to the mapping:",
+      "where $d$ is the hidden layer size of the encoders. Introducing matrix notations $\\mathbf {S}\\in \\mathbb {R}^{d \\times n}$ and $\\mathbf {P}\\in \\mathbb {R}^{d \\times n}$, which concatenate the pooled representations of all $n$ sentences for each side in the source-pivot corpus, we rewrite Equation DISPLAY_FORM24 as:",
+      "which can be easily computed by the singular value decomposition (SVD) for a closed-form solution, if we put an orthogonality constraint on $\\mathbf {M}$ BIBREF14. The resulting optimization is also called Procrustes problem.",
+      "The learned mapping is multiplied to encoder outputs of all positions in the final source$\\rightarrow $target tuning step. With this mapping, the source encoder emits sentence representations that lie in a similar space of the pivot encoder. Since the target decoder is pre-trained for pivot$\\rightarrow $target and accustomed to receive the pivot encoder outputs, it should process the mapped encoder outputs better than the original source encoder outputs."
+    ],
+    [
+      "As a third technique, we modify the source$\\rightarrow $pivot pre-training procedure to force the encoder to have cross-linguality over source and pivot languages; modeling source and pivot sentences in the same mathematical space. We achieve this by an additional autoencoding objective from a pivot sentence to the same pivot sentence (Figure FIGREF27).",
+      "The encoder is fed with sentences of both source and pivot languages, which are processed by a shared decoder that outputs only the pivot language. In this way, the encoder is learned to produce representations in a shared space regardless of the input language, since they are used in the same decoder. This cross-lingual space facilitates smoother learning of the final source$\\rightarrow $target model, because the decoder is pre-trained to translate the pivot language.",
+      "The same input/output in autoencoding encourages, however, merely copying the input; it is said to be not proper for learning complex structure of the data domain BIBREF19. Denoising autoencoder addresses this by corrupting the input sentences by artificial noises BIBREF20. Learning to reconstruct clean sentences, it encodes linguistic structures of natural language sentences, e.g., word order, better than copying. Here are the noise types we use BIBREF21:",
+      "Drop tokens randomly with a probability $p_\\mathrm {del}$",
+      "Replace tokens with a <BLANK> token randomly with a probability $p_\\mathrm {rep}$",
+      "Permute the token positions randomly so that the difference between an original index and its new index is less than or equal to $d_\\mathrm {per}$",
+      "We set $p_\\mathrm {del}=0.1$, $p_\\mathrm {rep}=0.1$, and $d_\\mathrm {per}=3$ in our experiments.",
+      "",
+      "The key idea of all three methods is to build a closer connection between the pre-trained encoder and decoder via a pivot language. The difference is in when we do this job: Cross-lingual encoder (Section SECREF26) changes the encoder pre-training stage (source$\\rightarrow $pivot), while step-wise pre-training (Section SECREF14) modifies decoder pre-training stage (pivot$\\rightarrow $target). Pivot adapter (Section SECREF18) is applied after all pre-training steps."
+    ],
+    [
+      "We evaluate the proposed transfer learning techniques in two non-English language pairs of WMT 2019 news translation tasks: French$\\rightarrow $German and German$\\rightarrow $Czech.",
+      "",
+      "Data We used the News Commentary v14 parallel corpus and newstest2008-2010 test sets as the source-target training data for both tasks. The newstest sets were oversampled four times. The German$\\rightarrow $Czech task was originally limited to unsupervised learning (using only monolingual corpora) in WMT 2019, but we relaxed this constraint by the available parallel data. We used newstest2011 as a validation set and newstest2012/newstest2013 as the test sets.",
+      "Both language pairs have much abundant parallel data in source-pivot and pivot-target with English as the pivot language. Detailed corpus statistics are given in Table TABREF33.",
+      "",
+      "Preprocessing We used the Moses tokenizer and applied true-casing on all corpora. For all transfer learning setups, we learned byte pair encoding (BPE) BIBREF22 for each language individually with 32k merge operations, except for cross-lingual encoder training with joint BPE only over source and pivot languages. This is for modularity of pre-trained models: for example, a French$\\rightarrow $English model trained with joint French/English/German BPE could be transferred smoothly to a French$\\rightarrow $German model, but would not be optimal for a transfer to e.g., a French$\\rightarrow $Korean model. Once we pre-train an NMT model with separate BPE vocabularies, we can reuse it for various final language pairs without wasting unused portion of subword vocabularies (e.g., German-specific tokens in building a French$\\rightarrow $Korean model).",
+      "On the contrary, baselines used joint BPE over all languages with also 32k merges.",
+      "",
+      "Model and Training The 6-layer base Transformer architecture BIBREF23 was used for all of our experiments. Batch size was set to 4,096 tokens. Each checkpoint amounts to 10k updates for pre-training and 20k updates for fine-tuning.",
+      "Each model was optimized with Adam BIBREF24 with an initial learning rate of 0.0001, which was multiplied by 0.7 whenever perplexity on the validation set was not improved for three checkpoints. When it was not improved for eight checkpoints, we stopped the training. The NMT model training and transfer were done with the OpenNMT toolkit BIBREF25.",
+      "Pivot adapter was trained using the Muse toolkit BIBREF26, which was originally developed for bilingual word embeddings but we adjusted for matching sentence representations.",
+      "",
+      "Baselines We thoroughly compare our approaches to the following baselines:",
+      "Direct source$\\rightarrow $target: A standard NMT model trained on given source$\\rightarrow $target parallel data.",
+      "Multilingual: A single, shared NMT model for multiple translation directions BIBREF6.",
+      "Many-to-many: Trained for all possible directions among source, target, and pivot languages.",
+      "Many-to-one: Trained for only the directions to target language, i.e., source$\\rightarrow $target and pivot$\\rightarrow $target, which tends to work better than many-to-many systems BIBREF27.",
+      "",
+      "In Table TABREF34, we report principal results after fine-tuning the pre-trained models using source-target parallel data.",
+      "As for baselines, multilingual models are better than a direct NMT model. The many-to-many models surpass the many-to-one models; since both tasks are in a low-resource setup, the model gains a lot from related language pairs even if the target languages do not match.",
+      "Plain transfer of pre-trained encoder/decoder without additional techniques (Figure FIGREF10) shows a nice improvement over the direct baseline: up to +2.7% Bleu for French$\\rightarrow $German and +5.2% Bleu for German$\\rightarrow $Czech. Pivot adapter provides an additional boost of maximum +0.7% Bleu or -0.7% Ter.",
+      "Cross-lingual encoder pre-training is proved to be not effective in the plain transfer setup. It shows no improvements over plain transfer in French$\\rightarrow $German, and 0.4% Bleu worse performance in German$\\rightarrow $Czech. We conjecture that the cross-lingual encoder needs a lot more data to be fine-tuned for another decoder, where the encoder capacity is basically divided into two languages at the beginning of the fine-tuning. On the other hand, the pivot adapter directly improves the connection to an individually pre-trained decoder, which works nicely with small fine-tuning data.",
+      "Pivot adapter gives an additional improvement on top of the cross-lingual encoder; up to +0.4% Bleu in French$\\rightarrow $German and +0.6% Bleu in German$\\rightarrow $Czech. In this case, we extract source and pivot sentence representations from the same shared encoder for training the adapter.",
+      "Step-wise pre-training gives a big improvement up to +1.2% Bleu or -1.6% Ter against plain transfer in French$\\rightarrow $German. It shows the best performance in both tasks when combined with the cross-lingual encoder: up to +1.2% Bleu in French$\\rightarrow $German and +2.6% Bleu in German$\\rightarrow $Czech, compared to the multilingual baseline. Step-wise pre-training prevents the cross-lingual encoder from degeneration, since the pivot$\\rightarrow $target pre-training (Step 2 in Section SECREF14) also learns the encoder-decoder connection with a large amount of data \u2014 in addition to the source$\\rightarrow $target tuning step afterwards.",
+      "Note that the pivot adapter, which inserts an extra layer between the encoder and decoder, is not appropriate after the step-wise pre-training; the decoder is already trained to correlate well with the pre-trained encoder. We experimented with the pivot adapter on top of step-wise pre-trained models \u2014 with or without cross-lingual encoder \u2014 but obtained detrimental results.",
+      "Compared to pivot translation (Table TABREF43), our best results are also clearly better in French $\\rightarrow $German and comparable in German$\\rightarrow $Czech."
+    ],
+    [
+      "In this section, we conduct ablation studies on the variants of our methods and see how they perform in different data conditions."
+    ],
+    [
+      "Firstly, we compare variants of the pivot adapter (Section SECREF18) in Table TABREF40. The row \u201cNone\u201d shows that a randomly initialized linear layer already guides the pre-trained encoder/decoder to harmonize with each other. Of course, when we train the adapter to map source encoder outputs to pivot encoder outputs, the performance gets better. For compressing encoder outputs over positions, average-pooling is better than max-pooling. We observed the same trend in the other test set and in French$\\rightarrow $German.",
+      "We also tested nonlinear pivot adapter, e.g., a 2-layer feedforward network with ReLU activations, but the performance was not better than just a linear adapter."
+    ],
+    [
+      "Table TABREF42 verifies that the noisy input in autoencoding is indeed beneficial to our cross-lingual encoder. It improves the final translation performance by maximum +2.1% Bleu, compared to using the copying autoencoding objective.",
+      "As the training data for autoencoding, we also compare between purely monolingual data and the pivot side of the source-pivot parallel data. By the latter, one can expect a stronger signal for a joint encoder representation space, since two different inputs (in source/pivot languages) are used to produce the exactly same output sentence (in pivot language). The results also tell that there are slight but consistent improvements by using the pivot part of the parallel data.",
+      "Again, we performed these comparisons in the other test set and German$\\rightarrow $Czech, observing the same tendency in results."
+    ],
+    [
+      "If we do not have an access to any source-target parallel data (zero-resource), non-English language pairs have two options for still building a working NMT system, given source-English and target-English parallel data:",
+      "Zero-shot: Perform source$\\rightarrow $target translation using models which have not seen any source-target parallel sentences, e.g., multilingual models or pivoting (Section SECREF2.UNKREF7).",
+      "Pivot-based synthetic data: Generate synthetic source-target parallel data using source$\\leftrightarrow $English and target$\\leftrightarrow $English models (Section SECREF2.UNKREF8). Use this data to train a model for source$\\rightarrow $target.",
+      "Table TABREF43 shows how our pre-trained models perform in zero-resource scenarios with the two options. Note that, unlike Table TABREF34, the multilingual baselines exclude source$\\rightarrow $target and target$\\rightarrow $source directions. First of all, plain transfer, where the encoder and the decoder are pre-trained separately, is poor in zero-shot scenarios. It simply fails to connect different representation spaces of the pre-trained encoder and decoder. In our experiments, neither pivot adapter nor cross-lingual encoder could enhance the zero-shot translation of plain transfer.",
+      "Step-wise pre-training solves this problem by changing the decoder pre-training to familiarize itself with representations from an already pre-trained encoder. It achieves zero-shot performance of 11.5% Bleu in French$\\rightarrow $German and 6.5% Bleu in German$\\rightarrow $Czech (newstest2013), while showing comparable or better fine-tuned performance against plain transfer (see also Table TABREF34).",
+      "With the pre-trained cross-lingual encoder, the zero-shot performance of step-wise pre-training is superior to that of pivot translation in French$\\rightarrow $German with only a single model. It is worse than pivot translation in German$\\rightarrow $Czech. We think that the data size of pivot-target is critical in pivot translation; relatively huge data for English$\\rightarrow $Czech make the pivot translation stronger. Note again that, nevertheless, pivoting (second row) is very poor in efficiency since it performs decoding twice with the individual models.",
+      "For the second option (pivot-based synthetic data), we compare our methods against the sentence-level beam search version of the teacher-student framework BIBREF4, with which we generated 10M synthetic parallel sentence pairs. We also tried other variants of chen2017teacher, e.g., $N$-best hypotheses with weights, but there were no consistent improvements.",
+      "Due to enormous bilingual signals, the model trained with the teacher-student synthetic data outperforms pivot translation. If tuned with the same synthetic data, our pre-trained model performs even better (last row), achieving the best zero-resource results on three of the four test sets.",
+      "We also evaluate our best German$\\rightarrow $Czech zero-resource model on newstest2019 and compare it with the participants of the WMT 2019 unsupervised news translation task. Ours yield 17.2% Bleu, which is much better than the best single unsupervised system of the winner of the task (15.5%) BIBREF28. We argue that, if one has enough source-English and English-target parallel data for a non-English language pair, it is more encouraged to adopt pivot-based transfer learning than unsupervised MT \u2014 even if there is no source-target parallel data. In this case, unsupervised MT unnecessarily restricts the data condition to using only monolingual data and its high computational cost does not pay off; simple pivot-based pre-training steps are more efficient and effective."
+    ],
+    [
+      "We also study the effect of pivot-based transfer learning in more data-rich scenarios: 1) with large synthetic source-target data (German$\\rightarrow $Czech), and 2) with larger real source-target data in combination with the synthetic data (French$\\rightarrow $German). We generated synthetic parallel data using pivot-based back-translation BIBREF3: 5M sentence pairs for German$\\rightarrow $Czech and 9.1M sentence pairs for French$\\rightarrow $German. For the second scenario, we also prepared 2.3M more lines of French$\\rightarrow $German real parallel data from Europarl v7 and Common Crawl corpora.",
+      "Table TABREF47 shows our transfer learning results fine-tuned with a combination of given parallel data and generated synthetic parallel data. The real source-target parallel data are oversampled to make the ratio of real and synthetic data to be 1:2. As expected, the direct source$\\rightarrow $target model can be improved considerably by training with large synthetic data.",
+      "Plain pivot-based transfer outperforms the synthetic data baseline by up to +1.9% Bleu or -3.3% Ter. However, the pivot adapter or cross-lingual encoder gives marginal or inconsistent improvements over the plain transfer. We suppose that the entire model can be tuned sufficiently well without additional adapter layers or a well-curated training process, once we have a large source-target parallel corpus for fine-tuning."
+    ],
+    [
+      "In this paper, we propose three effective techniques for transfer learning using pivot-based parallel data. The principle is to pre-train NMT models with source-pivot and pivot-target parallel data and transfer the source encoder and the target decoder. To resolve the input/output discrepancy of the pre-trained encoder and decoder, we 1) consecutively pre-train the model for source$\\rightarrow $pivot and pivot$\\rightarrow $target, 2) append an additional layer after the source encoder which adapts the encoder output to the pivot language space, or 3) train a cross-lingual encoder over source and pivot languages.",
+      "Our methods are suitable for most of the non-English language pairs with lots of parallel data involving English. Experiments in WMT 2019 French$\\rightarrow $German and German$\\rightarrow $Czech tasks show that our methods significantly improve the final source$\\rightarrow $target translation performance, outperforming multilingual models by up to +2.6% Bleu. The methods are applicable also to zero-resource language pairs, showing a strong performance in the zero-shot setting or with pivot-based synthetic data. We claim that our methods expand the advances in NMT to many more non-English language pairs that are not yet studied well.",
+      "Future work will be zero-shot translation without step-wise pre-training, i.e., combining individually pre-trained encoders and decoders freely for a fast development of NMT systems for a new non-English language pair."
+    ],
+    [
+      "",
+      "This work has received funding from the European Research Council (ERC) (under the European Union's Horizon 2020 research and innovation programme, grant agreement No 694537, project \"SEQCLAS\") and eBay Inc. The work reflects only the authors' views and none of the funding agencies is responsible for any use that may be made of the information it contains."
+    ]
+  ]
+}
+```
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+Name of Paper: Pivot-based Transfer Learning for Neural Machine Translation between Non-English Languages
+
+Question: What are multilingual models that were outperformed in performed experiment?
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+Name of Paper: Image Captioning: Transforming Objects into Words
+
+Question: What are the common captioning metrics?
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+Name of Paper: Open-World Knowledge Graph Completion
+
+Question: Can the model add new relations to the knowledge graph, or just new entities?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Closed-World Knowledge Graph Completion",
+    "ConMask: A Content Masking Model for Open-World KGC",
+    "Relationship-Dependent Content Masking",
+    "Target Fusion",
+    "Semantic Averaging",
+    "Loss Function",
+    "Experiments",
+    "Settings",
+    "Data Sets",
+    "Open-World Entity Prediction",
+    "Closed-World Entity Prediction",
+    "Discussion",
+    "Conclusion and Future Work"
+  ],
+  "paragraphs": [
+    [
+      "Knowledge Graphs (KGs) are a special type of information network that represents knowledge using RDF-style triples $\\langle h$ , $r$ , $t\\rangle $ , where $h$ represents some head entity and $r$ represents some relationship that connects $h$ to some tail entity $t$ . In this formalism a statement like \u201cSpringfield is the capital of Illinois\u201d can be represented as $\\langle $ Springfield, capitalOf, Illinois $\\rangle $ . Recently, a variety of KGs, such as DBPedia BIBREF0 , and ConceptNet BIBREF1 , have been curated in the service of fact checking BIBREF2 , question answering BIBREF3 , entity linking BIBREF4 , and for many other tasks BIBREF5 . Despite their usefulness and popularity, KGs are often noisy and incomplete. For example, DBPedia, which is generated from Wikipedia's infoboxes, contains $4.6$ million entities, but half of these entities contain less than 5 relationships.",
+      "Based on this observation, researchers aim to improve the accuracy and reliability of KGs by predicting the existence (or probability) of relationships. This task is often called Knowledge Graph Completion (KGC). Continuing the example from above, suppose the relationship capitalOf is missing between Indianapolis and Indiana; the KGC task might predict this missing relationship based on the topological similarity between this part of the KG and the part containing Springfield and Illinois. Progress in vector embeddings originating with word2vec has produced major advancements in the KGC task. Typical embedding-based KGC algorithms like TransE BIBREF6 and others learn low-dimensional representations (i.e., embeddings) for entities and relationships using topological features. These models are able to predict the existence of missing relationships thereby \u201ccompleting\u201d the KG.",
+      "Existing KGC models implicitly operate under the Closed-World Assumption BIBREF7 in which all entities and relationships in the KG cannot be changed \u2013 only discovered. We formally define the Closed-word KGC task as follows:",
+      "Definition 1 Given an incomplete Knowledge Graph $\\mathcal {G}=(\\mathbf {E},\\mathbf {R},\\mathbf {T})$ , where $\\mathbf {E}$ , $\\mathbf {R}$ , and $\\mathbf {T}$ are the entity set, relationship set, and triple set respectively, Closed-World Knowledge Graph Completion completes $\\mathcal {G}$ by finding a set of missing triples $\\mathbf {T^\\prime }=\\lbrace \\langle h,r,t\\rangle | h\\in \\mathbf {E}, r \\in \\mathbf {R}, t \\in \\mathbf {E}, \\langle h,r,t\\rangle \\notin \\mathbf {T}\\rbrace $ in the incomplete Knowledge Graph $\\mathcal {G}$ .",
+      "Closed-world KGC models heavily rely on the connectivity of the existing KG and are best able to predict relationships between existing, well-connected entities. Unfortunately, because of their strict reliance on the connectivity of the existing KG, closed-world KGC models are unable to predict the relationships of poorly connected or new entities. Therefore, we assess that closed-world KGC is most suitable for fixed or slowly evolving KGs.",
+      "However, most real-world KGs evolve quickly with new entities and relationships being added by the minute. For example, in the 6 months between DBPedia's October 2015 release and its April 2016 release $36,340$ new English entities were added \u2013 a rate of 200 new entities per day. Recall that DBPedia merely tracks changes to Wikipedia infoboxes, so these updates do not include newly added articles without valid infobox data. Because of the accelerated growth of online information, repeatedly re-training closed-world models every day (or hour) has become impractical.",
+      "In the present work we borrow the idea of open-world assumption from probabilistic database literature BIBREF8 and relax the closed-world assumption to develop an Open-World Knowledge Graph Completion model capable of predicting relationships involving unseen entities or those entities that have only a few connections. Formally we define the open-world KGC task as follows:",
+      "Definition 2 Given an incomplete Knowledge Graph $\\mathcal {G}=(\\mathbf {E},\\mathbf {R},\\mathbf {T})$ , where $\\mathbf {E}$ , $\\mathbf {R}$ , and $\\mathbf {T}$ are the entity set, relationship set, and triple set respectively, Open-World Knowledge Graph Completion completes $\\mathcal {G}$ by finding a set of missing triples $\\mathbf {T^\\prime }=\\lbrace \\langle h,r,t\\rangle | \\langle h,r,t\\rangle \\notin \\mathbf {T}, h \\in \\mathbf {E}^i, t\\in \\mathbf {E}^i, r \\in \\mathbf {R} \\rbrace $ in the incomplete Knowledge Graph $\\mathcal {G}$ where $\\mathbf {E}^i$ is an entity superset.",
+      "In Defn. \"Closed-World Knowledge Graph Completion\" we relax the constraint on the triple set $\\mathbf {T^\\prime }$ so that triples in $\\mathbf {T^\\prime }$ can contain entities that are absent from the original entity set $\\mathbf {E}$ .",
+      "Closed-world KGC models learn entity and relationship embedding vectors by updating an initially random vector based on the KG's topology. Therefore, any triple $\\langle h,r,t\\rangle \\in \\mathbf {T^\\prime }$ such that $h\\notin \\mathbf {E}$ or $t\\notin \\mathbf {E}$ will only ever be represented by its initial random vector because its absence does not permit updates from any inference function. In order to predict the missing connections for unseen entities, it is necessary to develop alternative features to replace the topological features used by closed-world models.",
+      "Text content is a natural substitute for the missing topological features of disconnected or newly added entities. Indeed, most KGs such as FreeBase BIBREF9 , DBPedia BIBREF0 , and SemMedDB BIBREF10 were either directly extracted from BIBREF11 , BIBREF12 , or are built in parallel to some underlying textual descriptions. However, open-world KGC differs from the standard information extraction task because 1) Rather than extracting triples from a large text corpus, the goal of open-world KGC is to discover missing relationships; and 2) Rather than a pipeline of independent subtasks like Entity Linking BIBREF13 and Slotfilling BIBREF14 , etc., open-world KGC is a holistic task that operates as a single model.",
+      "Although it may seem intuitive to simply include an entity's description into an existing KGC model, we find that learning useful vector embeddings from unstructured text is much more challenging than learning topology-embeddings as in the closed-world task. First, in closed-world KGC models, each entity will have a unique embedding, which is learned from its directly connected neighbors; whereas open-world KGC models must fuse entity embeddings with the word embeddings of the entity's description. These word embeddings must be updated by entities sharing the same words regardless of their connectivity status. Second, because of the inclusion of unstructured content, open-world models are likely to include noisy or redundant information.",
+      "With respect to these challenges, the present work makes the following contributions:",
+      "Before introduce the ConMask model, we first present preliminary material by describing relevant KGC models. Then we describe the methodology, data sets, and a robust case study of closed-world and open-world KGC tasks. Finally, we draw conclusions and offer suggestions for future work."
+    ],
+    [
+      "A variety of models have been developed to solve the closed-world KGC task. The most fundamental and widely used model is a translation-based Representation Learning (RL) model called TransE BIBREF6 . TransE assumes there exists a simple function that can translate the embedding of the head entity to the embedding of some tail entity via some relationship: ",
+      "$$\\mathbf {h} + \\mathbf {r} = \\mathbf {t},$$   (Eq. 5) ",
+      "where $\\mathbf {h}$ , $\\mathbf {r}$ and $\\mathbf {t}$ are embeddings of head entity, relationship, and tail entity respectively. Based on this function, many other KGC models improve the expressive power of Eq. 5 by introducing more relationship-dependent parameters. TransR BIBREF15 , for example, augments Eq. 5 to $\\mathbf {h}\\mathbf {M}_{r} + \\mathbf {r} = \\mathbf {t}\\mathbf {M}_{r}$ where $\\mathbf {M}_{r}$ is a relationship-dependent entity embedding transformation.",
+      "In order to train the KGC models, TransE defines an energy-based loss function as ",
+      "$$\\mathcal {L}(\\mathbf {T}) = \\Sigma _{\\langle h,r,t\\rangle \\in \\mathbf {T}}[\\gamma + E(\\langle h,r,t\\rangle ) - E(\\langle h^\\prime , r^\\prime , t^\\prime \\rangle )]_{+},$$   (Eq. 6) ",
+      "where the energy function $E(\\langle h,r,t\\rangle ) = \\parallel \\mathbf {h} + \\mathbf {r} - \\mathbf {t}\\parallel _{L_{n}}$ measures the closeness of the given triple, $\\langle h,r,t\\rangle $ is some triple that exists in the triple set $\\mathbf {T}$ of an incomplete KG $\\mathcal {G}$ , and $\\langle h^\\prime , r^\\prime , t^\\prime \\rangle $ is a \u201ccorrupted\u201d triple derived by randomly replacing one part of $\\langle h,r,t\\rangle $ so that it does not exist in $\\mathbf {T}$ .",
+      "In other recent work, ProjE BIBREF16 considered closed-world KGC to be a type of ranking task and applied a list-wise ranking loss instead of Eq. 6 . Other closed-world models such as PTransE BIBREF17 and dORC BIBREF18 maintain a simple translation function and use complex topological features like extended-length paths and \u201cone-relation-circle\u201d structures to improve predictive performance.",
+      "Unlike topology-based models, which have been studied extensively, there has been little work that utilizes text information for KGC. Neural Tensor Networks (NTN) BIBREF19 uses the averaged word embedding of an entity to initialize the entity representations. DKRL BIBREF20 uses the combined distance between topology-embeddings and text-embeddings as its energy function. Jointly BIBREF21 combines the topology-embeddings and text-embeddings first using a weighted sum and then calculates the $L_{n}$ distance between the translated head entity and tail entity. However, gains in predictive performance from these joint-learning models are rather small compared to advances in topology-based models.",
+      "Furthermore, the aforementioned models are all closed-world KGC models, which can only learn meaningful representations for entities that are present during training and are well connected within the KG. These models have no mechanism by which new entities can be connected with the existing KG as required in open-world KGC.",
+      "In the present work, we present an open-world KGC model called ConMask that uses primarily text features to learn entity and relationship embeddings. Compared to topology-based and joint-learning models, ConMask can generate representations for unseen entities if they share the same vocabulary with entities seen during training. To properly handle one-to-many and many-to-one relationships, we also apply a relationship-dependent content masking layer to generate entity embeddings."
+    ],
+    [
+      "In this section we describe the architecture and the modelling decisions of the ConMask model. To illustrate how this model works, we begin by presenting an actual example as well as the top-ranked target entity inferred by the ConMask model:",
+      "Example Task: Complete triple $\\langle $ Ameen Sayani, residence, ? $\\rangle $ , where Ameen Sayani is absent from the KG.",
+      "Snippet of Entity Description: \u201c... Ameen Sayani was introduced to All India Radio, Bombay, by his brother Hamid Sayani. Ameen participated in English programmes there for ten years ...\u201d.",
+      "Predicted Target Entity: Mumbai.",
+      "In this example, if a human reader were asked to find the residence of Ameen Sayani, a popular radio personality in India, from the entity description, then the human reader is unlikely to read the entire text from beginning to end. Instead, the reader might skim the description looking for contextual clues such as family or work-related information. Here, Ameen's workplace All India Radio is located in Bombay, so the human reader may infer that Ameen is a resident of Bombay. A human reader may further reason that because Bombay has recently changed its name to Mumbai, then Mumbai would be the (correct) target entity.",
+      "Here and throughout the present work, we denote the missing entity as the target entity, which can be either the head or the tail of a triple.",
+      "We decompose the reasoning process described above into three steps: 1) Locating information relevant to the task, 2) Implicit reasoning based on the context and the relevant text, and 3) Resolving the relevant text to the proper target entity. The ConMask model is designed to mimic this process. Thus, ConMask consists of three components:",
+      "ConMask selects words that are related to the given relationship to mitigate the inclusion of irrelevant and noisy words. From the relevant text, ConMask then uses fully convolutional network (FCN) to extract word-based embeddings. Finally, it compares the extracted embeddings to existing entities in the KG to resolve a ranked list of target entities. The overall structure of ConMask is illustrated in Fig. 1 . Later subsections describe the model in detail."
+    ],
+    [
+      "In open-world KGC, we cannot rely solely on the topology of the KG to guide our model. Instead, it is natural to consider extracting useful information from text in order to infer new relationships in the KG. The task of extracting relationships among entities from text is often called relation extraction BIBREF22 . Recent work in this area tends to employ neural networks such as CNN BIBREF21 or abstract meaning representations (AMRs) BIBREF23 to learn a unified kernel to remove noise and extract the relationship-agnostic entity representations. For open-world KGC, it may be possible to create a model with relationship-dependent CNN kernels. But this type of model would significantly increase the number of parameters and may overfit on rare relationships.",
+      "In the proposed ConMask model we developed an alternative approach called relationship-dependent content masking. The goal is to pre-process the input text in order to select small relevant snippets based on the given relationship \u2013 thereby masking irrelevant text. The idea of content masking is inspired by the attention mechanism used by recurrent neural network (RNN) models BIBREF24 , which is widely applied to NLP tasks. In a typical attention-based RNN model, each output stage of a recurrent cell is assigned an attention score.",
+      "In ConMask, we use a similar idea to select the most related words given some relationship and mask irrelevant words by assigning a relationship-dependent similarity score to words in the given entity description. We formally define relationship-dependent content masking as: ",
+      "$$\\tau (\\phi (e), \\psi (r)) = \\mathbf {W}_{\\phi (e)} \\circ f_{w}(\\mathbf {W}_{\\phi (e)}, \\mathbf {W}_{\\psi (r)}),$$   (Eq. 13) ",
+      "where $e$ is an entity, $r$ is some relationship, $\\phi $ and $\\psi $ are the description and name mapping functions respectively that return a word vector representing the description or the name of an entity or relationship. $\\mathbf {W}_{\\phi (e)} \\in \\mathbb {R}^{|\\phi (e)|\\times k}$ is the description matrix of $e$ in which each row represents a $k$ dimensional embedding for a word in $\\phi (e)$ in order, $\\mathbf {W}_{\\psi (r)} \\in \\mathbb {R}^{|\\psi (r)|\\times k}$ is the name matrix of $r$ in which each row represents a $r$0 dimensional embedding for a word in the title of relationship $r$1 , $r$2 is row-wise product, and $r$3 calculates the masking weight for each row, i.e., the embedding of each word, in $r$4 .",
+      "The simplest way to generate these weights is by calculating a similarity score between each word in entity description $\\phi (e)$ and the words in relationship name $\\psi (r)$ . We call this simple function Maximal Word-Relationship Weights (MWRW) and define it as: ",
+      "$$\\begin{adjustbox}{max width=0.92}\nf_{w}^{\\textrm {MWRW}}\\left(\\mathbf {W}_{\\phi (e)}, \\mathbf {W}_{\\psi (r)}\\right)_{[i]} = \\mathsf {max}_j\\left(\\frac{\\sum \\limits _m^k \\mathbf {W}_{\\phi (e)[i,m]} \\mathbf {W}_{\\psi (r)[j,m]}}{\\sqrt{\\sum \\limits _m^k \\mathbf {W}_{\\phi (e)[i,m]}^2}\\sqrt{\\sum \\limits _m^k \\mathbf {W}_{\\psi (r)[j,m]}^2}}\\right),\n\\end{adjustbox}$$   (Eq. 14) ",
+      "where the weight of the $i^{\\textrm {th}}$ word in $\\phi (e)$ is the largest cosine similarity score between the $i^{\\textrm {th}}$ word embedding in $\\mathbf {W}_{\\phi (e)}$ and the word embedding matrix of $\\psi (r)$ in $\\mathbf {W}_{\\psi (r)}$ .",
+      "This function assigns a lower weight to words that are not relevant to the given relationship and assigns higher scores to the words that appear in the relationship or are semantically similar to the relationship. For example, when inferring the target of the partial triple $\\langle $ Michelle Obama, AlmaMater, ? $\\rangle $ , MWRW will assign high weights to words like Princeton, Harvard, and University, which include the words that describe the target of the relationship. However the words that have the highest scores do not always represent the actual target but, instead, often represent words that are similar to the relationship name itself. A counter-example is shown in Fig. 2 , where, given the relationship spouse, the word with the highest MWRW score is married. Although spouse is semantically similar to married, it does not answer the question posed by the partial triple. Instead, we call words with high MWRW weights indicator words because the correct target-words are usually located nearby. In the example-case, we can see that the correct target Barack Obama appears after the indicator word married.",
+      "In order to assign the correct weights to the target words, we improve the content masking by using Maximal Context-Relationship Weights (MCRW) to adjust the weights of each word based on its context: ",
+      "$$\\begin{adjustbox}{max width=0.92}\nf_{w}\\left(\\mathbf {W}_{\\phi (e)}, \\mathbf {W}_{\\psi (r)}\\right)_{[i]} = \\max \\left(f_{w}^{\\textrm {MWRW}}\\left(\\mathbf {W}_{\\phi (e)}, \\mathbf {W}_{\\psi (r)}\\right)_{[i-k_m:i]}\\right),\n\\end{adjustbox}$$   (Eq. 15) ",
+      "in which the weight of the $i^{th}$ word in $\\phi (e)$ equals the maximum MWRW score of the $i^{th}$ word itself and previous $k_m$ words. From a neural network perspective, the re-weighting function $f_w$ can also be viewed as applying a row-wise max reduction followed by a 1-D max-pooling with a window size of $k_m$ on the matrix product of $\\mathbf {W}_{\\phi (e)}$ and $\\mathbf {W}_{\\psi (r)}^{T}$ .",
+      "To recap, the relationship-dependent content masking process described here assigns importance weights to words in an entity's description based on the similarity between each word's context and the given relationship. After non-relevant content is masked, the model needs to learn a single embedding vector from the masked content matrix to compare with the embeddings of candidate target entities."
+    ],
+    [
+      "Here we describe how ConMask extracts word-based entity embeddings. We call this process the target fusion function $\\xi $ , which distills an embedding using the output of Eq. 13 .",
+      "Initially, we looked for solutions to this problem in recurrent neural networks (RNNs) of various forms. Despite their popularity in NLP-related tasks, recent research has found that RNNs are not good at performing \u201cextractive\u201d tasks BIBREF25 . RNNs do not work well in our specific setting because the input of the Target Fusion is a masked content matrix, which means most of the stage inputs would be zero and hence hard to train.",
+      "In this work we decide to use fully convolutional neural network (FCN) as the target fusion structure. A CNN-based structure is well known for its ability to capture peak values using convolution and pooling. Therefore FCN is well suited to extract useful information from the weighted content matrix. Our adaptation of FCNs yields the target fusion function $\\xi $ , which generates a $k$ -dimensional embedding using the output of content masking $\\tau (\\phi (e),$ $\\psi (r))$ where $e$ is either a head or tail entity from a partial triple.",
+      "Figure 3 shows the overall architecture of the target fusion process and its dependent content masking process. The target fusion process has three FCN layers. In each layer, we first use two 1-D convolution operators to perform affine transformation, then we apply $sigmoid$ as the activation function to the convoluted output followed by batch normalization BIBREF26 and max-pooling. The last FCN layer uses mean-pooling instead of max-pooling to ensure the output of the target fusion layer always return a single $k$ -dimensional embedding.",
+      "Note that the FCN used here is different from the one that typically used in computer vision tasks BIBREF27 . Rather than reconstructing the input, as is typical in CV, the goal of target fusion is to extract the embedding w.r.t given relationship, therefore we do not have the de-convolution operations. Another difference is that we reduce the number of embeddings by half after each FCN layer but do not increase the number of channels, i.e., the embedding size. This is because the input weighted matrix is a sparse matrix with a large portion of zero values, so we are essentially fusing peak values from the input matrix into a single embedding representing the target entity."
+    ],
+    [
+      "Although it is possible to use target fusion to generate all entity embeddings used in ConMask, such a process would result in a large number of parameters. Furthermore, because the target fusion function is an extraction function it would be odd to apply it to entity names where no extraction is necessary. So, we also employ a simple semantic averaging function $\\eta (\\mathbf {W}) = \\frac{1}{k_{l}}\\Sigma _{i}^{k_{l}}\\mathbf {W}_{[i,:]}$ that combines word embeddings to represent entity names and for generating background representations of other textual features, where $\\mathbf {W} \\in \\mathcal {R}^{k_l\\times k}$ is the input embedding matrix from the entity description $\\phi (\\cdot )$ or the entity or relationship name $\\psi (\\cdot )$ .",
+      "To recap: at this point in the model we have generated entity embeddings through the content masking and target fusion operations. The next step is to define a loss function that finds one or more entities in the KG that most closely match the generated embedding."
+    ],
+    [
+      "To speed up the training and take to advantage of the performance boost associated with a list-wise ranking loss function BIBREF16 , we designed a partial list-wise ranking loss function that has both positive and negative target sampling: ",
+      "$$\\mathcal {L}(h,r,t)={\\left\\lbrace \\begin{array}{ll}\n\\sum \\limits _{h_+ \\in E^+} -\\frac{\\log (S(h_+, r, t, E^+\\cup E^-))}{|E^+|}, p_c > 0.5\\\\\n\\sum \\limits _{t_+ \\in E^+} -\\frac{\\log (S(h, r, t_+, E^+\\cup E^-))}{|E^+|}, p_c \\le 0.5\\\\\n\\end{array}\\right.},$$   (Eq. 21) ",
+      "where $p_c$ is the corruption probability drawn from an uniform distribution $U[0,1]$ such that when $p_c > 0.5$ we keep the input tail entity $t$ but do positive and negative sampling on the head entity and when $p_c \\le 0.5$ we keep the input head entity $h$ intact and do sampling on the tail entity. $E^+$ and $E^-$ are the sampled positive and negative entity sets drawn from the positive and negative target distribution $P_+$ and $P_-$ respectively. Although a type-constraint or frequency-based distribution may yield better results, here we follow the convention and simply apply a simple uniform distribution for both $U[0,1]$0 and $U[0,1]$1 . When $U[0,1]$2 , $U[0,1]$3 is a uniform distribution of entities in $U[0,1]$4 and $U[0,1]$5 is an uniform distribution of entities in $U[0,1]$6 . On the other hand when $U[0,1]$7 , $U[0,1]$8 is an uniform distribution of entities in $U[0,1]$9 and $p_c > 0.5$0 is an uniform distribution of entities in $p_c > 0.5$1 . The function $p_c > 0.5$2 in Eq. 21 is the softmax normalized output of ConMask: ",
+      "$$S(h,r,t,E^\\pm ) = {\\left\\lbrace \\begin{array}{ll}\n\\frac{\\exp (\\textrm {ConMask}(h,r,t))}{\\sum \\limits _{e\\in E^\\pm }\\exp (\\textrm {ConMask}(e, r, t))}, p_c > 0.5 \\\\\n\\frac{\\exp (\\textrm {ConMask}(h,r,t))}{ \\sum \\limits _{e\\in E^\\pm }\\exp (\\textrm {ConMask}(h, r, e))}, p_c \\le 0.5 \\\\\n\\end{array}\\right.}.$$   (Eq. 22) ",
+      "Note that Eq. 21 is actually a generalized form of the sampling process used by most existing KGC models. When $|E_+|=1$ and $|E_-|=1$ , the sampling method described in Eq. 21 is the same as the triple corruption used by TransE BIBREF6 , TransR BIBREF15 , TransH BIBREF28 , and many other closed-world KGC models. When $|E_+| = |\\lbrace t|\\langle h,r,t\\rangle \\in \\mathbf {T}\\rbrace |$ , which is the number of all true triples given a partial triple $\\langle h$ , $r$ , ? $\\rangle $ , Eq. 21 is the same as ProjE_listwise BIBREF16 ."
+    ],
+    [
+      "The previous section described the design decisions and modelling assumptions of ConMask. In this section we present the results of experiments performed on old and new data sets in both open-world and closed-world KGC tasks."
+    ],
+    [
+      "Training parameters were set empirically but without fine-tuning. We set the word embedding size $k=200$ , maximum entity content and name length $k_c=k_n=512$ . The word embeddings are from the publicly available pre-trained 200-dimensional GloVe embeddings BIBREF29 . The content masking window size $k_m=6$ , number of FCN layers $k_{fcn}=3$ where each layer has 2 convolutional layers and a BN layer with a moving average decay of $0.9$ followed by a dropout with a keep probability $p=0.5$ . Max-pooling in each FCN layer has a pool size and stride size of 2. The mini-batch size used by ConMask is $k_b=200$ . We use Adam as the optimizer with a learning rate of $10^{-2}$ . The target sampling set sizes for $|E_+|$ and $|E_-|$ are 1 and 4 respectively. All open-world KGC models were run for at most 200 epochs. All compared models used their default parameters.",
+      "ConMask is implemented in TensorFlow. The source code is available at https://github.com/bxshi/ConMask."
+    ],
+    [
+      "The Freebase 15K (FB15k) data set is widely used in KGC. But FB15k is fraught with reversed- or synonym-triples BIBREF30 and does not provide sufficient textual information for content-based KGC methods to use. Due to the limited text content and the redundancy found in the FB15K data set, we introduce two new data sets DBPedia50k and DBPedia500k for both open-world and closed-world KGC tasks. Statistics of all data sets are shown in Tab. 2 .",
+      "The methodology used to evaluate the open-world and closed-world KGC tasks is similar to the related work. Specifically, we randomly selected $90\\%$ of the entities in the KG and induced a KG subgraph using the selected entities, and from this reduced KG, we further removed $10\\%$ of the relationships, i.e., graph-edges, to create KG $_\\textrm {train}$ . All other triples not included in KG $_\\textrm {train}$ are held out for the test set."
+    ],
+    [
+      "For the open-world KGC task, we generated a test set from the $10\\%$ of entities that were held out of KG $_\\textrm {train}$ . This held out set has relationships that connect the test entities to the entities in KG $_\\textrm {train}$ . So, given a held out entity-relationship partial triple (that was not seen during training), our goal is to predict the correct target entity within KG $_\\textrm {train}$ .",
+      "To mitigate the excessive cost involved in computing scores for all entities in the KG, we applied a target filtering method to all KGC models. Namely, for a given partial triple $\\langle h$ , $r$ , ? $\\rangle $ or $\\langle $ ?, $r$ , $t \\rangle $ , if a target entity candidate has not been connected via relationship $r$ before in the training set, then it is skipped, otherwise we use the KGC model to calculate the actual ranking score. Simply put, this removes relationship-entity combinations that have never before been seen and are likely to represent nonsensical statements. The experiment results are shown in Tab. 1 .",
+      "As a naive baseline we include the target filtering baseline method in Tab. 1 , which assigns random scores to all the entities that pass the target filtering. Semantic Averaging is a simplified model which uses contextual features only. DKRL is a two-layer CNN model that generates entity embeddings with entity description BIBREF20 . We implemented DKRL ourselves and removed the structural-related features so it can work under open-world KGC settings.",
+      "We find that the extraction features in ConMask do boost mean rank performance by at least $60\\%$ on both data sets compared to the extraction-free Semantic Averaging. Interestingly, the performance boost on the larger DBPedia500k data set is more significant than the smaller DBPedia50k, which indicates that the extraction features are able to find useful textual information from the entity descriptions."
+    ],
+    [
+      "Because the open-world assumption is less restrictive than the closed-world assumption, it is possible for ConMask to perform closed-world tasks, even though it was not designed to do so. So in Tab. 3 we also compare the ConMask model with other closed-world methods on the standard FB15k data set as well as the two new data sets. Results from TransR are missing from the DBPedia500k data set because the model did not complete training after 5 days.",
+      "We find that ConMask sometimes outperforms closed-world methods on the closed-world task. ConMask especially shows improvement on the DBPedia50k data set; this is probably because the random sampling procedure used to create DBPedia50k generates a sparse graph. closed-world KGC models, which rely exclusively on structural features, have a more difficult time with sub-sampled KGs."
+    ],
+    [
+      "In this section we elaborate on some actual prediction results and show examples that highlight the strengths and limitations of the ConMask model.",
+      "Table 4 shows 4 KGC examples. In each case, ConMask was provided the head and the relationship and asked to predict the tail entity. In most cases ConMask successfully ranks the correct entities within the top-3 results. Gabrielle Stanton's notableWork is an exception. Although Stanton did work on Star Trek, DBPedia indicates that her most notable work is actually The Vampire Diaries, which ranked $4^{\\textrm {th}}$ . The reason for this error is because the indicator word for The Vampire Diaries was \u201cconsulting producer\u201d, which was not highly correlated to the relationship name \u201cnotable work\u201d from the model's perspective.",
+      "Another interesting result was the prediction given from the partial triple $\\langle $ The Time Machine, writer, ? $\\rangle $ . The ConMask model ranked the correct screenwriter David Duncan as the $2^{\\textrm {nd}}$ candidate, but the name \u201cDavid Duncan\u201d does not actually appear in the film's description. Nevertheless, the ConMask model was able to capture the correct relationship because the words \u201cThe Time Machine\u201d appeared in the description of David Duncan as one of his major works.",
+      "Although ConMask outperforms other KGC models on metrics such as Mean Rank and MRR, it still has some limitations and room for improvement. First, due to the nature of the relationship-dependent content masking, some entities with names that are similar to the given relationships, such as the Language-entity in the results of the languageFamily-relationship and the Writer-entity in the results of the writer-relationship, are ranked with a very high score. In most cases the correct target entity will be ranked above relationship-related entities. Yet, these entities still hurt the overall performance. It may be easy to apply a filter to modify the list of predicted target entities so that entities that are same as the relationship will be rearranged. We leave this task as a matter for future work."
+    ],
+    [
+      "In the present work we introduced a new open-world Knowledge Graph Completion model ConMask that uses relationship-dependent content masking, fully convolutional neural networks, and semantic averaging to extract relationship-dependent embeddings from the textual features of entities and relationships in KGs. Experiments on both open-world and closed-world KGC tasks show that the ConMask model has good performance in both tasks. Because of problems found in the standard KGC data sets, we also released two new DBPedia data sets for KGC research and development.",
+      "The ConMask model is an extraction model which currently can only predict relationships if the requisite information is expressed in the entity's description. The goal for future work is to extend ConMask with the ability to find new or implicit relationships."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: Using Gaussian Processes for Rumour Stance Classification in Social Media
+
+Question: Why is a Gaussian process an especially appropriate method for this classification problem?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Rumour Definition",
+    "Descriptive Analysis of Rumours in Social Media"
+  ],
+  "paragraphs": [
+    [
+      "There is an increasing need to interpret and act upon rumours spreading quickly through social media during breaking news, where new reports are released piecemeal and often have an unverified status at the time of posting. Previous research has posited the damage that the diffusion of false rumours can cause in society, and that corrections issued by news organisations or state agencies such as the police may not necessarily achieve the desired effect sufficiently quickly BIBREF0 , BIBREF1 . Being able to determine the accuracy of reports is therefore crucial in these scenarios. However, the veracity of rumours in circulation is usually hard to establish BIBREF2 , since as many views and testimonies as possible need to be assembled and examined in order to reach a final judgement. Examples of rumours that were later disproven, after being widely circulated, include a 2010 earthquake in Chile, where rumours of a volcano eruption and a tsunami warning in Valparaiso spawned on Twitter BIBREF3 . Another example is the England riots in 2011, where false rumours claimed that rioters were going to attack Birmingham's Children's Hospital and that animals had escaped from London Zoo BIBREF4 .",
+      "Previous work by ourselves and others has argued that looking at how users in social media orient to rumours is a crucial first step towards making an informed judgement on the veracity of a rumourous report BIBREF5 , BIBREF6 , BIBREF3 . For example, in the case of the riots in England in August 2011, Procter et al. manually analysed the stance expressed by users in social media towards rumours BIBREF4 . Each tweet discussing a rumour was manually categorised as supporting, denying or questioning it. It is obvious that manual methods have their disadvantages in that they do not scale well; the ability to perform stance categorisation of tweets in an automated way would be of great use in tracking rumours, flagging those that are largely denied or questioned as being more likely to be false.",
+      "Determining the stance of social media posts automatically has been attracting increasing interest in the scientific community in recent years, as this is a useful first step towards more in-depth rumour analysis:",
+      "Work on automatic rumour stance classification, however, is still in its infancy, with some methods ignoring temporal ordering and rumour identities (e.g. BIBREF10 ), while others being rule-based and thus with unclear generalisability to new rumours BIBREF7 .",
+      "Our work advances the state-of-the-art in tweet-level stance classification through multi-task learning and Gaussian Processes. This article substantially extends our earlier short paper BIBREF11 , fistly by using a second dataset, which enables us to test the generalisability of our results. Secondly, a comparison against additional baseline classifiers and recent state-of-the-art approaches has been added to the experimental section. Lastly, we carried out a more thorough analysis of the results, including now per-class performance scores, which furthers our understanding of rumour stance classification.",
+      "In comparison to the state-of-the-art, our approach is novel in several crucial aspects:",
+      "Based on the assumption of a common underlying linguistic signal in rumours on different topics, we build a transfer learning system based on Gaussian Processes, that can classify stance in newly emerging rumours. The paper reports results on two different rumour datasets and explores two different experimental settings \u2013 without any training data and with very limited training data. We refer to these as:",
+      "Our results demonstrate that Gaussian Process-based, multi-task learning leads to significantly improved performance over state-of-the-art methods and competitive baselines, as demonstrated on two very different datasets. The classifier relying on Gaussian Processes performs particularly well over the rest of the baseline classifiers in the Leave Part Out setting, proving that it does particularly well in determining the distribution of supporting, denying and questioning tweets associated with a rumour. Estimating the distribution of stances is the key aspect for which our classifier performs especially well compared to the baseline classifiers."
+    ],
+    [
+      "This section provides a more in-depth motivation of the rumour stance detection task and an overview of the state-of-the-art methods and their limitations. First, however, let us start by introducing the formal definition of a rumour."
+    ],
+    [
+      "There have been multiple attempts at defining rumours in the literature. Most of them are complementary to one another, with slight variations depending on the context of their analyses. The core concept that most researchers agree on matches the definition that major dictionaries provide, such as the Oxford English Dictionary defining a rumour as \u201ca currently circulating story or report of uncertain or doubtful truth\u201d. For instance, DiFonzo and Bordia BIBREF12 defined rumours as \u201cunverified and instrumentally relevant information statements in circulation.\u201d",
+      "Researchers have long looked at the properties of rumours to understand their diffusion patterns and to distinguish them from other kinds of information that people habitually share BIBREF13 . Allport and Postman BIBREF2 claimed that rumours spread due to two factors: people want to find meaning in things and, when faced with ambiguity, people try to find meaning by telling stories. The latter factor also explains why rumours tend to change in time by becoming shorter, sharper and more coherent. This is the case, it is argued, because in this way rumours explain things more clearly. On the other hand, Rosnow BIBREF14 claimed that there are four important factors for rumour transmission. Rumours must be outcome-relevant to the listener, must increase personal anxiety, be somewhat credible and be uncertain. Furthermore, Shibutani BIBREF15 defined rumours to be \u201ca recurrent form of communication through which men [sic] caught together in an ambiguous situation attempt to construct a meaningful interpretation of it by pooling their intellectual resources. It might be regarded as a form of collective problem-solving\u201d.",
+      "In contrast with these three theories, Guerin and Miyazaki BIBREF16 state that a rumour is a form of relationship-enhancing talk. Building on their previous work, they recall that many ways of talking serve the purpose of forming and maintaining social relationships. Rumours, they say, can be explained by such means.",
+      "In our work, we adhere to the widely accepted fact that rumours are unverified pieces of information. More specifically, following BIBREF5 , we regard a rumour in the context of breaking news, as a \u201ccirculating story of questionable veracity, which is apparently credible but hard to verify, and produces sufficient skepticism and/or anxiety so as to motivate finding out the actual truth\u201d."
+    ],
+    [
+      "One particularly influential piece of work in the field of rumour analysis in social media is that by Mendoza et al. BIBREF3 . By manually analysing the data from the earthquake in Chile in 2010, the authors selected 7 confirmed truths and 7 false rumours, each consisting of close to 1000 tweets or more. The veracity value of the selected stories was corroborated by using reliable sources. Each tweet from each of the news items was manually classified into one of the following classes: affirmation, denial, questioning, unknown or unrelated. In this way, each tweet was classified according to the position it showed towards the topic it was about. The study showed that a much higher percentage of tweets about false rumours are shown to deny the respective rumours (approximately 50%). This is in contrast to rumours later proven to be true, where only 0.3% of tweets were denials. Based on this, authors claimed that rumours can be detected using aggregate analysis of the stance expressed in tweets.",
+      "Recent research put together in a special issue on rumours and social media BIBREF17 also shows the increasing interest of the scientific community in the topic. BIBREF18 proposed an agenda for research that establishes an interdisciplinary methodology to explore in full the propagation and regulation of unverified content on social media. BIBREF19 described an approach for geoparsing social media posts in real-time, which can be of help to determine the veracity of rumours by tracking down the poster's location. The contribution of BIBREF20 to rumour resolution is to build an automated system that rates the level of trust of users in social media, hence enabling to get rid of users with low reputation. Complementary to these approaches, our objective is to determine the stance of tweets towards a rumour, which can then be aggregated to establish an overall veracity score for the rumour.",
+      "Another study that shows insightful conclusions with respect to stance towards rumours is that by Procter et al. BIBREF4 . The authors conducted an analysis of a large dataset of tweets related to the riots in the UK, which took place in August 2011. The dataset collected in the riots study is one of the two used in our experiments, and we describe it in more detail in section \"Datasets\" . After grouping the tweets into topics, where each represents a rumour, they were manually categorised into different classes, namely:",
+      "media reports, which are tweets sent by mainstream media accounts or journalists connected to media,",
+      "pictures, being tweets uploading a link to images,",
+      "rumours, being tweets claiming or counter claiming something without giving any source,",
+      "reactions, consisting of tweets being responses of users to the riots phenomenon or specific event related to the riots.",
+      "Besides categorisation of tweets by type, Procter et al. also manually categorised the accounts posting tweets into different types, such as mainstream media, only on-line media, activists, celebrities, bots, among others.",
+      "What is interesting for the purposes of our work is that the authors observed the following four-step pattern recurrently occurring across the collected rumours:",
+      "a rumour is initiated by someone claiming it may be true,",
+      "a rumour spreads together with its reformulations,",
+      "counter claims appear,",
+      "a consensus emerges about the credibility of the rumour.",
+      "This leads the authors to the conclusion that the process of 'inter-subjective sense making' by Twitter users plays a key role in exposing false rumours. This finding, together with subsequent work by Tolmie et al. into the conversational characteristics of microblogging BIBREF6 has motivated our research into automating stance classification as a methodology for accelerating this process.",
+      "Qazvinian et al. BIBREF10 conducted early work on rumour stance classification. They introduced a system that analyzes a set of tweets associated with a given topic predefined by the user. Their system would then classify each of the tweets as supporting, denying or questioning a tweet. We have adopted this scheme in terms of the different types of stance in the work we report here. However, their work ended up merging denying and questioning tweets for each rumour into a single class, converting it into a 2-way classification problem of supporting vs denying-or-questioning. Instead, we keep those classes separate and, following Procter et al., we conduct a 3-way classification BIBREF21 .",
+      "Another important characteristic that differentiates Qazvinian et al.'s work from ours is that they looked at support and denial on longstanding rumours, such as the fact that many people conjecture whether Barack Obama is a Muslim or not. By contrast, we look at rumours that emerge in the context of fast-paced, breaking news situations, where new information is released piecemeal, often with statements that employ hedging words such as \u201creportedly\u201d or \u201caccording to sources\u201d to make it clear that the information is not fully verified at the time of posting. This is a very different scenario from that in Qazvinian et al.'s work as the emergence of rumourous reports can lead to sudden changes in vocabulary, leading to situations that might not have been observed in the training data.",
+      "Another aspect that we deal with differently in our work, aiming to make it more realistically applicable to a real world scenario, is that we apply the method to each rumour separately. Ultimately, our goal is to classify new, emerging rumours, which can differ from what the classifier has observed in the training set. Previous work ignored this separation of rumours, by pooling together tweets from all the rumours in their collections, both in training and test data. By contrast, we consider the rumour stance classification problem as a form of transfer learning and seek to classify unseen rumours by training the classifier from previously labelled rumours. We argue that this makes a more realistic classification scenario towards implementing a real-world rumour-tracking system.",
+      "Following a short gap, there has been a burst of renewed interest in this task since 2015. For example, Liu et al. BIBREF9 introduce rule-based methods for stance classification, which were shown to outperform the approach by BIBREF10 . Similarly, BIBREF7 use regular expressions instead of an automated method for rumour stance classification. Hamidian and Diab BIBREF22 use Tweet Latent Vectors to assess the ability of performing 2-way classification of the stance of tweets as either supporting or denying a rumour. They study the extent to which a model trained on historical tweets can be used for classifying new tweets on the same rumour. This, however, limits the method's applicability to long-running rumours only.",
+      "The work closest to ours in terms of aims is Zeng et al. BIBREF23 , who explored the use of three different classifiers for automated rumour stance classification on unseen rumours. In their case, classifiers were set up on a 2-way classification problem dealing with tweets that support or deny rumours. In the present work, we extend this research by performing 3-way classification that also deals with tweets that question the rumours. Moreover, we adopt the three classifiers used in their work, namely Random Forest, Naive Bayes and Logistic Regression, as baselines in our work.",
+      "Lastly, researchers BIBREF7 , BIBREF24 have focused on the related task of detecting rumours in social media. While a rumour detection system could well be the step that is applied prior to our stance classification system, here we assume that rumours have already been identified to focus on the subsequent step of determining stances.",
+      "Individual tweets may discuss the same rumour in different ways, where each user expresses their own stance towards the rumour. Within this scenario, we define the tweet level rumour stance classification task as that in which a classifier has to determine the stance of each tweet towards the rumour. More specifically, given the tweet $t_i$ as input, the classifier has to determine which of the set $Y = \\lbrace supporting, denying, questioning\\rbrace $ applies to the tweet, $y(t_i) \\in Y$ .",
+      "Here we define the task as a supervised classification problem, where the classifier is trained from a labelled set of tweets and is applied to tweets on a new, unseen set of rumours.",
+      "Let $R$ be a set of rumours, each of which consists of tweets discussing it, $\\forall _{r \\in R}$ $T_r$ $= \\lbrace t^r_1, \\cdots , t^r_{r_n}\\rbrace $ . $T = \\cup _{r \\in R} T_r$ is the complete set of tweets from all rumours. Each tweet is classified as supporting, denying or questioning with respect to its rumour: $y(t_i) \\in \\lbrace s, d, q\\rbrace $ .",
+      "We formulate the problem in two different settings. First, we consider the Leave One Out (LOO) setting, which means that for each rumour $r \\in R$ , we construct the test set equal to $T_r$ and the training set equal to $T \\setminus T_r$ . This is the most challenging scenario, where the test set contains an entirely unseen rumour.",
+      "The second setting is Leave Part Out (LPO). In this formulation, a very small number of initial tweets from the target rumour is added to the training set $\\lbrace t^r_1, \\cdots , t^r_{{{r_k}}}\\rbrace $ . This scenario becomes applicable typically soon after a rumour breaks out and journalists have started monitoring and analysing the related tweet stream. The experimental section investigates how the number of initial training tweets influences classification performance on a fixed test set, namely: $\\lbrace t^r_{{{r_l}}{}}, \\cdots , t^r_{r_n}\\rbrace $ , $l>k$ .",
+      "The tweet-level stance classification problem here assumes that tweets from the training set are already labelled with the rumour discussed and the attitude expressed towards that. This information can be acquired either via manual annotation as part of expert analysis, as is the case with our dataset, or automatically, e.g. using pattern-based rumour detection BIBREF7 . Our method is then used to classify the stance expressed in each new tweet from the test set.",
+      "We evaluate our work on two different datasets, which we describe below. We use two recent datasets from previous work for our study, both of which adapt to our needs. We do not use the dataset by BIBREF10 given that it uses a different annotation scheme limited to two categories of stances.",
+      "The reason why we use the two datasets separately instead of combining them is that they have very different characteristics. Our experiments, instead, enable us to assess the ability of our classifier to deal with these different characteristics.",
+      "The first dataset consists of several rumours circulating on Twitter during the England riots in 2011 (see Table 2 ). The dataset was collected by tracking a long set of keywords associated with the event. The dataset was analysed and annotated manually as supporting, questioning, or denying a rumour, by a team of social scientists studying the role of social media during the riots BIBREF4 .",
+      "As can be seen from the dataset overview in Table 2 , different rumours exhibit varying proportions of supporting, denying and questioning tweets, which was also observed in other studies of rumours BIBREF3 , BIBREF10 . These variations in the number of instances for each class across rumours posits the challenge of properly modelling a rumour stance classifier. The classifier needs to be able to deal with a test set where the distribution of classes can be very different to that observed in the training set.",
+      "Thus, we perform 7-fold cross-validation in the experiments, each fold having six rumours in the training set, and the remaining rumour in the test set.",
+      "The seven rumours were as follows BIBREF4 :",
+      "Rioters had attacked London Zoo and released the animals.",
+      "Rioters were gathering to attack Birmingham's Children's Hospital.",
+      "Rioters had set the London Eye on fire.",
+      "Police had beaten a sixteen year old girl.",
+      "The Army was being mobilised in London to deal with the rioters.",
+      "Rioters had broken into a McDonalds and set about cooking their own food.",
+      "A store belonging to the Miss Selfridge retail group had been set on fire in Manchester.",
+      "Additionally, we use another rumour dataset associated with five different events, which was collected as part of the PHEME FP7 research project and described in detail in BIBREF5 , BIBREF25 . Note that the authors released datasets for nine events, but here we remove non-English datasets, as well as small English datasets each of which includes only 1 rumour, as opposed to the 40+ rumours in each of the datasets that we are using. We summarise the details of the five events we use from this dataset in Table 3 .",
+      "In contrast to the England riots dataset, the PHEME datasets were collected by tracking conversations initiated by rumourous tweets. This was done in two steps. First, we collected tweets that contained a set of keywords associated with a story unfolding in the news. We will be referring to the latter as an event. Next, we sampled the most retweeted tweets, on the basis that rumours by definition should be \u201ca circulation story which produces sufficient skepticism or anxiety\u201d. This allows us to filter potentially rumourous tweets and collect conversations initiated by those. Conversations were tracked by collecting replies to tweets and, therefore, unlike the England riots, this dataset also comprises replying tweets by definition. This is an important characteristic of the dataset, as one would expect that replies are generally shorter and potentially less descriptive than the source tweets that initiated the conversation. We take this difference into consideration when performing the analysis of our results.",
+      "This dataset includes tweets associated with the following five events:",
+      "Ferguson unrest: Citizens of Ferguson in Michigan, USA, protested after the fatal shooting of an 18-year-old African American, Michael Brown, by a white police officer on August 9, 2014.",
+      "Ottawa shooting: Shootings occurred on Ottawa's Parliament Hill in Canada, resulting in the death of a Canadian soldier on October 22, 2014.",
+      "Sydney siege: A gunman held as hostages ten customers and eight employees of a Lindt chocolate caf\u00e9 located at Martin Place in Sydney, Australia, on December 15, 2014.",
+      "Charlie Hebdo shooting: Two brothers forced their way into the offices of the French satirical weekly newspaper Charlie Hebdo in Paris, killing 11 people and wounding 11 more, on January 7, 2015.",
+      "Germanwings plane crash: A passenger plane from Barcelona to D\u00fcsseldorf crashed in the French Alps on March 24, 2015, killing all passengers and crew on board. The plane was ultimately found to have been deliberately crashed by the co-pilot of the plane.",
+      "In this case, we perform 5-fold cross-validation, having four events in the training set and the remaining event in the test set for each fold.",
+      "This section details the features and evaluation measures used in our experiments on tweet level stance classification.",
+      "We begin by describing the classifiers we use for our experimentation, including Gaussian Processes, as well as a set of competitive baseline classifiers that we use for comparison.",
+      "Gaussian Processes are a Bayesian non-parametric machine learning framework that has been shown to work well for a range of NLP problems, often beating other state-of-the-art methods BIBREF26 , BIBREF27 , BIBREF28 , BIBREF29 .",
+      "A Gaussian Process defines a prior over functions, which combined with the likelihood of data points gives rise to a posterior over functions explaining the data. The key concept is a kernel function, which specifies how outputs correlate as a function of the input. Thus, from a practitioner's point of view, a key step is to choose an appropriate kernel function capturing the similarities between inputs.",
+      "We use Gaussian Processes as this probabilistic kernelised framework avoids the need for expensive cross-validation for hyperparameter selection. Instead, the marginal likelihood of the data can be used for hyperparameter selection.",
+      "The central concept of Gaussian Process Classification (GPC; BIBREF30 ) is a latent function $f$ over inputs $\\mathbf {x}$ : $f(\\mathbf {x}) \\sim \\ \\mathcal {GP}(m(\\mathbf {x}), k(\\mathbf {x}, \\mathbf {x}^{\\prime }))$ , where $m$ is the mean function, assumed to be 0 and $k$ is the kernel function, specifying the degree to which the outputs covary as a function of the inputs. We use a linear kernel, $k(\\mathbf {x}, \\mathbf {x}^{\\prime }) = \\sigma ^2 \\mathbf {x}^{\\top }\\mathbf {x}^{\\prime }$ . The latent function is then mapped by the probit function $\\Phi (f)$ into the range $[0, 1]$ , such that the resulting value can be interpreted as $p(y=1 | \\mathbf {x})$ .",
+      "The GPC posterior is calculated as $\np(f^* | X, \\mathbf {y}, \\mathbf {x_*}) = \\int p(f^* | X, \\mathbf {x_*}, \\mathbf {f}) \\frac{p(\\mathbf {y} | \\mathbf {f})p(\\mathbf {f})}{p(\\mathbf {y}|X)} d\\mathbf {f} \\, \\!,\n$ ",
+      "where $p(\\mathbf {y}|\\mathbf {f}) = \\displaystyle \\prod _{j=1}^{n} \\Phi (f_j)^{y_j} (1 - \\Phi (f_j))^{1-y_j}$ is the Bernoulli likelihood of class $y$ . After calculating the above posterior from the training data, this is used in prediction, i.e., $\np(y_* \\!=\\! 1|X, \\mathbf {y}, \\mathbf {x_*}) \\!=\\!\\!\n\\int \\Phi \\left(f_*\\right)p\\left(f_*|X, \\mathbf {y}, \\mathbf {x_*}\\right)df_* \\, .\n$ ",
+      "The above integrals are intractable and approximation techniques are required to solve them. There exist various methods to deal with calculating the posterior; here we use Expectation Propagation (EP; BIBREF31 ). In EP, the posterior is approximated by a fully factorised distribution, where each component is assumed to be an unnormalised Gaussian.",
+      "In order to conduct multi-class classification, we perform a one-vs-all classification for each label and then assign the one with the highest likelihood, amongst the three (supporting, denying, questioning). We choose this method due to interpretability of results, similar to recent work on occupational class classification BIBREF29 .",
+      "In the Leave-Part-Out (LPO) setting initial labelled tweets from the target rumour are observed as well, as opposed to the Leave-One-Out (LOO) setting. In the case of LPO, we propose to weigh the importance of tweets from the reference rumours depending on how similar their characteristics are to the tweets from the target rumour available for training. To handle this with GPC, we use a multiple output model based on the Intrinsic Coregionalisation Model (ICM; BIBREF32 ). This model has already been applied successfully to NLP regression problems BIBREF28 and it can also be applied to classification ones. ICM parametrizes the kernel by a matrix which represents the extent of covariance between pairs of tasks. The complete kernel takes form of $\nk((\\mathbf {x}, d), (\\mathbf {x}^{\\prime }, d^{\\prime })) = k_{data}(\\mathbf {x}, \\mathbf {x}^{\\prime }) B_{d, d^{\\prime }} \\, ,\n$ ",
+      "where B is a square coregionalisation matrix, $d$ and $d^{\\prime }$ denote the tasks of the two inputs and $k_{data}$ is a kernel for comparing inputs $\\mathbf {x}$ and $\\mathbf {x}^{\\prime }$ (here, linear). We parametrize the coregionalisation matrix $B=\\kappa  I+vv^T$ , where $v$ specifies the correlation between tasks and the vector $\\mathbf {\\kappa }$ controls the extent of task independence. Note that in case of LOO setting this model does not provide useful information, since no target rumour data is available to estimate similarity to other rumours.",
+      "We tune hyperparameters $\\mathbf {v}$ , $\\kappa $ and $\\sigma ^2$ by maximizing evidence of the model $p(\\mathbf {y}|X)$ , thus having no need for a validation set.",
+      "We consider GPs in three different settings, varying in what data the model is trained on and what kernel it uses. The first setting (denoted GP) considers only target rumour data for training. The second (GPPooled) additionally considers tweets from reference rumours (i.e. other than the target rumour). The third setting is GPICM, where an ICM kernel is used to weight influence from tweets from reference rumours.",
+      "To assess and compare the efficiency of Gaussian Processes for rumour stance classification, we also experimented with five more baseline classifiers, all of which were implemented using the scikit Python package BIBREF33 : (1) majority classifier, which is a naive classifier that labels all the instances in the test set with the most common class in the training set, (2) logistic regression (MaxEnt), (3) support vector machines (SVM), (4) naive bayes (NB) and (5) random forest (RF). The selection of these baselines is in line with the classifiers used in recent research on stance classification BIBREF23 , who found that random forests, followed by logistic regression, performed best.",
+      "We conducted a series of preprocessing steps in order to address data sparsity. All words were converted to lowercase; stopwords have been removed; all emoticons were replaced by words; and stemming was performed. In addition, multiple occurrences of a character were replaced with a double occurrence BIBREF34 , to correct for misspellings and lengthenings, e.g., looool. All punctuation was also removed, except for ., ! and ?, which we hypothesize to be important for expressing emotion. Lastly, usernames were removed as they tend to be rumour-specific, i.e., very few users comment on more than one rumour. After preprocessing the text data, we use either the resulting bag of words (BOW) feature representation and replace all words with their Brown cluster ids (Brown). Brown clustering is a hard hierarchical clustering method BIBREF35 . It clusters words based on maximizing the probability of the words under the bigram language model, where words are generated based on their clusters. In previous work it has been shown that Brown clusters yield better performance than directly using the BOW features BIBREF11 .",
+      "In our experiments, the clusters used were obtained using 1000 clusters acquired from a large scale Twitter corpus BIBREF36 , from which we can learn Brown clusters aimed at representing a generalisable Twitter vocabulary. Retweets are removed from the training set to prevent bias BIBREF37 . More details on the Brown clusters that we used as well as the words that are part of each cluster are available online.",
+      "During the experimentation process, we also tested additional features, including the use of the bag of words instead of the Brown clusters, as well as using word embeddings trained from the training sets BIBREF38 . However, results turned out to be substantially poorer than those we obtained with the Brown clusters. We conjecture that this was due to the little data available to train the word embeddings; further exploring use of word embeddings trained from larger training datasets is left future work. In order to focus on our main objective of proving the effectiveness of a multi-task learning approach, as well as for clarity purposes, since the number of approaches to show in the figures increases if we also consider the BOW features, we only show results for the classifiers relying on Brown clusters as features.",
+      "Accuracy is often deemed a suitable evaluation measure to assess the performance of a classifier on a multi-class classification task. However, the classes are clearly imbalanced in our case, with varying tendencies towards one of the classes in each of the rumours. We argue that in these scenarios the sole evaluation based on accuracy is insufficient, and further measurement is needed to account for category imbalance. This is especially necessary in our case, as a classifier that always predicts the majority class in an imbalanced dataset will achieve high accuracy, even if the classifier is useless in practice. To tackle this, we use both micro-averaged and macro-averaged F1 scores. Note that the micro-averaged F1 score is equivalent to the well-known accuracy measure, while the macro-averaged F1 score complements it by measuring performance assigning the same weight to each category.",
+      "Both of the measures rely on precision (Equation 50 ) and recall (Equation 51 ) to compute the final F1 score. ",
+      "$$\\text{Precision}_k = \\frac{tp_k}{tp_k+fp_k}$$   (Eq. 50) ",
+      "$$\\text{Recall}_k = \\frac{tp_k}{tp_k+fn_k}$$   (Eq. 51) ",
+      "where $tp_k$ (true positives) refer to the number of instances correctly classified in class $k$ , $fp_k$ is the number of instances incorrectly classified in class $k$ , and $fn_k$ is the number of instances that actually belong to class $k$ but were not classified as such.",
+      "The above equations can be used to compute precision and recall for a specific class. Precision and recall for all the classes in a problem with $c$ classes are computed differently if they are microaveraged (see Equations 52 and 53 ) or macroaveraged (see Equations 54 and 55 ). ",
+      "$$\\text{Precision}_{\\text{micro}} = \\frac{\\sum _{k = 1}^{c} tp_k}{\\sum _{k = 1}^{c} tp_k + \\sum _{k = 1}^{c} fp_k}$$   (Eq. 52) ",
+      "$$\\text{Recall}_{\\text{micro}} = \\frac{\\sum _{k = 1}^{c} tp_k}{\\sum _{k = 1}^{c} tp_k + \\sum _{k = 1}^{c} fn_k}$$   (Eq. 53) ",
+      "After computing microaveraged and macroveraged precision and recall, the final F1 score is computed in the same way, i.e., calculating the harmonic mean of the precision and recall in question (see Equation 56 ). ",
+      "$$\\text{F1} = \\frac{2 \\times \\text{Precision} \\times \\text{Recall}}{\\text{Precision} + \\text{Recall}}$$   (Eq. 56) ",
+      "After computing the F1 score for each fold, we compute the micro-averaged score across folds.",
+      "First, we look at the results on each dataset separately. Then we complement the analysis by aggregating the results from both datasets, which leads to further understanding the performance of our classifiers on rumour stance classification.",
+      "We show the results for the LOO and LPO settings in the same figure, distinguished by the training size displayed in the X axis. In all the cases, labelled tweets from the remainder of the rumours (rumours other than the test/targer rumour) are used for training, and hence the training size shown in the X axis is in addition to those. Note that the training size refers to the number of labelled instances that the classifier is making use of from the target rumour. Thus, a training size of 0 indicates the LOO setting, while training sizes from 10 to 50 pertain to the LPO setting.",
+      "Figure 1 and Table 4 show how micro-averaged and macro-averaged F1 scores for the England riots dataset change as the number of tweets from the target rumour used for training increases. We observe that, as initially expected, the performance of most of the methods improves as the number of labelled training instances from the target rumour increases. This increase is especially remarkable with the GP-ICM method, which gradually increases after having as few as 10 training instances. GP-ICM's performance keeps improving as the number of training instances approaches 50 Two aspects stand out from analysing GP-ICM's performance:",
+      "It performs poorly in terms of micro-averaged F1 when no labelled instances from the target rumour are used. However, it makes very effective use of the labelled training instances, overtaking the rest of the approaches and achieving the best results. This proves the ability of GP-ICM to make the most of the labelled instances from the target rumour, which the rest of the approaches struggle with.",
+      "Irrespective of the number of labelled instances, GP-ICM is robust when evaluated in terms of macro-averaged F1. This means that GP-ICM is managing to determine the distribution of classes effectively, assigning labels to instances in the test set in a way that is better distributed than the rest of the classifier.",
+      "Despite the saliency of GP-ICM, we notice that two other baseline approaches, namely MaxEnt and RF, achieve competitive results that are above the rest of the baselines, but still perform worse than GP-ICM.",
+      "The results from the PHEME dataset are shown in Figure 2 and Table 5 . Overall, we can observe that results are lower in this case than they were for the riots dataset. The reason for this can be attributed to the following two observations: on the one hand, each fold pertaining to a different event in the PHEME dataset means that the classifier encounters a new event in the classification, where it will likely find new vocabulary, which may be more difficult to classify; on the other hand, the PHEME dataset is more prominently composed of tweets that are replying to others, which are likely shorter and less descriptive on their own and hence more difficult to get meaningful features from. Despite the additional difficulty in this dataset, we are interested in exploring if the same trend holds across classifiers, from which we can generalise the analysis to different types of classifiers.",
+      "One striking difference with respect to the results from the riots dataset is that, in this case, the classifiers, including GP-ICM, are not gaining as much from the inclusion of labelled instances from the target rumour. This is likely due to the heterogeneity of each of the events in the PHEME dataset. Here a diverse set of rumourous newsworthy pieces of information are discussed pertaining to the selected events as they unfold. By contrast, each rumour in the riots dataset is more homogeneous, as each rumour focuses on a specific story.",
+      "Interestingly, when we compare the performance of different classifiers, we observe that GP-ICM again outperforms the rest of the approaches, both in terms of micro-averaged and macro-averaged F1 scores. While the micro-averaged F1 score does not increase as the number of training instances increases, we can see a slight improvement in terms of macro-averaged F1. This improvement suggests that GP-ICM does still take advantage of the labelled training instances to boost performance, in this case by better distributing the predicted labels.",
+      "Again, as we observed in the case of the riots dataset, two baselines stand out, MaxEnt and RF. They are very close to the performance of GP-ICM for the PHEME dataset, event outperforming it in a few occasions. In the following subsection we take a closer look at the differences among the three classifiers.",
+      "We delve into the results of the best-performing classifiers, namely GP-ICM, MaxEnt and RF, looking at their per-class performance. This will help us understand when they perform well and where it is that GP-ICM stands out achieving the best results.",
+      "Tables 6 and 7 show per-class F1 measures for the aforementioned three best-performing classifiers for the England riots dataset and the PHEME dataset, respectively. They also show statistics of the mis-classifications that the classifiers made, in the form of percentage of deviations towards the other classes.",
+      "Looking at the per-class performance analysis, we observe that the performance of GP-ICM varies when we look into Precision and Recall. Still, in all the dataset-class pairs, GP-ICM performs best in terms of either Precision or Recall, even though never in both. Moreover, it is generally the best in terms of F1, achieving the best Precision and Recall. The only exception is with MaxEnt classifying questioning tweets more accurately in terms of F1 for the England riots.",
+      "When we look at the deviations, we see that all the classifiers suffer from the datasets being imbalanced towards supporting tweets. This results in all classifiers classifying numerous instances as supporting, while they are actually denying or questioning. This is a known problem in rumour diffusion, as previous studies have found that people barely deny or question rumours but generally tend to support them irrespective of their actual veracity value BIBREF5 . While we have found that GP-ICM can tackle the imbalance issue quite effectively and better than other classifiers, this caveat posits the need for further research in dealing with the striking majority of supporting tweets in the context of rumours in social media.",
+      "Experimentation with two different approaches based on Gaussian Processes (GP and GP-ICM) and comparison with respect to a set of competitive baselines over two rumour datasets enables us to gain generalisable insight on rumour stance classification on Twitter. This is reinforced by the fact that the two datasets are very different from each other. The first dataset, collected during the England riots in 2011, is a single event that we have split into folds, each fold belonging to a separate rumour within the event; hence, all the rumours are part of the same event. The second dataset, collected within the PHEME project, includes tweets for a set of five newsworthy events, where each event has been assigned a separate fold; therefore, the classifier needs to learn from four events and test on a new, unknown event, which has proven more challenging.",
+      "Results are generally consistent across datasets, which enables us to generalise conclusions well. We observe that while GP itself does not suffice to achieve competitive results, GP-ICM does instead help boost the performance of the classifier substantially to even outperform the rest of the baselines in the majority of the cases.",
+      "GP-ICM has proven to consistently perform well in both datasets, despite their very different characteristics, being competitive not only in terms of micro-averaged F1, but also in terms of macro-averaged F1. GP-ICM manages to balance the varying class distributions effectively, showing that its performance is above the rest of the baselines in accurately determining the distribution of classes. This is very important in this task of rumour stance classification, owing to the fact that even if a classifier that is 100% accurate is unlikely, a classifier that accurately guesses the overall distribution of classes can be of great help. If a classifier makes a good estimation of the number of denials in an aggregated set of tweets, it can be useful to flag those potentially false rumours with high level of confidence.",
+      "Another factor that stands out from GP-ICM is its capacity to perform well when a few labelled instances of the target rumour are leveraged in the training phase. GP-ICM effectively exploits the knowledge garnered from the few instances from the target rumour, outperforming the rest of the baselines even when its performance was modest when no labelled instances were used from the target rumour.",
+      "In light of these results, we deem GP-ICM the most competitive approach to use when one can afford to get a few instances labelled from the target rumour. The labels from the target rumour can be obtained in practice in different ways: (1) having someone in-house (e.g. journalists monitoring breaking news stories) label a few instances prior to running the classifier, (2) making use of resources for human computation such as crowdsourcing platforms to outsource the labelling work, or (3) developing techniques that will attempt to classify the first few instances, incorporating in the training set those for which a classification with high level of confidence has been produced. The latter presents an ambitious avenue for future work that could help alleviate the labelling task.",
+      "On the other hand, in the absence of labelled data from the target rumour, which is the case of the LOO setting, the effectiveness of the GP-ICM classifier is not as prominent. For this scenario, other classifiers such as MaxEnt and Random Forests have proven more competitive and one could see them as better options. However, we do believe that the remarkable difference that the reliance on the LPO setting produces is worth exploiting where possible.",
+      "Social media is becoming an increasingly important tool for maintaining social resilience: individuals use it to express opinions and follow events as they unfold; news media organisations use it as a source to inform their coverage of these events; and government agencies, such as the emergency services, use it to gather intelligence to help in decision-making and in advising the public about how they should respond BIBREF1 . While previous research has suggested that mechanisms for exposing false rumours are implicit in the ways in which people use social media BIBREF4 , it is nevertheless critically important to explore if there are ways in which computational tools can help to accelerate these mechanisms so that misinformation and disinformation can be targeted more rapidly, and the benefits of social media to society maintained BIBREF8 .",
+      "As a first step to achieving this aim, this paper has investigated the problem of classifying the different types of stance expressed by individuals in tweets about rumours. First, we considered a setting where no training data from the target rumours is available (LOO). Without access to annotated examples of the target rumour the learning problem becomes very difficult. We showed that in the supervised domain adaptation setting (LPO), even annotating a small number of tweets helps to achieve better results. Moreover, we demonstrated the benefits of a multi-task learning approach, as well as that Brown cluster features are more useful for the task than simple bag of words.",
+      "Findings from previous work, such as BIBREF39 , BIBREF4 , have suggested that the aggregate stance of individual users is correlated with actual rumour veracity. Hence, the next step in our own work will be to make use of the classifier for the stance expressed in the reactions of individual Twitter users in order to predict the actual veracity of the rumour in question. Another interesting direction for future work would be the addition of non-textual features to the classifier. For example, the rumour diffusion patterns BIBREF40 may be a useful cue for stance classification.",
+      "This work is partially supported by the European Union under grant agreement No. 611233 Pheme. The work was implemented using the GPy toolkit BIBREF41 . This research utilised Queen Mary's MidPlus computational facilities, supported by QMUL Research-IT and funded by EPSRC grant EP/K000128/1."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: Determining the Scale of Impact from Denial-of-Service Attacks in Real Time Using Twitter
+
+Question: Was performance of the weakly-supervised model compared to the performance of a supervised model?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Approach",
+    "Approach ::: Data Collection",
+    "Approach ::: Preprocessing",
+    "Approach ::: Create LDA Models",
+    "Approach ::: The attack topics",
+    "Approach ::: The attack tweets",
+    "Approach ::: Optional Classifier Layer",
+    "Approach ::: Measure the Severity",
+    "Experiments",
+    "Experiments ::: Term Definition",
+    "Experiments ::: Experiment Dataset",
+    "Experiments ::: The Attack Topics",
+    "Experiments ::: The Attack Tweets",
+    "Experiments ::: Generalization",
+    "Experiments ::: Impact Estimation",
+    "Experiments ::: Parameter Tuning",
+    "Discussion",
+    "Conclusion",
+    "Additional Result for Parameter Tuning"
+  ],
+  "paragraphs": [
+    [
+      "Denial of Service attacks are explicit attempts to stop legitimate users from accessing specific network systems BIBREF0. Attackers try to exhaust network resources like bandwidth, or server resources like CPU and memory. As a result, the targeted system slows down or becomes unusable BIBREF1. On-line service providers like Bank Of America, Facebook and Reddit are often the target of such attacks and the frequency and scale of those attacks has increased rapidly in recent years BIBREF2.",
+      "To address this problem, there is ample previous work on methods to detect and handle Denial of Service attacks, especially Distributed Denial of Service attacks. D-WARD BIBREF3 is a scheme that tries to locate a DDoS attacks at the source by monitoring inbound and outbound traffic of a network and comparing it with predefined \"normal\" values. Some IP Traceback mechanisms BIBREF4 were developed to trace back to the attack source from the victim's end. Still other methods try to deploy a defensive scheme in an entire network to detect and respond to an attack at intermediate sub-networks. Watchers BIBREF5 is an example of this approach.",
+      "Despite all the new models and techniques to prevent or handle cyber attacks, DDoS attacks keep evolving. Services are still being attacked frequently and brought down from time to time. After a service is disrupted, it is crucial for the provider to assess the scale of the outage impact.",
+      "In this paper, we present a novel approach to solve this problem. No matter how complex the network becomes or what methods the attackers use, a denial of service attack always results in legitimate users being unable to access the network system or slowing down their access and they are usually willing to reveal this information on social media plaforms. Thus legitimate user feedback can be a reliable indicator about the severity level of the service outage. Thus we split this problem into two parts namely by first isolating the tweet stream that is likely related to a DoS attack and then measuring the impact of attack by analyzing the extracted tweets.",
+      "A central challenge to measure the impact is how to figure out the scale of the effect on users as soon as possible so that appropriate action can be taken. Another difficulty is given the huge number of users of a service, how to effectively get and process the user feedback. With the development of Social Networks, especially micro blogs like Twitter, users post many life events in real time which can help with generating a fast response. Another advantage of social networks is that they are widely used. Twitter claims that they had 313 million monthly active users in the second quarter of 2016 BIBREF6. This characteristic will enlarge the scope of detection and is extremely helpful when dealing with cross domain attacks because tweets from multiple places can be leveraged. The large number of users of social networks will also guarantee the sensitivity of the model. However, because of the large number of users, a huge quantity of tweets will be generated in a short time, making it difficult to manually annotate the tweets, which makes unsupervised or weakly-supervised models much more desirable.",
+      "In the Twitter data that we collected there are three kinds of tweets. Firstly are tweets that are actually about a cyberattack. For example, someone tweeted \"Can't sign into my account for bank of America after hackers infiltrated some accounts.\" on September 19, 2012 when a attack on the website happened. Secondly are tweets about some random complaints about an entity like \"Death to Bank of America!!!! RIP my Hello Kitty card... \" which also appeared on that day. Lastly are tweets about other things related to the bank. For example, another tweet on the same day is \"Should iget an account with bank of america or welsfargo?\".",
+      "To find out the scale of impact from an attack, we must first pick out the tweets that are about the attack. Then using the ratio and number of attack tweets, an estimation of severity can be generated. To solve the problem of detecting Denial of Service attacks from tweets, we constructed a weakly-supervised Natural Language Processing (NLP) based model to process the feeds. More generally, this is a new event detection model. We hypothesize that new topics are attack topics. The hypothesis would not always hold and this issue will be handled by a later module. The first step of the model is to detect topics in one time window of the tweets using Latent Dirichlet Allocation BIBREF7. Then, in order to get a score for each of the topics, the topics in the current time window are compared with the topics in the previous time window using Symmetric Kullback-Leibler Divergence (KL Divergence) BIBREF8. After that, a score for each tweet in the time window is computed using the distribution of topics for the tweet and the score of the topics. We're looking for tweets on new topics through time. While the experiments show promising results, precision can be further increased by adding a layer of a supervised classifier trained with attack data at the expense of recall.",
+      "Following are the contributions in this paper:",
+      "A dataset of annotated tweets extracted from Twitter during DoS attacks on a variety organizations from differing domains such as banking (like Bank Of America) and technology.",
+      "A weakly-supervised approach to identifying detect likely DoS service related events on twitter in real-time.",
+      "A score to measure impact of the DoS attack based on the frequency of user complaints about the event.",
+      "The rest of this paper is organized as follows: In section 2, previous work regarding DDoS attack detection and new event detection will be discussed. In section 3, we describe the how the data was collected. We also present the model we created to estimate the impact of DDoS attacks from Twitter feeds. In section 4, the experiments are described and the results are provided. In section 5 we discuss some additional questions. Finally, section 6 concludes our paper and describes future work."
+    ],
+    [
+      "Denial of Service (DoS) attacks are a major threat to Internet security, and detecting them has been a core task of the security community for more than a decade. There exists significant amount of prior work in this domain. BIBREF9, BIBREF10, BIBREF11 all introduced different methods to tackle this problem. The major difference between this work and previous ones are that instead of working on the data of the network itself, we use the reactions of users on social networks to identify an intrusion.",
+      "Due to the widespread use of social networks, they have become an important platform for real-world event detection in recent years BIBREF12. BIBREF13 defined the task of new event detection as \"identifying the first story on topics of interest through constantly monitoring news streams\". Atefeh et al. BIBREF14 provided a comprehensive overview of event detection methods that have been applied to twitter data. We will discuss some of the approaches that are closely related to our work. Weng et al. BIBREF15 used a wavelet-signal clustering method to build a signal for individual words in the tweets that was dependent high frequency words that repeated themselves. The signals were clustered to detect events. Sankaranarayanan et al. BIBREF16 presented an unsupervised news detection method based on naive Bayes classifiers and on-line clustering. BIBREF17 described an unsupervised method to detect general new event detection using Hierarchical divisive clustering. Phuvipadawat et al. BIBREF18 discussed a pipeline to collect, cluster, rank tweets and ultimately track events. They computed the similarity between tweets using TF-IDF. The Stanford Named Entity Recognizer was used to identify nouns in the tweets providing additional features while computing the TF-IDF score. Petrovi\u0107 et al. BIBREF19 tried to detect events on a large web corpus by applying a modified locality sensitive hashing technique and clustering documents (tweets) together. Benson et al. BIBREF20 created a graphical model that learned a latent representation for twitter messages, ultimately generating a canonical value for each event. Tweet-scan BIBREF21 was a method to detect events in a specific geo-location. After extracting features such as name, time and location from the tweet, the method used DB-SCAN to cluster the tweets and Hierarchical Dirichlet Process to model the topics in the tweets. Badjatiya et. al. BIBREF22 applied deep neural networks to detect events. They showed different architectures such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (LSTM based) and FastText outperform standard n-gram and TF-IDF models. Burel et al. BIBREF23 created a Dual-CNN that had an additional channel to model the named entities in tweets apart from the pretrained word vectors from GloVe BIBREF24 or Word2Vec BIBREF25.",
+      "Thus most event detection models can be grouped into three main categories of methods i.e. TF-IDF based methods, approaches that model topics in tweets and deep neural network based algorithms. One of the main challenges against applying a neural network model is the the requirement of a large annotated corpus of tweets. Our corpus of tweets is comparatively small. Hence we build our pipeline by modeling the topics learned from tweets.",
+      "The previous work that is most similar to ours was BIBREF26. We both used Latent Dirichlet Allocation (LDA) to get the topics of the document, the difference was they only run LDA on the hash-tag of the tweets while we try to get the topics in the tweets by running it on the whole document.",
+      "Latent Dirichlet Allocation BIBREF7 was a method to get topics from a corpus. In our work, we used the technique to acquire the values of some of the variables in our equation. A variation of it, Hierarchically Supervised Latent Dirichlet Allocation BIBREF27 was used in the evaluation."
+    ],
+    [
+      "Figure FIGREF4 outlines the entire pipeline of the model from preprocessing tweets to modeling them and finally detecting / ranking future tweets that are related to a DoS issue and measuring its severity."
+    ],
+    [
+      "To collect the tweets, we first gathered a list of big DDoS attacks happened from 2012 to 2014. Then for each attack on the list, we collected all the tweets from one week before the attack to the attack day that contains the name of the entity attacked."
+    ],
+    [
+      "The following preprocessing procedure were applied to the corpus of tweets:",
+      "Remove all the meta-data like time stamp, author, and so on. These meta-data could provide useful information, but only the content of the tweet was used for now.",
+      "Lowercase all the text",
+      "Use an English stop word list to filter out stop words.",
+      "The last two steps are commonly used technique when preprocessing text."
+    ],
+    [
+      "Now we try to find out a quantitative representation of the corpus. To do that, the preprocessed tweets about one attack will be divided into two groups. One is on the attack day and the other is the tweets one week before it. The first set will be called $D_a$ and the other one $D_b$. This step will create two separate LDA models for $D_a$ and $D_b$ using the Genism library BIBREF28. The first Model will be called $M_a$ and the other one $M_b$.",
+      "Latent Dirichlet allocation (LDA) is a generative probabilistic topic modeling model. Figure FIGREF11 is its plate notation. The meaning of different parameters $M$, $N$, $\\alpha $, $\\beta $, $\\theta $, $z$ and $w$ is also described there.",
+      "We used the LDA algorithm implemented by the Gensim library. One of the most important parameters of the LDA algorithm is the number of topics $N_t$ in the corpus. To determine that we introduced the following formula:",
+      "where $N_d$ is the number of tweets in the corpus. $\\alpha $ is a constant and we used $\\alpha $=10 in our experiments. The logic behind the equation is discussed in section 5."
+    ],
+    [
+      "Then we want to find out how the new topics are different from the history topics or, in other words, how topics in $M_a$ differ from topics in $M_b$. We define the Symmetric Kullback-Leibler divergence for topic $T_j$ in Model $M_a$ as:",
+      "Where n is the number of topics in Model $M_b$, $T_m^{^{\\prime }}$ is the $m^{th}$ topic in Model $M_b$ and $D_kl (X,Y)$ is the original Kullback-Leibler Divergence for discrete probability distributions which defined as :",
+      "Where $X(i)$ and $Y(i)$ are the probability of token $i$ in topics $X$ and $Y$ respectively. This is similar to the Jensen-Shannon divergence.",
+      "So for each topic $T_j$ in Model $M_a$ its difference to topics in $M_b$ is determined by its most similar topic in $M_b$.",
+      "The topics from the attack day model $M_a$ are ranked by their Symmetric Kullback-Leibler divergence to topics from the non-attack day model $M_b$. An example of selected attack topics is provided in section 4.3."
+    ],
+    [
+      "This subsection is about how to find specific tweets that are about a network attack. The tweets are selected based on the relative score $S$. The score for tweet $t_i$ is defined as:",
+      "Where $n$ is the number of topics on the attack day, $P_{i,j}$ is the probability that topic $j$ appears in tweet $t_i$ in the attack day LDA model, and $SKL_j$ is the Symmetric Kullback-Leibler divergence for topic $j$. The higher the score the more likely it is related to an attack event."
+    ],
+    [
+      "Because annotated data is not needed, the model we described before can be regarded as a weakly-supervised model to detect new events on twitter in a given time period. To label tweets as attack tweets, one assumption must be true, which is that the new event in that time period is a cyber attack. Unfortunately, that is usually not true. Thus, an optional classifier layer can be used to prevent false positives.",
+      "By using a decision tree model we want to find out whether the weakly-supervised part of the model can simplify the problem enough that a simple classification algorithm like a decision tree can have a good result. Additionally, it is easy to find out the reasoning underline a decision tree model so that we will know what the most important features are.",
+      "The decision tree classifier is trained on the bag of words of collected tweets and the labels are manually annotated. We limit the minimum samples in each leaf to be no less than 4 so that the tree won't overfit. Other than that, a standard Classification and Regression Tree (CART) BIBREF29 implemented by scikit-learn BIBREF30 was used. The classifier was only trained on the training set (tweets about Bank of America on 09/19/2012), so that the test results do not overestimate accuracy."
+    ],
+    [
+      "The definition of severity varies from different network services and should be studied case by case.",
+      "For the sake of completeness, we propose this general formula:",
+      "In the equation above, $\\beta $ is a parameter from 0 to 1 which determines the weight of the two parts. $N_{attack}$ is the number of attack tweets found. $N_{all}$ means the number of all tweets collected in the time period. And $N_{user}$ is the number of twitter followers of the network service.",
+      "An interesting future work is to find out the quantitative relation between SeverityLevel score and the size of the actual DDoS attack."
+    ],
+    [
+      "In this section we experimentally study the proposed attack tweet detection models and report the evaluation results."
+    ],
+    [
+      "We used precision and recall for evaluation:",
+      "Precision: Out of all of the tweets that are marked as attack tweets, the percentage of tweets that are actually attack tweets. Or true positive over true positive plus false positive.",
+      "Recall: Out of all of the actual attack tweets, the percentage of tweets that are labeled as attack tweets. Or true positive over true positive plus false negative."
+    ],
+    [
+      "We collected tweets related to five different DDoS attacks on three different American banks. For each attack, all the tweets containing the bank's name posted from one week before the attack until the attack day were collected. There are in total 35214 tweets in the dataset. Then the collected tweets were preprocessed as mentioned in the preprocessing section.",
+      "The following attacks were used in the dataset:",
+      "Bank of America attack on 09/19/2012.",
+      "Wells Fargo Bank attack on 09/19/2012.",
+      "Wells Fargo Bank attack on 09/25/2012.",
+      "PNC Bank attack on 09/19/2012.",
+      "PNC Bank attack on 09/26/2012."
+    ],
+    [
+      "Only the tweets from the Bank of America attack on 09/19/2012 were used in this experiment. The tweets before the attack day and on the attack day were used to train the two LDA models mentioned in the approach section.",
+      "The top, bottom 4 attack topics and their top 10 words are shown in table 1 and 2.",
+      "As shown in table 1, there are roughly 4 kinds of words in the attack topics. First is the name of the entity we are watching. In this case, it is Bank of America. Those words are in every tweet, so they get very high weight in the topics, while not providing useful information. Those words can be safely discarded or added to the stop word list. The second type of words are general cybersecurity words like website, outage, hackers, slowdown and so on. Those words have the potential to become an indicator. When topics with those words appears, it is likely that there exists an attack. The third kind are words related to the specific attack but not attacks in general. Those words can provide details about the attack, but it is hard to identify them without reading the full tweets. In our example, the words movie and sacrilegious are in this group. That is because the DDoS attack on Bank of America was in response to the release of a controversial sacrilegious film. The remaining words are non-related words. The higher the weights of them in a topic, the less likely the topic is actually about a DDoS attack.",
+      "The results showed that except the 3rd topic, the top 4 topics have high weight on related words and the number of the forth type of words are smaller than the first three types of words. There are no high weight words related to security in the bottom 4 topics. We can say that the high SKL topics are about cyber attacks."
+    ],
+    [
+      "In this subsection we discuss the experiment on the attack tweets found in the whole dataset. As stated in section 3.3, the whole dataset was divided into two parts. $D_a$ contained all of the tweets collected on the attack day of the five attacks mentioned in section 4.2. And $D_b$ contained all of the tweets collected before the five attacks. There are 1180 tweets in $D_a$ and 7979 tweets in $D_b$. The tweets on the attack days ($D_a$) are manually annotated and only 50 percent of those tweets are actually about a DDoS attack.",
+      "The 5 tweets that have the highest relative score in the dataset are:",
+      "jiwa mines and miner u.s. bancorp, pnc latest bank websites to face access issues: (reuters) - some u.s. bancorp... http://bit.ly/p5xpmz",
+      "u.s. bancorp, pnc latest bank websites to face access issues: (reuters) - some u.s. bancorp and pnc financial...",
+      "@pncvwallet nothing pnc sucks fat d ur lucky there's 3 pnc's around me or your bitchassness wouldnt have my money",
+      "business us bancorp, pnc latest bank websites to face access issues - reuters news",
+      "forex business u.s. bancorp, pnc latest bank websites to face access issues http://dlvr.it/2d9ths",
+      "The precision when labeling the first x ranked tweets as attack tweet is shown in the figure FIGREF39. The x-axis is the number of ranked tweets treated as attack tweets. And the y-axis is the corresponding precision. The straight line in figures FIGREF39, FIGREF43 and FIGREF51 is the result of a supervised LDA algorithm which is used as a baseline. Supervised LDA achieved 96.44 percent precision with 10 fold cross validation.",
+      "The result shows that if the model is set to be more cautious about labeling a tweet as an attack tweet, a small x value, higher precision, even comparable to supervised model can be achieved. However as the x value increases the precision drops eventually.",
+      "Figure FIGREF40 shows the recall of the same setting. We can find out that the recall increases as the model becomes more bold, at the expense of precision.",
+      "Figure FIGREF41 is the detection error trade-off graph to show the relation between precision and recall more clearly (missed detection rate is the precision)."
+    ],
+    [
+      "In this subsection we evaluate how good the model generalizes. To achieve that, the dataset is divided into two groups, one is about the attacks on Bank of America and the other group is about PNC and Wells Fargo. The only difference between this experiment and the experiment in section 4.4 is the dataset. In this experiment setting $D_a$ contains only the tweets collected on the days of attack on PNC and Wells Fargo. $D_b$ only contains the tweets collected before the Bank of America attack. There are 590 tweets in $D_a$ and 5229 tweets in $D_b$. In this experiment, we want to find out whether a model trained on Bank of America data can make good classification on PNC and Wells Fargo data.",
+      "Figures FIGREF43 and FIGREF44 will show the precision and recall of the model in this experiment setting. A detection error trade-off graph (Figure FIGREF45) is also provided.",
+      "The result is similar to the whole dataset setting from the previous section. The smaller the x value is, the higher the precision and lower the recall, vice versa. The precision is also comparable to the supervised model when a small x is chosen. This shows that the model generalized well."
+    ],
+    [
+      "Using the result from last section, we choose to label the first 40 tweets as attack tweets. The number 40 can be decided by either the number of tweets labeled as attack tweets by the decision tree classifier or the number of tweets that have a relative score S higher than a threshold. The PNC and Wells Fargo bank have 308.3k followers combined as of July 2018. According to eqution (5) from section 3.6, the severity Level can be computed.",
+      "The score would have a range from 6.78 * $10^{-2}$ to 1.30 * $10^{-3}$, depending on the value of $\\beta $. This means that it could be a fairly important event because more than six percent of tweets mentioning the banks are talking about the DDoS attack. However it could also be a minor attack because only a tiny portion of the people following those banks are complaining about the outage. The value of $\\beta $ should depend on the provider's own definition of severity."
+    ],
+    [
+      "This model has two parameters that need to be provided. One is $\\alpha $ which is needed to determine the number of topics parameter $N_t$, and the other is whether to use the optional decision tree filter.",
+      "Figures FIGREF49 and FIGREF50 provide experimental results on the model with different combinations of parameters. We selected four combinations that have the best and worst performance. All of the results can be found in appendix. The model was trained on Bank of America tweets and tested on PNC and Wells Fargo tweets like in section 4.5. In the figure, different lines have different values of $\\alpha $ which ranges from 5 to 14 and the x axis is the number of ranked tweets labeled as attack tweets which have a range of 1 to 100 and the y-axis is the precision or recall of the algorithm and should be a number from 0 to 1.",
+      "The results shows the decision tree layer increases precision at the cost of recall. The model's performance differs greatly with different $\\alpha $ values while there lacks a good way to find the optimal one."
+    ],
+    [
+      "In this section, we will discuss two questions.",
+      "Firstly, we want to briefly discuss how good humans do on this task. What we find out is though humans perform well on most of the tweets, some tweets have proven to be challenging without additional information. In this experiment, we asked 18 members of our lab to classify 34 tweets picked from human annotated ones. There are only two tweets which all the 18 answers agree with each other. And there are two tweets that got exactly the same number of votes on both sides. The two tweets are \"if these shoes get sold out before i can purchase them, i'ma be so mad that i might just switch banks! @bankofamerica fix yourself!\" and \"nothing's for sure, but if i were a pnc accountholder, i'd get my online banking business done today: http://lat.ms/uv3qlo\".",
+      "The second question we want to talk about is how to find out the optimal number of topics in each of the two LDA models. As shown in the parameter tuning section, the number of topics parameter greatly affects the performance of the model. We've tried several ways to figure out the number of topics. First a set number of topics for different corpora. We tried 30 different topic numbers on the Bank of America dataset and chose the best one, and then tested it on the PNC data. The result shows that this method does not perform well on different datasets. We think it is because the number of topics should be a function of the number of documents or number of words in the corpus. Then we tried to let the model itself determines the parameter. There are some LDA variations that can do automatic number of topic inference. The one we chose is the Hierarchical Dirichlet Process (HDP) mixture model, which is a nonparametric Bayesian approach to clustering grouped data and a natural nonparametric generalization of Latent Dirichlet Allocation BIBREF31. However it does not perform very well. Its precision is shown in figure FIGREF51 and recall is shown in figure FIGREF52.",
+      "We think the reason for this kind of performance might be that tweets, with the restriction of 140 characters, have very different properties than usual documents like news or articles. The last method is what was proposed in this paper. An $\\alpha $ equals 10 is what we chose and did a good job on the experiments. But it is only an empirical result."
+    ],
+    [
+      "In this paper, we proposed a novel weakly-supervised model with optional supervised classifier layer to determine the impact of a Denial-of-Service attack in real time using twitter. The approach computes an anomaly score based on the distribution of new topics and their KL divergence to the historical topics. Then we tested the model on same and different entities to check the model's performance and how well it generalize. Our experiment result showed that the model achieved decent result on finding out tweets related to a DDoS attack even comparable to a supervised model baseline. And it could generalize to different entities within the same domain. Using the attack tweets, we could get an estimation of the impact of the attack with a proposed formula.",
+      "There remain some interesting open questions for future research. For example, it is important to figure out a way to find out the optimal number of topics in the dataset. We would also be interested to see how well this model will perform on other kind of event detection task if the optional classifier layer changes accordingly."
+    ],
+    [
+      "Figures FIGREF53 and FIGREF54 provide all of the experimental results on the model with different combinations of parameters."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: Introducing RONEC -- the Romanian Named Entity Corpus
+
+Question: How did they determine the distinct classes?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Introduction ::: Related corpora",
+    "Introduction ::: Related corpora ::: ROCO corpus",
+    "Introduction ::: Related corpora ::: ROMBAC corpus",
+    "Introduction ::: Related corpora ::: CoRoLa corpus",
+    "Corpus Description",
+    "Corpus Description ::: BRAT format",
+    "Corpus Description ::: CoNLL-U Plus format",
+    "Classes and Annotation Methodology",
+    "Classes and Annotation Methodology ::: PERSON",
+    "Classes and Annotation Methodology ::: NAT_REL_POL",
+    "Classes and Annotation Methodology ::: ORGANIZATION",
+    "Classes and Annotation Methodology ::: GPE",
+    "Classes and Annotation Methodology ::: LOC",
+    "Classes and Annotation Methodology ::: FACILITY",
+    "Classes and Annotation Methodology ::: PRODUCT",
+    "Classes and Annotation Methodology ::: EVENT",
+    "Classes and Annotation Methodology ::: LANGUAGE",
+    "Classes and Annotation Methodology ::: WORK_OF_ART",
+    "Classes and Annotation Methodology ::: DATETIME",
+    "Classes and Annotation Methodology ::: PERIOD",
+    "Classes and Annotation Methodology ::: MONEY",
+    "Classes and Annotation Methodology ::: QUANTITY",
+    "Classes and Annotation Methodology ::: NUMERIC_VALUE",
+    "Classes and Annotation Methodology ::: ORDINAL",
+    "Conclusions"
+  ],
+  "paragraphs": [
+    [
+      "Language resources are an essential component in entire R&D domains. From the humble but vast repositories of monolingual texts that are used by the newest language modeling approaches like BERT and GPT, to parallel corpora that allows our machine translation systems to inch closer to human performance, to the more specialized resources like WordNets that encode semantic relations between nodes, these resources are necessary for the general advancement of Natural Language Processing, which eventually evolves into real apps and services we are (already) taking for granted.",
+      "We introduce RONEC - the ROmanian Named Entity Corpus, a free, open-source resource that contains annotated named entities in copy-right free text.",
+      "A named entity corpus is generally used for Named Entity Recognition (NER): the identification of entities in text such as names of persons, locations, companies, dates, quantities, monetary values, etc. This information would be very useful for any number of applications: from a general information extraction system down to task-specific apps such as identifying monetary values in invoices or product and company references in customer reviews.",
+      "We motivate the need for this corpus primarily because, for Romanian, there is no other such corpus. This basic necessity has sharply arisen as we, while working on a different project, have found out there are no usable resources to help us in an Information Extraction task: we were unable to extract people, locations or dates/values. This constituted a major road-block, with the only solution being to create such a corpus ourselves. As the corpus was out-of-scope for this project, the work was done privately, outside the umbrella of any authors' affiliations - this is why we are able to distribute this corpus completely free.",
+      "The current landscape in Romania regarding language resources is relatively unchanged from the outline given by the META-NET project over six years ago. The in-depth analysis performed in this European-wide Horizon2020-funded project revealed that the Romanian language falls in the \"fragmentary support\" category, just above the last, \"weak/none\" category (see the language/support matrix in BIBREF3). This is why, in 2019/2020, we are able to present the first NER resource for Romanian."
+    ],
+    [
+      "We note that, while fragmentary, there are a few related language resources available, but none that specifically target named entities:"
+    ],
+    [
+      "ROCO BIBREF4 is a Romanian journalistic corpus that contains approx. 7.1M tokens. It is rich in proper names, numerals and named entities. The corpus has been automatically annotated at word-level with morphosyntactic information (MSD annotations)."
+    ],
+    [
+      "Released in 2016, ROMBAC BIBREF5 is a Romanian text corpus containing 41M words divided in relatively equal domains like journalism, legalese, fiction, medicine, etc. Similarly to ROCO, it is automatically annotated at word level with MSD descriptors."
+    ],
+    [
+      "The much larger and recently released CoRoLa corpus BIBREF6 contains over 1B words, similarly automatically annotated.",
+      "In all these corpora the named entities are not a separate category - the texts are morphologically and syntactically annotated and all proper nouns are marked as such - NP - without any other annotation or assigned category. Thus, these corpora cannot be used in a true NER sense. Furthermore, annotations were done automatically with a tokenizer/tagger/parser, and thus are of slightly lower quality than one would expect of a gold-standard corpus."
+    ],
+    [
+      "The corpus, at its current version 1.0 is composed of 5127 sentences, annotated with 16 classes, for a total of 26377 annotated entities. The 16 classes are: PERSON, NAT_REL_POL, ORG, GPE, LOC, FACILITY, PRODUCT, EVENT, LANGUAGE, WORK_OF_ART, DATETIME, PERIOD, MONEY, QUANTITY, NUMERIC_VALUE and ORDINAL.",
+      "It is based on copyright-free text extracted from Southeast European Times (SETimes). The news portal has published \u201cnews and views from Southeast Europe\u201d in ten languages, including Romanian. SETimes has been used in the past for several annotated corpora, including parallel corpora for machine translation. For RONEC we have used a hand-picked selection of sentences belonging to several categories (see table TABREF16 for stylistic examples).",
+      "The corpus contains the standard diacritics in Romanian: letters \u0219 and \u021b are written with a comma, not with a cedilla (like \u015f and \u0163). In Romanian many older texts are written with cedillas instead of commas because full Unicode support in Windows came much later than the classic extended Ascii which only contained the cedilla letters.",
+      "The 16 classes are inspired by the OntoNotes5 corpus BIBREF7 as well as the ACE (Automatic Content Extraction) English Annotation Guidelines for Entities Version 6.6 2008.06.13 BIBREF8. Each class will be presented in detail, with examples, in the section SECREF3 A summary of available classes with word counts for each is available in table TABREF18.",
+      "The corpus is available in two formats: BRAT and CoNLL-U Plus."
+    ],
+    [
+      "As the corpus was developed in the BRAT environment, it was natural to keep this format as-is. BRAT is an online environment for collaborative text annotation - a web-based tool where several people can mark words, sub-word pieces, multiple word expressions, can link them together by relations, etc. The back-end format is very simple: given a text file that contains raw sentences, in another text file every annotated entity is specified by the start/end character offset as well as the entity type, one per line. RONEC is exported in the BRAT format as ready-to-use in the BRAT annotator itself. The corpus is pre-split into sub-folders, and contains all the extra files such as the entity list, etc, needed to directly start an eventual edit/extension of the corpus.",
+      "Example (raw/untokenized) sentences:",
+      "Tot \u00een cadrul etapei a 2-a, a avut loc \u00eent\u00e2lnirea Vardar Skopje - S.C. Pick Szeged, care s-a \u00eencheiat la egalitate, 24 - 24.",
+      "I s-a decernat Premiul Nobel pentru literatur\u0103 pe anul 1959.",
+      "Example annotation format:",
+      "T1 ORDINAL 21 26 a 2-a",
+      "T2 ORGANIZATION 50 63 Vardar Skopje",
+      "T3 ORGANIZATION 66 82 S.C. Pick Szeged",
+      "T4 NUMERIC_VALUE 116 118 24",
+      "T5 NUMERIC_VALUE 121 123 24",
+      "T6 DATETIME 175 184 anul 1959"
+    ],
+    [
+      "The CoNLL-U Plus format extends the standard CoNLL-U which is used to annotate sentences, and in which many corpora are found today. The CoNLL-U format annotates one word per line with 10 distinct \"columns\" (tab separated):",
+      "nolistsep",
+      "ID: word index;",
+      "FORM: unmodified word from the sentence;",
+      "LEMMA: the word's lemma;",
+      "UPOS: Universal part-of-speech tag;",
+      "XPOS: Language-specific part-of-speech tag;",
+      "FEATS: List of morphological features from the universal feature inventory or from a defined language-specific extension;",
+      "HEAD: Head of the current word, which is either a value of ID or zero;",
+      "DEPREL: Universal dependency relation to the HEAD or a defined language-specific subtype of one;",
+      "DEPS: Enhanced dependency graph in the form of a list of head-deprel pairs;",
+      "MISC: Miscellaneous annotations such as space after word.",
+      "The CoNLL-U Plus extends this format by allowing a variable number of columns, with the restriction that the columns are to be defined in the header. For RONEC, we define our CoNLL-U Plus format as the standard 10 columns plus another extra column named RONEC:CLASS. This column has the following format: nolistsep",
+      "[noitemsep]",
+      "each named entity has a distinct id in the sentence, starting from 1; as an entity can span several words, all words that belong to it have the same id (no relation to word indexes)",
+      "the first word belonging to an entity also contains its class (e.g. word \"John\" in entity \"John Smith\" will be marked as \"1:PERSON\")",
+      "a non-entity word is marked with an asterisk *",
+      "Table TABREF37 shows the CoNLL-U Plus format where for example \"a 2-a\" is an ORDINAL entity spanning 3 words. The first word \"a\" is marked in this last column as \"1:ORDINAL\" while the following words just with the id \"1\".",
+      "The CoNLL-U Plus format we provide was created as follows: (1) annotate the raw sentences using the NLP-Cube tool for Romanian (it provides everything from tokenization to parsing, filling in all attributes in columns #1-#10; (2) align each token with the human-made entity annotations from the BRAT environment (the alignment is done automatically and is error-free) and fill in column #11."
+    ],
+    [
+      "For the English language, we found two \"categories\" of NER annotations to be more prominent: CoNLL- and ACE-style. Because CoNLL only annotates a few classes (depending on the corpora, starting from the basic three: PERSON, ORGANIZATION and LOCATION, up to seven), we chose to follow the ACE-style with 18 different classes. After analyzing the ACE guide we have settled on 16 final classes that seemed more appropriate for Romanian, seen in table TABREF18.",
+      "In the following sub-sections we will describe each class in turn, with a few examples. Some examples have been left in Romanian while some have been translated in English for the reader's convenience. In the examples at the end of each class' description, translations in English are colored for easier reading."
+    ],
+    [
+      "Persons, including fictive characters. We also mark common nouns that refer to a person (or several), including pronouns (us, them, they), but not articles (e.g. in \"an individual\" we don't mark \"an\"). Positions are not marked unless they directly refer to the person: \"The presidential counselor has advised ... that a new counselor position is open.\", here we mark \"presidential counselor\" because it refers to a person and not the \"counselor\" at the end of the sentence as it refers only to a position.",
+      "",
+      "Locul doi i-a revenit rom\u00e2ncei Otilia Aionesei, o elev\u0103 de 17 ani.",
+      "green!55!blueThe second place was won by Otilia Aionesei, a 17 year old student.",
+      "",
+      "Ministrul bulgar pentru afaceri europene, Meglena Kuneva ...",
+      "green!55!blueThe Bulgarian Minister for European Affairs, Meglena Kuneva ...",
+      ""
+    ],
+    [
+      "These are nationalities or religious or political groups. We include words that indicate the nationality of a person, group or product/object. Generally words marked as NAT_REl_POL are adjectives.",
+      "",
+      "avionul american",
+      "green!55!bluethe American airplane",
+      "",
+      "Grupul olandez",
+      "green!55!bluethe Dutch group",
+      "",
+      "Grecii i\u0219i vor alege pre\u0219edintele.",
+      "green!55!blueThe Greeks will elect their president.",
+      ""
+    ],
+    [
+      "Companies, agencies, institutions, sports teams, groups of people. These entities must have an organizational structure. We only mark full organizational entities, not fragments, divisions or sub-structures.",
+      "",
+      "Universitatea Politehnica Bucure\u0219ti a decis ...",
+      "green!55!blueThe Politehnic University of Bucharest has decided ...",
+      "",
+      "Adobe Inc. a lansat un nou produs.",
+      "green!55!blueAdobe Inc. has launched a new product.",
+      ""
+    ],
+    [
+      "Geo-political entities: countries, counties, cities, villages. GPE entities have all of the following components: (1) a population, (2) a well-defined governing/organizing structure and (3) a physical location. GPE entities are not sub-entities (like a neighbourhood from a city).",
+      "",
+      "Armin van Buuren s-a n\u0103scut \u00een Leiden.",
+      "green!55!blueArmin van Buuren was born in Leiden.",
+      "",
+      "U.S.A. ramane indiferent\u0103 amenin\u021b\u0103rilor Coreei de Nord.",
+      "green!55!blueU.S.A. remains indifferent to North Korea's threats.",
+      ""
+    ],
+    [
+      "Non-geo-political locations: mountains, seas, lakes, streets, neighbourhoods, addresses, continents, regions that are not GPEs. We include regions such as Middle East, \"continents\" like Central America or East Europe. Such regions include multiple countries, each with its own government and thus cannot be GPEs.",
+      "",
+      "Pe DN7 Petro\u0219ani-Ob\u00e2r\u0219ia Lotrului carosabilul era umed, acoperit (cca 1 cm) cu z\u0103pad\u0103, iar de la Ob\u00e2r\u0219ia Lotrului la sta\u021biunea Vidra, stratul de z\u0103pad\u0103 era de 5-6 cm.",
+      "green!55!blueOn DN7 Petro\u0219ani-Ob\u00e2r\u0219ia Lotrului the road was wet, covered (about 1cm) with snow, and from Ob\u00e2r\u0219ia Lotrului to Vidra resort the snow depth was around 5-6 cm.",
+      "",
+      "Produsele comercializate \u00een Europa de Est au o calitate inferioar\u0103 celor din vest.",
+      "green!55!blueProducts sold in East Europe have a lower quality than those sold in the west.",
+      ""
+    ],
+    [
+      "Buildings, airports, highways, bridges or other functional structures built by humans. Buildings or other structures which house people, such as homes, factories, stadiums, office buildings, prisons, museums, tunnels, train stations, etc., named or not. Everything that falls within the architectural and civil engineering domains should be labeled as a FACILITY. We do not mark structures composed of multiple (and distinct) sub-structures, like a named area that is composed of several buildings, or \"micro\"-structures such as an apartment (as it a unit of an apartment building). However, larger, named functional structures can still be marked (such as \"terminal X\" of an airport).",
+      "",
+      "Autostrada A2 a intrat \u00een repara\u021bii pe o band\u0103, \u00eens\u0103 pe A1 nu au fost \u00eenc\u0103 \u00eencepute lucr\u0103rile.",
+      "green!55!blueRepairs on one lane have commenced on the A2 highway, while on A1 no works have started yet.",
+      "",
+      "Aeroportul Henri Coand\u0103 ar putea sa fie extins cu un nou terminal.",
+      "green!55!blueHenri Coand\u0103 Airport could be extended with a new terminal.",
+      ""
+    ],
+    [
+      "Objects, cars, food, items, anything that is a product, including software (such as Photoshop, Word, etc.). We don't mark services or processes. With very few exceptions (such as software products), PRODUCT entities have to have physical form, be directly man-made. We don't mark entities such as credit cards, written proofs, etc. We don't include the producer's name unless it's embedded in the name of the product.",
+      "",
+      "Ma\u0219ina cump\u0103rat\u0103 este o Mazda.",
+      "green!55!blueThe bought car is a Mazda.",
+      "",
+      "S-au cump\u0103rat 5 Ford Taurus \u0219i 2 autobuze Volvo.",
+      "green!55!blue5 Ford Taurus and 2 Volvo buses have been acquired.",
+      ""
+    ],
+    [
+      "Named events: Storms (e.g.:\"Sandy\"), battles, wars, sports events, etc. We don't mark sports teams (they are ORGs), matches (e.g. \"Steaua-Rapid\" will be marked as two separate ORGs even if they refer to a football match between the two teams, but the match is not specific). Events have to be significant, with at least national impact, not local.",
+      "",
+      "R\u0103zboiul cel Mare, R\u0103zboiul Na\u021biunilor, denumit, \u00een timpul celui de Al Doilea R\u0103zboi Mondial, Primul R\u0103zboi Mondial, a fost un conflict militar de dimensiuni mondiale.",
+      "green!55!blueThe Great War, War of the Nations, as it was called during the Second World War, the First World War was a global-scale military conflict.",
+      ""
+    ],
+    [
+      "This class represents all languages.",
+      "",
+      "Rom\u00e2nii din Rom\u00e2nia vorbesc rom\u00e2n\u0103.",
+      "green!55!blueRomanians from Romania speak Romanian.",
+      "",
+      "\u00cen Moldova se vorbe\u0219te rusa \u0219i rom\u00e2na.",
+      "green!55!blueIn Moldavia they speak Russian and Romanian.",
+      ""
+    ],
+    [
+      "Books, songs, TV shows, pictures; everything that is a work of art/culture created by humans. We mark just their name. We don't mark laws.",
+      "",
+      "Accesul la Mona Lisa a fost temporar interzis vizitatorilor.",
+      "green!55!blueAccess to Mona Lisa was temporarily forbidden to visitors.",
+      "",
+      "\u00cen aceast\u0103 sear\u0103 la Vrei sa Fii Miliardar vom avea un invitat special.",
+      "green!55!blueThis evening in Who Wants To Be A Millionaire we will have a special guest.",
+      ""
+    ],
+    [
+      "Date and time values. We will mark full constructions, not parts, if they refer to the same moment (e.g. a comma separates two distinct DATETIME entities only if they refer to distinct moments). If we have a well specified period (e.g. \"between 20-22 hours\") we mark it as PERIOD, otherwise less well defined periods are marked as DATETIME (e.g.: \"last summer\", \"September\", \"Wednesday\", \"three days\"); Ages are marked as DATETIME as well. Prepositions are not included.",
+      "",
+      "Te rog s\u0103 vii aici \u00een cel mult o or\u0103, nu m\u00e2ine sau poim\u00e2ine.",
+      "green!55!bluePlease come here in one hour at most, not tomorrow or the next day.",
+      "",
+      "Actul s-a semnat la orele 16.",
+      "green!55!blueThe paper was signed at 16 hours.",
+      "",
+      "August este o lun\u0103 secetoas\u0103.",
+      "green!55!blueAugust is a dry month.",
+      "",
+      "Pe data de 20 martie \u00eentre orele 20-22 va fi oprit\u0103 alimentarea cu curent.",
+      "green!55!blueOn the 20th of March, between 20-22 hours, electricity will be cut-off.",
+      ""
+    ],
+    [
+      "Periods/time intervals. Periods have to be very well marked in text. If a period is not like \"a-b\" then it is a DATETIME.",
+      "",
+      "Spectacolul are loc \u00eentre 1 \u0219i 3 Aprilie.",
+      "green!55!blueThe show takes place between 1 and 3 April.",
+      "",
+      "\u00cen prima jum\u0103tate a lunii iunie va avea loc evenimentul de dou\u0103 zile.",
+      "green!55!blueIn the first half of June the two-day event will take place.",
+      ""
+    ],
+    [
+      "Money, monetary values, including units (e.g. USD, $, RON, lei, francs, pounds, Euro, etc.) written with number or letters. Entities that contain any monetary reference, including measuring units, will be marked as MONEY (e.g. 10$/sqm, 50 lei per hour). Words that are not clear values will not be marked, such as \"an amount of money\", \"he received a coin\".",
+      "",
+      "Primarul a semnat un contract \u00een valoare de 10 milioane lei noi, echivalentul a aproape 2.6m EUR.",
+      "green!55!blueThe mayor signed a contract worth 10 million new lei, equivalent of almost 2.6m EUR.",
+      ""
+    ],
+    [
+      "Measurements, such as weight, distance, etc. Any type of quantity belongs in this class.",
+      "",
+      "Conduc\u0103torul auto avea peste 1g/ml alcool \u00een s\u00e2nge, fiind oprit deoarece a fost prins cu peste 120 km/h \u00een localitate.",
+      "green!55!blueThe car driver had over 1g/ml blood alcohol, and was stopped because he was caught speeding with over 120km/h in the city.",
+      ""
+    ],
+    [
+      "Any numeric value (including phone numbers), written with letters or numbers or as percents, which is not MONEY, QUANTITY or ORDINAL.",
+      "",
+      "Raportul XII-2 arat\u0103 4 552 de investitori, iar structura de portofoliu este: cont curent 0,05%, certificate de trezorerie 66,96%, depozite bancare 13,53%, obliga\u021biuni municipale 19,46%.",
+      "green!55!blueThe XII-2 report shows 4 552 investors, and the portfolio structure is: current account 0,05%, treasury bonds 66,96%, bank deposits 13,53%, municipal bonds 19,46%.",
+      ""
+    ],
+    [
+      "The first, the second, last, 30th, etc.; An ordinal must imply an order relation between elements. For example, \"second grade\" does not involve a direct order relation; it indicates just a succession in grades in a school system.",
+      "",
+      "Primul loc a fost ocupat de echipa Germaniei.",
+      "green!55!blueThe first place was won by Germany's team.",
+      "",
+      "",
+      "",
+      "The corpus creation process involved a small number of people that have voluntarily joined the initiative, with the authors of this paper directing the work. Initially, we searched for NER resources in Romanian, and found none. Then we looked at English resources and read the in-depth ACE guide, out of which a 16-class draft evolved. We then identified a copy-right free text from which we hand-picked sentences to maximize the amount of entities while maintaining style balance. The annotation process was a trial-and-error, with cycles composed of annotation, discussing confusing entities, updating the annotation guide schematic and going through the corpus section again to correct entities following guide changes. The annotation process was done online, in BRAT. The actual annotation involved 4 people, has taken about 6 months (as work was volunteer-based, we could not have reached for 100% time commitment from the people involved), and followed the steps:",
+      "nolistsep",
+      "Each person would annotate the full corpus (this included the cycles of shaping up the annotation guide, and re-annotation). Inter-annotator agreement (ITA) at this point was relatively low, at 60-70%, especially for a number of classes.",
+      "We then automatically merged all annotations, with the following criterion: if 3 of the 4 annotators agreed on an entity (class&start-stop), then it would go unchanged; otherwise mark the entity (longest span) as CONFLICTED.",
+      "Two teams were created, each with two persons. Each team annotated the full corpus again, starting from the previous step. At this point, class-average ITA has risen to over 85%.",
+      "Next, the same automatic merging happened, this time entities remained unchanged if both annotations agreed.",
+      "Finally, one of the authors went through the full corpus one more time, correcting disagreements.",
+      "We would like to make a few notes regarding classes and inter-annotator agreements:",
+      "nolistsep",
+      "[noitemsep]",
+      "Classes like ORGANIZATION, NAT_REL_POL, LANGUAGE or GPEs have the highest ITA, over 98%. They are pretty clear and distinct from other classes.",
+      "The DATETIME class also has a high ITA, with some overlap with PERIOD: annotators could fall-back if they were not sure that an expression was a PERIOD and simply mark it as DATETIME.",
+      "WORK_OF_ART and EVENTs have caused some problems because the scope could not be properly defined from just one sentence. For example, a fair in a city could be a local event, but could also be a national periodic event.",
+      "MONEY, QUANTITY and ORDINAL all are more specific classes than NUMERIC_VALUE. So, in cases where a numeric value has a unit of measure by it, it should become a QUANTITY, not a NUMERIC_VALUE. However, this \"specificity\" has created some confusion between these classes, just like with DATETIME and PERIOD.",
+      "The ORDINAL class is a bit ambiguous, because, even though it ranks \"higher\" than NUMERIC_VALUE, it is the least diverse, most of the entities following the same patterns.",
+      "PRODUCT and FACILITY classes have the lowest ITA by far (less than 40% in the first annotation cycle, less than 70% in the second). We actually considered removing these classes from the annotation process, but to try to mimic the OntoNotes classes as much as possible we decided to keep them in. There were many cases where the annotators disagreed about the scope of words being facilities or products. Even in the ACE guidelines these two classes are not very well \"documented\" with examples of what is and what is not a PRODUCT or FACILITY. Considering that these classes are, in our opinion, of the lowest importance among all the classes, a lower ITA was accepted.",
+      "Finally, we would like to address the \"semantic scope\" of the entities - for example, for class PERSON, we do not annotate only proper nouns (NPs) but basically any reference to a person (e.g. through pronouns \"she\", job position titles, common nouns such as \"father\", etc.). We do this because we would like a high-coverage corpus, where entities are marked as more semantically-oriented rather than syntactically - in the same way ACE entities are more encompassing than CoNLL entities. We note that, for example, if one would like strict proper noun entities, it is very easy to extract from a PERSON multi-word entity only those words which are syntactically marked (by any tagger) as NPs."
+    ],
+    [
+      "We have presented RONEC - the first Named Entity Corpus for the Romanian language. At its current version, in its 5127 sentences we have 26377 annotated entities in 16 different classes. The corpus is based on copy-right free text, and is released as open-source, free to use and extend.",
+      "We hope that in time this corpus will grow in size and mature towards a strong resource for Romanian. For this to happen we have released the corpus in two formats: CoNLL-U PLus, which is a text-based tab-separated pre-tokenized and annotated format that is simple to use, and BRAT, which is practically plug-and-play into the BRAT web annotation tool where anybody can add and annotate new sentences. Also, in the GitHub repo there are automatic alignment and conversion script to and from the two formats so they could easily be exported between.",
+      "Finally, we have also provided an annotation guide that we will improve, and in time evolve into a full annotation document like the ACE Annotation Guidelines for Entities V6.6 BIBREF8."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2471/instruction.md b/qasper-2471/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..401357e187e488487191e5a1f957e788eeac0d6c
--- /dev/null
+++ b/qasper-2471/instruction.md
@@ -0,0 +1,78 @@
+Name of Paper: A General-Purpose Tagger with Convolutional Neural Networks
+
+Question: Do they jointly tackle multiple tagging problems?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Model",
+    "Character Composition Model",
+    "Context Encoding Model",
+    "Hyper-parameters",
+    "Data",
+    "Tasks",
+    "Setups",
+    "Results",
+    "Unnormalized Text",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Recently, character composition models have shown great success in many NLP tasks, mainly because of their robustness in dealing with out-of-vocabulary (OOV) words by capturing sub-word informations. Among the character composition models, bidirectional long short-term memory (LSTM) models and convolutional neural networks (CNN) are widely applied in many tasks, e.g. part-of-speech (POS) tagging BIBREF0 , BIBREF1 , named entity recognition BIBREF2 , language modeling BIBREF3 , BIBREF4 , machine translation BIBREF5 and dependency parsing BIBREF6 , BIBREF7 .",
+      "In this paper, we present a state-of-the-art general-purpose tagger that uses CNNs both to compose word representations from characters and to encode context information for tagging. We show that the CNN model is more capable than the LSTM model for both functions, and more stable for unseen or unnormalized words, which is the main benefit of character composition models.",
+      "Yu:2017 compared the performance of CNN and LSTM as character composition model for dependency parsing, and concluded that CNN performs better than LSTM. In this paper, we show that this is also the case for POS tagging. Furthermore, we extend the scope to morphological tagging and supertagging, in which the tag set is much larger and long-distance dependencies between words are more important.",
+      "In these three tagging tasks, we compare our tagger with the bilstm-aux tagger BIBREF1 and the CRF-based morphological tagger MarMot BIBREF8 . The CNN tagger shows robust performance accross the three tasks, and achieves the highest average accuracy in all tasks. It (significantly) outperforms LSTM in morphological tagging, and outperforms both baselines in supertagging by a large margin.",
+      "To test the robustness of the taggers against the OOV problem, we also conduct experiments using artificially constructed unnormalized text by corrupting words in the normal dev set. Again, the CNN tagger outperforms the two baselines by a very large margin.",
+      "Therefore we conclude that our CNN tagger is a robust state-of-the-art general-purpose tagger that can effectively compose word representation from characters and encode context information."
+    ],
+    [
+      "Our proposed CNN tagger has two main components: the character composition model and the context encoding model. Both components are essentially CNN models, capturing different levels of information: the first CNN captures morphological information from character n-grams, the second one captures contextual information from word n-grams. Figure FIGREF2 shows a diagram of both models of the tagger."
+    ],
+    [
+      "The character composition model is similar to Yu:2017, where several convolution filters are used to capture character n-grams of different sizes. The outputs of each convolution filter are fed through a max pooling layer, and the pooling outputs are concatenated to represent the word."
+    ],
+    [
+      "The context encoding model captures the context information of the target word by scanning through the word representations of its context window. The word representation could be only word embeddings ( INLINEFORM0 ), only composed vectors ( INLINEFORM1 ) or the concatenation of both ( INLINEFORM2 )",
+      "A context window consists of N words to both sides of the target word and the target word itself. To indicate the target word, we concatenate a binary feature to each of the word representations with 1 indicating the target and 0 otherwise, similar to Vu:2016. Additional to the binary feature, we also concatenate a position embedding to encode the relative position of each context word, similar to Gehring:2017."
+    ],
+    [
+      "For the character composition model, we take a fixed input size of 32 characters for each word, with padding on both sides or cutting from the middle if needed. We apply four convolution filters with sizes of 3, 5, 7, and 9. Each filter has an output channel of 25 dimensions, thus the composed vector is 100-dimensional. We apply Gaussian noise with standard deviation of 0.1 is applied on the composed vector during training.",
+      "For the context encoding model, we take a context window of 15 (7 words to both sides of the target word) as input and predict the tag of the target word. We also apply four convolution filters with sizes of 2, 3, 4 and 5, each filter is stacked by another filter with the same size, and the output has 128 dimensions, thus the context representation is 512-dimensional. We apply one 512-dimensional hidden layer with ReLU non-linearity before the prediction layer. We apply dropout with probability of 0.1 after the hidden layer during training.",
+      "The model is trained with averaged stochastic gradient descent with a learning rate of 0.1, momentum of 0.9 and mini-batch size of 100. We apply L2 regularization with a rate of INLINEFORM0 on all the parameters of the network except the embeddings."
+    ],
+    [
+      "We use treebanks from version 1.2 of Universal Dependencies (UD), and in the case of several treebanks for one language, we only use the canonical treebank. There are in total 22 treebanks, as in Plank:2016. Each treebank splits into train, dev, and test sets, we use the dev sets for early stop, and test on the test sets."
+    ],
+    [
+      "We evaluate our method on three tagging tasks: POS tagging (Pos), morphological tagging (Morph) and supertagging (Stag).",
+      "For POS tagging we use Universal POS tags, which is an extension of Petrov:2012. The universal tag set tries to capture the \u201cuniversal\u201d properties of words and facilitate cross-lingual learning. Therefore the tag set is very coarse and leaves out most of the language-specific properties to morphological features.",
+      "Morphological tags encode the language-specific morphological features of the words, e.g., number, gender, case. They are represented in the UD treebanks as one string which contains several key-value pairs of morphological features.",
+      "Supertags BIBREF9 are tags that encode more syntactic information than standard POS tags, e.g. the head direction or the subcategorization frame. We use dependency-based supertags BIBREF10 which are extracted from the dependency treebanks. Adding such tags into feature models of statistical dependency parsers significantly improves their performance BIBREF11 , BIBREF12 . Supertags can be designed with different levels of granularity. We use the standard Model 1 from Ouchi:2014, where each tag consists of head direction, dependency label and dependent direction. Even with the basic supertag model, the Stag task is more difficult than Pos and Morph because it generally requires taking long-distance dependencies between words into consideration.",
+      "We select these tasks as examples for tagging applications because they differ strongly in tag set sizes. Generally, the Pos set sizes for all the languages are no more than 17 and Stag set sizes are around 200. When treating morphological features as a string (i.e. not splitting into key-value pairs), the sizes of the Morph tag sets range from about 100 up to 2000."
+    ],
+    [
+      "As baselines to our models, we take the two state-of-the-art taggers MarMot (denoted as CRF) and bilstm-aux (denoted as LSTM). We train the taggers with the recommended hyper-parameters from the documentation.",
+      "To ensure a fair comparison (especially between LSTM and CNN), we generally treat the three tasks equally, and do not apply task-specific tuning on them, i.e., using the same features and same model hyper-parameters in each single task. Also, we do not use any pre-trained word embeddings.",
+      "For the LSTM tagger, we use the recommended hyper-parameters in the documentation including 64-dimensional word embeddings ( INLINEFORM0 ) and 100-dimensional composed vectors ( INLINEFORM1 ). We train the INLINEFORM2 , INLINEFORM3 and INLINEFORM4 models as in Plank:2016. We train the CNN taggers with the same dimensionalities for word representations.",
+      "For the CRF tagger, we predict Pos and Morph jointly as in the standard setting for MarMot, which performs much better than with separate predictions, as shown in Mueller:2013 and in our preliminary experiments. Also, it splits the morphological tags into key-value pairs, whereas the neural taggers treat the whole string as a tag. We predict Stag as a separate task."
+    ],
+    [
+      "The test results for the three tasks are shown in Table TABREF17 in three groups. The first group of seven columns are the results for Pos, where both LSTM and CNN have three variations of input features: word only ( INLINEFORM0 ), character only ( INLINEFORM1 ) and both ( INLINEFORM2 ). For Morph and Stag, we only use the INLINEFORM3 setting for both LSTM and CNN.",
+      "On macro-average, three taggers perform close in the Pos task, with the CNN tagger being slightly better. In the Morph task, CNN is again slightly ahead of CRF, while LSTM is about 2 points behind. In the Stag task, CNN outperforms both taggers by a large margin: 2 points higher than LSTM and 8 points higher than CRF.",
+      "While considering the input features of the LSTM and CNN taggers, both taggers perform close with only INLINEFORM0 as input, which suggests that the two taggers are comparable in encoding context for tagging Pos. However, with only INLINEFORM1 , CNN performs much better than LSTM (95.54 vs. 92.61), and close to INLINEFORM2 (96.18). Also, INLINEFORM3 consistently outperforms INLINEFORM4 for all languages. This suggests that the CNN model alone is capable of learning most of the information that the word-level model can learn, while the LSTM model is not.",
+      "The more interesting cases are Morph and Stag, where CNN performs much higher than LSTM. We hypothesize three possible reasons to explain the considerably large difference. First, the LSTM tagger may be more sensitive to hyper-parameters and requires task specific tuning. We use the same setting which is tuned for the Pos task, thus it underperforms in the other tasks. Second, the LSTM tagger may not deal well with large tag sets. The tag set size for Morph are larger than Pos in orders of magnitudes, especially for Czech, Basque, Finnish and Slovene, all of which have more than 1000 distinct Morph tags in the training data, and the LSTM performs poorly on these languages. Third, the LSTM has theoretically unlimited access to all the tokens in the sentence, but in practice it might not learn the context as good as the CNN. In the LSTM model, the information of long-distance contexts will gradually fade away during the recurrence, whereas in the CNN model, all words are treated equally as long as they are in the context window. Therefore the LSTM underperforms in the Stag task, where the information from long-distance context is more important."
+    ],
+    [
+      "It is a common scenario to use a model trained with news data to process text from social media, which could include intentional or unintentional misspellings. Unfortunately, we do not have social media data to test the taggers. However, we design an experiment to simulate unnormalized text, by systematically editing the words in the dev sets with three operations: insertion, deletion and substitution. For example, if we modify a word abcdef at position 2 (0-based), the modified words would be abxcdef, abdef, and abxdef, where x is a random character from the alphabet of the language.",
+      "For each operation, we create a group of modified dev sets, where all words longer than two characters are edited by the operation with a probability of 0.25, 0.5, 0.75, or 1. For each language, we use the models trained on the normal training sets and predict Pos for the three groups of modified dev set. The average accuracies are shown in Figure FIGREF19 .",
+      "Generally, all models suffer from the increasing degrees of unnormalized texts, but CNN always suffers the least. In the extreme case where almost all words are unnormalized, CNN performs 4 to 8 points higher than LSTM and 4 to 11 points higher than CRF. This suggests that the CNN is more robust to misspelt words. While looking into the specific cases of misspelling, CNN is more sensitive to insertion and deletion, while CRF and LSTM are more sensitive to substitution."
+    ],
+    [
+      "In this paper, we propose a general-purpose tagger that uses two CNNs for both character composition and context encoding. On the universal dependency treebanks (v1.2), the tagger achieves state-of-the-art results for POS tagging and morphological tagging, and to the best of our knowledge, it also performs best for supertagging. The tagger works well across different tagging tasks without tuning the hyper-parameters, and it is also robust against unnormalized text."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2476/instruction.md b/qasper-2476/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..7b09332142cf864a2ffe4c58d25f92afc508936a
--- /dev/null
+++ b/qasper-2476/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Principles for Developing a Knowledge Graph of Interlinked Events from News Headlines on Twitter
+
+Question: Which news organisations are the headlines sourced from?
\ No newline at end of file
diff --git a/qasper-2477/instruction.md b/qasper-2477/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..3e39221b2f621691bfeababe493c62bc860365b7
--- /dev/null
+++ b/qasper-2477/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs
+
+Question: What meta-information is being transferred?
\ No newline at end of file
diff --git a/qasper-2478/instruction.md b/qasper-2478/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..a4ec0c55281a337086dc1a601687678b7020838a
--- /dev/null
+++ b/qasper-2478/instruction.md
@@ -0,0 +1,134 @@
+Name of Paper: Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs
+
+Question: What datasets are used to evaluate the approach?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Knowledge Graph Embedding",
+    "Meta-Learning",
+    "Task Formulation",
+    "Method",
+    "Relation-Meta Learner",
+    "Embedding Learner",
+    "Training Objective",
+    "Experiments",
+    "Datasets and Evaluation Metrics",
+    "Implementation",
+    "Results",
+    "Ablation Study",
+    "Facts That Affect MetaR's Performance",
+    "Conclusion",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "A knowledge graph is composed by a large amount of triples in the form of $(head\\; entity,\\, relation,\\, tail\\; entity)$ ( $(h, r, t)$ in short), encoding knowledge and facts in the world. Many KGs have been proposed BIBREF0 , BIBREF1 , BIBREF2 and applied to various applications BIBREF3 , BIBREF4 , BIBREF5 .",
+      "Although with huge amount of entities, relations and triples, many KGs still suffer from incompleteness, thus knowledge graph completion is vital for the development of KGs. One of knowledge graph completion tasks is link prediction, predicting new triples based on existing ones. For link prediction, KG embedding methods BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 are promising ways. They learn latent representations, called embeddings, for entities and relations in continuous vector space and accomplish link prediction via calculation with embeddings.",
+      "The effectiveness of KG embedding methods is promised by sufficient training examples, thus results are much worse for elements with a few instances during training BIBREF10 . However, few-shot problem widely exists in KGs. For example, about 10% of relations in Wikidata BIBREF0 have no more than 10 triples. Relations with a few instances are called few-shot relations. In this paper, we devote to discuss few-shot link prediction in knowledge graphs, predicting tail entity $t$ given head entity $h$ and relation $r$ by only observing $K$ triples about $r$ , usually $K$ is small. Figure 1 depicts an example of 3-shot link prediction in KGs.",
+      "To do few-shot link prediction, BIBREF11 made the first trial and proposed GMatching, learning a matching metric by considering both learned embeddings and one-hop graph structures, while we try to accomplish few-shot link prediction from another perspective based on the intuition that the most important information to be transferred from a few existing instances to incomplete triples should be the common and shared knowledge within one task. We call such information relation-specific meta information and propose a new framework Meta Relational Learning (MetaR) for few-shot link prediction. For example, in Figure 1 , relation-specific meta information related to the relation CEOof or CountryCapital will be extracted and transferred by MetaR from a few existing instances to incomplete triples.",
+      "The relation-specific meta information is helpful in the following two perspectives: 1) transferring common relation information from observed triples to incomplete triples, 2) accelerating the learning process within one task by observing only a few instances. Thus we propose two kinds of relation-specific meta information: relation meta and gradient meta corresponding to afore mentioned two perspectives respectively. In our proposed framework MetaR, relation meta is the high-order representation of a relation connecting head and tail entities. Gradient meta is the loss gradient of relation meta which will be used to make a rapid update before transferring relation meta to incomplete triples during prediction.",
+      "Compared with GMatching BIBREF11 which relies on a background knowledge graph, our MetaR is independent with them, thus is more robust as background knowledge graphs might not be available for few-shot link prediction in real scenarios.",
+      "We evaluate MetaR with different settings on few-shot link prediction datasets. MetaR achieves state-of-the-art results, indicating the success of transferring relation-specific meta information in few-shot link prediction tasks. In summary, main contributions of our work are three-folds:"
+    ],
+    [
+      "One target of MetaR is to learn the representation of entities fitting the few-shot link prediction task and the learning framework is inspired by knowledge graph embedding methods. Furthermore, using loss gradient as one kind of meta information is inspired by MetaNet BIBREF12 and MAML BIBREF13 which explore methods for few-shot learning by meta-learning. From these two points, we regard knowledge graph embedding and meta-learning as two main kinds of related work."
+    ],
+    [
+      "Knowledge graph embedding models map relations and entities into continuous vector space. They use a score function to measure the truth value of each triple $(h, r, t)$ . Same as knowledge graph embedding, our MetaR also need a score function, and the main difference is that representation for $r$ is the learned relation meta in MetaR rather than embedding of $r$ as in normal knowledge graph embedding methods.",
+      "One line of work is started by TransE BIBREF6 with distance score function. TransH BIBREF14 and TransR BIBREF15 are two typical models using different methods to connect head, tail entities and their relations. DistMult BIBREF9 and ComplEx BIBREF8 are derived from RESCAL BIBREF7 , trying to mine latent semantics in different ways. There are also some others like ConvE BIBREF16 using convolutional structure to score triples and models using additional information such as entity types BIBREF17 and relation paths BIBREF18 . BIBREF19 comprehensively summarize the current popular knowledge graph embedding methods.",
+      "Traditional embedding models are heavily rely on rich training instances BIBREF20 , BIBREF11 , thus are limited to do few-shot link prediction. Our MetaR is designed to fill this vulnerability of existing embedding models."
+    ],
+    [
+      "Meta-learning seeks for the ability of learning quickly from only a few instances within the same concept and adapting continuously to more concepts, which are actually the rapid and incremental learning that humans are very good at.",
+      "Several meta-learning models have been proposed recently. Generally, there are three kinds of meta-learning methods so far: (1) Metric-based meta-learning BIBREF21 , BIBREF22 , BIBREF23 , BIBREF11 , which tries to learn a matching metric between query and support set generalized to all tasks, where the idea of matching is similar to some nearest neighbors algorithms. Siamese Neural Network BIBREF21 is a typical method using symmetric twin networks to compute the metric of two inputs. GMatching BIBREF11 , the first trial on one-shot link prediction in knowledge graphs, learns a matching metric based on entity embeddings and local graph structures which also can be regarded as a metric-based method. (2) Model-based method BIBREF24 , BIBREF12 , BIBREF25 , which uses a specially designed part like memory to achieve the ability of learning rapidly by only a few training instances. MetaNet BIBREF12 , a kind of memory augmented neural network (MANN), acquires meta information from loss gradient and generalizes rapidly via its fast parameterization. (3) Optimization-based approach BIBREF13 , BIBREF26 , which gains the idea of learning faster by changing the optimization algorithm. Model-Agnostic Meta-Learning BIBREF13 abbreviated as MAML is a model-agnostic algorithm. It firstly updates parameters of task-specific learner, and meta-optimization across tasks is performed over parameters by using above updated parameters, it's like \u201ca gradient through a gradient\".",
+      "As far as we know, work proposed by BIBREF11 is the first research on few-shot learning for knowledge graphs. It's a metric-based model which consists of a neighbor encoder and a matching processor. Neighbor encoder enhances the embedding of entities by their one-hop neighbors, and matching processor performs a multi-step matching by a LSTM block."
+    ],
+    [
+      "In this section, we present the formal definition of a knowledge graph and few-shot link prediction task. A knowledge graph is defined as follows:",
+      "Definition 3.1 (Knowledge Graph $\\mathcal {G}$ ) A knowledge graph $\\mathcal {G} = \\lbrace  \\mathcal {E}, \\mathcal {R}, \\mathcal {TP}\\rbrace $ . $\\mathcal {E}$ is the entity set. $\\mathcal {R}$ is the relation set. And $\\mathcal {TP} = \\lbrace  (h, r, t)\\in \\mathcal {E} \\times \\mathcal {R} \\times \\mathcal {E}\\rbrace  $ is the triple set.",
+      "And a few-shot link prediction task in knowledge graphs is defined as:",
+      "Definition 3.2 (Few-shot link prediction task $\\mathcal {T}$ ) With a knowledge graph $\\mathcal {G} = \\lbrace  \\mathcal {E}, \\mathcal {R}, \\mathcal {TP}\\rbrace $ , given a support set $\\mathcal {S}_r = \\lbrace (h_i, t_i)\\in \\mathcal {E} \\times \\mathcal {E} | (h_i, r, t_i) \\in \\mathcal {TP} \\rbrace $ about relation $r\\in \\mathcal {R}$ , where $|\\mathcal {S}_r | = K$ , predicting the tail entity linked with relation $r$ to head entity $h_j$ , formulated as $r:(h_j, ?)$ , is called K-shot link prediction.",
+      "As defined above, a few-shot link prediction task is always defined for a specific relation. During prediction, there usually is more than one triple to be predicted, and with support set $\\mathcal {S}_r$ , we call the set of all triples to be predicted as query set $\\mathcal {Q}_r = \\lbrace  r:(h_j, ?)\\rbrace $ .",
+      "The goal of a few-shot link prediction method is to gain the capability of predicting new triples about a relation $r$ with only observing a few triples about $r$ . Thus its training process is based on a set of tasks $\\mathcal {T}_{train}=\\lbrace \\mathcal {T}_{i}\\rbrace _{i=1}^{M}$ where each task $\\mathcal {T}_{i} = \\lbrace \\mathcal {S}_i, \\mathcal {Q}_i\\rbrace $ corresponds to an individual few-shot link prediction task with its own support and query set. Its testing process is conducted on a set of new tasks $\\mathcal {T}_{test} = \\lbrace \\mathcal {T}_{j}\\rbrace _{j=1}^{N}$ which is similar to $\\mathcal {T}_{train}$ , other than that $\\mathcal {T}_{j} \\in \\mathcal {T}_{test}$ should be about relations that have never been seen in $\\mathcal {T}_{train}$ .",
+      "Table 1 gives a concrete example of the data during learning and testing for few-shot link prediction."
+    ],
+    [
+      "To make one model gain the few-shot link prediction capability, the most important thing is transferring information from support set to query set and there are two questions for us to think about: (1) what is the most transferable and common information between support set and query set and (2) how to learn faster by only observing a few instances within one task. For question (1), within one task, all triples in support set and query set are about the same relation, thus it is naturally to suppose that relation is the key common part between support and query set. For question (2), the learning process is usually conducted by minimizing a loss function via gradient descending, thus gradients reveal how the model's parameters should be changed. Intuitively, we believe that gradients are valuable source to accelerate learning process.",
+      "Based on these thoughts, we propose two kinds of meta information which are shared between support set and query set to deal with above problems:",
+      "In order to extract relation meta and gradient mate and incorporate them with knowledge graph embedding to solve few-shot link prediction, our proposal, MetaR, mainly contains two modules:",
+      "The overview and algorithm of MetaR are shown in Figure 2 and Algorithm \"Method\" . Next, we introduce each module of MetaR via one few-shot link prediction task $\\mathcal {T}_r = \\lbrace  \\mathcal {S}_r, \\mathcal {Q}_r\\rbrace $ .",
+      "[tb] 1 Learning of MetaR [1] Training tasks $\\mathcal {T}_{train}$ Embedding layer $emb$ ; Parameter of relation-meta learner $\\phi $ not done Sample a task $\\mathcal {T}_r={\\lbrace \\mathcal {S}_r, \\mathcal {Q}_r\\rbrace }$ from $\\mathcal {T}_{train}$ Get $\\mathit {R}$ from $\\mathcal {S}_{r}$ (Equ. 18 , Equ. 19 ) Compute loss in $\\mathcal {S}_{r}$ (Equ. 22 ) Get $\\mathit {G}$ by gradient of $\\mathit {R}$ (Equ. 23 ) Update $emb$0 by $emb$1 (Equ. 24 ) Compute loss in $emb$2 (Equ. 26 ) Update $emb$3 and $emb$4 by loss in $emb$5 "
+    ],
+    [
+      "To extract the relation meta from support set, we design a relation-meta learner to learn a mapping from head and tail entities in support set to relation meta. The structure of this relation-meta learner can be implemented as a simple neural network.",
+      "In task $\\mathcal {T}_r$ , the input of relation-meta learner is head and tail entity pairs in support set $\\lbrace (h_i, t_i) \\in \\mathcal {S}_r\\rbrace $ . We firstly extract entity-pair specific relation meta via a $L$ -layers fully connected neural network, ",
+      "$$\\begin{aligned}\n\\mathbf {x}^0 &= \\mathbf {h}_i \\oplus \\mathbf {t}_i \\\\\n\\mathbf {x}^l &= \\sigma ({\\mathbf {W}^{l}\\mathbf {x}^{l-1} + b^l}) \\\\\n\\mathit {R}_{(h_i, t_i)} &= {\\mathbf {W}^{L}\\mathbf {x}^{L-1} + b^L}\n\n\\end{aligned}$$   (Eq. 18) ",
+      "where $\\mathbf {h}_i \\in \\mathbb {R}^{d}$ and $\\mathbf {t}_i \\in \\mathbb {R}^{d}$ are embeddings of head entity $h_i$ and tail entity $t_i$ with dimension $d$ respectively. $L$ is the number of layers in neural network, and $l \\in \\lbrace 1, \\dots , L-1 \\rbrace $ . $\\mathbf {W}^l$ and $\\mathbf {b}^l$ are weights and bias in layer $l$ . We use LeakyReLU for activation $\\mathbf {t}_i \\in \\mathbb {R}^{d}$0 . $\\mathbf {t}_i \\in \\mathbb {R}^{d}$1 represents the concatenation of vector $\\mathbf {t}_i \\in \\mathbb {R}^{d}$2 and $\\mathbf {t}_i \\in \\mathbb {R}^{d}$3 . Finally, $\\mathbf {t}_i \\in \\mathbb {R}^{d}$4 represent the relation meta from specific entity pare $\\mathbf {t}_i \\in \\mathbb {R}^{d}$5 and $\\mathbf {t}_i \\in \\mathbb {R}^{d}$6 .",
+      "With multiple entity-pair specific relation meta, we generate the final relation meta in current task via averaging all entity-pair specific relation meta in current task, ",
+      "$$\\mathit {R}_{\\mathcal {T}_r} = \\frac{\\sum _{i=1}^{K}\\mathit {R}_{(h_i, t_i)}}{K}$$   (Eq. 19) "
+    ],
+    [
+      "As we want to get gradient meta to make a rapid update on relation meta, we need a score function to evaluate the truth value of entity pairs under specific relations and also the loss function for current task. We apply the key idea of knowledge graph embedding methods in our embedding learner, as they are proved to be effective on evaluating truth value of triples in knowledge graphs.",
+      "In task $\\mathcal {T}_r$ , we firstly calculate the score for each entity pairs $(h_i, t_i)$ in support set $\\mathcal {S}_r$ as follows: ",
+      "$$s_{(h_i, t_i)} = \\Vert  \\mathbf {h}_i + {\\mathit {R}_{\\mathcal {T}_r}} - \\mathbf {t}_i \\Vert $$   (Eq. 21) ",
+      "where $\\Vert  \\mathbf {x}\\Vert $ represents the L2 norm of vector $\\mathbf {x}$ . We design the score function inspired by TransE BIBREF6 which assumes the head entity embedding $\\mathbf {h}$ , relation embedding $\\mathbf {r}$ and tail entity embedding $\\mathbf {t}$ for a true triple $(h, r, t)$ satisfying $\\mathbf {h} + \\mathbf {r} = \\mathbf {t}$ . Thus the score function is defined according to the distance between $\\mathbf {h} + \\mathbf {r} $ and $\\mathbf {t}$ . Transferring to our few-show link prediction task, we replace the relation embedding $\\mathbf {r}$ with relation meta $\\mathbf {x}$0 as there is no direct general relation embeddings in our task and $\\mathbf {x}$1 can be regarded as the relation embedding for current task $\\mathbf {x}$2 .",
+      "With score function for each triple, we set the following loss, ",
+      "$$L(\\mathcal {S}_r) = \\sum _{(h_i, t_i)\\in \\mathcal {S}_r} [\\gamma +s_{(h_i, t_i)}-s_{(h_i, t_i^{\\prime })}]_{+}$$   (Eq. 22) ",
+      "where $[x]_{+}$ represents the positive part of $x$ and $\\gamma $ represents margin which is a hyperparameter. $s_{(h_i, t_i^{\\prime })}$ is the score for negative sample $(h_i, t_i^{\\prime })$ corresponding to current positive entity pair $(h_i, t_i) \\in \\mathcal {S}_r$ , where $(h_i, r, t_i^{\\prime }) \\notin \\mathcal {G}$ .",
+      " $L(\\mathcal {S}_r)$ should be small for task $\\mathcal {T}_r$ which represents the model can properly encode truth values of triples. Thus gradients of parameters indicate how should the parameters be updated. Thus we regard the gradient of $\\mathit {R}_{\\mathcal {T}_r}$ based on $L(\\mathcal {S}_r)$ as gradient meta $\\mathit {G}_{\\mathcal {T}_r}$ : ",
+      "$$\\vspace{-2.84526pt}\n\\mathit {G}_{\\mathcal {T}_r} = \\nabla _{\\mathit {R}_{\\mathcal {T}_r}} L(\\mathcal {S}_r)$$   (Eq. 23) ",
+      "Following the gradient update rule, we make a rapid update on relation meta as follows: ",
+      "$$\\mathit {R}^\\prime _{\\mathcal {T}_r} = \\mathit {R}_{\\mathcal {T}_r} - \\beta \\mathit {G}_{\\mathcal {T}_r}$$   (Eq. 24) ",
+      "where $\\beta $ indicates the step size of gradient meta when operating on relation meta.",
+      "When scoring the query set by embedding learner, we use updated relation meta. After getting the updated relation meta $\\mathit {R}^\\prime $ , we transfer it to samples in query set $\\mathcal {Q}_r = \\lbrace (h_j, t_j) \\rbrace $ and calculate their scores and loss of query set, following the same way in support set: ",
+      "$$s_{(h_j, t_j)} = \\Vert  \\mathbf {h}_j + \\mathit {R}_{\\mathcal {T}_r}^\\prime - \\mathbf {t}_j \\Vert $$   (Eq. 25) ",
+      "$$L(\\mathcal {Q}_r) = \\sum _{(h_j, t_j)\\in \\mathcal {Q}_r}[\\gamma +s_{(h_j, t_j)}-s_{(h_j, t_j^{\\prime })}]_{+}$$   (Eq. 26) ",
+      "where $L(\\mathcal {Q}_r)$ is our training objective to be minimized. We use this loss to update the whole model."
+    ],
+    [
+      "During training, our objective is to minimize the following loss $L$ which is the sum of query loss for all tasks in one minibatch: ",
+      "$$L = \\sum _{(\\mathcal {S}_r, \\mathcal {Q}_r)\\in \\mathcal {T}_{train}} L(\\mathcal {Q}_r)$$   (Eq. 28) "
+    ],
+    [
+      "With MetaR, we want to figure out following things: 1) can MetaR accomplish few-shot link prediction task and even perform better than previous model? 2) how much relation-specific meta information contributes to few-shot link prediction? 3) is there any requirement for MetaR to work on few-shot link prediction? To do these, we conduct the experiments on two few-shot link prediction datasets and deeply analyze the experiment results ."
+    ],
+    [
+      "We use two datasets, NELL-One and Wiki-One which are constructed by BIBREF11 . NELL-One and Wiki-One are derived from NELL BIBREF2 and Wikidata BIBREF0 respectively. Furthermore, because these two benchmarks are firstly tested on GMatching which consider both learned embeddings and one-hop graph structures, a background graph is constructed with relations out of training/validation/test sets for obtaining the pre-train entity embeddings and providing the local graph for GMatching.",
+      "Unlike GMatching using background graph to enhance the representations of entities, our MetaR can be trained without background graph. For NELL-One and Wiki-One which have background graph originally, we can make use of such background graph by fitting it into training tasks or using it to train embeddings to initialize entity representations. Overall, we have three kinds of dataset settings, shown in Table 3 . For setting of BG:In-Train, in order to make background graph included in training tasks, we sample tasks from triples in background graph and original training set, rather than sampling from only original training set.",
+      "Note that these three settings don't violate the task formulation of few-shot link prediction in KGs. The statistics of NELL-One and Wiki-One are shown in Table 2 .",
+      "We use two traditional metrics to evaluate different methods on these datasets, MRR and Hits@N. MRR is the mean reciprocal rank and Hits@N is the proportion of correct entities ranked in the top N in link prediction."
+    ],
+    [
+      "During training, mini-batch gradient descent is applied with batch size set as 64 and 128 for NELL-One and Wiki-One respectively. We use Adam BIBREF27 with the initial learning rate as 0.001 to update parameters. We set $\\gamma = 1$ and $\\beta = 1$ . The number of positive and negative triples in query set is 3 and 10 in NELL-One and Wiki-One. Trained model will be applied on validation tasks each 1000 epochs, and the current model parameters and corresponding performance will be recorded, after stopping, the model that has the best performance on Hits@10 will be treated as final model. For number of training epoch, we use early stopping with 30 patient epochs, which means that we stop the training when the performance on Hits@10 drops 30 times continuously. Following GMatching, the embedding dimension of NELL-One is 100 and Wiki-One is 50. The sizes of two hidden layers in relation-meta learner are 500, 200 and 250, 100 for NELL-One and Wiki-One."
+    ],
+    [
+      "The results of two few-shot link prediction tasks, including 1-shot and 5-shot, on NELL-One and Wiki-One are shown in Table 4 . The baseline in our experiment is GMatching BIBREF11 , which made the first trial on few-shot link prediction task and is the only method that we can find as baseline. In this table, results of GMatching with different KG embedding initialization are copied from the original paper. Our MetaR is tested on different settings of datasets introduced in Table 3 .",
+      "In Table 4 , our model performs better with all evaluation metrics on both datasets. Specifically, for 1-shot link prediction, MetaR increases by 33%, 28.1%, 29.2% and 27.8% on MRR, Hits@10, Hits@5 and Hits@1 on NELL-One, and 41.4%, 18.8%, 37.9% and 62.2% on Wiki-One, with average improvement of 29.53% and 40.08% respectively. For 5-shot, MetaR increases by 29.9%, 40.5%, 32.6% and 17.5% on MRR, Hits@10, Hits@5 and Hits@1 on NELL-One with average improvement of 30.13%.",
+      "Thus for the first question we want to explore, the results of MetaR are no worse than GMatching, indicating that MetaR has the capability of accomplishing few-shot link prediction. In parallel, the impressive improvement compared with GMatching demonstrates that the key idea of MetaR, transferring relation-specific meta information from support set to query set, works well on few-shot link prediction task.",
+      "Furthermore, compared with GMatching, our MetaR is independent with background knowledge graphs. We test MetaR on 1-shot link prediction in partial NELL-One and Wiki-One which discard the background graph, and get the results of 0.279 and 0.348 on Hits@10 respectively. Such results are still comparable with GMatching in fully datasets with background."
+    ],
+    [
+      "We have proved that relation-specific meta information, the key point of MetaR, successfully contributes to few-shot link prediction in previous section. As there are two kinds of relation-specific meta information in this paper, relation meta and gradient meta, we want to figure out how these two kinds of meta information contribute to the performance. Thus, we conduct an ablation study with three settings. The first one is our complete MetaR method denoted as standard. The second one is removing the gradient meta by transferring un-updated relation meta directly from support set to query set without updating it via gradient meta, denoted as -g. The third one is removing the relation meta further which makes the model rebase to a simple TransE embedding model, denoted as -g -r. The result under the third setting is copied from BIBREF11 . It uses the triples from background graph, training tasks and one-shot training triples from validation/test set, so it's neither BG:Pre-Train nor BG:In-Train. We conduct the ablation study on NELL-one with metric Hit@10 and results are shown in Table 5 .",
+      "Table 5 shows that removing gradient meta decreases 29.3% and 15% on two dataset settings, and further removing relation meta continuous decreases the performance with 55% and 72% compared to the standard results. Thus both relation meta and gradient meta contribute significantly and relation meta contributes more than gradient meta. Without gradient meta and relation meta, there is no relation-specific meta information transferred in the model and it almost doesn't work. This also illustrates that relation-specific meta information is important and effective for few-shot link prediction task."
+    ],
+    [
+      "We have proved that both relation meta and gradient meta surely contribute to few-shot link prediction. But is there any requirement for MetaR to ensure the performance on few-shot link prediction? We analyze this from two points based on the results, one is the sparsity of entities and the other is the number of tasks in training set.",
+      "The sparsity of entities We notice that the best result of NELL-One and Wiki-One appears in different dataset settings. With NELL-One, MetaR performs better on BG:In-Train dataset setting, while with Wiki-One, it performs better on BG:Pre-Train. Performance difference between two dataset settings is more significant on Wiki-One.",
+      "Most datasets for few-shot task are sparse and the same with NELL-One and Wiki-One, but the entity sparsity in these two datasets are still significantly different, which is especially reflected in the proportion of entities that only appear in one triple in training set, $82.8$ % and $37.1$ % in Wiki-One and NELL-One respectively. Entities only have one triple during training will make MetaR unable to learn good representations for them, because entity embeddings heavily rely on triples related to them in MetaR. Only based on one triple, the learned entity embeddings will include a lot of bias. Knowledge graph embedding method can learn better embeddings than MetaR for those one-shot entities, because entity embeddings can be corrected by embeddings of relations that connect to it, while they can't in MetaR. This is why the best performance occurs in BG:Pre-train setting on Wiki-One, pre-train entity embeddings help MetaR overcome the low-quality on one-shot entities.",
+      "The number of tasks From the comparison of MetaR's performance between with and without background dataset setting on NELL-One, we find that the number of tasks will affect MetaR's performance significantly. With BG:In-Train, there are 321 tasks during training and MetaR achieves 0.401 on Hits@10, while without background knowledge, there are 51, with 270 less, and MetaR achieves 0.279. This makes it reasonable that why MetaR achieves best performance on BG:In-Train with NELL-One. Even NELL-One has $37.1$ % one-shot entities, adding background knowledge into dataset increases the number of training tasks significantly, which complements the sparsity problem and contributes more to the task.",
+      "Thus we conclude that both the sparsity of entities and number of tasks will affect performance of MetaR. Generally, with more training tasks, MetaR performs better and for extremely sparse dataset, pre-train entity embeddings are preferred."
+    ],
+    [
+      "We propose a meta relational learning framework to do few-shot link prediction in KGs, and we design our model to transfer relation-specific meta information from support set to query set. Specifically, using relation meta to transfer common and important information, and using gradient meta to accelerate learning. Compared to GMatching which is the only method in this task, our method MetaR gets better performance and it is also independent with background knowledge graphs. Based on experimental results, we analyze that the performance of MetaR will be affected by the number of training tasks and sparsity of entities. We may consider obtaining more valuable information about sparse entities in few-shot link prediction in KGs in the future."
+    ],
+    [
+      "We want to express gratitude to the anonymous reviewers for their hard work and kind comments, which will further improve our work in the future. This work is funded by NSFC 91846204/61473260, national key research program YS2018YFB140004, and Alibaba CangJingGe(Knowledge Engine) Research Plan."
+    ]
+  ]
+}
+```
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+Name of Paper: SimplerVoice: A Key Message&Visual Description Generator System for Illiteracy
+
+Question: Does their solution involve connecting images and text?
\ No newline at end of file
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+Name of Paper: Modelling Semantic Categories using Conceptual Neighborhood
+
+Question: How they indentify conceptual neighbours?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Model Description",
+    "Model Description ::: Step 1: Predicting Conceptual Neighborhood from Embeddings",
+    "Model Description ::: Step 1: Predicting Conceptual Neighborhood from Embeddings ::: Generating Distant Supervision Labels",
+    "Model Description ::: Step 2: Predicting Conceptual Neighborhood from Text",
+    "Model Description ::: Step 3: Category Induction",
+    "Experiments",
+    "Experiments ::: Experimental setting ::: Taxonomy",
+    "Experiments ::: Quantitative Results",
+    "Experiments ::: Qualitative Analysis",
+    "Conclusions"
+  ],
+  "paragraphs": [
+    [
+      "Vector space embeddings are commonly used to represent entities in fields such as machine learning (ML) BIBREF0, natural language processing (NLP) BIBREF1, information retrieval (IR) BIBREF2 and cognitive science BIBREF3. An important point, however, is that such representations usually represent both individuals and categories as vectors BIBREF4, BIBREF5, BIBREF6. Note that in this paper, we use the term category to denote natural groupings of individuals, as it is used in cognitive science, with individuals referring to the objects from the considered domain of discourse. For example, the individuals carrot and cucumber belong to the vegetable category. We use the term entities as an umbrella term covering both individuals and categories.",
+      "Given that a category corresponds to a set of individuals (i.e. its instances), modelling them as (possibly imprecise) regions in the embedding space seems more natural than using vectors. In fact, it has been shown that the vector representations of individuals that belong to the same category are indeed often clustered together in learned vector space embeddings BIBREF7, BIBREF8. The view of categories being regions is also common in cognitive science BIBREF3. However, learning region representations of categories is a challenging problem, because we typically only have a handful of examples of individuals that belong to a given category. One common assumption is that natural categories can be modelled using convex regions BIBREF3, which simplifies the estimation problem. For instance, based on this assumption, BIBREF9 modelled categories using Gaussian distributions and showed that these distributions can be used for knowledge base completion. Unfortunately, this strategy still requires a relatively high number of training examples to be successful.",
+      "However, when learning categories, humans do not only rely on examples. For instance, there is evidence that when learning the meaning of nouns, children rely on the default assumption that these nouns denote mutually exclusive categories BIBREF10. In this paper, we will in particular take advantage of the fact that many natural categories are organized into so-called contrast sets BIBREF11. These are sets of closely related categories which exhaustively cover some sub-domain, and which are assumed to be mutually exclusive; e.g. the set of all common color names, the set $\\lbrace \\text{fruit},\\text{vegetable}\\rbrace $ or the set $\\lbrace \\text{NLP}, \\text{IR}, \\text{ML}\\rbrace $. Categories from the same contrast set often compete for coverage. For instance, we can think of the NLP domain as consisting of research topics that involve processing textual information which are not covered by the IR and ML domains. Categories which compete for coverage in this way are known as conceptual neighbors BIBREF12; e.g. NLP and IR, red and orange, fruit and vegetable. Note that the exact boundary between two conceptual neighbors may be vague (e.g. tomato can be classified as fruit or as vegetable).",
+      "In this paper, we propose a method for learning region representations of categories which takes advantage of conceptual neighborhood, especially in scenarios where the number of available training examples is small. The main idea is illustrated in Figure FIGREF2, which depicts a situation where we are given some examples of a target category $C$ as well as some related categories $N_1,N_2,N_3,N_4$. If we have to estimate a region from the examples of $C$ alone, the small elliptical region shown in red would be a reasonable choice. More generally, a standard approach would be to estimate a Gaussian distribution from the given examples. However, vector space embeddings typically have hundreds of dimensions, while the number of known examples of the target category is often far lower (e.g. 2 or 3). In such settings we will almost inevitably underestimate the coverage of the category. However, in the example from Figure FIGREF2, if we take into account the knowledge that $N_1,N_2,N_3,N_4$ are conceptual neighbors of $C$, the much larger, shaded region becomes a more natural choice for representing $C$. Indeed, the fact that e.g. $C$ and $N_1$ are conceptual neighbors suggests that any point in between the examples of these categories needs to be contained either in the region representing $C$ or the region representing $N_1$. In the spirit of prototype approaches to categorization BIBREF13, without any further information it makes sense to assume that their boundary is more or less half-way in between the known examples.",
+      "The contribution of this paper is two-fold. First, we propose a method for identifying conceptual neighbors from text corpora. We essentially treat this problem as a standard text classification problem, by relying on categories with large numbers of training examples to generate a suitable distant supervision signal. Second, we show that the predicted conceptual neighbors can effectively be used to learn better category representations."
+    ],
+    [
+      "In distributional semantics, categories are frequently modelled as vectors. For example, BIBREF14 study the problem of deciding for a word pair $(i,c)$ whether $i$ denotes an instance of the category $c$, which they refer to as instantiation. They treat this problem as a binary classification problem, where e.g. the pair (AAAI, conference) would be a positive example, while (conference, AAAI) and (New York, conference) would be negative examples. Different from our setting, their aim is thus essentially to model the instantiation relation itself, similar in spirit to how hypernymy has been modelled in NLP BIBREF15, BIBREF16. To predict instantiation, they use a simple neural network model which takes as input the word vectors of the input pair $(i,c)$. They also experiment with an approach that instead models a given category as the average of the word vectors of its known instances and found that this led to better results.",
+      "A few authors have already considered the problem of learning region representations of categories. Most closely related, BIBREF17 model ontology concepts using Gaussian distributions. In BIBREF18 DBLP:conf/ecai/JameelS16, a model is presented which embeds Wikipedia entities such that entities which have the same WikiData type are characterized by some region within a low-dimensional subspace of the embedding. Within the context of knowledge graph embedding, several approaches have been proposed that essentially model semantic types as regions BIBREF19, BIBREF20. A few approaches have also been proposed for modelling word meaning using regions BIBREF21, BIBREF22 or Gaussian distributions BIBREF23. Along similar lines, several authors have proposed approaches inspired by probabilistic topic modelling, which model latent topics using Gaussians BIBREF24 or related distributions BIBREF25.",
+      "On the other hand, the notion of conceptual neighborhood has been covered in most detail in the field of spatial cognition, starting with the influential work of BIBREF12. In computational linguistics, moreover, this representation framework aligns with lexical semantics traditions where word meaning is constructed in terms of semantic decomposition, i.e. lexical items being minimally decomposed into structured forms (or templates) rather than sets of features BIBREF26, effectively mimicking a sort of conceptual neighbourhood. In Pustejovsky's generative lexicon, a set of \u201csemantic devices\u201d is proposed such that they behave in semantics similarly as grammars do in syntax. Specifically, this framework considers the qualia structure of a lexical unit as a set of expressive semantic distinctions, the most relevant for our purposes being the so-called formal role, which is defined as \u201cthat which distinguishes the object within a larger domain\u201d, e.g. shape or color. This semantic interplay between cognitive science and computational linguistics gave way to the term lexical coherence, which has been used for contextualizing the meaning of words in terms of how they relate to their conceptual neighbors BIBREF27, or by providing expressive lexical semantic resources in the form of ontologies BIBREF28."
+    ],
+    [
+      "Our aim is to introduce a model for learning region-based category representations which can take advantage of knowledge about the conceptual neighborhood of that category. Throughout the paper, we focus in particular on modelling categories from the BabelNet taxonomy BIBREF29, although the proposed method can be applied to any resource which (i) organizes categories in a taxonomy and (ii) provides examples of individuals that belong to these categories. Selecting BabelNet as our use case is a natural choice, however, given its large scale and the fact that it integrates many lexical and ontological resources.",
+      "As the possible conceptual neighbors of a given BabelNet category $C$, we consider all its siblings in the taxonomy, i.e. all categories $C_1,...,C_k$ which share a direct parent with $C$. To select which of these siblings are most likely to be conceptual neighbors, we look at mentions of these categories in a text corpus. As an illustrative example, consider the pair (hamlet,village) and the following sentence:",
+      "In British geography, a hamlet is considered smaller than a village and ...",
+      "From this sentence, we can derive that hamlet and village are disjoint but closely related categories, thus suggesting that they are conceptual neighbors. However, training a classifier that can identify conceptual neighbors from such sentences is complicated by the fact that conceptual neighborhood is not covered in any existing lexical resource, to the best of our knowledge, which means that large sets of training examples are not readily available. To address this lack of training data, we rely on a distant supervision strategy. The central insight is that for categories with a large number of known instances, we can use the embeddings of these instances to check whether two categories are conceptual neighbors. In particular, our approach involves the following three steps:",
+      "Identify pairs of categories that are likely to be conceptual neighbors, based on the vector representations of their known instances.",
+      "Use the pairs from Step 1 to train a classifier that can recognize sentences which indicate that two categories are conceptual neighbors.",
+      "Use the classifier from Step 2 to predict which pairs of BabelNet categories are conceptual neighbors and use these predictions to learn category representations.",
+      "Note that in Step 1 we can only consider BabelNet categories with a large number of instances, while the end result in Step 3 is that we can predict conceptual neighborhood for categories with only few known instances. We now discuss the three aforementioned steps one by one."
+    ],
+    [
+      "Our aim here is to generate distant supervision labels for pairs of categories, indicating whether they are likely to be conceptual neighbors. These labels will then be used in Section SECREF12 to train a classifier for predicting conceptual neighborhood from text.",
+      "Let $A$ and $B$ be siblings in the BabelNet taxonomy. If enough examples of individuals belonging to these categories are provided in BabelNet, we can use these instances to estimate high-quality representations of $A$ and $B$, and thus estimate whether they are likely to be conceptual neighbors. In particular, we split the known instances of $A$ into a training set $I^A_{\\textit {train}}$ and test set $I^A_{\\textit {test}}$, and similar for $B$. We then train two types of classifiers. The first classifier estimates a Gaussian distribution for each category, using the training instances in $I^A_{\\textit {train}}$ and $I^B_{\\textit {train}}$ respectively. This should provide us with a reasonable representation of $A$ and $B$ regardless of whether they are conceptual neighbors. In the second approach, we first learn a Gaussian distribution from the joint set of training examples $I^A_{\\textit {train}} \\cup I^B_{\\textit {train}}$ and then train a logistic regression classifier to separate instances from $A$ and $B$. In particular, note that in this way, we directly impose the requirement that the regions modelling $A$ and $B$ are adjacent in the embedding space (intuitively corresponding to two halves of a Gaussian distribution). We can thus expect that the second approach should lead to better predictions than the first approach if $A$ and $B$ are conceptual neighbors and to worse predictions if they are not. In particular, we propose to use the relative performance of the two classifiers as the required distant supervision signal for predicting conceptual neighborhood.",
+      "We now describe the two classification models in more detail, after which we explain how these models are used to generate the distant supervision labels.",
+      "Gaussian Classifier The first classifier follows the basic approach from BIBREF17, where Gaussian distributions were similarly used to model WikiData categories. In particular, we estimate the probability that an individual $e$ with vector representation $\\mathbf {e}$ is an instance of the category $A$ as follows:",
+      "where $\\lambda _A$ is the prior probability of belonging to category $A$, the likelihood $f(\\mathbf {e} | A)$ is modelled as a Gaussian distribution and $f(\\mathbf {e})$ will also be modelled as a Gaussian distribution. Intuitively, we think of the Gaussian $f(. | A)$ as defining a soft region, modelling the category $A$. Given the high-dimensional nature of typical vector space embeddings, we use a mean field approximation:",
+      "Where $d$ is the number of dimensions in the vector space embedding, $e_i$ is the $i^{\\textit {th}}$ coordinate of $\\mathbf {e}$, and $f_i(. | A)$ is a univariate Gaussian. To estimate the parameters $\\mu _i$ and $\\sigma _i^2$ of this Gaussian, we use a Bayesian approach with a flat prior:",
+      "where $G(e_i;\\mu _i,\\sigma _i^2)$ represents the Gaussian distribution with mean $\\mu _i$ and variance $\\sigma _i^2$ and NI$\\chi ^{2}$ is the normal inverse-$\\chi ^{2}$ distribution. In other words, instead of using a single estimate of the mean $\\mu $ and variance $\\sigma _2$ we average over all plausible choices of these parameters. The use of the normal inverse-$\\chi ^{2}$ distribution for the prior on $\\mu _i$ and $\\sigma _i^2$ is a common choice, which has the advantage that the above integral simplifies to a Student-t distribution. In particular, we have:",
+      "where we assume $I^A_{\\textit {train}}= \\lbrace a_1,...,a_n\\rbrace $, $a_i^j$ denotes the $i^{\\textit {th}}$ coordinate of the vector embedding of $a_j$, $\\overline{x_i} = \\frac{1}{n}\\sum _{j=1}^n a_i^j$ and $t_{n-1}$ is the Student t-distribution with $n-1$ degrees of freedom. The probability $f(\\mathbf {e})$ is estimated in a similar way, but using all BabelNet instances. The prior $\\lambda _A$ is tuned based on a validation set. Finally, we classify $e$ as a positive example if $P(A|\\mathbf {e}) > 0.5$.",
+      "GLR Classifier. We first train a Gaussian classifier as in Section UNKREF9, but now using the training instances of both $A$ and $B$. Let us denote the probability predicted by this classifier as $P(A\\cup B | \\textbf {e})$. The intuition is that entities for which this probability is high should either be instances of $A$ or of $B$, provided that $A$ and $B$ are conceptual neighbors. If, on the other hand, $A$ and $B$ are not conceptual neighbors, relying on this assumption is likely to lead to errors (i.e. there may be individuals whose representation is in between $A$ and $B$ which are not instances of either), which is what we need for generating the distant supervision labels. If $P(A\\cup B | \\textbf {e}) > 0.5$, we assume that $e$ either belongs to $A$ or to $B$. To distinguish between these two cases, we train a logistic regression classifier, using the instances from $I^A_{\\textit {train}}$ as positive examples and those from $I^B_{\\textit {train}}$ as negative examples. Putting everything together, we thus classify $e$ as a positive example for $A$ if $P(A\\cup B | \\textbf {e})>0.5$ and $e$ is classified as a positive example by the logistic regression classifier. Similarly, we classfiy $e$ as a positive example for $B$ if $P(A\\cup B | \\textbf {e})>0.5$ and $e$ is classified as a negative example by the logistic regression classifier. We will refer to this classification model as GLR (Gaussian Logistic Regression)."
+    ],
+    [
+      "To generate the distant supervision labels, we consider a ternary classification problem for each pair of siblings $A$ and $B$. In particular, the task is to decide for a given individual $e$ whether it is an instance of $A$, an instance of $B$, or an instance of neither (where only disjoint pairs $A$ and $B$ are considered). For the Gaussian classifier, we predict $A$ iff $P(A|\\mathbf {e})>0.5$ and $P(A|\\mathbf {e}) > P(B|\\mathbf {e})$. For the GLR classifier, we predict $A$ if $P(A\\cup B|\\mathbf {e}) >0.5$ and the associated logistic regression classifier predicts $A$. The condition for predicting $B$ is analogous. The test examples for this ternary classification problem consist of the elements from $I^A_{\\textit {test}}$ and $I^B_{\\textit {test}}$, as well as some negative examples (i.e. individuals that are neither instances of $A$ nor $B$). To select these negative examples, we first sample instances from categories that have the same parent as $A$ and $B$, choosing as many such negative examples as we have positive examples. Second, we also sample the same number of negative examples from randomly selected categories in the taxonomy.",
+      "Let $F^1_{AB}$ be the F1 score achieved by the Gaussian classifier and $F^2_{AB}$ the F1 score of the GLR classifier. Our hypothesis is that $F^1_{AB} \\ll F^2_{AB}$ suggests that $A$ and $B$ are conceptual neighbors, while $F^1_{AB} \\gg F^2_{AB}$ suggests that they are not. This intuition is captured in the following score:",
+      "where we consider $A$ and $B$ to be conceptual neighbors if $s_{AB}\\gg 0.5$."
+    ],
+    [
+      "We now consider the following problem: given two BabelNet categories $A$ and $B$, predict whether they are likely to be conceptual neighbors based on the sentences from a text corpus in which they are both mentioned. To train such a classifier, we use the distant supervision labels from Section SECREF8 as training data. Once this classifier has been trained, we can then use it to predict conceptual neighborhood for categories for which only few instances are known.",
+      "To find sentences in which both $A$ and $B$ are mentioned, we rely on a disambiguated text corpus in which mentions of BabelNet categories are explicitly tagged. Such a disambiguated corpus can be automatically constructed, using methods such as the one proposed by BIBREF30 mancini-etal-2017-embedding, for instance. For each pair of candidate categories, we thus retrieve all sentences where they co-occur. Next, we represent each extracted sentence as a vector. To this end, we considered two possible strategies:",
+      "Word embedding averaging: We compute a sentence embedding by simply averaging the word embeddings of each word within the sentence. Despite its simplicity, this approach has been shown to provide competitive results BIBREF31, in line with more expensive and sophisticated methods e.g. based on LSTMs.",
+      "Contextualized word embeddings: The recently proposed contextualized embeddings BIBREF32, BIBREF33 have already proven successful in a wide range of NLP tasks. Instead of providing a single vector representation for all words irrespective of the context, contextualized embeddings predict a representation for each word occurrence which depends on its context. These representations are usually based on pre-trained language models. In our setting, we extract the contextualized embeddings for the two candidate categories within the sentence. To obtain this contextualized embedding, we used the last layer of the pre-trained language model, which has been shown to be most suitable for capturing semantic information BIBREF34, BIBREF35. We then use the concatenation of these two contextualized embeddings as the representation of the sentence.",
+      "For both strategies, we average their corresponding sentence-level representations across all sentences in which the same two candidate categories are mentioned. Finally, we train an SVM classifier on the resulting vectors to predict for the pair of siblings $(A,B)$ whether $s_{AB}> 0.5$ holds."
+    ],
+    [
+      "Let $C$ be a category and assume that $N_1,...,N_k$ are conceptual neighbors of this category. Then we can model $C$ by generalizing the idea underpinning the GLR classifier. In particular, we first learn a Gaussian distribution from all the instances of $C$ and $N_1,...,N_k$. This Gaussian model allows us to estimate the probability $P(C\\cup N_1\\cup ...\\cup N_k \\,|\\, \\mathbf {e})$ that $e$ belongs to one of $C,N_1,...,N_k$. If this probability is sufficiently high (i.e. higher than 0.5), we use a multinomial logistic regression classifier to decide which of these categories $e$ is most likely to belong to. Geometrically, we can think of the Gaussian model as capturing the relevant local domain, while the multinomial logistic regression model carves up this local domain, similar as in Figure FIGREF2.",
+      "In practice, we do not know with certainty which categories are conceptual neighbors of $C$. Instead, we select the $k$ categories (for some fixed constant $k$), among all the siblings of $C$, which are most likely to be conceptual neighbors, according to the text classifier from Section SECREF12."
+    ],
+    [
+      "The central problem we consider is category induction: given some instances of a category, predict which other individuals are likely to be instances of that category. When enough instances are given, standard approaches such as the Gaussian classifier from Section UNKREF9, or even a simple SVM classifier, can perform well on this task. For many categories, however, we only have access to a few instances, either because the considered ontology is highly incomplete or because the considered category only has few actual instances. The main research question which we want to analyze is whether (predicted) conceptual neighborhood can help to obtain better category induction models in such cases. In Section SECREF16, we first provide more details about the experimental setting that we followed. Section SECREF23 then discusses our main quantitative results. Finally, in Section SECREF26 we present a qualitative analysis."
+    ],
+    [
+      "As explained in Section SECREF3, we used BabelNet BIBREF29 as our reference taxonomy. BabelNet is a large-scale full-fledged taxonomy consisting of heterogeneous sources such as WordNet BIBREF36, Wikidata BIBREF37 and WiBi BIBREF38, making it suitable to test our hypothesis in a general setting.",
+      "Vector space embeddings. Both the distant labelling method from Section SECREF8 and the category induction model itself need access to vector representations of the considered instances. To this end, we used the NASARI vectors, which have been learned from Wikipedia and are already linked to BabelNet BIBREF1.",
+      "BabelNet category selection. To test our proposed category induction model, we consider all BabelNet categories with fewer than 50 known instances. This is motivated by the view that conceptual neighborhood is mostly useful in cases where the number of known instances is small. For each of these categories, we split the set of known instances into 90% for training and 10% for testing. To tune the prior probability $\\lambda _A$ for these categories, we hold out 10% from the training set as a validation set.",
+      "The conceptual neighbors among the considered test categories are predicted using the classifier from Section SECREF12. To obtain the distant supervision labels needed to train that classifier, we consider all BabelNet categories with at least 50 instances. This ensures that the distant supervision labels are sufficiently accurate and that there is no overlap with the categories which are used for evaluating the model.",
+      "Text classifier training. As the text corpus to extract sentences for category pairs we used the English Wikipedia. In particular, we used the dump of November 2014, for which a disambiguated version is available online. This disambiguated version was constructed using the shallow disambiguation algorithm of BIBREF30 mancini-etal-2017-embedding. As explained in Section SECREF12, for each pair of categories we extracted all the sentences where they co-occur, including a maximum window size of 10 tokens between their occurrences, and 10 tokens to the left and right of the first and second category within the sentence, respectively. For the averaging-based sentence representations we used the 300-dimensional pre-trained GloVe word embeddings BIBREF39. To obtain the contextualized representations we used the pre-trained 768-dimensional BERT-base model BIBREF33..",
+      "The text classifier is trained on 3,552 categories which co-occur at least once in the same sentence in the Wikipedia corpus, using the corresponding scores $s_{AB}$ as the supervision signal (see Section SECREF12). To inspect how well conceptual neighborhood can be predicted from text, we performed a 10-fold cross validation over the training data, removing for this experiment the unclear cases (i.e., those category pairs with $s_{AB}$ scores between $0.4$ and $0.6$). We also considered a simple baselineWE based on the number of co-occurring sentences for each pairs, which we might expect to be a reasonably strong indicator of conceptual neighborhood, i.e. the more often two categories are mentiond in the same sentence, the more likely that they are conceptual neighbors. The results for this cross-validation experiment are summarized in Table TABREF22. Surprisingly, perhaps, the word vector averaging method seems more robust overall, while being considerably faster than the method using BERT. The results also confirm the intuition that the number of co-occurring sentences is positively correlated with conceptual neighborhood, although the results for this baseline are clearly weaker than those for the proposed classifiers.",
+      "Baselines. To put the performance of our model in perspective, we consider three baseline methods for category induction. First, we consider the performance of the Gaussian classifier from Section UNKREF9, as a representative example of how well we can model each category when only considering their given instances; this model will be referred to as Gauss. Second, we consider a variant of the proposed model in which we assume that all siblings of the category are conceptual neighbors; this model will be referred to as Multi. Third, we consider a variant of our model in which the neighbors are selected based on similarity. To this end, we represent each BabelNet as their vector from the NASARI space. From the set of siblings of the target category $C$, we then select the $k$ categories whose vector representation is most similar to that of $C$, in terms of cosine similarity. This baseline will be referred to as Similarity$_k$, with $k$ the number of selected neighbors.",
+      "We refer to our model as SECOND-WEA$_k$ or SECOND-BERT$_k$ (SEmantic categories with COnceptual NeighborhooD), depending on whether the word embedding averaging strategy is used or the method using BERT."
+    ],
+    [
+      "Our main results for the category induction task are summarized in Table TABREF24. In this table, we show results for different choices of the number of selected conceptual neighbors $k$, ranging from 1 to 5. As can be seen from the table, our approach substantially outperforms all baselines, with Multi being the most competitive baseline. Interestingly, for the Similarity baseline, the higher the number of neighbors, the more the performance approaches that of Multi. The relatively strong performance of Multi shows that using the siblings of a category in the BabelNet taxonomy is in general useful. However, as our results show, better results can be obtained by focusing on the predicted conceptual neighbors only. It is interesting to see that even selecting a single conceptual neighbor is already sufficient to substantially outperform the Gaussian model, although the best results are obtained for $k=4$. Comparing the WEA and BERT variants, it is notable that BERT is more successful at selecting the single best conceptual neighbor (reflected in an F1 score of 47.0 compared to 41.9). However, for $k \\ge 2$, the results of the WEA and BERT are largely comparable."
+    ],
+    [
+      "To illustrate how conceptual neighborhood can improve classification results, Fig. FIGREF25 shows the two first principal components of the embeddings of the instances of three BabelNet categories: Songbook, Brochure and Guidebook. All three categories can be considered to be conceptual neighbors. Brochure and Guidebook are closely related categories, and we may expect there to exist borderline cases between them. This can be clearly seen in the figure, where some instances are located almost exactly on the boundary between the two categories. On the other hand, Songbook is slightly more separated in the space. Let us now consider the left-most data point from the Songbook test set, which is essentially an outlier, being more similar to instances of Guidebook than typical Songbook instances. When using a Gaussian model, this data point would not be recognised as a plausible instance. When incorporating the fact that Brochure and Guidebook are conceptual neighbors of Songbook, however, it is more likely to be classified correctly.",
+      "To illustrate the notion of conceptual neighborhood itself, Table TABREF27 displays some selected category pairs from the training set (i.e. the category pairs that were used to train the text classifier), which intuitively correspond to conceptual neighbors. The left column contains some selected examples of category pairs with a high $s_{AB}$ score of at least 0.9. As these examples illustrate, we found that a high $s_{AB}$ score was indeed often predictive of conceptual neighborhood. As the right column of this table illustrates, there are several category pairs with a lower $s_{AB}$ score of around 0.5 which intuitively still seem to correspond to conceptual neighbors. When looking at category pairs with even lower scores, however, conceptual neighborhood becomes rare. Moreover, while there are several pairs with high scores which are not actually conceptual neighbors (e.g. the pair Actor \u2013 Makup Artist), they tend to be categories which are still closely related. This means that the impact of incorrectly treating them as conceptual neighbors on the performance of our method is likely to be limited. On the other hand, when looking at category pairs with a very low confidence score we find many unrelated pairs, which we can expect to be more harmful when considered as conceptual neighbors, as the combined Gaussian will then cover a much larger part of the space. Some examples of such pairs include Primary school \u2013 Financial institution, Movie theatre \u2013 Housing estate, Corporate title \u2013 Pharaoh and Fraternity \u2013 Headquarters.",
+      "Finally, in Tables TABREF28 and TABREF29, we show examples of the top conceptual neighbors that were selected for some categories from the test set. Table TABREF28 shows examples of BabelNet categories for which the F1 score of our SECOND-WEA$_1$ classifier was rather low. As can be seen, the conceptual neighbors that were chosen in these cases are not suitable. For instance, Bachelor's degree is a near-synonym of Undergraduate degree, hence assuming them to be conceptual neighbors would clearly be detrimental. In contrast, when looking at the examples in Table TABREF29, where categories are shown with a higher F1 score, we find examples of conceptual neighbors that are intuitively much more meaningful."
+    ],
+    [
+      "We have studied the role of conceptual neighborhood for modelling categories, focusing especially on categories with a relatively small number of instances, for which standard modelling approaches are challenging. To this end, we have first introduced a method for predicting conceptual neighborhood from text, by taking advantage of BabelNet to implement a distant supervision strategy. We then used the resulting classifier to identify the most likely conceptual neighbors of a given target category, and empirically showed that incorporating these conceptual neighbors leads to a better performance in a category induction task.",
+      "In terms of future work, it would be interesting to look at other types of lexical relations that can be predicted from text. One possible strategy would be to predict conceptual betweenness, where a category $B$ is said to be between $A$ and $C$ if $B$ has all the properties that $A$ and $C$ have in common BIBREF40 (e.g. we can think of wine as being conceptually between beer and rum). In particular, if $B$ is predicted to be conceptually between $A$ and $C$ then we would also expect the region modelling $B$ to be between the regions modelling $A$ and $C$.",
+      "Acknowledgments. Jose Camacho-Collados, Luis Espinosa-Anke and Steven Schockaert were funded by ERC Starting Grant 637277. Zied Bouraoui was supported by CNRS PEPS INS2I MODERN."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2483/instruction.md b/qasper-2483/instruction.md
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+Name of Paper: The Transference Architecture for Automatic Post-Editing
+
+Question: What experiment result led to conclussion that reducing the number of layers of the decoder does not matter much?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Research",
+    "Transference Model for APE",
+    "Experiments",
+    "Experiments ::: Data",
+    "Experiments ::: Experiment Setup",
+    "Experiments ::: Hyper-parameter Setup",
+    "Results",
+    "Results ::: Baselines",
+    "Results ::: Single-Encoder Transformer for APE",
+    "Results ::: Transference Transformer for APE",
+    "Results ::: Analysis of Error Patterns",
+    "Results ::: Discussion",
+    "Conclusions"
+  ],
+  "paragraphs": [
+    [
+      "The performance of state-of-the-art MT systems is not perfect, thus, human interventions are still required to correct machine translated texts into publishable quality translations BIBREF0. Automatic post-editing (APE) is a method that aims to automatically correct errors made by MT systems before performing actual human post-editing (PE) BIBREF1, thereby reducing the translators' workload and increasing productivity BIBREF2. APE systems trained on human PE data serve as MT post-processing modules to improve the overall performance. APE can therefore be viewed as a 2nd-stage MT system, translating predictable error patterns in MT output to their corresponding corrections. APE training data minimally involves MT output ($mt$) and the human post-edited ($pe$) version of $mt$, but additionally using the source ($src$) has been shown to provide further benefits BIBREF3, BIBREF4, BIBREF5.",
+      "To provide awareness of errors in $mt$ originating from $src$, attention mechanisms BIBREF6 allow modeling of non-local dependencies in the input or output sequences, and importantly also global dependencies between them (in our case $src$, $mt$ and $pe$). The transformer architecture BIBREF7 is built solely upon such attention mechanisms completely replacing recurrence and convolutions. The transformer uses positional encoding to encode the input and output sequences, and computes both self- and cross-attention through so-called multi-head attentions, which are facilitated by parallelization. Such multi-head attention allows to jointly attend to information at different positions from different representation subspaces, e.g. utilizing and combining information from $src$, $mt$, and $pe$.",
+      "In this paper, we present a multi-source neural APE architecture called transference. Our model contains a source encoder which encodes $src$ information, a second encoder ($enc_{src \\rightarrow mt}$) which takes the encoded representation from the source encoder ($enc_{src}$), combines this with the self-attention-based encoding of $mt$ ($enc_{mt}$), and prepares a representation for the decoder ($dec_{pe}$) via cross-attention. Our second encoder ($enc_{src \\rightarrow mt}$) can also be viewed as a standard transformer decoding block, however, without masking, which acts as an encoder. We thus recombine the different blocks of the transformer architecture and repurpose them for the APE task in a simple yet effective way. The suggested architecture is inspired by the two-step approach professional translators tend to use during post-editing: first, the source segment is compared to the corresponding translation suggestion (similar to what our $enc_{src \\rightarrow mt}$ is doing), then corrections to the MT output are applied based on the encountered errors (in the same way that our $dec_{pe}$ uses the encoded representation of $enc_{src \\rightarrow mt}$ to produce the final translation).",
+      "The paper makes the following contributions: (i) we propose a new multi-encoder model for APE that consists only of standard transformer encoding and decoding blocks, (ii) by using a mix of self- and cross-attention we provide a representation of both $src$ and $mt$ for the decoder, allowing it to better capture errors in $mt$ originating from $src$; this advances the state-of-the-art in APE in terms of BLEU and TER, and (iii), we analyze the effect of varying the number of encoder and decoder layers BIBREF8, indicating that the encoders contribute more than decoders in transformer-based neural APE."
+    ],
+    [
+      "Recent advances in APE research are directed towards neural APE, which was first proposed by Pal:2016:ACL and junczysdowmunt-grundkiewicz:2016:WMT for the single-source APE scenario which does not consider $src$, i.e. $mt \\rightarrow pe$. In their work, junczysdowmunt-grundkiewicz:2016:WMT also generated a large synthetic training dataset through back translation, which we also use as additional training data. Exploiting source information as an additional input can help neural APE to disambiguate corrections applied at each time step; this naturally leads to multi-source APE ($\\lbrace src, mt\\rbrace \\rightarrow pe$). A multi-source neural APE system can be configured either by using a single encoder that encodes the concatenation of $src$ and $mt$ BIBREF9 or by using two separate encoders for $src$ and $mt$ and passing the concatenation of both encoders' final states to the decoder BIBREF10. A few approaches to multi-source neural APE were proposed in the WMT 2017 APE shared task. Junczysdowmunt:2017:WMT combine both $mt$ and $src$ in a single neural architecture, exploring different combinations of attention mechanisms including soft attention and hard monotonic attention. Chatterjee-EtAl:2017:WMT2 built upon the two-encoder architecture of multi-source models BIBREF10 by means of concatenating both weighted contexts of encoded $src$ and $mt$. Varis-bojar:2017:WMT compared two multi-source models, one using a single encoder with concatenation of $src$ and $mt$ sentences, and a second one using two character-level encoders for $mt$ and $src$ along with a character-level decoder.",
+      "Recently, in the WMT 2018 APE shared task, several adaptations of the transformer architecture have been presented for multi-source APE. pal-EtAl:2018:WMT proposed an APE model that uses three self-attention-based encoders. They introduce an additional joint encoder that attends over a combination of the two encoded sequences from $mt$ and $src$. tebbifakhr-EtAl:2018:WMT, the NMT-subtask winner of WMT 2018 ($wmt18^{nmt}_{best}$), employ sequence-level loss functions in order to avoid exposure bias during training and to be consistent with the automatic evaluation metrics. shin-lee:2018:WMT propose that each encoder has its own self-attention and feed-forward layer to process each input separately. On the decoder side, they add two additional multi-head attention layers, one for $src \\rightarrow mt$ and another for $src \\rightarrow pe$. Thereafter another multi-head attention between the output of those attention layers helps the decoder to capture common words in $mt$ which should remain in $pe$. The APE PBSMT-subtask winner of WMT 2018 ($wmt18^{smt}_{best}$) BIBREF11 also presented another transformer-based multi-source APE which uses two encoders and stacks an additional cross-attention component for $src \\rightarrow pe$ above the previous cross-attention for $mt \\rightarrow pe$. Comparing shin-lee:2018:WMT's approach with the winner system, there are only two differences in the architecture: (i) the cross-attention order of $src \\rightarrow mt$ and $src \\rightarrow pe$ in the decoder, and (ii) $wmt18^{smt}_{best}$ additionally shares parameters between two encoders."
+    ],
+    [
+      "We propose a multi-source transformer model called transference ($\\lbrace src,mt\\rbrace _{tr} \\rightarrow pe$, Figure FIGREF1), which takes advantage of both the encodings of $src$ and $mt$ and attends over a combination of both sequences while generating the post-edited sentence. The second encoder, $enc_{src \\rightarrow mt}$, makes use of the first encoder $enc_{src}$ and a sub-encoder $enc_{mt}$ for considering $src$ and $mt$. Here, the $enc_{src}$ encoder and the $dec_{pe}$ decoder are equivalent to the original transformer for neural MT. Our $enc_{src \\rightarrow mt}$ follows an architecture similar to the transformer's decoder, the difference being that no masked multi-head self-attention is used to process $mt$.",
+      "One self-attended encoder for $src$, $\\mathbf {s}$ = $(s_1, s_2, \\ldots , s_k)$, returns a sequence of continuous representations, $enc_{src}$, and a second self-attended sub-encoder for $mt$, $\\mathbf {m}$ = $(m_1, m_2, \\ldots , m_l)$, returns another sequence of continuous representations, $enc_{mt}$. Self-attention at this point provides the advantage of aggregating information from all of the words, including $src$ and $mt$, and successively generates a new representation per word informed by the entire $src$ and $mt$ context. The internal $enc_{mt}$ representation performs cross-attention over $enc_{src}$ and prepares a final representation ($enc_{src \\rightarrow mt}$) for the decoder ($dec_{pe}$). The decoder then generates the $pe$ output in sequence, $\\mathbf {p}$ = $(p_1, p_2, \\ldots , p_n)$, one word at a time from left to right by attending to previously generated words as well as the final representations ($enc_{src \\rightarrow mt}$) generated by the encoder.",
+      "To summarize, our multi-source APE implementation extends Vaswani:NIPS2017 by introducing an additional encoding block by which $src$ and $mt$ communicate with the decoder.",
+      "Our proposed approach differs from the WMT 2018 PBSMT winner system in several ways: (i) we use the original transformer's decoder without modifications; (ii) one of our encoder blocks ($enc_{src \\rightarrow mt}$) is identical to the transformer's decoder block but uses no masking in the self-attention layer, thus having one self-attention layer and an additional cross-attention for $src \\rightarrow mt$; and (iii) in the decoder layer, the cross-attention is performed between the encoded representation from $enc_{src \\rightarrow mt}$ and $pe$.",
+      "Our approach also differs from the WMT 2018 NMT winner system: (i) $wmt18^{nmt}_{best}$ concatenates the encoded representation of two encoders and passes it as the key to the attention layer of the decoder, and (ii), the system additionally employs sequence-level loss functions based on maximum likelihood estimation and minimum risk training in order to avoid exposure bias during training.",
+      "The main intuition is that our $enc_{src \\rightarrow mt}$ attends over the $src$ and $mt$ and informs the $pe$ to better capture, process, and share information between $src$-$mt$-$pe$, which efficiently models error patterns and the corresponding corrections. Our model performs better than past approaches, as the experiment section will show."
+    ],
+    [
+      "We explore our approach on both APE sub-tasks of WMT 2018, where the 1st-stage MT system to which APE is applied is either a phrase-based statistical machine translation (PBSMT) or a neural machine translation (NMT) model.",
+      "For the PBSMT task, we compare against four baselines: the raw SMT output provided by the 1st-stage PBSMT system, the best-performing systems from WMT APE 2018 ($\\mathbf {wmt18^{smt}_{best}}$), which are a single model and an ensemble model by junczysdowmunt-grundkiewicz:2018:WMT, as well as a transformer trying to directly translate from $src$ to $pe$ (Transformer ($\\mathbf {src \\rightarrow pe}$)), thus performing translation instead of APE. We evaluate the systems using BLEU BIBREF12 and TER BIBREF13.",
+      "For the NMT task, we consider two baselines: the raw NMT output provided by the 1st-stage NMT system and the best-performing system from the WMT 2018 NMT APE task ($\\mathbf {wmt18^{nmt}_{best}}$) BIBREF14.",
+      "Apart from the multi-encoder transference architecture described above ($\\lbrace src,mt\\rbrace _{tr} \\rightarrow pe$) and ensembling of this architecture, two simpler versions are also analyzed: first, a `mono-lingual' ($\\mathbf {mt \\rightarrow pe}$) APE model using only parallel $mt$\u2013$pe$ data and therefore only a single encoder, and second, an identical single-encoder architecture, however, using the concatenated $src$ and $mt$ text as input ($\\mathbf {\\lbrace src+mt\\rbrace \\rightarrow pe}$) BIBREF9."
+    ],
+    [
+      "For our experiments, we use the English\u2013German WMT 2016 BIBREF4, 2017 BIBREF5 and 2018 BIBREF15 APE task data. All these released APE datasets consist of English\u2013German triplets containing source English text ($src$) from the IT domain, the corresponding German translations ($mt$) from a 1st-stage MT system, and the corresponding human-post-edited version ($pe$). The sizes of the datasets (train; dev; test), in terms of number of sentences, are (12,000; 1,000; 2,000), (11,000; 0; 2,000), and (13,442; 1,000; 1,023), for the 2016 PBSMT, the 2017 PBSMT, and the 2018 NMT data, respectively. One should note that for WMT 2018, we carried out experiments only for the NMT sub-task and ignored the data for the PBSMT task.",
+      "Since the WMT APE datasets are small in size, we use `artificial training data' BIBREF16 containing 4.5M sentences as additional resources, 4M of which are weakly similar to the WMT 2016 training data, while 500K are very similar according to TER statistics.",
+      "For experimenting on the NMT data, we additionally use the synthetic eScape APE corpus BIBREF17, consisting of $\\sim $7M triples. For cleaning this noisy eScape dataset containing many unrelated language words (e.g. Chinese), we perform the following two steps: (i) we use the cleaning process described in tebbifakhr-EtAl:2018:WMT, and (ii) we use the Moses BIBREF18 corpus cleaning scripts with minimum and maximum number of tokens set to 1 and 100, respectively. After cleaning, we perform punctuation normalization, and then use the Moses tokenizer BIBREF18 to tokenize the eScape corpus with `no-escape' option. Finally, we apply true-casing. The cleaned version of the eScape corpus contains $\\sim $6.5M triplets."
+    ],
+    [
+      "To build models for the PBSMT tasks from 2016 and 2017, we first train a generic APE model using all the training data (4M + 500K + 12K + 11K) described in Section SECREF2. Afterwards, we fine-tune the trained model using the 500K artificial and 23K (12K + 11K) real PE training data. We use the WMT 2016 development data (dev2016) containing 1,000 triplets to validate the models during training. To test our system performance, we use the WMT 2016 and 2017 test data (test2016, test2017) as two sub-experiments, each containing 2,000 triplets ($src$, $mt$ and $pe$). We compare the performance of our system with the four different baseline systems described above: raw MT, $wmt18^{smt}_{best}$ single and ensemble, as well as Transformer ($src \\rightarrow pe$).",
+      "Additionally, we check the performance of our model on the WMT 2018 NMT APE task (where unlike in previous tasks, the 1st-stage MT system is provided by NMT): for this, we explore two experimental setups: (i) we use the PBSMT task's APE model as a generic model which is then fine-tuned to a subset (12k) of the NMT data ($\\lbrace src,mt\\rbrace ^{nmt}_{tr} \\rightarrow pe^{{generic, smt}}_{{}}$). One should note that it has been argued that the inclusion of SMT-specific data could be harmful when training NMT APE models BIBREF11. (ii), we train a completely new generic model on the cleaned eScape data ($\\sim $6.5M) along with a subset (12K) of the original training data released for the NMT task ($\\lbrace src,mt\\rbrace ^{nmt}_{tr} \\rightarrow pe^{{generic, nmt}}_{{}}$). The aforementioned 12K NMT data are the first 12K of the overall 13.4K NMT data. The remaining 1.4K are used as validation data. The released development set (dev2018) is used as test data for our experiment, alongside the test2018, for which we could only obtain results for a few models by the WMT 2019 task organizers. We also explore an additional fine-tuning step of $\\lbrace src,mt\\rbrace ^{nmt}_{tr} \\rightarrow pe^{{generic, nmt}}_{{}}$ towards the 12K NMT data (called $\\lbrace src,mt\\rbrace ^{nmt}_{tr} \\rightarrow pe^{{ft}}_{{}}$), and a model averaging the 8 best checkpoints of $\\lbrace src,mt\\rbrace ^{nmt}_{tr} \\rightarrow pe^{{ft}}_{{}}$, which we call $\\lbrace src,mt\\rbrace ^{nmt}_{tr} \\rightarrow pe^{{ft}}_{{avg}}$.",
+      "Last, we analyze the importance of our second encoder ($enc_{src \\rightarrow mt}$), compared to the source encoder ($enc_{src}$) and the decoder ($dec_{pe}$), by reducing and expanding the amount of layers in the encoders and the decoder. Our standard setup, which we use for fine-tuning, ensembling etc., is fixed to 6-6-6 for $N_{src}$-$N_{mt}$-$N_{pe}$ (cf. Figure FIGREF1), where 6 is the value that was proposed by Vaswani:NIPS2017 for the base model. We investigate what happens in terms of APE performance if we change this setting to 6-6-4 and 6-4-6. To handle out-of-vocabulary words and reduce the vocabulary size, instead of considering words, we consider subword units BIBREF19 by using byte-pair encoding (BPE). In the preprocessing step, instead of learning an explicit mapping between BPEs in the $src$, $mt$ and $pe$, we define BPE tokens by jointly processing all triplets. Thus, $src$, $mt$ and $pe$ derive a single BPE vocabulary. Since $mt$ and $pe$ belong to the same language (German) and $src$ is a close language (English), they naturally share a good fraction of BPE tokens, which reduces the vocabulary size to 28k."
+    ],
+    [
+      "We follow a similar hyper-parameter setup for all reported systems. All encoders (for $\\lbrace src,mt\\rbrace _{tr} \\rightarrow pe$), and the decoder, are composed of a stack of $N_{src} = N_{mt} = N_{pe} = 6$ identical layers followed by layer normalization. The learning rate is varied throughout the training process, and increasing for the first training steps $warmup_{steps} = 8000$ and afterwards decreasing as described in BIBREF7. All remaining hyper-parameters are set analogously to those of the transformer's base model, except that we do not perform checkpoint averaging. At training time, the batch size is set to 25K tokens, with a maximum sentence length of 256 subwords. After each epoch, the training data is shuffled. During decoding, we perform beam search with a beam size of 4. We use shared embeddings between $mt$ and $pe$ in all our experiments."
+    ],
+    [
+      "The results of our four models, single-source ($\\mathbf {mt \\rightarrow pe}$), multi-source single encoder ($\\mathbf {\\lbrace src + pe\\rbrace \\rightarrow pe}$), transference ($\\mathbf {\\lbrace src,mt\\rbrace ^{smt}_{tr} \\rightarrow pe}$), and ensemble, in comparison to the four baselines, raw SMT, $\\mathbf {wmt18^{smt}_{best}}$ BIBREF11 single and ensemble, as well as Transformer ($\\mathbf {src \\rightarrow pe}$), are presented in Table TABREF5 for test2016 and test2017. Table TABREF9 reports the results obtained by our transference model ($\\mathbf {\\lbrace src,mt\\rbrace ^{nmt}_{tr} \\rightarrow pe^{{}}_{{}}}$) on the WMT 2018 NMT data for dev2018 (which we use as a test set) and test2018, compared to the baselines raw NMT and $\\mathbf {wmt18^{nmt}_{best}}$."
+    ],
+    [
+      "The raw SMT output in Table TABREF5 is a strong black-box PBSMT system (i.e., 1st-stage MT). We report its performance observed with respect to the ground truth ($pe$), i.e., the post-edited version of $mt$. The original PBSMT system scores over 62 BLEU points and below 25 TER on test2016 and test2017.",
+      "Using a Transformer ($src \\rightarrow pe$), we test if APE is really useful, or if potential gains are only achieved due to the good performance of the transformer architecture. While we cannot do a full training of the transformer on the data that the raw MT engine was trained on due to the unavailability of the data, we use our PE datasets in an equivalent experimental setup as for all other models. The results of this system (Exp. 1.2 in Table TABREF5) show that the performance is actually lower across both test sets, -5.52/-9.43 absolute points in BLEU and +5.21/+7.72 absolute in TER, compared to the raw SMT baseline.",
+      "We report four results from $\\mathbf {wmt18^{smt}_{best}}$, (i) $wmt18^{smt}_{best}$ ($single$), which is the core multi-encoder implementation without ensembling but with checkpoint averaging, (ii) $wmt18^{smt}_{best}$ ($x4$) which is an ensemble of four identical `single' models trained with different random initializations. The results of $wmt18^{smt}_{best}$ ($single$) and $wmt18^{smt}_{best}$ ($x4$) (Exp. 1.3 and 1.4) reported in Table TABREF5 are from junczysdowmunt-grundkiewicz:2018:WMT. Since their training procedure slightly differs from ours, we also trained the $wmt18^{smt}_{best}$ system using exactly our experimental setup in order to make a fair comparison. This yields the baselines (iii) $wmt18^{smt,generic}_{best}$ ($single$) (Exp. 1.5), which is similar to $wmt18^{smt}_{best}$ ($single$), however, the training parameters and data are kept in line with our transference general model (Exp. 2.3) and (iv) $wmt18^{smt,ft}_{best}$ ($single$) (Exp. 1.6), which is also trained maintaining the equivalent experimental setup compared to the fine tuned version of the transference general model (Exp. 3.3). Compared to both raw SMT and Transformer ($src \\rightarrow pe$) we see strong improvements for this state-of-the-art model, with BLEU scores of at least 68.14 and TER scores of at most 20.98 across the PBSMT testsets. $wmt18^{smt}_{best}$, however, performs better in its original setup (Exp. 1.3 and 1.4) compared to our experimental setup (Exp. 1.5 and 1.6)."
+    ],
+    [
+      "The two transformer architectures $\\mathbf {mt \\rightarrow pe}$ and $\\mathbf {\\lbrace src+mt\\rbrace \\rightarrow pe}$ use only a single encoder. Table TABREF5 shows that $\\mathbf {mt \\rightarrow pe}$ (Exp. 2.1) provides better performance (+4.42 absolute BLEU on test2017) compared to the original SMT, while $\\mathbf {\\lbrace src+mt\\rbrace \\rightarrow pe}$ (Exp. 2.2) provides further improvements by additionally using the $src$ information. $\\mathbf {\\lbrace src+mt\\rbrace \\rightarrow pe}$ improves over $\\mathbf {mt \\rightarrow pe}$ by +1.62/+1.35 absolute BLEU points on test2016/test2017. After fine-tuning, both single encoder transformers (Exp. 3.1 and 3.2 in Table TABREF5) show further improvements, +0.87 and +0.31 absolute BLEU points, respectively, for test2017 and a similar improvement for test2016."
+    ],
+    [
+      "In contrast to the two models above, our transference architecture uses multiple encoders. To fairly compare to $wmt18^{smt}_{best}$, we retrain the $wmt18^{smt}_{best}$ system with our experimental setup (cf. Exp. 1.5 and 1.6 in Table TABREF5). $wmt18^{smt,generic}_{best}$ (single) is a generic model trained on all the training data; which is afterwards fine-tuned with 500K artificial and 23K real PE data ($wmt18^{smt,ft}_{best}$ (single)). It is to be noted that in terms of performance the data processing method described in junczysdowmunt-grundkiewicz:2018:WMT reported in Exp. 1.3 is better than ours (Exp. 1.6). The fine-tuned version of the $\\lbrace src,mt\\rbrace ^{smt}_{tr} \\rightarrow pe$ model (Exp. 3.3 in Table TABREF5) outperforms $wmt18^{smt}_{best}$ (single) (Exp. 1.3) in BLEU on both test sets, however, the TER score for test2016 increases. One should note that $wmt18^{smt}_{best}$ (single) follows the transformer base model, which is an average of five checkpoints, while our Exp. 3.3 is not. When ensembling the 4 best checkpoints of our $\\lbrace src,mt\\rbrace ^{smt}_{tr} \\rightarrow pe$ model (Exp. 4.1), the result beats the $wmt18^{smt}_{best}$ (x4) system, which is an ensemble of four different randomly initialized $wmt18^{smt}_{best}$ (single) systems. Our $\\mathbf {ensemble^{smt} (x3)}$ combines two $\\lbrace src,mt\\rbrace ^{smt}_{tr} \\rightarrow pe$ (Exp. 2.3) models initialized with different random weights with the ensemble of the fine-tuned transference model Exp3.3$^{smt}_{ens4ckpt}$(Exp. 4.1). This ensemble provides the best results for all datasets, providing roughly +1 BLEU point and -0.5 TER when comparing against $wmt18^{smt}_{best}$ (x4).",
+      "The results on the WMT 2018 NMT datasets (dev2018 and test2018) are presented in Table TABREF9. The raw NMT system serves as one baseline against which we compare the performance of the different models. We evaluate the system hypotheses with respect to the ground truth ($pe$), i.e., the post-edited version of $mt$. The baseline original NMT system scores 76.76 BLEU points and 15.08 TER on dev2018, and 74.73 BLEU points and 16.84 TER on test2018.",
+      "For the WMT 2018 NMT data we first test our $\\lbrace src,mt\\rbrace ^{nmt}_{tr} \\rightarrow pe^{{generic,smt}}_{{}}$ model, which is the model from Exp. 3.3 fine-tuned towards NMT data as described in Section SECREF3. Table TABREF9 shows that our PBSMT APE model fine-tuned towards NMT (Exp. 7) can even slightly improve over the already very strong NMT system by about +0.3 BLEU and -0.1 TER, although these improvements are not statistically significant.",
+      "The overall results improve when we train our model on eScape and NMT data instead of using the PBSMT model as a basis. Our proposed generic transference model (Exp. 8, $\\lbrace src,mt\\rbrace ^{nmt}_{tr} \\rightarrow pe^{{generic,nmt}}_{{}}$ shows statistically significant improvements in terms of BLEU and TER compared to the baseline even before fine-tuning, and further improvements after fine-tuning (Exp. 9, $\\lbrace src,mt\\rbrace ^{nmt}_{tr} \\rightarrow pe^{{ft}}_{{}}$). Finally, after averaging the 8 best checkpoints, our $\\lbrace src,mt\\rbrace ^{nmt}_{tr} \\rightarrow pe^{{ft}}_{{avg}}$ model (Exp. 10) also shows consistent improvements in comparison to the baseline and other experimental setups. Overall our fine-tuned model averaging the 8 best checkpoints achieves +1.02 absolute BLEU points and -0.69 absolute TER improvements over the baseline on test2018. Table TABREF9 also shows the performance of our model compared to the winner system of WMT 2018 ($wmt18^{nmt}_{best}$) for the NMT task BIBREF14. $wmt18^{nmt}_{best}$ scores 14.78 in TER and 77.74 in BLEU on the dev2018 and 16.46 in TER and 75.53 in BLEU on the test2018. In comparison to $wmt18^{nmt}_{best}$, our model (Exp. 10) achieves better scores in TER on both the dev2018 and test2018, however, in terms of BLEU our model scores slightly lower for dev2018, while some improvements are achieved on test2018.",
+      "The number of layers ($N_{src}$-$N_{mt}$-$N_{pe}$) in all encoders and the decoder for these results is fixed to 6-6-6. In Exp. 5.1, and 5.2 in Table TABREF5, we see the results of changing this setting to 6-6-4 and 6-4-6. This can be compared to the results of Exp. 2.3, since no fine-tuning or ensembling was performed for these three experiments. Exp. 5.1 shows that decreasing the number of layers on the decoder side does not hurt the performance. In fact, in the case of test2016, we got some improvement, while for test2017, the scores got slightly worse. In contrast, reducing the $enc_{src \\rightarrow mt}$ encoder block's depth (Exp. 5.2) does indeed reduce the performance for all four scores, showing the importance of this second encoder."
+    ],
+    [
+      "In Table TABREF11, we analyze and compare the best performing SMT ($ensemble^{smt} (x3)$) and NMT ($\\lbrace src,mt\\rbrace ^{nmt}_{tr} \\rightarrow pe^{{ft}}_{{avg}}$) model outputs with the original MT outputs on the WMT 2017 (SMT) APE test set and on the WMT 2018 (NMT) development set. Improvements are measured in terms of number of words which need to be (i) inserted (In), (ii) deleted (De), (iii) substituted (Su), and (iv) shifted (Sh), as per TER BIBREF13, in order to turn the MT outputs into reference translations. Our model provides promising results by significantly reducing the required number of edits (24% overall for PBSMT task and 3.6% for NMT task) across all edit operations, thereby leading to reduced post-editing effort and hence improving human post-editing productivity.",
+      "When comparing PBSMT to NMT, we see that stronger improvements are achieved for PBSMT, probably because the raw SMT is worse than the raw NMT. For PBSMT, similar results are achieved for In, De, and Sh, while less gains are obtained in terms of Su. For NMT, In is improved most, followed by Su, De, and last Sh. For shifts in NMT, the APE system even creates further errors, instead of reducing them, which is an issue we aim to prevent in the future."
+    ],
+    [
+      "The proposed transference architecture ($\\lbrace src,mt\\rbrace ^{smt}_{tr} \\rightarrow pe$, Exp. 2.3) shows slightly worse results than $wmt18^{smt}_{best}$ (single) (Exp. 1.3) before fine-tuning, and roughly similar results after fine-tuning (Exp. 3.3). After ensembling, however, our transference model (Exp. 4.2) shows consistent improvements when comparing against the best baseline ensemble $wmt18^{smt}_{best}$ (x4) (Exp. 1.4). Due to the unavailability of the sentence-level scores of $wmt18^{smt}_{best}$ (x4), we could not test if the improvements (roughly +1 BLEU, -0.5 TER) are statistically significant. Interestingly, our approach of taking the model optimized for PBSMT and fine-tuning it to the NMT task (Exp. 7) does not hurt the performance as was reported in the previous literature BIBREF11. In contrast, some small, albeit statistically insignificant improvements over the raw NMT baseline were achieved. When we train the transference architecture directly for the NMT task (Exp. 8), we get slightly better and statistically significant improvements compared to raw NMT. Fine-tuning this NMT model further towards the actual NMT data (Exp. 9), as well as performing checkpoint averaging using the 8 best checkpoints improves the results even further.",
+      "The reasons for the effectiveness of our approach can be summarized as follows. (1) Our $enc_{src \\rightarrow mt}$ contains two attention mechanisms: one is self-attention and another is cross-attention. The self-attention layer is not masked here; therefore, the cross-attention layer in $enc_{src \\rightarrow mt}$ is informed by both previous and future time-steps from the self-attended representation of $mt$ ($enc_{mt}$) and additionally from $enc_{src}$. As a result, each state representation of $enc_{src \\rightarrow mt}$ is learned from the context of $src$ and $mt$. This might produce better representations for $dec_{pe}$ which can access the combined context. In contrast, in $wmt18^{smt}_{best}$, the $dec_{pe}$ accesses representations from $src$ and $mt$ independently, first using the representation from $mt$ and then using that of $src$. (2) The position-wise feed-forward layer in our $enc_{src \\rightarrow mt}$ of the transference model requires processing information from two attention modules, while in the case of $wmt18^{smt}_{best}$, the position-wise feed-forward layer in $dec_{pe}$ needs to process information from three attention modules, which may increase the learning difficulty of the feed-forward layer. (3) Since $pe$ is a post-edited version of $mt$, sharing the same language, $mt$ and $pe$ are quite similar compared to $src$. Therefore, attending over a fine-tuned representation from $mt$ along with $src$, which is what we have done in this work, might be a reason for the better results than those achieved by attending over $src$ directly. Evaluating the influence of the depth of our encoders and decoder show that while the decoder depth appears to have limited importance, reducing the encoder depth indeed hurts performance which is in line with domhan-2018-much."
+    ],
+    [
+      "In this paper, we presented a multi-encoder transformer-based APE model that repurposes the standard transformer blocks in a simple and effective way for the APE task: first, our transference architecture uses a transformer encoder block for $src$, followed by a decoder block without masking on $mt$ that effectively acts as a second encoder combining $src \\rightarrow mt$, and feeds this representation into a final decoder block generating $pe$. The proposed model outperforms the best-performing system of WMT 2018 on the test2016, test2017, dev2018, and test2018 data and provides a new state-of-the-art in APE.",
+      "Taking a departure from traditional transformer-based encoders, which perform self-attention only, our second encoder also performs cross-attention to produce representations for the decoder based on both $src$ and $mt$. We also show that the encoder plays a more pivotal role than the decoder in transformer-based APE, which could also be the case for transformer-based generation tasks in general. Our architecture is generic and can be used for any multi-source task, e.g., multi-source translation or summarization, etc."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2484/instruction.md b/qasper-2484/instruction.md
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+Name of Paper: The Transference Architecture for Automatic Post-Editing
+
+Question: How much is performance hurt when using too small amount of layers in encoder?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Research",
+    "Transference Model for APE",
+    "Experiments",
+    "Experiments ::: Data",
+    "Experiments ::: Experiment Setup",
+    "Experiments ::: Hyper-parameter Setup",
+    "Results",
+    "Results ::: Baselines",
+    "Results ::: Single-Encoder Transformer for APE",
+    "Results ::: Transference Transformer for APE",
+    "Results ::: Analysis of Error Patterns",
+    "Results ::: Discussion",
+    "Conclusions"
+  ],
+  "paragraphs": [
+    [
+      "The performance of state-of-the-art MT systems is not perfect, thus, human interventions are still required to correct machine translated texts into publishable quality translations BIBREF0. Automatic post-editing (APE) is a method that aims to automatically correct errors made by MT systems before performing actual human post-editing (PE) BIBREF1, thereby reducing the translators' workload and increasing productivity BIBREF2. APE systems trained on human PE data serve as MT post-processing modules to improve the overall performance. APE can therefore be viewed as a 2nd-stage MT system, translating predictable error patterns in MT output to their corresponding corrections. APE training data minimally involves MT output ($mt$) and the human post-edited ($pe$) version of $mt$, but additionally using the source ($src$) has been shown to provide further benefits BIBREF3, BIBREF4, BIBREF5.",
+      "To provide awareness of errors in $mt$ originating from $src$, attention mechanisms BIBREF6 allow modeling of non-local dependencies in the input or output sequences, and importantly also global dependencies between them (in our case $src$, $mt$ and $pe$). The transformer architecture BIBREF7 is built solely upon such attention mechanisms completely replacing recurrence and convolutions. The transformer uses positional encoding to encode the input and output sequences, and computes both self- and cross-attention through so-called multi-head attentions, which are facilitated by parallelization. Such multi-head attention allows to jointly attend to information at different positions from different representation subspaces, e.g. utilizing and combining information from $src$, $mt$, and $pe$.",
+      "In this paper, we present a multi-source neural APE architecture called transference. Our model contains a source encoder which encodes $src$ information, a second encoder ($enc_{src \\rightarrow mt}$) which takes the encoded representation from the source encoder ($enc_{src}$), combines this with the self-attention-based encoding of $mt$ ($enc_{mt}$), and prepares a representation for the decoder ($dec_{pe}$) via cross-attention. Our second encoder ($enc_{src \\rightarrow mt}$) can also be viewed as a standard transformer decoding block, however, without masking, which acts as an encoder. We thus recombine the different blocks of the transformer architecture and repurpose them for the APE task in a simple yet effective way. The suggested architecture is inspired by the two-step approach professional translators tend to use during post-editing: first, the source segment is compared to the corresponding translation suggestion (similar to what our $enc_{src \\rightarrow mt}$ is doing), then corrections to the MT output are applied based on the encountered errors (in the same way that our $dec_{pe}$ uses the encoded representation of $enc_{src \\rightarrow mt}$ to produce the final translation).",
+      "The paper makes the following contributions: (i) we propose a new multi-encoder model for APE that consists only of standard transformer encoding and decoding blocks, (ii) by using a mix of self- and cross-attention we provide a representation of both $src$ and $mt$ for the decoder, allowing it to better capture errors in $mt$ originating from $src$; this advances the state-of-the-art in APE in terms of BLEU and TER, and (iii), we analyze the effect of varying the number of encoder and decoder layers BIBREF8, indicating that the encoders contribute more than decoders in transformer-based neural APE."
+    ],
+    [
+      "Recent advances in APE research are directed towards neural APE, which was first proposed by Pal:2016:ACL and junczysdowmunt-grundkiewicz:2016:WMT for the single-source APE scenario which does not consider $src$, i.e. $mt \\rightarrow pe$. In their work, junczysdowmunt-grundkiewicz:2016:WMT also generated a large synthetic training dataset through back translation, which we also use as additional training data. Exploiting source information as an additional input can help neural APE to disambiguate corrections applied at each time step; this naturally leads to multi-source APE ($\\lbrace src, mt\\rbrace \\rightarrow pe$). A multi-source neural APE system can be configured either by using a single encoder that encodes the concatenation of $src$ and $mt$ BIBREF9 or by using two separate encoders for $src$ and $mt$ and passing the concatenation of both encoders' final states to the decoder BIBREF10. A few approaches to multi-source neural APE were proposed in the WMT 2017 APE shared task. Junczysdowmunt:2017:WMT combine both $mt$ and $src$ in a single neural architecture, exploring different combinations of attention mechanisms including soft attention and hard monotonic attention. Chatterjee-EtAl:2017:WMT2 built upon the two-encoder architecture of multi-source models BIBREF10 by means of concatenating both weighted contexts of encoded $src$ and $mt$. Varis-bojar:2017:WMT compared two multi-source models, one using a single encoder with concatenation of $src$ and $mt$ sentences, and a second one using two character-level encoders for $mt$ and $src$ along with a character-level decoder.",
+      "Recently, in the WMT 2018 APE shared task, several adaptations of the transformer architecture have been presented for multi-source APE. pal-EtAl:2018:WMT proposed an APE model that uses three self-attention-based encoders. They introduce an additional joint encoder that attends over a combination of the two encoded sequences from $mt$ and $src$. tebbifakhr-EtAl:2018:WMT, the NMT-subtask winner of WMT 2018 ($wmt18^{nmt}_{best}$), employ sequence-level loss functions in order to avoid exposure bias during training and to be consistent with the automatic evaluation metrics. shin-lee:2018:WMT propose that each encoder has its own self-attention and feed-forward layer to process each input separately. On the decoder side, they add two additional multi-head attention layers, one for $src \\rightarrow mt$ and another for $src \\rightarrow pe$. Thereafter another multi-head attention between the output of those attention layers helps the decoder to capture common words in $mt$ which should remain in $pe$. The APE PBSMT-subtask winner of WMT 2018 ($wmt18^{smt}_{best}$) BIBREF11 also presented another transformer-based multi-source APE which uses two encoders and stacks an additional cross-attention component for $src \\rightarrow pe$ above the previous cross-attention for $mt \\rightarrow pe$. Comparing shin-lee:2018:WMT's approach with the winner system, there are only two differences in the architecture: (i) the cross-attention order of $src \\rightarrow mt$ and $src \\rightarrow pe$ in the decoder, and (ii) $wmt18^{smt}_{best}$ additionally shares parameters between two encoders."
+    ],
+    [
+      "We propose a multi-source transformer model called transference ($\\lbrace src,mt\\rbrace _{tr} \\rightarrow pe$, Figure FIGREF1), which takes advantage of both the encodings of $src$ and $mt$ and attends over a combination of both sequences while generating the post-edited sentence. The second encoder, $enc_{src \\rightarrow mt}$, makes use of the first encoder $enc_{src}$ and a sub-encoder $enc_{mt}$ for considering $src$ and $mt$. Here, the $enc_{src}$ encoder and the $dec_{pe}$ decoder are equivalent to the original transformer for neural MT. Our $enc_{src \\rightarrow mt}$ follows an architecture similar to the transformer's decoder, the difference being that no masked multi-head self-attention is used to process $mt$.",
+      "One self-attended encoder for $src$, $\\mathbf {s}$ = $(s_1, s_2, \\ldots , s_k)$, returns a sequence of continuous representations, $enc_{src}$, and a second self-attended sub-encoder for $mt$, $\\mathbf {m}$ = $(m_1, m_2, \\ldots , m_l)$, returns another sequence of continuous representations, $enc_{mt}$. Self-attention at this point provides the advantage of aggregating information from all of the words, including $src$ and $mt$, and successively generates a new representation per word informed by the entire $src$ and $mt$ context. The internal $enc_{mt}$ representation performs cross-attention over $enc_{src}$ and prepares a final representation ($enc_{src \\rightarrow mt}$) for the decoder ($dec_{pe}$). The decoder then generates the $pe$ output in sequence, $\\mathbf {p}$ = $(p_1, p_2, \\ldots , p_n)$, one word at a time from left to right by attending to previously generated words as well as the final representations ($enc_{src \\rightarrow mt}$) generated by the encoder.",
+      "To summarize, our multi-source APE implementation extends Vaswani:NIPS2017 by introducing an additional encoding block by which $src$ and $mt$ communicate with the decoder.",
+      "Our proposed approach differs from the WMT 2018 PBSMT winner system in several ways: (i) we use the original transformer's decoder without modifications; (ii) one of our encoder blocks ($enc_{src \\rightarrow mt}$) is identical to the transformer's decoder block but uses no masking in the self-attention layer, thus having one self-attention layer and an additional cross-attention for $src \\rightarrow mt$; and (iii) in the decoder layer, the cross-attention is performed between the encoded representation from $enc_{src \\rightarrow mt}$ and $pe$.",
+      "Our approach also differs from the WMT 2018 NMT winner system: (i) $wmt18^{nmt}_{best}$ concatenates the encoded representation of two encoders and passes it as the key to the attention layer of the decoder, and (ii), the system additionally employs sequence-level loss functions based on maximum likelihood estimation and minimum risk training in order to avoid exposure bias during training.",
+      "The main intuition is that our $enc_{src \\rightarrow mt}$ attends over the $src$ and $mt$ and informs the $pe$ to better capture, process, and share information between $src$-$mt$-$pe$, which efficiently models error patterns and the corresponding corrections. Our model performs better than past approaches, as the experiment section will show."
+    ],
+    [
+      "We explore our approach on both APE sub-tasks of WMT 2018, where the 1st-stage MT system to which APE is applied is either a phrase-based statistical machine translation (PBSMT) or a neural machine translation (NMT) model.",
+      "For the PBSMT task, we compare against four baselines: the raw SMT output provided by the 1st-stage PBSMT system, the best-performing systems from WMT APE 2018 ($\\mathbf {wmt18^{smt}_{best}}$), which are a single model and an ensemble model by junczysdowmunt-grundkiewicz:2018:WMT, as well as a transformer trying to directly translate from $src$ to $pe$ (Transformer ($\\mathbf {src \\rightarrow pe}$)), thus performing translation instead of APE. We evaluate the systems using BLEU BIBREF12 and TER BIBREF13.",
+      "For the NMT task, we consider two baselines: the raw NMT output provided by the 1st-stage NMT system and the best-performing system from the WMT 2018 NMT APE task ($\\mathbf {wmt18^{nmt}_{best}}$) BIBREF14.",
+      "Apart from the multi-encoder transference architecture described above ($\\lbrace src,mt\\rbrace _{tr} \\rightarrow pe$) and ensembling of this architecture, two simpler versions are also analyzed: first, a `mono-lingual' ($\\mathbf {mt \\rightarrow pe}$) APE model using only parallel $mt$\u2013$pe$ data and therefore only a single encoder, and second, an identical single-encoder architecture, however, using the concatenated $src$ and $mt$ text as input ($\\mathbf {\\lbrace src+mt\\rbrace \\rightarrow pe}$) BIBREF9."
+    ],
+    [
+      "For our experiments, we use the English\u2013German WMT 2016 BIBREF4, 2017 BIBREF5 and 2018 BIBREF15 APE task data. All these released APE datasets consist of English\u2013German triplets containing source English text ($src$) from the IT domain, the corresponding German translations ($mt$) from a 1st-stage MT system, and the corresponding human-post-edited version ($pe$). The sizes of the datasets (train; dev; test), in terms of number of sentences, are (12,000; 1,000; 2,000), (11,000; 0; 2,000), and (13,442; 1,000; 1,023), for the 2016 PBSMT, the 2017 PBSMT, and the 2018 NMT data, respectively. One should note that for WMT 2018, we carried out experiments only for the NMT sub-task and ignored the data for the PBSMT task.",
+      "Since the WMT APE datasets are small in size, we use `artificial training data' BIBREF16 containing 4.5M sentences as additional resources, 4M of which are weakly similar to the WMT 2016 training data, while 500K are very similar according to TER statistics.",
+      "For experimenting on the NMT data, we additionally use the synthetic eScape APE corpus BIBREF17, consisting of $\\sim $7M triples. For cleaning this noisy eScape dataset containing many unrelated language words (e.g. Chinese), we perform the following two steps: (i) we use the cleaning process described in tebbifakhr-EtAl:2018:WMT, and (ii) we use the Moses BIBREF18 corpus cleaning scripts with minimum and maximum number of tokens set to 1 and 100, respectively. After cleaning, we perform punctuation normalization, and then use the Moses tokenizer BIBREF18 to tokenize the eScape corpus with `no-escape' option. Finally, we apply true-casing. The cleaned version of the eScape corpus contains $\\sim $6.5M triplets."
+    ],
+    [
+      "To build models for the PBSMT tasks from 2016 and 2017, we first train a generic APE model using all the training data (4M + 500K + 12K + 11K) described in Section SECREF2. Afterwards, we fine-tune the trained model using the 500K artificial and 23K (12K + 11K) real PE training data. We use the WMT 2016 development data (dev2016) containing 1,000 triplets to validate the models during training. To test our system performance, we use the WMT 2016 and 2017 test data (test2016, test2017) as two sub-experiments, each containing 2,000 triplets ($src$, $mt$ and $pe$). We compare the performance of our system with the four different baseline systems described above: raw MT, $wmt18^{smt}_{best}$ single and ensemble, as well as Transformer ($src \\rightarrow pe$).",
+      "Additionally, we check the performance of our model on the WMT 2018 NMT APE task (where unlike in previous tasks, the 1st-stage MT system is provided by NMT): for this, we explore two experimental setups: (i) we use the PBSMT task's APE model as a generic model which is then fine-tuned to a subset (12k) of the NMT data ($\\lbrace src,mt\\rbrace ^{nmt}_{tr} \\rightarrow pe^{{generic, smt}}_{{}}$). One should note that it has been argued that the inclusion of SMT-specific data could be harmful when training NMT APE models BIBREF11. (ii), we train a completely new generic model on the cleaned eScape data ($\\sim $6.5M) along with a subset (12K) of the original training data released for the NMT task ($\\lbrace src,mt\\rbrace ^{nmt}_{tr} \\rightarrow pe^{{generic, nmt}}_{{}}$). The aforementioned 12K NMT data are the first 12K of the overall 13.4K NMT data. The remaining 1.4K are used as validation data. The released development set (dev2018) is used as test data for our experiment, alongside the test2018, for which we could only obtain results for a few models by the WMT 2019 task organizers. We also explore an additional fine-tuning step of $\\lbrace src,mt\\rbrace ^{nmt}_{tr} \\rightarrow pe^{{generic, nmt}}_{{}}$ towards the 12K NMT data (called $\\lbrace src,mt\\rbrace ^{nmt}_{tr} \\rightarrow pe^{{ft}}_{{}}$), and a model averaging the 8 best checkpoints of $\\lbrace src,mt\\rbrace ^{nmt}_{tr} \\rightarrow pe^{{ft}}_{{}}$, which we call $\\lbrace src,mt\\rbrace ^{nmt}_{tr} \\rightarrow pe^{{ft}}_{{avg}}$.",
+      "Last, we analyze the importance of our second encoder ($enc_{src \\rightarrow mt}$), compared to the source encoder ($enc_{src}$) and the decoder ($dec_{pe}$), by reducing and expanding the amount of layers in the encoders and the decoder. Our standard setup, which we use for fine-tuning, ensembling etc., is fixed to 6-6-6 for $N_{src}$-$N_{mt}$-$N_{pe}$ (cf. Figure FIGREF1), where 6 is the value that was proposed by Vaswani:NIPS2017 for the base model. We investigate what happens in terms of APE performance if we change this setting to 6-6-4 and 6-4-6. To handle out-of-vocabulary words and reduce the vocabulary size, instead of considering words, we consider subword units BIBREF19 by using byte-pair encoding (BPE). In the preprocessing step, instead of learning an explicit mapping between BPEs in the $src$, $mt$ and $pe$, we define BPE tokens by jointly processing all triplets. Thus, $src$, $mt$ and $pe$ derive a single BPE vocabulary. Since $mt$ and $pe$ belong to the same language (German) and $src$ is a close language (English), they naturally share a good fraction of BPE tokens, which reduces the vocabulary size to 28k."
+    ],
+    [
+      "We follow a similar hyper-parameter setup for all reported systems. All encoders (for $\\lbrace src,mt\\rbrace _{tr} \\rightarrow pe$), and the decoder, are composed of a stack of $N_{src} = N_{mt} = N_{pe} = 6$ identical layers followed by layer normalization. The learning rate is varied throughout the training process, and increasing for the first training steps $warmup_{steps} = 8000$ and afterwards decreasing as described in BIBREF7. All remaining hyper-parameters are set analogously to those of the transformer's base model, except that we do not perform checkpoint averaging. At training time, the batch size is set to 25K tokens, with a maximum sentence length of 256 subwords. After each epoch, the training data is shuffled. During decoding, we perform beam search with a beam size of 4. We use shared embeddings between $mt$ and $pe$ in all our experiments."
+    ],
+    [
+      "The results of our four models, single-source ($\\mathbf {mt \\rightarrow pe}$), multi-source single encoder ($\\mathbf {\\lbrace src + pe\\rbrace \\rightarrow pe}$), transference ($\\mathbf {\\lbrace src,mt\\rbrace ^{smt}_{tr} \\rightarrow pe}$), and ensemble, in comparison to the four baselines, raw SMT, $\\mathbf {wmt18^{smt}_{best}}$ BIBREF11 single and ensemble, as well as Transformer ($\\mathbf {src \\rightarrow pe}$), are presented in Table TABREF5 for test2016 and test2017. Table TABREF9 reports the results obtained by our transference model ($\\mathbf {\\lbrace src,mt\\rbrace ^{nmt}_{tr} \\rightarrow pe^{{}}_{{}}}$) on the WMT 2018 NMT data for dev2018 (which we use as a test set) and test2018, compared to the baselines raw NMT and $\\mathbf {wmt18^{nmt}_{best}}$."
+    ],
+    [
+      "The raw SMT output in Table TABREF5 is a strong black-box PBSMT system (i.e., 1st-stage MT). We report its performance observed with respect to the ground truth ($pe$), i.e., the post-edited version of $mt$. The original PBSMT system scores over 62 BLEU points and below 25 TER on test2016 and test2017.",
+      "Using a Transformer ($src \\rightarrow pe$), we test if APE is really useful, or if potential gains are only achieved due to the good performance of the transformer architecture. While we cannot do a full training of the transformer on the data that the raw MT engine was trained on due to the unavailability of the data, we use our PE datasets in an equivalent experimental setup as for all other models. The results of this system (Exp. 1.2 in Table TABREF5) show that the performance is actually lower across both test sets, -5.52/-9.43 absolute points in BLEU and +5.21/+7.72 absolute in TER, compared to the raw SMT baseline.",
+      "We report four results from $\\mathbf {wmt18^{smt}_{best}}$, (i) $wmt18^{smt}_{best}$ ($single$), which is the core multi-encoder implementation without ensembling but with checkpoint averaging, (ii) $wmt18^{smt}_{best}$ ($x4$) which is an ensemble of four identical `single' models trained with different random initializations. The results of $wmt18^{smt}_{best}$ ($single$) and $wmt18^{smt}_{best}$ ($x4$) (Exp. 1.3 and 1.4) reported in Table TABREF5 are from junczysdowmunt-grundkiewicz:2018:WMT. Since their training procedure slightly differs from ours, we also trained the $wmt18^{smt}_{best}$ system using exactly our experimental setup in order to make a fair comparison. This yields the baselines (iii) $wmt18^{smt,generic}_{best}$ ($single$) (Exp. 1.5), which is similar to $wmt18^{smt}_{best}$ ($single$), however, the training parameters and data are kept in line with our transference general model (Exp. 2.3) and (iv) $wmt18^{smt,ft}_{best}$ ($single$) (Exp. 1.6), which is also trained maintaining the equivalent experimental setup compared to the fine tuned version of the transference general model (Exp. 3.3). Compared to both raw SMT and Transformer ($src \\rightarrow pe$) we see strong improvements for this state-of-the-art model, with BLEU scores of at least 68.14 and TER scores of at most 20.98 across the PBSMT testsets. $wmt18^{smt}_{best}$, however, performs better in its original setup (Exp. 1.3 and 1.4) compared to our experimental setup (Exp. 1.5 and 1.6)."
+    ],
+    [
+      "The two transformer architectures $\\mathbf {mt \\rightarrow pe}$ and $\\mathbf {\\lbrace src+mt\\rbrace \\rightarrow pe}$ use only a single encoder. Table TABREF5 shows that $\\mathbf {mt \\rightarrow pe}$ (Exp. 2.1) provides better performance (+4.42 absolute BLEU on test2017) compared to the original SMT, while $\\mathbf {\\lbrace src+mt\\rbrace \\rightarrow pe}$ (Exp. 2.2) provides further improvements by additionally using the $src$ information. $\\mathbf {\\lbrace src+mt\\rbrace \\rightarrow pe}$ improves over $\\mathbf {mt \\rightarrow pe}$ by +1.62/+1.35 absolute BLEU points on test2016/test2017. After fine-tuning, both single encoder transformers (Exp. 3.1 and 3.2 in Table TABREF5) show further improvements, +0.87 and +0.31 absolute BLEU points, respectively, for test2017 and a similar improvement for test2016."
+    ],
+    [
+      "In contrast to the two models above, our transference architecture uses multiple encoders. To fairly compare to $wmt18^{smt}_{best}$, we retrain the $wmt18^{smt}_{best}$ system with our experimental setup (cf. Exp. 1.5 and 1.6 in Table TABREF5). $wmt18^{smt,generic}_{best}$ (single) is a generic model trained on all the training data; which is afterwards fine-tuned with 500K artificial and 23K real PE data ($wmt18^{smt,ft}_{best}$ (single)). It is to be noted that in terms of performance the data processing method described in junczysdowmunt-grundkiewicz:2018:WMT reported in Exp. 1.3 is better than ours (Exp. 1.6). The fine-tuned version of the $\\lbrace src,mt\\rbrace ^{smt}_{tr} \\rightarrow pe$ model (Exp. 3.3 in Table TABREF5) outperforms $wmt18^{smt}_{best}$ (single) (Exp. 1.3) in BLEU on both test sets, however, the TER score for test2016 increases. One should note that $wmt18^{smt}_{best}$ (single) follows the transformer base model, which is an average of five checkpoints, while our Exp. 3.3 is not. When ensembling the 4 best checkpoints of our $\\lbrace src,mt\\rbrace ^{smt}_{tr} \\rightarrow pe$ model (Exp. 4.1), the result beats the $wmt18^{smt}_{best}$ (x4) system, which is an ensemble of four different randomly initialized $wmt18^{smt}_{best}$ (single) systems. Our $\\mathbf {ensemble^{smt} (x3)}$ combines two $\\lbrace src,mt\\rbrace ^{smt}_{tr} \\rightarrow pe$ (Exp. 2.3) models initialized with different random weights with the ensemble of the fine-tuned transference model Exp3.3$^{smt}_{ens4ckpt}$(Exp. 4.1). This ensemble provides the best results for all datasets, providing roughly +1 BLEU point and -0.5 TER when comparing against $wmt18^{smt}_{best}$ (x4).",
+      "The results on the WMT 2018 NMT datasets (dev2018 and test2018) are presented in Table TABREF9. The raw NMT system serves as one baseline against which we compare the performance of the different models. We evaluate the system hypotheses with respect to the ground truth ($pe$), i.e., the post-edited version of $mt$. The baseline original NMT system scores 76.76 BLEU points and 15.08 TER on dev2018, and 74.73 BLEU points and 16.84 TER on test2018.",
+      "For the WMT 2018 NMT data we first test our $\\lbrace src,mt\\rbrace ^{nmt}_{tr} \\rightarrow pe^{{generic,smt}}_{{}}$ model, which is the model from Exp. 3.3 fine-tuned towards NMT data as described in Section SECREF3. Table TABREF9 shows that our PBSMT APE model fine-tuned towards NMT (Exp. 7) can even slightly improve over the already very strong NMT system by about +0.3 BLEU and -0.1 TER, although these improvements are not statistically significant.",
+      "The overall results improve when we train our model on eScape and NMT data instead of using the PBSMT model as a basis. Our proposed generic transference model (Exp. 8, $\\lbrace src,mt\\rbrace ^{nmt}_{tr} \\rightarrow pe^{{generic,nmt}}_{{}}$ shows statistically significant improvements in terms of BLEU and TER compared to the baseline even before fine-tuning, and further improvements after fine-tuning (Exp. 9, $\\lbrace src,mt\\rbrace ^{nmt}_{tr} \\rightarrow pe^{{ft}}_{{}}$). Finally, after averaging the 8 best checkpoints, our $\\lbrace src,mt\\rbrace ^{nmt}_{tr} \\rightarrow pe^{{ft}}_{{avg}}$ model (Exp. 10) also shows consistent improvements in comparison to the baseline and other experimental setups. Overall our fine-tuned model averaging the 8 best checkpoints achieves +1.02 absolute BLEU points and -0.69 absolute TER improvements over the baseline on test2018. Table TABREF9 also shows the performance of our model compared to the winner system of WMT 2018 ($wmt18^{nmt}_{best}$) for the NMT task BIBREF14. $wmt18^{nmt}_{best}$ scores 14.78 in TER and 77.74 in BLEU on the dev2018 and 16.46 in TER and 75.53 in BLEU on the test2018. In comparison to $wmt18^{nmt}_{best}$, our model (Exp. 10) achieves better scores in TER on both the dev2018 and test2018, however, in terms of BLEU our model scores slightly lower for dev2018, while some improvements are achieved on test2018.",
+      "The number of layers ($N_{src}$-$N_{mt}$-$N_{pe}$) in all encoders and the decoder for these results is fixed to 6-6-6. In Exp. 5.1, and 5.2 in Table TABREF5, we see the results of changing this setting to 6-6-4 and 6-4-6. This can be compared to the results of Exp. 2.3, since no fine-tuning or ensembling was performed for these three experiments. Exp. 5.1 shows that decreasing the number of layers on the decoder side does not hurt the performance. In fact, in the case of test2016, we got some improvement, while for test2017, the scores got slightly worse. In contrast, reducing the $enc_{src \\rightarrow mt}$ encoder block's depth (Exp. 5.2) does indeed reduce the performance for all four scores, showing the importance of this second encoder."
+    ],
+    [
+      "In Table TABREF11, we analyze and compare the best performing SMT ($ensemble^{smt} (x3)$) and NMT ($\\lbrace src,mt\\rbrace ^{nmt}_{tr} \\rightarrow pe^{{ft}}_{{avg}}$) model outputs with the original MT outputs on the WMT 2017 (SMT) APE test set and on the WMT 2018 (NMT) development set. Improvements are measured in terms of number of words which need to be (i) inserted (In), (ii) deleted (De), (iii) substituted (Su), and (iv) shifted (Sh), as per TER BIBREF13, in order to turn the MT outputs into reference translations. Our model provides promising results by significantly reducing the required number of edits (24% overall for PBSMT task and 3.6% for NMT task) across all edit operations, thereby leading to reduced post-editing effort and hence improving human post-editing productivity.",
+      "When comparing PBSMT to NMT, we see that stronger improvements are achieved for PBSMT, probably because the raw SMT is worse than the raw NMT. For PBSMT, similar results are achieved for In, De, and Sh, while less gains are obtained in terms of Su. For NMT, In is improved most, followed by Su, De, and last Sh. For shifts in NMT, the APE system even creates further errors, instead of reducing them, which is an issue we aim to prevent in the future."
+    ],
+    [
+      "The proposed transference architecture ($\\lbrace src,mt\\rbrace ^{smt}_{tr} \\rightarrow pe$, Exp. 2.3) shows slightly worse results than $wmt18^{smt}_{best}$ (single) (Exp. 1.3) before fine-tuning, and roughly similar results after fine-tuning (Exp. 3.3). After ensembling, however, our transference model (Exp. 4.2) shows consistent improvements when comparing against the best baseline ensemble $wmt18^{smt}_{best}$ (x4) (Exp. 1.4). Due to the unavailability of the sentence-level scores of $wmt18^{smt}_{best}$ (x4), we could not test if the improvements (roughly +1 BLEU, -0.5 TER) are statistically significant. Interestingly, our approach of taking the model optimized for PBSMT and fine-tuning it to the NMT task (Exp. 7) does not hurt the performance as was reported in the previous literature BIBREF11. In contrast, some small, albeit statistically insignificant improvements over the raw NMT baseline were achieved. When we train the transference architecture directly for the NMT task (Exp. 8), we get slightly better and statistically significant improvements compared to raw NMT. Fine-tuning this NMT model further towards the actual NMT data (Exp. 9), as well as performing checkpoint averaging using the 8 best checkpoints improves the results even further.",
+      "The reasons for the effectiveness of our approach can be summarized as follows. (1) Our $enc_{src \\rightarrow mt}$ contains two attention mechanisms: one is self-attention and another is cross-attention. The self-attention layer is not masked here; therefore, the cross-attention layer in $enc_{src \\rightarrow mt}$ is informed by both previous and future time-steps from the self-attended representation of $mt$ ($enc_{mt}$) and additionally from $enc_{src}$. As a result, each state representation of $enc_{src \\rightarrow mt}$ is learned from the context of $src$ and $mt$. This might produce better representations for $dec_{pe}$ which can access the combined context. In contrast, in $wmt18^{smt}_{best}$, the $dec_{pe}$ accesses representations from $src$ and $mt$ independently, first using the representation from $mt$ and then using that of $src$. (2) The position-wise feed-forward layer in our $enc_{src \\rightarrow mt}$ of the transference model requires processing information from two attention modules, while in the case of $wmt18^{smt}_{best}$, the position-wise feed-forward layer in $dec_{pe}$ needs to process information from three attention modules, which may increase the learning difficulty of the feed-forward layer. (3) Since $pe$ is a post-edited version of $mt$, sharing the same language, $mt$ and $pe$ are quite similar compared to $src$. Therefore, attending over a fine-tuned representation from $mt$ along with $src$, which is what we have done in this work, might be a reason for the better results than those achieved by attending over $src$ directly. Evaluating the influence of the depth of our encoders and decoder show that while the decoder depth appears to have limited importance, reducing the encoder depth indeed hurts performance which is in line with domhan-2018-much."
+    ],
+    [
+      "In this paper, we presented a multi-encoder transformer-based APE model that repurposes the standard transformer blocks in a simple and effective way for the APE task: first, our transference architecture uses a transformer encoder block for $src$, followed by a decoder block without masking on $mt$ that effectively acts as a second encoder combining $src \\rightarrow mt$, and feeds this representation into a final decoder block generating $pe$. The proposed model outperforms the best-performing system of WMT 2018 on the test2016, test2017, dev2018, and test2018 data and provides a new state-of-the-art in APE.",
+      "Taking a departure from traditional transformer-based encoders, which perform self-attention only, our second encoder also performs cross-attention to produce representations for the decoder based on both $src$ and $mt$. We also show that the encoder plays a more pivotal role than the decoder in transformer-based APE, which could also be the case for transformer-based generation tasks in general. Our architecture is generic and can be used for any multi-source task, e.g., multi-source translation or summarization, etc."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2485/instruction.md b/qasper-2485/instruction.md
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+++ b/qasper-2485/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: The Transference Architecture for Automatic Post-Editing
+
+Question: What was previous state of the art model for automatic post editing?
\ No newline at end of file
diff --git a/qasper-2495/instruction.md b/qasper-2495/instruction.md
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+++ b/qasper-2495/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Deep Neural Machine Translation with Linear Associative Unit
+
+Question: Do they use the same architecture as LSTM-s and GRUs with just replacing with the LAU unit?
\ No newline at end of file
diff --git a/qasper-2502/instruction.md b/qasper-2502/instruction.md
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+++ b/qasper-2502/instruction.md
@@ -0,0 +1,119 @@
+Name of Paper: Speakers account for asymmetries in visual perspective so listeners don't have to
+
+Question: Was this experiment done in a lab?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Experiment 1: Speaker behavior under uncertainty",
+    "Methods",
+    "Behavioral results",
+    "Model comparison",
+    "Experiment 2: Comparing confederates to natural speakers",
+    "Results",
+    "General Discussion",
+    "Acknowledgements",
+    "Author contributions",
+    "Appendix A: Derivation of qualitative model predictions",
+    "Appendix B: Quantitative model fit for Exp. 1",
+    "Appendix C: Multi-stage bootstrap procedure for Expt. 2"
+  ],
+  "paragraphs": [
+    [
+      "Our success as a social species depends on our ability to understand, and be understood by, different communicative partners across different contexts. Theory of mind\u2014the ability to represent and reason about others' mental states\u2014is considered to be the key mechanism that supports such context-sensitivity in our everyday social interactions. Being able to reason about what others see, want, and think allows us to make more accurate predictions about their future behavior in different contexts and adjust our own behaviors accordingly BIBREF0 . Over the past two decades, however, there has been sustained debate over the extent to which adults actually make of use theory of mind in communication.",
+      "On one hand, accounts of language use in the tradition of BIBREF1 and BIBREF2 , BIBREF3 implicitly assume a fundamental and pervasive role for theory of mind mechanisms. The meaning of an utterance is established against a backdrop of inference, intention, and common ground: knowledge that is taken to be shared by both parties BIBREF4 , BIBREF5 . This view of adults as natural mind-readers is consistent with extensive evidence from the psycholinguistics literature: for instance, we spontaneously calibrate our referential expressions to our intended audiences BIBREF6 and make use of partner-specific history BIBREF7 , BIBREF8 . Yet in other cases the evidence appears to be more consistent with a more egocentric or \u201creflexively mind-blind\u201d view of language processing BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 . Under this view, although adults have the ability to deploy theory of mind, it is effortful and costly to do so. Thus people may initially anchor on their own perspective and only adjust to account for other perspectives when a problem arises and when sufficient cognitive resources are available.",
+      "Much of this debate has centered around the influential director-matcher paradigm, a variant of classic reference games BIBREF13 where a confederate speaker gives participants instructions about how to move objects around a grid. By introducing an asymmetry in visual access\u2014certain cells of the grid are covered such that participants can see objects that the speaker cannot (e.g. Fig. 1 )\u2014 BIBREF14 designed a task to expose cases where participants (listeners) either succeed or fail to take into account what the speaker sees. In particular, BIBREF14 argued that if listeners were reliably using theory of mind, they would only consider mutually visible objects as possible referents. For instance, on one trial a roll of Scotch tape was mutually visible and a cassette tape was hidden from the speaker's view. When the confederate speaker produced an ambiguous utterance, \u201ctape,\u201d participants should still interpret it as a reference to the mutually visible object even if it fits the hidden object better; the idea is that a speaker who cannot see an object wouldn't possibly be referring to it.",
+      "While the visual asymmetries constructed by BIBREF14 may provide the starkest test of this hypothesis, variations on this basic paradigm have manipulated other dimensions of non-visual knowledge asymmetry, including those based on spoken information BIBREF15 , BIBREF16 , spatial cues BIBREF17 , BIBREF18 , private pre-training on object labels BIBREF19 , cultural background BIBREF20 , and other task-relevant information BIBREF21 , BIBREF22 . Questions about speaker perspective-taking during production have similarly been explored by reversing the direction of the asymmetry so the speaker has private knowledge that the listener does not and examining whether this private information leaks into their utterances BIBREF23 , BIBREF24 , BIBREF25 , BIBREF26 , BIBREF27 , BIBREF28 , BIBREF29 . Numerous rounds of reinterpretation and methodological criticism have puzzled over seemingly contradictory findings in this sprawling body of work: some studies find strong evidence consistent with an egocentric view\u2014listeners initially consider and even attempt to move such objects\u2014while others find that information from the speaker's perspective is integrated from the very earliest stages of processing BIBREF30 , BIBREF31 .",
+      "Recent computational models have begun to unify this literature under a probabilistic framework. For instance, some models assume that listeners BIBREF32 and speakers BIBREF33 simultaneously integrate their own perspective with that of their partner, leading to behavior that lies between purely egocentric and purely guided by common ground. These constraint-based models BIBREF34 , BIBREF35 introduce a probabilistic weighting parameter between the two domains of reference and show that an intermediate weighting explains the gradient of communicative behavior better than a purely egocentric or purely perspective-adopting model. Yet these constraint-based models leave open a key puzzle for rational models of language use: why do people use the proportion they do in a given context? In other words, while different factors influencing the weighting have been proposed, no formal mechanism yet explains why incorporating egocentric knowledge would be adaptive when full common ground is available.",
+      "We argue in this paper for a resource rational account of perspective-taking in communication BIBREF36 , BIBREF37 . In a communicative interaction with another agent, the participants share the goal of successfully being understood while minimizing joint effort BIBREF38 , BIBREF4 . If theory of mind use is indeed effortful and cognitively demanding to some degree BIBREF39 , BIBREF40 , BIBREF41 , then the question for a rational agent is when and how to best allocate its cognitive resources to achieve its goals. This sets up a natural division of labor between the speaker and listener in how the effort should be shared, which in principle admits many solutions. Rather than being guided by rigid heuristics, individuals may rationally and adaptively calibrate their perspective-taking based on expectations about their partner's likely behavior. Critically, these expectations may themselves be derived from a targeted use of theory of mind.",
+      "Here, we explore one particular source of expectations derived from Gricean expectations of informativity, which have been largely neglected by prior work in the perspective-taking literature BIBREF42 . Just as making sense of an agent's physical behaviors requires a broad, accurate mental model of how the agent's visual access, beliefs, and intentions translate into motor plans BIBREF43 , BIBREF44 , making sense of an agent's linguistic behaviors depends on an accurate model of what a speaker would say, or what a listener would understand, in different situations BIBREF45 , BIBREF46 , BIBREF47 , BIBREF48 , BIBREF49 . From this perspective, theory of mind use not only incorporates people\u2019s mental models of a partner\u2019s knowledge or visual access but also their inferences about how their partner would behave in a communicative context. To instantiate this account, we elaborate the family of probabilistic weighting models by proposing that theory of mind use under knowledge asymmetries not only involves integrating a partner's knowledge but also recursive reasoning about how they will likely produce or interpret utterances in particular communicative contexts BIBREF50 .",
+      "The Gricean notion of cooperativity BIBREF3 , BIBREF4 refers to the idea that speakers try to avoid saying things that are confusing or unnecessarily complicated given the current context, and that listeners expect this. For instance, imagine trying to help someone spot your dog at a busy dog park. It may be literally correct to call it a \u201cdog,\u201d but as a cooperative speaker you would understand that the listener would have trouble disambiguating the referent from many other dogs. Likewise, the listener would reasonably expect you to say something more informative than \u201cdog\u201d in this context. You may therefore prefer to use a more specific or informative expressions, like \u201cthe little terrier with the blue collar.\u201d BIBREF7 , BIBREF51 . Critically, you might do so even when you happen to see only one dog at the moment, but know there are likely to be other dogs from the listener's point of view. In the presence of uncertainty about their partner's visual context, a cooperative speaker may tend toward additional specificity.",
+      "Now, what level of specificity is pragmatically appropriate in the particular director-matcher task used by BIBREF52 ? This task requires the speaker to generate a description such that a listener can identify the correct object among distractors, even though several cells are hidden from the speaker's view (e.g. Fig. 2 , bottom). It is thus highly salient to the speaker that there are hidden objects she cannot see but her partner can. Gricean reasoning, as realized by recent formal models BIBREF46 , BIBREF47 , BIBREF49 , predicts that a speaker in this context will compensate for her uncertainty about the listener's visual context by increasing the informativity of her utterance beyond what she would produce in a completely shared context. (See Appendix A for a formal model of pragmatic reasoning in this situation and a mathematical derivation of the informativity prediction.). The director-matcher task used by BIBREF52 is therefore not only challenging for the listener; it also requires a sophisticated use of theory of mind, vis a vis pragmatic reasoning, on the part of the speaker, to understand that the listener may expect her to increase the informativity of her utterance. While extensive prior work has examined how speakers adjust their utterances, or not, depending on their own private information, it remains untested how they pragmatically compensate for their lack of access to the listener's private information by flexibly modifying their informativity.",
+      "In the following experiments, we ask whether people, as speakers, show such sensitivity to their own uncertainty about their partner's visual access. Furthermore, we suggest that such sensitivity (and the listener's expectations about this sensitivity) can help us understand why listeners in prior work (e.g., in the Director-Matcher task) made frequent errors. A listener's rational reliance on the speaker's informativity, which allows them to efficiently neglect the speaker's visual access under cognitive load, may backfire and lead to errors when paired with a confederate speaker who violates Gricean expectations. First, we directly test our model's prediction by manipulating the presence and absence of occlusions in a simple, interactive, natural-language reference game. Second, we conduct a replication of BIBREF52 with an additional unscripted condition to evaluate whether the scripted referring expressions used by confederate speakers in prior work accord with what a real speaker would say in the same interactive context BIBREF54 , BIBREF55 , BIBREF56 . If confederate speakers were using scripts that were uncooperative and underinformative compared to what speakers naturally say, this previously unrecognized violation of Gricean expectations may have implications for the rational basis of listener errors. Our main goal here is to directly establish the adaptive pragmatic behavior of speakers. It is important to note that our broader claim about the source of listener errors emerges from establishing the plausibility of a resource-rational basis for perspective-neglect, showing that speakers are adaptive (Exp.1) and listeners indeed make more errors when speakers violate their expectations (Exp.2); causally manipulating listener expectations is beyond the scope of the current work. We return to the broader implications and predictions of this account in the discussion."
+    ],
+    [
+      "How does an unscripted speaker change her communicative behavior when there is uncertainty about exactly what her partner can see? To address this question empirically, we randomly assigned participants to the roles of speaker and listener and paired them over the web to play an interactive communication task BIBREF57 ."
+    ],
+    [
+      "We recruited 102 pairs of participants from Amazon Mechanical Turk and randomly assigned speaker and listener roles. After we removed 7 games that disconnected part-way through and 12 additional games according to our pre-registered exclusion criteria (due to being non-native English speakers, reporting confusion about the instructions, or clearly violating the instructions), we were left with a sample of 83 full games.",
+      "On each trial, both players were presented with a $3\\times 3$ grid containing objects. One target object was privately highlighted for the speaker, who freely typed a message into a chat box in order to get the listener to click the intended referent. The objects varied along three discrete features (shape, texture, and color), each of which took four discrete values (64 possible objects). See Appendix Fig. 7 for a screenshot of the interface.",
+      "There were four types of trials, forming a within-pair $2 \\times 2$ factorial design. We manipulated the presence or absence of occlusions and the closeness of shared distractors to the target (see Fig. 2 ). On `shared' trials, all objects were seen by both participants, but on `hidden' trials, two cells of the grid were covered with occluders (curtains) such that only the listener could see the contents of the cell. On `far' trials, the target is the only object with a particular shape; on `close' trials, there is also a shared distractor with the target's shape, differing only in color or texture.",
+      "In order to make it clear to the speaker that there could really be objects behind the occluders without providing a statistical cue to their identity or quantity on any particular trial, we randomized the total number of distractors in the grid on each trial (between 2 and 4) as well as the number of those distractors covered by curtains (1 or 2). If there were only two distractors, we did not allow both of them to be covered: there was always at least one visible distractor. Each trial type appeared 6 times for a total of 24 trials, and the sequence of trials was pseudo-randomized such that no trial type appeared more than twice in each block of eight trials. Participants were instructed to use visual properties of the objects rather than spatial locations in the grid.",
+      "Finally, we collected mouse-tracking data analogous to the eye-tracking common in referential paradigms. We asked the matcher to wait until the director sent a message; when the message was received, the matcher clicked a small circle in the center of the grid to show the objects and proceed with the trial. We recorded at 100Hz from the matcher's mouse in the decision window after this click, until the point where they clicked and started to drag one of the objects. While we did not intend to analyze these data for Exp. 1, we anticipated using it in our second experiment below and wanted to use the same procedure across experiments for consistency.",
+      "We recruited 200 pairs of participants from Amazon Mechanical Turk. 58 pairs were unable to complete the game due to a server outage. Following our preregistered exclusion criteria, we removed 24 games who reported confusion, violated our instructions, or made multiple errors on filler items, as well as 2 additional games containing non-native English speakers. This left 116 pairs in our final sample.",
+      "The materials and procedure were chosen to be as faithful as possible to those reported in BIBREF52 while allowing for interaction over the web. Directors used a chat box to communicate where to move a privately cued target object in a $4 \\times 4$ grid (see Fig. 1 ). The listener then attempted to click and drag the intended object. In each of 8 objects sets, mostly containing filler objects, one target belonged to a `critical pair' of objects, such as a visible cassette tape and a hidden roll of tape that could both plausibly be called `the tape.'",
+      "We displayed instructions to the director as a series of arrows pointing from some object to a neighboring unoccupied cell. Trials were blocked into eight sets of objects, with four instructions each. As in BIBREF52 , we collected baseline performance by replacing the hidden alternative (e.g. a roll of tape) with a filler object that did not fit the critical instruction (e.g. a battery) in half of the critical pairs. The assignment of items to conditions was randomized across participants, and the order of conditions was randomized under the constraint that the same condition would not be used on more than two consecutive items. All object sets, object placements, and corresponding instruction sets were fixed across participants. In case of a listener error, the object was placed back in its original position; both participants were given feedback and asked to try again.",
+      "We used a between-subject design to compare the scripted labels used by confederate directors in prior work against what participants naturally say in the same role. For participants assigned to the director role in the `scripted' condition, a pre-scripted message using the precise wording from BIBREF52 automatically appeared in their chat box on half of trials (the 8 critical trials as well as nearly half of the fillers). Hence, the scripted condition served as a direct replication. To maintain an interactive environment, the director could freely produce referring expressions on the remainder of filler trials. In the `unscripted' condition, directors were unrestricted and free to send whatever messages they deemed appropriate on all trials. In addition to analyzing messages sent through the chat box and errors made by matchers (listeners), we collected mouse-tracking data in analogy to the eye-tracking common in these paradigms."
+    ],
+    [
+      "Our primary measure of speaker behavior is the length (in words) of naturally produced referring expressions sent through the chat box. We tested differences in speaker behavior across conditions using a mixed-effect regression of context and occlusion on the number of words produced, with maximal random effect structure containing intercept, slopes, and interaction. First, as a baseline, we examined the simple effect of close vs. far contexts in trials with no occlusions. We found that speakers used significantly more words on average when there was a distractor in context that shared the same shape as the target ( $b = 0.56, t = 5.1, p < 0.001$ ; see Fig. 3 A). This replicates the findings of prior studies in experimental pragmatics BIBREF7 , BIBREF58 . Next, we turn to the simple effect of occlusion in far contexts (which are most similar to the displays used in the director-matcher task which we adopt in Exp. 2 BIBREF52 ). Speakers used 1.25 additional words on average when they knew their partner could potentially see additional objects ( $t = 7.5, p < 0.001$ ). Finally, we found a significant interaction ( $b = -0.49, t = 3.8, p <0.001$ ) where the effect of occlusion was larger in far contexts, likely indicating a ceiling on the level of informativity required to individuate objects in our simple stimulus space.",
+      "What are these additional words used for? As a secondary analysis, we annotated each utterance based on which of the three object features were mentioned (shape, texture, color). Because speakers nearly always mentioned shape (e.g. `star', `triangle') as the head noun of their referring expression regardless of context ( $\\sim 99\\%$ of trials), differences in utterance length across conditions must be due to differentially mentioning the other two features (color and texture). To test this observation, we ran separate mixed-effect logistic regressions for color and texture predicting mention from context; due to convergence issues, the maximum random effect structure supported by our data contains only speaker-level intercepts and slopes for the occlusion effect. We found simple effects of occlusion in far contexts for both features ( $b = 1.33, z = 2.9, p = 0.004$ for color; $b = 4.8, z = 6.4, p < 0.001$ for texture, see Fig. 3 B). In other words, in displays like the left column of Fig. 2 where the target was the only `star', speakers were somewhat more likely to produce the star's color\u2014and much more likely to produce its texture\u2014when there were occlusions present, even though shape alone is sufficient to disambiguate the target from visible distractors in both cases. Finally, we note that listener errors were rare: 88% of listeners made only one or fewer errors (out of 24 trials), and there was no significant difference in error rates across the four conditions ( $\\chi ^2(3) = 1.23, p = 0.74$ ). We test the connections between context-sensitive speaker behavior and listener error rates more explicitly in Exp. 2."
+    ],
+    [
+      "While our behavioral results provide qualitative support for a Gricean account over an egocentric account, formalizing these two accounts in computational models allows a stronger test of our hypothesis by generating graded quantitative predictions. We formalized both accounts in the probabilistic Rational Speech Act (RSA) framework BIBREF47 , BIBREF46 , BIBREF49 , BIBREF59 , BIBREF48 , which has successfully captured a variety of other pragmatic phenomena. In this framework, speakers are decision-theoretic agents attempting to (soft-)maximize a utility function balancing parsimony (i.e., a preference for shorter, simpler utterances) with informativeness (i.e., the likelihood of an imagined listener agent having the intended interpretation). The only difference between the two accounts in the RSA framework is how the asymmetry in visual access is handled: the `occlusion-blind' speaker simply assumes that the listener sees the same objects as she herself sees, while the `occlusion-sensitive' speaker represents uncertainty over her partner's visual context. In particular, she assumes a probability distribution over the possible objects that might be hidden behind the occlusions and attempts to be informative on average. The two models have the same four free parameters: a speaker optimality parameter controlling the soft-max temperature, and three parameters controlling the costs of producing the features of shape, color, and texture (see Appendix B for details).",
+      "We conducted a Bayesian data analysis to infer these parameters conditioning on our empirical data, and computed a Bayes Factor to compare the models. We found extremely strong support for the occlusion-sensitive model relative to the occlusion-blind model ( $BF = 2.2 \\times 10^{209}$ ; see Appendix Fig. 8 for likelihoods). To examine the pattern of behavior of each model, we computed the posterior predictive on the expected number of features mentioned in each trial type of our design. While the occlusion-blind speaker model successfully captured the simple effect of close vs. far contexts, it failed to account for behavior in the presence of occlusions. The occlusion-sensitive model, on the other hand, accurately accounted for the full pattern of results (see Fig 4 ). Finally, we examined parameter posteriors for the occlusion-sensitive model (see Appendix Fig. 9 ): the inferred production cost for texture was significantly higher than that for the other features, reflecting the asymmetry in production of texture relative to color."
+    ],
+    [
+      "Experiment 1 directly tested the hypothesis that speakers increase their specificity in contexts with asymmetry in visual access. We found that speakers are not only context-sensitive in choosing referring expressions that distinguish target from distractors in a shared context, but are occlusion-sensitive, adaptively compensating for uncertainty. Critically, this resulted in systematic differences in behavior across the occlusion conditions that are difficult to explain under an egocentric theory: in the presence of occlusions, speakers were spontaneously willing to spend additional time and keystrokes to give further information beyond what they produce in the corresponding unoccluded contexts, even though that information is equally redundant given the visible objects in their display.",
+      "These results validate our prediction that speakers appropriately increase their level of specificity in contexts containing occlusions. In Experiment 2, we recruited pairs of participants for an online, interactive version of the original director-matcher task BIBREF52 which used occluded contexts to demonstrate limits on visual perspective-taking for the listener. Given the results of Exp. 1, we predicted that participants in the director role (i.e. speakers) would naturally provide more informative referring expressions than the confederate directors used in prior work. This would suggest that the confederate directors in prior work were pragmatically infelicitous, violating listeners' expectations. This violation of listeners' cooperative expectations may have led to detrimental consequences for listener performance."
+    ],
+    [
+      "Our scripted condition successfully replicated the results of BIBREF52 with even stronger effects: listeners incorrectly moved the hidden object on approximately 50% of critical trials. However, on unscripted trials, the listener error rate dropped by more than half, $p_1 = 0.51, p_2 = 0.20, \\chi ^2(1) = 43, p < 0.001$ (Fig. 5 A). While we found substantial heterogeneity in error rates across object sets (just 3 of the 8 object sets accounted for the vast majority of remaining unscripted errors; see Appendix Fig. 10 ), listeners in the unscripted condition made fewer errors for nearly every critical item. In a maximal logistic model with fixed effect of condition, random intercepts for each dyad, and random slopes and intercepts for each object set, we found a significant difference in error rates across conditions ( $z = 2.6, p = 0.008$ ).",
+      "Even if participants in the unscripted condition make fewer actual errors, they may still be considering the hidden object just as often on trials where they go on to make correct responses. As a proxy for the eye-tracking analyses reported by BIBREF52 , we conducted a mouse-tracking analysis. We computed the mean (logged) amount of time spent hovering over the hidden distractor and found a significant interaction between condition and the contents of the hidden cell ( $t = 3.59, p <0.001$ ; Fig. 5 B) in a mixed-effects regression using dyad-level and object-level random intercepts and slopes for the difference from baseline. Listeners in the scripted condition spent more time hovering over the hidden cell when it contained a confusable distractor relative to baseline, again replicating BIBREF52 . In the unscripted condition there was no difference from baseline.",
+      "Next, we test whether these improvements in listener performance in the unscripted condition are accompanied by more informative speaker behavior than the scripted utterances allowed. The simplest measure of speaker informativity is the raw number of words used in referring expressions. Compared to the scripted referring expressions, speakers in the unscripted condition used significantly more words to refer to critical objects ( $b = 0.54, t = 2.6, p=0.019$ in a mixed-effects regression on difference scores using a fixed intercept and random intercepts for object and dyads). However, this is a coarse measure: for example, the shorter \u201cPyrex glass\u201d may be more specific than \u201clarge measuring glass\u201d despite using fewer words. For a more direct measure, we extracted the referring expressions generated by speakers in all critical trials and standardized spelling and grammar, yielding 122 unique labels after including scripted utterances.",
+      "We then recruited an independent sample of 20 judges on Amazon Mechanical Turk to rate how well each label fit the target and hidden distractor objects on a slider from \u201cstrongly disagree\u201d (meaning the label \u201cdoesn't match the object at all\u201d) to \u201cstrongly agree\u201d (meaning the label \u201cmatches the object perfectly\u201d). They were shown objects in the context of the full grid (with no occlusions) such that they could feasibly judge spatial or relative references like \u201cbottom block.\u201d We excluded 4 judges for guessing with response times $< 1s$ . Inter-rater reliability was relatively high, with intra-class correlation coefficient of $0.54\\, (95\\% CI = [0.47, 0.61])$ . We computed the informativity of an utterance (the tape) as the difference in how well it was judged to apply to the target (the cassette tape) relative to the distractor object (the roll of tape).",
+      "Our primary measure of interest is the difference in informativity across scripted and unscripted utterances. We found that speakers in the unscripted condition systematically produced more informative utterances than the scripted utterances ( $d = 0.5$ , 95% bootstrapped CI = $[0.27, 0.77], p < .001$ ; see Appendix C for details). Scripted labels fit the hidden distractor just as well or better than the target, but unscripted labels fit the target better and the hidden distractor much worse (see Fig. 6 A). In other words, the scripted labels used in BIBREF52 were less informative than expressions speakers would normally produce to refer to the same object in this context.",
+      "These results strongly suggest that the speaker's informativity influences listener accuracy. In support of this hypothesis, we found a strong negative correlation between informativity and error rates across items and conditions: listeners make fewer errors when utterances are a better fit for the target relative to the distractor ( $\\rho = -0.81$ , bootstrapped 95% CI $= [-0.9, -0.7]$ ; Fig. 6 B). This result suggests that listener behavior is driven by an expectation of speaker informativity: listeners interpret utterances proportionally to how well they fit objects in context."
+    ],
+    [
+      "Are human adults expert mind-readers, or fundamentally egocentric? The longstanding debate over the role of theory of mind in communication has largely centered around whether listeners (or speakers) with private information consider their partner's perspective BIBREF30 , BIBREF16 . Our work presents a more nuanced picture of how a speaker and a listener use theory of mind to modulate their pragmatic expectations. The Gricean cooperative principle emphasizes a natural division of labor in how the joint effort of being cooperative is shared BIBREF4 , BIBREF60 . It can be asymmetric when one partner is expected to, and able to, take on more complex reasoning than the other, in the form of visual perspective-taking, pragmatic inference, or avoiding further exchanges of clarification and repair. One such case is when the speaker has uncertainty over what the listener can see, as in the director-matcher task. Our Rational Speech Act (RSA) formalization of cooperative reasoning in this context predicts that speakers (directors) naturally increase the informativity of their referring expressions to hedge against the increased risk of misunderstanding; Exp. 1 presents direct evidence in support of this hypothesis.",
+      "Importantly, when the director (speaker) is expected to be appropriately informative, communication can be successful even when the matcher (listener) does not reciprocate the effort. If visual perspective-taking is effortful and cognitively demanding BIBREF39 , the matcher will actually minimize joint effort by not taking the director's visual perspective. This suggests a less egocentric explanation of when and why listeners neglect the speaker's visual perspective; they do so when they expect the speaker to disambiguate referents sufficiently. While adaptive in most natural communicative contexts, such neglect might backfire and lead to errors when the speaker (inexplicably) violates this expectation. From this point of view, the \u201cfailure\u201d of listener theory of mind in these tasks is not really a failure; instead, it suggests that both speakers and listeners may use theory of mind to know when (and how much) they should expect others to be cooperative and informative, and subsequently allocate their resources accordingly BIBREF36 . Exp. 2 is consistent with this hypothesis; when directors used underinformative scripted instructions (taken from prior work), listeners made significantly more errors than when speakers were allowed to provide referring expressions at their natural level of informativity, and speaker informativeness strongly modulated listener error rates.",
+      "Our work adds to the growing literature on the debate over the role of pragmatics in the director-matcher task. A recent study questions the communicative nature of the task itself by showing that selective attention alone is sufficient for successful performance on this task, and that listeners become suspicious of the director's visual access when the director shows unexpectedly high levels of specificity in their referring expressions BIBREF61 . Our results further sbolster the argument that pragmatic reasoning about appropriate levels of informativity is an integral aspect of theory of mind use in the director-matcher task (and communication more generally). Note however that in BIBREF61 , participants became suspicious, while in our study participants overtrusted the speaker to be informative; a more detailed look at differences between experimental paradigms, as well as further experimental work, is necessary to better understand why participants had different expectations about the speaker. Prior work also suggests that although speakers tend to be over-informative in their referring expressions BIBREF62 a number of situational factors (e.g., perceptual saliency of referents) can modulate this tendency. Our work hints at an additional principle that guides speaker informativity: speakers maintain uncertainty about the listener's visual context and their ability to disambiguate the referent in that context.",
+      "Additionally, while our model builds on probabilistic models weighting different perspectives BIBREF32 , BIBREF33 , we leave the formal integration of resource-rational recursive reasoning mechanisms with perspective-weighting mechanisms for future work. While BIBREF33 focused on cases where the speaker has private information unknown to the listener, our model focuses on the reverse case: how speakers behave when they know that the listener has additional private information BIBREF52 . Furthermore, whether the allocation of resources, and ensuing perspective neglect, is a fixed strategy or one that adjusts dynamically remains an open question: given sufficient evidence of an unusually underinformative partner, listeners may realize that vigilance about which objects are occluded yields a more effective strategy for the immediate interaction. An important direction for future work is to directly explore listener adaptability in adjusting their use of visual perspective-taking as a function of Gricean expectations for a given partner BIBREF63 , BIBREF64 .",
+      "In sum, our findings suggest that language use is well-adapted to contexts of uncertainty and knowledge asymmetry. The pragmatic use of theory of mind to establish division of labor is also critical for other forms of social cooperation, including pedagogy BIBREF65 and team-based problem solving BIBREF66 , BIBREF67 . Enriching our notion of theory of mind use to encompass these pragmatic expectations, not only expectations about what our partner knows or desires, may shed new light on the flexibility of social interaction more broadly."
+    ],
+    [
+      "This manuscript is based in part on work presented at the 38th Annual Conference of the Cognitive Science Society. The first author is supported by a NSF Graduate Research Fellowship and a Stanford Graduate Fellowship. A pilot of expt. 2 was originally conducted under the supervision of Michael Frank, with early input from Desmond Ong. We\u2019re grateful to Boaz Keysar for providing select materials for our replication. This work was supported by ONR grants N00014-13-1-0788 and N00014-13- 1-0287, and a James S. McDonnell Foundation Scholar Award to NDG."
+    ],
+    [
+      "R.X.D.H. and N.D.G. initially formulated project. R.X.D.H. performed experiments, analyzed data, and performed computational modeling. All authors planned experiments, interpreted result, and wrote the paper. Unless otherwise mentioned, all analyses and materials were preregistered at https://osf.io/qwkmp/. Code and materials for reproducing the experiment as well as all data and analysis scripts are open and available at https://github.com/hawkrobe/pragmatics_of_perspective_taking."
+    ],
+    [
+      "Our experiments are motivated by the Gricean observation that speakers should attempt to be more informative when there is an asymmetry in visual access, such that their partner sees something they do not. In this appendix, we formalize this scenario in a computational model of communication as recursive social reasoning and prove that the predicted increase in informativity qualitatively holds under fairly unrestrictive conditions.",
+      "Following recent advances in the Rational Speech Act (RSA) framework, we define a speaker as a decision-theoretic agent who must choose a referring expression $u$ to refer to a target object $o$ in a context $C$ by (soft)-maximizing a utility function $U$ : $S(u | o, C) \\propto \\exp \\lbrace \\alpha U(u; o, C)\\rbrace $ ",
+      "Definition The basic utility used in RSA models captures the informativeness of each utterance to an imagined literal listener agent $L$ who is attempting to select the target object from alternatives in context: $U_{basic}(u; o, C) = \\log L(o | u, C)$ ",
+      "This information-theoretic expression measures how certain the listener becomes about the intended object after hearing the utterance. The literal listener is assumed to update their beliefs about the target object according to Bayesian inference, conditioning on the literal meaning of the utterance being true of it: $L(o | u, C) \\propto \\mathcal {L}(o,u) P(o)$ ",
+      "where normalization takes place over objects $o \\in C$ and $\\mathcal {L}$ represents the lexical semantics of $u$ . If $u$ is true of $o$ then $\\mathcal {L}(o,u) = 1$ ; otherwise, $\\mathcal {L}(o,u) = 0$ .",
+      "This basic setup assumes that the speaker reasons about a listener sharing the same context $C$ in common ground. How should it be extended to handle asymmetries in visual access between the speaker and listener, where the speaker has uncertainty over the possible distractors behind the occlusions? In the RSA framework, speaker uncertainty is represented straightforwardly by a prior over the state of the world: for example, BIBREF48 examined a case where the speaker has limited perceptual access to the objects they are describing. For the director-matcher task, we construct this prior by positing a space of alternative objects $\\mathcal {O}$ , introducing uncertainty $P(o_h)$ over which object $o_h \\in \\mathcal {O}$ , if any, is hidden behind an occlusion, and marginalizing over these alternatives when reasoning about the listener.",
+      "Definition This gives us a utility for conditions of asymmetries in visual access: $U_{asym}(u; o, C) =\\sum _{o_h \\in \\mathcal {O}} P(o_h) \\log L(o | u, C \\cup o_h)$ ",
+      "where $C$ denotes the set of objects in context that the speaker perceives.",
+      "We define \u201cspecificity\u201d extensionally, in the sense that if $u_0$ is more specific than $u_1$ , then the objects for which $u_0$ is true is a subset of the objects for which $u_1$ is true:",
+      "Definition Utterance $u_0$ is said to be more specific than $u_1$ iff $\\mathcal {L}(u_0, o_h) \\le \\mathcal {L}(u_1, o_h)\\ \\forall o_h \\in \\mathcal {O}$ and there exists a subset of objects $\\mathcal {O}^* \\subset \\mathcal {O}$ such that $\\sum _{o^* \\in \\mathcal {O}^*} P(o^*) > 0$ and $\\mathcal {L}(u_0, o^*) < \\mathcal {L}(u_1, o^*)$ for $o* \\in \\mathcal {O}^*$ .",
+      "We now show that the recursive reasoning model predicts that speakers should prefer more informative utterances in contexts with occlusions. In other words, that the asymmetry utility leads to a preference for more specific referring expressions than the basic utility.",
+      "Theorem If $u_0$ is more specific than $u_1$ , then the following holds for any target $o^t$ and shared context $C$ : $\n\\frac{S_{asym}(u_0 | o^t, C)}{S_{asym}(u_1| o^t, C)}\n>\n\\frac{S_{basic}(u_0 | o^t, C)}{S_{basic}(u_1 | o^t, C)}\n$ ",
+      "Since $S(u_0|o^t, C)/S(u_1|o^t, C) = \\exp (\\alpha \\cdot (U(u_0; o^t, C) - U(u_1;o^t,C)))$ it is sufficient to show $\nU_{asym}(u_0 ; o, C) - U_{asym}(u_1; o, C)\n>\nU_{basic}(u_0 ; o, C) - U_{basic}(u_1 ; o, C)\n$ ",
+      "We first break apart the sum on the left-hand side: ",
+      "$$U_{asym}(u_0 | o^t, C) - U_{asym}(u_1 | o^t, C)\n&=& \\displaystyle \\sum _{o_h \\in \\mathcal {O}} p(o_h)\\left[\\log L(o | u_0, C\\cup o_h) - \\log L(o|u_1, C \\cup o_h)\\right] \\\\\n& = & \\displaystyle \\sum _{o^*\\in \\mathcal {O}^*} p(o^*) \\log \\frac{L(o^t|u_0, C\\cup o^*)}{L(o^t|u_1, C\\cup o^*)} \\\\\n& & + \\displaystyle \\sum _{o_h\\in \\mathcal {O}\\setminus \\mathcal {O}^*} p(o_h) \\log \\frac{L(o^t|u_0, C\\cup o_h)}{L(o^t|u_1, C\\cup o_h)} $$   (Eq. 9) ",
+      "By the definition of \u201cmore specific\u201d and because we defined $o^*\\in \\mathcal {O^*}$ to be precisely the subset of objects for which $\\mathcal {L}(u_0, o^*) < \\mathcal {L}(u_1, o^*)$ , for objects $o_h$ in the complementary set $\\mathcal {O} \\setminus \\mathcal {O^*}$ we have $\\mathcal {L}(u_0, o_h) = \\mathcal {L}(u_1, o_h)$ . Therefore, for , $L(o^t | u_i, C \\cup o_h) = L(o^t | u_i, C)$ , giving us $\\log \\frac{L(o^t | u_0, C)}{L(o^t|u_1, C)}\\sum _{o_h\\in \\mathcal {O}\\setminus \\mathcal {O}^*}p(o_h)$ ",
+      "For the ratio in 9 , we can substitute the definition of the listener $L$ and simplify: $\n\\begin{array}{rcl}\n\\displaystyle \\frac{L(o^t|u_0, C\\cup o^*)}{L(o^t|u_1, C\\cup o^*)}\n& = & \\displaystyle \\frac{\\mathcal {L}(o^t, u_0) [\\sum _{o\\in C \\cup o^*}\\mathcal {L}(o,u_1)]}{\\mathcal {L}(o^t, u_1) [\\sum _{o\\in C \\cup o^*}\\mathcal {L}(o,u_0)]} \\\\[.5cm]\n& = & \\displaystyle \\frac{\\mathcal {L}(o^t, u_0) [\\sum _{o\\in C}\\mathcal {L}(o,u_1) + \\mathcal {L}(o^*, u_1)]}{\\mathcal {L}(o^t, u_1) [\\sum _{o\\in C}\\mathcal {L}(o,u_0) + \\mathcal {L}(o^*, u_0)]} \\\\[.5cm]\n& < & \\displaystyle \\frac{\\mathcal {L}(o^t, u_0) [\\sum _{o\\in C}\\mathcal {L}(o,u_1)]}{\\mathcal {L}(o^t, u_1) [\\sum _{o\\in C}\\mathcal {L}(o,u_0)]} \\\\[.5cm]\n& = & \\displaystyle \\frac{L(o^t|u_0, C)}{L(o^t|u_1, C)}\n\\end{array}\n$ ",
+      "Thus, $\n\\begin{array}{rcl}\nU_{asym}(u_0 | o^t, C) - U_{asym}(u_1 | o^t, C) & < & \\log \\frac{L(o^t | u_0, C)}{L(o^t|u_1, C)}\\left(\\displaystyle \\sum _{o^*\\in \\mathcal {O}^*}p(o^*) + \\displaystyle \\sum _{o_h\\in \\mathcal {O}\\setminus \\mathcal {O}^*}p(o_h)\\right) \\\\\n&=& \\log L(o^t | u_0, C) - \\log L(o^t | u_1, C) \\\\\n&=& U_{basic}(u_0 | o^t, C) - U_{basic}(u_1 | o^t, C)\n\\end{array}\n$ ",
+      "Note that this proof also holds when an utterance-level cost term $\\textrm {cost}(u)$ penalizing longer or more effortful utterances is incorporated into the utilities $\n\\begin{array}{lcl}\nU_{asym}(u; o, C_s) & = & \\sum _{o_h \\in \\mathcal {O}} \\log L_0(o | u, C_s \\cup o_h)P(o_h) - \\textrm {cost}(u) \\\\\nU_{basic}(u; o, C) & = & \\log L(o | u, C) - \\textrm {cost}(u)\n\\end{array}\n$ ",
+      "since the same constant appears on both sides of inequality. In principle, it can also be extended to real-valued meanings $\\mathcal {L}$ , though additional assumptions must be made."
+    ],
+    [
+      "In addition to the qualitative predictions derived in the previous section, our speaker model makes direct quantitative predictions about Exp. 1 data. Here, we describe the details of a Bayesian Data Analysis evaluating this model on the empirical data, and comparing it to an occlusion-blind model which does not reason about possible hidden objects.",
+      "Because there were no differences observed in production based on the particular levels of target features (e.g. whether the target was blue or red), we collapse across these details and only feed the model which features of each distractor differed from the target on each trial. After this simplification, there were only 4 possible contexts: far contexts, where the distractors differed in every dimension, and three varieties of close contexts, where the critical distractor differed in only shape, shape and color, or shape and texture. In addition, we included in the model information about whether each trial had cells occluded or not.",
+      "The space of utterances used in our speaker model is derived from our feature annotations: for each trial, the speaker model selected among 7 utterances referring to each combination of features: only mentioning the target's shape, only mentioning the target's color, mentioning the shape and the color, and so on. For the set of alternative objects $\\mathcal {O}$ , we used the full 64-object stimulus space used in our experiment design, and we placed a uniform prior over these objects such that the occlusion-sensitive speaker assumed they were equally likely to be hidden.",
+      "Our model has four free parameters which we infer from the data using Bayesian inference. The speaker optimality parameter, $\\alpha $ , is a soft-max temperature such that at $\\alpha = 1$ , the speaker produces utterances directly proportional to their utility, and as $\\alpha \\rightarrow \\infty $ the speaker maximizes. In addition, to account for the differential production of the three features (see Fig. 2B), we assume separate production costs for each feature: a texture cost $c_t$ , a color cost $c_c$ , and a shape cost $c_s$ . We use (uninformative) uniform priors for all parameters: $\n\\begin{array}{rcl}\n\\alpha & \\sim & \\textrm {Unif}(0,50) \\\\\nc_t, c_c, c_s & \\sim & \\textrm {Unif}(0,10)\n\\end{array}\n$ ",
+      "We compute speaker predictions for a particular parameter setting using (nested) enumeration and infer the posterior over parameters using MCMC. We discard 5000 burn-in samples and then take 5000 samples from the posterior with a lag of 2. Our posterior predictives are computed from these posteriors by taking the expected number of features produced by the speaker marginalizing over parameters and possible non-critical distractors in context (this captures the statistics of our experimental contexts, where there was always a distractor sharing the same color or texture but a different shape as the target). Finally, to precisely compute the Bayes Factor, we enumerated over a discrete grid of parameter values in the prior. We implemented our models and conducted inference in the probabilistic programming language WebPPL (Goodman & Stuhlmuller, 2014). All code necessary to reproduce our model results are available at the project github: https://github.com/hawkrobe/pragmatics_of_perspective_taking."
+    ],
+    [
+      "The statistical dependency structure of our ratings was more complex than standard mixed-effect model packages are designed to handle and the summary statistic we needed for our test was a simple difference score across conditions, so we instead implemented a simple multi-stage, non-parametric bootstrap scheme to appropriately account for different sources of variance. In particular, we needed to control for effects of judge, item, and speaker.",
+      "First, to control for the repeated measurements of each judge rating the informativity of all labels, we resampled our set of sixteen judge ids with replacement. For each label, we then computed informativity as the difference between the target and distractor fits within every judge's ratings, and took the mean across our bootstrapped sample of judges. Next, we controlled for item effects by resampling our eight item ids with replacement. Finally, we resampled speakers from pairs within each condition (scripted vs. unscripted), and looked up the mean informativity of each utterance they produced for each of the resampled set of items. Now, we can take the mean within each condition and compute the difference across conditions, which is our desired test statistic. We repeated this multi-stage resampling procedure 1000 times to get the bootstrapped distribution of our test statistic that we reported in the main text. Individual errors bars in Fig. 4 are derived from the same procedure but without taking difference scores."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: Domain Adaptation via Teacher-Student Learning for End-to-End Speech Recognition
+
+Question: How long is new model trained on 3400 hours of data?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Attention-Based Encoder-Decoder (AED) Model",
+    "T/S Learning for Unsupervised Domain Adaptation of AED",
+    "Adaptive T/S (AT/S) Learning for Supervised Domain Adaptation of AED",
+    "Experiments",
+    "Experiments ::: Data Preparation",
+    "Experiments ::: AED Baseline System",
+    "Experiments ::: Unsupervised Domain Adaptation with T/S Learning",
+    "Experiments ::: Supervised Domain Adaptation with AT/S Learning",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Recently, with the advancement of deep learning, great progress has been made in end-to-end (E2E) automatic speech recognition (ASR). With the goal of directly mapping a sequence of speech frames to a sequence of output tokens, an E2E ASR system incorporates the acoustic model, language model and pronunciation model of a conventional ASR system into a single deep neural network (DNN). The most dominant approaches for E2E ASR include connectionist temporal classification (CTC) BIBREF0, BIBREF1, recurrent neural network transducer (RNNT) BIBREF2 and attention-based encoder-decoder (AED) models BIBREF3, BIBREF4, BIBREF5.",
+      "However, the performance of E2E ASR degrades significantly when an acoustic mismatch exists between training and test conditions. An intuitive solution is domain adaptation where a well-trained source-domain E2E model is adapted to the data in the target domain. Different from speaker adaption, domain adaptation allows for the usage of a large amount of adaptation data in both source and target domains.",
+      "There has been plenty of domain adaptation methods for hybrid systems that we can leverage for adapting E2E systems. One popular approach is the adversarial learning in which an intermediate deep feature BIBREF6, BIBREF7, BIBREF8 or a front-end speech feature BIBREF9, BIBREF10 is learned to be invariant to the shifts between source and target domains. Adversarial domain adaptation is suitable for the situation where no transcription or parallel adaptation data in both domains are available. It can also effectively suppress the environment BIBREF11, BIBREF12, BIBREF13 and speaker BIBREF14, BIBREF15 variability during domain adaptation. However, in speech area, a parallel sequence of target-domain data can be easily simulated from the source-domain data such that the speech from both domains are frame-by-frame synchronized. To take advantage of this, teacher-student (T/S) learning BIBREF16 was proposed for the unsupervised domain adaptation of acoustic models in DNN-hidden Markov model (HMM) hybrid systems BIBREF17. In T/S learning, the Kullback-Leibler (KL) divergence between the output senone distributions of teacher and student acoustic models given parallel source and target domain data at the input is minimized by updating only the student model parameters. T/S training was shown to outperform the cross entropy training directly using the hard label in the target domain BIBREF17, BIBREF18, BIBREF19, BIBREF20, BIBREF21.",
+      "One drawback of unsupervised T/S learning is that, the teacher model is not perfect and will sometimes make inaccurate predictions that mislead the student model toward suboptimal directions. To overcome this, one-hot ground-truth labels are used to compensate for teacher's imperfections. Hinton et al. proposed interpolated T/S (IT/S) learning BIBREF22 to interpolate the teacher's soft class posteriors with one-hot ground truth using a pair of globally fixed weights. However, the optimal weights are data-dependent and can only be determined through careful tuning on a dev set. More recently, conditional T/S (CT/S) learning was proposed in BIBREF20 where the student model selectively chooses to learn from either the teacher or the ground truth depending on whether the teacher's prediction is correct or not. CT/S does not disturb the statistical relationships among classes naturally embedded in the class posteriors and achieves significant word error rate (WER) improvement over T/S for domain adaptation on CHiME-3 dataset BIBREF23.",
+      "In this work, we focus on the domain adaptation of AED models for E2E ASR by using T/S learning which was previously applied to learn small-footprint AED models in BIBREF24, BIBREF25, BIBREF26 by distilling knowledge from a large powerful teacher AED. For unsupervised domain adaptation, we extend T/S learning to AED models by introducing a two-level knowledge transfer: in addition to learning from the teacher's soft token posteriors, the student AED also conditions its decoder on the one-best token sequence decoded by the teacher AED.",
+      "We further propose an adaptive T/S (AT/S) learning method to improve T/S learning using ground-truth labels. By taking advantage of both IT/S and CT/S, AT/S adaptively assigns a pair of weights to the teacher's soft token posteriors and the one-hot ground-truth label at each decoder step depending on the confidence scores on each of the labels. The confidence scores are dynamically estimated as a function of soft and one-hot labels. The student AED learns from an adaptive linear combination of both labels. AT/S inherits the linear interpolation of soft and one-hot labels from IT/S and borrows from CT/S the judgement on the credibility of both knowledge sources before merging them. It is expected to achieve improved performance over the other T/S methods for domain adaptation. As a general deep learning method, AT/S can be widely applied to the domain adaptation or model compression of any DNN.",
+      "With 3400 hours close-talk and far-field Microsoft Cortana data for domain adaptation, T/S learning achieves up to 24.9% and 6.3% relative WER gains over close-talk and far-field baseline AEDs, respectively. AT/S improves the close-talk and far-field AEDs by 28.2% and 10.3%, respectively, consistently outperforming IT/S and CT/S."
+    ],
+    [
+      "In this work, we perform domain adaptation on AED models BIBREF3, BIBREF4, BIBREF5. AED model was first introduced in BIBREF27, BIBREF28 for neural machine translation. Without any conditional independence assumption as in CTC BIBREF0, AED was successfully applied to to E2E ASR in BIBREF3, BIBREF4, BIBREF5 and has recently achieved superior performance to conventional hybrid systems in BIBREF29.",
+      "AED directly models the conditional probability distribution $P(\\mathbf {Y} | \\mathbf {X})$ over sequences of output tokens $\\mathbf {Y}=\\lbrace y_1, \\ldots , y_L\\rbrace $ given a sequence of input speech frames $\\mathbf {X}=\\lbrace \\mathbf {x}_1, \\ldots , \\mathbf {x}_N\\rbrace $ as below:",
+      "To achieve this, the AED model incorporates an encoder, a decoder and an attention network. The encoder maps a sequence of input speech frames $\\mathbf {X}$ into a sequence of high-level features $\\mathbf {H} = \\lbrace \\mathbf {h}_1, \\ldots , \\mathbf {h}_N\\rbrace $ through an RNN. An attention network is used to determine which encoded features in $\\mathbf {H}$ should be attended to predict the output label $y_l$ and to generate a context vector $\\mathbf {z}_l$ as a linear combination of $\\mathbf {H}$ BIBREF3. A decoder is used to model $P(\\mathbf {Y}|\\mathbf {H})$ which is equivalent to $P(\\mathbf {Y}|\\mathbf {X})$. At each time step $t$, the decoder RNN takes the sum of the previous token embedding $\\mathbf {e}_{l-1}$ and the context vector $\\mathbf {z}_{l-1}$ as the input to predict the conditional probability of each token, i.e., $P(u | \\mathbf {Y}_{0:l-1}, \\mathbf {H}), u \\in \\mathbb {U}$, at the decoder step $l$, where $\\mathbb {U}$ is the set of all the output tokens:",
+      "In Eq. (DISPLAY_FORM2) and Eq. (), we sum together the $\\mathbf {z}_l$ and $\\mathbf {q}_l$ (or $\\mathbf {e}_t$) instead of concatenation, because, by summation, we get a lower-dimensional combined vector than concatenation, saving the number of parameters by half for the subsequent projection operation. In our experiments, concatenation does not improve the performance even with more parameters. where $\\mathbf {q}_l$ is the hidden state of the decoder RNN. bias $\\mathbf {b}_y$ and the matrix $K_y$ are learnable parameters.",
+      "An AED model is trained to minimize the following cross-entropy (CE) loss on the training corpus $\\mathbb {T_r}$.",
+      "where $\\mathbf {Y}^G = \\lbrace y^G_1, \\ldots , y^G_{L^G}\\rbrace $ is the sequence of grouth-truth tokens, $L^G$ represents the number of elements in $\\mathbf {Y}^G$ and $\\theta $ denotes all the model parameters in AED."
+    ],
+    [
+      "For unsupervised domain adaptation, we want to make use of a large amount of unlabeled data that is widely available. As shown in Fig. FIGREF4, with T/S learning, only two sequences of parallel data are required: an input sequence of source-domain speech frames to the teacher AED $\\mathbf {X}^T=\\lbrace \\mathbf {x}^T_{1}, \\ldots , \\mathbf {x}^T_{N}\\rbrace $ and an input sequence of target-domain speech frames to the student model $\\mathbf {X}^S=\\lbrace \\mathbf {x}^S_{1}, \\ldots , \\mathbf {x}^S_{N}\\rbrace $. $\\mathbf {X}^T$ and $\\mathbf {X}^S$ are parallel to each other, i.e, each pair of $\\mathbf {x}^S_n$ and $\\mathbf {x}^T_n, \\forall n \\in \\lbrace 1, \\ldots , N\\rbrace $ are frame-by-frame synchronized. For most domain adaptation tasks in ASR, such as adapting from clean to noisy speech, close-talk to far-field speech, wide-band to narrow-band speech, the parallel data in the target domain can be easily simulated from the data in the source domain BIBREF17, BIBREF19.",
+      "Our goal is to train a student AED that can accurately predict the tokens of the target-domain data by forcing the student to emulate the behaviors of the teacher. To achieve this, we minimize the Kullback-Leibler (KL) divergence between the token-level output distributions of the teacher and the student AEDs given the parrallel data $\\mathbf {X}^T$ and $\\mathbf {X}^S$ are fed as the input to the AEDs. The KL divergence between the token-level output distributions of the teacher and student AEDs are formulated below",
+      "where $\\mathbf {Y}^{T}=\\lbrace y^T_1, \\ldots , y^T_{L^T}\\rbrace $ is the sequence of one-best token sequence decoded by the teacher AED as follows",
+      "where $L^T$ is the number of tokens in $\\mathbf {Y}^T$, and $\\mathbf {\\theta }^{T}$, $\\mathbf {\\theta }^{S}$ denote all the parameters in the teacher and student AED models, respectively. Note that, for unsupervised domain adaptation, the teacher AED can only condition its decoder on the token $y^T_{l-1}$ predicted at the previous step since the ground-truth labels $\\mathbf {Y}^G$ are not available. We minimize the KL divergence with respect to $\\theta ^S$ while keeping $\\theta ^T$ fixed on the adaptation data corpus $\\mathbb {A}$, which is equivalent to minimizing the token-level T/S loss function below:",
+      "The steps of token-level T/S learning for unsupervised domain adaptation of AED model are summarized as follows:",
+      "Clone the student AED from a teacher AED well-trained with transcribed source-domain data by minimizing Eq. (DISPLAY_FORM3).",
+      "Forward-propagate the source-domain data $\\mathbf {X}^T$ through the teacher AED, generate teacher's one-best token sequence $\\mathbf {Y}^T$ using Eq. (DISPLAY_FORM6) and teacher's soft posteriors for each decoder step $P(u|\\mathbf {Y}^T_{0:l-1},\\mathbf {X}^T; \\mathbf {\\theta }^{T}), u \\in \\mathbb {U}$ by Eqs. (DISPLAY_FORM2) and ().",
+      "Forward-propagate the target-domain data $\\mathbf {X}^S$ (parallel to $\\mathbf {X}^T$) through the student AED, generate student's soft posteriors for each teacher's decoder step $P(u|\\mathbf {Y}^T_{0:l-1},$ $\\mathbf {X}^S; \\mathbf {\\theta }^{S}), u \\in \\mathbb {U}$ by Eqs. (DISPLAY_FORM2) and ().",
+      "Compute error signal of the T/S loss function in Eq. (DISPLAY_FORM7) , back-propagate the error through student AED and update the parameters of the student AED.",
+      "Repeat Steps UNKREF9 to UNKREF11 until convergence.",
+      "After T/S learning, only the adapted student AED is used for testing and the teacher AED is discarded.",
+      "From Eqs. (DISPLAY_FORM6) and (DISPLAY_FORM7), to extend T/S learning to AED-based E2E models, two levels of knowledge transfer are involved: 1) the student learns from the teacher's soft token posteriors $P(u|\\mathbf {Y}^T_{0:l-1}, \\mathbf {X}^T; \\mathbf {\\theta }^T)$ at each decoder step; 2) the student AED conditions its decoder on the previous token $y^T_{l-1}$ predicted by the teacher to make the current prediction.",
+      "Sequence-level T/S learning BIBREF24, BIBREF30 is another method for unsupervised domain adaptation in which a KL divergence between the sequence-level output distributions of the teacher and student AEDs are minimized. Equivalently, we minimize the sequence-level T/S loss function below with respect to $\\theta ^S$",
+      "where $\\mathbb {V}$ is the set of all possible token sequences and the teacher's sequence-level output distribution $P(\\mathbf {V} | \\mathbf {X}^T)$ is approximated by $\\mathbb {1}[\\mathbf {V}=\\mathbf {Y}^T]$ for easy implementation. $\\mathbb {1}[\\cdot ]$ is an indicator function which equals to 1 if the condition in the squared bracket is satisfied and 0 otherwise.",
+      "From Eq. (DISPLAY_FORM13), we see that only one level of knowledge transfer exists in sequence-level T/S, i.e., the one-best token sequence $\\mathbf {Y}^T$ decoded by the teacher AED. The student AED learns from $\\mathbf {Y}^T$ and conditions its decoder on it at each step. Different from token-level T/S, in sequence-level T/S, one-hot labels in $\\mathbf {Y}^T$ are used as training targets of the student AED instead of the soft token posteriors."
+    ],
+    [
+      "In this section, we want to make good use of the ground-truth labels of the adaptation data to further improve the T/S domain adaptation. Note that different from unsupervised T/S in Section SECREF3, in supervised domain adaptation, the teacher AED conditions its decoder on the ground-truth token instead of its previous decoding result because the token transcription $\\mathbf {Y}^G$ is available in addition to $\\mathbf {X}^S$ and $\\mathbf {X}^T$.",
+      "One shortcoming of unsupervised T/S learning is that the teacher model can sporadically predict inaccurate token posteriors which misleads the student AED towards suboptimal performance. One-hot ground-truth labels can be utilized to alleviate this issue. One possible solution is the interpolated T/S (IT/S) learning BIBREF22 in which a weighted sum of teacher's soft posteriors and the one-hot ground truth is used as the target to train the student AED. A pair of global weights summed to be one is applied to each pair of soft and one-hot labels. However, the optimal global weights are hard to determine because they are data-dependent and need to be carefully tuned on a dev set.",
+      "To address this issue, conditional T/S learning (CT/S) BIBREF20 was proposed recently in which the student selectively chooses to learn from either the teacher AED or the ground truth conditioned on whether the teacher AED can correctly predict the ground-truth labels. CT/S have shown significant WER improvements over T/S and IT/S for both domain and speaker adaptation on CHiME-3 dataset. However, in CT/S, the student is still not \u201csmart\u201d enough because, for each token, the student AED solely relies on either the teacher's posteriors or the ground truth instead of dynamically extracting useful knowledge from both.",
+      "To further improve the effectiveness of knowledge transfer, we propose an adaptive teacher-student (AT/S) learning method by taking advantage of both CT/S and IT/S. As shown in Fig. FIGREF14, instead of assigning a fixed pair of soft weight $w$ and one-hot weight $(1-w)$ for all the decoder steps, we adaptively weight the teacher's soft posteriors at the $l^\\text{th}$ decoder step, $P(u|\\mathbf {Y}^G_{0:l-1},\\mathbf {X}^T;\\mathbf {\\theta }^{T}), u\\in \\mathbb {U}$, by $w_l \\in [0,1]$ and the one-hot vector of the $l^\\text{th}$ token in the ground-truth sequence $\\mathbf {Y}^G$ by $(1-w_l)$.",
+      "In order to quantify the value of the knowledge to be transferred, $w_l$ should be positively correlated with a confidence score $c_l$ on the teacher's prediction on token posteriors, while $(1-w_l)$ should be positively correlated with a confidence score on the ground truth $d_l$. To achieve this, we compute $w_l$ by normalizing $c_l$ against its summation with $d_l$.",
+      "It is in general true that the higher posterior $P(y_l^G|\\mathbf {Y}^G_{0:l-1},$ $\\mathbf {X}^T;\\theta ^T)$ a teacher assigns to the correct (ground-truth) token $y_l^G$, the more accurate the teacher's soft posteriors are at this decoder step. Therefore, the confidence score $c_l$ on teacher's soft posteriors $P(u|\\mathbf {Y}^G_{0:l-1}, \\mathbf {X}^T; \\theta ^T), u\\in \\mathbb {U}$ can be any monotonically increasing function of the correct token posterior predicted by the teacher $P(y^G_l|\\mathbf {Y}^G_{0:l-1}, \\mathbf {X}^T; \\theta ^T)$, while the confidence score $d_l$ on the one-hot ground truth can be any monotonically increasing function of $(1-P(y^G_l|\\mathbf {Y}^G_{0:l-1}, \\mathbf {X}^T;$ $\\theta ^T))$ as follow",
+      "where both $f_1$ and $f_2$ are any monotonically increasing functions on the interval $[0, 1]$. In this work, we simply assume that $f_1$ and $f_2$ are both power functions of the same form, i.e., $f_1(x) = f_2(x) = x^{\\lambda }, \\; \\lambda > 0 $. Note that $w_l$ equals to $P(y^G_l|\\mathbf {Y}^G_{0:l-1}, \\mathbf {X}^T; \\theta ^T)$ when $\\lambda =1$.",
+      "In AT/S, a linear combination of the teacher's soft posteriors and the one-hot ground truth weighted by $w_l$ and $(1- w_l)$, respectively, is used as the training target for the student AED at each decoder step. The AT/S loss function is formulated as",
+      "The steps of AT/S learning for supervised domain adaptation of AED model are summarized as follows:",
+      "Perform token-level unsupervised T/S adaptation by following the steps in Section SECREF3 as the initialization.",
+      "Forward-propagate the parallel source and target domain data $\\mathbf {X}^T$ and $\\mathbf {X}^S$ through the teacher and student AEDs, generate teacher and student's soft posteriors $P(u|\\mathbf {Y}^G_{0:l-1},\\mathbf {X}^T; \\mathbf {\\theta }^{T})$ and $P(u|\\mathbf {Y}^G_{0:l-1},$ $\\mathbf {X}^S; \\mathbf {\\theta }^{S}), u \\in \\mathbb {U}$ for each decoder step by Eqs. (DISPLAY_FORM2) and ().",
+      "Compute the confidence scores $c_l$ and $d_l$ for teacher's soft posteriors and one-hot vector of ground truth $y^G_l$ by Eqs. (DISPLAY_FORM16) and (), compute the adaptive weight $w_l$ by Eq. (DISPLAY_FORM15).",
+      "Compute error signal of the AT/S loss function in Eq. (DISPLAY_FORM17) , back-propagate the error through student AED and update the parameters of the student AED.",
+      "Repeat Steps UNKREF9 to UNKREF11 until convergence.",
+      "AT/S is superior to IT/S in that the combination weights for soft and one-hot labels at each decoder step are adaptively assigned according to the confidence score on both labels. AT/S will degenerate to IT/S if the combination weights $w_l$ are fixed globally. Compared to CT/S, in AT/S, the student always adaptively learns from both the teacher's soft posteriors and the one-hot ground truth rather than choosing either of them depending on the correctness of teacher's prediction."
+    ],
+    [
+      "We adapt a close-talk AED model to the far-field data through various T/S learning methods with parallel close-talk and far-field Microsoft Cortana data for E2E ASR."
+    ],
+    [
+      "For both training and adaptation, close-talk data consisting of 3400 hours of Microsoft live US English Cortana utterances are collected through a number of deployed speech services including voice search and SMD. We simulate 3400 hours of far-field Microsoft Cortana data by convolving the close-talk signal with different room impulse responses and adding various environmental noise for both training and adaptation. The 3400 hours far-field data is parallel with the 3400 hours close-talk data. We collect 17.5k far-field utterances (about 19 hours) from Harman Kardon (HK) speaker as the test set.",
+      "80-dimensional log Mel filter bank features are extracted from the training, adaptation and test speech every 10 ms over a 25 ms window. We stack 3 consecutive frames and stride the stacked frame by 30 ms, to form a sequence of 240-dimensional input speech frames. We first generate 34k mixed-units consisting of words and multi-letter units as in BIBREF31 based on the training transcription and then tokenize the training, adaptation transcriptions correspondingly. We insert a special token <space> between every two adjacent words to indicate the word boundary and add <sos>, <eos> to the beginning and end of each utterance, respectively."
+    ],
+    [
+      "We first train an AED model predicting 34k mixed units with 3400 hours close-talk training data and it ground-truth labels for E2E ASR as in BIBREF32, BIBREF33, BIBREF34. The encoder is a bi-directional gated recurrent units (GRU)-recurrent neural network (RNN) BIBREF27, BIBREF35 with 6 hidden layers, each with 512 hidden units. We use GRU instead of long short-term memory (LSTM) BIBREF36, BIBREF37 for RNN because it has less parameters and is trained faster than LSTM with no loss of performance. Layer normalization BIBREF38 is applied for each encoder hidden layer. Each mixed unit is represented as a 512-dimensional embedding vector. The decoder is a uni-directional GRU-RNN with 2 hidden layers, each with 512 hidden units. The 34k-dimensional output layer of the decoder predicts the posteriors of all the mixed units in the vocabulary. During training, scheduled sampling BIBREF39 is applied to the decoder with a sampling probability starting at 0.0 and gradually increasing to 0.4 BIBREF29. Dropout BIBREF40 with a probability of 0.1 is used in both encoder and decoder. A label-smoothed cross-entropy BIBREF41 loss is minimized during training. Greedy decoding is performed to generate the ASR transcription. We use PyTorch BIBREF42 toolkit for the experiments. Table TABREF25 shows that the close-talk AED model achieves 7.58% and 17.39% WERs on a close-talk Cortana test set used in BIBREF33 and the far-field HK speaker test set, respectively.",
+      "Using the well-trained close-talk AED as the initialization, we then train a far-field AED with 3400 hours far-field data and its ground-truth labels by following the same procedure. When evaluated on the HK speaker test set, the baseline far-field AED achieves 13.93% WER for ASR as in Table TABREF25."
+    ],
+    [
+      "We adapt the close-talk baseline AED to the 3400 hours far-field data using token and sequence level T/S learning as discussed in Section SECREF3. To achieve this, we feed the 3400 hours close-talk adaptation data as the input to the teacher AED and the 3400 hours parallel far-field adaptation data as the input to the student AED. The student AED conditions its decoder on one-best token sequences generated by the teacher AED through greedy decoding. In token-level T/S, the soft posteriors generated by the teacher serve as the training targets of the student while in sequence-level T/S, the one-best sequences decoded by the teacher are used the targets.",
+      "As shown in Table TABREF25, the token-level T/S achieves 13.06% WER on HK speaker test set, which is 24.9% and 6.25% relative improvements over the close-talk and far-field AED models, respectively. The sequence-level T/S achieves 14.00% WER, which is 19.5% relative improvement over the close-talk AED model. The sequence-level T/S performs slightly worse than the far-field AED trained with ground-truth labels because the one-best decoding from the teacher AED is not always reliable to serve as the training targets for the student model. The sequence-level T/S can be improved by using multiple decoded hypotheses generated by the teacher AED as the training targets as in BIBREF25, BIBREF26. We did not perform N-best decoding because it will drastically increase the computational cost and will consumes much more adaptation time than the other T/S methods. The 6.7% relative WER gain obtained by token-level T/S over sequence-level T/S shows the benefit of using soft posteriors generated by the teacher AED as the training target at each decoder step when a reliable ground-truth transcription is not available.",
+      "The 6.3% relative WER gain of token T/S over far-field AED baseline shows that the unsupervised T/S learning with no ground-truth labels can significantly outperform the supervised domain adaptation with such information available. Compared to the one-hot labels, the soft posteriors accurately models the inherent statistical relationships among different token classes in addition to the token identity encoded by a one-hot vector. It proves to be a more powerful target for the student to learn from which is consistent with what was observed in BIBREF17, BIBREF18, BIBREF19, BIBREF20, BIBREF21."
+    ],
+    [
+      "As discussed in Section SECREF4, we want to further improve the T/S learning by using one-hot ground-truth labels when they are available. As in BIBREF22, we perform IT/S learning for supervised domain adaptation by using the linear interpolation of soft posterior and one-hot ground truth as the training target of the student. The interpolation weights are globally fixed at 0.5 and 0.5 for all decoder steps. By following BIBREF20, we also conduct CT/S for supervised domain adaptation where soft posteriors are used as the training target of the student if the teacher's prediction is correct at the current decoder step, otherwise the one-hot ground truth is used as the target. Finally, AT/S domain adaptation is performed by adaptively adjusting the weights assigned to the soft and one-hot labels at each decoder step as in Eqs. (DISPLAY_FORM15) to (). We explore using different power functions as $f_1(x)$ and $f_2(x)$ to compute the confidence scores by adjusting $\\lambda $. For all the above supervised T/S learning methods, the 3400 hours close-talk and 3400 hours far-field parallel adaptation data is fed as the input to the teacher and student AEDs, respectively.",
+      "As shown in Table TABREF25, IT/S with $w = 0.2$ achieves 13.95% WER on HK speaker test set which is 25.5%, 7.0% and 0.8% relative improvements over the close-talk, far-field and token-level T/S adapted AED models, respectively. With a 12.82% WER, CT/S relatively improves the close-talk, far-field and token-level T/S adapted AED models by 26.3%, 8.0% and 1.8% respectively. Among different $\\lambda $s for AT/S, the best WER is 12.49%, which is 28.2%, 10.3% and 4.4% relative gains over close-talk, far-field and token-level T/S adapted AEDs. The minimum WER is reached when $\\lambda =0.25$ and $c_l = P(y_l|\\mathbf {Y}^G_{0:l-1}, \\mathbf {X}^T; \\theta ^T)^{0.25}$. Compared to $\\lambda >1$, AT/S works better for $\\lambda \\in [0, 1]$ when confidence scores $c_l$, $d_l$ are both concave functions of the correct token posterior and the sum of incorrect token posteriors, respectively. All the IT/S, CT/S and AT/S outperform the unsupervised T/S learning indicating that the one-hot ground truth can further improve T/S domain adaptation when it is properly used. AT/S achieves the largest gain in supervised domain adaptation methods showing the superiority of adaptively extracting useful knowledge from both the soft and one-hot labels depending on their confidence scores."
+    ],
+    [
+      "In this paper, we extend T/S learning to unsupervised domain adaptation of AED models for E2E ASR. T/S learning requires only unlabeled parallel source and target domain data as the input to the teacher and student AEDs, respectively. In T/S, the student AED conditions its decoder on the one-best token sequences generated by the teacher. The teacher's soft posteriors and decoded one-hot tokens are used as the training target of the student AED for token-level and sequence-level T/S learning, respectively.",
+      "For supervised domain adaption, we propose adaptive T/S learning in which the student always learns from a linear combination of the teacher's soft posteriors and the one-hot ground truth. The combination weights are adaptively computed at each decoder step based on the confidence scores on both knowledge sources.",
+      "Domain adaptation is conducted on 3400 hours close-talk and 3400 hours far-field Microsoft Cortana data. Token-level T/S achieves 6.3% relative WER improvement over the baseline far-field AED model trained with CE criterion. By making use of the ground-truth labels, AT/S further improves the token-level T/S by 4.4% relative and achieves a total 10.3% relative gain over the far-field AED. AT/S also consistently outperforms IT/S and CT/S showing the advantage of learning from both the teacher and the ground truth as well as the adaptive adjustment of the combination weights."
+    ]
+  ]
+}
+```
\ No newline at end of file
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+Name of Paper: HAS-QA: Hierarchical Answer Spans Model for Open-domain Question Answering
+
+Question: How much does HAS-QA improve over baselines?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Works",
+    "Probabilistic Views of OpenQA",
+    "HAS-QA Model",
+    "Question Aware Context Encoder",
+    "Conditional Span Predictor",
+    "Multiple Spans Aggregator",
+    "Paragraph Quality Estimator",
+    "Datasets",
+    "Experimental Settings",
+    "Overall Results",
+    "Model Analysis",
+    "Conclusions",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "Open-domain question answering (OpenQA) aims to seek answers for a broad range of questions from a large knowledge sources, e.g., structured knowledge bases BIBREF0 , BIBREF1 and unstructured documents from search engine BIBREF2 . In this paper we focus on the OpenQA task with the unstructured knowledge sources retrieved by search engine.",
+      "Inspired by the reading comprehension (RC) task flourishing in the area of natural language processing BIBREF3 , BIBREF4 , BIBREF5 , some recent works have viewed OpenQA as an RC task, and directly applied the existing RC models to it BIBREF6 , BIBREF7 , BIBREF3 , BIBREF8 . However, these RC models do not well fit for the OpenQA task.",
+      "Firstly, they directly omit the paragraphs without answer string. RC task assumes that the given paragraph contains the answer string (Figure 1 top), however, it is not valid for the OpenQA task (Figure 1 bottom). That's because the paragraphs to provide answer for an OpenQA question is collected from a search engine, where each retrieved paragraph is merely relevant to the question. Therefore, it contains many paragraphs without answer string, for instance, in Figure 1 Paragraph2. When applying RC models to OpenQA task, we have to omit these paragraphs in the training phase. However, during the inference phase, when model meets one paragraph without answer string, it will pick out a text span as an answer span with high confidence, since RC model has no evidence to justify whether a paragraph contains the answer string.",
+      "Secondly, they only consider the first answer span in the paragraph, but omit the remaining rich multiple answer spans. In RC task, the answer and its positions in the paragraph are provided by the annotator in the training data. Therefore RC models only need to consider the unique answer span, e.g., in SQuAD BIBREF9 . However, the OpenQA task only provides the answer string as the ground-truth. Therefore, multiple answer spans are detected in the given paragraph, which cannot be considered by the traditional RC models. Take Figure 1 as an example, all text spans contain `fat' are treated as answer span, so we detect two answer spans in Paragraph1.",
+      "Thirdly, they assume that the start position and end position of an answer span is independent. However, the end position is evidently related with the start position, especially when there are multiple answer spans in a paragraph. Therefore, it may introduce some problems when using such independence assumption. For example, the detected end position may correspond to another answer span, rather than the answer span located by the start position. In Figure 1 Paragraph1, `fat in their $\\cdots $ insulating effect fat' has a high confidence to be an answer span under independence assumption.",
+      "In this paper, we propose a Hierarchical Answer Span Model, named HAS-QA, based on a new three-level probabilistic formulation of OpenQA task, as shown in Figure 2 .",
+      "At the question level, the conditional probability of the answer string given a question and a collection of paragraphs, named answer probability, is defined as the product of the paragraph probability and conditional answer probability, based on the law of total probability.",
+      "At the paragraph level, paragraph probability is defined as the degree to which a paragraph can answer the question. This probability is used to measure the quality of a paragraph and targeted to tackle the first problem mentioned, i.e. identify the useless paragraphs. For calculation, we first apply bidirectional GRU and an attention mechanism on the question aware context embedding to obtain a score. Then, we normalize the scores across the multiple paragraphs. In the training phase, we adopt a negative sampling strategy for optimization. Conditional answer probability is the conditional probability that a text string is the answer given the paragraph. Considering multiple answer spans in a paragraph, the conditional answer probability can be further represented as the aggregation of several span probability, defined later. In this paper, four types of functions, i.e. HEAD, RAND, MAX and SUM, are used for aggregation.",
+      "At the span level, span probability represents the probability that a text span in a paragraph is the answer span. Similarly to previous work BIBREF3 , span probability can be computed as the product of two location probability, i.e., location start probability and location end probability. Then a conditional pointer network is proposed to model the probabilistic dependences between the start and end positions, by making generation of end position depended on the start position directly, rather than internal representation of start position BIBREF10 .",
+      "The contributions of this paper include:",
+      "1) a probabilistic formulation of the OpenQA task, based on the a three-level hierarchical structure, i.e. the question level, the paragraph level and the answer span level;",
+      "2) the proposal of an end-to-end HAS-QA model to implement the three-level probabilistic formulation of OpenQA task (Section \"HAS-QA Model\" ), which tackles the three problems of direct applying existing RC models to OpenQA;",
+      "3) extensive experiments on QuasarT, TriviaQA and SearchQA datasets, which show that HAS-QA outperforms traditional RC baselines and recent OpenQA baselines."
+    ],
+    [
+      "Research in reading comprehension grows rapidly, and many successful RC models have been proposed BIBREF11 , BIBREF4 , BIBREF3 in this area. Recently, some works have treated OpenQA task as an RC task and directly applied existing RC models. In this section, we first review the approach of typical RC models, then introduce some recent OpenQA models which are directly based on the RC approach.",
+      "RC models typically have two components: context encoder and answer decoder. Context encoder is used to obtain the embeddings of questions, paragraphs and their interactions. Most of recent works are based on the attention mechanism and its extensions. The efficient way is to treat the question as a key to attention paragraph BIBREF3 , BIBREF6 . Adding the attention from paragraph to question BIBREF4 , BIBREF5 , enriches the representations of context encoder. Some works BIBREF12 , BIBREF13 , BIBREF8 find that self-attention is useful for RC task. Answer decoder aims to generate answer string based on the context embeddings. There exist two sorts of approaches, generate answer based on the entail word vocabulary BIBREF14 and retrieve answer from the current paragraph. Almost all works in RC task choose the retrieval-based method. Some of them use two independently position classifiers BIBREF6 , BIBREF15 , the others use the pointer networks BIBREF3 , BIBREF4 , BIBREF12 , BIBREF13 . An answer length limitation is applied in these models, i.e. omit the text span longer than 8. We find that relaxing length constrain leads to performance drop.",
+      "Some recent works in OpenQA research directly introduce RC model to build a pure data driven pipline. DrQA BIBREF6 is the earliest work that applies RC model in OpenQA task. However, its RC model is trained using typical RC dataset SQuAD BIBREF9 , which turns to be over-confidence about its predicted results even if the candidate paragraphs contain no answer span. R ${}^3$ BIBREF16 introduces a ranker model to rerank the original paragraph list, so as to improve the input quality of the following RC model. The training data of the RC model is solely limited to the paragraphs containing the answer span and the first appeared answer span location is chosen as the ground truth. Shared-Norm BIBREF8 applied a shared-norm trick which considers paragraphs without answer span in training RC models. The trained RC model turns to be robust for the useless paragraphs and generates the lower span scores for them. However, it assumes that the start and the end positions of an answer span are independent, which is not suitable for modeling multiple answer spans in one paragraph.",
+      "Therefore, we realize that the existing OpenQA models rarely consider the differences between RC and OpenQA task. In this paper, we directly model the OpenQA task based on a probabilistic formulation, in order to identify the useless paragraphs and utilize the multiple answer spans."
+    ],
+    [
+      "In OpenQA task, the question $Q$ and its answer string $A$ are given. Entering question $Q$ into a search engine, top $K$ relevant paragraphs are returned, denote as a list $\\mathbf {P} = [P_1,\\dots , P_K]$ . The target of OpenQA is to find the maximum probability of $P(A|Q, \\mathbf {P})$ , named answer probability for short. We can see the following three characteristics of OpenQA:",
+      "1) we cannot guarantee that paragraph retrieved by search engine contains the answer span for the question, so the paragraphs without answer span have to be deleted when using the above RC models. However, these paragraphs are useful for distinguishing the quality of paragraphs in training. More importantly, the quality of a paragraph plays an important role in determining the answer probability in the inference phase. It is clear that directly applying RC models fails to meet this requirement.",
+      "2) only answer string is provided, while the location of the answer string is unknown. That means there may be many answer spans in the paragraph. It is well known that traditional RC models are only valid for a single answer span. To tackle this problem, the authors of BIBREF7 propose a distantly supervised method to use the first exact match location of answer string in the paragraph as the ground-truth answer span. However, this method omit the valuable multiple answer spans information, which may be important for the calculation of the answer probability.",
+      "3) the start and end positions are coupled together to determine a specific answer span, since there may be multiple answer spans. However, existing RC models usually assume that the start and end positions are independent. That's because there is only one answer span in the RC scenario. This may introduce serious problem in the OpenQA task. For example, if we do not consider the relations between the start and end position, the end position may be another answer span's end position, instead of the one determined by the start position. Therefore, it is not appropriate to assume independence between start and end positions.",
+      "In this paper, we propose to tackle the above three problems. Firstly, according to the law of total probability, the answer probability can be rewritten as the following form. ",
+      "$$P(A|Q, \\mathbf {P})\\! =\\! \\sum _{i=1}^{K} P(P_i|Q, \\mathbf {P}) P(A|Q, P_i).$$   (Eq. 4) ",
+      "We name $P(P_i|Q, \\mathbf {P})$ and $P(A|Q, P_i)$ as the paragraph probability and conditional answer probability, respectively. We can see that the paragraph probability measures the quality of paragraph $P_i$ across the list $\\mathbf {P}$ , while the conditional answer probability measures the probability that string $A$ is an answer string given paragraph $P_i$ .",
+      "The conditional answer probability can be treated as a function of multiple span probabilities $\\lbrace P(L_j(A)|Q, P_i)\\rbrace _j$ , as shown in Eq 5 . ",
+      "$$\\begin{aligned}\nP(A|Q, P_i) &:= \\mathcal {F}(\\lbrace P(L_j(A)|Q, P_i)\\rbrace _j), \\\\\n&j \\in [1, |\\mathcal {L}(A,P_i)|],\n\\end{aligned}$$   (Eq. 5) ",
+      "where the aggregation function $\\mathcal {F}$ treats a list of spans $\\mathcal {L}(A,P_i)$ as input, and $|\\mathcal {L}(A,P_i)|$ denotes the number of the text spans contain the string $A$ . A proper aggregation function makes use of all the answer spans information in OpenQA task. Previous work BIBREF7 can be treated as a special case, which uses a function of selecting first match span as the aggregation function $\\mathcal {F}$ .",
+      "The span probability $P(L_j(A)|Q, P_i)$ represents the probability that a text span $L_j(A)$ in the paragraph $P_i$ is an answer span. We further decompose it into the product of location start probability $P(L^s_j(A)|Q, P_i)$ and location end probability $P(L^e_j(A)|Q, P_i, L^s_j(A))$ , shown in Eq 6 . ",
+      "$$\\begin{aligned}\nP(L_j(A)|Q, P_i) = &P(L^s_j(A)|Q, P_i) \\\\\n\\cdot &P(L^e_j(A)|Q, P_i, L^s_j(A)). \\end{aligned}$$   (Eq. 6) ",
+      "Some previous work such as DrQA BIBREF6 treats them as the two independently position classification tasks, thus $L^{s}(A)$ and $L^{e}(A)$ are modeled by two different functions. Match-LSTM BIBREF3 treats them as the pointer networks BIBREF10 . The difference is that $L^{e}(A)$ is the function of the hidden state of $L^{s}(A)$ , denote as $\\mathbf {M^s}$ . However, $L^{s}(A)$ and $L^{e}(A)$ are still independent in probabilistic view, because $L^{e}(A)$ depends on the hidden state $\\mathbf {M^s}$ , not the start position $L^{s}(A)$ . In this paper, the span positions $L^{e}(A)$0 and $L^{e}(A)$1 are determined by the question $L^{e}(A)$2 and the paragraph $L^{e}(A)$3 . Specially, end position $L^{e}(A)$4 is also conditional on start position $L^{e}(A)$5 directly. With this conditional probability, we can naturally remove the answer length limitation.",
+      "With above formulation, we find that RC task is a special case of OpenQA task, where we set the number of paragraph $K$ to 1, set the paragraph probability to constant number 1, treat $P(A|Q,P){=}P(L(A)|Q, P)$ , $P(L(A)|Q, P){=}P(L^{s}(A)|Q, P)P(L^{e}(A)|Q, P)$ , where $P$ is the idealized paragraph that contain the answer string $A$ , and the right position $L(A)$ is also known."
+    ],
+    [
+      "In this section, we propose a Hierarchical Answer Span Model (HAS-QA) for OpenQA task, based on the probabilistic view of OpenQA in Section \"Probabilistic Views of OpenQA\" . HAS-QA has four components: question aware context encoder, conditional span predictor, multiple spans aggregator and paragraph quality estimator. We will introduce them one by one."
+    ],
+    [
+      "The question aware context embeddings $\\mathbf {C}$ is generated by the context encoder, while HAS-QA do not limit the use of context encoder. We choose a simple but efficient context encoder in this paper. It takes advantage of previous works BIBREF8 , BIBREF3 , which contains the character-level embedding enhancement, the bi-directional attention mechanism BIBREF4 and the self-attention mechanism BIBREF12 . We briefly describe the process below .",
+      "Word Embeddings: use size 300 pre-trained GloVe BIBREF17 word embeddings.",
+      "Char Embeddings: encode characters in size 20, which are learnable. Then obtain the embedding of each word by convolutional layer and max pooling layer.",
+      "Context Embeddings: concatenate word embeddings and char embeddings, and apply bi-directional GRU BIBREF18 to obtain the context embeddings. Both question and paragraph get their own context embeddings.",
+      "Question Aware Context Embeddings: use bi-directional attention mechanism from the BiDAF BIBREF4 to build question aware context embeddings. Additionally, we subsequently apply a layer of self-attention to get the final question aware context embeddings.",
+      "After the processes above, we get the final question aware context embeddings, denoted $\\mathbf {C} \\in \\mathbb {R}^{n \\times r}$ , where $n$ is the length of the paragraph and $r$ is size of the embedding."
+    ],
+    [
+      "Conditional span predictor defines the span probability for each text span in a paragraph using a conditional pointer network.",
+      "We first review the answer decoder in traditional RC models. It mainly has two types: two independently position classifiers (IndCls) and the pointer networks (PtrNet). Both of these approaches generate a distribution of start position $\\mathbf {p^s} \\in \\mathbb {R}^n$ and a distribution of end position $\\mathbf {p^e} \\in \\mathbb {R}^n$ , where $n$ is the length of the paragraph. Starting from the context embeddings $\\mathbf {C}$ , two intermedia representations $\\mathbf {M^s} \\in \\mathbb {R}^{n \\times 2d}$ and $\\mathbf {M^e} \\in \\mathbb {R}^{n \\times 2d}$ are generated using two bidirectional GRUs with the output dimension $d$ . ",
+      "$$ \\mathbf {M^s} &= \\mathrm {BiGRU}(\\mathbf {C})\\\\\n \\textrm {IndCls:}\\; \\mathbf {M^e} &= \\mathrm {BiGRU}(\\mathbf {C}), \\\\\n \\textrm {PtrNet:}\\; \\mathbf {M^e} &= \\mathrm {BiGRU}([\\mathbf {C}, \\mathbf {M^s}]).$$   (Eq. 10) ",
+      "Then an additional Softmax function is used to generate the final positional distributions, ",
+      "$$ \n\\begin{aligned}\n&\\mathbf {p^s}\\! =\\! \\mathrm {softmax}(\\mathbf {M^s}w_s), \\\\\n&\\mathbf {p^e}\\! =\\! \\mathrm {softmax}(\\mathbf {M^e}w_e).\n\\end{aligned}$$   (Eq. 11) ",
+      "where $w_s, w_e \\in \\mathbb {R}^{2d}$ denotes the linear transformation parameters.",
+      "As mentioned in Section \"Probabilistic Views of OpenQA\" , IndCls and PtrNet both treat start and end position as probabilistic independent. Given the independent start and end positions can not distinguish the different answer spans in a paragraph properly, so it is necessary to build a conditional model for them. Therefore, we proposed a conditional pointer network which directly feed the start position to the process of generating the end position: ",
+      "$$ \n\\begin{aligned}\n\\mathbf {M^e_j} &= \\mathrm {BiGRU}([\\mathbf {C}, \\mathbf {M^s}, \\mathrm {OneHot}(L^s_j)]), \\\\\n\\mathbf {p^e_j} &= \\mathrm {softmax}(\\mathbf {M^e_j}w_e),\n\\end{aligned}$$   (Eq. 12) ",
+      "where $L^s_j$ denotes the start position selected from the start positional distribution $\\mathbf {p^s}$ and $\\mathrm {OneHot}(\\cdot )$ denotes the transformation from a position index to an one-hot vector.",
+      "In the training phase, we are given the start and end positions of each answer span, denote as $L^s_j$ and $L^e_j$ . The span probability is: ",
+      "$$ \nP(L_j(A)|Q, P_i) = s_j = \\mathbf {p^s}[L^s_j] \\cdot \\mathbf {p^e_j}[L^e_j].$$   (Eq. 13) ",
+      "In the inference phase, we first select the start position $L^s_j$ from the start distribution $\\mathbf {p^s}$ . Then we yield its corresponding end distribution $\\mathbf {p^e_j}$ using Eq 12 , and select the end position $L^e_j$ from it. Finally, we get the span probability using Eq 13 ."
+    ],
+    [
+      "Multiple span aggregator is used to build the relations among multiple answer spans and outputs the conditional answer probability. In this paper, we design four types of aggregation functions $\\mathcal {F}$ : ",
+      "$$ \n\\begin{aligned}\n&\\textrm {HEAD:} \\; P(A|Q, P_i) = s_1 \\\\\n&\\textrm {RAND:} \\; P(A|Q, P_i) = \\textrm {Random}(s_j) \\\\\n&\\textrm {MAX:} \\;\\;\\; P(A|Q, P_i) = \\max _j\\nolimits (s_j) \\\\\n&\\textrm {SUM:} \\;\\;\\; P(A|Q, P_i) = \\sum _j\\nolimits (s_j) \\\\\n\\end{aligned}$$   (Eq. 15) ",
+      "where $s_j$ denotes the span probability defined in Eq 13 , $s_1$ denotes the first match answer span and $\\textrm {Random}$ denotes a stochastic function for randomly choosing an answer span.",
+      "Different aggregation functions represent different assumptions about the distribution of the oracle answer spans in a paragraph. The oracle answer span represents the answer of the question that can be merely determined by its context, e.g. in Figure 1 , the first answer span `fat' is the oracle answer span, while the second one is not, because we could retrieval the answer directly, if we have read `concentrating body fat in their humps'.",
+      "HEAD operation simply chooses the first match span probability as the conditional answer probability, which simulates the answer preprocessing in previous works BIBREF16 , BIBREF7 . This function only encourages the first match answer span as the oracle, while punishes the others. It can be merely worked in a paragraph with definition, such as first paragraph in WikiPedia.",
+      "RAND operation randomly chooses a span probability as the conditional answer probability. This function assumes that all answer spans are equally important, and must be treated as oracle. However, balancing the probabilities of answer spans is hard. It can be used in paraphrasing answer spans appear in a list.",
+      "MAX operation chooses the maximum span probability as the conditional answer probability. This function assumes that only one answer span is the oracle. It can be used in a noisy paragraph, especially for those retrieved by a search engine.",
+      "SUM operation sums all the span probabilities as the conditional answer probability. This function assumes that one or more answer spans are the oracle. It can be used in a broad range of scenarios, for its relatively weak assumption.",
+      "In the training phase, all annotated answer spans contain the same answer string $A$ , we directly apply the Eq 15 to obtain the conditional answer probability in paragraph level.",
+      "In the inference phase, we treat the top $K$ span probabilities $s_j$ as the input of the aggregation function. However, we have to check all possible start and end positions to get the precise top $K$ span probabilities. Instead, we use a beam search strategy BIBREF19 which only consider the top $K_1$ start positions and the top $K_2$ end positions, where $K_1 K_2 \\ge K$ . Different span probabilities $s_j$ represent variance answer strings $A_t$ . Following the definition in Eq 15 , we group them by different answer strings respectively."
+    ],
+    [
+      "Paragraph quality estimator takes the useless paragraphs into consideration, which implements the paragraph probability $P(P_i|Q, \\mathbf {P})$ directly.",
+      "Firstly, we use an attention-based network to generate a quality score, denotes as $\\hat{q}_i$ , in order to measure the quality of the given paragraph $P_i$ . ",
+      "$$ \n\\begin{aligned}\n&\\mathbf {M^c} = \\textrm {BiGRU}(\\mathbf {C}),\\\\\n&\\hat{q}_i = (\\mathbf {M^c}^{\\top } \\cdot \\mathbf {p^s}) \\cdot w_c.\n\\end{aligned}$$   (Eq. 17) ",
+      "where $\\mathbf {M^c} \\in \\mathbb {R}^{n \\times 2d}$ is the intermedia representation obtained by applying bidirectional GRU on the context embedding $\\mathbf {C}$ . Then, let start distribution $\\mathbf {p^s} \\in \\mathbb {R}^n$ as a key to attention $\\mathbf {M^c}$ and transform it to 1-d value using weight $w_c \\in \\mathbb {R}^{2d}$ . Finally, we get the quality score $\\hat{q}_i$ . Paragraph probabilities $P(P_i|Q, \\mathbf {P})$ are generated by normalizing across $\\mathbf {P}$ , ",
+      "$$ \nP(P_i|Q, \\mathbf {P})\\! =\\! q_i =\\! \\frac{\\exp (\\hat{q}_i)}{\\sum _{P_j \\in \\mathbf {P}} \\exp (\\hat{q}_j)}.$$   (Eq. 18) ",
+      "In the training phase, we conduct a negative sampling strategy with one negative sample, for efficient training. Thus a pair of paragraphs, $P^+$ as positive and $P^-$ as negative, are used to approximate $q^+ \\approx P(P^+|Q, [P^+, P^-])$ and $q^- \\approx P(P^-|Q, [P^+, P^-])$ .",
+      "In the inference phase, the probability $q_i$ is obtained by normalizing across all the retrieved paragraphs $\\mathbf {P}$ .",
+      "[h] HAS-QA Model in Training Phase [1] $Q$ : question; $A$ : answer string;",
+      " $\\mathbf {P}$ : retrieved paragraphs;",
+      " $\\mathcal {L}$ : loss function",
+      " $P^+$ , $P^-$ in $\\mathbf {P}$ : Get answer locations $\\mathbf {L^s}$ , $\\mathbf {L^e}$ for $P^+$ ; Get the context embedding $\\mathbf {C}$ ; Compute $\\mathbf {p^s}$ ; (Eq 11 ) $L^s_j, L^e_j$ in $\\mathbf {L^s}, \\mathbf {L^e}$ : $P^-$0 ; Compute $P^-$1 ; (Eq 12 ) $P^-$2 ; $P^-$3 ; Apply function: $P^-$4 ; Compute $P^-$5 in $P^-$6 ; (Eq 17 , Eq 18 ) $P^-$7 ; $P^-$8 .",
+      "[h] HAS-QA Model in Inference Phase [1] $Q$ : question; $\\mathbf {P}$ : retrieved paragraphs; $A_{best}$ : answer string",
+      " $P_i$ in $\\mathbf {P}$ : Get the context embedding $\\mathbf {C}$ ; Compute $\\mathbf {p^s}$ ; (Eq 11 ) $L^s_j$ in Top- $K_1$ $\\mathbf {p^s}$ : $p^s_j \\leftarrow \\mathbf {p^s}[L^s_j]$ ; Compute $\\mathbf {p^e_j}$ ; (Eq 12 ) $L^e_{jk}$ in Top- $\\mathbf {P}$0 $\\mathbf {P}$1 : $\\mathbf {P}$2 ; $\\mathbf {P}$3 ; Group $\\mathbf {P}$4 by extracted answer string $\\mathbf {P}$5 ; Apply function: $\\mathbf {P}$6 ; Compute $\\mathbf {P}$7 ; (Eq 17 )",
+      "Normalize $\\lbrace \\hat{q}_i\\rbrace $ get $\\lbrace q_i\\rbrace $ ; (Eq 18 ) $S(A_t) \\leftarrow \\sum _i q_i \\cdot p^{A_t}_i$ ; $A_{best} \\leftarrow \\arg \\max (S(A_t))$ .",
+      "Above all, we describe our model with Algorithm \"Paragraph Quality Estimator\" in the training phase and Algorithm \"Paragraph Quality Estimator\" in the inference phase."
+    ],
+    [
+      "We evaluate our model on three OpenQA datasets, QuasarT BIBREF21 , TriviaQA BIBREF7 and SearchQA BIBREF22 .",
+      "QuasarT: consists of 43k open-domain trivia questions whose answers obtained from various internet sources. ClueWeb09 BIBREF23 serves as the background corpus for providing evidences paragraphs. We choose the Long version, which is truncated to 2048 characters and 20 paragraphs for each question.",
+      "TriviaQA: consists of 95k open-domain question-answer pairs authored by trivia enthusiasts and independently gathered evidence documents from Bing Web Search and Wikipedia, six per question on average. We focus on the open domain setting contains unfiltered documents.",
+      "SearchQA: is based on a Jeopardy! questions and collects about top 50 web page snippets from Google search engine for each question.",
+      "As we can see in Table 1 , there exist amounts of negative paragraphs which contains no answer span, especially in TriviaQA and SearchQA. For all datasets, more than 4 answer spans averagely obtained per paragraph. These statistics illustrate that problems mentioned above exist in OpenQA datasets."
+    ],
+    [
+      "For RC baseline models GA BIBREF11 , BiDAF BIBREF4 and AQA BIBREF20 , their experimental results are collected from published papers BIBREF22 , BIBREF7 .",
+      "The DrQA BIBREF6 , R ${}^3$ BIBREF16 and Shared-Norm BIBREF8 are evaluated using their released code.",
+      "Our model adopts the same data preprocessing and question context encoder presented in BIBREF8 . In training step, we use the Adadelta optimizer BIBREF24 with the batch size of 30, and we choose the model performed the best on develop set . The hidden dimension of GRU is 200, and the dropout ratio is 0.8. We use 300 dimensional word embeddings pre-trained by GloVe (released by BIBREF17 ) and do not fine-tune in training step. Additionally, 20 dimensional character embeddings are left as learnable parameters. In inference step, for baseline models we set the answer length limitation to 8, while for our models it is unlimited. We analyze different answer length limitation settings in the Section UID31 . The parameters of beam search are $K_1=3$ and $K_2=1$ ."
+    ],
+    [
+      "The experimental results on three OpenQA datasets are shown in Table 2 . It concludes as follow:",
+      "1) HAS-QA outperforms traditional RC baselines with a large gap, such as GA, BiDAF, AQA listed in the first part. For example, in QuasarT, it improves 16.8% in EM score and 20.4% in F1 score. As RC task is just a special case of OpenQA task. Some experiments on standard SQuAD dataset(dev-set) BIBREF9 show that HAS-QA yields EM/F1:0.719/0.798, which is comparable with the best released single model Reinforced Mnemonic Reader BIBREF25 in the leaderboard (dev-set) EM/F1:0.721/0.816. Our performance is slightly worse because Reinforced Mnemonic Reader directly use the accurate answer span, while we use multiple distantly supervised answer spans. That may introduce noises in the setting of SQuAD, since only one span is accurate.",
+      "2) HAS-QA outperforms recent OpenQA baselines, such as DrQA, R ${}^3$ and Shared-Norm listed in the second part. For example, in QuasarT, it improves 4.6% in EM score and 3.5% in F1 score."
+    ],
+    [
+      "In this subsection, we analyze our model by answering the following fine-grained analytic questions:",
+      "1) What advantages does HAS-QA have via modeling answer span using the conditional pointer network?",
+      "2) How much does HAS-QA gain from modeling multiple answer spans in a paragraph?",
+      "3) How does the paragraph quality work in HAS-QA?",
+      "The following three parts are used to answer these questions respectively.",
+      "In order to demonstrate the effect of the conditional pointer networks, we compare Shared-Norm, which uses pointer networks, with our model. Then, we gradually remove the answer length limitation, from restricting 4 words to 128 words until no limitation (denote as $\\infty $ ). Finally, we draw the tendency of the EM performance and average predicted answer length according to the different answer length limitations.",
+      "As shown in Figure 3 (TopLeft), the performance of Shared-Norm decreases when removing the answer length limitation, while the performance of HAS-QA first increases then becomes stable. In Figure 3 (TopRight), we find that the average predicted answer length increases in Shared-Norm when removing the answer length limitation. However, our model stably keeps average about 1.8 words, where the oracle average answer length is about 1.9 words. Example in Figure 3 (Bottom) illustrates that start/end pointers in Shared-Norm search their own optimal positions independently, such as two `Louis' in paragraph. It leads to an unreasonable answer span prediction.",
+      "The effects of utilizing multiple answer spans lay into two aspects, 1) choose the aggregation functions in training phase, and 2) select the parameters of beam search in inference phase.",
+      "In the training phase, we evaluate four types of aggregation functions introduced in Section \"Multiple Spans Aggregator\" . The experimental results on QuasarT dataset, shown in Table 3 , demonstrate the superiority of SUM and MAX operations. They take advantages of using multiple answer spans for training and improve about 6% - 10% in EM comparing to the HEAD operation. The performance of MAX operation is a little better than the SUM operation. The failure of RAND operation, mainly comes down to the conflicting training samples. Therefore, simple way to make use of multiple answer spans may not improve the performance.",
+      "In the inference phase, Table 4 shows the effects of parameters in beam search. We find that the larger $K_1$ yields the better performance, while $K_2$ seems irrelevant to the performance. As a conclusion, we choose the parameters $K_1=3, K_2=1$ to balance the performance and the speed.",
+      "The paragraph probability is efficient to measure the quality of paragraphs, especially for that containing useless paragraphs.",
+      "Figure 4 (Left) shows that with the increasing number of given paragraphs which ordered by the rank of a search engine, EM performance of HAS-QA sustainably grows. However, EM performance of Shared-Norm stops increasing at about 15 paragraphs and our model without paragraph quality (denotes PosOnly) stops increasing at about 5 paragraphs. So that with the help of paragraph probability, model performance can be improved by adding more evidence paragraphs.",
+      "We also evaluate the Mean Average Precision (MAP) score between the predicted scores and the label whether a paragraph contains answer spans (Figure 4 (Right)). The paragraph probability in our model outperforms PosOnly and Shared-Norm, so that it can rank the high quality paragraphs in the front of the given paragraph list."
+    ],
+    [
+      "In this paper, we point out three distinct characteristics of OpenQA, which make it inappropriate to directly apply existing RC models to this task. In order to tackle these problems, we first propose a new probabilistic formulation of OpenQA, where the answer probability is written as the question, paragraph and span, three-level structure. In this formulation, RC can be treated as a special case. Then, Hierarchical Answer Spans Model (HAS-QA) is designed to implement this structure. Specifically, a paragraph quality estimator makes it robust for the paragraphs without answer spans; a multiple span aggregator points out that it is necessary to combine the contributions of multiple answer spans in a paragraph, and a conditional span predictor is proposed to model the dependence between the start and end positions of each answer span. Experiments on public OpenQA datasets, including QuasarT, TriviaQA and SearchQA, show that HAS-QA significantly outperforms traditional RC baselines and recent OpenQA baselines."
+    ],
+    [
+      "This work was funded by the National Natural Science Foundation of China (NSFC) under Grants No. 61773362, 61425016, 61472401, 61722211, and 61872338, the Youth Innovation Promotion Association CAS under Grants No. 20144310, and 2016102, and the National Key R&D Program of China under Grants No. 2016QY02D0405."
+    ]
+  ]
+}
+```
\ No newline at end of file
diff --git a/qasper-2505/instruction.md b/qasper-2505/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..83ad47c6e329f6a9ab4feeb408ed7379d74d697a
--- /dev/null
+++ b/qasper-2505/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Question Answering and Question Generation as Dual Tasks
+
+Question: What does "explicitly leverages their probabilistic correlation to guide the training process of both models" mean?
\ No newline at end of file
diff --git a/qasper-2532/instruction.md b/qasper-2532/instruction.md
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--- /dev/null
+++ b/qasper-2532/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Progressive Joint Modeling in Unsupervised Single-channel Overlapped Speech Recognition
+
+Question: How is the discriminative training formulation different from the standard ones?
\ No newline at end of file
diff --git a/qasper-2533/instruction.md b/qasper-2533/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..3876314634c85cba31a6b0021615c9c6f78b02fb
--- /dev/null
+++ b/qasper-2533/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Progressive Joint Modeling in Unsupervised Single-channel Overlapped Speech Recognition
+
+Question: How are the two datasets artificially overlapped?
\ No newline at end of file
diff --git a/qasper-2535/instruction.md b/qasper-2535/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..44ff360f3ed2b12137a60f50be4cb85e6a9e0b90
--- /dev/null
+++ b/qasper-2535/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: NIHRIO at SemEval-2018 Task 3: A Simple and Accurate Neural Network Model for Irony Detection in Twitter
+
+Question: What type of lexical, syntactic, semantic and polarity features are used?
\ No newline at end of file
diff --git a/qasper-2551/instruction.md b/qasper-2551/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..9704f391c1ece6ce9e7fe687ede1a4d7dc84ff55
--- /dev/null
+++ b/qasper-2551/instruction.md
@@ -0,0 +1,3 @@
+Name of Paper: Learning End-to-End Goal-Oriented Dialog
+
+Question: How large is the Dialog State Tracking Dataset?
\ No newline at end of file
diff --git a/qasper-2556/instruction.md b/qasper-2556/instruction.md
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index 0000000000000000000000000000000000000000..0f7bce84b4f3c4362a5105985112b492550cf7e0
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+++ b/qasper-2556/instruction.md
@@ -0,0 +1,125 @@
+Name of Paper: Question Answering via Integer Programming over Semi-Structured Knowledge
+
+Question: How is the semi-structured knowledge base created?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "QA as Subgraph Optimization",
+    "Semi-Structured Knowledge as Tables",
+    "QA as a Search for Desirable Support Graphs",
+    "ILP Formulation",
+    "Evaluation",
+    "Solvers",
+    "Results",
+    "Ablation Study",
+    "Question Perturbation",
+    "Conclusion",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "Answering questions posed in natural language is a fundamental AI task, with a large number of impressive QA systems built over the years. Today's Internet search engines, for instance, can successfully retrieve factoid style answers to many natural language queries by efficiently searching the Web. Information Retrieval (IR) systems work under the assumption that answers to many questions of interest are often explicitly stated somewhere BIBREF0 , and all one needs, in principle, is access to a sufficiently large corpus. Similarly, statistical correlation based methods, such as those using Pointwise Mutual Information or PMI BIBREF1 , work under the assumption that many questions can be answered by looking for words that tend to co-occur with the question words in a large corpus.",
+      "While both of these approaches help identify correct answers, they are not suitable for questions requiring reasoning, such as chaining together multiple facts in order to arrive at a conclusion. Arguably, such reasoning is a cornerstone of human intelligence, and is a key ability evaluated by standardized science exams given to students. For example, consider a question from the NY Regents 4th Grade Science Test:",
+      "We would like a QA system that, even if the answer is not explicitly stated in a document, can combine basic scientific and geographic facts to answer the question, e.g., New York is in the north hemisphere; the longest day occurs during the summer solstice; and the summer solstice in the north hemisphere occurs in June (hence the answer is June). Figure 1 illustrates how our system approaches this, with the highlighted support graph representing its line of reasoning.",
+      "Further, we would like the system to be robust under simple perturbations, such as changing New York to New Zealand (in the southern hemisphere) or changing an incorrect answer option to an irrelevant word such as \u201clast\u201d that happens to have high co-occurrence with the question text.",
+      "To this end, we propose a structured reasoning system, called TableILP, that operates over a semi-structured knowledge base derived from text and answers questions by chaining multiple pieces of information and combining parallel evidence. The knowledge base consists of tables, each of which is a collection of instances of an $n$ -ary relation defined over natural language phrases. E.g., as illustrated in Figure 1 , a simple table with schema (country, hemisphere) might contain the instance (United States, Northern) while a ternary table with schema (hemisphere, orbital event, month) might contain (North, Summer Solstice, June). TableILP treats lexical constituents of the question $Q$ , as well as cells of potentially relevant tables $T$ , as nodes in a large graph $\\mathcal {G}_{Q,T}$ , and attempts to find a subgraph $G$ of $\\mathcal {G}_{Q,T}$ that \u201cbest\u201d supports an answer option. The notion of best support is captured via a number of structural and semantic constraints and preferences, which are conveniently expressed in the Integer Linear Programming (ILP) formalism. We then use an off-the-shelf ILP optimization engine called SCIP BIBREF3 to determine the best supported answer for $Q$ .",
+      "Following a recently proposed AI challenge BIBREF4 , we evaluate TableILP on unseen elementary-school science questions from standardized tests. Specifically, we consider a challenge set BIBREF2 consisting of all non-diagram multiple choice questions from 6 years of NY Regents 4th grade science exams. In contrast to a state-of-the-art structured inference method BIBREF5 for this task, which used Markov Logic Networks (MLNs) BIBREF6 , TableILP achieves a significantly (+14% absolute) higher test score. This suggests that a combination of a rich and fine-grained constraint language, namely ILP, even with a publicly available solver is more effective in practice than various MLN formulations of the task. Further, while the scalability of the MLN formulations was limited to very few (typically one or two) selected science rules at a time, our approach easily scales to hundreds of relevant scientific facts. It also complements the kind of questions amenable to IR and PMI techniques, as is evidenced by the fact that a combination (trained using simple Logistic Regression BIBREF2 ) of TableILP with IR and PMI results in a significant (+10% absolute) boost in the score compared to IR alone.",
+      "Our ablation study suggests that combining facts from multiple tables or multiple rows within a table plays an important role in TableILP's performance. We also show that TableILP benefits from the table structure, by comparing it with an IR system using the same knowledge (the table rows) but expressed as simple sentences; TableILP scores significantly (+10%) higher. Finally, we demonstrate that our approach is robust to a simple perturbation of incorrect answer options: while the simple perturbation results in a relative drop of 20% and 33% in the performance of IR and PMI methods, respectively, it affects TableILP's performance by only 12%."
+    ],
+    [
+      " BIBREF2 aristo2016:combining proposed an ensemble approach for the science QA task, demonstrating the effectiveness of a combination of information retrieval, statistical association, rule-based reasoning, and an ILP solver operating on semi-structured knowledge. Our ILP system extends their model with additional constraints and preferences (e.g., semantic relation matching), substantially improving QA performance.",
+      "A number of systems have been developed for answering factoid questions with short answers (e.g., \u201cWhat is the capital of France?\u201d) using document collections or databases (e.g., Freebase BIBREF7 , NELL BIBREF8 ), for example BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 , BIBREF15 . However, many science questions have answers that are not explicitly stated in text, and instead require combining information together. Conversely, while there are AI systems for formal scientific reasoning (e.g., BIBREF16 , BIBREF17 ), they require questions to be posed in logic or restricted English. Our goal here is a system that operates between these two extremes, able to combine information while still operating with natural language.",
+      "The task of Recognizing Textual Entailment (RTE) BIBREF18 , BIBREF19 is also closely related, as QA can be cast as entailment (Does corpus entail question+answer? BIBREF20 ). However, RTE has primarily focused on the task of linguistic equivalence, and has not addressed questions where some form of scientific reasoning is required. Recent work on Natural Logic BIBREF21 , BIBREF22 has extended RTE to account for the logical structure within language. Our work can be seen as going one step further, to add a layer of structured reasoning on top of this; in fact, we use an RTE engine as a basic subroutine for comparing individual table cells in our ILP formulation.",
+      "ILP based discrete optimization has been successful in several NLP tasks BIBREF23 , BIBREF24 , BIBREF25 , BIBREF26 , BIBREF27 . While our ILP formulation also operates on natural language text, our focus is on the use of a specific semi-structured table representation for QA. BIBREF28 cohen2000:joins studied tables with natural language text requiring soft matching, with a focus on efficiently computing the top few candidates given a database query. In contrast, our system, given a natural language question, (implicitly) seeks to generate a query that produces the most supported answer."
+    ],
+    [
+      "We begin with our knowledge representation formalism, followed by our treatment of QA as an optimal subgraph selection problem over such knowledge, and then briefly describe our ILP model for subgraph selection."
+    ],
+    [
+      "We use semi-structured knowledge represented in the form of $n$ -ary predicates over natural language text BIBREF2 . Formally, a $k$ -column table in the knowledge base is a predicate $r(x_1, x_2, \\ldots , x_k)$ over strings, where each string is a (typically short) natural language phrase. The column headers capture the table schema, akin to a relational database. Each row in the table corresponds to an instance of this predicate. For example, a simple country-hemisphere table represents the binary predicate $r_{\\text{ctry-hems}}(c,h)$ with instances such as (Australia, Southern) and (Canada, Northern). Since table content is specified in natural language, the same entity is often represented differently in different tables, posing an additional inference challenge.",
+      "Although techniques for constructing this knowledge base are outside the scope of this paper, we briefly mention them. Tables were constructed using a mixture of manual and semi-automatic techniques. First, the table schemas were manually defined based on the syllabus, study guides, and training questions. Tables were then populated both manually and semi-automatically using IKE BIBREF29 , a table-building tool that performs interactive, bootstrapped relation extraction over a corpus of science text. In addition, to augment these tables with the broad knowledge present in study guides that doesn't always fit the manually defined table schemas, we ran an Open IE BIBREF30 pattern-based subject-verb-object (SVO) extractor from BIBREF31 clark2014:akbc over several science texts to populate three-column Open IE tables. Methods for further automating table construction are under development."
+    ],
+    [
+      "We treat question answering as the task of pairing the question with an answer such that this pair has the best support in the knowledge base, measured in terms of the strength of a \u201csupport graph\u201d defined as follows.",
+      "Given a multiple choice question $Q$ and tables $T$ , we can define a labeled undirected graph $\\mathcal {G}_{Q,T}$ over nodes $\\mathcal {V}$ and edges $\\mathcal {E}$ as follows. We first split $Q$ into lexical constituents (e.g., non-stopword tokens, or chunks) $\\mathbf {q} = \\lbrace q_\\ell \\rbrace $ and answer options $\\mathbf {a} = \\lbrace a_{m}\\rbrace $ . For each table $T_{i}$ , we consider its cells $\\mathbf {t} = \\lbrace t_{ijk}\\rbrace $ as well as column headers $T$0 . The nodes of $T$1 are then $T$2 . For presentation purposes, we will equate a graph node with the lexical entity it represents (such as a table cell or a question constituent). The undirected edges of $T$3 are $T$4 excluding edges both whose endpoints are within a single table.",
+      "Informally, an edge denotes (soft) equality between a question or answer node and a table node, or between two table nodes. To account for lexical variability (e.g., that tool and instrument are essentially equivalent) and generalization (e.g., that a dog is an animal), we replace string equality with a phrase-level entailment or similarity function $w : \\mathcal {E} \\rightarrow [0,1]$ that labels each edge $e \\in \\mathcal {E}$ with an associated score $w(e)$ . We use entailment scores (directional) from $\\mathbf {q}$ to $\\mathbf {t} \\cup \\mathbf {h}$ and from $\\mathbf {t} \\cup \\mathbf {h}$ to $\\mathbf {a}$ , and similarity scores (symmetric) between two nodes in $\\mathbf {t}$ . In the special case of column headers across two tables, the score is (manually) set to either 0 or 1, indicating whether this corresponds to a meaningful join.",
+      "Intuitively, we would like the support graph for an answer option to be connected, and to include nodes from the question, the answer option, and at least one table. Since each table row represents a coherent piece of information but cells within a row do not have any edges in $\\mathcal {G}_{Q,T}$ (the same holds also for cells and the corresponding column headers), we use the notion of an augmented subgraph to capture the underlying table structure. Let $G = (V,E)$ be a subgraph of $\\mathcal {G}_{Q,T}$ . The augmented subgraph $G^+$ is formed by adding to $G$ edges $(v_1,v_2)$ such that $v_1$ and $v_2$ are in $V$ and they correspond to either the same row (possibly the header row) of a table in $T$ or to a cell and the corresponding column header.",
+      "Definition 1 A support graph $G = G(Q,T,a_{m})$ for a question $Q$ , tables $T$ , and an answer option $a_{m}$ is a subgraph $(V,E)$ of $\\mathcal {G}_{Q,T}$ with the following basic properties:",
+      " $V \\cap \\mathbf {a} = \\lbrace a_{m}\\rbrace , \\ V \\cap \\mathbf {q} \\ne \\phi , \\ V \\cap \\mathbf {t} \\ne \\phi $ ;",
+      " $w(e) > 0$ for all $e \\in E$ ;",
+      "if $e \\in E \\cap (\\mathbf {t} \\times \\mathbf {t})$ then there exists a corresponding $e^{\\prime } \\in E \\cap (\\mathbf {h} \\times \\mathbf {h})$ involving the same columns; and",
+      "the augmented subgraph $G^+$ is connected.",
+      "A support graph thus connects the question constituents to a unique answer option through table cells and (optionally) table headers corresponding to the aligned cells. A given question and tables give rise to a large number of possible support graphs, and the role of the inference process will be to choose the \u201cbest\" one under a notion of desirable support graphs developed next. We do this through a number of additional structural and semantic properties; the more properties the support graph satisfies, the more desirable it is."
+    ],
+    [
+      "We model the above support graph search for QA as an ILP optimization problem, i.e., as maximizing a linear objective function over a finite set of variables, subject to a set of linear inequality constraints. A summary of the model is given below. We note that the ILP objective and constraints aren't tied to the particular domain of evaluation; they represent general properties that capture what constitutes a well supported answer for a given question.",
+      "Table 1 summarizes the notation for various elements of the problem, such as $t_{ijk}$ for cell $(j,k)$ of table $i$ . All core variables in the ILP model are binary, i.e., have domain $\\lbrace 0,1\\rbrace $ . For each element, the model has a unary variable capturing whether this element is part of the support graph $G$ , i.e., it is \u201cactive\u201d. For instance, row $r_{ij}$ is active if at least one cell in row $j$ of table $i$ is in $G$ . The model also has pairwise \u201calignment\u201d variables, capturing edges of $\\mathcal {G}_{Q,T}$ . The alignment variable for an edge $(j,k)$0 in $(j,k)$1 is associated with the corresponding weight $(j,k)$2 , and captures whether $(j,k)$3 is included in $(j,k)$4 . To improve efficiency, we create a pairwise variable for $(j,k)$5 only if $(j,k)$6 is larger than a certain threshold. These unary and pairwise variables are then used to define various types of constraints and preferences, as discussed next.",
+      "To make the definitions clearer, we introduce all basic variables used in our optimization in Table 2 , and will use them later to define constraints explicitly. We use the notation $x\\left(.\\right)$ to refer to a unary variable parameterized by a single element of the optimization, and $y\\left(., .\\right)$ to refer to a pairwise variable parameterized by a pair of elements. Unary variables represent the presence of a specific element as a node in the support graph $G$ . For example $x\\left(T_{i}\\right) = 1$ if and only if the table $T_{i}$ is active in $G$ . Similarly, $y\\left(t_{ijk}, q_\\ell \\right)= 1$ if and only if the corresponding edge is present in $G$ , which we alternatively refer to as an alignment between cell $(j,k)$ of table $i$ and the $y\\left(., .\\right)$0 -th constituent of the question.",
+      "As previously mentioned, in practice we do not create all possible pairwise variables. Instead we choose the pairs alignment score $w(e)$ exceeds a pre-set threshold. For example, we create $y\\left(t_{ijk}, t_{i^{\\prime }j^{\\prime }k^{\\prime }}\\right)$ only if $w(t_{ijk}, t_{i^{\\prime }j^{\\prime }k^{\\prime }}) \\ge \\textsc {MinCellCellAlignment}$ .",
+      "The objective function is a weighted linear sum over all variables instantiated for a given question answering problem. A small set of auxiliary variables is defined for linearizing complicated constraints. Constraints are a significant part of our model, used for imposing the desired behavior on the support graph. Due to lack of space, we discuss only a representative subset here.",
+      "Some constraints relate variables to each other. For example, unary variables are defined through constraints that relate them to the corresponding pairwise variables. For instance, for active row variable $x\\left(r_{ij}\\right)$ , we ensure that it is 1 if and only if at least one cell in row $j$ is active: $\nx\\left(r_{ij}\\right) \\ge y\\left(t_{ijk}, *\\right),\\ \\ \\ \\forall (t_{ijk}, *) \\in \\mathcal {R}_{ij}, \\forall i, j, k,\n$ ",
+      "where $\\mathcal {R}_{ij}$ is collection of pairwise variables with one end in row $j$ of table $i$ .",
+      "In the remainder of this section, we outline some of the important characteristics we expect in our model, and provide details of a few illustrative constraints. ",
+      "Consider the following question:",
+      "Which characteristic helps a fox find food? (A) sense of smell (B) thick fur (C) long tail (D) pointed teeth",
+      "In order to answer such lookup-style questions, we generally seek a row with the highest aggregate alignment to question constituents. We achieve this by incorporating the question-table alignment variables with the alignment scores, $w(e)$ , as coefficients and the active question constituents variable with a constant coefficient in the objective function. Since any additional question-table edge with a positive entailment score (even to irrelevant tables) in the support graph would result in an increase in the score, we disallow tables with alignments only to the question (or only to a choice) and add a small penalty for every table used in order to reduce noise in the support graph. We also limit the maximum number of alignments of a question constituent and table cells, in order to prevent one constituent or cell from having a large influence on the objective function and thereby the solution: $\n\\sum _{(*, q_\\ell ) \\in \\mathcal {Q}_l } y\\left(*, q_\\ell \\right) \\le \\textsc {MaxAlignmentsPerQCons}, \\forall l\n$ ",
+      "where $\\mathcal {Q}_l$ is the set of all pairwise variables with one end in the question constituent $\\ell $ . ",
+      "For certain questions, evidence needs to be combined from multiple rows of a table. For example,",
+      "Sleet, rain, snow, and hail are forms of (A) erosion (B) evaporation (C) groundwater (D) precipitation",
+      "To answer this question, we need to combine evidence from multiple table entries from the weather terms table, (term, type), namely (sleet, precipitation), (rain, precipitation), (snow, precipitation), and (hail, precipitation). To achieve this, we allow multiple active rows in the support graph. Similar to the basic constraints, we limit the maximum number of active rows per table and add a penalty for every active row to ensure only relevant rows are considered for reasoning : $\n\\sum _{j} x\\left(r_{ij}\\right) \\le \\textsc {MaxRowsPerTable}, \\forall i\n$ ",
+      " To encourage only coherent parallel evidence within a single table, we limit our support graph to always use the same columns across multiple rows within a table, i.e., every active row has the active cells corresponding to the same set of columns.",
+      "Questions requiring chaining of evidence from multiple tables, such as the example in Figure 1 , are typically the most challenging in this domain. Chaining can be viewed as performing a join between two tables. We introduce alignments between cells across columns in pairs of tables to allow for chaining of evidence. To help minimize potential noise introduced by chaining irrelevant facts, we add a penalty for every inter-table alignment and also rely on the 0/1 weights of header-to-header edges to ensure only semantically meaningful table joins are considered.",
+      "Our constraints so far have only looked at the content of the table cells, or the structure of the support graph, without explicitly considering the semantics of the table schema. By using alignments between the question and column headers (i.e., type information), we exploit the table schema to prefer alignments to columns relevant to the \u201ctopic\u201d of the question. In particular, for questions of the form \u201cwhich X $\\ldots $ \u201d, we prefer answers that directly entail X or are connected to cells that entail X. However, this is not sufficient for questions such as:",
+      "What is one way to change water from a liquid to a solid? (A) decrease the temperature (B) increase the temperature (C) decrease the mass (D) increase the mass",
+      "Even if we select the correct table, say $r_\\text{change-init-fin}(c, i, f)$ that describes the initial and final states for a phase change event, both choice (A) and choice (B) would have the exact same score in the presence of table rows (increase temperature, solid, liquid) and (decrease temperature, liquid, solid). The table, however, does have the initial vs. final state structure. To capture this semantic structure, we annotate pairs of columns within certain tables with the semantic relationship present between them. In this example, we would annotate the phase change table with the relations: changeFrom $(c, i)$ , changeTo $(c, f)$ , and fromTo $(i, f)$ .",
+      "Given such semantic relations for table schemas, we can now impose a preference towards question-table alignments that respect these relations. We associate each semantic relation with a set of linguistic patterns describing how it might be expressed in natural language. TableILP then uses these patterns to spot possible mentions of the relations in the question $Q$ . We then add the soft constraint that for every pair of active columns in a table (with an annotated semantic relation) aligned to a pair of question constituents, there should be a valid expression of that relation in $Q$ between those constituents. In our example, we would match the relation fromTo(liquid, solid) in the table to \u201cliquid to a solid\u201d in the question via the pattern \u201cX to a Y\" associated with fromTo(X,Y), and thereby prefer aligning with the correct row (decrease temperature, liquid, solid)."
+    ],
+    [
+      "We compare our approach to three existing methods, demonstrating that it outperforms the best previous structured approach BIBREF5 and produces a statistically significant improvement when used in combination with IR-based methods BIBREF2 . For evaluations, we use a 2-core 2.5 GHz Amazon EC2 linux machine with 16 GB RAM.",
+      "Question Set. We use the same question set as BIBREF2 aristo2016:combining, which consists of all non-diagram multiple-choice questions from 12 years of the NY Regents 4th Grade Science exams. The set is split into 108 development questions and 129 hidden test questions based on the year they appeared in (6 years each). All numbers reported below are for the hidden test set, except for question perturbation experiments which relied on the 108 development questions.",
+      "Test scores are reported as percentages. For each question, a solver gets a score of 1 if it chooses the correct answer and $1/k$ if it reports a $k$ -way tie that includes the correct answer. On the 129 test questions, a score difference of 9% (or 7%) is statistically significant at the 95% (or 90%, resp.) confidence interval based on the binomial exact test BIBREF33 .",
+      "Corpora. We work with three knowledge corpora:"
+    ],
+    [
+      "TableILP (our approach). Given a question $Q$ , we select the top 7 tables from the Table Corpus using the the standard TF-IDF score of $Q$ with tables treated as bag-of-words documents. For each selected table, we choose the 20 rows that overlap with $Q$ the most. This filtering improves efficiency and reduces noise. We then generate an ILP and solve it using the open source SCIP engine BIBREF3 , returning the active answer option $a_{m}$ from the optimal solution. To check for ties, we disable $a_{m}$ , re-solve the ILP, and compare the score of the second-best answer, if any, with that of $a_m$ .",
+      "MLN Solver (structured inference baseline). We consider the current state-of-the-art structured reasoning method developed for this specific task by BIBREF5 aristo2015:mln. We compare against their best performing system, namely Praline, which uses Markov Logic Networks BIBREF6 to (a) align lexical elements of the question with probabilistic first-order science rules and (b) to control inference. We use the entire set of 47,000 science rules from their original work, which were also derived from same domain-targeted sources as the ones used in our Sentence Corpus.",
+      "IR Solver (information retrieval baseline). We use the IR baseline by BIBREF2 aristo2016:combining, which selects the answer option that has the best matching sentence in a corpus. Specifically, for each answer option $a_i$ , the IR solver sends $q + a_i$ as a query to a search engine (we use Lucene) on the Sentence Corpus, and returns the search engine's score for the top retrieved sentence $s$ , where $s$ must have at least one non-stopword overlap with $q$ , and at least one with $a_i$ . The option with the highest Lucene score is returned as the answer.",
+      "PMI Solver (statistical co-occurrence baseline). We use the PMI-based approach by BIBREF2 aristo2016:combining, which selects the answer option that most frequently co-occurs with the question words in a corpus. Specifically, it extracts unigrams, bigrams, trigrams, and skip-bigrams from the question and each answer option. For a pair $(x,y)$ of $n$ -grams, their pointwise mutual information (PMI) BIBREF1 in the corpus is defined as $\\log \\frac{p(x,y)}{p(x)p(y)}$ where $p(x,y)$ is the co-occurrence frequency of $x$ and $y$ (within some window) in the corpus. The solver returns the answer option that has the largest average PMI in the Web Corpus, calculated over all pairs of question $n$ -grams and answer option $n$ -grams."
+    ],
+    [
+      "We first compare the accuracy of our approach against the previous structured (MLN-based) reasoning solver. We also compare against IR(tables), an IR solver using table rows expressed as sentences, thus embodying an unstructured approach operating on the same knowledge as TableILP.",
+      "As Table 3 shows, among the two structured inference approaches, TableILP outperforms the MLN baseline by 14%. The preliminary ILP system reported by BIBREF2 aristo2016:combining achieves only a score of 43.8% on this question set. Further, given the same semi-structured knowledge (i.e., the Table Corpus), TableILP is substantially (+10%) better at exploiting the structure than the IR(tables) baseline, which, as mentioned above, uses the same data expressed as sentences.",
+      "While their overall score is similar, TableILP and IR-based methods clearly approach QA very differently. To assess whether TableILP adds any new capabilities, we considered the 50 (out of 129) questions incorrectly answered by PMI solver (ignoring tied scores). On these unseen but arguably more difficult questions, TableILP answered 27 questions correctly, achieving a score of 54% compared to the random chance of 25% for 4-way multiple-choice questions. Results with IR solver were similar: TableILP scored 24.75 on the 52 questions incorrectly answered by IR (i.e., 47.6% accuracy).",
+      "This analysis highlights the complementary strengths of these solvers. Following BIBREF2 aristo2016:combining, we create an ensemble of TableILP, IR, and PMI solvers, combining their answer predictions using a simple Logistic Regression model trained on the development set. This model uses 4 features derived from each solver's score for each answer option, and 11 features derived from TableILP's support graphs. Table 4 shows the results, with the final combination at 69% representing a significant improvement over individual solvers.",
+      "Table 5 summarizes various ILP and support graph statistics for TableILP, averaged across all test questions.",
+      "The optimization model has around 50 high-level constraints, which result, on average, in around 4000 inequalities over 1000 variables. Model creation, which includes computing pairwise entailment scores using WordNet, takes 1.9 seconds on average per question, and the resulting ILP is solved by the SCIP engine in 2.1 seconds (total for all four options), using around 1,300 LP iterations for each option. Thus, TableILP takes only 4 seconds to answer a question using multiple rows across multiple tables (typically 140 rows in total), as compared to 17 seconds needed by the MLN solver for reasoning with four rules (one per answer option).",
+      "While the final support graph on this question set relies mostly on a single table to answer the question, it generally combines information from more than two rows (2.3 on average) for reasoning. This suggests parallel evidence is more frequently used on this dataset than evidence chaining."
+    ],
+    [
+      "To quantify the importance of various components of our system, we performed several ablation experiments, summarized in Table 6 and described next.",
+      "No Multiple Row Inference: We modify the ILP constraints to limit inference to a single row (and hence a single table), thereby disallowing parallel evidence and evidence chaining (Section \"ILP Formulation\" ). This drops the performance by 10.5%, highlighting the importance of being able to combine evidence from multiple rows (which would correspond to multiple sentences in a corpus) from one or more tables.",
+      "No Relation matching: To assess the importance of considering the semantics of the table, we remove the requirement of matching the semantic relation present between columns of a table with its lexicalization in the question (Section \"ILP Formulation\" ). The 6% drop indicates TableILP relies strongly on the table semantics to ensure creating meaningful inferential chains.",
+      "No Open IE tables: To evaluate the impact of relatively unstructured knowledge from a large corpus, we removed the tables containing Open IE extractions (Section \"Conclusion\" ). The 9% drop in the score shows that this knowledge is important and TableILP is able to exploit it even though it has a very simple triple structure. This opens up the possibility of extending our approach to triples extracted from larger knowledge bases.",
+      "No Lexical Entailment: Finally, we test the effect of changing the alignment metric $w$ (Section \"Conclusion\" ) from WordNet based scores to a simple asymmetric word-overlap measured as $\\mathit {score}(T, H) = \\frac{|T \\cap H|}{|H|}$ . Relying on just word-matching results in an 11% drop, which is consistent with our knowledge often being defined in terms of generalities."
+    ],
+    [
+      "One desirable property of QA systems is robustness to simple variations of a question, especially when a variation would make the question arguably easier for humans.",
+      "To assess this, we consider a simple, automated way to perturb each 4-way multiple-choice question: (1) query Microsoft's Bing search engine (www.bing.com) with the question text and obtain the text snippet of the top 2,000 hits; (2) create a list of strings by chunking and tokenizing the results; (3) remove stop words and special characters, as well as any words (or their lemma) appearing in the question; (4) sort the remaining strings based on their frequency; and (5) replace the three incorrect answer options in the question with the most frequently occurring strings, thereby generating a new question. For instance:",
+      "In New York State, the longest period of daylight occurs during which month? (A) eastern (B) June (C) history (D) years",
+      "As in this example, the perturbations (italicized) are often not even of the correct \u201ctype\u201d, typically making them much easier for humans. They, however, still remain difficult for solvers.",
+      "For each of the 108 development questions, we generate 10 new perturbed questions, using the 30 most frequently occurring words in step (5) above. While this approach can introduce new answer options that should be considered correct as well, only 3% of the questions in a random sample exhibited this behavior. Table 7 shows the performance of various solvers on the resulting 1,080 perturbed questions. As one might expect, the PMI approach suffers the most at a 33% relative drop. TableILP's score drops as well (since answer type matching isn't perfect), but only by 12%, attesting to its higher resilience to simple question variation."
+    ],
+    [
+      "Answering real science questions is a challenging task because they are posed in natural language, require extensive domain knowledge, and often require combining multiple facts together. We presented TableILP, a system that can answer such questions, using a semi-structured knowledge base. We treat QA as a subgraph selection problem and then formulate this as an ILP optimization. Most importantly, this formulation allows multiple, semi-formally expressed facts to be combined to answer questions, a capability outside the scope of IR-based QA systems. In our experiments, this approach significantly outperforms both the previous best attempt at structured reasoning for this task, and an IR engine provided with the same knowledge. It also significantly boosts performance when combined with unstructured methods (IR and PMI). These results suggest that the approach is both viable and promising for natural language question answering."
+    ],
+    [
+      "D.K. is in part supported by AI2 and Google. The authors would like to thank Christos Christodoulopoulos, Sujay Jauhar, Sam Skjonsberg, and the Aristo Team at AI2 for invaluable discussions and insights. "
+    ]
+  ]
+}
+```
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+Name of Paper: Hate Speech Detection on Vietnamese Social Media Text using the Bidirectional-LSTM Model
+
+Question: What social media platform does the data come from?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Bi-LSTM model for Vietnamese Hate Speech Detection",
+    "Bi-LSTM model for Vietnamese Hate Speech Detection ::: Long Short-Term Memory",
+    "Bi-LSTM model for Vietnamese Hate Speech Detection ::: Bidirectional Long Short-Term Memory",
+    "Pre-Processing",
+    "Experiments ::: Dataset and Word Embeddings",
+    "Experiments ::: Evaluation on each Model",
+    "Experiments ::: Experimental Results",
+    "Conclusion and Future Work",
+    "Acknowledgment"
+  ],
+  "paragraphs": [
+    [
+      "In recent years, social networking has grown and become prevalent with every people, it makes easy for people to interact and share with each other. However, every problem has two sides. It also has some negative issues, hate speech is a hot topic in the domain of social media. With the freedom of speech on social networks and anonymity on the internet, some people are free to comment on hate and insults. Hate speech can have an adverse effect on human behavior as well as directly affect society. We don't manually delete each of those comments, which is time-consuming and boring. This spurs research to build an automated system that detects hate speech and eliminates them. With that system, we can detect and eliminate hate speech and thus reduce their spread on social media. With Vietnamese, we can use methods to apply specific extraction techniques manually and in combination with string labeling algorithms such as Conditional Random Field (CRF)[1], Model Hidden Markov (HMM)[2] or Entropy[3]. However, we have to choose the features manually to bring the model with high accuracy. Deep Neural Network architectures can handle the weaknesses of the above methods. In this report we apply Bidirectional Long Short-Term Memory (Bi-LSTM) to build the model. Also combined with the word embedding matrix to increase the accuracy of the model.",
+      "The rest of the paper is organized as follows. In section 2, we presented the related work. In section 3, we described our Bi-LSTM system. In sections 4 and 5, we presented the experimental process and results. Finally, section 6 gives conclusions about the work."
+    ],
+    [
+      "Gao and Huang (2017)[4] used BiLSTMs with attention mechanism 372 to detect hate speech. They illustrated that the Bi-directional LSTM model with attention mechanism achieves the high performance. They hypothesize that this is because hate indicator phrases are often concentrated in a small region of a comment, which is especially the case for long comments. Davidson et al. (2017)[5] train a model to differentiate among three classes: containing hate speech, only offensive language, or neither.Jing Qian, Mai ElSherief, Elizabeth Belding, William Yang Wang (2018) [6] worked on classifying a tweet as racist, sexist or neither by multiple deep learning architectures. ABARUAH at SemEval-2019 [7] presented the results obtained using bi-directional long short-term memory (BiLSTM) with and without attention and Logistic Regression (LR) models for multilingual detection of hate speech against immigrants and women in Twitter. Animesh Koratana and Kevin Hu [8] use many machine learning models to detect toxic words, in which the Bi-Lstm model got the highest performance. Malmasi and Zampieri (2017)[9] made a similar study to compare the performance of different features in detecting hate speech."
+    ],
+    [
+      "As mentioned previously, we propose a framework based on the ensemble of Bi-LSTM models to perform hate speech detection with the provided dataset. Besides, we also implemented some more models to compare and find the optimal model for the task."
+    ],
+    [
+      "LSTM takes words from an input sentence in a distributed word representation format. LSTM's network architecture includes memory cells and ports that allow the storage or retrieval of information. These gates help the LSTM memory cell to perform a write, read and reset operation. They enable the LSTM memory cell to store and access information over a period of time."
+    ],
+    [
+      "One drawback of LSTM architecture[10] is that they are only considering the previous context. However, the identification of a word depends not only on the previous context but also on the subsequent context. Bidirectional LSTM (Bi-LSTM)[11] was created to overcome this weakness. A Bi-LSTM architecture usually contains two single LSTM networks used simultaneously and independently to model input chains in two directions: forward LSTM and backward LSTM."
+    ],
+    [
+      "The pre-processing performed on the text includes the following:",
+      "The comments were converted to lowercase.",
+      "The URLs, mentions (@) and non-alphabetic characters are removed (number, excess whitespace).",
+      "Several stopwords were removed from the comments. We don't remove all stopword because having a few stopwords affect the results.",
+      "A few characters that don't affect the results are replaced by an empty string.",
+      "Using Tokenizer to convert each comment into a sequence of integers."
+    ],
+    [
+      "VLSP Shared Task 2019: Hate Speech Detection on Social Networks: This dataset includes 25431 items in csv format, the dataset was divided into two file, training dataset with 20345 items and test dataset with 5086 items. Each data line of training dataset is assigned 1 of 3 labels CLEAN, OFFENSIVE or HATE. The test dataset is not assign label. The statistic summarization of the given training dataset is described in Table I.",
+      "Hate speech (HATE) contains the abusive language, which often bears the purpose of insulting individuals or groups, and can include hate speech, derogatory and offensive language. An item is identified as hate speech if it (1) targets individuals or groups on the basis of their characteristics; (2) demonstrates a clear intention to incite harm, or to promote hatred; (3) may or may not use offensive or profane words.",
+      "Offensive but not hate speech (OFFENSIVE) is an item (posts/comments) may contain offensive words but it does not target individuals or groups on the basis of their characteristics.",
+      "Neither offensive nor hate speech (CLEAN) is a normal item. It's conversations, expressing emotions normally. It does not contain offensive language or hate speech.",
+      "In this paper, we use two different word embeddings to compare and find out the best word embedding such as Word2Vec [17] and FastText [16]. We used pre-trained vector with large dimensions to increase the accuracy of the model. Through experiments we found FastText achieved better results.",
+      "For this public dataset, we find that the dataset is an unbalanced dataset. The CLEAN label has the highest rate with 91.49% and the HATE label is lowest with 3.49%. Therefore, it is difficult and challenging to find a good model for this task."
+    ],
+    [
+      "For problems of this type, there are many models suitable to handle such as: SVM, Bi-LTSM, LR, GRU, CNN and etc. To solve this problem, we implement four different models (SVM, LR, Bi-LSTM, and GRU) to compare and find the most suitable one. To evaluate the four models on this task, we divide the training dataset into two parts training, testing rate of 80%, 20% respectively.",
+      "The details of our models are provided below.",
+      "1. Support Vector Machine (SVM)",
+      "Support Vector Machines (SVMs) are a popular machine learning method for classification, regression, and other learning tasks [13]. It is often used for two-class classification problems. For this problem, it has three labels, so we use the SVM to classify twice, two label at a time.Firstly, we classify two label 0 and 1, we achieved accuracy, precision, recall, and F1-score rates of 96.00%, 93.37%, 98.96%, and 96.08% respectively, on training dataset. Second time, we classify two label 1 and 2, we achieved accuracy, precision, recall, and F1-score rates of 84.34%, 87.38%, 78.86%, and 82.90% respectively. We find that this model doesn't classify well for two labels 1 and 2. Moreover, when we check this model with the public-test, it brings the result as not good as we expected with 63.87%.",
+      "2. Logistic Regression (LR)",
+      "Logistic regression is basically a supervised classification algorithm. In a classification problem, the target variable(or output), can take only discrete values for a given set of features(or inputs) [14]. We have applied it to this problem as follows: Firstly, we use the TfidfVectorizer tool to convert text into feature vectors that are used as input for the model. Then, we used the Logistic Regression model to predict the classification results. When checking it on training datasets, we achieved accuracy, precision, recall, and F1-score rates of 94.17%, 88.87%, 55.54%, and 64.15% respectively. We also try submitting this model's result on the system, the result is worse we thought with 51.15%",
+      "3. Gated Recurrent Units (GRU)",
+      "The Recurrent Neural Network (RNN) handles the variable-length sequence by having a recurrent hidden state whose activation at each time is dependent on that of the previous time [15]. The GRU is a variant of RNN and it only has two inputs. We have used it into this problem as follows: We have used it with word embeddings Fasttext [16]. First, we use Tokenizer() for sequences because GRU is good at processing long sequences. Then, we have applied this model to the problem. We achieved accuracy, precision, recall, and F1-score rates of 94.61%, 67.12%, 59.66%, and 64.15% respectively, on the training dataset. When we check this model with the public dataset, it brings the result quite good with 65.01%",
+      "4. Bidirectional Long Short-Term Memory (Bi-LSTM)",
+      "The LSTM is a famous variant of RNN. The Bidirectional Long Short Term Memory can be trained using all available input information in the past and future of a specific time frame. We have applied it with word embeddings Fasttext [16] and baomoi.vn.model.txt [17]. As follows:",
+      "As well as the GRU, we also used GloVe Embedding for sequences and applied this model for the problem. When using word embeddings baomoi.vn.model.txt [17], we achieved accuracy, precision, recall, and F1-score rates of 93.26%, 90.74%, 50.30%, and 53.62% respectively, on the training dataset. The same with word embeddings Fasttext [16], we achieved accuracy, precision, recall, and F1-score rates of 95.67%, 85.61%, 67.36%, and 73.84% respectively, on the training dataset. We find that when combining the Bi-LSTM with fasttext will bring the result better. When we check it with the public dataset, it brings the result good with 71.43%"
+    ],
+    [
+      "After conducting experiments on many models, we obtained the following results on public-test, shown in Table TABREF16.",
+      "We achieved the best result with Bi-LSTM, ranking the 2nd of the scoreboard on the public-test set shown in Table TABREF17. However, our result ranks the 6th of the scoreboard on the private-test set."
+    ],
+    [
+      "In this paper, we have presented our approach to address Vietnamese hate speech detection task proposed at the VLSP Shared Task 2019. We develop the system using Bidirectional Long Short Memory for classifying three different labels in this task. We participate in this and evaluate the performance of our system on this dataset. As a result, our result is 71.43% of F1-score, ranking the 2nd of the scoreboard on the public-test set.",
+      "In the future work, we plan to address this problem in different ways to enhance the performance of this task. We will investigate experiments both in traditional machine learning and types of deep learning for this problem. In addition, we also analyze experimental results on this task to choose the efficient approach such as the hybrid approach which combines machine learning and rule-based approaches to boost the result of detecting hate speech on Vietnamese social media text."
+    ],
+    [
+      "We would like to thank the VLSP Shared Task 2019 organizers for their really hard work and providing the dataset of Vietnamese Hate Speech Detection on social networks for our experiments."
+    ]
+  ]
+}
+```
\ No newline at end of file