Add files using upload-large-folder tool

qasper-0111/instruction.md

@@ -0,0 +1,47 @@
+Name of Paper: Diachronic Topics in New High German Poetry
+
+Question: What is the algorithm used for the classification tasks?
+
+## 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."
+    ]
+  ]
+}
+```

qasper-0116/instruction.md

@@ -0,0 +1,98 @@
+Name of Paper: Important Attribute Identification in Knowledge Graph
+
+Question: What user generated text data do you use?
+
+## 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."
+    ]
+  ]
+}
+```

qasper-0120/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: What Drives the International Development Agenda? An NLP Analysis of the United Nations General Debate 1970-2016
+
+Question: What are the country-specific drivers of international development rhetoric?

qasper-0129/instruction.md

@@ -0,0 +1,82 @@
+Name of Paper: Improving Spoken Language Understanding By Exploiting ASR N-best Hypotheses
+
+Question: Which ASR system(s) is used in this work?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Baseline, Oracle and Direct Models ::: Baseline and Oracle",
+    "Baseline, Oracle and Direct Models ::: Direct Models",
+    "Integration of N-BEST Hypotheses",
+    "Integration of N-BEST Hypotheses ::: Hypothesized Text Concatenation",
+    "Integration of N-BEST Hypotheses ::: Hypothesis Embedding Concatenation",
+    "Experiment ::: Dataset",
+    "Experiment ::: Performance on Entire Test Set",
+    "Experiment ::: Performance Comparison among Various Subsets",
+    "Experiment ::: Improvements on Different Domains and Different Numbers of Hypotheses",
+    "Experiment ::: Intent Classification",
+    "Conclusions and Future Work",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "Currently, voice-controlled smart devices are widely used in multiple areas to fulfill various tasks, e.g. playing music, acquiring weather information and booking tickets. The SLU system employs several modules to enable the understanding of the semantics of the input speeches. When there is an incoming speech, the ASR module picks it up and attempts to transcribe the speech. An ASR model could generate multiple interpretations for most speeches, which can be ranked by their associated confidence scores. Among the $n$-best hypotheses, the top-1 hypothesis is usually transformed to the NLU module for downstream tasks such as domain classification, intent classification and named entity recognition (slot tagging). Multi-domain NLU modules are usually designed hierarchically BIBREF0. For one incoming utterance, NLU modules will firstly classify the utterance as one of many possible domains and the further analysis on intent classification and slot tagging will be domain-specific.",
+      "In spite of impressive development on the current SLU pipeline, the interpretation of speech could still contain errors. Sometimes the top-1 recognition hypothesis of ASR module is ungrammatical or implausible and far from the ground-truth transcription BIBREF1, BIBREF2. Among those cases, we find one interpretation exact matching with or more similar to transcription can be included in the remaining hypotheses ($2^{nd}- n^{th}$).",
+      "To illustrate the value of the $2^{nd}- n^{th}$ hypotheses, we count the frequency of exact matching and more similar (smaller edit distance compared to the 1st hypothesis) to transcription for different positions of the $n$-best hypotheses list. Table TABREF1 exhibits the results. For the explored dataset, we only collect the top 5 interpretations for each utterance ($n = 5$). Notably, when the correct recognition exists among the 5 best hypotheses, 50% of the time (sum of the first row's percentages) it occurs among the $2^{nd}-5^{th}$ positions. Moreover, as shown by the second row in Table TABREF1, compared to the top recognition hypothesis, the other hypotheses can sometimes be more similar to the transcription.",
+      "Over the past few years, we have observed the success of reranking the $n$-best hypotheses BIBREF1, BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9, BIBREF10 before feeding the best interpretation to the NLU module. These approaches propose the reranking framework by involving morphological, lexical or syntactic features BIBREF8, BIBREF9, BIBREF10, speech recognition features like confidence score BIBREF1, BIBREF4, and other features like number of tokens, rank position BIBREF1. They are effective to select the best from the hypotheses list and reduce the word error rate (WER) BIBREF11 of speech recognition.",
+      "Those reranking models could benefit the first two cases in Table TABREF2 when there is an utterance matching with transcription. However, in other cases like the third row, it is hard to integrate the fragmented information in multiple hypotheses.",
+      "This paper proposes various methods integrating $n$-best hypotheses to tackle the problem. To the best of our knowledge, this is the first study that attempts to collectively exploit the $n$-best speech interpretations in the SLU system. This paper serves as the basis of our $n$-best-hypotheses-based SLU system, focusing on the methods of integration for the hypotheses. Since further improvements of the integration framework require considerable setup and descriptions, where jointly optimized tasks (e.g. transcription reconstruction) trained with multiple ways (multitask BIBREF12, multistage learning BIBREF13) and more features (confidence score, rank position, etc.) are involved, we leave those to a subsequent article.",
+      "This paper is organized as follows. Section SECREF2 introduces the Baseline, Oracle and Direct models. Section SECREF3 describes proposed ways to integrate $n$-best hypotheses during training. The experimental setup and results are described in Section SECREF4. Section SECREF5 contains conclusions and future work."
+    ],
+    [
+      "The preliminary architecture is shown in Fig. FIGREF4. For a given transcribed utterance, it is firstly encoded with Byte Pair Encoding (BPE) BIBREF14, a compression algorithm splitting words to fundamental subword units (pairs of bytes or BPs) and reducing the embedded vocabulary size. Then we use a BiLSTM BIBREF15 encoder and the output state of the BiLSTM is regarded as a vector representation for this utterance. Finally, a fully connected Feed-forward Neural Network (FNN) followed by a softmax layer, labeled as a multilayer perceptron (MLP) module, is used to perform the domain/intent classification task based on the vector.",
+      "For convenience, we simplify the whole process in Fig.FIGREF4 as a mapping $BM$ (Baseline Mapping) from the input utterance $S$ to an estimated tag's probability $p(\\tilde{t})$, where $p(\\tilde{t}) \\leftarrow BM(S)$. The $Baseline$ is trained on transcription and evaluated on ASR 1st best hypothesis ($S=\\text{ASR}\\ 1^{st}\\ \\text{best})$. The $Oracle$ is trained on transcription and evaluated on transcription ($S = \\text{Transcription}$). We name it Oracle simply because we assume that hypotheses are noisy versions of transcription."
+    ],
+    [
+      "Besides the Baseline and Oracle, where only ASR 1-best hypothesis is considered, we also perform experiments to utilize ASR $n$-best hypotheses during evaluation. The models evaluating with $n$-bests and a BM (pre-trained on transcription) are called Direct Models (in Fig. FIGREF7):",
+      "Majority Vote. We apply the BM model on each hypothesis independently and combine the predictions by picking the majority predicted label, i.e. Music.",
+      "",
+      "Sort by Score. After parallel evaluation on all hypotheses, sort the prediction by the corresponding confidence score and choose the one with the highest score, i.e. Video.",
+      "",
+      "Rerank (Oracle). Since the current rerank models (e.g., BIBREF1, BIBREF3, BIBREF4) attempt to select the hypothesis most similar to transcription, we propose the Rerank (Oracle), which picks the hypothesis with the smallest edit distance to transcription (assume it is the $a$-th best) during evaluation and uses its corresponding prediction."
+    ],
+    [
+      "All the above mentioned models apply the BM trained on one interpretation (transcription). Their abilities to take advantage of multiple interpretations are actually not trained. As a further step, we propose multiple ways to integrate the $n$-best hypotheses during training. The explored methods can be divided into two groups as shown in Fig. FIGREF11. Let $H_1, H_2,..., H_n $ denote all the hypotheses from ASR and $bp_{H_k, i} \\in BPs$ denotes the $i$-th pair of bytes (BP) in the $k^{th}$ best hypothesis. The model parameters associated with the two possible ways both contain: embedding $e_{bp}$ for pairs of bytes, BiLSTM parameters $\\theta $ and MLP parameters $W, b$."
+    ],
+    [
+      "The basic integration method (Combined Sentence) concatenates the $n$-best hypothesized text. We separate hypotheses with a special delimiter ($<$SEP$>$). We assume BPE totally produces $m$ BPs (delimiters are not split during encoding). Suppose the $n^{th}$ hypothesis has $j$ pairs. The entire model can be formulated as:",
+      "In Eqn. DISPLAY_FORM13, the connected hypotheses and separators are encoded via BiLSTM to a sequence of hidden state vectors. Each hidden state vector, e.g. $h_1$, is the concatenation of forward $h_{1f}$ and backward $h_{1b}$ states. The concatenation of the last state of the forward and backward LSTM forms the output vector of BiLSTM (concatenation denoted as $[,]$). Then, in Eqn. DISPLAY_FORM14, the MLP module defines the probability of a specific tag (domain or intent) $\\tilde{t}$ as the normalized activation ($\\sigma $) output after linear transformation of the output vector."
+    ],
+    [
+      "The concatenation of hypothesized text leverages the $n$-best list by transferring information among hypotheses in an embedding framework, BiLSTM. However, since all the layers have access to both the preceding and subsequent information, the embedding among $n$-bests will influence each other, which confuses the embedding and makes the whole framework sensitive to the noise in hypotheses.",
+      "As the second group of integration approaches, we develop models, PoolingAvg/Max, on the concatenation of hypothesis embedding, which isolate the embedding process among hypotheses and summarize the features by a pooling layer. For each hypothesis (e.g., $i^{th}$ best in Eqn. DISPLAY_FORM16 with $j$ pairs of bytes), we could get a sequence of hidden states from BiLSTM and obtain its final output state by concatenating the first and last hidden state ($h_{output_i}$ in Eqn. DISPLAY_FORM17). Then, we stack all the output states vertically as shown in Eqn. SECREF15. Note that in the real data, we will not always have a fixed size of hypotheses list. For a list with $r$ ($<n$) interpretations, we get the embedding for each of them and pad with the embedding of the first best hypothesis until a fixed size $n$. When $r\\ge n$, we only stack the top $n$ embeddings. We employ $h_{output_1}$ for padding to enhance the influence of the top 1 hypothesis, which is more reliable. Finally, one unified representation could be achieved via Pooling (Max/Avg pooling with $n$ by 1 sliding window and stride 1) on the concatenation and one score could be produced per possible tag for the given task."
+    ],
+    [
+      "We conduct our experiments on $\\sim $ 8.7M annotated anonymised user utterances. They are annotated and derived from requests across 23 domains."
+    ],
+    [
+      "Table TABREF24 shows the relative error reduction (RErr) of Baseline, Oracle and our proposed models on the entire test set ($\\sim $ 300K utterances) for multi-class domain classification. We can see among all the direct methods, predicting based on the hypothesis most similar to the transcription (Rerank (Oracle)) is the best.",
+      "As for the other models attempting to integrate the $n$-bests during training, PoolingAvg gets the highest relative improvement, 14.29%. It as well turns out that all the integration methods outperform direct models drastically. This shows that having access to $n$-best hypotheses during training is crucial for the quality of the predicted semantics."
+    ],
+    [
+      "To further detect the reason for improvements, we split the test set into two parts based on whether ASR first best agrees with transcription and evaluate separately. Comparing Table TABREF26 and Table TABREF27, obviously the benefits of using multiple hypotheses are mainly gained when ASR 1st best disagrees with the transcription. When ASR 1st best agrees with transcription, the proposed integration models can also keep the performance. Under that condition, we can still improve a little (3.56%) because, by introducing multiple ASR hypotheses, we could have more information and when the transcription/ASR 1st best does not appear in the training set's transcriptions, its $n$-bests list may have similar hypotheses included in the training set's $n$-bests. Then, our integration model trained on $n$-best hypotheses as well has clue to predict. The series of comparisons reveal that our approaches integrating the hypotheses are robust to the ASR errors and whenever the ASR model makes mistakes, we can outperform more significantly."
+    ],
+    [
+      "Among all the 23 domains, we choose 8 popular domains for further comparisons between the Baseline and the best model of Table TABREF24, PoolingAvg. Fig. FIGREF29 exhibits the results. We could find the PoolingAvg consistently improves the accuracy for all 8 domains.",
+      "In the previous experiments, the number of utilized hypotheses for each utterance during evaluation is five, which means we use the top 5 interpretations when the size of ASR recognition list is not smaller than 5 and use all the interpretations otherwise. Changing the number of hypotheses while evaluation, Fig. FIGREF30 shows a monotonic increase with the access to more hypotheses for the PoolingAvg and PoolingMax (Sort by Score is shown because it is the best achievable direct model while the Rerank (Oracle) is not realistic). The growth becomes gentle after four hypotheses are leveraged."
+    ],
+    [
+      "Since another downstream task, intent classification, is similar to domain classification, we just show the best model in domain classification, PoolingAvg, on domain-specific intent classification for three popular domains due to space limit. As Table TABREF32 shows, the margins of using multiple hypotheses with PoolingAvg are significant as well."
+    ],
+    [
+      "This paper improves the SLU system robustness to ASR errors by integrating $n$-best hypotheses in different ways, e.g. the aggregation of predictions from hypotheses or the concatenation of hypothesis text or embedding. We can achieve significant classification accuracy improvements over production-quality baselines on domain and intent classifications, 14% to 25% relative gains. The improvement is more significant for a subset of testing data where ASR first best is different from transcription. We also observe that with more hypotheses utilized, the performance can be further improved. In the future, we aim to employ additional features (e.g. confidence scores for hypotheses or tokens) to integrate $n$-bests more efficiently, where we can train a function $f$ to obtain a weight for each hypothesis embedding before pooling. Another direction is using deep learning framework to embed the word lattice BIBREF16 or confusion network BIBREF17, BIBREF18, which can provide a compact representation of multiple hypotheses and more information like times, in the SLU system."
+    ],
+    [
+      "We would like to thank Junghoo (John) Cho for proofreading."
+    ]
+  ]
+}
+```

qasper-0312/instruction.md

@@ -0,0 +1,116 @@
+Name of Paper: DENS: A Dataset for Multi-class Emotion Analysis
+
+Question: How many annotators were there?
+
+## 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."
+    ]
+  ]
+}
+```

qasper-0323/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Tag-based Multi-Span Extraction in Reading Comprehension
+
+Question: What approach did previous models use for multi-span questions?

qasper-0324/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Tag-based Multi-Span Extraction in Reading Comprehension
+
+Question: How they use sequence tagging to answer multi-span questions?

qasper-0544/instruction.md

@@ -0,0 +1,41 @@
+Name of Paper: How Does Language Influence Documentation Workflow? Unsupervised Word Discovery Using Translations in Multiple Languages
+
+Question: What are the different bilingual models employed?
+
+## 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."
+    ]
+  ]
+}
+```

qasper-0572/instruction.md

@@ -0,0 +1,115 @@
+Name of Paper: Representation of Constituents in Neural Language Models: Coordination Phrase as a Case Study
+
+Question: What is the best performance achieved by supervised models?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Methods ::: Psycholinguistics Paradigm",
+    "Methods ::: Models Tested ::: Recurrent Neural Network (RNN) Language Models",
+    "Methods ::: Models Tested ::: ActionLSTM",
+    "Methods ::: Models Tested ::: Generative Recurrent Neural Network Grammars (RNNG)",
+    "Experiment 1: Non-coordination Agreement",
+    "Experiment 2: Simple Coordination",
+    "Experiment 2: Simple Coordination ::: Number Agreement",
+    "Experiment 2: Simple Coordination ::: Gender Agreement",
+    "Experiment 3: Complex Coordination",
+    "Experiment 3: Complex Coordination ::: Complex Coordination Control",
+    "Experiment 3: Complex Coordination ::: Complex Coordination Critical",
+    "Experiment 4: Inverted Coordination",
+    "Discussion",
+    "Acknowledgments",
+    "The Effect of Annotation Schemes",
+    "PTB/FTB Agreement Patterns"
+  ],
+  "paragraphs": [
+    [
+      "Humans deploy structure-sensitive expectations to guide processing during natural language comprehension BIBREF0. While it has been shown that neural language models show similar structure-sensitivity in their predictions about upcoming material BIBREF1, BIBREF2, previous work has focused on dependencies that are conditioned by features attached to a single word, such as subject number BIBREF3, BIBREF4 or wh-question words BIBREF5. There has been no systematic investigation into models' ability to compute phrase-level features\u2014features that are attached to a set of words\u2014and whether models can deploy these more abstract properties to drive downstream expectations.",
+      "In this work, we assess whether state-of-the-art neural models can compute and employ phrase-level gender and number features of coordinated subject Noun Phrases (CoordNPs) with two nouns. Typical syntactic phrases are endocentric: they are headed by a single child, whose features determine the agreement requirements for the entire phrase. In Figure FIGREF1, for example, the word star heads the subject NP The star; since star is singular, the verb must be singular. CoordNPs lack endocentricity: neither conjunct NP solely determines the features of the NP as a whole. Instead, these feature values are determined by compositional rules sensitive to the features of the conjuncts and the identity of the coordinator. In Figure FIGREF1, because the coordinator is and, the subject NP number is plural even though both conjuncts (the star and the moon) are singular. As this case demonstrates, the agreement behavior for CoordNPs must be driven by more abstract, constituent-level representations, and cannot be reduced to features hosted on a single lexical item.",
+      "We use four suites of experiments to assess whether neural models are able to build up phrase-level representations of CoordNPs on the fly and deploy them to drive humanlike behavior. First, we present a simple control experiment to show that models can represent number and gender features of non-coordinate NPs (Non-coordination Agreement). Second, we show that models modulate their expectations for downstream verb number based on the CoordNP's coordinating conjunction combined with the features of the coordinated nouns (Simple Coordination). We rule out the possibility that models are using simple heuristics by designing a set of stimuli where a simple heuristic would fail due to structural ambiguity (Complex Coordination). The striking success for all models in this experiment indicates that even neural models with no explicit hierarchical bias, trained on a relatively small amount of text are able to learn fine-grained and robust generalizations about the interaction between CoordNPs and local syntactic context. Finally, we use subject\u2013auxiliary inversion to test whether an upstream lexical item modulates model expectation for the phrasal-level features of a downstream CoordNP (Inverted Coordination). Here, we find that all models are insensitive to the fine-grained features of this particular syntactic context. Overall, our results indicate that neural models can learn fine-grained information about the interaction of Coordinated NPs and local syntactic context, but their behavior remains unhumanlike in many key respects."
+    ],
+    [
+      "To determine whether state-of-the-art neural architectures are capable of learning humanlike CoordNP/verb agreement properties, we adopt the psycholinguistics paradigm for model assessment. In this paradigm the models are tested using hand-crafted sentences designed to test underlying network knowledge. The assumption here is that if a model implicitly learns humanlike linguistic knowledge during training, its expectations for upcoming words should qualitatively match human expectations in novel contexts. For example, BIBREF1 and BIBREF6 assessed how well neural models had learned the subject/verb number agreement by feeding them with the prefix The keys to the cabinet .... If the models predicted the grammatical continuation are over the ungrammatical continuation is, they can be said to have learned the number agreement insofar as the number of the head noun and not the number of the distractor noun, cabinet, drives expectations about the number of the matrix verb.",
+      "If models are able to robustly modulate their expectations based on the internal components of the CoordNP, this will provide evidence that the networks are building up a context-sensitive phrase-level representation. We quantify model expectations as surprisal values. Surprisal is the negative log-conditional probability $S(x_i) = -\\log _2 p(x_i|x_1 \\dots x_{i-1})$ of a sentence's $i^{th}$ word $x_i$ given the previous words. Surprisal tells us how strongly $x_i$ is expected in context and is known to correlate with human processing difficulty BIBREF7, BIBREF0, BIBREF8. In the CoordNP/Verb agreement studies presented here, cases where the proceeding context sets high expectation for a number-inflected verb form $w_i$, (e.g. singular `is') we would expect $S(w_i)$ to be lower than its number-mismatched counterpart (e.g. plural `are')."
+    ],
+    [
+      "are trained to output the probability distribution of the upcoming word given a context, without explicitly representing the structure of the context BIBREF9, BIBREF10. We trained two two-layer recurrent neural language models with long short-term memory architecture BIBREF11 on a relatively small corpus. The first model, referred as `LSTM (PTB)' in the following sections, was trained on the sentences from Penn Treebank BIBREF12. The second model, referred as `LSTM (FTB)', was trained on the sentences from French Treebank BIBREF13. We set the size of input word embedding and LSTM hidden layer of both models as 256.",
+      "We also compare LSTM language models trained on large corpora. We incorporate two pretrained English language models: one trained on the Billion Word benchmark (referred as `LSTM (1B)') from BIBREF14, and the other trained on English Wikipedia (referred as `LSTM (enWiki)') from BIBREF3. For French, we trained a large LSTM language model (referred as `LSTM (frWaC)') on a random subset (about 4 million sentences, 138 million word tokens) of the frWaC dataset BIBREF15. We set the size of the input embeddings and hidden layers to 400 for the LSTM (frWaC) model since it is trained on a large dataset."
+    ],
+    [
+      "models the linearized bracketed tree structure of a sentence by learning to predict the next action required to construct a phrase-structure parse BIBREF16. The action space consists of three possibilities: open a new non-terminal node and opening bracket; generate a terminal node; and close a bracket. To compute surprisal values for a given token, we approximate $P(w_i|w_{1\\cdots i-1)}$ by marginalizing over the most-likely partial parses found by word-synchronous beam search BIBREF17."
+    ],
+    [
+      "jointly model the word sequence as well as the underlying syntactic structure BIBREF18. Following BIBREF19, we estimate surprisal using word-synchronous beam search BIBREF17. We use the same hyper-parameter settings as BIBREF18.",
+      "The annotation schemes used to train the syntactically-supervised models differ slightly between French and English. In the PTB (English) CoordNPs are flat structures bearing an `NP' label. In FTB (French), CoordNPs are binary-branching, labeled as NPs, except for the phrasal node dominating the coordinating conjunction, which is labeled `COORD'. We examine the effects of annotation schemes on model performance in Appendix SECREF8."
+    ],
+    [
+      "In order to provide a baseline for following experiments, here we assess whether the models tested have learned basic representations of number and gender features for non-coordinated Noun Phrases. We test number agreement in English and French as well as gender agreement in French. Both English and French have two grammatical number feature: singular (sg) and plural (pl). French has two grammatical gender features: masculine (m) and feminine (f).",
+      "The experimental materials include sentences where the subject NPs contain a single noun which can either match with the matrix verb (in the case of number agreement) or a following predicative adjective (in the case of gender agreement). Conditions are given in Table TABREF9 and Table TABREF10. We measure model behavior by computing the plural expectation, or the surprisal of the singular continuation minus the surprisal of the plural continuation for each condition and took the average for each condition. We expect a positive plural expectation in the Npl conditions and a negative plural expectation in the Nsg conditions. For gender expectation we compute a gender expectation, which is S(feminine continuation) $-$ S(masculine continuation). We measure surprisal at the verbs and predicative adjectives themselves.",
+      "The results for this experiment are in Figure FIGREF11, with the plural expectation and gender expectation on the y-axis and conditions on the x-axis. For this and subsequent experiments error bars represent 95% confidence intervals for across-item means. For number agreement, all the models in English and French show positive plural expectation when the head noun is plural and negative plural expectation when it is singular. For gender agreement, however, only the LSTM (frWaC) shows modulation of gender expectation based on the gender of the head noun. This is most likely due to the lower frequency of predicative adjectives compared to matrix verbs in the corpus."
+    ],
+    [
+      "In this section, we test whether neural language models can use grammatical features hosted on multiple components of a coordination phrase\u2014the coordinated nouns as well as the coordinating conjunction\u2014to drive downstream expectations. We test number agreement in both English and French and gender agreement in French."
+    ],
+    [
+      "In simple subject/verb number agreement, the number features of the CoordNP are determined by the coordinating conjunction and the number features of the two coordinated NPs. CoordNPs formed by and are plural and thus require plural verbs; CoordNPs formed by or allow either plural or singular verbs, often with the number features of the noun linearly closest to the verb playing a more important role, although this varies cross-linguistically BIBREF20. Forced-choice preference experiments in BIBREF21 reveal that English native speakers prefer singular agreement when the closest conjunct in an or-CoordNP is singular and plural agreement when the closest conjunct is plural. In French, both singular and plural verbs are possible when two singular NPs are joined via disjunction BIBREF22.",
+      "In order to assess whether the neural models learn the basic CoordNP licensing for English, we adapted 37 items from BIBREF21, along the 16 conditions outlined in Table TABREF14. Test items consist of the sentence preamble, followed by either the singular or plural BE verb, half the time in present tense (is/are) and half the time in past tense (was/were). We measured the plural expectation, following the procedure in Section SECREF3. We created 24 items using the same conditions as the English experiment to test the models trained in French, using the 3rd person singular and plural form of verb aller, `to go' (va, vont). Within each item, nouns match in gender; across all conditions half the nouns are masculine, half feminine.",
+      "The results for this experiment can be seen in Figure FIGREF12, with the results for English on the left and French on the right. The results for and are on the top row, or on the bottom row. For all figures the y-axis shows the plural expectation, or the difference in surprisal between the singular condition and the plural condition. Turning first to English-and (Figure FIGREF12), all models show plural expectation (the bars are significantly greater than zero) in the pl_and_pl and sg_and_pl conditions, as expected. For the pl_and_sg condition, only the LSTM (enWiki) and ActionLSTM are greater than zero, indicating humanlike behavior. For the sg_and_sg condition, only the LSTM (enWiki) model shows the correct plural expectation. For the French-and (Figure FIGREF12), all models show positive plural expectation in all conditions, as expected, except for the LSTM (FTB) in the sg_and_sg condition.",
+      "Examining the results for English-or, we find that all models demonstrate humanlike expectation in the pl_or_pl and sg_or_pl conditions. The LSTM (1B), LSTM (PTB), and RNNG models show zero or negative singular expectation for the pl_or_sg conditions, as expected. However the LSTM (enWiki) and ActionLSTM models show positive plural expectation in this condition, indicating that they have not learned the humanlike generalizations. All models show significantly negative plural expectation in the sg_or_sg condition, as expected. In the French-or cases, models show almost identical behavior to the and conditions, except the LSTM (frWaC) shows smaller plural expectation when singular nouns are linearly proximal to the verb.",
+      "These results indicate moderate success at learning coordinate NP agreement, however this success may be the result of an overly simple heuristic. It appears that expectation for both plural and masculine continuations are driven by a linear combination of the two nominal number/gender features transferred into log-probability space, with the earlier noun mattering less than the later noun. A model that optimally captures human grammatical preferences should show no or only slight difference across conditions in the surprisal differential for the and conditions, and be greater than zero in all cases. Yet, all the models tested show gradient performance based on the number of plural conjuncts."
+    ],
+    [
+      "In French, if two nouns are coordinated with et (and-coordination), agreement must be masculine if there is one masculine element in the coordinate structure. If the nouns are coordinated with ou (or-coordination), both masculine and feminine agreement is acceptable BIBREF23, BIBREF24. Although linear proximity effects have been tested for a number of languages that employ grammatical gender, as in e.g. Slavic languages BIBREF25, there is no systematic study for French.",
+      "To assess whether the French neural models learned humanlike gender agreement, we created 24 test items, following the examples in Table TABREF16, and measured the masculine expectation. In our test items, the coordinated subject NP is followed by a predicative adjective, which either takes on masculine or feminine gender morphology.",
+      "Results from the experiment can be seen in Figure FIGREF17. No models shows qualitative difference based on the coordinator, and only the LSTM (frWaC) shows significant behavior difference between conditions. Here, we find positive masculine expectation in the m_and_m and f_and_m conditions, and negative masculine expectation in the f_and_f condition, as expected. However, in the m_and_f condition, the masculine expectation is not significantly different from zero, where we would expect it to be positive. In the or-coordination conditions, following our expectation, masculine expectation is positive when both conjuncts are masculine and negative when both are feminine. For the LSTM (FTB) and ActionLSTM models, the masculine expectation is positive (although not significantly so) in all conditions, consistent with results in Section SECREF3."
+    ],
+    [
+      "One possible explanation for the results presented in the previous section is that the models are using a `bag of features' approach to plural and masculine licensing that is opaque to syntactic context: Following a coordinating conjunction surrounded by nouns, models simply expect the following verb to be plural, proportionally to the number of plural nouns.",
+      "In this section, we control for this potential confound by conducting two experiments: In the Complex Coordination Control experiments we assess models' ability to extend basic CoordNP licensing into sententially-embedded environments, where the CoordNP can serve as an embedded subject. In the Complex Coordination Critical experiments, we leverage the sentential embedding environment to demonstrate that when the CoordNPs cannot plausibly serve as the subject of the embedded phrase, models are able to suppress the previously-demonstrated expectations set up by these phrases. These results demonstrate that models are not following a simple strategy for predicting downstream number and gender features, but are building up CoordNP representations on the fly, conditioned on the local syntactic context."
+    ],
+    [
+      "Following certain sentential-embedding verbs, CoordNPs serve unambiguously as the subject of the verb's sentence complement and should trigger number agreement behavior in the main verb of the embedded clause, similar to the behavior presented in SECREF13. To assess this, we use the 37 test items in English and 24 items in French in section SECREF13, following the conditions in Table TABREF19 (for number agreement), testing only and coordination. For gender agreement, we use the same test items and conditions for and coordination in Section SECREF15, but with the Coordinated NPs embedded in a context similar to SECREF18. As before, we derived the plural expectation by measuring the difference in surprisal between the singular and plural continuations and the gender expectation by computing the difference in surprisal between the masculine and feminine predicates.",
+      ". Je croyais que les prix et les d\u00e9penses \u00e9taient importants/importantes.",
+      "I thought that the.pl price.mpl and the.pl expense.fpl were important.mpl/fpl",
+      "I thought that the prices and the expenses were important.",
+      "The results for the control experiments can be seen in Figure FIGREF20, with English number agreement on the top row, French number agreement in the middle row and French gender agreement on the bottom. The y-axis shows either plural or masculine expectation, with the various conditions along the x-axis. For English number agreement, we find that the models behave similarly as they do for simple coordination contexts. All models show significant plural expectation when the closest noun is plural, with only two models demonstrating plural expectation in the sg_and_sg case. The French number agreement tests show similar results, with all models except LSTM (FTB) demonstrating significant plural prediction in all cases. Turning to French gender agreement, only the LSTM (frWaC) shows sensitivity to the various conditions, with positive masculine expectation in the m_and_m condition and negative expectation in the f_and_f condition, as expected. These results indicate that the behavior shown in Section SECREF13 extends to more complex syntactic environments\u2014in this case to sentential embeddings. Interestingly, for some models, such as the LSTM (1B), behavior is more humanlike when the CoordNP serves as the subject of an embedded sentence. This may be because the model, which has a large number of hidden states and may be extra sensitive to fine-grained syntactic information carried on lexical items BIBREF2, is using the complementizer, that, to drive more robust expectations."
+    ],
+    [
+      "In order to assess whether the models' strategy for CoordNP/verb number agreement is sensitive to syntactic context, we contrast the results presented above to those from a second, critical experiment. Here, two coordinated nouns follow a verb that cannot take a sentential complement, as in the examples given in Table TABREF23. Of the two possible continuations\u2014are or is\u2014the plural is only grammatically licensed when the second of the two conjuncts is plural. In these cases, the plural continuation may lead to a final sentence where the first noun serves as the verb's object and the second introduces a second main clause coordinated with the first, as in I fixed the doors and the windows are still broken. For the same reason, the singular-verb continuation is only licensed when the noun immediately following and is singular.",
+      "We created 37 test items in both English and French, and calculated the plural expectation. If the models were following a simple strategy to drive CoordNP/verb number agreement, then we should see either no difference in plural expectation across the four conditions or behavior no different from the control experiment. If, however, the models are sensitive to the licensing context, we should see a contrast based solely on the number features of the second conjunct, where plural expectation is positive when the second conjunct is plural, and negative otherwise.",
+      "Experimental items for a critical gender test were created similarly, as in Example SECREF22. As with plural agreement, gender expectation should be driven solely by the second conjunct: For the f_and_m and m_and_m conditions, the only grammatical continuation is one where the adjectival predicate bears masculine gender morphology. Conversely, for the m_and_f or f_and_f conditions, the only grammatical continuation is one where the adjectival predicate bears feminine morphology. As in SECREF13, we created 24 test items and measured the gender expectation by calculating the difference in surprisal between the masculine and feminine continuations.",
+      ". Nous avons accept\u00e9 les prix et les d\u00e9penses \u00e9taient importants/importantes.",
+      "we have accepted the.pl price.mpl and the expense.fpl were important.mpl/fpl",
+      "We have accepted the prices and the expenses were important.",
+      "The results from the critical experiments are in Figure FIGREF21, with the English number agreement on the top row, French number agreement in the middle and gender expectation on the bottom row. Here the y-axis shows either plural expectation or masculine expectation, with the various conditions are on the x-axis. The results here are strikingly different from those in the control experiments. For number agreement, all models in both languages show strong plural expectation in conditions where the second noun is plural (blue and green bars), as they do in the control experiments. Crucially, when the second noun is singular, the plural expectation is significantly negative for all models (save for the French LSTM (FTB) pl_and_sg condition). Turning to gender agreement, only the LSTM (frWaC) model shows differentiation between the four conditions tested. However, whereas the f_and_m and m_and_f gender expectations are not significantly different from zero in the control condition, in the critical condition they pattern with the purely masculine and purely feminine conditions, indicating that, in this syntactic context, the model has successfully learned to base gender expectation solely off of the second noun.",
+      "These results are inconsistent with a simple `bag of features' strategy that is insensitive to local syntactic context. They indicate that both models can interpret the same string as either a coordinated noun phrase, or as an NP object and the start of a coordinated VP with the second NP as its subject."
+    ],
+    [
+      "In addition to using phrase-level features to drive expectation about downstream lexical items, human processors can do the inverse\u2014use lexical features to drive expectations about upcoming syntactic chunks. In this experiment, we assess whether neural models use number features hosted on a verb to modulate their expectations for upcoming CoordNPs.",
+      "To assess whether neural language models learn inverted coordination rules, we adapted items from Section SECREF13 in both English (37 items) and French (24 items), following the paradigm in Table TABREF24. The first part of the phrase contains either a plural or singular verb and a plural or singular noun. In this case, we sample the surprisal for the continuations and (or is grammatical in all conditions, so it is omitted from this study). Our expectation is that `and' is less surprising in the Vpl_Nsg condition than in the Vsg_Nsg condition, where a CoordNP is not licensed by the grammar in either French or English (as in *What is the pig and the cat eating?). We also expect lower surprisal for and in the Vpl_Nsg condition, where it is obligatory for a grammatical continuation, than in the Vpl_Npl condition, where it is optional.",
+      "For French experimental items, the question is embedded into a sentential-complement taking verb, following Example SECREF6, due to the fact that unembedded subject-verb inverted questions sound very formal and might be relatively rare in the training data.",
+      ". Je me demande o\u00f9 vont le maire et",
+      "I myself ask where go.3PL the.MSG mayor.MSG and",
+      "The results for both languages are shown in Figure FIGREF25, with the surprisal at the coordinator on the y-axis and the various conditions on the x-axis. No model in either language shows a signficant difference in surprisal between the Vpl_Nsg and Vpl_Npl conditions or between the Vpl_Nsg and Vsg_Nsg conditions. The LSTM (1B) shows significant difference between the Vpl_Nsg and Vpl_Npl conditions, but in the opposite direction than expected, with the coordinator less surprising in the latter condition. These results indicate that the models are unable to use the fine-grained context sensitivity to drive expectations for CoordNPs, at least in the inversion setting."
+    ],
+    [
+      "The experiments presented here extend and refine a line of research investigating what linguistic knowledge is acquired by neural language models. Previous studies have demonstrated that sequential models trained on a simple regime of optimizing the next word can learn long-distance syntactic dependencies in impressive detail. Our results provide complimentary insights, demonstrating that a range of model architectures trained on a variety of datasets can learn fine-grained information about the interaction of CoordNPs and local syntactic context, but their behavior remains unhumanlike in many key ways. Furthermore, to our best knowledge, this work presents the first psycholinguistic analysis of neural language models trained on French, a high-resource language that has so far been under-investigated in this line of research.",
+      "In the simple coordination experiment, we demonstrated that models were able to capture some of the agreement behaviors of humans, although their performance deviated in crucial aspects. Whereas human behavior is best modeled as a `percolation' process, the neural models appear to be using a linear combination of NP constituent number to drive CoordNP/verb number agreement, with the second noun weighted more heavily than the first. In these experiments, supervision afforded by the RNNG and ActionLSTM models did not translate into more robust or humanlike learning outcomes. The complex coordination experiments provided evidence that the neural models tested were not using a simple `bag of features' strategy, but were sensitive to syntactic context. All models tested were able to interpret material that had similar surface form in ways that corresponded to two different tree-structural descriptions, based on local context. The inverted coordination experiment provided a contrasting example, in which models were unable to modulate expectations based on subtleties in the syntactic environment.",
+      "Across all our experiments, the French models performed consistently better on subject/verb number agreement than on subject/predicate gender agreement. Although there are likely more examples of subject/verb number agreement in the French training data, gender agreement is syntactically mandated and widespread in French. It remains an open question why all but one of the models tested were unable to leverage the numerous examples of gender agreement seen in various contexts during training to drive correct subject/predicate expectations."
+    ],
+    [
+      "This project is supported by a grant of Labex EFL ANR-10-LABX-0083 (and Idex ANR-18-IDEX-0001) for AA and MIT\u2013IBM AI Laboratory and the MIT\u2013SenseTimeAlliance on Artificial Intelligence for RPL. We would like to thank the anonymous reviewers for their comments and Anne Abeill\u00e9 for her advice and feedback."
+    ],
+    [
+      "This section further investigates the effects of CoordNP annotation schemes on the behaviors of structurally-supervised models. We test whether an explicit COORD phrasal tag improves model performance. We trained two additional RNNG models on 38,546 sentences from the Penn Treebank annotated with two different schemes: The first, RNNG (PTB-control) was trained with the original Penn Treebank annotation. The second, RNNG (PTB-coord), was trained on the same sentences, but with an extended coordination annotation scheme, meant to employ the scheme employed in the FTB, adapted from BIBREF26. We stripped empty categories from their scheme and only kept the NP-COORD label for constituents inside a coordination structure. Figure FIGREF26 illustrates the detailed annotation differences between two datasets. We tested both models on all the experiments presented in Sections SECREF3-SECREF6 above.",
+      "Turning to the results of these six experiments: We see little difference between the two models in the Non-coordination agreement experiment. For the Complex coordination control and Complex coordination critical experiments, both models are largely the same as well. However, in the Simple and-coordination and Simple or-coordination experiments the values for all conditions are shifted upwards for the RNNG PTB-coord model, indicating higher over-all preference for the plural continuation. Furthermore, the range of values is reduced in the RNNG PTB-coord model, compared to the RNNG PTB-control model. These results indicate that adding an explicit COORD phrasal label does not drastically change model performance: Both models still appear to be using a linear combination of number features to drive plural vs. singular expectation. However, the explicit representation has made the interior of the coordination phrase more opaque to the model (each feature matters less) and has slightly shifted model preference towards plural continuations. In this sense, the PTB-coord model may have learned a generalization about CoordNPs, but this generalization remains unlike the ones learned by humans."
+    ],
+    [
+      "We present statistics of subject/predicate agreement patterns in the Penn Treebank (PTB) and French Treebank (FTB) in Table TABREF28 and TABREF29."
+    ]
+  ]
+}
+```

qasper-0575/instruction.md

@@ -0,0 +1,81 @@
+Name of Paper: Investigating Linguistic Pattern Ordering in Hierarchical Natural Language Generation
+
+Question: What evaluation metrics are used?
+
+## 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."
+    ]
+  ]
+}
+```

qasper-0581/instruction.md

@@ -0,0 +1,82 @@
+Name of Paper: Detecting Potential Topics In News Using BERT, CRF and Wikipedia
+
+Question: Which news corpus is used?
+
+## 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."
+    ]
+  ]
+}
+```

qasper-0586/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: How Far are We from Effective Context Modeling ? An Exploratory Study on Semantic Parsing in Context
+
+Question: What context modelling methods are evaluated?

qasper-0740/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Learning Twitter User Sentiments on Climate Change with Limited Labeled Data
+
+Question: Do they report results only on English data?

qasper-0747/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: A multimodal deep learning approach for named entity recognition from social media
+
+Question: What are the baseline state of the art models?

qasper-0749/instruction.md

@@ -0,0 +1,95 @@
+Name of Paper: Uncover Sexual Harassment Patterns from Personal Stories by Joint Key Element Extraction and Categorization
+
+Question: What model did they use?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Data Collection and Annotation",
+    "Proposed Models",
+    "Proposed Models ::: CNN Based Joint Learning Models",
+    "Proposed Models ::: BiLSTM Based Joint Learning Models",
+    "Experiments and Results ::: Experimental Settings",
+    "Experiments and Results ::: Results and Discussions",
+    "Patterns of Sexual Harassment",
+    "Conclusions",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "Sexual violence, including harassment, is a pervasive, worldwide problem with a long history. This global problem has finally become a mainstream issue thanks to the efforts of survivors and advocates. Statistics show that girls and women are put at high risk of experiencing harassment. Women have about a 3 in 5 chance of experiencing sexual harassment, whereas men have slightly less than 1 in 5 chance BIBREF0, BIBREF1, BIBREF2. While women in developing countries are facing distinct challenges with sexual violence BIBREF3, however sexual violence is ubiquitous. In the United States, for example, there are on average >300,000 people who are sexually assaulted every year BIBREF4. Additionally, these numbers could be underestimated, due to reasons like guilt, blame, doubt and fear, which stopped many survivors from reporting BIBREF5. Social media can be a more open and accessible channel for those who have experienced harassment to be empowered to freely share their traumatic experiences and to raise awareness of the vast scale of sexual harassment, which then allows us to understand and actively address abusive behavior as part of larger efforts to prevent future sexual harassment. The deadly gang rape of a medical student on a Delhi bus in 2012 was a catalyst for protest and action, including the development of Safecity, which uses online and mobile technology to work towards ending sexual harassment and assault. More recently, the #MeToo and #TimesUp movements, further demonstrate how reporting personal stories on social media can raise awareness and empower women. Millions of people around the world have come forward and shared their stories. Instead of being bystanders, more and more people become up-standers, who take action to protest against sexual harassment online. The stories of people who experienced harassment can be studied to identify different patterns of sexual harassment, which can enable solutions to be developed to make streets safer and to keep women and girls more secure when navigating city spaces BIBREF6. In this paper, we demonstrated the application of natural language processing (NLP) technologies to uncover harassment patterns from social media data. We made three key contributions:",
+      "1. Safecity is the largest publicly-available online forum for reporting sexual harassment BIBREF6. We annotated about 10,000 personal stories from Safecity with the key elements, including information of harasser (i.e. the words describing the harasser), time, location and the trigger words (i.e. the phrases indicate the harassment that occurred). The key elements are important for studying the patterns of harassment and victimology BIBREF5, BIBREF7. Furthermore, we also associated each story with five labels that characterize the story in multiple dimensions (i.e. age of harasser, single/multiple harasser(s), type of harasser, type of location and time of day). The annotation data are available online.",
+      "2. We proposed joint learning NLP models that use convolutional neural network (CNN) BIBREF8 and bi-directional long short-term memory (BiLSTM) BIBREF9, BIBREF10 as basic units. Our models can automatically extract the key elements from the sexual harassment stories and at the same time categorize the stories in different dimensions. The proposed models outperformed the single task models, and achieved higher than previously reported accuracy in classifications of harassment forms BIBREF6.",
+      "3. We uncovered significant patterns from the categorized sexual harassment stories."
+    ],
+    [
+      "Conventional surveys and reports are often used to study sexual harassment, but harassment on these is usually under-reported BIBREF2, BIBREF5. The high volume of social media data available online can provide us a much larger collection of firsthand stories of sexual harassment. Social media data has already been used to analyze and predict distinct societal and health issues, in order to improve the understanding of wide-reaching societal concerns, including mental health, detecting domestic abuse, and cyberbullying BIBREF11, BIBREF12, BIBREF13, BIBREF14.",
+      "There are a very limited number of studies on sexual harassment stories shared online. Karlekar and Bansal karlekar2018safecity were the first group to our knowledge that applied NLP to analyze large amount ( $\\sim $10,000) of sexual harassment stories. Although their CNN-RNN classification models demonstrated high performance on classifying the forms of harassment, only the top 3 majority forms were studied. In order to study the details of the sexual harassment, the trigger words are crucial. Additionally, research indicated that both situational factors and person (or individual difference) factors contribute to sexual harassment BIBREF15. Therefore, the information about perpetrators needs to be extracted as well as the location and time of events. Karlekar and Bansal karlekar2018safecity applied several visualization techniques in order to capture such information, but it was not obtained explicitly. Our preliminary research demonstrated automatic extraction of key element and story classification in separate steps BIBREF16. In this paper, we proposed joint learning NLP models to directly extract the information of the harasser, time, location and trigger word as key elements and categorize the harassment stories in five dimensions as well. Our approach can provide an avenue to automatically uncover nuanced circumstances informing sexual harassment from online stories."
+    ],
+    [
+      "We obtained 9,892 stories of sexual harassment incidents that was reported on Safecity. Those stories include a text description, along with tags of the forms of harassment, e.g. commenting, ogling and groping. A dataset of these stories was published by Karlekar and Bansal karlekar2018safecity. In addition to the forms of harassment, we manually annotated each story with the key elements (i.e. \u201charasser\", \u201ctime\", \u201clocation\", \u201ctrigger\"), because they are essential to uncover the harassment patterns. An example is shown in Figure FIGREF3. Furthermore, we also assigned each story classification labels in five dimensions (Table TABREF4). The detailed definitions of classifications in all dimensions are explained below.",
+      "Age of Harasser: Individual difference such as age can affect harassment behaviors. Therefore, we studied the harassers in two age groups, young and adult. Young people in this paper refer to people in the early 20s or younger.",
+      "Single/Multiple Harasser(s): Harassers may behave differently in groups than they do alone.",
+      "Type of Harasser: Person factors in harassment include the common relationships or titles of the harassers. Additionally, the reactions of people who experience harassment may vary with the harassers' relations to themselves BIBREF5. We defined 10 groups with respects to the harassers' relationships or titles. We put conductors and drivers in one group, as they both work on the public transportation. Police and guards are put in the same category, because they are employed to provide security. Manager, supervisors, and colleagues are in the work-related group. The others are described by their names.",
+      "Type of Location: It will be helpful to reveal the places where harassment most frequently occurs BIBREF7, BIBREF6. We defined 14 types of locations. \u201cStation/stop\u201d refers to places where people wait for public transportation or buy tickets. Private places include survivors' or harassers' home, places of parties and etc. The others are described by their names.",
+      "Time of Day: The time of an incident may be reported as \u201cin evening\u201d or at a specific time, e.g. \u201c10 pm\u201d. We considered that 5 am to 6 pm as day time, and the rest of the day as the night.",
+      "Because many of the stories collected are short, many do not contain all of the key elements. For example, \u201cA man came near to her tried to be physical with her .\u201d. The time and location are unknown from the story. In addition, the harassers were strangers to those they harassed in many cases. For instance, \u201cMy friend was standing in the queue to pay bill and was ogled by a group of boys.\u201d, we can only learn that there were multiple young harassers, but the type of harasser is unclear. The missing information is hence marked as \u201cunspecified\u201d. It is different from the label \u201cother\", which means the information is provided but the number of them is too small to be represented by a group, for example, a \u201ctrader\u201d.",
+      "All the data were labeled by two annotators with training. Inter-rater agreement was measured by Cohen's kappa coefficient, ranging from 0.71 to 0.91 for classifications in different dimensions and 0.75 for key element extraction (details can refer to Table 1 in supplementary file). The disagreements were reviewed by a third annotator and a final decision was made."
+    ],
+    [
+      "The key elements can be very informative when categorizing the incidents. For instance, in Figure 1, with identified key elements, one can easily categorize the incident in dimensions of \u201cage of harasser\u201d (adult), \u201csingle/multiple harasser(s)\u201d (single), \u201ctype of harasser\u201d (unspecified), \u201ctype of location\u201d (park) , \u201ctime of day\u201d (day time). Therefore, we proposed two joint learning schemes to extract the key elements and categorize the incidents together. In the models' names, \u201cJ\u201d, \u201cA\u201d, \u201cSA\u201d stand for joint learning, attention, and supervised attention, respectively."
+    ],
+    [
+      "In Figure FIGREF6, the first proposed structure consists of two layers of CNN modules.",
+      "J-CNN: To predict the type of key element, it is essential for the CNN model to capture the context information around each word. Therefore, the word along with its surrounding context of a fixed window size was converted into a context sequence. Assuming a window size of $2l + 1$ around the target word $w_0$, the context sequence is $[(w_{-l}, w_{-l+1},...w_0, ...w_{l-1},w_l)]$, where $w_i (i \\in [-l,l])$ stands for the $ith$ word from $w_0$.",
+      "Because the context of the two consecutive words in the original text are only off by one position, it will be difficult for the CNN model to detect the difference. Therefore, the position of each word in this context sequence is crucial information for the CNN model to make the correct predictions BIBREF17. That position was embedded as a $p$ dimensional vector, where $p$ is a hyperparameter. The position embeddings were learned at the training stage. Each word in the original text was then converted into a sequence of the concatenation of word and position embeddings. Such sequence was fed into the CNN modules in the first layer of the model, which output the high level word representation ($h_i, i\\in [0,n-1]$, where n is the number of input words). The high level word representation was then passed into a fully connected layer, to predict the key element type for the word. The CNN modules in this layer share the same parameters.",
+      "We input the sequence of high level word representations ($h_i$) from the first layer into another layer of multiple CNN modules to categorize the harassment incident in each dimension (Figure FIGREF6). Inside each CNN module, the sequence of word representations were first passed through a convolution layer to generate a sequence of new feature vectors ($C =[c_0,c_1,...c_q]$). This vector sequence ($C$) was then fed into a max pooling layer. This is followed by a fully connected layer. Modules in this layer do not share parameters across classification tasks.",
+      "J-ACNN: We also experimented with attentive pooling, by replacing the max pooling layer. The attention layer aggregates the sequence of feature vectors ($C$) by measuring the contribution of each vector to form the high level representation of the harassment story. Specifically,",
+      "That is, a fully connected layer with non-linear activation was applied to each vector $c_{i}$ to get its hidden representation $u_{i}$. The similarity of $u_{i}$ with a context vector $u_{w}$ was measured and get normalized through a softmax function, as the importance weight $\\alpha _{i}$. The final representation of the incident story $v$ was an aggregation of all the feature vectors weighted by $\\alpha _{i}$. $W_{\\omega }$, $b_{\\omega }$ and $u_{w}$ were learned during training.",
+      "The final representation ($v$) was passed into one fully connected layer for each classification task. We also applied different attention layers for different classifications, because the classification modules categorize the incident in different dimensions, their focuses vary. For example, to classify \u201ctime of day\u201d, one needs to focus on the time phrases, but pays more attention to harassers when classifying \u201cage of harasser\u201d.",
+      "J-SACNN: To further exploit the information of the key elements, we applied supervision BIBREF18 to the attentive pooling layer, with the annotated key element types of the words as ground truth. For instance, in classification of \u201cage of harasser\u201d, the ground truth attention labels for words with key element types of \u201charasser\u201d are 1 and others are 0. To conform to the CNN structure, we applied convolution to the sequence of ground truth attention labels, with the same window size ($w$) that was applied to the word sequence (Eq. DISPLAY_FORM11).",
+      "where $\\circ $ is element-wise multiplication, $e_t$ is the ground truth attention label, and the $W \\in R^{w\\times 1}$ is a constant matrix with all elements equal to 1. $\\alpha ^{*}$ was normalized through a softmax function and used as ground truth weight values of the vector sequence ($C$) output from the convolution layer. The loss was calculated between learned attention $\\alpha $ and $\\alpha ^{*}$ (Eq. DISPLAY_FORM12), and added to the total loss."
+    ],
+    [
+      "J-BiLSTM: The model input the sequence of word embeddings to the BiLSTM layer. To extract key elements, the hidden states from the forward and backward LSTM cells were concatenated and used as word representations to predict the key element types.",
+      "To classify the harassment story in different dimensions, concatenation of the forward and backward final states of BiLSTM layer was used as document level representation of the story.",
+      "J-ABiLSTM: We also experimented on BiLSTM model with the attention layer to aggregate the outputs from BiLSTM layer (Figure FIGREF7). The aggregation of the outputs was used as document level representation.",
+      "J-SABiLSTM: Similarly, we experimented with the supervised attention.",
+      "In all the models, softmax function was used to calculate the probabilities at the prediction step, and the cross entropy losses from extraction and classification tasks were added together. In case of supervised attention, the loss defined in Eq. DISPLAY_FORM12 was added to the total loss as well. We applied the stochastic gradient descent algorithm with mini-batches and the AdaDelta update Rule (rho=0.95 and epsilon=1e-6) BIBREF19, BIBREF20. The gradients were computed using back-propagation. During training, we also optimized the word and position embeddings."
+    ],
+    [
+      "Data Splits: We used the same splits of train, develop, and test sets used by Karlekar and Bansal BIBREF6, with 7201, 990 and 1701 stories, respectively. In this study, we only considered single label classifications.",
+      "Baseline Models: CNN and BiLSTM models that perform classification and extraction separately were used as baseline models. In classification, we also experimented with BiLSTM with the attention layer. To demonstrate that the improvement came from joint learning structure rather the two layer structure in J-CNN, we investigated the same model structure without training on key element extraction. We use J-CNN* to denote it.",
+      "Preprocess: All the texts were converted to lowercase and preprocessed by removing non-alphanumeric characters, excluding \u201c. ! ? \u201d . The word embeddings were pre-trained using fastText BIBREF21 with dimension equaling 100.",
+      "Hyperparameters: For the CNN model, the filter size was chosen to be (1,2,3,4), with 50 filters per filter size. Batch size was set to 50 and the dropout rate was 0.5. The BiLSTM model comprises two layers of one directional LSTM. Every LSTM cell has 50 hidden units. The dropout rate was 0.25. Attention size was 50."
+    ],
+    [
+      "We compared joint learning models with the single task models. Results are averages from five experiments. Although not much improvement was achieved in key element extraction (Figure TABREF16), classification performance improved significantly with joint learning schemes (Table TABREF17). Significance t-test results are shown in Table 2 in the supplementary file.",
+      "BiLSTM Based Models: Joint learning BiLSTM with attention outperformed single task BiLSTM models. One reason is that it directed the attention of the model to the correct part of the text. For example,",
+      "S1: \u201c foogreen!1.7003483371809125 foowhen foogreen!3.4324652515351772 fooi foogreen!10.76661329716444 foowas foogreen!20.388443022966385 fooreturning foogreen!9.704475291073322 foomy foogreen!6.052316632121801 foohome foogreen!2.477810252457857 fooafter foogreen!3.5612427163869143 foofinishing foogreen!4.7736018896102905 foomy foogreen!4.634172189980745 fooclass foogreen!0.6899426807649434 foo. foogreen!0.35572052001953125 fooi foogreen!0.3427551419008523 foowas foogreen!0.293194578262046 fooin foogreen!0.2028885210165754 fooqueue foogreen!0.10553237370913848 footo foogreen!0.19472737039905041 fooget foogreen!0.44946340494789183 fooon foogreen!0.5511227645911276 foothe foogreen!2.056689700111747 foomicro foogreen!2.597035141661763 foobus foogreen!2.5683704297989607 fooand foogreen!4.6382867731153965 foothere foogreen!9.827975183725357 foowas foogreen!21.346069872379303 fooa foogreen!22.295180708169937 foogirl foogreen!11.672522872686386 fooopposite foogreen!8.892465382814407 footo foogreen!18.20233091711998 foome foogreen!13.192926533520222 foojust foogreen!26.24184638261795 foothen foogreen!40.2555949985981 fooa foogreen!30.108729377388954 fooyoung foogreen!115.02625793218613 fooman foogreen!93.40204298496246 footried foogreen!58.68498980998993 footo foogreen!144.01434361934662 footouch foogreen!108.82275551557541 fooher foogreen!80.9452086687088 fooon foogreen!47.26015031337738 foothe foogreen!47.71501570940018 foobreast foogreen!19.392695277929306 foo.\u201d",
+      "S2: \u201c foogreen!0.2212507533840835 foowhen foogreen!0.26129744946956635 fooi foogreen!0.3014186804648489 foowas foogreen!0.314583390718326 fooreturning foogreen!0.23829322890378535 foomy foogreen!0.018542312318459153 foohome foogreen!0.06052045864635147 fooafter foogreen!0.3865368489641696 foofinishing foogreen!0.5127551266923547 foomy foogreen!0.569560332223773 fooclass foogreen!0.037081812479300424 foo. foogreen!0.061129467212595046 fooi foogreen!0.12043083552271128 foowas foogreen!0.2053432835964486 fooin foogreen!0.038308095099637285 fooqueue foogreen!0.05270353358355351 footo foogreen!0.07939991337480024 fooget foogreen!0.14962266141083091 fooon foogreen!0.11444976553320885 foothe foogreen!0.013002995729038958 foomicro foogreen!0.016201976904994808 foobus foogreen!0.14046543219592422 fooand foogreen!0.12413455988280475 foothere foogreen!0.18423641449771821 foowas foogreen!0.3394613158889115 fooa foogreen!1.0372470133006573 foogirl foogreen!0.20553644571918994 fooopposite foogreen!0.2821453963406384 footo foogreen!0.5574009846895933 foome foogreen!0.2709480468183756 foojust foogreen!0.2582515007816255 foothen foogreen!0.9223996312357485 fooa foogreen!788.9420390129089 fooyoung foogreen!199.1765946149826 fooman foogreen!0.39259070763364434 footried foogreen!0.27069455245509744 footo foogreen!0.5092779756523669 footouch foogreen!0.7033208385109901 fooher foogreen!0.6793316570110619 fooon foogreen!0.5892394692637026 foothe foogreen!0.4084075626451522 foobreast foogreen!0.14951340563129634 foo.\u201d",
+      "S3: \u201c foogreen!0.23944019631017 foowhen foogreen!0.16698541003279388 fooi foogreen!0.3381385176908225 foowas foogreen!0.21315943740773946 fooreturning foogreen!0.3222442464902997 foomy foogreen!0.8483575657010078 foohome foogreen!0.10339960863348097 fooafter foogreen!0.2440519310766831 foofinishing foogreen!0.39699181797914207 foomy foogreen!1.2218113988637924 fooclass foogreen!0.1232976937899366 foo. foogreen!0.10928708070423454 fooi foogreen!0.2562549489084631 foowas foogreen!0.8099888218566775 fooin foogreen!2.9650430660694838 fooqueue foogreen!0.507337914314121 footo foogreen!0.727736041881144 fooget foogreen!0.7367140497080982 fooon foogreen!0.711284636054188 foothe foogreen!194.2763775587082 foomicro foogreen!786.8869304656982 foobus foogreen!0.4422159108798951 fooand foogreen!0.43104542419314384 foothere foogreen!0.4694198723882437 foowas foogreen!0.5085613229312003 fooa foogreen!0.4430979897733778 foogirl foogreen!0.36199347232468426 fooopposite foogreen!0.31067250529304147 footo foogreen!0.2927705936599523 foome foogreen!0.24646619567647576 foojust foogreen!0.23911069729365408 foothen foogreen!0.11775700113503262 fooa foogreen!0.002219072712250636 fooyoung foogreen!0.0019248132048232947 fooman foogreen!0.32698659924790263 footried foogreen!0.3118939639534801 footo foogreen!0.5727249081246555 footouch foogreen!0.5670131067745388 fooher foogreen!0.7104063988663256 fooon foogreen!0.6698771030642092 foothe foogreen!0.4756081907544285 foobreast foogreen!0.26600153069011867 foo.\u201d",
+      "In S1, the regular BiLSTM with attention model for classification on \u201cage of harasser\u201d put some attention on phrases other than the harasser, and hence aggregated noise. This could explain why the regular BiLSTM model got lower performance than the CNN model. However, when training with key element extractions, it put almost all attention on the harasser \u201cyoung man\u201d (S2), which helped the model make correct prediction of \u201cyoung harasser\u201d. When predicting the \u201ctype of location\u201d (S3), the joint learning model directed its attention to \u201cmicro bus\u201d.",
+      "CNN Based Models: Since CNN is efficient for capturing the most useful information BIBREF22, it is quite suitable for the classification tasks in this study. It achieved better performance than the BiLSTM model. The joint learning method boosted the performance even higher. This is because the classifications are related to the extracted key elements, and the word representation learned by the first layer of CNNs (Figure FIGREF6) is more informative than word embedding. By plotting of t-SNEs BIBREF23 of the two kinds of word vectors, we can see the word representations in the joint learning model made the words more separable (Figure 1 in supplementary file). In addition, no improvement was found with the J-CNN* model, which demonstrated the joint learning with extraction is essential for the improvement.",
+      "With supervised attentive pooling, the model can get additional knowledge from key element labels. It helped the model in cases when certain location phrases were mentioned but the incidents did not happen at those locations. For instance, \u201cI was followed on my way home .\u201d, max pooling will very likely to predict it as \u201cprivate places\u201d. But, it is actually unknown. In other cases, with supervised attentive pooling, the model can distinguish \u201cmetro\u201d and \u201cmetro station\u201d, which are \u201ctransportation\u201d and \u201cstop/station\u201d respectively. Therefore, the model further improved on classifications on \u201ctype of location\u201d with supervised attention in terms of macro F1. For some tasks, like \u201ctime of day\u201d, there are fewer cases with such disambiguation and hence max pooling worked well. Supervised attention improved macro F1 in location and harasser classifications, because it made more correct predictions in cases that mentioned location and harasser. But the majority did not mention them. Therefore, the accuracy of J-SACNN did not increase, compared with the other models.",
+      "Classification on Harassment Forms: In Table TABREF18, we also compared the performance of binary classifications on harassment forms with the results reported by Karlekar and Bansal karlekar2018safecity. Joint learning models achieved higher accuracy. In some harassment stories, the whole text or a span of the text consists of trigger words of multiple forms, such as \u201cstare, whistles, start to sing, commenting\u201d. The supervised attention mechanism will force the model to look at all such words rather than just the one related to the harassment form for classification and hence it can introduce noise. This can explain why J-SACNN got lower accuracy in two of the harassment form classifications, compared to J-ACNN. In addition, J-CNN model did best in \u201cogling\u201d classification."
+    ],
+    [
+      "We plotted the distribution of harassment incidents in each categorization dimension (Figure FIGREF19). It displays statistics that provide important evidence as to the scale of harassment and that can serve as the basis for more effective interventions to be developed by authorities ranging from advocacy organizations to policy makers. It provides evidence to support some commonly assumed factors about harassment: First, we demonstrate that harassment occurred more frequently during the night time than the day time. Second, it shows that besides unspecified strangers (not shown in the figure), conductors and drivers are top the list of identified types of harassers, followed by friends and relatives.",
+      "Furthermore, we uncovered that there exist strong correlations between the age of perpetrators and the location of harassment, between the single/multiple harasser(s) and location, and between age and single/multiple harasser(s) (Figure FIGREF20). The significance of the correlation is tested by chi-square independence with p value less than 0.05. Identifying these patterns will enable interventions to be differentiated for and targeted at specific populations. For instance, the young harassers often engage in harassment activities as groups. This points to the influence of peer pressure and masculine behavioral norms for men and boys on these activities. We also found that the majority of young perpetrators engaged in harassment behaviors on the streets. These findings suggest that interventions with young men and boys, who are readily influenced by peers, might be most effective when education is done peer-to-peer. It also points to the locations where such efforts could be made, including both in schools and on the streets. In contrast, we found that adult perpetrators of sexual harassment are more likely to act alone. Most of the adult harassers engaged in harassment on public transportation. These differences in adult harassment activities and locations, mean that interventions should be responsive to these factors. For example, increasing the security measures on transit at key times and locations.",
+      "In addition, we also found that the correlations between the forms of harassment with the age, single/multiple harasser, type of harasser, and location (Figure FIGREF21). For example, young harassers are more likely to engage in behaviors of verbal harassment, rather than physical harassment as compared to adults. It was a single perpetrator that engaged in touching or groping more often, rather than groups of perpetrators. In contrast, commenting happened more frequently when harassers were in groups. Last but not least, public transportation is where people got indecently touched most frequently both by fellow passengers and by conductors and drivers. The nature and location of the harassment are particularly significant in developing strategies for those who are harassed or who witness the harassment to respond and manage the everyday threat of harassment. For example, some strategies will work best on public transport, a particular closed, shared space setting, while other strategies might be more effective on the open space of the street.",
+      "These results can provide valuable information for all members of the public. Sharing stories of harassment has been found by researchers to shift people\u2019s cognitive and emotional orientation towards their traumatic experiences BIBREF24. Greater awareness of patterns and scale of harassment experiences promises to ensure those who have been subjected to this violence that they are not alone, empowering others to report incidents, and ensuring them that efforts are being made to prevent others from experiencing the same harassment. These results also provide various authorities tools to identify potential harassment patterns and to make more effective interventions to prevent further harassment incidents. For instance, the authorities can increase targeted educational efforts at youth and adults, and be guided in utilizing limited resources the most effectively to offer more safety measures, including policing and community-based responses. For example, focusing efforts on highly populated public transportation during the nighttime, when harassment is found to be most likely to occur."
+    ],
+    [
+      "We provided a large number of annotated personal stories of sexual harassment. Analyzing and identifying the social patterns of harassment behavior is essential to changing these patterns and social tolerance for them. We demonstrated the joint learning NLP models with strong performances to automatically extract key elements and categorize the stories. Potentiality, the approaches and models proposed in this study can be applied to sexual harassment stories from other sources, which can process and summarize the harassment stories and help those who have experienced harassment and authorities to work faster, such as by automatically filing reports BIBREF6. Furthermore, we discovered meaningful patterns in the situations where harassment commonly occurred. The volume of social media data is huge, and the more we can extract from these data, the more powerful we can be as part of the efforts to build a safer and more inclusive communities. Our work can increase the understanding of sexual harassment in society, ease the processing of such incidents by advocates and officials, and most importantly, raise awareness of this urgent problem."
+    ],
+    [
+      "We thank the Safecity for granting the permission of using the data."
+    ]
+  ]
+}
+```

qasper-0776/instruction.md

@@ -0,0 +1,127 @@
+Name of Paper: Multi-style Generative Reading Comprehension
+
+Question: Does their model also take the expected answer style as input?
+
+## 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."
+    ]
+  ]
+}
+```

qasper-0778/instruction.md

@@ -0,0 +1,127 @@
+Name of Paper: Multi-style Generative Reading Comprehension
+
+Question: Is there exactly one "answer style" per dataset?
+
+## 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."
+    ]
+  ]
+}
+```

qasper-0912/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Identifying and Understanding User Reactions to Deceptive and Trusted Social News Sources
+
+Question: What is the architecture of their model?

qasper-0915/instruction.md

@@ -0,0 +1,112 @@
+Name of Paper: Discriminative Acoustic Word Embeddings: Recurrent Neural Network-Based Approaches
+
+Question: Which dimensionality do they use for their embeddings?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related work",
+    "Approach",
+    "Training",
+    "EXPERIMENTS",
+    "Classification network details",
+    "Siamese network details",
+    "Results",
+    "Effect of model structure",
+    "Effect of embedding dimensionality",
+    "Effect of training vocabulary",
+    "Visualization of embeddings",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Many speech processing tasks \u2013 such as automatic speech recognition or spoken term detection \u2013 hinge on associating segments of speech signals with word labels. In most systems developed for such tasks, words are broken down into sub-word units such as phones, and models are built for the individual units. An alternative, which has been considered by some researchers, is to consider each entire word segment as a single unit, without assigning parts of it to sub-word units. One motivation for the use of whole-word approaches is that they avoid the need for sub-word models. This is helpful since, despite decades of work on sub-word modeling BIBREF0 , BIBREF1 , it still poses significant challenges. For example, speech processing systems are still hampered by differences in conversational pronunciations BIBREF2 . A second motivation is that considering whole words at once allows us to consider a more flexible set of features and reason over longer time spans.",
+      "Whole-word approaches typically involve, at some level, template matching. For example, in template-based speech recognition BIBREF3 , BIBREF4 , word scores are computed from dynamic time warping (DTW) distances between an observed segment and training segments of the hypothesized word. In query-by-example search, putative matches are typically found by measuring the DTW distance between the query and segments of the search database BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 . In other words, whole-word approaches often boil down to making decisions about whether two segments are examples of the same word or not.",
+      "An alternative to DTW that has begun to be explored is the use of acoustic word embeddings (AWEs), or vector representations of spoken word segments. AWEs are representations that can be learned from data, ideally such that the embeddings of two segments corresponding to the same word are close, while embeddings of segments corresponding to different words are far apart. Once word segments are represented via fixed-dimensional embeddings, computing distances is as simple as measuring a cosine or Euclidean distance between two vectors.",
+      "There has been some, thus far limited, work on acoustic word embeddings, focused on a number of embedding models, training approaches, and tasks BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 , BIBREF15 , BIBREF16 . In this paper we explore new embedding models based on recurrent neural networks (RNNs), applied to a word discrimination task related to query-by-example search. RNNs are a natural model class for acoustic word embeddings, since they can handle arbitrary-length sequences. We compare several types of RNN-based embeddings and analyze their properties. Compared to prior embeddings tested on the same task, our best models achieve sizable improvements in average precision."
+    ],
+    [
+      "We next briefly describe the most closely related prior work.",
+      "Maas et al. BIBREF9 and Bengio and Heigold BIBREF10 used acoustic word embeddings, based on convolutional neural networks (CNNs), to generate scores for word segments in automatic speech recognition. Maas et al. trained CNNs to predict (continuous-valued) embeddings of the word labels, and used the resulting embeddings to define feature functions in a segmental conditional random field BIBREF17 rescoring system. Bengio and Heigold also developed CNN-based embeddings for lattice rescoring, but with a contrastive loss to separate embeddings of a given word from embeddings of other words.",
+      "Levin et al. BIBREF11 developed unsupervised embeddings based on representing each word as a vector of DTW distances to a collection of reference word segments. This representation was subsequently used in several applications: a segmental approach for query-by-example search BIBREF12 , lexical clustering BIBREF18 , and unsupervised speech recognition BIBREF19 . Voinea et al. BIBREF15 developed a representation also based on templates, in their case phone templates, designed to be invariant to specific transformations, and showed their robustness on digit classification.",
+      "Kamper et al. BIBREF13 compared several types of acoustic word embeddings for a word discrimination task related to query-by-example search, finding that embeddings based on convolutional neural networks (CNNs) trained with a contrastive loss outperformed the reference vector approach of Levin et al. BIBREF11 as well as several other CNN and DNN embeddings and DTW using several feature types. There have now been a number of approaches compared on this same task and data BIBREF11 , BIBREF20 , BIBREF21 , BIBREF22 . For a direct comparison with this prior work, in this paper we use the same task and some of the same training losses as Kamper et al., but develop new embedding models based on RNNs.",
+      "The only prior work of which we are aware using RNNs for acoustic word embeddings is that of Chen et al. BIBREF16 and Chung et al. BIBREF14 . Chen et al. learned a long short-term memory (LSTM) RNN for word classification and used the resulting hidden state vectors as a word embedding in a query-by-example task. The setting was quite specific, however, with a small number of queries and speaker-dependent training. Chung et al. BIBREF14 worked in an unsupervised setting and trained single-layer RNN autoencoders to produce embeddings for a word discrimination task. In this paper we focus on the supervised setting, and compare a variety of RNN-based structures trained with different losses.",
+      ""
+    ],
+    [
+      "",
+      "An acoustic word embedding is a function that takes as input a speech segment corresponding to a word, INLINEFORM0 , where each INLINEFORM1 is a vector of frame-level acoustic features, and outputs a fixed-dimensional vector representing the segment, INLINEFORM2 . The basic embedding model structure we use is shown in Fig. FIGREF1 . The model consists of a deep RNN with some number INLINEFORM3 of stacked layers, whose final hidden state vector is passed as input to a set of INLINEFORM4 of fully connected layers; the output of the final fully connected layer is the embedding INLINEFORM5 .",
+      "The RNN hidden state at each time frame can be viewed as a representation of the input seen thus far, and its value in the last time frame INLINEFORM0 could itself serve as the final word embedding. The fully connected layers are added to account for the fact that some additional transformation may improve the representation. For example, the hidden state may need to be larger than the desired word embedding dimension, in order to be able to \"remember\" all of the needed intermediate information. Some of that information may not be needed in the final embedding. In addition, the information maintained in the hidden state may not necessarily be discriminative; some additional linear or non-linear transformation may help to learn a discriminative embedding.",
+      "Within this class of embedding models, we focus on Long Short-Term Memory (LSTM) networks BIBREF23 and Gated Recurrent Unit (GRU) networks BIBREF24 . These are both types of RNNs that include a mechanism for selectively retaining or discarding information at each time frame when updating the hidden state, in order to better utilize long-term context. Both of these RNN variants have been used successfully in speech recognition BIBREF25 , BIBREF26 , BIBREF27 , BIBREF28 .",
+      "In an LSTM RNN, at each time frame both the hidden state INLINEFORM0 and an associated \u201ccell memory\" vector INLINEFORM1 , are updated and passed on to the next time frame. In other words, each forward edge in Figure FIGREF1 can be viewed as carrying both the cell memory and hidden state vectors. The updates are modulated by the values of several gating vectors, which control the degree to which the cell memory and hidden state are updated in light of new information in the current frame. For a single-layer LSTM network, the updates are as follows:",
+      " INLINEFORM0 ",
+      "where INLINEFORM0 , and INLINEFORM1 are all vectors of the same dimensionality, INLINEFORM2 , and INLINEFORM3 are learned weight matrices of the appropriate sizes, INLINEFORM4 and INLINEFORM5 are learned bias vectors, INLINEFORM6 is a componentwise logistic activation, and INLINEFORM7 refers to the Hadamard (componentwise) product.",
+      "Similarly, in a GRU network, at each time step a GRU cell determines what components of old information are retained, overwritten, or modified in light of the next step in the input sequence. The output from a GRU cell is only the hidden state vector. A GRU cell uses a reset gate INLINEFORM0 and an update gate INLINEFORM1 as described below for a single-layer network: INLINEFORM2 ",
+      "where INLINEFORM0 , and INLINEFORM1 are all the same dimensionality, INLINEFORM2 , and INLINEFORM3 are learned weight matrices of the appropriate size, and INLINEFORM4 , INLINEFORM5 and INLINEFORM6 are learned bias vectors.",
+      "All of the above equations refer to single-layer networks. In a deep network, with multiple stacked layers, the same update equations are used in each layer, with the state, cell, and gate vectors replaced by layer-specific vectors INLINEFORM0 and so on for layer INLINEFORM1 . For all but the first layer, the input INLINEFORM2 is replaced by the hidden state vector from the previous layer INLINEFORM3 .",
+      "For the fully connected layers, we use rectified linear unit (ReLU) BIBREF29 activation, except for the final layer which depends on the form of supervision and loss used in training.",
+      ""
+    ],
+    [
+      "We train the RNN-based embedding models using a set of pre-segmented spoken words. We use two main training approaches, inspired by prior work but with some differences in the details. As in BIBREF13 , BIBREF10 , our first approach is to use the word labels of the training segments and train the networks to classify the word. In this case, the final layer of INLINEFORM0 is a log-softmax layer. Here we are limited to the subset of the training set that has a sufficient number of segments per word to train a good classifier, and the output dimensionality is equal to the number of words (but see BIBREF13 for a study of varying the dimensionality in such a classifier-based embedding model by introducing a bottleneck layer). This model is trained end-to-end and is optimized with a cross entropy loss. Although labeled data is necessarily limited, the hope is that the learned models will be useful even when applied to spoken examples of words not previously seen in the training data. For words not seen in training, the embeddings should correspond to some measure of similarity of the word to the training words, measured via the posterior probabilities of the previously seen words. In the experiments below, we examine this assumption by analyzing performance on words that appear in the training data compared to those that do not.",
+      "The second training approach, based on earlier work of Kamper et al. BIBREF13 , is to train \"Siamese\" networks BIBREF30 . In this approach, full supervision is not needed; rather, we use weak supervision in the form of pairs of segments labeled as same or different. The base model remains the same as before\u2014an RNN followed by a set of fully connected layers\u2014but the final layer is no longer a softmax but rather a linear activation layer of arbitrary size. In order to learn the parameters, we simultaneously feed three word segments through three copies of our model (i.e. three networks with shared weights). One input segment is an \u201canchor\", INLINEFORM0 , the second is another segment with the same word label, INLINEFORM1 , and the third is a segment corresponding to a different word label, INLINEFORM2 . Then, the network is trained using a \u201ccos-hinge\" loss:",
+      " DISPLAYFORM0 ",
+      "where INLINEFORM0 is the cosine distance between INLINEFORM1 . Unlike cross entropy training, here we directly aim to optimize relative (cosine) distance between same and different word pairs. For tasks such as query-by-example search, this training loss better respects our end objective, and can use more data since neither fully labeled data nor any minimum number of examples of each word should be needed.",
+      ""
+    ],
+    [
+      "",
+      "Our end goal is to improve performance on downstream tasks requiring accurate word discrimination. In this paper we use an intermediate task that more directly tests whether same- and different-word pairs have the expected relationship. and that allows us to compare to a variety of prior work. Specifically, we use the word discrimination task of Carlin et al. BIBREF20 , which is similar to a query-by-example task where the word segmentations are known. The evaluation consists of determining, for each pair of evaluation segments, whether they are examples of the same or different words, and measuring performance via the average precision (AP). We do this by measuring the cosine similarity between their acoustic word embeddings and declaring them to be the same if the distance is below a threshold. By sweeping the threshold, we obtain a precision-recall curve from which we compute the AP.",
+      "The data used for this task is drawn from the Switchboard conversational English corpus BIBREF31 . The word segments range from 50 to 200 frames in length. The acoustic features in each frame (the input to the word embedding models INLINEFORM0 ) are 39-dimensional MFCCs+ INLINEFORM1 + INLINEFORM2 . We use the same train, development, and test partitions as in prior work BIBREF13 , BIBREF11 , and the same acoustic features as in BIBREF13 , for as direct a comparison as possible. The train set contains approximately 10k example segments, while dev and test each contain approximately 11k segments (corresponding to about 60M pairs for computing the dev/test AP). As in BIBREF13 , when training the classification-based embeddings, we use a subset of the training set containing all word types with a minimum of 3 occurrences, reducing the training set size to approximately 9k segments.",
+      "When training the Siamese networks, the training data consists of all of the same-word pairs in the full training set (approximately 100k pairs). For each such training pair, we randomly sample a third example belonging to a different word type, as required for the INLINEFORM0 loss.",
+      ""
+    ],
+    [
+      "Our classifier-based embeddings use LSTM or GRU networks with 2\u20134 stacked layers and 1\u20133 fully connected layers. The final embedding dimensionality is equal to the number of unique word labels in the training set, which is 1061. The recurrent hidden state dimensionality is fixed at 512 and dropout BIBREF32 between stacked recurrent layers is used with probability INLINEFORM0 . The fully connected hidden layer dimensionality is fixed at 1024. Rectified linear unit (ReLU) non-linearities and dropout with INLINEFORM1 are used between fully-connected layers. However, between the final recurrent hidden state output and the first fully-connected layer no non-linearity or dropout is applied. These settings were determined through experiments on the development set.",
+      "The classifier network is trained with a cross entropy loss and optimized using stochastic gradient descent (SGD) with Nesterov momentum BIBREF33 . The learning rate is initialized at 0.1 and is reduced by a factor of 10 according to the following heuristic: If 99% of the current epoch's average batch loss is greater than the running average of batch losses over the last 3 epochs, this is considered a plateau; if there are 3 consecutive plateau epochs, then the learning rate is reduced. Training stops when reducing the learning rate no longer improves dev set AP. Then, the model from the epoch corresponding to the the best dev set AP is chosen. Several other optimizers\u2014Adagrad BIBREF34 , Adadelta BIBREF35 , and Adam BIBREF36 \u2014were explored in initial experiments on the dev set, but all reported results were obtained using SGD with Nesterov momentum.",
+      ""
+    ],
+    [
+      "For experiments with Siamese networks, we initialize (warm-start) the networks with the tuned classification network, removing the final log-softmax layer and replacing it with a linear layer of size equal to the desired embedding dimensionality. We explored embeddings with dimensionalities between 8 and 2048. We use a margin of 0.4 in the cos-hinge loss.",
+      "In training the Siamese networks, each training mini-batch consists of INLINEFORM0 triplets. INLINEFORM1 triplets are of the form INLINEFORM2 where INLINEFORM3 and INLINEFORM4 are examples of the same class (a pair from the 100k same-word pair set) and INLINEFORM5 is a randomly sampled example from a different class. Then, for each of these INLINEFORM6 triplets INLINEFORM7 , an additional triplet INLINEFORM8 is added to the mini-batch to allow all segments to serve as anchors. This is a slight departure from earlier work BIBREF13 , which we found to improve stability in training and performance on the development set.",
+      "In preliminary experiments, we compared two methods for choosing the negative examples INLINEFORM0 during training, a uniform sampling approach and a non-uniform one. In the case of uniform sampling, we sample INLINEFORM1 uniformly at random from the full set of training examples with labels different from INLINEFORM2 . This sampling method requires only word-pair supervision. In the case of non-uniform sampling, INLINEFORM3 is sampled in two steps. First, we construct a distribution INLINEFORM4 over word labels INLINEFORM5 and sample a different label from it. Second, we sample an example uniformly from within the subset with the chosen label. The goal of this method is to speed up training by targeting pairs that violate the margin constraint. To construct the multinomial PMF INLINEFORM6 , we maintain an INLINEFORM7 matrix INLINEFORM8 , where INLINEFORM9 is the number of unique word labels in training. Each word label corresponds to an integer INLINEFORM10 INLINEFORM11 [1, INLINEFORM12 ] and therefore a row in INLINEFORM13 . The values in a row of INLINEFORM14 are considered similarity scores, and we can retrieve the desired PMF for each row by normalizing by its sum.",
+      "At the start of each epoch, we initialize INLINEFORM0 with 0's along the diagonal and 1's elsewhere (which reduces to uniform sampling). For each training pair INLINEFORM1 , we update INLINEFORM2 for both INLINEFORM3 and INLINEFORM4 :",
+      " INLINEFORM0 ",
+      "The PMFs INLINEFORM0 are updated after the forward pass of an entire mini-batch. The constant INLINEFORM1 enforces a potentially stronger constraint than is used in the INLINEFORM2 loss, in order to promote diverse sampling. In all experiments, we set INLINEFORM3 . This is a heuristic approach, and it would be interesting to consider various alternatives. Preliminary experiments showed that the non-uniform sampling method outperformed uniform sampling, and in the following we report results with non-uniform sampling.",
+      "We optimize the Siamese network model using SGD with Nesterov momentum for 15 epochs. The learning rate is initialized to 0.001 and dropped every 3 epochs until no improvement is seen on the dev set. The final model is taken from the epoch with the highest dev set AP. All models were implemented in Torch BIBREF37 and used the rnn library of BIBREF38 .",
+      ""
+    ],
+    [
+      " Based on development set results, our final embedding models are LSTM networks with 3 stacked layers and 3 fully connected layers, with output dimensionality of 1024 in the case of Siamese networks. Final test set results are given in Table TABREF7 . We include a comparison with the best prior results on this task from BIBREF13 , as well as the result of using standard DTW on the input MFCCs (reproduced from BIBREF13 ) and the best prior result using DTW, obtained with frame features learned with correlated autoencoders BIBREF21 . Both classifier and Siamese LSTM embedding models outperform all prior results on this task of which we are aware.",
+      "We next analyze the effects of model design choices, as well as the learned embeddings themselves.",
+      ""
+    ],
+    [
+      "Table TABREF10 shows the effect on development set performance of the number of stacked layers INLINEFORM0 , the number of fully connected layers INLINEFORM1 , and LSTM vs. GRU cells, for classifier-based embeddings. The best performance in this experiment is achieved by the LSTM network with INLINEFORM2 . However, performance still seems to be improving with additional layers, suggesting that we may be able to further improve performance by adding even more layers of either type. However, we fixed the model to INLINEFORM3 in order to allow for more experimentation and analysis within a reasonable time.",
+      "Table TABREF10 reveals an interesting trend. When only one fully connected layer is used, the GRU networks outperform the LSTMs given a sufficient number of stacked layers. On the other hand, once we add more fully connected layers, the LSTMs outperform the GRUs. In the first few lines of Table TABREF10 , we use 2, 3, and 4 layer stacks of LSTMs and GRUs while holding fixed the number of fully-connected layers at INLINEFORM0 . There is clear utility in stacking additional layers; however, even with 4 stacked layers the RNNs still underperform the CNN-based embeddings of BIBREF13 until we begin adding fully connected layers.",
+      "After exploring a variety of stacked RNNs, we fixed the stack to 3 layers and varied the number of fully connected layers. The value of each additional fully connected layer is clearly greater than that of adding stacked layers. All networks trained with 2 or 3 fully connected layers obtain more than 0.4 AP on the development set, while stacked RNNs with 1 fully connected layer are at around 0.3 AP or less. This may raise the question of whether some simple fully connected model may be all that is needed; however, previous work has shown that this approach is not competitive BIBREF13 , and convolutional or recurrent layers are needed to summarize arbitrary-length segments into a fixed-dimensional representation.",
+      ""
+    ],
+    [
+      "For the Siamese networks, we varied the output embedding dimensionality, as shown in Fig. FIGREF11 . This analysis shows that the embeddings learned by the Siamese RNN network are quite robust to reduced dimensionality, outperforming the classifier model for all dimensionalities 32 or higher and outperforming previously reported dev set performance with CNN-based embeddings BIBREF13 for all dimensionalities INLINEFORM0 .",
+      ""
+    ],
+    [
+      "We might expect the learned embeddings to be more accurate for words that are seen in training than for ones that are not. Fig. FIGREF11 measures this effect by showing performance as a function of the number of occurrences of the dev words in the training set. Indeed, both model types are much more successful for in-vocabulary words, and their performance improves the higher the training frequency of the words. However, performance increases more quickly for the Siamese network than for the classifier as training frequency increases. This may be due to the fact that, if a word type occurs at least INLINEFORM0 times in the classifier training set, then it occurs at least INLINEFORM1 times in the Siamese paired training data.",
+      ""
+    ],
+    [
+      "In order to gain a better qualitative understanding of the differences between clasiffier and Siamese-based embeddings, and of the learned embedding space more generally, we plot a two-dimensional visualization of some of our learned embeddings via t-SNE BIBREF40 in Fig. FIGREF12 . For both classifier and Siamese embeddings, there is a marked difference in the quality of clusters formed by embeddings of words that were previously seen vs. previously unseen in training. However, the Siamese network embeddings appear to have better relative distances between word clusters with similar and dissimilar pronunciations. For example, the word programs appears equidistant from problems and problem in the classifier-based embedding space, but in the Siamese embedding space problems falls between problem and programs. Similarly, the cluster for democracy shifts with respect to actually and especially to better respect differences in pronunciation. More study of learned embeddings, using more data and word types, is needed to confirm such patterns in general. Improvements in unseen word embeddings from the classifier embedding space to the Siamese embedding space (such as for democracy, morning, and basketball) are a likely result of optimizing the model for relative distances between words.",
+      ""
+    ],
+    [
+      "",
+      "Our main finding is that RNN-based acoustic word embeddings outperform prior approaches, as measured via a word discrimination task related to query-by-example search. Our best results are obtained with deep LSTM RNNs with a combination of several stacked layers and several fully connected layers, optimized with a contrastive Siamese loss. Siamese networks have the benefit that, for any given training data set, they are effectively trained on a much larger set, in the sense that they measure a loss and gradient for every possible pair of data points. Our experiments suggest that the models could still be improved with additional layers. In addition, we have found that, for the purposes of acoustic word embeddings, fully connected layers are very important and have a more significant effect per layer than stacked layers, particularly when trained with the cross entropy loss function.",
+      "These experiments represent an initial exploration of sequential neural models for acoustic word embeddings. There are a number of directions for further work. For example, while our analyses suggest that Siamese networks are better than classifier-based models at embedding previously unseen words, our best embeddings are still much poorer for unseen words. Improvements in this direction may come from larger training sets, or may require new models that better model the shared structure between words. Other directions for future work include additional forms of supervision and training, as well as application to downstream tasks."
+    ]
+  ]
+}
+```

qasper-0923/instruction.md

@@ -0,0 +1,105 @@
+Name of Paper: Paraphrase Generation from Latent-Variable PCFGs for Semantic Parsing
+
+Question: What are the baselines?
+
+## 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."
+    ]
+  ]
+}
+```

qasper-1008/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Gated Convolutional Neural Networks for Domain Adaptation
+
+Question: Does the fact that GCNs can perform well on this tell us that the task is simpler than previously thought?

qasper-1030/instruction.md

@@ -0,0 +1,137 @@
+Name of Paper: Analysing Coreference in Transformer Outputs
+
+Question: Which three neural machine translation systems are analyzed?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Background and Related Work ::: Coreference",
+    "Background and Related Work ::: Translation studies",
+    "Background and Related Work ::: Coreference in MT",
+    "Systems, Methods and Resources ::: State-of-the-art NMT",
+    "Systems, Methods and Resources ::: State-of-the-art NMT ::: S1",
+    "Systems, Methods and Resources ::: State-of-the-art NMT ::: S2",
+    "Systems, Methods and Resources ::: State-of-the-art NMT ::: S3",
+    "Systems, Methods and Resources ::: Test data under analysis",
+    "Systems, Methods and Resources ::: Manual annotation process",
+    "Results and Analyses ::: Chain features",
+    "Results and Analyses ::: MT quality at system level",
+    "Results and Analyses ::: Error analysis",
+    "Results and Analyses ::: Error analysis ::: Predefined error categories",
+    "Results and Analyses ::: Error analysis ::: Additional error types",
+    "Results and Analyses ::: Error analysis ::: Types of erroneous mentions",
+    "Summary and Conclusions",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "In the present paper, we analyse coreference in the output of three neural machine translation systems (NMT) that were trained under different settings. We use a transformer architecture BIBREF0 and train it on corpora of different sizes with and without the specific coreference information. Transformers are the current state-of-the-art in NMT BIBREF1 and are solely based on attention, therefore, the kind of errors they produce might be different from other architectures such as CNN or RNN-based ones. Here we focus on one architecture to study the different errors produced only under different data configurations.",
+      "Coreference is an important component of discourse coherence which is achieved in how discourse entities (and events) are introduced and discussed. Coreference chains contain mentions of one and the same discourse element throughout a text. These mentions are realised by a variety of linguistic devices such as pronouns, nominal phrases (NPs) and other linguistic means. As languages differ in the range of such linguistic means BIBREF2, BIBREF3, BIBREF4, BIBREF5 and in their contextual restrictions BIBREF6, these differences give rise to problems that may result in incoherent (automatic) translations. We focus on coreference chains in English-German translations belonging to two different genres. In German, pronouns, articles and adjectives (and some nouns) are subject to grammatical gender agreement, whereas in English, only person pronouns carry gender marking. An incorrect translation of a pronoun or a nominal phrase may lead to an incorrect relation in a discourse and will destroy a coreference chain. Recent studies in automatic coreference translation have shown that dedicated systems can lead to improvements in pronoun translation BIBREF7, BIBREF8. However, standard NMT systems work at sentence level, so improvements in NMT translate into improvements on pronouns with intra-sentential antecedents, but the phenomenon of coreference is not limited to anaphoric pronouns, and even less to a subset of them. Document-level machine translation (MT) systems are needed to deal with coreference as a whole. Although some attempts to include extra-sentential information exist BIBREF9, BIBREF10, BIBREF11, BIBREF12, the problem is far from being solved. Besides that, some further problems of NMT that do not seem to be related to coreference at first glance (such as translation of unknown words and proper names or the hallucination of additional words) cause coreference-related errors.",
+      "In our work, we focus on the analysis of complete coreference chains, manually annotating them in the three translation variants. We also evaluate them from the point of view of coreference chain translation. The goal of this paper is two-fold. On the one hand, we are interested in various properties of coreference chains in these translations. They include total number of chains, average chain length, the size of the longest chain and the total number of annotated mentions. These features are compared to those of the underlying source texts and also the corresponding human translation reference. On the other hand, we are also interested in the quality of coreference translations. Therefore, we define a typology of errors, and and chain members in MT output are annotated as to whether or not they are correct. The main focus is on such errors as gender, number and case of the mentions, but we also consider wrong word selection or missing words in a chain. Unlike previous work, we do not restrict ourselves to pronouns. Our analyses show that there are further errors that are not directly related to coreference but consequently have an influence on the correctness of coreference chains.",
+      "The remainder of the paper is organised as follows. Section SECREF2 introduces the main concepts and presents an overview of related MT studies. Section SECREF3 provides details on the data, systems used and annotation procedures. Section SECREF4 analyses the performance of our transformer systems on coreferent mentions. Finally we summarise and draw conclusions in Section SECREF5."
+    ],
+    [
+      "Coreference is related to cohesion and coherence. The latter is the logical flow of inter-related ideas in a text, whereas cohesion refers to the text-internal relationship of linguistic elements that are overtly connected via lexico-grammatical devices across sentences BIBREF13. As stated by BIBREF14, this connectedness of texts implies dependencies between sentences. And if these dependencies are neglected in translation, the output text no longer has the property of connectedness which makes a sequence of sentences a text. Coreference expresses identity to a referent mentioned in another textual part (not necessarily in neighbouring sentences) contributing to text connectedness. An addressee is following the mentioned referents and identifies them when they are repeated. Identification of certain referents depends not only on a lexical form, but also on other linguistic means, e.g. articles or modifying pronouns BIBREF15. The use of these is influenced by various factors which can be language-dependent (range of linguistic means available in grammar) and also context-independent (pragmatic situation, genre). Thus, the means of expressing reference differ across languages and genres. This has been shown by some studies in the area of contrastive linguistics BIBREF6, BIBREF3, BIBREF5. Analyses in cross-lingual coreference resolution BIBREF16, BIBREF17, BIBREF18, BIBREF19 show that there are still unsolved problems that should be addressed."
+    ],
+    [
+      "Differences between languages and genres in the linguistic means expressing reference are important for translation, as the choice of an appropriate referring expression in the target language poses challenges for both human and machine translation. In translation studies, there is a number of corpus-based works analysing these differences in translation. However, most of them are restricted to individual phenomena within coreference. For instance, BIBREF20 analyse abstract anaphors in English-German translations. To our knowledge, they do not consider chains. BIBREF21 in their contrastive analysis of potential coreference chain members in English-German translations, describe transformation patterns that contain different types of referring expressions. However, the authors rely on automatic tagging and parsing procedures and do not include chains into their analysis. The data used by BIBREF4 and BIBREF22 contain manual chain annotations. The authors focus on different categories of anaphoric pronouns in English-Czech translations, though not paying attention to chain features (e.g. their number or size).",
+      "Chain features are considered in a contrastive analysis by BIBREF6. Their study concerns different phenomena in a variety of genres in English and German comparable texts. Using contrastive interpretations, they suggest preferred translation strategies from English into German, i.e. translators should use demonstrative pronouns instead of personal pronouns (e.g. dies/das instead of es/it) when translating from English into German and vice versa. However, corpus-based studies show that translators do not necessarily apply such strategies. Instead, they often preserve the source language anaphor's categories BIBREF20 which results in the shining through effects BIBREF23. Moreover, due to the tendency of translators to explicitly realise meanings in translations that were implicit in the source texts BIBREF24, translations are believed to contain more (explicit) referring expressions, and subsequently, more (and longer) coreference chains.",
+      "Therefore, in our analysis, we focus on the chain features related to the phenomena of shining through and explicitation. These features include number of mentions, number of chains, average chain length and the longest chain size. Machine-translated texts are compared to their sources and the corresponding human translations in terms of these features. We expect to find shining through and explicitation effects in automatic translations."
+    ],
+    [
+      "As explained in the introduction, several recent works tackle the automatic translation of pronouns and also coreference BIBREF25, BIBREF26 and this has, in part, motivated the creation of devoted shared tasks and test sets to evaluate the quality of pronoun translation BIBREF7, BIBREF27, BIBREF28, BIBREF29.",
+      "But coreference is a wider phenomenon that affects more linguistic elements. Noun phrases also appear in coreference chains but they are usually studied under coherence and consistency in MT. BIBREF30 use topic modelling to extract coherence chains in the source, predict them in the target and then promote them as translations. BIBREF31 use word embeddings to enforce consistency within documents. Before these works, several methods to post-process the translations and even including a second decoding pass were used BIBREF32, BIBREF33, BIBREF34, BIBREF35.",
+      "Recent NMT systems that include context deal with both phenomena, coreference and coherence, but usually context is limited to the previous sentence, so chains as a whole are never considered. BIBREF10 encode both a source and a context sentence and then combine them to obtain a context-aware input. The same idea was implemented before by BIBREF36 where they concatenate a source sentence with the previous one to include context. Caches BIBREF37, memory networks BIBREF38 and hierarchical attention methods BIBREF39 allow to use a wider context. Finally, our work is also related to BIBREF40 and BIBREF41 where their oracle translations are similar to the data-based approach we introduce in Section SECREF4."
+    ],
+    [
+      "Our NMT systems are based on a transformer architecture BIBREF0 as implemented in the Marian toolkit BIBREF42 using the transformer big configuration.",
+      "We train three systems (S1, S2 and S3) with the corpora summarised in Table TABREF5. The first two systems are transformer models trained on different amounts of data (6M vs. 18M parallel sentences as seen in the Table). The third system includes a modification to consider the information of full coreference chains throughout a document augmenting the sentence to be translated with this information and it is trained with the same amount of sentence pairs as S1. A variant of the S3 system participated in the news machine translation of the shared task held at WMT 2019 BIBREF43."
+    ],
+    [
+      "is trained with the concatenation of Common Crawl, Europarl, a cleaned version of Rapid and the News Commentary corpus. We oversample the latter in order to have a significant representation of data close to the news genre in the final corpus."
+    ],
+    [
+      "uses the same data as S1 with the addition of a filtered portion of Paracrawl. This corpus is known to be noisy, so we use it to create a larger training corpus but it is diluted by a factor 4 to give more importance to high quality translations."
+    ],
+    [
+      "S3 uses the same data as S1, but this time enriched with the cross- and intra-sentential coreference chain markup as described below. The information is included as follows.",
+      "Source documents are annotated with coreference chains using the neural annotator of Stanford CoreNLP BIBREF44. The tool detects pronouns, nominal phrases and proper names as mentions in a chain. For every mention, CoreNLP extracts its gender (male, female, neutral, unknown), number (singular, plural, unknown), and animacy (animate, inanimate, unknown). This information is not added directly but used to enrich the single sentence-based MT training data by applying a set of heuristics implemented in DocTrans:",
+      "We enrich pronominal mentions with the exception of \"I\" with the head (main noun phrase) of the chain. The head is cleaned by removing articles and Saxon genitives and we only consider heads with less than 4 tokens in order to avoid enriching a word with a full sentence",
+      "We enrich nominal mentions including proper names with the gender of the head",
+      "The head itself is enriched with she/he/it/they depending on its gender and animacy",
+      "The enrichment is done with the addition of tags as shown in the examples:",
+      "I never cook with $<$b_crf$>$ salt $<$e_crf$>$ it.",
+      "",
+      "$<$b_crf$>$ she $<$e_crf$>$ Biles arrived late.",
+      "In the first case heuristic 1 is used, salt is the head of the chain and it is prepended to the pronoun. The second example shows a sentence where heuristic 2 has been used and the proper name Biles has now information about the gender of the person it is referring to.",
+      "Afterwards, the NMT system is trained at sentence level in the usual way. The data used for the three systems is cleaned, tokenised, truecased with Moses scripts and BPEd with subword-nmt using separated vocabularies with 50 k subword units each. The validation set ($news2014$) and the test sets described in the following section are pre-processed in the same way."
+    ],
+    [
+      "As one of our aims is to compare coreference chain properties in automatic translation with those of the source texts and human reference, we derive data from ParCorFull, an English-German corpus annotated with full coreference chains BIBREF46. The corpus contains ca. 160.7 thousand tokens manually annotated with about 14.9 thousand mentions and 4.7 thousand coreference chains. For our analysis, we select a portion of English news texts and TED talks from ParCorFull and translate them with the three NMT systems described in SECREF4 above. As texts considerably differ in their length, we select 17 news texts (494 sentences) and four TED talks (518 sentences). The size (in tokens) of the total data set under analysis \u2013 source (src) and human translations (ref) from ParCorFull and the automatic translations produced within this study (S1, S2 and S3) are presented in Table TABREF20.",
+      "Notably, automatic translations of TED talks contain more words than the corresponding reference translation, which means that machine-translated texts of this type have also more potential tokens to enter in a coreference relation, and potentially indicating a shining through effect. The same does not happen with the news test set."
+    ],
+    [
+      "The English sources and their corresponding human translations into German were already manually annotated for coreference chains. We follow the same scheme as BIBREF47 to annotate the MT outputs with coreference chains. This scheme allows the annotator to define each markable as a certain mention type (pronoun, NP, VP or clause). The mentions can be defined further in terms of their cohesive function (antecedent, anaphoric, cataphoric, comparative, substitution, ellipsis, apposition). Antecedents can either be marked as simple or split or as entity or event. The annotation scheme also includes pronoun type (personal, possessive, demonstrative, reflexive, relative) and modifier types of NPs (possessive, demonstrative, definite article, or none for proper names), see BIBREF46 for details. The mentions referring to the same discourse item are linked between each other. We use the annotation tool MMAX2 BIBREF48 which was also used for the annotation of ParCorFull.",
+      "In the next step, chain members are annotated for their correctness. For the incorrect translations of mentions, we include the following error categories: gender, number, case, ambiguous and other. The latter category is open, which means that the annotators can add their own error types during the annotation process. With this, the final typology of errors also considered wrong named entity, wrong word, missing word, wrong syntactic structure, spelling error and addressee reference.",
+      "The annotation of machine-translated texts was integrated into a university course on discourse phenomena. Our annotators, well-trained students of linguistics, worked in small groups on the assigned annotation tasks (4-5 texts, i.e. 12-15 translations per group). At the beginning of the annotation process, the categories under analysis were discussed within the small groups and also in the class. The final versions of the annotation were then corrected by the instructor."
+    ],
+    [
+      "First, we compare the distribution of several chain features in the three MT outputs, their source texts and the corresponding human translations.",
+      "Table TABREF20 shows that, overall, all machine translations contain a greater number of annotated mentions in both news texts and TED talks than in the annotated source (src and src$_{\\rm CoreNLP}$) and reference (ref) texts. Notice that src$_{\\rm CoreNLP}$ \u2014where coreferences are not manually but automatically annotated with CoreNLP\u2014 counts also the tokens that the mentions add to the sentences, but not the tags. The larger number of mentions may indicate a strong explicitation effect observed in machine-translated texts. Interestingly, CoreNLP detects a similar number of mentions in both genres, while human annotators clearly marked more chains for TED than for news. Both genres are in fact quite different in nature; whereas only $37\\%$ of the mentions are pronominal in news texts (343 out of 915), the number grows to $58\\%$ for TED (577 out of 989), and this could be an indicator of the difficulty of the genres for NMT systems. There is also a variation in terms of chain number between translations of TED talks and news. While automatic translations of news texts contain more chains than the corresponding human annotated sources and references, machine-translated TED talks contain less chains than the sources and human translations. However, there is not much variation between the chain features of the three MT outputs. The chains are also longer in machine-translated output than in reference translations as can be seen by the number of mentions per chain and the length of the longest chain."
+    ],
+    [
+      "We evaluate the quality of the three transformer engines with two automatic metrics, BLEU BIBREF49 and METEOR BIBREF50. Table TABREF25 shows the scores in two cases: all, when the complete texts are evaluated and coref, when only the subset of sentences that have been augmented in S3 are considered \u2013 265 out of 494 for news and 239 out of 518 for TED. For news, the best system is that trained on more data, S2; but for TED talks S3 with less data has the best performance.",
+      "The difference between the behaviour of the systems can be related to the different genres. We have seen that news are dominated by nominal mentions while TED is dominated by pronominal ones. Pronouns mostly need coreference information to be properly translated, while noun phrases can be improved simply because more instances of the nouns appear in the training data. With this, S3 improves the baseline S1 in +1.1 BLEU points for TED$_{coref}$ but -0.2 BLEU points for news$_{coref}$.",
+      "However, even if the systems differ in the overall performance, the change is not related to the number of errors in coreference chains. Table TABREF25 also reports the number of mistakes in the translation of coreferent mentions. Whereas the number of errors correlates with translation quality (as measured by BLEU) for news$_{coref}$ this is not the case of TED$_{coref}$."
+    ],
+    [
+      "The total distribution for the 10 categories of errors defined in Section SECREF23 can be seen in Figure FIGREF29. Globally, the proportion of errors due to our closed categories (gender, number, case and ambiguous) is larger for TED talks than for news (see analysis in Section SECREF28). Gender is an issue with all systems and genres which does not get solved by the addition of more data. Additionally, news struggle with wrong words and named entities; for this genre the additional error types (see analysis in Section SECREF30) represent around 60% of the errors of S1/S3 to be compared to the 40% of TED talks."
+    ],
+    [
+      "0.4em 0.4Within our predefined closed categories (gender, number, case and ambiguous), the gender errors belong to the most frequent errors. They include wrong gender translation of both pronouns, as sie (\u201cher\u201d) instead of ihn (\u201chim\u201d) in example SECREF28 referring to the masculine noun Mindestlohn, and nominal phrases, as der Stasi instead of die Stasi, where a masculine form of the definite article is used instead of a feminine one, in example SECREF28.",
+      ".src: [The current minimum wage] of 7.25 US dollars is a pittance... She wants to raise [it] to 15 dollars an hour.",
+      "S3: [Der aktuelle Mindestlohn] von 7,25 US-Dollar sei Almosen... Sie m\u00f6chte [sie] auf 15 Dollar pro Stunde erh\u00f6hen.",
+      ". src: ...let's have a short look at the history of [the Stasi], because it is really important for understanding [its] self-conception.",
+      "S2: Lassen sie uns... einen kurzen Blick auf die Geschichte [des Stasi] werfen denn es wirklich wichtig, [seine] Selbstauffassung zu verstehen.",
+      "The gender-related errors are common to all the automatic translations. Interestingly, systems S1 and S3 have more problems with gender in translations of TED talks, whereas they do better in translating news, which leads us to assume that this is a data-dependent issue: while the antecedent for news is in the same sentence it is not for TED talks. A closer look at the texts with a high number of gender problems confirms this assumption \u2014they contain references to females who were translated with male forms of nouns and pronouns (e.g. Mannschaftskapit\u00e4n instead of Mannschaftskapit\u00e4nin).",
+      "We also observe errors related to gender for the cases of explicitation in translation. Some impersonal English constructions not having direct equivalents in German are translated with personal constructions, which requires an addition of a pronoun. Such cases of explicitation were automatically detected in parallel data in BIBREF21, BIBREF2. They belong to the category of obligatory explicitation, i.e. explicitation dictated by differences in the syntactic and semantic structure of languages, as defined by BIBREF51. An MT system tends to insert a male form instead of a female one even if it's marked as feminine (S3 adds the feminine form she as markup), as illustrated in example SECREF28 where the automatic translation contains the masculine pronoun er (\u201che\u201d) instead of sie (\u201cshe\u201d).",
+      ". src: [Biles] earned the first one on Tuesday while serving as the exclamation point to retiring national team coordinator Martha Karolyi's going away party.",
+      "ref: [Biles] holte die erste Medaille am Dienstag, w\u00e4hrend [sie] auf der Abschiedsfeier der sich in Ruhestand begehenden Mannschaftskoordinatorin Martha Karolyi als Ausrufezeichen diente.",
+      "S2: [Biles] verdiente den ersten am Dienstag, w\u00e4hrend [er] als Ausrufezeichen f\u00fcr den pensionierten Koordinator der Nationalmannschaft, Martha Karolyi, diente.",
+      "Another interesting case of a problem related to gender is the dependence of the referring expressions on grammatical restrictions in German. In example SECREF28, the source chain contains the pronoun him referring to both a 6-year-old boy and The child. In German, these two nominal phrases have different gender (masculine vs. neutral). The pronoun has grammatical agreement with the second noun of the chain (des Kindes) and not its head (ein 6 Jahre alter Junge).",
+      ". src: Police say [a 6-year-old boy] has been shot in Philadelphia... [The child]'s grandparents identified [him] to CBS Philadelphia as [Mahaj Brown].",
+      "S1: Die Polizei behauptet, [ein 6 Jahre alter Junge] sei in Philadelphia erschossen worden... Die Gro\u00dfeltern [des Kindes] identifizierten [ihn] mit CBS Philadelphia als [Mahaj Brown].",
+      "Case- and number-related errors are less frequent in our data. However, translations of TED talks with S2 contain much more number-related errors than other outputs. Example SECREF28 illustrates this error type which occurs within a sentence. The English source contains the nominal chain in singular the cost \u2013 it, whereas the German correspondence Kosten has a plural form and requires a plural pronoun (sie). However, the automatic translation contains the singular pronoun es.",
+      ". src: ...to the point where [the cost] is now below 1,000 dollars, and it's confidently predicted that by the year 2015 [it] will be below 100 dollars...",
+      "S2: bis zu dem Punkt, wo [die Kosten] jetzt unter 1.000 Dollar liegen, und es ist zuversichtlich, dass [es] bis zum Jahr 2015 unter 100 Dollar liegen wird...",
+      "Ambiguous cases often contain a combination of errors or they are difficult to categorise due to the ambiguity of the source pronouns, as the pronoun it in example SECREF28 which may refer either to the noun trouble or even the clause Democracy is in trouble is translated with the pronoun sie (feminine). In case of the first meaning, the pronoun would be correct, but the form of the following verb should be in plural. In case of a singular form, we would need to use a demonstrative pronoun dies (or possibly the personal pronoun es).",
+      ". src: Democracy is in trouble... and [it] comes in part from a deep dilemma...",
+      "S2: Die Demokratie steckt in Schwierigkeiten ... und [sie] r\u00fchrt teilweise aus einem tiefen Dilemma her..."
+    ],
+    [
+      "At first glance, the error types discussed in this section do not seem to be related to coreference \u2014a wrong translation of a noun can be traced back to the training data available and the way NMT deals with unknown words. However, a wrong translation of a noun may result in its invalidity to be a referring expression for a certain discourse item. As a consequence, a coreference chain is damaged. We illustrate a chain with a wrong named entity translation in example SECREF30. The source chain contains five nominal mentions referring to an American gymnast Aly Raisman: silver medalist \u2013 \u201cFinal Five\u201d teammate \u2013 Aly Raisman \u2013 Aly Raisman \u2013 Raisman. All the three systems used different names. Example SECREF30 illustrates the translation with S2, where Aly Donovan and Aly Encence were used instead of Aly Raisman, and the mention Raisman disappears completely from the chain.",
+      ". src: Her total of 62.198 was well clear of [silver medalist] and [\u201cFinal Five\u201d teammate] [Aly Raisman]...United States' Simone Biles, left, and [Aly Raisman] embrace after winning gold and silver respectively... [Raisman]'s performance was a bit of revenge from four years ago, when [she] tied...",
+      "S2: Ihre Gesamtmenge von 62.198 war deutlich von [Silbermedaillengewinner] und [\u201cFinal Five\u201d Teamkollegen] [Aly Donovan]... Die Vereinigten Staaten Simone Biles, links und [Aly Encence] Umarmung nach dem Gewinn von Gold und Silber... Vor vier Jahren, als [sie]...",
+      "Example SECREF30 illustrates translation of the chain The scaling in the opposite direction \u2013 that scale. The noun phrases Die Verlagerung in die entgegengesetzte Richtung (\u201cthe shift in the opposite direction\u201d) and dieses Ausma\u00df (\u201cextent/scale\u201d) used in the S1 output do not corefer (cf. Wachstum in die entgegengesetzte Richtung and Wachstum in the reference translation). Notice that these cases with long noun phrases are not tackled by S3 either.",
+      ". src: [The scaling in the opposite direction]...drive the structure of business towards the creation of new kinds of institutions that can achieve [that scale].",
+      "ref: [Wachstum in die entgegengesetzte Richtung]... steuert die Struktur der Gesch\u00e4fte in Richtung Erschaffung von neuen Institutionen, die [dieses Wachstum] erreichen k\u00f6nnen.",
+      "S1: [Die Verlagerung in die entgegengesetzte Richtung]... treibt die Struktur der Unternehmen in Richtung der Schaffung neuer Arten von Institutionen, die [dieses Ausma\u00df] erreichen k\u00f6nnen."
+    ],
+    [
+      "Finally, we also analyse the types of the mentions marked as errors. They include either nominal phrases or pronouns. Table TABREF32 shows that there is a variation between the news texts and TED talks in terms of these features. News contain more erroneous nominal phrases, whereas TED talks contain more pronoun-related errors. Whereas both the news and the TED talks have more errors in translating anaphors, there is a higher proportion of erroneous antecedents in the news than in the TED talks.",
+      "It is also interesting to see that S3 reduces the percentage of errors in anaphors for TED, but has a similar performance to S2 on news."
+    ],
+    [
+      "We analysed coreferences in the translation outputs of three transformer systems that differ in the training data and in whether they have access to explicit intra- and cross-sentential anaphoric information (S3) or not (S1, S2). We see that the translation errors are more dependent on the genre than on the nature of the specific NMT system: whereas news (with mainly NP mentions) contain a majority of errors related to wrong word selection, TED talks (with mainly pronominal mentions) are prone to accumulate errors on gender and number.",
+      "System S3 was specifically designed to solve this issue, but we cannot trace the improvement from S1 to S3 by just counting the errors and error types, as some errors disappear and others emerge: coreference quality and automatic translation quality do not correlate in our analysis on TED talks. As a further improvement to address the issue, we could add more parallel data to our training corpus with a higher density of coreference chains such as movie subtitles or parallel TED talks.",
+      "We also characterised the originals and translations according to coreference features such as total number of chains and mentions, average chain length and size of the longest chain. We see how NMT translations increase the number of mentions about $30\\%$ with respect to human references showing even a more marked explicitation effect than human translations do. As future work, we consider a more detailed comparison of the human and machine translations, and analyse the purpose of the additional mentions added by the NMT systems. It would be also interesting to evaluate of the quality of the automatically computed coreferences chains used for S3."
+    ],
+    [
+      "The annotation work was performed at Saarland University. We thank Anna Felsing, Francesco Fernicola, Viktoria Henn, Johanna Irsch, Kira Janine Jebing, Alicia Lauer, Friederike Lessau and Christina Pollkl\u00e4sener for performing the manual annotation of the NMT outputs. The project on which this paper is based was partially funded by the German Federal Ministry of Education and Research under the funding code 01IW17001 (Deeplee) and by the German Research Foundation (DFG) as part of SFB 1102 Information Density and Linguistic Encoding. Responsibility for the content of this publication is with the authors."
+    ]
+  ]
+}
+```

qasper-1037/instruction.md

@@ -0,0 +1,69 @@
+Name of Paper: Contextualized Word Embeddings Enhanced Event Temporal Relation Extraction for Story Understanding
+
+Question: Do they report results only on English data?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Models",
+    "Data",
+    "Implementation Details",
+    "Result and Analysis",
+    "Temporal Relation Data",
+    "Feature-based Models",
+    "Neural Network Model",
+    "Conclusion",
+    "Acknowledgement"
+  ],
+  "paragraphs": [
+    [
+      "Event temporal relation understanding is a major component of story/narrative comprehension. It is an important natural language understanding (NLU) task with broad applications to downstream tasks such as story understanding BIBREF0 , BIBREF1 , BIBREF2 , question answering BIBREF3 , BIBREF4 , and text summarization BIBREF5 , BIBREF6 .",
+      "The goal of event temporal relation extraction is to build a directed graph where nodes correspond to events, and edges reflect temporal relations between the events. Figure FIGREF1 illustrates an example of such a graph for the text shown above. Different types of edges specify different temporal relations: the event assassination is before slaughtered, slaughtered is included in rampage, and the relation between rampage and war is vague.",
+      "Modeling event temporal relations is crucial for story/narrative understanding and storytelling, because a story is typically composed of a sequence of events BIBREF7 . Several story corpora are thus annotated with various event-event relations to understand commonsense event knowledge. CaTeRS BIBREF8 is created by annotating 320 five-sentence stories sampled from ROCStories BIBREF7 dataset. RED BIBREF9 contains annotations of rich relations between event pairs for storyline understanding, including co-reference and partial co-reference relations, temporal; causal, and sub-event relations.",
+      "Despite multiple productive research threads on temporal and causal relation modeling among events BIBREF10 , BIBREF11 , BIBREF12 and event relation annotation for story understanding BIBREF8 , the intersection of these two threads seems flimsy. To the best of our knowledge, no event relation extraction results have been reported on CaTeRS and RED.",
+      "We apply neural network models that leverage recent advances in contextualized embeddings (BERT BIBREF13 ) to event-event relation extraction tasks for CaTeRS and RED. Our goal in this paper is to increase understanding of how well the state-of-the-art event relation models work for story/narrative comprehension.",
+      "In this paper, we report the first results of event temporal relation extraction on two under-explored story comprehension datasets: CaTeRS and RED. We establish strong baselines with neural network models enhanced by recent breakthrough of contextualized embeddings, BERT BIBREF13 . We summarize the contributions of the paper as follows:"
+    ],
+    [
+      "We investigate both neural network-based models and traditional feature-based models. We briefly introduce them in this section."
+    ],
+    [
+      "is created by annotating 1600 sentences of 320 five-sentence stories sampled from ROCStories BIBREF7 dataset. CaTeRS contains both temporal and causal relations in an effort to understand and predict commonsense relations between events.",
+      "As demonstrated in Table TABREF16 , we split all stories into 220 training and 80 test. We do not construct the development set because the dataset is small. Note that some relations have compounded labels such as \u201cCAUSE_BEFORE\u201d, \u201cENABLE_BEFORE\u201d, etc. We only take the temporal portion of the annotations.",
+      "annotates a wide range of relations of event pairs including their coreference and partial coreference relations, and temporal, causal and subevent relationships. We split data according to the standard train, development, test sets, and only focus on the temporal relations.",
+      "The common issue of these two datasets is that they are not densely annotated \u2013 not every pair of events is annotated with a relation. We provide one way to handle negative (unannotated) pairs in this paper. When constructing negative examples, we take all event pairs that occur within the same or neighboring sentences with no annotations, labeling them as \u201cNONE\u201d. The negative to positive samples ratio is 1.00 and 11.5 for CaTeRS and RED respectively. Note that RED data has much higher negative ratio (as shown in Table TABREF16 ) because it contains longer articles, more complicated sentence structures, and richer entity types than CaTeRS where all stories consist of 5 (mostly short) sentences.",
+      "In both the development and test sets, we add all negative pairs as candidates for the relation prediction. During training, the number of negative pairs we add is based on a hyper-parameter that we tune to control the negative-to-positive sample ratio.",
+      "To justify our decision of selecting negative pairs within the same or neighboring sentences, we show the distribution of distances across positive sentence pairs in Table TABREF18 . Although CaTeRS data has pair distance more evenly distributed than RED, we observe that the vast majority (85.87% and 93.99% respectively) of positive pairs have sentence distance less than or equal to one.",
+      "To handle negative pairs that are more than two sentences away, we automatically predict all out-of-window pairs as \u201cNONE\u201d. This means that some positive pairs will be automatically labeled as negative pairs. Since the percentage of out-of-window positive pairs is small, we believe the impact on performance is small. We can investigate expanding the prediction window in future research, but the trade-off is that we will get more negative pairs that are hard to predict."
+    ],
+    [
+      "CAEVO consists of both linguistic-rule-based sieves and feature-based trainable sieves. We train CAEVO sieves with our train set and evaluate them on both dev and test sets. CAEVO is an end-to-end system that automatically annotates both events and relations. In order to resolve label annotation mismatch between CAEVO and our gold data, we create our own final input files to CAEVO system. Default parameter settings are used when running the CAEVO system.",
+      "In an effort of building a general model and reducing the number of hand-crafted features, we leverage pre-trained (GloVe 300) embeddings in place of linguistic features. The only linguistic feature we use in our experiment is token distance. We notice in our experiments that hidden layer size, dropout ratio and negative sample ratio impact model performance significantly. We conduct grid search to find the best hyper-parameter combination according to the performance of the development set.",
+      "Note that since the CaTeRS data is small and there is no standard train, development, and test splits, we conduct cross-validation on training data to choose the best hyper-parameters and predict on test. For RED data, the standard train, development, test splits are used.",
+      "As we mentioned briefly in the introduction, using BERT output as word embeddings could provide an additional performance boost in our NN architecture. We pre-process our raw data by feeding original sentences into a pre-trained BERT model and output the last layer of BERT as token representations. In this experiment, we fix the negative sample ratio according to the result obtained from the previous step and only search for the best hidden layer size and dropout ratio."
+    ],
+    [
+      "Table TABREF25 contains the best hyper-parameters and Table TABREF26 contains micro-average F1 scores for both datasets on dev and test sets. We only consider positive pairs, i.e. correct predictions on NONE pairs are excluded for evaluation. In general, the baseline model CAEVO is outperformed by both NN models, and NN model with BERT embedding achieves the greatest performance. We now provide more detailed analysis and discussion for each dataset."
+    ],
+    [
+      "Collecting dense TempRel corpora with event pairs fully annotated has been reported challenging since annotators could easily overlook some pairs BIBREF18 , BIBREF19 , BIBREF10 . TimeBank BIBREF20 is an example with events and their relations annotated sparsely. TB-Dense dataset mitigates this issue by forcing annotators to examine all pairs of events within the same or neighboring sentences. However, densely annotated datasets are relatively small both in terms of number of documents and event pairs, which restricts the complexity of machine learning models used in previous research."
+    ],
+    [
+      "The series of TempEval competitions BIBREF21 , BIBREF22 , BIBREF23 have attracted many research interests in predicting event temporal relations. Early attempts by BIBREF24 , BIBREF21 , BIBREF25 , BIBREF26 only use pair-wise classification models. State-of-the-art local methods, such as ClearTK BIBREF27 , UTTime BIBREF28 , and NavyTime BIBREF29 improve on earlier work by feature engineering with linguistic and syntactic rules. As we mention in the Section 2, CAEVO is the current state-of-the-art system for feature-based temporal event relation extraction BIBREF10 . It's widely used as the baseline for evaluating TB-Dense data. We adopt it as our baseline for evaluating CaTeRS and RED datasets. Additionally, several models BramsenDLB2006, ChambersJ2008, DoLuRo12, NingWuRo18, P18-1212 have successfully incorporated global inference to impose global prediction consistency such as temporal transitivity."
+    ],
+    [
+      "Neural network-based methods have been employed for event temporal relation extraction BIBREF14 , BIBREF15 , BIBREF16 , BIBREF12 which achieved impressive results. However, the dataset they focus on is TB-Dense. We have explored neural network models on CaTeRS and RED, which are more related to story narrative understanding and generation.",
+      "In our NN model, we also leverage Bidrectional Encoder Representations from Transformers (BERT) BIBREF30 which has shown significant improvement in many NLP tasks by allowing fine-tuning of pre-trained language representations. Unlike the Generative Pre-trained Transformer (OpenAI GPT) BIBREF31 , BERT uses a biderctional Transformer BIBREF32 instead of a unidirectional (left-to-right) Transformer to incorporate context from both directions. As mentioned earlier, we do not fine-tune BERT in our experiments and simply leverage the last layer as our contextualized word representations."
+    ],
+    [
+      "We established strong baselines for two story narrative understanding datasets: CaTeRS and RED. We have shown that neural network-based models can outperform feature-based models with wide margins, and we conducted an ablation study to show that contextualized representation learning can boost performance of NN models. Further research can focus on more systematic study or build stronger NN models over the same datasets used in this work. Exploring possibilities to directly apply temporal relation extraction to enhance performance of story generation systems is another promising research direction."
+    ],
+    [
+      "We thank the anonymous reviewers for their constructive comments, as well as the members of the USC PLUS lab for their early feedback. This work is supported by Contract W911NF-15-1-0543 with the US Defense Advanced Research Projects Agency (DARPA)."
+    ]
+  ]
+}
+```

qasper-1039/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Contextualized Word Embeddings Enhanced Event Temporal Relation Extraction for Story Understanding
+
+Question: Do the BERT-based embeddings improve results?

qasper-1202/instruction.md

@@ -0,0 +1,93 @@
+Name of Paper: Weakly Supervised Domain Detection
+
+Question: What domains are detected in this paper?
+
+## 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."
+    ]
+  ]
+}
+```

qasper-1205/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Harry Potter and the Action Prediction Challenge from Natural Language
+
+Question: Do they literally just treat this as "predict the next spell that appears in the text"?

qasper-1453/instruction.md

@@ -0,0 +1,94 @@
+Name of Paper: A Continuous Relaxation of Beam Search for End-to-end Training of Neural Sequence Models
+
+Question: Do they compare partially complete sequences (created during steps of beam search) to gold/target sequences?
+
+## 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."
+    ]
+  ]
+}
+```

qasper-1454/instruction.md

@@ -0,0 +1,94 @@
+Name of Paper: A Continuous Relaxation of Beam Search for End-to-end Training of Neural Sequence Models
+
+Question: Which loss metrics do they try in their new training procedure evaluated on the output of beam search?
+
+## 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."
+    ]
+  ]
+}
+```

qasper-1496/instruction.md

@@ -0,0 +1,118 @@
+Name of Paper: Question Answering by Reasoning Across Documents with Graph Convolutional Networks
+
+Question: What baseline did they compare Entity-GCN to?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Method",
+    "Dataset and task abstraction",
+    "Reasoning on an entity graph",
+    "Node annotations",
+    "Entity relational graph convolutional network",
+    "Experiments",
+    "Comparison",
+    "Ablation study",
+    "Error analysis",
+    "Related work",
+    "Conclusion",
+    "Acknowledgments",
+    "Architecture",
+    "Training details"
+  ],
+  "paragraphs": [
+    [
+      "The long-standing goal of natural language understanding is the development of systems which can acquire knowledge from text collections. Fresh interest in reading comprehension tasks was sparked by the availability of large-scale datasets, such as SQuAD BIBREF1 and CNN/Daily Mail BIBREF2 , enabling end-to-end training of neural models BIBREF3 , BIBREF4 , BIBREF5 . These systems, given a text and a question, need to answer the query relying on the given document. Recently, it has been observed that most questions in these datasets do not require reasoning across the document, but they can be answered relying on information contained in a single sentence BIBREF6 . The last generation of large-scale reading comprehension datasets, such as a NarrativeQA BIBREF7 , TriviaQA BIBREF8 , and RACE BIBREF9 , have been created in such a way as to address this shortcoming and to ensure that systems relying only on local information cannot achieve competitive performance.",
+      "Even though these new datasets are challenging and require reasoning within documents, many question answering and search applications require aggregation of information across multiple documents. The WikiHop dataset BIBREF0 was explicitly created to facilitate the development of systems dealing with these scenarios. Each example in WikiHop consists of a collection of documents, a query and a set of candidate answers (Figure 1 ). Though there is no guarantee that a question cannot be answered by relying just on a single sentence, the authors ensure that it is answerable using a chain of reasoning crossing document boundaries.",
+      "Though an important practical problem, the multi-hop setting has so far received little attention. The methods reported by BIBREF0 approach the task by merely concatenating all documents into a single long text and training a standard RNN-based reading comprehension model, namely, BiDAF BIBREF3 and FastQA BIBREF6 . Document concatenation in this setting is also used in Weaver BIBREF10 and MHPGM BIBREF11 . The only published paper which goes beyond concatenation is due to BIBREF12 , where they augment RNNs with jump-links corresponding to co-reference edges. Though these edges provide a structural bias, the RNN states are still tasked with passing the information across the document and performing multi-hop reasoning.",
+      "Instead, we frame question answering as an inference problem on a graph representing the document collection. Nodes in this graph correspond to named entities in a document whereas edges encode relations between them (e.g., cross- and within-document coreference links or simply co-occurrence in a document). We assume that reasoning chains can be captured by propagating local contextual information along edges in this graph using a graph convolutional network (GCN) BIBREF13 .",
+      "The multi-document setting imposes scalability challenges. In realistic scenarios, a system needs to learn to answer a query for a given collection (e.g., Wikipedia or a domain-specific set of documents). In such scenarios one cannot afford to run expensive document encoders (e.g., RNN or transformer-like self-attention BIBREF14 ), unless the computation can be preprocessed both at train and test time. Even if (similarly to WikiHop creators) one considers a coarse-to-fine approach, where a set of potentially relevant documents is provided, re-encoding them in a query-specific way remains the bottleneck. In contrast to other proposed methods (e.g., BIBREF12 , BIBREF10 , BIBREF3 ), we avoid training expensive document encoders.",
+      "In our approach, only a small query encoder, the GCN layers and a simple feed-forward answer selection component are learned. Instead of training RNN encoders, we use contextualized embeddings (ELMo) to obtain initial (local) representations of nodes. This implies that only a lightweight computation has to be performed online, both at train and test time, whereas the rest is preprocessed. Even in the somewhat contrived WikiHop setting, where fairly small sets of candidates are provided, the model is at least 5 times faster to train than BiDAF. Interestingly, when we substitute ELMo with simple pre-trained word embeddings, Entity-GCN still performs on par with many techniques that use expensive question-aware recurrent document encoders.",
+      "Despite not using recurrent document encoders, the full Entity-GCN model achieves over 2% improvement over the best previously-published results. As our model is efficient, we also reported results of an ensemble which brings further 3.6% of improvement and only 3% below the human performance reported by BIBREF0 . Our contributions can be summarized as follows:"
+    ],
+    [
+      "In this section we explain our method. We first introduce the dataset we focus on, WikiHop by BIBREF0 , as well as the task abstraction. We then present the building blocks that make up our Entity-GCN model, namely, an entity graph used to relate mentions to entities within and across documents, a document encoder used to obtain representations of mentions in context, and a relational graph convolutional network that propagates information through the entity graph."
+    ],
+    [
+      "The WikiHop dataset comprises of tuples $\\langle q, S_q, C_q, a^\\star \\rangle $ where: $q$ is a query/question, $S_q$ is a set of supporting documents, $C_q$ is a set of candidate answers (all of which are entities mentioned in $S_q$ ), and $a^\\star \\in C_q$ is the entity that correctly answers the question. WikiHop is assembled assuming that there exists a corpus and a knowledge base (KB) related to each other. The KB contains triples $\\langle s, r, o \\rangle $ where $s$ is a subject entity, $o$ an object entity, and $r$ a unidirectional relation between them. BIBREF0 used Wikipedia as corpus and Wikidata BIBREF15 as KB. The KB is only used for constructing WikiHop: BIBREF0 retrieved the supporting documents $q$0 from the corpus looking at mentions of subject and object entities in the text. Note that the set $q$1 (not the KB) is provided to the QA system, and not all of the supporting documents are relevant for the query but some of them act as distractors. Queries, on the other hand, are not expressed in natural language, but instead consist of tuples $q$2 where the object entity is unknown and it has to be inferred by reading the support documents. Therefore, answering a query corresponds to finding the entity $q$3 that is the object of a tuple in the KB with subject $q$4 and relation $q$5 among the provided set of candidate answers $q$6 .",
+      "The goal is to learn a model that can identify the correct answer $a^\\star $ from the set of supporting documents $S_q$ . To that end, we exploit the available supervision to train a neural network that computes scores for candidates in $C_q$ . We estimate the parameters of the architecture by maximizing the likelihood of observations. For prediction, we then output the candidate that achieves the highest probability. In the following, we present our model discussing the design decisions that enable multi-step reasoning and an efficient computation."
+    ],
+    [
+      "In an offline step, we organize the content of each training instance in a graph connecting mentions of candidate answers within and across supporting documents. For a given query $q = \\langle s, r, ? \\rangle $ , we identify mentions in $S_q$ of the entities in $C_q \\cup \\lbrace s\\rbrace $ and create one node per mention. This process is based on the following heuristic:",
+      "we consider mentions spans in $S_q$ exactly matching an element of $C_q \\cup \\lbrace s\\rbrace $ . Admittedly, this is a rather simple strategy which may suffer from low recall.",
+      "we use predictions from a coreference resolution system to add mentions of elements in $C_q \\cup \\lbrace s\\rbrace $ beyond exact matching (including both noun phrases and anaphoric pronouns). In particular, we use the end-to-end coreference resolution by BIBREF16 .",
+      "we discard mentions which are ambiguously resolved to multiple coreference chains; this may sacrifice recall, but avoids propagating ambiguity.",
+      "To each node $v_i$ , we associate a continuous annotation $\\mathbf {x}_i \\in \\mathbb {R}^D$ which represents an entity in the context where it was mentioned (details in Section \"Node annotations\" ). We then proceed to connect these mentions i) if they co-occur within the same document (we will refer to this as DOC-BASED edges), ii) if the pair of named entity mentions is identical (MATCH edges\u2014these may connect nodes across and within documents), or iii) if they are in the same coreference chain, as predicted by the external coreference system (COREF edges). Note that MATCH edges when connecting mentions in the same document are mostly included in the set of edges predicted by the coreference system. Having the two types of edges lets us distinguish between less reliable edges provided by the coreference system and more reliable (but also more sparse) edges given by the exact-match heuristic. We treat these three types of connections as three different types of relations. See Figure 2 for an illustration. In addition to that, and to prevent having disconnected graphs, we add a fourth type of relation (COMPLEMENT edge) between any two nodes that are not connected with any of the other relations. We can think of these edges as those in the complement set of the entity graph with respect to a fully connected graph.",
+      "Our model then approaches multi-step reasoning by transforming node representations (Section \"Node annotations\" for details) with a differentiable message passing algorithm that propagates information through the entity graph. The algorithm is parameterized by a graph convolutional network (GCN) BIBREF13 , in particular, we employ relational-GCNs BIBREF17 , an extended version that accommodates edges of different types. In Section \"Entity relational graph convolutional network\" we describe the propagation rule.",
+      "Each step of the algorithm (also referred to as a hop) updates all node representations in parallel. In particular, a node is updated as a function of messages from its direct neighbours, and a message is possibly specific to a certain relation. At the end of the first step, every node is aware of every other node it connects directly to. Besides, the neighbourhood of a node may include mentions of the same entity as well as others (e.g., same-document relation), and these mentions may have occurred in different documents. Taking this idea recursively, each further step of the algorithm allows a node to indirectly interact with nodes already known to their neighbours. After $L$ layers of R-GCN, information has been propagated through paths connecting up to $L+1$ nodes.",
+      "We start with node representations $\\lbrace \\mathbf {h}_i^{(0)}\\rbrace _{i=1}^N$ , and transform them by applying $L$ layers of R-GCN obtaining $\\lbrace \\mathbf {h}_i^{(L)}\\rbrace _{i=1}^N$ . Together with a representation $\\mathbf {q}$ of the query, we define a distribution over candidate answers and we train maximizing the likelihood of observations. The probability of selecting a candidate $c \\in C_q$ as an answer is then ",
+      "$$ \nP(c|q, C_q, S_q) \\propto \\exp \\left(\\max _{i \\in \\mathcal {M}_c} f_o([\\mathbf {q}, \\mathbf {h}^{(L)}_i]) \\right)\\;,$$   (Eq. 16) ",
+      "where $f_o$ is a parameterized affine transformation, and $\\mathcal {M}_c$ is the set of node indices such that $i\\in \\mathcal {M}_c$ only if node $v_i$ is a mention of $c$ . The $\\max $ operator in Equation 16 is necessary to select the node with highest predicted probability since a candidate answer is realized in multiple locations via different nodes."
+    ],
+    [
+      "Keeping in mind we want an efficient model, we encode words in supporting documents and in the query using only a pre-trained model for contextualized word representations rather than training our own encoder. Specifically, we use ELMo BIBREF20 , a pre-trained bi-directional language model that relies on character-based input representation. ELMo representations, differently from other pre-trained word-based models (e.g., word2vec BIBREF21 or GloVe BIBREF22 ), are contextualized since each token representation depends on the entire text excerpt (i.e., the whole sentence).",
+      "We choose not to fine tune nor propagate gradients through the ELMo architecture, as it would have defied the goal of not having specialized RNN encoders. In the experiments, we will also ablate the use of ELMo showing how our model behaves using non-contextualized word representations (we use GloVe).",
+      "ELMo encodings are used to produce a set of representations $\\lbrace \\mathbf {x}_i\\rbrace _{i=1}^N$ , where $\\mathbf {x}_i \\in \\mathbb {R}^D$ denotes the $i$ th candidate mention in context. Note that these representations do not depend on the query yet and no trainable model was used to process the documents so far, that is, we use ELMo as a fixed pre-trained encoder. Therefore, we can pre-compute representation of mentions once and store them for later use.",
+      "ELMo encodings are used to produce a query representation $\\mathbf {q} \\in \\mathbb {R}^K$ as well. Here, $\\mathbf {q}$ is a concatenation of the final outputs from a bidirectional RNN layer trained to re-encode ELMo representations of words in the query. The vector $\\mathbf {q}$ is used to compute a query-dependent representation of mentions $\\lbrace \\mathbf { \\hat{x}}_i\\rbrace _{i=1}^N$ as well as to compute a probability distribution over candidates (as in Equation 16 ). Query-dependent mention encodings $\\mathbf {\\hat{x}}_i = f_x(\\mathbf {q}, \\mathbf {x}_i)$ are generated by a trainable function $f_x$ which is parameterized by a feed-forward neural network."
+    ],
+    [
+      "Our model uses a gated version of the original R-GCN propagation rule. At the first layer, all hidden node representation are initialized with the query-aware encodings $\\mathbf {h}_i^{(0)} = \\mathbf {\\hat{x}}_i$ . Then, at each layer $0\\le \\ell \\le L$ , the update message $\\mathbf {u}_i^{(\\ell )}$ to the $i$ th node is a sum of a transformation $f_s$ of the current node representation $\\mathbf {h}^{(\\ell )}_i$ and transformations of its neighbours: ",
+      "$$\\mathbf {u}^{(\\ell )}_i = f_s(\\mathbf {h}^{(\\ell )}_i) + \\frac{1}{|\\mathcal {N}_i|} \\sum _{j \\in \\mathcal {N}_i} \\sum _{r \\in \\mathcal {R}_{ij}} f_r(\\mathbf {h}_j^{(\\ell )})\\;,$$   (Eq. 22) ",
+      "where $\\mathcal {N}_i$ is the set of indices of nodes neighbouring the $i$ th node, $\\mathcal {R}_{ij}$ is the set of edge annotations between $i$ and $j$ , and $f_r$ is a parametrized function specific to an edge type $r\\in \\mathcal {R}$ . Recall the available relations from Section \"Ablation study\" , namely, $\\mathcal {R} =\\lbrace $ DOC-BASED, MATCH, COREF, COMPLEMENT $\\rbrace $ .",
+      "A gating mechanism regulates how much of the update message propagates to the next step. This provides the model a way to prevent completely overwriting past information. Indeed, if all necessary information to answer a question is present at a layer which is not the last, then the model should learn to stop using neighbouring information for the next steps. Gate levels are computed as ",
+      "$$\\mathbf {a}^{(\\ell )}_i = \\sigma \\left( f_a\\left([\\mathbf {u}^{(\\ell )}_i, \\mathbf {h}^{(\\ell )}_i ]\\right) \\right) \\;,$$   (Eq. 23) ",
+      "where $\\sigma (\\cdot )$ is the sigmoid function and $f_a$ a parametrized transformation. Ultimately, the updated representation is a gated combination of the previous representation and a non-linear transformation of the update message: ",
+      "$$\\mathbf {h}^{(\\ell + 1)}_i = \\phi (\\mathbf {u}^{(\\ell )}_i) \\odot \\mathbf {a}^{(\\ell )}_i + \\mathbf {h}^{(\\ell )}_i \\odot (1 - \\mathbf {a}^{(\\ell )}_i ) \\;,$$   (Eq. 24) ",
+      "where $\\phi (\\cdot )$ is any nonlinear function (we used $\\tanh $ ) and $\\odot $ stands for element-wise multiplication. All transformations $f_*$ are affine and they are not layer-dependent (since we would like to use as few parameters as possible to decrease model complexity promoting efficiency and scalability)."
+    ],
+    [
+      "In this section, we compare our method against recent work as well as preforming an ablation study using the WikiHop dataset BIBREF0 . See Appendix \"Implementation and experiments details\" in the supplementary material for a description of the hyper-parameters of our model and training details."
+    ],
+    [
+      "In this experiment, we compare our Enitity-GCN against recent prior work on the same task. We present test and development results (when present) for both versions of the dataset in Table 2 . From BIBREF0 , we list an oracle based on human performance as well as two standard reading comprehension models, namely BiDAF BIBREF3 and FastQA BIBREF6 . We also compare against Coref-GRU BIBREF12 , MHPGM BIBREF11 , and Weaver BIBREF10 . Additionally, we include results of MHQA-GRN BIBREF23 , from a recent arXiv preprint describing concurrent work. They jointly train graph neural networks and recurrent encoders. We report single runs of our two best single models and an ensemble one on the unmasked test set (recall that the test set is not publicly available and the task organizers only report unmasked results) as well as both versions of the validation set.",
+      "Entity-GCN (best single model without coreference edges) outperforms all previous work by over 2% points. We additionally re-ran BiDAF baseline to compare training time: when using a single Titan X GPU, BiDAF and Entity-GCN process 12.5 and 57.8 document sets per second, respectively. Note that BIBREF0 had to use BiDAF with very small state dimensionalities (20), and smaller batch size due to the scalability issues (both memory and computation costs). We compare applying the same reductions. Eventually, we also report an ensemble of 5 independently trained models. All models are trained on the same dataset splits with different weight initializations. The ensemble prediction is obtained as $\\arg \\max \\limits _c \\prod \\limits _{i=1}^5 P_i(c|q, C_q, S_q)$ from each model."
+    ],
+    [
+      "To help determine the sources of improvements, we perform an ablation study using the publicly available validation set (see Table 3 ). We perform two groups of ablation, one on the embedding layer, to study the effect of ELMo, and one on the edges, to study how different relations affect the overall model performance.",
+      "We argue that ELMo is crucial, since we do not rely on any other context encoder. However, it is interesting to explore how our R-GCN performs without it. Therefore, in this experiment, we replace the deep contextualized embeddings of both the query and the nodes with GloVe BIBREF22 vectors (insensitive to context). Since we do not have any component in our model that processes the documents, we expect a drop in performance. In other words, in this ablation our model tries to answer questions without reading the context at all. For example, in Figure 1 , our model would be aware that \u201cStockholm\u201d and \u201cSweden\u201d appear in the same document but any context words, including the ones encoding relations (e.g., \u201cis the capital of\u201d) will be hidden. Besides, in the masked case all mentions become `unknown' tokens with GloVe and therefore the predictions are equivalent to a random guess. Once the strong pre-trained encoder is out of the way, we also ablate the use of our R-GCN component, thus completely depriving the model from inductive biases that aim at multi-hop reasoning.",
+      "The first important observation is that replacing ELMo by GloVe (GloVe with R-GCN in Table 3 ) still yields a competitive system that ranks far above baselines from BIBREF0 and even above the Coref-GRU of BIBREF12 , in terms of accuracy on (unmasked) validation set. The second important observation is that if we then remove R-GCN (GloVe w/o R-GCN in Table 3 ), we lose 8.0 points. That is, the R-GCN component pushes the model to perform above Coref-GRU still without accessing context, but rather by updating mention representations based on their relation to other ones. These results highlight the impact of our R-GCN component.",
+      "In this experiment we investigate the effect of the different relations available in the entity graph and processed by the R-GCN module. We start off by testing our stronger encoder (i.e., ELMo) in absence of edges connecting mentions in the supporting documents (i.e., using only self-loops \u2013 No R-GCN in Table 3 ). The results suggest that WikipHop genuinely requires multihop inference, as our best model is 6.1% and 8.4% more accurate than this local model, in unmasked and masked settings, respectively. However, it also shows that ELMo representations capture predictive context features, without being explicitly trained for the task. It confirms that our goal of getting away with training expensive document encoders is a realistic one.",
+      "We then inspect our model's effectiveness in making use of the structure encoded in the graph. We start naively by fully-connecting all nodes within and across documents without distinguishing edges by type (No relation types in Table 3 ). We observe only marginal improvements with respect to ELMo alone (No R-GCN in Table 3 ) in both the unmasked and masked setting suggesting that a GCN operating over a naive entity graph would not add much to this task and a more informative graph construction and/or a more sophisticated parameterization is indeed needed.",
+      "Next, we ablate each type of relations independently, that is, we either remove connections of mentions that co-occur in the same document (DOC-BASED), connections between mentions matching exactly (MATCH), or edges predicted by the coreference system (COREF). The first thing to note is that the model makes better use of DOC-BASED connections than MATCH or COREF connections. This is mostly because i) the majority of the connections are indeed between mentions in the same document, and ii) without connecting mentions within the same document we remove important information since the model is unaware they appear closely in the document. Secondly, we notice that coreference links and complement edges seem to play a more marginal role. Though it may be surprising for coreference edges, recall that the MATCH heuristic already captures the easiest coreference cases, and for the rest the out-of-domain coreference system may not be reliable. Still, modelling all these different relations together gives our Entity-GCN a clear advantage. This is our best system evaluating on the development. Since Entity-GCN seems to gain little advantage using the coreference system, we report test results both with and without using it. Surprisingly, with coreference, we observe performance degradation on the test set. It is likely that the test documents are harder for the coreference system.",
+      "We do perform one last ablation, namely, we replace our heuristic for assigning edges and their labels by a model component that predicts them. The last row of Table 3 (Induced edges) shows model performance when edges are not predetermined but predicted. For this experiment, we use a bilinear function $f_e(\\mathbf {\\hat{x}}_i, \\mathbf {\\hat{x}}_j) = \\sigma \\left( \\mathbf {\\hat{x}}^\\top _i \\mathbf {W}_e \\mathbf {\\hat{x}}_j \\right)$ that predicts the importance of a single edge connecting two nodes $i,j$ using the query-dependent representation of mentions (see Section \"Node annotations\" ). The performance drops below `No R-GCN' suggesting that it cannot learn these dependencies on its own.",
+      "Most results are stronger for the masked settings even though we do not apply the coreference resolution system in this setting due to masking. It is not surprising as coreferred mentions are labeled with the same identifier in the masked version, even if their original surface forms did not match ( BIBREF0 used Wikipedia links for masking). Indeed, in the masked version, an entity is always referred to via the same unique surface form (e.g., MASK1) within and across documents. In the unmasked setting, on the other hand, mentions to an entity may differ (e.g., \u201cUS\u201d vs \u201cUnited States\u201d) and they might not be retrieved by the coreference system we are employing, making the task harder for all models. Therefore, as we rely mostly on exact matching when constructing our graph for the masked case, we are more effective in recovering coreference links on the masked rather than unmasked version.",
+      "In Figure 3 , we show how the model performance goes when the input graph is large. In particular, how Entity-GCN performs as the number of candidate answers or the number of nodes increases."
+    ],
+    [
+      "In this section we provide an error analysis for our best single model predictions. First of all, we look at which type of questions our model performs well or poorly. There are more than 150 query types in the validation set but we filtered the three with the best and with the worst accuracy that have at least 50 supporting documents and at least 5 candidates. We show results in Table 4 . We observe that questions regarding places (birth and death) are considered harder for Entity-GCN. We then inspect samples where our model fails while assigning highest likelihood and noticed two principal sources of failure i) a mismatch between what is written in Wikipedia and what is annotated in Wikidata, and ii) a different degree of granularity (e.g., born in \u201cLondon\u201d vs \u201cUK\u201d could be considered both correct by a human but not when measuring accuracy). See Table 6 in the supplement material for some reported samples.",
+      "Secondly, we study how the model performance degrades when the input graph is large. In particular, we observe a negative Pearson's correlation (-0.687) between accuracy and the number of candidate answers. However, the performance does not decrease steeply. The distribution of the number of candidates in the dataset peaks at 5 and has an average of approximately 20. Therefore, the model does not see many samples where there are a large number of candidate entities during training. Differently, we notice that as the number of nodes in the graph increases, the model performance drops but more gently (negative but closer to zero Pearson's correlation). This is important as document sets can be large in practical applications. See Figure 3 in the supplemental material for plots.",
+      "In Table 6 , we report three samples from WikiHop development set where out Entity-GCN fails. In particular, we show two instances where our model presents high confidence on the answer, and one where is not. We commented these samples explaining why our model might fail in these cases."
+    ],
+    [
+      "In previous work, BiDAF BIBREF3 , FastQA BIBREF6 , Coref-GRU BIBREF12 , MHPGM BIBREF11 , and Weaver / Jenga BIBREF10 have been applied to multi-document question answering. The first two mainly focus on single document QA and BIBREF0 adapted both of them to work with WikiHop. They process each instance of the dataset by concatenating all $d \\in S_q$ in a random order adding document separator tokens. They trained using the first answer mention in the concatenated document and evaluating exact match at test time. Coref-GRU, similarly to us, encodes relations between entity mentions in the document. Instead of using graph neural network layers, as we do, they augment RNNs with jump links corresponding to pairs of corefereed mentions. MHPGM uses a multi-attention mechanism in combination with external commonsense relations to perform multiple hops of reasoning. Weaver is a deep co-encoding model that uses several alternating bi-LSTMs to process the concatenated documents and the query.",
+      "Graph neural networks have been shown successful on a number of NLP tasks BIBREF24 , BIBREF25 , BIBREF26 , including those involving document level modeling BIBREF27 . They have also been applied in the context of asking questions about knowledge contained in a knowledge base BIBREF28 . In schlichtkrull2017modeling, GCNs are used to capture reasoning chains in a knowledge base. Our work and unpublished concurrent work by BIBREF23 are the first to study graph neural networks in the context of multi-document QA. Besides differences in the architecture, BIBREF23 propose to train a combination of a graph recurrent network and an RNN encoder. We do not train any RNN document encoders in this work."
+    ],
+    [
+      "We designed a graph neural network that operates over a compact graph representation of a set of documents where nodes are mentions to entities and edges signal relations such as within and cross-document coreference. The model learns to answer questions by gathering evidence from different documents via a differentiable message passing algorithm that updates node representations based on their neighbourhood. Our model outperforms published results where ablations show substantial evidence in favour of multi-step reasoning. Moreover, we make the model fast by using pre-trained (contextual) embeddings."
+    ],
+    [
+      "We would like to thank Johannes Welbl for helping to test our system on WikiHop. This project is supported by SAP Innovation Center Network, ERC Starting Grant BroadSem (678254) and the Dutch Organization for Scientific Research (NWO) VIDI 639.022.518. Wilker Aziz is supported by the Dutch Organisation for Scientific Research (NWO) VICI Grant nr. 277-89-002."
+    ],
+    [
+      "See table 5 for an outline of Entity-GCN architectural detail. Here the computational steps",
+      "ELMo embeddings are a concatenation of three 1024-dimensional vectors resulting in 3072-dimensional input vectors $\\lbrace \\mathbf {x}_i\\rbrace _{i=1}^N$ .",
+      "For the query representation $\\mathbf {q}$ , we apply 2 bi-LSTM layers of 256 and 128 hidden units to its ELMo vectors. The concatenation of the forward and backward states results in a 256-dimensional question representation.",
+      "ELMo embeddings of candidates are projected to 256-dimensional vectors, concatenated to the $\\mathbf {q}$ , and further transformed with a two layers MLP of 1024 and 512 hidden units in 512-dimensional query aware entity representations $\\lbrace \\mathbf {\\hat{x}}_i\\rbrace _{i=1}^N \\in \\mathbb {R}^{512}$ .",
+      "All transformations $f_*$ in R-GCN-layers are affine and they do maintain the input and output dimensionality of node representations the same (512-dimensional).",
+      "Eventually, a 2-layers MLP with [256, 128] hidden units takes the concatenation between $\\lbrace \\mathbf {h}_i^{(L)}\\rbrace _{i=1}^N$ and $\\mathbf {q}$ to predict the probability that a candidate node $v_i$ may be the answer to the query $q$ (see Equation 16 ).",
+      "During preliminary trials, we experimented with different numbers of R-GCN-layers (in the range 1-7). We observed that with WikiHop, for $L \\ge 3$ models reach essentially the same performance, but more layers increase the time required to train them. Besides, we observed that the gating mechanism learns to keep more and more information from the past at each layer making unnecessary to have more layers than required."
+    ],
+    [
+      "We train our models with a batch size of 32 for at most 20 epochs using the Adam optimizer BIBREF29 with $\\beta _1=0.9$ , $\\beta _2=0.999$ and a learning rate of $10^{-4}$ . To help against overfitting, we employ dropout (drop rate $\\in {0, 0.1, 0.15, 0.2, 0.25}$ ) BIBREF30 and early-stopping on validation accuracy. We report the best results of each experiment based on accuracy on validation set."
+    ]
+  ]
+}
+```

qasper-1498/instruction.md

@@ -0,0 +1,118 @@
+Name of Paper: Question Answering by Reasoning Across Documents with Graph Convolutional Networks
+
+Question: Did they use a relation extraction method to construct the edges in the graph?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Method",
+    "Dataset and task abstraction",
+    "Reasoning on an entity graph",
+    "Node annotations",
+    "Entity relational graph convolutional network",
+    "Experiments",
+    "Comparison",
+    "Ablation study",
+    "Error analysis",
+    "Related work",
+    "Conclusion",
+    "Acknowledgments",
+    "Architecture",
+    "Training details"
+  ],
+  "paragraphs": [
+    [
+      "The long-standing goal of natural language understanding is the development of systems which can acquire knowledge from text collections. Fresh interest in reading comprehension tasks was sparked by the availability of large-scale datasets, such as SQuAD BIBREF1 and CNN/Daily Mail BIBREF2 , enabling end-to-end training of neural models BIBREF3 , BIBREF4 , BIBREF5 . These systems, given a text and a question, need to answer the query relying on the given document. Recently, it has been observed that most questions in these datasets do not require reasoning across the document, but they can be answered relying on information contained in a single sentence BIBREF6 . The last generation of large-scale reading comprehension datasets, such as a NarrativeQA BIBREF7 , TriviaQA BIBREF8 , and RACE BIBREF9 , have been created in such a way as to address this shortcoming and to ensure that systems relying only on local information cannot achieve competitive performance.",
+      "Even though these new datasets are challenging and require reasoning within documents, many question answering and search applications require aggregation of information across multiple documents. The WikiHop dataset BIBREF0 was explicitly created to facilitate the development of systems dealing with these scenarios. Each example in WikiHop consists of a collection of documents, a query and a set of candidate answers (Figure 1 ). Though there is no guarantee that a question cannot be answered by relying just on a single sentence, the authors ensure that it is answerable using a chain of reasoning crossing document boundaries.",
+      "Though an important practical problem, the multi-hop setting has so far received little attention. The methods reported by BIBREF0 approach the task by merely concatenating all documents into a single long text and training a standard RNN-based reading comprehension model, namely, BiDAF BIBREF3 and FastQA BIBREF6 . Document concatenation in this setting is also used in Weaver BIBREF10 and MHPGM BIBREF11 . The only published paper which goes beyond concatenation is due to BIBREF12 , where they augment RNNs with jump-links corresponding to co-reference edges. Though these edges provide a structural bias, the RNN states are still tasked with passing the information across the document and performing multi-hop reasoning.",
+      "Instead, we frame question answering as an inference problem on a graph representing the document collection. Nodes in this graph correspond to named entities in a document whereas edges encode relations between them (e.g., cross- and within-document coreference links or simply co-occurrence in a document). We assume that reasoning chains can be captured by propagating local contextual information along edges in this graph using a graph convolutional network (GCN) BIBREF13 .",
+      "The multi-document setting imposes scalability challenges. In realistic scenarios, a system needs to learn to answer a query for a given collection (e.g., Wikipedia or a domain-specific set of documents). In such scenarios one cannot afford to run expensive document encoders (e.g., RNN or transformer-like self-attention BIBREF14 ), unless the computation can be preprocessed both at train and test time. Even if (similarly to WikiHop creators) one considers a coarse-to-fine approach, where a set of potentially relevant documents is provided, re-encoding them in a query-specific way remains the bottleneck. In contrast to other proposed methods (e.g., BIBREF12 , BIBREF10 , BIBREF3 ), we avoid training expensive document encoders.",
+      "In our approach, only a small query encoder, the GCN layers and a simple feed-forward answer selection component are learned. Instead of training RNN encoders, we use contextualized embeddings (ELMo) to obtain initial (local) representations of nodes. This implies that only a lightweight computation has to be performed online, both at train and test time, whereas the rest is preprocessed. Even in the somewhat contrived WikiHop setting, where fairly small sets of candidates are provided, the model is at least 5 times faster to train than BiDAF. Interestingly, when we substitute ELMo with simple pre-trained word embeddings, Entity-GCN still performs on par with many techniques that use expensive question-aware recurrent document encoders.",
+      "Despite not using recurrent document encoders, the full Entity-GCN model achieves over 2% improvement over the best previously-published results. As our model is efficient, we also reported results of an ensemble which brings further 3.6% of improvement and only 3% below the human performance reported by BIBREF0 . Our contributions can be summarized as follows:"
+    ],
+    [
+      "In this section we explain our method. We first introduce the dataset we focus on, WikiHop by BIBREF0 , as well as the task abstraction. We then present the building blocks that make up our Entity-GCN model, namely, an entity graph used to relate mentions to entities within and across documents, a document encoder used to obtain representations of mentions in context, and a relational graph convolutional network that propagates information through the entity graph."
+    ],
+    [
+      "The WikiHop dataset comprises of tuples $\\langle q, S_q, C_q, a^\\star \\rangle $ where: $q$ is a query/question, $S_q$ is a set of supporting documents, $C_q$ is a set of candidate answers (all of which are entities mentioned in $S_q$ ), and $a^\\star \\in C_q$ is the entity that correctly answers the question. WikiHop is assembled assuming that there exists a corpus and a knowledge base (KB) related to each other. The KB contains triples $\\langle s, r, o \\rangle $ where $s$ is a subject entity, $o$ an object entity, and $r$ a unidirectional relation between them. BIBREF0 used Wikipedia as corpus and Wikidata BIBREF15 as KB. The KB is only used for constructing WikiHop: BIBREF0 retrieved the supporting documents $q$0 from the corpus looking at mentions of subject and object entities in the text. Note that the set $q$1 (not the KB) is provided to the QA system, and not all of the supporting documents are relevant for the query but some of them act as distractors. Queries, on the other hand, are not expressed in natural language, but instead consist of tuples $q$2 where the object entity is unknown and it has to be inferred by reading the support documents. Therefore, answering a query corresponds to finding the entity $q$3 that is the object of a tuple in the KB with subject $q$4 and relation $q$5 among the provided set of candidate answers $q$6 .",
+      "The goal is to learn a model that can identify the correct answer $a^\\star $ from the set of supporting documents $S_q$ . To that end, we exploit the available supervision to train a neural network that computes scores for candidates in $C_q$ . We estimate the parameters of the architecture by maximizing the likelihood of observations. For prediction, we then output the candidate that achieves the highest probability. In the following, we present our model discussing the design decisions that enable multi-step reasoning and an efficient computation."
+    ],
+    [
+      "In an offline step, we organize the content of each training instance in a graph connecting mentions of candidate answers within and across supporting documents. For a given query $q = \\langle s, r, ? \\rangle $ , we identify mentions in $S_q$ of the entities in $C_q \\cup \\lbrace s\\rbrace $ and create one node per mention. This process is based on the following heuristic:",
+      "we consider mentions spans in $S_q$ exactly matching an element of $C_q \\cup \\lbrace s\\rbrace $ . Admittedly, this is a rather simple strategy which may suffer from low recall.",
+      "we use predictions from a coreference resolution system to add mentions of elements in $C_q \\cup \\lbrace s\\rbrace $ beyond exact matching (including both noun phrases and anaphoric pronouns). In particular, we use the end-to-end coreference resolution by BIBREF16 .",
+      "we discard mentions which are ambiguously resolved to multiple coreference chains; this may sacrifice recall, but avoids propagating ambiguity.",
+      "To each node $v_i$ , we associate a continuous annotation $\\mathbf {x}_i \\in \\mathbb {R}^D$ which represents an entity in the context where it was mentioned (details in Section \"Node annotations\" ). We then proceed to connect these mentions i) if they co-occur within the same document (we will refer to this as DOC-BASED edges), ii) if the pair of named entity mentions is identical (MATCH edges\u2014these may connect nodes across and within documents), or iii) if they are in the same coreference chain, as predicted by the external coreference system (COREF edges). Note that MATCH edges when connecting mentions in the same document are mostly included in the set of edges predicted by the coreference system. Having the two types of edges lets us distinguish between less reliable edges provided by the coreference system and more reliable (but also more sparse) edges given by the exact-match heuristic. We treat these three types of connections as three different types of relations. See Figure 2 for an illustration. In addition to that, and to prevent having disconnected graphs, we add a fourth type of relation (COMPLEMENT edge) between any two nodes that are not connected with any of the other relations. We can think of these edges as those in the complement set of the entity graph with respect to a fully connected graph.",
+      "Our model then approaches multi-step reasoning by transforming node representations (Section \"Node annotations\" for details) with a differentiable message passing algorithm that propagates information through the entity graph. The algorithm is parameterized by a graph convolutional network (GCN) BIBREF13 , in particular, we employ relational-GCNs BIBREF17 , an extended version that accommodates edges of different types. In Section \"Entity relational graph convolutional network\" we describe the propagation rule.",
+      "Each step of the algorithm (also referred to as a hop) updates all node representations in parallel. In particular, a node is updated as a function of messages from its direct neighbours, and a message is possibly specific to a certain relation. At the end of the first step, every node is aware of every other node it connects directly to. Besides, the neighbourhood of a node may include mentions of the same entity as well as others (e.g., same-document relation), and these mentions may have occurred in different documents. Taking this idea recursively, each further step of the algorithm allows a node to indirectly interact with nodes already known to their neighbours. After $L$ layers of R-GCN, information has been propagated through paths connecting up to $L+1$ nodes.",
+      "We start with node representations $\\lbrace \\mathbf {h}_i^{(0)}\\rbrace _{i=1}^N$ , and transform them by applying $L$ layers of R-GCN obtaining $\\lbrace \\mathbf {h}_i^{(L)}\\rbrace _{i=1}^N$ . Together with a representation $\\mathbf {q}$ of the query, we define a distribution over candidate answers and we train maximizing the likelihood of observations. The probability of selecting a candidate $c \\in C_q$ as an answer is then ",
+      "$$ \nP(c|q, C_q, S_q) \\propto \\exp \\left(\\max _{i \\in \\mathcal {M}_c} f_o([\\mathbf {q}, \\mathbf {h}^{(L)}_i]) \\right)\\;,$$   (Eq. 16) ",
+      "where $f_o$ is a parameterized affine transformation, and $\\mathcal {M}_c$ is the set of node indices such that $i\\in \\mathcal {M}_c$ only if node $v_i$ is a mention of $c$ . The $\\max $ operator in Equation 16 is necessary to select the node with highest predicted probability since a candidate answer is realized in multiple locations via different nodes."
+    ],
+    [
+      "Keeping in mind we want an efficient model, we encode words in supporting documents and in the query using only a pre-trained model for contextualized word representations rather than training our own encoder. Specifically, we use ELMo BIBREF20 , a pre-trained bi-directional language model that relies on character-based input representation. ELMo representations, differently from other pre-trained word-based models (e.g., word2vec BIBREF21 or GloVe BIBREF22 ), are contextualized since each token representation depends on the entire text excerpt (i.e., the whole sentence).",
+      "We choose not to fine tune nor propagate gradients through the ELMo architecture, as it would have defied the goal of not having specialized RNN encoders. In the experiments, we will also ablate the use of ELMo showing how our model behaves using non-contextualized word representations (we use GloVe).",
+      "ELMo encodings are used to produce a set of representations $\\lbrace \\mathbf {x}_i\\rbrace _{i=1}^N$ , where $\\mathbf {x}_i \\in \\mathbb {R}^D$ denotes the $i$ th candidate mention in context. Note that these representations do not depend on the query yet and no trainable model was used to process the documents so far, that is, we use ELMo as a fixed pre-trained encoder. Therefore, we can pre-compute representation of mentions once and store them for later use.",
+      "ELMo encodings are used to produce a query representation $\\mathbf {q} \\in \\mathbb {R}^K$ as well. Here, $\\mathbf {q}$ is a concatenation of the final outputs from a bidirectional RNN layer trained to re-encode ELMo representations of words in the query. The vector $\\mathbf {q}$ is used to compute a query-dependent representation of mentions $\\lbrace \\mathbf { \\hat{x}}_i\\rbrace _{i=1}^N$ as well as to compute a probability distribution over candidates (as in Equation 16 ). Query-dependent mention encodings $\\mathbf {\\hat{x}}_i = f_x(\\mathbf {q}, \\mathbf {x}_i)$ are generated by a trainable function $f_x$ which is parameterized by a feed-forward neural network."
+    ],
+    [
+      "Our model uses a gated version of the original R-GCN propagation rule. At the first layer, all hidden node representation are initialized with the query-aware encodings $\\mathbf {h}_i^{(0)} = \\mathbf {\\hat{x}}_i$ . Then, at each layer $0\\le \\ell \\le L$ , the update message $\\mathbf {u}_i^{(\\ell )}$ to the $i$ th node is a sum of a transformation $f_s$ of the current node representation $\\mathbf {h}^{(\\ell )}_i$ and transformations of its neighbours: ",
+      "$$\\mathbf {u}^{(\\ell )}_i = f_s(\\mathbf {h}^{(\\ell )}_i) + \\frac{1}{|\\mathcal {N}_i|} \\sum _{j \\in \\mathcal {N}_i} \\sum _{r \\in \\mathcal {R}_{ij}} f_r(\\mathbf {h}_j^{(\\ell )})\\;,$$   (Eq. 22) ",
+      "where $\\mathcal {N}_i$ is the set of indices of nodes neighbouring the $i$ th node, $\\mathcal {R}_{ij}$ is the set of edge annotations between $i$ and $j$ , and $f_r$ is a parametrized function specific to an edge type $r\\in \\mathcal {R}$ . Recall the available relations from Section \"Ablation study\" , namely, $\\mathcal {R} =\\lbrace $ DOC-BASED, MATCH, COREF, COMPLEMENT $\\rbrace $ .",
+      "A gating mechanism regulates how much of the update message propagates to the next step. This provides the model a way to prevent completely overwriting past information. Indeed, if all necessary information to answer a question is present at a layer which is not the last, then the model should learn to stop using neighbouring information for the next steps. Gate levels are computed as ",
+      "$$\\mathbf {a}^{(\\ell )}_i = \\sigma \\left( f_a\\left([\\mathbf {u}^{(\\ell )}_i, \\mathbf {h}^{(\\ell )}_i ]\\right) \\right) \\;,$$   (Eq. 23) ",
+      "where $\\sigma (\\cdot )$ is the sigmoid function and $f_a$ a parametrized transformation. Ultimately, the updated representation is a gated combination of the previous representation and a non-linear transformation of the update message: ",
+      "$$\\mathbf {h}^{(\\ell + 1)}_i = \\phi (\\mathbf {u}^{(\\ell )}_i) \\odot \\mathbf {a}^{(\\ell )}_i + \\mathbf {h}^{(\\ell )}_i \\odot (1 - \\mathbf {a}^{(\\ell )}_i ) \\;,$$   (Eq. 24) ",
+      "where $\\phi (\\cdot )$ is any nonlinear function (we used $\\tanh $ ) and $\\odot $ stands for element-wise multiplication. All transformations $f_*$ are affine and they are not layer-dependent (since we would like to use as few parameters as possible to decrease model complexity promoting efficiency and scalability)."
+    ],
+    [
+      "In this section, we compare our method against recent work as well as preforming an ablation study using the WikiHop dataset BIBREF0 . See Appendix \"Implementation and experiments details\" in the supplementary material for a description of the hyper-parameters of our model and training details."
+    ],
+    [
+      "In this experiment, we compare our Enitity-GCN against recent prior work on the same task. We present test and development results (when present) for both versions of the dataset in Table 2 . From BIBREF0 , we list an oracle based on human performance as well as two standard reading comprehension models, namely BiDAF BIBREF3 and FastQA BIBREF6 . We also compare against Coref-GRU BIBREF12 , MHPGM BIBREF11 , and Weaver BIBREF10 . Additionally, we include results of MHQA-GRN BIBREF23 , from a recent arXiv preprint describing concurrent work. They jointly train graph neural networks and recurrent encoders. We report single runs of our two best single models and an ensemble one on the unmasked test set (recall that the test set is not publicly available and the task organizers only report unmasked results) as well as both versions of the validation set.",
+      "Entity-GCN (best single model without coreference edges) outperforms all previous work by over 2% points. We additionally re-ran BiDAF baseline to compare training time: when using a single Titan X GPU, BiDAF and Entity-GCN process 12.5 and 57.8 document sets per second, respectively. Note that BIBREF0 had to use BiDAF with very small state dimensionalities (20), and smaller batch size due to the scalability issues (both memory and computation costs). We compare applying the same reductions. Eventually, we also report an ensemble of 5 independently trained models. All models are trained on the same dataset splits with different weight initializations. The ensemble prediction is obtained as $\\arg \\max \\limits _c \\prod \\limits _{i=1}^5 P_i(c|q, C_q, S_q)$ from each model."
+    ],
+    [
+      "To help determine the sources of improvements, we perform an ablation study using the publicly available validation set (see Table 3 ). We perform two groups of ablation, one on the embedding layer, to study the effect of ELMo, and one on the edges, to study how different relations affect the overall model performance.",
+      "We argue that ELMo is crucial, since we do not rely on any other context encoder. However, it is interesting to explore how our R-GCN performs without it. Therefore, in this experiment, we replace the deep contextualized embeddings of both the query and the nodes with GloVe BIBREF22 vectors (insensitive to context). Since we do not have any component in our model that processes the documents, we expect a drop in performance. In other words, in this ablation our model tries to answer questions without reading the context at all. For example, in Figure 1 , our model would be aware that \u201cStockholm\u201d and \u201cSweden\u201d appear in the same document but any context words, including the ones encoding relations (e.g., \u201cis the capital of\u201d) will be hidden. Besides, in the masked case all mentions become `unknown' tokens with GloVe and therefore the predictions are equivalent to a random guess. Once the strong pre-trained encoder is out of the way, we also ablate the use of our R-GCN component, thus completely depriving the model from inductive biases that aim at multi-hop reasoning.",
+      "The first important observation is that replacing ELMo by GloVe (GloVe with R-GCN in Table 3 ) still yields a competitive system that ranks far above baselines from BIBREF0 and even above the Coref-GRU of BIBREF12 , in terms of accuracy on (unmasked) validation set. The second important observation is that if we then remove R-GCN (GloVe w/o R-GCN in Table 3 ), we lose 8.0 points. That is, the R-GCN component pushes the model to perform above Coref-GRU still without accessing context, but rather by updating mention representations based on their relation to other ones. These results highlight the impact of our R-GCN component.",
+      "In this experiment we investigate the effect of the different relations available in the entity graph and processed by the R-GCN module. We start off by testing our stronger encoder (i.e., ELMo) in absence of edges connecting mentions in the supporting documents (i.e., using only self-loops \u2013 No R-GCN in Table 3 ). The results suggest that WikipHop genuinely requires multihop inference, as our best model is 6.1% and 8.4% more accurate than this local model, in unmasked and masked settings, respectively. However, it also shows that ELMo representations capture predictive context features, without being explicitly trained for the task. It confirms that our goal of getting away with training expensive document encoders is a realistic one.",
+      "We then inspect our model's effectiveness in making use of the structure encoded in the graph. We start naively by fully-connecting all nodes within and across documents without distinguishing edges by type (No relation types in Table 3 ). We observe only marginal improvements with respect to ELMo alone (No R-GCN in Table 3 ) in both the unmasked and masked setting suggesting that a GCN operating over a naive entity graph would not add much to this task and a more informative graph construction and/or a more sophisticated parameterization is indeed needed.",
+      "Next, we ablate each type of relations independently, that is, we either remove connections of mentions that co-occur in the same document (DOC-BASED), connections between mentions matching exactly (MATCH), or edges predicted by the coreference system (COREF). The first thing to note is that the model makes better use of DOC-BASED connections than MATCH or COREF connections. This is mostly because i) the majority of the connections are indeed between mentions in the same document, and ii) without connecting mentions within the same document we remove important information since the model is unaware they appear closely in the document. Secondly, we notice that coreference links and complement edges seem to play a more marginal role. Though it may be surprising for coreference edges, recall that the MATCH heuristic already captures the easiest coreference cases, and for the rest the out-of-domain coreference system may not be reliable. Still, modelling all these different relations together gives our Entity-GCN a clear advantage. This is our best system evaluating on the development. Since Entity-GCN seems to gain little advantage using the coreference system, we report test results both with and without using it. Surprisingly, with coreference, we observe performance degradation on the test set. It is likely that the test documents are harder for the coreference system.",
+      "We do perform one last ablation, namely, we replace our heuristic for assigning edges and their labels by a model component that predicts them. The last row of Table 3 (Induced edges) shows model performance when edges are not predetermined but predicted. For this experiment, we use a bilinear function $f_e(\\mathbf {\\hat{x}}_i, \\mathbf {\\hat{x}}_j) = \\sigma \\left( \\mathbf {\\hat{x}}^\\top _i \\mathbf {W}_e \\mathbf {\\hat{x}}_j \\right)$ that predicts the importance of a single edge connecting two nodes $i,j$ using the query-dependent representation of mentions (see Section \"Node annotations\" ). The performance drops below `No R-GCN' suggesting that it cannot learn these dependencies on its own.",
+      "Most results are stronger for the masked settings even though we do not apply the coreference resolution system in this setting due to masking. It is not surprising as coreferred mentions are labeled with the same identifier in the masked version, even if their original surface forms did not match ( BIBREF0 used Wikipedia links for masking). Indeed, in the masked version, an entity is always referred to via the same unique surface form (e.g., MASK1) within and across documents. In the unmasked setting, on the other hand, mentions to an entity may differ (e.g., \u201cUS\u201d vs \u201cUnited States\u201d) and they might not be retrieved by the coreference system we are employing, making the task harder for all models. Therefore, as we rely mostly on exact matching when constructing our graph for the masked case, we are more effective in recovering coreference links on the masked rather than unmasked version.",
+      "In Figure 3 , we show how the model performance goes when the input graph is large. In particular, how Entity-GCN performs as the number of candidate answers or the number of nodes increases."
+    ],
+    [
+      "In this section we provide an error analysis for our best single model predictions. First of all, we look at which type of questions our model performs well or poorly. There are more than 150 query types in the validation set but we filtered the three with the best and with the worst accuracy that have at least 50 supporting documents and at least 5 candidates. We show results in Table 4 . We observe that questions regarding places (birth and death) are considered harder for Entity-GCN. We then inspect samples where our model fails while assigning highest likelihood and noticed two principal sources of failure i) a mismatch between what is written in Wikipedia and what is annotated in Wikidata, and ii) a different degree of granularity (e.g., born in \u201cLondon\u201d vs \u201cUK\u201d could be considered both correct by a human but not when measuring accuracy). See Table 6 in the supplement material for some reported samples.",
+      "Secondly, we study how the model performance degrades when the input graph is large. In particular, we observe a negative Pearson's correlation (-0.687) between accuracy and the number of candidate answers. However, the performance does not decrease steeply. The distribution of the number of candidates in the dataset peaks at 5 and has an average of approximately 20. Therefore, the model does not see many samples where there are a large number of candidate entities during training. Differently, we notice that as the number of nodes in the graph increases, the model performance drops but more gently (negative but closer to zero Pearson's correlation). This is important as document sets can be large in practical applications. See Figure 3 in the supplemental material for plots.",
+      "In Table 6 , we report three samples from WikiHop development set where out Entity-GCN fails. In particular, we show two instances where our model presents high confidence on the answer, and one where is not. We commented these samples explaining why our model might fail in these cases."
+    ],
+    [
+      "In previous work, BiDAF BIBREF3 , FastQA BIBREF6 , Coref-GRU BIBREF12 , MHPGM BIBREF11 , and Weaver / Jenga BIBREF10 have been applied to multi-document question answering. The first two mainly focus on single document QA and BIBREF0 adapted both of them to work with WikiHop. They process each instance of the dataset by concatenating all $d \\in S_q$ in a random order adding document separator tokens. They trained using the first answer mention in the concatenated document and evaluating exact match at test time. Coref-GRU, similarly to us, encodes relations between entity mentions in the document. Instead of using graph neural network layers, as we do, they augment RNNs with jump links corresponding to pairs of corefereed mentions. MHPGM uses a multi-attention mechanism in combination with external commonsense relations to perform multiple hops of reasoning. Weaver is a deep co-encoding model that uses several alternating bi-LSTMs to process the concatenated documents and the query.",
+      "Graph neural networks have been shown successful on a number of NLP tasks BIBREF24 , BIBREF25 , BIBREF26 , including those involving document level modeling BIBREF27 . They have also been applied in the context of asking questions about knowledge contained in a knowledge base BIBREF28 . In schlichtkrull2017modeling, GCNs are used to capture reasoning chains in a knowledge base. Our work and unpublished concurrent work by BIBREF23 are the first to study graph neural networks in the context of multi-document QA. Besides differences in the architecture, BIBREF23 propose to train a combination of a graph recurrent network and an RNN encoder. We do not train any RNN document encoders in this work."
+    ],
+    [
+      "We designed a graph neural network that operates over a compact graph representation of a set of documents where nodes are mentions to entities and edges signal relations such as within and cross-document coreference. The model learns to answer questions by gathering evidence from different documents via a differentiable message passing algorithm that updates node representations based on their neighbourhood. Our model outperforms published results where ablations show substantial evidence in favour of multi-step reasoning. Moreover, we make the model fast by using pre-trained (contextual) embeddings."
+    ],
+    [
+      "We would like to thank Johannes Welbl for helping to test our system on WikiHop. This project is supported by SAP Innovation Center Network, ERC Starting Grant BroadSem (678254) and the Dutch Organization for Scientific Research (NWO) VIDI 639.022.518. Wilker Aziz is supported by the Dutch Organisation for Scientific Research (NWO) VICI Grant nr. 277-89-002."
+    ],
+    [
+      "See table 5 for an outline of Entity-GCN architectural detail. Here the computational steps",
+      "ELMo embeddings are a concatenation of three 1024-dimensional vectors resulting in 3072-dimensional input vectors $\\lbrace \\mathbf {x}_i\\rbrace _{i=1}^N$ .",
+      "For the query representation $\\mathbf {q}$ , we apply 2 bi-LSTM layers of 256 and 128 hidden units to its ELMo vectors. The concatenation of the forward and backward states results in a 256-dimensional question representation.",
+      "ELMo embeddings of candidates are projected to 256-dimensional vectors, concatenated to the $\\mathbf {q}$ , and further transformed with a two layers MLP of 1024 and 512 hidden units in 512-dimensional query aware entity representations $\\lbrace \\mathbf {\\hat{x}}_i\\rbrace _{i=1}^N \\in \\mathbb {R}^{512}$ .",
+      "All transformations $f_*$ in R-GCN-layers are affine and they do maintain the input and output dimensionality of node representations the same (512-dimensional).",
+      "Eventually, a 2-layers MLP with [256, 128] hidden units takes the concatenation between $\\lbrace \\mathbf {h}_i^{(L)}\\rbrace _{i=1}^N$ and $\\mathbf {q}$ to predict the probability that a candidate node $v_i$ may be the answer to the query $q$ (see Equation 16 ).",
+      "During preliminary trials, we experimented with different numbers of R-GCN-layers (in the range 1-7). We observed that with WikiHop, for $L \\ge 3$ models reach essentially the same performance, but more layers increase the time required to train them. Besides, we observed that the gating mechanism learns to keep more and more information from the past at each layer making unnecessary to have more layers than required."
+    ],
+    [
+      "We train our models with a batch size of 32 for at most 20 epochs using the Adam optimizer BIBREF29 with $\\beta _1=0.9$ , $\\beta _2=0.999$ and a learning rate of $10^{-4}$ . To help against overfitting, we employ dropout (drop rate $\\in {0, 0.1, 0.15, 0.2, 0.25}$ ) BIBREF30 and early-stopping on validation accuracy. We report the best results of each experiment based on accuracy on validation set."
+    ]
+  ]
+}
+```

qasper-1659/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Coarse-grain Fine-grain Coattention Network for Multi-evidence Question Answering
+
+Question: How big is the test set?

qasper-1661/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Unsupervised Question Decomposition for Question Answering
+
+Question: What off-the-shelf QA model was used to answer sub-questions?

qasper-1666/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Simultaneous Neural Machine Translation using Connectionist Temporal Classification
+
+Question: Which model architecture do they use to build a model?

qasper-1692/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: BERT Can See Out of the Box: On the Cross-modal Transferability of Text Representations
+
+Question: How are multimodal representations combined?

qasper-1695/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Facet-Aware Evaluation for Extractive Text Summarization
+
+Question: What is a facet?

qasper-1805/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: TLT-school: a Corpus of Non Native Children Speech
+
+Question: How is the proficiency score calculated?

qasper-1833/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions
+
+Question: what is the size of BoolQ dataset?

qasper-1834/instruction.md

@@ -0,0 +1,78 @@
+Name of Paper: Database of Parliamentary Speeches in Ireland, 1919-2013
+
+Question: what processing was done on the speeches before being parsed?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Overview of Database Content",
+    "Analyzing the Content of Parliamentary Debates",
+    "The Content of Budget Speeches in Historical Perspective",
+    "Estimation of Finance Ministers' Policy Positions",
+    "Speakers' Policy Position in the 2008 Budget Debate",
+    "Ministers' policy position in the 26th government",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Almost all political decisions and political opinions are, in one way or another, expressed in written or spoken texts. Great leaders in history become famous for their ability to motivate the masses with their speeches; parties publish policy programmes before elections in order to provide information about their policy objectives; parliamentary decisions are discussed and deliberated on the floor in order to exchange opinions; members of the executive in most political systems are legally obliged to provide written or verbal answers to questions from legislators; and citizens express their opinions about political events on internet blogs or in public online chats. Political texts and speeches are everywhere that people express their political opinions and preferences.",
+      "It is not until recently that social scientists have discovered the potential of analyzing political texts to test theories of political behavior. One reason is that systematically processing large quantities of textual data to retrieve information is technically challenging. Computational advances in natural language processing have greatly facilitated this task. Adaptation of such techniques in social science \u2013 for example, Wordscore BIBREF0 , BIBREF1 or Wordfish BIBREF2 \u2013 now enable researchers to systematically compare documents with one another and extract relevant information from them. Applied to party manifestos, for which most of these techniques have been developed, these methods can be used to evaluate the similarity or dissimilarity between manifestos, which can then be used to derive estimates about parties' policy preferences and their ideological distance to each other.",
+      "One area of research that increasingly makes use of quantitative text methods are studies of legislative behavior and parliaments BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 . Only a few parliaments in the world use roll-call votes (the recording of each legislator's decision in a floor vote) that allow for the monitoring of individual members' behavior. In all other cases, contributions to debates are the only outcome that can be observed from individual members. Using such debates for social science research, however, is often limited by data availability. Although most parliaments keep written records of parliamentary debates and often make such records electronically available, they are never published in formats that facilitate social science research. A significant amount of labor is usually required to collect, clean and organize parliamentary records before they can be used for analytical purposes, often requiring technical skills that many social scientists lack.",
+      "The purpose of this paper is to present a new database of parliamentary debates to overcome precisely this barrier. Our database contains all debates as well as questions and answers in D\u00e1il \u00c9ireann, covering almost a century of political discourse from 1919 to 2013. These debates are organized in a way that allows users to search by date, topics or speaker. More importantly, and lacking in the official records of parliamentary debates, we have identified all speakers and linked their debate contributions to the information on party affiliation and constituencies from the official members database. This enables researchers to retrieve member-specific speeches on particular topics or within a particular timeframe. Furthermore, all data can be retrieved and stored in formats that can be accessed using commonly used statistical software packages.",
+      "In addition to documenting this database, we also present three applications in which we make use of the new data (Section SECREF3 ). In the first study, we analyze budget speeches delivered by all finance ministers from 1922 to 2008 (Section SECREF11 ) and show how the policy agenda and ministers' policy preferences have changed over time (Section SECREF16 ). In the second application we compare contributions that were made on one particular topic: the 2008 budget debate (Section SECREF20 ). Here we demonstrate how text analytics can be used to estimate members' policy preferences on a dimension that represents pro- versus anti-government attitudes. Finally, we estimate all contributions from members of the 26th government that formed as a coalition between Fianna F\u00e1il and the Progressive Democrats in 2002. Here we estimate the policy positions of all cabinet ministers on a pro- versus anti-spending dimension and show that positions on this dimension are highly correlated with the actual spending levels of each ministerial department (Section SECREF25 )."
+    ],
+    [
+      "Parliamentary debates in D\u00e1il \u00c9ireann are collected by the Oireachtas' Debates Office and published as the Official Record. The Debates Office records and transcribes all debates and then publishes them both in printed as well as in digital form. All debates are then published on Oireachtas' website as single HTML files. At the time of writing, the official debates website contains 549,292 HTML files. The content of all these HTML files forms the data source for our database. It is obviously impossible to hand-code that much information. We therefore wrote a computer script that automated the processing of all files. This script is able to find all debate contributions and the names of all speakers in each file. In addition, it retrieves the date as well as the topic of each debate.",
+      "As already explained above, the official online version of the Official Records does not provide information about speakers besides their name. Each speaker's name is \u201chard coded\u201d into the HTML files and not linked to the information in the official members database. In addition, speaker names are not coded consistently, hence making it difficult to collect speeches from a particular deputy. Our goal was to identify every single speaker name that appears in the Official Record and integrate parliamentary speeches with information about deputies' party affiliation, constituency, age and profession from the official members database into a single database. We therefore used an automated record-linkage procedure to identify every single speaker.",
+      "The final database contains all debates and written answers from the first meeting of the D\u00e1il on 21 January 1919 through to 28 March 2013, covering every D\u00e1il session that has met during this period. In total, the database contains 4,443,713 individual contributions by 1,178 TDs. The data is organized in a way that facilitates analysis for substantive questions of interest to social scientists. Every row in the data set is one contribution with columns containing information on the following variables:"
+    ],
+    [
+      "In the previous section, we have explained the structure of the database. In the following three sections we demonstrate how the data can be used for social science research. We do this by demonstrating three different applications. In the first application, we analyze the budget speeches of all finance ministers from 1922 to 2008. Budget speeches are delivered by Finance Ministers once a year, with the exception of emergency budgets. Analyzing this data, we show how policy agendas and ministers' fiscal preferences have changed over time. In the second application, we construct a data set that resembles a cross-sectional analysis as we retrieve all speeches from one particular year and on one particular topic from our database: the 2008 budget debate. This data structure enables us to estimate the policy positions of all speakers who contributed to the budget debate and to compare how similar or dissimilar their preferences were. We find that policy positions are clustered into two groups: the government and the opposition; but we also find considerable variation within each group. Finally, we take all contributions made during the term of one government and use the data to estimate the policy positions of all cabinet members on a dimension representing pro- versus anti-spending. We demonstrate the validity of estimated policy positions by comparing them against actual spending levels of each cabinet ministers' department and show that the two measures are almost perfectly correlated with each other."
+    ],
+    [
+      "The quantitative analysis of text is primarily based on the proposition that preference profiles of speakers can be constructed from their word frequencies BIBREF15 , BIBREF16 . This makes word frequencies the most important data input to almost all existing methods of text analysis. Word frequencies can be easily visualized as word clouds. These word clouds show the most frequently used words in a text with font size being proportional to frequency of appearance. Despite their simplicity, word clouds can be used as a first descriptive view of the data. Here we look at word clouds for the speeches made by Irish Ministers for Finance. We have extracted the budget speeches of all finance ministers from our database, the first being Cosgrave's speech in April 1923, and the latest being Lenihan's speech in October 2008. In total, there are 90 speeches given by 23 different finance ministers for whom we have generated word clouds as shown in Figure FIGREF12 .",
+      "One way to look at Figure FIGREF12 is to consider that each individual word cloud panel presents a snapshot into the preference profiles of individual ministers. With taxation being the key instrument of fiscal policy it is unsurprising that the word \u201ctax\u201d is on average the most frequently used word across all Ministers for Finance. We can also discern that frequency of references to \u201cgovernment\u201d has been uneven over time with relatively high usage in the 1960s to 1980s and then subsequent decline (apart from Quinn's tenure) until the later speeches of Cowen and particularly Lenihan.",
+      "What is more clearly evident is the change in the number of unique words used by different ministers. This reflects the fact that some budget speeches were very short, while others were long and covered many distinct topics. The easiest example is to compare speeches by two consecutive ministers: Cowen and Lenihan. Word clouds reflect the sheer multitude of problems facing the country that needed to be addressed by Lenihan compared to the relatively \u201cquieter\u201d (on average) three budgets delivered by Cowen.",
+      "Overall, while catchy word clouds can only be used as easy first-cut visualizations of the data, rather than methods for any meaningful analysis. One thing that becomes readily apparent from Figure FIGREF12 is that word clouds do not facilitate systematic comparison of documents and their content with one another. Next, we show how our data facilitates the application of relatively simple text analysis techniques to answer more complex empirical questions without the ambiguity in interpretation that is inherent in word clouds."
+    ],
+    [
+      "Wordfish BIBREF2 is a method that combines Item Response Theory BIBREF17 with text classification. Wordfish assumes that there is a latent policy dimension and that each author has a position on this dimension. Words are assumed to be distributed over this dimension such that INLINEFORM0 , where INLINEFORM1 is the count of word INLINEFORM2 in document INLINEFORM3 at time INLINEFORM4 . The functional form of the model is assumed to be INLINEFORM5 ",
+      "where INLINEFORM0 are fixed effects to control for differences in the length of speeches and INLINEFORM1 are fixed effects to control for the fact that some words are used more often than others in all documents. INLINEFORM2 are the estimates of authors' position on the latent dimension and INLINEFORM3 are estimates of word-weights that are determined by how important specific words are in discriminating documents from each other. In this model each document is treated as a separate actor's position and all positions are estimated simultaneously. If a minister maintains a similar position from one budget speech to the next, this means that words with similar frequencies were used over time. At the same time any movement detected by the model towards a position held by, for example, his predecessor, means that the minister's word choice is now much closer to his predecessor than to his own word usage in the previous budget speech. The identification strategy for the model also sets the mean of all positions to 0 and the standard deviation to 1, thus allowing over time a change in positions relative to the mean with the total variance of all positions over time fixed BIBREF2 . Effectively this standardizes the results and allows for the comparison of positions over time on a comparable scale.",
+      "Before including documents in the analysis, we have removed all numbers, punctuation marks, and stop words. In addition, we follow the advice in BIBREF18 and delete words that appear in less than 20% of all speeches. We do this in order to prevent words that are specific to a small time period (and hence only appear in a few speeches) from having a large impact on discriminating speeches from each other. Figure FIGREF17 shows the results of estimation, with an overlaid regression line.",
+      "The results in Figure FIGREF17 indicate a concept drift \u2013 the gradual change over time of the underlying concept behind the text categorization class BIBREF19 . In the political science text scaling literature, this issue is known as agenda shift BIBREF18 . In supervised learning models like Wordscore, this problem has typically been dealt with by estimating text models separately for each time period BIBREF20 , BIBREF21 , where the definition of the dimensions remains stable through the choice of training documents. However, this approach is not easily transferrable to inductive techniques like Wordfish, where there may be substantively different policy dimensions at different time periods, rendering comparison of positions over time challenging, if not impossible. A clear presence of the concept drift issue in Wordfish estimation should be a cautionary note for using the approach with time series data, even though the original method was specifically designed to deal with time-series data as indicated in the title of the paper BIBREF2 .",
+      "Looking at Figure FIGREF17 we can also observe that some ministers have similar preference profiles while others differ significantly. For example, Ahern and Reynolds are very similar in their profile but differ from a group consisting of Quinn, McCreevy, Cowen, and Lenihan who are very close to each other. There also appears to be a dramatic shift in agenda between the tenures of Lynch and Haughey (and also during Taoiseach Lynch's delivery of the budget speech for the Minister for Finance Charles Haughey in 1970). Overall, it appears that topics covered in budget speeches develop in waves, with clear bands formed by, for example, Lenihan, Cowen, McCreevy and Quin; Ahern and Reynolds; MacSharry, Dukes, Bruton, Fitzgerald, O'Kennedy and Colley; R. Ryan, Colley, Lynch (for Haughey); MacEntee, McGilligan and Aiken; Blythe and MacEntee.",
+      "One intuitive interpretation of our Wordfish results is that budget speeches by finance ministers are related to underlying macroeconomic dynamics in the country. We consider the relationship between estimated policy positions of Minsters and three core economic indicators: unemployment, inflation, and per capita GDP growth rates. Figure FIGREF18 shows the three economic indicators, inflation (1923\u20132008), GDP growth (annual %; 1961\u20132008) and unemployment rate (1956\u20132008), over time.",
+      "Figure FIGREF19 show Ministers' estimated positions plotted against the three indicators.",
+      "As expected, the results presented here show that the policy positions of some Ministers can be partly explained by the contemporaneous economic situation in the country. However, the fact that some of the Ministers are clear outliers highlights the effect of individual characteristics on policy-making. One of the avenues for research that arises from this exercise is to analyze the determinants of these individual idiosyncrasies, possibly looking at education, class, and previous ministerial career. Such questions can now be easily investigated by researchers using our database."
+    ],
+    [
+      "In the previous section, we used budget speeches from each year and compared them over time. In this section, we restrict the analysis to a single year but take multiple speeches made on the same topic. More specifically, we estimate the preferences of all speakers who participated in the debate over the 2008 budget. We extract these speeches from the database by selecting all contributions to the topic \u201cFinancial Resolution\u201d in year 2007. This leaves us with a total of 22 speakers from all five parties. Table TABREF22 shows the speeches included in the analysis.",
+      "To estimate speakers' position we use Wordscore BIBREF1 \u2013 a version of the Naive Bayes classifier that is deployed for text categorization problems BIBREF22 . In a similar application, BIBREF1 have already demonstrated that Wordscore can be effectively used to derive estimates of TDs policy positions. As in the example above, we pre-process documents by removing all numbers and interjections.",
+      "Wordscore uses two documents with well-known positions as reference texts (training set). The positions of all other documents are then estimated by comparing them to these reference documents. The underlying idea is that a document that, in terms of word frequencies, is similar to a reference document was produced by an author with similar preferences. The selection of reference documents furthermore determines the (assumed) underlying dimension for which documents' positions are estimated. For example, using two opposing documents on climate change would scale documents on the underlying dimension \u201cclimate politics\u201d. It has also been shown that under certain assumptions the Wordscore algorithm is related to the Wordfish algorithm used in the previous section BIBREF23 .",
+      "We assume that contributions in budget debates have the underlying dimension of being either pro or contra the current government. Our interpretation from reading the speeches is that, apart from the budget speech itself, all other speeches largely either attack or defend the incumbent government and to a lesser extent debate the issues of the next budget. We can therefore use contributions during the budget debate as an indicator for how much a speaker is supporting or opposing the current government, here consisting of Fianna F\u00e1il and the Green Party. As our reference texts we therefore chose the speeches of Bertie Ahern (Taoiseach) and Enda Kenny (FG party leader). The former should obviously be strongly supportive of the government while the latter, as party leader of the largest opposition party, should strongly oppose it. Figure FIGREF24 shows estimated positions for all speakers grouped by party affiliation.",
+      "The estimated positions are clustered into two groups, one representing the government and one the opposition. Within the government cluster, Deputy Batt O'Keeffe (Minister of State at the Department of Environment, Heritage and Local Government) is estimated to be the most supportive speaker for the government, while Deputy Pat Carey (Minister of State at the Department of Community, Rural and Gaeltacht Affairs) and Deputy Sean Ardagh are estimated to be relatively closer to the opposition. Deputy John Gormley, leader of the Green party and Minister for the Environment, Heritage and Local Government in the FF-Green coalition, is estimated to be in the centre of the government cluster. Among all positions in the opposition cluster, the speech of R\u00f3is\u00edn Shortall is the closest to the government side, with Neville being the farthest out."
+    ],
+    [
+      "The government cabinet in parliamentary democracies is at the core of political decision making, yet it is difficult to model intra-cabinet bargaining as the preferences of most cabinet members are unknown. Cabinet decisions are usually made behind closed doors and the doctrine of joint cabinet responsibility prevents ministers from publicly opposing decisions, even if they disagree with them. Using ministers' speeches and their responses during question times offer a unique opportunity to infer their preferences on policy dimensions of interest. In our final application we estimate policy positions for all cabinet members in the 26th government. The dimension on which positions are estimated represents pro- versus contra-government spending (or spending left-right). We show that estimated positions are highly correlated with departments' actual spending, which means that estimated positions are not only meaningful but can also be used to predict actual policy-making.",
+      "The 26th government was formed as a coalition between Fianna F\u00e1il and the Progressive Democrats after the election for the 29th D\u00e1il in 2002. The cabinet was reshuffled on 29 September 2004 and we only include ministers' speeches until that date. Table TABREF26 lists all cabinet members (and their portfolios) included in our analysis.",
+      "To estimate ministers' policy positions, we retrieve the complete record of each minister's contribution in parliament from the first meeting on 6 June 2002 until the date of the reshuffle. On average, each minister made 3,643 contributions with an average number of 587,077 words. Table TABREF27 provides summary statistics for all ministers, sorted by total word count.",
+      "We again use Wordscore BIBREF0 , BIBREF1 to estimate positions as it allows us to define the underlying policy dimension by choosing appropriate reference texts. We estimate positions on a social-economic left-right dimension that reflects pro- versus contra-government spending. We therefore use contributions by Mary Coughlan (Minister for Social and Family Affairs) and Charlie McCreevy (Minister for Finance) as reference texts, assuming that the former is more in favor of spending than the latter. Figure FIGREF28 shows the results of estimation grouped by the two parties.",
+      "As expected, we find that the two PD members, Mary Harney and Michael McDowell, are at the right side of the dimension. We estimate the most left-wing members to be \u00c9amon \u00d3 Cu\u00edv (Minister for Community, Rural and Gaeltacht Affairs), Noel Dempsey (Minister for Education and Science), and Miche\u00e1l Martin (Minister for Health and Children). The most right-wing members are John O'Donoghue (Minister for Arts, Sport and Tourism), Charlie McCreevy (whose contributions we used as right-wing reference text), and Michael Smith (Minister for Defense).",
+      "How valid are these estimated positions? In order to have substantive meaning, our estimates should be able to predict political decisions on the same policy dimension. We therefore use ministers' estimated positions to predict their departmental spending level BIBREF3 . Our outcome variable is each department's spending as share of the total budget in 2004 modeled as a function of estimated policy positions. We conjecture that more left-wing ministers should have higher spending levels than right-wing ministers, which we test by estimating DISPLAYFORM0 ",
+      "via ordinary least-square regression. Figure FIGREF31 shows the two variables plotted against each other together with the estimated regression line from equation EQREF29 . In one analysis shown we include all cabinet members. In the other, we exclude non-spending departments with small budgets, such as the office of the Taoiseach or the Department of Foreign Affairs.",
+      "Figure FIGREF31 reveals that there is a negative, albeit weak, relationship between estimated positions and spending, with more left-wing cabinet members having higher spending levels than right-wing members. The correlation between the two variables is -0.53 ( INLINEFORM0 ) which is not significant at the 0.05 level. However, if we only take members from high-spending departments into account (second pane in Figure FIGREF31 ) we find a significant linear relationship between the two variables with a correlation coefficient of -0.95 ( INLINEFORM1 ). This result provides some level of validation for our data and analysis.",
+      "These results also open up an intriguing question about the endogeneity of observable policy preferences of ministers. Do higher spending portfolios receive more pro-spending ministers or do ministers adapt their policy preferences after appointment and literally grow into the job? This and related questions are outside the scope of this paper and can be pursued by researchers with the help of our database of parliamentary speeches."
+    ],
+    [
+      "Policy preferences of individual politicians (ministers or TDs in general), are inherently unobservable. However, we have abundant data on speeches made by political actors. The latest developments in automated text analysis techniques allow us to estimate the policy positions of individual actors from these speeches.",
+      "In relation to Irish political actors such estimation has been hindered by the structure of the available data. While all speeches made in D\u00e1il \u00c9ireann are dutifully recorded, the architecture of the data set, where digitized versions of speeches are stored, makes it impossible to apply any of the existing text analysis software. Speeches are currently stored by D\u00e1il \u00c9ireann in more than half a million separate HTML files with entries that are not related to one another.",
+      "In this paper we present a new database of speeches that was created with the purpose of allowing the estimation of policy preferences of individual politicians. For that reason we created a relational database where speeches are related to the members database and structured in terms of dates, topics of debates, and names of speakers, their constituency and party affiliation. This gives the necessary flexibility to use available text scaling methods in order to estimate the policy positions of actors.",
+      "We also present several examples for which this data can be used. We show how to estimate the policy positions of all Irish Ministers for Finance, and highlight how this can lead to interesting research questions in estimating the determinants of their positions. We show that for some ministers the position can be explained by the country's economic performance, while the preferences of other ministers seem to be idiosyncratic. In another example we estimate positions of individual TDs in a budget debate, followed by the estimation of policy positions of cabinet members of the 26th Government.",
+      "With the introduction of our database, we aim to make text analysis an easy and accessible tool for social scientists engaged in empirical research on policy-making that requires estimation of policy preferences of political actors."
+    ]
+  ]
+}
+```

qasper-2010/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: A Full End-to-End Semantic Role Labeler, Syntax-agnostic Over Syntax-aware?
+
+Question: Are there syntax-agnostic SRL models before?

qasper-2017/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 were potentially hateful messages identified?

qasper-2021/instruction.md

@@ -0,0 +1,177 @@
+Name of Paper: An analysis of the utility of explicit negative examples to improve the syntactic abilities of neural language models
+
+Question: What neural language models are explored?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Target Task and Setup",
+    "Target Task and Setup ::: Syntactic evaluation task",
+    "Target Task and Setup ::: Syntactic evaluation task ::: @!START@BIBREF0@!END@ test set",
+    "Target Task and Setup ::: Language models",
+    "Target Task and Setup ::: Language models ::: Training data",
+    "Target Task and Setup ::: Language models ::: Baseline LSTM-LM",
+    "Learning with Negative Examples",
+    "Learning with Negative Examples ::: Notations",
+    "Learning with Negative Examples ::: Negative Example Losses ::: Binary-classification loss",
+    "Learning with Negative Examples ::: Negative Example Losses ::: Unlikelihood loss",
+    "Learning with Negative Examples ::: Negative Example Losses ::: Sentence-level margin loss",
+    "Learning with Negative Examples ::: Negative Example Losses ::: Token-level margin loss",
+    "Learning with Negative Examples ::: Parameters",
+    "Learning with Negative Examples ::: Scope of Negative Examples",
+    "Experiments on Additional Losses",
+    "Experiments on Additional Losses ::: Naive LSTM-LMs perform well",
+    "Experiments on Additional Losses ::: Higher margin value is effective",
+    "Experiments on Additional Losses ::: Which additional loss works better?",
+    "Limitations of LSTM-LMs",
+    "Limitations of LSTM-LMs ::: Setup",
+    "Limitations of LSTM-LMs ::: Results",
+    "Do models generalize explicit supervision, or just memorize it?",
+    "Do models generalize explicit supervision, or just memorize it? ::: Setup",
+    "Do models generalize explicit supervision, or just memorize it? ::: Results",
+    "Conclusion",
+    "Acknowledges"
+  ],
+  "paragraphs": [
+    [
+      "intro",
+      "Despite not being exposed to explicit syntactic supervision, neural language models (LMs), such as recurrent neural networks, are able to generate fluent and natural sentences, suggesting that they induce syntactic knowledge about the language to some extent. However, it is still under debate whether such induced knowledge about grammar is robust enough to deal with syntactically challenging constructions such as long-distance subject-verb agreement. So far, the results for RNN language models (RNN-LMs) trained only with raw text are overall negative; prior work has reported low performance on the challenging test cases BIBREF0 even with the massive size of the data and model BIBREF1, or argue the necessity of an architectural change to track the syntactic structure explicitly BIBREF2, BIBREF3. Here the task is to evaluate whether a model assigns a higher likelihood on a grammatically correct sentence (UNKREF3) over an incorrect sentence (UNKREF5) that is minimally different from the original one BIBREF4.",
+      "",
+      ".5ex",
+      "The author that the guards like laughs.",
+      ".5ex",
+      "The author that the guards like laugh.",
+      "",
+      "In this paper, to obtain a new insight into the syntactic abilities of neural LMs, in particular RNN-LMs, we perform a series of experiments under a different condition from the prior work. Specifically, we extensively analyze the performance of the models that are exposed to explicit negative examples. In this work, negative examples are the sentences or tokens that are grammatically incorrect, such as (UNKREF5) above.",
+      "Since these negative examples provide a direct learning signal on the task at test time it may not be very surprising if the task performance goes up. We acknowledge this, and argue that our motivation for this setup is to deepen understanding, in particular the limitation or the capacity of the current architectures, which we expect can be reached with such strong supervision. Another motivation is engineering: we could exploit negative examples in different ways, and establishing a better way will be of practical importance toward building an LM or generator that can be robust on particular linguistic constructions.",
+      "The first research question we pursue is about this latter point: what is a better method to utilize negative examples that help LMs to acquire robustness on the target syntactic constructions? Regarding this point, we find that adding additional token-level loss trying to guarantee a margin between log-probabilities for the correct and incorrect words (e.g., $\\log p(\\textrm {laughs} | h)$ and $\\log p(\\textrm {laugh} | h)$ for (UNKREF3)) is superior to the alternatives. On the test set of BIBREF0, we show that LSTM language models (LSTM-LMs) trained by this loss reach near perfect level on most syntactic constructions for which we create negative examples, with only a slight increase of perplexity about 1.0 point.",
+      "Past work conceptually similar to us is BIBREF5, which, while not directly exploiting negative examples, trains an LM with additional explicit supervision signals to the evaluation task. They hypothesize that LSTMs do have enough capacity to acquire robust syntactic abilities but the learning signals given by the raw text are weak, and show that multi-task learning with a binary classification task to predict the upcoming verb form (singular or plural) helps models aware of the target syntax (subject-verb agreement). Our experiments basically confirm and strengthen this argument, with even stronger learning signals from negative examples, and we argue this allows to evaluate the true capacity of the current architectures. In our experiments (Section exp), we show that our margin loss achieves higher syntactic performance.",
+      "Another relevant work on the capacity of LSTMs is BIBREF6, which shows that by distilling from syntactic LMs BIBREF7, LSTM-LMs can be robust on syntax. We show that our LMs with the margin loss outperforms theirs in most of the aspects, further strengthening the capacity of LSTMs, and also discuss the limitation.",
+      "The latter part of this paper is a detailed analysis of the trained models and introduced losses. Our second question is about the true limitation of LSTM-LMs: are there still any syntactic constructions that the models cannot handle robustly even with our direct learning signals? This question can be seen as a fine-grained one raised by BIBREF5 with a stronger tool and improved evaluation metric. Among tested constructions, we find that syntactic agreement across an object relative clause (RC) is challenging. To inspect whether this is due to the architectural limitation, we train another LM on a dataset, on which we unnaturally augment sentences involving object RCs. Since it is known that object RCs are relatively rare compared to subject RCs BIBREF8, frequency may be the main reason for the lower performance. Interestingly, even when increasing the number of sentences with an object RC by eight times (more than twice of sentences with a subject RC), the accuracy does not reach the same level as agreement across a subject RC. This result suggests an inherent difficulty to track a syntactic state across an object RC for sequential neural architectures.",
+      "We finally provide an ablation study to understand the encoded linguistic knowledge in the models learned with the help of our method. We experiment under reduced supervision at two different levels: (1) at a lexical level, by not giving negative examples on verbs that appear in the test set; (2) at a construction level, by not giving negative examples about a particular construction, e.g., verbs after a subject RC. We observe no huge score drops by both. This suggests that our learning signals at a lexical level (negative words) strengthen the abstract syntactic knowledge about the target constructions, and also that the models can generalize the knowledge acquired by negative examples to similar constructions for which negative examples are not explicitly given. The result also implies that negative examples do not have to be complete and can be noisy, which will be appealing from an engineering perspective."
+    ],
+    [
+      "The most common evaluation metric of an LM is perplexity. Although neural LMs achieve impressive perplexity BIBREF9, it is an average score across all tokens and does not inform the models' behaviors on linguistically challenging structures, which are rare in the corpus. This is the main motivation to separately evaluate the models' syntactic robustness by a different task."
+    ],
+    [
+      "task As introduced in Section intro, the task for a model is to assign a higher probability to the grammatical sentence over the ungrammatical one, given a pair of minimally different sentences at a critical position affecting the grammaticality. For example, (UNKREF3) and (UNKREF5) only differ at a final verb form, and to assign a higher probability to (UNKREF3), models need to be aware of the agreement dependency between author and laughs over an RC."
+    ],
+    [
+      "While initial work BIBREF4, BIBREF10 has collected test examples from naturally occurring sentences, this approach suffers from the coverage issue, as syntactically challenging examples are relatively rare. We use the test set compiled by BIBREF0, which consists of synthetic examples (in English) created by a fixed vocabulary and a grammar. This approach allows us to collect varieties of sentences with complex structures.",
+      "The test set is divided by a necessary syntactic ability. Many are about different patterns of subject-verb agreement, including local (UNKREF8) and non-local ones across a prepositional phrase or a subject/object RC, and coordinated verb phrases (UNKREF9). (UNKREF1) is an example of agreement across an object RC.",
+      "",
+      "The senators smile/*smiles.",
+      "The senators like to watch television shows and are/*is twenty three years old.",
+      "Previous work has shown that non-local agreement is particularly challenging for sequential neural models BIBREF0.",
+      "The other patterns are reflexive anaphora dependencies between a noun and a reflexive pronoun (UNKREF10), and on negative polarity items (NPIs), such as ever, which requires a preceding negation word (e.g., no and none) at an appropriate scope (UNKREF11):",
+      "",
+      "The authors hurt themselves/*himself.",
+      "No/*Most authors have ever been popular.",
+      "",
+      "Note that NPI examples differ from the others in that the context determining the grammaticality of the target word (No/*Most) does not precede it. Rather, the grammaticality is determined by the following context. As we discuss in Section method, this property makes it difficult to apply training with negative examples for NPIs for most of the methods studied in this work.",
+      "All examples above (UNKREF1\u2013UNKREF11) are actual test sentences, and we can see that since they are synthetic some may sound somewhat unnatural. The main argument behind using this dataset is that even not very natural, they are still strictly grammatical, and an LM equipped with robust syntactic abilities should be able to handle them as human would do."
+    ],
+    [
+      "lm"
+    ],
+    [
+      "Following the practice, we train LMs on the dataset not directly relevant to the test set. Throughout the paper, we use an English Wikipedia corpus assembled by BIBREF10, which has been used as training data for the present task BIBREF0, BIBREF6, consisting of 80M/10M/10M tokens for training/dev/test sets. It is tokenized and rare words are replaced by a single unknown token, amounting to the vocabulary size of 50,000."
+    ],
+    [
+      "Since our focus in this paper is an additional loss exploiting negative examples (Section method), we fix the baseline LM throughout the experiments. Our baseline is a three-layer LSTM-LM with 1,150 hidden units at internal layers trained with the standard cross-entropy loss. Word embeddings are 400-dimensional, and input and output embeddings are tied BIBREF11. Deviating from some prior work BIBREF0, BIBREF1, we train LMs at sentence level as in sequence-to-sequence models BIBREF12. This setting has been employed in some previous work BIBREF3, BIBREF6.",
+      "Parameters are optimized by SGD. For regularization, we apply dropout on word embeddings and outputs of every layer of LSTMs, with weight decay of 1.2e-6, and anneal the learning rate by 0.5 if the validation perplexity does not improve successively, checking every 5,000 mini-batches. Mini-batch size, dropout weight, and initial learning rate are tuned by perplexity on the dev set of Wikipedia dataset.",
+      "The size of our three-layer LM is the same as the state-of-the-art LSTM-LM at document-level BIBREF9. BIBREF0's LSTM-LM is two-layer with 650 hidden units and word embeddings. Comparing two, since the word embeddings of our models are smaller (400 vs. 650) the total model sizes are comparable (40M for ours vs. 39M for theirs). Nonetheless, we will see in the first experiment that our carefully tuned three-layer model achieves much higher syntactic performance than their model (Section exp), being a stronger baseline to our extensions, which we introduce next."
+    ],
+    [
+      "method",
+      "Now we describe four additional losses for exploiting negative examples. The first two are existing ones, proposed for a similar purpose or under a different motivation. As far as we know, the latter two have not appeared in past work.",
+      "We note that we create negative examples by modifying the original Wikipedia training sentences. As a running example, let us consider the case where sentence (UNKREF19) exists in a mini-batch, from which we create a negative example (UNKREF21).",
+      "",
+      ".5ex",
+      "An industrial park with several companies is located in the close vicinity.",
+      ".5ex",
+      "An industrial park with several companies are located in the close vicinity."
+    ],
+    [
+      "By a target word, we mean a word for which we create a negative example (e.g., is). We distinguish two types of negative examples: a negative token and a negative sentence; the former means a single incorrect word (e.g., are)."
+    ],
+    [
+      "This is proposed by BIBREF5 to complement a weak inductive bias in LSTM-LMs for learning syntax. It is multi-task learning across the cross-entropy loss ($L_{lm}$) and an additional loss ($L_{add}$):",
+      "where $\\beta $ is a relative weight for $L_{add}$. Given outputs of LSTMs, a linear and binary softmax layers predict whether the next token is singular or plural. $L_{add}$ is a loss for this classification, only defined for the contexts preceding a target token $x_{i}$:",
+      "where $x_{1:i} = x_1 \\cdots x_{i}$ is a prefix sequence and $\\mathbf {h^*}$ is a set of all prefixes ending with a target word (e.g., An industrial park with several companies is) in the training data. $\\textrm {num}(x) \\in \\lbrace \\textrm {singular, plural} \\rbrace $ is a function returning the number of $x$. In practice, for each mini-batch for $L_{lm}$, we calculate $L_{add}$ for the same set of sentences and add these two to obtain a total loss for updating parameters.",
+      "As we mentioned in Section intro, this loss does not exploit negative examples explicitly; essentially a model is only informed of a key position (target word) that determines the grammaticality. This is rather an indirect learning signal, and we expect that it does not outperform the other approaches."
+    ],
+    [
+      "This is recently proposed BIBREF15 for resolving the repetition issue, a known problem for neural text generators BIBREF16. Aiming at learning a model that can suppress repetition, they introduce an unlikelihood loss, which is an additional loss at a token level and explicitly penalizes choosing words previously appeared in the current context.",
+      "We customize their loss for negative tokens $x_i^*$ (e.g., are in (UNKREF21)). Since this loss is added at token-level, instead of Eq. () the total loss is $L_{lm}$, which we modify as:",
+      "where $\\textrm {neg}_t(\\cdot )$ returns negative tokens for a target $x_i$. $\\alpha $ controls the weight. $\\mathbf {x}$ is a sentence in the training data $D$. The unlikelihood loss strengthens the signal to penalize undesirable words in a context by explicitly reducing the likelihood of negative tokens $x_i^*$. This is more direct learning signal than the binary classification loss."
+    ],
+    [
+      "We propose a different loss, in which the likelihoods for correct and incorrect sentences are more tightly coupled. As in the binary classification loss, the total loss is given by Eq. (). We consider the following loss for $L_{add}$:",
+      "where $\\delta $ is a margin value between the log-likelihood of original sentence $\\mathbf {x}$ and negative sentences $\\lbrace \\mathbf {x}_j^* \\rbrace $. $\\textrm {neg}_s(\\cdot )$ returns a set of negative sentences by modifying the original one. Note that we change only one token for each $\\mathbf {x}_j^*$, and thus may obtain multiple negative sentences from one $\\mathbf {x}$ when it contains multiple target tokens (e.g., she leaves there but comes back ...).",
+      "Comparing to the unlikelihood loss, not only decreasing the likelihood of a negative example, this loss tries to guarantee a minimal difference between the two likelihoods. The learning signal of this loss seems stronger in this sense; however, the token-level supervision is missing, which may provide a more direct signal to learn a clear contrast between correct and incorrect words. This is an empirical problem we pursue in the experiments."
+    ],
+    [
+      "Our final loss is a combination of the previous two, by replacing $g(x_i)$ in the unlikelihood loss by a margin loss:"
+    ],
+    [
+      "Each method employs a few additional hyperparameters. For the binary classification ($\\beta $) and unlikelihood ($\\alpha $) losses, we select their values from $\\lbrace 1,10,100,1000\\rbrace $ that achieve the best average syntactic performance (we find $\\alpha =1000, \\beta =1$). For the two margin losses, we fix $\\beta =1.0$ and $\\alpha =1.0$ and only see the effects of margin values."
+    ],
+    [
+      "scope Since our goal is to understand to what extent LMs can be sensitive to the target syntactic constructions by giving explicit supervision via negative examples, we only prepare negative examples on the constructions that are directly tested at evaluation. Specifically, we mark the following words in the training data, and create negative examples:",
+      "",
+      "To create negative examples on subject-verb agreement, we mark all present verbs and change their numbers.",
+      "",
+      "We also create negative examples on reflexive anaphora, by flipping between {themselves}$\\leftrightarrow ${himself, herself}.",
+      "These two are both related to the syntactic number of a target word. For binary classification we regard both as a target word, apart from the original work that only deals with subject-verb agreement BIBREF5. We use a single common linear layer for both constructions.",
+      "In this work, we do not create negative examples for NPIs. This is mainly for technical reasons. Among four losses, only the sentence-level margin loss can correctly handle negative examples for NPIs, essentially because other losses are token-level. For NPIs, left contexts do not have information to decide the grammaticality of the target token (a quantifier; no, most, etc.) (Section task). Instead, in this work, we use NPI test cases as a proxy to see possible negative (or positive) impacts as compensation for specially targeting some constructions. We will see that in particular for our margin losses, such negative effects are very small."
+    ],
+    [
+      "exp",
+      "We first see the overall performance of baseline LMs as well as the effects of additional losses. Throughout the experiments, for each setting, we train five models from different random seeds and report the average score and standard deviation."
+    ],
+    [
+      "The main accuracy comparison across target constructions for different settings is presented in Table main. We first notice that our baseline LSTM-LMs (Section lm) perform much better than BIBREF0's LM. A similar observation is recently made by BIBREF6. This suggests that the original work underestimates the true syntactic ability induced by LSTM-LMs. The table also shows the results by their distilled LSTMs from RNNGs (Section intro)."
+    ],
+    [
+      "For the two types of margin loss, which margin value should we use? Figure margin reports average accuracies within the same types of constructions. For both token and sentence-levels, the task performance increases with $\\delta $, but a too large value (15) causes a negative effect, in particular on reflexive anaphora. There is an increase of perplexity by both methods. However, this effect is much smaller for the token-level loss. In the following experiments, we fix the margin value to 10 for both, which achieves the best syntactic performance."
+    ],
+    [
+      "We see a clear tendency that our token-level margin achieves overall better performance. Unlikelihood loss does not work unless we choose a huge weight parameter ($\\alpha =1000$), but it does not outperform ours, with a similar value of perplexity. The improvements by binary-classification loss are smaller, indicating that the signals are weaker than other methods with explicit negative examples. Sentence-level margin loss is conceptually advantageous in that it can deal with any types of negative examples defined in a sentence including NPIs. We see that it is often competitive with token-level margin loss, but we see relatively a large increase of perplexity (4.9 points). This increase is observed by even smaller values (Figure margin). Understanding the cause of this degradation as well as alleviating it is an important future direction."
+    ],
+    [
+      "orc In Table main, the accuracies on dependencies across an object RC are relatively low. The central question in this experiment is whether this low performance is due to the limitation of current architectures, or other factors such as frequency. We base our discussion on the contrast between object (UNKREF45) and subject (UNKREF46) RCs:",
+      "",
+      "The authors (that) the chef likes laugh.",
+      "The authors that like the chef laugh.",
+      "Importantly, the accuracies for a subject RC are more stable, reaching 99.8% with the token-level margin loss, although the content words used in the examples are common.",
+      "It is known that object RCs are less frequent than subject RCs BIBREF8, BIBREF18, and it could be the case that the use of negative examples still does not fully alleviate this factor. Here, to understand the true limitation of the current LSTM architecture, we try to eliminate such other factors as much as possible under a controlled experiment."
+    ],
+    [
+      "We first inspect the frequencies of object and subject RCs in the training data, by parsing them with the state-of-the-art Berkeley neural parser BIBREF19. In total, while subject RCs occur 373,186 times, object RCs only occur 106,558 times. We create three additional training datasets by adding sentences involving object RCs to the original Wikipedia corpus (Section lm). To this end, we randomly pick up 30 million sentences from Wikipedia (not overlapped to any sentences in the original corpus), parse by the same parser, and filter sentences containing an object RC, amounting to 680,000 sentences. Among the test cases about object RCs, we compare accuracies on subject-verb agreement, to make a comparison with subject RCs. We also evaluate on \u201canimate only\u201d subset, which has a correspondence to the test cases for subject RC with only differences in word order and inflection (like (UNKREF45) and (UNKREF46); see footnote FOOTREF47). Of particular interest to us is the accuracy on these animate cases. Since the vocabularies are exactly the same, we hypothesize that the accuracy will reach the same level as that on subject RCs with our augmentation."
+    ],
+    [
+      "However, for both all and animate cases, accuracies are below those for subject RCs (Figure orc). Although we see improvements from the original score (93.7), the highest average accuracy by the token-level margin loss on \u201canimate\u201d subset is 97.1 (\u201cwith that\u201d), not beyond 99%. This result indicates some architectural limitation of LSTM-LMs in handling object RCs robustly at a near perfect level. Answering why the accuracy does not reach (almost) 100%, perhaps with other empirical properties or inductive biases BIBREF20, BIBREF21 is future work."
+    ],
+    [
+      "One distinguishing property of our margin loss, in particular token-level loss, is that it is highly lexical, making contrast explicitly between correct and incorrect words. This direct signal may make models acquire very specialized knowledge about each target word, not very generalizable one across similar words and occurring contexts. In this section, to get insights into the transferability of syntactic knowledge induced by our margin losses, we provide an ablation study by removing certain negative examples during training."
+    ],
+    [
+      "We perform two kinds of ablation. For token-level ablation (-Token), we avoid creating negative examples for all verbs that appear as a target verb in the test set. Another is construction-level (-Pattern), by removing all negative examples occurring in a particular syntactic pattern. We ablate a single construction at a time for -Pattern, from four non-local subject-verb dependencies (across a prepositional phrase (PP), subject RC, object RC, and long verb phrase (VP)). We hypothesize that models are less affected by token-level ablation, as knowledge transfer across words appearing in similar contexts is promoted by language modeling objective. We expect that construction-level supervision would be necessary to induce robust syntactic knowledge, as perhaps different phrases, e.g., a PP and a VP, are processed differently."
+    ],
+    [
+      "Figure ablation is the main results. Across models, we restrict the evaluation on four non-local dependency constructions, which we selected as ablation candidates as well. For a model with -Pattern, we evaluate only on examples of construction ablated in the training (see caption). To our surprise, both -Token and -Pattern have similar effects, except \u201cAcross an ORC\u201d, on which the degradation by -Pattern is larger. This may be related to the inherent difficulty of object RCs for LSTM-LMs that we verified in Section orc. For such particularly challenging constructions, models may need explicit supervision signals. We observe lesser score degradation by ablating prepositional phrases and subject RCs. This suggests that, for example, the syntactic knowledge strengthened for prepositional phrases with negative examples could be exploited to learn the syntactic patterns about subject RCs, even when direct learning signals on subject RCs are missing.",
+      "We see approximately 10.0 points score degradation on long VP coordination by both ablations. Does this mean that long VPs are particularly hard in terms of transferability? We find that the main reason for this drop, relative to other cases, are rather technical, essentially due to the target verbs used in the test cases. See Table vpcoordfirst,secondvp, which show that failed cases for the ablated models are often characterized by the existence of either like or likes. Excluding these cases (\u201cother verbs\u201d in Table secondvp), the accuracies reach 99.2 and 98.0 by -Token and -Pattern, respectively. These verbs do not appear in the test cases of other tested constructions. This result suggests that the transferability of syntactic knowledge to a particular word may depend on some characteristics of that word. We conjecture that the reason of weak transferability to likes and like is that they are polysemous; e.g., in the corpus, like is much more often used as a preposition and being used as a present tense verb is rare. This types of issues due to frequency may be one reason of lessening the transferability. In other words, like can be seen as a challenging verb to learn its usage only from the corpus, and our margin loss helps for such cases."
+    ],
+    [
+      "We have shown that by exploiting negative examples explicitly, the syntactic abilities of LSTM-LMs greatly improve, demonstrating a new capacity of handling syntax robustly. Given a success of our approach using negative examples, and our final analysis for transferability, which indicates that the negative examples do not have to be complete, one interesting future direction is to extend our approach to automatically inducing negative examples themselves in some way, possibly with orthographic and/or distributional indicators or others."
+    ],
+    [
+      "We would like to thank Naho Orita and the members of Computational Psycholinguistics Tokyo for their valuable suggestions and comments. This paper is based on results obtained from projects commissioned by the New Energy and Industrial Technology Development Organization (NEDO)."
+    ]
+  ]
+}
+```

qasper-2028/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Bag of Tricks for Efficient Text Classification
+
+Question: Which datasets are used for evaluation?

qasper-2213/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Build it Break it Fix it for Dialogue Safety: Robustness from Adversarial Human Attack
+
+Question: What evaluation metric is used?

qasper-2222/instruction.md

@@ -0,0 +1,64 @@
+Name of Paper: Impact of Coreference Resolution on Slot Filling
+
+Question: What is the task of slot filling?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related work",
+    "Slot filling task",
+    "Coreference resolution for slot filling",
+    "Coreference resource",
+    "Analysis of coreference resolution errors",
+    "Experiments with end-to-end system",
+    "Conclusion",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "Coreference resolution systems group noun phrases (mentions) that refer to the same entity into the same chain. Mentions can be full names (e.g., John Miller), pronouns (e.g., he), demonstratives (e.g., this), comparatives (e.g., the first) or descriptions of the entity (e.g. the 40-year-old) BIBREF0 . Although coreference resolution has been a research focus for several years, systems are still far away from being perfect. Nevertheless, there are many tasks in natural language processing (NLP) which would benefit from coreference information, such as information extraction, question answering or summarization BIBREF1 . In BIBREF2 , for example, we showed that coreference information can also be incorporated into word embedding training. In general, coreference resolution systems can be used as a pre-processing step or as a part of a pipeline of different modules.",
+      "Slot Filling is an information extraction task which has become popular in the last years BIBREF3 . It is a shared task organized by the Text Analysis Conference (TAC). The task aims at extracting information about persons, organizations or geo-political entities from a large collection of news, web and discussion forum documents. An example is \u201cSteve Jobs\u201d for the slot \u201cX founded Apple\u201d. Thinking of a text passage like \u201cSteve Jobs was an American businessman. In 1976, he co-founded Apple\u201d, it is clear that coreference resolution can play an important role for finding the correct slot filler value.",
+      "In this study, we investigate how coreference resolution could help to improve performance on slot filling and which challenges exist. Furthermore, we present how we pre-processed the TAC source corpus with a coreference resolution system in order to be able to run the slot filling system more efficiently. In addition to this paper, we also publish the results of this pre-processing since it required long computation time and much resources."
+    ],
+    [
+      "The slot filling task has been organized since 2009. The top ranked systems of the last years achieved F1 scores of 37.28 (2013) BIBREF4 , 36.77 (2014) BIBREF5 and 31.48 (2015). In 2015, the task has been merged with the Cold Start track of the same conference. This led to several changes in the number of relations, the evaluation documents and the outputs expected from the systems BIBREF6 .",
+      "Previous studies and error analyses have shown that coreference resolution is an important component to increase the recall of slot filling systems BIBREF7 , BIBREF8 , BIBREF9 . analysis2012 identified coreference failures as the second most frequent error source of slot filling systems (after inference failures). In most cases, nominal anaphors were not resolved correctly. analysisRecall investigated possible causes of recall loss in a slot filling system. They described that coreference resolution provided higher recall but might be inefficient since it requires a lot of time and resources. Moreover, they argued that the overall results of a slot filling system might be better without coreference resolution since it can have a negative impact on precision. In contrast, our experiments in this study show that the increased number of true positives when using coreference resolution has a much higher impact on the final results. For coping with the problem of time-consuming coreference resolution, we prepared and publish KBPchains, a coreference resource for slot filling."
+    ],
+    [
+      "The main idea of slot filling is to extend a knowledge base by extracting pre-defined relations between (named) entities from text data. Systems are provided with a large collection of text documents and a query file including entities and the relations to find in the text. As output, they have to provide the second argument for each relation. For entity \u201cApple\u201d and relation \u201corg:founded_by\u201d, for example, the systems need to extract \u201cSteve Jobs\u201d, \u201cSteve Wozniak\u201d and \u201cRonald Wayne\u201d along with text passages for justification.",
+      "This task combines several NLP challenges like information retrieval, information extraction, relation classification and knowledge inference. Until 2014, the slot filling shared task included 41 relations (25 for persons and 16 for organizations) BIBREF3 . Since 2015, these relations have been extended to all possible inverse relations which introduced a new query entity type (geo-political entity) and augmented the set of relations to 64 (27 for persons, 20 for organizations and 17 for geo-political entities) BIBREF6 . Table 1 provides exemplary relations for the different entity types.",
+      "The input for a slot filling system is an xml query containing the name and type of the entity, an exemplary occurence of the entity in the document collection and the slot to be filled. The expected output of the system contains, i.a., a provenance for the slot filler in the document collection, the slot filler itself, the type of the filler ( $\\in {PER, ORG, GPE, STRING}$ ), its offsets in the document collection, as well as a confidence value of the system.",
+      "The document collection from which the slot fillers should be extracted is quite large: until 2014, it consisted of about 2.1 million documents, in 2015 the number was reduced to about 50,000 documents. The documents comprise newswire, web and discussion forum texts. Therefore, the slot filling task is more than relation extraction for pre-defined relations: It also includes challenges like information retrieval and coping with different genres.",
+      "Most slot filling systems are a pipeline of different components, such as query expansion, information retrieval, candidate extraction, candidate classification and postprocessing. Figure 1 depicts a typical system. We performed a detailed analysis of the errors of these components and found that one of the most important sources of error is failure of coreference resolution in the candidate extraction step."
+    ],
+    [
+      "In our study, we have identified two main reasons why coreference resolution can improve slot filling performance. The first reason is that both arguments of a relation can be pronouns referring to the entity or filler in question. Consider the relation \u201cper:parents\u201d and the sentence \u201cBill is the father of Jane.\u201d Both entities \u201cBill\u201d and \u201cJane\u201d might have been mentioned in sentences before and could now be replaced by pronouns: \u201cHe is the father of Jane\u201d, \u201cBill is her father\u201d or \u201cHe is her father\u201d. If a slot filling system only extracts sentences with appearances of the full name of a person, it might miss many relevant sentences which can reduce the recall of the whole system drastically. As analysisRecall pointed out, the recall losses cannot be recovered by subsequent pipeline modules.",
+      "The second reason is that coreference resolution can provide slot fillers \u201cfor free\u201d: If a phrase like \u201cThe Hawaii-born\u201d is coreferent to the entity in question, it not only provides an additional sentence with information about the entity but also directly the location of birth (without the need of classification). Similar phrases can provide the age, a title or the religion of a person or the location of headquarters of an organization."
+    ],
+    [
+      "As motivated above, coreference information is a very important resource for participants of the slot filling task or related knowledge base population tasks on the same documents. Since we found that the coreference resolution component is one of the bottlenecks which considerably slows down our slot filling pipeline, we have pre-processed the TAC source corpus by tagging its documents using Stanford CoreNLP BIBREF10 . We call this resource of coreference chains KBPchains and share it (in the form of document-offset spans) on our website. Although CoreNLP is publicly available, KBPchains will save researchers much time and resources (cf., analysisRecall who mentioned the need for efficient coreference resolution when processing the large slot filling corpora). Table 2 lists statistics about the extracted coreference chains and their mentions. In addition to the minimum, maximum, average and median numbers of chains per document, mentions per chain and words per mention, we also report the number of mentions which are pronouns, the number of singletons (chains consisting of only one mention) and the number of chains with only identical mentions."
+    ],
+    [
+      "Coreference resolution systems produce acceptable results but are still far away from being perfect. In an analysis of the results of Stanford CoreNLP on the TAC source corpus in the context of our slot filling system, we found the following flaws being most prominent: Wrongly linked pronoun chains, unlinked pronoun chains and no recognition of coreferent phrases like \u201cthe 42-year-old\u201d, \u201cthe author\u201d or \u201cthe California-based company\u201d. In the following, we describe the effect of these failures on the slot filling system.",
+      "Wronly linked pronoun chains. If a pronoun chain is wrongly linked to the entity in question, all sentences with pronouns of this chain will be extracted as sentences containing information about the entity. This increases the number of falsely extracted sentences and as a result also the number of possible filler candidates. All those false positive filler candidates will be passed to the candidate evaluation module and can easily lead to a lower precision in the final output. (Either because the candidate evaluation makes a wrong decision, too or because \u2013 in the worst case \u2013 the relation in question holds between the pronoun and the filler candidate but not between the entity in question and the filler candidate.)",
+      "Unlinked pronoun chains. If a coreference chain consists of only pronouns without any entity mention, the slot filling system cannot decide to which entity it belongs to and will omit it. If the pronouns of the chain are coreferent to the entity in question, the chance that the slot filling system misses information which are relevant to the slot in question is quite high. As a result, the recall of the end-to-end system will be reduced. A solution to this problem could be a post-processing of these unlinked pronoun chains, a challenge we will investigate in the future.",
+      "No recognition of nominal anaphors. Phrases like \u201cthe 42-year-old\u201d or \u201cthe California-based company\u201d may occur directly after a sentence with the entity in question but are often not recognized as being coreferent to it. However, if they refer to this entity, they first contain possibly relevant information (like the age of a person). Second, the sentence in which they appear could mention additional information about the entity. Omitting these sentences and these phrases can therefore reduce the recall of the slot filling system. In our system, we cope with these cases by explicitely looking for such phrases in the sentence following a mention of the entity in question.",
+      "Additional findings. We perform a manual analysis of the extracted coreference chains in ten randomly chosen documents with the following results."
+    ],
+    [
+      "In order to investigate the impact of coreference resolution on slot filling empirically, we perform end-to-end experiments on the TAC evaluation data from 2015. Our system with coreference resolution was one of the top-performing systems in the official evaluations 2015 BIBREF11 . It follows the pipeline shown in Figure 1 . For a more detailed descriptions of its component, see BIBREF11 . Table 3 shows its results with (+) and without (-) coreference resolution in the candidate extraction component.",
+      "The number of true positives is reduced considerably (from 361 to 321) when the system does not use coreference information. The number of false positives is also lower, but the final results show that the impact of the number of true positives is larger since it affects both precision and recall: The F1 score drops by more than 6 points when omitting coreference resolution.",
+      "To conclude, in order to provide the classification and postprocessing modules with a recall as high as possible, coreference resolution is a crucial part of the system. Despite of the errors identified in Section \"Analysis of coreference resolution errors\" , an automatic coreference system still performs well enough to improve the performance on slot filling."
+    ],
+    [
+      "In this work, we analyzed the impact of coreference resolution on the NLP task slot filling. We showed that coreference information improves the slot filling system performance and outlined the most important challenges we have discovered in an analysis of coreference resolution errors. Since the TAC source corpus is very large, we will publish KBPchains, a resource containing the coreference chains which we have extracted automatically."
+    ],
+    [
+      "Heike Adel is a recipient of the Google European Doctoral Fellowship in Natural Language Processing and this research is supported by this fellowship. This work was also supported by DFG (grant SCHU 2246/4-2)."
+    ]
+  ]
+}
+```

qasper-2225/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Fully Automated Fact Checking Using External Sources
+
+Question: How are the potentially relevant text fragments identified?

qasper-2445/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Casting a Wide Net: Robust Extraction of Potentially Idiomatic Expressions
+
+Question: What dictionaries are used for automatic extraction of PIEs?

qasper-2473/instruction.md

@@ -0,0 +1,78 @@
+Name of Paper: A General-Purpose Tagger with Convolutional Neural Networks
+
+Question: How do they confirm their model working well on out-of-vocabulary 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."
+    ]
+  ]
+}
+```

qasper-2474/instruction.md

@@ -0,0 +1,78 @@
+Name of Paper: Towards Machine Comprehension of Spoken Content: Initial TOEFL Listening Comprehension Test by Machine
+
+Question: What approach does this work propose for the new task?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Task Definition and Contributions",
+    "Proposed Approach",
+    "Question Representation",
+    "Story Attention Module",
+    "Hopping",
+    "Answer Selection",
+    "Experimental Setup",
+    "Baselines",
+    "Results",
+    "Analysis on a typical example",
+    "Conclusions"
+  ],
+  "paragraphs": [
+    [
+      "With the popularity of shared videos, social networks, online course, etc, the quantity of multimedia or spoken content is growing much faster beyond what human beings can view or listen to. Accessing large collections of multimedia or spoken content is difficult and time-consuming for humans, even if these materials are more attractive for humans than plain text information. Hence, it will be great if the machine can automatically listen to and understand the spoken content, and even visualize the key information for humans. This paper presents an initial attempt towards the above goal: machine comprehension of spoken content. In an initial task, we wish the machine can listen to and understand an audio story, and answer the questions related to that audio content. TOEFL listening comprehension test is for human English learners whose native language is not English. This paper reports how today's machine can perform with such a test.",
+      "The listening comprehension task considered here is highly related to Spoken Question Answering (SQA) BIBREF0 , BIBREF1 . In SQA, when the users enter questions in either text or spoken form, the machine needs to find the answer from some audio files. SQA usually worked with ASR transcripts of the spoken content, and used information retrieval (IR) techniques BIBREF2 or relied on knowledge bases BIBREF3 to find the proper answer. Sibyl BIBREF4 , a factoid SQA system, used some IR techniques and utilized several levels of linguistic information to deal with the task. Question Answering in Speech Transcripts (QAST) BIBREF5 , BIBREF6 , BIBREF7 has been a well-known evaluation program of SQA for years. However, most previous works on SQA mainly focused on factoid questions like \u201cWhat is name of the highest mountain in Taiwan?\u201d. Sometimes this kind of questions may be correctly answered by simply extracting the key terms from a properly chosen utterance without understanding the given spoken content. More difficult questions that cannot be answered without understanding the whole spoken content seemed rarely dealt with previously.",
+      "With the fast development of deep learning, neural networks have successfully applied to speech recognition BIBREF8 , BIBREF9 , BIBREF10 or NLP tasks BIBREF11 , BIBREF12 . A number of recent efforts have explored various ways to understand multimedia in text form BIBREF13 , BIBREF14 , BIBREF15 , BIBREF16 , BIBREF17 , BIBREF18 . They incorporated attention mechanisms BIBREF16 with Long Short-Term Memory based networks BIBREF19 . In Question Answering field, most of the works focused on understanding text documents BIBREF20 , BIBREF21 , BIBREF22 , BIBREF23 . Even though BIBREF24 tried to answer the question related to the movie, they only used the text and image in the movie for that. It seems that none of them have studied and focused on comprehension of spoken content yet."
+    ],
+    [
+      "In this paper, we develop and propose a new task of machine comprehension of spoken content which was never mentioned before to our knowledge. We take TOEFL listening comprehension test as an corpus for this work. TOEFL is an English examination which tests the knowledge and skills of academic English for English learners whose native languages is not English. In this examination, the subjects would first listen to an audio story around five minutes and then answer several question according to that story. The story is related to the college life such as conversation between the student and the professor or a lecture in the class. Each question has four choices where only one is correct. An real example in the TOEFL examination is shown in Fig. 1 . The upper part is the manual transcription of a small part of the audio story. The questions and four choices are listed too. The correct choice to the question in Fig. 1 is choice A. The questions in TOEFL are not simple even for a human with relatively good knowledge because the question cannot be answered by simply matching the words in the question and in the choices with those in the story, and key information is usually buried by many irrelevant utterances. To answer the questions like \u201cWhy does the student go to professor's office?\", the listeners have to understand the whole audio story and draw the inferences to answer the question correctly. As a result, this task is believed to be very challenging for the state-of-the-art spoken language understanding technologies.",
+      "We propose a listening comprehension model for the task defined above, the Attention-based Multi-hop Recurrent Neural Network (AMRNN) framework, and show that this model is able to perform reasonably well for the task. In the proposed approach, the audio of the stories is first transcribed into text by ASR, and the proposed model is developed to process the transcriptions for selecting the correct answer out of 4 choices given the question. The initial experiments showed that the proposed model achieves encouraging scores on the TOEFL listening comprehension test. The attention-mechanism proposed in this paper can be applied on either word or sentence levels. We found that sentence-level attention achieved better results on the manual transcriptions without ASR errors, but word-level attention outperformed the sentence-level on ASR transcriptions with errors."
+    ],
+    [
+      "The overall structure of the proposed model is in Fig 2 . The input of model includes the transcriptions of an audio story, a question and four answer choices, all represented as word sequences. The word sequence of the input question is first represented as a question vector $V_Q$ in Section \"Experiments\" . With the question vector $V_Q$ , the attention mechanism is applied to extract the question-related information from the story in Section \"Story Attention Module\" . The machine then goes through the story by the attention mechanism several times and obtain an answer selection vector $V_{Q_n}$ in Section \"Hopping\" . This answer selection vector $V_{Q_n}$ is finally used to evaluate the confidence of each choice in Section \"Answer Selection\" , and the choice with the highest score is taken as the output. All the model parameters in the above procedure are jointly trained with the target where 1 for the correct choice and 0 otherwise."
+    ],
+    [
+      "Fig. 3 (A) shows the procedure of encoding the input question into a vector representation $V_Q$ . The input question is a sequence of T words, $w_1,w_2,...,w_T$ , every word $W_{i}$ represented in 1-Of-N encoding. A bidirectional Gated Recurrent Unit (GRU) network BIBREF25 , BIBREF26 , BIBREF27 takes one word from the input question sequentially at a time. In Fig 3 (A), the hidden layer output of the forward GRU (green rectangle) at time index $t$ is denoted by $y_{f}(t)$ , and that of the backward GRU (blue rectangle) is by $y_{b}(t)$ . After looking through all the words in the question, the hidden layer output of forward GRU network at the last time index $y_{f}(T)$ , and that of backward GRU network at the first time index $y_{b}(1)$ , are concatenated to form the question vector representation $V_{Q}$ , or $V_{Q} = [y_{f}(T) \\Vert  y_{b}(1)]$ ."
+    ],
+    [
+      "Fig. 3 (B) shows the attention mechanism which takes the question vector $V_Q$ obtained in Fig. 3 (A) and the story transcriptions as the input to encode the whole story into a story vector representation $V_{S}$ . The story transcription is a very long word sequence with many sentences, so we only show two sentences each with 4 words for simplicity. There is a bidirectional GRU in Fig 3 (B) encoding the whole story into a story vector representation $V_{S}$ . The word vector representation of the $t$ -th word $S_{t}$ is constructed by concatenating the hidden layer outputs of forward and backward GRU networks, that is $S_t = [y_{f}(t) \\Vert  y_{b}(t)]$ . Then the attention value $\\alpha _t$ for each time index ${t}$ is the cosine similarity between the question vector $V_{Q}$ and the word vector representation $S_{t}$ of each word, $V_{S}$0 . With attention values $V_{S}$2 , there can be two different attention mechanisms, word-level and sentence-level, to encode the whole story into the story vector representations $V_{S}$3 .",
+      "Word-level Attention: We normalize all the attention values $\\alpha _t$ into $\\alpha _t^\\prime $ such that they sum to one over the whole story. Then all the word vector $S_{t}$ from the bidirectional GRU network for every word in the story are weighted with this normalized attention value $\\alpha _{t}^\\prime $ and sum to give the story vector, that is $V_{S} = \\sum _{t}\\alpha _{t}^{\\prime }S_{t}$ .",
+      "Sentence-level Attention: Sentence-level attention means the model collects the information only at the end of each sentence. Therefore, the normalization is only performed over those words at the end of the sentences to obtain $\\alpha _t^{\\prime \\prime }$ . The story vector representation is then $V_{S} = \\sum _{t=eos}\\alpha _t^{\\prime \\prime }*S_{t}$ , where only those words at the end of sentences (eos) contribute to the weighted sum. So $V_{S} = \\alpha _4^{\\prime \\prime }*S_4 + \\alpha _8^{\\prime \\prime }*S_8$ in the example of the Fig. 3 "
+    ],
+    [
+      "The overall picture of the proposed model is shown in Fig 2 , in which Fig. 3 (A) and (B) are component modules (labeled as Fig. 3 (A) and (B)) of the complete proposed model. In the left of Fig. 2 , the input question is first converted into a question vector $V_{Q_0}$ by the module in Fig. 3 (A). This $V_{Q_0}$ is used to compute the attention values $\\alpha _{t}$ to obtain the story vector $V_{S_1}$ by the module in Fig. 3 (B). Then $V_{Q_0}$ and $V_{S_1}$ are summed to form a new question vector $V_{Q_1}$ . This process is called the first hop (hop 1) in Fig. 2 . The output of the first hop $V_{Q_1}$ can be used to compute the new attention to obtain a new story vector $V_{S_1}$ . This can be considered as the machine going over the story again to re-focus the story with a new question vector. Again, $V_{Q_1}$ and $V_{Q_0}$0 are summed to form $V_{Q_0}$1 (hop 2). After $V_{Q_0}$2 hops ( $V_{Q_0}$3 should be pre-defined), the output of the last hop $V_{Q_0}$4 is used for the answer selection in the Section \"Answer Selection\" ."
+    ],
+    [
+      "As in the upper part of Fig. 2 , the same way previously used to encode the question into $V_Q$ in Fig. 3 (A) is used here to encode four choice into choice vector representations $V_A$ , $V_B$ , $V_C$ , $V_D$ . Then the cosine similarity between the output of the last hop $V_{Q_n}$ and the choice vectors are computed, and the choice with highest similarity is chosen."
+    ],
+    [
+      " $\\bullet $ Dataset Collection: The collected TOEFL dataset included 963 examples in total (717 for training, 124 for validation, 122 for testing). Each example included a story, a question and 4 choices. Besides the audio recording of each story, the manual transcriptions of the story are also available. We used a pydub library BIBREF28 to segment the full audio recording into utterances. Each audio recording has 57.9 utterances in average. There are in average 657.7 words in a story, 12.01 words in question and 10.35 words in each choice.",
+      " $\\bullet $ Speech Recognition: We used the CMU speech recognizer - Sphinx BIBREF29 to transcribe the audio story. The recognition word error rate (WER) was 34.32%.",
+      " $\\bullet $ Pre-processing: We used a pre-trained 300 dimension glove vector model BIBREF30 to obtain the vector representation for each word. Each utterance in the stories, question and each choice can be represented as a fixed length vector by adding the vectors of the all component words. Before training, we pruned the utterances in the story whose vector representation has cosine distance far from the question's. The percentage of the pruned utterances was determined by the performance of the model on the development set. The vector representations of utterances, questions and choices were only used in this pre-processing stage and the baseline approaches in Section \"Baselines\" , not used in the proposed model.",
+      " $\\bullet $ Training Details: The size of the hidden layer for both the forward and backward GRU networks were 128. All the bidirectional GRU networks in the proposed model shared the same set of parameters to avoid overfitting. We used RmsProp BIBREF31 with initial learning rate of 1e-5 with momentum 0.9. Dropout rate was 0.2. Batch size was 40. The number of hop was tuned from 1 to 3 by development set."
+    ],
+    [
+      "We compared the proposed model with some commonly used simple baselines in BIBREF24 and the memory network BIBREF16 . $\\bullet $ Choice Length: The most naive baseline is to select the choices based on the number of words in it without listening to the stories and looking at the questions. This included: (i) selecting the longest choice, (ii) selecting the shortest choice or (iii) selecting the choice with the length most different from the rest choices. $\\bullet $ Within-Choices similarity: With the vector representations for the choices in pre-processing of Section \"Experimental Setup\" , we computed the cosine distance among the four choices and selected the one which is (i) the most similar to or (ii) the most different from the others.",
+      " $\\bullet $ Question and Choice Similarity: With the vector representations for the choices and questions in pre-processing of Section \"Experimental Setup\" , the choice with the highest cosine similarity to the question is selected. $\\bullet $ Sliding Window BIBREF24 , BIBREF32 : This model try to found a window of $W$ utterances in the story with the maximum similarity to the question. The similarity between a window of utterances and a question was the averaged cosine similarity of the utterances in the window and the question by their glove vector representation. After obtaining the window with the largest cosine similarity to the question, the confidence score of each choice is the average cosine similarity between the utterances in the window and the choice. The choice with the highest score is selected as the answer.",
+      " $\\bullet $ Memory Network BIBREF16 : We implemented the memory network with some modifications for this task to find out if memory network was able to deal it. The original memory network didn't have the embedding module for the choices, so we used the module for question in the memory network to embed the choices. Besides, in order to have the memory network select the answer out of four choices, instead of outputting a word in its original version, we computed the cosine similarity between the the output of the last hop and the choices to select the closest choice as the answer. We shared all the parameters of embedding layers in the memory network for avoiding overfitting. Without this modification, very poor results were obtained on the testing set. The embedding size of the memory network was set 128, stochastic gradient descent was used as BIBREF16 with initial learning rate of 0.01. Batch size was 40. The size of hop was tuned from 1 to 3 by development set."
+    ],
+    [
+      "We used the accuracy (number of question answered correctly / total number of questions) as our evaluation metric. The results are showed in Table 1 . We trained the model on the manual transcriptions of the stories, while tested the model on the testing set with both manual transcriptions (column labelled \u201cManual\u201d) and ASR transcriptions (column labelled \u201cASR\u201d).",
+      " $\\bullet $ Choice Length: Part (a) shows the performance of three models for selecting the answer with the longest, shortest or most different length, ranging from 23% to 35%.",
+      " $\\bullet $ Within Choices similarity: Part (b) shows the performance of two models for selecting the choice which is most similar to or the most different from the others. The accuracy are 36.09% and 27.87% respectively.",
+      " $\\bullet $ Question and Choice Similarity: In part (c), selecting the choice which is the most similar to the question only yielded 24.59%, very close to randomly guess.",
+      " $\\bullet $ Sliding Window: Part (d) for sliding window is the first baseline model considering the transcription of the stories. We tried the window size {1,2,3,5,10,15,20,30} and found the best window size to be 5 on the development set. This implied the useful information for answering the questions is probably within 5 sentences. The performance of 31.15% and 33.61% with and without ASR errors respectively tells how ASR errors affected the results, and the task here is too difficult for this approach to get good results.",
+      " $\\bullet $ Memory Network: The results of memory network in part (e) shows this task is relatively difficult for it, even though memory network was successful in some other tasks. However, the performance of 39.17% accuracy was clearly better than all approaches mentioned above, and it's interesting that this result was independent of the ASR errors and the reason is under investigation. The performance was 31% accuracy when we didn't use the shared embedding layer in the memory network.",
+      " $\\bullet $ AMRNN model: The results of the proposed model are listed in part (f), respectively for the attention mechanism on word-level and sentence-level. Without the ASR errors, the proposed model with sentence-level attention gave an accuracy as high as 51.67%, and slightly lower for word-level attention. It's interesting that without ASR errors, sentence-level attention is about 2.5% higher than word-level attention. Very possibly because that getting the information from the whole sentence is more useful than listening carefully at every words, especially for the conceptual and high-level questions in this task. Paying too much attention to every single word may be a bit noisy. On the other hand, the 34.32% ASR errors affected the model on sentence-level more than on word-level. This is very possibly because the incorrectly recognized words may seriously change the meaning of the whole sentences. However, with attention on word-level, when a word is incorrectly recognized, the model may be able to pay attention on other correctly recognized words to compensate for ASR errors and still come up with correct answer."
+    ],
+    [
+      "Fig 4 shows the visualization of the attention weights obtained for a typical example story in the testing set, with the proposed AMRNN model using word-level or sentence-level attention on manual or ASR transcriptions respectively. The darker the color, the higher the weights. Only a small part of the story is shown where the response of the model made good difference. This story was mainly talking about the thick cloud and some mysteries on Venus. The question for this story is \u201cWhat is a possible origin of Venus'clouds?\" and the correct choice is \u201cGases released as a result of volcanic activity\". In the manual transcriptions cases (left half of Fig 4 ), both models, with word-level or sentence-level attention, answered the question right and focused on the core and informative words/sentences to the question. The sentence-level model successfully captured the sentence including \u201c...volcanic eruptions often omits gases.\u201d; while the word-level model captured some important key words like \u201cvolcanic eruptions\", \u201cemit gases\". However, in ASR cases (right half of Fig 4 ), the ASR errors misled both models to put some attention on some irrelevant words/sentences. The sentence-level model focus on the irrelevant sentence \u201cIn other area, you got canyons...\"; while the word-level model focused on some irrelevant words \u201ccanyons\", \u201crift malaise\", but still capture some correct important words like \u201cvolcanic\" or \u201ceruptions\" to answer correctly. By the darkness of the color, we can observe that the problem caused by ASR errors was more serious for the sentence-level attention when capturing the key concepts needed for the question. This may explain why in part (f) of Table 1 we find degradation caused by ASR errors was less for word-level model than for sentence-level model."
+    ],
+    [
+      "In this paper we create a new task with the TOEFL corpus. TOEFL is an English examination, where the English learner is asked to listen to a story up to 5 minutes and then answer some corresponding questions. The learner needs to do deduction, logic and summarization for answering the question. We built a model which is able to deal with this challenging task. On manual transcriptions, the proposed model achieved 51.56% accuracy, while the very capable memory network got only 39.17% accuracy. Even on ASR transcriptions with WER of 34.32%, the proposed model still yielded 48.33% accuracy. We also found that although sentence-level attention achieved the best results on the manual transcription, word-level attention outperformed the sentence-level when there were ASR errors."
+    ]
+  ]
+}
+```

qasper-2480/instruction.md

@@ -0,0 +1,93 @@
+Name of Paper: SimplerVoice: A Key Message&Visual Description Generator System for Illiteracy
+
+Question: Which model do they use to generate key messages?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "System Design",
+    "Overview",
+    "Object2Text",
+    "Text2Visual",
+    "Evaluation",
+    "Case Study",
+    "Prototype System",
+    "Experiment",
+    "Conclusion",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "Illiteracy has been one of the most serious pervasive problems all over the world. According to the U. S. Department of Education, the National Center for Education Statistics, approximately 32 million adults in the United States are not able to read, which is about 14% of the entire adult population BIBREF0 . Additionally, 44% of the 2.4 million students in the U. S. federally funded adult education programs are English as a second language (ESL) students, and about 185,000 of them are at the lowest ESL level, beginning literacy BIBREF1 . While low-literate adults lack the ability to read and to understand text, particularly, the low-literate ESL adult learners also face the dual challenge of developing basic literacy skills which includes decoding, comprehending, and producing print, along with English proficiency, represent different nationalities and cultural backgrounds BIBREF2 . Hence, illiteracy is shown as a significant barrier that results in a person's struggling in every aspect of his or her daily life activity.",
+      "While there have not been any solutions to completely solve the illiteracy problem, recent developments of data science and artificial intelligence have brought a great opportunity to study how to support low-literate people in their lives. In this work, we propose SimplerVoice: a system that is able to generate key messages, and visual description for illiteracy. SimplerVoice could present easier-to-understand representations of complex objects to low-literate adult users, which helps them gain more confidence in navigating their own daily lives.",
+      "While the recent technology such as Google Goggles, Amazon's Flow, etc. proposed methods to parse the complex objects using image recognition, augmented reality techniques into the objects names, then to search for URLs of the objects information, the main challenges of SimplerVoice are to generate and retrieve simple, yet informative text, and visual description for illiterate people. This includes supporting adult basic education (ABE), and the English as a second language acquisition (SLA) training by performing natural language processing, and information retrieval techniques, such as: automatically generating sensible texts, word-sense-disambiguation and image-sense-disambiguation mechanism, and retrieving the optimal visual components. We propose the overall framework, and demonstrate the system in a case study of grocery shopping where SimplerVoice generates key text, and visual manual of how to use grocery products. The system prototype are also provided, and the empirical evaluation shows that SimplerVoice is able to provide users with simple text, and visual components which adequately convey the product's usage.",
+      "The organization of the paper is as follows. First, we have a quick review of previous works of text-to-image synthesis field in Section SECREF2 . In Section SECREF3 , we show our system design, including 4 parts as Input Retrieval, Object2Text, Text2Visual, and Output Display, along with the challenges of each components, and the proposed solution. We report the empirical evaluation of the proposed methods using real-world datasets for a case study in Section SECREF4 . Finally, Section SECREF5 concludes this paper, and states future work directions."
+    ],
+    [
+      "In the field of ABE and SLA, researchers have conducted a number of studies to assist low-literate learners in their efforts to acquire literacy and language skills by reading interventions, and providing specific instructions through local education agencies, community colleges and educational organizations BIBREF3 , BIBREF1 .",
+      "In augmentative and alternative communication (AAC) study, text-to-picture systems were proposed in BIBREF4 , BIBREF5 . BIBREF4 used a lookup table to transliterate each word in a sentence into an icon which resulted in a sequence of icons. Because the resulting icons sequence might be difficult to comprehend, the authors in BIBREF5 introduced a system using a concatenative or \u201dcollage\u201d approach to select and display the pictures corresponding to the text.",
+      "To generate images from text, the authors in BIBREF6 proposed an approach to automatically generate a large number of images for specified object classes that downloads all contents from a Web search query, then, removes irrelevant components, and re-ranks the remainder. However, the study did not work on action-object interaction classes, which might be needed to describe an object.",
+      "Another direction is to link the text to a database of pictographs. BIBREF7 introduced a text-to-pictograph translation system that is used in an on-line platform for augmentative and alternative communication. The text-to-pictograph was built, and evaluated on email text messages. Furthermore, an extended study of this work was provided in BIBREF8 which improved the Dutch text-to-pictograph through word sense disambiguation.",
+      "Recently, there have been studies that proposed to use deep generative adversarial networks to perform text-to-image synthesis BIBREF9 , BIBREF10 . However, these techniques might still have the limitation of scalability, or image resolution restriction."
+    ],
+    [
+      "In this section, we describe the system design, and workflow of SimplerVoice (Figure FIGREF1 ). SimplerVoice has 4 main components: input retrieval, object2text, text2visual, and output display. Figure FIGREF1 provides the overall structure of SimplerVoice system."
+    ],
+    [
+      "Given an object as the target, SimplerVoice, first, retrieves the target input in either of 3 representations: (1) object's title as text, (2) object's shape as image, or (3) other forms, e.g. object's information from scanned barcode, speech from users, etc. Based on the captured input, the system, then, generates a query string/sequence of text which is the key message describing the object's usage. Due to low-literates' lack of reading capability, the generated text requires not only informativeness, but also simplicity, and clarity. Therefore, we propose to use the \"S-V-O\" query's canonical representation as below:",
+      "[Subject] + [Verb-ing] + (with) + [Object Type/Category]",
+      "The intuition of this query representation is that the generated key message should be able to describe the action of a person using, or interacting with the target object. Moreover, the simple \"S-V-O\" model has been proposed to use in other studies BIBREF11 , BIBREF12 , BIBREF13 since it is able to provide adequate semantics meaning. The detail of generating the S-V-O query is provided in Section SECREF3 .",
+      "Once the query is constructed, SimplerVoice converts the query text into visual forms. There is a variety of visual formats to provide users: photos, icons, pictographs, etc. These visual components can be obtained by different means, such as: using search engine, mapping query/ontology to a database of images. However, the key point is to choose the optimal display for illiteracy which is described in Section SECREF12 . The result of SimplerVoice is provided further in Section SECREF4 ."
+    ],
+    [
+      "This section discusses the process of generating key message from the object's input. Based on the retrieved input, we can easily obtain the object's title through searching in database, or using search engine; hence, we assume that the input of object2text is the object's title. The workflow of object2text is provided in Figure FIGREF4 . S-V-O query is constructed by the 3 steps below.",
+      "In order to find the object type, SimplerVoice, first, builds an ontology-based knowledge tree. Then, the system maps the object with a tree's leaf node based on the object's title. For instance, given the object's title as \u201cThomas' Plain Mini Bagels\", SimplerVoice automatically defines that the object category is \u201cbagel\". Note that both the knowledge tree, and the mapping between object and object category are obtained based on text-based searching / crawling web, or through semantic webs' content. Figure FIGREF6 shows an example of the sub-tree for object category \"bagel\". While the mapped leaf node is the O in our S-V-O model, the parents nodes describe the more general object categories, and the neighbors indicate other objects' types which are similar to the input object. All the input object's type, the direct parents category, and the neighbors' are, then, put in the next step: generating verbs (V).",
+      "We propose to use 2 methods to generate the suitable verbs for the target object: heuristics-based, and n-grams model. In detail, SimplerVoice has a set of rule-based heuristics for the objects. For instance, if the object belongs to a \"food | drink\" category, the verb is generated as \"eat | drink\". Another example is the retrieved \"play\" verb if input object falls into \"toy\" category. However, due to the complexity of object's type, heuristics-based approach might not cover all the contexts of object. As to solve this, an n-grams model is applied to generate a set of verbs for the target object. An n-gram is a contiguous sequence of n items from a given speech, or text string. N-grams model has been extensively used for various tasks in text mining, and natural language processing field BIBREF14 , BIBREF15 . Here, we use the Google Books n-grams database BIBREF16 , BIBREF17 to generate a set of verbs corresponding to the input object's usage. Given a noun, n-grams model can provide a set of words that have the highest frequency of appearance followed by the noun in the database of Google Books. For an example, \"eaten\", \"toasted\", \"are\", etc. are the words which are usually used with \"bagel\". To get the right verb form, after retrieving the words from n-grams model, SimplerVoice performs word stemming BIBREF18 on the n-grams' output.",
+      "Word-sense disambiguation: In the real-world case, a word could have multiple meanings. This fact may affects the process of retrieving the right verb set. Indeed, word-sense disambiguation has been a challenging problem in the field of nature language processing. An example of the ambiguity is the object \"cookie\". The word \"cookie\" has 2 meanings: one is \"a small, flat, sweet food made from flour and sugar\" (context of biscuit), another is \"a piece of information stored on your computer about Internet documents that you have looked at\" (context of computing). Each meaning results in different verb lists, such as: \"eat\", \"bake\" for biscuit cookie, and \"use\", \"store\" for computing cookie. In order to solve the ambiguity, we propose to take advantage of the built ontology tree.",
+      "In detail, SimplerVoice uses the joint verb set of 3 types of nouns: the input object, the parents, and the neighbors as the 3 noun types are always in the same context of ontology. Equation EQREF8 shows the word-sense disambiguation mechanism with INLINEFORM0 (Object) indicates the verb set of an object generated by heuristics, and n-grams model: DISPLAYFORM0 ",
+      "Low-informative verbs: In order to ensure the quality of generated verbs, SimplerVoice maintains a list of restricted verbs that need to be filtered out. There are a lot of general, and low-informative verbs generated by n-grams model such as \"be\", \"have\", \"use\", etc. as these verbs are highly used in daily sentences/conversation. The restricted verb list could help to ensure the right specificity aspect. Hence, we modify ( EQREF8 ) into ( EQREF9 ). The process of word-sense disambiguation, and low-informative verb filtering is provided in Figure FIGREF10 : DISPLAYFORM0 ",
+      "The approach to generate the subject (S) is similar to the verb (V). SimplerVoice also uses heuristics, and n-grams model to find the suitable actor S. In regard to heuristics method, we apply a rule-based method via the object's title, and object's category to generate S since there are objects only used by a specific group of S. For an example, if the object's title contains the word \"woman, women\", the S will be \"Woman\"; of if the object belongs to the \"baby\" product category, the S will be \"Baby\". Additionally, n-grams model also generates pronouns that frequently appear with the noun O. The pronouns output could help identify the right subject S, e.g. \"she\" - \"woman, girl\", \"he\" - \"man, boy\", etc. If there exists both \"she\", and \"he\" in the generated pronoun set, the system picks either of them."
+    ],
+    [
+      "Once the S-V-O is generated, Text2Visual provides users with visual components that convey the S-V-O text meanings.",
+      "One simple solution to perform Text2Visual is to utilize existing conventional Web search engines. SimplerVoice retrieves top image results using S-V-O as the search query. However, there could be image sense ambiguity in displaying the result from search engine. For instance, if the object is \"Swiss Cheese\", user might not distinguish between \"Swiss Cheese\", and the general \"Cheese\" images. To solve the image sense ambiguity issue, the authors in BIBREF5 suggests to display multiple images to guide human perception onto the right target object's meaning.",
+      "Additionally, since SimplerVoice is designed for illiteracy, the system needs to display the optimal visual component suitable for low-literate people. In BIBREF19 , the authors study the effectiveness of different types of audio-visual representations for illiterate computer users. While there is no difference between dynamic and static imagery (mixed result in different use cases), hand-drawn or cartoons are shown to be easier for low-literate users to understand than photorealistic representations. Therefore, SimplerVoice also provides users with pictographs display along with images. We use the Sclera database of pictographs BIBREF20 . Each S-V-O word is mapped with a corresponding Sclera pictograph file. The detail of how to perform the mapping is discussed in BIBREF7 . Intuitively, the process is described as: first, the system manually links a subset of words with pictographs' filenames; then, if the manual link is missing, the word is linked to the close synset using WordNet (Figure FIGREF15 )."
+    ],
+    [
+      "In this section, we demonstrate the effectiveness of SimplerVoice system in a case study of grocery shopping. The section organization is as follows: first, we describe the real dataset, and setup that SimplerVoice uses; second, we provide the prototype system which is a built application for end-users; finally, we show the results of SimplerVoice along with users feedback."
+    ],
+    [
+      "In the case study of grocery products shopping, we use a database of INLINEFORM0 products' description crawled from multiple sources. Each product description contains 4 fields: UPC code, product's title, ontology path of product category, and URL link of the product. Since it is recommended to utilize various devices of technology, such as computers or smart phones in adult ESL literacy education BIBREF21 , we build a mobile application of SimplerVoice for illiterate users. The goal of SimplerVoice is to support users with key message, & simple visual components of how to use the products given the scanned barcode (UPC code), or products' name retrieved from parsing products images taken by end-users' phone cameras. Section SECREF17 shows our SimplerVoice application description."
+    ],
+    [
+      "There are 2 means to retrieve the object's input through SimplerVoice application: text filling, or taking photos of barcode / products' labels (Figure FIGREF18 ). SimplerVoice automatically reads the target grocery product's name, and proceeds to the next stage.",
+      "Based on the built-in ontology tree, SimplerVoice, then, finds the object's category, the parent, and the neighboring nodes. The next step is to generate the S-V-O message (e.g. Table TABREF19 ), and visual description (e.g. Figure FIGREF20 ) of product's usage. Figure FIGREF22 shows an example of the result of SimplerVoice system for product \"H-E-B Bakery Cookies by the Pound\" from a grocery store: (1) the product description, (2) key messages, and (3) visual components. The product description includes the product's categories searched on the grocery store's website BIBREF22 , the parent node's, and the neighbors - similar products' categories. The S-V-O query, or key message for \"H-E-B Bakery Cookies by the Pound\" is generated as \"Woman eating cookies\". Additionally, we support users with language translation into Spanish for convenience, and provides different levels of reading. Each reading level has a different level of difficulty: The higher the reading level is, the more advanced the texts are. The reason of breaking the texts into levels is to encourage low-literate users learning how to read. Next to the key messages are the images, and pictographs."
+    ],
+    [
+      "To evaluate our system, we compared SimplerVoice to the original product description / package (baseline 1) and the top images result from search engines of the same product (baseline 2). Given a set of products, we generated the key message & visual description of each product using 3 approaches below. An example of the 3 approaches is provided in Fig. FIGREF23 .",
+      "Baseline 1: We captured and displayed the product package photos and the product title text as product description.",
+      "Baseline 2: The product description was retrieved by search engine using the product titles, and then presented to the users as the top images result from Google and Bing. We also provided the product title along with the images.",
+      "SimplerVoice: We shown the generated key messages (Tab. TABREF19 ), and visual description including 2 components: photorealistics images and pictographs (Fig. FIGREF20 ) from SimplerVoice system.",
+      "Intuitively, baseline 1 shows how much information a user would receive from the products' packages without prior knowledge of the products while baseline 2 might provide additional information by showing top images from search engines. With the baseline 2, we attempt to measure whether merely adding \"relevant\" or \"similar\" products' images would be sufficient to improve the end-users' ability to comprehend the product's intended use. Moreover, with SimplerVoice, we test if our system could provide users with the proper visual components to help them understand the products' usage based on the proposed techniques, and measure the usefulness of SimplerVoice's generated description.",
+      "We evaluated the effectiveness & interpretability of 3 above approaches by conducting a controlled user study with 15 subjects who were Vietnamese native and did not speak/comprehend English. A dataset of random 20 U.S. products including products' title, UPC code, and product package images were chosen to be displayed in the user study. Note that the 15 participated subjects had not used the 20 products before and were also not familiar with the packaged products including the chosen 20 products; hence, they were \"illiterate\" in terms of comprehending English and in terms of having used any of the products although they might be literate in Vietnamese.",
+      "Each participated user was shown the product description generated from each approach, and was asked to identify what the products were and how to use them. The users' responses were then recorded in Vietnamese and were assigned to a score if they \"matched\" the correct answer by 3 experts who were bilingual in English and Vietnamese. In this study, we used the \"mean opinion score\" (MOS) BIBREF23 , BIBREF24 to measure the effectiveness: how similar a response were comparing to the correct product's usage. The MOS score range is 1-5 (1-Bad, 2-Poor, 3-Fair, 4-Good, 5-Excellent) with 1 means incorrect product usage interpretability - the lowest level of effectiveness and 5 means correct product usage interpretability - the highest effectiveness level. The assigned scores corresponding to responses were aggregated over all participated subjects and over the 3 experts. The result of the score is reported in the next section Result.",
+      "Table TABREF21 shows the MOS scores indicating the performance of 3 approaches. The mean of MOS scores of baseline 1 is the lowest one: 2.57 (the standard deviation (stdev) is 1.17), the baseline 2 mean score is slightly higher than the baseline 1's: 2.86 (the stdev is 1.27), while SimplerVoice evaluation score is the highest one: 4.82 (the stdev is 0.35) which means the most effective approach to provide users with products' usage. Additionally, a paired-samples t-test was conducted to compare the MOS scores of users' responses among all products using baseline 1 and SimplerVoice system. There was a significant difference in the scores for baseline 1 (Mean = 2.57, Stdev = 1.17) and SimplerVoice (Mean = 4.82, Stdev = 0.35); t= -8.18224, p =1.19747e-07. These results show that there is a statistically significant difference in the MOS means between baseline 1 and SimplerVoice and that SimplerVoice performs more effectively than baseline 1 over different types of products.",
+      "Baseline 1 scores ranges from 1 to 4.25 over all products as some products are easily to guess based on product package images, such as bagels, pretzels, soda, etc. while some products packages might cause confusion, such as shoe dye, wax cube, vinegar, etc. For an example, all participated users were able to recognize the \"Always Bagels Cinnamon Raisin Bagels\" product as \"a type of bread\" and its usage as \"eating\" using baseline 1 while the \"ScentSationals Wild Raspberry Fragrance Wax Cubes\" product were mostly incorrectly recognized as a type of \"candy\" for \"eating\".",
+      "Baseline 2 scores range over all products is 1 - 4.7. The baseline 2 has higher score than baseline 1 since users were provided more information with the top result product images from search engine. For instance, given the \"Fiesta Cinnamon Sticks\" product, most users' responses were \"a type of pastries - cannoli\" for \"eating\" based on baseline 1. Since baseline 2 provided more photos of cinnamon sticks without the packaging, the users were able to recognize the products as cinnamon. Moreover, the score of baseline 2 is only slightly higher than baseline 1 because search engines mostly return similar images from product package, hence, might provide only little additional information to the participants.",
+      "SimplerVoice scores ranges from 3.75 to 5 which is higher than baseline 1, and baseline 2. SimplerVoice score has low standard deviation indicating the consistent effectiveness along different types of products. While performing the user study, we also notice that the culture differences is an important factor to the result. For an example, the product has lowest score is the \"Heinz Distilled White Vinegar\" since there were participated users who have never used vinegar before. These participants are from the rural Northern Vietnam area where people might have not known the vinegar product."
+    ],
+    [
+      "In this work, we introduce SimplerVoice: a key message & visual description generator system for illiteracy. To our best knowledge, SimplerVoice is the first system framework to combine multiple AI techniques, particularly in the field of natural language processing, and information retrieval, to support low-literate users including low-literate ESL learners building confidence on their own lives, and to encourage them to improve their reading skills. Although awareness by itself does not solve the problem of illiteracy, the system can be put in different contexts for education goals. SimplerVoice might be a valuable tool for both educational systems, and daily usage.",
+      "The SimplerVoice system was evaluated and shown to achieve higher performance score comparing to other approaches. Moreover, we also introduced the SimplerVoice mobile application and have the application used by participants in the Literacy Coalition of Central Texas's SPARK program BIBREF25 . We received positive end-users' feedback for the prototype, and plan to add more features for SimplerVoice.",
+      "One of the future work is to improve the input retrieval of the system, so that SimplerVoice can automatically recognize the object through the object's shape. Another direction is to extend the work in other different real-world use cases, and demonstrate its effectiveness on those case studies."
+    ],
+    [
+      "This research was conducted under the auspices of the IBM Science for Social Good initiative. The authors would like to thank Christian O. Harris and Heng Luo for discussions."
+    ]
+  ]
+}
+```

qasper-2487/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: An Annotation Scheme of A Large-scale Multi-party Dialogues Dataset for Discourse Parsing and Machine Comprehension
+
+Question: Did they experiment on the proposed task?